From 89f5a746a3191977f5bd001f449a4f577acfb914 Mon Sep 17 00:00:00 2001
From: Christopher Moroney <christopher.moroney1@gmail.com>
Date: Thu, 30 Apr 2026 11:15:12 -0700
Subject: [PATCH 01/23] learning-paths faq and summary workflow test

---
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 tools/generate_summary_faq.py                 | 809 ++++++++++++++++++
 424 files changed, 1864 insertions(+), 1 deletion(-)
 create mode 100644 .github/workflows/generate-summary-faq.yml
 create mode 100644 reports/generated-summary-faq/latest-run.yml
 create mode 100644 themes/arm-design-system-hugo-theme/layouts/partials/learning-paths/generated-summary-faq.html
 create mode 100644 tools/generate_summary_faq.py

diff --git a/.github/workflows/generate-summary-faq.yml b/.github/workflows/generate-summary-faq.yml
new file mode 100644
index 0000000000..9273ccadd2
--- /dev/null
+++ b/.github/workflows/generate-summary-faq.yml
@@ -0,0 +1,137 @@
+name: Generate Learning Path Summary and FAQ
+
+on:
+  workflow_dispatch:
+    inputs:
+      paths:
+        description: "Optional comma or newline separated Learning Path directories or _index.md files. Leave blank to process all eligible paths."
+        required: false
+        type: string
+      limit:
+        description: "Optional limit when paths is empty. Use 0 to process all eligible Learning Paths."
+        required: false
+        default: "0"
+        type: string
+      require_flag:
+        description: "Only process Learning Paths where generate_summary_faq is true."
+        required: true
+        default: true
+        type: boolean
+      dry_run:
+        description: "Generate the report without changing any _index.md files."
+        required: true
+        default: false
+        type: boolean
+      commit_changes:
+        description: "Commit changed _index.md files and the central report back to the selected branch."
+        required: true
+        default: true
+        type: boolean
+
+permissions:
+  contents: write
+
+jobs:
+  generate_summary_faq:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out current repo
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+
+      - name: Install Python dependencies
+        run: python -m pip install pyyaml
+
+      - name: Generate summary and FAQ content
+        env:
+          INPUT_PATHS: ${{ inputs.paths }}
+          INPUT_LIMIT: ${{ inputs.limit }}
+          INPUT_REQUIRE_FLAG: ${{ inputs.require_flag }}
+          INPUT_DRY_RUN: ${{ inputs.dry_run }}
+          REPORT_FILE: reports/generated-summary-faq/latest-run.yml
+          RUN_URL: ${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}
+          GIT_REF_NAME: ${{ github.ref_name }}
+          GIT_SHA: ${{ github.sha }}
+          GITHUB_ACTOR_NAME: ${{ github.actor }}
+        run: |
+          CMD=(
+            python tools/generate_summary_faq.py
+            --path-filter "$INPUT_PATHS"
+            --limit "$INPUT_LIMIT"
+            --report-file "$REPORT_FILE"
+            --run-url "$RUN_URL"
+            --git-ref "$GIT_REF_NAME"
+            --git-sha "$GIT_SHA"
+            --actor "$GITHUB_ACTOR_NAME"
+          )
+
+          if [ "$INPUT_REQUIRE_FLAG" != "true" ]; then
+            CMD+=(--allow-unflagged)
+          fi
+
+          if [ "$INPUT_DRY_RUN" = "true" ]; then
+            CMD+=(--dry-run)
+          else
+            CMD+=(--write)
+          fi
+
+          "${CMD[@]}"
+
+      - name: Upload latest run report
+        uses: actions/upload-artifact@v4
+        with:
+          name: generated-summary-faq-report
+          path: reports/generated-summary-faq/latest-run.yml
+          retention-days: 14
+
+      - name: Configure git
+        if: ${{ !inputs.dry_run && inputs.commit_changes }}
+        run: |
+          git config user.name "GitHub Actions Summary FAQ Bot"
+          git config user.email "<>"
+
+      - name: Commit generated content
+        if: ${{ !inputs.dry_run && inputs.commit_changes }}
+        env:
+          TARGET_REF: ${{ github.ref_name }}
+        run: |
+          git add content/learning-paths reports/generated-summary-faq/latest-run.yml
+          git commit -m "Generate Learning Path summary and FAQ content" && git push origin HEAD:$TARGET_REF || echo "No changes to commit"
+
+      - name: Add workflow summary
+        if: always()
+        run: |
+          python - <<'PY' >> "$GITHUB_STEP_SUMMARY"
+          import pathlib
+          import yaml
+
+          report_path = pathlib.Path("reports/generated-summary-faq/latest-run.yml")
+          print("Generated summary/FAQ report: `reports/generated-summary-faq/latest-run.yml`")
+          print("")
+
+          if not report_path.exists():
+              print("Report file was not created.")
+              raise SystemExit(0)
+
+          report = yaml.safe_load(report_path.read_text(encoding="utf-8")) or {}
+          latest = report.get("latest_run", {})
+          totals = latest.get("totals", {})
+          print("## Run totals")
+          print("")
+          print(f"- Processed: {totals.get('processed', 0)}")
+          print(f"- Added: {totals.get('added', 0)}")
+          print(f"- Updated: {totals.get('updated', 0)}")
+          print(f"- Unchanged: {totals.get('unchanged', 0)}")
+          print(f"- Errors: {totals.get('errors', 0)}")
+          print("")
+          print("## Paths")
+          print("")
+          for entry in latest.get("paths", []):
+              print(f"- {entry.get('status', 'unknown')}: {entry.get('path', '')}")
+          PY
diff --git a/archetypes/learning-path/_index.md b/archetypes/learning-path/_index.md
index ec8f0e4589..cf781f3648 100644
--- a/archetypes/learning-path/_index.md
+++ b/archetypes/learning-path/_index.md
@@ -13,6 +13,10 @@ prerequisites:
     - PLACEHOLDER PREREQ 1
     - PLACEHOLDER PREREQ 2
 
+# Optional: set to true to include this Learning Path in the manual
+# generated summary/FAQ GitHub Action.
+generate_summary_faq: false
+
 author: PLACEHOLDER NAME
 
 ### Tags
diff --git a/content/learning-paths/automotive/intro/_index.md b/content/learning-paths/automotive/intro/_index.md
index 20aa7cb6da..acf3ebbbe1 100644
--- a/content/learning-paths/automotive/intro/_index.md
+++ b/content/learning-paths/automotive/intro/_index.md
@@ -16,6 +16,8 @@ draft: true
 cascade:
     draft: true
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/automotive/openadkit1_container/_index.md b/content/learning-paths/automotive/openadkit1_container/_index.md
index cb0edace8b..0222646880 100644
--- a/content/learning-paths/automotive/openadkit1_container/_index.md
+++ b/content/learning-paths/automotive/openadkit1_container/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - An Arm Neoverse cloud instance, or a local Arm Neoverse Linux computer with at least 16 CPUs and 32GB of RAM
     - Familiarity with Docker and Docker Compose
 
+generate_summary_faq: true
+
 author: Odin Shen
 
 ### Tags
diff --git a/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md b/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
index 32e15dd30a..3c8dcd74d0 100644
--- a/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
+++ b/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - Completion of the [Deploy Open AD Kit containerized autonomous driving simulation on Arm Neoverse](/learning-paths/automotive/openadkit1_container/) Learning Path
     - Basic familiarity with Docker
 
+generate_summary_faq: true
+
 author: 
     - Odin Shen
     - Julien Jayat
diff --git a/content/learning-paths/automotive/system76-auto/_index.md b/content/learning-paths/automotive/system76-auto/_index.md
index fc1e11f441..0526e6a102 100644
--- a/content/learning-paths/automotive/system76-auto/_index.md
+++ b/content/learning-paths/automotive/system76-auto/_index.md
@@ -14,6 +14,8 @@ learning_objectives:
 prerequisites:
     - A System76 Thelio Astra desktop computer running Ubuntu 24.04. 
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/automotive/zenacssdebug/_index.md b/content/learning-paths/automotive/zenacssdebug/_index.md
index ff16aba0ac..6c594fd158 100644
--- a/content/learning-paths/automotive/zenacssdebug/_index.md
+++ b/content/learning-paths/automotive/zenacssdebug/_index.md
@@ -18,6 +18,8 @@ prerequisites:
   - Arm Development Studio 2024.1 or later with a valid license - for support see the [Install Guide for Arm DS](/install-guides/armds) 
   - Basic understanding of the Arm Zena CSS software stack, Armv8-A/Armv9-A cores, and Linux
 
+generate_summary_faq: true
+
 author: Ronan Synnott
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/_example-learning-path/_index.md b/content/learning-paths/cross-platform/_example-learning-path/_index.md
index 5603e466b2..5a7b7a201c 100644
--- a/content/learning-paths/cross-platform/_example-learning-path/_index.md
+++ b/content/learning-paths/cross-platform/_example-learning-path/_index.md
@@ -16,6 +16,8 @@ learning_objectives:
 prerequisites:
     - A [GitHub](https://github.com/) account
 
+generate_summary_faq: true
+
 author: Zach Lasiuk
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/adler32/_index.md b/content/learning-paths/cross-platform/adler32/_index.md
index ebf0fcc4fb..e0c67bcc0f 100644
--- a/content/learning-paths/cross-platform/adler32/_index.md
+++ b/content/learning-paths/cross-platform/adler32/_index.md
@@ -14,6 +14,8 @@ prerequisites:
     - An Arm computer running Linux with the GNU compiler (gcc) installed.
     - Visual Studio Code with the GitHub Copilot extension installed. 
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md b/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md
index 33c689e7c4..1415bd64fc 100644
--- a/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md
+++ b/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - Basic familiarity with Python, PyTest, and container concepts
     - Familiarity with the [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) specification
 
+generate_summary_faq: true
+
 author: Neethu Elizabeth Simon
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/avh_cicd/_index.md b/content/learning-paths/cross-platform/avh_cicd/_index.md
index b222e60628..0ac6911d36 100644
--- a/content/learning-paths/cross-platform/avh_cicd/_index.md
+++ b/content/learning-paths/cross-platform/avh_cicd/_index.md
@@ -14,6 +14,8 @@ learning_objectives:
 prerequisites:
     - Some familiarity with CI/CD concepts is assumed
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/avh_cicd2/_index.md b/content/learning-paths/cross-platform/avh_cicd2/_index.md
index dbd9e8ca13..22183ead55 100644
--- a/content/learning-paths/cross-platform/avh_cicd2/_index.md
+++ b/content/learning-paths/cross-platform/avh_cicd2/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - This learning path builds on [Integrate Arm Virtual Hardware into CI/CD workflow 1](/learning-paths/cross-platform/avh_cicd/).
     - Valid AWS and GitHub accounts are required
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ##### Tags
diff --git a/content/learning-paths/cross-platform/cca_rme/_index.md b/content/learning-paths/cross-platform/cca_rme/_index.md
index e951bbc9a9..a7631ce474 100644
--- a/content/learning-paths/cross-platform/cca_rme/_index.md
+++ b/content/learning-paths/cross-platform/cca_rme/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - Some understanding of the Arm architecture
     - Arm Development Studio, 2023.0 or later
 
+generate_summary_faq: true
+
 author: Ronan Synnott
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md b/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md
index 760e05f318..ceb824d226 100644
--- a/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md
+++ b/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md
@@ -16,6 +16,8 @@ learning_objectives:
 prerequisites:
     - An Arm computer running Linux. You can also use a virtual machine from a [cloud service provider](/learning-paths/servers-and-cloud-computing/csp/).
 
+generate_summary_faq: true
+
 author: Kieran Hejmadi
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/docker-build-cloud/_index.md b/content/learning-paths/cross-platform/docker-build-cloud/_index.md
index b54bf8a88f..931f7e553d 100644
--- a/content/learning-paths/cross-platform/docker-build-cloud/_index.md
+++ b/content/learning-paths/cross-platform/docker-build-cloud/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - A GitHub account
     - A Docker Hub account
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/docker/_index.md b/content/learning-paths/cross-platform/docker/_index.md
index 57e2098b50..ac98750bfb 100644
--- a/content/learning-paths/cross-platform/docker/_index.md
+++ b/content/learning-paths/cross-platform/docker/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - A Windows, macOS, or Linux computer with Docker installed, any architecture can be used
     - An Arm Linux server with Docker installed 
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md b/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md
index 54ed81adec..3bdbf73d18 100644
--- a/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md
+++ b/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 - Familiarity with C programming, with a good understanding of pointers.
 - A Linux machine to run the example code.
 
+generate_summary_faq: true
+
 author: David Spickett
 
 test_images:
diff --git a/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md b/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md
index 992decceec..af925344f5 100644
--- a/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md
+++ b/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md
@@ -14,6 +14,8 @@ learning_objectives:
 prerequisites:
     - An Arm-based computer running Linux and a recent version of a C++ compiler (Clang or GCC).
 
+generate_summary_faq: true
+
 author: Konstantinos Margaritis
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/ernie_moe_v9/_index.md b/content/learning-paths/cross-platform/ernie_moe_v9/_index.md
index be39782954..43557bed5f 100644
--- a/content/learning-paths/cross-platform/ernie_moe_v9/_index.md
+++ b/content/learning-paths/cross-platform/ernie_moe_v9/_index.md
@@ -15,6 +15,8 @@ learning_objectives:
 prerequisites:
     - An Armv9 device with at least 32 GB of available disk space, for example, Radxa Orion O6
 
+generate_summary_faq: true
+
 author: Odin Shen
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/floating-point-behavior/_index.md b/content/learning-paths/cross-platform/floating-point-behavior/_index.md
index 8f02a1c599..3b4b83615e 100644
--- a/content/learning-paths/cross-platform/floating-point-behavior/_index.md
+++ b/content/learning-paths/cross-platform/floating-point-behavior/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - Access to an x86 and an Arm Linux machine.
     - Familiarity with floating-point numbers.
 
+generate_summary_faq: true
+
 author: 
     - Kieran Hejmadi
     - Jason Andrews
diff --git a/content/learning-paths/cross-platform/function-multiversioning/_index.md b/content/learning-paths/cross-platform/function-multiversioning/_index.md
index 81b88182e3..d4149c7f80 100644
--- a/content/learning-paths/cross-platform/function-multiversioning/_index.md
+++ b/content/learning-paths/cross-platform/function-multiversioning/_index.md
@@ -21,6 +21,8 @@ prerequisites:
     - Familiarity with Arm assembly.
     - A LLVM 20 compiler with runtime library support or GCC 16.
 
+generate_summary_faq: true
+
 author: Alexandros Lamprineas
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/github-arm-runners/_index.md b/content/learning-paths/cross-platform/github-arm-runners/_index.md
index ba6149aec7..5705781038 100644
--- a/content/learning-paths/cross-platform/github-arm-runners/_index.md
+++ b/content/learning-paths/cross-platform/github-arm-runners/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - A GitHub account (a Team or Enterprise Cloud plan is required for private repositories).
     - A Docker Hub account.
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md b/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md
index f11a5914eb..67a6b02947 100644
--- a/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md
+++ b/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md
@@ -18,6 +18,8 @@ learning_objectives:
 prerequisites:
     - A GitLab account (free tier includes Arm64 runner access)
 
+generate_summary_faq: true
+
 author: Mohamed Ismail
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/gitlab/_index.md b/content/learning-paths/cross-platform/gitlab/_index.md
index ef9095d0f4..184f08fb95 100644
--- a/content/learning-paths/cross-platform/gitlab/_index.md
+++ b/content/learning-paths/cross-platform/gitlab/_index.md
@@ -18,6 +18,8 @@ prerequisites:
     - A computer with [Google Cloud CLI](/install-guides/gcloud) and [kubectl](/install-guides/kubectl/)installed.
     - A valid GitLab account
 
+generate_summary_faq: true
+
 author: Pranay Bakre
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/integer-vs-floats/_index.md b/content/learning-paths/cross-platform/integer-vs-floats/_index.md
index f7912d8b5d..33d7a42e5b 100644
--- a/content/learning-paths/cross-platform/integer-vs-floats/_index.md
+++ b/content/learning-paths/cross-platform/integer-vs-floats/_index.md
@@ -13,6 +13,8 @@ learning_objectives:
 prerequisites:
     - An Arm computer running Linux and a recent version of a C++ compiler (Clang or GCC) installed
 
+generate_summary_faq: true
+
 author: Konstantinos Margaritis
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/intrinsics/_index.md b/content/learning-paths/cross-platform/intrinsics/_index.md
index 88d0dd032b..7625f6a171 100644
--- a/content/learning-paths/cross-platform/intrinsics/_index.md
+++ b/content/learning-paths/cross-platform/intrinsics/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 - An Arm based machine or [cloud instance](/learning-paths/servers-and-cloud-computing/csp/) running Ubuntu Linux.
 - Optionally, an `x86_64` machine also running Ubuntu.
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 test_images:
diff --git a/content/learning-paths/cross-platform/ipexplorer/_index.md b/content/learning-paths/cross-platform/ipexplorer/_index.md
index 4c6484dbe6..8089c48fd6 100644
--- a/content/learning-paths/cross-platform/ipexplorer/_index.md
+++ b/content/learning-paths/cross-platform/ipexplorer/_index.md
@@ -14,6 +14,8 @@ prerequisites:
     - An Arm account that can access IP Explorer
     - (Optional) A Linux machine with the desired compilers installed
 
+generate_summary_faq: true
+
 author: Ronan Synnott
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/kleidiai-explainer/_index.md b/content/learning-paths/cross-platform/kleidiai-explainer/_index.md
index f7e786dc91..ec9346d2dc 100644
--- a/content/learning-paths/cross-platform/kleidiai-explainer/_index.md
+++ b/content/learning-paths/cross-platform/kleidiai-explainer/_index.md
@@ -14,6 +14,8 @@ prerequisites:
     - An Arm-based Linux machine that implements the Int8 Matrix Multiplication (*i8mm*) architecture feature. The example in this Learning Path is run on an AWS Graviton 3 instance. Instructions on setting up an Arm-based server are [found here](/learning-paths/servers-and-cloud-computing/csp/aws/).
     - A basic understanding of linear algebra terminology, such as dot product and matrix multiplication.
 
+generate_summary_faq: true
+
 author: Zach Lasiuk
 ### Tags
 skilllevels: Introductory 
diff --git a/content/learning-paths/cross-platform/llm-fine-tuning-for-web-applications/_index.md b/content/learning-paths/cross-platform/llm-fine-tuning-for-web-applications/_index.md
index 51398b5edc..4aad6e5871 100644
--- a/content/learning-paths/cross-platform/llm-fine-tuning-for-web-applications/_index.md
+++ b/content/learning-paths/cross-platform/llm-fine-tuning-for-web-applications/_index.md
@@ -27,6 +27,8 @@ prerequisites:
     - Basic understanding of machine learning and deep learning. 
     - Familiarity with deep learning frameworks such as PyTorch and Hugging Face Transformers. 
 
+generate_summary_faq: true
+
 author: Parichay Das
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/loop-reflowing/_index.md b/content/learning-paths/cross-platform/loop-reflowing/_index.md
index a80d144c41..14a763660e 100644
--- a/content/learning-paths/cross-platform/loop-reflowing/_index.md
+++ b/content/learning-paths/cross-platform/loop-reflowing/_index.md
@@ -12,6 +12,8 @@ learning_objectives:
 prerequisites:
     - An Arm computer running Linux and a recent version of Clang or the GNU compiler (gcc) installed.
 
+generate_summary_faq: true
+
 author: Konstantinos Margaritis
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/matrix/_index.md b/content/learning-paths/cross-platform/matrix/_index.md
index b30e9fd1b7..59a9c4e490 100644
--- a/content/learning-paths/cross-platform/matrix/_index.md
+++ b/content/learning-paths/cross-platform/matrix/_index.md
@@ -19,6 +19,8 @@ prerequisites:
     - A build system [GNU Make](https://www.gnu.org/software/make/) or [Ninja](https://ninja-build.org/).
     - A documentation generator [Doxygen](https://www.doxygen.nl/).
 
+generate_summary_faq: true
+
 author: Arnaud de Grandmaison
 
 
diff --git a/content/learning-paths/cross-platform/mca-godbolt/_index.md b/content/learning-paths/cross-platform/mca-godbolt/_index.md
index 7d76a87d59..0c6276bb8a 100644
--- a/content/learning-paths/cross-platform/mca-godbolt/_index.md
+++ b/content/learning-paths/cross-platform/mca-godbolt/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - Familiarity with Arm assembly.
     - LLVM version 16 or newer, which includes support for Neoverse V2.
 
+generate_summary_faq: true
+
 author: Asher Dobrescu
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/mcp-ai-agent/_index.md b/content/learning-paths/cross-platform/mcp-ai-agent/_index.md
index 511d27dae6..4f4920bfe1 100644
--- a/content/learning-paths/cross-platform/mcp-ai-agent/_index.md
+++ b/content/learning-paths/cross-platform/mcp-ai-agent/_index.md
@@ -18,6 +18,8 @@ prerequisites:
     - Basic understanding of Large Language Models (LLMs) and how they are used in local inference.
     - Understanding of AI agents and the OpenAI Agent SDK (or similar frameworks).
 
+generate_summary_faq: true
+
 author: Andrew Choi
 
 skilllevels: Introductory
diff --git a/content/learning-paths/cross-platform/memory-latency/_index.md b/content/learning-paths/cross-platform/memory-latency/_index.md
index 7945e6ff98..762e2cca28 100644
--- a/content/learning-paths/cross-platform/memory-latency/_index.md
+++ b/content/learning-paths/cross-platform/memory-latency/_index.md
@@ -13,6 +13,8 @@ learning_objectives:
 prerequisites:
     - An Arm computer running Linux with recent versions of Clang or GCC installed.
 
+generate_summary_faq: true
+
 author: Konstantinos Margaritis
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md b/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md
index b1d4951ae9..884d46ab83 100644
--- a/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md
+++ b/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md
@@ -18,6 +18,8 @@ prerequisites:
     - Familiarity with the Linux command line, Git, and building C++ projects with CMake
     - Internet access to download source code, model assets, and sample data
 
+generate_summary_faq: true
+
 author: Odin Shen
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md b/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md
index 71c81d366d..ed135f1463 100644
--- a/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md
+++ b/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md
@@ -24,6 +24,8 @@ prerequisites:
     - Installation of Android Development Studio and adb (if you're targeting an Android phone with SME2 support)
     - Compiler support for SME2 instructions (for example, LLVM 18 or later with SME2 backend support)
 
+generate_summary_faq: true
+
 author: Arnaud de Grandmaison
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/psa-tfm/_index.md b/content/learning-paths/cross-platform/psa-tfm/_index.md
index 86c0c9babb..e0d6476541 100644
--- a/content/learning-paths/cross-platform/psa-tfm/_index.md
+++ b/content/learning-paths/cross-platform/psa-tfm/_index.md
@@ -19,6 +19,8 @@ prerequisites:
     - Ubuntu host or access to AWS
     - Optional MPS3 FPGA prototyping board
 
+generate_summary_faq: true
+
 author: Ronan Synnott
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md b/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md
index 3b6cd4c1a6..87acf975aa 100644
--- a/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md
+++ b/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md
@@ -22,6 +22,8 @@ prerequisites:
     - For the OS, you can use Windows, Linux, or macOS.
   
 
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/remoteit/_index.md b/content/learning-paths/cross-platform/remoteit/_index.md
index 72dd2b2e1a..57f5f641ae 100644
--- a/content/learning-paths/cross-platform/remoteit/_index.md
+++ b/content/learning-paths/cross-platform/remoteit/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - A device/computer to which you would like remote access. A device can be a Windows, Mac, or Linux computer including development kits such as Raspberry Pi or cloud-hosted such as within Arm Virtual Hardware or within AWS. You will need a method to control this device before Remote.It is deployed which can be local access or access via another remote connectivity solution (Remote Desktop, VPN, etc.)
     - Determine if your device that you would like to access remotely also needs to make connections to other Remote.It devices.
 
+generate_summary_faq: true
+
 author: Brenda Strech
 
 further_reading:
diff --git a/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md b/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
index 675b0136aa..271c852998 100644
--- a/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
+++ b/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
@@ -13,6 +13,8 @@ learning_objectives:
 prerequisites:
     - An Arm computer running Linux OS and a recent version of compiler (Clang or GCC) installed
 
+generate_summary_faq: true
+
 author: Konstantinos Margaritis
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/rust_armds/_index.md b/content/learning-paths/cross-platform/rust_armds/_index.md
index 4cb9e746c7..861964d222 100644
--- a/content/learning-paths/cross-platform/rust_armds/_index.md
+++ b/content/learning-paths/cross-platform/rust_armds/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - An installation of Arm Development Studio.
     - A basic understanding of Rust programming.
 
+generate_summary_faq: true
+
 author: Ronan Synnott
 
 
diff --git a/content/learning-paths/cross-platform/simd-info-demo/_index.md b/content/learning-paths/cross-platform/simd-info-demo/_index.md
index 313febf26d..f6d9a50383 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/_index.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/_index.md
@@ -14,6 +14,8 @@ prerequisites:
     - A basic understanding of SIMD.
     - Access to an Arm platform with a SIMD-supported engine, installed with recent versions of a C compiler such as Clang or GCC.
 
+generate_summary_faq: true
+
 author: 
     - Georgios Mermigkis
     - Konstantinos Margaritis
diff --git a/content/learning-paths/cross-platform/simd-loops/_index.md b/content/learning-paths/cross-platform/simd-loops/_index.md
index f9c64f92d0..1e339ec86a 100644
--- a/content/learning-paths/cross-platform/simd-loops/_index.md
+++ b/content/learning-paths/cross-platform/simd-loops/_index.md
@@ -18,6 +18,8 @@ prerequisites:
     - Some familiarity with SIMD programming and Neon intrinsics
     - Recent toolchains that support SVE/SME (GCC 13+ or Clang 16+ recommended)
 
+generate_summary_faq: true
+
 author:
     - Alejandro Martinez Vicente
     - Mohamad Najem
diff --git a/content/learning-paths/cross-platform/simd-on-rust/_index.md b/content/learning-paths/cross-platform/simd-on-rust/_index.md
index 219fe1da68..2654001092 100644
--- a/content/learning-paths/cross-platform/simd-on-rust/_index.md
+++ b/content/learning-paths/cross-platform/simd-on-rust/_index.md
@@ -16,6 +16,8 @@ learning_objectives:
 prerequisites:
     - An Arm-based computer with recent versions of a C compiler (Clang or GCC) and a Rust compiler installed
 
+generate_summary_faq: true
+
 author: Konstantinos Margaritis
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md b/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md
index e55d43e8b3..f159f77ccf 100644
--- a/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md
+++ b/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md
@@ -19,6 +19,9 @@ prerequisites:
   - Basic familiarity with ExecuTorch or PyTorch
   - Optionally, an Android device with Armv9 and SME2 support for on-device testing (if used, configure power management settings to ensure consistent performance measurements)
     
+
+generate_summary_faq: true
+
 author: Jason Zhu, Tyler Mullenbach, Damien Dooley 
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md b/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md
index 04c94b8e14..4741ec7517 100644
--- a/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md
+++ b/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md
@@ -19,6 +19,8 @@ prerequisites:
   - Basic knowledge of communication protocols (MQTT, HTTP, and others)
   - Familiarity with edge-cloud architectures and data-flow orchestration
 
+generate_summary_faq: true
+
 author: Tinkerblox
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/topdown-compare/_index.md b/content/learning-paths/cross-platform/topdown-compare/_index.md
index 044f20ac10..6b5d1abb24 100644
--- a/content/learning-paths/cross-platform/topdown-compare/_index.md
+++ b/content/learning-paths/cross-platform/topdown-compare/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - Access to Arm Neoverse V2 and Intel x86 Linux systems to run the code example
     - Basic understanding of CPU pipeline concepts and performance bottlenecks
 
+generate_summary_faq: true
+
 author:
     - Jason Andrews
 
diff --git a/content/learning-paths/cross-platform/vectorization-comparison/_index.md b/content/learning-paths/cross-platform/vectorization-comparison/_index.md
index 9b056affba..60938e38f3 100644
--- a/content/learning-paths/cross-platform/vectorization-comparison/_index.md
+++ b/content/learning-paths/cross-platform/vectorization-comparison/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - Familiarity with vector extensions, SIMD programming, and compiler intrinsics
     - Access to Linux systems with Neon and SVE support
 
+generate_summary_faq: true
+
 author:
     - Jason Andrews
 
diff --git a/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md b/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md
index 52739047e5..112b87e586 100644
--- a/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md
+++ b/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md
@@ -13,6 +13,8 @@ learning_objectives:
 prerequisites:
     - An Arm computer running Linux and a recent version of Clang or the GNU compiler (gcc) installed.
 
+generate_summary_faq: true
+
 author: Konstantinos Margaritis
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md b/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
index 86099b8ad1..fe7f754cf5 100644
--- a/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
+++ b/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
@@ -19,6 +19,9 @@ prerequisites:
     - An installation of [Visual Studio](/install-guides/vs-woa/).
     - An installation of [Git](/install-guides/git-woa/).
   
+
+generate_summary_faq: true
+
 author: Przemyslaw Wirkus
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/woa_azure/_index.md b/content/learning-paths/cross-platform/woa_azure/_index.md
index ea70d41434..89d52c9b08 100644
--- a/content/learning-paths/cross-platform/woa_azure/_index.md
+++ b/content/learning-paths/cross-platform/woa_azure/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - An Azure Cloud account.
     - An RDP client to connect to your Windows on Arm instance.  For more info on RDP clients, see [Remote Desktop clients for Remote Desktop Services and remote PCs](https://learn.microsoft.com/en-us/windows-server/remote/remote-desktop-services/clients/remote-desktop-clients) to get started.
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md b/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
index 290a354ce7..3069941cc7 100644
--- a/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
+++ b/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - At least two local Cortex-A devices running Linux, such as Raspberry Pi 4 or Pi 5. You can also use Arm servers or cloud instances
     - Experience with ROS 2 applications
 
+generate_summary_faq: true
+
 author: 
     - Odin Shen
     - William Liang
diff --git a/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md b/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
index 45d8cdaa2e..fd980bb6b9 100644
--- a/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - Basic understanding of System on Chip design
     - A 'Zybo Z7-10' development board 
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md b/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
index 82d729997b..28dad0b0f8 100644
--- a/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
@@ -20,6 +20,8 @@ prerequisites:
     - A development machine running macOS on Apple Silicon with Visual Studio Code installed
     - An AWS account or access to an Arm-based cloud instance for native Arm compilation
 
+generate_summary_faq: true
+
 author: Gabriel Peterson
 
 skilllevels: Advanced
diff --git a/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md b/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
index 947bcf1751..7738cda490 100644
--- a/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
@@ -20,6 +20,8 @@ prerequisites:
     - A [PIR sensor](https://www.amazon.com/HiLetgo-HC-SR501-Infrared-Sensor-Arduino/dp/B07KZW86YR/ref=sr_1_3?keywords=pir+sensor&qid=1698432931&sr=8-3) for detecting motion
     - A [peizo-electric buzzer](https://www.amazon.com/mxuteuk-Electronic-Computers-Printers-Components/dp/B07VK1GJ9X/ref=sr_1_4?crid=2FAXYI17HZKDB&keywords=piezo+buzzer&qid=1698432968&sprefix=peizo%2Caps%2C148&sr=8-4) for signaling motion
 
+generate_summary_faq: true
+
 author: Michael Hall
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/armds/_index.md b/content/learning-paths/embedded-and-microcontrollers/armds/_index.md
index c1f0d5a3cc..f8f36b5112 100644
--- a/content/learning-paths/embedded-and-microcontrollers/armds/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/armds/_index.md
@@ -14,6 +14,8 @@ learning_objectives:
 prerequisites:
     - Some familiarity with embedded programming is assumed
 
+generate_summary_faq: true
+
 author: Ronan Synnott
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/asm/_index.md b/content/learning-paths/embedded-and-microcontrollers/asm/_index.md
index 84d2584eb0..b6214317d3 100644
--- a/content/learning-paths/embedded-and-microcontrollers/asm/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/asm/_index.md
@@ -4,6 +4,8 @@ title: Write Arm Assembler functions
 minutes_to_complete: 60
 description: Learn how to write mixed C and assembly programs for Cortex-M microcontrollers using Keil MDK, following Arm Procedure Call Standard conventions.
 
+generate_summary_faq: true
+
 author: Ronan Synnott
 
 who_is_this_for: This is an introductory topic for software developers who are interested in programming microcontrollers with C/Assembly.
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
index b09108b413..6f732a3e7c 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
@@ -18,6 +18,8 @@ prerequisites:
     - A Linux machine with root access
     - Some familiarity with embedded Linux
 
+generate_summary_faq: true
+
 author: Michael Hall
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
index de36c5ccab..0a660c0194 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - An Arm Virtual Hardware account
     - Some familiarity with embedded Linux
 
+generate_summary_faq: true
+
 author: Michael Hall
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
index dbb782b672..74657a0cde 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
@@ -16,6 +16,8 @@ learning_objectives:
 prerequisites:
     - Some familiarity with embedded programming is assumed
 
+generate_summary_faq: true
+
 author: Ronan Synnott
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
index 796dab64d5..3af79edfdf 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - Some familiarity with AI/ML software development 
     - An Amazon Web Services(AWS) [account](https://aws.amazon.com/) to subscribe [Arm Virtual Hardware](https://aws.amazon.com/marketplace/pp/prodview-urbpq7yo5va7g) Amazon Machine Image(AMI)
 
+generate_summary_faq: true
+
 author: Liliya Wu
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
index e46b2ea320..0d8540fa6b 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
@@ -14,6 +14,8 @@ prerequisites:
     - A valid [AWS](https://aws.amazon.com/) account
     - Some familiarity with Python
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md b/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
index 46927e2945..a471947503 100644
--- a/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
@@ -20,6 +20,8 @@ prerequisites:
     - A machine with Python 3 and Visual Studio Code installed
     - An active Azure account with sufficient permissions to create resources (such as IoT Hub, Functions, and Cosmos DB)
 
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md b/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md
index cdfc0afdef..312ba5e0f8 100644
--- a/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md
@@ -17,6 +17,8 @@ learning_objectives:
 prerequisites:
     - Some familiarity with embedded programming is assumed
 
+generate_summary_faq: true
+
 author: Ronan Synnott
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md b/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
index e7b080d3b7..d0d6b4ba52 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - A valid account with [Arm Virtual Hardware](https://app.avh.arm.com/login)
     - An Arm Linux host machine (if you want to build your own runtime and container image)
 
+generate_summary_faq: true
+
 author: Basma El Gaabouri
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md
index d11be462bb..b42fc46a2c 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md
@@ -14,6 +14,8 @@ prerequisites:
     - An installation of [Arm Keil MDK](/install-guides/mdk) or [Arm Development Studio](/install-guides/armds) (MDK recommended)
     - Some familiarity with CMSIS is assumed
 
+generate_summary_faq: true
+
 author: Ronan Synnott
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md
index cec00d4064..811f41e73b 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - Installation of [Arm Keil Studio for VS Code](/install-guides/keilstudio_vs)
     - Some familiarity with CMSIS is assumed
 
+generate_summary_faq: true
+
 author: Ronan Synnott
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md b/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
index cfb93d6a0f..65da1e5235 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
@@ -19,6 +19,8 @@ prerequisites:
     - Prior exposure to CMSIS-DSP.
     - Python installed on your machine.
 
+generate_summary_faq: true
+
 author: Christophe Favergeon
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md b/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
index 2ee1c4fd28..37aa7cf0a1 100644
--- a/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
@@ -16,6 +16,8 @@ learning_objectives:
 prerequisites:
     - Basic knowledge and familiarity with Cortex-M processors.
 
+generate_summary_faq: true
+
 author: Uma Ramalingam
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md b/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
index a27ab3b219..e2f5b73aaf 100644
--- a/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
@@ -14,6 +14,8 @@ learning_objectives:
 prerequisites:
     - Basic familiarity with Keil MDK
 
+generate_summary_faq: true
+
 author: Ronan Synnott
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md b/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
index e4fd04b169..66292c2e24 100644
--- a/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
@@ -15,6 +15,8 @@ learning_objectives:
 prerequisites:
     - Basic familiarity with Python and the command line
 
+generate_summary_faq: true
+
 author:
     - Kavya Sri Chennoju
     - Annie Tallund
diff --git a/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md b/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md
index 79c2841558..f3d4aa9827 100644
--- a/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - Basic familiarity with command-line tools
     - (Optional) A Raspberry Pi for testing a full device-to-device (D2D) setup
 
+generate_summary_faq: true
+
 author: 
     - Annie Tallund
     - Kavya Sri Chennoju
diff --git a/content/learning-paths/embedded-and-microcontrollers/docker/_index.md b/content/learning-paths/embedded-and-microcontrollers/docker/_index.md
index e5b4fbf236..7796fa1c0c 100644
--- a/content/learning-paths/embedded-and-microcontrollers/docker/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/docker/_index.md
@@ -14,6 +14,8 @@ learning_objectives:
 
 prerequisites:
 
+generate_summary_faq: true
+
 author: Ronan Synnott
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/edge/_index.md b/content/learning-paths/embedded-and-microcontrollers/edge/_index.md
index 1a667cb3dd..7c0eeeee84 100644
--- a/content/learning-paths/embedded-and-microcontrollers/edge/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/edge/_index.md
@@ -19,6 +19,8 @@ prerequisites:
   - The [Arduino IDE](/install-guides/arduino-pico/) with the RP2040 board support package installed on your computer.
   - An [Arduino Nano RP2040 Connect board](https://store.arduino.cc/products/arduino-nano-rp2040-connect-with-headers).
 
+generate_summary_faq: true
+
 author: Bright Edudzi Gershon Kordorwu
 ### Tags
 skilllevels: Introductory
diff --git a/content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/_index.md b/content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/_index.md
index 40957ee28b..748e84fe0e 100644
--- a/content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/_index.md
@@ -24,6 +24,8 @@ prerequisites:
     - An SSH client and familiarity with the Linux command line
     - Basic understanding of ML concepts
 
+generate_summary_faq: true
+
 author: Doug Anson
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md b/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md
index bdd760c3a0..aab03554e8 100644
--- a/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - Familiarity with C programming on microcontrollers
     - STM32 B-L475E-IOT01A2 board
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/intro/_index.md b/content/learning-paths/embedded-and-microcontrollers/intro/_index.md
index 2dc3dbaaa5..ec441b826f 100644
--- a/content/learning-paths/embedded-and-microcontrollers/intro/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/intro/_index.md
@@ -14,6 +14,8 @@ learning_objectives:
 prerequisites:
     - None
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md b/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
index 1adc728422..e75e8c348d 100644
--- a/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
@@ -18,6 +18,8 @@ prerequisites:
     - A Linux computer
 
 
+generate_summary_faq: true
+
 author: Dominica Abena O. Amanfo
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md b/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
index d32270b5e3..5b2eee15ee 100644
--- a/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - Some familiarity with embedded programming
     - An AWS account (required for Arm Virtual Hardware)
 
+generate_summary_faq: true
+
 author: Ronan Synnott
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md b/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
index 82ecff78f1..269efa37d0 100644
--- a/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - A microSD card (64GB UHS-1 or larger is recommended)
     - A MIPI CSI-2 camera, with a 22 pin connector on at least one end
 
+generate_summary_faq: true
+
 author: Gabriel Peterson
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md b/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
index fb4720d580..e296fa793e 100644
--- a/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - Some familiarity with embedded programming is assumed
     - An [Arm Account](https://developer.arm.com/register) is required
 
+generate_summary_faq: true
+
 author: Christopher Seidl 
 
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md b/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
index 2aa2f64c88..4a233a048b 100644
--- a/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
@@ -20,6 +20,8 @@ prerequisites:
     - A USB-C cable for the board's **DBG** serial connection.
     - A USB-C power supply/cable for the board's **POWER** port.
 
+generate_summary_faq: true
+
 author: Waheed Brown
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md b/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md
index f2663baf57..ce32ec3a09 100644
--- a/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - Basic understanding of Assembly and C programming.
    
 
+generate_summary_faq: true
+
 author: Qixiang Xu
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md b/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md
index ad5f76c7f6..9d169547a0 100644
--- a/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md
@@ -16,6 +16,8 @@ learning_objectives:
 prerequisites:
     - A Raspberry Pi 5 running Raspberry Pi OS.
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/migration/_index.md b/content/learning-paths/embedded-and-microcontrollers/migration/_index.md
index 6eeeb5d1a2..f711378403 100644
--- a/content/learning-paths/embedded-and-microcontrollers/migration/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/migration/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - Knowledge about building workflows
     - Access to an aarch64 or x86_64 machine running Linux
 
+generate_summary_faq: true
+
 author: Kasper Mecklenburg
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md b/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md
index 756d7e1e53..0017708d39 100644
--- a/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - Some familiarity with embedded programming
     - A Linux host machine running Ubuntu
 
+generate_summary_faq: true
+
 author: Ronan Synnott
 
 ### RS: Learning Path hidden until AWS instance updated
diff --git a/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md b/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md
index 11dafb081a..591d0d4f22 100644
--- a/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md
@@ -8,6 +8,8 @@ armips:
 - Ethos-U
 - Corstone
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 learning_objectives:
diff --git a/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md b/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md
index bbd5baab94..0726c6a92b 100644
--- a/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md
@@ -14,6 +14,8 @@ learning_objectives:
 prerequisites:
     - Some familiarity with embedded debug
 
+generate_summary_faq: true
+
 author: Ronan Synnott
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md b/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
index bd639809b4..680c6561bb 100644
--- a/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
@@ -18,6 +18,8 @@ prerequisites:
     - Basic knowledge of Machine Learning concepts
     - A host computer to compile ExecuTorch libraries
 
+generate_summary_faq: true
+
 author: 
 - Waheed Brown
 - Fidel Makatia Omusilibwa
diff --git a/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md b/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
index 420b4203c2..9e5b944008 100644
--- a/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - A good understanding of [CMSIS-Packs](https://open-cmsis-pack.github.io/Open-CMSIS-Pack-Spec/main/html/index.html).
     - A CMSIS-Pack that contains device support and was created for CMSIS v5.
 
+generate_summary_faq: true
+
 author: Christopher Seidl
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md b/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md
index afdee0caec..0b44e3e597 100644
--- a/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - A CMSIS v5 based project.
     - A basic understanding of the CMSIS-Pack system.
 
+generate_summary_faq: true
+
 author: Christopher Seidl
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md b/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md
index 428dfaf822..9be7d3e78f 100644
--- a/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md
@@ -19,6 +19,8 @@ prerequisites:
   - Electronic components (breadboard, LEDs, resistors, jumper wires) for GPIO testing
   - Familiarity with Python programming, Raspberry Pi GPIO pinout, and basic electronics
 
+generate_summary_faq: true
+
 author: Fidel Makatia Omusilibwa
 
 skilllevels: Introductory
diff --git a/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md b/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md
index 25764f7cd6..b0c5ad859d 100644
--- a/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md
@@ -20,6 +20,8 @@ prerequisites:
     - A microSD card with at least 16GB of storage
     - A Linux compatible USB microphone and USB speakers or a USB audio device with a microphone and speakers
 
+generate_summary_faq: true
+
 author: Gabriel Peterson
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
index f26cad441f..a95e5009f9 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
@@ -20,6 +20,8 @@ prerequisites:
     - An Arm Linux machine or an [Arm cloud instance](/learning-paths/servers-and-cloud-computing/csp/).
     - A Raspberry Pi 5.
 
+generate_summary_faq: true
+
 author: Annie Tallund
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
index c75e157b25..f6065f01c0 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
@@ -18,6 +18,8 @@ prerequisites:
     - A Raspberry Pi 3 or 4 board
 
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md
index 27fce11cf8..4eac210275 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - A Raspberry Pi 4 board
     - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider.
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
index 71497432b9..bf1ac75005 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - Raspberry Pi Pico board.
     - Raspberry Pi 3, 4, 400, or 5 as a development computer.
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md b/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
index c6a4d74023..e71fc958bd 100644
--- a/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
@@ -20,6 +20,8 @@ prerequisites:
     - Arm-based Linux target device (such as a Raspberry Pi, BeagleBone, or similar board) with Secure Shell (SSH) access
     - A host machine that meets [Buildroot system requirements](https://buildroot.org/downloads/manual/manual.html#requirement)
 
+generate_summary_faq: true
+
 author: Yahya Abouelseoud
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md b/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
index 6a78c8682d..a9fde867a3 100644
--- a/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - Familiarity with C programming on microcontrollers
     - STM32 B-L475E-IOT01A2 board
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md b/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md
index ce7b75065d..8a640a45f6 100644
--- a/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 - Some familiarity with embedded C programming
 - A machine running Ubuntu Linux
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 test_images:
diff --git a/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md b/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md
index 1f91e67355..16cab88242 100644
--- a/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md
@@ -19,6 +19,8 @@ prerequisites:
   - Completion of the Learning Path [Introduction to TinyML on Arm using PyTorch and ExecuTorch](/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/)
   - An x86 Linux host machine or VM running Ubuntu 22.04 or later
 
+generate_summary_faq: true
+
 author: Dominica Abena O. Amanfo
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md b/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md
index 65540f0e67..73d602e923 100644
--- a/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md
@@ -18,6 +18,8 @@ prerequisites:
     - Comfortable with Windows 
     - NXP LPCXpresso55S69 board
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md b/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
index dc976ecfc0..ccea6fa53a 100644
--- a/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
@@ -25,6 +25,8 @@ prerequisites:
     - PETG filament. Others can work, but PETG allows some flex without the risk of snapping
     
 
+generate_summary_faq: true
+
 author: Gabriel Peterson
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md b/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
index 3be98ec17e..43198ce4c1 100644
--- a/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
@@ -7,6 +7,8 @@ description: Learn how to debug microcontrollers using µVision with basic run/s
 minutes_to_complete: 90
 # Always measured in minutes. Should be an integer, to complete the learning path (not read it).
 
+generate_summary_faq: true
+
 author: Christopher Seidl
 
 who_is_this_for: >
diff --git a/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md b/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
index 403455b359..6a58c14d3d 100644
--- a/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
@@ -18,6 +18,8 @@ prerequisites:
     - Install [uv2csolution](https://arm-software.github.io/MDK-Toolbox/01_installation/) for the command line flow.
     - The &micro;Vision project must use Arm Compiler 6 as the default toolchain. Arm Compiler 5 is not supported.
 
+generate_summary_faq: true
+
 author: Christopher Seidl
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md b/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
index ba6325bd0b..aa26cf6139 100644
--- a/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
@@ -19,6 +19,8 @@ prerequisites:
     - A basic understanding of the [development tools for Arm Cortex-M](https://developer.arm.com/Tools%20and%20Software/)
     - Command line access to your machine
 
+generate_summary_faq: true
+
 author: Christopher Seidl
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md b/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md
index 78bade2a50..c11033ba91 100644
--- a/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md
@@ -18,6 +18,8 @@ prerequisites:
     - A Linux or macOS computer with Python 3 installed
 
 
+generate_summary_faq: true
+
 author: Waheed Brown
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md b/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md
index 9345b95d4a..7e2a64a338 100644
--- a/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - Some familiarity with embedded Linux.
     - A linux machine running Ubuntu 22.04 with at least 60 GB of disk space.
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md b/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md
index 9242d47167..f588f57f20 100644
--- a/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md
@@ -20,6 +20,8 @@ prerequisites:
     - A USB-C cable.
     - An x86 Linux machine, or a Mac running macOS.
 
+generate_summary_faq: true
+
 author:
     - Chaodong Gong
     - Alex Su
diff --git a/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md b/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md
index 3fbd7577e5..c0da032f21 100644
--- a/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 - Some familiarity with embedded C programming
 - A Linux machine running Ubuntu, or an AWS account to use [Arm Virtual Hardware](https://www.arm.com/products/development-tools/simulation/virtual-hardware)
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 test_images:
diff --git a/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md b/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md
index 17958ae66e..9b52e4bd5a 100644
--- a/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md
@@ -20,6 +20,8 @@ prerequisites:
     - A Cortex-M development board
     - Windows 10+ (64-bit), macOS with Homebrew, or Linux (preferably Ubuntu 20.04+)
 
+generate_summary_faq: true
+
 author: 
     - Ayoub Bourjilat
     - Odin Shen
diff --git a/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md b/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md
index e4697b98c3..caca4ec0df 100644
--- a/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md
+++ b/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md
@@ -18,6 +18,8 @@ prerequisites:
     - A ChromeOS device with the Linux development environment enabled. The Lenovo Chromebook Plus 14 is recommended. 
     - Basic knowledge of the Linux command line
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md
index 4c917ffa39..e6ad66f867 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md
@@ -19,6 +19,8 @@ prerequisites:
     - Experience with Python scripting and virtual environments
     - Basic understanding of reinforcement learning concepts (rewards, policies, episodes)
 
+generate_summary_faq: true
+
 author:
     - Johnny Nunez
     - Odin Shen
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md
index ac01762ed0..52b8698bb7 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md
@@ -21,6 +21,8 @@ prerequisites:
     - Experience building software from source using CMake and make
 
 
+generate_summary_faq: true
+
 author: Odin Shen
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md
index ac8e6828bd..1798e1fc87 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md
@@ -16,6 +16,8 @@ learning_objectives:
 prerequisites:
     - An NVIDIA DGX Spark system with at least 15 GB of available disk space
 
+generate_summary_faq: true
+
 author: Odin Shen
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md
index 84b60e7996..6d1744280e 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md
@@ -18,6 +18,8 @@ prerequisites:
     - An NVIDIA DGX Spark system with at least 15 GB of available disk space
     - A USB microphone for audio input
 
+generate_summary_faq: true
+
 author: Odin Shen
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/docker-models/_index.md b/content/learning-paths/laptops-and-desktops/docker-models/_index.md
index c2577deaec..33e6e3b67d 100644
--- a/content/learning-paths/laptops-and-desktops/docker-models/_index.md
+++ b/content/learning-paths/laptops-and-desktops/docker-models/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - Basic understanding of Docker CLI and concepts.
     - Familiarity with LLM concepts.
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/electron/_index.md b/content/learning-paths/laptops-and-desktops/electron/_index.md
index ed96d99677..e878a43b89 100644
--- a/content/learning-paths/laptops-and-desktops/electron/_index.md
+++ b/content/learning-paths/laptops-and-desktops/electron/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - Node.js for Arm64. You can find the [Node.js installer](https://nodejs.org/dist/v20.10.0/node-v20.10.0-arm64.msi).
     - Any code editor; we recommend using [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user).
 
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md b/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md
index f603d2583c..3fd42c6465 100644
--- a/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md
+++ b/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - A GitHub account. 
     - Familiarity with GitHub Actions.
 
+generate_summary_faq: true
+
 author: 
     - Pareena Verma
 
diff --git a/content/learning-paths/laptops-and-desktops/hyper-v/_index.md b/content/learning-paths/laptops-and-desktops/hyper-v/_index.md
index 02305a24f0..18fa19c149 100644
--- a/content/learning-paths/laptops-and-desktops/hyper-v/_index.md
+++ b/content/learning-paths/laptops-and-desktops/hyper-v/_index.md
@@ -13,6 +13,8 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 with [Hyper-V](/install-guides/hyper-v/) installed.
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/intro/_index.md b/content/learning-paths/laptops-and-desktops/intro/_index.md
index f9fb290aad..884811bf22 100644
--- a/content/learning-paths/laptops-and-desktops/intro/_index.md
+++ b/content/learning-paths/laptops-and-desktops/intro/_index.md
@@ -14,6 +14,8 @@ learning_objectives:
 prerequisites:
     - Nothing
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 
diff --git a/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md b/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md
index 7c6c7b6263..1cb93acc24 100644
--- a/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md
+++ b/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 - Xcode command line tools installed
 - Basic familiarity with using the Terminal and command-line tools
 
+generate_summary_faq: true
+
 author: Jett Zhou
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md b/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md
index 33fe3de5dd..f730d83392 100644
--- a/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md
+++ b/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md
@@ -14,6 +14,8 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md b/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md
index cd44279f19..52bce4f37e 100644
--- a/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md
+++ b/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 - Basic knowledge of how MTE works. Refer to the [Learn about Memory Tagging Extension Learning Path](/learning-paths/mobile-graphics-and-gaming/mte/)
 - Knowledge of how a dynamic memory allocator can be implemented. Refer to [Write a Dynamic Memory Allocator Learning Path](/learning-paths/cross-platform/dynamic-memory-allocator/).
 
+generate_summary_faq: true
+
 author: David Spickett
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md b/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
index 53f38c2447..eabeb91a07 100644
--- a/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
+++ b/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - A Pinebook Pro laptop
     - A microSD card (8GB or greater; class 10 or faster)
 
+generate_summary_faq: true
+
 author: Gabriel Peterson
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md b/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
index 442dba508f..18e1a1e0be 100644
--- a/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
+++ b/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - Hugging Face account and access token
     - NVIDIA DGX Spark workstation
 
+generate_summary_faq: true
+
 author: Michael Hall
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md b/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
index db4f66a3bd..c034b6a5a4 100644
--- a/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
+++ b/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - A DockerHub account. You can [set up a free DockerHub account](https://hub.docker.com/signup).
     - A GitHub account. You can [sign up for GitHub](https://github.com/signup).
 
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/_index.md b/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
index 4e81727e43..9a15c3b1ff 100644
--- a/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
@@ -14,6 +14,8 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm machine such as the Lenovo Thinkpad X13s, or an [Azure virtual machine](/learning-paths/cross-platform/woa_azure/).
 
+generate_summary_faq: true
+
 author: Koki Mitsunami
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md b/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md
index 96d7f78f99..566f02f323 100644
--- a/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11
     - A code editor such as [Visual Studio Code for Windows on Arm](https://code.visualstudio.com/docs/?dv=win32arm64user) 
 
+generate_summary_faq: true
+
 author: Ruifeng Wang
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md b/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
index d6bff51118..b44d9ce777 100644
--- a/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
@@ -16,6 +16,8 @@ learning_objectives:
 prerequisites:
     - An Arm Linux system with KVM support and a minimum of 8GB RAM and 50GB free disk space
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md b/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
index 20a6fe8d8c..f6f7d155ca 100644
--- a/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
@@ -14,6 +14,8 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11.
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md b/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
index 97fc28f0af..7a412418ef 100644
--- a/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
@@ -17,6 +17,9 @@ prerequisites:
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is suitable.
     - Visual Studio 2022 with Arm build tools. [Refer to this guide for the installation steps](https://developer.arm.com/documentation/102528/0100/Install-Visual-Studio).
     
+
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md b/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md
index a2b2d02a3e..c754b50a5c 100644
--- a/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - '[Qt framework](https://www.qt.io/) or [Qt for Open Source Development](https://www.qt.io/download-open-source)'
 
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md b/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
index 18cad09cec..2cda7edd89 100644
--- a/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - .NET 8 SDK for [arm64](https://dotnet.microsoft.com/en-us/download/dotnet/thank-you/sdk-8.0.100-windows-arm64-installer).
     - Any code editor, we recommend using [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user).
 
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
index 18737fd53e..227fbec10c 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - A Windows-on-Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows-on-Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - Any code editor. Visual Studio Code is suitable.
 
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md
index f0dbbb012a..c8d059813e 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is suitable.
     - Completion of the [Create IoT applications with Windows on Arm and AWS IoT Core](/learning-paths/laptops-and-desktops/win_aws_iot/) Learning Path.
 
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md
index 24220f983b..3b4ace477f 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md
@@ -18,6 +18,8 @@ prerequisites:
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is suitable.
     - Completion of the [Create IoT applications with Windows on Arm and AWS IoT Core](/learning-paths/laptops-and-desktops/win_aws_iot/) Learning Path.
 
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md
index 80934cd32c..a31da07551 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is suitable.
     - Completion of the [Create IoT applications with Windows on Arm and AWS IoT Core](/learning-paths/laptops-and-desktops/win_aws_iot/) Learning Path.
 
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md
index 4ee34f9e3c..2b9d85a305 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is suitable.    
     - Completion of the [Use AWS Lambda for IoT applications](/learning-paths/laptops-and-desktops/win_aws_iot_lambda/) Learning Path.
 
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/win_cef/_index.md b/content/learning-paths/laptops-and-desktops/win_cef/_index.md
index 51a888d6b6..1a5c50d769 100644
--- a/content/learning-paths/laptops-and-desktops/win_cef/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_cef/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - Visual Studio 2022.
 
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/win_forms/_index.md b/content/learning-paths/laptops-and-desktops/win_forms/_index.md
index 899069712c..3b9259b4f0 100644
--- a/content/learning-paths/laptops-and-desktops/win_forms/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_forms/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - Visual Studio 2022 with .NET Desktop Development workload
 
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/win_net/_index.md b/content/learning-paths/laptops-and-desktops/win_net/_index.md
index 4e185c9ae7..47350d4c40 100644
--- a/content/learning-paths/laptops-and-desktops/win_net/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_net/_index.md
@@ -13,6 +13,8 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/win_net8/_index.md b/content/learning-paths/laptops-and-desktops/win_net8/_index.md
index 3a97f70a62..e31f6de8f4 100644
--- a/content/learning-paths/laptops-and-desktops/win_net8/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_net8/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - .NET 8 SDK for [x64](https://dotnet.microsoft.com/en-us/download/dotnet/thank-you/sdk-8.0.100-windows-x64-installer) and [arm64](https://dotnet.microsoft.com/en-us/download/dotnet/thank-you/sdk-8.0.100-windows-arm64-installer).
     - Any code editor, we recommend using [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user).
 
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md b/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md
index f73d38bcb2..0c7ab00db1 100644
--- a/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - Visual Studio 2022 with .NET Multi-platform App UI development and Universal Windows Platform development installed.
 
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md b/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md
index 20798446c8..c7efd7058b 100644
--- a/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md
@@ -13,6 +13,8 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as a Lenovo Thinkpad X13 running Windows 11, or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
 
+generate_summary_faq: true
+
 author: Barbara Corriero
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md b/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md
index b99d6943d8..608a5cbc98 100644
--- a/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - Familiarity with C++ development and compiling programs from the command line
     - A Windows on Arm machine with [Visual Studio](/install-guides/vs-woa/) and the C++ desktop development tools installed
 
+generate_summary_faq: true
+
 author: Tom Dunkle
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/win_python/_index.md b/content/learning-paths/laptops-and-desktops/win_python/_index.md
index b5bbf99b3a..a554eb48ed 100644
--- a/content/learning-paths/laptops-and-desktops/win_python/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_python/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - Any code editor, we recommend using [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user).
     - Visual Studio 2022 with Arm build tools. [Refer to this guide for the installation steps](https://developer.arm.com/documentation/102528/0100/Install-Visual-Studio)
 
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/win_python_onnx/_index.md b/content/learning-paths/laptops-and-desktops/win_python_onnx/_index.md
index f4e05b13e0..9fe0e22375 100644
--- a/content/learning-paths/laptops-and-desktops/win_python_onnx/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_python_onnx/_index.md
@@ -19,6 +19,8 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/). 
     - Any code editor like [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user).    
 
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md b/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md
index 9892d02631..968ba3a4dc 100644
--- a/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - A valid [GitHub account](https://github.com/) to complete this Learning Path.
     
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md b/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md
index 825f7e454e..0698ae926c 100644
--- a/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md
@@ -16,6 +16,9 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).    
     - Refer to [Visual Studio 2022 with Arm build tools](/install-guides/vs-woa).
     
+
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/win_winui3/_index.md b/content/learning-paths/laptops-and-desktops/win_winui3/_index.md
index 4ff89128ed..8f5cafc395 100644
--- a/content/learning-paths/laptops-and-desktops/win_winui3/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_winui3/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - Visual Studio 2022 with .NET desktop development and Universal Windows Platform development installed.
 
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/win_wpf/_index.md b/content/learning-paths/laptops-and-desktops/win_wpf/_index.md
index 145c6345d9..9d96b79cbf 100644
--- a/content/learning-paths/laptops-and-desktops/win_wpf/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_wpf/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - Visual Studio 2022 with .NET desktop development installed.
 
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md b/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md
index 6c5d26aa1d..8de3cae8a6 100644
--- a/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - Visual Studio 2022 with .NET desktop development and Universal Windows Platform development installed.
 
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md b/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md
index f566170487..0966904605 100644
--- a/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md
@@ -14,6 +14,8 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11.
 
+generate_summary_faq: true
+
 author: Odin Shen
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md b/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md
index 458231ea0a..0ce64a21aa 100644
--- a/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - Valid GitHub account
     - Microsoft Azure account (if using virtual machine)
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md
index 799cd849a3..6cfe810369 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md
@@ -17,6 +17,8 @@ prerequisites:
   - A desktop or laptop running Windows on Arm.
   - Visual Studio 2022 Community Edition, WindowsPerf, WindowsPerf Visual Studio extension, and Windows Performance Analyzer (WPA) installed. 
 
+generate_summary_faq: true
+
 author: 
   - Nader Zouaoui
 
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf/_index.md
index a730a9bd29..2521cc0be3 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf/_index.md
@@ -14,6 +14,8 @@ learning_objectives:
 prerequisites:
     - Windows on Arm desktop or development machine
 
+generate_summary_faq: true
+
 author: Ronan Synnott
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md
index 9ed0724d13..4153bd1684 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - Windows on Arm desktop or development machine with [WindowsPerf installed](/install-guides/wperf)
     - Windows x86_64 desktop machine with [Visual Studio 2022 Community Edition](https://visualstudio.microsoft.com/vs/) installed.
 
+generate_summary_faq: true
+
 author: Przemyslaw Wirkus
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
index 8b877bf037..03d2615e52 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
@@ -14,6 +14,8 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm laptop with WindowsPerf, Windows Performance Analyzer (WPA), and the WPA plugin installed.
 
+generate_summary_faq: true
+
 author: Alaaeddine Chakroun
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/wsl2/_index.md b/content/learning-paths/laptops-and-desktops/wsl2/_index.md
index 5971e0fe31..2c7b07347d 100644
--- a/content/learning-paths/laptops-and-desktops/wsl2/_index.md
+++ b/content/learning-paths/laptops-and-desktops/wsl2/_index.md
@@ -19,6 +19,8 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11. 
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md b/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md
index 888f8f251a..5e59debb17 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - Knowledge of the Vulkan API.
     - A Vulkan application that creates and uses images. This Learning Path shows how to use an API Sample in the [Khronos Vulkan Samples repository](https://github.com/KhronosGroup/Vulkan-Samples/blob/main/scripts/README.adoc#generate-api-sample) as an example.
 
+generate_summary_faq: true
+
 author: Jose-Emilio Munoz-Lopez
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md b/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md
index b19e5ce4e4..ed69565b0f 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md
@@ -14,6 +14,8 @@ learning_objectives:
 prerequisites:
     - A computer running Arm Linux or macOS with Docker installed
 
+generate_summary_faq: true
+
 author: Arnaud de Grandmaison
 
 test_images:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md b/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
index ccc2bc5a4f..fc4ce3ee67 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
@@ -20,6 +20,8 @@ prerequisites:
     - Arm Performance Studio installed. Follow the [Arm Performance Studio install guide](/install-guides/ams) for instructions.
     - Android SDK Platform tools installed. Required for the Android Debug bridge (adb).
 
+generate_summary_faq: true
+
 author: Ronan Synnott
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md b/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md
index f34d5a7c0e..fb38d83666 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md
@@ -18,6 +18,8 @@ prerequisites:
     - Download and install Arm Performance Studio from [Product Download Hub](https://developer.arm.com/downloads/view/MOBST-PRO0). It is supported on Windows, Linux, and macOS host platforms.
     - Download and install [Android SDK Platform tools](https://developer.android.com/studio/releases/platform-tools.html). Required for [Android Debug bridge (adb)](https://developer.android.com/studio/command-line/adb).
 
+generate_summary_faq: true
+
 author: Julie Gaskin
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md
index 755fdd1bd3..4b11b7053d 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - An Android phone for testing, in Developer Mode, with USB cable for connection
     - Basic familiarity with Kotlin and Android app development
 
+generate_summary_faq: true
+
 author: Ben Clark
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
index f2fcaa8356..3f0acba0c2 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - Basic C++ knowledge
     - Android Studio with Android Emulator
 
+generate_summary_faq: true
+
 author: Éliás Bálint, Dawid Borycki, Steve Suzuki
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_neon/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_neon/_index.md
index 665b99c264..c5b7565460 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_neon/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_neon/_index.md
@@ -20,6 +20,8 @@ prerequisites:
     - A x86_64 or Apple M1 development machine with Android Studio installed.
     - A 64-bit Arm powered smartphone running Android.
 
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md
index f7f92aa3a3..72bef3336e 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - A development machine with [Android Studio](https://developer.android.com/studio) installed. 
     - An Android smartphone.
 
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md
index c26bcdaf4d..42b567eaef 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md
@@ -17,6 +17,8 @@ prerequisites:
    - An Android smartphone.
    - Familiarity with OpenCV, review [Create Computer Vision Applications with OpenCV on Android Devices](/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/) before starting.
 
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
index 73b266258a..1eba1ed578 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - Familiarity with Android development concepts.
     - An Android smartphone.
 
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md
index 0dbe5be9d5..c1984ec277 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - Knowledge of [Neon](https://developer.arm.com/documentation/102474/latest)
     - Knowledge of [Scalable Vector Extension (SVE)](https://developer.arm.com/documentation/101726/4-0)
 
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
index acc2cb13d0..56db74c3b7 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
@@ -25,6 +25,8 @@ prerequisites:
     - Arm Performance Studio.
     - Python 3.10 or later.
 
+generate_summary_faq: true
+
 author:
     - Varun Chari
     - Albin Bernhardsson
diff --git a/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md b/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
index ccb0e28d32..60e30668c4 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 - An Android mobile device that has a Mali GPU with hardware ray tracing support.
 - A USB cable to connect the mobile device to your computer.
 
+generate_summary_faq: true
+
 author: Owen Wu
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md b/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md
index 63d046f5fb..a6a5eed980 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - A Windows x86_64 development machine with at least 16GB of RAM.
     - An Android phone with at least 8GB of RAM. This learning path was tested on Samsung Galaxy S24.
 
+generate_summary_faq: true
+
 author: Koki Mitsunami
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md b/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md
index aedcf69697..fde29d71d6 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md
@@ -20,6 +20,8 @@ prerequisites:
     - Basic knowledge of Modern Android Architecture. See [Modern Android App Architecture](https://developer.android.com/courses/pathways/android-architecture).
     - Basic knowledge of Kotlin programming language, including [Coroutines](https://kotlinlang.org/docs/coroutines-overview.html) and [Kotlin Flows](https://kotlinlang.org/docs/flow.html).
 
+generate_summary_faq: true
+
 author: Han Yin
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md b/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md
index 7432ef6110..18400d1c28 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md
@@ -22,6 +22,8 @@ prerequisites:
     - Java 17 JDK. Follow the steps in [Java 17 JDK](https://www.oracle.com/java/technologies/javase/jdk17-archive-downloads.html) to download and install JDK for host.
     - Python 3.10.
 
+generate_summary_faq: true
+
 author: 
     - Varun Chari
     - Pareena Verma
diff --git a/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md b/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md
index e189e65e7f..0fe535107e 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md
@@ -22,6 +22,8 @@ prerequisites:
     - Python 3.10 or later
     - A [Hugging Face](https://huggingface.co/) account with access to Meta Llama models
 
+generate_summary_faq: true
+
 author: Parichay Das
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md b/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md
index 4f9b979aff..ee4ba1faea 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md
@@ -19,6 +19,8 @@ prerequisites:
     - A USB cable to connect your computer to your Google Pixel 8.
     - Android Debug Bridge (adb) installed on your device. If needed, follow the steps in the [Android Debug Bridge](https://developer.android.com/tools/adb) documentation.
 
+generate_summary_faq: true
+
 author: Roberto Lopez Mendez
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/gemma4-kleidiai-sme2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/gemma4-kleidiai-sme2/_index.md
index 6969febd5c..9e2feca315 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/gemma4-kleidiai-sme2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/gemma4-kleidiai-sme2/_index.md
@@ -19,6 +19,8 @@ prerequisites:
     - A SME2 device (macOS M4 on Apple Silicon)
     - Git, Homebrew, and Xcode Command Line Tools
 
+generate_summary_faq: true
+
 author: Annie Tallund
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md
index 551b954c4c..6ed550d3a9 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md
@@ -14,6 +14,8 @@ prerequisites:
     - A game project that uses advanced rendering features (such as hardware ray tracing) that stretch the performance capabilities of everyday smartphones.
     - A development machine with Git installed.
 
+generate_summary_faq: true
+
 author: Julie Gaskin
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md
index 9e3b4d3257..b1af579458 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - Recent Android device, such as a mobile phone or tablet
     - Desktop computer capable of running Unity
 
+generate_summary_faq: true
+
 author: Joshua Marshall-Law
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md
index b9fbecd3f9..89ce4b186f 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md
@@ -13,6 +13,8 @@ prerequisites:
     - Familiarity with Godot
     - Familiarity with Arm Performance Studio tools
 
+generate_summary_faq: true
+
 author: Albin Bernhardsson, Julie Gaskin
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md b/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md
index b70082794e..bd3f17d53d 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md
@@ -14,6 +14,8 @@ prerequisites:
 - An Android mobile device that has a Mali GPU with hardware ray tracing support.
 - A USB cable to connect the mobile device to your computer.
 
+generate_summary_faq: true
+
 author: Owen Wu
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md b/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md
index cf1a0a868a..15c2b844a0 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md
@@ -11,6 +11,8 @@ learning_objectives:
 prerequisites:
     - None
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md b/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md
index 8cdfd61acf..4a407564cd 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - Basic understanding of quantization concepts for neural networks
     - (Optional) Access to an Arm CPU with SME2 support (Linux or Android) for hands-on verification steps
 
+generate_summary_faq: true
+
 author: Zenon Zhilong Xiu
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md b/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md
index 2c897bd210..10dba075f5 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - An x86_64 Linux machine running Ubuntu with approximately 500 MB of free space, or a docker daemon that can build and run a provided x86_64 Dockerfile.
     - An Android phone with support for i8mm (tested on Google Pixel 8 Pro).
 
+generate_summary_faq: true
+
 author: 
     - Pareena Verma
     - Joe Stech
diff --git a/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md b/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md
index ed9fd41666..deb3ff3a72 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md
@@ -14,6 +14,8 @@ prerequisites:
     - A development machine running Ubuntu or Debian Linux with `x86_64` architecture
     - An Android device with an Arm GPU
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ##### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md b/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md
index 9a9690030b..dbb9aa1a13 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 - An Arm64 Linux development machine 
 - An Android device that supports Arm SME2 architecture features - see this [list of devices with SME2 support](/learning-paths/cross-platform/multiplying-matrices-with-sme2/1-get-started/#devices)
 
+generate_summary_faq: true
+
 author: Jiaming Guo
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md b/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md
index 9058c10cf3..c1d31c697e 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md
@@ -16,6 +16,8 @@ prerequisites:
   - An x86_64 Linux host machine running Ubuntu, with at least 15 GB of free disk space
   - An Arm64 target system with support for SME or SME2 - see the Learning Path [Devices with native SME2 support](/learning-paths/cross-platform/multiplying-matrices-with-sme2/1-get-started/#devices)
 
+generate_summary_faq: true
+
 author: Qixiang Xu
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md b/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md
index 897c817a2e..91009496ec 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - A development machine running Ubuntu 22.04, with a CUDA-capable NVIDIA® GPU
     - CUDA Toolkit version 11.8 or later
 
+generate_summary_faq: true
+
 author: Annie Tallund
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md b/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md
index 30eb008802..b6fe39967c 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md
@@ -12,6 +12,8 @@ learning_objectives:
 prerequisites:
     - An AArch64 Linux development machine. Cloud instances can be used, refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ##### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md b/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md
index b3431499c4..d4200d48fc 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md
@@ -18,6 +18,8 @@ prerequisites:
     - A USB cable to connect your Google Pixel 8 to your desktop machine
     - Android Debug Bridge (adb) installed on your device. Follow the steps in https://developer.android.com/tools/adb to install Android SDK Platform Tools. The adb tool is included in this package.
 
+generate_summary_faq: true
+
 author: Roberto Lopez Mendez
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md b/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md
index ad838f597e..4f8f215c1c 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md
@@ -18,6 +18,8 @@ prerequisites:
     - Visual Studio with C++ game development tools
     - A C++ Unreal project (such as the Third Person template)
 
+generate_summary_faq: true
+
 author: 
 - Annie Tallund
 - Richard Burton
diff --git a/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md b/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md
index 256eaf6875..411181ca79 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md
@@ -20,6 +20,8 @@ prerequisites:
     - Visual Studio (with Desktop Development with C++ and .NET desktop build tools)
 
 
+generate_summary_faq: true
+
 author: Annie Tallund
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md b/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md
index 7f9ed7bd8e..19075e50fe 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md
@@ -19,6 +19,8 @@ prerequisites:
   - An Arm64 device such as a Raspberry Pi or Android smartphone
   - Android Studio (required only for the final deployment section)
 
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md b/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md
index f10f85d242..088e436e33 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md
@@ -14,6 +14,8 @@ prerequisites:
     - Understanding of vertex attributes.
     - Familiarity with Arm Frame Advisor (part of Arm Performance Studio).
 
+generate_summary_faq: true
+
 author:
     - Andrew Kilroy
     - Peter Harris
diff --git a/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md
index 011f29c2de..e185a806e5 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - Git, CMake, and Android Debug Bridge (ADB) installed on your host machine
     - An Android device with Arm SME2 support for running and profiling the executable
 
+generate_summary_faq: true
+
 author: Zenon Zhilong Xiu
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md
index d5e63aad63..3fff9361cf 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - Basic understanding of machine learning model inference
     - Familiarity with Android NDK and cross-compilation
 
+generate_summary_faq: true
+
 author: Zenon Zhilong Xiu
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md b/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md
index 8bb50b5ebd..71ab0ec630 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - Android Studio Profiler.
   
 
+generate_summary_faq: true
+
 author: Ben Clark
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md b/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md
index 9fc9f36906..fb78d49a88 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - Basic knowledge of Unity and programming concepts
     - The setup described in the Learning Path [Get started with Unity on Android](/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android)
 
+generate_summary_faq: true
+
 author: Joshua Marshall-Law
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md b/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md
index c196c74fdb..5b418f25a9 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - Basic PyTorch model training and evaluation experience
     - A development machine with Python 3.10+ and PyTorch installed that runs ExecuTorch
 
+generate_summary_faq: true
+
 author:
 - Richard Burton
 - Annie Tallund
diff --git a/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md b/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md
index a7d89a8f8f..8cefa6582f 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - Knowledge of the Vulkan API.
     - A Vulkan renderer. Most code is generic and should be easy to incorporate into any deferred PBR renderer.
 
+generate_summary_faq: true
+
 author: Iago Calvo Lista
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md b/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md
index b08030fb62..933ab7eced 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - If you wish to analyze your own applications you will need a supported Android device.
     - Some basic familiarity with Frame Advisor. Review the [Frame Advisor](/learning-paths/mobile-graphics-and-gaming/ams/fa/) section in [Get started with Arm Performance Studio for mobile](/learning-paths/mobile-graphics-and-gaming/ams/).
 
+generate_summary_faq: true
+
 author: Mark Thurman
 
 further_reading:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md
index bf761bd8ba..510fb8f170 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md
@@ -18,6 +18,8 @@ prerequisites:
     - A [HuggingFace](https://huggingface.co/) account.
     - An Android phone in [developer mode](https://developer.android.com/studio/debug/dev-options) and a cable to connect it to your development machine.
 
+generate_summary_faq: true
+
 author:
     - Nina Drozd
     - Gian Marco Iodice
diff --git a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md
index efca28426d..19eef9a4d3 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - A [Hugging Face](https://huggingface.co/) account
     - An Android phone in [developer mode](https://developer.android.com/studio/debug/dev-options) with at least 8 GB of RAM and a cable to connect it to your development machine
 
+generate_summary_faq: true
+
 author:
     - Adnan AlSinan
     - Pareena Verma
diff --git a/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md b/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md
index 7dd9539e84..45f39754f0 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - An ethernet connection
     - A mouse and keyboard connected to the Orange Pi
 
+generate_summary_faq: true
+
 author: Gabriel Peterson
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md b/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md
index e83fce3c76..1170a231c8 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - Familiarity with Unity and the Unity Profiler
     - Familiarity with Arm Performance Studio tools
 
+generate_summary_faq: true
+
 author: Julie Gaskin
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md b/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md
index 8f01dc7664..a29019d729 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md
@@ -18,6 +18,8 @@ prerequisites:
     - Desktop computer capable of running Unity
     - Unity version compatible with Unity Burst compiler 1.5 or later
 
+generate_summary_faq: true
+
 author: Ben Clark, Joshua Marshall-Law
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md b/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md
index d1f5732c2a..16cba88282 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md
@@ -15,6 +15,8 @@ prerequisites:
 - An Android mobile device that has a 64-bit processor and supports at least Android 8.
 - A USB cable to connect the mobile device to your computer.
 
+generate_summary_faq: true
+
 author: Arm
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md b/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md
index 56df416150..8c66d23ab8 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - A development machine with [Android Studio](https://developer.android.com/studio) installed.
     - A smartphone running Android with support for `i8mm` and `dotprod` instructions.
 
+generate_summary_faq: true
+
 author:
     - Shuheng Deng
     - Yiyang Fan
diff --git a/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md b/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md
index f346de0c4a..05059e4a97 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md
@@ -18,6 +18,8 @@ prerequisites:
     - This Learning Path was tested on a Vivo X300 Pro.
     - A development machine with [Android Studio](https://developer.android.com/studio) installed.
 
+generate_summary_faq: true
+
 author:
     - Arnaud de Grandmaison
     - Nina Drozd
diff --git a/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md b/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md
index 5abc5709e9..8453adedba 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md
@@ -19,6 +19,8 @@ prerequisites:
     - A working microphone for voice input.
     - Basic Python and command-line knowledge.
 
+generate_summary_faq: true
+
 author: Bhanu Arya
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md
index fbe072b627..f0354735be 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 
 
+generate_summary_faq: true
+
 author: Annie Tallund
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md b/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
index d46face880..d178d1f221 100644
--- a/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - Basic understanding of Python and prompt engineering.
     - Understanding of LLM fundamentals.
 
+generate_summary_faq: true
+
 author: Andrew Choi
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/aks/_index.md b/content/learning-paths/servers-and-cloud-computing/aks/_index.md
index 663fde582c..e4c7500608 100644
--- a/content/learning-paths/servers-and-cloud-computing/aks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/aks/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - An Azure account
     - A machine with [Terraform](/install-guides/terraform/), [Azure CLI](/install-guides/azure-cli), and [Kubectl](/install-guides/kubectl/) installed
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md b/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
index 72bd8dc6b4..57d9d21201 100644
--- a/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
@@ -19,6 +19,8 @@ prerequisites:
   - Basic understanding of data formats such as Parquet or ORC
   - Familiarity with Linux command-line operations
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
index 910d15913d..21ef4ea2dd 100644
--- a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - An [AWS account](https://aws.amazon.com/) with permission to launch Graviton4 (`c8g.4xlarge` or larger) instances
     - Basic familiarity with Linux and SSH
 
+generate_summary_faq: true
+
 author: Julien Simon
 
 # Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
index f5b6ee43b5..6416bc59fd 100644
--- a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - A [Google Cloud account](https://console.cloud.google.com/) with permission to launch Axion (`c4a-standard-16` or larger) instances
     - Basic familiarity with Linux and SSH
 
+generate_summary_faq: true
+
 author: Julien Simon
 
 # Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
index 1b2d3f59b2..ae29e74b1a 100644
--- a/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
@@ -21,6 +21,8 @@ prerequisites:
   - Basic understanding of Git and GitHub workflows
   - Familiarity with basic Linux command-line usage
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md b/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
index b0f17ea6de..399505be69 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - Access to an x86 and an Arm cloud instance (virtual machine).
     - Proficiency in C++ programming.
 
+generate_summary_faq: true
+
 author: Kieran Hejmadi
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md b/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
index b5a8484ec0..a8bd5fd4e5 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
@@ -17,6 +17,9 @@ prerequisites:
     - An AI-powered IDE such as VS Code, Copilot in VS Code, Kiro (IDE or CLI) or Codex
     - Basic familiarity with Docker and C/C++ development
     - Access to an Arm-based cloud instance or local Arm computer running Linux or macOS
+
+generate_summary_faq: true
+
 author: Joe Stech
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md b/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
index d743026940..350a0129d5 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
@@ -19,6 +19,8 @@ prerequisites:
     - Familiarity with Linux development environments and basic embedded or cloud deployment concepts
     - Experience building applications with GCC and CMake
 
+generate_summary_faq: true
+
 author: Daniel Schleicher
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md b/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
index 34a9e54b05..38e512b19f 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
@@ -17,6 +17,8 @@ learning_objectives:
 prerequisites:
   - Access to an Arm-based Linux system running Ubuntu, Debian, or CentOS.
 
+generate_summary_faq: true
+
 author: Geremy Cohen
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md b/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
index 0f520fb7fc..ebf536cb72 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
@@ -14,6 +14,8 @@ learning_objectives:
 prerequisites:
     - An Arm computer running Linux. A bare metal or cloud metal instance is best because they expose more counters. You can use a virtual machine (VM), but fewer counters may be available. These instructions have been tested on the `a1.metal` instance type.
 
+generate_summary_faq: true
+
 author: Julio Suarez
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md b/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
index 791749039f..b4cd8c75fb 100644
--- a/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - An Amazon Web Services (AWS) account
     - A local computer with Docker, AWS CLI, and AWS Copilot CLI installed
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md b/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
index dc5856ddcf..139e24573f 100644
--- a/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - An Amazon Web Services (AWS) [account](https://aws.amazon.com/)
     - A computer with [Terraform](/install-guides/terraform) installed
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
index a1a518139f..213ab24986 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - Azure CLI installed on your local machine - see the [Azure CLI install guide](/install-guides/azure-cli/)
     - An SSH key pair for authentication
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
index d0d91d66a2..b3aab4f6a7 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - A GitHub account.
     - A machine with [Terraform](/install-guides/terraform/),[Azure CLI](/install-guides/azure-cli), and [Kubectl](/install-guides/kubectl/) installed.
 
+generate_summary_faq: true
+
 author: Pranay Bakre
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
index ce5f0f11fa..84f5d4f83c 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - An Azure account
     - A computer with Terraform installed
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
index f49eecb3d3..2f62b922e7 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
@@ -19,6 +19,9 @@ prerequisites:
     - A [Microsoft Azure](https://azure.microsoft.com/) account with permission to create resources, including instances using Cobalt 100 processors
     - A Linux machine with [QEMU](https://www.qemu.org/download/) and the [Azure CLI](/install-guides/azure-cli/) installed and authenticated
     
+
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md b/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
index 3e18e61160..8744ee2ce4 100644
--- a/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
@@ -14,6 +14,8 @@ learning_objectives:
 prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an on-premise Arm server.
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md b/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
index 127574e7a1..1999927599 100644
--- a/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from an appropriate
   cloud service provider.
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 
diff --git a/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md b/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
index 5613cce72f..3e8956ae86 100644
--- a/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
@@ -23,6 +23,8 @@ prerequisites:
     - A system with with sufficient hardware performance counters to use the [TopDown](/install-guides/topdown-tool) methodology. This typically requires running on bare metal rather than a virtualized environment.
 
 
+generate_summary_faq: true
+
 author: Paschalis Mpeis
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md b/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
index fe8d987995..6ffb7664a8 100644
--- a/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
@@ -16,6 +16,8 @@ learning_objectives:
 prerequisites:
   - An Arm-based Linux system with [BOLT](/install-guides/bolt/) and [Linux Perf](/install-guides/perf/) installed
 
+generate_summary_faq: true
+
 author: Gayathri Narayana Yegna Narayanan
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/bolt/_index.md b/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
index 62ad8ac5e7..023207d5b3 100644
--- a/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - An Arm based system running Linux with [BOLT](/install-guides/bolt/) and [Linux Perf](/install-guides/perf/) installed. The Linux kernel should be version 5.15 or later. Earlier kernel versions can be used, but some Linux Perf features may be limited or not available. For [SPE](./bolt-spe) the version should be 6.14 or later.
     - (Optional) A second, more powerful Linux system to build the software executable and run BOLT.
 
+generate_summary_faq: true
+
 author: Jonathan Davies
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
index bf29a05cb4..9e29f53af0 100644
--- a/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
@@ -19,6 +19,8 @@ prerequisites:
   - Familiarity with [Docker](https://docs.docker.com/get-started/) and container concepts
   - A [GitHub account](https://github.com/join) to host your application repository
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
index 064994fafa..bbe81cc8da 100644
--- a/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
@@ -17,6 +17,8 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Familiarity with Cassandra architecture, replication, and [Cassandra partitioning and event-driven I/O](https://cassandra.apache.org/doc/stable/cassandra/architecture/)
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
index ccc83cd8a2..d7e54770f6 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
@@ -16,6 +16,8 @@ learning_objectives:
 prerequisites:
     - An AArch64 or x86_64 computer running Linux or macOS. You can use cloud instances, refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
 
+generate_summary_faq: true
+
 author:
     - Pareena Verma
     - Arnaud de Grandmaison
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
index 7d71f78977..12c69588e9 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
@@ -19,6 +19,8 @@ prerequisites:
     - Completion of the [Run an application in a Realm using the Arm Confidential Computing Architecture (CCA)](/learning-paths/servers-and-cloud-computing/cca-container/) Learning Path
     - Completion of the [Run an end-to-end Attestation Flow](/learning-paths/servers-and-cloud-computing/cca-essentials/) Learning Path
 
+generate_summary_faq: true
+
 author: Arnaud de Grandmaison
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
index 4902aebd5f..2dbfa458af 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - Completion of [Get Started with CCA Attestation and Veraison](/learning-paths/servers-and-cloud-computing/cca-veraison) Learning Path.
     - Completion of the [Run an application in a Realm using the Arm Confidential Computing Architecture (CCA)](/learning-paths/servers-and-cloud-computing/cca-container/) Learning Path.
 
+generate_summary_faq: true
+
 author: 
     - Arnaud de Grandmaison
     - Paul Howard
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
index ecf8975ba0..f8a11643c8 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
@@ -15,6 +15,8 @@ prerequisites:
   - An AArch64 or x86_64 computer running Linux or macOS. Cloud-based instances can also be used; see the [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/)
   - Completion of the [Run an end-to-end Attestation with Arm CCA and Trustee](/learning-paths/servers-and-cloud-computing/cca-trustee) Learning Path
 
+generate_summary_faq: true
+
 author:
   - Anton Antonov
 
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
index 14bfba12d5..dcc6686f72 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
@@ -16,6 +16,8 @@ prerequisites:
   - Completion of the [Get started with CCA attestation and Veraison](/learning-paths/servers-and-cloud-computing/cca-veraison) Learning Path
   - Completion of the [Run an end-to-end attestation flow with Arm CCA](/learning-paths/servers-and-cloud-computing/cca-essentials/) Learning Path
 
+generate_summary_faq: true
+
 author:
   - Anton Antonov
 
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
index c86b25869a..155f538235 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
@@ -14,6 +14,8 @@ prerequisites:
     - An [AWS account](/learning-paths/servers-and-cloud-computing/csp/aws/) with access to AWS services.
     - An x86 computer running Ubuntu or Arch Linux, authorized for AWS access. If you're using another build environment, you'll need to configure the toolchains for cross-compilation.
 
+generate_summary_faq: true
+
 author: Paul Howard
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md
index 00bd5df482..b36efc5d35 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md
@@ -19,6 +19,8 @@ prerequisites:
     - An Arm-based or x86 computer running Ubuntu. You can use a server instance from a cloud service provider of your choice.
 
 
+generate_summary_faq: true
+
 author: Paul Howard
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
index c78bed17a5..20d4267042 100644
--- a/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
@@ -23,6 +23,8 @@ prerequisites:
     [runners](https://circleci.com/docs/guides/execution-runner/runner-overview/)
 
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md b/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md
index ea22c2c97e..224cf75708 100644
--- a/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md
@@ -16,6 +16,8 @@ prerequisites:
   - A CircleCI account
   - Basic understanding of CircleCI workflows, jobs and resource classes
 
+generate_summary_faq: true
+
 author: Annie Tallund
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/clair/_index.md b/content/learning-paths/servers-and-cloud-computing/clair/_index.md
index bc66e670cb..3cb1b9bd3b 100644
--- a/content/learning-paths/servers-and-cloud-computing/clair/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/clair/_index.md
@@ -14,6 +14,8 @@ learning_objectives:
 prerequisites:
     - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an Arm server with recent versions of Docker and Go installed.
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
index 9cbd7743f0..97bb6f17db 100644
--- a/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
@@ -21,6 +21,8 @@ prerequisites:
   - Basic familiarity with [ClickHouse](https://clickhouse.com/)
   - Basic understanding of databases and SQL
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md b/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
index aa647ef87d..31fa72425a 100644
--- a/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
@@ -13,6 +13,8 @@ learning_objectives:
 prerequisites:
     - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an on-premise Arm server.
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
index a505aa3dc9..372af7a1cf 100644
--- a/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - A Microsoft Azure subscription with permissions to create virtual machines and networking resources
     - Basic familiarity with SSH
 
+generate_summary_faq: true
+
 author: Joe Stech
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md b/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
index 4dc1e25cdc..d0f56423cf 100644
--- a/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
@@ -14,6 +14,8 @@ prerequisites:
     - An [AWS account](/learning-paths/servers-and-cloud-computing/csp/aws/) for accessing AWS cloud services.
     - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or any Arm server, laptop, or single-board computer running [Docker](/install-guides/docker/) used to run the created images
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/codec/_index.md b/content/learning-paths/servers-and-cloud-computing/codec/_index.md
index d6537e5e55..ab59c9dc64 100644
--- a/content/learning-paths/servers-and-cloud-computing/codec/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/codec/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from an appropriate
   cloud service provider. This Learning Path has been verified on AWS EC2 and Oracle cloud services, running `Ubuntu Linux 20.04.`
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 test_images:
diff --git a/content/learning-paths/servers-and-cloud-computing/codec1/_index.md b/content/learning-paths/servers-and-cloud-computing/codec1/_index.md
index 9fca28bcbc..da1eb44f17 100644
--- a/content/learning-paths/servers-and-cloud-computing/codec1/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/codec1/_index.md
@@ -2,6 +2,8 @@
 title: Run the AV1 and VP9 codecs on Arm Linux
 description: Learn how to build and run the AV1 and VP9 video codecs on Arm Linux systems with performance benchmarking across various resolutions and encoding configurations.
 
+generate_summary_faq: true
+
 author: Odin Shen
 
 minutes_to_complete: 30
diff --git a/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
index 975c69094f..3a21af57f6 100644
--- a/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
@@ -17,6 +17,8 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled  
   - Basic familiarity with [Couchbase](https://www.couchbase.com/)
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md b/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
index ae01e0a945..0dc4351095 100644
--- a/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
@@ -14,6 +14,8 @@ prerequisites:
     - Basic understanding of C++.
     - Basic understanding of compilers.
 
+generate_summary_faq: true
+
 author: Kieran Hejmadi
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md b/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md
index 86357e23a2..f12d8deb54 100644
--- a/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md
@@ -14,6 +14,8 @@ prerequisites:
     - Basic C++ understanding.
     - Access to an Arm-based Linux machine.
 
+generate_summary_faq: true
+
 author: Kieran Hejmadi
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md b/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
index d67bdd205d..c6ea2ea125 100644
--- a/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
@@ -14,6 +14,8 @@ prerequisites:
     - Access to Arm Performix
     - Basic understanding of C++
 
+generate_summary_faq: true
+
 author: Kieran Hejmadi
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/csp/_index.md b/content/learning-paths/servers-and-cloud-computing/csp/_index.md
index 665353918d..bbf20fdd77 100644
--- a/content/learning-paths/servers-and-cloud-computing/csp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/csp/_index.md
@@ -2,6 +2,8 @@
 title: Get started with Arm-based cloud instances
 description: Learn how to start an Arm-based virtual machine instance from major cloud service providers and verify the Arm architecture is being used.
 
+generate_summary_faq: true
+
 author: Ronan Synnott
 
 minutes_to_complete: 15
diff --git a/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md b/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
index 5f4d8dafea..0eb8491a6e 100644
--- a/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
@@ -14,6 +14,8 @@ learning_objectives:
 prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud provider or an on-premise Arm server. This Learning Path was tested on an AWS Graviton4 r8g.24xlarge instance.
 
+generate_summary_faq: true
+
 author:
     - Tianyu Li
 
diff --git a/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md b/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md
index e878c3cb6c..dc2101b951 100644
--- a/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an Arm Linux server.
     - Familiarity with Linux.
 
+generate_summary_faq: true
+
 author: Kieran Hejmadi
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md
index b7f2113c58..d6404f3a8b 100644
--- a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - Familiarity with the Learning Path [Deploy a Large Language Model (LLM) chatbot with llama.cpp using KleidiAI on Arm servers](/learning-paths/servers-and-cloud-computing/llama-cpu)
     - Familiarity with AWS
 
+generate_summary_faq: true
+
 author: 
     - Aryan Bhusari
     - Joe Stech
diff --git a/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md
index 3f9157de78..8ccc4fe4e8 100644
--- a/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md
@@ -21,6 +21,8 @@ prerequisites:
   - Basic familiarity with [Django](https://www.djangoproject.com/)
   - Basic understanding of containers and Kubernetes concepts
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/django/_index.md b/content/learning-paths/servers-and-cloud-computing/django/_index.md
index 3785d40fc0..9368057d44 100644
--- a/content/learning-paths/servers-and-cloud-computing/django/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/django/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - Be comfortable with SSH/Linux terminal and basic system administration tasks.
     - To install both [Nginx](/learning-paths/servers-and-cloud-computing/nginx/) and [PostgreSQL](/learning-paths/servers-and-cloud-computing/postgresql/)
 
+generate_summary_faq: true
+
 author: Diego Russo
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md b/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
index 106b9b4afa..01ba3b0dd5 100644
--- a/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
@@ -14,6 +14,8 @@ learning_objectives:
 prerequisites:
     - Any [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider (CSP), or an on-premise Arm server with at least 400GB of RAM and 800 GB of disk space.
 
+generate_summary_faq: true
+
 author: 
     - Phalani Paladugu
     - Annie Tallund
diff --git a/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md b/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
index d4edfe75ae..ba1b904ff5 100644
--- a/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
@@ -22,6 +22,9 @@ prerequisites:
     - A GitHub account with a personal access token
     - A machine with at least 8 GB RAM (16 GB recommended)
     - Basic familiarity with Docker, C++, and SIMD intrinsics concepts
+
+generate_summary_faq: true
+
 author: Ajeet Singh Raina
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md b/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md
index 180d3d86b3..8891752bc6 100644
--- a/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md
@@ -20,6 +20,8 @@ prerequisites:
     - GCC installed (Linux) or access to a cross-compiler
     - OrchardCore application created using the .NET CLI or Visual Studio
 
+generate_summary_faq: true
+
 author: Joe Stech
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
index 585ca76560..ce12af13cf 100644
--- a/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
@@ -18,6 +18,8 @@ prerequisites:
   - Familiarity with SSH and remote server access
   - Basic understanding of cloud infrastructure and monitoring concepts
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/ecs/_index.md b/content/learning-paths/servers-and-cloud-computing/ecs/_index.md
index 5a414e08a4..a87e262ecc 100644
--- a/content/learning-paths/servers-and-cloud-computing/ecs/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ecs/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - An AWS account
     - A computer with Docker, AWS CLI, and Terraform installed
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md b/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
index 7d99e46899..0626d66a18 100644
--- a/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - A computer with [Amazon eksctl CLI](/install-guides/eksctl) and [kubectl](/install-guides/kubectl/)installed.
     - Docker installed on local computer [Docker](/install-guides/docker)
 
+generate_summary_faq: true
+
 author: Pranay Bakre
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/eks/_index.md b/content/learning-paths/servers-and-cloud-computing/eks/_index.md
index 6b3f2b9d30..5a5bfc1e22 100644
--- a/content/learning-paths/servers-and-cloud-computing/eks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/eks/_index.md
@@ -14,6 +14,8 @@ learning_objectives:
 prerequisites:
     - An Amazon Web Services (AWS) [account](https://aws.amazon.com/)
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md
index c80e6afd11..0092892fe6 100644
--- a/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md
@@ -17,6 +17,8 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free?utm_source=google&hl=en) account with billing enabled
   - Familiarity with networking concepts and the [Envoy architecture](https://www.envoyproxy.io/docs/envoy/latest/)
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/envoy/_index.md b/content/learning-paths/servers-and-cloud-computing/envoy/_index.md
index f163198839..af49c9a452 100644
--- a/content/learning-paths/servers-and-cloud-computing/envoy/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/envoy/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - To run Envoy as a web server, you will need at least one [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an on-premises Arm server.
     - Network settings (firewalls and security groups) which allow communication on port 22 (SSH) and port 80 (HTTP).
 
+generate_summary_faq: true
+
 author: Zhengjun Xing
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md
index 49d9ba3553..a4d1c5de9d 100644
--- a/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - Cloud or bare-metal installation of an Envoy service
     - Review [Learn how to deploy Envoy](/learning-paths/servers-and-cloud-computing/envoy/) if you do not already have an Envoy setup
 
+generate_summary_faq: true
+
 author: Zhengjun Xing
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md b/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md
index ccb5711f2e..6a2b68e993 100644
--- a/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md
@@ -19,6 +19,8 @@ prerequisites:
     - Some familiarity with running linux command line commands.
     - Some familiarity with using a gdb debugger.
 
+generate_summary_faq: true
+
 author: Kristof Beyls
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md b/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
index efdea1f510..3d840d2869 100644
--- a/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - A basic understanding of cache coherency and its impact on performance.
     - Familiarity with Linux Perf tools.
 
+generate_summary_faq: true
+
 author: Kieran Hejmadi
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md b/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md
index ca5b2d9779..a7234fe1df 100644
--- a/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - An AWS account with permissions to create EC2 instances
     - Familiarity with basic Linux administration and SSH
 
+generate_summary_faq: true
+
 author: Geremy Cohen
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md b/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md
index 709c07746a..b65dc87c30 100644
--- a/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - Access to an [AWS Graviton4, Google Axion, or Azure Cobalt 100 virtual machine from a cloud service provider](/learning-paths/servers-and-cloud-computing/csp/)
     - Some familiarity with SIMD programming and SVE intrinsics
 
+generate_summary_faq: true
+
 author: 
 - Arnaud Grasset
 - Claudio Martino
diff --git a/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md
index 14e3e33043..8e64c6cfc9 100644
--- a/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md
@@ -16,6 +16,8 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with [Apache Flink](https://flink.apache.org/) and its runtime environment
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/flink/_index.md b/content/learning-paths/servers-and-cloud-computing/flink/_index.md
index 628ed4cc8f..5005196a26 100644
--- a/content/learning-paths/servers-and-cloud-computing/flink/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/flink/_index.md
@@ -14,6 +14,8 @@ learning_objectives:
 prerequisites:
 - An Arm based instance server from a cloud service provider.
 
+generate_summary_faq: true
+
 author: Ying Yu
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md b/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md
index b96c3470c0..13565ea1a5 100644
--- a/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md
@@ -19,6 +19,8 @@ prerequisites:
   - Basic understanding of machine learning pipelines
 
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
index 204faf230a..909c15e266 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
@@ -21,6 +21,8 @@ prerequisites:
     - 'C# Extension for Visual Studio Code: https://marketplace.visualstudio.com/items?itemName=ms-dotnettools.csharp'
     - '[Install Docker on Arm64](/install-guides/docker/docker-desktop/)'
 
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md
index 3d452100db..efa651eed8 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - 'Azure subscription. Use this link to sign up for a free account: https://azure.microsoft.com/en-us/free/.'
     - 'Complete the [first learning path](/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1) of this series.'  
 
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
index 6c34cb9107..baa7ad24b1 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
@@ -14,6 +14,9 @@ prerequisites:
     - 'Azure subscription. Use this link to sign up for a free account: https://azure.microsoft.com/en-us/free/.'
     - 'Complete the [first](/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1) and [second](/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2) learning paths of this series.'
   
+
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md
index 592493d999..8c91af3bc6 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md
@@ -18,6 +18,8 @@ prerequisites:
     - 'Node.js (details provided in this learning path)'
     - 'Azure CLI (details provided in this learning path)'
 
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/funasr/_index.md b/content/learning-paths/servers-and-cloud-computing/funasr/_index.md
index 8fea725936..181d383c6f 100644
--- a/content/learning-paths/servers-and-cloud-computing/funasr/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/funasr/_index.md
@@ -14,6 +14,8 @@ learning_objectives:
 prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider, or a local Arm Linux computer with at least 8 CPUs and 16GB of RAM.
 
+generate_summary_faq: true
+
 author: Odin Shen
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
index 2476b5bfd2..70e4ee483b 100644
--- a/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
@@ -18,6 +18,8 @@ prerequisites:
   - Basic familiarity with [Kubernetes](https://kubernetes.io/)
   - Familiarity with container concepts ([Docker](https://www.docker.com/))
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md b/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
index e69f61bcd3..db75a9af46 100644
--- a/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - An Arm Linux system (cloud instance, on-premises hardware, or a virtual machine)
     - A recent version of the [GCC toolchain](/install-guides/gcc/)
 
+generate_summary_faq: true
+
 author: Victor Do Nascimento
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/gcp/_index.md
index 2ae1d5b12e..f4e336802d 100644
--- a/content/learning-paths/servers-and-cloud-computing/gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gcp/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - A computer with [Terraform](/install-guides/terraform) installed.
     - A computer with [Google Cloud CLI](/install-guides/gcloud) installed.
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md b/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
index e8ed45b0cc..bfe99ae41d 100644
--- a/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
@@ -13,6 +13,8 @@ learning_objectives:
 prerequisites:
 - An Arm computer running Linux. You can use a cloud instance, refer to [Get started with Arm-based cloud instances](/learning-paths/servers-and-cloud-computing/csp/).
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 skilllevels: Introductory
diff --git a/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md b/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
index 57758520db..debe29381c 100644
--- a/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
@@ -18,6 +18,8 @@ prerequisites:
     - A Docker Hub account for storing container images.
     - Familiarity with the concepts of ML and continuous integration and deployment (CI/CD).
 
+generate_summary_faq: true
+
 author:
     - Pareena Verma
     - Annie Tallund
diff --git a/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md b/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
index fa5bb49430..17254aa768 100644
--- a/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
@@ -14,6 +14,8 @@ prerequisites:
     - An [Amazon Web Services account](/learning-paths/servers-and-cloud-computing/csp/aws/).
     - A GitHub account (personal or organizational).
 
+generate_summary_faq: true
+
 author: Cyril Rohr
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md b/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md
index c416bbcbdf..8e2ef43fdd 100644
--- a/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md
@@ -15,6 +15,8 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free?utm_source=google&hl=en) account with billing enabled
   - A GitHub account; you can [sign up for GitHub](https://github.com/signup)
 
+generate_summary_faq: true
+
 author: Annie Tallund
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
index a2bd77f270..f01a552b00 100644
--- a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - A local Linux or macOS computer with Docker, Kubernetes CLI (kubectl), Google Cloud CLI (gcloud), and Git installed, or access to Google Cloud Shell
     - Basic familiarity with Docker, Kubernetes, and gcloud
 
+generate_summary_faq: true
+
 author: 
    - Rani Chowdary Mandepudi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
index 9f8b5a67dd..07e7f1153b 100644
--- a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - A computer with [Google Cloud CLI](/install-guides/gcloud) and [kubectl](/install-guides/kubectl/)installed.
     - An existing Google Kubernetes Engine (GKE) cluster with x86-based nodes
 
+generate_summary_faq: true
+
 author: Pranay Bakre
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/gke/_index.md b/content/learning-paths/servers-and-cloud-computing/gke/_index.md
index 6695b69ef1..3e283dabc2 100644
--- a/content/learning-paths/servers-and-cloud-computing/gke/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gke/_index.md
@@ -13,6 +13,8 @@ prerequisites:
     - A Google Cloud account
     - A computer with the following tools installed`:` Terraform, Google Cloud CLI (gcloud), Kubernetes CLI (kubectl)
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md b/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
index 893dca8c60..faede922a0 100644
--- a/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
@@ -15,6 +15,8 @@ prerequisites:
 - An Arm based instance from a cloud service provider.
 - Review the learning path on [LSE](/learning-paths/servers-and-cloud-computing/lse/)
 
+generate_summary_faq: true
+
 author: Ying Yu
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md b/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
index f8ffd3d80d..92fc321e52 100644
--- a/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - A [Google Cloud account](https://console.cloud.google.com/). This Learning Path can be run on any cloud provider or on-premises, but it focuses on Google Cloud’s Axion Arm64-based instances.
     - A local machine with [Google Cloud CLI](/install-guides/gcloud/) installed
 
+generate_summary_faq: true
+
 author: Geremy Cohen
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md
index 7212cbc2f4..75eed034b2 100644
--- a/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - Basic familiarity with the [Go programming language](https://go.dev/) and cloud deployment practices
     - Understanding of Linux command line and virtual machine management
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md
index d8536492a0..44f427f7d5 100644
--- a/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md
@@ -22,6 +22,9 @@ prerequisites:
   - Basic understanding of [Helm](https://helm.sh/docs/topics/architecture/) and Kubernetes manifests
   - Familiarity with basic Linux command-line usage
     
+
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/intro/_index.md b/content/learning-paths/servers-and-cloud-computing/intro/_index.md
index 50ba7b1075..2b067b887b 100644
--- a/content/learning-paths/servers-and-cloud-computing/intro/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/intro/_index.md
@@ -13,6 +13,8 @@ learning_objectives:
 prerequisites:
     - None
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md b/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
index 11b941193e..9567886078 100644
--- a/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - An Arm computer running Linux
     - Some familiarity with the Linux command line
 
+generate_summary_faq: true
+
 author: Kiel Friedt
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md b/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
index fafab92491..cc0bc92e45 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - Basic understanding of Java.
     - An [installation of Java](/install-guides/java/) on your machine.
 
+generate_summary_faq: true
+
 author: Kieran Hejmadi
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
index 893ba4ba07..0aaa578d0c 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
@@ -14,6 +14,8 @@ learning_objectives:
 prerequisites:
     - A [Google Cloud](https://cloud.google.com/) account with access to Axion based instances (C4A).
 
+generate_summary_faq: true
+
 author: Joe Stech
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
index 94b9486329..89a9ecdf42 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - A [Microsoft Azure](https://azure.microsoft.com/) account with access to Cobalt 100 based instances (Dpsv6)
 
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
index c03707d7f9..f39077837d 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
@@ -15,6 +15,8 @@ prerequisites:
   - Access to both Arm-based and x86-based computers running Ubuntu (you can use cloud-based server instances)
   - Basic familiarity with Java applications and performance profiling using flame graphs
 
+generate_summary_faq: true
+
 author: 
   - Ying Yu
   - Martin Ma
diff --git a/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md b/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
index 5139e59e87..f42c01c987 100644
--- a/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
@@ -21,6 +21,8 @@ prerequisites:
     - Basic understanding of Linux command line
     - Familiarity with CI/CD concepts and [Jenkins fundamentals](https://www.jenkins.io/doc/book/pipeline/)
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
index 6b87e68ecd..6fd26f10e2 100644
--- a/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - Basic understanding of Linux command line
     - Familiarity with the [Apache Kafka architecture](https://kafka.apache.org/) and deployment practices on Arm64 platforms
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/kafka/_index.md b/content/learning-paths/servers-and-cloud-computing/kafka/_index.md
index 4bc43b7f9e..41ff740f45 100644
--- a/content/learning-paths/servers-and-cloud-computing/kafka/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kafka/_index.md
@@ -17,6 +17,8 @@ learning_objectives:
 prerequisites:
     - Seven physical Arm machines or cloud instances with either Ubuntu or Debian installed. 
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md b/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
index 0ed3acc520..ebd7e8749c 100644
--- a/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
@@ -17,6 +17,8 @@ prerequisites:
   - Kubectl and Helm installed 
   - Access to the Kedify Service dashboard to obtain your Organization ID and API key (sign up at [Kedify dashboard](https://dashboard.kedify.io/))
 
+generate_summary_faq: true
+
 author: Zbynek Roubalik
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md b/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
index 0591aeaad6..755b247093 100644
--- a/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider, an on-premises Arm server, or a Linux virtual machine on your Arm device. 
     - Familiarity with SSH, the Linux command line, and basic system administration tasks.
 
+generate_summary_faq: true
+
 author: 
     - Diego Russo
     - Leandro Nunes
diff --git a/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md b/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
index 5b0dea05d8..8665eaa486 100644
--- a/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
@@ -18,6 +18,8 @@ prerequisites:
     - Understanding of kernel images and modules
     - Familiarity with GRUB bootloader and initramfs
 
+generate_summary_faq: true
+
 author: Geremy Cohen
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md b/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
index 00448a94fd..3adc059fca 100644
--- a/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
@@ -16,6 +16,8 @@ learning_objectives:
 prerequisites:
     - A running Kubernetes cluster accessible with `kubectl`.
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md b/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
index c3d672126d..c3937b1e2a 100644
--- a/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
@@ -13,6 +13,9 @@ learning_objectives:
 prerequisites:
     - A computer with [Terraform](/install-guides/terraform/) and the [AWS CLI](/install-guides/aws-cli/) installed. 
     
+
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md b/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
index e57a4b8c70..8ed9856b80 100644
--- a/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - An Arm server or virtual machine instance from a cloud service provider with Ubuntu installed
     - Knowledge of how to build a MySQL server and run the sysbench benchmark test
 
+generate_summary_faq: true
+
 author: Bolt Liu
 
 skilllevels: Advanced
diff --git a/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md b/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
index 017889b601..9fe7bb869f 100644
--- a/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
@@ -14,6 +14,8 @@ learning_objectives:
 prerequisites:
     - An AWS Graviton4 r8g.16xlarge instance to test Arm performance optimizations, or any [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an on-premise Arm server.
 
+generate_summary_faq: true
+
 author:
     - Pareena Verma
     - Jason Andrews
diff --git a/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md b/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
index 4717c62d7e..70de57361e 100644
--- a/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
@@ -19,6 +19,8 @@ prerequisites:
     - A basic understanding of Streamlit.
     - A basic understanding of LLM fundamentals.
 
+generate_summary_faq: true
+
 author: Nobel Chowdary Mandepudi
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
index fb87055082..4e28ca2a90 100644
--- a/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
@@ -20,6 +20,8 @@ prerequisites:
     - Knowledge of Arm Streamline usage
     - An Arm Neoverse or Cortex-A hardware platform running Linux or Android
 
+generate_summary_faq: true
+
 author: 
     - Zenon Zhilong Xiu
     - Odin Shen
diff --git a/content/learning-paths/servers-and-cloud-computing/lse/_index.md b/content/learning-paths/servers-and-cloud-computing/lse/_index.md
index b78e9c0898..2ba1f4db38 100644
--- a/content/learning-paths/servers-and-cloud-computing/lse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/lse/_index.md
@@ -14,6 +14,8 @@ learning_objectives:
 prerequisites:
     - An [AWS account](/learning-paths/servers-and-cloud-computing/csp/aws/) to access instance types with different AWS Graviton processors. If you don't have an AWS account, you can substitute other Arm Linux computers.
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md b/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
index f7fd16118a..b9061dd401 100644
--- a/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - Cloud service provider accounts for each service you want to use including AWS, Azure, and GCP
     - A local computer with [Docker](/install-guides/docker/), [Terraform](/install-guides/terraform/), [AWS CLI](/install-guides/aws-cli/), [Azure CLI](/install-guides/azure-cli/), [Google Cloud CLI](/install-guides/gcloud/), and [Ansible](/install-guides/ansible/) installed
 
+generate_summary_faq: true
+
 author: Jason Andrews
 ### Tags
 skilllevels: Introductory
diff --git a/content/learning-paths/servers-and-cloud-computing/memcached/_index.md b/content/learning-paths/servers-and-cloud-computing/memcached/_index.md
index ecd15889b3..012ffb4766 100644
--- a/content/learning-paths/servers-and-cloud-computing/memcached/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memcached/_index.md
@@ -14,6 +14,8 @@ learning_objectives:
 prerequisites:
 - An Arm based instance from an appropriate cloud service provider.
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 test_images:
diff --git a/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md b/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
index b8385753f5..6e2d63a46f 100644
--- a/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 - A Google Cloud [account](https://console.cloud.google.com/)
 - A machine with [Terraform](/install-guides/terraform/), [AWS CLI](/install-guides/aws-cli), [Google Cloud CLI](/install-guides/gcloud), [Azure CLI](/install-guides/azure-cli), [AWS IAM authenticator](https://docs.aws.amazon.com/eks/latest/userguide/install-aws-iam-authenticator.html), and [Ansible](/install-guides/ansible/) installed
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 
diff --git a/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md b/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
index ecc23c1fa6..14280f5e1b 100644
--- a/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
@@ -19,6 +19,8 @@ prerequisites:
     - Arm System Characterization Tool (ASCT) installed on each system
     - A good understanding of CPU memory subsystems, including cache hierarchies, cache lines, and DRAM in the memory hierarchy
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 skilllevels: Advanced
diff --git a/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md b/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
index 53ac133551..2a62ac2cac 100644
--- a/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
@@ -19,6 +19,8 @@ prerequisites:
     - Familiarity with general-purpose registers.
     - Familiarity with memory barriers, including Acquire-Release Semantics.
 
+generate_summary_faq: true
+
 author: Julio Suarez
 
 skilllevels: Advanced
diff --git a/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md b/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
index 078aa543ed..de38d50403 100644
--- a/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
@@ -14,6 +14,8 @@ prerequisites:
     - Basic understanding of networking principles such as Transmission Control Protocol/Internet Protocol (TCP/IP) and User Datagram Protocol (UDP).
     - Access to two [Arm-based cloud instances](/learning-paths/servers-and-cloud-computing/csp/).
 
+generate_summary_faq: true
+
 author: Kieran Hejmadi
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md b/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
index 224b2c6ff1..b0046f7d30 100644
--- a/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
@@ -16,6 +16,8 @@ learning_objectives:
 prerequisites:
     - Access to an [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) for testing and validation.
 
+generate_summary_faq: true
+
 author: 
     - Odin Shen
     - Jun He
diff --git a/content/learning-paths/servers-and-cloud-computing/migration/_index.md b/content/learning-paths/servers-and-cloud-computing/migration/_index.md
index d8a14d50d5..b450d4c2fc 100644
--- a/content/learning-paths/servers-and-cloud-computing/migration/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/migration/_index.md
@@ -16,6 +16,8 @@ learning_objectives:
 prerequisites:
     - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider.
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
index beb0d61ed2..3bf52a4e73 100644
--- a/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - An AWS Graviton3 C7g.2xlarge instance, or any [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp) from a cloud service provider or an on-premise Arm server.
     - A [Zilliz account](https://zilliz.com/cloud?utm_source=partner&utm_medium=referral&utm_campaign=2024-10-24_web_arm-dev-hub-data-loading_arm), which you can sign up for with a free trial.
 
+generate_summary_faq: true
+
 author: Chen Zhang
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
index 577587d4f2..83f23fba17 100644
--- a/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
@@ -18,6 +18,8 @@ prerequisites:
   - Familiarity with SSH and remote server access
   - Basic understanding of cloud storage concepts
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md b/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
index a4baf68cfa..cd163c3473 100644
--- a/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
@@ -15,6 +15,8 @@ prerequisites:
 - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from an appropriate
   cloud service provider or an on-premise Arm server.
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 test_images:
diff --git a/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
index cf421025a2..2c9f76afe4 100644
--- a/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - A [Microsoft Azure](https://azure.microsoft.com/) account with access to Cobalt 100 (Dpsv6) instances
     - Familiarity with the [MongoDB architecture](https://www.mongodb.com/) and deployment practices on Arm64 platforms
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
index 2d6dc48469..be5b20e7b6 100644
--- a/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
@@ -15,6 +15,8 @@ learning_objectives:
 prerequisites:
      - A [Google Cloud Platform (GCP)](https://cloud.google.com/free?utm_source=google&hl=en) account with billing enabled
 
+generate_summary_faq: true
+
 author: Annie Tallund
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md b/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
index ea51b4e610..92fc2e608e 100644
--- a/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Analyze the performance of MongoDB on Arm servers
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 minutes_to_complete: 30
diff --git a/content/learning-paths/servers-and-cloud-computing/mpi/_index.md b/content/learning-paths/servers-and-cloud-computing/mpi/_index.md
index bb0d8b8b82..abf75d9c2b 100644
--- a/content/learning-paths/servers-and-cloud-computing/mpi/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mpi/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - Some understanding of C, Python, and Linux commands
     - An Arm computer running Linux. Cloud instances can be used, refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
 
+generate_summary_faq: true
+
 author: Florent Lebeau
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md b/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md
index d61e186f0a..80bc56b8b7 100644
--- a/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md
@@ -2,6 +2,9 @@
 title: Control floating-point accuracy modes in Arm Performance Libraries
 
 minutes_to_complete: 20
+
+generate_summary_faq: true
+
 author: Joana Cruz
 
 who_is_this_for: This is an introductory topic for developers who want to use the different accuracy modes for vectorized math functions in Libamath, a component of Arm Performance Libraries.
diff --git a/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md b/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md
index 48c602c4db..185de52bb1 100644
--- a/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md
@@ -17,6 +17,9 @@ prerequisites:
     - An [Azure account](https://azure.microsoft.com/en-us/free/)
     - A local machine with [`jq`](https://jqlang.org/download/), [`curl`](https://curl.se/download.html), [`wrk`](https://github.com/wg/wrk), [Azure CLI](/install-guides/azure-cli/), and [`kubectl`](/install-guides/kubectl/) installed
    
+
+generate_summary_faq: true
+
 author:
     - Geremy Cohen
 
diff --git a/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md b/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md
index 7865526d64..b14c3b558d 100644
--- a/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - The [GKE Cloud Plugin](https://cloud.google.com/kubernetes-engine/docs/how-to/cluster-access-for-kubectl#gcloud) installed.
    
 
+generate_summary_faq: true
+
 author:
     - Geremy Cohen
 
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md
index f6e8b14479..828ee780e0 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - A [Microsoft Azure](https://azure.microsoft.com/) account with access to Cobalt 100 based instances (Dpsv6)
     - Familiarity with relational databases and the basics of [MySQL](https://dev.mysql.com/doc/refman/8.0/en/introduction.html)
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql-lns-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql-lns-azure/_index.md
index 5bc27c3431..e6f7caf151 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql-lns-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql-lns-azure/_index.md
@@ -21,6 +21,8 @@ prerequisites:
     - Basic familiarity with SSH and MySQL command-line tools
 
 
+generate_summary_faq: true
+
 author: Doug Anson
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql/_index.md
index 235b79b0ba..1278194647 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql/_index.md
@@ -13,6 +13,8 @@ prerequisites:
     - An Arm based instance from a cloud service provider, or an on-premise Arm server.
     - If you do not have an Arm node, the next section discusses some options.
 
+generate_summary_faq: true
+
 author: Jason Andrews
 ### Tags
 skilllevels: Introductory
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md
index 51e2c28332..46d7f60e3d 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md
@@ -13,6 +13,8 @@ prerequisites:
     - Basic knowledge of [MySQL databases](https://www.mysql.com/)
     - Two Arm servers running Ubuntu 22.04, one for the MySQL server and the other for the Sysbench client
 
+generate_summary_faq: true
+
 author: Bolt Liu
 
 skilllevels: Introductory
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md
index 4c66347fb5..19d481d325 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md
@@ -11,6 +11,8 @@ learning_objectives:
 prerequisites:
     - Bare-metal or cloud [installation of MySQL](/learning-paths/servers-and-cloud-computing/mysql/)
 
+generate_summary_faq: true
+
 author: Julio Suarez
 
 skilllevels: Advanced
diff --git a/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md b/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md
index 833dc52cc3..97c135bb9a 100644
--- a/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md
@@ -19,6 +19,8 @@ prerequisites:
     - Understanding of firmware boot stages and SoC-level architecture
     - Docker installed, or a GitHub Codespaces-compatible development environment
 
+generate_summary_faq: true
+
 author:
     - Odin Shen
     - Ann Cheng
diff --git a/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md b/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md
index 96cd0d3c95..004a0d0e21 100644
--- a/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from AWS or GCP.
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is an example of a suitable editor.    
 
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md
index 6eba7132e3..7409e44d5a 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md
@@ -15,6 +15,8 @@ learning_objectives:
 prerequisites:
     - A [Microsoft Azure](https://azure.microsoft.com/) account with access to Cobalt 100 based instances (Dpsv6)
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx/_index.md
index 29f14ce283..8967d22424 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - To create a reverse proxy or API gateway you will need at least three Arm based instances from a cloud service provider or at least three on-premises Arm servers.
     - Network settings (firewalls and security groups) which allow communication on port 22 (SSH) and port 443 (HTTPS).
 
+generate_summary_faq: true
+
 author: Julio Suarez
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
index 97032c8bf6..b16ebaaecc 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - A cloud or bare-metal installation of a Nginx file server or load balancer.
     - If you do not already have a Nginx setup, a review of [Learn how to deploy Nginx](/learning-paths/servers-and-cloud-computing/nginx/).
 
+generate_summary_faq: true
+
 author: Julio Suarez
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md b/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md
index c7be9fb269..7d8931b128 100644
--- a/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md
@@ -12,6 +12,8 @@ learning_objectives:
 prerequisites:
     - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an on-premise Arm server.
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md
index fe438b0011..f9a639a71f 100644
--- a/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md
@@ -19,6 +19,8 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Familiarity with networking concepts and [Node.js event-driven architecture](https://nodejs.org/en/docs/guides/event-loop-timers-and-nexttick)
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md b/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md
index 531dbb1244..1b631610ad 100644
--- a/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md
@@ -12,6 +12,8 @@ prerequisites:
     - An OCI account
     - A computer with Terraform installed
 
+generate_summary_faq: true
+
 author: Frédéric -lefred- Descamps
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md
index c7de8e0c29..555831dea7 100644
--- a/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - Basic understanding of Python and machine learning concepts
     - Familiarity with [ONNX Runtime](https://onnxruntime.ai/docs/) and Azure cloud services
 
+generate_summary_faq: true
+
 author: Pareena Verma    
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/onnx/_index.md b/content/learning-paths/servers-and-cloud-computing/onnx/_index.md
index db04a08937..dd9c2c25b4 100644
--- a/content/learning-paths/servers-and-cloud-computing/onnx/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/onnx/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - Knowledge of Large Language Model (LLM) fundamentals.
 
 
+generate_summary_faq: true
+
 author: Nobel Chowdary Mandepudi
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md b/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md
index 0f6122ee73..4cd3f3b354 100644
--- a/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md
@@ -18,6 +18,8 @@ prerequisites:
   - Working knowledge of Docker, Git, and common Linux terminal tools
   - Basic understanding of the server firmware stack (such as UEFI, BMC, and TF-A)
 
+generate_summary_faq: true
+
 author:
   - Odin Shen
   - Ken Zhang
diff --git a/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md b/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md
index 0d06231e55..feed3a1556 100644
--- a/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - An Arm Linux (aarch64) server, such as an AWS Graviton3 instance
     - Basic understanding of C++ and CMake
 
+generate_summary_faq: true
+
 author: Kieran Hejmadi
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/openshift/_index.md b/content/learning-paths/servers-and-cloud-computing/openshift/_index.md
index 45e9301f9a..f1cd2e0679 100644
--- a/content/learning-paths/servers-and-cloud-computing/openshift/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openshift/_index.md
@@ -14,6 +14,8 @@ prerequisites:
   - Familiarity with the `oc` CLI, container fundamentals, and basic Tekton concepts (Task, Pipeline, PipelineRun)
   - Cluster access with cluster-admin or equivalent permissions to configure nodes and pipelines
 
+generate_summary_faq: true
+
 author: Jeff Young
 
 # Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md
index 05c955e7ca..71f165c9a4 100644
--- a/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md
@@ -21,6 +21,8 @@ prerequisites:
   - Familiarity with SSH and remote server access
   - Basic understanding of cloud computing and virtualization concepts
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md b/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md
index 20b563b6d4..ea666bd900 100644
--- a/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md
@@ -16,6 +16,8 @@ prerequisites:
   - Basic familiarity with Python and Flask
   - Basic understanding of containers and Kubernetes concepts
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/pac/_index.md b/content/learning-paths/servers-and-cloud-computing/pac/_index.md
index f635ab6a56..75df90a3ca 100644
--- a/content/learning-paths/servers-and-cloud-computing/pac/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pac/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - If needed, review [Get started with Arm-based cloud instances](/learning-paths/servers-and-cloud-computing/csp/) to learn how to deploy Arm in the cloud. These learning paths also point to more advanced learning paths that show how to automate the deployment of Arm instances at different cloud providers.
     
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md b/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md
index d60e5915c8..dc65cc1de5 100644
--- a/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md
@@ -18,6 +18,9 @@ prerequisites:
     - Access to an Arm-based cloud instance running Linux, such as an AWS Graviton3 instance
     - Access to Arm Performix configured with the remote Arm target. See the [Arm Performix install guide](/install-guides/performix/) for setup instructions
     - Basic understanding of C++
+
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md b/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md
index 8e8adb5de5..858febb90a 100644
--- a/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - Familiarity with Linux command line
     - Basic understanding of CPU performance concepts
 
+generate_summary_faq: true
+
 author:
 - Brendan Long
 - Kieran Hejmadi
diff --git a/content/learning-paths/servers-and-cloud-computing/performix-system-characterization/_index.md b/content/learning-paths/servers-and-cloud-computing/performix-system-characterization/_index.md
index 350d9c71b0..51d9c8985b 100644
--- a/content/learning-paths/servers-and-cloud-computing/performix-system-characterization/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/performix-system-characterization/_index.md
@@ -19,6 +19,8 @@ learning_objectives:
 prerequisites:
     - A Arm Linux target machine accessible via SSH to characterize.
 
+generate_summary_faq: true
+
 author:
 - Brendan Long
 - David Wong
diff --git a/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md
index d21dfdc3aa..2852738926 100644
--- a/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md
@@ -16,6 +16,9 @@ learning_objectives:
 prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with web servers and PHP scripting
+
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md b/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md
index 9723089cad..29f8c77eff 100644
--- a/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - Understanding of build systems and computer architecture concepts
     - Familiarity with Linux command-line tools
 
+generate_summary_faq: true
+
 author: Kieran Hejmadi
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md b/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md
index 5605a590b1..0047348874 100644
--- a/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - An Arm-based computer running Linux.
     - Some familiarity with Linux application performance analysis.
 
+generate_summary_faq: true
+
 author: Gayathri Narayana Yegna Narayanan
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md
index e3bf304229..f9250dee6b 100644
--- a/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md
@@ -18,6 +18,8 @@ prerequisites:
   - Familiarity with SSH and remote server access
   - Basic understanding of databases and SQL
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 description: Deploy PostgreSQL on Azure Cobalt 100 Arm64 virtual machines, load a relational schema with transactional data, and benchmark and optimize query performance using pgbench and pg_stat_statements.
diff --git a/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md b/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md
index 3937b08cb4..2e40332f04 100644
--- a/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md
@@ -13,6 +13,8 @@ prerequisites:
     - An Arm based instance from a cloud service provider, or an on-premise Arm server.
     - If you do not have an Arm node, the next section discusses some options.
 
+generate_summary_faq: true
+
 author: Jason Andrews
 ### Tags
 skilllevels: Introductory
diff --git a/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md
index e86f03a5ea..a12eef6ec0 100644
--- a/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md
@@ -11,6 +11,8 @@ learning_objectives:
 prerequisites:
     - Bare-metal or cloud [installation of PostgreSQL](/learning-paths/servers-and-cloud-computing/postgresql/)
 
+generate_summary_faq: true
+
 author: Julio Suarez
 
 test_images:
diff --git a/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md b/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
index 90e8a303dd..3c409a66d7 100644
--- a/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
@@ -13,6 +13,8 @@ prerequisites:
     - An Arm-based system (bare metal server, cloud instance, or developer board) running Linux with kernel version 5.8.0 or later.
     - Root access, or the ability to run the sudo command.
 
+generate_summary_faq: true
+
 author: Graham Woodward
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md b/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md
index 61dafba20a..a60f30dc1e 100644
--- a/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md
@@ -12,6 +12,8 @@ learning_objectives:
 prerequisites:
     - An Arm Neoverse-based (N1, N2 or V1) computer running Linux. For your host OS, you can use Amazon Linux 2023 or newer, Debian 10 or newer, RHEL 8 or newer, or Ubuntu 20.04 or newer.
 
+generate_summary_faq: true
+
 author: Julie Gaskin
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md
index dffca64bc8..7da95247a3 100644
--- a/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md
@@ -10,6 +10,9 @@ learning_objectives:
   - Install Puppet on a SUSE Arm64 C4A instance
   - Verify Puppet by applying a test manifest and confirming successful resource creation on Arm64  
   - Benchmark Puppet by measuring catalog compile time, apply speed, and resource usage on Arm64
+
+generate_summary_faq: true
+
 author: Pareena Verma
 
 prerequisites:
diff --git a/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md b/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md
index d61d0e6efc..b844a4bcaf 100644
--- a/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md
@@ -15,6 +15,8 @@ learning_objectives:
 prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) with at least 16 CPUs from a cloud service provider or an on-premise Arm server.
 
+generate_summary_faq: true
+
 author:
     - Nikhil Gupta
     - Pareena Verma
diff --git a/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md
index ed8e0aec57..af5dfa24c4 100644
--- a/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md
@@ -19,6 +19,8 @@ prerequisites:
   - Basic understanding of machine learning embeddings
   - Familiarity with Linux command-line operations
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md
index e413aa9de4..a527948408 100644
--- a/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md
@@ -19,6 +19,8 @@ prerequisites:
   - Basic understanding of message queues and messaging concepts (publishers, consumers)
   - Familiarity with Linux command-line operations
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/rag/_index.md b/content/learning-paths/servers-and-cloud-computing/rag/_index.md
index 530fe9e21b..5db432d5cf 100644
--- a/content/learning-paths/servers-and-cloud-computing/rag/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rag/_index.md
@@ -19,6 +19,8 @@ prerequisites:
     - Basic knowledge of vector databases.
     - Understanding of LLM fundamentals.
 
+generate_summary_faq: true
+
 author: Nobel Chowdary Mandepudi
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/ran/_index.md b/content/learning-paths/servers-and-cloud-computing/ran/_index.md
index 17c923bae1..a726d206de 100644
--- a/content/learning-paths/servers-and-cloud-computing/ran/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ran/_index.md
@@ -15,6 +15,8 @@ prerequisites:
 - An Arm computer running Linux. Cloud instances can be used, refer to the list of
   [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
 
+generate_summary_faq: true
+
 author: Ronan Synnott
 
 test_images:
diff --git a/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md
index 29ea132d25..e98bce6a1a 100644
--- a/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md
@@ -16,6 +16,8 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with Python and distributed systems concepts
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md
index 132629be1d..fc3ad585ed 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md
@@ -18,6 +18,8 @@ prerequisites:
   - Familiarity with SSH and remote server access
   - Basic understanding of databases, caching, and messaging systems
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md b/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md
index b979afbda1..da3c4ad565 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md
@@ -15,6 +15,8 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with [Redis](https://redis.io/) 
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/redis/_index.md b/content/learning-paths/servers-and-cloud-computing/redis/_index.md
index 21d2ccff3d..0284b323b9 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis/_index.md
@@ -14,6 +14,8 @@ prerequisites:
     - An Arm based instance from a cloud service provider, or an on-premise Arm server.
     - If you do not have an Arm node, the next section discusses some options.
 
+generate_summary_faq: true
+
 author: Elham Harirpoush
 ### Tags
 skilllevels: Introductory
diff --git a/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md b/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md
index 4168eebc40..8c51e74518 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - A Google Cloud [account](https://console.cloud.google.com/)
     - A machine with [Terraform](/install-guides/terraform/), [AWS CLI](/install-guides/aws-cli), [Google Cloud CLI](/install-guides/gcloud), [Azure CLI](/install-guides/azure-cli), [AWS IAM authenticator](https://docs.aws.amazon.com/eks/latest/userguide/install-aws-iam-authenticator.html), and [Ansible](/install-guides/ansible/) installed
 
+generate_summary_faq: true
+
 author: Jason Andrews
 ### Tags
 skilllevels: Advanced
diff --git a/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md
index 89c619eec2..0b9e2c12d4 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md
@@ -14,6 +14,8 @@ prerequisites:
     - Cloud or bare-metal installation of an Redis file server
     - Review [Learn how to deploy Redis](/learning-paths/servers-and-cloud-computing/redis/) if you do not already have Redis setup
 
+generate_summary_faq: true
+
 author: Elham Harirpoush
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md b/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md
index fdea0ad482..48c4d115d7 100644
--- a/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - A Fixed Virtual Platform (FVP).
     - A basic understanding of Neoverse Reference Design (RD) platform boot.
 
+generate_summary_faq: true
+
 author: Daniel Nguyen
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md b/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md
index 53598fecaa..551ec005f9 100644
--- a/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - Some understanding of the Linux command line.
     - Optionally a basic understanding of Docker and containers.
 
+generate_summary_faq: true
+
 author: 
     - Tom Pilar
     - Daniel Nguyen
diff --git a/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md b/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md
index a6eb3bd9a5..dfb6ca58f3 100644
--- a/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md
@@ -2,6 +2,9 @@
 title: Enable reproducible math functions across vector extensions with Arm Performance Libraries
 
 minutes_to_complete: 10
+
+generate_summary_faq: true
+
 author: Joana Cruz
 
 who_is_this_for: This is an introductory topic for developers who want to produce reproducible code across vector extensions using math functions in Libamath, a component of Arm Performance Libraries. 
diff --git a/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md b/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md
index 78ef4179a6..c0f007d925 100644
--- a/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md
@@ -14,6 +14,8 @@ prerequisites:
     - An aarch64 or x86_64 computer running Ubuntu 22.04. Cloud instances can be used, refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
     - If you use a client application to access your computer running Ubuntu, make sure that X11 forwarding is enabled.
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md b/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md
index d81741888e..c81715936a 100644
--- a/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md
@@ -14,6 +14,8 @@ prerequisites:
     - Any Arm Neoverse N2-based or Arm Neoverse V2-based instance running Ubuntu 22.04 LTS from a cloud service provider or an on-premise Arm server. 
     - For the server, at least four cores and 16GB of RAM, with disk storage configured up to at least 32 GB. 
 
+generate_summary_faq: true
+
 author: Tianyu Li
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md b/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md
index 81d7ae18f4..c95dcada3f 100644
--- a/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md
@@ -16,6 +16,8 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with Ruby programming, the Rails framework, and the [PostgreSQL Relational Database](https://www.postgresql.org/)
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md
index 50a5485bf6..b07eaa8e7b 100644
--- a/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md
@@ -17,6 +17,8 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled  
   - Basic familiarity with [Rust](https://www.rust-lang.org/)
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md b/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md
index 14432138bc..450c55d9bc 100644
--- a/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md
@@ -14,6 +14,8 @@ prerequisites:
     - An AWS account.
     - A computer with Docker, Terraform, the Amazon eksctl command-line interface, and kubectl installed.
 
+generate_summary_faq: true
+
 author: 
     - Pranay Bakre
     - Masoud Koleini
diff --git a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md
index 4f2cf849fb..16983bae60 100644
--- a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md
@@ -13,6 +13,8 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).   
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is suitable.    
 
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md
index c637c0ca08..6f3f209196 100644
--- a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md
@@ -14,6 +14,8 @@ prerequisites:
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is suitable.
     - Completion of [Deploy AWS services using the Serverless Framework](/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/).
 
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md
index ad60326a1d..b409d392d4 100644
--- a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md
@@ -14,6 +14,8 @@ prerequisites:
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is suitable.
     - Completion of the Learning Path that shows you how to [Deploy AWS services using the Serverless Framework](/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/).
 
+generate_summary_faq: true
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/snappy/_index.md b/content/learning-paths/servers-and-cloud-computing/snappy/_index.md
index f1d99399bf..f7bb4289ab 100644
--- a/content/learning-paths/servers-and-cloud-computing/snappy/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/snappy/_index.md
@@ -15,6 +15,8 @@ prerequisites:
 - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from an appropriate
   cloud service provider.
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 test_images:
diff --git a/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md b/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md
index fce28125a7..e432b93aff 100644
--- a/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md
@@ -15,6 +15,8 @@ prerequisites:
     - A basic understanding of Snort's operation and configuration.
     
 
+generate_summary_faq: true
+
 author: Preema Merlin Dsouza
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md
index 59c9669d88..f4fa3f808a 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - A machine with [Docker](/install-guides/docker/) installed
     - Familiarity with distributed computing concepts and the [Apache Spark architecture](https://spark.apache.org/docs/latest/)
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md
index f017a4288c..f3ef3beef3 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md
@@ -15,6 +15,8 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free?utm_source=google&hl=en) account with billing enabled
   - Familiarity with distributed computing concepts and the [Apache Spark architecture](https://spark.apache.org/docs/latest/)
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/spark-velox-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/spark-velox-cobalt/_index.md
index 7317b853a9..591b2d5163 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark-velox-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark-velox-cobalt/_index.md
@@ -22,6 +22,8 @@ prerequisites:
   - Familiarity with SSH and remote server access
   - Basic understanding of distributed systems and Apache Spark
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/spark/_index.md b/content/learning-paths/servers-and-cloud-computing/spark/_index.md
index 904a80cf16..1cb62dcd41 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark/_index.md
@@ -13,6 +13,8 @@ prerequisites:
     - An Amazon Web Services (AWS) [account](https://aws.amazon.com/)
     - A machine with [Terraform](/install-guides/terraform/), [AWS CLI](/install-guides/aws-cli/), [AWS IAM authenticator](https://docs.aws.amazon.com/eks/latest/userguide/install-aws-iam-authenticator.html), and [Ansible](/install-guides/ansible/) installed 
 
+generate_summary_faq: true
+
 author: Jason Andrews
 ### Tags
 skilllevels: Introductory
diff --git a/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md b/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md
index 5fbbca7a44..e2db85b711 100644
--- a/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md
@@ -14,6 +14,8 @@ prerequisites:
     - An AWS account
     - A Remote.It account
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/sve/_index.md b/content/learning-paths/servers-and-cloud-computing/sve/_index.md
index 5a43876fff..0a6f5500be 100644
--- a/content/learning-paths/servers-and-cloud-computing/sve/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sve/_index.md
@@ -14,6 +14,8 @@ prerequisites:
     - General knowledge about SIMD processing, vectorization or Arm Neon.
     - An Arm computer running Linux. Cloud instances can be used, refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
 
+generate_summary_faq: true
+
 author: Florent Lebeau
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md b/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md
index 8f0adad89a..32cbc13934 100644
--- a/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md
@@ -16,6 +16,8 @@ learning_objectives:
 prerequisites:
 - Access to an [AWS Graviton4, Google Axion, or Azure Cobalt 100 virtual machine](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider.
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 
diff --git a/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md b/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md
index a1fa7ec098..fcbec7b9f0 100644
--- a/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md
@@ -13,6 +13,8 @@ learning_objectives:
 prerequisites:
     - An Arm-based system (bare metal server, cloud instance, developer board) running Linux 
 
+generate_summary_faq: true
+
 author: James Whitaker
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
index 6a16182926..a35f6aae94 100644
--- a/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
@@ -14,6 +14,8 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with [TensorFlow](https://www.tensorflow.org/)
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md b/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md
index a382ac0ed9..f0171ea08b 100644
--- a/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md
@@ -12,6 +12,8 @@ learning_objectives:
 prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an on-premise Arm server.
 
+generate_summary_faq: true
+
 author: ThirdAI
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md
index 38457558a2..069c857033 100644
--- a/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md
@@ -15,6 +15,8 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with SQL, Python, and Grafana
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md b/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md
index 9edf15ab66..1463568ac9 100644
--- a/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md
@@ -14,6 +14,8 @@ learning_objectives:
 prerequisites:
     - An Arm Neoverse N1 computer running Linux. A bare metal or cloud metal instance is best because they expose more counters. You can use a virtual machine (VM), but it may offer fewer counters and some commands might not succeed. These instructions have been tested on the `a1.metal` instance type.
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md b/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md
index 532d80c30a..ace2a9eea9 100644
--- a/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md
@@ -13,6 +13,8 @@ learning_objectives:
 prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an on-premise Arm server.
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md
index af5b873cd0..83f8348def 100644
--- a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md
@@ -13,6 +13,8 @@ prerequisites:
     - Some familiarity with performance analysis.
     - The ability to read Arm assembly code.
 
+generate_summary_faq: true
+
 author: Johanna Skinnider
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md
index 2decd7e628..37f9f656e7 100644
--- a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md
@@ -14,6 +14,8 @@ prerequisites:
     - Knowledge of performance analysis. 
     - The ability to read Arm assembly code.
 
+generate_summary_faq: true
+
 author: Johanna Skinnider
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md b/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md
index e08910024c..295df8896a 100644
--- a/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md
@@ -17,6 +17,8 @@ prerequisites:
   - Basic knowledge of Linux command-line operations
   - Familiarity with GitHub Actions runners
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md b/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md
index 47170a0754..9a2ce9023e 100644
--- a/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - Access to an x86_64 bare-metal server running Ubuntu 24.04 to run `wrk2`
     - Basic familiarity with Java applications
 
+generate_summary_faq: true
+
 author: Ying Yu, Ker Liu, Rui Chang
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md
index 8ca2cab57f..10ec4db9b2 100644
--- a/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md
@@ -16,6 +16,8 @@ prerequisites:
   - Basic familiarity with [TypeScript](https://www.typescriptlang.org/) and Node.js runtime environment
 
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md b/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md
index 92be71c700..1f7051acff 100644
--- a/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md
@@ -13,6 +13,8 @@ prerequisites:
     - Access to both an Arm and an x86-based cloud instance.
     - Intermediate understanding of C++, compilers, and Linux. 
 
+generate_summary_faq: true
+
 author: Kieran Hejmadi
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/vLLM-quant/_index.md b/content/learning-paths/servers-and-cloud-computing/vLLM-quant/_index.md
index 3800c186d8..8fe82c690c 100644
--- a/content/learning-paths/servers-and-cloud-computing/vLLM-quant/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vLLM-quant/_index.md
@@ -22,6 +22,8 @@ prerequisites:
     - Familiarity with Python and basic understanding of transformer models and quantization techniques.
     - An active Hugging Face account with access to the target model.
 
+generate_summary_faq: true
+
 author: 
    - Rani Chowdary Mandepudi
    - Phalani Paladugu
diff --git a/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md b/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md
index 722a058137..fb6e85873f 100644
--- a/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md
@@ -14,6 +14,8 @@ learning_objectives:
 prerequisites:
     - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an Arm server with Ubuntu 20.04 or Ubuntu 22.04 installed.
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md b/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
index 591fbc0b15..f84ff7b4d4 100644
--- a/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - An Arm-based Linux server (Ubuntu 22.04+ recommended) with a minimum of 32 vCPUs, 64 GB RAM, and 64 GB free disk space
     - Python 3.12 and basic familiarity with Hugging Face Transformers and quantization
 
+generate_summary_faq: true
+
 author:
    - Nikhil Gupta
 
diff --git a/content/learning-paths/servers-and-cloud-computing/vllm/_index.md b/content/learning-paths/servers-and-cloud-computing/vllm/_index.md
index 4231effc49..bb9edd18b6 100644
--- a/content/learning-paths/servers-and-cloud-computing/vllm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vllm/_index.md
@@ -14,6 +14,8 @@ learning_objectives:
 prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider, or a local Arm Linux computer with at least 8 CPUs and 16 GB RAM.
 
+generate_summary_faq: true
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md b/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
index e93891e74a..bd6523437a 100644
--- a/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Run the vvenc H.266 encoder on Arm servers
 
+generate_summary_faq: true
+
 author: Willen Yang
 
 minutes_to_complete: 20
diff --git a/content/learning-paths/servers-and-cloud-computing/whisper/_index.md b/content/learning-paths/servers-and-cloud-computing/whisper/_index.md
index 2842c87f36..0e5867246d 100644
--- a/content/learning-paths/servers-and-cloud-computing/whisper/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/whisper/_index.md
@@ -18,6 +18,8 @@ prerequisites:
     - Familiarity with machine learning concepts.
     - Familiarity with the fundamentals of the Whisper ASR Model.
 
+generate_summary_faq: true
+
 author: Nobel Chowdary Mandepudi
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md b/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
index 6aa31bd865..0ff40028ea 100644
--- a/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
@@ -7,6 +7,8 @@ prerequisites:
     - An OCI account
     - An Arm compute instance deployed on OCI with Oracle Linux
 
+generate_summary_faq: true
+
 author: Frédéric -lefred- Descamps
 
 who_is_this_for: This is an introductory topic for developers who want to install WordPress on Oracle Cloud Infrastructure (OCI) using always free tier.
diff --git a/content/learning-paths/servers-and-cloud-computing/zlib/_index.md b/content/learning-paths/servers-and-cloud-computing/zlib/_index.md
index 1a4f2cb5cd..e9b43205f0 100644
--- a/content/learning-paths/servers-and-cloud-computing/zlib/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/zlib/_index.md
@@ -15,6 +15,8 @@ learning_objectives:
 prerequisites:
 - An Arm Linux computer or an [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider running Ubuntu 22.04 or Ubuntu 24.04.
 
+generate_summary_faq: true
+
 author: Pareena Verma
 
 test_images:
diff --git a/reports/generated-summary-faq/latest-run.yml b/reports/generated-summary-faq/latest-run.yml
new file mode 100644
index 0000000000..ed6a25239a
--- /dev/null
+++ b/reports/generated-summary-faq/latest-run.yml
@@ -0,0 +1,21 @@
+latest_run:
+  timestamp: ""
+  mode: ""
+  require_enable_flag: true
+  path_filter: ""
+  limit: 0
+  run_url: ""
+  git_ref: ""
+  git_sha: ""
+  actor: ""
+  template_version: summary-faq-v1
+  totals:
+    processed: 0
+    added: 0
+    updated: 0
+    unchanged: 0
+    skipped: 0
+    errors: 0
+    removed: 0
+  paths: []
+history: []
diff --git a/themes/arm-design-system-hugo-theme/layouts/partials/learning-paths/generated-summary-faq.html b/themes/arm-design-system-hugo-theme/layouts/partials/learning-paths/generated-summary-faq.html
new file mode 100644
index 0000000000..0af1b2d171
--- /dev/null
+++ b/themes/arm-design-system-hugo-theme/layouts/partials/learning-paths/generated-summary-faq.html
@@ -0,0 +1,39 @@
+{{/*
+Render a generated summary paragraph and FAQ block for Learning Path introduction pages.
+
+Expected front-matter shape:
+  generated_summary_faq:
+    summary: ...
+    faqs:
+      - question: ...
+        answer: ...
+*/}}
+
+{{ $generated := .Params.generated_summary_faq }}
+{{ if $generated }}
+    {{ $summary := $generated.summary }}
+    {{ $faqs := $generated.faqs }}
+
+    {{ if or $summary $faqs }}
+        <div id="generated-summary-faq" class="u-margin-top-2">
+            {{ with $summary }}
+                <h3>Summary</h3>
+                <div class="u-margin-left-1">
+                    {{ . | markdownify }}
+                </div>
+            {{ end }}
+
+            {{ with $faqs }}
+                <h3 class="u-margin-top-2">Frequently asked questions</h3>
+                {{ range . }}
+                    <details class="u-margin-left-1 u-margin-bottom-1">
+                        <summary>{{ .question }}</summary>
+                        <div class="u-margin-top-1 u-margin-left-1">
+                            {{ .answer | markdownify }}
+                        </div>
+                    </details>
+                {{ end }}
+            {{ end }}
+        </div>
+    {{ end }}
+{{ end }}
diff --git a/themes/arm-design-system-hugo-theme/layouts/partials/learning-paths/introduction.html b/themes/arm-design-system-hugo-theme/layouts/partials/learning-paths/introduction.html
index ca1d7fa7ae..b6facffe89 100644
--- a/themes/arm-design-system-hugo-theme/layouts/partials/learning-paths/introduction.html
+++ b/themes/arm-design-system-hugo-theme/layouts/partials/learning-paths/introduction.html
@@ -35,4 +35,5 @@ <h3 class="u-margin-top-2">Prerequisites</h3>
                 <li class="u-margin-left-2">{{. | markdownify}}</li>
             {{ end }}
         </ul>
-</div>
\ No newline at end of file
+        {{ partial "learning-paths/generated-summary-faq.html" . }}
+</div>
diff --git a/tools/generate_summary_faq.py b/tools/generate_summary_faq.py
new file mode 100644
index 0000000000..971854b690
--- /dev/null
+++ b/tools/generate_summary_faq.py
@@ -0,0 +1,809 @@
+#!/usr/bin/env python3
+
+"""
+Generate summary and FAQ content for Learning Path _index.md files.
+
+This script is intentionally template-driven for the first iteration so it can
+run in CI without external AI dependencies. It:
+
+- selects eligible Learning Paths using a front-matter flag or explicit paths
+- generates a managed `generated_summary_faq` front-matter block
+- updates `_index.md` files in place when requested
+- writes a central run report with per-path change details
+
+Managed front-matter contract:
+
+    generate_summary_faq: true
+
+    # START generated_summary_faq
+    generated_summary_faq:
+      template_version: summary-faq-v1
+      generated_at: "2026-04-30T19:40:00Z"
+      generator: template
+      source_hash: "..."
+      summary: >-
+        ...
+      faqs:
+        - question: What will you accomplish in this Learning Path?
+          answer: >-
+            ...
+    # END generated_summary_faq
+"""
+
+from __future__ import annotations
+
+import argparse
+import copy
+import hashlib
+import json
+import os
+import re
+import sys
+from dataclasses import dataclass
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any, Dict, Iterable, List, Sequence
+
+import yaml
+
+
+REPO_ROOT = Path(__file__).resolve().parent.parent
+LEARNING_PATH_ROOT = REPO_ROOT / "content" / "learning-paths"
+DEFAULT_REPORT_PATH = REPO_ROOT / "reports" / "generated-summary-faq" / "latest-run.yml"
+
+ENABLE_FLAG = "generate_summary_faq"
+GENERATED_KEY = "generated_summary_faq"
+MANAGED_START = "# START generated_summary_faq"
+MANAGED_END = "# END generated_summary_faq"
+TEMPLATE_VERSION = "summary-faq-v1"
+DEFAULT_HISTORY_LIMIT = 20
+
+
+class BlockString(str):
+    """Marker type so YAML emits readable folded blocks for long prose."""
+
+
+class ReadableDumper(yaml.SafeDumper):
+    """YAML dumper that keeps generated prose readable in front matter/report files."""
+
+
+def _block_string_presenter(dumper: yaml.SafeDumper, data: BlockString) -> yaml.nodes.ScalarNode:
+    return dumper.represent_scalar("tag:yaml.org,2002:str", str(data), style=">")
+
+
+def _string_presenter(dumper: yaml.SafeDumper, data: str) -> yaml.nodes.ScalarNode:
+    if "\n" in data:
+        return dumper.represent_scalar("tag:yaml.org,2002:str", data, style=">")
+    return dumper.represent_scalar("tag:yaml.org,2002:str", data)
+
+
+ReadableDumper.add_representer(BlockString, _block_string_presenter)
+ReadableDumper.add_representer(str, _string_presenter)
+ReadableDumper.ignore_aliases = lambda self, data: True
+
+
+@dataclass
+class MarkdownDocument:
+    path: Path
+    raw_text: str
+    front_matter_text: str
+    content: str
+    metadata: Dict[str, Any]
+
+
+@dataclass
+class StepPage:
+    path: Path
+    title: str
+    weight: int
+    metadata: Dict[str, Any]
+    content: str
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Generate summary/FAQ front matter for Learning Paths.")
+    parser.add_argument(
+        "--path-filter",
+        default="",
+        help="Optional comma/newline-separated list of Learning Path directories or _index.md files.",
+    )
+    parser.add_argument(
+        "--limit",
+        type=int,
+        default=0,
+        help="Maximum number of Learning Paths to process when no explicit path filter is provided. 0 means no limit.",
+    )
+    parser.add_argument(
+        "--allow-unflagged",
+        action="store_true",
+        help=f"Process explicit or discovered Learning Paths even when `{ENABLE_FLAG}` is not true.",
+    )
+    parser.add_argument(
+        "--write",
+        action="store_true",
+        help="Persist generated front-matter updates back to each _index.md file.",
+    )
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="Compute results and print a summary without writing Learning Path files.",
+    )
+    parser.add_argument(
+        "--report-file",
+        default=str(DEFAULT_REPORT_PATH),
+        help="Path to the central run report YAML file.",
+    )
+    parser.add_argument(
+        "--no-write-report",
+        action="store_true",
+        help="Skip writing the central report file.",
+    )
+    parser.add_argument(
+        "--history-limit",
+        type=int,
+        default=DEFAULT_HISTORY_LIMIT,
+        help="Maximum number of historical run entries to retain in the central report file.",
+    )
+    parser.add_argument("--run-url", default="", help="Optional GitHub Actions run URL to store in the report.")
+    parser.add_argument("--git-ref", default="", help="Optional Git ref or branch name to store in the report.")
+    parser.add_argument("--git-sha", default="", help="Optional commit SHA to store in the report.")
+    parser.add_argument("--actor", default="", help="Optional workflow actor to store in the report.")
+
+    args = parser.parse_args()
+
+    if args.write and args.dry_run:
+        parser.error("Use either --write or --dry-run, not both.")
+    if not args.write and not args.dry_run:
+        args.dry_run = True
+
+    return args
+
+
+def read_markdown_document(path: Path, require_front_matter: bool = True) -> MarkdownDocument:
+    raw_text = path.read_text(encoding="utf-8")
+    match = re.match(r"\A---\s*\n(.*?)\n---\s*\n?(.*)\Z", raw_text, re.DOTALL)
+    if not match:
+        if require_front_matter:
+            raise ValueError(f"{path} does not contain valid YAML front matter.")
+        return MarkdownDocument(
+            path=path,
+            raw_text=raw_text,
+            front_matter_text="",
+            content=raw_text,
+            metadata={},
+        )
+
+    front_matter_text = match.group(1)
+    content = match.group(2)
+    metadata = yaml.safe_load(front_matter_text) or {}
+    if not isinstance(metadata, dict):
+        raise ValueError(f"{path} front matter did not parse as a mapping.")
+
+    return MarkdownDocument(
+        path=path,
+        raw_text=raw_text,
+        front_matter_text=front_matter_text,
+        content=content,
+        metadata=metadata,
+    )
+
+
+def fallback_title_from_content(content: str, path: Path) -> str:
+    for line in content.splitlines():
+        stripped = line.strip()
+        if not stripped:
+            continue
+        heading_match = re.match(r"^#{1,6}\s+(.*)$", stripped)
+        if heading_match:
+            return compact_whitespace(heading_match.group(1))
+    return compact_whitespace(path.stem.replace("-", " ").replace("_", " ").title())
+
+
+def normalize_path_filter(path_filter: str) -> List[Path]:
+    raw_items = [item.strip() for item in re.split(r"[\n,]+", path_filter) if item.strip()]
+    resolved: List[Path] = []
+
+    for item in raw_items:
+        candidate = Path(item)
+        if not candidate.is_absolute():
+            candidate = (REPO_ROOT / candidate).resolve()
+
+        if candidate.is_dir():
+            candidate = candidate / "_index.md"
+
+        if candidate.name != "_index.md" or not candidate.exists():
+            raise FileNotFoundError(f"Could not resolve Learning Path index from '{item}'.")
+
+        resolved.append(candidate)
+
+    return resolved
+
+
+def discover_learning_path_indexes() -> List[Path]:
+    indexes = sorted(LEARNING_PATH_ROOT.glob("*/*/_index.md"))
+    return [path for path in indexes if path.is_file()]
+
+
+def has_enable_flag(doc: MarkdownDocument) -> bool:
+    return bool(doc.metadata.get(ENABLE_FLAG))
+
+
+def is_draft(doc: MarkdownDocument) -> bool:
+    return bool(doc.metadata.get("draft", False))
+
+
+def load_steps(index_path: Path) -> List[StepPage]:
+    steps: List[StepPage] = []
+    for md_path in index_path.parent.glob("*.md"):
+        doc = read_markdown_document(md_path, require_front_matter=False)
+        weight = doc.metadata.get("weight", 99999)
+        if not isinstance(weight, int):
+            try:
+                weight = int(weight)
+            except Exception:
+                weight = 99999
+
+        title = str(doc.metadata.get("title", "")).strip() or fallback_title_from_content(doc.content, md_path)
+        steps.append(
+            StepPage(
+                path=md_path,
+                title=title,
+                weight=weight,
+                metadata=doc.metadata,
+                content=doc.content,
+            )
+        )
+
+    steps.sort(key=lambda step: (step.weight, step.path.name))
+    return steps
+
+
+def compact_whitespace(value: str) -> str:
+    return re.sub(r"\s+", " ", value).strip()
+
+
+def ensure_sentence(value: str) -> str:
+    cleaned = compact_whitespace(value)
+    if not cleaned:
+        return ""
+    if cleaned[-1] not in ".!?":
+        cleaned += "."
+    return cleaned
+
+
+def strip_markdown_links(text: str) -> str:
+    text = re.sub(r"\[([^\]]+)\]\([^)]+\)", r"\1", text)
+    text = re.sub(r"`([^`]+)`", r"\1", text)
+    return compact_whitespace(text)
+
+
+def normalize_audience(value: str) -> str:
+    cleaned = compact_whitespace(value)
+    patterns = [
+        r"^This Learning Path is for\s+",
+        r"^This learning path is for\s+",
+        r"^This topic is for\s+",
+        r"^This is an? [^.]*? topic for\s+",
+        r"^This is for\s+",
+    ]
+    for pattern in patterns:
+        cleaned = re.sub(pattern, "", cleaned, flags=re.IGNORECASE)
+    return cleaned.rstrip(". ")
+
+
+def normalize_objective_for_sentence(objective: str) -> str:
+    cleaned = compact_whitespace(objective).rstrip(". ")
+    if not cleaned:
+        return ""
+    return cleaned[0].lower() + cleaned[1:] if len(cleaned) > 1 else cleaned.lower()
+
+
+def natural_join(items: Sequence[str], conjunction: str = "and") -> str:
+    cleaned = [compact_whitespace(str(item)) for item in items if compact_whitespace(str(item))]
+    if not cleaned:
+        return ""
+    if len(cleaned) == 1:
+        return cleaned[0]
+    if len(cleaned) == 2:
+        return f"{cleaned[0]} {conjunction} {cleaned[1]}"
+    return f"{', '.join(cleaned[:-1])}, {conjunction} {cleaned[-1]}"
+
+
+def semicolon_join(items: Sequence[str]) -> str:
+    cleaned = [compact_whitespace(str(item)) for item in items if compact_whitespace(str(item))]
+    return "; ".join(cleaned)
+
+
+def unique_strings(items: Iterable[Any]) -> List[str]:
+    seen = set()
+    ordered: List[str] = []
+    for item in items:
+        text = compact_whitespace(str(item))
+        if text and text not in seen:
+            ordered.append(text)
+            seen.add(text)
+    return ordered
+
+
+def as_list(value: Any) -> List[Any]:
+    if value is None:
+        return []
+    if isinstance(value, list):
+        return value
+    return [value]
+
+
+def build_platform_sentence(metadata: Dict[str, Any]) -> str:
+    tools = unique_strings(as_list(metadata.get("tools_software_languages")))
+    operating_systems = unique_strings(as_list(metadata.get("operatingsystems")))
+    arm_ips = [item for item in unique_strings(as_list(metadata.get("armips"))) if item.lower() != "all"]
+    cloud_providers = unique_strings(as_list(metadata.get("cloud_service_providers")))
+
+    parts: List[str] = []
+
+    if tools:
+        parts.append(f"tools and technologies such as {natural_join(tools[:5])}")
+    if operating_systems:
+        parts.append(f"{natural_join(operating_systems[:4])} environments")
+    if arm_ips:
+        parts.append(f"Arm platforms including {natural_join(arm_ips[:4])}")
+    if cloud_providers:
+        parts.append(f"cloud platforms such as {natural_join(cloud_providers[:4])}")
+
+    if not parts:
+        return ""
+
+    return ensure_sentence(f"It focuses on {natural_join(parts, conjunction='and')}")
+
+
+def build_step_sentence(steps: Sequence[StepPage]) -> str:
+    visible_titles = [
+        step.title
+        for step in steps
+        if step.path.name not in {"_index.md", "_next-steps.md", "_review.md", "_demo.md"}
+        and not step.metadata.get("hide_from_navpane", False)
+        and step.title
+    ]
+    if not visible_titles:
+        return ""
+    selected = visible_titles[:5]
+    return ensure_sentence(f"The main steps cover {natural_join(selected)}")
+
+
+def build_summary(metadata: Dict[str, Any], steps: Sequence[StepPage]) -> str:
+    title = compact_whitespace(str(metadata.get("title", "This Learning Path")))
+    description = ensure_sentence(str(metadata.get("description", "")).strip())
+    audience = normalize_audience(str(metadata.get("who_is_this_for", "")).strip())
+    objectives = [normalize_objective_for_sentence(item) for item in as_list(metadata.get("learning_objectives"))]
+    objectives = [objective for objective in objectives if objective]
+
+    sentences: List[str] = []
+
+    if description:
+        sentences.append(description)
+    else:
+        sentences.append(ensure_sentence(f"{title} walks you through an end-to-end Arm software workflow"))
+
+    if audience:
+        sentences.append(ensure_sentence(f"It is designed for {audience}"))
+
+    if objectives:
+        sentences.append(ensure_sentence(f"By the end, you will be able to {natural_join(objectives[:3])}"))
+
+    platform_sentence = build_platform_sentence(metadata)
+    if platform_sentence:
+        sentences.append(platform_sentence)
+
+    step_sentence = build_step_sentence(steps)
+    if step_sentence:
+        sentences.append(step_sentence)
+
+    return " ".join(sentence for sentence in sentences if sentence)
+
+
+def build_faqs(metadata: Dict[str, Any], steps: Sequence[StepPage]) -> List[Dict[str, str]]:
+    description = ensure_sentence(str(metadata.get("description", "")).strip())
+    audience_raw = ensure_sentence(str(metadata.get("who_is_this_for", "")).strip())
+    prerequisites = [str(item).strip() for item in as_list(metadata.get("prerequisites")) if str(item).strip()]
+    objectives = [normalize_objective_for_sentence(item) for item in as_list(metadata.get("learning_objectives"))]
+    objectives = [objective for objective in objectives if objective]
+
+    tools = unique_strings(as_list(metadata.get("tools_software_languages")))
+    operating_systems = unique_strings(as_list(metadata.get("operatingsystems")))
+    arm_ips = [item for item in unique_strings(as_list(metadata.get("armips"))) if item.lower() != "all"]
+    cloud_providers = unique_strings(as_list(metadata.get("cloud_service_providers")))
+
+    visible_titles = [
+        step.title
+        for step in steps
+        if step.path.name not in {"_index.md", "_next-steps.md", "_review.md", "_demo.md"}
+        and not step.metadata.get("hide_from_navpane", False)
+        and step.title
+    ]
+
+    accomplishment_parts: List[str] = []
+    if objectives:
+        accomplishment_parts.append(ensure_sentence(f"You will {natural_join(objectives[:3])}"))
+    if description:
+        accomplishment_parts.append(description)
+
+    prerequisites_answer = (
+        ensure_sentence(f"Before you start, make sure you have the following: {semicolon_join(prerequisites)}")
+        if prerequisites
+        else "There are no explicit prerequisites listed for this Learning Path."
+    )
+
+    coverage_parts: List[str] = []
+    if tools:
+        coverage_parts.append(f"tools and languages including {natural_join(tools[:5])}")
+    if operating_systems:
+        coverage_parts.append(f"{natural_join(operating_systems[:4])} environments")
+    if arm_ips:
+        coverage_parts.append(f"Arm platforms such as {natural_join(arm_ips[:4])}")
+    if cloud_providers:
+        coverage_parts.append(f"cloud platforms such as {natural_join(cloud_providers[:4])}")
+
+    structure_answer = (
+        ensure_sentence(f"The Learning Path is organized around {natural_join(visible_titles[:5])}")
+        if visible_titles
+        else "The Learning Path follows the standard introduction, guided steps, and next steps structure."
+    )
+
+    faqs = [
+        {
+            "question": "What will you accomplish in this Learning Path?",
+            "answer": " ".join(part for part in accomplishment_parts if part)
+            or "You will work through an Arm-focused workflow and finish with a concrete outcome.",
+        },
+        {
+            "question": "Who is this Learning Path for?",
+            "answer": audience_raw or "This Learning Path is written for Arm software developers.",
+        },
+        {
+            "question": "What do you need before you start?",
+            "answer": prerequisites_answer,
+        },
+        {
+            "question": "Which tools, languages, or platforms does it cover?",
+            "answer": ensure_sentence(f"It covers {natural_join(coverage_parts, conjunction='and')}")
+            if coverage_parts
+            else "It focuses on the tools, platforms, and steps listed in the Learning Path itself.",
+        },
+        {
+            "question": "How is the Learning Path structured?",
+            "answer": structure_answer,
+        },
+    ]
+
+    return faqs
+
+
+def build_source_hash(metadata: Dict[str, Any], steps: Sequence[StepPage]) -> str:
+    relevant = {
+        "title": metadata.get("title"),
+        "description": metadata.get("description"),
+        "who_is_this_for": metadata.get("who_is_this_for"),
+        "learning_objectives": metadata.get("learning_objectives"),
+        "prerequisites": metadata.get("prerequisites"),
+        "tools_software_languages": metadata.get("tools_software_languages"),
+        "operatingsystems": metadata.get("operatingsystems"),
+        "armips": metadata.get("armips"),
+        "cloud_service_providers": metadata.get("cloud_service_providers"),
+        "step_titles": [
+            {
+                "file": step.path.name,
+                "title": step.title,
+                "weight": step.weight,
+                "hidden": bool(step.metadata.get("hide_from_navpane", False)),
+            }
+            for step in steps
+        ],
+    }
+    payload = json.dumps(relevant, sort_keys=True, ensure_ascii=False)
+    return hashlib.sha256(payload.encode("utf-8")).hexdigest()
+
+
+def build_generated_block(metadata: Dict[str, Any], steps: Sequence[StepPage]) -> Dict[str, Any]:
+    generated_at = datetime.now(timezone.utc).replace(microsecond=0).isoformat().replace("+00:00", "Z")
+    summary = build_summary(metadata, steps)
+    faqs = build_faqs(metadata, steps)
+
+    wrapped_faqs = []
+    for faq in faqs:
+        wrapped_faqs.append(
+            {
+                "question": faq["question"],
+                "answer": BlockString(faq["answer"]),
+            }
+        )
+
+    return {
+        "template_version": TEMPLATE_VERSION,
+        "generated_at": generated_at,
+        "generator": "template",
+        "source_hash": build_source_hash(metadata, steps),
+        "summary": BlockString(summary),
+        "faqs": wrapped_faqs,
+    }
+
+
+def make_managed_yaml_block(generated_block: Dict[str, Any]) -> str:
+    serializable = {GENERATED_KEY: copy.deepcopy(generated_block)}
+    yaml_block = yaml.dump(
+        serializable,
+        Dumper=ReadableDumper,
+        sort_keys=False,
+        allow_unicode=True,
+        width=92,
+        default_flow_style=False,
+    ).rstrip()
+
+    lines = [MANAGED_START, yaml_block, MANAGED_END]
+    return "\n".join(lines)
+
+
+def insert_or_replace_managed_block(front_matter_text: str, generated_block: Dict[str, Any]) -> str:
+    managed_block = make_managed_yaml_block(generated_block)
+    marker_pattern = re.compile(
+        rf"(?ms)^[ \t]*{re.escape(MANAGED_START)}\n.*?^[ \t]*{re.escape(MANAGED_END)}[ \t]*\n?"
+    )
+
+    if marker_pattern.search(front_matter_text):
+        updated = marker_pattern.sub(managed_block + "\n", front_matter_text).rstrip()
+        return updated
+
+    insertion_patterns = [
+        re.compile(r"(?m)^author:\s"),
+        re.compile(r"(?m)^### Tags\s*$"),
+        re.compile(r"(?m)^### FIXED, DO NOT MODIFY\s*$"),
+    ]
+
+    for pattern in insertion_patterns:
+        match = pattern.search(front_matter_text)
+        if match:
+            insert_at = match.start()
+            prefix = front_matter_text[:insert_at].rstrip()
+            suffix = front_matter_text[insert_at:].lstrip("\n")
+            parts = [part for part in [prefix, managed_block, suffix] if part]
+            return "\n\n".join(parts).rstrip()
+
+    return (front_matter_text.rstrip() + "\n\n" + managed_block).rstrip()
+
+
+def rebuild_markdown(doc: MarkdownDocument, updated_front_matter_text: str) -> str:
+    content = doc.content.lstrip("\n")
+    return f"---\n{updated_front_matter_text.rstrip()}\n---\n\n{content}"
+
+
+def classify_faq_changes(existing: Dict[str, Any], new: Dict[str, Any]) -> Dict[str, Any]:
+    existing_faqs = existing.get("faqs") or []
+    new_faqs = new.get("faqs") or []
+
+    existing_by_question = {
+        faq.get("question"): compact_whitespace(str(faq.get("answer", "")))
+        for faq in existing_faqs
+        if isinstance(faq, dict) and faq.get("question")
+    }
+    new_by_question = {
+        faq.get("question"): compact_whitespace(str(faq.get("answer", "")))
+        for faq in new_faqs
+        if isinstance(faq, dict) and faq.get("question")
+    }
+
+    added_questions = [question for question in new_by_question if question not in existing_by_question]
+    removed_questions = [question for question in existing_by_question if question not in new_by_question]
+    updated_questions = [
+        question
+        for question in new_by_question
+        if question in existing_by_question and new_by_question[question] != existing_by_question[question]
+    ]
+
+    return {
+        "before_count": len(existing_by_question),
+        "after_count": len(new_by_question),
+        "added_questions": added_questions,
+        "removed_questions": removed_questions,
+        "updated_questions": updated_questions,
+    }
+
+
+def classify_change(existing: Dict[str, Any] | None, new: Dict[str, Any]) -> Dict[str, Any]:
+    if existing is None:
+        faq_changes = classify_faq_changes({}, new)
+        return {
+            "status": "added",
+            "summary_changed": True,
+            "faq_changed": bool(new.get("faqs")),
+            "faq_changes": faq_changes,
+        }
+
+    existing_for_compare = copy.deepcopy(existing)
+    new_for_compare = copy.deepcopy(new)
+
+    existing_for_compare.pop("generated_at", None)
+    new_for_compare.pop("generated_at", None)
+
+    summary_changed = compact_whitespace(str(existing_for_compare.get("summary", ""))) != compact_whitespace(
+        str(new_for_compare.get("summary", ""))
+    )
+
+    faq_changes = classify_faq_changes(existing_for_compare, new_for_compare)
+    faq_changed = bool(
+        faq_changes["added_questions"] or faq_changes["removed_questions"] or faq_changes["updated_questions"]
+    )
+
+    status = "updated" if existing_for_compare != new_for_compare else "unchanged"
+    return {
+        "status": status,
+        "summary_changed": summary_changed,
+        "faq_changed": faq_changed,
+        "faq_changes": faq_changes,
+    }
+
+
+def report_path_for_output(path: Path) -> str:
+    try:
+        return str(path.relative_to(REPO_ROOT))
+    except ValueError:
+        return str(path)
+
+
+def build_run_report(
+    args: argparse.Namespace,
+    processed_paths: Sequence[Path],
+    per_path_results: Sequence[Dict[str, Any]],
+) -> Dict[str, Any]:
+    totals = {
+        "processed": len(processed_paths),
+        "added": sum(1 for result in per_path_results if result["status"] == "added"),
+        "updated": sum(1 for result in per_path_results if result["status"] == "updated"),
+        "unchanged": sum(1 for result in per_path_results if result["status"] == "unchanged"),
+        "skipped": sum(1 for result in per_path_results if result["status"] == "skipped"),
+        "errors": sum(1 for result in per_path_results if result["status"] == "error"),
+        "removed": 0,
+    }
+
+    return {
+        "timestamp": datetime.now(timezone.utc).replace(microsecond=0).isoformat().replace("+00:00", "Z"),
+        "mode": "write" if args.write else "dry-run",
+        "require_enable_flag": not args.allow_unflagged,
+        "path_filter": args.path_filter or "",
+        "limit": args.limit,
+        "run_url": args.run_url or "",
+        "git_ref": args.git_ref or "",
+        "git_sha": args.git_sha or "",
+        "actor": args.actor or "",
+        "template_version": TEMPLATE_VERSION,
+        "totals": totals,
+        "paths": per_path_results,
+    }
+
+
+def write_report(report_file: Path, run_report: Dict[str, Any], history_limit: int) -> None:
+    report_file.parent.mkdir(parents=True, exist_ok=True)
+
+    existing_history: List[Dict[str, Any]] = []
+    if report_file.exists():
+        try:
+            current = yaml.safe_load(report_file.read_text(encoding="utf-8")) or {}
+            existing_history = current.get("history", []) or []
+        except Exception:
+            existing_history = []
+
+    history = [copy.deepcopy(run_report)] + existing_history
+    history = history[:history_limit]
+
+    payload = {
+        "latest_run": copy.deepcopy(run_report),
+        "history": history,
+    }
+
+    report_text = yaml.dump(
+        payload,
+        Dumper=ReadableDumper,
+        sort_keys=False,
+        allow_unicode=True,
+        width=100,
+        default_flow_style=False,
+    )
+    report_file.write_text(report_text, encoding="utf-8")
+
+
+def print_result_summary(run_report: Dict[str, Any]) -> None:
+    totals = run_report["totals"]
+    print(
+        "Processed {processed} Learning Paths: "
+        "{added} added, {updated} updated, {unchanged} unchanged, {errors} errors.".format(**totals)
+    )
+    for result in run_report["paths"]:
+        status = result["status"]
+        print(f"- {status.upper():9s} {result['path']}")
+
+
+def select_learning_paths(args: argparse.Namespace) -> List[Path]:
+    explicit_paths = normalize_path_filter(args.path_filter) if args.path_filter else []
+    selected = explicit_paths or discover_learning_path_indexes()
+    filtered: List[Path] = []
+
+    for index_path in selected:
+        doc = read_markdown_document(index_path)
+        if is_draft(doc):
+            continue
+        if not args.allow_unflagged and not has_enable_flag(doc):
+            continue
+        filtered.append(index_path)
+
+    if not explicit_paths and args.limit > 0:
+        filtered = filtered[: args.limit]
+
+    return filtered
+
+
+def process_learning_path(index_path: Path, args: argparse.Namespace) -> Dict[str, Any]:
+    try:
+        doc = read_markdown_document(index_path)
+        steps = load_steps(index_path)
+        existing_generated = doc.metadata.get(GENERATED_KEY)
+        if existing_generated is not None and not isinstance(existing_generated, dict):
+            raise ValueError(f"{GENERATED_KEY} in {index_path} must be a mapping when present.")
+
+        new_generated = build_generated_block(doc.metadata, steps)
+        change = classify_change(existing_generated, new_generated)
+
+        updated_front_matter = insert_or_replace_managed_block(doc.front_matter_text, new_generated)
+        updated_markdown = rebuild_markdown(doc, updated_front_matter)
+        changed_on_disk = updated_markdown != doc.raw_text
+
+        if args.write and changed_on_disk:
+            index_path.write_text(updated_markdown, encoding="utf-8")
+
+        preview_summary = compact_whitespace(strip_markdown_links(str(new_generated.get("summary", ""))))
+        preview_summary = preview_summary[:200] + ("..." if len(preview_summary) > 200 else "")
+
+        return {
+            "path": report_path_for_output(index_path),
+            "status": change["status"] if changed_on_disk else "unchanged",
+            "changed_on_disk": changed_on_disk,
+            "summary_changed": change["summary_changed"],
+            "faq_changed": change["faq_changed"],
+            "faq_changes": change["faq_changes"],
+            "summary_preview": preview_summary,
+            "source_hash": new_generated["source_hash"],
+        }
+    except Exception as exc:
+        return {
+            "path": report_path_for_output(index_path),
+            "status": "error",
+            "error": str(exc),
+        }
+
+
+def main() -> int:
+    args = parse_args()
+    selected_paths = select_learning_paths(args)
+
+    if not selected_paths:
+        print("No Learning Paths matched the current selection rules.")
+        run_report = build_run_report(args, [], [])
+        if not args.no_write_report:
+            write_report(Path(args.report_file), run_report, args.history_limit)
+            print(f"Wrote report to {report_path_for_output(Path(args.report_file))}")
+        return 0
+
+    results = [process_learning_path(path, args) for path in selected_paths]
+    run_report = build_run_report(args, selected_paths, results)
+
+    if not args.no_write_report:
+        write_report(Path(args.report_file), run_report, args.history_limit)
+        print(f"Wrote report to {report_path_for_output(Path(args.report_file))}")
+
+    print_result_summary(run_report)
+
+    if run_report["totals"]["errors"] > 0:
+        return 1
+
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

From 4c5f94dd928413a40b0c163d7103c241b18b5f99 Mon Sep 17 00:00:00 2001
From: GitHub Actions Summary FAQ Bot <>
Date: Thu, 30 Apr 2026 18:58:22 +0000
Subject: [PATCH 02/23] Generate Learning Path summary and FAQ content

---
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 .../mariadb/_index.md                         |    47 +
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 .../multiarch_ollama_on_gke/_index.md         |    47 +
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 .../openbmc-rdv3/_index.md                    |    53 +
 .../openrng-with-performix/_index.md          |    50 +-
 .../openshift/_index.md                       |    37 +
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 .../php-on-gcp/_index.md                      |    45 +
 .../pinning-threads/_index.md                 |    46 +
 .../pmuv3_plugin_learning_path/_index.md      |    45 +-
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 .../postgresql/_index.md                      |    38 +-
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 reports/generated-summary-faq/latest-run.yml  | 16321 +++++++++++++++-
 408 files changed, 34187 insertions(+), 27 deletions(-)

diff --git a/content/learning-paths/automotive/openadkit1_container/_index.md b/content/learning-paths/automotive/openadkit1_container/_index.md
index 0222646880..e8cb96684c 100644
--- a/content/learning-paths/automotive/openadkit1_container/_index.md
+++ b/content/learning-paths/automotive/openadkit1_container/_index.md
@@ -17,6 +17,52 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
+  summary: >-
+    Learn how to deploy and run containerized autonomous driving simulations using Autoware Open
+    AD Kit on Arm Neoverse with Docker, demonstrating SOAFEE-based Shift-Left development workflows.
+    It is designed for automotive developers, aimed at helping them accelerate autonomous driving
+    software development before automotive hardware is available. By the end, you will be able
+    to understand the SOAFEE architecture and its role in supporting Shift-Left software development
+    strategies to optimize the autonomous driving development process, use the Autoware Open AD
+    Kit simulation environment, and run containerized workloads on Arm Neoverse processors with
+    Docker, supporting execution on both cloud-based and on-premise servers. It focuses on tools
+    and technologies such as Python, Docker, and ROS 2, Linux environments, and Arm platforms
+    including Neoverse. The main steps cover About Software-Defined Vehicles and SOAFEE, Learn
+    about ROS 2 and Open AD Kit, Set up Open AD Kit, and Run the Open AD Kit demo.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand the SOAFEE architecture and its role in supporting Shift-Left software
+      development strategies to optimize the autonomous driving development process, use the Autoware
+      Open AD Kit simulation environment, and run containerized workloads on Arm Neoverse processors
+      with Docker, supporting execution on both cloud-based and on-premise servers. Learn how
+      to deploy and run containerized autonomous driving simulations using Autoware Open AD Kit
+      on Arm Neoverse with Docker, demonstrating SOAFEE-based Shift-Left development workflows.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for automotive developers, aimed at helping them accelerate
+      autonomous driving software development before automotive hardware is available.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm Neoverse cloud instance, or a
+      local Arm Neoverse Linux computer with at least 16 CPUs and 32GB of RAM; Familiarity with
+      Docker and Docker Compose.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Python, Docker, and ROS 2, Linux environments, and
+      Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around About Software-Defined Vehicles and SOAFEE, Learn
+      about ROS 2 and Open AD Kit, Set up Open AD Kit, and Run the Open AD Kit demo.
+# END generated_summary_faq
+
 author: Odin Shen
 
 ### Tags
@@ -58,3 +104,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md b/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
index 3c8dcd74d0..ac9fc9ca3b 100644
--- a/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
+++ b/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
@@ -18,6 +18,61 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 92a6dac2b1674a44cd623a0c8c3189b38438124a6067ed7ca776999ff1d8b5bf
+  summary: >-
+    Learn how to implement functional safety isolation for autonomous driving systems on Arm Neoverse
+    using DDS-based communication, containerized deployment, and ISO 26262 compliance principles.
+    It is designed for automotive engineers developing safety-critical systems. You'll learn how
+    to accelerate ISO 26262-compliant development workflows using Arm-based cloud compute, containerized
+    simulation, and DDS-based communication. By the end, you will be able to apply functional
+    safety principles, including risk prevention, fault detection, and ASIL compliance, to build
+    robust, certifiable automotive systems, use DDS and a publish-subscribe architecture for low-latency,
+    scalable, and fault-tolerant communication in autonomous driving systems, and implement distributed
+    development by separating the simulation platform into independent, safety-isolated components.
+    It focuses on tools and technologies such as Python, Docker, ROS 2, and DDS, Linux environments,
+    and Arm platforms including Neoverse. The main steps cover Why functional safety matters in
+    software systems, Understand functional safety risks, Apply ISO 26262 and ASIL levels, Implement
+    safety-critical isolation using safety island architecture, and Functional safety for automotive
+    software development.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will apply functional safety principles, including risk prevention, fault detection,
+      and ASIL compliance, to build robust, certifiable automotive systems, use DDS and a publish-subscribe
+      architecture for low-latency, scalable, and fault-tolerant communication in autonomous driving
+      systems, and implement distributed development by separating the simulation platform into
+      independent, safety-isolated components. Learn how to implement functional safety isolation
+      for autonomous driving systems on Arm Neoverse using DDS-based communication, containerized
+      deployment, and ISO 26262 compliance principles.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is for automotive engineers developing safety-critical systems. You'll
+      learn how to accelerate ISO 26262-compliant development workflows using Arm-based cloud
+      compute, containerized simulation, and DDS-based communication.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Access to two Arm-based Neoverse cloud
+      instances, or a local Arm Neoverse Linux system with at least 16 CPUs and 32 GB of RAM;
+      Completion of the [Deploy Open AD Kit containerized autonomous driving simulation on Arm
+      Neoverse](/learning-paths/automotive/openadkit1_container/) Learning Path; Basic familiarity
+      with Docker.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Python, Docker, ROS 2, and DDS, Linux environments,
+      and Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Why functional safety matters in software systems,
+      Understand functional safety risks, Apply ISO 26262 and ASIL levels, Implement safety-critical
+      isolation using safety island architecture, and Functional safety for automotive software
+      development.
+# END generated_summary_faq
+
 author: 
     - Odin Shen
     - Julien Jayat
@@ -77,3 +132,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/automotive/system76-auto/_index.md b/content/learning-paths/automotive/system76-auto/_index.md
index 0526e6a102..e42ec5ff8a 100644
--- a/content/learning-paths/automotive/system76-auto/_index.md
+++ b/content/learning-paths/automotive/system76-auto/_index.md
@@ -16,6 +16,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 2b758d2dcf28a683ab164e28578a736d6d730b81dfbc01a6765619052fcdebd0
+  summary: >-
+    Learn how to build and run the Arm Automotive Solutions Software Reference Stack locally on
+    the System76 Thelio Astra desktop using Multipass virtualization and Yocto build tools. It
+    is designed for automotive developers interested in local development using the System76 Thelio
+    Astra Linux desktop computer. By the end, you will be able to create an efficient automotive
+    development environment on the System76 Thelio Astra desktop and build and run the Arm Automotive
+    Solutions Software Reference Stack locally. It focuses on tools and technologies such as Multipass,
+    Yocto, Docker, and Git, Linux environments, and Arm platforms including Neoverse. The main
+    steps cover Thelio Astra, Set up an automotive development environment, Arm Automotive Solutions
+    Software Reference Stack, Build the Arm Automotive Solutions Software Reference Stack, and
+    Parsec.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create an efficient automotive development environment on the System76 Thelio Astra
+      desktop and build and run the Arm Automotive Solutions Software Reference Stack locally.
+      Learn how to build and run the Arm Automotive Solutions Software Reference Stack locally
+      on the System76 Thelio Astra desktop using Multipass virtualization and Yocto build tools.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for automotive developers interested in local development
+      using the System76 Thelio Astra Linux desktop computer.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A System76 Thelio Astra desktop computer
+      running Ubuntu 24.04.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Multipass, Yocto, Docker, and Git, Linux environments,
+      and Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Thelio Astra, Set up an automotive development environment,
+      Arm Automotive Solutions Software Reference Stack, Build the Arm Automotive Solutions Software
+      Reference Stack, and Parsec.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
@@ -48,3 +91,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/automotive/zenacssdebug/_index.md b/content/learning-paths/automotive/zenacssdebug/_index.md
index 6c594fd158..07c5167009 100644
--- a/content/learning-paths/automotive/zenacssdebug/_index.md
+++ b/content/learning-paths/automotive/zenacssdebug/_index.md
@@ -20,6 +20,53 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 8dccdbdd4d9727f3466987ce918d9df0ed9d4d4f28cd613162bacab01d9c18f3
+  summary: >-
+    Learn how to debug the Arm Zena CSS Reference Software Stack using Arm Development Studio
+    on a Fixed Virtual Platform, covering RSE, Safety Island, and Linux kernel debugging workflows.
+    It is designed for This introductory topic is for software developers who want to use Arm
+    Development Studio to explore and debug the Arm Zena Compute Subsystem (CSS) Reference Software
+    Stack on a Fixed Virtual Platform (FVP). By the end, you will be able to set up and save a
+    debug configuration for the Arm Zena CSS FVP, start Runtime Security Engine (RSE) debug at
+    reset and step through early boot, and attach to and debug Safety Island (SI) firmware. It
+    focuses on tools and technologies such as Arm Development Studio, Arm Zena CSS, and FVP, Linux
+    environments, and Arm platforms including Cortex-A and Cortex-R. The main steps cover Getting
+    started, Launch the FVP, Configure the model, Create debug connections, and Debug RSE from
+    reset.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will set up and save a debug configuration for the Arm Zena CSS FVP, start Runtime Security
+      Engine (RSE) debug at reset and step through early boot, and attach to and debug Safety
+      Island (SI) firmware. Learn how to debug the Arm Zena CSS Reference Software Stack using
+      Arm Development Studio on a Fixed Virtual Platform, covering RSE, Safety Island, and Linux
+      kernel debugging workflows.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This introductory topic is for software developers who want to use Arm Development Studio
+      to explore and debug the Arm Zena Compute Subsystem (CSS) Reference Software Stack on a
+      Fixed Virtual Platform (FVP).
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Ubuntu 22.04 host machine; Arm Development
+      Studio 2024.1 or later with a valid license - for support see the [Install Guide for Arm
+      DS](/install-guides/armds); Basic understanding of the Arm Zena CSS software stack, Armv8-A/Armv9-A
+      cores, and Linux.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Arm Development Studio, Arm Zena CSS, and FVP, Linux
+      environments, and Arm platforms such as Cortex-A and Cortex-R.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Getting started, Launch the FVP, Configure the model,
+      Create debug connections, and Debug RSE from reset.
+# END generated_summary_faq
+
 author: Ronan Synnott
 
 ### Tags
@@ -52,3 +99,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/_example-learning-path/_index.md b/content/learning-paths/cross-platform/_example-learning-path/_index.md
index 5a7b7a201c..1f6ea18b81 100644
--- a/content/learning-paths/cross-platform/_example-learning-path/_index.md
+++ b/content/learning-paths/cross-platform/_example-learning-path/_index.md
@@ -18,6 +18,42 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: a58d5eb4c4256f3e4f4374344ef0e73836e8b51ac7223b0a5238b22137ec0819
+  summary: >-
+    Learn what type of content belongs in a Learning Path and how to format it. It is designed
+    for content creators and software developers who want to share Arm related information as
+    a step-by-step guide called a Learning Path. By the end, you will be able to understand what
+    type of content belongs in a Learning Path, set up the required tools for Learning Path creation,
+    and write and format your own Learning Path using markdown. It focuses on tools and technologies
+    such as Hugo. The main steps cover Learning Path basics, Learning Path setup, Create a new
+    Learning Path, Modify Learning Path metadata, and Contribute.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand what type of content belongs in a Learning Path, set up the required
+      tools for Learning Path creation, and write and format your own Learning Path using markdown.
+      Learn what type of content belongs in a Learning Path and how to format it.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This topic is for content creators and software developers who want to share Arm related
+      information as a step-by-step guide called a Learning Path.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [GitHub](https://github.com/) account.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Hugo.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Learning Path basics, Learning Path setup, Create
+      a new Learning Path, Modify Learning Path metadata, and Contribute.
+# END generated_summary_faq
+
 author: Zach Lasiuk
 
 ### Tags
@@ -43,3 +79,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/adler32/_index.md b/content/learning-paths/cross-platform/adler32/_index.md
index e0c67bcc0f..a4a5d82b8f 100644
--- a/content/learning-paths/cross-platform/adler32/_index.md
+++ b/content/learning-paths/cross-platform/adler32/_index.md
@@ -16,6 +16,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 37b19106fba70423ba8c58f82097d9251f0ae208e882c852f9c4531afcebb46c
+  summary: >-
+    Learn how to use GitHub Copilot to write Neon intrinsics that accelerate the Adler32 checksum
+    algorithm on Arm platforms, achieving significant performance improvements over standard C
+    implementations. It is designed for C/C++ developers who are interested in using GitHub Copilot
+    to improve performance using Neon intrinsics. By the end, you will be able to use GitHub Copilot
+    to write Neon intrinsics that accelerate the Adler32 checksum algorithm. It focuses on tools
+    and technologies such as GCC and Runbook, Linux environments, and Arm platforms including
+    Neoverse and Cortex-A. The main steps cover About Neon and Adler32, Create a C Version of
+    Adler32, Create a test program, Create a Makefile, and Build and run the test program.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will use GitHub Copilot to write Neon intrinsics that accelerate the Adler32 checksum
+      algorithm. Learn how to use GitHub Copilot to write Neon intrinsics that accelerate the
+      Adler32 checksum algorithm on Arm platforms, achieving significant performance improvements
+      over standard C implementations.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for C/C++ developers who are interested in using GitHub Copilot
+      to improve performance using Neon intrinsics.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm computer running Linux with the
+      GNU compiler (gcc) installed.; Visual Studio Code with the GitHub Copilot extension installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including GCC and Runbook, Linux environments, and Arm platforms
+      such as Neoverse and Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around About Neon and Adler32, Create a C Version of Adler32,
+      Create a test program, Create a Makefile, and Build and run the test program.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
@@ -58,3 +98,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md b/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md
index 1415bd64fc..13392da4fe 100644
--- a/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md
+++ b/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md
@@ -19,6 +19,53 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 597877722e5f277e5558e29ecd33878ac2262b70a84a9769325b3c3b0ce06e58
+  summary: >-
+    Learn how to automate integration testing of MCP servers using Testcontainers and PyTest,
+    with hands-on examples and GitHub Actions CI/CD configuration. It is designed for software
+    developers and QA engineers who want to automate integration testing of Model Context Protocol
+    (MCP) servers using Testcontainers and PyTest. By the end, you will be able to set up Testcontainers
+    with PyTest for containerized testing of MCP servers, write and run integration tests that
+    validate MCP server functionality, and configure GitHub Actions to automate MCP server testing
+    in CI/CD pipelines. It focuses on tools and technologies such as Python, Pytest, Docker, GitHub
+    Actions, and Testcontainers, Linux, macOS, and Windows environments, and Arm platforms including
+    Neoverse and Cortex-A. The main steps cover Introduction to MCP server testing, Set up your
+    testing environment, Run a basic Testcontainers example, Write integration tests for MCP servers,
+    and Configure GitHub Actions for CI/CD.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will set up Testcontainers with PyTest for containerized testing of MCP servers, write
+      and run integration tests that validate MCP server functionality, and configure GitHub Actions
+      to automate MCP server testing in CI/CD pipelines. Learn how to automate integration testing
+      of MCP servers using Testcontainers and PyTest, with hands-on examples and GitHub Actions
+      CI/CD configuration.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers and QA engineers who want to automate
+      integration testing of Model Context Protocol (MCP) servers using Testcontainers and PyTest.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A computer with [Docker](/install-guides/docker/)
+      and Python 3.11 or later installed; Basic familiarity with Python, PyTest, and container
+      concepts; Familiarity with the [Model Context Protocol (MCP)](https://modelcontextprotocol.io/)
+      specification.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Python, Pytest, Docker, GitHub Actions, and Testcontainers,
+      Linux, macOS, and Windows environments, and Arm platforms such as Neoverse and Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Introduction to MCP server testing, Set up your testing
+      environment, Run a basic Testcontainers example, Write integration tests for MCP servers,
+      and Configure GitHub Actions for CI/CD.
+# END generated_summary_faq
+
 author: Neethu Elizabeth Simon
 
 ### Tags
@@ -69,3 +116,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/avh_cicd/_index.md b/content/learning-paths/cross-platform/avh_cicd/_index.md
index 0ac6911d36..5f7cd642d1 100644
--- a/content/learning-paths/cross-platform/avh_cicd/_index.md
+++ b/content/learning-paths/cross-platform/avh_cicd/_index.md
@@ -16,6 +16,44 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: b6a7439a701f6ae09ce8c61f02150a61fa6ecc1151050e903492e16794999676
+  summary: >-
+    Learn how to integrate Arm Virtual Hardware into a GitHub Actions CI/CD workflow for automated
+    embedded software testing and validation. It is designed for embedded software developers
+    new to Arm Virtual Hardware and its features. By the end, you will be able to prepare a GitHub
+    repository and integrate AVH into a CI/CD flow with GitHub Actions. It focuses on tools and
+    technologies such as Arm Virtual Hardware and GitHub, Baremetal environments, and Arm platforms
+    including Cortex-M. The main steps cover Prepare GitHub repository for CI/CD development and
+    Self-hosted runner.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will prepare a GitHub repository and integrate AVH into a CI/CD flow with GitHub Actions.
+      Learn how to integrate Arm Virtual Hardware into a GitHub Actions CI/CD workflow for automated
+      embedded software testing and validation.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for embedded software developers new to Arm Virtual Hardware
+      and its features.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Some familiarity with CI/CD concepts
+      is assumed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Arm Virtual Hardware and GitHub, Baremetal environments,
+      and Arm platforms such as Cortex-M.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Prepare GitHub repository for CI/CD development and
+      Self-hosted runner.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
@@ -51,3 +89,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/avh_cicd2/_index.md b/content/learning-paths/cross-platform/avh_cicd2/_index.md
index 22183ead55..713cb7c343 100644
--- a/content/learning-paths/cross-platform/avh_cicd2/_index.md
+++ b/content/learning-paths/cross-platform/avh_cicd2/_index.md
@@ -17,6 +17,44 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 251b6edf6a016f9fc73eb35216f56b2001465fbca8253bd7b6e6f9f4afcd25e6
+  summary: >-
+    Learn how to integrate Arm Virtual Hardware with AWS and GitHub Actions for automated CI/CD
+    workflows, including CloudFormation setup and test automation. It is designed for DevOps integrating
+    AVH into their CI/CD flows. By the end, you will be able to prepare AWS account for GitHub
+    integration and integrate Arm Virtual Hardware into CI/CD flow with GitHub Actions. It focuses
+    on tools and technologies such as Arm Virtual Hardware and GitHub, Baremetal environments,
+    and Arm platforms including Cortex-M. The main steps cover Prepare AWS account for GitHub
+    integration and Automate build and validation example.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will prepare AWS account for GitHub integration and integrate Arm Virtual Hardware into
+      CI/CD flow with GitHub Actions. Learn how to integrate Arm Virtual Hardware with AWS and
+      GitHub Actions for automated CI/CD workflows, including CloudFormation setup and test automation.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for DevOps integrating AVH into their CI/CD flows.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: This learning path builds on [Integrate
+      Arm Virtual Hardware into CI/CD workflow 1](/learning-paths/cross-platform/avh_cicd/).;
+      Valid AWS and GitHub accounts are required.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Arm Virtual Hardware and GitHub, Baremetal environments,
+      and Arm platforms such as Cortex-M.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Prepare AWS account for GitHub integration and Automate
+      build and validation example.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ##### Tags
@@ -53,3 +91,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/cca_rme/_index.md b/content/learning-paths/cross-platform/cca_rme/_index.md
index a7631ce474..110bfa417d 100644
--- a/content/learning-paths/cross-platform/cca_rme/_index.md
+++ b/content/learning-paths/cross-platform/cca_rme/_index.md
@@ -17,6 +17,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: a1685fb43efecb9690d03c7f8ee64ab20ae8aece91190e2223705106568b1038
+  summary: >-
+    Learn how to use Arm Development Studio to explore Realm Management Extension (RME) and Arm
+    Confidential Compute Architecture (CCA) through a bare-metal example running on the Arm Architecture
+    Envelope Model. It is designed for developers interested in learning the concepts of Realm
+    Management Extension and the Arm Confidential Compute Architecture (CCA). By the end, you
+    will be able to understand the Arm Confidential Compute Architecture (CCA) and understand
+    a simple bare-metal example provided with Arm Development Studio. It focuses on tools and
+    technologies such as Trusted Firmware, Arm Development Studio, RME, CCA, and Runbook, Linux
+    and Android environments, and Arm platforms including Neoverse, Cortex-A, and Armv9-A. The
+    main steps cover Arm Confidential Compute Architecture and Bare-metal example.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand the Arm Confidential Compute Architecture (CCA) and understand a simple
+      bare-metal example provided with Arm Development Studio. Learn how to use Arm Development
+      Studio to explore Realm Management Extension (RME) and Arm Confidential Compute Architecture
+      (CCA) through a bare-metal example running on the Arm Architecture Envelope Model.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers interested in learning the concepts of Realm
+      Management Extension and the Arm Confidential Compute Architecture (CCA).
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Some understanding of the Arm architecture;
+      Arm Development Studio, 2023.0 or later.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Trusted Firmware, Arm Development Studio, RME, CCA,
+      and Runbook, Linux and Android environments, and Arm platforms such as Neoverse, Cortex-A,
+      and Armv9-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Arm Confidential Compute Architecture and Bare-metal
+      example.
+# END generated_summary_faq
+
 author: Ronan Synnott
 
 ### Tags
@@ -67,3 +109,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md b/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md
index ceb824d226..b319d83984 100644
--- a/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md
+++ b/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md
@@ -18,6 +18,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: a639e60da034fd04e891c9236e9fe5dab47cca87f6174828275ef5a76c43c32e
+  summary: >-
+    Learn how to optimize C++ loop performance on Arm by providing boundary information to the
+    compiler, enabling SIMD vectorization and reducing runtime through compile-time context. It
+    is designed for C++ developers who want to improve the runtime of loops using existing knowledge
+    of the loop size. By the end, you will be able to learn how to communicate loop size constraints
+    to the compiler for better optimization, understand how providing compile-time context can
+    improve runtime performance, and implement techniques to express loop boundaries that enable
+    better code generation. It focuses on tools and technologies such as CPP and Runbook, Linux
+    environments, and Arm platforms including Neoverse and Cortex-A. The main steps cover Understand
+    developer knowledge for compiler optimizations, Baseline loop implementation, and Optimize
+    loops using boundary information.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will learn how to communicate loop size constraints to the compiler for better optimization,
+      understand how providing compile-time context can improve runtime performance, and implement
+      techniques to express loop boundaries that enable better code generation. Learn how to optimize
+      C++ loop performance on Arm by providing boundary information to the compiler, enabling
+      SIMD vectorization and reducing runtime through compile-time context.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for C++ developers who want to improve the runtime of loops
+      using existing knowledge of the loop size.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm computer running Linux. You can
+      also use a virtual machine from a [cloud service provider](/learning-paths/servers-and-cloud-computing/csp/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including CPP and Runbook, Linux environments, and Arm platforms
+      such as Neoverse and Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Understand developer knowledge for compiler optimizations,
+      Baseline loop implementation, and Optimize loops using boundary information.
+# END generated_summary_faq
+
 author: Kieran Hejmadi
 
 ### Tags
@@ -55,3 +98,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/docker-build-cloud/_index.md b/content/learning-paths/cross-platform/docker-build-cloud/_index.md
index 931f7e553d..4c027811d0 100644
--- a/content/learning-paths/cross-platform/docker-build-cloud/_index.md
+++ b/content/learning-paths/cross-platform/docker-build-cloud/_index.md
@@ -18,6 +18,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 9a94219b689b8e22a175c6067c6bb6d139d6ad22f3aac77c78b6b38970d5a5eb
+  summary: >-
+    Learn how to build multi-architecture Docker images for Arm and x86 using Docker Build Cloud,
+    with GitHub Actions automation for faster builds without emulation. It is designed for software
+    developers who want to learn how to use Docker Build Cloud. By the end, you will be able to
+    build Arm images and multi-architecture images with Docker Build Cloud and use GitHub Actions
+    to automate image builds. It focuses on tools and technologies such as Docker, Linux environments,
+    and Arm platforms including Neoverse. The main steps cover Setup and build images with Docker
+    Build Cloud and Use GitHub Actions with Docker Build Cloud.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build Arm images and multi-architecture images with Docker Build Cloud and use
+      GitHub Actions to automate image builds. Learn how to build multi-architecture Docker images
+      for Arm and x86 using Docker Build Cloud, with GitHub Actions automation for faster builds
+      without emulation.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to learn how to use Docker
+      Build Cloud.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A computer with Docker installed. This
+      can be Windows, macOS, or Linux. Any architecture can be used.; A GitHub account; A Docker
+      Hub account.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Docker, Linux environments, and Arm platforms such
+      as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Setup and build images with Docker Build Cloud and
+      Use GitHub Actions with Docker Build Cloud.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
@@ -54,3 +94,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/docker/_index.md b/content/learning-paths/cross-platform/docker/_index.md
index ac98750bfb..cf5bf3d1b1 100644
--- a/content/learning-paths/cross-platform/docker/_index.md
+++ b/content/learning-paths/cross-platform/docker/_index.md
@@ -19,6 +19,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 058f779bc7dd9faef294a57f4524bf525046d757e21a287744825fb9b290935e
+  summary: >-
+    Learn how to build, run, and share multi-architecture Docker images for Arm and x86 platforms
+    using buildx, manifest, and remote builders. It is designed for software developers who want
+    to learn about Docker for the Arm architecture. By the end, you will be able to build, run,
+    and share Docker images, perform multi-architecture builds using Docker buildx, and use a
+    remote server to build a Docker image for the Arm architecture. It focuses on tools and technologies
+    such as Docker, Linux environments, and Arm platforms including Neoverse and Cortex-A. The
+    main steps cover Build, run, and share a Docker image, Build multi-architecture images with
+    Docker buildx, Perform remote Docker builds on an Arm server, Use Docker manifest to create
+    multi-architecture images, and Check container images for multi-architecture support.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build, run, and share Docker images, perform multi-architecture builds using Docker
+      buildx, and use a remote server to build a Docker image for the Arm architecture. Learn
+      how to build, run, and share multi-architecture Docker images for Arm and x86 platforms
+      using buildx, manifest, and remote builders.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to learn about Docker for
+      the Arm architecture.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows, macOS, or Linux computer
+      with Docker installed, any architecture can be used; An Arm Linux server with Docker installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Docker, Linux environments, and Arm platforms such
+      as Neoverse and Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Build, run, and share a Docker image, Build multi-architecture
+      images with Docker buildx, Perform remote Docker builds on an Arm server, Use Docker manifest
+      to create multi-architecture images, and Check container images for multi-architecture support.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
@@ -64,3 +106,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md b/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md
index 3bdbf73d18..2dfbc8e773 100644
--- a/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md
+++ b/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md
@@ -18,6 +18,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: c59308676849aea9b7cfce8c48c7d6e1ca072ef6fb38fb193a34aa5b2597b9b9
+  summary: >-
+    Learn how to implement a dynamic memory allocator in C, understanding heap management and
+    how malloc and free work under the hood with practical examples. It is designed for software
+    developers learning about dynamic memory allocation for the first time, and who may have used
+    malloc and free in C programming. It also provides a starting point to explore more advanced
+    memory allocation topics. By the end, you will be able to explain how dynamic memory allocation
+    and the C heap works, write a simple dynamic memory allocator, and explain some of the risks
+    of heap allocation in general. It focuses on tools and technologies such as C and Runbook,
+    Linux environments, and Arm platforms including Cortex-A and Neoverse. The main steps cover
+    Dynamic memory allocation, Design a dynamic memory allocator, Implement a dynamic memory allocator,
+    and Memory allocation summary.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will explain how dynamic memory allocation and the C heap works, write a simple dynamic
+      memory allocator, and explain some of the risks of heap allocation in general. Learn how
+      to implement a dynamic memory allocator in C, understanding heap management and how malloc
+      and free work under the hood with practical examples.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers learning about dynamic memory allocation
+      for the first time, and who may have used malloc and free in C programming. It also provides
+      a starting point to explore more advanced memory allocation topics.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Familiarity with C programming, with
+      a good understanding of pointers.; A Linux machine to run the example code.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including C and Runbook, Linux environments, and Arm platforms
+      such as Cortex-A and Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Dynamic memory allocation, Design a dynamic memory
+      allocator, Implement a dynamic memory allocator, and Memory allocation summary.
+# END generated_summary_faq
+
 author: David Spickett
 
 test_images:
@@ -60,3 +103,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md b/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md
index af925344f5..859d00ad26 100644
--- a/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md
+++ b/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md
@@ -16,6 +16,44 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 39c5e146afbfc74c3076d2cf3f1a67ddc0e4715f39b2cff1ccf76b288415bdc0
+  summary: >-
+    Learn how to use the Eigen linear algebra library on Arm systems with ASIMD and SVE vectorization,
+    including building TensorFlow with SVE support for optimized performance. It is designed for
+    C/C++ developers who want to create high performance applications using the Eigen linear algebra
+    library. By the end, you will be able to describe how to use Eigen on Arm systems and build
+    TensorFlow with SVE on Arm systems. It focuses on tools and technologies such as GCC, Clang,
+    and Runbook, Linux environments, and Arm platforms including Cortex-A and Neoverse. The main
+    steps cover About Eigen, Eigen examples, Eigen on Arm, and Build and Run TensorFlow with SVE.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will describe how to use Eigen on Arm systems and build TensorFlow with SVE on Arm systems.
+      Learn how to use the Eigen linear algebra library on Arm systems with ASIMD and SVE vectorization,
+      including building TensorFlow with SVE support for optimized performance.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for C/C++ developers who want to create high performance applications
+      using the Eigen linear algebra library.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm-based computer running Linux
+      and a recent version of a C++ compiler (Clang or GCC).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including GCC, Clang, and Runbook, Linux environments, and
+      Arm platforms such as Cortex-A and Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around About Eigen, Eigen examples, Eigen on Arm, and Build
+      and Run TensorFlow with SVE.
+# END generated_summary_faq
+
 author: Konstantinos Margaritis
 
 ### Tags
@@ -59,3 +97,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/ernie_moe_v9/_index.md b/content/learning-paths/cross-platform/ernie_moe_v9/_index.md
index 43557bed5f..116f1d39dc 100644
--- a/content/learning-paths/cross-platform/ernie_moe_v9/_index.md
+++ b/content/learning-paths/cross-platform/ernie_moe_v9/_index.md
@@ -17,6 +17,55 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: e835a550c955b13805c7859ff64e3b8d7fdee68222569225c53a0f15db72f046
+  summary: >-
+    Learn how to deploy ERNIE-4.5 Mixture of Experts models on Armv9 devices using llama.cpp,
+    compare PT and Thinking variants, and measure Armv9-specific hardware optimization impact.
+    It is designed for developers and engineers who want to deploy Mixture of Experts (MoE) models,
+    such as ERNIE 4.5, on edge devices. MoE architectures allow large LLMs with 21 billion or
+    more parameters to run with only a fraction of their weights active per inference, making
+    them ideal for resource constrained environments. By the end, you will be able to deploy MoE
+    models like ERNIE-4.5 on edge devices using llama.cpp, compare inference behavior between
+    ERNIE-4.5 PT and Thinking versions, and measure performance impact of Armv9-specific hardware
+    optimizations. It focuses on tools and technologies such as Python, CPP, Bash, and llama.cpp,
+    Linux environments, and Arm platforms including Cortex-A. The main steps cover Understand
+    Mixture of Experts architecture for edge deployment, Set up llama.cpp on an Armv9 development
+    board, Compare ERNIE model behavior and expert routing, and Optimize performance with Armv9
+    hardware features.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will deploy MoE models like ERNIE-4.5 on edge devices using llama.cpp, compare inference
+      behavior between ERNIE-4.5 PT and Thinking versions, and measure performance impact of Armv9-specific
+      hardware optimizations. Learn how to deploy ERNIE-4.5 Mixture of Experts models on Armv9
+      devices using llama.cpp, compare PT and Thinking variants, and measure Armv9-specific hardware
+      optimization impact.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for developers and engineers who want to deploy Mixture of Experts
+      (MoE) models, such as ERNIE 4.5, on edge devices. MoE architectures allow large LLMs with
+      21 billion or more parameters to run with only a fraction of their weights active per inference,
+      making them ideal for resource constrained environments.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Armv9 device with at least 32 GB
+      of available disk space, for example, Radxa Orion O6.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Python, CPP, Bash, and llama.cpp, Linux environments,
+      and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Understand Mixture of Experts architecture for edge
+      deployment, Set up llama.cpp on an Armv9 development board, Compare ERNIE model behavior
+      and expert routing, and Optimize performance with Armv9 hardware features.
+# END generated_summary_faq
+
 author: Odin Shen
 
 ### Tags
@@ -60,3 +109,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/floating-point-behavior/_index.md b/content/learning-paths/cross-platform/floating-point-behavior/_index.md
index 3b4b83615e..0ed2cb95f5 100644
--- a/content/learning-paths/cross-platform/floating-point-behavior/_index.md
+++ b/content/learning-paths/cross-platform/floating-point-behavior/_index.md
@@ -18,6 +18,53 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 6427d4466fcd34f7275d16820cbfa2afa3815e1f0306c02e5011b2a4a6afa984
+  summary: >-
+    Learn how Arm and x86 floating-point implementations follow IEEE 754 standards, identify rare
+    undefined behavior differences, and write portable code across architectures. It is designed
+    for This is a topic for developers who are porting applications from x86 to Arm and want to
+    understand floating-point behavior across these architectures. Both architectures provide
+    reliable and consistent floating-point computation following the IEEE 754 standard. By the
+    end, you will be able to understand that Arm and x86 produce identical results for all well-defined
+    floating-point operations, recognize that differences only occur in special undefined cases
+    permitted by IEEE 754, and learn to recognize floating-point differences and make your code
+    portable across architectures. It focuses on tools and technologies such as CPP, Linux environments,
+    and Arm platforms including Cortex-A and Neoverse. The main steps cover Floating-point representation,
+    Overflow in floating-point to integer conversion, and Precision and floating-point instruction
+    considerations.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand that Arm and x86 produce identical results for all well-defined floating-point
+      operations, recognize that differences only occur in special undefined cases permitted by
+      IEEE 754, and learn to recognize floating-point differences and make your code portable
+      across architectures. Learn how Arm and x86 floating-point implementations follow IEEE 754
+      standards, identify rare undefined behavior differences, and write portable code across
+      architectures.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is a topic for developers who are porting applications from x86 to Arm and want to
+      understand floating-point behavior across these architectures. Both architectures provide
+      reliable and consistent floating-point computation following the IEEE 754 standard.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Access to an x86 and an Arm Linux machine.;
+      Familiarity with floating-point numbers.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including CPP, Linux environments, and Arm platforms such
+      as Cortex-A and Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Floating-point representation, Overflow in floating-point
+      to integer conversion, and Precision and floating-point instruction considerations.
+# END generated_summary_faq
+
 author: 
     - Kieran Hejmadi
     - Jason Andrews
@@ -54,3 +101,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/function-multiversioning/_index.md b/content/learning-paths/cross-platform/function-multiversioning/_index.md
index d4149c7f80..c91dafe330 100644
--- a/content/learning-paths/cross-platform/function-multiversioning/_index.md
+++ b/content/learning-paths/cross-platform/function-multiversioning/_index.md
@@ -23,6 +23,53 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 1c1d745bd1af535315d5edd1b2b9214c96419d46abde3af8fa75bdde299bb65d
+  summary: >-
+    Learn how to optimize C/C++ applications using function multiversioning on Arm64 targets with
+    GCC or LLVM, enabling automatic runtime selection of hardware-optimized function versions.
+    It is designed for developers interested in optimizing their C/C++ applications across Arm64
+    targets. By the end, you will be able to use hardware features to tune your applications at
+    function level, create multiple versions of C/C++ functions for the targets that you intend
+    to run applications on, and assist the compiler in generating optimal code for the targets,
+    or provide your own optimized versions at source level. It focuses on tools and technologies
+    such as C, CPP, and Runbook, Linux, Android, and macOS environments, and Arm platforms including
+    Cortex-A and Neoverse. The main steps cover About function multiversioning, Example 1 - code
+    generation, Example 2 - runtime using ACLE intrinsics, Example 3 - inline assembly at runtime,
+    and Compatibility with streaming mode.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will use hardware features to tune your applications at function level, create multiple
+      versions of C/C++ functions for the targets that you intend to run applications on, and
+      assist the compiler in generating optimal code for the targets, or provide your own optimized
+      versions at source level. Learn how to optimize C/C++ applications using function multiversioning
+      on Arm64 targets with GCC or LLVM, enabling automatic runtime selection of hardware-optimized
+      function versions.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for developers interested in optimizing their C/C++ applications
+      across Arm64 targets.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Basic knowledge of GNU function attributes.;
+      Familiarity with indirect functions (ifuncs).; Basic knowledge of loop vectorization.; Familiarity
+      with Arm assembly.; A LLVM 20 compiler with runtime library support or GCC 16.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including C, CPP, and Runbook, Linux, Android, and macOS environments,
+      and Arm platforms such as Cortex-A and Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around About function multiversioning, Example 1 - code generation,
+      Example 2 - runtime using ACLE intrinsics, Example 3 - inline assembly at runtime, and Compatibility
+      with streaming mode.
+# END generated_summary_faq
+
 author: Alexandros Lamprineas
 
 ### Tags
@@ -62,3 +109,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/github-arm-runners/_index.md b/content/learning-paths/cross-platform/github-arm-runners/_index.md
index 5705781038..05e8037ae9 100644
--- a/content/learning-paths/cross-platform/github-arm-runners/_index.md
+++ b/content/learning-paths/cross-platform/github-arm-runners/_index.md
@@ -17,6 +17,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 3557d534c51f81839cc353ebbd600ec588a60197d2c27c9a58e97c25017d07e4
+  summary: >-
+    Learn how to use GitHub Actions with Arm-hosted runners to build multi-architecture container
+    images for arm64 and amd64 platforms and automate deployment to Docker Hub. It is designed
+    for software developers who want to learn how to use Arm-hosted runners for GitHub Actions
+    jobs. By the end, you will be able to build Arm images and multi-architecture images with
+    Arm-hosted runners and use GitHub Actions to automate image builds. It focuses on tools and
+    technologies such as GitHub, Docker, and Runbook, Linux environments, and Arm platforms including
+    Neoverse. The main steps cover Build options for multi-architecture container images, Arm-hosted
+    runners for public repositories, Create a new Arm-hosted runner for private repositories,
+    and Run GitHub Actions jobs on the Arm-hosted runner.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build Arm images and multi-architecture images with Arm-hosted runners and use
+      GitHub Actions to automate image builds. Learn how to use GitHub Actions with Arm-hosted
+      runners to build multi-architecture container images for arm64 and amd64 platforms and automate
+      deployment to Docker Hub.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to learn how to use Arm-hosted
+      runners for GitHub Actions jobs.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A GitHub account (a Team or Enterprise
+      Cloud plan is required for private repositories).; A Docker Hub account.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including GitHub, Docker, and Runbook, Linux environments,
+      and Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Build options for multi-architecture container images,
+      Arm-hosted runners for public repositories, Create a new Arm-hosted runner for private repositories,
+      and Run GitHub Actions jobs on the Arm-hosted runner.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
@@ -58,3 +100,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md b/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md
index 67a6b02947..2180d3d3a5 100644
--- a/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md
+++ b/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md
@@ -20,6 +20,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: a6ad703d9d894da06ed4aa3725fcc9006d70050cef3f0a3ef9a758bcf4523289
+  summary: >-
+    Learn how to build GitLab CI/CD pipelines using GitLab-hosted Arm64 runners to containerize
+    C applications, store images in GitLab Container Registry, and deploy on Arm infrastructure.
+    It is designed for DevOps engineers who want to build CI/CD pipelines on Arm-based infrastructure
+    using GitLab-hosted runners. By the end, you will be able to create a GitLab project with
+    CI/CD configuration, configure pipeline stages to use Arm64 runners, and build and containerize
+    applications for Arm64 architecture. It focuses on tools and technologies such as GitLab,
+    Docker, and C, Linux environments, Arm platforms including Neoverse, and cloud platforms such
+    as Google Cloud. The main steps cover Build and use GitLab-hosted Arm runners for CI/CD, Create
+    a GitLab project, Build and configure your Arm64 CI/CD pipeline, and Run pipeline and verify
+    results.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create a GitLab project with CI/CD configuration, configure pipeline stages to
+      use Arm64 runners, and build and containerize applications for Arm64 architecture. Learn
+      how to build GitLab CI/CD pipelines using GitLab-hosted Arm64 runners to containerize C
+      applications, store images in GitLab Container Registry, and deploy on Arm infrastructure.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for DevOps engineers who want to build CI/CD pipelines on
+      Arm-based infrastructure using GitLab-hosted runners.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A GitLab account (free tier includes
+      Arm64 runner access).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including GitLab, Docker, and C, Linux environments, Arm platforms
+      such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Build and use GitLab-hosted Arm runners for CI/CD,
+      Create a GitLab project, Build and configure your Arm64 CI/CD pipeline, and Run pipeline
+      and verify results.
+# END generated_summary_faq
+
 author: Mohamed Ismail
 
 ### Tags
@@ -68,3 +111,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/gitlab/_index.md b/content/learning-paths/cross-platform/gitlab/_index.md
index 184f08fb95..e39fd0c541 100644
--- a/content/learning-paths/cross-platform/gitlab/_index.md
+++ b/content/learning-paths/cross-platform/gitlab/_index.md
@@ -20,6 +20,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: af9eb1c426e1844e2fd20215b7012d95c1efaf737f1d7b5be828931528342316
+  summary: >-
+    Learn how to build a GitLab CI/CD pipeline using Google Axion-based self-hosted runners. It
+    is designed for DevOps professionals who are looking to build a CI/CD pipeline with GitLab
+    on Google Axion based self-hosted GitLab runners. By the end, you will be able to create a
+    Google Axion based GitLab self-hosted runner, build a CI/CD pipeline with multi-architecture
+    support, and build multi-architecture docker images using native GitLab runners on x86 and
+    Arm. It focuses on tools and technologies such as Kubernetes, Docker, and GitLab, Linux environments,
+    Arm platforms including Neoverse, and cloud platforms such as Google Cloud. The main steps
+    cover Create a Google Axion-based GitLab self-hosted runner and Automate the build and deployment
+    of a multi-arch application with GitLab CI/CD.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create a Google Axion based GitLab self-hosted runner, build a CI/CD pipeline with
+      multi-architecture support, and build multi-architecture docker images using native GitLab
+      runners on x86 and Arm. Learn how to build a GitLab CI/CD pipeline using Google Axion-based
+      self-hosted runners.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for DevOps professionals who are looking to build a CI/CD pipeline
+      with GitLab on Google Axion based self-hosted GitLab runners.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud account](https://console.cloud.google.com/).
+      Create an account if needed.; A computer with [Google Cloud CLI](/install-guides/gcloud)
+      and [kubectl](/install-guides/kubectl/)installed.; A valid GitLab account.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Kubernetes, Docker, and GitLab, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Create a Google Axion-based GitLab self-hosted runner
+      and Automate the build and deployment of a multi-arch application with GitLab CI/CD.
+# END generated_summary_faq
+
 author: Pranay Bakre
 
 ### Tags
@@ -68,3 +110,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/integer-vs-floats/_index.md b/content/learning-paths/cross-platform/integer-vs-floats/_index.md
index 33d7a42e5b..59bdf34566 100644
--- a/content/learning-paths/cross-platform/integer-vs-floats/_index.md
+++ b/content/learning-paths/cross-platform/integer-vs-floats/_index.md
@@ -15,6 +15,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 1895767d551ba0fa249c5002d3e2e471acacdec06ddb7c2f5314a2fa0df94f2e
+  summary: >-
+    Learn how to identify and fix potential problems with integer and floating-point conversions
+    in C/C++ code on Arm, including explicit conversions, implicit conversions, and type demotion
+    issues. It is designed for C/C++ developers who are interested in learning about the intricacies
+    of conversions between floating-point numbers and integers. By the end, you will be able to
+    learn how to identify and fix potential problems in integer/float conversions in C/C++ on
+    Arm. It focuses on tools and technologies such as GCC, Clang, and Runbook, Linux environments,
+    and Arm platforms including Aarch64, Armv8-a, and Armv9-a. The main steps cover An introduction
+    to integer and floating-point data types, Integer and floating-point conversions, More on
+    implicit conversions, and Data type demotions.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will learn how to identify and fix potential problems in integer/float conversions in
+      C/C++ on Arm. Learn how to identify and fix potential problems with integer and floating-point
+      conversions in C/C++ code on Arm, including explicit conversions, implicit conversions,
+      and type demotion issues.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for C/C++ developers who are interested in learning about the
+      intricacies of conversions between floating-point numbers and integers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm computer running Linux and a
+      recent version of a C++ compiler (Clang or GCC) installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including GCC, Clang, and Runbook, Linux environments, and
+      Arm platforms such as Aarch64, Armv8-a, and Armv9-a.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around An introduction to integer and floating-point data
+      types, Integer and floating-point conversions, More on implicit conversions, and Data type
+      demotions.
+# END generated_summary_faq
+
 author: Konstantinos Margaritis
 
 ### Tags
@@ -68,3 +110,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/intrinsics/_index.md b/content/learning-paths/cross-platform/intrinsics/_index.md
index 7625f6a171..17e66128eb 100644
--- a/content/learning-paths/cross-platform/intrinsics/_index.md
+++ b/content/learning-paths/cross-platform/intrinsics/_index.md
@@ -20,6 +20,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: ecf51d9a9085f95dedda9c0cfbfa4d6350d0f68d81b61f9461bc51070abd0b69
+  summary: >-
+    Learn how to port architecture-specific intrinsics to Arm processors. It is designed for software
+    developers interested in porting architecture specific intrinsics to Arm processors. By the
+    end, you will be able to describe what intrinsics are and how to find them in code and evaluate
+    options and use header-only libraries to port architecture-specific intrinsics to Arm. It
+    focuses on tools and technologies such as Neon, SVE, Intrinsics, and Runbook, Linux environments,
+    and Arm platforms including Neoverse and Cortex-A. The main steps cover Code Migration to
+    Arm, Use sse2neon to port code to Arm, Use SIMD Everywhere to port code to Arm, and Find intrinsics
+    in large code bases.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will describe what intrinsics are and how to find them in code and evaluate options
+      and use header-only libraries to port architecture-specific intrinsics to Arm. Learn how
+      to port architecture-specific intrinsics to Arm processors.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers interested in porting architecture specific
+      intrinsics to Arm processors.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Some understanding of SIMD concepts.;
+      An Arm based machine or [cloud instance](/learning-paths/servers-and-cloud-computing/csp/)
+      running Ubuntu Linux.; Optionally, an `x86_64` machine also running Ubuntu.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Neon, SVE, Intrinsics, and Runbook, Linux environments,
+      and Arm platforms such as Neoverse and Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Code Migration to Arm, Use sse2neon to port code to
+      Arm, Use SIMD Everywhere to port code to Arm, and Find intrinsics in large code bases.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 test_images:
@@ -71,3 +111,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/ipexplorer/_index.md b/content/learning-paths/cross-platform/ipexplorer/_index.md
index 8089c48fd6..4b18e42b17 100644
--- a/content/learning-paths/cross-platform/ipexplorer/_index.md
+++ b/content/learning-paths/cross-platform/ipexplorer/_index.md
@@ -16,6 +16,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 47cd598f6e33c12d3729c319a1d6a7afcd748de7acd6faea639f2e8600a085ed
+  summary: >-
+    Learn how to run custom software benchmarks on IP Explorer simulation platforms and compare
+    performance across Arm Cortex-M processors using cycle count analysis. It is designed for
+    IP Explorer users using the software simulation platforms available. By the end, you will
+    be able to run a pre-installed example on IP Explorer simulation platform, create your own
+    example benchmark, and upload and run your benchmark. It focuses on tools and technologies
+    such as IP Explorer, Baremetal environments, and Arm platforms including Cortex-A, Cortex-R,
+    and Cortex-M. The main steps cover Run a pre-installed example and compare performance, Create
+    a custom example, and Upload and run your custom example.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will run a pre-installed example on IP Explorer simulation platform, create your own
+      example benchmark, and upload and run your benchmark. Learn how to run custom software benchmarks
+      on IP Explorer simulation platforms and compare performance across Arm Cortex-M processors
+      using cycle count analysis.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for IP Explorer users using the software simulation platforms
+      available.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm account that can access IP Explorer;
+      (Optional) A Linux machine with the desired compilers installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including IP Explorer, Baremetal environments, and Arm platforms
+      such as Cortex-A, Cortex-R, and Cortex-M.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Run a pre-installed example and compare performance,
+      Create a custom example, and Upload and run your custom example.
+# END generated_summary_faq
+
 author: Ronan Synnott
 
 ### Tags
@@ -52,3 +92,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/kleidiai-explainer/_index.md b/content/learning-paths/cross-platform/kleidiai-explainer/_index.md
index ec9346d2dc..76839a6f88 100644
--- a/content/learning-paths/cross-platform/kleidiai-explainer/_index.md
+++ b/content/learning-paths/cross-platform/kleidiai-explainer/_index.md
@@ -16,6 +16,52 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 907255b3c2c1086b421abe4a1e378b68533de4524a4f4dd81138545a2ff1a5b5
+  summary: >-
+    Learn how to use KleidiAI micro-kernels to accelerate AI inference performance through optimized
+    matrix multiplication on Arm processors with architecture features like i8mm. It is designed
+    for developers who want to learn how to use KleidiAI to accelerate the execution of Generative
+    AI workloads on hardware. By the end, you will be able to describe how basic math operations
+    power Large Language Models, describe how the KleidiAI micro-kernels speed up Generative AI
+    inference performance, and run a basic C++ matrix multiplication example to showcase the speedup
+    that KleidiAI micro-kernels can deliver. It focuses on tools and technologies such as CPP,
+    Generative AI, Neon, and Runbook, Linux environments, and Arm platforms including Cortex-A
+    and Neoverse. The main steps cover KleidiAI and matrix multiplication, KleidiAI in a real
+    software stack, and Quantizing and packing micro-kernels.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will describe how basic math operations power Large Language Models, describe how the
+      KleidiAI micro-kernels speed up Generative AI inference performance, and run a basic C++
+      matrix multiplication example to showcase the speedup that KleidiAI micro-kernels can deliver.
+      Learn how to use KleidiAI micro-kernels to accelerate AI inference performance through optimized
+      matrix multiplication on Arm processors with architecture features like i8mm.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers who want to learn how to use KleidiAI to accelerate
+      the execution of Generative AI workloads on hardware.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm-based Linux machine that implements
+      the Int8 Matrix Multiplication (*i8mm*) architecture feature. The example in this Learning
+      Path is run on an AWS Graviton 3 instance. Instructions on setting up an Arm-based server
+      are [found here](/learning-paths/servers-and-cloud-computing/csp/aws/).; A basic understanding
+      of linear algebra terminology, such as dot product and matrix multiplication.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including CPP, Generative AI, Neon, and Runbook, Linux environments,
+      and Arm platforms such as Cortex-A and Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around KleidiAI and matrix multiplication, KleidiAI in a
+      real software stack, and Quantizing and packing micro-kernels.
+# END generated_summary_faq
+
 author: Zach Lasiuk
 ### Tags
 skilllevels: Introductory 
@@ -59,3 +105,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/loop-reflowing/_index.md b/content/learning-paths/cross-platform/loop-reflowing/_index.md
index 14a763660e..d28f826d17 100644
--- a/content/learning-paths/cross-platform/loop-reflowing/_index.md
+++ b/content/learning-paths/cross-platform/loop-reflowing/_index.md
@@ -14,6 +14,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 4218b8b0c1dee3862bb9765a83dfed0cb38555d1da7425e791c5c16b17f98c21
+  summary: >-
+    Learn how to optimize C/C++ code using compiler autovectorization techniques including loop
+    modifications, restrict qualifiers, and conditional handling for Arm processors. It is designed
+    for C/C++ developers who are interested in taking advantage of autovectorization in compilers.
+    By the end, you will be able to modify loops to take advantage of autovectorization in compilers.
+    It focuses on tools and technologies such as GCC, Clang, and Runbook, Linux environments,
+    and Arm platforms including Neoverse and Cortex-A. The main steps cover An introduction to
+    autovectorization, Autovectorization using the restrict keyword, Autovectorization limits,
+    Autovectorization and conditionals, and Autovectorization on Arm.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will modify loops to take advantage of autovectorization in compilers. Learn how to
+      optimize C/C++ code using compiler autovectorization techniques including loop modifications,
+      restrict qualifiers, and conditional handling for Arm processors.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for C/C++ developers who are interested in taking advantage of
+      autovectorization in compilers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm computer running Linux and a
+      recent version of Clang or the GNU compiler (gcc) installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including GCC, Clang, and Runbook, Linux environments, and
+      Arm platforms such as Neoverse and Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around An introduction to autovectorization, Autovectorization
+      using the restrict keyword, Autovectorization limits, Autovectorization and conditionals,
+      and Autovectorization on Arm.
+# END generated_summary_faq
+
 author: Konstantinos Margaritis
 
 ### Tags
@@ -62,3 +102,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/matrix/_index.md b/content/learning-paths/cross-platform/matrix/_index.md
index 59a9c4e490..569e5fe2eb 100644
--- a/content/learning-paths/cross-platform/matrix/_index.md
+++ b/content/learning-paths/cross-platform/matrix/_index.md
@@ -21,6 +21,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 5611b6d1eebbea167d1860e3ac7f4f7910584561cbb18c3ed696aa27215edce9
+  summary: >-
+    Learn how to develop and test a modern C++ library using CMake, GoogleTest, and matrix processing
+    as a practical example on Arm platforms. It is designed for developers who want to learn how
+    to develop a library in modern C++ on Arm, using matrix processing as an example. By the end,
+    you will be able to develop a new C++ library and test a C++ library, ensuring it does not
+    regress functionally. It focuses on tools and technologies such as CPP, GCC, Clang, CMake,
+    and Google Test, Linux, macOS, and Windows environments, and Arm platforms including Neoverse
+    and Cortex-A. The main steps cover Laying the foundations, Test the library, Start coding,
+    and Implement matrix operations.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will develop a new C++ library and test a C++ library, ensuring it does not regress
+      functionally. Learn how to develop and test a modern C++ library using CMake, GoogleTest,
+      and matrix processing as a practical example on Arm platforms.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for developers who want to learn how to develop a library in modern
+      C++ on Arm, using matrix processing as an example.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm-based computer running Linux,
+      macOS, or Windows.; An intermediate understanding of C++ programming.; A suitable Integrated
+      Development Environment (IDE).; The [CMake](/install-guides/cmake/) build tool.; A C++ compiler
+      with C++17 support.; A build system [GNU Make](https://www.gnu.org/software/make/) or [Ninja](https://ninja-build.org/).;
+      A documentation generator [Doxygen](https://www.doxygen.nl/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including CPP, GCC, Clang, CMake, and Google Test, Linux,
+      macOS, and Windows environments, and Arm platforms such as Neoverse and Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Laying the foundations, Test the library, Start coding,
+      and Implement matrix operations.
+# END generated_summary_faq
+
 author: Arnaud de Grandmaison
 
 
@@ -66,3 +108,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/mca-godbolt/_index.md b/content/learning-paths/cross-platform/mca-godbolt/_index.md
index 0c6276bb8a..f0aa724a83 100644
--- a/content/learning-paths/cross-platform/mca-godbolt/_index.md
+++ b/content/learning-paths/cross-platform/mca-godbolt/_index.md
@@ -17,6 +17,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: c86322d497541344f92907315793ab990669aa08bef994b0ce9ff1e32a5ba055
+  summary: >-
+    Learn how to use llvm-mca with Compiler Explorer to analyze Arm assembly performance, estimate
+    hardware resource pressure, and diagnose performance issues. It is designed for developers
+    who want to diagnose performance issues of Arm programs using LLVM Machine Code Analyzer (MCA)
+    and Compiler Explorer. By the end, you will be able to estimate the hardware resource pressure
+    and the number of cycles taken to execute your code snippet using llvm-mca, describe how this
+    estimate can help diagnose possible performance issues, and use Compiler Explorer to run llvm-mca.
+    It focuses on tools and technologies such as Assembly, llvm-mca, and Runbook, Linux, Windows,
+    and macOS environments, and Arm platforms including Cortex-A and Neoverse. The main steps
+    cover Background, Run MCA with Arm assembly, and Use MCA with Compiler Explorer.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will estimate the hardware resource pressure and the number of cycles taken to execute
+      your code snippet using llvm-mca, describe how this estimate can help diagnose possible
+      performance issues, and use Compiler Explorer to run llvm-mca. Learn how to use llvm-mca
+      with Compiler Explorer to analyze Arm assembly performance, estimate hardware resource pressure,
+      and diagnose performance issues.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers who want to diagnose performance issues of
+      Arm programs using LLVM Machine Code Analyzer (MCA) and Compiler Explorer.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Familiarity with Arm assembly.; LLVM
+      version 16 or newer, which includes support for Neoverse V2.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Assembly, llvm-mca, and Runbook, Linux, Windows,
+      and macOS environments, and Arm platforms such as Cortex-A and Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Background, Run MCA with Arm assembly, and Use MCA
+      with Compiler Explorer.
+# END generated_summary_faq
+
 author: Asher Dobrescu
 
 ### Tags
@@ -56,3 +98,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/mcp-ai-agent/_index.md b/content/learning-paths/cross-platform/mcp-ai-agent/_index.md
index 4f4920bfe1..c393e958b5 100644
--- a/content/learning-paths/cross-platform/mcp-ai-agent/_index.md
+++ b/content/learning-paths/cross-platform/mcp-ai-agent/_index.md
@@ -20,6 +20,56 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 39d489081bfa22125a4046c17d5c00a32c0d7b298cba7b6a12373cf3cf6bac04
+  summary: >-
+    Learn how to deploy a Model Context Protocol server on Raspberry Pi 5 and use the OpenAI Agent
+    SDK to create AI agents with custom tools for local inference. It is designed for LLM and
+    IoT developers who want to run and interact with AI agents on edge devices like the Raspberry
+    Pi 5. You'll learn how to deploy a lightweight Model Context Protocol (MCP) server and use
+    the OpenAI Agent SDK to create and register tools for intelligent local inference. By the
+    end, you will be able to deploy a lightweight Model Context Protocol (MCP) server on Raspberry
+    Pi 5 for local AI agent execution, use the OpenAI Agent SDK to interact with a local AI agent,
+    and design and register custom tools for the agent tasks. It focuses on tools and technologies
+    such as Python, AI, Raspberry Pi, and MCP, Linux environments, and Arm platforms including
+    Cortex-A. The main steps cover Introduction to Model Context Protocol (MCP) and Python uv
+    package for local AI agents, Set up an MCP server on Raspberry Pi 5, and Build and run an
+    AI agent on your development machine.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will deploy a lightweight Model Context Protocol (MCP) server on Raspberry Pi 5 for
+      local AI agent execution, use the OpenAI Agent SDK to interact with a local AI agent, and
+      design and register custom tools for the agent tasks. Learn how to deploy a Model Context
+      Protocol server on Raspberry Pi 5 and use the OpenAI Agent SDK to create AI agents with
+      custom tools for local inference.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is for LLM and IoT developers who want to run and interact with AI agents
+      on edge devices like the Raspberry Pi 5. You'll learn how to deploy a lightweight Model
+      Context Protocol (MCP) server and use the OpenAI Agent SDK to create and register tools
+      for intelligent local inference.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Raspberry Pi 5](https://www.raspberrypi.com/products/raspberry-pi-5/)
+      with a Linux-based OS installed.; Familiarity with Python programming and prompt engineering
+      techniques.; Basic understanding of Large Language Models (LLMs) and how they are used in
+      local inference.; Understanding of AI agents and the OpenAI Agent SDK (or similar frameworks).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Python, AI, Raspberry Pi, and MCP, Linux environments,
+      and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Introduction to Model Context Protocol (MCP) and Python
+      uv package for local AI agents, Set up an MCP server on Raspberry Pi 5, and Build and run
+      an AI agent on your development machine.
+# END generated_summary_faq
+
 author: Andrew Choi
 
 skilllevels: Introductory
@@ -56,3 +106,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/memory-latency/_index.md b/content/learning-paths/cross-platform/memory-latency/_index.md
index 762e2cca28..802e4e3831 100644
--- a/content/learning-paths/cross-platform/memory-latency/_index.md
+++ b/content/learning-paths/cross-platform/memory-latency/_index.md
@@ -15,6 +15,47 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 72e5ffe5091311850d17f30ed3ab4bd7487cbbd862d0d8751cc73bd91fff00e2
+  summary: >-
+    Learn how to reduce memory latency impact in applications using cache alignment and prefetching
+    techniques on Arm processors for improved performance. It is designed for Arm developers who
+    want to learn about memory latency and cache usage in application programming. By the end,
+    you will be able to explain the importance of memory latency and how to reduce its impact,
+    identify how cache alignment impacts performance, and use cache prefetching to improve performance.
+    It focuses on tools and technologies such as GCC, Clang, and Runbook, Linux environments,
+    and Arm platforms including Cortex-A and Neoverse. The main steps cover About memory latency,
+    How latency impacts performance - part 1, How latency impacts performance - part 2, Cache
+    alignment, and Cache prefetching.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will explain the importance of memory latency and how to reduce its impact, identify
+      how cache alignment impacts performance, and use cache prefetching to improve performance.
+      Learn how to reduce memory latency impact in applications using cache alignment and prefetching
+      techniques on Arm processors for improved performance.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for Arm developers who want to learn about memory latency
+      and cache usage in application programming.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm computer running Linux with recent
+      versions of Clang or GCC installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including GCC, Clang, and Runbook, Linux environments, and
+      Arm platforms such as Cortex-A and Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around About memory latency, How latency impacts performance
+      - part 1, How latency impacts performance - part 2, Cache alignment, and Cache prefetching.
+# END generated_summary_faq
+
 author: Konstantinos Margaritis
 
 ### Tags
@@ -63,3 +104,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md b/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md
index 884d46ab83..e9fa29d886 100644
--- a/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md
+++ b/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md
@@ -20,6 +20,60 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: bf3183bd62b590d4930f0bcf9c9dc836bbc5206a991be7bec5e18e9c20942352
+  summary: >-
+    Learn how to build MNN on an Armv9 system, run text, vision, and audio prompts with a multimodal
+    Omni model, and combine image and audio inputs into a single-shot retail restock ticket workflow.
+    It is designed for developers and engineers who want to run multimodal image, audio, and text
+    models on Armv9 Linux systems using MNN as a portable, CPU-first inference runtime. It is
+    aimed at readers who are comfortable building software from source and want a reproducible
+    on-device workflow without quantization or heterogeneous scheduling. By the end, you will
+    be able to build MNN natively on an Armv9 Linux system for multimodal inference, verify a
+    CPU-only Omni model workflow with text, vision, and audio prompts, and create a reproducible
+    multimodal application flow that combines image and audio inputs into an actionable restock
+    ticket. It focuses on tools and technologies such as CMake, CPP, and Bash, Linux environments,
+    and Arm platforms including Cortex-A. The main steps cover Run multimodal inference with MNN
+    on Armv9, Build MNN and prepare an Omni model on Armv9, Validate text-only inference with
+    an Omni model on Armv9, Run a vision retail shelf audit with MNN Omni, and Convert spoken
+    restock notes into structured tickets with MNN Omni.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build MNN natively on an Armv9 Linux system for multimodal inference, verify a
+      CPU-only Omni model workflow with text, vision, and audio prompts, and create a reproducible
+      multimodal application flow that combines image and audio inputs into an actionable restock
+      ticket. Learn how to build MNN on an Armv9 system, run text, vision, and audio prompts with
+      a multimodal Omni model, and combine image and audio inputs into a single-shot retail restock
+      ticket workflow.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is for developers and engineers who want to run multimodal image, audio,
+      and text models on Armv9 Linux systems using MNN as a portable, CPU-first inference runtime.
+      It is aimed at readers who are comfortable building software from source and want a reproducible
+      on-device workflow without quantization or heterogeneous scheduling.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Armv9 Linux device with at least
+      32 GB of available disk space, for example a Radxa Orion O6; Familiarity with the Linux
+      command line, Git, and building C++ projects with CMake; Internet access to download source
+      code, model assets, and sample data.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including CMake, CPP, and Bash, Linux environments, and Arm
+      platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Run multimodal inference with MNN on Armv9, Build
+      MNN and prepare an Omni model on Armv9, Validate text-only inference with an Omni model
+      on Armv9, Run a vision retail shelf audit with MNN Omni, and Convert spoken restock notes
+      into structured tickets with MNN Omni.
+# END generated_summary_faq
+
 author: Odin Shen
 
 ### Tags
@@ -69,3 +123,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md b/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md
index ed135f1463..c40c97b4fb 100644
--- a/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md
+++ b/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md
@@ -26,6 +26,58 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: eb01b77f36323331c080615edcbddbf8cb56cf005f2249f1ea309ab1dbec8616
+  summary: >-
+    Learn how to implement and optimize matrix multiplication using Arm's Scalable Matrix Extension
+    2 (SME2) with assembly and intrinsics, including benchmarking and validation on Arm hardware.
+    It is designed for This Learning Path is an advanced topic for developers who want to accelerate
+    the performance of matrix multiplication using Arm's Scalable Matrix Extension Version 2 (SME2).
+    By the end, you will be able to implement a baseline matrix multiplication kernel in C without
+    SME2, use SME2 assembly instructions to accelerate matrix multiplication performance, and
+    use SME2 intrinsics to vectorize and optimize matrix multiplication. It focuses on tools and
+    technologies such as C, Clang, LLVM, and SME2, Linux, macOS, and Windows environments, and
+    Arm platforms including Arm C1. The main steps cover Overview, Set up your SME2 development
+    environment, Test your SME2 development environment, Streaming mode and ZA state in SME, and
+    Vanilla matrix multiplication.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will implement a baseline matrix multiplication kernel in C without SME2, use SME2 assembly
+      instructions to accelerate matrix multiplication performance, and use SME2 intrinsics to
+      vectorize and optimize matrix multiplication. Learn how to implement and optimize matrix
+      multiplication using Arm's Scalable Matrix Extension 2 (SME2) with assembly and intrinsics,
+      including benchmarking and validation on Arm hardware.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is an advanced topic for developers who want to accelerate the performance
+      of matrix multiplication using Arm's Scalable Matrix Extension Version 2 (SME2).
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Working knowledge of Arm’s SVE and SME2
+      instruction sets; Intermediate proficiency with the C programming language and the Armv9-A
+      assembly language; A computer running Linux, macOS, or Windows; Installations of Git, CMake
+      and Ninja for project setup; A platform that supports SME2 - see the list of [devices with
+      SME2 support](/learning-paths/cross-platform/multiplying-matrices-with-sme2/1-get-started/#devices)
+      or an emulator to run code with SME2 instructions; Installation of Docker for SME2 emulation
+      (if you don't have SME2 available); Installation of Android Development Studio and adb (if
+      you're targeting an Android phone with SME2 support); Compiler support for SME2 instructions
+      (for example, LLVM 18 or later with SME2 backend support).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including C, Clang, LLVM, and SME2, Linux, macOS, and Windows
+      environments, and Arm platforms such as Arm C1.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview, Set up your SME2 development environment,
+      Test your SME2 development environment, Streaming mode and ZA state in SME, and Vanilla
+      matrix multiplication.
+# END generated_summary_faq
+
 author: Arnaud de Grandmaison
 
 ### Tags
@@ -105,3 +157,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md b/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md
index 87acf975aa..ac5de0604b 100644
--- a/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md
+++ b/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md
@@ -24,6 +24,53 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 1251465f13b67ee80292b66ef22069a1b6cc2ca67bb602cdd5e393a278576ec8
+  summary: >-
+    Learn how to create and train a PyTorch neural network for MNIST digit classification, optimize
+    it with quantization and fusing, and deploy it in an Android application with performance
+    measurement. It is designed for software developers interested in learning how to use PyTorch
+    to create and train a feedforward neural network for digit classification, and also software
+    developers interested in learning how to use and apply optimizations to the trained model
+    in an Android application. By the end, you will be able to prepare a PyTorch development environment,
+    download and prepare the MNIST dataset, and create and train a neural network architecture
+    using PyTorch. It focuses on tools and technologies such as Android Studio and Visual Studio
+    Code, Windows, Linux, and macOS environments, and Arm platforms including Cortex-A and Neoverse.
+    The main steps cover Prepare a PyTorch Development Environment, Create a PyTorch model for
+    MNIST, About PyTorch Model Training, Perform Training and Save the Model, and Deploy the Model
+    for Inference.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will prepare a PyTorch development environment, download and prepare the MNIST dataset,
+      and create and train a neural network architecture using PyTorch. Learn how to create and
+      train a PyTorch neural network for MNIST digit classification, optimize it with quantization
+      and fusing, and deploy it in an Android application with performance measurement.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers interested in learning how to use PyTorch
+      to create and train a feedforward neural network for digit classification, and also software
+      developers interested in learning how to use and apply optimizations to the trained model
+      in an Android application.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A machine that can run Python3, Visual
+      Studio Code, and Android Studio.; For the OS, you can use Windows, Linux, or macOS.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Android Studio and Visual Studio Code, Windows,
+      Linux, and macOS environments, and Arm platforms such as Cortex-A and Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Prepare a PyTorch Development Environment, Create
+      a PyTorch model for MNIST, About PyTorch Model Training, Perform Training and Save the Model,
+      and Deploy the Model for Inference.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
@@ -67,3 +114,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/remoteit/_index.md b/content/learning-paths/cross-platform/remoteit/_index.md
index 57f5f641ae..c2e51be9a7 100644
--- a/content/learning-paths/cross-platform/remoteit/_index.md
+++ b/content/learning-paths/cross-platform/remoteit/_index.md
@@ -19,6 +19,54 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 927cfebb8ebf9595922dad115c9a8d10900e43c4f80e73e6102a71e3e4ca2da1
+  summary: >-
+    Learn how to install and configure Remote.It for secure remote device access using SSH and
+    other services, with proxy and peer-to-peer connection options. It is designed for software
+    developers who want to use Remote.It to establish private network connections between users
+    and devices or devices to device. By the end, you will be able to install Remote.It on target
+    devices (devices you would like to access remotely), access your Remote.It enabled devices
+    from anywhere, and understand the different types of network connections (proxy vs. Peer to
+    peer). It focuses on tools and technologies such as Remote.It, Linux, Windows, and macOS environments,
+    and Arm platforms including Neoverse and Cortex-A. The main steps cover Remote.It Packages,
+    Installing the Remote.It Device Package, Remote.It CLI, and Connections.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install Remote.It on target devices (devices you would like to access remotely),
+      access your Remote.It enabled devices from anywhere, and understand the different types
+      of network connections (proxy vs. Peer to peer). Learn how to install and configure Remote.It
+      for secure remote device access using SSH and other services, with proxy and peer-to-peer
+      connection options.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to use Remote.It to establish
+      private network connections between users and devices or devices to device.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows, macOS, or Linux computer
+      which you will use to configure your devices as well as connect to your remote devices.;
+      A device/computer to which you would like remote access. A device can be a Windows, Mac,
+      or Linux computer including development kits such as Raspberry Pi or cloud-hosted such as
+      within Arm Virtual Hardware or within AWS. You will need a method to control this device
+      before Remote.It is deployed which can be local access or access via another remote connectivity
+      solution (Remote Desktop, VPN, etc.); Determine if your device that you would like to access
+      remotely also needs to make connections to other Remote.It devices.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Remote.It, Linux, Windows, and macOS environments,
+      and Arm platforms such as Neoverse and Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Remote.It Packages, Installing the Remote.It Device
+      Package, Remote.It CLI, and Connections.
+# END generated_summary_faq
+
 author: Brenda Strech
 
 further_reading:
@@ -70,3 +118,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md b/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
index 271c852998..6a8b527cfa 100644
--- a/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
+++ b/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
@@ -15,6 +15,43 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 9825df99004e981fadce5884b40b2152f4e43064cbba2cd2129fd90006090678
+  summary: >-
+    Learn how to use the C99 restrict keyword to indicate non-overlapping memory regions and enable
+    better compiler optimizations for vectorization on Arm platforms. It is designed for C developers
+    who are interested in software optimization. By the end, you will be able to learn the importance
+    of using the `restrict` keyword in C correctly. It focuses on tools and technologies such
+    as GCC, Clang, SVE2, and Runbook, Linux environments, and Arm platforms including Aarch64,
+    Armv8-a, and Armv9-a. The main steps cover What problem does restrict solve?, Another example
+    with SVE2, and When can you use restrict.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will learn the importance of using the `restrict` keyword in C correctly. Learn how
+      to use the C99 restrict keyword to indicate non-overlapping memory regions and enable better
+      compiler optimizations for vectorization on Arm platforms.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for C developers who are interested in software optimization.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm computer running Linux OS and
+      a recent version of compiler (Clang or GCC) installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including GCC, Clang, SVE2, and Runbook, Linux environments,
+      and Arm platforms such as Aarch64, Armv8-a, and Armv9-a.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around What problem does restrict solve?, Another example
+      with SVE2, and When can you use restrict.
+# END generated_summary_faq
+
 author: Konstantinos Margaritis
 
 ### Tags
@@ -58,3 +95,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/rust_armds/_index.md b/content/learning-paths/cross-platform/rust_armds/_index.md
index 861964d222..819f2acf70 100644
--- a/content/learning-paths/cross-platform/rust_armds/_index.md
+++ b/content/learning-paths/cross-platform/rust_armds/_index.md
@@ -17,6 +17,44 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: f237cd239e5886b21bd5bee88dcd9f95d88eda9a5c2eea363cc9db252e0c6e9f
+  summary: >-
+    Learn how to build an embedded Rust application for Arm processors, run it on a Fixed Virtual
+    Platform, and debug it using Arm Development Studio. It is designed for embedded application
+    developers to get started with Rust. By the end, you will be able to build an embedded application
+    in Rust, run the application on a Fixed Virtual Platform (FVP), and debug the application
+    with Arm Development Studio. It focuses on tools and technologies such as IP Explorer, Baremetal
+    environments, and Arm platforms including Cortex-A, Cortex-R, and Cortex-M. The main steps
+    cover Install tools and build an example and Run the example on FVP and debug with Arm Debugger.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build an embedded application in Rust, run the application on a Fixed Virtual Platform
+      (FVP), and debug the application with Arm Development Studio. Learn how to build an embedded
+      Rust application for Arm processors, run it on a Fixed Virtual Platform, and debug it using
+      Arm Development Studio.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for embedded application developers to get started with Rust.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An installation of Arm Development Studio.;
+      A basic understanding of Rust programming.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including IP Explorer, Baremetal environments, and Arm platforms
+      such as Cortex-A, Cortex-R, and Cortex-M.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Install tools and build an example and Run the example
+      on FVP and debug with Arm Debugger.
+# END generated_summary_faq
+
 author: Ronan Synnott
 
 
@@ -57,3 +95,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/simd-info-demo/_index.md b/content/learning-paths/cross-platform/simd-info-demo/_index.md
index f6d9a50383..ae0582f89d 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/_index.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/_index.md
@@ -16,6 +16,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 7a40efa6d83b4629888f9622260e6e9aa9192db836b203fa4bf388cbe636b7e6
+  summary: >-
+    Learn how to use SIMD.info to port SIMD intrinsics across Arm architectures, including navigation,
+    search, and comparison features for finding equivalent instructions. It is designed for software
+    developers who are interested in porting SIMD code across Arm platforms. By the end, you will
+    be able to describe how to use SIMD.info's tools and features, such as navigation, search,
+    and comparison, to simplify the process of finding equivalent SIMD intrinsics between architectures
+    to improve code portability. It focuses on tools and technologies such as GCC, Clang, Rust,
+    and Runbook, Linux environments, and Arm platforms including AArch64, Armv8-A, and Armv9-A.
+    The main steps cover Overview, SIMD.info Features, Example Program, Porting Process, and Code
+    Verification.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will describe how to use SIMD.info's tools and features, such as navigation, search,
+      and comparison, to simplify the process of finding equivalent SIMD intrinsics between architectures
+      to improve code portability. Learn how to use SIMD.info to port SIMD intrinsics across Arm
+      architectures, including navigation, search, and comparison features for finding equivalent
+      instructions.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is for software developers who are interested in porting SIMD code across
+      Arm platforms.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A basic understanding of SIMD.; Access
+      to an Arm platform with a SIMD-supported engine, installed with recent versions of a C compiler
+      such as Clang or GCC.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including GCC, Clang, Rust, and Runbook, Linux environments,
+      and Arm platforms such as AArch64, Armv8-A, and Armv9-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview, SIMD.info Features, Example Program, Porting
+      Process, and Code Verification.
+# END generated_summary_faq
+
 author: 
     - Georgios Mermigkis
     - Konstantinos Margaritis
@@ -55,3 +98,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/simd-loops/_index.md b/content/learning-paths/cross-platform/simd-loops/_index.md
index 1e339ec86a..528806a548 100644
--- a/content/learning-paths/cross-platform/simd-loops/_index.md
+++ b/content/learning-paths/cross-platform/simd-loops/_index.md
@@ -20,6 +20,54 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: b1c43e1bf971db4582ca358c98dab2c7e6e047d6c79bfcc0db148bc575f33679
+  summary: >-
+    Learn how to write high-performance SIMD code using the SIMD Loops project, with hands-on
+    examples demonstrating SVE, SVE2, and SME2 features on Arm processors. It is designed for
+    software developers who want to learn how to use the full range of features available in SVE,
+    SVE2, and SME2 to improve software performance on Arm processors. By the end, you will be
+    able to improve SIMD code performance using Scalable Vector Extension (SVE) and Scalable Matrix
+    Extension (SME), describe what SIMD Loops contains and how kernels are organized across scalar,
+    Neon, SVE, SVE2, and SME2 variants, and build and run a selected kernel with the provided
+    runner and validate correctness against the C reference. It focuses on tools and technologies
+    such as C, CPP, GCC, Clang, and SME2, Linux and macOS environments, and Arm platforms including
+    Neoverse and Cortex-A. The main steps cover About Single Instruction, Multiple Data loops,
+    Using SIMD Loops, Code example, and Learning with SIMD Loops.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will improve SIMD code performance using Scalable Vector Extension (SVE) and Scalable
+      Matrix Extension (SME), describe what SIMD Loops contains and how kernels are organized
+      across scalar, Neon, SVE, SVE2, and SME2 variants, and build and run a selected kernel with
+      the provided runner and validate correctness against the C reference. Learn how to write
+      high-performance SIMD code using the SIMD Loops project, with hands-on examples demonstrating
+      SVE, SVE2, and SME2 features on Arm processors.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers who want to learn how to use the full
+      range of features available in SVE, SVE2, and SME2 to improve software performance on Arm
+      processors.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An AArch64 computer running Linux or
+      macOS. You can use cloud instances, refer to [Get started with Arm-based cloud instances](/learning-paths/servers-and-cloud-computing/csp/)
+      for a list of cloud service providers; Some familiarity with SIMD programming and Neon intrinsics;
+      Recent toolchains that support SVE/SME (GCC 13+ or Clang 16+ recommended).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including C, CPP, GCC, Clang, and SME2, Linux and macOS environments,
+      and Arm platforms such as Neoverse and Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around About Single Instruction, Multiple Data loops, Using
+      SIMD Loops, Code example, and Learning with SIMD Loops.
+# END generated_summary_faq
+
 author:
     - Alejandro Martinez Vicente
     - Mohamad Najem
@@ -107,3 +155,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/simd-on-rust/_index.md b/content/learning-paths/cross-platform/simd-on-rust/_index.md
index 2654001092..1a97bf3820 100644
--- a/content/learning-paths/cross-platform/simd-on-rust/_index.md
+++ b/content/learning-paths/cross-platform/simd-on-rust/_index.md
@@ -18,6 +18,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: a051c519c1a4969f30d5a81e46823e77f0c15f163011b3a38f4c05104d853249
+  summary: >-
+    Learn how to write SIMD code in Rust on Arm platforms using Neon intrinsics, portable SIMD
+    abstractions, and optimize performance with architecture-specific instructions. It is designed
+    for software developers who want to take advantage of SIMD code on Arm systems using Rust.
+    By the end, you will be able to write SIMD code with Rust using std::arch and Neon intrinsics
+    on Arm, use portable SIMD abstractions with std::simd for cross-platform code, and apply feature
+    detection and target attributes for architecture-specific optimizations. It focuses on tools
+    and technologies such as GCC, Clang, Rust, and Runbook, Linux environments, and Arm platforms
+    including Cortex-A and Neoverse. The main steps cover Introduction to Rust, Arm SIMD on Rust,
+    Inlining Intrinsics, Matrix transpose, and A more complicated example.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will write SIMD code with Rust using std::arch and Neon intrinsics on Arm, use portable
+      SIMD abstractions with std::simd for cross-platform code, and apply feature detection and
+      target attributes for architecture-specific optimizations. Learn how to write SIMD code
+      in Rust on Arm platforms using Neon intrinsics, portable SIMD abstractions, and optimize
+      performance with architecture-specific instructions.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers who want to take advantage of SIMD code
+      on Arm systems using Rust.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm-based computer with recent versions
+      of a C compiler (Clang or GCC) and a Rust compiler installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including GCC, Clang, Rust, and Runbook, Linux environments,
+      and Arm platforms such as Cortex-A and Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Introduction to Rust, Arm SIMD on Rust, Inlining Intrinsics,
+      Matrix transpose, and A more complicated example.
+# END generated_summary_faq
+
 author: Konstantinos Margaritis
 
 ### Tags
@@ -66,3 +108,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md b/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md
index f159f77ccf..0b6dd600ed 100644
--- a/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md
+++ b/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md
@@ -22,6 +22,55 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 36f7dfe13c8c940abbaad2b61620b3b1c6d97f580de15e573b45ef7760753797
+  summary: >-
+    Learn how to profile and optimize ExecuTorch models using SME2 acceleration on Arm platforms,
+    including operator-level analysis and performance bottleneck identification. It is designed
+    for developers and performance engineers who deploy ExecuTorch models on Arm devices and want
+    to understand and reduce inference latency. By the end, you will be able to understand how
+    SME2 acceleration changes the performance profile of ExecuTorch models by reducing compute-bound
+    bottlenecks, interpret operator-level and operator-category breakdowns (for example, convolution,
+    GEMM, data movement, and other operators), and identify which operators benefit most from
+    SME2 acceleration and which operators become the new performance bottlenecks. It focuses on
+    tools and technologies such as ExecuTorch, Python, CMake, and SME2, macOS and Android environments,
+    and Arm platforms including Cortex-A and Arm C1. The main steps cover Explore ExecuTorch profiling
+    with SME2, Set up the ExecuTorch profiling environment, Export PyTorch models and analyze
+    performance, and Automate profiling workflows with AI agents.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand how SME2 acceleration changes the performance profile of ExecuTorch
+      models by reducing compute-bound bottlenecks, interpret operator-level and operator-category
+      breakdowns (for example, convolution, GEMM, data movement, and other operators), and identify
+      which operators benefit most from SME2 acceleration and which operators become the new performance
+      bottlenecks. Learn how to profile and optimize ExecuTorch models using SME2 acceleration
+      on Arm platforms, including operator-level analysis and performance bottleneck identification.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for developers and performance engineers who deploy ExecuTorch
+      models on Arm devices and want to understand and reduce inference latency.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Apple Silicon macOS host with Python
+      3.9 or later and CMake 3.29 or later; Basic familiarity with ExecuTorch or PyTorch; Optionally,
+      an Android device with Armv9 and SME2 support for on-device testing (if used, configure
+      power management settings to ensure consistent performance measurements).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including ExecuTorch, Python, CMake, and SME2, macOS and Android
+      environments, and Arm platforms such as Cortex-A and Arm C1.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Explore ExecuTorch profiling with SME2, Set up the
+      ExecuTorch profiling environment, Export PyTorch models and analyze performance, and Automate
+      profiling workflows with AI agents.
+# END generated_summary_faq
+
 author: Jason Zhu, Tyler Mullenbach, Damien Dooley 
 
 ### Tags
@@ -64,3 +113,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md b/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md
index 4741ec7517..1593e53000 100644
--- a/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md
+++ b/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md
@@ -21,6 +21,60 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 09142517ecc64fba821062e1af4db3ac9d72f9adcd3f10c12d6fd5a4cf3daaf4
+  summary: >-
+    Learn how to deploy Tinkerblox UltraEdge HPC-I for edge AI and mixed workloads on Arm platforms,
+    including installation and configuration on Debian, Ubuntu, and Yocto systems. It is designed
+    for business, R&D, and engineering teams seeking to optimize CPU and GPU infrastructure utilization
+    while reducing total cost of ownership on edge and constrained environments. It's ideal for
+    innovation and development teams building next-generation AI workloads using alternative runtime
+    environments and packaging technologies. By the end, you will be able to understand the layered
+    architecture of UltraEdge core, boost, and prime, build applications using the UltraEdge MicroStack,
+    and deploy the MicroPacs on Linux-based compute systems and scale to cloud or data-center
+    environments. It focuses on tools and technologies such as Tinkerblox, Linux and other environments,
+    Arm platforms including Neoverse, and cloud platforms such as Google Cloud. The main steps
+    cover Understand UltraEdge HPC-I architecture for edge AI and mixed workloads, Provision a
+    Google Axion C4A VM for Yocto image builds on Arm, Build and install Yocto images for NXP
+    S32G-VNP-GLDBOX3 with UltraEdge, Install UltraEdge on Debian and Ubuntu for Edge AI workloads,
+    and Run and manage UltraEdge HPC-I for AI and mixed workloads on Arm.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand the layered architecture of UltraEdge core, boost, and prime, build
+      applications using the UltraEdge MicroStack, and deploy the MicroPacs on Linux-based compute
+      systems and scale to cloud or data-center environments. Learn how to deploy Tinkerblox UltraEdge
+      HPC-I for edge AI and mixed workloads on Arm platforms, including installation and configuration
+      on Debian, Ubuntu, and Yocto systems.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for business, R&D, and engineering teams seeking to optimize CPU
+      and GPU infrastructure utilization while reducing total cost of ownership on edge and constrained
+      environments. It's ideal for innovation and development teams building next-generation AI
+      workloads using alternative runtime environments and packaging technologies.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Experience using Linux on embedded or
+      SBC platforms; Understanding of container runtimes (containerd) and CNI networking; Basic
+      knowledge of communication protocols (MQTT, HTTP, and others); Familiarity with edge-cloud
+      architectures and data-flow orchestration.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Tinkerblox, Linux and other environments, Arm platforms
+      such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Understand UltraEdge HPC-I architecture for edge AI
+      and mixed workloads, Provision a Google Axion C4A VM for Yocto image builds on Arm, Build
+      and install Yocto images for NXP S32G-VNP-GLDBOX3 with UltraEdge, Install UltraEdge on Debian
+      and Ubuntu for Edge AI workloads, and Run and manage UltraEdge HPC-I for AI and mixed workloads
+      on Arm.
+# END generated_summary_faq
+
 author: Tinkerblox
 
 ### Tags
@@ -57,3 +111,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/topdown-compare/_index.md b/content/learning-paths/cross-platform/topdown-compare/_index.md
index 6b5d1abb24..3a4132280d 100644
--- a/content/learning-paths/cross-platform/topdown-compare/_index.md
+++ b/content/learning-paths/cross-platform/topdown-compare/_index.md
@@ -19,6 +19,59 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 5f4b05e3d962476c67c4a4400c8fa71f04412f3f0354d5c3123f38af57791304
+  summary: >-
+    Learn how to compare Arm Neoverse and Intel x86 top-down performance analysis methodologies
+    using PMU counters, Linux Perf, and topdown-tool to identify bottlenecks across architectures.
+    It is designed for software developers and performance engineers who want to understand the
+    similarities and differences between Arm Neoverse and Intel x86 top-down performance analysis
+    using PMU counters, Linux Perf, and the topdown-tool. By the end, you will be able to compare
+    Intel x86 multi-level hierarchical methodology with Arm Neoverse micro-architecture exploration
+    methodology, execute performance analysis using Linux Perf on x86 and topdown-tool on Arm
+    systems, and analyze Backend Bound, Frontend Bound, Bad Speculation, and Retiring categories
+    across both architectures. It focuses on tools and technologies such as GCC, Clang, Perf,
+    and topdown-tool, Linux environments, and Arm platforms including Neoverse. The main steps
+    cover Analyze Intel x86 and Arm Neoverse top-down performance methodologies, Understand Intel
+    x86 multilevel hierarchical top-down analysis, Understand Arm Neoverse top-down analysis,
+    Evaluate cross-platform PMU counter differences, and Measure cross-platform performance with
+    topdown-tool and Perf PMU counters.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will compare Intel x86 multi-level hierarchical methodology with Arm Neoverse micro-architecture
+      exploration methodology, execute performance analysis using Linux Perf on x86 and topdown-tool
+      on Arm systems, and analyze Backend Bound, Frontend Bound, Bad Speculation, and Retiring
+      categories across both architectures. Learn how to compare Arm Neoverse and Intel x86 top-down
+      performance analysis methodologies using PMU counters, Linux Perf, and topdown-tool to identify
+      bottlenecks across architectures.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers and performance engineers who want to
+      understand the similarities and differences between Arm Neoverse and Intel x86 top-down
+      performance analysis using PMU counters, Linux Perf, and the topdown-tool.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Familiarity with performance analysis
+      on Linux systems using Perf and PMU counters; Access to Arm Neoverse V2 and Intel x86 Linux
+      systems to run the code example; Basic understanding of CPU pipeline concepts and performance
+      bottlenecks.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including GCC, Clang, Perf, and topdown-tool, Linux environments,
+      and Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Analyze Intel x86 and Arm Neoverse top-down performance
+      methodologies, Understand Intel x86 multilevel hierarchical top-down analysis, Understand
+      Arm Neoverse top-down analysis, Evaluate cross-platform PMU counter differences, and Measure
+      cross-platform performance with topdown-tool and Perf PMU counters.
+# END generated_summary_faq
+
 author:
     - Jason Andrews
 
@@ -65,3 +118,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/vectorization-comparison/_index.md b/content/learning-paths/cross-platform/vectorization-comparison/_index.md
index 60938e38f3..f27210ac12 100644
--- a/content/learning-paths/cross-platform/vectorization-comparison/_index.md
+++ b/content/learning-paths/cross-platform/vectorization-comparison/_index.md
@@ -18,6 +18,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 26450ae17f7ed4242c52456c2780ffce5fad36b56dfc4a8482e8f236f855e134
+  summary: >-
+    Learn how to migrate x86-64 SIMD code to Arm64 by mapping Intel SSE/AVX to Arm Neon, SVE,
+    and SME, with code examples and migration strategies using autovectorization or intrinsics.
+    It is designed for developers migrating vectorized (SIMD) code from x86-64 to Arm64. By the
+    end, you will be able to identify how Arm vector extensions including Neon, Scalable Vector
+    Extension (SVE), and Scalable Matrix Extension (SME) map to vector extensions from other architectures
+    and plan a migration strategy using autovectorization, intrinsics, or library substitution.
+    It focuses on tools and technologies such as GCC and Clang, Linux environments, and Arm platforms
+    including Neoverse. The main steps cover Migrate SIMD code to the Arm architecture and Explore
+    vector extension code examples.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will identify how Arm vector extensions including Neon, Scalable Vector Extension (SVE),
+      and Scalable Matrix Extension (SME) map to vector extensions from other architectures and
+      plan a migration strategy using autovectorization, intrinsics, or library substitution.
+      Learn how to migrate x86-64 SIMD code to Arm64 by mapping Intel SSE/AVX to Arm Neon, SVE,
+      and SME, with code examples and migration strategies using autovectorization or intrinsics.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for developers migrating vectorized (SIMD) code from x86-64 to
+      Arm64.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Familiarity with vector extensions,
+      SIMD programming, and compiler intrinsics; Access to Linux systems with Neon and SVE support.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including GCC and Clang, Linux environments, and Arm platforms
+      such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Migrate SIMD code to the Arm architecture and Explore
+      vector extension code examples.
+# END generated_summary_faq
+
 author:
     - Jason Andrews
 
@@ -84,5 +126,3 @@ layout: "learningpathall"       # All files under learning paths have this same
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
 
-   
-
diff --git a/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md b/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md
index 112b87e586..cbf784bf84 100644
--- a/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md
+++ b/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md
@@ -15,6 +15,45 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 14a22194d3fc73ebebb62db9c7cfbeabe0bd9acdaf340b2df52072be65d98655
+  summary: >-
+    Learn how to optimize SIMD performance on Arm by restructuring data layouts from Array-of-Structures
+    to Structure-of-Arrays, with practical examples using Neon and SVE intrinsics. It is designed
+    for C/C++ developers who are interested in improving the performance of SIMD code. By the
+    end, you will be able to comprehend the importance of data layout when writing SIMD code.
+    It focuses on tools and technologies such as GCC, Clang, and Runbook, Linux environments,
+    and Arm platforms including Neoverse and Cortex-A. The main steps cover What exactly is data
+    layout?, Improve data alignment, Increase complexity, Write hand optimized SIMD code, and
+    Structure of arrays.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will comprehend the importance of data layout when writing SIMD code. Learn how to optimize
+      SIMD performance on Arm by restructuring data layouts from Array-of-Structures to Structure-of-Arrays,
+      with practical examples using Neon and SVE intrinsics.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for C/C++ developers who are interested in improving the performance
+      of SIMD code.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm computer running Linux and a
+      recent version of Clang or the GNU compiler (gcc) installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including GCC, Clang, and Runbook, Linux environments, and
+      Arm platforms such as Neoverse and Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around What exactly is data layout?, Improve data alignment,
+      Increase complexity, Write hand optimized SIMD code, and Structure of arrays.
+# END generated_summary_faq
+
 author: Konstantinos Margaritis
 
 ### Tags
@@ -59,3 +98,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md b/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
index fe7f754cf5..9d4f7f31ee 100644
--- a/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
+++ b/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
@@ -22,6 +22,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: bf887ef2481b51cf6c32e49e3eb1d5577346b7b9b1e4d6a604c559597b5306f0
+  summary: >-
+    Learn how to sample and profile CPU instructions using WindowsPerf with Arm Statistical Profiling
+    Extension (SPE) on Windows on Arm, demonstrated with CPython workload analysis. It is designed
+    for developers who would like to learn about sampling CPU instructions with WindowsPerf and
+    the Arm Statistical Profiling Extension (SPE). By the end, you will be able to use WindowsPerf
+    with a native Windows on Arm workload, describe the basic concepts of sampling with Arm SPE,
+    and explore the WindowsPerf command line. It focuses on tools and technologies such as WindowsPerf,
+    Python, and perf, Windows environments, and Arm platforms including Neoverse and Cortex-A.
+    The main steps cover Overview of Arm Statistical Profiling Extension, Setup, WindowsPerf Sample
+    using SPE, WindowsPerf Record using SPE, and Summary.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will use WindowsPerf with a native Windows on Arm workload, describe the basic concepts
+      of sampling with Arm SPE, and explore the WindowsPerf command line. Learn how to sample
+      and profile CPU instructions using WindowsPerf with Arm Statistical Profiling Extension
+      (SPE) on Windows on Arm, demonstrated with CPython workload analysis.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers who would like to learn about sampling CPU
+      instructions with WindowsPerf and the Arm Statistical Profiling Extension (SPE).
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows on Arm desktop or development
+      machine, with CPU support for SPE.; An installation of [WindowsPerf](/install-guides/wperf).;
+      An installation of [Visual Studio](/install-guides/vs-woa/).; An installation of [Git](/install-guides/git-woa/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including WindowsPerf, Python, and perf, Windows environments,
+      and Arm platforms such as Neoverse and Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview of Arm Statistical Profiling Extension, Setup,
+      WindowsPerf Sample using SPE, WindowsPerf Record using SPE, and Summary.
+# END generated_summary_faq
+
 author: Przemyslaw Wirkus
 
 ### Tags
@@ -103,3 +145,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/woa_azure/_index.md b/content/learning-paths/cross-platform/woa_azure/_index.md
index 89d52c9b08..93b9b8e62f 100644
--- a/content/learning-paths/cross-platform/woa_azure/_index.md
+++ b/content/learning-paths/cross-platform/woa_azure/_index.md
@@ -17,6 +17,45 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 367566dfec0a76685b1504c2c1a996e50c55e67b2f4c5515057c0f0f1384ef98
+  summary: >-
+    Learn how to create and connect to a Windows on Arm virtual machine in Microsoft Azure using
+    the Azure Marketplace and RDP. It is designed for software developers interested using Windows
+    on Arm in the Azure cloud. By the end, you will be able to start a Windows on Arm virtual
+    machine in Azure cloud and discover all Arm-based image offerings in the Azure Image Marketplace.
+    It focuses on Windows environments, Arm platforms including Neoverse, and cloud platforms
+    such as Microsoft Azure. The main steps cover Create Windows on Arm virtual machine in Azure
+    cloud.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will start a Windows on Arm virtual machine in Azure cloud and discover all Arm-based
+      image offerings in the Azure Image Marketplace. Learn how to create and connect to a Windows
+      on Arm virtual machine in Microsoft Azure using the Azure Marketplace and RDP.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers interested using Windows on Arm in
+      the Azure cloud.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Azure Cloud account.; An RDP client
+      to connect to your Windows on Arm instance. For more info on RDP clients, see [Remote Desktop
+      clients for Remote Desktop Services and remote PCs](https://learn.microsoft.com/en-us/windows-server/remote/remote-desktop-services/clients/remote-desktop-clients)
+      to get started.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers Windows environments, Arm platforms such as Neoverse, and cloud platforms such
+      as Microsoft Azure.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Create Windows on Arm virtual machine in Azure cloud.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
@@ -50,3 +89,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md b/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
index 3069941cc7..141695a40e 100644
--- a/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
+++ b/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
@@ -19,6 +19,59 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: a56dce27c6a846bcf35b6e9505142f1445b22ff71530f1189700049d7b14e994
+  summary: >-
+    Learn how to build and deploy distributed Zenoh systems on Arm devices like Raspberry Pi,
+    using pub/sub, storage, and queryable models for scalable robotics and IoT applications. It
+    is designed for robotics developers, industrial automation engineers, and IoT system architects
+    who are building distributed, scalable, and low-latency applications. Whether you're using
+    the Robot Operating System (ROS), developing autonomous systems, or designing multi-node communication
+    frameworks, you can use Eclipse Zenoh on Arm-based platforms, both in the cloud and on local
+    devices like Raspberry Pi. By the end, you will be able to understand Zenoh's architecture
+    and how it integrates pub/sub, storage, querying, and computation models, build and run Zenoh
+    examples on both Arm servers and Raspberry Pi, and set up and deploy a multi-node Zenoh system.
+    It focuses on tools and technologies such as ROS 2, C, Raspberry Pi, Zenoh, and Rust, Linux
+    environments, and Arm platforms including Cortex-A and Neoverse. The main steps cover Build
+    scalable communication systems with Eclipse Zenoh, Get started with Zenoh on Raspberry Pi
+    and Arm Linux, Containerize and deploy Zenoh across multiple Raspberry Pi devices, Run a simple
+    Zenoh pub/sub example, and Run a Zenoh storage and query example.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand Zenoh's architecture and how it integrates pub/sub, storage, querying,
+      and computation models, build and run Zenoh examples on both Arm servers and Raspberry Pi,
+      and set up and deploy a multi-node Zenoh system. Learn how to build and deploy distributed
+      Zenoh systems on Arm devices like Raspberry Pi, using pub/sub, storage, and queryable models
+      for scalable robotics and IoT applications.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is for robotics developers, industrial automation engineers, and IoT
+      system architects who are building distributed, scalable, and low-latency applications.
+      Whether you're using the Robot Operating System (ROS), developing autonomous systems, or
+      designing multi-node communication frameworks, you can use Eclipse Zenoh on Arm-based platforms,
+      both in the cloud and on local devices like Raspberry Pi.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: At least two local Cortex-A devices
+      running Linux, such as Raspberry Pi 4 or Pi 5. You can also use Arm servers or cloud instances;
+      Experience with ROS 2 applications.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including ROS 2, C, Raspberry Pi, Zenoh, and Rust, Linux environments,
+      and Arm platforms such as Cortex-A and Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Build scalable communication systems with Eclipse
+      Zenoh, Get started with Zenoh on Raspberry Pi and Arm Linux, Containerize and deploy Zenoh
+      across multiple Raspberry Pi devices, Run a simple Zenoh pub/sub example, and Run a Zenoh
+      storage and query example.
+# END generated_summary_faq
+
 author: 
     - Odin Shen
     - William Liang
@@ -65,3 +118,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md b/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
index fd980bb6b9..8116740d6b 100644
--- a/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
@@ -19,6 +19,50 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: d8c48c293b2d316d5e68e18ab9e81157ad114a64cf2efa6951374a55d25238d6
+  summary: >-
+    Learn how to design and integrate a custom AXI-Lite peripheral with a Cortex-A9 processor
+    on the Zybo Z7-10 board using Vivado, configuring GPIOs to control LEDs based on switch inputs.
+    It is designed for software developers interested in System on Chip Design. By the end, you
+    will be able to configure and integrate an AXI-Lite peripheral with a Cortex-A9 Processing
+    System, program the Cortex-A9 processor to read the state of switches and control the LEDs
+    using a C program, and demonstrate a basic functional system that lights up the LEDs based
+    on the status of the switches. It focuses on tools and technologies such as C, Baremetal environments,
+    and Arm platforms including Cortex-A. The main steps cover Setup a Workspace in Xilinx Vivado,
+    Create a custom AXI4 Peripheral, Connect AXI4 Peripheral to ZYNQ Processing System, and Generate
+    the bitstream and write your application using Vitis IDE.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will configure and integrate an AXI-Lite peripheral with a Cortex-A9 Processing System,
+      program the Cortex-A9 processor to read the state of switches and control the LEDs using
+      a C program, and demonstrate a basic functional system that lights up the LEDs based on
+      the status of the switches. Learn how to design and integrate a custom AXI-Lite peripheral
+      with a Cortex-A9 processor on the Zybo Z7-10 board using Vivado, configuring GPIOs to control
+      LEDs based on switch inputs.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers interested in System on Chip Design.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Some familiarity with Verilog; Basic
+      understanding of System on Chip design; A 'Zybo Z7-10' development board.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including C, Baremetal environments, and Arm platforms such
+      as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Setup a Workspace in Xilinx Vivado, Create a custom
+      AXI4 Peripheral, Connect AXI4 Peripheral to ZYNQ Processing System, and Generate the bitstream
+      and write your application using Vitis IDE.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
@@ -47,3 +91,4 @@ learning_path_main_page: "yes"  # Indicates this should be surfaced when looking
 
 # Prereqs
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md b/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
index 28dad0b0f8..9d066b9ec5 100644
--- a/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
@@ -22,6 +22,54 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 8585bb59efa67b3a92314e4382339fc5c0a14bb458931c0d98f2748379003857
+  summary: >-
+    Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit and run inference
+    on the Ethos-U85 NPU. It is designed for embedded developers who want to deploy a neural network
+    model to an Arm Cortex-M55 microcontroller using ExecuTorch and an Ethos-U85 NPU. By the end,
+    you will be able to compile a MobileNetV2 model for the Ethos-U85 NPU using ExecuTorch's ahead-of-time
+    (AOT) compiler on an Arm-based cloud instance, build ExecuTorch static libraries for bare-metal
+    Cortex-M55 targets, and configure CMSIS project files, memory layout, and linker scripts for
+    an ML workload on the Alif Ensemble E8. It focuses on tools and technologies such as ExecuTorch,
+    PyTorch, GCC, CMSIS-Toolbox, and Python, Baremetal environments, and Arm platforms including
+    Cortex-M and Ethos-U. The main steps cover Set up the Alif Ensemble E8 DevKit, Compile the
+    model on an Arm cloud instance, Create the image classification firmware project, Add the
+    application code, and Configure memory layout and flash settings.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will compile a MobileNetV2 model for the Ethos-U85 NPU using ExecuTorch's ahead-of-time
+      (AOT) compiler on an Arm-based cloud instance, build ExecuTorch static libraries for bare-metal
+      Cortex-M55 targets, and configure CMSIS project files, memory layout, and linker scripts
+      for an ML workload on the Alif Ensemble E8. Deploy a MobileNetV2 image classification model
+      to an Alif Ensemble E8 DevKit and run inference on the Ethos-U85 NPU.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for embedded developers who want to deploy a neural network model
+      to an Arm Cortex-M55 microcontroller using ExecuTorch and an Ethos-U85 NPU.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Experience with C/C++ and embedded development
+      concepts; An [Alif Ensemble E8 DevKit](https://alifsemi.com/support/kits/ensemble-e8devkit/)
+      with a USB-C cable; A SEGGER J-Link debug probe (included in the DevKit); A development
+      machine running macOS on Apple Silicon with Visual Studio Code installed; An AWS account
+      or access to an Arm-based cloud instance for native Arm compilation.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including ExecuTorch, PyTorch, GCC, CMSIS-Toolbox, and Python,
+      Baremetal environments, and Arm platforms such as Cortex-M and Ethos-U.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Set up the Alif Ensemble E8 DevKit, Compile the model
+      on an Arm cloud instance, Create the image classification firmware project, Add the application
+      code, and Configure memory layout and flash settings.
+# END generated_summary_faq
+
 author: Gabriel Peterson
 
 skilllevels: Advanced
@@ -62,3 +110,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md b/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
index 7738cda490..8e45fd48a4 100644
--- a/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
@@ -22,6 +22,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 3ddb97eb97a4c7ede7951410086198ee793a9d452a79b607f211873971bd375d
+  summary: >-
+    Learn how to build a motion-detection device with Raspberry Pi Pico (RP2040 Cortex-M0+) using
+    Arduino IDE, PIR sensors, and interrupt-driven programming on baremetal. It is designed for
+    software developers interested in embedded programming. By the end, you will be able to understand
+    the basics of embedded programming, know the differences between embedded and application
+    development, and write a simple embedded application. It focuses on tools and technologies
+    such as Arduino, Baremetal environments, and Arm platforms including Cortex-M. The main steps
+    cover About Embedded Programming, Application Programming, Embedded Programming, Embedded
+    Programming on Arm, and Build a smart device prototype.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand the basics of embedded programming, know the differences between embedded
+      and application development, and write a simple embedded application. Learn how to build
+      a motion-detection device with Raspberry Pi Pico (RP2040 Cortex-M0+) using Arduino IDE,
+      PIR sensors, and interrupt-driven programming on baremetal.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers interested in embedded programming.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: The [Arduino IDE with the RP2040 board
+      support package](/install-guides/arduino-pico/) installed on your computer; A [Raspberry
+      Pi Pico](https://www.raspberrypi.com/products/raspberry-pi-pico/) board; A [PIR sensor](https://www.amazon.com/HiLetgo-HC-SR501-Infrared-Sensor-Arduino/dp/B07KZW86YR/ref=sr_1_3?keywords=pir+sensor&qid=1698432931&sr=8-3)
+      for detecting motion; A [peizo-electric buzzer](https://www.amazon.com/mxuteuk-Electronic-Computers-Printers-Components/dp/B07VK1GJ9X/ref=sr_1_4?crid=2FAXYI17HZKDB&keywords=piezo+buzzer&qid=1698432968&sprefix=peizo%2Caps%2C148&sr=8-4)
+      for signaling motion.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Arduino, Baremetal environments, and Arm platforms
+      such as Cortex-M.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around About Embedded Programming, Application Programming,
+      Embedded Programming, Embedded Programming on Arm, and Build a smart device prototype.
+# END generated_summary_faq
+
 author: Michael Hall
 
 ### Tags
@@ -51,3 +93,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/armds/_index.md b/content/learning-paths/embedded-and-microcontrollers/armds/_index.md
index f8f36b5112..d371f70cc5 100644
--- a/content/learning-paths/embedded-and-microcontrollers/armds/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/armds/_index.md
@@ -16,6 +16,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 0103f51d42c230dbe75ff5b78ac15a33dfd2f2c0f4906fb665a8dd681512d2e1
+  summary: >-
+    Learn how to import and build example projects in Arm Development Studio and debug embedded
+    applications using Fixed Virtual Platforms (FVPs) or hardware with DSTREAM debug probes. It
+    is designed for embedded software developers new to Arm Development Studio. By the end, you
+    will be able to import and build an example project, debug the example code running on a Fixed
+    Virtual Platform (FVP), and debug the example code running on a board with a DSTREAM debug
+    probe. It focuses on tools and technologies such as Arm Development Studio, Arm Compiler for
+    Embedded, Arm Fast Models, and DSTREAM, Baremetal environments, and Arm platforms including
+    Cortex-A, Cortex-R, Cortex-M, and Neoverse. The main steps cover Import and build example
+    project, Debug the example, and Other compilers and project types.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will import and build an example project, debug the example code running on a Fixed
+      Virtual Platform (FVP), and debug the example code running on a board with a DSTREAM debug
+      probe. Learn how to import and build example projects in Arm Development Studio and debug
+      embedded applications using Fixed Virtual Platforms (FVPs) or hardware with DSTREAM debug
+      probes.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for embedded software developers new to Arm Development Studio.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Some familiarity with embedded programming
+      is assumed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Arm Development Studio, Arm Compiler for Embedded,
+      Arm Fast Models, and DSTREAM, Baremetal environments, and Arm platforms such as Cortex-A,
+      Cortex-R, Cortex-M, and Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Import and build example project, Debug the example,
+      and Other compilers and project types.
+# END generated_summary_faq
+
 author: Ronan Synnott
 
 ### Tags
@@ -56,3 +98,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/asm/_index.md b/content/learning-paths/embedded-and-microcontrollers/asm/_index.md
index b6214317d3..6cae2fa488 100644
--- a/content/learning-paths/embedded-and-microcontrollers/asm/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/asm/_index.md
@@ -6,6 +6,47 @@ description: Learn how to write mixed C and assembly programs for Cortex-M micro
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 24245102b7b6cefd0d4f67fbfaff1fb27c913de33665929ec3cb15293a1b3b51
+  summary: >-
+    Learn how to write mixed C and assembly programs for Cortex-M microcontrollers using Keil
+    MDK, following Arm Procedure Call Standard conventions. It is designed for software developers
+    who are interested in programming microcontrollers with C/Assembly. By the end, you will be
+    able to write a mixed C program and assembly language subroutines for the microcontroller,
+    call the subroutines written in assembly in a C function, and use Arm register calling conventions
+    when writing subroutines in assembly language. It focuses on tools and technologies such as
+    Keil MDK, Baremetal environments, and Arm platforms including Cortex-M. The main steps cover
+    Keil MDK versions, Setting up a Project in Keil Studio (VS Code), Setting up a Project in
+    Keil MDK (μVision), and Writing assembly functions.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will write a mixed C program and assembly language subroutines for the microcontroller,
+      call the subroutines written in assembly in a C function, and use Arm register calling conventions
+      when writing subroutines in assembly language. Learn how to write mixed C and assembly programs
+      for Cortex-M microcontrollers using Keil MDK, following Arm Procedure Call Standard conventions.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who are interested in programming
+      microcontrollers with C/Assembly.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Some familiarity with C/Assembly.; An
+      installation of Keil MDK.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Keil MDK, Baremetal environments, and Arm platforms
+      such as Cortex-M.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Keil MDK versions, Setting up a Project in Keil Studio
+      (VS Code), Setting up a Project in Keil MDK (μVision), and Writing assembly functions.
+# END generated_summary_faq
+
 author: Ronan Synnott
 
 who_is_this_for: This is an introductory topic for software developers who are interested in programming microcontrollers with C/Assembly.
@@ -43,5 +84,5 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # Indicates this should be surfaced when looking for related content. Only set for _index.md of learning path content.
 # ================================================================================
-
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
index 6f732a3e7c..b845c0441d 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
@@ -20,6 +20,45 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 983afd3fcc565266c8f12588086e36d736540e23d0e748c94b5d2a45ab53d6ab
+  summary: >-
+    Learn how to create a custom Balena OS image, run it on Arm Virtual Hardware, and deploy IoT
+    applications to a virtual Raspberry Pi 4 device. It is designed for embedded software developers
+    interested in Balena OS. By the end, you will be able to start a Raspberry Pi Arm Virtual
+    Hardware instance, create a Balena OS image for Arm Virtual Hardware, and deploy a pre-built
+    Balena Hub application. It focuses on tools and technologies such as Arm Virtual Hardware,
+    balenaCloud, Raspberry Pi, and BalenaOS, Linux environments, and Arm platforms including Cortex-A.
+    The main steps cover Prepare a custom Balena OS image, Install Balena OS on AVH, and Deploy
+    an application to your device.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will start a Raspberry Pi Arm Virtual Hardware instance, create a Balena OS image for
+      Arm Virtual Hardware, and deploy a pre-built Balena Hub application. Learn how to create
+      a custom Balena OS image, run it on Arm Virtual Hardware, and deploy IoT applications to
+      a virtual Raspberry Pi 4 device.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for embedded software developers interested in Balena OS.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Balena Cloud account; An Arm Virtual
+      Hardware account; A Linux machine with root access; Some familiarity with embedded Linux.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Arm Virtual Hardware, balenaCloud, Raspberry Pi,
+      and BalenaOS, Linux environments, and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Prepare a custom Balena OS image, Install Balena OS
+      on AVH, and Deploy an application to your device.
+# END generated_summary_faq
+
 author: Michael Hall
 
 ### Tags
@@ -61,3 +100,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
index 0a660c0194..0b1a61fa69 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
@@ -18,6 +18,45 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 85845fd4a47960bca053aed8d87c1d1442a7f5de0be5caff349fc92c6980b07e
+  summary: >-
+    Learn how to set up AWS IoT Greengrass Core on Arm Virtual Hardware and deploy AWS Greengrass
+    components to a virtual Raspberry Pi 4 device. It is designed for embedded software developers
+    interested in AWS IoT Greengrass. By the end, you will be able to start a Raspberry Pi Arm
+    Virtual Hardware instance and deploy pre-built AWS IoT Greengrass components on Arm Virtual
+    Hardware. It focuses on tools and technologies such as Arm Virtual Hardware, AWS IoT Greengrass,
+    and Raspberry Pi, Linux environments, and Arm platforms including Cortex-A. The main steps
+    cover Set up your accounts and create a virtual device and Deploy an AWS IoT Greengrass component
+    to your device.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will start a Raspberry Pi Arm Virtual Hardware instance and deploy pre-built AWS IoT
+      Greengrass components on Arm Virtual Hardware. Learn how to set up AWS IoT Greengrass Core
+      on Arm Virtual Hardware and deploy AWS Greengrass components to a virtual Raspberry Pi 4
+      device.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for embedded software developers interested in AWS IoT Greengrass.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Amazon AWS account; An Arm Virtual
+      Hardware account; Some familiarity with embedded Linux.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Arm Virtual Hardware, AWS IoT Greengrass, and Raspberry
+      Pi, Linux environments, and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Set up your accounts and create a virtual device and
+      Deploy an AWS IoT Greengrass component to your device.
+# END generated_summary_faq
+
 author: Michael Hall
 
 ### Tags
@@ -59,3 +98,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
index 74657a0cde..368b1c3313 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
@@ -18,6 +18,47 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: bd39d50c93219201ead5de5da627447a91a82ea9c65e232350facd0c3516bedc
+  summary: >-
+    Learn how to build Matter reference examples on Arm Virtual Hardware, demonstrate device communication,
+    and automate testing with GitHub Actions CI/CD workflows. It is designed for embedded software
+    developers new to Arm Virtual Hardware. By the end, you will be able to instantiate Arm Virtual
+    Hardware instances, build and run Matter examples on Arm Virtual Hardware, and demonstrate
+    communication between two virtual hardware targets. It focuses on tools and technologies such
+    as Matter, Arm Virtual Hardware, and GitHub, Linux environments, and Arm platforms including
+    Cortex-A. The main steps cover Prepare AVH instances of Raspberry Pi 4, Build and run Matter
+    examples on Arm Virtual Hardware, Manage development in a CI/CD workflow with Self-Hosted
+    Runner, and Control Arm Virtual Hardware with API.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will instantiate Arm Virtual Hardware instances, build and run Matter examples on Arm
+      Virtual Hardware, and demonstrate communication between two virtual hardware targets. Learn
+      how to build Matter reference examples on Arm Virtual Hardware, demonstrate device communication,
+      and automate testing with GitHub Actions CI/CD workflows.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for embedded software developers new to Arm Virtual Hardware.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Some familiarity with embedded programming
+      is assumed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Matter, Arm Virtual Hardware, and GitHub, Linux
+      environments, and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Prepare AVH instances of Raspberry Pi 4, Build and
+      run Matter examples on Arm Virtual Hardware, Manage development in a CI/CD workflow with
+      Self-Hosted Runner, and Control Arm Virtual Hardware with API.
+# END generated_summary_faq
+
 author: Ronan Synnott
 
 ### Tags
@@ -53,3 +94,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
index 3af79edfdf..330eefb1b2 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
@@ -19,6 +19,47 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 398077be1406d0787b0a5d8dc254472da0d125b51b0907769cd4a3516ce9111b
+  summary: >-
+    Learn how to export and compile a PaddleOCR text recognition model using TVMC and deploy it
+    on the Arm Corstone-300 FVP with Cortex-M55 processors. It is designed for software developers
+    interested in using PaddlePaddle for Arm Cortex-M processors. By the end, you will be able
+    to export Paddle inference model, compile Paddle inference model with TVMC, and deploy on
+    the AVH Corstone-300 platform with Arm Cortex-M55. It focuses on tools and technologies such
+    as Arm Virtual Hardware, GCC, Paddle, and TVMC, Baremetal environments, and Arm platforms
+    including Cortex-M and Corstone. The main steps cover Overview of OCR and Deploy the PaddleOCR
+    model.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will export Paddle inference model, compile Paddle inference model with TVMC, and deploy
+      on the AVH Corstone-300 platform with Arm Cortex-M55. Learn how to export and compile a
+      PaddleOCR text recognition model using TVMC and deploy it on the Arm Corstone-300 FVP with
+      Cortex-M55 processors.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers interested in using PaddlePaddle for
+      Arm Cortex-M processors.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Some familiarity with embedded programming;
+      Some familiarity with AI/ML software development; An Amazon Web Services(AWS) [account](https://aws.amazon.com/)
+      to subscribe [Arm Virtual Hardware](https://aws.amazon.com/marketplace/pp/prodview-urbpq7yo5va7g)
+      Amazon Machine Image(AMI).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Arm Virtual Hardware, GCC, Paddle, and TVMC, Baremetal
+      environments, and Arm platforms such as Cortex-M and Corstone.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview of OCR and Deploy the PaddleOCR model.
+# END generated_summary_faq
+
 author: Liliya Wu
 
 ### Tags
@@ -61,3 +102,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
index 0d8540fa6b..e2d5710eaa 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
@@ -16,6 +16,42 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: aaff31b8917320c53825f3e7410b703bc7c7e273b1963fd80727b6b874f50566
+  summary: >-
+    Learn how to create and integrate a virtual LED peripheral using the Virtual IO interface
+    of Arm Virtual Hardware to simulate real-world peripherals. It is designed for software developers
+    new to Arm Virtual Hardware and its features. By the end, you will be able to create and integrate
+    an LED peripheral with the Virtual IO (VIO) interface of AVH. It focuses on tools and technologies
+    such as Arm Virtual Hardware, Baremetal environments, and Arm platforms including Cortex-M
+    and Corstone. The main steps cover Create a peripheral using Virtual Input/Output (VIO).
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create and integrate an LED peripheral with the Virtual IO (VIO) interface of AVH.
+      Learn how to create and integrate a virtual LED peripheral using the Virtual IO interface
+      of Arm Virtual Hardware to simulate real-world peripherals.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers new to Arm Virtual Hardware and its
+      features.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A valid [AWS](https://aws.amazon.com/)
+      account; Some familiarity with Python.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Arm Virtual Hardware, Baremetal environments, and
+      Arm platforms such as Cortex-M and Corstone.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Create a peripheral using Virtual Input/Output (VIO).
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
@@ -45,3 +81,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md b/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
index a471947503..f76ab44deb 100644
--- a/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
@@ -22,6 +22,53 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 0e653bdbf5e5b08a6a00ba68e5ed215a0082e22c634d7d632d088851d48bb01c
+  summary: >-
+    Learn how to build a complete IoT solution in Azure that streams, stores, monitors, aggregates,
+    and visualizes telemetry data from Arm devices using IoT Hub, Stream Analytics, Cosmos DB,
+    and Azure Functions. It is designed for developers who want to build a comprehensive IoT solution
+    in Azure that streams, stores, monitors, aggregates, and visualizes telemetry data from Arm
+    IoT devices. By the end, you will be able to set up and configure Azure IoT Hub for device
+    communication, register an IoT device and stream telemetry data using the Azure IoT SDK, and
+    route IoT data to Azure services using Azure Stream Analytics. It focuses on tools and technologies
+    such as Python, Azure, and Visual Studio Code, Windows, Linux, and macOS environments, and
+    Arm platforms including Cortex-A. The main steps cover Overview, Create Azure IoT Hub, Build
+    a Python-based IoT telemetry simulator, Process IoT telemetry in real time with Azure Stream
+    Analytics, and Store data in Azure Cosmos DB with Azure Stream Analytics.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will set up and configure Azure IoT Hub for device communication, register an IoT device
+      and stream telemetry data using the Azure IoT SDK, and route IoT data to Azure services
+      using Azure Stream Analytics. Learn how to build a complete IoT solution in Azure that streams,
+      stores, monitors, aggregates, and visualizes telemetry data from Arm devices using IoT Hub,
+      Stream Analytics, Cosmos DB, and Azure Functions.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for developers who want to build a comprehensive IoT solution
+      in Azure that streams, stores, monitors, aggregates, and visualizes telemetry data from
+      Arm IoT devices.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A machine with Python 3 and Visual Studio
+      Code installed; An active Azure account with sufficient permissions to create resources
+      (such as IoT Hub, Functions, and Cosmos DB).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Python, Azure, and Visual Studio Code, Windows,
+      Linux, and macOS environments, and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview, Create Azure IoT Hub, Build a Python-based
+      IoT telemetry simulator, Process IoT telemetry in real time with Azure Stream Analytics,
+      and Store data in Azure Cosmos DB with Azure Stream Analytics.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
@@ -60,3 +107,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md b/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md
index 312ba5e0f8..42b46506ba 100644
--- a/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md
@@ -19,6 +19,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 6521f17d1a10ab8871d13917923f7f64320f69301b4c0c5f5b528163d3cb43c6
+  summary: >-
+    Learn how to create, build, and run a bare-metal embedded application for Armv8-A processors
+    using Arm Compiler for Embedded and Fixed Virtual Platforms, including basic exception handling.
+    It is designed for embedded software developers new to Armv8-A processors and/or the Arm Compiler
+    for Embedded. By the end, you will be able to create and build an example project, run example
+    on Fixed Virtual Platform (FVP), and understand basic boot code and other syntax. It focuses
+    on tools and technologies such as Arm Development Studio, Arm Compiler for Embedded, and Arm
+    Fast Models, Baremetal environments, and Arm platforms including Cortex-A. The main steps
+    cover Create and build a Hello World example project, Write a reset handler, Modify the example
+    to use the UART for printf output, Create event-driven application (1), and Create event-driven
+    application (2).
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create and build an example project, run example on Fixed Virtual Platform (FVP),
+      and understand basic boot code and other syntax. Learn how to create, build, and run a bare-metal
+      embedded application for Armv8-A processors using Arm Compiler for Embedded and Fixed Virtual
+      Platforms, including basic exception handling.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for embedded software developers new to Armv8-A processors
+      and/or the Arm Compiler for Embedded.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Some familiarity with embedded programming
+      is assumed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Arm Development Studio, Arm Compiler for Embedded,
+      and Arm Fast Models, Baremetal environments, and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Create and build a Hello World example project, Write
+      a reset handler, Modify the example to use the UART for printf output, Create event-driven
+      application (1), and Create event-driven application (2).
+# END generated_summary_faq
+
 author: Ronan Synnott
 
 ### Tags
@@ -50,3 +93,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md b/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
index d0d6b4ba52..1f285b270f 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
@@ -19,6 +19,51 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 3394bf994039e441d57dced2abb64d725077d4672e0e68dc71f1de50e58f0408
+  summary: >-
+    Learn how to deploy containerized embedded applications and firmware onto an Arm Cortex-M
+    core from a Cortex-A core using containerd, K3s, and the hybrid-runtime on Arm Virtual Hardware.
+    It is designed for developers interested in learning how to deploy software (embedded applications
+    and firmware) onto other processors in the system, using Linux running on the application
+    core. By the end, you will be able to deploy a containerized embedded application onto an
+    Arm Cortex-M core from an Arm Cortex-A core using containerd and K3s, build a firmware container
+    image, and build the hybrid-runtime components. It focuses on tools and technologies such
+    as Docker, Arm Virtual Hardware, K3s, and Containerd, Linux environments, and Arm platforms
+    including Cortex-M and Cortex-A. The main steps cover Motivation, Hybrid container runtime,
+    AVH device setup, Deploy firmware container using `containerd`, and Deploy SMARTER Demo using
+    K3s.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will deploy a containerized embedded application onto an Arm Cortex-M core from an Arm
+      Cortex-A core using containerd and K3s, build a firmware container image, and build the
+      hybrid-runtime components. Learn how to deploy containerized embedded applications and firmware
+      onto an Arm Cortex-M core from a Cortex-A core using containerd, K3s, and the hybrid-runtime
+      on Arm Virtual Hardware.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This learning path is for developers interested in learning how to deploy software (embedded
+      applications and firmware) onto other processors in the system, using Linux running on the
+      application core.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A valid account with [Arm Virtual Hardware](https://app.avh.arm.com/login);
+      An Arm Linux host machine (if you want to build your own runtime and container image).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Docker, Arm Virtual Hardware, K3s, and Containerd,
+      Linux environments, and Arm platforms such as Cortex-M and Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Motivation, Hybrid container runtime, AVH device setup,
+      Deploy firmware container using `containerd`, and Deploy SMARTER Demo using K3s.
+# END generated_summary_faq
+
 author: Basma El Gaabouri
 
 ### Tags
@@ -58,3 +103,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md
index b42fc46a2c..bb84798e4a 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md
@@ -16,6 +16,44 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: d8c73d658fa7a8051131276b8567cbd7376aac3b8f6e87c8ecdf224443c3ef99
+  summary: >-
+    Learn how to create, build, and debug an RTX5 RTOS-based application using Keil μVision with
+    CMSIS-RTOS2 API and Event Recorder for embedded Cortex-M development. It is designed for software
+    developers new to RTOS development. By the end, you will be able to implement a basic RTOS-based
+    application. It focuses on tools and technologies such as Keil RTX RTOS, Keil MDK, and Arm
+    Development Studio, RTOS environments, and Arm platforms including Cortex-M. The main steps
+    cover Create and setup Keil MDK project, Initialize the operating system, Create RTOS threads,
+    Build and run the application, and Using Event Recorder.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will implement a basic RTOS-based application. Learn how to create, build, and debug
+      an RTX5 RTOS-based application using Keil μVision with CMSIS-RTOS2 API and Event Recorder
+      for embedded Cortex-M development.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers new to RTOS development.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An installation of [Arm Keil MDK](/install-guides/mdk)
+      or [Arm Development Studio](/install-guides/armds) (MDK recommended); Some familiarity with
+      CMSIS is assumed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Keil RTX RTOS, Keil MDK, and Arm Development Studio,
+      RTOS environments, and Arm platforms such as Cortex-M.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Create and setup Keil MDK project, Initialize the
+      operating system, Create RTOS threads, Build and run the application, and Using Event Recorder.
+# END generated_summary_faq
+
 author: Ronan Synnott
 
 ### Tags
@@ -47,3 +85,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md
index 811f41e73b..c946a635e9 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md
@@ -18,6 +18,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: f4d1ab3448590c5654e2479937c9eec3e378d05229b29a45b8675139f40ac177
+  summary: >-
+    Learn how to create, configure, and debug an RTX5 RTOS application using Keil Studio for VS
+    Code with CMSIS-RTOS2 API for embedded Cortex-M development. It is designed for software developers
+    new to RTOS development. By the end, you will be able to understand the basics of RTX-based
+    RTOS application development, configure and manage an RTOS project in Keil Studio, including
+    defining the memory map, selecting software components, and setting up debugging configurations
+    for Cortex-M processors, and create and manage multiple threads within an RTX5 RTOS application.
+    It focuses on tools and technologies such as Keil RTX RTOS, Keil MDK, and Arm Development
+    Studio, RTOS environments, and Arm platforms including Cortex-M. The main steps cover Create
+    csolution project, Initialize the Operating System, Create RTOS Threads, and Build and run
+    the application.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand the basics of RTX-based RTOS application development, configure and
+      manage an RTOS project in Keil Studio, including defining the memory map, selecting software
+      components, and setting up debugging configurations for Cortex-M processors, and create
+      and manage multiple threads within an RTX5 RTOS application. Learn how to create, configure,
+      and debug an RTX5 RTOS application using Keil Studio for VS Code with CMSIS-RTOS2 API for
+      embedded Cortex-M development.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers new to RTOS development.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Installation of [Arm Keil Studio for
+      VS Code](/install-guides/keilstudio_vs); Some familiarity with CMSIS is assumed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Keil RTX RTOS, Keil MDK, and Arm Development Studio,
+      RTOS environments, and Arm platforms such as Cortex-M.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Create csolution project, Initialize the Operating
+      System, Create RTOS Threads, and Build and run the application.
+# END generated_summary_faq
+
 author: Ronan Synnott
 
 ### Tags
@@ -49,3 +92,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md b/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
index 65da1e5235..10553de353 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
@@ -21,6 +21,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 69526ee8b840c8f5d77d49349d2f0cc7ff4594fec5e4311984ef72a0672d3462
+  summary: >-
+    Learn how to prototype and port DSP algorithms using the CMSIS-DSP Python package, mapping
+    Python code to efficient C implementations for embedded Cortex-M and Cortex-A platforms. It
+    is designed for developers looking to integrate the CMSIS-DSP library into their applications
+    using Python. By the end, you will be able to use the CMSIS-DSP Python package to prototype
+    DSP algorithms, understand how the Python API maps to the C implementation, and build and
+    port a complex DSP application using CMSIS-DSP. It focuses on tools and technologies such
+    as CMSIS-DSP, Python, C, Jupyter Notebook, and NumPy, Linux, Windows, and macOS environments,
+    and Arm platforms including Cortex-M and Cortex-A. The main steps cover CMSIS-DSP Python package,
+    Set up environment, Load an audio file, Write a simple VAD, and Write a noise suppression
+    algorithm.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will use the CMSIS-DSP Python package to prototype DSP algorithms, understand how the
+      Python API maps to the C implementation, and build and port a complex DSP application using
+      CMSIS-DSP. Learn how to prototype and port DSP algorithms using the CMSIS-DSP Python package,
+      mapping Python code to efficient C implementations for embedded Cortex-M and Cortex-A platforms.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for developers looking to integrate the CMSIS-DSP library into
+      their applications using Python.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Familiarity with Python and digital
+      signal processing concepts.; Working knowledge of C.; Prior exposure to CMSIS-DSP.; Python
+      installed on your machine.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including CMSIS-DSP, Python, C, Jupyter Notebook, and NumPy,
+      Linux, Windows, and macOS environments, and Arm platforms such as Cortex-M and Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around CMSIS-DSP Python package, Set up environment, Load
+      an audio file, Write a simple VAD, and Write a noise suppression algorithm.
+# END generated_summary_faq
+
 author: Christophe Favergeon
 
 ### Tags
@@ -69,3 +112,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md b/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
index 37aa7cf0a1..fb5aa6aea1 100644
--- a/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
@@ -18,6 +18,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 0b7a5895a87de83903c223215845b6c840602663838c0682f46e50d139d69c59
+  summary: >-
+    Learn how to implement context switching operations on Arm Cortex-M processors using the Memory
+    Protection Unit and SysTick exception in a bare-metal environment. It is designed for software
+    developers who would like to learn about context switching operations on Cortex-M processors
+    in a bare-metal environment. By the end, you will be able to understand the basics of context
+    switching, learn how to program the Memory Protection Unit (MPU), and learn how to use the
+    SysTick exception with context switching operations. It focuses on tools and technologies
+    such as CMSIS and Arm Development Studio, Baremetal environments, and Arm platforms including
+    Cortex-M. The main steps cover Example Arm DS project to demonstrate context switching operations.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand the basics of context switching, learn how to program the Memory Protection
+      Unit (MPU), and learn how to use the SysTick exception with context switching operations.
+      Learn how to implement context switching operations on Arm Cortex-M processors using the
+      Memory Protection Unit and SysTick exception in a bare-metal environment.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who would like to learn about context
+      switching operations on Cortex-M processors in a bare-metal environment.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Basic knowledge and familiarity with
+      Cortex-M processors.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including CMSIS and Arm Development Studio, Baremetal environments,
+      and Arm platforms such as Cortex-M.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Example Arm DS project to demonstrate context switching
+      operations.
+# END generated_summary_faq
+
 author: Uma Ramalingam
 
 ### Tags
@@ -47,3 +87,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md b/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
index e2f5b73aaf..ce90159dcb 100644
--- a/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
@@ -16,6 +16,41 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: f989929b11c48df42c2701dc71516cec0b8637b4c639c3dbbf6ac5e7440c8a56
+  summary: >-
+    Learn how to set up and use code coverage in Keil MDK with FVPs to verify that your embedded
+    application tests execute all code paths. It is designed for embedded software developers
+    new to the code-coverage feature in Keil MDK. By the end, you will be able to set up project
+    execution on FVP and understand basics of the Code Coverage report. It focuses on tools and
+    technologies such as Keil MDK and FVP, Baremetal and RTOS environments, and Arm platforms
+    including Cortex-M. The main steps cover What is Code Coverage? and Set up Code Coverage.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will set up project execution on FVP and understand basics of the Code Coverage report.
+      Learn how to set up and use code coverage in Keil MDK with FVPs to verify that your embedded
+      application tests execute all code paths.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for embedded software developers new to the code-coverage
+      feature in Keil MDK.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Basic familiarity with Keil MDK.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Keil MDK and FVP, Baremetal and RTOS environments,
+      and Arm platforms such as Cortex-M.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around What is Code Coverage? and Set up Code Coverage.
+# END generated_summary_faq
+
 author: Ronan Synnott
 
 ### Tags
@@ -51,3 +86,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md b/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
index 66292c2e24..c78af8ce06 100644
--- a/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
@@ -17,6 +17,53 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 9d9e4c170fa318a9875c888c2c812ceb27c59f56c4b0dd7e0ae87dd31bb5783c
+  summary: >-
+    Device-to-Device communication with Device Connect walks you through an end-to-end Arm software
+    workflow. It is designed for developers wiring up heterogeneous edge fleets, where devices
+    need a shared way to find each other and a shared way to be controlled by agents. Device Connect
+    provides this communication protocol between agents and devices, and standardizes how devices
+    from different vendors advertise themselves and exchange structured messages, so both peer
+    devices and AI agents can discover and invoke them through the same driver model. You'll use
+    it to stand up peer-to-peer communication between two devices, with no broker or cloud service
+    in between. By the end, you will be able to understand Device Connect Edge SDK primitives,
+    set up a Python environment for Device Connect with no hardware required, and build two simulated
+    devices. It focuses on tools and technologies such as Python, Linux, macOS, and Windows environments,
+    and Arm platforms including Cortex-A. The main steps cover Why device-to-device at the edge,
+    Device Connect developer model, and Set up D2D communication between a sensor and a monitor.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand Device Connect Edge SDK primitives, set up a Python environment for
+      Device Connect with no hardware required, and build two simulated devices.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers wiring up heterogeneous edge fleets, where
+      devices need a shared way to find each other and a shared way to be controlled by agents.
+      Device Connect provides this communication protocol between agents and devices, and standardizes
+      how devices from different vendors advertise themselves and exchange structured messages,
+      so both peer devices and AI agents can discover and invoke them through the same driver
+      model. You'll use it to stand up peer-to-peer communication between two devices, with no
+      broker or cloud service in between.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Basic familiarity with Python and the
+      command line.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Python, Linux, macOS, and Windows environments,
+      and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Why device-to-device at the edge, Device Connect developer
+      model, and Set up D2D communication between a sensor and a monitor.
+# END generated_summary_faq
+
 author:
     - Kavya Sri Chennoju
     - Annie Tallund
@@ -53,3 +100,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md b/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md
index f3d4aa9827..d106f39422 100644
--- a/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md
@@ -19,6 +19,57 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: c31d398d3ce965a4193fca45cd025182d788a2fc603fbe8c828dfbd5a1c599ba
+  summary: >-
+    Learn how to connect AI agents to Arm-based edge devices using Device Connect for structured
+    device access and Strands for agent orchestration, with examples for both simulated and physical
+    robots. It is designed for software developers who want to connect AI agents to edge devices.
+    You'll use Device Connect, Arm's platform for structured device access, and Strands, AWS's
+    open-source agent SDK. The examples cover both physical and simulated devices. By the end,
+    you will be able to understand how Device Connect and Strands work together to give AI agents
+    structured access to Arm-based edge devices, set up a Python environment with the Device Connect
+    SDK and agent tools installed from source, and start a simulated robot that registers itself
+    on the local network and is discovered automatically by an agent. It focuses on tools and
+    technologies such as Python, Docker, and strands-agents, Linux and macOS environments, and
+    Arm platforms including Cortex-A and Neoverse. The main steps cover Learn Device Connect and
+    Strands architecture for edge devices, Set up the Device Connect and Strands developer environment,
+    Run device discovery and agent control examples, and Run with full Device Connect infrastructure
+    (optional).
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand how Device Connect and Strands work together to give AI agents structured
+      access to Arm-based edge devices, set up a Python environment with the Device Connect SDK
+      and agent tools installed from source, and start a simulated robot that registers itself
+      on the local network and is discovered automatically by an agent. Learn how to connect AI
+      agents to Arm-based edge devices using Device Connect for structured device access and Strands
+      for agent orchestration, with examples for both simulated and physical robots.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to connect AI agents to edge
+      devices. You'll use Device Connect, Arm's platform for structured device access, and Strands,
+      AWS's open-source agent SDK. The examples cover both physical and simulated devices.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A development machine with git installed;
+      Basic familiarity with command-line tools; (Optional) A Raspberry Pi for testing a full
+      device-to-device (D2D) setup.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Python, Docker, and strands-agents, Linux and macOS
+      environments, and Arm platforms such as Cortex-A and Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Learn Device Connect and Strands architecture for
+      edge devices, Set up the Device Connect and Strands developer environment, Run device discovery
+      and agent control examples, and Run with full Device Connect infrastructure (optional).
+# END generated_summary_faq
+
 author: 
     - Annie Tallund
     - Kavya Sri Chennoju
@@ -63,3 +114,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/docker/_index.md b/content/learning-paths/embedded-and-microcontrollers/docker/_index.md
index 7796fa1c0c..45a814a875 100644
--- a/content/learning-paths/embedded-and-microcontrollers/docker/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/docker/_index.md
@@ -16,6 +16,42 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: a4b522c46c68d3c6f5c4af7c76b00c7bde48bb638147c201376bc19a4de79cca
+  summary: >-
+    Learn how to create a Dockerfile, build a Docker image with Arm Compiler for Embedded and
+    Fixed Virtual Platforms, and test the containerized Arm development environment. It is designed
+    for embedded software developers new to Docker. By the end, you will be able to create and
+    understand a Dockerfile, build Docker image, and test the image. It focuses on tools and technologies
+    such as Docker, Arm Development Studio, Arm Compiler for Embedded, and Arm Fast Models, Baremetal
+    environments, and Arm platforms including Cortex-A, Cortex-R, Cortex-M, and Neoverse. The
+    main steps cover Create Dockerfile and build docker image.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create and understand a Dockerfile, build Docker image, and test the image. Learn
+      how to create a Dockerfile, build a Docker image with Arm Compiler for Embedded and Fixed
+      Virtual Platforms, and test the containerized Arm development environment.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for embedded software developers new to Docker.
+  - question: What do you need before you start?
+    answer: >-
+      There are no explicit prerequisites listed for this Learning Path.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Docker, Arm Development Studio, Arm Compiler for
+      Embedded, and Arm Fast Models, Baremetal environments, and Arm platforms such as Cortex-A,
+      Cortex-R, Cortex-M, and Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Create Dockerfile and build docker image.
+# END generated_summary_faq
+
 author: Ronan Synnott
 
 ### Tags
@@ -52,3 +88,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/edge/_index.md b/content/learning-paths/embedded-and-microcontrollers/edge/_index.md
index 7c0eeeee84..aa2f5574f7 100644
--- a/content/learning-paths/embedded-and-microcontrollers/edge/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/edge/_index.md
@@ -21,6 +21,52 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 8a7ec29d10a89649662ebb0fa4f14712984786902b6e7343a195abb1f3cbc00b
+  summary: >-
+    Learn how to collect and preprocess audio data using Edge Impulse, train an audio classification
+    model, and deploy it to the Arduino Nano RP2040 to control LEDs based on voice commands. It
+    is designed for beginners in Edge AI and TinyML, including developers, engineers, hobbyists,
+    AI/ML enthusiasts, and researchers working with embedded AI and IoT. By the end, you will
+    be able to understand the basics of Edge AI and TinyML, collect and preprocess audio data
+    using Edge Impulse, and train and deploy an audio classification model on the Arduino Nano
+    RP2040. It focuses on tools and technologies such as Edge Impulse, tinyML, Edge AI, and Arduino,
+    Baremetal environments, and Arm platforms including Cortex-M. The main steps cover Overview,
+    Train and deploy a TinyML audio classifier with Edge Impulse, Board connection and IDE setup,
+    and Program your first TinyML device.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand the basics of Edge AI and TinyML, collect and preprocess audio data
+      using Edge Impulse, and train and deploy an audio classification model on the Arduino Nano
+      RP2040. Learn how to collect and preprocess audio data using Edge Impulse, train an audio
+      classification model, and deploy it to the Arduino Nano RP2040 to control LEDs based on
+      voice commands.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is for beginners in Edge AI and TinyML, including developers, engineers,
+      hobbyists, AI/ML enthusiasts, and researchers working with embedded AI and IoT.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Completion of [Embedded programming
+      with Arduino on the Raspberry Pi Pico](/learning-paths/embedded-and-microcontrollers/arduino-pico/)
+      if you're an absolute beginner.; An [Edge Impulse Studio](https://studio.edgeimpulse.com/signup)
+      account.; The [Arduino IDE](/install-guides/arduino-pico/) with the RP2040 board support
+      package installed on your computer.; An [Arduino Nano RP2040 Connect board](https://store.arduino.cc/products/arduino-nano-rp2040-connect-with-headers).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Edge Impulse, tinyML, Edge AI, and Arduino, Baremetal
+      environments, and Arm platforms such as Cortex-M.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview, Train and deploy a TinyML audio classifier
+      with Edge Impulse, Board connection and IDE setup, and Program your first TinyML device.
+# END generated_summary_faq
+
 author: Bright Edudzi Gershon Kordorwu
 ### Tags
 skilllevels: Introductory
@@ -58,3 +104,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md b/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md
index aab03554e8..b7e053b84e 100644
--- a/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md
@@ -18,6 +18,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 66024a2e3da90dcda298bf66b5de07952ed1e355d22172bc2812c46ad4fcda7f
+  summary: >-
+    Develop a image classification neural network model and deploy it on an STM32 B-L475E-IOT01A2
+    board. It is designed for embedded software developers interested in building neural network
+    models for microcontrollers. By the end, you will be able to build a convolution neural network(CNN)
+    model for image classification and run the CNN model on an STM32 B-L475E-IOT01A2 board using
+    STM Cube AI. It focuses on tools and technologies such as TensorFlow and STM32, Baremetal
+    environments, and Arm platforms including Cortex-M. The main steps cover Prepare environment,
+    Build an image classification NN model trained with the CIFAR-10 dataset, Deploy the image
+    classification NN model on STM32, and Run the image classification NN model on STM32.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build a convolution neural network(CNN) model for image classification and run
+      the CNN model on an STM32 B-L475E-IOT01A2 board using STM Cube AI. Develop a image classification
+      neural network model and deploy it on an STM32 B-L475E-IOT01A2 board.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for embedded software developers interested in building neural
+      network models for microcontrollers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Familiarity with ML concepts; Familiarity
+      with C programming on microcontrollers; STM32 B-L475E-IOT01A2 board.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including TensorFlow and STM32, Baremetal environments, and
+      Arm platforms such as Cortex-M.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Prepare environment, Build an image classification
+      NN model trained with the CIFAR-10 dataset, Deploy the image classification NN model on
+      STM32, and Run the image classification NN model on STM32.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
@@ -48,3 +88,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/intro/_index.md b/content/learning-paths/embedded-and-microcontrollers/intro/_index.md
index ec441b826f..be48880b6e 100644
--- a/content/learning-paths/embedded-and-microcontrollers/intro/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/intro/_index.md
@@ -16,6 +16,42 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: ac69e979101f6befe55abee1429299c163c70b9a886d87a8f64779babd9fe5af
+  summary: >-
+    Learn where Arm architecture is used in microcontrollers and discover microcontroller hardware
+    options for software development on Arm Cortex-M processors. It is designed for software developers
+    working on microcontroller applications and new to the Arm architecture. By the end, you will
+    be able to understand where the Arm architecture is used in microcontrollers and find microcontroller
+    hardware to use for software development. It focuses on Baremetal and RTOS environments and
+    Arm platforms including Cortex-M and Ethos-U. The main steps cover Arm in Microcontrollers,
+    Find Arm hardware, and Other learning resources.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand where the Arm architecture is used in microcontrollers and find microcontroller
+      hardware to use for software development. Learn where Arm architecture is used in microcontrollers
+      and discover microcontroller hardware options for software development on Arm Cortex-M processors.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers working on microcontroller applications
+      and new to the Arm architecture.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: None.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers Baremetal and RTOS environments and Arm platforms such as Cortex-M and Ethos-U.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Arm in Microcontrollers, Find Arm hardware, and Other
+      learning resources.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
@@ -47,3 +83,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md b/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
index e75e8c348d..553fbdf988 100644
--- a/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
@@ -20,6 +20,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: aa49e4a1651367965e79d36c5f6af16e9b341c392d48cf051f24cbb21070e972
+  summary: >-
+    Learn what differentiates TinyML from other AI domains, explore Arm-based edge devices for
+    TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed Virtual
+    Platform. It is designed for developers and data scientists new to Tiny Machine Learning (TinyML)
+    who want to explore its potential using PyTorch and ExecuTorch. By the end, you will be able
+    to describe what differentiates TinyML from other AI domains, describe the benefits of deploying
+    AI models on Arm-based edge devices, and identify suitable Arm-based devices for TinyML applications.
+    It focuses on tools and technologies such as Arm Virtual Hardware, FVP, Python, PyTorch, and
+    ExecuTorch, Linux environments, and Arm platforms including Cortex-A, Cortex-M, and Ethos-U.
+    The main steps cover Overview, Install ExecuTorch, Set up the Corstone-320 FVP, and Build
+    a simple PyTorch model.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will describe what differentiates TinyML from other AI domains, describe the benefits
+      of deploying AI models on Arm-based edge devices, and identify suitable Arm-based devices
+      for TinyML applications. Learn what differentiates TinyML from other AI domains, explore
+      Arm-based edge devices for TinyML, and set up a development environment using ExecuTorch
+      and Corstone-320 Fixed Virtual Platform.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers and data scientists new to Tiny Machine Learning
+      (TinyML) who want to explore its potential using PyTorch and ExecuTorch.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Basic knowledge of Machine Learning
+      concepts; A Linux computer.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Arm Virtual Hardware, FVP, Python, PyTorch, and
+      ExecuTorch, Linux environments, and Arm platforms such as Cortex-A, Cortex-M, and Ethos-U.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview, Install ExecuTorch, Set up the Corstone-320
+      FVP, and Build a simple PyTorch model.
+# END generated_summary_faq
+
 author: Dominica Abena O. Amanfo
 
 ### Tags
@@ -65,3 +108,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md b/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
index 5b2eee15ee..386986d2ed 100644
--- a/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
@@ -17,6 +17,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: 11fc64eb1a0595b1d8e625e465be9fa87a4de04f59492004204ccbe61a92a5b7
+  summary: >-
+    Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300 virtual hardware
+    to understand complete IoT software stack construction. It is designed for embedded software
+    developers interested in learning how a complete IoT software stack is constructed. By the
+    end, you will be able to build examples from Open-IoT-SDK and run the examples on Corstone-300
+    virtual hardware. It focuses on tools and technologies such as Arm Virtual Hardware, FVP,
+    and Arm Compiler for Embedded, Baremetal and RTOS environments, and Arm platforms including
+    Cortex-M, Ethos-U, and Corstone. The main steps cover Build and run Open-IoT-SDK examples
+    and Enable AWS connectivity.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build examples from Open-IoT-SDK and run the examples on Corstone-300 virtual hardware.
+      Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300 virtual hardware
+      to understand complete IoT software stack construction.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for embedded software developers interested in learning how
+      a complete IoT software stack is constructed.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Some familiarity with embedded programming;
+      An AWS account (required for Arm Virtual Hardware).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Arm Virtual Hardware, FVP, and Arm Compiler for
+      Embedded, Baremetal and RTOS environments, and Arm platforms such as Cortex-M, Ethos-U,
+      and Corstone.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Build and run Open-IoT-SDK examples and Enable AWS
+      connectivity.
+# END generated_summary_faq
+
 author: Ronan Synnott
 
 ### Tags
@@ -56,3 +96,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md b/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
index 269efa37d0..8578f43fd0 100644
--- a/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
@@ -18,6 +18,47 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:15Z'
+  generator: template
+  source_hash: fdf582f1b54f372768a2c896e1da152c50d22c3bd238848253cdbe18cc68222d
+  summary: >-
+    Learn how to set up a Jetson Orin Nano with a MIPI CSI-2 camera and perform real-time object
+    detection from live video and image files using DetectNet and TensorRT. It is designed for
+    developers interested in integrating object detection into their applications. By the end,
+    you will be able to set up a Jetson Orin Nano with a MIPI CSI-2 camera for object detection
+    and detect objects from both live video and image files. It focuses on tools and technologies
+    such as DetectNet, TensorRT, and Docker, Linux environments, and Arm platforms including Cortex-A.
+    The main steps cover Set up your Jetson Orin Nano, Launch the image classification Docker
+    container, and Detect objects in video and images.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will set up a Jetson Orin Nano with a MIPI CSI-2 camera for object detection and detect
+      objects from both live video and image files. Learn how to set up a Jetson Orin Nano with
+      a MIPI CSI-2 camera and perform real-time object detection from live video and image files
+      using DetectNet and TensorRT.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers interested in integrating object detection
+      into their applications.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Jetson Orin Nano](https://developer.nvidia.com/embedded/learn/jetson-orin-nano-devkit-user-guide/index.html);
+      A microSD card (64GB UHS-1 or larger is recommended); A MIPI CSI-2 camera, with a 22 pin
+      connector on at least one end.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including DetectNet, TensorRT, and Docker, Linux environments,
+      and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Set up your Jetson Orin Nano, Launch the image classification
+      Docker container, and Detect objects in video and images.
+# END generated_summary_faq
+
 author: Gabriel Peterson
 
 ### Tags
@@ -59,3 +100,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md b/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
index e296fa793e..a30174efd1 100644
--- a/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
@@ -17,6 +17,40 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: f3a01adf18ad93027b6ab61ceb5b0c470e6b5c298f9d4f944989a96ff64eec81
+  summary: >-
+    Learn how to import, build, and debug your first Keil Studio Cloud project. It is designed
+    for embedded software developers new to Keil Studio Cloud. By the end, you will be able to
+    import and build an example project and run the example on Arm Virtual Hardware. It focuses
+    on tools and technologies such as Arm Compiler for Embedded, Arm Virtual Hardware, and CMSIS,
+    Baremetal and RTOS environments, and Arm platforms including Cortex-M. The main steps cover
+    Work with an example project.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will import and build an example project and run the example on Arm Virtual Hardware.
+      Learn how to import, build, and debug your first Keil Studio Cloud project.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for embedded software developers new to Keil Studio Cloud.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Some familiarity with embedded programming
+      is assumed; An [Arm Account](https://developer.arm.com/register) is required.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Arm Compiler for Embedded, Arm Virtual Hardware,
+      and CMSIS, Baremetal and RTOS environments, and Arm platforms such as Cortex-M.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Work with an example project.
+# END generated_summary_faq
+
 author: Christopher Seidl 
 
 
@@ -59,3 +93,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md b/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
index 4a233a048b..2d67caba71 100644
--- a/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
@@ -22,6 +22,51 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 567387e8e513458a360f74c14d3f4d02c4af392bc573620e605c93976e4d8b4f
+  summary: >-
+    Learn how to boot and configure the NXP FRDM i.MX 93 Arm board with Linux, create a user with
+    sudo access, connect to WiFi using ConnMan, and transfer files over the network. It is designed
+    for embedded developers and ML engineers who want to boot an NXP FRDM i.MX 93 board, connect
+    over serial, enable WiFi, and transfer files for on-device development on Arm. By the end,
+    you will be able to boot the NXP FRDM i.MX 93 board and log in to Linux over a serial console,
+    create a non-root Linux user with sudo access for development workflows, and connect the board
+    to WiFi using ConnMan. It focuses on tools and technologies such as Bash, systemd, picocom,
+    ConnMan, and OpenSSH, Linux and macOS environments, and Arm platforms including Cortex-A.
+    The main steps cover Set up the board, Set up a Linux user and connect to WiFi, Transfer files
+    to the board, and (Optional) Enable Persistent WiFi.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will boot the NXP FRDM i.MX 93 board and log in to Linux over a serial console, create
+      a non-root Linux user with sudo access for development workflows, and connect the board
+      to WiFi using ConnMan. Learn how to boot and configure the NXP FRDM i.MX 93 Arm board with
+      Linux, create a user with sudo access, connect to WiFi using ConnMan, and transfer files
+      over the network.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for embedded developers and ML engineers who want to boot
+      an NXP FRDM i.MX 93 board, connect over serial, enable WiFi, and transfer files for on-device
+      development on Arm.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An NXP [FRDM i.MX 93](https://www.nxp.com/design/design-center/development-boards-and-designs/frdm-i-mx-93-development-board:FRDM-IMX93)
+      board.; A computer running Linux or macOS.; A USB-C cable for the board's **DBG** serial
+      connection.; A USB-C power supply/cable for the board's **POWER** port.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Bash, systemd, picocom, ConnMan, and OpenSSH, Linux
+      and macOS environments, and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Set up the board, Set up a Linux user and connect
+      to WiFi, Transfer files to the board, and (Optional) Enable Persistent WiFi.
+# END generated_summary_faq
+
 author: Waheed Brown
 
 ### Tags
@@ -68,3 +113,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md b/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md
index ce32ec3a09..b3bf07f2a0 100644
--- a/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md
@@ -17,6 +17,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 5943d817827bef274f175f7c14249cad769b2160094b3c65d7476aca6cbf0a1d
+  summary: >-
+    Learn how to boot a Linux software stack on Arm Fixed Virtual Platforms (FVPs), then debug
+    Trusted Firmware-A and the Linux kernel using Arm Development Studio. It is designed for developers
+    who want to run Linux on Arm Fixed Virtual Platforms (FVPs) and debug both Trusted Firmware-A
+    and the Linux kernel using Arm Development Studio. By the end, you will be able to boot and
+    run a Linux software stack on an Arm Fixed Virtual Platform (FVP) and debug Trusted Firmware-A
+    and the Linux kernel using Arm Development Studio. It focuses on tools and technologies such
+    as Arm Development Studio, C, and Assembly, Linux environments, and Arm platforms including
+    Cortex-A. The main steps cover Introduction to Arm Fixed Virtual Platforms (FVPs), Configure
+    Trusted Firmware-A build flags to include cpu_ops support, Modify the device tree for CPU
+    FVPs, Run the Linux software stack on an FVP, and Debug the software stack.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will boot and run a Linux software stack on an Arm Fixed Virtual Platform (FVP) and
+      debug Trusted Firmware-A and the Linux kernel using Arm Development Studio. Learn how to
+      boot a Linux software stack on Arm Fixed Virtual Platforms (FVPs), then debug Trusted Firmware-A
+      and the Linux kernel using Arm Development Studio.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This topic is for developers who want to run Linux on Arm Fixed Virtual Platforms (FVPs)
+      and debug both Trusted Firmware-A and the Linux kernel using Arm Development Studio.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Linux-based x86-64 host computer with
+      Arm Development Studio installed.; Basic understanding of Assembly and C programming.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Arm Development Studio, C, and Assembly, Linux environments,
+      and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Introduction to Arm Fixed Virtual Platforms (FVPs),
+      Configure Trusted Firmware-A build flags to include cpu_ops support, Modify the device tree
+      for CPU FVPs, Run the Linux software stack on an FVP, and Debug the software stack.
+# END generated_summary_faq
+
 author: Qixiang Xu
 
 ### Tags
@@ -53,3 +96,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md b/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md
index 9d169547a0..8e1a9d37bb 100644
--- a/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md
@@ -18,6 +18,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: aa6252610c0603acfdfc8d4555bb7da93cbdd5b9d92ba140c5dc5f63d23e2b07
+  summary: >-
+    Learn how to install the Python version of llama.cpp on a Raspberry Pi 5, download an LLM
+    from Hugging Face, assess memory and performance, and run the model using Python bindings.
+    It is designed for anyone interested in running a local Large Language Model on a Raspberry
+    Pi 5. By the end, you will be able to install the Python version of llama.cpp on your Raspberry
+    Pi 5, download an LLM from Hugging Face, and assess LLM memory size and performance. It focuses
+    on tools and technologies such as LLM, Generative AI, Raspberry Pi, Python, and Hugging Face,
+    Linux environments, and Arm platforms including Cortex-A. The main steps cover Run a Large
+    Language Model (LLM) chatbot on a Raspberry Pi 5.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install the Python version of llama.cpp on your Raspberry Pi 5, download an LLM
+      from Hugging Face, and assess LLM memory size and performance. Learn how to install the
+      Python version of llama.cpp on a Raspberry Pi 5, download an LLM from Hugging Face, assess
+      memory and performance, and run the model using Python bindings.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for anyone interested in running a local Large Language Model
+      on a Raspberry Pi 5.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Raspberry Pi 5 running Raspberry Pi
+      OS.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including LLM, Generative AI, Raspberry Pi, Python, and Hugging
+      Face, Linux environments, and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Run a Large Language Model (LLM) chatbot on a Raspberry
+      Pi 5.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
@@ -56,3 +96,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/migration/_index.md b/content/learning-paths/embedded-and-microcontrollers/migration/_index.md
index f711378403..e999cff896 100644
--- a/content/learning-paths/embedded-and-microcontrollers/migration/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/migration/_index.md
@@ -19,6 +19,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 81a15ff0d5579be9529a8c9c444be57521ebc48bbf5234f042f5a47a8a709b5c
+  summary: >-
+    Learn the software migration methodology for porting Linux workloads from x86_64 to aarch64,
+    including using Arm compilers, porting compiler intrinsics, and deploying applications in
+    containers. It is designed for embedded software developers looking at migrating Linux workloads
+    to aarch64. By the end, you will be able to understand software migration methodology, use
+    different Arm compilers and libraries, and port applications containing compiler intrinsics.
+    It focuses on tools and technologies such as GCC, Arm Compiler for Linux, Docker, and Neon,
+    Linux environments, and Arm platforms including Cortex-A and Neoverse. The main steps cover
+    Porting methodology, Porting analysis, Development environment, Application porting, and Run
+    and evaluate.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand software migration methodology, use different Arm compilers and libraries,
+      and port applications containing compiler intrinsics. Learn the software migration methodology
+      for porting Linux workloads from x86_64 to aarch64, including using Arm compilers, porting
+      compiler intrinsics, and deploying applications in containers.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for embedded software developers looking at migrating Linux workloads
+      to aarch64.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Introductory understanding of software
+      containers; Knowledge about building workflows; Access to an aarch64 or x86_64 machine running
+      Linux.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including GCC, Arm Compiler for Linux, Docker, and Neon, Linux
+      environments, and Arm platforms such as Cortex-A and Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Porting methodology, Porting analysis, Development
+      environment, Application porting, and Run and evaluate.
+# END generated_summary_faq
+
 author: Kasper Mecklenburg
 
 ### Tags
@@ -63,3 +105,4 @@ learning_path_main_page: "yes"  # Indicates this should be surfaced when looking
 
 # Prereqs
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md b/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md
index 0017708d39..688e1fd653 100644
--- a/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md
@@ -17,6 +17,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: acf43df3c6f344b55bb413b7483d60e26d0e137489ac148bca64500e443140ab
+  summary: >-
+    Learn how to build examples from the Machine Learning Evaluation Kit (MLEK) and run them on
+    the Arm Ecosystem FVP for machine learning application development on microcontrollers. It
+    is designed for embedded software developers interested in machine learning applications.
+    By the end, you will be able to build examples from Machine Learning Evaluation Kit (MLEK)
+    and run the examples on Arm Ecosystem FVP. It focuses on tools and technologies such as Arm
+    Virtual Hardware, FVP, GCC, and Arm Compiler for Embedded, Baremetal environments, and Arm
+    platforms including Cortex-M, Ethos-U, and Corstone. The main steps cover Build the ML Evaluation
+    Kit examples, Install Arm Ecosystem FVP, and Run the examples on the FVP.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build examples from Machine Learning Evaluation Kit (MLEK) and run the examples
+      on Arm Ecosystem FVP. Learn how to build examples from the Machine Learning Evaluation Kit
+      (MLEK) and run them on the Arm Ecosystem FVP for machine learning application development
+      on microcontrollers.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for embedded software developers interested in machine learning
+      applications.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Some familiarity with embedded programming;
+      A Linux host machine running Ubuntu.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Arm Virtual Hardware, FVP, GCC, and Arm Compiler
+      for Embedded, Baremetal environments, and Arm platforms such as Cortex-M, Ethos-U, and Corstone.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Build the ML Evaluation Kit examples, Install Arm
+      Ecosystem FVP, and Run the examples on the FVP.
+# END generated_summary_faq
+
 author: Ronan Synnott
 
 ### RS: Learning Path hidden until AWS instance updated
@@ -57,3 +97,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md b/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md
index 591d0d4f22..9563a14295 100644
--- a/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md
@@ -10,6 +10,50 @@ armips:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 0cfaa21be515b980097232ed38092b80d046df308120887e5503513a3384a1d7
+  summary: >-
+    Learn how to understand and select physical and virtual hardware targets for ML application
+    development with Cortex-M and Ethos-U, identify software tools, and find example applications.
+    It is designed for embedded software developers interested in learning about machine learning.
+    By the end, you will be able to understand and select physical and virtual hardware targets
+    for ML application development with Cortex-M and Ethos-U, identify and install software tools
+    used for machine learning applications on microcontrollers, and find and learn from existing
+    example applications. It focuses on tools and technologies such as FVP, Arm Virtual Hardware,
+    GCC, Arm Compiler for Embedded, and MPS3, Baremetal environments, and Arm platforms including
+    Cortex-M, Ethos-U, and Corstone. The main steps cover Overview, Development platforms, and
+    Software development considerations.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand and select physical and virtual hardware targets for ML application
+      development with Cortex-M and Ethos-U, identify and install software tools used for machine
+      learning applications on microcontrollers, and find and learn from existing example applications.
+      Learn how to understand and select physical and virtual hardware targets for ML application
+      development with Cortex-M and Ethos-U, identify software tools, and find example applications.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for embedded software developers interested in learning about
+      machine learning.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Some familiarity with microcontroller
+      software development.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including FVP, Arm Virtual Hardware, GCC, Arm Compiler for
+      Embedded, and MPS3, Baremetal environments, and Arm platforms such as Cortex-M, Ethos-U,
+      and Corstone.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview, Development platforms, and Software development
+      considerations.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 learning_objectives:
@@ -60,3 +104,4 @@ weight: 1
 layout: learningpathall
 learning_path_main_page: 'yes'
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md b/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md
index 0726c6a92b..a1225d1907 100644
--- a/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md
@@ -16,6 +16,44 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: d2c403c7fd1001dbd985697c9eb018951a955358c2be39c727183a87a3dfdf51
+  summary: >-
+    Learn how to create debug configurations for virtual platforms and development boards in Arm
+    Development Studio, including setting up connections for Fast Models and DSTREAM debug probes.
+    It is designed for embedded software developers new to Arm Development Studio. By the end,
+    you will be able to create a debug configuration for a virtual platform and create a debug
+    configuration for a development board. It focuses on tools and technologies such as Arm Development
+    Studio, Arm Fast Models, and DSTREAM, Baremetal environments, and Arm platforms including
+    Cortex-A, Cortex-R, Cortex-M, and Neoverse. The main steps cover Debug connection to Arm Fast
+    Models and Debug connection with Arm DSTREAM.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create a debug configuration for a virtual platform and create a debug configuration
+      for a development board. Learn how to create debug configurations for virtual platforms
+      and development boards in Arm Development Studio, including setting up connections for Fast
+      Models and DSTREAM debug probes.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for embedded software developers new to Arm Development Studio.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Some familiarity with embedded debug.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Arm Development Studio, Arm Fast Models, and DSTREAM,
+      Baremetal environments, and Arm platforms such as Cortex-A, Cortex-R, Cortex-M, and Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Debug connection to Arm Fast Models and Debug connection
+      with Arm DSTREAM.
+# END generated_summary_faq
+
 author: Ronan Synnott
 
 ### Tags
@@ -55,3 +93,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md b/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
index 680c6561bb..6197856db8 100644
--- a/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
@@ -20,6 +20,57 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: be2b3571d4d2ed75a16bc97589abc22c970d83be868b7e94bb60962a4ba853da
+  summary: >-
+    Learn how to bring up ExecuTorch executor_runner firmware on the NXP FRDM i.MX 93 Cortex-M33
+    using Linux RemoteProc, compile .pte models for Ethos-U65, and run inference with NPU acceleration.
+    It is designed for developers and data scientists new to TinyML who want to observe ExecuTorch
+    performance on a physical device. By the end, you will be able to bring up a custom ExecuTorch
+    `executor_runner` firmware on the FRDM i.MX 93 Cortex-M33 using Linux RemoteProc, compile
+    an ExecuTorch `.pte` model for Ethos-U65 and run inference with NPU acceleration, and understand
+    how heterogeneous Arm systems split responsibilities across application cores, microcontrollers,
+    and NPUs. It focuses on tools and technologies such as Baremetal, Python, PyTorch, ExecuTorch,
+    and Arm Compute Library, Linux and macOS environments, and Arm platforms including Cortex-A,
+    Cortex-M, and Ethos-U. The main steps cover Understand ExecuTorch deployment on NXP with Ethos-U,
+    Boot the NXP FRDM i.MX 93 board, Set up the ExecuTorch build environment, Build and install
+    ExecuTorch, and Build ExecuTorch models for Ethos-U65.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will bring up a custom ExecuTorch `executor_runner` firmware on the FRDM i.MX 93 Cortex-M33
+      using Linux RemoteProc, compile an ExecuTorch `.pte` model for Ethos-U65 and run inference
+      with NPU acceleration, and understand how heterogeneous Arm systems split responsibilities
+      across application cores, microcontrollers, and NPUs. Learn how to bring up ExecuTorch executor_runner
+      firmware on the NXP FRDM i.MX 93 Cortex-M33 using Linux RemoteProc, compile .pte models
+      for Ethos-U65, and run inference with NPU acceleration.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers and data scientists new to TinyML who want
+      to observe ExecuTorch performance on a physical device.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An NXP [FRDM i.MX 93](https://www.nxp.com/design/design-center/development-boards-and-designs/frdm-i-mx-93-development-board:FRDM-IMX93)
+      development board; A USB Mini-B to USB Type-A cable, or a USB Mini-B to USB Type-C cable;
+      Completion of [Use Linux on an NXP FRDM i.MX 93 board](/learning-paths/embedded-and-microcontrollers/linux-nxp-board/)
+      (Linux setup, login access, and file transfer); Basic knowledge of Machine Learning concepts;
+      A host computer to compile ExecuTorch libraries.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Baremetal, Python, PyTorch, ExecuTorch, and Arm
+      Compute Library, Linux and macOS environments, and Arm platforms such as Cortex-A, Cortex-M,
+      and Ethos-U.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Understand ExecuTorch deployment on NXP with Ethos-U,
+      Boot the NXP FRDM i.MX 93 board, Set up the ExecuTorch build environment, Build and install
+      ExecuTorch, and Build ExecuTorch models for Ethos-U65.
+# END generated_summary_faq
+
 author: 
 - Waheed Brown
 - Fidel Makatia Omusilibwa
@@ -67,3 +118,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md b/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
index 9e5b944008..67809f20b6 100644
--- a/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
@@ -17,6 +17,41 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 8039812773c6c1ac45cceb4039cee801e627d67871b625283c1bb5b3fa2960b3
+  summary: >-
+    Learn how to migrate a CMSIS v5-based CMSIS-Pack with device support to CMSIS v6 and update
+    example projects for compatibility with the new CMSIS version. It is designed for maintainers
+    of CMSIS-Packs with device support. By the end, you will be able to migrate a CMSIS v5-based
+    CMSIS-Pack with device support to CMSIS v6 and update example projects. It focuses on tools
+    and technologies such as CMSIS and CMSIS-Toolbox, Baremetal and RTOS environments, and Arm
+    platforms including Cortex-M. The main steps cover Device support and Example projects.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will migrate a CMSIS v5-based CMSIS-Pack with device support to CMSIS v6 and update
+      example projects. Learn how to migrate a CMSIS v5-based CMSIS-Pack with device support to
+      CMSIS v6 and update example projects for compatibility with the new CMSIS version.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for maintainers of CMSIS-Packs with device support.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A good understanding of [CMSIS-Packs](https://open-cmsis-pack.github.io/Open-CMSIS-Pack-Spec/main/html/index.html).;
+      A CMSIS-Pack that contains device support and was created for CMSIS v5.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including CMSIS and CMSIS-Toolbox, Baremetal and RTOS environments,
+      and Arm platforms such as Cortex-M.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Device support and Example projects.
+# END generated_summary_faq
+
 author: Christopher Seidl
 
 ### Tags
@@ -50,3 +85,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md b/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md
index 0b44e3e597..f6afaa0d75 100644
--- a/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md
@@ -18,6 +18,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 7a192506fc8a15423d1257fe92e7655053e38be85aad8310dae29602e483fbba
+  summary: >-
+    Learn how to migrate CMSIS v5 projects to CMSIS v6 by identifying supported toolchains, installing
+    required CMSIS-Packs, and selecting the necessary software components. It is designed for
+    embedded developers who want to migrate their projects to CMSIS v6. By the end, you will be
+    able to identify the supported toolchains, install the required CMSIS-Packs, and select the
+    software components needed to migrate your projects to CMSIS v6. It focuses on tools and technologies
+    such as CMSIS and CMSIS-Toolbox, Baremetal and RTOS environments, and Arm platforms including
+    Cortex-M. The main steps cover Supported toolchains, Required CMSIS-Packs, Device mapping,
+    Project format conversion, and Troubleshooting.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will identify the supported toolchains, install the required CMSIS-Packs, and select
+      the software components needed to migrate your projects to CMSIS v6. Learn how to migrate
+      CMSIS v5 projects to CMSIS v6 by identifying supported toolchains, installing required CMSIS-Packs,
+      and selecting the necessary software components.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for embedded developers who want to migrate their projects to
+      CMSIS v6.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A CMSIS v5 based project.; A basic understanding
+      of the CMSIS-Pack system.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including CMSIS and CMSIS-Toolbox, Baremetal and RTOS environments,
+      and Arm platforms such as Cortex-M.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Supported toolchains, Required CMSIS-Packs, Device
+      mapping, Project format conversion, and Troubleshooting.
+# END generated_summary_faq
+
 author: Christopher Seidl
 
 ### Tags
@@ -53,3 +93,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md b/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md
index 9be7d3e78f..53b67bc5e5 100644
--- a/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md
@@ -21,6 +21,60 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: a0145c77b3d4a8bb25a32c62adaa3ad378e65fccbe6db88d9a46a569897d238a
+  summary: >-
+    Learn how to run large language models locally on the Raspberry Pi 5 using Ollama, control
+    GPIO-connected devices, and deploy a privacy-first web-based smart home assistant without
+    cloud services. It is designed for edge AI developers, Raspberry Pi hobbyists, and software
+    engineers who want to build privacy-first smart home assistants. You’ll learn how to run large
+    language models (LLMs) locally on the Raspberry Pi 5 using Ollama, control GPIO-connected
+    devices, and deploy a web-based assistant without relying on cloud services. By the end, you
+    will be able to understand how the Arm architecture enables efficient, private, and responsive
+    LLM inference, run a smart home assistant on Raspberry Pi 5 with local LLM integration, and
+    wire and control physical devices (for example, LEDs) using Raspberry Pi GPIO pins. It focuses
+    on tools and technologies such as Python, Ollama, FastAPI, and Raspberry Pi, Linux environments,
+    and Arm platforms including Cortex-A. The main steps cover Run LLMs locally on Raspberry Pi
+    5 for Edge AI, Set up software dependencies on Raspberry Pi 5 for Ollama and LLMs, Test Raspberry
+    Pi 5 GPIO pins for smart home devices, and Build and Run a Smart Home Assistant on Raspberry
+    Pi 5 with LLMs.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand how the Arm architecture enables efficient, private, and responsive
+      LLM inference, run a smart home assistant on Raspberry Pi 5 with local LLM integration,
+      and wire and control physical devices (for example, LEDs) using Raspberry Pi GPIO pins.
+      Learn how to run large language models locally on the Raspberry Pi 5 using Ollama, control
+      GPIO-connected devices, and deploy a privacy-first web-based smart home assistant without
+      cloud services.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for edge AI developers, Raspberry Pi hobbyists, and software
+      engineers who want to build privacy-first smart home assistants. You’ll learn how to run
+      large language models (LLMs) locally on the Raspberry Pi 5 using Ollama, control GPIO-connected
+      devices, and deploy a web-based assistant without relying on cloud services.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm-based single board computer (for
+      example, Raspberry Pi 5 running Raspberry Pi OS); Electronic components (breadboard, LEDs,
+      resistors, jumper wires) for GPIO testing; Familiarity with Python programming, Raspberry
+      Pi GPIO pinout, and basic electronics.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Python, Ollama, FastAPI, and Raspberry Pi, Linux
+      environments, and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Run LLMs locally on Raspberry Pi 5 for Edge AI, Set
+      up software dependencies on Raspberry Pi 5 for Ollama and LLMs, Test Raspberry Pi 5 GPIO
+      pins for smart home devices, and Build and Run a Smart Home Assistant on Raspberry Pi 5
+      with LLMs.
+# END generated_summary_faq
+
 author: Fidel Makatia Omusilibwa
 
 skilllevels: Introductory
@@ -67,3 +121,4 @@ weight: 1 # _index.md always has weight of 1 to order correctly
 layout: "learningpathall" # All files under learning paths have this same wrapper
 learning_path_main_page: "yes" # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md b/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md
index b0c5ad859d..f0aa241dc8 100644
--- a/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md
@@ -22,6 +22,50 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: ed2034f5c95c4148fca355f6d545219c320f2e73267a0725934c792ebc4c0c59
+  summary: >-
+    Learn how to build a voice-controlled bot on a Raspberry Pi that listens for a wake word,
+    converts speech to text using Google Speech Recognition, sends requests to ChatGPT's API,
+    and plays audio responses. It is designed for This is an introductory project for developers
+    interested in integrating a Chatbot (namely ChatGPT) into Raspberry Pi projects. By the end,
+    you will be able to run a bot on a Raspberry Pi that will listen to you and respond to what
+    you say, learn how to listen for a keyword and wake a program when the keyword is heard, and
+    convert speech from the microphone to text using Google Speech Recognition. It focuses on
+    tools and technologies such as ChatGPT, Porcupine, and Python, Linux environments, and Arm
+    platforms including Cortex-A. The main steps cover Initial setup, Configure and test audio,
+    Create the Python application, and Run and test the bot.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will run a bot on a Raspberry Pi that will listen to you and respond to what you say,
+      learn how to listen for a keyword and wake a program when the keyword is heard, and convert
+      speech from the microphone to text using Google Speech Recognition. Learn how to build a
+      voice-controlled bot on a Raspberry Pi that listens for a wake word, converts speech to
+      text using Google Speech Recognition, sends requests to ChatGPT's API, and plays audio responses.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory project for developers interested in integrating a Chatbot (namely
+      ChatGPT) into Raspberry Pi projects.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Raspberry Pi 4 or 5 (earlier models
+      may also work); A microSD card with at least 16GB of storage; A Linux compatible USB microphone
+      and USB speakers or a USB audio device with a microphone and speakers.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including ChatGPT, Porcupine, and Python, Linux environments,
+      and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Initial setup, Configure and test audio, Create the
+      Python application, and Run and test the bot.
+# END generated_summary_faq
+
 author: Gabriel Peterson
 
 ### Tags
@@ -59,3 +103,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
index a95e5009f9..4b43ebd63b 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
@@ -22,6 +22,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 9cd2c3082c4874dc596e1b7b59c3dc2143aaf1ce3e66948ab8b6af190ffa3776
+  summary: >-
+    Learn how to compile the Llama 3 large language model using ExecuTorch, deploy it to a Raspberry
+    Pi 5, and understand techniques for running LLMs in embedded environments. It is designed
+    for anyone interested in running the Llama 3 model on a Raspberry Pi 5, and learning about
+    techniques for running large language models (LLMs) in an embedded environment. By the end,
+    you will be able to use Docker to run Raspberry Pi OS on an Arm Linux server, compile a Large
+    Language Model (LLM) using ExecuTorch, and deploy the Llama 3 model on an edge device. It
+    focuses on tools and technologies such as LLM, Generative AI, Raspberry Pi, Hugging Face,
+    and ExecuTorch, Linux environments, and Arm platforms including Cortex-A. The main steps cover
+    Set up the development environment, Set up ExecuTorch, Set up Llama 3, and Run the model on
+    a Raspberry Pi 5.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will use Docker to run Raspberry Pi OS on an Arm Linux server, compile a Large Language
+      Model (LLM) using ExecuTorch, and deploy the Llama 3 model on an edge device. Learn how
+      to compile the Llama 3 large language model using ExecuTorch, deploy it to a Raspberry Pi
+      5, and understand techniques for running LLMs in embedded environments.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for anyone interested in running the Llama 3 model on a Raspberry
+      Pi 5, and learning about techniques for running large language models (LLMs) in an embedded
+      environment.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm Linux machine or an [Arm cloud
+      instance](/learning-paths/servers-and-cloud-computing/csp/).; A Raspberry Pi 5.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including LLM, Generative AI, Raspberry Pi, Hugging Face,
+      and ExecuTorch, Linux environments, and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Set up the development environment, Set up ExecuTorch,
+      Set up Llama 3, and Run the model on a Raspberry Pi 5.
+# END generated_summary_faq
+
 author: Annie Tallund
 
 ### Tags
@@ -67,3 +110,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
index f6065f01c0..142e21db4e 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
@@ -20,6 +20,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 17721158fe10e97314af364f138c32b7bcf53fdc88fe11fffeb5765f11e26b79
+  summary: >-
+    Learn how to reduce compile time for embedded Linux projects by installing a Raspberry Pi
+    OS file system on an Arm server, building the MXNet machine learning framework, and transferring
+    it to a Raspberry Pi. It is designed for software developers who want to reduce compile time
+    for embedded Linux software projects. By the end, you will be able to install a Raspberry
+    Pi OS file system on an Arm server, reduce compile time for a Linux application, the MXNet
+    machine learning framework, and transfer the compiled MXNet application to a Raspberry Pi
+    and test it. It focuses on tools and technologies such as Raspberry Pi and MXNet, Linux environments,
+    and Arm platforms including Neoverse and Cortex-A72. The main steps cover User change, User
+    change, and User change.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install a Raspberry Pi OS file system on an Arm server, reduce compile time for
+      a Linux application, the MXNet machine learning framework, and transfer the compiled MXNet
+      application to a Raspberry Pi and test it. Learn how to reduce compile time for embedded
+      Linux projects by installing a Raspberry Pi OS file system on an Arm server, building the
+      MXNet machine learning framework, and transferring it to a Raspberry Pi.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers who want to reduce compile time for embedded
+      Linux software projects.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm computer running Linux. Cloud
+      instances can be used, refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).;
+      A Raspberry Pi 3 or 4 board.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Raspberry Pi and MXNet, Linux environments, and
+      Arm platforms such as Neoverse and Cortex-A72.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around User change, User change, and User change.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
@@ -47,3 +89,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md
index 4eac210275..d72b350b0f 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md
@@ -17,6 +17,45 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 5e5fad1563b67ed38d1cf3399d8e0d62162e76cae8d6848c3be7638f67cd037c
+  summary: >-
+    Learn how to build and run multiple software examples on the Raspberry Pi 4, including TensorFlow
+    and Docker applications, and compare its performance to Arm cloud servers. It is designed
+    for software developers interested in the Raspberry Pi 4. By the end, you will be able to
+    build and run multiple software examples on the Raspberry Pi 4 and compare and contrast the
+    Raspberry Pi 4 to an Arm cloud server. It focuses on tools and technologies such as Raspberry
+    Pi, TensorFlow, and Docker, Linux environments, and Arm platforms including Cortex-A and Neoverse.
+    The main steps cover Introduction to the Raspberry Pi 4, Setup a Raspberry Pi 4 and an Arm
+    cloud instance, Identifying the hardware, Linux Kernel Compile, and TensorFlow.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build and run multiple software examples on the Raspberry Pi 4 and compare and
+      contrast the Raspberry Pi 4 to an Arm cloud server. Learn how to build and run multiple
+      software examples on the Raspberry Pi 4, including TensorFlow and Docker applications, and
+      compare its performance to Arm cloud servers.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers interested in the Raspberry Pi 4.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Raspberry Pi 4 board; An [Arm based
+      instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Raspberry Pi, TensorFlow, and Docker, Linux environments,
+      and Arm platforms such as Cortex-A and Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Introduction to the Raspberry Pi 4, Setup a Raspberry
+      Pi 4 and an Arm cloud instance, Identifying the hardware, Linux Kernel Compile, and TensorFlow.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
@@ -49,3 +88,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
index bf1ac75005..4eef1751b3 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
@@ -19,6 +19,43 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: d9fe3cfc8f7a7092f40786763fc28e371697e4b57dba99b2c9b191dae4273911
+  summary: >-
+    Setup tools and start programming with Raspberry Pi Pico. It is designed for embedded software
+    developers new to Raspberry Pi Pico. By the end, you will be able to install the Raspberry
+    Pi Pico SDK, run a hello world example, and measure application performance. It focuses on
+    tools and technologies such as Raspberry Pi, Baremetal environments, and Arm platforms including
+    Cortex-M. The main steps cover How do I install the Raspberry Pi Pico SDK?, How do I run Hello
+    World for Raspberry Pi Pico?, How do I measure RPi Pico Application Performance?, and How
+    do I debug RPi Pico applications?
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install the Raspberry Pi Pico SDK, run a hello world example, and measure application
+      performance. Setup tools and start programming with Raspberry Pi Pico.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for embedded software developers new to Raspberry Pi Pico.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Raspberry Pi Pico board.; Raspberry
+      Pi 3, 4, 400, or 5 as a development computer.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Raspberry Pi, Baremetal environments, and Arm platforms
+      such as Cortex-M.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around How do I install the Raspberry Pi Pico SDK?, How do
+      I run Hello World for Raspberry Pi Pico?, How do I measure RPi Pico Application Performance?,
+      and How do I debug RPi Pico applications?
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
@@ -48,3 +85,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md b/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
index e71fc958bd..3cadd093f8 100644
--- a/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
@@ -22,6 +22,53 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 0b8de63a15d3d77b9c9972103b1317d6c48907875ac625c3d1c1b8db5360879a
+  summary: >-
+    Learn how to profile Linux kernel modules using Arm Streamline to identify performance bottlenecks,
+    analyze both out-of-tree and in-tree modules, and use Statistical Profiling Extension (SPE)
+    for deeper insights. It is designed for developers and performance engineers interested in
+    profiling Linux kernel performance. By the end, you will be able to understand why profiling
+    Linux kernel modules is important for performance and stability, set up and use Arm Streamline
+    to profile the Linux kernel, and profile both out-of-tree and in-tree kernel modules on Arm-based
+    systems. It focuses on tools and technologies such as Arm Streamline, Arm Performance Studio,
+    Linux kernel, and Performance analysis, Linux environments, and Arm platforms including Cortex-A.
+    The main steps cover Profile Linux kernel modules with Arm Streamline, Set up your environment,
+    Build the out-of-tree kernel module, Profile the out-of-tree kernel module, and Integrate
+    a custom character device driver into the Linux kernel.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand why profiling Linux kernel modules is important for performance and
+      stability, set up and use Arm Streamline to profile the Linux kernel, and profile both out-of-tree
+      and in-tree kernel modules on Arm-based systems. Learn how to profile Linux kernel modules
+      using Arm Streamline to identify performance bottlenecks, analyze both out-of-tree and in-tree
+      modules, and use Statistical Profiling Extension (SPE) for deeper insights.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for developers and performance engineers interested in profiling
+      Linux kernel performance.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Basic understanding of Linux kernel
+      development and module programming; Arm-based Linux target device (such as a Raspberry Pi,
+      BeagleBone, or similar board) with Secure Shell (SSH) access; A host machine that meets
+      [Buildroot system requirements](https://buildroot.org/downloads/manual/manual.html#requirement).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Arm Streamline, Arm Performance Studio, Linux kernel,
+      and Performance analysis, Linux environments, and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Profile Linux kernel modules with Arm Streamline,
+      Set up your environment, Build the out-of-tree kernel module, Profile the out-of-tree kernel
+      module, and Integrate a custom character device driver into the Linux kernel.
+# END generated_summary_faq
+
 author: Yahya Abouelseoud
 
 ### Tags
@@ -61,3 +108,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md b/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
index a9fde867a3..d47b6de88b 100644
--- a/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
@@ -18,6 +18,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: b5714494f00fff407c39e6f2f7bf37de94cde61d7569ba147412fec1b2f537c2
+  summary: >-
+    Build a letter recognition neural network model using TensorFlow and deploy it on an STM32
+    B-L475E-IOT01A2 board. It is designed for software developers interested in building network
+    models for microcontrollers. By the end, you will be able to build a letter recognition neural
+    network(NN) model using TensorFlow framework and run the NN model on an STM32 B-L475E-IOT01A2
+    board using STM32CubeAI. It focuses on tools and technologies such as TensorFlow and STM32,
+    Baremetal environments, and Arm platforms including Cortex-M. The main steps cover Prepare
+    development environment, Collect training data, Train the model, Feature extraction, and Run
+    the model on development board.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build a letter recognition neural network(NN) model using TensorFlow framework
+      and run the NN model on an STM32 B-L475E-IOT01A2 board using STM32CubeAI. Build a letter
+      recognition neural network model using TensorFlow and deploy it on an STM32 B-L475E-IOT01A2
+      board.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers interested in building network models
+      for microcontrollers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Familiarity with ML concepts; Familiarity
+      with C programming on microcontrollers; STM32 B-L475E-IOT01A2 board.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including TensorFlow and STM32, Baremetal environments, and
+      Arm platforms such as Cortex-M.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Prepare development environment, Collect training
+      data, Train the model, Feature extraction, and Run the model on development board.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
@@ -48,3 +88,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md b/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md
index 8a640a45f6..3f36b46b8d 100644
--- a/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md
@@ -18,6 +18,42 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 8d8f9df559ff1b1570dc98baf640fc2d4c4d225d69f22529e1881b62d4017752
+  summary: >-
+    Learn how to build and run the reference Trusted Firmware-M tests and example application
+    on Arm Fixed Virtual Platforms for secure microcontroller development. It is designed for
+    software developers new to Trusted Firmware-M. By the end, you will be able to build and run
+    the reference TF-M tests and example application. It focuses on tools and technologies such
+    as Arm Virtual Hardware, FVP, TrustZone, and Trusted Firmware, Baremetal environments, and
+    Arm platforms including Cortex-M and Corstone. The main steps cover Build and run TF-M tests
+    and example application.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build and run the reference TF-M tests and example application. Learn how to build
+      and run the reference Trusted Firmware-M tests and example application on Arm Fixed Virtual
+      Platforms for secure microcontroller development.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers new to Trusted Firmware-M.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Some familiarity with embedded C programming;
+      A machine running Ubuntu Linux.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Arm Virtual Hardware, FVP, TrustZone, and Trusted
+      Firmware, Baremetal environments, and Arm platforms such as Cortex-M and Corstone.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Build and run TF-M tests and example application.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 test_images:
@@ -62,5 +98,5 @@ further_reading:
 weight: 1                       # _index.md always has weight of 1 to order correctly
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
-
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md b/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md
index 16cab88242..4f87e24738 100644
--- a/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md
@@ -21,6 +21,51 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 20c500fd5174c9901058676d4619981ee1c8a8cf8e331affea8c0292112651c9
+  summary: >-
+    Learn how to train a CNN image classification model using PyTorch, convert it to ExecuTorch
+    format, and run it as an interactive mini-game on Arm-based edge devices. It is designed for
+    machine learning developers who want to deploy TinyML models on Arm-based edge devices using
+    PyTorch and ExecuTorch. By the end, you will be able to train a small Convolutional Neural
+    Network (CNN) for image classification using PyTorch, use synthetic data generation for training
+    a model when real data is limited, and convert and optimize a PyTorch model to an ExecuTorch
+    program (`.pte`) for Arm-based devices. It focuses on tools and technologies such as tinyML,
+    Computer Vision, Edge AI, CNN, and PyTorch, Linux environments, and Arm platforms including
+    Cortex-M and Ethos-U. The main steps cover Set up your environment, Train and Test the rock-paper-scissors
+    Model, and Run the model on Corstone-320 FVP.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will train a small Convolutional Neural Network (CNN) for image classification using
+      PyTorch, use synthetic data generation for training a model when real data is limited, and
+      convert and optimize a PyTorch model to an ExecuTorch program (`.pte`) for Arm-based devices.
+      Learn how to train a CNN image classification model using PyTorch, convert it to ExecuTorch
+      format, and run it as an interactive mini-game on Arm-based edge devices.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for machine learning developers who want to deploy TinyML
+      models on Arm-based edge devices using PyTorch and ExecuTorch.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Basic understanding of machine learning
+      concepts; Familiarity with Python and the PyTorch library; Completion of the Learning Path
+      [Introduction to TinyML on Arm using PyTorch and ExecuTorch](/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/);
+      An x86 Linux host machine or VM running Ubuntu 22.04 or later.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including tinyML, Computer Vision, Edge AI, CNN, and PyTorch,
+      Linux environments, and Arm platforms such as Cortex-M and Ethos-U.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Set up your environment, Train and Test the rock-paper-scissors
+      Model, and Run the model on Corstone-320 FVP.
+# END generated_summary_faq
+
 author: Dominica Abena O. Amanfo
 
 ### Tags
@@ -56,3 +101,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md b/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md
index 73d602e923..979e77534b 100644
--- a/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md
@@ -20,6 +20,44 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 6c81f3c9595df1128ca6f4f89446f093826d2242fa789ffa2d37465854be1f8e
+  summary: >-
+    Learn how to install Keil MDK Tools, run a TrustZone hello world example on the NXP LPCXpresso55S69
+    board, and understand security state switching and secure function calls. It is designed for
+    software developers new to using TrustZone. By the end, you will be able to install the Keil
+    MDK Tools, run a hello world TrustZone example, and understand switching of security states.
+    It focuses on tools and technologies such as TrustZone, Arm Compiler for Embedded, and Keil
+    MDK, Baremetal environments, and Arm platforms including Cortex-M. The main steps cover Run
+    TrustZone Hello World example and Breaking down the application.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install the Keil MDK Tools, run a hello world TrustZone example, and understand
+      switching of security states. Learn how to install Keil MDK Tools, run a TrustZone hello
+      world example on the NXP LPCXpresso55S69 board, and understand security state switching
+      and secure function calls.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers new to using TrustZone.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Familiar with C programming on microcontrollers;
+      Comfortable with Windows; NXP LPCXpresso55S69 board.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including TrustZone, Arm Compiler for Embedded, and Keil MDK,
+      Baremetal environments, and Arm platforms such as Cortex-M.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Run TrustZone Hello World example and Breaking down
+      the application.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
@@ -51,3 +89,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md b/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
index ccea6fa53a..69e6e99f05 100644
--- a/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
@@ -27,6 +27,55 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 57e05139cdea1de2f4a4ce239d701f0cef634b6ac11fd437cba47cc071e14098
+  summary: >-
+    Learn how to acquire and print materials, assemble a universal SBC rack mount system in a
+    4U chassis, and install single board computers in the racks using 3D-printed parts. It is
+    designed for software developers and hobbyists who want to build a rack mount system for housing
+    single board computers. By the end, you will be able to acquire and print the required materials,
+    assemble and install the universal SBC rack mount system in a 4U chassis, and install single
+    board computers in the racks. It focuses on tools and technologies such as Fusion 360, Linux
+    environments, and Arm platforms including Cortex-A. The main steps cover Print the Required
+    Parts, Assembly Instructions for the Chassis Bays, and Assembly Instructions for the Chassis
+    Bays.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will acquire and print the required materials, assemble and install the universal SBC
+      rack mount system in a 4U chassis, and install single board computers in the racks. Learn
+      how to acquire and print materials, assemble a universal SBC rack mount system in a 4U chassis,
+      and install single board computers in the racks using 3D-printed parts.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers and hobbyists who want to build a
+      rack mount system for housing single board computers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: 3D printer; Hack saw or chop saw to
+      cut threaded steel rods; 4U server chassis with the insides removed. For example, Rosewill
+      RSV-L4500 4U Industrial Rack-Mount Server Chassis; 8-32 stainless steel threaded rods at
+      least 405 mm long. 4 x 405 mm long rods are also required for each bay row. [Example part](https://www.mcmaster.com/98847A009/);
+      8-32 stainless steel hex nut. 8 per bay row. [Example part](https://www.mcmaster.com/91841A009/);
+      8-32 stainless steel wing nut. 8 per bay row. [Example part](https://www.mcmaster.com/92001A291/);
+      \#8 stainless steel washer. 8 per bay row. [Example part](https://www.mcmaster.com/90107A010/);
+      18-8 stainless steel socket head screw. 4 per card. [Example part](https://www.mcmaster.com/91292A016/);
+      18-8 stainless steel hex nut. 4 per card. [Example part](https://www.mcmaster.com/91828A113/);
+      PETG filament. Others can work, but PETG allows some flex without the risk of snapping.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Fusion 360, Linux environments, and Arm platforms
+      such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Print the Required Parts, Assembly Instructions for
+      the Chassis Bays, and Assembly Instructions for the Chassis Bays.
+# END generated_summary_faq
+
 author: Gabriel Peterson
 
 ### Tags
@@ -61,3 +110,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md b/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
index 43198ce4c1..14a43e49b3 100644
--- a/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
@@ -9,6 +9,50 @@ minutes_to_complete: 90
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 27ced3e9f69dbe51e75dcf13999ae1745a994137f31c86d6f17d4de341c7fa2a
+  summary: >-
+    Learn how to debug microcontrollers using µVision with basic run/stop debug, advanced techniques
+    using Event Recorder and Serial Wire Viewer, ETM Trace for performance analysis, and power
+    measurement with ULINKplus. It is designed for software developers who want to debug microcontrollers
+    using µVision. By the end, you will be able to use basic run/stop debug, learn advanced debug
+    techniques using Event Recorder and Serial Wire Viewer, and learn to use ETM Trace for optimum
+    performance. It focuses on tools and technologies such as Keil MDK and FVP, RTOS and Baremetal
+    environments, and Arm platforms including Cortex-M. The main steps cover Use basic run/stop
+    debug, Debug using Event Recorder, Debug using Serial Wire Viewer, Advanced debug with ETM
+    trace, and Measure Power with ULINKplus.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will use basic run/stop debug, learn advanced debug techniques using Event Recorder
+      and Serial Wire Viewer, and learn to use ETM Trace for optimum performance. Learn how to
+      debug microcontrollers using µVision with basic run/stop debug, advanced techniques using
+      Event Recorder and Serial Wire Viewer, ETM Trace for performance analysis, and power measurement
+      with ULINKplus.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers who want to debug microcontrollers using
+      µVision.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Some familiarity with embedded programming
+      is assumed; An [Arm Account](https://developer.arm.com/register) is required; A Windows
+      machine; Installation of [Arm Keil MDK](/install-guides/mdk/) with an active MDK-Community
+      license; Installation of the [Corstone-300 Ecosystem FVP](/install-guides/fm_fvp/eco_fvp/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Keil MDK and FVP, RTOS and Baremetal environments,
+      and Arm platforms such as Cortex-M.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Use basic run/stop debug, Debug using Event Recorder,
+      Debug using Serial Wire Viewer, Advanced debug with ETM trace, and Measure Power with ULINKplus.
+# END generated_summary_faq
+
 author: Christopher Seidl
 
 who_is_this_for: >
diff --git a/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md b/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
index 6a58c14d3d..c9766cc9a1 100644
--- a/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
@@ -20,6 +20,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 179b0318bbef9cef31ca6bb82de853597a609f61d6868aaaa85cfb40a27a051a
+  summary: >-
+    Learn how to import, convert, and build uvprojx-based projects to csolution format using Keil
+    Studio, µVision, and command-line tools for CMSIS-Toolbox compatibility. It is designed for
+    This is a topic for users of µVision who want to migrate to the new project format (csolution)
+    required by CMSIS-Toolbox. By the end, you will be able to import, convert, and build uvprojx-based
+    projects in Keil Studio, convert uvprojx-based projects in µVision, and convert and build
+    uvprojx-based projects on the command line. It focuses on tools and technologies such as Keil
+    MDK and CMSIS-Toolbox, Windows, Linux, and macOS environments, and Arm platforms including
+    Cortex-M. The main steps cover Using Keil Studio, Using µVision, and Using the command line.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will import, convert, and build uvprojx-based projects in Keil Studio, convert uvprojx-based
+      projects in µVision, and convert and build uvprojx-based projects on the command line. Learn
+      how to import, convert, and build uvprojx-based projects to csolution format using Keil
+      Studio, µVision, and command-line tools for CMSIS-Toolbox compatibility.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is a topic for users of µVision who want to migrate to the new project format (csolution)
+      required by CMSIS-Toolbox.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Install [Keil Studio](/install-guides/keilstudio_vs/)
+      on your machine.; Install [µVision](/install-guides/mdk/) on your machine.; Install [uv2csolution](https://arm-software.github.io/MDK-Toolbox/01_installation/)
+      for the command line flow.; The &micro;Vision project must use Arm Compiler 6 as the default
+      toolchain. Arm Compiler 5 is not supported.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Keil MDK and CMSIS-Toolbox, Windows, Linux, and
+      macOS environments, and Arm platforms such as Cortex-M.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Using Keil Studio, Using µVision, and Using the command
+      line.
+# END generated_summary_faq
+
 author: Christopher Seidl
 
 ### Tags
@@ -59,3 +101,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md b/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
index aa26cf6139..ba0f4e2e52 100644
--- a/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
@@ -21,6 +21,47 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 0c3422e1cd571e6abff676c28ec32c2ec69a626a406167f9166e57daa6829252
+  summary: >-
+    Learn how to install vcpkg, initialize it, create vcpkg-configuration.json files, use vcpkg
+    for tool management, activate tool licensing, and remove vcpkg for reproducible command-line
+    tool installations. It is designed for software developers who want to create reproducible
+    tool installations on the command line. By the end, you will be able to install vcpkg, initialize
+    vcpkg, and create a vcpkg-configuration.json file. It focuses on tools and technologies such
+    as vcpkg, Linux, Windows, and macOS environments, and Arm platforms including Cortex-M. The
+    main steps cover Install vcpkg, Initialize vcpkg, Create a vcpkg-configuration.json file,
+    Use vcpkg, and Activate a license.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install vcpkg, initialize vcpkg, and create a vcpkg-configuration.json file. Learn
+      how to install vcpkg, initialize it, create vcpkg-configuration.json files, use vcpkg for
+      tool management, activate tool licensing, and remove vcpkg for reproducible command-line
+      tool installations.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to create reproducible tool
+      installations on the command line.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A basic understanding of the [development
+      tools for Arm Cortex-M](https://developer.arm.com/Tools%20and%20Software/); Command line
+      access to your machine.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including vcpkg, Linux, Windows, and macOS environments, and
+      Arm platforms such as Cortex-M.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Install vcpkg, Initialize vcpkg, Create a vcpkg-configuration.json
+      file, Use vcpkg, and Activate a license.
+# END generated_summary_faq
+
 author: Christopher Seidl
 
 ### Tags
@@ -61,3 +102,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md b/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md
index c11033ba91..c0c44a9f70 100644
--- a/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md
@@ -20,6 +20,52 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: dcdbc4cecc376ed4c0df9377d13cb4fe792ad7c68acfe0610d75cf41898804ff
+  summary: >-
+    Learn how to identify Arm-based targets for TinyML, install Fixed Virtual Platforms, deploy
+    ExecuTorch models on Corstone-320 FVP, and visualize model execution using the FVP graphical
+    interface. It is designed for developers and data scientists who are new to TinyML and want
+    to visualize ExecuTorch model performance on virtual Arm hardware. By the end, you will be
+    able to identify Arm-based targets suitable for TinyML workloads, install and configure Fixed
+    Virtual Platforms (FVPs), and deploy a TinyML model using ExecuTorch on a Corstone-320 FVP.
+    It focuses on tools and technologies such as Arm Virtual Hardware, FVP, Python, PyTorch, and
+    ExecuTorch, Linux and macOS environments, and Arm platforms including Cortex-A, Cortex-M,
+    and Ethos-U. The main steps cover Overview, Understand the ExecuTorch workflow, Set up your
+    ExecuTorch environment, Set up the Corstone-320 Fixed Virtual Platform, and Deploy and run
+    Mobilenet V2 on the Corstone-320 FVP.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will identify Arm-based targets suitable for TinyML workloads, install and configure
+      Fixed Virtual Platforms (FVPs), and deploy a TinyML model using ExecuTorch on a Corstone-320
+      FVP. Learn how to identify Arm-based targets for TinyML, install Fixed Virtual Platforms,
+      deploy ExecuTorch models on Corstone-320 FVP, and visualize model execution using the FVP
+      graphical interface.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers and data scientists who are new to TinyML and
+      want to visualize ExecuTorch model performance on virtual Arm hardware.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Familiarity with basic machine learning
+      concepts; A Linux or macOS computer with Python 3 installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Arm Virtual Hardware, FVP, Python, PyTorch, and
+      ExecuTorch, Linux and macOS environments, and Arm platforms such as Cortex-A, Cortex-M,
+      and Ethos-U.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview, Understand the ExecuTorch workflow, Set
+      up your ExecuTorch environment, Set up the Corstone-320 Fixed Virtual Platform, and Deploy
+      and run Mobilenet V2 on the Corstone-320 FVP.
+# END generated_summary_faq
+
 author: Waheed Brown
 
 ### Tags
@@ -67,3 +113,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md b/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md
index 7e2a64a338..f6121490d0 100644
--- a/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md
@@ -17,6 +17,42 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 3bcfc51edbab56e3c8416f27045a61b069333e6f0cd130e8689b9a4d9fde1dd6
+  summary: >-
+    Introduction to building a minimal Yocto Linux image and running it on 64-bit Qemu Arm target.
+    It is designed for software developers interested in learning the basics of building Yocto
+    Linux for embedded Arm targets. By the end, you will be able to build a minimal Yocto Linux
+    image for generic 64-bit Arm target and run the built Yocto image on Qemu. It focuses on tools
+    and technologies such as Yocto Project and QEMU, Linux environments, and Arm platforms including
+    Cortex-A. The main steps cover How do I get started with Yocto Linux on Qemu?
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build a minimal Yocto Linux image for generic 64-bit Arm target and run the built
+      Yocto image on Qemu. Introduction to building a minimal Yocto Linux image and running it
+      on 64-bit Qemu Arm target.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers interested in learning the basics
+      of building Yocto Linux for embedded Arm targets.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Some familiarity with embedded Linux.;
+      A linux machine running Ubuntu 22.04 with at least 60 GB of disk space.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Yocto Project and QEMU, Linux environments, and
+      Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around How do I get started with Yocto Linux on Qemu?
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
@@ -54,3 +90,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md b/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md
index f588f57f20..0f81c01c19 100644
--- a/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md
@@ -22,6 +22,51 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: b0cb917bdcfb601c054e6cac93b2d04aca3ffe84b5a1963288fd7b89dd9bad3b
+  summary: >-
+    Learn how to run a YOLO object detection model on the Himax WiseEye2 module, build the Himax
+    SDK, update firmware, and connect to the Grove Vision AI module for computer vision applications.
+    It is designed for developers who would like to learn about how to run a computer vision application
+    on an embedded device from Himax. By the end, you will be able to run a You-Only-Look-Once
+    (YOLO) object detection model on a Himax WiseEye2 module, build the Himax Software Development
+    Kit (SDK) and generate a firmware image file, and update firmware on the Himax WiseEye2. It
+    focuses on tools and technologies such as Himax SDK, Python, and Hugging Face, Linux and macOS
+    environments, and Arm platforms including Cortex-M55 and Ethos-U55. The main steps cover Overview,
+    Set up the environment, Build the firmware, Flash firmware onto the microcontroller, and Run
+    additional models in the web toolkit.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will run a You-Only-Look-Once (YOLO) object detection model on a Himax WiseEye2 module,
+      build the Himax Software Development Kit (SDK) and generate a firmware image file, and update
+      firmware on the Himax WiseEye2. Learn how to run a YOLO object detection model on the Himax
+      WiseEye2 module, build the Himax SDK, update firmware, and connect to the Grove Vision AI
+      module for computer vision applications.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers who would like to learn about how to run a
+      computer vision application on an embedded device from Himax.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Seeed Grove Vision AI Module V2](https://www.seeedstudio.com/Grove-Vision-AI-Module-V2-p-5851.html)
+      development board.; An [OV5647-62 Camera Module](https://www.seeedstudio.com/OV5647-69-1-FOV-Camera-module-for-Raspberry-Pi-3B-4B-p-5484.html).;
+      A Flexible Printed Circuit (FPC) cable.; A USB-C cable.; An x86 Linux machine, or a Mac
+      running macOS.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Himax SDK, Python, and Hugging Face, Linux and macOS
+      environments, and Arm platforms such as Cortex-M55 and Ethos-U55.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview, Set up the environment, Build the firmware,
+      Flash firmware onto the microcontroller, and Run additional models in the web toolkit.
+# END generated_summary_faq
+
 author:
     - Chaodong Gong
     - Alex Su
@@ -62,3 +107,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md b/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md
index c0da032f21..cedd1359fb 100644
--- a/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md
@@ -18,6 +18,41 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 89f599ab33721a2d578d4fc39e505b5aed8d930aabac71f22d1ba28bfc4a5cf8
+  summary: >-
+    Learn how to build and run Zephyr RTOS applications on the Arm Corstone-300 Fixed Virtual
+    Platform using Arm Virtual Hardware. It is designed for software developers getting started
+    with the Zephyr RTOS. By the end, you will be able to build and run Zephyr applications on
+    the Corstone-300. It focuses on tools and technologies such as Zephyr, Arm Virtual Hardware,
+    and FVP, RTOS environments, and Arm platforms including Cortex-M. The main steps cover Build
+    and run Zephyr applications.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build and run Zephyr applications on the Corstone-300. Learn how to build and run
+      Zephyr RTOS applications on the Arm Corstone-300 Fixed Virtual Platform using Arm Virtual
+      Hardware.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers getting started with the Zephyr RTOS.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Some familiarity with embedded C programming;
+      A Linux machine running Ubuntu, or an AWS account to use [Arm Virtual Hardware](https://www.arm.com/products/development-tools/simulation/virtual-hardware).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Zephyr, Arm Virtual Hardware, and FVP, RTOS environments,
+      and Arm platforms such as Cortex-M.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Build and run Zephyr applications.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 test_images:
@@ -57,5 +92,5 @@ further_reading:
 weight: 1                       # _index.md always has weight of 1 to order correctly
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
+---
 
----
\ No newline at end of file
diff --git a/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md b/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md
index 9b52e4bd5a..f193703313 100644
--- a/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md
@@ -22,6 +22,50 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 808f1c99409f9ca3bb72419eaf950bc097f1c7af6c2dcade6385be97fdbbf713
+  summary: >-
+    Learn how to install Workbench for Zephyr extension in VS Code, set up the complete Zephyr
+    development environment, create and build Zephyr applications, debug embedded systems, and
+    perform memory usage analysis. It is designed for embedded developers targeting Arm-based
+    platforms with the Zephyr RTOS using the Workbench for Zephyr extension for VS Code. By the
+    end, you will be able to install and configure the Workbench for Zephyr extension in VS Code,
+    set up a complete Zephyr development environment including the SDK and toolchain, and create,
+    build, and debug Zephyr applications using hands-on examples. It focuses on tools and technologies
+    such as Zephyr and C, RTOS environments, and Arm platforms including Cortex-M. The main steps
+    cover Set up your environment, Build a Zephyr application with Zephyr workbench, and Analyze
+    and debug a Zephyr application.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install and configure the Workbench for Zephyr extension in VS Code, set up a complete
+      Zephyr development environment including the SDK and toolchain, and create, build, and debug
+      Zephyr applications using hands-on examples. Learn how to install Workbench for Zephyr extension
+      in VS Code, set up the complete Zephyr development environment, create and build Zephyr
+      applications, debug embedded systems, and perform memory usage analysis.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for embedded developers targeting Arm-based platforms with
+      the Zephyr RTOS using the Workbench for Zephyr extension for VS Code.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Basic familiarity with embedded C programming;
+      Visual Studio Code; A Cortex-M development board; Windows 10+ (64-bit), macOS with Homebrew,
+      or Linux (preferably Ubuntu 20.04+).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Zephyr and C, RTOS environments, and Arm platforms
+      such as Cortex-M.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Set up your environment, Build a Zephyr application
+      with Zephyr workbench, and Analyze and debug a Zephyr application.
+# END generated_summary_faq
+
 author: 
     - Ayoub Bourjilat
     - Odin Shen
@@ -55,3 +99,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md b/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md
index caca4ec0df..7c05084cbf 100644
--- a/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md
+++ b/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md
@@ -20,6 +20,50 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 137e974aee0ccba78e3375a1c8179af392e54a0304cfecdcd53cf4ac5c38917b
+  summary: >-
+    Learn how to create and run Ubuntu containers on ChromeOS Crostini using LXC with file sharing
+    and GUI application support on Arm-based Chromebooks. It is designed for software developers
+    who want to install Ubuntu and other Linux distributions on their Arm-based Chromebook with
+    ChromeOS file sharing and GUI support. By the end, you will be able to create and run an Ubuntu
+    24.04 container on ChromeOS Crostini using LXC and Termina shell, set up ChromeOS integration
+    for file sharing and GUI applications, and manage LXC containers on ChromeOS. It focuses on
+    tools and technologies such as Ubuntu, ChromeOS environments, and Arm platforms including
+    Cortex-A. The main steps cover Create an Ubuntu 24.04 container on ChromeOS, Integrate ChromeOS
+    with Linux containers, Enable desktop applications, and Manage Linux containers with additional
+    commands.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create and run an Ubuntu 24.04 container on ChromeOS Crostini using LXC and Termina
+      shell, set up ChromeOS integration for file sharing and GUI applications, and manage LXC
+      containers on ChromeOS. Learn how to create and run Ubuntu containers on ChromeOS Crostini
+      using LXC with file sharing and GUI application support on Arm-based Chromebooks.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is for software developers who want to install Ubuntu and other Linux
+      distributions on their Arm-based Chromebook with ChromeOS file sharing and GUI support.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A ChromeOS device with the Linux development
+      environment enabled. The Lenovo Chromebook Plus 14 is recommended.; Basic knowledge of the
+      Linux command line.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Ubuntu, ChromeOS environments, and Arm platforms
+      such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Create an Ubuntu 24.04 container on ChromeOS, Integrate
+      ChromeOS with Linux containers, Enable desktop applications, and Manage Linux containers
+      with additional commands.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
@@ -48,3 +92,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md
index e6ad66f867..6a43695829 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md
@@ -21,6 +21,58 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: eda533c727ea7094202e8784bf1ed2240cfea2573cefc73aca1a86797840778c
+  summary: >-
+    Learn how to build and deploy high-fidelity robotic simulations and reinforcement learning
+    pipelines using Isaac Sim and Isaac Lab on Arm-based NVIDIA DGX Spark with Grace-Blackwell
+    architecture. It is designed for robotics developers, simulation engineers, and AI researchers
+    who want to run high-fidelity robotic simulations and reinforcement learning (RL) pipelines
+    using NVIDIA Isaac Sim and Isaac Lab on Arm-based NVIDIA DGX Spark system powered by the Grace–Blackwell
+    (GB10) architecture. By the end, you will be able to describe the roles of Isaac Sim and Isaac
+    Lab within a robotics simulation and RL pipeline, build and configure Isaac Sim and Isaac
+    Lab on an Arm-based DGX Spark system, and launch and control a robot simulation in Isaac Sim
+    using Python. It focuses on tools and technologies such as Python, Bash, IsaacSim, and IsaacLab,
+    Linux environments, and Arm platforms including Cortex-X and Cortex-A. The main steps cover
+    Explore Isaac Sim and Isaac Lab for robotic workflows on DGX Spark, Set up Isaac Sim and Isaac
+    Lab on DGX Spark, Run and Understand a Sample Robot Simulation with Isaac Sim, and Train a
+    Humanoid Locomotion Policy with Isaac Lab on DGX Spark.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will describe the roles of Isaac Sim and Isaac Lab within a robotics simulation and
+      RL pipeline, build and configure Isaac Sim and Isaac Lab on an Arm-based DGX Spark system,
+      and launch and control a robot simulation in Isaac Sim using Python. Learn how to build
+      and deploy high-fidelity robotic simulations and reinforcement learning pipelines using
+      Isaac Sim and Isaac Lab on Arm-based NVIDIA DGX Spark with Grace-Blackwell architecture.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for robotics developers, simulation engineers, and AI researchers
+      who want to run high-fidelity robotic simulations and reinforcement learning (RL) pipelines
+      using NVIDIA Isaac Sim and Isaac Lab on Arm-based NVIDIA DGX Spark system powered by the
+      Grace–Blackwell (GB10) architecture.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A NVIDIA DGX Spark system with at least
+      50 GB of free disk space; Familiarity with Linux command-line tools; Experience with Python
+      scripting and virtual environments; Basic understanding of reinforcement learning concepts
+      (rewards, policies, episodes).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Python, Bash, IsaacSim, and IsaacLab, Linux environments,
+      and Arm platforms such as Cortex-X and Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Explore Isaac Sim and Isaac Lab for robotic workflows
+      on DGX Spark, Set up Isaac Sim and Isaac Lab on DGX Spark, Run and Understand a Sample Robot
+      Simulation with Isaac Sim, and Train a Humanoid Locomotion Policy with Isaac Lab on DGX
+      Spark.
+# END generated_summary_faq
+
 author:
     - Johnny Nunez
     - Odin Shen
@@ -69,3 +121,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md
index 52b8698bb7..86ed79bfa5 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md
@@ -23,6 +23,57 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: ada333cc887badfd57815708ef93e172543da74f2c995b46a916817917e92394
+  summary: >-
+    Learn how to build and optimize quantized LLMs using llama.cpp on NVIDIA DGX Spark with Grace-Blackwell
+    architecture, leveraging Armv9 SIMD acceleration. It is designed for AI practitioners, performance
+    engineers, and system architects who want to learn how to deploy and optimize quantized large
+    language models (LLMs) on NVIDIA DGX Spark systems powered by the Grace-Blackwell (GB10) architecture.
+    By the end, you will be able to describe the Grace–Blackwell (GB10) architecture and its support
+    for efficient AI inference, build CUDA-enabled and CPU-only versions of llama.cpp for flexible
+    deployment, and validate the functionality of both builds on the DGX Spark platform. It focuses
+    on tools and technologies such as Python, C, Bash, and llama.cpp, Linux environments, and
+    Arm platforms including Cortex-A and Cortex-X. The main steps cover Explore Grace Blackwell
+    architecture for efficient quantized LLM inference, Verify your Grace Blackwell system readiness
+    for AI inference, Build the GPU version of llama.cpp on GB10, Build the CPU version of llama.cpp
+    on GB10, and Analyze CPU instruction mix using Process Watch.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will describe the Grace–Blackwell (GB10) architecture and its support for efficient
+      AI inference, build CUDA-enabled and CPU-only versions of llama.cpp for flexible deployment,
+      and validate the functionality of both builds on the DGX Spark platform. Learn how to build
+      and optimize quantized LLMs using llama.cpp on NVIDIA DGX Spark with Grace-Blackwell architecture,
+      leveraging Armv9 SIMD acceleration.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for AI practitioners, performance engineers, and system architects
+      who want to learn how to deploy and optimize quantized large language models (LLMs) on NVIDIA
+      DGX Spark systems powered by the Grace-Blackwell (GB10) architecture.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Access to an NVIDIA DGX Spark system
+      with at least 15 GB of available disk space; Familiarity with command-line interfaces and
+      basic Linux operations; Understanding of CUDA programming basics and GPU/CPU compute concepts;
+      Basic knowledge of quantized large language models (LLMs) and machine learning inference;
+      Experience building software from source using CMake and make.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Python, C, Bash, and llama.cpp, Linux environments,
+      and Arm platforms such as Cortex-A and Cortex-X.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Explore Grace Blackwell architecture for efficient
+      quantized LLM inference, Verify your Grace Blackwell system readiness for AI inference,
+      Build the GPU version of llama.cpp on GB10, Build the CPU version of llama.cpp on GB10,
+      and Analyze CPU instruction mix using Process Watch.
+# END generated_summary_faq
+
 author: Odin Shen
 
 ### Tags
@@ -63,3 +114,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md
index 1798e1fc87..6bccdd15dd 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md
@@ -18,6 +18,57 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: facf9c504a3caceb62ef898a04a6760e8aafeb7e802e5727cb80d3a7e7e344d0
+  summary: >-
+    Learn how to build a Retrieval-Augmented Generation (RAG) pipeline on NVIDIA DGX Spark combining
+    Arm Grace CPU orchestration with Blackwell GPU-accelerated inference using llama.cpp. It is
+    designed for developers who want to build a Retrieval-Augmented Generation (RAG) pipeline
+    on the NVIDIA DGX Spark platform. You'll learn how Arm-based Grace CPUs handle document retrieval
+    and orchestration, while Blackwell GPUs speed up large language model inference using the
+    open-source llama.cpp REST server. This is a great fit if you're interested in combining Arm
+    CPU management with GPU-accelerated AI workloads. By the end, you will be able to describe
+    how a RAG system combines document retrieval and language model generation, deploy a hybrid
+    CPU-GPU RAG pipeline on the GB10 platform using open-source tools, and use the llama.cpp REST
+    Server for GPU-accelerated inference with CPU-managed retrieval. It focuses on tools and technologies
+    such as Python, llama.cpp, and Hugging Face, Linux environments, and Arm platforms including
+    Cortex-A. The main steps cover Explore building a RAG pipeline on Arm-based Grace–Blackwell
+    systems, Configure the RAG development environment and models, Add documents to the RAG vector
+    database, Build and run the RAG pipeline, and Monitor unified memory performance.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will describe how a RAG system combines document retrieval and language model generation,
+      deploy a hybrid CPU-GPU RAG pipeline on the GB10 platform using open-source tools, and use
+      the llama.cpp REST Server for GPU-accelerated inference with CPU-managed retrieval. Learn
+      how to build a Retrieval-Augmented Generation (RAG) pipeline on NVIDIA DGX Spark combining
+      Arm Grace CPU orchestration with Blackwell GPU-accelerated inference using llama.cpp.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for developers who want to build a Retrieval-Augmented Generation
+      (RAG) pipeline on the NVIDIA DGX Spark platform. You'll learn how Arm-based Grace CPUs handle
+      document retrieval and orchestration, while Blackwell GPUs speed up large language model
+      inference using the open-source llama.cpp REST server. This is a great fit if you're interested
+      in combining Arm CPU management with GPU-accelerated AI workloads.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An NVIDIA DGX Spark system with at least
+      15 GB of available disk space.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Python, llama.cpp, and Hugging Face, Linux environments,
+      and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Explore building a RAG pipeline on Arm-based Grace–Blackwell
+      systems, Configure the RAG development environment and models, Add documents to the RAG
+      vector database, Build and run the RAG pipeline, and Monitor unified memory performance.
+# END generated_summary_faq
+
 author: Odin Shen
 
 ### Tags
@@ -57,3 +108,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md
index 6d1744280e..32f275f4e8 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md
@@ -20,6 +20,52 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 4ccde526ec4dd9fc18672e162e067108c7251161c1da0375a0bb0374a2f3a4ea
+  summary: >-
+    Learn how to build an offline voice assistant combining speech-to-text via faster-whisper
+    and text generation via vLLM on Arm-based DGX Spark for privacy-focused deployments. It is
+    designed for developers and ML engineers who want to build private, offline voice assistant
+    systems on Arm-based servers such as DGX Spark. By the end, you will be able to explain the
+    architecture of an offline voice chatbot pipeline combining speech-to-text (STT) and vLLM,
+    capture and segment real-time audio using PyAudio and Voice Activity Detection (VAD), and
+    transcribe speech using faster-whisper and generate replies using vLLM. It focuses on tools
+    and technologies such as Docker and Python, Linux environments, and Arm platforms including
+    Neoverse. The main steps cover Build an offline voice assistant with whisper and vLLM, Install
+    faster-whisper for local speech recognition, Build a real-time STT pipeline on CPU, Fine-tune
+    segmentation parameters, and Build a real-time offline voice chatbot using STT and vLLM.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will explain the architecture of an offline voice chatbot pipeline combining speech-to-text
+      (STT) and vLLM, capture and segment real-time audio using PyAudio and Voice Activity Detection
+      (VAD), and transcribe speech using faster-whisper and generate replies using vLLM. Learn
+      how to build an offline voice assistant combining speech-to-text via faster-whisper and
+      text generation via vLLM on Arm-based DGX Spark for privacy-focused deployments.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for developers and ML engineers who want to build private, offline
+      voice assistant systems on Arm-based servers such as DGX Spark.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An NVIDIA DGX Spark system with at least
+      15 GB of available disk space; A USB microphone for audio input.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Docker and Python, Linux environments, and Arm platforms
+      such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Build an offline voice assistant with whisper and
+      vLLM, Install faster-whisper for local speech recognition, Build a real-time STT pipeline
+      on CPU, Fine-tune segmentation parameters, and Build a real-time offline voice chatbot using
+      STT and vLLM.
+# END generated_summary_faq
+
 author: Odin Shen
 
 ### Tags
@@ -57,3 +103,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/docker-models/_index.md b/content/learning-paths/laptops-and-desktops/docker-models/_index.md
index 33e6e3b67d..c6bb2909aa 100644
--- a/content/learning-paths/laptops-and-desktops/docker-models/_index.md
+++ b/content/learning-paths/laptops-and-desktops/docker-models/_index.md
@@ -18,6 +18,47 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: eae0a23635e7a025e1a73baaf5ccbd01f2c031ec76725c68893ca02190e36deb
+  summary: >-
+    Learn how to run pre-trained AI models locally using Docker Model Runner and build containerized
+    applications integrating large language models. It is designed for software developers and
+    AI enthusiasts who want to run pre-trained AI models locally using Docker Model Runner. By
+    the end, you will be able to run AI models locally using Docker Model Runner and build containerized
+    applications that integrate Large Language Models (LLMs). It focuses on tools and technologies
+    such as Docker, Python, and LLM, Windows and macOS environments, and Arm platforms including
+    Neoverse and Cortex-A. The main steps cover Run AI models using Docker Model Runner and Run
+    a containerized AI chat app with Docker Compose.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will run AI models locally using Docker Model Runner and build containerized applications
+      that integrate Large Language Models (LLMs). Learn how to run pre-trained AI models locally
+      using Docker Model Runner and build containerized applications integrating large language
+      models.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is for software developers and AI enthusiasts who want to run pre-trained AI models
+      locally using Docker Model Runner.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Docker Desktop (version 4.40 or later)
+      installed on a system with at least 16GB of RAM (recommended).; Basic understanding of Docker
+      CLI and concepts.; Familiarity with LLM concepts.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Docker, Python, and LLM, Windows and macOS environments,
+      and Arm platforms such as Neoverse and Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Run AI models using Docker Model Runner and Run a
+      containerized AI chat app with Docker Compose.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/electron/_index.md b/content/learning-paths/laptops-and-desktops/electron/_index.md
index e878a43b89..4814ba1eab 100644
--- a/content/learning-paths/laptops-and-desktops/electron/_index.md
+++ b/content/learning-paths/laptops-and-desktops/electron/_index.md
@@ -18,6 +18,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 8491a5e83e9e6436721f6078085e9b367121d19fdf228634dba859b3e1a0802a
+  summary: >-
+    Learn how to develop and build cross-platform desktop applications using the Electron Framework
+    on Windows on Arm devices. It is designed for developers who want to learn how to develop
+    cross-platform desktop applications using the Electron Framework on Windows on Arm (WoA).
+    By the end, you will be able to implement a sample application using the electron framework
+    on a Windows on Arm machine and learn how to create a multi platform build of the application.
+    It focuses on tools and technologies such as JavaScript, HTML, and Visual Studio Code, Windows
+    environments, and Arm platforms including Cortex-A. The main steps cover Create the application
+    using the electron framework and Build the cross-platform application.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will implement a sample application using the electron framework on a Windows on Arm
+      machine and learn how to create a multi platform build of the application. Learn how to
+      develop and build cross-platform desktop applications using the Electron Framework on Windows
+      on Arm devices.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This learning path is for developers who want to learn how to develop cross-platform desktop
+      applications using the Electron Framework on Windows on Arm (WoA).
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows on Arm computer such as the
+      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
+      Node.js for Arm64. You can find the [Node.js installer](https://nodejs.org/dist/v20.10.0/node-v20.10.0-arm64.msi).;
+      Any code editor; we recommend using [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including JavaScript, HTML, and Visual Studio Code, Windows
+      environments, and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Create the application using the electron framework
+      and Build the cross-platform application.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
@@ -53,3 +95,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md b/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md
index 3fd42c6465..20e92d83af 100644
--- a/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md
+++ b/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md
@@ -18,6 +18,47 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 7e108d774eb0fd0b2b72fa8b5d65e1efec0ff4ecf3d80d4e511e82cdf3862284
+  summary: >-
+    Learn how to automate Windows application builds on Arm architecture using GitHub Arm-hosted
+    runners and GitHub Actions workflows. It is designed for This introductory tutorial is for
+    software developers looking to automate Windows application builds on Arm architecture using
+    GitHub Actions. By the end, you will be able to describe GitHub Arm-hosted Windows runners,
+    configure workflows to run on Arm-hosted runners, and automate Windows application builds
+    with GitHub Actions. It focuses on tools and technologies such as GitHub, Visual Studio, MSBuild,
+    and Arm Performance Libraries, Windows environments, and Arm platforms including Cortex-A.
+    The main steps cover Introduction to GitHub Arm-hosted Runners and Automate the Build of Windows
+    Applications.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will describe GitHub Arm-hosted Windows runners, configure workflows to run on Arm-hosted
+      runners, and automate Windows application builds with GitHub Actions. Learn how to automate
+      Windows application builds on Arm architecture using GitHub Arm-hosted runners and GitHub
+      Actions workflows.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This introductory tutorial is for software developers looking to automate Windows application
+      builds on Arm architecture using GitHub Actions.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A GitHub account.; Familiarity with
+      GitHub Actions.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including GitHub, Visual Studio, MSBuild, and Arm Performance
+      Libraries, Windows environments, and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Introduction to GitHub Arm-hosted Runners and Automate
+      the Build of Windows Applications.
+# END generated_summary_faq
+
 author: 
     - Pareena Verma
 
diff --git a/content/learning-paths/laptops-and-desktops/hyper-v/_index.md b/content/learning-paths/laptops-and-desktops/hyper-v/_index.md
index 18fa19c149..1a09ea6de1 100644
--- a/content/learning-paths/laptops-and-desktops/hyper-v/_index.md
+++ b/content/learning-paths/laptops-and-desktops/hyper-v/_index.md
@@ -15,6 +15,41 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 829130636ec6969f791826ef731b38f7bb87c025d910218822a113ecdef62306
+  summary: >-
+    Learn how to create and manage Arm-based Linux virtual machines using Hyper-V on Windows on
+    Arm devices. It is designed for software developers who want to use Linux virtual machines
+    with Windows on Arm devices. By the end, you will be able to create Arm-based Linux virtual
+    machines using Hyper-V. It focuses on tools and technologies such as Hyper-V, Windows and
+    Linux environments, and Arm platforms including Cortex-A. The main steps cover Create a Linux
+    virtual machine using Hyper-V.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create Arm-based Linux virtual machines using Hyper-V. Learn how to create and
+      manage Arm-based Linux virtual machines using Hyper-V on Windows on Arm devices.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to use Linux virtual machines
+      with Windows on Arm devices.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows on Arm computer such as the
+      Lenovo Thinkpad X13s running Windows 11 with [Hyper-V](/install-guides/hyper-v/) installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Hyper-V, Windows and Linux environments, and Arm
+      platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Create a Linux virtual machine using Hyper-V.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
@@ -41,3 +76,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/intro/_index.md b/content/learning-paths/laptops-and-desktops/intro/_index.md
index 884811bf22..7bead73e66 100644
--- a/content/learning-paths/laptops-and-desktops/intro/_index.md
+++ b/content/learning-paths/laptops-and-desktops/intro/_index.md
@@ -16,6 +16,41 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 5a5e4437a7e71182ede45ee091ae49117446fd1c34b02be92df75a41f4fd1f0d
+  summary: >-
+    Learn where the Arm architecture is used in desktop and laptop computers and find hardware
+    for software development on Arm platforms. It is designed for developers working on laptops
+    and desktops and new to the Arm architecture. By the end, you will be able to understand where
+    the Arm architecture is used in desktop and laptop computers and find desktop and laptop hardware
+    to use for software development. It focuses on Linux, Windows, and ChromeOS environments.
+    The main steps cover Arm in Laptops and Desktops and Find Arm hardware.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand where the Arm architecture is used in desktop and laptop computers and
+      find desktop and laptop hardware to use for software development. Learn where the Arm architecture
+      is used in desktop and laptop computers and find hardware for software development on Arm
+      platforms.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers working on laptops and desktops and new to
+      the Arm architecture.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Nothing.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers Linux, Windows, and ChromeOS environments.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Arm in Laptops and Desktops and Find Arm hardware.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 
@@ -43,3 +78,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md b/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md
index 1cb93acc24..433f3a7025 100644
--- a/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md
+++ b/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md
@@ -19,6 +19,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 3e93048b46c24514a89ccc2f277b53111a419c049b53662e1461be38a22e83f7
+  summary: >-
+    Learn how to build, test, and verify KleidiCV with Scalable Matrix Extensions (SME) on Apple
+    Silicon Macs for accelerated computer vision performance. It is designed for software developers
+    who want to build and test KleidiCV on macOS. By the end, you will be able to install and
+    compile KleidiCV on macOS, run KleidiCV example tests, and enable Scalable Matrix Extensions
+    (SME) and verify increased SME performance. It focuses on tools and technologies such as KleidiCV
+    and C, macOS environments, and Arm platforms including Cortex-A. The main steps cover Download
+    and build KleidiCV software and Test KleidiCV and verify SME backend support.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install and compile KleidiCV on macOS, run KleidiCV example tests, and enable Scalable
+      Matrix Extensions (SME) and verify increased SME performance. Learn how to build, test,
+      and verify KleidiCV with Scalable Matrix Extensions (SME) on Apple Silicon Macs for accelerated
+      computer vision performance.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to build and test KleidiCV
+      on macOS.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Mac with Apple Silicon (M4 generation
+      or newer); Xcode command line tools installed; Basic familiarity with using the Terminal
+      and command-line tools.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including KleidiCV and C, macOS environments, and Arm platforms
+      such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Download and build KleidiCV software and Test KleidiCV
+      and verify SME backend support.
+# END generated_summary_faq
+
 author: Jett Zhou
 
 ### Tags
@@ -54,3 +94,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md b/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md
index f730d83392..cf3114d47c 100644
--- a/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md
+++ b/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md
@@ -16,6 +16,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 631134875aac73e168b60b86d1ca7b4e898196f98cb2825a4238f62129d2d862
+  summary: >-
+    Learn how to configure the LLVM toolchain with Visual Studio to build native Windows on Arm
+    applications using the open-source PuTTY project. It is designed for software developers doing
+    native development on Windows on Arm computers. By the end, you will be able to configure
+    the native LLVM toolchain with Visual Studio to compile for Windows on Arm and build open-source
+    PuTTY application for Windows on Arm using the native LLVM toolchain. It focuses on tools
+    and technologies such as LLVM and Visual Studio Code, Windows environments, and Arm platforms
+    including Cortex-A. The main steps cover Build a native windows application using LLVM for
+    Windows on Arm.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will configure the native LLVM toolchain with Visual Studio to compile for Windows on
+      Arm and build open-source PuTTY application for Windows on Arm using the native LLVM toolchain.
+      Learn how to configure the LLVM toolchain with Visual Studio to build native Windows on
+      Arm applications using the open-source PuTTY project.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers doing native development on Windows
+      on Arm computers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows on Arm computer such as the
+      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including LLVM and Visual Studio Code, Windows environments,
+      and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Build a native windows application using LLVM for
+      Windows on Arm.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
@@ -46,3 +86,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md b/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md
index 52bce4f37e..e3a38ab113 100644
--- a/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md
+++ b/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md
@@ -18,6 +18,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 87d4afe4ce7f0cef113cd61fd712fde073cca0eaafbe86a2066b76a117328d11
+  summary: >-
+    Learn how to apply Arm Memory Tagging Extension (MTE) to protect dynamic memory allocations
+    and prevent common memory use errors. It is designed for software developers who want to learn
+    how to use the Memory Tagging Extension (MTE) to protect dynamic memory allocations. By the
+    end, you will be able to learn how to apply MTE to an existing memory allocator and understand
+    how MTE can prevent common memory use errors. It focuses on tools and technologies such as
+    MTE, Linux, and C, Linux environments, and Arm platforms including Cortex-A. The main steps
+    cover Why Use Memory Tagging?, Implement Memory Tagging for a Dynamic Memory Allocator, Memory
+    Tagging Changes, Preventing Mistakes By Using Memory Tagging, and Memory Tagged Allocation
+    Summary.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will learn how to apply MTE to an existing memory allocator and understand how MTE can
+      prevent common memory use errors. Learn how to apply Arm Memory Tagging Extension (MTE)
+      to protect dynamic memory allocations and prevent common memory use errors.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers who want to learn how to use the Memory
+      Tagging Extension (MTE) to protect dynamic memory allocations.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Linux computer.; Basic knowledge of
+      how MTE works. Refer to the [Learn about Memory Tagging Extension Learning Path](/learning-paths/mobile-graphics-and-gaming/mte/);
+      Knowledge of how a dynamic memory allocator can be implemented. Refer to [Write a Dynamic
+      Memory Allocator Learning Path](/learning-paths/cross-platform/dynamic-memory-allocator/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including MTE, Linux, and C, Linux environments, and Arm platforms
+      such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Why Use Memory Tagging?, Implement Memory Tagging
+      for a Dynamic Memory Allocator, Memory Tagging Changes, Preventing Mistakes By Using Memory
+      Tagging, and Memory Tagged Allocation Summary.
+# END generated_summary_faq
+
 author: David Spickett
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md b/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
index eabeb91a07..3e287ebf5b 100644
--- a/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
+++ b/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
@@ -18,6 +18,45 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: e1befed4cafab0eaee29a31c2f259a6a90a14d9f8230c570b6c10a9840b761d5
+  summary: >-
+    Learn how to install and configure Arch Linux for Arm with the i3 window manager and Neovim
+    editor on the Pinebook Pro laptop. It is designed for developers who want to use the Pinebook
+    Pro as an Arm Linux development machine. By the end, you will be able to install and configure
+    Arch Linux for Arm, install and configure the i3 window manager, and install and configure
+    the Neovim editor. It focuses on tools and technologies such as i3, Alacritty, and Neovim,
+    Linux environments, and Arm platforms including Cortex-A72 and Cortex-A53. The main steps
+    cover How do I install Arch Linux?, How do you install the i3 Windows Manager?, and How do
+    I install and configure Neovim?
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install and configure Arch Linux for Arm, install and configure the i3 window manager,
+      and install and configure the Neovim editor. Learn how to install and configure Arch Linux
+      for Arm with the i3 window manager and Neovim editor on the Pinebook Pro laptop.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for developers who want to use the Pinebook Pro as an Arm Linux
+      development machine.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Pinebook Pro laptop; A microSD card
+      (8GB or greater; class 10 or faster).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including i3, Alacritty, and Neovim, Linux environments, and
+      Arm platforms such as Cortex-A72 and Cortex-A53.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around How do I install Arch Linux?, How do you install the
+      i3 Windows Manager?, and How do I install and configure Neovim?
+# END generated_summary_faq
+
 author: Gabriel Peterson
 
 ### Tags
@@ -54,3 +93,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md b/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
index 18e1a1e0be..4a7076f778 100644
--- a/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
+++ b/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
@@ -19,6 +19,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: aa2a78baf3e52172e37506c3f75254968d775b4eb516f9696a0a6998aba50e97
+  summary: >-
+    Learn how to fine-tune large language models using PyTorch and Hugging Face on NVIDIA DGX
+    Spark to improve domain-specific accuracy. It is designed for AI developers and ML engineers
+    who want to fine-tune large language models using PyTorch and Hugging Face on the NVIDIA DGX
+    Spark platform. By the end, you will be able to understand how fine-tuning teaches a model
+    domain-specific knowledge, prepare a custom JSONL dataset for supervised fine-tuning, and
+    fine-tune Llama 3.2 3B on Raspberry Pi datasheet content using PyTorch and Hugging Face. It
+    focuses on tools and technologies such as Python, PyTorch, Docker, and Hugging Face, Linux
+    environments, and Arm platforms including Cortex-A and Neoverse. The main steps cover Set
+    up your NVIDIA DGX Spark, Understand fine-tuning, Fine-tune a model with PyTorch and Hugging
+    Face, and Test your fine-tuned model with vLLM.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand how fine-tuning teaches a model domain-specific knowledge, prepare a
+      custom JSONL dataset for supervised fine-tuning, and fine-tune Llama 3.2 3B on Raspberry
+      Pi datasheet content using PyTorch and Hugging Face. Learn how to fine-tune large language
+      models using PyTorch and Hugging Face on NVIDIA DGX Spark to improve domain-specific accuracy.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for AI developers and ML engineers who want to fine-tune large
+      language models using PyTorch and Hugging Face on the NVIDIA DGX Spark platform.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Hugging Face account and access token;
+      NVIDIA DGX Spark workstation.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Python, PyTorch, Docker, and Hugging Face, Linux
+      environments, and Arm platforms such as Cortex-A and Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Set up your NVIDIA DGX Spark, Understand fine-tuning,
+      Fine-tune a model with PyTorch and Hugging Face, and Test your fine-tuned model with vLLM.
+# END generated_summary_faq
+
 author: Michael Hall
 
 ### Tags
@@ -67,3 +109,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md b/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
index c034b6a5a4..a7c8ac56bd 100644
--- a/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
+++ b/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
@@ -19,6 +19,47 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: a851b2f81b6aac54aef84acf7537a1cc6b99f66ce31ffd26631d6a966401e4ed
+  summary: >-
+    Learn how to create a CI/CD pipeline in GitHub using self-hosted Arm64 runners to build and
+    push Docker images to DockerHub. It is designed for software developers and IT practitioners
+    who want to learn how to use GitHub Actions for CI/CD purposes. By the end, you will be able
+    to create a CI/CD pipeline in GitHub, use a self-hosted runner, and build and push the Docker
+    image to DockerHub. It focuses on tools and technologies such as .NET and Visual Studio Code,
+    Linux environments, and Arm platforms including Cortex-A. The main steps cover Background:
+    GitHub Actions and CI/CD, Further Context, Setting up the DockerHub Repository, Prepare GitHub
+    Repository, and Prepare the runner.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create a CI/CD pipeline in GitHub, use a self-hosted runner, and build and push
+      the Docker image to DockerHub. Learn how to create a CI/CD pipeline in GitHub using self-hosted
+      Arm64 runners to build and push Docker images to DockerHub.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is for software developers and IT practitioners who want to learn how
+      to use GitHub Actions for CI/CD purposes.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm64-powered machine, either virtual
+      or physical. This Learning Path demonstration uses an Arm64-powered VM with Ubuntu 22.04.;
+      A DockerHub account. You can [set up a free DockerHub account](https://hub.docker.com/signup).;
+      A GitHub account. You can [sign up for GitHub](https://github.com/signup).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including .NET and Visual Studio Code, Linux environments,
+      and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Background: GitHub Actions and CI/CD, Further Context,
+      Setting up the DockerHub Repository, Prepare GitHub Repository, and Prepare the runner.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
@@ -52,3 +93,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/_index.md b/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
index 9a15c3b1ff..fd6ca8af2d 100644
--- a/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
@@ -16,6 +16,44 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: d24bf30aef690451942789bb66df63b7a3483ecc3cd2c4b3e60ce15fad90cb91
+  summary: >-
+    Learn how to build the OpenCV library for Windows on Arm devices and develop computer vision
+    applications using OpenCV. It is designed for software developers who want to build and develop
+    applications on Windows on Arm devices using OpenCV. By the end, you will be able to build
+    the OpenCV library for Windows on Arm devices and develop applications using OpenCV. It focuses
+    on tools and technologies such as Visual Studio, Clang, OpenCV, and CPP, Windows environments,
+    and Arm platforms including Cortex-A. The main steps cover OpenCV and Compilers for Windows
+    on Arm, Setup, Build OpenCV Applications with Clang, and Build OpenCV Applications with MSVC.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build the OpenCV library for Windows on Arm devices and develop applications using
+      OpenCV. Learn how to build the OpenCV library for Windows on Arm devices and develop computer
+      vision applications using OpenCV.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers who want to build and develop applications
+      on Windows on Arm devices using OpenCV.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows on Arm machine such as the
+      Lenovo Thinkpad X13s, or an [Azure virtual machine](/learning-paths/cross-platform/woa_azure/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Visual Studio, Clang, OpenCV, and CPP, Windows environments,
+      and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around OpenCV and Compilers for Windows on Arm, Setup, Build
+      OpenCV Applications with Clang, and Build OpenCV Applications with MSVC.
+# END generated_summary_faq
+
 author: Koki Mitsunami
 
 ### Tags
@@ -54,3 +92,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md b/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md
index 566f02f323..5ce63e889d 100644
--- a/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md
@@ -18,6 +18,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: fc0d79605640cc8e7a36070a044323d6e278335484949921d0aa4e9cd163d4bd
+  summary: >-
+    Learn how to measure application resource usage, benchmark video encoding tasks, and monitor
+    CPU, memory, and power consumption on Windows on Arm using FFmpeg and PowerShell. It is designed
+    for developers who want to measure resource usage of applications on Windows on Arm devices
+    using FFmpeg. By the end, you will be able to measure application resource usage using FFmpeg
+    and PowerShell, benchmark a video encoding task, and monitor CPU, memory, and power consumption
+    during a video decode task. It focuses on tools and technologies such as FFmpeg and PowerShell,
+    Windows environments, and Arm platforms including Cortex-A. The main steps cover Set up FFmpeg
+    and encode a test video, Track system resource usage on Windows on Arm with PowerShell, and
+    Measure power usage on Windows on Arm with PowerShell.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will measure application resource usage using FFmpeg and PowerShell, benchmark a video
+      encoding task, and monitor CPU, memory, and power consumption during a video decode task.
+      Learn how to measure application resource usage, benchmark video encoding tasks, and monitor
+      CPU, memory, and power consumption on Windows on Arm using FFmpeg and PowerShell.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers who want to measure resource usage of applications
+      on Windows on Arm devices using FFmpeg.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows on Arm computer such as the
+      Lenovo Thinkpad X13s running Windows 11; A code editor such as [Visual Studio Code for Windows
+      on Arm](https://code.visualstudio.com/docs/?dv=win32arm64user).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including FFmpeg and PowerShell, Windows environments, and
+      Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Set up FFmpeg and encode a test video, Track system
+      resource usage on Windows on Arm with PowerShell, and Measure power usage on Windows on
+      Arm with PowerShell.
+# END generated_summary_faq
+
 author: Ruifeng Wang
 
 ### Tags
@@ -55,3 +98,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md b/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
index b44d9ce777..f13f6f3e32 100644
--- a/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
@@ -18,6 +18,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 915f6eb5e95bd42ed09b727b4599855d965125e67a8761fbd48a124e9e74b1bc
+  summary: >-
+    Learn how to automate Windows on Arm VM creation on Arm Linux systems using QEMU, KVM, and
+    Bash scripts for development and testing. It is designed for developers and system administrators
+    who want to automate Windows on Arm virtual machine (VM) creation on Arm Linux systems using
+    QEMU and KVM. By the end, you will be able to understand the process of creating a Windows
+    on Arm virtual machine using Bash scripts, run scripts for VM creation and management, and
+    troubleshoot common VM setup and runtime issues. It focuses on tools and technologies such
+    as QEMU, KVM, Bash, and RDP, Linux and Windows environments, and Arm platforms including Neoverse
+    and Cortex-A. The main steps cover Check system requirements, Understand and customize Windows
+    on Arm VM automation scripts, Create a Windows on Arm virtual machine, and Run a Windows on
+    Arm virtual machine.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand the process of creating a Windows on Arm virtual machine using Bash
+      scripts, run scripts for VM creation and management, and troubleshoot common VM setup and
+      runtime issues. Learn how to automate Windows on Arm VM creation on Arm Linux systems using
+      QEMU, KVM, and Bash scripts for development and testing.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers and system administrators who want to automate
+      Windows on Arm virtual machine (VM) creation on Arm Linux systems using QEMU and KVM.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm Linux system with KVM support
+      and a minimum of 8GB RAM and 50GB free disk space.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including QEMU, KVM, Bash, and RDP, Linux and Windows environments,
+      and Arm platforms such as Neoverse and Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Check system requirements, Understand and customize
+      Windows on Arm VM automation scripts, Create a Windows on Arm virtual machine, and Run a
+      Windows on Arm virtual machine.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
@@ -51,3 +94,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md b/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
index f6f7d155ca..c10d4af748 100644
--- a/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
@@ -16,6 +16,44 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 4069c5bce1ce4b689a7a67d740fc077dc55c9b0bfbab392f5984ba0bdd9e59c3
+  summary: >-
+    Learn how to build native Arm applications and migrate x86/x64 applications to Arm using Arm64EC
+    on Windows on Arm devices. It is designed for software developers who want to use Arm64EC
+    with Windows on Arm devices. By the end, you will be able to build native Arm applications
+    and migrate x86 or x64 applications to Arm using Arm64EC and compare the performance of a
+    simple application using different build configurations. It focuses on tools and technologies
+    such as Arm64EC and Visual Studio, Windows environments, and Arm platforms including Cortex-A.
+    The main steps cover Build an application on Windows 11 using Arm64EC.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build native Arm applications and migrate x86 or x64 applications to Arm using
+      Arm64EC and compare the performance of a simple application using different build configurations.
+      Learn how to build native Arm applications and migrate x86/x64 applications to Arm using
+      Arm64EC on Windows on Arm devices.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to use Arm64EC with Windows
+      on Arm devices.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows on Arm computer such as the
+      Lenovo Thinkpad X13s running Windows 11.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Arm64EC and Visual Studio, Windows environments,
+      and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Build an application on Windows 11 using Arm64EC.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
@@ -42,3 +80,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md b/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
index 7a412418ef..6d34a4b1c4 100644
--- a/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
@@ -20,6 +20,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 3b3ecd451ed8b634c7fbe194248cdf1d33432633efbcbef5de713495041ff425
+  summary: >-
+    Learn how to port Qt-based Python desktop applications with C/C++ dependencies to Arm64 using
+    Arm64EC on Windows on Arm. It is designed for developers who want to learn how to port their
+    applications to Arm64 using Arm64EC. By the end, you will be able to build a Qt-based Python
+    desktop application, create C/C++ dependencies and use them in the Qt-based Python app, and
+    learn how to port the C/C++ based dependencies to Arm64 using Arm64EC. It focuses on tools
+    and technologies such as C, CPP, and Qt, Windows environments, and Arm platforms including
+    Cortex-A. The main steps cover Application, Porting using CMake, and Porting using MSBuild.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build a Qt-based Python desktop application, create C/C++ dependencies and use
+      them in the Qt-based Python app, and learn how to port the C/C++ based dependencies to Arm64
+      using Arm64EC. Learn how to port Qt-based Python desktop applications with C/C++ dependencies
+      to Arm64 using Arm64EC on Windows on Arm.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers who want to learn how to port their applications
+      to Arm64 using Arm64EC.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows on Arm computer such as the
+      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
+      Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user)
+      is suitable.; Visual Studio 2022 with Arm build tools. [Refer to this guide for the installation
+      steps](https://developer.arm.com/documentation/102528/0100/Install-Visual-Studio).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including C, CPP, and Qt, Windows environments, and Arm platforms
+      such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Application, Porting using CMake, and Porting using
+      MSBuild.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
@@ -55,3 +97,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md b/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md
index c754b50a5c..9a2b4c2e2a 100644
--- a/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md
@@ -17,6 +17,44 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 63389742eced4df89f85bdf56a01e489e52a9702d446b557f8f55312f9d31f20
+  summary: >-
+    Learn how to build and run Qt-based desktop applications on Windows on Arm and investigate
+    native Arm64 performance improvements. It is designed for software developers who want to
+    use the native performance of the Qt framework for building desktop applications on Windows
+    on Arm (WoA). By the end, you will be able to build and run a Qt-based desktop application
+    and investigate performance improvements gained by running on Arm64. It focuses on tools and
+    technologies such as C, CPP, and Qt, Windows environments, and Arm platforms including Cortex-A.
+    The main steps cover Qt application.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build and run a Qt-based desktop application and investigate performance improvements
+      gained by running on Arm64. Learn how to build and run Qt-based desktop applications on
+      Windows on Arm and investigate native Arm64 performance improvements.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to use the native performance
+      of the Qt framework for building desktop applications on Windows on Arm (WoA).
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows on Arm computer such as the
+      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
+      [Qt framework](https://www.qt.io/) or [Qt for Open Source Development](https://www.qt.io/download-open-source).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including C, CPP, and Qt, Windows environments, and Arm platforms
+      such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Qt application.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
@@ -48,3 +86,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md b/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
index 2cda7edd89..9401fca61b 100644
--- a/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
@@ -19,6 +19,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: e60ce02e9d1872da06bc5bfeb18c7ec65f2bc17defb2b75292f930fb3ad41711
+  summary: >-
+    Learn how to build and run an ASP.NET Core 8 web server application with Web API and dependency
+    injection services on Windows on Arm. It is designed for developers who are interested in
+    building a web server for a headless IoT applications. By the end, you will be able to build
+    and run an ASP.NET Core 8 application, create a Web API, and create and use services using
+    the dependency injection. It focuses on tools and technologies such as .NET and Visual Studio
+    Code, Windows environments, and Arm platforms including Cortex-A. The main steps cover Create
+    a ASP.NET Core Web API project and Build, run, and access the web server.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build and run an ASP.NET Core 8 application, create a Web API, and create and use
+      services using the dependency injection. Learn how to build and run an ASP.NET Core 8 web
+      server application with Web API and dependency injection services on Windows on Arm.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for developers who are interested in building a web server for
+      a headless IoT applications.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows on Arm computer such as the
+      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
+      .NET 8 SDK for [arm64](https://dotnet.microsoft.com/en-us/download/dotnet/thank-you/sdk-8.0.100-windows-arm64-installer).;
+      Any code editor, we recommend using [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including .NET and Visual Studio Code, Windows environments,
+      and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Create a ASP.NET Core Web API project and Build, run,
+      and access the web server.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
@@ -53,3 +93,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
index 227fbec10c..e88e8b3f2c 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
@@ -18,6 +18,47 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: cddfb7b83e82f0daa513558b1e7ee09b55c63e2ff95675d67be7d4408d391aa4
+  summary: >-
+    Learn how to create Node.js IoT applications that stream sensor data from Windows on Arm devices
+    to AWS IoT Core using MQTT. It is designed for developers who want to learn how to create
+    IoT applications using Windows on Arm and AWS IoT Core. By the end, you will be able to create
+    a Node.js that streams synthesized sensor data to AWS cloud, register a device in AWS IoT
+    Core, and send data from a device to AWS IoT Core. It focuses on tools and technologies such
+    as Node.js and Visual Studio, Windows environments, and Arm platforms including Cortex-A.
+    The main steps cover AWS IoT Core, Connect the emulator to AWS IoT Core and stream data, and
+    Testing the data stream.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create a Node.js that streams synthesized sensor data to AWS cloud, register a
+      device in AWS IoT Core, and send data from a device to AWS IoT Core. Learn how to create
+      Node.js IoT applications that stream sensor data from Windows on Arm devices to AWS IoT
+      Core using MQTT.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This learning path is for developers who want to learn how to create IoT applications using
+      Windows on Arm and AWS IoT Core.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows-on-Arm computer such as the
+      Lenovo Thinkpad X13s running Windows 11 or a Windows-on-Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
+      Any code editor. Visual Studio Code is suitable.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Node.js and Visual Studio, Windows environments,
+      and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around AWS IoT Core, Connect the emulator to AWS IoT Core
+      and stream data, and Testing the data stream.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
@@ -48,3 +89,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md
index c8d059813e..5da7ef0b27 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md
@@ -19,6 +19,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: f25597c6c9e69e09e9a86fa7c02d0ace9c347f293a21a11c00a6d3498300052c
+  summary: >-
+    Learn how to configure AWS IoT Core rules to parse MQTT messages and store IoT data in Amazon
+    DynamoDB from Windows on Arm devices. It is designed for developers who are interested in
+    using Amazon DynamoDB as a database for storing data. By the end, you will be able to gain
+    familiarity with Amazon DynamoDB, be able to run the IoT application that streams data to
+    AWS IoT Core, and be able to create the rule that parses messages from AWS IoT Core and writes
+    them to DynamoDB. It focuses on tools and technologies such as .NET and Visual Studio Code,
+    Windows environments, and Arm platforms including Cortex-A. The main steps cover Background
+    and Create a ASP.NET Core Web API project.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will gain familiarity with Amazon DynamoDB, be able to run the IoT application that
+      streams data to AWS IoT Core, and be able to create the rule that parses messages from AWS
+      IoT Core and writes them to DynamoDB. Learn how to configure AWS IoT Core rules to parse
+      MQTT messages and store IoT data in Amazon DynamoDB from Windows on Arm devices.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for developers who are interested in using Amazon DynamoDB as
+      a database for storing data.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows on Arm computer such as the
+      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
+      Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user)
+      is suitable.; Completion of the [Create IoT applications with Windows on Arm and AWS IoT
+      Core](/learning-paths/laptops-and-desktops/win_aws_iot/) Learning Path.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including .NET and Visual Studio Code, Windows environments,
+      and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Background and Create a ASP.NET Core Web API project.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
@@ -53,3 +95,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md
index 3b4ace477f..9fd53c4cc6 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md
@@ -20,6 +20,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: f685f45be9e5fc05b278e28590f9d421a920d43cc36ccb572545de4eaf4a799a
+  summary: >-
+    Learn how to process IoT data using AWS Lambda functions triggered by AWS IoT Core messages
+    from Windows on Arm devices. It is designed for developers who are interested in using AWS
+    Lambda for processing data streamed by IoT applications and devices. By the end, you will
+    be able to describe how to use AWS Lambda for IoT applications running on Arm64, process data
+    from IoT devices, and describe the serverless compute services in AWS. It focuses on tools
+    and technologies such as .NET and Visual Studio Code, Windows environments, and Arm platforms
+    including Cortex-A. The main steps cover Overview of Learning Path, Background, Create a Rule,
+    Implement Lambda Function, and Summary.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will describe how to use AWS Lambda for IoT applications running on Arm64, process data
+      from IoT devices, and describe the serverless compute services in AWS. Learn how to process
+      IoT data using AWS Lambda functions triggered by AWS IoT Core messages from Windows on Arm
+      devices.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for developers who are interested in using AWS Lambda for processing
+      data streamed by IoT applications and devices.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows on Arm computer such as the
+      a Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
+      Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user)
+      is suitable.; Completion of the [Create IoT applications with Windows on Arm and AWS IoT
+      Core](/learning-paths/laptops-and-desktops/win_aws_iot/) Learning Path.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including .NET and Visual Studio Code, Windows environments,
+      and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview of Learning Path, Background, Create a Rule,
+      Implement Lambda Function, and Summary.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
@@ -54,3 +97,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md
index a31da07551..31c04522d7 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md
@@ -18,6 +18,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: f5a93346a0fd55659b7c0a6df501db97742ec71755b6443051b654f3ce871cdf
+  summary: >-
+    Learn how to implement AWS Lambda functions that process and aggregate IoT data stored in
+    DynamoDB tables from Windows on Arm devices. It is designed for developers who are interested
+    in using AWS Lambda for processing data stored in DynamoDB. By the end, you will be able to
+    implement an AWS Lambda function that processes data stored in a DynamoDB table and learn
+    how to work with DynamoDB to scan and aggregate records. It focuses on tools and technologies
+    such as Node.js and Visual Studio Code, Windows environments, and Arm platforms including
+    Cortex-A. The main steps cover Background, Create the AWS Lambda Function, Implement the AWS
+    Lambda Function, and Test Lambda Function.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will implement an AWS Lambda function that processes data stored in a DynamoDB table
+      and learn how to work with DynamoDB to scan and aggregate records. Learn how to implement
+      AWS Lambda functions that process and aggregate IoT data stored in DynamoDB tables from
+      Windows on Arm devices.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for developers who are interested in using AWS Lambda for processing
+      data stored in DynamoDB.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows on Arm computer such as the
+      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
+      Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user)
+      is suitable.; Completion of the [Create IoT applications with Windows on Arm and AWS IoT
+      Core](/learning-paths/laptops-and-desktops/win_aws_iot/) Learning Path.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Node.js and Visual Studio Code, Windows environments,
+      and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Background, Create the AWS Lambda Function, Implement
+      the AWS Lambda Function, and Test Lambda Function.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
@@ -52,3 +95,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md
index 2b9d85a305..5ddbb77140 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md
@@ -18,6 +18,47 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 32186a4879e98aa113f461d2a2c705dee099404ed2020ef6fdb981a28bb0c0c3
+  summary: >-
+    Learn how to create a static website hosted on Amazon S3 that interacts with AWS Lambda functions
+    to display IoT data from Windows on Arm devices. It is designed for developers who are interested
+    in using Amazon Web Services (AWS) S3 for hosting their IoT websites. By the end, you will
+    be able to gain familiarity with Amazon S3 and create a static website that interacts with
+    AWS Lambda. It focuses on tools and technologies such as Node.js and Visual Studio Code, Windows
+    environments, and Arm platforms including Cortex-A. The main steps cover Background, Static
+    website, Add AWS Lambda Endpoint, and Deploy website to Amazon S3.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will gain familiarity with Amazon S3 and create a static website that interacts with
+      AWS Lambda. Learn how to create a static website hosted on Amazon S3 that interacts with
+      AWS Lambda functions to display IoT data from Windows on Arm devices.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for developers who are interested in using Amazon Web Services
+      (AWS) S3 for hosting their IoT websites.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows on Arm computer such as the
+      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
+      Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user)
+      is suitable.; Completion of the [Use AWS Lambda for IoT applications](/learning-paths/laptops-and-desktops/win_aws_iot_lambda/)
+      Learning Path.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Node.js and Visual Studio Code, Windows environments,
+      and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Background, Static website, Add AWS Lambda Endpoint,
+      and Deploy website to Amazon S3.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
@@ -52,3 +93,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/win_cef/_index.md b/content/learning-paths/laptops-and-desktops/win_cef/_index.md
index 1a5c50d769..b78abd0050 100644
--- a/content/learning-paths/laptops-and-desktops/win_cef/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_cef/_index.md
@@ -17,6 +17,44 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 5df8cc6d859c505ad79f8297056b06233956c92203a6d2352d9be8e498a42547
+  summary: >-
+    Learn how to create and build Chromium Embedded Framework desktop applications using CMake
+    and web technologies on Windows on Arm. It is designed for developers who want to learn how
+    to use web technologies for developing Desktop apps on Windows on Arm (WoA). By the end, you
+    will be able to create and build a Chromium Embedded Framework project using CMake and modify
+    and style the application. It focuses on tools and technologies such as CPP, CMake, HTML,
+    JavaScript, and CSS, Windows environments, and Arm platforms including Cortex-A. The main
+    steps cover Create a Chromium Embedded Framework Project.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create and build a Chromium Embedded Framework project using CMake and modify and
+      style the application. Learn how to create and build Chromium Embedded Framework desktop
+      applications using CMake and web technologies on Windows on Arm.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This learning path is for developers who want to learn how to use web technologies for developing
+      Desktop apps on Windows on Arm (WoA).
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows on Arm computer such as the
+      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
+      Visual Studio 2022.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including CPP, CMake, HTML, JavaScript, and CSS, Windows environments,
+      and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Create a Chromium Embedded Framework Project.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
@@ -50,3 +88,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/win_forms/_index.md b/content/learning-paths/laptops-and-desktops/win_forms/_index.md
index 3b9259b4f0..0d094690dd 100644
--- a/content/learning-paths/laptops-and-desktops/win_forms/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_forms/_index.md
@@ -17,6 +17,45 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: d43413097704af29b9233dfe33fb675ffd5c6d5a172154d6a7542e77d6625c00
+  summary: >-
+    Learn how to create and build Windows Forms applications and measure code execution performance
+    on Arm64. It is designed for developers who want to learn how to create Windows Forms applications
+    on Windows on Arm (WoA). By the end, you will be able to create and build a Windows Forms
+    application and measure code execution performance on Arm64. It focuses on tools and technologies
+    such as Windows Forms, C#, and .NET, Windows environments, and Arm platforms including Cortex-A.
+    The main steps cover Create an application using Windows Forms and Compare the performance
+    results.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create and build a Windows Forms application and measure code execution performance
+      on Arm64. Learn how to create and build Windows Forms applications and measure code execution
+      performance on Arm64.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This learning path is for developers who want to learn how to create Windows Forms applications
+      on Windows on Arm (WoA).
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows on Arm computer such as the
+      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
+      Visual Studio 2022 with .NET Desktop Development workload.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Windows Forms, C#, and .NET, Windows environments,
+      and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Create an application using Windows Forms and Compare
+      the performance results.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
@@ -48,3 +87,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/win_net/_index.md b/content/learning-paths/laptops-and-desktops/win_net/_index.md
index 47350d4c40..f5a1c0372b 100644
--- a/content/learning-paths/laptops-and-desktops/win_net/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_net/_index.md
@@ -15,6 +15,44 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 9cc8f77f98bc8f4afb7b77344e42615e420be421f85583f9fc4f5ec76516e6eb
+  summary: >-
+    Learn how to build and run a .NET 6 Windows Presentation Foundation (WPF) application on Windows
+    on Arm machines. It is designed for software developers doing native development on Windows
+    on Arm computers. By the end, you will be able to build and run a .NET 6 Windows Presentation
+    Foundation (WPF) application on a Windows on Arm machine. It focuses on tools and technologies
+    such as .NET and Visual Studio, Windows environments, and Arm platforms including Cortex-A.
+    The main steps cover Build a native windows application using .NET 6 framework for Windows
+    on Arm.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build and run a .NET 6 Windows Presentation Foundation (WPF) application on a Windows
+      on Arm machine. Learn how to build and run a .NET 6 Windows Presentation Foundation (WPF)
+      application on Windows on Arm machines.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers doing native development on Windows
+      on Arm computers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows on Arm computer such as the
+      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including .NET and Visual Studio, Windows environments, and
+      Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Build a native windows application using .NET 6 framework
+      for Windows on Arm.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
@@ -45,3 +83,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/win_net8/_index.md b/content/learning-paths/laptops-and-desktops/win_net8/_index.md
index e31f6de8f4..76c4bf8ad6 100644
--- a/content/learning-paths/laptops-and-desktops/win_net8/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_net8/_index.md
@@ -19,6 +19,47 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 218fd5b33e104360d5de9f632f9338e2f24041ea70f7a01fad0af6be2a11619c
+  summary: >-
+    Learn how to build, run, and benchmark .NET 8 Console applications to measure performance
+    on Windows on Arm devices. It is designed for developers who want to benchmark the performance
+    of the .NET 8 applications on Windows on Arm (WoA). By the end, you will be able to build
+    and run .NET 8 Console Applications, benchmark .NET applications, and implement custom performance
+    benchmarks. It focuses on tools and technologies such as .NET, Visual Studio, and Visual Studio
+    Code, Windows environments, and Arm platforms including Cortex-A. The main steps cover Measuring
+    performance of the .NET apps and Custom benchmarks.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build and run .NET 8 Console Applications, benchmark .NET applications, and implement
+      custom performance benchmarks. Learn how to build, run, and benchmark .NET 8 Console applications
+      to measure performance on Windows on Arm devices.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This learning path is for developers who want to benchmark the performance of the .NET 8
+      applications on Windows on Arm (WoA).
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows on Arm computer such as the
+      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
+      .NET 8 SDK for [x64](https://dotnet.microsoft.com/en-us/download/dotnet/thank-you/sdk-8.0.100-windows-x64-installer)
+      and [arm64](https://dotnet.microsoft.com/en-us/download/dotnet/thank-you/sdk-8.0.100-windows-arm64-installer).;
+      Any code editor, we recommend using [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including .NET, Visual Studio, and Visual Studio Code, Windows
+      environments, and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Measuring performance of the .NET apps and Custom
+      benchmarks.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
@@ -62,3 +103,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md b/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md
index 0c7ab00db1..9b1a2448d7 100644
--- a/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md
@@ -17,6 +17,45 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 037509ebca3a7c5a58bef247532919cd8196c7993e946ce519585cdc82073daa
+  summary: >-
+    Learn how to create and build cross-platform .NET MAUI applications and measure code execution
+    performance uplift on Arm64. It is designed for developers who want to learn how to create
+    cross-platform applications with .NET MAUI and leverage performance improvements on Arm64.
+    By the end, you will be able to create and build a .NET MAUI application and measure code
+    execution performance uplift on Arm64. It focuses on tools and technologies such as .NET,
+    C#, and Visual Studio, Windows environments, and Arm platforms including Cortex-A. The main
+    steps cover Create a .NET MAUI Project and Implement the application.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create and build a .NET MAUI application and measure code execution performance
+      uplift on Arm64. Learn how to create and build cross-platform .NET MAUI applications and
+      measure code execution performance uplift on Arm64.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This learning path is for developers who want to learn how to create cross-platform applications
+      with .NET MAUI and leverage performance improvements on Arm64.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows on Arm computer such as the
+      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
+      Visual Studio 2022 with .NET Multi-platform App UI development and Universal Windows Platform
+      development installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including .NET, C#, and Visual Studio, Windows environments,
+      and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Create a .NET MAUI Project and Implement the application.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
@@ -52,3 +91,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md b/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md
index c7efd7058b..a7dc42b292 100644
--- a/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md
@@ -15,6 +15,47 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 606702e7afc9a29976d027eceab021570cf3f91ec408ef2dd2051df0b05d9bda
+  summary: >-
+    Learn how to build ONNX Runtime with the Generate() API and run Phi-3 model inference with
+    KleidiAI acceleration on Windows on Arm. It is designed for developers looking to build ONNX
+    Runtime for Windows on Arm (WoA) and leverage the Generate() API to run Phi-3 inference with
+    KleidiAI acceleration. By the end, you will be able to build ONNX Runtime and enable the Generate()
+    API for Windows on Arm and run inference with a Phi-3 model using ONNX Runtime with KleidiAI
+    acceleration. It focuses on tools and technologies such as Visual Studio, CPP, Python, Git,
+    and CMake, Windows environments, and Arm platforms including Cortex-A. The main steps cover
+    Set up your Environment, Build ONNX Runtime, Build ONNX Runtime Generate() API, and Run Phi3
+    Model.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build ONNX Runtime and enable the Generate() API for Windows on Arm and run inference
+      with a Phi-3 model using ONNX Runtime with KleidiAI acceleration. Learn how to build ONNX
+      Runtime with the Generate() API and run Phi-3 model inference with KleidiAI acceleration
+      on Windows on Arm.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for developers looking to build ONNX Runtime for Windows on Arm
+      (WoA) and leverage the Generate() API to run Phi-3 inference with KleidiAI acceleration.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows on Arm computer such as a
+      Lenovo Thinkpad X13 running Windows 11, or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Visual Studio, CPP, Python, Git, and CMake, Windows
+      environments, and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Set up your Environment, Build ONNX Runtime, Build
+      ONNX Runtime Generate() API, and Run Phi3 Model.
+# END generated_summary_faq
+
 author: Barbara Corriero
 
 ### Tags
@@ -52,3 +93,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md b/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md
index 608a5cbc98..437309a084 100644
--- a/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md
@@ -18,6 +18,50 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: b975cc83674410ec50ca49a31744eee4ac5a63c994dd28e46ed84f7afda0568d
+  summary: >-
+    Learn how to apply Profile-Guided Optimization (PGO) to build performance-tuned C++ binaries
+    and measure improvements using Google Benchmark on Windows on Arm. It is designed for software
+    developers who want to optimize C++ application performance on Windows on Arm using Profile-Guided
+    Optimization (PGO). By the end, you will be able to microbenchmark a function using Google
+    Benchmark, apply profile-guided optimization to build performance-tuned binaries for Windows
+    on Arm, and measure and compare performance improvements from PGO-optimized builds. It focuses
+    on tools and technologies such as C, MSVC, Google Benchmark, and PGO, Windows environments,
+    and Arm platforms including Cortex-A. The main steps cover Understand Profile-Guided Optimization,
+    Understand Google Benchmark basics, Create a baseline benchmark, and Apply Profile-Guided
+    Optimization.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will microbenchmark a function using Google Benchmark, apply profile-guided optimization
+      to build performance-tuned binaries for Windows on Arm, and measure and compare performance
+      improvements from PGO-optimized builds. Learn how to apply Profile-Guided Optimization (PGO)
+      to build performance-tuned C++ binaries and measure improvements using Google Benchmark
+      on Windows on Arm.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to optimize C++ application
+      performance on Windows on Arm using Profile-Guided Optimization (PGO).
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Familiarity with C++ development and
+      compiling programs from the command line; A Windows on Arm machine with [Visual Studio](/install-guides/vs-woa/)
+      and the C++ desktop development tools installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including C, MSVC, Google Benchmark, and PGO, Windows environments,
+      and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Understand Profile-Guided Optimization, Understand
+      Google Benchmark basics, Create a baseline benchmark, and Apply Profile-Guided Optimization.
+# END generated_summary_faq
+
 author: Tom Dunkle
 
 ### Tags
@@ -59,3 +103,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/win_python/_index.md b/content/learning-paths/laptops-and-desktops/win_python/_index.md
index a554eb48ed..f8b81c71ef 100644
--- a/content/learning-paths/laptops-and-desktops/win_python/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_python/_index.md
@@ -18,6 +18,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: d953214fe33ad89a683f79e7c29ce9348e5169e6529b808804329cb35060b431
+  summary: >-
+    Learn how to build Python applications on Windows on Arm and leverage native Arm64 performance
+    for platform-dependent packages. It is designed for developers who are interested in building
+    Python applications on Arm. By the end, you will be able to understand the platform-dependency
+    of Python packages and leverage native Arm64 for Python applications. It focuses on tools
+    and technologies such as Python and Visual Studio Code, Windows environments, and Arm platforms
+    including Cortex-A. The main steps cover Platform-specificity of the Python packages and Build
+    the application.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand the platform-dependency of Python packages and leverage native Arm64
+      for Python applications. Learn how to build Python applications on Windows on Arm and leverage
+      native Arm64 performance for platform-dependent packages.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers who are interested in building Python applications
+      on Arm.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows on Arm computer such as the
+      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
+      Any code editor, we recommend using [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user).;
+      Visual Studio 2022 with Arm build tools. [Refer to this guide for the installation steps](https://developer.arm.com/documentation/102528/0100/Install-Visual-Studio).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Python and Visual Studio Code, Windows environments,
+      and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Platform-specificity of the Python packages and Build
+      the application.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
@@ -48,3 +88,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md b/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md
index 968ba3a4dc..15d5353822 100644
--- a/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md
@@ -18,6 +18,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 055e3a3d8c0dc717e2246e3e8e7ebb00c7d2cea3ff9faabf7125ca1ebfff7a31
+  summary: >-
+    Learn how to configure Windows Sandbox as a self-hosted GitHub Actions runner to build and
+    run .NET 8 WPF applications in CI/CD workflows. It is designed for software developers who
+    are developing applications on Windows on Arm computers. By the end, you will be able to configure
+    Windows Sandbox as a self-hosted GitHub Actions runner and build and run a .NET 8 Windows
+    Presentation Foundation (WPF) application using a self-hosted GitHub Actions runner in your
+    CI/CD workflow. It focuses on tools and technologies such as .NET, Visual Studio, and Windows
+    Sandbox, Windows environments, and Arm platforms including Cortex-A. The main steps cover
+    Configure Windows Sandbox as your GitHub Actions self-hosted Arm64 runner and Build and run
+    the .NET application using the GitHub Actions workflow.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will configure Windows Sandbox as a self-hosted GitHub Actions runner and build and
+      run a .NET 8 Windows Presentation Foundation (WPF) application using a self-hosted GitHub
+      Actions runner in your CI/CD workflow. Learn how to configure Windows Sandbox as a self-hosted
+      GitHub Actions runner to build and run .NET 8 WPF applications in CI/CD workflows.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who are developing applications on
+      Windows on Arm computers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows on Arm computer such as the
+      Lenovo Thinkpad X13s running Windows 11 Version 22H2 which has [Windows Sandbox enabled](/install-guides/windows-sandbox-woa).;
+      A valid [GitHub account](https://github.com/) to complete this Learning Path.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including .NET, Visual Studio, and Windows Sandbox, Windows
+      environments, and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Configure Windows Sandbox as your GitHub Actions self-hosted
+      Arm64 runner and Build and run the .NET application using the GitHub Actions workflow.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
@@ -49,3 +91,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md b/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md
index 0698ae926c..907b3c9a62 100644
--- a/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md
@@ -19,6 +19,43 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: cacf4c6fc3cd3c2a9ab194f7a04981fd4820fca5482317a06c6b9fa02a1c9da2
+  summary: >-
+    Learn how to create C/C++ Win32 DLLs and port them to Arm64 for use in Windows console applications.
+    It is designed for developers who want to learn how to port their Win32 applications to Arm64.
+    By the end, you will be able to create C/C++ Win32 DLL, use Win32 DLL in the Console App,
+    and learn how to port the C/C++ Win32 DLL to Arm64. It focuses on tools and technologies such
+    as C and CPP, Windows environments, and Arm platforms including Cortex-A. The main steps cover
+    Porting Win32 code to Arm64.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create C/C++ Win32 DLL, use Win32 DLL in the Console App, and learn how to port
+      the C/C++ Win32 DLL to Arm64. Learn how to create C/C++ Win32 DLLs and port them to Arm64
+      for use in Windows console applications.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers who want to learn how to port their Win32 applications
+      to Arm64.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows on Arm computer such as the
+      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
+      Refer to [Visual Studio 2022 with Arm build tools](/install-guides/vs-woa).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including C and CPP, Windows environments, and Arm platforms
+      such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Porting Win32 code to Arm64.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
@@ -53,3 +90,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/win_winui3/_index.md b/content/learning-paths/laptops-and-desktops/win_winui3/_index.md
index 8f5cafc395..9abbcf0a8b 100644
--- a/content/learning-paths/laptops-and-desktops/win_winui3/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_winui3/_index.md
@@ -17,6 +17,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 383dcf17bce86b24a2d9c8d0982fa6e9ddf954955c16b420463ada951753dbfa
+  summary: >-
+    Learn how to create and build Windows UI Library (WinUI) applications and measure code execution
+    performance on Arm64. It is designed for developers who want to learn how to create cross-platform
+    applications and leverage performance improvements on Arm64. By the end, you will be able
+    to create and build a Windows UI Library (WinUI) application and measure code execution performance
+    on Arm64. It focuses on tools and technologies such as WinUI 3, C#, .NET, and Visual Studio,
+    Windows environments, and Arm platforms including Cortex-A. The main steps cover Creating
+    an application and Comparing the performance on various platforms.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create and build a Windows UI Library (WinUI) application and measure code execution
+      performance on Arm64. Learn how to create and build Windows UI Library (WinUI) applications
+      and measure code execution performance on Arm64.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This learning path is for developers who want to learn how to create cross-platform applications
+      and leverage performance improvements on Arm64.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows on Arm computer such as the
+      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
+      Visual Studio 2022 with .NET desktop development and Universal Windows Platform development
+      installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including WinUI 3, C#, .NET, and Visual Studio, Windows environments,
+      and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Creating an application and Comparing the performance
+      on various platforms.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
@@ -49,3 +89,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/win_wpf/_index.md b/content/learning-paths/laptops-and-desktops/win_wpf/_index.md
index 9d96b79cbf..9042d6f240 100644
--- a/content/learning-paths/laptops-and-desktops/win_wpf/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_wpf/_index.md
@@ -17,6 +17,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: cc1a494e7b03672122ff84073b677a93659eca7df601b3f77c7e7fd536cc1af9
+  summary: >-
+    Learn how to create and build Windows Presentation Foundation (WPF) applications and measure
+    code execution performance uplift on Arm64. It is designed for developers who want to learn
+    how to create desktop applications and leverage performance improvements on Arm64. By the
+    end, you will be able to create and build a Windows Presentation Foundation (WPF) application
+    and measure code execution performance uplift on Arm64. It focuses on tools and technologies
+    such as Windows Presentation Foundation, C#, .NET, and Visual Studio, Windows environments,
+    and Arm platforms including Cortex-A. The main steps cover Create an application using Windows
+    Presentation Foundation (WPF) and Run the application and compare execution times.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create and build a Windows Presentation Foundation (WPF) application and measure
+      code execution performance uplift on Arm64. Learn how to create and build Windows Presentation
+      Foundation (WPF) applications and measure code execution performance uplift on Arm64.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This learning path is for developers who want to learn how to create desktop applications
+      and leverage performance improvements on Arm64.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows on Arm computer such as the
+      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
+      Visual Studio 2022 with .NET desktop development installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Windows Presentation Foundation, C#, .NET, and Visual
+      Studio, Windows environments, and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Create an application using Windows Presentation Foundation
+      (WPF) and Run the application and compare execution times.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
@@ -49,3 +89,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md b/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md
index 8de3cae8a6..de33462585 100644
--- a/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md
@@ -18,6 +18,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 16b7f8c67050ea336402791c0ecca2c688d5da9373c571986e12dcf646c8093c
+  summary: >-
+    Learn how to create and build Xamarin Forms applications using the MVVM pattern and measure
+    code execution performance uplift on Arm64. It is designed for developers who want to learn
+    how to create cross-platform applications and leverage performance improvements on Arm64.
+    By the end, you will be able to create and build an Xamarin Forms application, measure code
+    execution performance uplift on Arm64, and learn how to use the Model-View-ViewModel (MVVM)
+    architectural pattern. It focuses on tools and technologies such as Xamarin Forms, C#, .NET,
+    and Visual Studio, Windows environments, and Arm platforms including Cortex-A. The main steps
+    cover Create an application using Xamarin Forms and Implement logic with Model View ViewModel.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create and build an Xamarin Forms application, measure code execution performance
+      uplift on Arm64, and learn how to use the Model-View-ViewModel (MVVM) architectural pattern.
+      Learn how to create and build Xamarin Forms applications using the MVVM pattern and measure
+      code execution performance uplift on Arm64.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This learning path is for developers who want to learn how to create cross-platform applications
+      and leverage performance improvements on Arm64.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows on Arm computer such as the
+      Lenovo Thinkpad X13s running Windows 11 or a a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
+      Visual Studio 2022 with .NET desktop development and Universal Windows Platform development
+      installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Xamarin Forms, C#, .NET, and Visual Studio, Windows
+      environments, and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Create an application using Xamarin Forms and Implement
+      logic with Model View ViewModel.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
@@ -50,3 +92,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md b/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md
index 0966904605..87acc6bc0b 100644
--- a/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md
@@ -16,6 +16,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: f058f628cefb7766ef0728e594c9ef5b57bde97c1850395786d54cc591271503
+  summary: >-
+    Learn how to develop Windows on Arm applications using Visual Studio and optimize performance
+    with Arm Performance Libraries. It is designed for software developers who want to improve
+    the performance of Windows on Arm applications using Arm Performance Libraries. By the end,
+    you will be able to develop a Windows on Arm application using Microsoft Visual Studio and
+    utilize Arm Performance Libraries to optimize the performance of an application. It focuses
+    on tools and technologies such as Visual Studio, C#, .NET, and Arm Performance Libraries,
+    Windows environments, and Arm platforms including Cortex-A. The main steps cover Before you
+    begin, Create and Run a Windows on Arm application, Git setup, Build and Profile an Application
+    with Spin the Cube and Visual Studio, and Use Arm Performance Libraries to Optimize Performance.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will develop a Windows on Arm application using Microsoft Visual Studio and utilize
+      Arm Performance Libraries to optimize the performance of an application. Learn how to develop
+      Windows on Arm applications using Visual Studio and optimize performance with Arm Performance
+      Libraries.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to improve the performance
+      of Windows on Arm applications using Arm Performance Libraries.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows on Arm computer such as the
+      Lenovo Thinkpad X13s running Windows 11.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Visual Studio, C#, .NET, and Arm Performance Libraries,
+      Windows environments, and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Before you begin, Create and Run a Windows on Arm
+      application, Git setup, Build and Profile an Application with Spin the Cube and Visual Studio,
+      and Use Arm Performance Libraries to Optimize Performance.
+# END generated_summary_faq
+
 author: Odin Shen
 
 ### Tags
@@ -47,3 +89,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md b/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md
index 0ce64a21aa..4bf5ea82c6 100644
--- a/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md
@@ -18,6 +18,44 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 828e4022f387698d2736d94010de5d93efa9f38ffd3a2e4f15252410e9ccedb2
+  summary: >-
+    Get started with GitHub CI/CD development flow on a Windows on Arm machine (or virtual machine).
+    It is designed for software developers interested in running their CI flows on Windows on
+    Arm machines. By the end, you will be able to setup a CI/CD flow with GitHub Actions to use
+    Windows on Arm as the self-hosted runner host and run a simple GitHub Actions workflow. It
+    focuses on tools and technologies such as GitHub, Windows environments, and Arm platforms
+    including Neoverse. The main steps cover Setup GitHub Self-hosted Runner and Create and run
+    simple workflow.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will setup a CI/CD flow with GitHub Actions to use Windows on Arm as the self-hosted
+      runner host and run a simple GitHub Actions workflow. Get started with GitHub CI/CD development
+      flow on a Windows on Arm machine (or virtual machine).
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers interested in running their CI flows
+      on Windows on Arm machines.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Some familiarity with CI/CD concepts
+      is assumed; Valid GitHub account; Microsoft Azure account (if using virtual machine).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including GitHub, Windows environments, and Arm platforms
+      such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Setup GitHub Self-hosted Runner and Create and run
+      simple workflow.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
@@ -51,3 +89,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md
index 6cfe810369..a3c6719af5 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md
@@ -19,6 +19,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 1dd709b14537e06c80b9cdee58729cfa7066f44f0de4ceabe5450b33288731aa
+  summary: >-
+    Learn how to install and use the WindowsPerf Visual Studio extension to generate counting
+    and sampling reports and analyze performance data in Windows Performance Analyzer. It is designed
+    for software developers using Visual Studio on Windows on Arm who want to integrate WindowsPerf
+    into their development flow. By the end, you will be able to install and use the WindowsPerf
+    Visual Studio extension, generate a counting report and explore the data, and review the report
+    in Windows Performance Analyzer (WPA). It focuses on tools and technologies such as WindowsPerf,
+    perf, and Visual Studio, Windows environments, and Arm platforms including Cortex-A. The main
+    steps cover Configure your development tools, Use the counting feature, Use the sampling feature,
+    and The SPE Feature.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install and use the WindowsPerf Visual Studio extension, generate a counting report
+      and explore the data, and review the report in Windows Performance Analyzer (WPA). Learn
+      how to install and use the WindowsPerf Visual Studio extension to generate counting and
+      sampling reports and analyze performance data in Windows Performance Analyzer.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers using Visual Studio on Windows on
+      Arm who want to integrate WindowsPerf into their development flow.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A desktop or laptop running Windows
+      on Arm.; Visual Studio 2022 Community Edition, WindowsPerf, WindowsPerf Visual Studio extension,
+      and Windows Performance Analyzer (WPA) installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including WindowsPerf, perf, and Visual Studio, Windows environments,
+      and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Configure your development tools, Use the counting
+      feature, Use the sampling feature, and The SPE Feature.
+# END generated_summary_faq
+
 author: 
   - Nader Zouaoui
 
@@ -70,3 +112,4 @@ weight: 1 # _index.md always has weight of 1 to order correctly
 layout: "learningpathall" # All files under learning paths have this same wrapper
 learning_path_main_page: "yes" # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf/_index.md
index 2521cc0be3..98693e403e 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf/_index.md
@@ -16,6 +16,42 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 61a6390d42ce6bfc37a3bb981710dc23b8566070733f7979f9ec37bc63ab7d78
+  summary: >-
+    Learn how to install WindowsPerf on Windows on Arm machines and generate sample performance
+    reports for CPU profiling. It is designed for software developers working on laptops and desktops
+    and new to the Arm architecture. By the end, you will be able to install WindowsPerf on Windows
+    on Arm machine and generate a sample report. It focuses on tools and technologies such as
+    WindowsPerf, Windows environments, and Arm platforms including Cortex-A and Neoverse. The
+    main steps cover WindowsPerf and WindowsPerf cheat sheet.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install WindowsPerf on Windows on Arm machine and generate a sample report. Learn
+      how to install WindowsPerf on Windows on Arm machines and generate sample performance reports
+      for CPU profiling.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers working on laptops and desktops and
+      new to the Arm architecture.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Windows on Arm desktop or development
+      machine.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including WindowsPerf, Windows environments, and Arm platforms
+      such as Cortex-A and Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around WindowsPerf and WindowsPerf cheat sheet.
+# END generated_summary_faq
+
 author: Ronan Synnott
 
 ### Tags
@@ -65,3 +101,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md
index 4153bd1684..757e79fd8a 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md
@@ -19,6 +19,45 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: e23de84e7e05d771072ab799f5cc802476fd5e3c23da41a202c9c50fe9980e25
+  summary: >-
+    Learn how to use WindowsPerf for performance sampling on Windows on Arm, build CPython from
+    sources, and analyze native workload performance. It is designed for developers keen to understand
+    sampling and who are new to the Arm architecture. By the end, you will be able to use WindowsPerf
+    with native Windows on Arm workload, understand the basics of sampling, and explore the WindowsPerf
+    command line. It focuses on tools and technologies such as WindowsPerf, Python, and perf,
+    Windows environments, and Arm platforms including Cortex-A. The main steps cover CPython Sampling
+    Example Overview, WindowsPerf sample example, and WindowsPerf record example.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will use WindowsPerf with native Windows on Arm workload, understand the basics of sampling,
+      and explore the WindowsPerf command line. Learn how to use WindowsPerf for performance sampling
+      on Windows on Arm, build CPython from sources, and analyze native workload performance.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers keen to understand sampling and who are new
+      to the Arm architecture.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Windows on Arm desktop or development
+      machine with [WindowsPerf installed](/install-guides/wperf); Windows x86_64 desktop machine
+      with [Visual Studio 2022 Community Edition](https://visualstudio.microsoft.com/vs/) installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including WindowsPerf, Python, and perf, Windows environments,
+      and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around CPython Sampling Example Overview, WindowsPerf sample
+      example, and WindowsPerf record example.
+# END generated_summary_faq
+
 author: Przemyslaw Wirkus
 
 ### Tags
@@ -93,3 +132,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
index 03d2615e52..0f3a1280cc 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
@@ -16,6 +16,44 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 1fd4d85855933a5dfc5edf5ae3abf5f4388d94c9ee69aff12b77eb766e88301f
+  summary: >-
+    Learn how to import WindowsPerf data in Windows Performance Analyzer (WPA) and visualize timeline
+    and telemetry data using the WPA plugin. It is designed for software developers interested
+    in using the Windows Performance Analyzer (WPA) plugin for performance analysis. By the end,
+    you will be able to import WindowsPerf data as a .json file in WPA and visualize the timeline
+    and telemetry data in WPA using the WPA plugin. It focuses on tools and technologies such
+    as WindowsPerf, perf, and Windows Performance Analyzer, Windows environments, and Arm platforms
+    including Cortex-A and Neoverse. The main steps cover Visualize data from WindowsPerf using
+    WPA.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will import WindowsPerf data as a .json file in WPA and visualize the timeline and telemetry
+      data in WPA using the WPA plugin. Learn how to import WindowsPerf data in Windows Performance
+      Analyzer (WPA) and visualize timeline and telemetry data using the WPA plugin.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers interested in using the Windows Performance
+      Analyzer (WPA) plugin for performance analysis.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows on Arm laptop with WindowsPerf,
+      Windows Performance Analyzer (WPA), and the WPA plugin installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including WindowsPerf, perf, and Windows Performance Analyzer,
+      Windows environments, and Arm platforms such as Cortex-A and Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Visualize data from WindowsPerf using WPA.
+# END generated_summary_faq
+
 author: Alaaeddine Chakroun
 
 ### Tags
@@ -67,3 +105,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/laptops-and-desktops/wsl2/_index.md b/content/learning-paths/laptops-and-desktops/wsl2/_index.md
index 2c7b07347d..0ae90fcaf9 100644
--- a/content/learning-paths/laptops-and-desktops/wsl2/_index.md
+++ b/content/learning-paths/laptops-and-desktops/wsl2/_index.md
@@ -21,6 +21,47 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 03d816ff419e6e5b1533f95e9d4ffa087b9c0c9aefeee112f67b70223c8aedc3
+  summary: >-
+    Learn how to configure and run WSL with Linux distributions, graphical applications, remote
+    desktop, and development tools on Windows on Arm computers. It is designed for Software developers
+    with Windows on Arm computers doing Linux or cloud native development. By the end, you will
+    be able to configure and run WSL with various Linux distributions, run graphical Linux applications
+    on Windows, and use ssh to connect to WSL. It focuses on tools and technologies such as WSL
+    and Visual Studio Code, Windows and Linux environments, and Arm platforms including Cortex-A.
+    The main steps cover Configure and run WSL with various Linux distributions, Run graphical
+    Linux applications, Enable systemd in WSL, Use SSH to connect to WSL, and Connect to WSL using
+    RDP and VNC.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will configure and run WSL with various Linux distributions, run graphical Linux applications
+      on Windows, and use ssh to connect to WSL. Learn how to configure and run WSL with Linux
+      distributions, graphical applications, remote desktop, and development tools on Windows
+      on Arm computers.
+  - question: Who is this Learning Path for?
+    answer: >-
+      Software developers with Windows on Arm computers doing Linux or cloud native development.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows on Arm computer such as the
+      Lenovo Thinkpad X13s running Windows 11.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including WSL and Visual Studio Code, Windows and Linux environments,
+      and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Configure and run WSL with various Linux distributions,
+      Run graphical Linux applications, Enable systemd in WSL, Use SSH to connect to WSL, and
+      Connect to WSL using RDP and VNC.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
@@ -52,3 +93,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md b/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md
index 5e59debb17..5e3d7c5cdd 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md
@@ -19,6 +19,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: e4512ad20b372f616409199c51a38d95cb116ec6cf49d9004348d6bb8ee4014d
+  summary: >-
+    Learn how to enable and verify Arm Fixed Rate Compression in Vulkan applications on Android
+    devices to reduce memory footprint and bandwidth. It is designed for Software developers of
+    Android applications and mobile games who are interested in learning how to enable Arm Fixed
+    Rate Compression (AFRC) to improve performance. By the end, you will be able to query for
+    fixed-rate compression support, specify what compression to use, and verify that compression
+    is applied. It focuses on tools and technologies such as Vulkan, Android environments, and
+    Arm platforms including Mali and Immortalis. The main steps cover What is fixed-rate compression?,
+    How to run the code examples, Vulkan Extensions, Query for compression support, and Request
+    fixed-rate compression.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will query for fixed-rate compression support, specify what compression to use, and
+      verify that compression is applied. Learn how to enable and verify Arm Fixed Rate Compression
+      in Vulkan applications on Android devices to reduce memory footprint and bandwidth.
+  - question: Who is this Learning Path for?
+    answer: >-
+      Software developers of Android applications and mobile games who are interested in learning
+      how to enable Arm Fixed Rate Compression (AFRC) to improve performance.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An appropriate Android device (e.g.,
+      Google Pixel 8) supporting the required Vulkan extensions.; Knowledge of the Vulkan API.;
+      A Vulkan application that creates and uses images. This Learning Path shows how to use an
+      API Sample in the [Khronos Vulkan Samples repository](https://github.com/KhronosGroup/Vulkan-Samples/blob/main/scripts/README.adoc#generate-api-sample)
+      as an example.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Vulkan, Android environments, and Arm platforms
+      such as Mali and Immortalis.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around What is fixed-rate compression?, How to run the code
+      examples, Vulkan Extensions, Query for compression support, and Request fixed-rate compression.
+# END generated_summary_faq
+
 author: Jose-Emilio Munoz-Lopez
 
 ### Tags
@@ -56,3 +99,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md b/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md
index ed69565b0f..7e225b2c7b 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md
@@ -16,6 +16,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: dee181248ecc8cb40e3ad76642fdb216cbf1e7610dde2f2605ba04f111b8926a
+  summary: >-
+    Learn how to build and optimize AI-powered camera pipeline applications on Arm Linux using
+    KleidiAI, KleidiCV, and SME2 to accelerate denoising, background blur, and low-light effects.
+    It is designed for This introductory topic is for mobile and computer-vision developers, camera
+    pipeline engineers, and performance-minded practitioners who want to optimize real-time camera
+    effects on Arm using KleidiAI and KleidiCV. By the end, you will be able to build and run
+    AI-powered camera pipeline applications and use SME2 to improve the performance of real-time
+    camera pipelines. It focuses on tools and technologies such as CPP, Docker, and SME2, Linux
+    and macOS environments, and Arm platforms including Cortex-A and Arm C1. The main steps cover
+    Prerequisites, Overview, Build the pipelines, Run the pipelines, and Performance.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build and run AI-powered camera pipeline applications and use SME2 to improve the
+      performance of real-time camera pipelines. Learn how to build and optimize AI-powered camera
+      pipeline applications on Arm Linux using KleidiAI, KleidiCV, and SME2 to accelerate denoising,
+      background blur, and low-light effects.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This introductory topic is for mobile and computer-vision developers, camera pipeline engineers,
+      and performance-minded practitioners who want to optimize real-time camera effects on Arm
+      using KleidiAI and KleidiCV.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A computer running Arm Linux or macOS
+      with Docker installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including CPP, Docker, and SME2, Linux and macOS environments,
+      and Arm platforms such as Cortex-A and Arm C1.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Prerequisites, Overview, Build the pipelines, Run
+      the pipelines, and Performance.
+# END generated_summary_faq
+
 author: Arnaud de Grandmaison
 
 test_images:
@@ -76,3 +118,4 @@ weight: 1                       # _index.md always has a weight of 1 to order co
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md b/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
index fc4ce3ee67..b87cd6d6ae 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
@@ -22,6 +22,50 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 3d1a743c1b3ee617f52191fc9c9fd33a9d44454ac079f00b6f532e2865103377
+  summary: >-
+    Learn how to use each of the tools supplied with Arm Performance Studio (formerly known as
+    Arm Mobile Studio). It is designed for Android application and games developers new to Arm
+    Performance Studio. By the end, you will be able to learn the basic features of each component
+    of Arm Performance Studio and get started profiling and optimizing your application. It focuses
+    on tools and technologies such as Arm Performance Studio and Arm Mobile Studio, Android environments,
+    and Arm platforms including Cortex-A, Mali, and Immortalis. The main steps cover What is Arm
+    Performance Studio?, Setup tasks, Arm Streamline example capture, Streamline with your application,
+    and Performance Advisor example report.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will learn the basic features of each component of Arm Performance Studio and get started
+      profiling and optimizing your application. Learn how to use each of the tools supplied with
+      Arm Performance Studio (formerly known as Arm Mobile Studio).
+  - question: Who is this Learning Path for?
+    answer: >-
+      Android application and games developers new to Arm Performance Studio.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Android device.; Arm Performance
+      Studio supports applications built with OpenGL ES versions 2.0 to 3.2, or Vulkan versions
+      1.0 to 1.2.; For OpenGL ES applications, your device must be running Android 10 or later.;
+      For Vulkan applications, your device must be running Android 9 or later.; A debuggable build
+      of your application.; Arm Performance Studio installed. Follow the [Arm Performance Studio
+      install guide](/install-guides/ams) for instructions.; Android SDK Platform tools installed.
+      Required for the Android Debug bridge (adb).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Arm Performance Studio and Arm Mobile Studio, Android
+      environments, and Arm platforms such as Cortex-A, Mali, and Immortalis.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around What is Arm Performance Studio?, Setup tasks, Arm
+      Streamline example capture, Streamline with your application, and Performance Advisor example
+      report.
+# END generated_summary_faq
+
 author: Ronan Synnott
 
 ### Tags
@@ -86,3 +130,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md b/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md
index fb38d83666..3ad8f3be24 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md
@@ -20,6 +20,52 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: d5406f24ab38522b21e348ed3829ede7b1bcdce28e81ec4fddebbec280d2cb89
+  summary: >-
+    Learn how to capture frame data from Android applications and analyze performance inefficiencies
+    using Frame Advisor in Arm Performance Studio. It is designed for Android application developers
+    who want to learn how to use Frame Advisor. By the end, you will be able to capture data from
+    a significant frame in your application and find inefficiencies in the application with Frame
+    Advisor. It focuses on tools and technologies such as Frame Advisor, Android environments,
+    and Arm platforms including Mali GPUs and Immortalis GPUs. The main steps cover What is Frame
+    Advisor?, Capture a trace, Analyze draw calls, Analyze frame construction, and Analyze mesh
+    geometry.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will capture data from a significant frame in your application and find inefficiencies
+      in the application with Frame Advisor. Learn how to capture frame data from Android applications
+      and analyze performance inefficiencies using Frame Advisor in Arm Performance Studio.
+  - question: Who is this Learning Path for?
+    answer: >-
+      Android application developers who want to learn how to use Frame Advisor.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Android device. These [devices](https://developer.arm.com/Tools%20and%20Software/Arm%20Mobile%20Studio#Supported-Devices)
+      have been tested internally within Arm and confirmed to work with Arm Performance Studio.;
+      Arm Performance Studio supports applications built with OpenGL ES versions 2.0 to 3.2 or
+      Vulkan versions 1.0 to 1.2. For OpenGL ES applications, your device must be running Android
+      10 or later. For Vulkan applications, your device must be running Android 9 or later.; A
+      debuggable build of your application.; Download and install Arm Performance Studio from
+      [Product Download Hub](https://developer.arm.com/downloads/view/MOBST-PRO0). It is supported
+      on Windows, Linux, and macOS host platforms.; Download and install [Android SDK Platform
+      tools](https://developer.android.com/studio/releases/platform-tools.html). Required for
+      [Android Debug bridge (adb)](https://developer.android.com/studio/command-line/adb).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Frame Advisor, Android environments, and Arm platforms
+      such as Mali GPUs and Immortalis GPUs.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around What is Frame Advisor?, Capture a trace, Analyze draw
+      calls, Analyze frame construction, and Analyze mesh geometry.
+# END generated_summary_faq
+
 author: Julie Gaskin
 
 ### Tags
@@ -57,3 +103,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md
index 4b11b7053d..ddd2f1e8b7 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md
@@ -18,6 +18,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: e63ac917c218aa5e5981f96a9821567d0f8ba9761d5ce6e4c9d7b6dea7ed5c5f
+  summary: >-
+    Learn how to build an Android chatbot app using Arm's AI Chat library to run GGUF models on-device
+    with optimized performance on Arm CPUs. It is designed for developers who want to add a local,
+    on-device LLM chat experience using Arm's AI Chat library, Kotlin, and Android Studio. By
+    the end, you will be able to create a simple Android chatbot app scaffold in Android Studio
+    and load a mobile-friendly GGUF model on-device and run streamed chat inference. It focuses
+    on tools and technologies such as Kotlin, Neon, SVE2, SME2, and LLM, Android environments,
+    and Arm platforms including Arm AI Chat library. The main steps cover Create the Android project,
+    Configure the AI Chat library dependency, Create the UI layouts and message adapter, Implement
+    the main activity logic, and Download a model and run the app.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create a simple Android chatbot app scaffold in Android Studio and load a mobile-friendly
+      GGUF model on-device and run streamed chat inference. Learn how to build an Android chatbot
+      app using Arm's AI Chat library to run GGUF models on-device with optimized performance
+      on Arm CPUs.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers who want to add a local, on-device LLM chat
+      experience using Arm's AI Chat library, Kotlin, and Android Studio.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Android development environment with
+      Android Studio installed; An Android phone for testing, in Developer Mode, with USB cable
+      for connection; Basic familiarity with Kotlin and Android app development.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Kotlin, Neon, SVE2, SME2, and LLM, Android environments,
+      and Arm platforms such as Arm AI Chat library.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Create the Android project, Configure the AI Chat
+      library dependency, Create the UI layouts and message adapter, Implement the main activity
+      logic, and Download a model and run the app.
+# END generated_summary_faq
+
 author: Ben Clark
 
 ### Tags
@@ -69,3 +112,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
index 3f0acba0c2..9974cf9c5a 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
@@ -18,6 +18,51 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: fa04ff97605ae93b17c056c397c37f6fc17cf6f4853bfabe374610ede002e3df
+  summary: >-
+    Learn how to build real-time image processing pipelines using Halide on Android, combining
+    operations for improved performance in Kotlin applications. It is designed for developers
+    interested in learning how to use Halide for image processing. By the end, you will be able
+    to learn the basics of Halide and set up your development environment, build a simple real-time
+    image processing pipeline with Halide, and make your image processing faster by combining
+    operations in Halide. It focuses on tools and technologies such as Android Studio, Halide,
+    CPP, Kotlin, and CMake, Android environments, and Arm platforms including Cortex-A and Cortex-X.
+    The main steps cover Install and configure Halide for Arm development, Build a simple camera
+    image processing workflow, Apply operator fusion in Halide for real-time image processing,
+    Generate optimized Halide pipelines for Android using ahead-of-time cross-compilation, and
+    Integrate Halide into an Android project with Kotlin.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will learn the basics of Halide and set up your development environment, build a simple
+      real-time image processing pipeline with Halide, and make your image processing faster by
+      combining operations in Halide. Learn how to build real-time image processing pipelines
+      using Halide on Android, combining operations for improved performance in Kotlin applications.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers interested in learning how to use Halide for
+      image processing.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Basic C++ knowledge; Android Studio
+      with Android Emulator.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Android Studio, Halide, CPP, Kotlin, and CMake,
+      Android environments, and Arm platforms such as Cortex-A and Cortex-X.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Install and configure Halide for Arm development,
+      Build a simple camera image processing workflow, Apply operator fusion in Halide for real-time
+      image processing, Generate optimized Halide pipelines for Android using ahead-of-time cross-compilation,
+      and Integrate Halide into an Android project with Kotlin.
+# END generated_summary_faq
+
 author: Éliás Bálint, Dawid Borycki, Steve Suzuki
 
 ### Tags
@@ -58,3 +103,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md
index 72bef3336e..37eb76fa2f 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md
@@ -17,6 +17,45 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 2137cec46fe8d57f11e9e4011306a140bba7d8a5b2326a746193c1794a148f75
+  summary: >-
+    Learn how to create and configure an Android project with OpenCV support to process camera
+    images for computer vision applications. It is designed for developers who are interested
+    in creating Computer Vision Applications with OpenCV on Android Devices. By the end, you will
+    be able to describe what OpenCV is, and what it can offer, create and configure a project
+    to add OpenCV support, and process camera images using OpenCV. It focuses on tools and technologies
+    such as Android, Android Studio, Kotlin, and Java, Windows environments, and Arm platforms
+    including Cortex-A. The main steps cover Overview, Create a project and add OpenCV, Get camera
+    images using OpenCV, Process Images, and Summary.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will describe what OpenCV is, and what it can offer, create and configure a project
+      to add OpenCV support, and process camera images using OpenCV. Learn how to create and configure
+      an Android project with OpenCV support to process camera images for computer vision applications.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers who are interested in creating Computer Vision
+      Applications with OpenCV on Android Devices.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A development machine with [Android
+      Studio](https://developer.android.com/studio) installed.; An Android smartphone.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Android, Android Studio, Kotlin, and Java, Windows
+      environments, and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview, Create a project and add OpenCV, Get camera
+      images using OpenCV, Process Images, and Summary.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
@@ -53,3 +92,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md
index 42b567eaef..c6782e9721 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md
@@ -19,6 +19,47 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 3a120620bbc56e796aa45fbe01aa1455fbee085a1a4cf0d055416b7e95e72d0d
+  summary: >-
+    Learn how to implement face detection on Android devices using OpenCV, camera frame retrieval,
+    and Haar cascade classifiers. It is designed for developers who are interested in creating
+    Computer Vision applications with OpenCV on Android devices. By the end, you will be able
+    to describe how you can use OpenCV for face detection, use OpenCV to retrieve camera frames,
+    and use Haar cascade classifier for face detection. It focuses on tools and technologies such
+    as Android, Android Studio, and Kotlin, Windows and macOS environments, and Arm platforms
+    including Cortex-A. The main steps cover Background, Create a project, add OpenCV, and read
+    camera frames, and Face detection.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will describe how you can use OpenCV for face detection, use OpenCV to retrieve camera
+      frames, and use Haar cascade classifier for face detection. Learn how to implement face
+      detection on Android devices using OpenCV, camera frame retrieval, and Haar cascade classifiers.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers who are interested in creating Computer Vision
+      applications with OpenCV on Android devices.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A development machine with [Android
+      Studio](https://developer.android.com/studio) installed.; An Android smartphone.; Familiarity
+      with OpenCV, review [Create Computer Vision Applications with OpenCV on Android Devices](/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/)
+      before starting.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Android, Android Studio, and Kotlin, Windows and
+      macOS environments, and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Background, Create a project, add OpenCV, and read
+      camera frames, and Face detection.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
@@ -55,3 +96,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
index 1eba1ed578..9d758ab205 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
@@ -19,6 +19,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 8e05fb124ad18194fba2ed83adae3e52673b2fad41af998ca8d0aa8cb9785f5e
+  summary: >-
+    Learn how to accelerate OpenCV-based Android applications using KleidiCV for enhanced computer
+    vision performance. It is designed for developers who are interested in creating Computer
+    Vision applications with OpenCV and KleidiCV on Android Devices. By the end, you will be able
+    to describe what KleidiCV is, and what it can offer, create and configure a project to add
+    OpenCV support, and process images using OpenCV functionality. It focuses on tools and technologies
+    such as Android, Android Studio, Kotlin, and Java, Android environments, and Arm platforms
+    including Cortex-A. The main steps cover Overview, Create a project and add OpenCV, Define
+    the UI, Processing the Images, and Summary.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will describe what KleidiCV is, and what it can offer, create and configure a project
+      to add OpenCV support, and process images using OpenCV functionality. Learn how to accelerate
+      OpenCV-based Android applications using KleidiCV for enhanced computer vision performance.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers who are interested in creating Computer Vision
+      applications with OpenCV and KleidiCV on Android Devices.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A development machine with [Android
+      Studio](https://developer.android.com/studio) installed.; Familiarity with Android development
+      concepts.; An Android smartphone.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Android, Android Studio, Kotlin, and Java, Android
+      environments, and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview, Create a project and add OpenCV, Define
+      the UI, Processing the Images, and Summary.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
@@ -54,3 +94,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md
index c1984ec277..4d64fd8b8d 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md
@@ -18,6 +18,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: be91053c5abcdd669ff8da7e66b2f717c4adaac27b3fb44ea073b69a68d2dba7
+  summary: >-
+    Get started with Scalable Vector Extension 2 (SVE2) on Android walks you through an end-to-end
+    Arm software workflow. It is designed for software developers interested in learning how to
+    use the Scalable Vector Extension 2 (SVE2) on Arm powered mobile devices running Android.
+    By the end, you will be able to enable Scalable Vector Extension 2 (SVE2) support in Android
+    Studio, implement an Android application that uses the Android Native Development Kit (NDK)
+    to calculate the fused multiply-add (FMA), and measure the performance uplift by using SVE2
+    intrinsics. It focuses on tools and technologies such as Android Studio, Android environments,
+    and Arm platforms including Cortex-A. The main steps cover Enable SVE2 support in Android
+    Studio and Implement vector operations.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will enable Scalable Vector Extension 2 (SVE2) support in Android Studio, implement
+      an Android application that uses the Android Native Development Kit (NDK) to calculate the
+      fused multiply-add (FMA), and measure the performance uplift by using SVE2 intrinsics.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers interested in learning how to use
+      the Scalable Vector Extension 2 (SVE2) on Arm powered mobile devices running Android.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A x86_64 or Apple development machine
+      with Android Studio installed.; A 64-bit Arm powered smartphone running Android.; Knowledge
+      of Single instruction Multi Data (SIMD); Knowledge of [Neon](https://developer.arm.com/documentation/102474/latest);
+      Knowledge of [Scalable Vector Extension (SVE)](https://developer.arm.com/documentation/101726/4-0).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Android Studio, Android environments, and Arm platforms
+      such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Enable SVE2 support in Android Studio and Implement
+      vector operations.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
@@ -57,3 +99,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
index 56db74c3b7..f2f8dbf0e9 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
@@ -27,6 +27,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: 8f58429f12e40b53e8a2e0d7eb260f22fbb7ac869ca64554a906ddcd28c9fd75
+  summary: >-
+    Learn how to integrate Dawn WebGPU in an Android application, render 3D objects, and profile
+    the application using Streamline. It is designed for developers who are building GPU-based
+    Android applications and are interested in experimenting with WebGPU. By the end, you will
+    be able to describe the benefits of WebGPU, describe the benefits of using Dawn, and set up
+    a WebGPU development environment. It focuses on tools and technologies such as Java, Kotlin,
+    CPP, and Python, macOS, Linux, Windows, and Android environments, and Arm platforms including
+    Cortex-A. The main steps cover Overview of WebGPU, Set up a development environment, Create
+    an application with Dawn, Using Dawn WebGPU APIs in the application, and Render a simple 3D
+    object.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will describe the benefits of WebGPU, describe the benefits of using Dawn, and set up
+      a WebGPU development environment. Learn how to integrate Dawn WebGPU in an Android application,
+      render 3D objects, and profile the application using Streamline.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers who are building GPU-based Android applications
+      and are interested in experimenting with WebGPU.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Basic knowledge of graphics APIs and
+      experience in developing Android graphics applications.; A development machine with Android
+      Studio, Blender, and Arm Streamline installed.; An Android phone in developer mode.; Android
+      Studio.; Arm Performance Studio.; Python 3.10 or later.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Java, Kotlin, CPP, and Python, macOS, Linux, Windows,
+      and Android environments, and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview of WebGPU, Set up a development environment,
+      Create an application with Dawn, Using Dawn WebGPU APIs in the application, and Render a
+      simple 3D object.
+# END generated_summary_faq
+
 author:
     - Varun Chari
     - Albin Bernhardsson
@@ -86,3 +129,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md b/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
index 60e30668c4..2360a3899e 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
@@ -18,6 +18,50 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:16Z'
+  generator: template
+  source_hash: ef70ed2089132df657bd1ebfb9a66a39934d8a118b1e73974148ce11c104fef5
+  summary: >-
+    Learn how to optimize hardware ray tracing with Lumen on Android devices powered by Arm Mali
+    GPUs to maximize performance. It is designed for Unreal Engine developers interested in optimizing
+    hardware ray tracing with Lumen on android devices. By the end, you will be able to learn
+    about ray tracing, understand what an acceleration structure is, and learn about the best
+    practices for getting the maximum performance of hardware ray tracing on Lumen for Arm devices.
+    It focuses on tools and technologies such as Unreal Engine, Android environments, and Arm
+    platforms including Immortalis-G715 and Immortalis-G720. The main steps cover Lumen and Ray
+    Tracing, Acceleration Structure, Only Add Important Objects into Ray Tracing, Take Full Advantage
+    of Instancing, and Optimize Acceleration Structure.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will learn about ray tracing, understand what an acceleration structure is, and learn
+      about the best practices for getting the maximum performance of hardware ray tracing on
+      Lumen for Arm devices. Learn how to optimize hardware ray tracing with Lumen on Android
+      devices powered by Arm Mali GPUs to maximize performance.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for Unreal Engine developers interested in optimizing hardware
+      ray tracing with Lumen on android devices.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A computer capable of running [Unreal
+      Engine 5.3 or later version](https://www.unrealengine.com/en-US/download).; An Android mobile
+      device that has a Mali GPU with hardware ray tracing support.; A USB cable to connect the
+      mobile device to your computer.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Unreal Engine, Android environments, and Arm platforms
+      such as Immortalis-G715 and Immortalis-G720.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Lumen and Ray Tracing, Acceleration Structure, Only
+      Add Important Objects into Ray Tracing, Take Full Advantage of Instancing, and Optimize
+      Acceleration Structure.
+# END generated_summary_faq
+
 author: Owen Wu
 
 ### Tags
@@ -54,3 +98,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md b/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md
index a6a5eed980..1ee843e955 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md
@@ -17,6 +17,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: deeb745d72457138cb81252c421205cd8dfb43b5e29ceee3a15c5842cc90cc33
+  summary: >-
+    Learn how to build ONNX Runtime and the generate() API for Android to run a Phi-3 model on
+    Arm-based smartphones. It is designed for software developers interested in learning how to
+    build an Android chat app with ONNX Runtime and ONNX Runtime Generate() API. By the end, you
+    will be able to build ONNX Runtime and ONNX Runtime generate() API for Android and run a Phi-3
+    model using ONNX Runtime on an Arm-based smartphone. It focuses on tools and technologies
+    such as Kotlin, CPP, ONNX Runtime, Android, and Hugging Face, Windows and Android environments,
+    and Arm platforms including Cortex-A. The main steps cover Create a development environment,
+    Build ONNX Runtime, Build ONNX Runtime Generate() API, Run a benchmark on an Android phone,
+    and Build and run an Android chat app.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build ONNX Runtime and ONNX Runtime generate() API for Android and run a Phi-3
+      model using ONNX Runtime on an Arm-based smartphone. Learn how to build ONNX Runtime and
+      the generate() API for Android to run a Phi-3 model on Arm-based smartphones.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers interested in learning how to build an
+      Android chat app with ONNX Runtime and ONNX Runtime Generate() API.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows x86_64 development machine
+      with at least 16GB of RAM.; An Android phone with at least 8GB of RAM. This learning path
+      was tested on Samsung Galaxy S24.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Kotlin, CPP, ONNX Runtime, Android, and Hugging
+      Face, Windows and Android environments, and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Create a development environment, Build ONNX Runtime,
+      Build ONNX Runtime Generate() API, Run a benchmark on an Android phone, and Build and run
+      an Android chat app.
+# END generated_summary_faq
+
 author: Koki Mitsunami
 
 ### Tags
@@ -58,3 +100,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md b/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md
index fde29d71d6..bed45cbf8b 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md
@@ -22,6 +22,56 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 13ff69755e51c6d7c355616f95703b3f2cefaedcf1f8dbeab31fe2e3db9aec74
+  summary: >-
+    Learn how to build a hands-free selfie Android application using MediaPipe multimodal AI,
+    Kotlin flows, CameraX, and MVVM architecture. It is designed for mobile application developers
+    interested in learning how to build an Android selfie application with Modern MediaPipe Multimodal
+    AI, Kotlin flows, and CameraX, using the Modern Android Development (MAD) architecture design.
+    By the end, you will be able to architect a modern hands-free selfie Android app with MediaPipe,
+    leverage lifecycle-aware components within the Model-View-ViewModel (MVVM) architecture, and
+    combine MediaPipe's face landmark detection and gesture recognition for integration in a multimodel
+    selfie solution. It focuses on tools and technologies such as Android Studio, Kotlin, and
+    MediaPipe, Android environments, and Arm platforms including Cortex-A and Mali GPU. The main
+    steps cover Set up the Development Environment, Manage Camera Permissions, Integrate MediaPipe
+    solutions, Manage UI state with ViewModel, and Use SharedFlow to View Events.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will architect a modern hands-free selfie Android app with MediaPipe, leverage lifecycle-aware
+      components within the Model-View-ViewModel (MVVM) architecture, and combine MediaPipe's
+      face landmark detection and gesture recognition for integration in a multimodel selfie solution.
+      Learn how to build a hands-free selfie Android application using MediaPipe multimodal AI,
+      Kotlin flows, CameraX, and MVVM architecture.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for mobile application developers interested in learning how
+      to build an Android selfie application with Modern MediaPipe Multimodal AI, Kotlin flows,
+      and CameraX, using the Modern Android Development (MAD) architecture design.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A development machine with [Android
+      Studio](https://developer.android.com/studio) installed.; A recent Arm-powered Android phone
+      with a front-facing camera and a USB data cable.; Familiarity with Android development concepts.;
+      Basic knowledge of Modern Android Architecture. See [Modern Android App Architecture](https://developer.android.com/courses/pathways/android-architecture).;
+      Basic knowledge of Kotlin programming language, including [Coroutines](https://kotlinlang.org/docs/coroutines-overview.html)
+      and [Kotlin Flows](https://kotlinlang.org/docs/flow.html).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Android Studio, Kotlin, and MediaPipe, Android environments,
+      and Arm platforms such as Cortex-A and Mali GPU.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Set up the Development Environment, Manage Camera
+      Permissions, Integrate MediaPipe solutions, Manage UI state with ViewModel, and Use SharedFlow
+      to View Events.
+# END generated_summary_faq
+
 author: Han Yin
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md b/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md
index 18400d1c28..7288ca6232 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md
@@ -24,6 +24,56 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 688b5b6eddc0480fa732308802d225696371c22a468bb6b140c6b74908222bbc
+  summary: >-
+    Learn how to build an Android chat application with Llama models using ExecuTorch, XNNPACK,
+    and KleidiAI for accelerated performance on Arm smartphones. It is designed for software developers
+    interested in learning how to build an Android chat app with Llama, KleidiAI, ExecuTorch,
+    and XNNPACK. By the end, you will be able to set up an ExecuTorch development environment,
+    describe how ExecuTorch uses KleidiAI kernels to accelerate performance on Arm-based platforms,
+    and describe how 4-bit groupwise PTQ quantization reduces model size without significantly
+    sacrificing model accuracy. It focuses on tools and technologies such as Java, CPP, Python,
+    Hugging Face, and ExecuTorch, macOS and Android environments, and Arm platforms including
+    Cortex-A. The main steps cover Create a development environment, ExecuTorch Setup, Understanding
+    Llama models, Prepare Llama models for ExecuTorch, and Run Benchmark on Android phone.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will set up an ExecuTorch development environment, describe how ExecuTorch uses KleidiAI
+      kernels to accelerate performance on Arm-based platforms, and describe how 4-bit groupwise
+      PTQ quantization reduces model size without significantly sacrificing model accuracy. Learn
+      how to build an Android chat application with Llama models using ExecuTorch, XNNPACK, and
+      KleidiAI for accelerated performance on Arm smartphones.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers interested in learning how to build
+      an Android chat app with Llama, KleidiAI, ExecuTorch, and XNNPACK.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Apple M1/M2 development machine with
+      Android Studio installed or a Linux machine with at least 16GB of RAM.; An Arm-powered smartphone
+      with the i8mm feature running Android, with 16GB of RAM.; A USB cable to connect your smartphone
+      to your development machine.; Android Debug Bridge (adb) installed on your device. Follow
+      the steps in [adb](https://developer.android.com/tools/adb) to install Android SDK Platform
+      Tools. The adb tool is included in this package.; Java 17 JDK. Follow the steps in [Java
+      17 JDK](https://www.oracle.com/java/technologies/javase/jdk17-archive-downloads.html) to
+      download and install JDK for host.; Python 3.10.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Java, CPP, Python, Hugging Face, and ExecuTorch,
+      macOS and Android environments, and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Create a development environment, ExecuTorch Setup,
+      Understanding Llama models, Prepare Llama models for ExecuTorch, and Run Benchmark on Android
+      phone.
+# END generated_summary_faq
+
 author: 
     - Varun Chari
     - Pareena Verma
@@ -67,3 +117,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md b/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md
index 0fe535107e..db98e92f5b 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md
@@ -24,6 +24,55 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 9ccf2cdd6406694d85c553204479d7ff1f688512056a3c76d4c85266cdc418b1
+  summary: >-
+    Learn how to build a customer support chatbot for Android using Llama 3.2, ExecuTorch, and
+    KleidiAI to run on-device inference on Arm platforms. It is designed for software developers
+    interested in building an on-device customer support chatbot for Android using Meta's Llama
+    models and the ExecuTorch runtime. By the end, you will be able to set up a development environment
+    for building and deploying ExecuTorch-based apps on Android, describe how ExecuTorch uses
+    KleidiAI kernels to accelerate performance on Arm-based platforms, and export a Llama 3.2
+    model to .pte format optimized for on-device inference. It focuses on tools and technologies
+    such as Java, Python, and ExecuTorch, macOS, Linux, and Android environments, and Arm platforms
+    including Cortex-A. The main steps cover Create a development environment, Set up ExecuTorch,
+    Understand Llama models, Prepare Llama models for ExecuTorch, and Run the chatbot on Android.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will set up a development environment for building and deploying ExecuTorch-based apps
+      on Android, describe how ExecuTorch uses KleidiAI kernels to accelerate performance on Arm-based
+      platforms, and export a Llama 3.2 model to .pte format optimized for on-device inference.
+      Learn how to build a customer support chatbot for Android using Llama 3.2, ExecuTorch, and
+      KleidiAI to run on-device inference on Arm platforms.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers interested in building an on-device
+      customer support chatbot for Android using Meta's Llama models and the ExecuTorch runtime.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Apple M1/M2/M3 development machine,
+      or a Linux machine with at least 16GB of RAM; An Arm-powered smartphone with the i8mm feature
+      running Android, with 16GB of RAM; A USB cable to connect your smartphone to your development
+      machine; Android Debug Bridge (adb) installed. Follow the steps in [adb](https://developer.android.com/tools/adb)
+      to install Android SDK Platform Tools; Java 17 JDK. Follow the steps in [Java SE 17 Archive
+      Downloads](https://www.oracle.com/java/technologies/javase/jdk17-archive-downloads.html)
+      to download and install JDK for your host; Python 3.10 or later; A [Hugging Face](https://huggingface.co/)
+      account with access to Meta Llama models.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Java, Python, and ExecuTorch, macOS, Linux, and
+      Android environments, and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Create a development environment, Set up ExecuTorch,
+      Understand Llama models, Prepare Llama models for ExecuTorch, and Run the chatbot on Android.
+# END generated_summary_faq
+
 author: Parichay Das
 
 ### Tags
@@ -68,3 +117,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md b/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md
index ee4ba1faea..189cbd8fd4 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md
@@ -21,6 +21,51 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 95d4fb855552939d1a0e9cccfe46333692ea291ee85dd08b816321db29ceebdc
+  summary: >-
+    Learn how to detect and debug memory safety bugs in Android applications using Arm Memory
+    Tagging Extension (MTE) on a Google Pixel 8 smartphone. It is designed for developers interested
+    in learning how to use the Arm Memory Tagging Extension (MTE) to detect memory safety bugs
+    with Android Studio on a Google Pixel 8 smartphone. By the end, you will be able to recognize
+    common memory safety bugs in Android applications, describe how you can use an Android MTE
+    Test app to implement common memory bugs, and build the MTE Test app in Android Studio. It
+    focuses on tools and technologies such as Android Studio and MTE, Android environments, and
+    Arm platforms including Cortex-A. The main steps cover Background, Implement memory safety
+    bugs with the Android app, Set up the app for debugging with MTE, and Debug in Android Studio
+    with MTE.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will recognize common memory safety bugs in Android applications, describe how you can
+      use an Android MTE Test app to implement common memory bugs, and build the MTE Test app
+      in Android Studio. Learn how to detect and debug memory safety bugs in Android applications
+      using Arm Memory Tagging Extension (MTE) on a Google Pixel 8 smartphone.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for developers interested in learning how to use the Arm Memory
+      Tagging Extension (MTE) to detect memory safety bugs with Android Studio on a Google Pixel
+      8 smartphone.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Google Pixel 8 smartphone.; Android
+      Studio installed on your development computer.; A USB cable to connect your computer to
+      your Google Pixel 8.; Android Debug Bridge (adb) installed on your device. If needed, follow
+      the steps in the [Android Debug Bridge](https://developer.android.com/tools/adb) documentation.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Android Studio and MTE, Android environments, and
+      Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Background, Implement memory safety bugs with the
+      Android app, Set up the app for debugging with MTE, and Debug in Android Studio with MTE.
+# END generated_summary_faq
+
 author: Roberto Lopez Mendez
 
 ### Tags
@@ -61,3 +106,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md
index 6ed550d3a9..ddac2ee308 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md
@@ -16,6 +16,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: b194edd6ff4ffc5e39e7daa995271d2ed81ec31c8e88ddf1b23a54eb06dd1d06
+  summary: >-
+    Get started with Arm Accuracy Super Resolution (Arm ASR) walks you through an end-to-end Arm
+    software workflow. It is designed for mobile, gaming, and graphics developers who want to
+    install and configure Arm Accuracy Super Resolution (Arm ASR) to enhance performance on complex
+    game content without sacrificing image quality. By the end, you will be able to describe Arm
+    Accuracy Super Resolution, integrate Arm ASR into your game project, and manage how Arm ASR
+    upscales content. It focuses on tools and technologies such as Unreal Engine, Android environments,
+    and Arm platforms including Mali and Immortalis. The main steps cover What is Arm Accuracy
+    Super Resolution?, Using Arm ASR in Unreal Engine, Using Arm ASR in a Custom Engine using
+    the Generic Library, and Acknowledgements and Licensing.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will describe Arm Accuracy Super Resolution, integrate Arm ASR into your game project,
+      and manage how Arm ASR upscales content.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is for mobile, gaming, and graphics developers who want to install and
+      configure Arm Accuracy Super Resolution (Arm ASR) to enhance performance on complex game
+      content without sacrificing image quality.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A game project that uses advanced rendering
+      features (such as hardware ray tracing) that stretch the performance capabilities of everyday
+      smartphones.; A development machine with Git installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Unreal Engine, Android environments, and Arm platforms
+      such as Mali and Immortalis.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around What is Arm Accuracy Super Resolution?, Using Arm
+      ASR in Unreal Engine, Using Arm ASR in a Custom Engine using the Generic Library, and Acknowledgements
+      and Licensing.
+# END generated_summary_faq
+
 author: Julie Gaskin
 
 ### Tags
@@ -61,3 +103,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md
index b1af579458..758015958d 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md
@@ -17,6 +17,43 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 23df12c0e165a4120fa25b5573437121bc7af141376758ccd051ddac14e180fb
+  summary: >-
+    Get started with Unity on Android walks you through an end-to-end Arm software workflow. It
+    is designed for Unity developers who want to target Android devices. By the end, you will
+    be able to set up with Unity development, build and deploy to an Android device, and launch
+    the Profiler tool to investigate performance issues. It focuses on tools and technologies
+    such as Unity and C#, Android environments, and Arm platforms including Cortex. The main steps
+    cover Set up, Sample project, Test on Android device, Introduction to profiling, and Profiling
+    on Android.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will set up with Unity development, build and deploy to an Android device, and launch
+      the Profiler tool to investigate performance issues.
+  - question: Who is this Learning Path for?
+    answer: >-
+      Unity developers who want to target Android devices.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Basic knowledge of game engines and
+      programming concepts; Recent Android device, such as a mobile phone or tablet; Desktop computer
+      capable of running Unity.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Unity and C#, Android environments, and Arm platforms
+      such as Cortex.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Set up, Sample project, Test on Android device, Introduction
+      to profiling, and Profiling on Android.
+# END generated_summary_faq
+
 author: Joshua Marshall-Law
 
 ### Tags
@@ -45,3 +82,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md
index 89ce4b186f..000ae5e6f3 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md
@@ -15,6 +15,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 44e7b5fe3fda52267dc1957319439895293276602b1f533de5665adaf6f7cafe
+  summary: >-
+    Profile Android game performance in Godot with Arm Performance Studio walks you through an
+    end-to-end Arm software workflow. It is designed for Godot developers targeting Android devices
+    who want to optimize game performance on Arm CPUs and Mali GPUs using Arm Performance Studio
+    tools. By the end, you will be able to install the Arm Performance Studio Integration extension
+    in Godot and annotate your Godot game with performance markers for profiling in Streamline
+    and Performance Advisor. It focuses on tools and technologies such as Godot and Arm Performance
+    Studio, Windows, macOS, and Linux environments, and Arm platforms including Cortex-A and Mali.
+    The main steps cover Profile your Godot game with Arm Performance Studio, Install the Arm
+    Performance Studio extension in Godot, Annotate Game Events for Profiling in Godot, Define
+    performance regions in Godot, and Use channels for threaded performance annotations.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install the Arm Performance Studio Integration extension in Godot and annotate
+      your Godot game with performance markers for profiling in Streamline and Performance Advisor.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for Godot developers targeting Android devices who want to
+      optimize game performance on Arm CPUs and Mali GPUs using Arm Performance Studio tools.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Familiarity with Godot; Familiarity
+      with Arm Performance Studio tools.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Godot and Arm Performance Studio, Windows, macOS,
+      and Linux environments, and Arm platforms such as Cortex-A and Mali.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Profile your Godot game with Arm Performance Studio,
+      Install the Arm Performance Studio extension in Godot, Annotate Game Events for Profiling
+      in Godot, Define performance regions in Godot, and Use channels for threaded performance
+      annotations.
+# END generated_summary_faq
+
 author: Albin Bernhardsson, Julie Gaskin
 
 ### Tags
@@ -58,3 +100,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md b/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md
index bd3f17d53d..8a583d095c 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md
@@ -16,6 +16,45 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 3f35558e0399cb4c851b120eaa2217a63c48336cbba96d23500d1b751e4aec63
+  summary: >-
+    How to Enable Hardware Ray Tracing on Lumen for Android Devices walks you through an end-to-end
+    Arm software workflow. It is designed for Unreal Engine developers interested in using hardware
+    ray tracing with Lumen on Arm devices. By the end, you will be able to learn about Lumen and
+    global illumination and enable hardware ray tracing on Lumen for Arm devices. It focuses on
+    tools and technologies such as Unreal Engine, Android environments, and Arm platforms including
+    Immortalis-G715 and Immortalis-G720. The main steps cover What is Lumen?, What is Global Illumination?,
+    How to Enable Lumen, and How to Enable Hardware Ray Tracing on Lumen.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will learn about Lumen and global illumination and enable hardware ray tracing on Lumen
+      for Arm devices.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for Unreal Engine developers interested in using hardware
+      ray tracing with Lumen on Arm devices.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A computer capable of running [Unreal
+      Engine 5.3 or later version](https://www.unrealengine.com/en-US/download).; An Android mobile
+      device that has a Mali GPU with hardware ray tracing support.; A USB cable to connect the
+      mobile device to your computer.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Unreal Engine, Android environments, and Arm platforms
+      such as Immortalis-G715 and Immortalis-G720.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around What is Lumen?, What is Global Illumination?, How
+      to Enable Lumen, and How to Enable Hardware Ray Tracing on Lumen.
+# END generated_summary_faq
+
 author: Owen Wu
 
 ### Tags
@@ -52,3 +91,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md b/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md
index 15c2b844a0..6cbb5e3e51 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md
@@ -13,6 +13,36 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: ab171b1ff6cfed7bce1cb8e50d4d9aeb4bee1972e8a409203cb438f94086a320
+  summary: >-
+    Get started with Arm hardware walks you through an end-to-end Arm software workflow. It is
+    designed for Developers new to the Arm architecture and looking for mobile hardware. By the
+    end, you will be able to find mobile hardware to use for software development. It focuses
+    on Android environments and Arm platforms including Cortex-A, Mali, and Immortalis. The main
+    steps cover Find Arm hardware.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will find mobile hardware to use for software development.
+  - question: Who is this Learning Path for?
+    answer: >-
+      Developers new to the Arm architecture and looking for mobile hardware.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: None.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers Android environments and Arm platforms such as Cortex-A, Mali, and Immortalis.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Find Arm hardware.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
@@ -40,3 +70,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md b/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md
index 4a407564cd..1936aaaae9 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md
@@ -18,6 +18,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 5a94138f74e7b2266c35dbd555f9966ca0ff29fdbd1842d4724e0da0cd4e46db
+  summary: >-
+    Understand KleidiAI SME2 matmul microkernels walks you through an end-to-end Arm software
+    workflow. It is designed for software developers, performance engineers, and AI practitioners.
+    By the end, you will be able to explain how a KleidiAI microkernel performs matrix multiplication
+    (matmul) with quantized data, identify how SME2 INT8 MOPA (matrix outer product accumulate)
+    instructions map to matmul work, and trace how quantization and packing feed an SME2 matmul
+    microkernel (using GGML Q4_0 and llama.cpp call stacks as a concrete example). It focuses
+    on tools and technologies such as C++, KleidiAI, llama.cpp, and SME2, Android and Linux environments,
+    and Arm platforms including Arm C1. The main steps cover Overview and setup, Matmul tiling
+    and packing, SME2 INT8 MOPA for matmul, Decode the SME2 matmul microkernel, and Repack RHS
+    weights (GGML Q4_0).
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will explain how a KleidiAI microkernel performs matrix multiplication (matmul) with
+      quantized data, identify how SME2 INT8 MOPA (matrix outer product accumulate) instructions
+      map to matmul work, and trace how quantization and packing feed an SME2 matmul microkernel
+      (using GGML Q4_0 and llama.cpp call stacks as a concrete example).
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers, performance engineers, and AI practitioners.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Basic understanding of general matrix
+      multiplication (GEMM) and matmul operations; Basic understanding of quantization concepts
+      for neural networks; (Optional) Access to an Arm CPU with SME2 support (Linux or Android)
+      for hands-on verification steps.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including C++, KleidiAI, llama.cpp, and SME2, Android and
+      Linux environments, and Arm platforms such as Arm C1.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview and setup, Matmul tiling and packing, SME2
+      INT8 MOPA for matmul, Decode the SME2 matmul microkernel, and Repack RHS weights (GGML Q4_0).
+# END generated_summary_faq
+
 author: Zenon Zhilong Xiu
 
 ### Tags
@@ -62,3 +105,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md b/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md
index 10dba075f5..800bd80944 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md
@@ -17,6 +17,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 0e51db9ceb25c31c42608f3c6744a4fa8f9d995805ed44a7d7fc83324889ea12
+  summary: >-
+    Learn how to run LLM inference on Android devices using MediaPipe with KleidiAI-enhanced Arm
+    i8mm features to benchmark the Gemma 2B model. It is designed for Android developers who want
+    to efficiently run LLMs on-device. By the end, you will be able to install the prerequisites
+    for cross-compiling new inference engines for Android, run LLM inference on an Android device
+    with the Gemma 2B model using the Google AI Edge's MediaPipe framework, and benchmark LLM
+    inference speed with and without the KleidiAI-enhanced Arm i8mm processor feature. It focuses
+    on tools and technologies such as Java, MediaPipe, Android SDK, Android NDK, and Bazel, Linux
+    environments, and Arm platforms including Cortex-A. The main steps cover Install dependencies,
+    Run the Gemma 2B model using MediaPipe with XNNPACK, and Benchmark the Gemma 2B Model with
+    KleidiAI.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install the prerequisites for cross-compiling new inference engines for Android,
+      run LLM inference on an Android device with the Gemma 2B model using the Google AI Edge's
+      MediaPipe framework, and benchmark LLM inference speed with and without the KleidiAI-enhanced
+      Arm i8mm processor feature. Learn how to run LLM inference on Android devices using MediaPipe
+      with KleidiAI-enhanced Arm i8mm features to benchmark the Gemma 2B model.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for Android developers who want to efficiently run LLMs on-device.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An x86_64 Linux machine running Ubuntu
+      with approximately 500 MB of free space, or a docker daemon that can build and run a provided
+      x86_64 Dockerfile.; An Android phone with support for i8mm (tested on Google Pixel 8 Pro).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Java, MediaPipe, Android SDK, Android NDK, and Bazel,
+      Linux environments, and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Install dependencies, Run the Gemma 2B model using
+      MediaPipe with XNNPACK, and Benchmark the Gemma 2B Model with KleidiAI.
+# END generated_summary_faq
+
 author: 
     - Pareena Verma
     - Joe Stech
@@ -62,3 +105,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md b/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md
index deb3ff3a72..e2d3e88877 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md
@@ -16,6 +16,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 68cdc7315795b6ffa794c0a1fd4cf325ab39ebb65e23efd27b7760ece76a2ec9
+  summary: >-
+    Learn how to build the libGPUInfo library using Android NDK and query configuration details
+    of Arm Mali or Immortalis GPUs on Android devices. It is designed for Android developers who
+    want to adjust application complexity to match device performance. By the end, you will be
+    able to build the libGPUInfo library using the Android NDK and run an example application
+    to query the configuration details of an Arm Mali or Arm Immortalis GPU. It focuses on tools
+    and technologies such as Android NDK and adb, Android environments, and Arm platforms including
+    Cortex-A, Mali, and Immortalis. The main steps cover Build and run an example application
+    to obtain Arm GPU configuration information.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build the libGPUInfo library using the Android NDK and run an example application
+      to query the configuration details of an Arm Mali or Arm Immortalis GPU. Learn how to build
+      the libGPUInfo library using Android NDK and query configuration details of Arm Mali or
+      Immortalis GPUs on Android devices.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for Android developers who want to adjust application complexity
+      to match device performance.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A development machine running Ubuntu
+      or Debian Linux with `x86_64` architecture; An Android device with an Arm GPU.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Android NDK and adb, Android environments, and Arm
+      platforms such as Cortex-A, Mali, and Immortalis.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Build and run an example application to obtain Arm
+      GPU configuration information.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ##### Tags
@@ -55,3 +95,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md b/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md
index dbb9aa1a13..c12d891d5c 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md
@@ -18,6 +18,50 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: a744c8118a5a3cb97eee7bee271f768bdd71b4286e723c38a7b4ff6cd9e08d18
+  summary: >-
+    Learn how to accelerate LiteRT model inference on Android using KleidiAI with SME2 instructions
+    and validate performance with the benchmark tool. It is designed for developers looking to
+    leverage Arm's Scalable Matrix Extension 2 (SME2) instructions to accelerate LiteRT model
+    inference on Android. By the end, you will be able to understand how KleidiAI integrates with
+    LiteRT, build the LiteRT benchmark tool and enable XNNPACK and KleidiAI with SME2 support
+    in LiteRT, and create LiteRT models that can be accelerated by SME2 through KleidiAI. It focuses
+    on tools and technologies such as C, Python, and SME2, Android environments, and Arm platforms
+    including Cortex-A, Cortex-X, and Arm C1. The main steps cover Explore LiteRT, XNNPACK, KleidiAI,
+    and SME2, Create LiteRT models, Build the LiteRT benchmark tool, and Benchmark the LiteRT
+    model.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand how KleidiAI integrates with LiteRT, build the LiteRT benchmark tool
+      and enable XNNPACK and KleidiAI with SME2 support in LiteRT, and create LiteRT models that
+      can be accelerated by SME2 through KleidiAI. Learn how to accelerate LiteRT model inference
+      on Android using KleidiAI with SME2 instructions and validate performance with the benchmark
+      tool.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for developers looking to leverage Arm's Scalable Matrix Extension
+      2 (SME2) instructions to accelerate LiteRT model inference on Android.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm64 Linux development machine;
+      An Android device that supports Arm SME2 architecture features - see this [list of devices
+      with SME2 support](/learning-paths/cross-platform/multiplying-matrices-with-sme2/1-get-started/#devices).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including C, Python, and SME2, Android environments, and Arm
+      platforms such as Cortex-A, Cortex-X, and Arm C1.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Explore LiteRT, XNNPACK, KleidiAI, and SME2, Create
+      LiteRT models, Build the LiteRT benchmark tool, and Benchmark the LiteRT model.
+# END generated_summary_faq
+
 author: Jiaming Guo
 
 ### Tags
@@ -58,3 +102,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md b/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md
index c1d31c697e..5213a1d272 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md
@@ -18,6 +18,55 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: b55d902389806f5e84e674e5ba1f1f2d9e38af370c289ad344eff2dad3475df1
+  summary: >-
+    Learn how to benchmark KleidiAI micro-kernels in ExecuTorch using SME/SME2 instructions on
+    Arm64 platforms with ETDump profiling and analysis. It is designed for developers, performance
+    engineers, and ML framework contributors who want to benchmark and optimize KleidiAI micro-kernels
+    within ExecuTorch to accelerate model inference on Arm64 platforms supporting SME/SME2 instructions.
+    By the end, you will be able to cross-compile ExecuTorch for Arm64 with XNNPACK and KleidiAI
+    enabled, including SME/SME2 instructions, build and export ExecuTorch models that can be accelerated
+    by KleidiAI using SME/SME2 instructions, and use the executor_runner tool to run kernel workloads
+    and collect ETDump profiling data. It focuses on tools and technologies such as Python, ExecuTorch,
+    XNNPACK, and KleidiAI, Linux environments, and Arm platforms including Cortex-A. The main
+    steps cover Set up your environment, Cross-Compile ExecuTorch for the AArch64 platform, Accelerate
+    ExecuTorch operators with KleidiAI micro-kernels, Create and quantize linear layer benchmark
+    model, and Create and quantize convolution layer benchmark model.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will cross-compile ExecuTorch for Arm64 with XNNPACK and KleidiAI enabled, including
+      SME/SME2 instructions, build and export ExecuTorch models that can be accelerated by KleidiAI
+      using SME/SME2 instructions, and use the executor_runner tool to run kernel workloads and
+      collect ETDump profiling data. Learn how to benchmark KleidiAI micro-kernels in ExecuTorch
+      using SME/SME2 instructions on Arm64 platforms with ETDump profiling and analysis.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for developers, performance engineers, and ML framework contributors
+      who want to benchmark and optimize KleidiAI micro-kernels within ExecuTorch to accelerate
+      model inference on Arm64 platforms supporting SME/SME2 instructions.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An x86_64 Linux host machine running
+      Ubuntu, with at least 15 GB of free disk space; An Arm64 target system with support for
+      SME or SME2 - see the Learning Path [Devices with native SME2 support](/learning-paths/cross-platform/multiplying-matrices-with-sme2/1-get-started/#devices).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Python, ExecuTorch, XNNPACK, and KleidiAI, Linux
+      environments, and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Set up your environment, Cross-Compile ExecuTorch
+      for the AArch64 platform, Accelerate ExecuTorch operators with KleidiAI micro-kernels, Create
+      and quantize linear layer benchmark model, and Create and quantize convolution layer benchmark
+      model.
+# END generated_summary_faq
+
 author: Qixiang Xu
 
 ### Tags
@@ -50,3 +99,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md b/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md
index 91009496ec..0206cde4c9 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md
@@ -19,6 +19,53 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: e145caae5b20f7165251d88e334ba22d90c8b35270902778a2b6fe0ed0b250f6
+  summary: >-
+    Learn how to fine-tune and evaluate Neural Super Sampling (NSS) models using PyTorch and Arm's
+    Model Gym API with hardware-aware optimization. It is designed for developers exploring neural
+    graphics and interested in training and deploying upscaling models like Neural Super Sampling
+    (NSS) using PyTorch and Arm’s hardware-aware backend. By the end, you will be able to understand
+    the principles of neural graphics and how it’s applied to game performance, learn how to fine-tune
+    and evaluate a neural network for Neural Super Sampling (NSS), and use the Model Gym Python
+    API and CLI to configure and train neural graphics models. It focuses on tools and technologies
+    such as PyTorch, Jupyter Notebook, Vulkan, and NX, Linux environments, and Arm platforms including
+    Mali. The main steps cover Install Model Gym and explore neural graphics examples, Set up
+    your environment, Launch the training notebook, Visualize your model with Model Explorer,
+    and Defining your own use cases.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand the principles of neural graphics and how it’s applied to game performance,
+      learn how to fine-tune and evaluate a neural network for Neural Super Sampling (NSS), and
+      use the Model Gym Python API and CLI to configure and train neural graphics models. Learn
+      how to fine-tune and evaluate Neural Super Sampling (NSS) models using PyTorch and Arm's
+      Model Gym API with hardware-aware optimization.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for developers exploring neural graphics and interested in training
+      and deploying upscaling models like Neural Super Sampling (NSS) using PyTorch and Arm’s
+      hardware-aware backend.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Basic understanding of PyTorch and machine
+      learning concepts; A development machine running Ubuntu 22.04, with a CUDA-capable NVIDIA®
+      GPU; CUDA Toolkit version 11.8 or later.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including PyTorch, Jupyter Notebook, Vulkan, and NX, Linux
+      environments, and Arm platforms such as Mali.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Install Model Gym and explore neural graphics examples,
+      Set up your environment, Launch the training notebook, Visualize your model with Model Explorer,
+      and Defining your own use cases.
+# END generated_summary_faq
+
 author: Annie Tallund
 
 ### Tags
@@ -62,3 +109,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md b/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md
index b6fe39967c..33eaeea436 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md
@@ -14,6 +14,43 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: a25cb73b70716e397d9477f8ab9729f4a094143d276e4de68cf8c33b273bb1ff
+  summary: >-
+    Learn how to run example C programs on AArch64 Linux to gain an introductory understanding
+    of the Arm Memory Tagging Extension (MTE). It is designed for developers who want to gain
+    some experience with the Arm Memory Tagging Extension (MTE). By the end, you will be able
+    to run an example C program to gain an introductory understanding of MTE. It focuses on tools
+    and technologies such as QEMU, Linux environments, and Arm platforms including Cortex-A. The
+    main steps cover Build and run an example application to learn about MTE.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will run an example C program to gain an introductory understanding of MTE. Learn how
+      to run example C programs on AArch64 Linux to gain an introductory understanding of the
+      Arm Memory Tagging Extension (MTE).
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers who want to gain some experience with the Arm
+      Memory Tagging Extension (MTE).
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An AArch64 Linux development machine.
+      Cloud instances can be used, refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including QEMU, Linux environments, and Arm platforms such
+      as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Build and run an example application to learn about
+      MTE.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ##### Tags
@@ -54,3 +91,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md b/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md
index d4200d48fc..36515c8bfc 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md
@@ -20,6 +20,50 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: b6a9aa558ec5062c829d61b28fb92e25d5517249c011eb29f03cc36775b6f9af
+  summary: >-
+    Learn how to enable Arm Memory Tagging Extension (MTE) on a Google Pixel 8 smartphone, trigger
+    memory bug crashes, and interpret bug reports. It is designed for developers interested in
+    learning how to enable Arm's Memory Tagging Extension (MTE) on Google's Pixel 8 smartphone
+    and also how to access a memory bug report. By the end, you will be able to enable MTE on
+    your Google Pixel 8 smartphone, understand how MTE works and learn how to make an application
+    crash when it encounters a memory bug, and access the memory bug report. It focuses on tools
+    and technologies such as MTE, adb, and Google Pixel 8, Android environments, and Arm platforms
+    including Cortex-A. The main steps cover Enabling MTE on Pixel 8, Understanding MTE, Testing
+    MTE, Creating the Bug Report, and The Bug Report.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will enable MTE on your Google Pixel 8 smartphone, understand how MTE works and learn
+      how to make an application crash when it encounters a memory bug, and access the memory
+      bug report. Learn how to enable Arm Memory Tagging Extension (MTE) on a Google Pixel 8 smartphone,
+      trigger memory bug crashes, and interpret bug reports.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers interested in learning how to enable Arm's
+      Memory Tagging Extension (MTE) on Google's Pixel 8 smartphone and also how to access a memory
+      bug report.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Google Pixel 8 smartphone; A USB cable
+      to connect your Google Pixel 8 to your desktop machine; Android Debug Bridge (adb) installed
+      on your device. Follow the steps in https://developer.android.com/tools/adb to install Android
+      SDK Platform Tools. The adb tool is included in this package.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including MTE, adb, and Google Pixel 8, Android environments,
+      and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Enabling MTE on Pixel 8, Understanding MTE, Testing
+      MTE, Creating the Bug Report, and The Bug Report.
+# END generated_summary_faq
+
 author: Roberto Lopez Mendez
 
 ### Tags
@@ -61,3 +105,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md b/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md
index 4f8f215c1c..f9f0974756 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md
@@ -20,6 +20,52 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 5ca059af36f0ba375701e76ce82b7803f01ae6beab313336b333bfbdebaa3b1a
+  summary: >-
+    Learn how to capture high-quality frame datasets from Unreal Engine 5.5 gameplay for training
+    and evaluating neural graphics models like Neural Super Sampling. It is designed for Unreal
+    Engine developers who want to generate high-quality frame datasets for training and evaluating
+    neural graphics models. By the end, you will be able to understand why Neural Graphics Data
+    Capture is useful in a neural graphics workflow, install and enable the Neural Graphics Data
+    Capture plugin in Unreal Engine 5.5, and configure a Level Blueprint to start and stop capture
+    with hotkeys. It focuses on tools and technologies such as Unreal Engine, Visual Studio, and
+    NX, Windows environments, and Arm platforms including Mali and Immortalis. The main steps
+    cover Benefits of Neural Graphics Data Capture for game developers, Install and enable the
+    plugin, Configure Level Blueprint capture controls, Run capture and verify outputs, and Capture
+    settings and troubleshooting.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand why Neural Graphics Data Capture is useful in a neural graphics workflow,
+      install and enable the Neural Graphics Data Capture plugin in Unreal Engine 5.5, and configure
+      a Level Blueprint to start and stop capture with hotkeys. Learn how to capture high-quality
+      frame datasets from Unreal Engine 5.5 gameplay for training and evaluating neural graphics
+      models like Neural Super Sampling.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is for Unreal Engine developers who want to generate high-quality frame
+      datasets for training and evaluating neural graphics models.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Windows 11; Unreal Engine 5.5 installed;
+      Visual Studio with C++ game development tools; A C++ Unreal project (such as the Third Person
+      template).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Unreal Engine, Visual Studio, and NX, Windows environments,
+      and Arm platforms such as Mali and Immortalis.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Benefits of Neural Graphics Data Capture for game
+      developers, Install and enable the plugin, Configure Level Blueprint capture controls, Run
+      capture and verify outputs, and Capture settings and troubleshooting.
+# END generated_summary_faq
+
 author: 
 - Annie Tallund
 - Richard Burton
@@ -69,3 +115,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md b/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md
index 411181ca79..6f5c2a460d 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md
@@ -22,6 +22,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 7ffbac59b340f3ef5119d2f5ba4a786fd15d521bb294f3a4213b083c9b4ce23d
+  summary: >-
+    Learn how to configure ML Extensions for Vulkan emulation and enable Neural Super Sampling
+    (NSS) in Unreal Engine for real-time upscaling. It is designed for developers experimenting
+    with neural graphics using Unreal Engine® and ML Extensions for Vulkan®. By the end, you will
+    be able to understand how Arm enables neural graphics for game development, configure ML extensions
+    for Vulkan emulation, and enable Neural Super Sampling (NSS) in Unreal Engine. It focuses
+    on tools and technologies such as Unreal Engine, Vulkan SDK, Visual Studio, and NX, Windows
+    environments, and Arm platforms including Mali and Immortalis. The main steps cover Introduction
+    to neural graphics and Neural Super Sampling (NSS), Setting up the emulation layers, Create
+    an example game, Run the example, and Using RenderDoc for Debugging and Analysis.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand how Arm enables neural graphics for game development, configure ML extensions
+      for Vulkan emulation, and enable Neural Super Sampling (NSS) in Unreal Engine. Learn how
+      to configure ML Extensions for Vulkan emulation and enable Neural Super Sampling (NSS) in
+      Unreal Engine for real-time upscaling.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers experimenting with neural graphics using Unreal
+      Engine® and ML Extensions for Vulkan®.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Windows 11; Unreal Engine 4.27 or 5.4
+      or 5.6 (with the Templates and Feature Pack enabled); Visual Studio (with Desktop Development
+      with C++ and .NET desktop build tools).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Unreal Engine, Vulkan SDK, Visual Studio, and NX,
+      Windows environments, and Arm platforms such as Mali and Immortalis.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Introduction to neural graphics and Neural Super Sampling
+      (NSS), Setting up the emulation layers, Create an example game, Run the example, and Using
+      RenderDoc for Debugging and Analysis.
+# END generated_summary_faq
+
 author: Annie Tallund
 
 ### Tags
@@ -66,3 +109,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md b/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md
index 19075e50fe..843074124f 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md
@@ -21,6 +21,55 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: aba1cea365c0c61e84fb545ada15a64ed52c099f1252dc6312f88ee82385d2d0
+  summary: >-
+    Learn how to build, optimize, and deploy machine learning models using ONNX Runtime on Arm64
+    platforms, including Raspberry Pi, cloud instances, and Android devices. It is designed for
+    developers who want to build, optimize, and deploy machine learning models using ONNX on Arm64-based
+    platforms such as Raspberry Pi, Arm-based laptops, cloud instances, or Android smartphones.
+    By the end, you will be able to explain what ONNX is and how it enables model portability
+    across ML frameworks, build and export a neural network model in Python to ONNX format, and
+    run inference using ONNX Runtime on Arm64 platforms. It focuses on tools and technologies
+    such as Python, PyTorch, TensorFlow, ONNX, and Android, Windows, Linux, macOS, and Android
+    environments, and Arm platforms including Cortex-A and Neoverse. The main steps cover Understand
+    ONNX fundamentals and architecture, Set up your development environment, Generate a synthetic
+    Sudoku digit dataset, Train the digit recognizer, and Run inference and evaluate the model.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will explain what ONNX is and how it enables model portability across ML frameworks,
+      build and export a neural network model in Python to ONNX format, and run inference using
+      ONNX Runtime on Arm64 platforms. Learn how to build, optimize, and deploy machine learning
+      models using ONNX Runtime on Arm64 platforms, including Raspberry Pi, cloud instances, and
+      Android devices.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for developers who want to build, optimize, and deploy machine
+      learning models using ONNX on Arm64-based platforms such as Raspberry Pi, Arm-based laptops,
+      cloud instances, or Android smartphones.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A development machine with Python 3.10
+      or 3.11 installed (Prebuilt ONNX Runtime packages for Arm platforms don't yet support Python
+      3.12); Basic familiarity with PyTorch or TensorFlow; An Arm64 device such as a Raspberry
+      Pi or Android smartphone; Android Studio (required only for the final deployment section).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Python, PyTorch, TensorFlow, ONNX, and Android,
+      Windows, Linux, macOS, and Android environments, and Arm platforms such as Cortex-A and
+      Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Understand ONNX fundamentals and architecture, Set
+      up your development environment, Generate a synthetic Sudoku digit dataset, Train the digit
+      recognizer, and Run inference and evaluate the model.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
@@ -67,3 +116,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md b/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md
index 088e436e33..18763a5704 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md
@@ -16,6 +16,43 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 586a505543df4237c943ea47210d735fafe7d5ed2c6ad18b1b99227987ef9b15
+  summary: >-
+    Learn how to optimize vertex representations and analyze Vertex Memory Efficiency using Arm
+    Frame Advisor for improved GPU performance on Android. It is designed for Android graphics
+    application developers aiming to enhance GPU performance through smarter vertex optimization.
+    By the end, you will be able to optimize vertex representations on Arm GPUs and analyze Vertex
+    Memory Efficiency using Arm Frame Advisor. It focuses on tools and technologies such as C
+    and CPP, Android environments, and Arm platforms including Immortalis and Mali. The main steps
+    cover Optimizing graphics vertex efficiency for Arm GPUs.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will optimize vertex representations on Arm GPUs and analyze Vertex Memory Efficiency
+      using Arm Frame Advisor. Learn how to optimize vertex representations and analyze Vertex
+      Memory Efficiency using Arm Frame Advisor for improved GPU performance on Android.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for Android graphics application developers aiming to enhance
+      GPU performance through smarter vertex optimization.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Understanding of vertex attributes.;
+      Familiarity with Arm Frame Advisor (part of Arm Performance Studio).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including C and CPP, Android environments, and Arm platforms
+      such as Immortalis and Mali.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Optimizing graphics vertex efficiency for Arm GPUs.
+# END generated_summary_faq
+
 author:
     - Andrew Kilroy
     - Peter Harris
@@ -62,3 +99,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md
index e185a806e5..2993ea9919 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md
@@ -19,6 +19,50 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 4962524245ec5e5e07d1207537208a22c2e9569f252f36d758c4f828d2104272
+  summary: >-
+    Learn how to build llama.cpp with KleidiAI and SME2 support to profile and accelerate LLM
+    inference performance on Android devices. It is designed for software developers, performance
+    engineers, and AI practitioners. By the end, you will be able to build llama.cpp with KleidiAI
+    and SME2 support, profile LLM inference performance on Android, and understand how KleidiAI
+    and SME2 accelerate LLM operators. It focuses on tools and technologies such as SME2, C++,
+    and llama.cpp, Android and Linux environments, and Arm platforms including Arm C1. The main
+    steps cover Understand how SME2 and KleidiAI accelerate LLM inference in llama.cpp, Trace
+    how KleidiAI and SME2 accelerate llama.cpp from model load to token decode, Build llama.cpp
+    with KleidiAI and SME2 enabled, and Measure SME2 acceleration in llama.cpp on Android.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build llama.cpp with KleidiAI and SME2 support, profile LLM inference performance
+      on Android, and understand how KleidiAI and SME2 accelerate LLM operators. Learn how to
+      build llama.cpp with KleidiAI and SME2 support to profile and accelerate LLM inference performance
+      on Android devices.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers, performance engineers, and AI practitioners.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Knowledge of KleidiAI and SME2; A Linux
+      host machine (x86_64 or aarch64) for building llama.cpp with the Arm GNU Toolchain; Git,
+      CMake, and Android Debug Bridge (ADB) installed on your host machine; An Android device
+      with Arm SME2 support for running and profiling the executable.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including SME2, C++, and llama.cpp, Android and Linux environments,
+      and Arm platforms such as Arm C1.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Understand how SME2 and KleidiAI accelerate LLM inference
+      in llama.cpp, Trace how KleidiAI and SME2 accelerate llama.cpp from model load to token
+      decode, Build llama.cpp with KleidiAI and SME2 enabled, and Measure SME2 acceleration in
+      llama.cpp on Android.
+# END generated_summary_faq
+
 author: Zenon Zhilong Xiu
 
 ### Tags
@@ -62,3 +106,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md
index 3fff9361cf..d71100089d 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md
@@ -19,6 +19,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 1645a1c65527da010edd6fefddad3c865eac0f9cad417ba59709a57bb9dc847a
+  summary: >-
+    Learn how to build ONNX Runtime with KleidiAI and SME2 support for Android and profile ONNX
+    model performance to compare acceleration improvements. It is designed for software developers,
+    performance engineers, and AI practitioners. By the end, you will be able to build ONNX Runtime
+    with KleidiAI and SME2 support for Android, profile ONNX model performance using benchmark
+    tools, and analyze how KleidiAI kernels accelerate ONNX operators with SME2. It focuses on
+    tools and technologies such as C++, ONNX Runtime, and SME2, Android and Linux environments,
+    and Arm platforms including Cortex-A and Arm C1. The main steps cover ONNX Runtime architecture
+    with SME2 acceleration, Integration of KleidiAI to ORT MLAS, Build ONNX Runtime with KleidiAI
+    and SME2 for Android, and Profile ONNX model performance.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build ONNX Runtime with KleidiAI and SME2 support for Android, profile ONNX model
+      performance using benchmark tools, and analyze how KleidiAI kernels accelerate ONNX operators
+      with SME2. Learn how to build ONNX Runtime with KleidiAI and SME2 support for Android and
+      profile ONNX model performance to compare acceleration improvements.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers, performance engineers, and AI practitioners.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Android device with Arm SME2 support;
+      Basic understanding of machine learning model inference; Familiarity with Android NDK and
+      cross-compilation.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including C++, ONNX Runtime, and SME2, Android and Linux environments,
+      and Arm platforms such as Cortex-A and Arm C1.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around ONNX Runtime architecture with SME2 acceleration,
+      Integration of KleidiAI to ORT MLAS, Build ONNX Runtime with KleidiAI and SME2 for Android,
+      and Profile ONNX model performance.
+# END generated_summary_faq
+
 author: Zenon Zhilong Xiu
 
 ### Tags
@@ -59,3 +101,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md b/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md
index 71ab0ec630..77b490794a 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md
@@ -19,6 +19,52 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 01138f6538c655f866fb034a983461b2ac9b793142775465233b2ec8ec18d0c5
+  summary: >-
+    Learn how to profile ML model execution times and application performance on Arm Android devices
+    using Arm Performance Studio and Android Studio Profiler. It is designed for software developers
+    who want to learn how to profile the performance of Machine Learning (ML) models running on
+    Arm devices. By the end, you will be able to profile the execution times of ML models on Arm
+    devices, profile ML application performance on Arm devices, and describe how profiling can
+    help optimize the performance of Machine Learning applications. It focuses on tools and technologies
+    such as Android Studio, LiteRT, and Hugging Face, Android and Linux environments, and Arm
+    platforms including Cortex-A, Mali, and Immortalis. The main steps cover Why should you profile
+    your ML application?, Profile your application with Streamline, Memory Profiling with Android
+    Studio, Profiling the Neural Network, and ML Profiling of a LiteRT model with ExecuteNetwork.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will profile the execution times of ML models on Arm devices, profile ML application
+      performance on Arm devices, and describe how profiling can help optimize the performance
+      of Machine Learning applications. Learn how to profile ML model execution times and application
+      performance on Arm Android devices using Arm Performance Studio and Android Studio Profiler.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to learn how to profile the
+      performance of Machine Learning (ML) models running on Arm devices.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm-powered Android smartphone, and
+      a USB cable to connect to it.; For profiling the ML inference, [Arm NN ExecuteNetwork](https://github.com/ARM-software/armnn/releases)
+      or [ExecuTorch](https://github.com/pytorch/executorch).; For profiling the application,
+      [Arm Performance Studio with Streamline](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio).;
+      Android Studio Profiler.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Android Studio, LiteRT, and Hugging Face, Android
+      and Linux environments, and Arm platforms such as Cortex-A, Mali, and Immortalis.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Why should you profile your ML application?, Profile
+      your application with Streamline, Memory Profiling with Android Studio, Profiling the Neural
+      Network, and ML Profiling of a LiteRT model with ExecuteNetwork.
+# END generated_summary_faq
+
 author: Ben Clark
 
 ### Tags
@@ -53,3 +99,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md b/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md
index fb78d49a88..09678b96f1 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md
@@ -19,6 +19,45 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 9950a58160736e0c60ad80ea602262347e2ba8a37a00e29f25dc96709026ea1f
+  summary: >-
+    Learn how to deploy Unity applications to Android, profile code running on Arm devices, and
+    analyze performance data for optimization. It is designed for Unity developers wanting to
+    analyze the performance of their apps on Android devices. By the end, you will be able to
+    deploy to Android, profile code running on an Android device, and analyze performance data.
+    It focuses on tools and technologies such as Unity and C#, Android environments, and Arm platforms
+    including armv8, aarch32, aarch64, and arm64. The main steps cover Analyzing the sample application,
+    Preparation, Inside the code, Profiling, and Collect performance data.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will deploy to Android, profile code running on an Android device, and analyze performance
+      data. Learn how to deploy Unity applications to Android, profile code running on Arm devices,
+      and analyze performance data for optimization.
+  - question: Who is this Learning Path for?
+    answer: >-
+      Unity developers wanting to analyze the performance of their apps on Android devices.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Recent Android device, such as a mobile
+      phone or tablet; Desktop computer capable of running Unity; Basic knowledge of Unity and
+      programming concepts; The setup described in the Learning Path [Get started with Unity on
+      Android](/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Unity and C#, Android environments, and Arm platforms
+      such as armv8, aarch32, aarch64, and arm64.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Analyzing the sample application, Preparation, Inside
+      the code, Profiling, and Collect performance data.
+# END generated_summary_faq
+
 author: Joshua Marshall-Law
 
 ### Tags
@@ -55,3 +94,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md b/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md
index 5b418f25a9..8c8b96ce1f 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md
@@ -18,6 +18,51 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 56d9d0fe9606e42281a2d2be52992c7a4b6846b208376c92e7fe3094647d1c70
+  summary: >-
+    Learn how to apply post-training quantization to PyTorch models using TorchAO and export INT8
+    models to .vgf format with the ExecuTorch Arm backend. It is designed for ML developers who
+    want to reduce latency and memory bandwidth by exporting INT8 models to the `.vgf` file format
+    using the ExecuTorch Arm backend. By the end, you will be able to explain when to use post-training
+    quantization (PTQ) vs quantization-aware training (QAT), prepare and quantize a PyTorch model
+    using TorchAO PT2E quantization APIs, and export the quantized model to TOSA and generate
+    a model artifact with the ExecuTorch Arm backend. It focuses on tools and technologies such
+    as ExecuTorch, TorchAO, Vulkan, TOSA, and NX, Linux, macOS, and Windows environments, and
+    Arm platforms including Mali. The main steps cover Explore PTQ and QAT for ExecuTorch INT8
+    deployment, Set up your environment for ExecuTorch quantization, Apply PTQ and export a quantized
+    VGF model, Apply QAT and export a quantized VGF model, and Inspect the graph with Model Explorer.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will explain when to use post-training quantization (PTQ) vs quantization-aware training
+      (QAT), prepare and quantize a PyTorch model using TorchAO PT2E quantization APIs, and export
+      the quantized model to TOSA and generate a model artifact with the ExecuTorch Arm backend.
+      Learn how to apply post-training quantization to PyTorch models using TorchAO and export
+      INT8 models to .vgf format with the ExecuTorch Arm backend.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for ML developers who want to reduce latency and memory bandwidth
+      by exporting INT8 models to the `.vgf` file format using the ExecuTorch Arm backend.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Basic PyTorch model training and evaluation
+      experience; A development machine with Python 3.10+ and PyTorch installed that runs ExecuTorch.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including ExecuTorch, TorchAO, Vulkan, TOSA, and NX, Linux,
+      macOS, and Windows environments, and Arm platforms such as Mali.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Explore PTQ and QAT for ExecuTorch INT8 deployment,
+      Set up your environment for ExecuTorch quantization, Apply PTQ and export a quantized VGF
+      model, Apply QAT and export a quantized VGF model, and Inspect the graph with Model Explorer.
+# END generated_summary_faq
+
 author:
 - Richard Burton
 - Annie Tallund
@@ -62,3 +107,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md b/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md
index 8cefa6582f..ea991a8190 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md
@@ -18,6 +18,50 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 78f862a7c46fd4591fec45f91d040eb3f1093291c0a7328dddd2203dad72db3f
+  summary: >-
+    Learn how to use the Vulkan ray tracing API to implement realistic shadows, reflections, and
+    refractions in Android applications. It is designed for Vulkan developers who are familiar
+    with rendering and are interested in deploying ray tracing in their applications. By the end,
+    you will be able to describe how the Vulkan ray tracing API works, describe how to use ray
+    tracing to implement realistic shadows, reflections, and refractions, and implement basic
+    ray tracing effects in a Vulkan renderer. It focuses on tools and technologies such as Vulkan,
+    Android environments, and Arm platforms including Mali and Immortalis. The main steps cover
+    What is ray tracing?, Setup: enabling ray tracing, Ray traversal: ray tracing pipeline versus
+    ray query, Acceleration structure, and Bindless materials.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will describe how the Vulkan ray tracing API works, describe how to use ray tracing
+      to implement realistic shadows, reflections, and refractions, and implement basic ray tracing
+      effects in a Vulkan renderer. Learn how to use the Vulkan ray tracing API to implement realistic
+      shadows, reflections, and refractions in Android applications.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is for Vulkan developers who are familiar with rendering and are interested
+      in deploying ray tracing in their applications.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An appropriate Android device that supports
+      the required Vulkan extensions (for example, Vivo X100).; Knowledge of the Vulkan API.;
+      A Vulkan renderer. Most code is generic and should be easy to incorporate into any deferred
+      PBR renderer.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Vulkan, Android environments, and Arm platforms
+      such as Mali and Immortalis.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around What is ray tracing?, Setup: enabling ray tracing,
+      Ray traversal: ray tracing pipeline versus ray query, Acceleration structure, and Bindless
+      materials.
+# END generated_summary_faq
+
 author: Iago Calvo Lista
 
 ### Tags
@@ -59,3 +103,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md b/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md
index 933ab7eced..28f9f553d2 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md
@@ -17,6 +17,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: e2f6f3005c119e6f6fe97612d4e2600849106f244bce729d56bfeeedb30637c2
+  summary: >-
+    Learn how to use Frame Advisor's Render Graph view to identify and resolve graphics performance
+    issues in Android applications. It is designed for Mobile application developers who wish
+    to improve graphics performance. By the end, you will be able to understand Frame Advisor's
+    Render Graph view and use the Render Graph view to identify and resolve performance issues
+    in your application. It focuses on tools and technologies such as OpenGL ES and Vulkan, Linux,
+    Windows, macOS, and Android environments, and Arm platforms including Mali and Immortalis.
+    The main steps cover What are render graphs?, Generating a render graph for your application,
+    Understanding your render graph, Problem solving – unused resources, and Problem solving –
+    unwanted execution nodes.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand Frame Advisor's Render Graph view and use the Render Graph view to identify
+      and resolve performance issues in your application. Learn how to use Frame Advisor's Render
+      Graph view to identify and resolve graphics performance issues in Android applications.
+  - question: Who is this Learning Path for?
+    answer: >-
+      Mobile application developers who wish to improve graphics performance.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Frame Advisor, part of Arm Performance
+      Studio, installed. Refer to the [Arm Performance Studio](/install-guides/ams/) install guide.;
+      If you wish to analyze your own applications you will need a supported Android device.;
+      Some basic familiarity with Frame Advisor. Review the [Frame Advisor](/learning-paths/mobile-graphics-and-gaming/ams/fa/)
+      section in [Get started with Arm Performance Studio for mobile](/learning-paths/mobile-graphics-and-gaming/ams/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including OpenGL ES and Vulkan, Linux, Windows, macOS, and
+      Android environments, and Arm platforms such as Mali and Immortalis.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around What are render graphs?, Generating a render graph
+      for your application, Understanding your render graph, Problem solving – unused resources,
+      and Problem solving – unwanted execution nodes.
+# END generated_summary_faq
+
 author: Mark Thurman
 
 further_reading:
@@ -63,3 +106,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md
index 510fb8f170..c15b3a2c48 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md
@@ -20,6 +20,52 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 388cab0dcb284c29fe2ad82f2b2934d9243c6356b2885d26db0a97c1a1d4d393
+  summary: >-
+    Learn how to convert and deploy the Stable Audio Open Small text-to-audio model to LiteRT
+    format for audio generation on Android devices and macOS. It is designed for developers looking
+    to deploy the Stable Audio Open Small text-to-audio model using LiteRT on an Android™ device
+    or on a reasonably modern platform with macOS®. By the end, you will be able to download and
+    test the Stable Audio Open Small model, convert the Stable Audio Open Small model to the LiteRT
+    (.tflite) format, and compile the application for an Arm CPU. It focuses on tools and technologies
+    such as CPP, Python, and Hugging Face, Linux and Android environments, and Arm platforms including
+    Cortex-A and Cortex-X. The main steps cover Set up your development environment, Download
+    and test the model, Convert Stable Audio Open Small model to LiteRT, Build LiteRT, and Create
+    a simple program for Android target.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will download and test the Stable Audio Open Small model, convert the Stable Audio Open
+      Small model to the LiteRT (.tflite) format, and compile the application for an Arm CPU.
+      Learn how to convert and deploy the Stable Audio Open Small text-to-audio model to LiteRT
+      format for audio generation on Android devices and macOS.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers looking to deploy the Stable Audio Open Small
+      text-to-audio model using LiteRT on an Android™ device or on a reasonably modern platform
+      with macOS®.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Linux-based x86 or macOS development
+      machine with at least 8 GB of RAM and 50 GB of disk space (tested on Ubuntu 22.04 with x86_64).;
+      A [HuggingFace](https://huggingface.co/) account.; An Android phone in [developer mode](https://developer.android.com/studio/debug/dev-options)
+      and a cable to connect it to your development machine.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including CPP, Python, and Hugging Face, Linux and Android
+      environments, and Arm platforms such as Cortex-A and Cortex-X.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Set up your development environment, Download and
+      test the model, Convert Stable Audio Open Small model to LiteRT, Build LiteRT, and Create
+      a simple program for Android target.
+# END generated_summary_faq
+
 author:
     - Nina Drozd
     - Gian Marco Iodice
@@ -66,3 +112,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md
index 19eef9a4d3..751ec8d814 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md
@@ -18,6 +18,52 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 7970846a399ddcdb488d90bf19cce3c6b74df6348e88914aed83661b2597fe7b
+  summary: >-
+    Learn how to convert the Stable Audio Open Small model to ExecuTorch format and build an audio
+    generation application for Android or macOS. It is designed for developers who want to deploy
+    the Stable Audio Open Small text-to-audio model using ExecuTorch on an Android device or macOS.
+    By the end, you will be able to download the Stable Audio Open Small model from Hugging Face,
+    convert the Stable Audio Open Small model to ExecuTorch (.pte) format, and build the audio
+    generation application for Arm CPUs. It focuses on tools and technologies such as CPP, Python,
+    Hugging Face, and ExecuTorch, Linux, Android, and macOS environments, and Arm platforms including
+    Cortex-A and Cortex-X. The main steps cover Set up your development environment, Download
+    the Stable Audio Open Small model, Convert the model to ExecuTorch format, Build and run on
+    macOS, and Build and run on Android.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will download the Stable Audio Open Small model from Hugging Face, convert the Stable
+      Audio Open Small model to ExecuTorch (.pte) format, and build the audio generation application
+      for Arm CPUs. Learn how to convert the Stable Audio Open Small model to ExecuTorch format
+      and build an audio generation application for Android or macOS.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers who want to deploy the Stable Audio Open Small
+      text-to-audio model using ExecuTorch on an Android device or macOS.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Linux-based x86 or macOS development
+      machine with at least 8 GB of RAM and 50 GB of disk space (tested on Ubuntu 22.04 with x86_64
+      and macOS with Apple Silicon); A [Hugging Face](https://huggingface.co/) account; An Android
+      phone in [developer mode](https://developer.android.com/studio/debug/dev-options) with at
+      least 8 GB of RAM and a cable to connect it to your development machine.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including CPP, Python, Hugging Face, and ExecuTorch, Linux,
+      Android, and macOS environments, and Arm platforms such as Cortex-A and Cortex-X.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Set up your development environment, Download the
+      Stable Audio Open Small model, Convert the model to ExecuTorch format, Build and run on
+      macOS, and Build and run on Android.
+# END generated_summary_faq
+
 author:
     - Adnan AlSinan
     - Pareena Verma
@@ -67,3 +113,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md b/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md
index 45f39754f0..3f4baba38a 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md
@@ -19,6 +19,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: dea8c3e15cca703f075c8fa9ba3daf873a90e3eb2ee9975dd05b973dad744b54
+  summary: >-
+    Learn how to build and install a Unity game on an Orange Pi 5 single-board computer running
+    Droid OS. It is designed for software developers who want to build and run a Unity game on
+    an Arm-based single board computer. By the end, you will be able to install Droid OS on an
+    Orange Pi 5, create a build of a Unity game to run on an Orange Pi, and install the Unity
+    game on the Orange Pi. It focuses on tools and technologies such as Unity, 7-Zip, and SDDiskTool,
+    Android environments, and Arm platforms including Cortex-A76 and Cortex-A55. The main steps
+    cover How do I install Droid OS on the Orange Pi 5?, How do I build my game in Unity to run
+    on the Orange Pi 5, and How do I install my Unity game onto the Orange Pi 5?
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install Droid OS on an Orange Pi 5, create a build of a Unity game to run on an
+      Orange Pi, and install the Unity game on the Orange Pi. Learn how to build and install a
+      Unity game on an Orange Pi 5 single-board computer running Droid OS.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to build and run a Unity
+      game on an Arm-based single board computer.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows PC to use Orange Pi's imaging
+      software, which is only available for Windows; An Orange Pi 5; A microSD card (16GB or greater;
+      class 10 or faster); An ethernet connection; A mouse and keyboard connected to the Orange
+      Pi.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Unity, 7-Zip, and SDDiskTool, Android environments,
+      and Arm platforms such as Cortex-A76 and Cortex-A55.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around How do I install Droid OS on the Orange Pi 5?, How
+      do I build my game in Unity to run on the Orange Pi 5, and How do I install my Unity game
+      onto the Orange Pi 5?
+# END generated_summary_faq
+
 author: Gabriel Peterson
 
 ### Tags
@@ -57,3 +99,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md b/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md
index 1170a231c8..afa59f4522 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md
@@ -17,6 +17,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 4a990e957658b03bac9306d5f8e6955734cc1d3b063f43c38ac525ed1bf95b79
+  summary: >-
+    Learn how to install Arm integration packages in Unity to view GPU metrics in Unity Profiler
+    and annotate games with markers for Arm Performance Studio. It is designed for Unity developers
+    who are targeting Android devices and want to get more insight into how their game performs
+    on devices with Arm CPUs and GPUs. By the end, you will be able to install the packages in
+    Unity, view Arm GPU metrics in the Unity Profiler, and annotate your Unity game with markers
+    that give context to a profile in Arm Performance Studio tools. It focuses on tools and technologies
+    such as Unity and Arm Performance Studio, Windows, macOS, and Linux environments, and Arm
+    platforms including Cortex-A and Mali. The main steps cover GPU metrics and Arm Performance
+    Studio Unity integrations.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install the packages in Unity, view Arm GPU metrics in the Unity Profiler, and
+      annotate your Unity game with markers that give context to a profile in Arm Performance
+      Studio tools. Learn how to install Arm integration packages in Unity to view GPU metrics
+      in Unity Profiler and annotate games with markers for Arm Performance Studio.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for Unity developers who are targeting Android devices and
+      want to get more insight into how their game performs on devices with Arm CPUs and GPUs.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Familiarity with Unity and the Unity
+      Profiler; Familiarity with Arm Performance Studio tools.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Unity and Arm Performance Studio, Windows, macOS,
+      and Linux environments, and Arm platforms such as Cortex-A and Mali.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around GPU metrics and Arm Performance Studio Unity integrations.
+# END generated_summary_faq
+
 author: Julie Gaskin
 
 ### Tags
@@ -64,3 +104,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md b/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md
index a29019d729..f6381afe92 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md
@@ -20,6 +20,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: f17ab3c4216495b88cee75a81612c11a4727d874114f914edf97c467f0d1c125
+  summary: >-
+    Learn how to use Arm Neon intrinsics in Unity C# scripts to optimize code on Android and collect
+    performance data using Unity Profiler. It is designed for Developers interested in leveraging
+    the Unity Machine Learning Agents toolkit on Arm devices. By the end, you will be able to
+    use Arm Neon intrinsics in your Unity C# scripts, optimize your code, and collect and compare
+    performance data using the Unity Profiler and Analyzer tools. It focuses on tools and technologies
+    such as Unity and C#, Android environments, and Arm platforms including armv8, aarch64, arm64,
+    and arm architecture. The main steps cover Set up, Arm Neon and the Unity Burst compiler,
+    The sample project, The plain (unoptimized) code, and The optimizations.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will use Arm Neon intrinsics in your Unity C# scripts, optimize your code, and collect
+      and compare performance data using the Unity Profiler and Analyzer tools. Learn how to use
+      Arm Neon intrinsics in Unity C# scripts to optimize code on Android and collect performance
+      data using Unity Profiler.
+  - question: Who is this Learning Path for?
+    answer: >-
+      Developers interested in leveraging the Unity Machine Learning Agents toolkit on Arm devices.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Basic knowledge of Unity and C#; Recent
+      Android device, such as a mobile phone or tablet; Desktop computer capable of running Unity;
+      Unity version compatible with Unity Burst compiler 1.5 or later.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Unity and C#, Android environments, and Arm platforms
+      such as armv8, aarch64, arm64, and arm architecture.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Set up, Arm Neon and the Unity Burst compiler, The
+      sample project, The plain (unoptimized) code, and The optimizations.
+# END generated_summary_faq
+
 author: Ben Clark, Joshua Marshall-Law
 
 ### Tags
@@ -56,3 +96,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md b/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md
index 16cba88282..a0ed7ee4f3 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md
@@ -17,6 +17,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 9cd19738cbe9cde618125b309bd4f2c57ad1799e807251fdaed09707bea2781d
+  summary: >-
+    Learn how to integrate Unity's Machine Learning Agents toolkit into games deployable to Arm-powered
+    Android devices. It is designed for Developers interested in leveraging the Unity Machine
+    Learning Agents toolkit on Arm devices. By the end, you will be able to get the Unity Machine
+    Learning (ML) Agents toolkit running in a game that is deployable to Arm-powered Android devices
+    and note - Instructions on how to deploy Unity games to an Arm-powered Android device and
+    how to profile them are included in separate Learning Paths. It focuses on tools and technologies
+    such as Unity, Android environments, and Arm platforms including Cortex-A. The main steps
+    cover Machine Learning in games, Install Unity and the project, The Dr Arm game, Machine Learning
+    in Unity, and The Unity project.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will get the Unity Machine Learning (ML) Agents toolkit running in a game that is deployable
+      to Arm-powered Android devices and note - Instructions on how to deploy Unity games to an
+      Arm-powered Android device and how to profile them are included in separate Learning Paths.
+      Learn how to integrate Unity's Machine Learning Agents toolkit into games deployable to
+      Arm-powered Android devices.
+  - question: Who is this Learning Path for?
+    answer: >-
+      Developers interested in leveraging the Unity Machine Learning Agents toolkit on Arm devices.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A computer capable of running Unity.
+      (Instructions are for Windows, but could be adapted to other platforms.); An Android mobile
+      device that has a 64-bit processor and supports at least Android 8.; A USB cable to connect
+      the mobile device to your computer.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Unity, Android environments, and Arm platforms such
+      as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Machine Learning in games, Install Unity and the project,
+      The Dr Arm game, Machine Learning in Unity, and The Unity project.
+# END generated_summary_faq
+
 author: Arm
 
 ### Tags
@@ -52,3 +95,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md b/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md
index 8c66d23ab8..d805106b93 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md
@@ -19,6 +19,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: e7d95c8e7210be2fe4520fe94ccbaa3e437cd0edfa5caca549afaee22fb8b377
+  summary: >-
+    Learn how to download, convert, and deploy Vision Transformers using the Mobile Neural Network
+    framework on Android with KleidiAI micro-kernels for optimized performance. It is designed
+    for developers who want to run Vision Transformers (ViT) efficiently on Android. By the end,
+    you will be able to download a Vision Large Language Model (LLM) from Hugging Face, convert
+    the model to the Mobile Neural Network (MNN) framework, and install an Android demo application
+    using the model to run an inference. It focuses on tools and technologies such as Android
+    Studio and KleidiAI, Android environments, and Arm platforms including Cortex-A. The main
+    steps cover Background, Environment setup and prepare model, Benchmark the Vision Transformer
+    performance with KleidiAI, and Build the MNN Command-line ViT Demo.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will download a Vision Large Language Model (LLM) from Hugging Face, convert the model
+      to the Mobile Neural Network (MNN) framework, and install an Android demo application using
+      the model to run an inference. Learn how to download, convert, and deploy Vision Transformers
+      using the Mobile Neural Network framework on Android with KleidiAI micro-kernels for optimized
+      performance.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is for developers who want to run Vision Transformers (ViT) efficiently
+      on Android.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A development machine with [Android
+      Studio](https://developer.android.com/studio) installed.; A smartphone running Android with
+      support for `i8mm` and `dotprod` instructions.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Android Studio and KleidiAI, Android environments,
+      and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Background, Environment setup and prepare model, Benchmark
+      the Vision Transformer performance with KleidiAI, and Build the MNN Command-line ViT Demo.
+# END generated_summary_faq
+
 author:
     - Shuheng Deng
     - Yiyang Fan
@@ -62,3 +105,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md b/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md
index 05059e4a97..35ad4e9e71 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md
@@ -20,6 +20,53 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 192f5919261cfef88c107576dc444d2db3a07fbad98100d9c758bb75670a5a00
+  summary: >-
+    Learn how to build and optimize a multimodal Voice Assistant application on Android using
+    KleidiAI and SME2 for accelerated performance. It is designed for developers who want to implement
+    a multimodal pipeline for a Voice Assistant application and accelerate the performance on
+    Android devices using KleidiAI and SME2. By the end, you will be able to learn about the multimodal
+    Voice Assistant pipeline and different components used, learn about the functionality of ML
+    components used and how these can be built and benchmarked on various platforms, and compile
+    and run a multimodal Voice Assistant example based on Android OS. It focuses on tools and
+    technologies such as Java, Kotlin, CPP, and SME2, Android, Linux, and macOS environments,
+    and Arm platforms including Cortex-A and Arm C1. The main steps cover Set up your environment,
+    Overview, Build the Voice Assistant, Run the Voice Assistant, and KleidiAI.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will learn about the multimodal Voice Assistant pipeline and different components used,
+      learn about the functionality of ML components used and how these can be built and benchmarked
+      on various platforms, and compile and run a multimodal Voice Assistant example based on
+      Android OS. Learn how to build and optimize a multimodal Voice Assistant application on
+      Android using KleidiAI and SME2 for accelerated performance.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers who want to implement a multimodal pipeline
+      for a Voice Assistant application and accelerate the performance on Android devices using
+      KleidiAI and SME2.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Android phone that supports the i8mm
+      Arm architecture feature (8-bit integer matrix multiplication).; An Android phone with support
+      for SME (Scalable Matrix Extension) instructions, required for SME performance checking;
+      This Learning Path was tested on a Vivo X300 Pro.; A development machine with [Android Studio](https://developer.android.com/studio)
+      installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Java, Kotlin, CPP, and SME2, Android, Linux, and
+      macOS environments, and Arm platforms such as Cortex-A and Arm C1.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Set up your environment, Overview, Build the Voice
+      Assistant, Run the Voice Assistant, and KleidiAI.
+# END generated_summary_faq
+
 author:
     - Arnaud de Grandmaison
     - Nina Drozd
@@ -62,3 +109,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md b/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md
index 8453adedba..6954a70392 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md
@@ -21,6 +21,53 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 2d8fca8838e759c3098358f131fb4b0884b0d80d5fdb2fd68719bd09fa4c3d32
+  summary: >-
+    Build an end-to-end, on-device voice assistant that understands both speech and emotion using
+    Whisper, HuBERT, ONNX Runtime, and a local LLM with llama.cpp on Arm. It is designed for developers,
+    ML practitioners, and game developers interested in building on-device AI applications, including
+    voice interfaces, real-time interactions with non-player characters (NPCs), and edge AI systems
+    powered by LLMs on Arm platforms. By the end, you will be able to build a voice-to-LLM pipeline
+    using Whisper and llama.cpp, train a voice sentiment classification model using HuBERT on
+    the RAVDESS dataset, and quantize the model and convert into ONNX Runtime for on-device inference.
+    It focuses on tools and technologies such as Python, Transformers, ONNX Runtime, llama.cpp,
+    and Gradio, Linux, Windows, and macOS environments, and Arm platforms including Cortex-A.
+    The main steps cover Understand voice sentiment analysis for on-device AI, Set up your environment,
+    Build the voice-to-LLM pipeline, Train the voice sentiment classification model, and Convert
+    and quantize the model.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build a voice-to-LLM pipeline using Whisper and llama.cpp, train a voice sentiment
+      classification model using HuBERT on the RAVDESS dataset, and quantize the model and convert
+      into ONNX Runtime for on-device inference. Build an end-to-end, on-device voice assistant
+      that understands both speech and emotion using Whisper, HuBERT, ONNX Runtime, and a local
+      LLM with llama.cpp on Arm.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is for developers, ML practitioners, and game developers interested in
+      building on-device AI applications, including voice interfaces, real-time interactions with
+      non-player characters (NPCs), and edge AI systems powered by LLMs on Arm platforms.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Python 3.9 or later for programming.;
+      A working microphone for voice input.; Basic Python and command-line knowledge.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Python, Transformers, ONNX Runtime, llama.cpp, and
+      Gradio, Linux, Windows, and macOS environments, and Arm platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Understand voice sentiment analysis for on-device
+      AI, Set up your environment, Build the voice-to-LLM pipeline, Train the voice sentiment
+      classification model, and Convert and quantize the model.
+# END generated_summary_faq
+
 author: Bhanu Arya
 
 ### Tags
@@ -63,3 +110,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md
index f0354735be..9164837502 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md
@@ -21,6 +21,50 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: af4f1c8964e0970c187643bcd0330a63a6ce66c38a2e6b4d2fc17857a12a3d7f
+  summary: >-
+    Learn how to set up ML Emulation Layers for Vulkan, run sample applications using ML extensions,
+    and debug the flow with RenderDoc. It is designed for engine developers interested in learning
+    about neural graphics using ML Extensions for Vulkan. By the end, you will be able to explain
+    the purpose of neural graphics and the role of ML Extensions for Vulkan, set up the ML Emulation
+    Layers for Vulkan to enable the extensions, and run a sample Vulkan application that uses
+    the extensions. It focuses on tools and technologies such as Vulkan, RenderDoc, and NX, Windows
+    environments, and Arm platforms including Mali. The main steps cover Run neural graphics workloads
+    with ML Extensions for Vulkan, Setting up the ML Emulation Layers for Vulkan, Simple Tensor
+    and Data Graph, Running a test with the Scenario Runner, and Use RenderDoc to debug and analyze
+    workloads.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will explain the purpose of neural graphics and the role of ML Extensions for Vulkan,
+      set up the ML Emulation Layers for Vulkan to enable the extensions, and run a sample Vulkan
+      application that uses the extensions. Learn how to set up ML Emulation Layers for Vulkan,
+      run sample applications using ML extensions, and debug the flow with RenderDoc.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for engine developers interested in learning about neural graphics
+      using ML Extensions for Vulkan.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Windows 11 development machine; Visual
+      Studio 2022; Visual Studio workload - Desktop development with C++; Visual Studio workload
+      - .NET desktop build tools.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Vulkan, RenderDoc, and NX, Windows environments,
+      and Arm platforms such as Mali.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Run neural graphics workloads with ML Extensions for
+      Vulkan, Setting up the ML Emulation Layers for Vulkan, Simple Tensor and Data Graph, Running
+      a test with the Scenario Runner, and Use RenderDoc to debug and analyze workloads.
+# END generated_summary_faq
+
 author: Annie Tallund
 
 ### Tags
@@ -66,3 +110,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md b/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
index d178d1f221..624d2c9acd 100644
--- a/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
@@ -19,6 +19,50 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 275878b5aa4c27dee394da12ad8b80e4c0d0235b3ddce66b1fe3f0e056ef3da9
+  summary: >-
+    Learn how to build and deploy an AI agent application on Arm servers using llama.cpp and llama-cpp-agent
+    with KleidiAI optimization for efficient LLM inference and function calling. It is designed
+    for software developers and ML engineers looking to deploy an optimized AI agent application.
+    By the end, you will be able to set up llama-cpp-python optimized for Arm servers, run optimized
+    Large Language Models (LLMs), and create custom functions for LLMs. It focuses on tools and
+    technologies such as Python, AWS Graviton, and AI, Linux environments, Arm platforms including
+    Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The
+    main steps cover Introduction to AI Agents and Agent Use Cases, Set Up Your Local Environment
+    to Run an AI Application, AI Agent Application, and Explore and Test Your AI Agent.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will set up llama-cpp-python optimized for Arm servers, run optimized Large Language
+      Models (LLMs), and create custom functions for LLMs. Learn how to build and deploy an AI
+      agent application on Arm servers using llama.cpp and llama-cpp-agent with KleidiAI optimization
+      for efficient LLM inference and function calling.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers and ML engineers looking to deploy
+      an optimized AI agent application.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/)
+      from a cloud service provider or an on-premise Arm server.; Basic understanding of Python
+      and prompt engineering.; Understanding of LLM fundamentals.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Python, AWS Graviton, and AI, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
+      Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Introduction to AI Agents and Agent Use Cases, Set
+      Up Your Local Environment to Run an AI Application, AI Agent Application, and Explore and
+      Test Your AI Agent.
+# END generated_summary_faq
+
 author: Andrew Choi
 
 ### Tags
@@ -58,3 +102,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/aks/_index.md b/content/learning-paths/servers-and-cloud-computing/aks/_index.md
index e4c7500608..a70afdc69b 100644
--- a/content/learning-paths/servers-and-cloud-computing/aks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/aks/_index.md
@@ -17,6 +17,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 01c5cc8930650a8d81d67d4c48b62e0f9d7b1ebfc2d09a552e11046fb982fc52
+  summary: >-
+    Learn how to automate the deployment of an Arm-based Kubernetes cluster on Azure AKS using
+    Terraform and deploy a sample WordPress application as a workload. It is designed for software
+    developers who want to deploy an Arm-based Kubernetes cluster using Azure Kubernetes Service
+    (AKS). By the end, you will be able to automate the deployment of an Arm-based AKS cluster
+    using Terraform and install Wordpress on AKS as an example workload. It focuses on tools and
+    technologies such as Terraform, Kubernetes, WordPress, and MySQL, Linux environments, Arm
+    platforms including Neoverse, and cloud platforms such as Microsoft Azure. The main steps
+    cover Deploy an Arm-based AKS Cluster using Terraform and Deploy a WordPress Example.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will automate the deployment of an Arm-based AKS cluster using Terraform and install
+      Wordpress on AKS as an example workload. Learn how to automate the deployment of an Arm-based
+      Kubernetes cluster on Azure AKS using Terraform and deploy a sample WordPress application
+      as a workload.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers who want to deploy an Arm-based Kubernetes
+      cluster using Azure Kubernetes Service (AKS).
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Azure account; A machine with [Terraform](/install-guides/terraform/),
+      [Azure CLI](/install-guides/azure-cli), and [Kubectl](/install-guides/kubectl/) installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Terraform, Kubernetes, WordPress, and MySQL, Linux
+      environments, Arm platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Deploy an Arm-based AKS Cluster using Terraform and
+      Deploy a WordPress Example.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
@@ -67,3 +107,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md b/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
index 57d9d21201..06e3da7c21 100644
--- a/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
@@ -21,6 +21,56 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 90b638385267e085ea07a70a1c23f16578aa3caaeabeca1f651bcdcac5bf38fb
+  summary: >-
+    Learn how to deploy Apache Arrow and Arrow Flight on Google Cloud Axion C4A processors for
+    high-throughput columnar data processing and low-latency data transport with MinIO integration.
+    It is designed for data engineers, platform engineers, and developers who aim to build high-performance
+    analytics pipelines on Arm64-based Google Cloud C4A Axion processors using Apache Arrow and
+    Arrow Flight. By the end, you will be able to deploy Apache Arrow–based data processing workloads
+    on Google Cloud C4A Axion processors, set up and run an Arrow Flight server for high-throughput,
+    low-latency data transport, and read and write columnar data formats such as Parquet and ORC
+    using Apache Arrow. It focuses on tools and technologies such as Apache Arrow, Arrow Flight,
+    Python, and MinIO, Linux environments, Arm platforms including Neoverse, and cloud platforms
+    such as Google Cloud. The main steps cover Get started with Apache Arrow and Arrow Flight
+    on Google Axion C4A, Create firewall rules on GCP for Apache Arrow, MinIO, and Arrow Flight,
+    Create a Google Axion C4A arm64 virtual machine on GCP, Set up Apache Arrow and MinIO on arm64,
+    and Analyze columnar data with Apache Arrow on arm64.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will deploy Apache Arrow–based data processing workloads on Google Cloud C4A Axion processors,
+      set up and run an Arrow Flight server for high-throughput, low-latency data transport, and
+      read and write columnar data formats such as Parquet and ORC using Apache Arrow. Learn how
+      to deploy Apache Arrow and Arrow Flight on Google Cloud Axion C4A processors for high-throughput
+      columnar data processing and low-latency data transport with MinIO integration.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for data engineers, platform engineers, and developers who
+      aim to build high-performance analytics pipelines on Arm64-based Google Cloud C4A Axion
+      processors using Apache Arrow and Arrow Flight.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
+      account with billing enabled; Basic familiarity with Python; Basic understanding of data
+      formats such as Parquet or ORC; Familiarity with Linux command-line operations.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Apache Arrow, Arrow Flight, Python, and MinIO, Linux
+      environments, Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Get started with Apache Arrow and Arrow Flight on
+      Google Axion C4A, Create firewall rules on GCP for Apache Arrow, MinIO, and Arrow Flight,
+      Create a Google Axion C4A arm64 virtual machine on GCP, Set up Apache Arrow and MinIO on
+      arm64, and Analyze columnar data with Apache Arrow on arm64.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ##### Tags
@@ -75,3 +125,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: yes
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
index 21ef4ea2dd..9ef5a60845 100644
--- a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
@@ -19,6 +19,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 964c1e6a87810dca686a7b3218433ce09d31bd92882db10af355e8c50bb6091c
+  summary: >-
+    Learn how to build llama.cpp, quantize the Arcee AFM-4.5B model, and run optimized inference
+    on AWS Graviton4 instances with perplexity-based quality evaluation. It is designed for developers
+    and ML engineers who want to deploy Arcee's AFM-4.5B small language model on AWS Graviton4
+    instances using Llama.cpp. By the end, you will be able to launch an Arm-based EC2 instance
+    on AWS Graviton4, build and install Llama.cpp from source, and download and quantize the AFM-4.5B
+    model from Hugging Face. It focuses on tools and technologies such as AWS, Hugging Face, Python,
+    and Llama.cpp, Linux environments, Arm platforms including Neoverse, and cloud platforms such
+    as AWS. The main steps cover Overview, Provision your Graviton4 environment, Configure your
+    Graviton4 environment, Build Llama.cpp, and Install Python dependencies.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will launch an Arm-based EC2 instance on AWS Graviton4, build and install Llama.cpp
+      from source, and download and quantize the AFM-4.5B model from Hugging Face. Learn how to
+      build llama.cpp, quantize the Arcee AFM-4.5B model, and run optimized inference on AWS Graviton4
+      instances with perplexity-based quality evaluation.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is for developers and ML engineers who want to deploy Arcee's AFM-4.5B
+      small language model on AWS Graviton4 instances using Llama.cpp.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An [AWS account](https://aws.amazon.com/)
+      with permission to launch Graviton4 (`c8g.4xlarge` or larger) instances; Basic familiarity
+      with Linux and SSH.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including AWS, Hugging Face, Python, and Llama.cpp, Linux
+      environments, Arm platforms such as Neoverse, and cloud platforms such as AWS.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview, Provision your Graviton4 environment, Configure
+      your Graviton4 environment, Build Llama.cpp, and Install Python dependencies.
+# END generated_summary_faq
+
 author: Julien Simon
 
 # Tags
@@ -66,3 +108,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
index 6416bc59fd..94ebd43bdb 100644
--- a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
@@ -19,6 +19,52 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: b2f60d2c31ef380e6e6ef3028671ad9242dd51c98f4a192f2da32bb05b94e185
+  summary: >-
+    Learn how to build llama.cpp, quantize the Arcee AFM-4.5B model, and run optimized inference
+    on Google Cloud Axion instances with perplexity-based quality evaluation. It is designed for
+    developers and ML engineers who want to deploy Arcee's AFM-4.5B small language model on Google
+    Cloud Axion instances using Llama.cpp. By the end, you will be able to launch an Arm-based
+    Compute Engine instance on Google Cloud Axion, build and install Llama.cpp from source, and
+    download and quantize the AFM-4.5B model from Hugging Face. It focuses on tools and technologies
+    such as Google Cloud, Hugging Face, Python, and Llama.cpp, Linux environments, Arm platforms
+    including Neoverse, and cloud platforms such as Google Cloud. The main steps cover AFM-4.5B
+    deployment on Google Cloud Axion with Llama.cpp, Provision a Google Cloud Axion Arm64 environment,
+    Configure your Google Cloud Axion Arm64 environment, Build Llama.cpp on Google Cloud Axion
+    Arm64, and Install Python dependencies for Llama.cpp.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will launch an Arm-based Compute Engine instance on Google Cloud Axion, build and install
+      Llama.cpp from source, and download and quantize the AFM-4.5B model from Hugging Face. Learn
+      how to build llama.cpp, quantize the Arcee AFM-4.5B model, and run optimized inference on
+      Google Cloud Axion instances with perplexity-based quality evaluation.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is for developers and ML engineers who want to deploy Arcee's AFM-4.5B
+      small language model on Google Cloud Axion instances using Llama.cpp.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud account](https://console.cloud.google.com/)
+      with permission to launch Axion (`c4a-standard-16` or larger) instances; Basic familiarity
+      with Linux and SSH.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Google Cloud, Hugging Face, Python, and Llama.cpp,
+      Linux environments, Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around AFM-4.5B deployment on Google Cloud Axion with Llama.cpp,
+      Provision a Google Cloud Axion Arm64 environment, Configure your Google Cloud Axion Arm64
+      environment, Build Llama.cpp on Google Cloud Axion Arm64, and Install Python dependencies
+      for Llama.cpp.
+# END generated_summary_faq
+
 author: Julien Simon
 
 # Tags
@@ -66,3 +112,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
index ae29e74b1a..5996cefbb4 100644
--- a/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
@@ -23,6 +23,57 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: c6106f21859f5a8e04bbd579db47c7177bb5371d4c7f306054e56e81ed9e89a1
+  summary: >-
+    Learn how to deploy and manage applications on Google Cloud GKE Arm64 clusters using Argo
+    CD GitOps workflows with automated sync and self-healing on Axion processors. It is designed
+    for developers and platform engineers who want hands-on experience implementing GitOps using
+    Argo CD on Arm64-based Google Kubernetes Engine (GKE) clusters running on Google Axion (C4A)
+    processors. By the end, you will be able to provision an Arm-based SUSE Linux Enterprise Server
+    (SLES) virtual machine on Google Cloud (C4A with Axion processors), create and connect to
+    a Google Kubernetes Engine (GKE) cluster running on Arm64 (Axion) nodes, and install and validate
+    Argo CD on an Arm-based GKE cluster. It focuses on tools and technologies such as Argo CD,
+    Kubernetes, kubectl, GKE, and Git, Linux environments, Arm platforms including Neoverse, and
+    cloud platforms such as Google Cloud. The main steps cover Get started with Argo CD on Google
+    Axion C4A (Arm-based), Create a Google Axion C4A virtual machine on Google Cloud, Prepare
+    a GKE cluster for Argo CD deployments, Install and access Argo CD on Arm64 GKE, and Deploy
+    applications using GitOps with Argo CD.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will provision an Arm-based SUSE Linux Enterprise Server (SLES) virtual machine on Google
+      Cloud (C4A with Axion processors), create and connect to a Google Kubernetes Engine (GKE)
+      cluster running on Arm64 (Axion) nodes, and install and validate Argo CD on an Arm-based
+      GKE cluster. Learn how to deploy and manage applications on Google Cloud GKE Arm64 clusters
+      using Argo CD GitOps workflows with automated sync and self-healing on Axion processors.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers and platform engineers who want hands-on experience
+      implementing GitOps using Argo CD on Arm64-based Google Kubernetes Engine (GKE) clusters
+      running on Google Axion (C4A) processors.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
+      account with billing enabled; Basic familiarity with [Kubernetes concepts](https://kubernetes.io/docs/concepts/);
+      Basic understanding of Git and GitHub workflows; Familiarity with basic Linux command-line
+      usage.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Argo CD, Kubernetes, kubectl, GKE, and Git, Linux
+      environments, Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Get started with Argo CD on Google Axion C4A (Arm-based),
+      Create a Google Axion C4A virtual machine on Google Cloud, Prepare a GKE cluster for Argo
+      CD deployments, Install and access Argo CD on Arm64 GKE, and Deploy applications using GitOps
+      with Argo CD.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ##### Tags
@@ -69,3 +120,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md b/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
index 399505be69..af9ee6a2e1 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
@@ -17,6 +17,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 3a4bc8b2ab548be507b6d528844b0593b27f2dab3ab3c9649e807abdf4887ce0
+  summary: >-
+    Learn how to write correct concurrent C++ code when porting applications from x86 to Arm by
+    understanding memory ordering differences and using best practices to avoid race conditions.
+    It is designed for C++ developers porting applications from x86 to Arm and optimizing performance.
+    By the end, you will be able to describe at a high level what a memory model does, and the
+    types of memory ordering, describe the differences between the Arm and x86 memory model, and
+    employ best practices for writing C++ on Arm to avoid race conditions. It focuses on tools
+    and technologies such as CPP, TSan, and Runbook, Linux environments, and Arm platforms including
+    Neoverse. The main steps cover Introduction to C++ Memory Models, The C++ Memory Model and
+    Atomics, Walk through a Race condition example, and Detecting race conditions.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will describe at a high level what a memory model does, and the types of memory ordering,
+      describe the differences between the Arm and x86 memory model, and employ best practices
+      for writing C++ on Arm to avoid race conditions. Learn how to write correct concurrent C++
+      code when porting applications from x86 to Arm by understanding memory ordering differences
+      and using best practices to avoid race conditions.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for C++ developers porting applications from x86 to Arm and optimizing
+      performance.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Access to an x86 and an Arm cloud instance
+      (virtual machine).; Proficiency in C++ programming.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including CPP, TSan, and Runbook, Linux environments, and
+      Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Introduction to C++ Memory Models, The C++ Memory
+      Model and Atomics, Walk through a Race condition example, and Detecting race conditions.
+# END generated_summary_faq
+
 author: Kieran Hejmadi
 
 ### Tags
@@ -48,3 +90,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md b/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
index a8bd5fd4e5..fc6668ef53 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
@@ -20,6 +20,52 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: b870ac5160d35ddb51955ca8379493e172787ced8125cde8ae79f7700e653a87
+  summary: >-
+    Learn how to automate x86-to-Arm application migration using the Arm MCP Server, with AI-assisted
+    compatibility checks, C++ code refactoring, and Docker-based validation on Arm cloud platforms.
+    It is designed for developers who want to use AI-powered tools to migrate x86 applications
+    to Arm-based cloud instances. By the end, you will be able to explain how the Arm MCP Server
+    enables AI-driven x86-to-Arm migration workflows, use AI-assisted checks to inspect Docker
+    images for Arm compatibility, and set up and use the Arm Cloud Migration Agent in GitHub Copilot
+    to automate x86-to-Arm code migration. It focuses on tools and technologies such as MCP, Docker,
+    CPP, and GitHub Copilot, Linux environments, and Arm platforms including Neoverse. The main
+    steps cover Understand the Arm MCP Server for AI-driven x86-to-Arm migration, Verify Docker
+    image compatibility with Arm using AI, Arm Cloud Migration Agent in GitHub Copilot, and Configure
+    other AI agents to automate Arm migration workflows.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will explain how the Arm MCP Server enables AI-driven x86-to-Arm migration workflows,
+      use AI-assisted checks to inspect Docker images for Arm compatibility, and set up and use
+      the Arm Cloud Migration Agent in GitHub Copilot to automate x86-to-Arm code migration. Learn
+      how to automate x86-to-Arm application migration using the Arm MCP Server, with AI-assisted
+      compatibility checks, C++ code refactoring, and Docker-based validation on Arm cloud platforms.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for developers who want to use AI-powered tools to migrate x86
+      applications to Arm-based cloud instances.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An AI-powered IDE such as VS Code, Copilot
+      in VS Code, Kiro (IDE or CLI) or Codex; Basic familiarity with Docker and C/C++ development;
+      Access to an Arm-based cloud instance or local Arm computer running Linux or macOS.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including MCP, Docker, CPP, and GitHub Copilot, Linux environments,
+      and Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Understand the Arm MCP Server for AI-driven x86-to-Arm
+      migration, Verify Docker image compatibility with Arm using AI, Arm Cloud Migration Agent
+      in GitHub Copilot, and Configure other AI agents to automate Arm migration workflows.
+# END generated_summary_faq
+
 author: Joe Stech
 
 ### Tags
@@ -67,3 +113,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md b/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
index 350a0129d5..9e5ee18884 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
@@ -21,6 +21,58 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 49b3f2b1464efb20f0c8fbcff46e9f30910d856567ca01c01dc348aa1c5740d1
+  summary: >-
+    Learn how to migrate C applications between Arm platforms using Kiro's AI-assisted tooling
+    to identify hardware dependencies and implement abstraction layers for cross-platform compatibility.
+    It is designed for experienced developers who need to migrate applications between Arm-based
+    platforms using AI-assisted tooling. You will work through a structured, repeatable migration
+    workflow using Kiro Arm SoC Migration Power, moving an application from AWS Graviton3 (Neoverse)
+    to Raspberry Pi 5 (Cortex-A). The techniques apply broadly to cloud-to-edge and cross-architecture
+    migrations across the Arm ecosystem. By the end, you will be able to install and configure
+    Kiro Arm SoC Migration Power, apply a structured migration workflow across Arm platforms,
+    and identify platform-specific and hardware-dependent code using AI-guided analysis. It focuses
+    on tools and technologies such as Kiro, AWS EC2, GCC, C, and CMake, Linux environments, Arm
+    platforms including Neoverse and Cortex-A, and cloud platforms such as AWS, Microsoft Azure,
+    Google Cloud, and Oracle. The main steps cover Install Arm SoC Migration Power, Develop on
+    source platform, Migrate using Arm SoC Migration Power, and Validate migration with testing.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install and configure Kiro Arm SoC Migration Power, apply a structured migration
+      workflow across Arm platforms, and identify platform-specific and hardware-dependent code
+      using AI-guided analysis. Learn how to migrate C applications between Arm platforms using
+      Kiro's AI-assisted tooling to identify hardware dependencies and implement abstraction layers
+      for cross-platform compatibility.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for experienced developers who need to migrate applications between
+      Arm-based platforms using AI-assisted tooling. You will work through a structured, repeatable
+      migration workflow using Kiro Arm SoC Migration Power, moving an application from AWS Graviton3
+      (Neoverse) to Raspberry Pi 5 (Cortex-A). The techniques apply broadly to cloud-to-edge and
+      cross-architecture migrations across the Arm ecosystem.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Access to both source and target Arm
+      platforms (for example, AWS Graviton3 and Raspberry Pi 5); Working knowledge of C programming;
+      Familiarity with Linux development environments and basic embedded or cloud deployment concepts;
+      Experience building applications with GCC and CMake.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Kiro, AWS EC2, GCC, C, and CMake, Linux environments,
+      Arm platforms such as Neoverse and Cortex-A, and cloud platforms such as AWS, Microsoft
+      Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Install Arm SoC Migration Power, Develop on source
+      platform, Migrate using Arm SoC Migration Power, and Validate migration with testing.
+# END generated_summary_faq
+
 author: Daniel Schleicher
 
 ### Tags
@@ -70,3 +122,4 @@ weight: 1
 layout: learningpathall
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md b/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
index 38e512b19f..57433583ce 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
@@ -19,6 +19,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 67004eb9a75926144eb6f75124104972b3cf20a57a22b698c9359d20db3eca02
+  summary: >-
+    Learn how to install and configure a Linux kernel with 64K page size support on Arm systems
+    to improve memory efficiency and performance for memory-intensive workloads. It is designed
+    for developers who want to modify the Linux kernel page size on Arm-based systems to improve
+    performance for memory-intensive workloads. By the end, you will be able to explain the differences
+    in page size configuration between Arm64 and x86 architectures, understand how page size affects
+    memory efficiency and system performance, and check the current memory page size on an Arm-based
+    Linux system. It focuses on tools and technologies such as bash, Linux environments, and Arm
+    platforms including Neoverse. The main steps cover Overview, Change page size on Ubuntu, Change
+    page size on Debian, and Change page size on CentOS.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will explain the differences in page size configuration between Arm64 and x86 architectures,
+      understand how page size affects memory efficiency and system performance, and check the
+      current memory page size on an Arm-based Linux system. Learn how to install and configure
+      a Linux kernel with 64K page size support on Arm systems to improve memory efficiency and
+      performance for memory-intensive workloads.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers who want to modify the Linux kernel page size
+      on Arm-based systems to improve performance for memory-intensive workloads.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Access to an Arm-based Linux system
+      running Ubuntu, Debian, or CentOS.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including bash, Linux environments, and Arm platforms such
+      as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview, Change page size on Ubuntu, Change page
+      size on Debian, and Change page size on CentOS.
+# END generated_summary_faq
+
 author: Geremy Cohen
 
 ### Tags
@@ -61,4 +103,5 @@ further_reading:
 weight: 1                       # _index.md always has weight of 1 to order correctly
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
----
\ No newline at end of file
+---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md b/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
index ebf536cb72..06a09f11ec 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
@@ -16,6 +16,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
+  summary: >-
+    Learn how to access and use Arm hardware performance counters and the system counter from
+    user space using PAPI, perf_event_open, and assembly code for performance instrumentation.
+    It is designed for software developers who want to instrument hardware event counters or the
+    system counter in software applications. By the end, you will be able to understand different
+    options for accessing counters from user space, use the system counter to measure time in
+    code, and use PAPI to instrument event counters in code. It focuses on tools and technologies
+    such as PAPI, perf, Assembly, GCC, and Runbook, Linux environments, and Arm platforms including
+    Neoverse. The main steps cover Counter access options, Use a system counter, Use PAPI for
+    counting, and Use perf_event_open for counting.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand different options for accessing counters from user space, use the system
+      counter to measure time in code, and use PAPI to instrument event counters in code. Learn
+      how to access and use Arm hardware performance counters and the system counter from user
+      space using PAPI, perf_event_open, and assembly code for performance instrumentation.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers who want to instrument hardware event
+      counters or the system counter in software applications.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm computer running Linux. A bare
+      metal or cloud metal instance is best because they expose more counters. You can use a virtual
+      machine (VM), but fewer counters may be available. These instructions have been tested on
+      the `a1.metal` instance type.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including PAPI, perf, Assembly, GCC, and Runbook, Linux environments,
+      and Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Counter access options, Use a system counter, Use
+      PAPI for counting, and Use perf_event_open for counting.
+# END generated_summary_faq
+
 author: Julio Suarez
 
 ### Tags
@@ -54,3 +97,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md b/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
index b4cd8c75fb..80254cc904 100644
--- a/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
@@ -17,6 +17,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: ced3882cf8be11a55304d2697f450a2c862a81d87317ab42298148755e9f272c
+  summary: >-
+    Learn how to package multi-architecture container applications and deploy them on AWS Fargate
+    with Graviton processors using the AWS Copilot CLI. It is designed for software developers
+    who want to learn how to use the command line to deploy Arm containers on AWS Fargate. By
+    the end, you will be able to package applications using a multi-architecture containers, deploy
+    containers on AWS Fargate with the AWS Copilot CLI, and configure Copilot to use AWS Graviton
+    processors. It focuses on tools and technologies such as Docker, Linux environments, Arm platforms
+    including Neoverse, and cloud platforms such as AWS. The main steps cover Containerize an
+    example application and Deploy with Copilot.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will package applications using a multi-architecture containers, deploy containers on
+      AWS Fargate with the AWS Copilot CLI, and configure Copilot to use AWS Graviton processors.
+      Learn how to package multi-architecture container applications and deploy them on AWS Fargate
+      with Graviton processors using the AWS Copilot CLI.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to learn how to use the command
+      line to deploy Arm containers on AWS Fargate.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Amazon Web Services (AWS) account;
+      A local computer with Docker, AWS CLI, and AWS Copilot CLI installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Docker, Linux environments, Arm platforms such as
+      Neoverse, and cloud platforms such as AWS.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Containerize an example application and Deploy with
+      Copilot.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
@@ -54,3 +94,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md b/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
index 139e24573f..c373e2ea71 100644
--- a/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
@@ -17,6 +17,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 087a4d56914e5b53cec5ad17e8d682e72d04ba6b394237c081efe4a3f939e04e
+  summary: >-
+    Learn how to automate the creation and deployment of AWS Graviton instances using Terraform
+    with jump server access for secure infrastructure management. It is designed for software
+    developers who are new to deploying Arm instances on AWS using Terraform. By the end, you
+    will be able to automate AWS EC2 instance creation using Terraform, deploy Arm instances on
+    AWS and provide access via Jump Server, and provide infrastructure basics, code knowledge
+    and files that could help with future learning paths. It focuses on tools and technologies
+    such as Terraform and Bastion, Linux environments, Arm platforms including Neoverse, and cloud
+    platforms such as AWS. The main steps cover Automate AWS instance creation using Terraform
+    and Deploy Arm instances on AWS and provide access via Jump Server.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will automate AWS EC2 instance creation using Terraform, deploy Arm instances on AWS
+      and provide access via Jump Server, and provide infrastructure basics, code knowledge and
+      files that could help with future learning paths. Learn how to automate the creation and
+      deployment of AWS Graviton instances using Terraform with jump server access for secure
+      infrastructure management.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who are new to deploying Arm instances
+      on AWS using Terraform.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Amazon Web Services (AWS) [account](https://aws.amazon.com/);
+      A computer with [Terraform](/install-guides/terraform) installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Terraform and Bastion, Linux environments, Arm platforms
+      such as Neoverse, and cloud platforms such as AWS.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Automate AWS instance creation using Terraform and
+      Deploy Arm instances on AWS and provide access via Jump Server.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
@@ -50,3 +92,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
index 213ab24986..be536fe154 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
@@ -19,6 +19,52 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 1682c0527bb49c417263286e2aba7c82fb9a27fa315ecc6b9ca10978b69cc236
+  summary: >-
+    Learn how to create and deploy Azure Resource Manager templates to provision Arm64-based Cobalt
+    100 virtual machines on Azure using the Azure CLI. It is designed for developers and DevOps
+    engineers who want to automate the deployment of Arm-based Azure Cobalt 100 virtual machines
+    using Azure Resource Manager templates. By the end, you will be able to structure an Azure
+    Resource Manager template with parameters, variables, and resources, specify Arm64 architecture
+    and Cobalt 100 Azure VM sizes, and deploy the template using Azure CLI and verify the deployment.
+    It focuses on tools and technologies such as Azure CLI and JSON, Linux environments, Arm platforms
+    including Neoverse, and cloud platforms such as Microsoft Azure. The main steps cover Getting
+    started with Azure Resource Manager, Create the Azure Resource Manager template, Deploy the
+    Azure Resource Manager template, and Connect to the Cobalt 100 VM and verify Arm64 architecture.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will structure an Azure Resource Manager template with parameters, variables, and resources,
+      specify Arm64 architecture and Cobalt 100 Azure VM sizes, and deploy the template using
+      Azure CLI and verify the deployment. Learn how to create and deploy Azure Resource Manager
+      templates to provision Arm64-based Cobalt 100 virtual machines on Azure using the Azure
+      CLI.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers and DevOps engineers who want to automate the
+      deployment of Arm-based Azure Cobalt 100 virtual machines using Azure Resource Manager templates.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Azure subscription with permissions
+      to create resource groups, virtual machines, and networking resources; Azure CLI installed
+      on your local machine - see the [Azure CLI install guide](/install-guides/azure-cli/); An
+      SSH key pair for authentication.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Azure CLI and JSON, Linux environments, Arm platforms
+      such as Neoverse, and cloud platforms such as Microsoft Azure.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Getting started with Azure Resource Manager, Create
+      the Azure Resource Manager template, Deploy the Azure Resource Manager template, and Connect
+      to the Cobalt 100 VM and verify Arm64 architecture.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
@@ -55,3 +101,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
index b3aab4f6a7..3cb1dddf85 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
@@ -18,6 +18,50 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: b5bd3abde8d9dd3146e0f11f4819ac510417ad7f707b4d0970c02fd63801b358
+  summary: >-
+    Learn how to configure a self-hosted GitHub runner on Azure Cobalt 100, create an AKS cluster
+    with Terraform, and deploy a .NET application using GitHub Actions CI/CD. It is designed for
+    software developers who want to develop cloud-native applications using GitHub Actions and
+    Azure Kubernetes Service (AKS), and run them on Microsoft Azure Cobalt 100 VMs. By the end,
+    you will be able to configure an Azure Cobalt 100 VM as a self-hosted GitHub runner, create
+    an AKS cluster with Arm-based Azure Cobalt 100 nodes using Terraform, and deploy a .NET application
+    to AKS with GitHub Actions using the self-hosted Arm64-based runner. It focuses on tools and
+    technologies such as .NET, Kubernetes, and Docker, Linux environments, Arm platforms including
+    Neoverse, and cloud platforms such as Microsoft Azure. The main steps cover Background and
+    Build and deploy a .NET application.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will configure an Azure Cobalt 100 VM as a self-hosted GitHub runner, create an AKS
+      cluster with Arm-based Azure Cobalt 100 nodes using Terraform, and deploy a .NET application
+      to AKS with GitHub Actions using the self-hosted Arm64-based runner. Learn how to configure
+      a self-hosted GitHub runner on Azure Cobalt 100, create an AKS cluster with Terraform, and
+      deploy a .NET application using GitHub Actions CI/CD.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers who want to develop cloud-native applications
+      using GitHub Actions and Azure Kubernetes Service (AKS), and run them on Microsoft Azure
+      Cobalt 100 VMs.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Microsoft Azure account.; A GitHub
+      account.; A machine with [Terraform](/install-guides/terraform/),[Azure CLI](/install-guides/azure-cli),
+      and [Kubectl](/install-guides/kubectl/) installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including .NET, Kubernetes, and Docker, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Background and Build and deploy a .NET application.
+# END generated_summary_faq
+
 author: Pranay Bakre
 
 ### Tags
@@ -63,3 +107,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
index 84f5d4f83c..4b7d26c192 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
@@ -18,6 +18,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 6713af3d70887933cf54c7fa36bdc88d71a51fa34fb29a4ce90636ff1de624c7
+  summary: >-
+    Learn how to automate the creation of Azure Arm virtual machines using Terraform. It is designed
+    for software developers who are new to deploying Arm instances on Azure using Terraform. By
+    the end, you will be able to automate Arm virtual machine creation using Terraform, deploy
+    Arm VMs on Azure and provide access via Jump Server, and provide infrastructure basics, code
+    knowledge and files that could help with future learning paths. It focuses on tools and technologies
+    such as Terraform and Bastion, Linux environments, Arm platforms including Neoverse, and cloud
+    platforms such as Microsoft Azure. The main steps cover Automate Azure VM creation with Terraform
+    and Deploy Arm VMs on Azure and provide access via Jump Server.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will automate Arm virtual machine creation using Terraform, deploy Arm VMs on Azure
+      and provide access via Jump Server, and provide infrastructure basics, code knowledge and
+      files that could help with future learning paths. Learn how to automate the creation of
+      Azure Arm virtual machines using Terraform.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who are new to deploying Arm instances
+      on Azure using Terraform.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Azure account; A computer with Terraform
+      installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Terraform and Bastion, Linux environments, Arm platforms
+      such as Neoverse, and cloud platforms such as Microsoft Azure.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Automate Azure VM creation with Terraform and Deploy
+      Arm VMs on Azure and provide access via Jump Server.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
@@ -56,3 +96,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
index 2f62b922e7..cc8cfbe57a 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
@@ -22,6 +22,50 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 413467e581e3561425229d0f6118975dbb596d6a9494544a54f0b9ab22c1b89d
+  summary: >-
+    Learn how to create a custom Azure Linux 3.0 VM image using QEMU, upload it to Azure Shared
+    Image Gallery, and deploy it on Arm-based Cobalt 100 processors. It is designed for developers
+    who want to run Azure Linux 3.0 on Arm-based Cobalt 100 processors in a custom virtual machine.
+    By the end, you will be able to use QEMU to create a raw disk image, boot a virtual machine
+    using an AArch64 ISO and install Azure Linux 3.0, and convert the raw disk image to VHD format.
+    It focuses on tools and technologies such as QEMU and Azure CLI, Linux environments, Arm platforms
+    including Neoverse, and cloud platforms such as Microsoft Azure. The main steps cover Build
+    and run Azure Linux 3.0 on an Arm-based Azure virtual machine, Create an Azure Linux image
+    for Arm, Transfer the image to Azure, and Start an Azure virtual machine with the new image.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will use QEMU to create a raw disk image, boot a virtual machine using an AArch64 ISO
+      and install Azure Linux 3.0, and convert the raw disk image to VHD format. Learn how to
+      create a custom Azure Linux 3.0 VM image using QEMU, upload it to Azure Shared Image Gallery,
+      and deploy it on Arm-based Cobalt 100 processors.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for developers who want to run Azure Linux 3.0 on Arm-based Cobalt
+      100 processors in a custom virtual machine.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Microsoft Azure](https://azure.microsoft.com/)
+      account with permission to create resources, including instances using Cobalt 100 processors;
+      A Linux machine with [QEMU](https://www.qemu.org/download/) and the [Azure CLI](/install-guides/azure-cli/)
+      installed and authenticated.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including QEMU and Azure CLI, Linux environments, Arm platforms
+      such as Neoverse, and cloud platforms such as Microsoft Azure.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Build and run Azure Linux 3.0 on an Arm-based Azure
+      virtual machine, Create an Azure Linux image for Arm, Transfer the image to Azure, and Start
+      an Azure virtual machine with the new image.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md b/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
index 8744ee2ce4..28a8cbd868 100644
--- a/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
@@ -16,6 +16,53 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: a4cf1d9161b3a32e29694415762eda419752e1c3144662d5e131b6553f0a58e3
+  summary: >-
+    Learn how to deploy and accelerate PyTorch NLP sentiment analysis models from Hugging Face
+    on Arm servers with BFloat16 fast math kernel optimization on Graviton3 processors. It is
+    designed for software developers who want to learn how to run and accelerate the performance
+    of Natural Language Processing (NLP) models on Arm-based servers. By the end, you will be
+    able to deploy PyTorch NLP Sentiment Analysis models from Hugging Face on Arm servers, evaluate
+    the performance of three NLP models using the Sentiment Analysis pipeline, and measure the
+    performance uplift of these models by enabling support for BFloat16 fast math kernels on Arm
+    Neoverse-based AWS Graviton3 Processors. It focuses on tools and technologies such as Python,
+    PyTorch, and Hugging Face, Linux environments, Arm platforms including Neoverse, and cloud
+    platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps cover Measure
+    and accelerate the performance of Natural Language Processing (NLP) models from Hugging Face
+    on Arm servers.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will deploy PyTorch NLP Sentiment Analysis models from Hugging Face on Arm servers,
+      evaluate the performance of three NLP models using the Sentiment Analysis pipeline, and
+      measure the performance uplift of these models by enabling support for BFloat16 fast math
+      kernels on Arm Neoverse-based AWS Graviton3 Processors. Learn how to deploy and accelerate
+      PyTorch NLP sentiment analysis models from Hugging Face on Arm servers with BFloat16 fast
+      math kernel optimization on Graviton3 processors.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to learn how to run and accelerate
+      the performance of Natural Language Processing (NLP) models on Arm-based servers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/)
+      from a cloud service provider or an on-premise Arm server.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Python, PyTorch, and Hugging Face, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
+      Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Measure and accelerate the performance of Natural
+      Language Processing (NLP) models from Hugging Face on Arm servers.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
@@ -61,3 +108,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md b/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
index 1999927599..7ce905a37e 100644
--- a/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
@@ -19,6 +19,52 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 1701b37580fe5d012a5e6fd322307742656a748dfb766fd48914011167386e95
+  summary: >-
+    Learn how to implement and benchmark bitmap scanning operations for database workloads using
+    scalar, Neon, and SVE instructions on Arm-based cloud instances. It is designed for database
+    developers, performance engineers, and anyone interested in optimizing data processing workloads
+    on Arm-based cloud instances. By the end, you will be able to understand bitmap scanning operations
+    in database systems, implement bitmap scanning with scalar, Neon, and SVE instructions, and
+    compare performance between different implementations. It focuses on tools and technologies
+    such as SVE, Neon, and Runbook, Linux environments, Arm platforms including Neoverse, and
+    cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps cover
+    Optimize bitmap scanning in databases with SVE and Neon on Arm servers, Build and manage a
+    bit vector in C, Implement scalar bitmap scanning in C, Vectorized bitmap scanning with Neon
+    and SVE, and Benchmarking bitmap scanning across implementations.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand bitmap scanning operations in database systems, implement bitmap scanning
+      with scalar, Neon, and SVE instructions, and compare performance between different implementations.
+      Learn how to implement and benchmark bitmap scanning operations for database workloads using
+      scalar, Neon, and SVE instructions on Arm-based cloud instances.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for database developers, performance engineers, and anyone
+      interested in optimizing data processing workloads on Arm-based cloud instances.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/)
+      from an appropriate cloud service provider.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including SVE, Neon, and Runbook, Linux environments, Arm
+      platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud,
+      and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Optimize bitmap scanning in databases with SVE and
+      Neon on Arm servers, Build and manage a bit vector in C, Implement scalar bitmap scanning
+      in C, Vectorized bitmap scanning with Neon and SVE, and Benchmarking bitmap scanning across
+      implementations.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 
@@ -55,3 +101,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md b/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
index 3e8956ae86..e0e11c3537 100644
--- a/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
@@ -25,6 +25,55 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 8654b656131bf1d529e11d85f874f7a81c01f7207340d2a606b6fd2d80bfad04
+  summary: >-
+    Learn how to identify optimization candidates and apply LLVM BOLT post-link optimization to
+    AArch64 binaries using BRBE, SPE, instrumentation, and PMU profiling techniques. It is designed
+    for developers who have compiled an AArch64 Linux application and want to evaluate whether
+    LLVM BOLT can improve its runtime performance. By the end, you will be able to identify whether
+    a program is a good candidate for code layout optimization, install LLVM BOLT on Linux, and
+    use LLVM BOLT to perform profile-guided post-link optimization of an AArch64 binary with poor
+    spatial locality. It focuses on tools and technologies such as BOLT and perf, Linux environments,
+    and Arm platforms including Neoverse and Cortex-A. The main steps cover Understand BOLT optimization
+    for Arm, Install BOLT on Linux, Prepare your environment, Identify programs for BOLT optimization,
+    and Optimize with BRBE profiling.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will identify whether a program is a good candidate for code layout optimization, install
+      LLVM BOLT on Linux, and use LLVM BOLT to perform profile-guided post-link optimization of
+      an AArch64 binary with poor spatial locality. Learn how to identify optimization candidates
+      and apply LLVM BOLT post-link optimization to AArch64 binaries using BRBE, SPE, instrumentation,
+      and PMU profiling techniques.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers who have compiled an AArch64 Linux application
+      and want to evaluate whether LLVM BOLT can improve its runtime performance.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An AArch64 system running Linux with
+      [perf](/install-guides/perf/) installed; Linux kernel version 6.17 or later to enable Branch
+      Record Buffer Extension ([BRBE profiling](/learning-paths/servers-and-cloud-computing/bolt-demo/brbe/));
+      Linux kernel version 6.14 or later for Arm Statistical Profiling Extension ([SPE profiling](/learning-paths/servers-and-cloud-computing/bolt-demo/spe/));
+      GCC version 13.3 or later to compile the example program ([GCC](/install-guides/gcc/) );
+      A system with with sufficient hardware performance counters to use the [TopDown](/install-guides/topdown-tool)
+      methodology. This typically requires running on bare metal rather than a virtualized environment.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including BOLT and perf, Linux environments, and Arm platforms
+      such as Neoverse and Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Understand BOLT optimization for Arm, Install BOLT
+      on Linux, Prepare your environment, Identify programs for BOLT optimization, and Optimize
+      with BRBE profiling.
+# END generated_summary_faq
+
 author: Paschalis Mpeis
 
 ### Tags
@@ -74,3 +123,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md b/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
index 6ffb7664a8..bd81ea77f3 100644
--- a/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
@@ -18,6 +18,50 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 84a8b96fe7df302e0a2a6e4645bbb6170b45a3e0b55e0ea3682ec47663d34819
+  summary: >-
+    Learn how to optimize Arm application binaries and shared libraries using BOLT profile instrumentation,
+    merge multiple profiles for improved coverage, and integrate optimized libraries. It is designed
+    for performance engineers and software developers targeting Arm platforms who want to optimize
+    application binaries and shared libraries using BOLT. By the end, you will be able to instrument
+    and optimize application binaries for individual workload features using BOLT, collect and
+    merge separate BOLT profiles to improve code coverage, and optimize shared libraries independently
+    of application binaries. It focuses on tools and technologies such as BOLT, perf, and Runbook,
+    Linux environments, and Arm platforms including Neoverse and Cortex-A. The main steps cover
+    Overview, Instrument MySQL with BOLT, Run a new workload using BOLT and merge the results,
+    Instrument shared libraries with BOLT, and Review the performance results.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will instrument and optimize application binaries for individual workload features using
+      BOLT, collect and merge separate BOLT profiles to improve code coverage, and optimize shared
+      libraries independently of application binaries. Learn how to optimize Arm application binaries
+      and shared libraries using BOLT profile instrumentation, merge multiple profiles for improved
+      coverage, and integrate optimized libraries.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for performance engineers and software developers targeting Arm
+      platforms who want to optimize application binaries and shared libraries using BOLT.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm-based Linux system with [BOLT](/install-guides/bolt/)
+      and [Linux Perf](/install-guides/perf/) installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including BOLT, perf, and Runbook, Linux environments, and
+      Arm platforms such as Neoverse and Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview, Instrument MySQL with BOLT, Run a new workload
+      using BOLT and merge the results, Instrument shared libraries with BOLT, and Review the
+      performance results.
+# END generated_summary_faq
+
 author: Gayathri Narayana Yegna Narayanan
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/bolt/_index.md b/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
index 023207d5b3..93b64ab511 100644
--- a/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
@@ -17,6 +17,51 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
+  summary: >-
+    Learn how to build, profile, and optimize Arm executables using BOLT post-link binary optimization
+    to improve application performance through code layout improvements. It is designed for software
+    developers who want to learn how to use BOLT on an Arm executable. By the end, you will be
+    able to build an application which is ready to be optimized by BOLT, profile an application
+    and collect performance information, and run BOLT to create an optimized executable. It focuses
+    on tools and technologies such as BOLT, perf, and Runbook, Linux environments, and Arm platforms
+    including Neoverse and Cortex-A. The main steps cover Overview of the BOLT optimization process,
+    Prepare your BOLT environment, Use BOLT with Samples, Use BOLT with ETM, and Use BOLT with
+    SPE.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build an application which is ready to be optimized by BOLT, profile an application
+      and collect performance information, and run BOLT to create an optimized executable. Learn
+      how to build, profile, and optimize Arm executables using BOLT post-link binary optimization
+      to improve application performance through code layout improvements.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to learn how to use BOLT
+      on an Arm executable.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm based system running Linux with
+      [BOLT](/install-guides/bolt/) and [Linux Perf](/install-guides/perf/) installed. The Linux
+      kernel should be version 5.15 or later. Earlier kernel versions can be used, but some Linux
+      Perf features may be limited or not available. For [SPE](./bolt-spe) the version should
+      be 6.14 or later.; (Optional) A second, more powerful Linux system to build the software
+      executable and run BOLT.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including BOLT, perf, and Runbook, Linux environments, and
+      Arm platforms such as Neoverse and Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview of the BOLT optimization process, Prepare
+      your BOLT environment, Use BOLT with Samples, Use BOLT with ETM, and Use BOLT with SPE.
+# END generated_summary_faq
+
 author: Jonathan Davies
 
 ### Tags
@@ -51,3 +96,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
index 9e29f53af0..1d4325a890 100644
--- a/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
@@ -21,6 +21,57 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 4bda206717eef380430009f859826d9bcf820442d13492cd3c22a114561e2917
+  summary: >-
+    Learn how to configure Buildkite agents on Google Axion C4A VMs to build and publish multi-architecture
+    Docker images using Docker Buildx for Arm and x86 platforms. It is designed for developers
+    who want to build and run multi-architecture Docker images with Buildkite on Arm-based Google
+    Cloud C4A virtual machines (VM) powered by Google Axion processors. By the end, you will be
+    able to provision an Arm-based VM on Google Cloud running either SUSE Linux Enterprise Server
+    or Ubuntu, install and configure Docker, Docker Buildx, and the Buildkite agent, and write
+    a Dockerfile to containerize a simple Flask-based Python application. It focuses on tools
+    and technologies such as Buildkite, Docker, and Docker Buildx, Linux environments, Arm platforms
+    including Neoverse, and cloud platforms such as Google Cloud. The main steps cover Discover
+    Buildkite on Google Axion C4A instances, Create a Google Axion C4A Arm virtual machine on
+    GCP, Install Buildkite on a Google Axion C4A Arm VM, Set up and connect Buildkite agent on
+    a Google Axion C4A Arm VM, and Create a Flask app and set up the Buildkite pipeline.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will provision an Arm-based VM on Google Cloud running either SUSE Linux Enterprise
+      Server or Ubuntu, install and configure Docker, Docker Buildx, and the Buildkite agent,
+      and write a Dockerfile to containerize a simple Flask-based Python application. Learn how
+      to configure Buildkite agents on Google Axion C4A VMs to build and publish multi-architecture
+      Docker images using Docker Buildx for Arm and x86 platforms.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers who want to build and run multi-architecture
+      Docker images with Buildkite on Arm-based Google Cloud C4A virtual machines (VM) powered
+      by Google Axion processors.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud Platform (GCP) account](https://cloud.google.com/free?utm_source=google&hl=en)
+      with billing enabled; Basic Linux system administration skills, including how to create
+      users, install packages, and manage services; Familiarity with [Docker](https://docs.docker.com/get-started/)
+      and container concepts; A [GitHub account](https://github.com/join) to host your application
+      repository.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Buildkite, Docker, and Docker Buildx, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Discover Buildkite on Google Axion C4A instances,
+      Create a Google Axion C4A Arm virtual machine on GCP, Install Buildkite on a Google Axion
+      C4A Arm VM, Set up and connect Buildkite agent on a Google Axion C4A Arm VM, and Create
+      a Flask app and set up the Buildkite pipeline.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ##### Tags
@@ -63,3 +114,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
index bbe81cc8da..80f89ca3e9 100644
--- a/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
@@ -19,6 +19,53 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: b8268d4df3b62aeb9943b750b436a936134d47745fa71db6cc08db8c84eb37be
+  summary: >-
+    Learn how to install and configure Apache Cassandra on Google Cloud Axion C4A Arm64 instances
+    and benchmark read/write performance using cassandra-stress. It is designed for software developers
+    migrating Cassandra workloads from x86_64 to Arm-based servers, specifically on Google Cloud
+    C4A virtual machines built on Axion processors. By the end, you will be able to provision
+    an Arm-based SUSE SLES virtual machine on Google Cloud (C4A with Axion processors), install
+    and configure Apache Cassandra on a SUSE Arm64 (C4A) instance, and validate Cassandra functionality
+    using CQLSH and baseline keyspace/table operations. It focuses on tools and technologies such
+    as Apache Cassandra, Java, cqlsh, and cassandra-stress, Linux environments, Arm platforms
+    including Neoverse, and cloud platforms such as Google Cloud. The main steps cover Get started
+    with Cassandra on Google Axion C4A, Create a Google Axion C4A Arm virtual machine, Install
+    Apache Cassandra, Test Cassandra baseline functionality, and Benchmark Cassandra performance.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will provision an Arm-based SUSE SLES virtual machine on Google Cloud (C4A with Axion
+      processors), install and configure Apache Cassandra on a SUSE Arm64 (C4A) instance, and
+      validate Cassandra functionality using CQLSH and baseline keyspace/table operations. Learn
+      how to install and configure Apache Cassandra on Google Cloud Axion C4A Arm64 instances
+      and benchmark read/write performance using cassandra-stress.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers migrating Cassandra workloads from
+      x86_64 to Arm-based servers, specifically on Google Cloud C4A virtual machines built on
+      Axion processors.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
+      account with billing enabled; Familiarity with Cassandra architecture, replication, and
+      [Cassandra partitioning and event-driven I/O](https://cassandra.apache.org/doc/stable/cassandra/architecture/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Apache Cassandra, Java, cqlsh, and cassandra-stress,
+      Linux environments, Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Get started with Cassandra on Google Axion C4A, Create
+      a Google Axion C4A Arm virtual machine, Install Apache Cassandra, Test Cassandra baseline
+      functionality, and Benchmark Cassandra performance.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ##### Tags
@@ -62,3 +109,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
index d7e54770f6..2485389627 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
@@ -18,6 +18,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 3947ebbac742399e70001e41ac5face92819bed0deb55aba3e2414f98432023e
+  summary: >-
+    Learn how to run the Arm CCA reference software stack on an FVP with RME support, create a
+    Realm virtual machine, and obtain attestation tokens for confidential computing. It is designed
+    for software developers who want to learn how to run their applications in a Realm using the
+    Arm Confidential Compute Architecture (CCA). By the end, you will be able to run the Arm reference
+    CCA software stack on an Armv-A AEM Base FVP (Fixed Virtual Platform) with support for RME
+    extensions, create a virtual machine in a Realm running guest Linux using a pre-built docker
+    container, and run a simple application in a Realm running guest Linux. It focuses on tools
+    and technologies such as GCC, FVP, RME, CCA, and Docker, Linux environments, and Arm platforms
+    including Neoverse. The main steps cover Overview: Realms, Run the Arm CCA stack using a pre-built
+    docker container, Run an application in a Realm, and Use memory encryption.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will run the Arm reference CCA software stack on an Armv-A AEM Base FVP (Fixed Virtual
+      Platform) with support for RME extensions, create a virtual machine in a Realm running guest
+      Linux using a pre-built docker container, and run a simple application in a Realm running
+      guest Linux. Learn how to run the Arm CCA reference software stack on an FVP with RME support,
+      create a Realm virtual machine, and obtain attestation tokens for confidential computing.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to learn how to run their
+      applications in a Realm using the Arm Confidential Compute Architecture (CCA).
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An AArch64 or x86_64 computer running
+      Linux or macOS. You can use cloud instances, refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including GCC, FVP, RME, CCA, and Docker, Linux environments,
+      and Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview: Realms, Run the Arm CCA stack using a pre-built
+      docker container, Run an application in a Realm, and Use memory encryption.
+# END generated_summary_faq
+
 author:
     - Pareena Verma
     - Arnaud de Grandmaison
@@ -67,3 +110,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
index 12c69588e9..192f0a002d 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
@@ -21,6 +21,54 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: e21f51feb101ad90245ecceab95fe13e5eb61958faf24dff818eddd11f484b9a
+  summary: >-
+    Learn how Arm CCA Realms interact with I/O devices using VirtIO paravirtualization, SWIOTLB
+    bounce buffers, and PCIe-TDISP secure device attach mechanisms with attestation. It is designed
+    for developers who want to understand how Arm CCA Realms interact with I/O devices using VirtIO,
+    bounce buffers, and secure device attach mechanisms. By the end, you will be able to define
+    device attach and distinguish VirtIO paravirtualized attach from secure physical device attach,
+    summarize what a Realm is and how RME isolates Realm memory, and describe how VirtIO enables
+    paravirtualized I/O without full device emulation. It focuses on tools and technologies such
+    as CCA, RME, and Docker, Linux and macOS environments, and Arm platforms including Neoverse
+    and Cortex-A. The main steps cover About CCA Realms, VirtIO for device attach, Bounce buffers
+    in Realms, and Exercise: observe bounce buffers in a Realm.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will define device attach and distinguish VirtIO paravirtualized attach from secure
+      physical device attach, summarize what a Realm is and how RME isolates Realm memory, and
+      describe how VirtIO enables paravirtualized I/O without full device emulation. Learn how
+      Arm CCA Realms interact with I/O devices using VirtIO paravirtualization, SWIOTLB bounce
+      buffers, and PCIe-TDISP secure device attach mechanisms with attestation.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for developers who want to understand how Arm CCA Realms interact
+      with I/O devices using VirtIO, bounce buffers, and secure device attach mechanisms.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An AArch64 or x86_64 computer running
+      Linux or macOS. You can also use a cloud instance from one of these [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).;
+      Completion of [Get Started with CCA Attestation and Veraison](/learning-paths/servers-and-cloud-computing/cca-veraison)
+      Learning Path; Completion of the [Run an application in a Realm using the Arm Confidential
+      Computing Architecture (CCA)](/learning-paths/servers-and-cloud-computing/cca-container/)
+      Learning Path; Completion of the [Run an end-to-end Attestation Flow](/learning-paths/servers-and-cloud-computing/cca-essentials/)
+      Learning Path.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including CCA, RME, and Docker, Linux and macOS environments,
+      and Arm platforms such as Neoverse and Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around About CCA Realms, VirtIO for device attach, Bounce
+      buffers in Realms, and Exercise: observe bounce buffers in a Realm.
+# END generated_summary_faq
+
 author: Arnaud de Grandmaison
 
 ### Tags
@@ -61,3 +109,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
index 2dbfa458af..d3af0335a3 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
@@ -18,6 +18,55 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: b032debbdfe82cbd017812cf671907520b146fd8684afce0c45c91e7f2287e18
+  summary: >-
+    Learn how to deploy a CCA realm on an FVP with RME support and connect it with attestation
+    services to create an end-to-end confidential computing workflow. It is designed for software
+    developers who want to learn how to run an end-to-end attestation flow with Arm's Confidential
+    Computing Architecture (CCA). By the end, you will be able to describe how you can use attestation
+    with Arm's Confidential Computing Architecture (CCA), deploy a simple workload in a CCA realm
+    on an Armv9-A AEM Base Fixed Virtual Platform (FVP) that has support for RME extensions, and
+    connect the workload with additional software services to create an end-to-end example that
+    uses attestation to unlock the confidential processing of data. It focuses on tools and technologies
+    such as GCC, FVP, RME, CCA, and Docker, Linux environments, and Arm platforms including Neoverse.
+    The main steps cover Overview of the Software Architecture and Run an end-to-end Attestation
+    with Arm CCA.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will describe how you can use attestation with Arm's Confidential Computing Architecture
+      (CCA), deploy a simple workload in a CCA realm on an Armv9-A AEM Base Fixed Virtual Platform
+      (FVP) that has support for RME extensions, and connect the workload with additional software
+      services to create an end-to-end example that uses attestation to unlock the confidential
+      processing of data. Learn how to deploy a CCA realm on an FVP with RME support and connect
+      it with attestation services to create an end-to-end confidential computing workflow.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers who want to learn how to run an end-to-end
+      attestation flow with Arm's Confidential Computing Architecture (CCA).
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An AArch64 or x86_64 computer running
+      Linux. You can use cloud instances, see this list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).;
+      Completion of [Get Started with CCA Attestation and Veraison](/learning-paths/servers-and-cloud-computing/cca-veraison)
+      Learning Path.; Completion of the [Run an application in a Realm using the Arm Confidential
+      Computing Architecture (CCA)](/learning-paths/servers-and-cloud-computing/cca-container/)
+      Learning Path.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including GCC, FVP, RME, CCA, and Docker, Linux environments,
+      and Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview of the Software Architecture and Run an end-to-end
+      Attestation with Arm CCA.
+# END generated_summary_faq
+
 author: 
     - Arnaud de Grandmaison
     - Paul Howard
@@ -65,3 +114,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
index f8a11643c8..9611ff919d 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
@@ -17,6 +17,53 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 649957b07b2bde05bc11047bd12bfc4f697c1a55eef1c3a25b5fafc95cda893d
+  summary: >-
+    Learn how to deploy Confidential Containers from encrypted images inside Arm CCA Realms using
+    Trustee services for attestation-based authorization on an FVP with RME support. It is designed
+    for developers who want to understand how Confidential Containers run in Arm CCA Realms. By
+    the end, you will be able to gain an overview of Confidential Containers and their role in
+    confidential computing, understand how Trustee services are used with Arm CCA attestation
+    to authorize and unlock confidential workloads, and deploy a Confidential Container from an
+    encrypted image inside an Arm CCA Realm using an Armv9-A AEM Base Fixed Virtual Platform (FVP)
+    with RME support. It focuses on tools and technologies such as FVP, RME, CCA, Docker, and
+    Veraison, Linux and macOS environments, and Arm platforms including Neoverse and Cortex-A.
+    The main steps cover Overview of Confidential Containers and Arm CCA Attestation with Trustee
+    and Run a confidential container with an encrypted image.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will gain an overview of Confidential Containers and their role in confidential computing,
+      understand how Trustee services are used with Arm CCA attestation to authorize and unlock
+      confidential workloads, and deploy a Confidential Container from an encrypted image inside
+      an Arm CCA Realm using an Armv9-A AEM Base Fixed Virtual Platform (FVP) with RME support.
+      Learn how to deploy Confidential Containers from encrypted images inside Arm CCA Realms
+      using Trustee services for attestation-based authorization on an FVP with RME support.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is for developers who want to understand how Confidential Containers
+      run in Arm CCA Realms.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An AArch64 or x86_64 computer running
+      Linux or macOS. Cloud-based instances can also be used; see the [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/);
+      Completion of the [Run an end-to-end Attestation with Arm CCA and Trustee](/learning-paths/servers-and-cloud-computing/cca-trustee)
+      Learning Path.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including FVP, RME, CCA, Docker, and Veraison, Linux and macOS
+      environments, and Arm platforms such as Neoverse and Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview of Confidential Containers and Arm CCA Attestation
+      with Trustee and Run a confidential container with an encrypted image.
+# END generated_summary_faq
+
 author:
   - Anton Antonov
 
@@ -63,3 +110,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
index dcc6686f72..13ffa361be 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
@@ -18,6 +18,54 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 563456f5d151c7ef59bf458f6ac971c321077475a0a78d3b5a3885b97157ba9e
+  summary: >-
+    Learn how to deploy a CCA realm workload on an FVP and connect it with Trustee services to
+    enable attestation-based confidential data processing. It is designed for software developers
+    who want to run an end-to-end attestation flow using Arm Confidential Compute Architecture
+    (CCA) and Trustee services. By the end, you will be able to describe how you can use attestation
+    with Arm's Confidential Computing Architecture (CCA) and Trustee services, deploy a simple
+    workload in a CCA realm on an Armv9-A AEM Base Fixed Virtual Platform (FVP) that has support
+    for RME extensions, and connect the workload with Trustee services to create an end-to-end
+    example that uses attestation to unlock the confidential processing of data. It focuses on
+    tools and technologies such as FVP, RME, CCA, Docker, and Veraison, Linux and macOS environments,
+    and Arm platforms including Neoverse and Cortex-A. The main steps cover Architecture overview
+    for Arm CCA Attestation with Trustee and Run an end-to-end Attestation with Arm CCA and Trustee.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will describe how you can use attestation with Arm's Confidential Computing Architecture
+      (CCA) and Trustee services, deploy a simple workload in a CCA realm on an Armv9-A AEM Base
+      Fixed Virtual Platform (FVP) that has support for RME extensions, and connect the workload
+      with Trustee services to create an end-to-end example that uses attestation to unlock the
+      confidential processing of data. Learn how to deploy a CCA realm workload on an FVP and
+      connect it with Trustee services to enable attestation-based confidential data processing.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is for software developers who want to run an end-to-end attestation
+      flow using Arm Confidential Compute Architecture (CCA) and Trustee services.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An AArch64 or x86_64 computer running
+      Linux or macOS; you can use cloud instances - see the [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/);
+      Completion of the [Get started with CCA attestation and Veraison](/learning-paths/servers-and-cloud-computing/cca-veraison)
+      Learning Path; Completion of the [Run an end-to-end attestation flow with Arm CCA](/learning-paths/servers-and-cloud-computing/cca-essentials/)
+      Learning Path.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including FVP, RME, CCA, Docker, and Veraison, Linux and macOS
+      environments, and Arm platforms such as Neoverse and Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Architecture overview for Arm CCA Attestation with
+      Trustee and Run an end-to-end Attestation with Arm CCA and Trustee.
+# END generated_summary_faq
+
 author:
   - Anton Antonov
 
@@ -62,3 +110,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
index 155f538235..95d6082bcf 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
@@ -16,6 +16,50 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 55b8032ceaf735d53b1157102a3a1da5aa5cb686e9ec51cf4896ded66d9bf263
+  summary: >-
+    Learn how to deploy a scalable Arm CCA attestation verifier service on AWS using Veraison
+    components with platform endorsement provisioning. It is designed for developers familiar
+    with CCA attestation and the Veraison project. You'll learn how to deploy a scalable CCA attestation
+    verifier service on AWS. By the end, you will be able to build an attestation service on AWS
+    using the Veraison project's components and set up Veraison as a verifier for Arm CCA attestation
+    tokens by provisioning CCA platform endorsements. It focuses on tools and technologies such
+    as CCA, RME, and Runbook, Linux environments, Arm platforms including Neoverse and Cortex-A,
+    and cloud platforms such as AWS. The main steps cover Overview, Prepare AWS Account, Create
+    the Domain and Certificate, Create the Veraison Deployment, and Add CCA Platform Endorsements
+    to Veraison.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build an attestation service on AWS using the Veraison project's components and
+      set up Veraison as a verifier for Arm CCA attestation tokens by provisioning CCA platform
+      endorsements. Learn how to deploy a scalable Arm CCA attestation verifier service on AWS
+      using Veraison components with platform endorsement provisioning.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is for developers familiar with CCA attestation and the Veraison project.
+      You'll learn how to deploy a scalable CCA attestation verifier service on AWS.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An [AWS account](/learning-paths/servers-and-cloud-computing/csp/aws/)
+      with access to AWS services.; An x86 computer running Ubuntu or Arch Linux, authorized for
+      AWS access. If you're using another build environment, you'll need to configure the toolchains
+      for cross-compilation.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including CCA, RME, and Runbook, Linux environments, Arm platforms
+      such as Neoverse and Cortex-A, and cloud platforms such as AWS.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview, Prepare AWS Account, Create the Domain and
+      Certificate, Create the Veraison Deployment, and Add CCA Platform Endorsements to Veraison.
+# END generated_summary_faq
+
 author: Paul Howard
 
 ### Tags
@@ -53,3 +97,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md
index b36efc5d35..afece7f2a8 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md
@@ -21,6 +21,47 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 9e6dee3aef79c1c65cc1a1f4fe3528b9c5542d8046f0b059ef703079ef964d77
+  summary: >-
+    Learn how to inspect and verify Arm CCA attestation tokens using command-line tools and the
+    open-source Veraison attestation verification service. It is designed for developers who would
+    like to learn about attestation in confidential computing, using Arm's Confidential Computing
+    Architecture (CCA). By the end, you will be able to describe the importance of attestation
+    in confidential computing, understand what a CCA attestation token is, and describe its format,
+    and inspect the contents of a CCA attestation token using command-line tools. It focuses on
+    tools and technologies such as CCA, RME, and Runbook, Linux environments, and Arm platforms
+    including Cortex-A. The main steps cover Using this Learning Path, CCA and Attestation, Veraison,
+    Download and inspect the attestation token, and Use the verification service.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will describe the importance of attestation in confidential computing, understand what
+      a CCA attestation token is, and describe its format, and inspect the contents of a CCA attestation
+      token using command-line tools. Learn how to inspect and verify Arm CCA attestation tokens
+      using command-line tools and the open-source Veraison attestation verification service.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is for developers who would like to learn about attestation in confidential
+      computing, using Arm's Confidential Computing Architecture (CCA).
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm-based or x86 computer running
+      Ubuntu. You can use a server instance from a cloud service provider of your choice.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including CCA, RME, and Runbook, Linux environments, and Arm
+      platforms such as Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Using this Learning Path, CCA and Attestation, Veraison,
+      Download and inspect the attestation token, and Use the verification service.
+# END generated_summary_faq
+
 author: Paul Howard
 
 ### Tags
@@ -60,3 +101,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
index 20d4267042..6567b60236 100644
--- a/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
@@ -25,6 +25,56 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: ec9cdca7aa9a5670f54ea3646f965149460d8e66d9d5d904e1b6dcf09867df39
+  summary: >-
+    Learn how to set up CircleCI self-hosted machine runners on Google Cloud Axion C4A SUSE VMs
+    and execute Arm-native CI/CD workflows using custom resource classes. It is designed for developers
+    and DevOps engineers looking to set up and run CircleCI Arm native workflows on SUSE Linux
+    Arm64 virtual machines (VMs), specifically on Google Cloud C4A with Axion processors, using
+    self-hosted runners. By the end, you will be able to provision a SUSE Arm64 virtual machine
+    on Google Cloud (C4A with Axion processors), install and configure CircleCI self-hosted machine
+    runners on Arm64, and create a cloud-native Node.js demo app to run on the self-hosted Arm
+    runner. It focuses on tools and technologies such as CircleCI, Node.js, npm, and Docker, Linux
+    environments, Arm platforms including Neoverse, and cloud platforms such as Google Cloud.
+    The main steps cover Get started with CircleCI on Google Axion C4A, Create a Google Axion
+    C4A Arm virtual machine on GCP, Install CircleCI, Create a resource class, and Install CircleCI
+    Machine Runner on SUSE Arm.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will provision a SUSE Arm64 virtual machine on Google Cloud (C4A with Axion processors),
+      install and configure CircleCI self-hosted machine runners on Arm64, and create a cloud-native
+      Node.js demo app to run on the self-hosted Arm runner. Learn how to set up CircleCI self-hosted
+      machine runners on Google Cloud Axion C4A SUSE VMs and execute Arm-native CI/CD workflows
+      using custom resource classes.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers and DevOps engineers looking to set up and
+      run CircleCI Arm native workflows on SUSE Linux Arm64 virtual machines (VMs), specifically
+      on Google Cloud C4A with Axion processors, using self-hosted runners.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
+      account with billing enabled; Basic familiarity with Linux command line, Node.js, and npm;
+      Basic understanding of CircleCI concepts such as [workflows](https://circleci.com/docs/guides/orchestrate/workflows/),
+      [jobs](https://circleci.com/docs/guides/orchestrate/jobs-steps/), [resource classes](https://circleci.com/docs/guides/execution-managed/resource-class-overview/),
+      and [runners](https://circleci.com/docs/guides/execution-runner/runner-overview/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including CircleCI, Node.js, npm, and Docker, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Get started with CircleCI on Google Axion C4A, Create
+      a Google Axion C4A Arm virtual machine on GCP, Install CircleCI, Create a resource class,
+      and Install CircleCI Machine Runner on SUSE Arm.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ##### Tags
@@ -73,3 +123,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md b/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md
index 224cf75708..196517ec1f 100644
--- a/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md
@@ -18,6 +18,52 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: e04982fd668765fa2ab25f943f020b8d2b4a5e9b5ff3a51b7431cc4d93730180
+  summary: >-
+    Learn how to install and configure CircleCI self-hosted machine runners on AWS Graviton Arm64
+    instances to execute CI/CD workflows natively on Arm. It is designed for developers and DevOps
+    engineers who want to set up and run CircleCI Arm native workflows on Linux Arm64 virtual
+    machines. You'll use AWS EC2 Graviton instances (Neoverse N1) and self-hosted runners. By
+    the end, you will be able to create an AWS EC2 Graviton Arm64 virtual machine, install and
+    configure CircleCI self-hosted machine runners on Arm64, and verify the runner by running
+    a simple workflow and test computation. It focuses on tools and technologies such as CircleCI,
+    Bash, and Git, Linux environments, Arm platforms including Neoverse, and cloud platforms such
+    as AWS. The main steps cover Get Started with CircleCI on AWS Graviton, Create an AWS EC2
+    Arm64 Graviton Instance, Install CircleCI CLI, Create a resource class in CircleCI, and Install
+    CircleCI machine runner on AWS Graviton.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create an AWS EC2 Graviton Arm64 virtual machine, install and configure CircleCI
+      self-hosted machine runners on Arm64, and verify the runner by running a simple workflow
+      and test computation. Learn how to install and configure CircleCI self-hosted machine runners
+      on AWS Graviton Arm64 instances to execute CI/CD workflows natively on Arm.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers and DevOps engineers who want to set up and
+      run CircleCI Arm native workflows on Linux Arm64 virtual machines. You'll use AWS EC2 Graviton
+      instances (Neoverse N1) and self-hosted runners.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An [AWS account](https://aws.amazon.com/free/)
+      with billing enabled; A CircleCI account; Basic understanding of CircleCI workflows, jobs
+      and resource classes.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including CircleCI, Bash, and Git, Linux environments, Arm
+      platforms such as Neoverse, and cloud platforms such as AWS.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Get Started with CircleCI on AWS Graviton, Create
+      an AWS EC2 Arm64 Graviton Instance, Install CircleCI CLI, Create a resource class in CircleCI,
+      and Install CircleCI machine runner on AWS Graviton.
+# END generated_summary_faq
+
 author: Annie Tallund
 
 ##### Tags
@@ -62,3 +108,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/clair/_index.md b/content/learning-paths/servers-and-cloud-computing/clair/_index.md
index 3cb1b9bd3b..59e3be4177 100644
--- a/content/learning-paths/servers-and-cloud-computing/clair/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/clair/_index.md
@@ -16,6 +16,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: e742eb44fa108bfcc4ee5a241414e6aa05e1fc0e1cceb589fb78a080f59b0d38
+  summary: >-
+    Learn how to install and run Clair on Arm servers using combined and distributed deployment
+    models to scan container images and generate vulnerability reports. It is designed for software
+    developers interested in scanning container images for vulnerabilities on Arm servers. By
+    the end, you will be able to install Clair on an Arm server, run Clair using combined and
+    distributed deployment models, and submit container images using the Clair CLI (command-line
+    interface) and generate vulnerability reports. It focuses on tools and technologies such as
+    Docker, Go, and Clair, Linux environments, Arm platforms including Neoverse, and cloud platforms
+    such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps cover Introduction
+    to Clair deployment models, Create a combined deployment, Create a distributed deployment,
+    and Generate vulnerability reports.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install Clair on an Arm server, run Clair using combined and distributed deployment
+      models, and submit container images using the Clair CLI (command-line interface) and generate
+      vulnerability reports. Learn how to install and run Clair on Arm servers using combined
+      and distributed deployment models to scan container images and generate vulnerability reports.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers interested in scanning container images
+      for vulnerabilities on Arm servers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/)
+      from a cloud service provider or an Arm server with recent versions of Docker and Go installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Docker, Go, and Clair, Linux environments, Arm platforms
+      such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Introduction to Clair deployment models, Create a
+      combined deployment, Create a distributed deployment, and Generate vulnerability reports.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
@@ -53,3 +95,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
index 97bb6f17db..bbf05c8dd2 100644
--- a/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
@@ -23,6 +23,57 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: e77cd1373236f39f360afe6844d642fde290960ccdad26544ff1e3342f2a980c
+  summary: >-
+    Learn how to deploy ClickHouse on Google Cloud Axion C4A processors and build a streaming
+    ETL pipeline using Apache Beam, Dataflow, and Pub/Sub for real-time analytics. It is designed
+    for developers deploying and optimizing ClickHouse on Arm-based Linux environments using Google
+    Cloud C4A virtual machines powered by Axion processors, to evaluate ClickHouse performance
+    and behavior on Arm-based infrastructure. By the end, you will be able to provision an Arm-based
+    SUSE SLES virtual machine on Google Cloud using C4A (Axion processors), configure Google Cloud
+    Pub/Sub for real-time log ingestion, and deploy and validate ClickHouse on a SUSE Linux Arm64
+    (Axion) VM. It focuses on tools and technologies such as ClickHouse, Apache Beam, Google Dataflow,
+    Google Cloud Pub/Sub, and Python 3.11, Linux environments, Arm platforms including Neoverse,
+    and cloud platforms such as Google Cloud. The main steps cover Get started with ClickHouse
+    on Google Cloud C4A Arm virtual machines, Create a Firewall Rule on GCP, Create a Google Axion
+    C4A Arm virtual machine on GCP, Set up GCP Pub/Sub and IAM for ClickHouse real-time analytics
+    on Axion, and Install ClickHouse.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will provision an Arm-based SUSE SLES virtual machine on Google Cloud using C4A (Axion
+      processors), configure Google Cloud Pub/Sub for real-time log ingestion, and deploy and
+      validate ClickHouse on a SUSE Linux Arm64 (Axion) VM. Learn how to deploy ClickHouse on
+      Google Cloud Axion C4A processors and build a streaming ETL pipeline using Apache Beam,
+      Dataflow, and Pub/Sub for real-time analytics.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers deploying and optimizing ClickHouse on Arm-based
+      Linux environments using Google Cloud C4A virtual machines powered by Axion processors,
+      to evaluate ClickHouse performance and behavior on Arm-based infrastructure.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
+      account with billing enabled; Basic familiarity with [ClickHouse](https://clickhouse.com/);
+      Basic understanding of databases and SQL.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including ClickHouse, Apache Beam, Google Dataflow, Google
+      Cloud Pub/Sub, and Python 3.11, Linux environments, Arm platforms such as Neoverse, and
+      cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Get started with ClickHouse on Google Cloud C4A Arm
+      virtual machines, Create a Firewall Rule on GCP, Create a Google Axion C4A Arm virtual machine
+      on GCP, Set up GCP Pub/Sub and IAM for ClickHouse real-time analytics on Axion, and Install
+      ClickHouse.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ##### Tags
@@ -67,3 +118,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md b/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
index 31fa72425a..44fdeefa0d 100644
--- a/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
@@ -15,6 +15,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 0d1390198cf2f0e87f509c5269fc2405cffcb2e57311024150d47e60a3273178
+  summary: >-
+    Learn how to install ClickHouse on Arm-based cloud instances and measure database performance
+    using ClickBench to determine appropriate instance configurations. It is designed for software
+    developers who want to use ClickHouse on Arm-based cloud instances. By the end, you will be
+    able to learn how to install and measure ClickHouse performance and determine the appropriate
+    instance configuration needed for your workloads. It focuses on tools and technologies such
+    as ClickHouse and ClickBench, Linux environments, Arm platforms including Neoverse, and cloud
+    platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps cover Run
+    ClickHouse and measure performance.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will learn how to install and measure ClickHouse performance and determine the appropriate
+      instance configuration needed for your workloads. Learn how to install ClickHouse on Arm-based
+      cloud instances and measure database performance using ClickBench to determine appropriate
+      instance configurations.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to use ClickHouse on Arm-based
+      cloud instances.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/)
+      from a cloud service provider or an on-premise Arm server.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including ClickHouse and ClickBench, Linux environments, Arm
+      platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud,
+      and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Run ClickHouse and measure performance.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
@@ -59,3 +99,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
index 372af7a1cf..693732d3ba 100644
--- a/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
@@ -18,6 +18,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: e4eb84292a669a8d73bff4b63d2fe3f70382dd873d023fc8ff24db5ad6178ab8
+  summary: >-
+    Learn how to deploy an Arm-based Cobalt 100 virtual machine on Azure, connect via SSH, and
+    configure network security group rules for external connectivity. It is designed for developers
+    and DevOps engineers who want to deploy an Arm-based virtual machine on Azure and expose an
+    application port to the internet. By the end, you will be able to deploy an Arm-based Cobalt
+    100 virtual machine (VM) on Microsoft Azure, connect to the Cobalt 100 VM using SSH, and configure
+    an inbound TCP port in the associated Network Security Group (NSG). It focuses on tools and
+    technologies such as Azure Portal and Azure CLI, Linux environments, Arm platforms including
+    Neoverse, and cloud platforms such as Microsoft Azure. The main steps cover Create the Cobalt
+    100 virtual machine, Open inbound ports in the Network Security Group, and Verify connectivity
+    to the Cobalt 100 VM.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will deploy an Arm-based Cobalt 100 virtual machine (VM) on Microsoft Azure, connect
+      to the Cobalt 100 VM using SSH, and configure an inbound TCP port in the associated Network
+      Security Group (NSG). Learn how to deploy an Arm-based Cobalt 100 virtual machine on Azure,
+      connect via SSH, and configure network security group rules for external connectivity.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers and DevOps engineers who want to deploy an
+      Arm-based virtual machine on Azure and expose an application port to the internet.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Microsoft Azure subscription with
+      permissions to create virtual machines and networking resources; Basic familiarity with
+      SSH.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Azure Portal and Azure CLI, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Create the Cobalt 100 virtual machine, Open inbound
+      ports in the Network Security Group, and Verify connectivity to the Cobalt 100 VM.
+# END generated_summary_faq
+
 author: Joe Stech
 
 ### Tags
@@ -57,3 +100,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md b/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
index d0f56423cf..c6c2d78799 100644
--- a/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
@@ -16,6 +16,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: e7ea48aaea0e25f624ea1d77c6252b0e537fdaeca3aacca560d7bc9d103bbbb3
+  summary: >-
+    Learn how to automate Docker image creation for Arm using AWS CodeBuild with GitHub integration
+    and run the images on any Arm system with Docker installed. It is designed for software developers
+    interested in using AWS CodeBuild to automate container build tasks. By the end, you will
+    be able to use a GitHub project and AWS CodeBuild to automate Docker image creation and pull
+    and run the created Docker images on any Arm computer with Docker installed. It focuses on
+    tools and technologies such as Docker and AWS CodeBuild, Linux environments, Arm platforms
+    including Neoverse, and cloud platforms such as AWS. The main steps cover Build Docker images
+    using AWS CodeBuild and Run Docker images from Docker Hub and AWS Elastic Container Registry
+    (ECR).
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will use a GitHub project and AWS CodeBuild to automate Docker image creation and pull
+      and run the created Docker images on any Arm computer with Docker installed. Learn how to
+      automate Docker image creation for Arm using AWS CodeBuild with GitHub integration and run
+      the images on any Arm system with Docker installed.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers interested in using AWS CodeBuild to automate
+      container build tasks.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An [AWS account](/learning-paths/servers-and-cloud-computing/csp/aws/)
+      for accessing AWS cloud services.; An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/)
+      from a cloud service provider or any Arm server, laptop, or single-board computer running
+      [Docker](/install-guides/docker/) used to run the created images.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Docker and AWS CodeBuild, Linux environments, Arm
+      platforms such as Neoverse, and cloud platforms such as AWS.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Build Docker images using AWS CodeBuild and Run Docker
+      images from Docker Hub and AWS Elastic Container Registry (ECR).
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
@@ -50,3 +93,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/codec/_index.md b/content/learning-paths/servers-and-cloud-computing/codec/_index.md
index ab59c9dc64..917de74f49 100644
--- a/content/learning-paths/servers-and-cloud-computing/codec/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/codec/_index.md
@@ -19,6 +19,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 908a750f2a891a0c4c9d1183c2130ac5ac0fdcfb4a45185cef6ed6da47c9aaa9
+  summary: >-
+    Learn how to build and run the x265 H.265 codec on Arm servers with performance benchmarking
+    across various video resolutions and encoding presets. It is designed for software developers
+    who want to build and run an x265 codec on Arm servers and measure performance. By the end,
+    you will be able to build x265 codec on Arm server and run x265 codec on Arm server with the
+    same video of various resolutions and encoding presets to measure the performance impact.
+    It focuses on tools and technologies such as x265, Linux environments, Arm platforms including
+    Neoverse, and cloud platforms such as AWS and Oracle. The main steps cover Build and Run x265
+    codec on Arm servers.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build x265 codec on Arm server and run x265 codec on Arm server with the same video
+      of various resolutions and encoding presets to measure the performance impact. Learn how
+      to build and run the x265 H.265 codec on Arm servers with performance benchmarking across
+      various video resolutions and encoding presets.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to build and run an x265
+      codec on Arm servers and measure performance.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/)
+      from an appropriate cloud service provider. This Learning Path has been verified on AWS
+      EC2 and Oracle cloud services, running `Ubuntu Linux 20.04.`.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including x265, Linux environments, Arm platforms such as
+      Neoverse, and cloud platforms such as AWS and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Build and Run x265 codec on Arm servers.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 test_images:
@@ -63,3 +103,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/codec1/_index.md b/content/learning-paths/servers-and-cloud-computing/codec1/_index.md
index da1eb44f17..c8a05e7325 100644
--- a/content/learning-paths/servers-and-cloud-computing/codec1/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/codec1/_index.md
@@ -4,6 +4,44 @@ description: Learn how to build and run the AV1 and VP9 video codecs on Arm Linu
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: c0643a788cdb0b3e33fe645fbb61d99a1899806e3ee197541c1eb8134b2876c1
+  summary: >-
+    Learn how to build and run the AV1 and VP9 video codecs on Arm Linux systems with performance
+    benchmarking across various resolutions and encoding configurations. It is designed for software
+    developers who want to build and run the VP9 and AV1 codecs on Arm servers and measure performance.
+    By the end, you will be able to build the AV1 and VP9 codecs on Arm Linux and run the AV1
+    and VP9 codecs on Arm Linux using example videos with various resolutions and encodings. It
+    focuses on Linux environments and Arm platforms including Neoverse and Cortex-A. The main
+    steps cover Build and Run the AV1 codec and Build and Run the VP9 codec.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build the AV1 and VP9 codecs on Arm Linux and run the AV1 and VP9 codecs on Arm
+      Linux using example videos with various resolutions and encodings. Learn how to build and
+      run the AV1 and VP9 video codecs on Arm Linux systems with performance benchmarking across
+      various resolutions and encoding configurations.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to build and run the VP9
+      and AV1 codecs on Arm servers and measure performance.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm Linux system or an [Arm based
+      instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers Linux environments and Arm platforms such as Neoverse and Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Build and Run the AV1 codec and Build and Run the
+      VP9 codec.
+# END generated_summary_faq
+
 author: Odin Shen
 
 minutes_to_complete: 30
@@ -53,5 +91,6 @@ further_reading:
 
 weight: 1
 layout: learningpathall
-learning_path_main_page: "yes" 
+learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
index 3a21af57f6..5330d0b111 100644
--- a/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
@@ -19,6 +19,52 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 0a7d76b8c944a50073c7524813acf83948155c7c61d9c92f9202eae1192c9600
+  summary: >-
+    Learn how to install and configure Couchbase on Google Cloud Axion C4A Arm64 instances and
+    benchmark read/write performance using YCSB workloads. It is designed for developers deploying
+    Couchbase workloads on Arm Linux environments, specifically using Google Cloud C4A virtual
+    machines (VM) powered by Axion processors. By the end, you will be able to provision an Arm-based
+    SUSE Linux Enterprise Server (SLES) virtual machine on Google Cloud (C4A with Axion processors),
+    install Couchbase Server on the SUSE Arm64 (C4A) instance, and verify Couchbase deployment
+    by accessing the web console, creating a test bucket, and confirming cluster health. It focuses
+    on tools and technologies such as Couchbase, Linux environments, Arm platforms including Neoverse,
+    and cloud platforms such as Google Cloud. The main steps cover Get started with Couchbase
+    on Google Axion C4A, Create a firewall rule on GCP, Create a Google Axion C4A Arm virtual
+    machine on GCP, Install Couchbase, and Perform Couchbase baseline testing.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will provision an Arm-based SUSE Linux Enterprise Server (SLES) virtual machine on Google
+      Cloud (C4A with Axion processors), install Couchbase Server on the SUSE Arm64 (C4A) instance,
+      and verify Couchbase deployment by accessing the web console, creating a test bucket, and
+      confirming cluster health. Learn how to install and configure Couchbase on Google Cloud
+      Axion C4A Arm64 instances and benchmark read/write performance using YCSB workloads.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers deploying Couchbase workloads on Arm Linux
+      environments, specifically using Google Cloud C4A virtual machines (VM) powered by Axion
+      processors.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
+      account with billing enabled; Basic familiarity with [Couchbase](https://www.couchbase.com/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Couchbase, Linux environments, Arm platforms such
+      as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Get started with Couchbase on Google Axion C4A, Create
+      a firewall rule on GCP, Create a Google Axion C4A Arm virtual machine on GCP, Install Couchbase,
+      and Perform Couchbase baseline testing.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ##### Tags
@@ -54,3 +100,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md b/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
index 0dc4351095..cc51e5439b 100644
--- a/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
@@ -16,6 +16,45 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 22f845b8ea4dbb9ffc63fafb76c17c79aedde14a28417d49e1ab0833bbbc1eba
+  summary: >-
+    Learn how to apply g++ compiler optimization techniques and flags to improve C++ application
+    performance on Arm systems with hands-on examples. It is designed for beginner C++ developers
+    who are looking to optimize applications on Arm-based cloud instances using compiler flags.
+    By the end, you will be able to compile a C++ program for a specific Arm target and use compiler
+    flags to manage optimizations. It focuses on tools and technologies such as CPP and Runbook,
+    Linux environments, Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft
+    Azure, Google Cloud, and Oracle. The main steps cover Compiler basics, Set up Your Environment,
+    Find specific Neoverse features, and Try an example application.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will compile a C++ program for a specific Arm target and use compiler flags to manage
+      optimizations. Learn how to apply g++ compiler optimization techniques and flags to improve
+      C++ application performance on Arm systems with hands-on examples.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is for beginner C++ developers who are looking to optimize applications
+      on Arm-based cloud instances using compiler flags.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Basic understanding of C++.; Basic understanding
+      of compilers.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including CPP and Runbook, Linux environments, Arm platforms
+      such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Compiler basics, Set up Your Environment, Find specific
+      Neoverse features, and Try an example application.
+# END generated_summary_faq
+
 author: Kieran Hejmadi
 
 ### Tags
@@ -47,3 +86,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md b/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md
index f12d8deb54..83a57fe7eb 100644
--- a/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md
@@ -16,6 +16,47 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 4e7de348514d0b5a742fa47bb85a2a5814fa9bd587478e7ef08365d16273f6bf
+  summary: >-
+    Learn how to apply profile-guided optimization to C++ applications on Arm systems and measure
+    performance improvements using Google Benchmark. It is designed for Developers looking to
+    optimize C++ performance based on runtime behavior. By the end, you will be able to microbenchmark
+    a function using Google Benchmark and apply profile-guided optimization to build performance-tuned
+    binaries. It focuses on tools and technologies such as Google Benchmark and Runbook, Linux
+    environments, Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft
+    Azure, Google Cloud, and Oracle. The main steps cover Profile-Guided Optimization, Google
+    Benchmark, Example operation, Using Profile Guided Optimization, and Incorporating PGO into
+    a GitHub Actions workflow.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will microbenchmark a function using Google Benchmark and apply profile-guided optimization
+      to build performance-tuned binaries. Learn how to apply profile-guided optimization to C++
+      applications on Arm systems and measure performance improvements using Google Benchmark.
+  - question: Who is this Learning Path for?
+    answer: >-
+      Developers looking to optimize C++ performance based on runtime behavior.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Basic C++ understanding.; Access to
+      an Arm-based Linux machine.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Google Benchmark and Runbook, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
+      Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Profile-Guided Optimization, Google Benchmark, Example
+      operation, Using Profile Guided Optimization, and Incorporating PGO into a GitHub Actions
+      workflow.
+# END generated_summary_faq
+
 author: Kieran Hejmadi
 
 ### Tags
@@ -52,3 +93,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md b/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
index c6ea2ea125..35403e8bdd 100644
--- a/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
@@ -16,6 +16,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 58dba071f70b4f85b87b3bd27b3a7ba3ff985f80079a42e05b797a998aeaf104
+  summary: >-
+    Learn how to profile and identify CPU hotspots in C++ applications on Arm Neoverse using Arm
+    Performix flame graphs to guide optimization. It is designed for software developers and performance
+    engineers who want to identify code hotspots in applications running on Arm Linux systems.
+    By the end, you will be able to run the Code Hotspots recipe in Arm Performix and identify
+    which functions consume the most CPU cycles and target them for optimization. It focuses on
+    tools and technologies such as Arm Performix, C++, and Runbook, Linux environments, and Arm
+    platforms including Neoverse. The main steps cover Understand flame graphs and profiling tools,
+    Build the example application, Profile baseline performance, and Optimize application performance.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will run the Code Hotspots recipe in Arm Performix and identify which functions consume
+      the most CPU cycles and target them for optimization. Learn how to profile and identify
+      CPU hotspots in C++ applications on Arm Neoverse using Arm Performix flame graphs to guide
+      optimization.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers and performance engineers who want
+      to identify code hotspots in applications running on Arm Linux systems.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Access to Arm Performix; Basic understanding
+      of C++.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Arm Performix, C++, and Runbook, Linux environments,
+      and Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Understand flame graphs and profiling tools, Build
+      the example application, Profile baseline performance, and Optimize application performance.
+# END generated_summary_faq
+
 author: Kieran Hejmadi
 
 ### Tags
@@ -54,3 +94,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/csp/_index.md b/content/learning-paths/servers-and-cloud-computing/csp/_index.md
index bbf20fdd77..299f11f452 100644
--- a/content/learning-paths/servers-and-cloud-computing/csp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/csp/_index.md
@@ -4,6 +4,45 @@ description: Learn how to start an Arm-based virtual machine instance from major
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: 9e94b69eabf35677c48db812bb85ea9cef184efc078a826b96954f540a45e915
+  summary: >-
+    Learn how to start an Arm-based virtual machine instance from major cloud service providers
+    and verify the Arm architecture is being used. It is designed for software developers who
+    are new to Arm-based cloud instances. By the end, you will be able to start an Arm-based instance
+    in the cloud and verify that the instance is using the Arm architecture. It focuses on Linux
+    environments, Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft
+    Azure, Google Cloud, and Oracle. The main steps cover Getting Started with AWS, Getting Started
+    with Microsoft Azure, Getting Started with Google Cloud Platform, Getting Started with Oracle
+    OCI, and Getting Started with Alibaba Cloud Services.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will start an Arm-based instance in the cloud and verify that the instance is using
+      the Arm architecture. Learn how to start an Arm-based virtual machine instance from major
+      cloud service providers and verify the Arm architecture is being used.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who are new to Arm-based cloud instances.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An account with your preferred cloud
+      service provider.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers Linux environments, Arm platforms such as Neoverse, and cloud platforms such as
+      AWS, Microsoft Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Getting Started with AWS, Getting Started with Microsoft
+      Azure, Getting Started with Google Cloud Platform, Getting Started with Oracle OCI, and
+      Getting Started with Alibaba Cloud Services.
+# END generated_summary_faq
+
 author: Ronan Synnott
 
 minutes_to_complete: 15
@@ -66,3 +105,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md b/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
index 0eb8491a6e..0fa004a725 100644
--- a/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
@@ -16,6 +16,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:17Z'
+  generator: template
+  source_hash: f600fcba0adea12ff1b8b092e75de553577d940dc3bf632e9a247cec22d364a4
+  summary: >-
+    Learn how to deploy and run the DeepSeek-R1 language model on Arm servers using llama.cpp
+    with quantization for efficient CPU inference. It is designed for developers who want to run
+    DeepSeek-R1 on Arm-based servers. By the end, you will be able to clone and build llama.cpp
+    on your Arm-based server, download a pre-quantized DeepSeek-R1 model from Hugging Face, and
+    run the model on your Arm CPU and benchmark its performance. It focuses on tools and technologies
+    such as LLM, Generative AI, and Python, Linux environments, Arm platforms including Neoverse,
+    and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps
+    cover Run a DeepSeek-R1 chatbot on Arm servers and Access the chatbot using the OpenAI-compatible
+    API.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will clone and build llama.cpp on your Arm-based server, download a pre-quantized DeepSeek-R1
+      model from Hugging Face, and run the model on your Arm CPU and benchmark its performance.
+      Learn how to deploy and run the DeepSeek-R1 language model on Arm servers using llama.cpp
+      with quantization for efficient CPU inference.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is for developers who want to run DeepSeek-R1 on Arm-based servers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/)
+      from a cloud provider or an on-premise Arm server. This Learning Path was tested on an AWS
+      Graviton4 r8g.24xlarge instance.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including LLM, Generative AI, and Python, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
+      Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Run a DeepSeek-R1 chatbot on Arm servers and Access
+      the chatbot using the OpenAI-compatible API.
+# END generated_summary_faq
+
 author:
     - Tianyu Li
 
@@ -63,3 +105,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md b/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md
index dc2101b951..571b1ecf75 100644
--- a/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md
@@ -17,6 +17,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: a1ec216948e7cfd4fc52815196bb3b99ab4e76c9c756aa9e9a8e3216ef5e7ce4
+  summary: >-
+    Learn how to use fio to microbenchmark storage performance on Arm systems and monitor storage
+    using iostat, iotop, and pidstat to identify bottlenecks. It is designed for developers looking
+    to optimize storage performance, reduce costs, identify bottlenecks, and evaluate storage
+    options when migrating applications across platforms. By the end, you will be able to describe
+    data flow through storage devices, monitor storage performance using tools like iostat, iotop,
+    and pidstat, and run fio to microbenchmark a block storage device. It focuses on tools and
+    technologies such as bash and Runbook, Linux environments, Arm platforms including Neoverse,
+    and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps
+    cover Fundamentals of storage systems, Analyzing I/O behavior with real workloads, and Benchmarking
+    block storage performance with fio.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will describe data flow through storage devices, monitor storage performance using tools
+      like iostat, iotop, and pidstat, and run fio to microbenchmark a block storage device. Learn
+      how to use fio to microbenchmark storage performance on Arm systems and monitor storage
+      using iostat, iotop, and pidstat to identify bottlenecks.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers looking to optimize storage performance, reduce
+      costs, identify bottlenecks, and evaluate storage options when migrating applications across
+      platforms.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/)
+      from a cloud service provider or an Arm Linux server.; Familiarity with Linux.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including bash and Runbook, Linux environments, Arm platforms
+      such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Fundamentals of storage systems, Analyzing I/O behavior
+      with real workloads, and Benchmarking block storage performance with fio.
+# END generated_summary_faq
+
 author: Kieran Hejmadi
 
 ### Tags
@@ -47,3 +90,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md
index d6404f3a8b..ad3edba10c 100644
--- a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md
@@ -19,6 +19,51 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 6ac0c7cf1ab4b3efa680acbf1e349b858bee5dc7992baf6689e1f293a83060a4
+  summary: >-
+    Run distributed LLM inference with llama.cpp across multiple AWS Graviton4 instances, covering
+    multi-node setup, coordination, and performance trade-offs. It is designed for This introductory
+    topic is for developers with some experience using llama.cpp who want to learn how to run
+    distributed inference on Arm-based servers. By the end, you will be able to set up a main
+    host and worker nodes with llama.cpp and run a large quantized model (for example, Llama 3.1
+    405B) with distributed CPU inference on Arm machines. It focuses on tools and technologies
+    such as LLM, Generative AI, and AWS, Linux environments, Arm platforms including Neoverse,
+    and cloud platforms such as AWS. The main steps cover Convert model to GGUF and quantize,
+    Configure the worker nodes, and Configure the master node.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will set up a main host and worker nodes with llama.cpp and run a large quantized model
+      (for example, Llama 3.1 405B) with distributed CPU inference on Arm machines. Run distributed
+      LLM inference with llama.cpp across multiple AWS Graviton4 instances, covering multi-node
+      setup, coordination, and performance trade-offs.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This introductory topic is for developers with some experience using llama.cpp who want
+      to learn how to run distributed inference on Arm-based servers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Three AWS c8g.4xlarge instances with
+      at least 500 GB of EBS storage; Python 3 installed on each instance; Access to Meta's gated
+      repository for the Llama 3.1 model family and a Hugging Face token to download models; Familiarity
+      with the Learning Path [Deploy a Large Language Model (LLM) chatbot with llama.cpp using
+      KleidiAI on Arm servers](/learning-paths/servers-and-cloud-computing/llama-cpu); Familiarity
+      with AWS.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including LLM, Generative AI, and AWS, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as AWS.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Convert model to GGUF and quantize, Configure the
+      worker nodes, and Configure the master node.
+# END generated_summary_faq
+
 author: 
     - Aryan Bhusari
     - Joe Stech
@@ -53,3 +98,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md
index 8ccc4fe4e8..11a116ef68 100644
--- a/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md
@@ -23,6 +23,57 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 6675df4c91126b157dcbf39c96a773130f1a95e2f5680913979da96f6f6c97cd
+  summary: >-
+    Learn how to deploy a production-grade Django REST API on Google Kubernetes Engine with Arm64
+    Axion node pools integrated with Google Cloud managed data services. It is designed for DevOps
+    engineers and software developers who want to deploy, operate, and benchmark a production-grade
+    Django REST API on Google Kubernetes Engine (GKE) running on Arm64 Axion processors, integrated
+    with managed Google Cloud data services. By the end, you will be able to provision Arm-based
+    Axion compute on Google Cloud using virtual machines and GKE node pools, package a Django
+    REST API into an Arm-native Docker container, and push container images to Google Artifact
+    Registry. It focuses on tools and technologies such as Django, Docker, Kubernetes, Google
+    Artifact Registry, and Cloud SQL (PostgreSQL), Linux environments, Arm platforms including
+    Neoverse, and cloud platforms such as Google Cloud. The main steps cover Get started with
+    Django on Google Axion C4A, Configure firewall rules for Django on Google Cloud, Create a
+    Google Axion C4A Arm virtual machine on GCP, Install Django on your Arm-based VM, and Verify
+    Django installation and run the development server.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will provision Arm-based Axion compute on Google Cloud using virtual machines and GKE
+      node pools, package a Django REST API into an Arm-native Docker container, and push container
+      images to Google Artifact Registry. Learn how to deploy a production-grade Django REST API
+      on Google Kubernetes Engine with Arm64 Axion node pools integrated with Google Cloud managed
+      data services.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for DevOps engineers and software developers who want to deploy,
+      operate, and benchmark a production-grade Django REST API on Google Kubernetes Engine (GKE)
+      running on Arm64 Axion processors, integrated with managed Google Cloud data services.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
+      account with billing enabled; Basic familiarity with [Django](https://www.djangoproject.com/);
+      Basic understanding of containers and Kubernetes concepts.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Django, Docker, Kubernetes, Google Artifact Registry,
+      and Cloud SQL (PostgreSQL), Linux environments, Arm platforms such as Neoverse, and cloud
+      platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Get started with Django on Google Axion C4A, Configure
+      firewall rules for Django on Google Cloud, Create a Google Axion C4A Arm virtual machine
+      on GCP, Install Django on your Arm-based VM, and Verify Django installation and run the
+      development server.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ##### Tags
@@ -77,3 +128,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: yes
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/django/_index.md b/content/learning-paths/servers-and-cloud-computing/django/_index.md
index 9368057d44..f263f06717 100644
--- a/content/learning-paths/servers-and-cloud-computing/django/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/django/_index.md
@@ -19,6 +19,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
+  summary: >-
+    Learn how to create a simple Django web application and deploy it on Arm machines using Nginx
+    and PostgreSQL. It is designed for engineers who want to deploy a Django based application
+    on Arm machines. By the end, you will be able to create a simple Django application, deploy
+    the Django application using Nginx and PostgreSQL, and verify that the Django application
+    is working correctly. It focuses on tools and technologies such as Django, Python, NGINX,
+    and PostgreSQL, Linux environments, Arm platforms including Neoverse, and cloud platforms
+    such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps cover Install the required
+    dependencies, Create the Django application, and Deploy the Django application.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create a simple Django application, deploy the Django application using Nginx and
+      PostgreSQL, and verify that the Django application is working correctly. Learn how to create
+      a simple Django web application and deploy it on Arm machines using Nginx and PostgreSQL.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for engineers who want to deploy a Django based application
+      on Arm machines.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: At least either an [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/)
+      from a cloud service provider, on-premises Arm server, or a Linux virtual machine on your
+      Arm device.; Sudo access to install dependencies and to modify system configuration files.;
+      Be comfortable with SSH/Linux terminal and basic system administration tasks.; To install
+      both [Nginx](/learning-paths/servers-and-cloud-computing/nginx/) and [PostgreSQL](/learning-paths/servers-and-cloud-computing/postgresql/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Django, Python, NGINX, and PostgreSQL, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
+      Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Install the required dependencies, Create the Django
+      application, and Deploy the Django application.
+# END generated_summary_faq
+
 author: Diego Russo
 
 ### Tags
@@ -62,3 +105,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md b/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
index 01ba3b0dd5..b9973b6bd0 100644
--- a/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
@@ -16,6 +16,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 82716c2de19d18a154c85d03b7f2ec01839284262914f7bb3ad04b18a105379d
+  summary: >-
+    Learn how to build and benchmark the Deep Learning Recommendation Model using PyTorch and
+    MLPerf on Arm Neoverse V2 processors. It is designed for software developers who want to set
+    up a pipeline in the cloud for recommendation models. You'll build and run the Deep Learning
+    Recommendation Model (DLRM) and benchmark its performance using MLPerf and PyTorch. By the
+    end, you will be able to build the Deep Learning Recommendation Model (DLRM) and run a modified
+    performant DLRMv2 benchmark and inspect the results. It focuses on tools and technologies
+    such as Docker, MLPerf, and Google Cloud, Linux environments, Arm platforms including Neoverse,
+    and cloud platforms such as AWS and Google Cloud. The main steps cover Overview and setup,
+    Download model weights and data, and Run the benchmark.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build the Deep Learning Recommendation Model (DLRM) and run a modified performant
+      DLRMv2 benchmark and inspect the results. Learn how to build and benchmark the Deep Learning
+      Recommendation Model using PyTorch and MLPerf on Arm Neoverse V2 processors.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to set up a pipeline in the
+      cloud for recommendation models. You'll build and run the Deep Learning Recommendation Model
+      (DLRM) and benchmark its performance using MLPerf and PyTorch.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Any [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/)
+      from a cloud service provider (CSP), or an on-premise Arm server with at least 400GB of
+      RAM and 800 GB of disk space.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Docker, MLPerf, and Google Cloud, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as AWS and Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview and setup, Download model weights and data,
+      and Run the benchmark.
+# END generated_summary_faq
+
 author: 
     - Phalani Paladugu
     - Annie Tallund
@@ -54,3 +96,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md b/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
index ba1b904ff5..170352aac4 100644
--- a/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
@@ -25,6 +25,60 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 80785022032bf4e3c65da682e698940a212ee1ee77386698889a9fafbed9f823
+  summary: >-
+    Learn how to use the Docker MCP Toolkit with the Arm MCP Server and GitHub Copilot to automate
+    container and code migration from x86 to Arm64. Through a hands-on example, migrate a legacy
+    C++ application with AVX2 intrinsics to Arm Neon. It is designed for developers and DevOps
+    engineers who want to automate the migration of containerized applications from x86 to Arm64
+    using AI-powered tools in the Docker MCP Toolkit. By the end, you will be able to describe
+    how the Model Context Protocol (MCP) enables AI coding assistants to invoke structured migration
+    tools through the Arm MCP server, explain how the Docker MCP Toolkit connects AI coding assistants
+    to Arm MCP server, and install and configure the Docker MCP Toolkit with the Arm MCP Server,
+    GitHub MCP Server, and Sequential Thinking MCP Server. It focuses on tools and technologies
+    such as Docker, MCP, GitHub Copilot, C++, and VS Code, Linux and macOS environments, and Arm
+    platforms including Neoverse. The main steps cover Simplify Arm migration with the Docker
+    MCP Toolkit and Arm MCP Server, Set up Docker MCP Toolkit with Arm, GitHub, and Sequential
+    Thinking servers, Examine x86 AVX2 intrinsics in the demo application, Automate x86 to Arm
+    migration with GitHub Copilot, and Validate the Arm64 migration and test containers.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will describe how the Model Context Protocol (MCP) enables AI coding assistants to invoke
+      structured migration tools through the Arm MCP server, explain how the Docker MCP Toolkit
+      connects AI coding assistants to Arm MCP server, and install and configure the Docker MCP
+      Toolkit with the Arm MCP Server, GitHub MCP Server, and Sequential Thinking MCP Server.
+      Learn how to use the Docker MCP Toolkit with the Arm MCP Server and GitHub Copilot to automate
+      container and code migration from x86 to Arm64. Through a hands-on example, migrate a legacy
+      C++ application with AVX2 intrinsics to Arm Neon.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for developers and DevOps engineers who want to automate the migration
+      of containerized applications from x86 to Arm64 using AI-powered tools in the Docker MCP
+      Toolkit.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Docker Desktop 4.59 or later with MCP
+      Toolkit enabled; VS Code with the GitHub Copilot extension; A GitHub account with a personal
+      access token; A machine with at least 8 GB RAM (16 GB recommended); Basic familiarity with
+      Docker, C++, and SIMD intrinsics concepts.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Docker, MCP, GitHub Copilot, C++, and VS Code, Linux
+      and macOS environments, and Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Simplify Arm migration with the Docker MCP Toolkit
+      and Arm MCP Server, Set up Docker MCP Toolkit with Arm, GitHub, and Sequential Thinking
+      servers, Examine x86 AVX2 intrinsics in the demo application, Automate x86 to Arm migration
+      with GitHub Copilot, and Validate the Arm64 migration and test containers.
+# END generated_summary_faq
+
 author: Ajeet Singh Raina
 
 ### Tags
@@ -70,3 +124,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md b/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md
index 8891752bc6..39efd68537 100644
--- a/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md
@@ -22,6 +22,51 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 08d8f0c86625ef41476d3a8b24bad9b0a0820797022ef847bf9bb17a976726a7
+  summary: >-
+    Learn how to build and run an OrchardCore CMS .NET application on Azure Cobalt 100 processors,
+    covering AnyCPU configuration and shared C library integration. It is designed for .NET developers
+    who want to take advantage of the performance and cost benefits of Azure Cobalt processors.
+    By the end, you will be able to build and run a basic OrchardCore CMS application, integrate
+    a simple C shared library into a .NET application, and configure architecture-agnostic builds
+    using AnyCPU. It focuses on tools and technologies such as .NET, Orchard Core, and C, Linux
+    environments, Arm platforms including Neoverse, and cloud platforms such as Microsoft Azure.
+    The main steps cover Build and run an OrchardCore CMS app on Azure Cobalt (Arm64), Integrate
+    a C shared library into your .NET OrchardCore app, Configure and run an OrchardCore app, and
+    Evaluate .NET performance across versions on Arm.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build and run a basic OrchardCore CMS application, integrate a simple C shared
+      library into a .NET application, and configure architecture-agnostic builds using AnyCPU.
+      Learn how to build and run an OrchardCore CMS .NET application on Azure Cobalt 100 processors,
+      covering AnyCPU configuration and shared C library integration.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for .NET developers who want to take advantage of the performance
+      and cost benefits of Azure Cobalt processors.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Microsoft Azure account with permissions
+      to deploy virtual machines; .NET SDK 8.0 or later; Basic knowledge of C and C#; GCC installed
+      (Linux) or access to a cross-compiler; OrchardCore application created using the .NET CLI
+      or Visual Studio.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including .NET, Orchard Core, and C, Linux environments, Arm
+      platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Build and run an OrchardCore CMS app on Azure Cobalt
+      (Arm64), Integrate a C shared library into your .NET OrchardCore app, Configure and run
+      an OrchardCore app, and Evaluate .NET performance across versions on Arm.
+# END generated_summary_faq
+
 author: Joe Stech
 
 ### Tags
@@ -61,3 +106,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
index ce12af13cf..16dce84efa 100644
--- a/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
@@ -20,6 +20,56 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 4ef29931bf19dc95bb586440796381725c271ef1b953dcf46d00ad9617eabbb1
+  summary: >-
+    Learn how to deploy Dynatrace OneAgent on Azure Cobalt 100 Arm64 virtual machines and configure
+    ActiveGate for secure infrastructure and application monitoring. It is designed for developers,
+    DevOps engineers, and platform engineers who want to implement infrastructure and application
+    monitoring using Dynatrace on Arm-based cloud environments. By the end, you will be able to
+    deploy Dynatrace OneAgent on Azure Cobalt 100 Arm64 virtual machines, configure Dynatrace
+    ActiveGate for secure monitoring communication, and monitor system resources, processes, and
+    services using Dynatrace. It focuses on tools and technologies such as Dynatrace, NGINX, and
+    ActiveGate, Linux environments, Arm platforms including Neoverse, and cloud platforms such
+    as Microsoft Azure. The main steps cover Overview of Azure Cobalt 100 and Dynatrace, Create
+    an Azure Cobalt 100 Arm64 virtual machine, Create a firewall rule on Azure, Install Dynatrace
+    OneAgent on Azure Ubuntu Arm64 virtual machine, and Install Dynatrace ActiveGate on Azure
+    Ubuntu Arm64 virtual machine.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will deploy Dynatrace OneAgent on Azure Cobalt 100 Arm64 virtual machines, configure
+      Dynatrace ActiveGate for secure monitoring communication, and monitor system resources,
+      processes, and services using Dynatrace. Learn how to deploy Dynatrace OneAgent on Azure
+      Cobalt 100 Arm64 virtual machines and configure ActiveGate for secure infrastructure and
+      application monitoring.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers, DevOps engineers, and platform engineers who
+      want to implement infrastructure and application monitoring using Dynatrace on Arm-based
+      cloud environments.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Microsoft Azure account](https://azure.microsoft.com/)
+      with access to Cobalt 100 based instances (Dpsv6); Basic knowledge of Linux command-line
+      operations; Familiarity with SSH and remote server access; Basic understanding of cloud
+      infrastructure and monitoring concepts.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Dynatrace, NGINX, and ActiveGate, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview of Azure Cobalt 100 and Dynatrace, Create
+      an Azure Cobalt 100 Arm64 virtual machine, Create a firewall rule on Azure, Install Dynatrace
+      OneAgent on Azure Ubuntu Arm64 virtual machine, and Install Dynatrace ActiveGate on Azure
+      Ubuntu Arm64 virtual machine.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
@@ -63,3 +113,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/ecs/_index.md b/content/learning-paths/servers-and-cloud-computing/ecs/_index.md
index a87e262ecc..f797106bfd 100644
--- a/content/learning-paths/servers-and-cloud-computing/ecs/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ecs/_index.md
@@ -17,6 +17,47 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: ef5f9e7c8844b20b9044b43f4758bc1d74374521093d7738a7f8832d21f1dcac
+  summary: >-
+    Learn how to create an AWS ECS cluster with Fargate and AWS Graviton processors, then create
+    and run containerized tasks on Arm infrastructure. It is designed for developers who want
+    to use AWS Graviton processors with Amazon Elastic Container Service (ECS). By the end, you
+    will be able to create an AWS ECS cluster with Fargate and AWS Graviton processors, create
+    and run an AWS ECS task, and use Terraform to automate deployment of an ECS cluster. It focuses
+    on tools and technologies such as Terraform and AWS Elastic Container Service (ECS), Linux
+    environments, Arm platforms including Neoverse, and cloud platforms such as AWS. The main
+    steps cover Deploy containers using ECS on AWS Graviton processors and Deploy ECS containers
+    on AWS Graviton processor using Terraform.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create an AWS ECS cluster with Fargate and AWS Graviton processors, create and
+      run an AWS ECS task, and use Terraform to automate deployment of an ECS cluster. Learn how
+      to create an AWS ECS cluster with Fargate and AWS Graviton processors, then create and run
+      containerized tasks on Arm infrastructure.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers who want to use AWS Graviton processors with
+      Amazon Elastic Container Service (ECS).
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An AWS account; A computer with Docker,
+      AWS CLI, and Terraform installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Terraform and AWS Elastic Container Service (ECS),
+      Linux environments, Arm platforms such as Neoverse, and cloud platforms such as AWS.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Deploy containers using ECS on AWS Graviton processors
+      and Deploy ECS containers on AWS Graviton processor using Terraform.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ##### Tags
@@ -51,3 +92,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # Indicates this should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md b/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
index 0626d66a18..2554ffa89a 100644
--- a/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
@@ -18,6 +18,51 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: e32bdb090c422d1fb5bb1f9bd3af56c55fdf8989e6a7fe6a101e90dcb6f3eadd
+  summary: >-
+    Learn how to use docker buildx and docker manifest to build and deploy multi-architecture
+    container images with x86/amd64 and arm64 support on Amazon EKS. It is designed for software
+    developers who want to understand how to build and deploy a multi-architecture application
+    with x86/amd64 and arm64-based container images on Amazon EKS. By the end, you will be able
+    to build x86/amd64 and arm64 container images with docker buildx and docker manifest, understand
+    the nuances of building a multi-architecture container image, and deploy a multi-arch container
+    application across multiple architectures in a single Amazon EKS cluster. It focuses on tools
+    and technologies such as Kubernetes and AWS Elastic Kubernetes Service (EKS), Linux environments,
+    Arm platforms including Neoverse, and cloud platforms such as AWS. The main steps cover Build
+    and deploy a multi-arch application on Amazon EKS.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build x86/amd64 and arm64 container images with docker buildx and docker manifest,
+      understand the nuances of building a multi-architecture container image, and deploy a multi-arch
+      container application across multiple architectures in a single Amazon EKS cluster. Learn
+      how to use docker buildx and docker manifest to build and deploy multi-architecture container
+      images with x86/amd64 and arm64 support on Amazon EKS.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers who want to understand how to build and
+      deploy a multi-architecture application with x86/amd64 and arm64-based container images
+      on Amazon EKS.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An [AWS account](https://aws.amazon.com/).
+      Create an account if needed.; A computer with [Amazon eksctl CLI](/install-guides/eksctl)
+      and [kubectl](/install-guides/kubectl/)installed.; Docker installed on local computer [Docker](/install-guides/docker).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Kubernetes and AWS Elastic Kubernetes Service (EKS),
+      Linux environments, Arm platforms such as Neoverse, and cloud platforms such as AWS.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Build and deploy a multi-arch application on Amazon
+      EKS.
+# END generated_summary_faq
+
 author: Pranay Bakre
 
 ### Tags
@@ -53,3 +98,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/eks/_index.md b/content/learning-paths/servers-and-cloud-computing/eks/_index.md
index 5a5bfc1e22..95de9c32c4 100644
--- a/content/learning-paths/servers-and-cloud-computing/eks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/eks/_index.md
@@ -16,6 +16,44 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 4f1c448eef66300e024bda27c420f9746047c0c4b76e8556d3d8693382206055
+  summary: >-
+    Learn how to provision an Amazon EKS cluster on Arm-based Graviton instances and deploy a
+    WordPress application with MySQL database. It is designed for software developers new to Kubernetes
+    on AWS who want to gain experience with cloud applications. By the end, you will be able to
+    provision an Amazon Elastic Kubernetes Service (EKS) cluster on Arm-based instances and deploy
+    Wordpress with MySQL on EKS. It focuses on tools and technologies such as AWS Elastic Kubernetes
+    Service (EKS), Kubernetes, SQL, MySQL, and WordPress, Linux environments, Arm platforms including
+    Neoverse, and cloud platforms such as AWS. The main steps cover Create an EKS cluster and
+    Deploy WordPress.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will provision an Amazon Elastic Kubernetes Service (EKS) cluster on Arm-based instances
+      and deploy Wordpress with MySQL on EKS. Learn how to provision an Amazon EKS cluster on
+      Arm-based Graviton instances and deploy a WordPress application with MySQL database.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers new to Kubernetes on AWS who want
+      to gain experience with cloud applications.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Amazon Web Services (AWS) [account](https://aws.amazon.com/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including AWS Elastic Kubernetes Service (EKS), Kubernetes,
+      SQL, MySQL, and WordPress, Linux environments, Arm platforms such as Neoverse, and cloud
+      platforms such as AWS.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Create an EKS cluster and Deploy WordPress.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
@@ -51,3 +89,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # Indicates this should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md
index 0092892fe6..3d15a290fd 100644
--- a/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md
@@ -19,6 +19,52 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: f36b1e9a45b6ec29d8041b70997550d917322cf9cdd456db26083169d21175ed
+  summary: >-
+    Learn how to install and configure Envoy proxy on Google Cloud Axion C4A Arm64 instances and
+    benchmark HTTP proxy performance with load testing. It is designed for This introductory topic
+    for software developers migrating Envoy Proxy workloads from x86_64 to Arm-based servers,
+    specifically on Google Cloud C4A virtual machines built on Axion processors. By the end, you
+    will be able to provision an Arm-based C4A VM on Google Cloud Platform (GCP), install and
+    configure Envoy Proxy on a C4A instance, and validate Envoy functionality with baseline tests.
+    It focuses on tools and technologies such as Envoy, Siege, Networking, and Service Mesh, Linux
+    environments, Arm platforms including Neoverse, and cloud platforms such as Google Cloud.
+    The main steps cover Get started with Envoy Proxy on Google Axion C4A (Arm Neoverse V2), Create
+    a Google Axion C4A Arm virtual machine on GCP, Deploy Envoy on Google Axion C4A Arm virtual
+    machines, Run baseline Envoy testing on a Google Axion C4A Arm VM, and Benchmark Envoy on
+    Google Cloud for Arm64 and x86_64 with Siege.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will provision an Arm-based C4A VM on Google Cloud Platform (GCP), install and configure
+      Envoy Proxy on a C4A instance, and validate Envoy functionality with baseline tests. Learn
+      how to install and configure Envoy proxy on Google Cloud Axion C4A Arm64 instances and benchmark
+      HTTP proxy performance with load testing.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This introductory topic for software developers migrating Envoy Proxy workloads from x86_64
+      to Arm-based servers, specifically on Google Cloud C4A virtual machines built on Axion processors.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free?utm_source=google&hl=en)
+      account with billing enabled; Familiarity with networking concepts and the [Envoy architecture](https://www.envoyproxy.io/docs/envoy/latest/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Envoy, Siege, Networking, and Service Mesh, Linux
+      environments, Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Get started with Envoy Proxy on Google Axion C4A (Arm
+      Neoverse V2), Create a Google Axion C4A Arm virtual machine on GCP, Deploy Envoy on Google
+      Axion C4A Arm virtual machines, Run baseline Envoy testing on a Google Axion C4A Arm VM,
+      and Benchmark Envoy on Google Cloud for Arm64 and x86_64 with Siege.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ##### Tags
@@ -62,3 +108,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/envoy/_index.md b/content/learning-paths/servers-and-cloud-computing/envoy/_index.md
index af49c9a452..f0f26d737b 100644
--- a/content/learning-paths/servers-and-cloud-computing/envoy/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/envoy/_index.md
@@ -17,6 +17,44 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: d492b6dce11b7d8f6591ff3b3ce9aa2c382a6a7a749add228c3ac1f6ae57e218
+  summary: >-
+    Learn how to build, install, and run Envoy proxy on Arm servers and configure it as a web
+    server for traffic management. It is designed for engineers who want to use Envoy on Arm.
+    By the end, you will be able to build, install, and run Envoy on Arm servers, setup Envoy
+    as a web server, and verify Envoy is working correctly. It focuses on tools and technologies
+    such as Envoy, Linux environments, Arm platforms including Neoverse, and cloud platforms such
+    as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps cover Build and install
+    Envoy and Run Envoy as a service.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build, install, and run Envoy on Arm servers, setup Envoy as a web server, and
+      verify Envoy is working correctly. Learn how to build, install, and run Envoy proxy on Arm
+      servers and configure it as a web server for traffic management.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for engineers who want to use Envoy on Arm.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: To run Envoy as a web server, you will
+      need at least one [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/)
+      from a cloud service provider or an on-premises Arm server.; Network settings (firewalls
+      and security groups) which allow communication on port 22 (SSH) and port 80 (HTTP).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Envoy, Linux environments, Arm platforms such as
+      Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Build and install Envoy and Run Envoy as a service.
+# END generated_summary_faq
+
 author: Zhengjun Xing
 
 ### Tags
@@ -52,3 +90,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md
index a4d1c5de9d..70c8dcf565 100644
--- a/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md
@@ -18,6 +18,43 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: cbbcc8fa33f864e422f8db7d58fba32c26f6070be2846b246837c63ccf37e1c7
+  summary: >-
+    Learn how to optimize Envoy proxy performance on Arm servers using Transparent Huge Pages
+    and Profile-Guided Optimization techniques. It is designed for software developers who want
+    to use Envoy on Arm. By the end, you will be able to tune Envoy by THP, tune Envoy with PGO,
+    and learn about kernel parameters that can impact Envoy performance. It focuses on tools and
+    technologies such as Envoy and Runbook, Linux environments, Arm platforms including Neoverse,
+    and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps
+    cover Tune Envoy by THP and Tune Envoy by PGO.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will tune Envoy by THP, tune Envoy with PGO, and learn about kernel parameters that
+      can impact Envoy performance. Learn how to optimize Envoy proxy performance on Arm servers
+      using Transparent Huge Pages and Profile-Guided Optimization techniques.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers who want to use Envoy on Arm.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Cloud or bare-metal installation of
+      an Envoy service; Review [Learn how to deploy Envoy](/learning-paths/servers-and-cloud-computing/envoy/)
+      if you do not already have an Envoy setup.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Envoy and Runbook, Linux environments, Arm platforms
+      such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Tune Envoy by THP and Tune Envoy by PGO.
+# END generated_summary_faq
+
 author: Zhengjun Xing
 
 ### Tags
@@ -51,3 +88,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md b/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md
index 6a2b68e993..40f9756f3f 100644
--- a/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md
@@ -21,6 +21,50 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 02eedcebf59a8ab506d4ebbf5bbe20ead367cf3c0700950db5a9059d2f671853
+  summary: >-
+    Learn how stack buffer overflow exploits work on AArch64 by analyzing stack frame layouts,
+    redirecting control flow, and understanding defense mechanisms. It is designed for software
+    developers interested in understanding how memory vulnerability-based exploits work on AArch64
+    and how to defend against them. By the end, you will be able to analyze the stack frame layout
+    to derive which field in user input overwrites the return address stored on the stack and
+    build a basic end-to-end exploit by changing the return address to an attacker-controlled
+    value. It focuses on tools and technologies such as Clang, C, Assembly, and Runbook, Linux
+    environments, and Arm platforms including AArch64. The main steps cover Introduction: "Smashing
+    the stack", Docker Setup, Frame Layout, Stack Buffer Overflow, and Redirect control flow.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will analyze the stack frame layout to derive which field in user input overwrites the
+      return address stored on the stack and build a basic end-to-end exploit by changing the
+      return address to an attacker-controlled value. Learn how stack buffer overflow exploits
+      work on AArch64 by analyzing stack frame layouts, redirecting control flow, and understanding
+      defense mechanisms.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers interested in understanding how memory
+      vulnerability-based exploits work on AArch64 and how to defend against them.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm computer running linux with [Docker](/install-guides/docker/)
+      installed.; Some familiarity with reading and writing basic C code and AArch64 assembly
+      code.; Some familiarity with running linux command line commands.; Some familiarity with
+      using a gdb debugger.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Clang, C, Assembly, and Runbook, Linux environments,
+      and Arm platforms such as AArch64.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Introduction: "Smashing the stack", Docker Setup,
+      Frame Layout, Stack Buffer Overflow, and Redirect control flow.
+# END generated_summary_faq
+
 author: Kristof Beyls
 
 ### Tags
@@ -60,3 +104,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md b/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
index 3d840d2869..ff8b206746 100644
--- a/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
@@ -18,6 +18,53 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: dde32f5d14a877313a8b0aafec58acb09cd1e77f4fc9138b9e1bd6d9fedf250a
+  summary: >-
+    Learn how to identify and fix false sharing issues using Perf C2C cache line analysis and
+    Arm Statistical Profiling Extension on Arm-based cloud systems. It is designed for performance-oriented
+    developers working on Arm-based cloud or server systems who want to optimize memory access
+    patterns and investigate cache inefficiencies using Perf C2C and Arm SPE. By the end, you
+    will be able to identify and fix false sharing issues using Perf C2C, a cache line analysis
+    tool, enable and use the Arm Statistical Profiling Extension (SPE) on Linux systems, and investigate
+    cache line performance with Perf C2C. It focuses on tools and technologies such as perf and
+    Runbook, Linux environments, Arm platforms including Neoverse, and cloud platforms such as
+    AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps cover Arm Statistical Profiling
+    Extension and false sharing, Set up your environment for Arm SPE and Perf C2C profiling, False
+    sharing example, and Perform root cause analysis with Perf C2C.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will identify and fix false sharing issues using Perf C2C, a cache line analysis tool,
+      enable and use the Arm Statistical Profiling Extension (SPE) on Linux systems, and investigate
+      cache line performance with Perf C2C. Learn how to identify and fix false sharing issues
+      using Perf C2C cache line analysis and Arm Statistical Profiling Extension on Arm-based
+      cloud systems.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This topic is for performance-oriented developers working on Arm-based cloud or server systems
+      who want to optimize memory access patterns and investigate cache inefficiencies using Perf
+      C2C and Arm SPE.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Access to an Arm-based cloud instance
+      with support for the Arm Statistical Profiling Extension (SPE).; A basic understanding of
+      cache coherency and its impact on performance.; Familiarity with Linux Perf tools.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including perf and Runbook, Linux environments, Arm platforms
+      such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Arm Statistical Profiling Extension and false sharing,
+      Set up your environment for Arm SPE and Perf C2C profiling, False sharing example, and Perform
+      root cause analysis with Perf C2C.
+# END generated_summary_faq
+
 author: Kieran Hejmadi
 
 ### Tags
@@ -53,3 +100,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md b/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md
index a7234fe1df..bab988463d 100644
--- a/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md
@@ -18,6 +18,50 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 978d3da8668e38758c138313c31809f3de5a8cefd1b7c2b47a536c0ee364b692
+  summary: >-
+    Learn how to build custom Linux kernels using tuxmake and Fastpath, then benchmark and compare
+    kernel versions on Arm-based EC2 instances. It is designed for software developers and performance
+    engineers who want to benchmark and compare different Linux kernel versions on Arm servers.
+    By the end, you will be able to build custom Linux kernels for Arm systems using tuxmake and
+    Fastpath, configure and provision Arm-based EC2 instances for kernel testing, and create and
+    execute test plans that compare kernel performance across versions. It focuses on tools and
+    technologies such as Fastpath, tuxmake, and Linux, Linux environments, Arm platforms including
+    Neoverse, and cloud platforms such as AWS. The main steps cover Understand the Fastpath kernel
+    benchmarking workflow, Set up the kernel build host, Set up the Fastpath host, Set up the
+    System Under Test, and Generate and execute the benchmark plan.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build custom Linux kernels for Arm systems using tuxmake and Fastpath, configure
+      and provision Arm-based EC2 instances for kernel testing, and create and execute test plans
+      that compare kernel performance across versions. Learn how to build custom Linux kernels
+      using tuxmake and Fastpath, then benchmark and compare kernel versions on Arm-based EC2
+      instances.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers and performance engineers who want to
+      benchmark and compare different Linux kernel versions on Arm servers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An AWS account with permissions to create
+      EC2 instances; Familiarity with basic Linux administration and SSH.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Fastpath, tuxmake, and Linux, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as AWS.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Understand the Fastpath kernel benchmarking workflow,
+      Set up the kernel build host, Set up the Fastpath host, Set up the System Under Test, and
+      Generate and execute the benchmark plan.
+# END generated_summary_faq
+
 author: Geremy Cohen
 
 ### Tags
@@ -54,3 +98,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md b/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md
index b65dc87c30..287f57db61 100644
--- a/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md
@@ -17,6 +17,50 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: a67c552b76df6323eccc331c9146d0fcbdb8ad222fe81dbf2e1c2339c7609b61
+  summary: >-
+    Learn how to implement exponential functions using Arm SVE intrinsics with the FEXPA instruction
+    for hardware-accelerated computations on Neoverse processors. It is designed for developers
+    interested in accelerating exponential function computations using Arm's Scalable Vector Extension
+    (SVE). The FEXPA instruction provides hardware acceleration for exponential calculations on
+    Arm Neoverse processors. By the end, you will be able to implement the exponential function
+    using SVE intrinsics and optimize the function with FEXPA. It focuses on tools and technologies
+    such as C and CPP, Linux and macOS environments, Arm platforms including Neoverse, and cloud
+    platforms such as AWS, Microsoft Azure, and Google Cloud. The main steps cover Learn exponential
+    function optimization techniques, Implement exponential with SVE intrinsics, Optimize with
+    FEXPA instruction, and Review benefits and next steps.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will implement the exponential function using SVE intrinsics and optimize the function
+      with FEXPA. Learn how to implement exponential functions using Arm SVE intrinsics with the
+      FEXPA instruction for hardware-accelerated computations on Neoverse processors.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers interested in accelerating exponential function
+      computations using Arm's Scalable Vector Extension (SVE). The FEXPA instruction provides
+      hardware acceleration for exponential calculations on Arm Neoverse processors.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Access to an [AWS Graviton4, Google
+      Axion, or Azure Cobalt 100 virtual machine from a cloud service provider](/learning-paths/servers-and-cloud-computing/csp/);
+      Some familiarity with SIMD programming and SVE intrinsics.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including C and CPP, Linux and macOS environments, Arm platforms
+      such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, and Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Learn exponential function optimization techniques,
+      Implement exponential with SVE intrinsics, Optimize with FEXPA instruction, and Review benefits
+      and next steps.
+# END generated_summary_faq
+
 author: 
 - Arnaud Grasset
 - Claudio Martino
diff --git a/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md
index 8e64c6cfc9..6239e32a75 100644
--- a/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md
@@ -18,6 +18,53 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: adcf2a1b8a4a77e5834e14a40e46e27b0cbe5e440fbf732a4366ce486d7fafb7
+  summary: >-
+    Learn how to install and configure Apache Flink on Google Cloud Axion C4A Arm64 instances
+    and benchmark stream processing performance with Nexmark. It is designed for developers deploying
+    and optimizing Apache Flink workloads on Linux/Arm64 environments, specifically using Google
+    Cloud C4A virtual machines powered by Axion processors. By the end, you will be able to provision
+    an Arm-based SUSE SLES virtual machine on Google Cloud (C4A with Axion processors), install
+    and configure Apache Flink on an Arm64 instance, and validate Flink functionality by starting
+    the cluster and running a baseline job. It focuses on tools and technologies such as Flink,
+    Java, and Maven, Linux environments, Arm platforms including Neoverse, and cloud platforms
+    such as Google Cloud. The main steps cover Get started with Apache Flink on Google Axion C4A,
+    Create a Google Axion C4A Arm virtual machine, Install Apache Flink, Test Flink baseline functionality,
+    and Benchmark Flink performance.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will provision an Arm-based SUSE SLES virtual machine on Google Cloud (C4A with Axion
+      processors), install and configure Apache Flink on an Arm64 instance, and validate Flink
+      functionality by starting the cluster and running a baseline job. Learn how to install and
+      configure Apache Flink on Google Cloud Axion C4A Arm64 instances and benchmark stream processing
+      performance with Nexmark.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers deploying and optimizing Apache Flink workloads
+      on Linux/Arm64 environments, specifically using Google Cloud C4A virtual machines powered
+      by Axion processors.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
+      account with billing enabled; Basic familiarity with [Apache Flink](https://flink.apache.org/)
+      and its runtime environment.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Flink, Java, and Maven, Linux environments, Arm
+      platforms such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Get started with Apache Flink on Google Axion C4A,
+      Create a Google Axion C4A Arm virtual machine, Install Apache Flink, Test Flink baseline
+      functionality, and Benchmark Flink performance.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ##### Tags
@@ -60,3 +107,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/flink/_index.md b/content/learning-paths/servers-and-cloud-computing/flink/_index.md
index 5005196a26..0ee57822f5 100644
--- a/content/learning-paths/servers-and-cloud-computing/flink/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/flink/_index.md
@@ -16,6 +16,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 974d71b1ee968e3aeabe900bfdd52ae4fbfdd0ca7dea420e6c1fc01f5475e8c1
+  summary: >-
+    Learn how to install and run Apache Flink on Arm servers and benchmark stream processing performance
+    using the Nexmark benchmark suite. It is designed for software developers using Flink as their
+    stream processing and batch processing framework on Arm servers. By the end, you will be able
+    to install and run Flink on an Arm server and benchmark the performance of Flink. It focuses
+    on tools and technologies such as Flink, Java, Nexmark, and Runbook, Linux environments, Arm
+    platforms including Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud,
+    and Oracle. The main steps cover Setup and Configure Flink, Setup and Config Nexmark, and
+    Benchmark Flink with nexmark-flink on Arm.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install and run Flink on an Arm server and benchmark the performance of Flink.
+      Learn how to install and run Apache Flink on Arm servers and benchmark stream processing
+      performance using the Nexmark benchmark suite.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers using Flink as their stream processing
+      and batch processing framework on Arm servers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm based instance server from a
+      cloud service provider.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Flink, Java, Nexmark, and Runbook, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
+      Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Setup and Configure Flink, Setup and Config Nexmark,
+      and Benchmark Flink with nexmark-flink on Arm.
+# END generated_summary_faq
+
 author: Ying Yu
 
 ### Tags
@@ -60,3 +100,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md b/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md
index 13565ea1a5..a79e99dd06 100644
--- a/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md
@@ -21,6 +21,54 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 85359b9210812675169f149c86bf11a1736ab838c011d18e876b246463d297ee
+  summary: >-
+    Learn how to build scalable machine learning workflow pipelines on Google Cloud C4A Axion
+    processors using Flyte for workflow orchestration and gRPC for distributed service communication.
+    It is designed for developers, data engineers, and ML engineers who want to build scalable
+    machine learning workflow pipelines on Arm64-based Google Cloud C4A Axion processors using
+    Flyte workflow orchestration and gRPC-based microservices. By the end, you will be able to
+    deploy Flyte workflow pipelines on Google Cloud C4A Axion processors, build distributed machine
+    learning pipelines using Flyte tasks, and implement gRPC-based services for feature engineering.
+    It focuses on tools and technologies such as Flyte, Python, and gRPC, Linux environments,
+    Arm platforms including Neoverse, and cloud platforms such as Google Cloud. The main steps
+    cover Understand Flyte and gRPC ML workflows on Google Axion, Create a Google Axion C4A Arm
+    virtual machine, Install Flyte and gRPC tools on Axion, Build a gRPC feature engineering service,
+    and Create ML Training Workflow.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will deploy Flyte workflow pipelines on Google Cloud C4A Axion processors, build distributed
+      machine learning pipelines using Flyte tasks, and implement gRPC-based services for feature
+      engineering. Learn how to build scalable machine learning workflow pipelines on Google Cloud
+      C4A Axion processors using Flyte for workflow orchestration and gRPC for distributed service
+      communication.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers, data engineers, and ML engineers who want
+      to build scalable machine learning workflow pipelines on Arm64-based Google Cloud C4A Axion
+      processors using Flyte workflow orchestration and gRPC-based microservices.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
+      account with billing enabled; Basic familiarity with Python; Basic understanding of machine
+      learning pipelines.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Flyte, Python, and gRPC, Linux environments, Arm
+      platforms such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Understand Flyte and gRPC ML workflows on Google Axion,
+      Create a Google Axion C4A Arm virtual machine, Install Flyte and gRPC tools on Axion, Build
+      a gRPC feature engineering service, and Create ML Training Workflow.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ##### Tags
@@ -69,3 +117,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: yes
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
index 909c15e266..2702cb90d4 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
@@ -23,6 +23,54 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 94866800acca2c5f9cd89f76f972af1a343aad5777b6844d49fac09eb764f580
+  summary: >-
+    Learn how to create an Arm64 Azure VM, install .NET SDK, containerize .NET applications, and
+    push Docker images to Azure Container Registry. It is designed for software developers interested
+    in learning how to deploy .NET applications to Microsoft Azure using Arm64-powered Virtual
+    Machines. You will also learn how to containerize .NET applications, and push Docker images
+    to the Azure Container Registry. By the end, you will be able to create a Virtual Machine
+    (VM) in Microsoft Azure, connect to the VM to install app dependencies, including SDK, and
+    create and run the .NET application. It focuses on tools and technologies such as .NET SDK
+    and C#, Linux environments, Arm platforms including Neoverse, and cloud platforms such as
+    Microsoft Azure. The main steps cover Motivation, Creating the Virtual Machine, Connecting
+    to the Virtual Machine, Installing application dependencies and running the application, and
+    Create a Dockerfile using Visual Studio Code.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create a Virtual Machine (VM) in Microsoft Azure, connect to the VM to install
+      app dependencies, including SDK, and create and run the .NET application. Learn how to create
+      an Arm64 Azure VM, install .NET SDK, containerize .NET applications, and push Docker images
+      to Azure Container Registry.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This learning path is for software developers interested in learning how to deploy .NET
+      applications to Microsoft Azure using Arm64-powered Virtual Machines. You will also learn
+      how to containerize .NET applications, and push Docker images to the Azure Container Registry.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A subscription to Azure. Use this link
+      to sign up for a free account: https://azure.microsoft.com/en-us/free/; Visual Studio Code:
+      https://code.visualstudio.com/download; Docker Extension for Visual Studio Code: https://code.visualstudio.com/docs/containers/overview;
+      C# Extension for Visual Studio Code: https://marketplace.visualstudio.com/items?itemName=ms-dotnettools.csharp;
+      [Install Docker on Arm64](/install-guides/docker/docker-desktop/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including .NET SDK and C#, Linux environments, Arm platforms
+      such as Neoverse, and cloud platforms such as Microsoft Azure.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Motivation, Creating the Virtual Machine, Connecting
+      to the Virtual Machine, Installing application dependencies and running the application,
+      and Create a Dockerfile using Visual Studio Code.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
@@ -64,3 +112,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md
index efa651eed8..dd26a59f14 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md
@@ -17,6 +17,50 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 3823ad3df8ae868acfd59b5b5b541240624572fe739aaf2275185d5cd3578032
+  summary: >-
+    Learn how to create and run Docker containers on Azure Container Instances for Arm64-based
+    containerized application deployment. It is designed for developers interested in learning
+    how to create and run a Docker container in Microsoft Azure using Azure Container Instances.
+    By the end, you will be able to create Azure Container Instances, run a Docker container in
+    Azure Container Instances, and enable Admin in Azure Container Registry, which is required
+    when you are deploying Docker containers from the Azure Container Registry. It focuses on
+    tools and technologies such as ASP.NET Core and Docker, Linux and Windows environments, Arm
+    platforms including Neoverse, and cloud platforms such as Microsoft Azure. The main steps
+    cover Azure Container Instances, Access the application, and Deploying a Docker container
+    from the Azure Container Registry.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create Azure Container Instances, run a Docker container in Azure Container Instances,
+      and enable Admin in Azure Container Registry, which is required when you are deploying Docker
+      containers from the Azure Container Registry. Learn how to create and run Docker containers
+      on Azure Container Instances for Arm64-based containerized application deployment.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers interested in learning how to create and run
+      a Docker container in Microsoft Azure using Azure Container Instances.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Azure subscription. Use this link to
+      sign up for a free account: https://azure.microsoft.com/en-us/free/.; Complete the [first
+      learning path](/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1)
+      of this series.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including ASP.NET Core and Docker, Linux and Windows environments,
+      Arm platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Azure Container Instances, Access the application,
+      and Deploying a Docker container from the Azure Container Registry.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
@@ -57,3 +101,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
index baa7ad24b1..7c5e0a221c 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
@@ -17,6 +17,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: a3347a62c3322d88b80fc7848fa5ee1d92ee86333c2a21891b958286f8b70935
+  summary: >-
+    Learn how to create an Azure Kubernetes Service cluster with Arm64 virtual machines and deploy
+    a containerized application to AKS. It is designed for This learning path is dedicated to
+    developers interested in learning how to deploy applications to the Azure Kubernetes Cluster
+    powered by arm64-based virtual machines. By the end, you will be able to create a Kubernetes
+    cluster using the Azure Kubernetes Service and deploy a containerized application to the Azure
+    Kubernetes Service. It focuses on tools and technologies such as ASP.NET Core, Docker, and
+    Kubernetes, Linux environments, Arm platforms including Neoverse, and cloud platforms such
+    as Microsoft Azure. The main steps cover Motivation, Create the Kubernetes cluster with Azure
+    Container Registry, Connecting to the cluster, and Deploying an application.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create a Kubernetes cluster using the Azure Kubernetes Service and deploy a containerized
+      application to the Azure Kubernetes Service. Learn how to create an Azure Kubernetes Service
+      cluster with Arm64 virtual machines and deploy a containerized application to AKS.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This learning path is dedicated to developers interested in learning how to deploy applications
+      to the Azure Kubernetes Cluster powered by arm64-based virtual machines.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Azure subscription. Use this link to
+      sign up for a free account: https://azure.microsoft.com/en-us/free/.; Complete the [first](/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1)
+      and [second](/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2) learning
+      paths of this series.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including ASP.NET Core, Docker, and Kubernetes, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Motivation, Create the Kubernetes cluster with Azure
+      Container Registry, Connecting to the cluster, and Deploying an application.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
@@ -58,3 +100,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md
index 8c91af3bc6..01bda1de59 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md
@@ -20,6 +20,50 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 1510c787979257e191196e13999e98c20ca24d0857f72075649c28520c74c576
+  summary: >-
+    Learn how to automate Azure resource deployment using Infrastructure as Code with Pulumi to
+    provision Azure Container Instances for containerized applications. It is designed for developers
+    interested in learning how to automate their cloud deployments using the Infrastructure as
+    Code (IaC). By the end, you will be able to automate the deployment of all the Azure resources
+    required to deploy a containerized application to the Azure Container Instance, set up Pulumi
+    for Infrastructure as Code (IaC), and automate the provisioning of the Azure resources. It
+    focuses on tools and technologies such as TypeScript and Docker, Windows environments, Arm
+    platforms including Neoverse, and cloud platforms such as Microsoft Azure. The main steps
+    cover Motivation, Pulumi, Pulumi project, and Resources declaration and deployment.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will automate the deployment of all the Azure resources required to deploy a containerized
+      application to the Azure Container Instance, set up Pulumi for Infrastructure as Code (IaC),
+      and automate the provisioning of the Azure resources. Learn how to automate Azure resource
+      deployment using Infrastructure as Code with Pulumi to provision Azure Container Instances
+      for containerized applications.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers interested in learning how to automate their
+      cloud deployments using the Infrastructure as Code (IaC).
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Azure subscription. Use this link to
+      sign up for a free account: https://azure.microsoft.com/en-us/free/; Visual Studio Code;
+      A free Pulumi account and Pulumi CLI (details provided in this learning path); Node.js (details
+      provided in this learning path); Azure CLI (details provided in this learning path).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including TypeScript and Docker, Windows environments, Arm
+      platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Motivation, Pulumi, Pulumi project, and Resources
+      declaration and deployment.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
@@ -60,3 +104,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/funasr/_index.md b/content/learning-paths/servers-and-cloud-computing/funasr/_index.md
index 181d383c6f..22830b32ef 100644
--- a/content/learning-paths/servers-and-cloud-computing/funasr/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/funasr/_index.md
@@ -16,6 +16,54 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 410ec28d257ce7aa1308f11a741aa87baea80daf1acb113c4149761144269022
+  summary: >-
+    Learn how to deploy the ModelScope FunASR Chinese automatic speech recognition model on Arm-based
+    servers with real-time transcription and sentiment analysis. It is designed for developers
+    interested in learning how to deploy the ModelScope FunASR Chinese Automatic Speech Recognition
+    (ASR) model on Arm-based servers. By the end, you will be able to leverage open-source large
+    language models and tools to build Chinese ASR applications, deploy real-time Chinese speech
+    recognition, punctuation restoration, and sentiment analysis using FunASR, and describe how
+    to accelerate ModelScope models on Arm-based servers for enhanced performance and efficiency.
+    It focuses on tools and technologies such as ModelScope, FunASR, LLM, Generative AI, and Python,
+    Linux environments, Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft
+    Azure, Google Cloud, and Oracle. The main steps cover Introduction to Automatic Speech Recognition,
+    ModelScope - an Open Source Pre-trained AI Models Hub, and Building ASR Applications with
+    ModelScope.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will leverage open-source large language models and tools to build Chinese ASR applications,
+      deploy real-time Chinese speech recognition, punctuation restoration, and sentiment analysis
+      using FunASR, and describe how to accelerate ModelScope models on Arm-based servers for
+      enhanced performance and efficiency. Learn how to deploy the ModelScope FunASR Chinese automatic
+      speech recognition model on Arm-based servers with real-time transcription and sentiment
+      analysis.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers interested in learning how to deploy the ModelScope
+      FunASR Chinese Automatic Speech Recognition (ASR) model on Arm-based servers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/)
+      from a cloud service provider, or a local Arm Linux computer with at least 8 CPUs and 16GB
+      of RAM.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including ModelScope, FunASR, LLM, Generative AI, and Python,
+      Linux environments, Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft
+      Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Introduction to Automatic Speech Recognition, ModelScope
+      - an Open Source Pre-trained AI Models Hub, and Building ASR Applications with ModelScope.
+# END generated_summary_faq
+
 author: Odin Shen
 
 ### Tags
@@ -64,3 +112,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
index 70e4ee483b..70d1a30155 100644
--- a/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
@@ -20,6 +20,56 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 85d3d64e0eb0c3cfa0eee29cf1e4199049a6dcbf69634e578dad2b5443ca2b7f
+  summary: >-
+    Learn how to install and configure Gardener Kubernetes management platform on Google Cloud
+    Axion C4A SUSE Arm64 instances and deploy workload clusters. It is designed for software developers
+    deploying and optimizing Gardener workloads on Linux Arm64 environments, specifically using
+    Google Cloud C4A virtual machines powered by Axion processors. By the end, you will be able
+    to provision an Arm-based SUSE Linux Enterprise Server (SLES) virtual machine on Google Cloud
+    (C4A with Axion processors), install and configure Gardener on a SUSE Arm64 (C4A) instance,
+    and deploy Garden, Seed, and Shoot clusters locally using Kubernetes in Docker (KinD). It
+    focuses on tools and technologies such as Gardener, Kubernetes, Docker, KinD, and Helm, Linux
+    environments, Arm platforms including Neoverse, and cloud platforms such as Google Cloud.
+    The main steps cover Get started with Gardener on Google Axion C4A (Arm Neoverse-V2), Create
+    a Google Axion C4A Arm virtual machine for Gardener, Install Gardener on your Arm-based SUSE
+    VM, Verify Gardener cluster health and functionality, and Benchmark Gardener cluster security
+    with kube-bench.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will provision an Arm-based SUSE Linux Enterprise Server (SLES) virtual machine on Google
+      Cloud (C4A with Axion processors), install and configure Gardener on a SUSE Arm64 (C4A)
+      instance, and deploy Garden, Seed, and Shoot clusters locally using Kubernetes in Docker
+      (KinD). Learn how to install and configure Gardener Kubernetes management platform on Google
+      Cloud Axion C4A SUSE Arm64 instances and deploy workload clusters.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers deploying and optimizing Gardener
+      workloads on Linux Arm64 environments, specifically using Google Cloud C4A virtual machines
+      powered by Axion processors.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
+      account with billing enabled; Basic familiarity with [Kubernetes](https://kubernetes.io/);
+      Familiarity with container concepts ([Docker](https://www.docker.com/)).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Gardener, Kubernetes, Docker, KinD, and Helm, Linux
+      environments, Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Get started with Gardener on Google Axion C4A (Arm
+      Neoverse-V2), Create a Google Axion C4A Arm virtual machine for Gardener, Install Gardener
+      on your Arm-based SUSE VM, Verify Gardener cluster health and functionality, and Benchmark
+      Gardener cluster security with kube-bench.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ##### Tags
@@ -70,3 +120,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md b/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
index db75a9af46..f5d119c4f2 100644
--- a/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
@@ -18,6 +18,45 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 2e53b87d4dc7a7d1984e3bbe035038a60e619aea2f5793cb3082f3b59084bbf9
+  summary: >-
+    Learn how to apply link-time optimization with the GCC toolchain to improve application performance
+    by optimizing across compilation units. It is designed for developers who want to improve
+    application performance using link-time optimization (LTO) with the GCC toolchain. By the
+    end, you will be able to understand how link-time optimization (LTO) works and when to apply
+    it, enable and configure LTO with GCC compiler flags, and evaluate the performance and code
+    size trade-offs of LTO. It focuses on tools and technologies such as GCC, Linux environments,
+    and Arm platforms including Neoverse and Cortex-A. The main steps cover An LTO Primer, Deploying
+    LTO, and Potential Gains.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand how link-time optimization (LTO) works and when to apply it, enable
+      and configure LTO with GCC compiler flags, and evaluate the performance and code size trade-offs
+      of LTO. Learn how to apply link-time optimization with the GCC toolchain to improve application
+      performance by optimizing across compilation units.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers who want to improve application performance
+      using link-time optimization (LTO) with the GCC toolchain.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm Linux system (cloud instance,
+      on-premises hardware, or a virtual machine); A recent version of the [GCC toolchain](/install-guides/gcc/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including GCC, Linux environments, and Arm platforms such
+      as Neoverse and Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around An LTO Primer, Deploying LTO, and Potential Gains.
+# END generated_summary_faq
+
 author: Victor Do Nascimento
 
 ### Tags
@@ -51,3 +90,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/gcp/_index.md
index f4e336802d..81f449a9f5 100644
--- a/content/learning-paths/servers-and-cloud-computing/gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gcp/_index.md
@@ -18,6 +18,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 24e2ffcf9b7cda919e6e864f18bb9424c0c328242c92f491c1b284e317ed7e1c
+  summary: >-
+    Learn how to automate the creation of Arm virtual machines on Google Cloud Platform using
+    Terraform with jump server access configuration. It is designed for anyone new to using Arm
+    virtual machines in the Google Cloud Platform (GCP). By the end, you will be able to automate
+    Arm virtual machine creation using Terraform, deploy Arm instances on GCP and provide access
+    via Jump Server, and provide infrastructure basics, code knowledge and files that could help
+    with future learning paths. It focuses on tools and technologies such as Terraform and Bastion,
+    Linux environments, Arm platforms including Neoverse, and cloud platforms such as Google Cloud.
+    The main steps cover Automate virtual machine creation with Terraform and Deploy Arm instances
+    on GCP and provide access via Jump Server.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will automate Arm virtual machine creation using Terraform, deploy Arm instances on
+      GCP and provide access via Jump Server, and provide infrastructure basics, code knowledge
+      and files that could help with future learning paths. Learn how to automate the creation
+      of Arm virtual machines on Google Cloud Platform using Terraform with jump server access
+      configuration.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for anyone new to using Arm virtual machines in the Google
+      Cloud Platform (GCP).
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud account](https://console.cloud.google.com/).
+      Create an account if needed.; A computer with [Terraform](/install-guides/terraform) installed.;
+      A computer with [Google Cloud CLI](/install-guides/gcloud) installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Terraform and Bastion, Linux environments, Arm platforms
+      such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Automate virtual machine creation with Terraform and
+      Deploy Arm instances on GCP and provide access via Jump Server.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ##### Tags
@@ -56,3 +99,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # Indicates this should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md b/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
index bfe99ae41d..d6455a8c61 100644
--- a/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
@@ -15,6 +15,43 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 85a0a44bde6f217bdfc7fd0660ed6a86279b24c7ede302307ef6efb2626d83d9
+  summary: >-
+    Run Geekbench on Arm systems to benchmark CPU performance, interpret the results, and compare
+    different Arm configurations. It is designed for software developers interested in comparing
+    the performance of Arm Linux computers using Geekbench. By the end, you will be able to learn
+    how to install and run Geekbench and use Geekbench to help determine the appropriate hardware
+    configuration for your workload. It focuses on tools and technologies such as Geekbench and
+    Runbook, Linux environments, and Arm platforms including Neoverse. The main steps cover Download
+    and run Geekbench.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will learn how to install and run Geekbench and use Geekbench to help determine the
+      appropriate hardware configuration for your workload. Run Geekbench on Arm systems to benchmark
+      CPU performance, interpret the results, and compare different Arm configurations.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers interested in comparing the performance
+      of Arm Linux computers using Geekbench.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm computer running Linux. You can
+      use a cloud instance, refer to [Get started with Arm-based cloud instances](/learning-paths/servers-and-cloud-computing/csp/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Geekbench and Runbook, Linux environments, and Arm
+      platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Download and run Geekbench.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 skilllevels: Introductory
@@ -53,3 +90,4 @@ weight: 1
 layout: learningpathall
 learning_path_main_page: 'yes'
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md b/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
index debe29381c..f814610a15 100644
--- a/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
@@ -20,6 +20,55 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 642b67e7ca3717f499ab73efedd924eeab29a0055fad81c2d60fda993dcea195
+  summary: >-
+    Learn how to set up Arm-hosted GitHub runners and train PyTorch ML models using the German
+    Traffic Sign Recognition Benchmark dataset with automated workflows. It is designed for software
+    developers interested in automation for Machine Learning (ML) tasks. By the end, you will
+    be able to set up an Arm-hosted GitHub runner, train and test a PyTorch ML model with the
+    German Traffic Sign Recognition Benchmark (GTSRB) dataset, and compare the performance of
+    two trained PyTorch ML models; one model compiled with OpenBLAS (Open Basic Linear Algebra
+    Subprograms Library) and oneDNN (Deep Neural Network Library), and the other model compiled
+    with Arm Compute Library (ACL). It focuses on tools and technologies such as Python, PyTorch,
+    ACL, and GitHub, Linux environments, and Arm platforms including Neoverse. The main steps
+    cover MLOps background, Understand neural network model training and testing, Automate training
+    and testing with GitHub Actions, Compare the performance of PyTorch backends, and Deploy the
+    application as a container.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will set up an Arm-hosted GitHub runner, train and test a PyTorch ML model with the
+      German Traffic Sign Recognition Benchmark (GTSRB) dataset, and compare the performance of
+      two trained PyTorch ML models; one model compiled with OpenBLAS (Open Basic Linear Algebra
+      Subprograms Library) and oneDNN (Deep Neural Network Library), and the other model compiled
+      with Arm Compute Library (ACL). Learn how to set up Arm-hosted GitHub runners and train
+      PyTorch ML models using the German Traffic Sign Recognition Benchmark dataset with automated
+      workflows.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers interested in automation for Machine
+      Learning (ML) tasks.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A GitHub account with access to Arm-hosted
+      GitHub runners.; A Docker Hub account for storing container images.; Familiarity with the
+      concepts of ML and continuous integration and deployment (CI/CD).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Python, PyTorch, ACL, and GitHub, Linux environments,
+      and Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around MLOps background, Understand neural network model
+      training and testing, Automate training and testing with GitHub Actions, Compare the performance
+      of PyTorch backends, and Deploy the application as a container.
+# END generated_summary_faq
+
 author:
     - Pareena Verma
     - Annie Tallund
@@ -60,3 +109,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md b/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
index 17254aa768..30489a3b3b 100644
--- a/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
@@ -16,6 +16,45 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: cc72ef1fe1fde9f4f9ea0769c0e04d749731b8073b2efc0e65d7f608665abc2d
+  summary: >-
+    Learn how to install RunsOn self-hosted runner manager in your AWS account to execute GitHub
+    Actions workflows on Arm runners. It is designed for developers who want to use Arm runners
+    offered by AWS to execute GitHub Actions workflows. By the end, you will be able to install
+    RunsOn, a self-hosted runner manager, in your AWS account and execute GitHub Actions workflows
+    on Arm runners. It focuses on tools and technologies such as AWS Cloud Formation, GitHub,
+    and AWS EC2, Linux environments, Arm platforms including Neoverse, and cloud platforms such
+    as AWS. The main steps cover About RunsOn and before you begin, Install RunsOn in your AWS
+    account, and Execute GitHub Actions workflows on Arm runners.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install RunsOn, a self-hosted runner manager, in your AWS account and execute GitHub
+      Actions workflows on Arm runners. Learn how to install RunsOn self-hosted runner manager
+      in your AWS account to execute GitHub Actions workflows on Arm runners.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is for developers who want to use Arm runners offered by AWS to execute
+      GitHub Actions workflows.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An [Amazon Web Services account](/learning-paths/servers-and-cloud-computing/csp/aws/).;
+      A GitHub account (personal or organizational).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including AWS Cloud Formation, GitHub, and AWS EC2, Linux
+      environments, Arm platforms such as Neoverse, and cloud platforms such as AWS.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around About RunsOn and before you begin, Install RunsOn
+      in your AWS account, and Execute GitHub Actions workflows on Arm runners.
+# END generated_summary_faq
+
 author: Cyril Rohr
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md b/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md
index 8e2ef43fdd..45e4604de1 100644
--- a/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md
@@ -17,6 +17,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: d5df467355a7792f7a04eafc4a6bbd2410353aca000cd2b1aec74a7ed93a9270
+  summary: >-
+    Learn how to provision a Google Axion C4A Arm virtual machine and set up a GitHub Actions
+    self-hosted runner for CI/CD workflows. It is designed for developers who want to deploy a
+    GitHub Actions self-hosted runner on an Arm-based Google Axion C4A instance. By the end, you
+    will be able to provision an Arm virtual machine on the Google Cloud Platform using the C4A
+    Google Axion instance family, set up and validate a GitHub Actions self-hosted runner on the
+    Arm virtual machine, and deploy a basic CI workflow with NGINX and verify execution on Arm
+    infrastructure. It focuses on tools and technologies such as GitHub Actions and GitHub CLI,
+    Linux environments, Arm platforms including Neoverse, and cloud platforms such as Google Cloud.
+    The main steps cover About Google Axion and GitHub Actions, Create the instance, Set up a
+    GitHub Self-Hosted Runner, and Deploy NGINX with the GitHub runner.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will provision an Arm virtual machine on the Google Cloud Platform using the C4A Google
+      Axion instance family, set up and validate a GitHub Actions self-hosted runner on the Arm
+      virtual machine, and deploy a basic CI workflow with NGINX and verify execution on Arm infrastructure.
+      Learn how to provision a Google Axion C4A Arm virtual machine and set up a GitHub Actions
+      self-hosted runner for CI/CD workflows.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers who want to deploy a GitHub Actions self-hosted
+      runner on an Arm-based Google Axion C4A instance.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free?utm_source=google&hl=en)
+      account with billing enabled; A GitHub account; you can [sign up for GitHub](https://github.com/signup).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including GitHub Actions and GitHub CLI, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around About Google Axion and GitHub Actions, Create the
+      instance, Set up a GitHub Self-Hosted Runner, and Deploy NGINX with the GitHub runner.
+# END generated_summary_faq
+
 author: Annie Tallund
 
 ##### Tags
@@ -64,3 +107,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # Indicates this should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
index f01a552b00..af07db6fa0 100644
--- a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
@@ -19,6 +19,55 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: cf981c67553824c7c57b6dda9c2953ac80926750676ac101e939f6bbff655ab5
+  summary: >-
+    Learn how to create dual-architecture GKE clusters with arm64 and amd64 node pools, build
+    multi-architecture Docker images, and migrate services to Google Axion processors. It is designed
+    for cloud, platform, and site reliability engineers who operate Kubernetes on Google Cloud
+    and need to build multi-architecture images and migrate services from x86 to Arm using Google
+    Axion processors. By the end, you will be able to prepare Dockerfiles for multi-architecture
+    builds by adding arm64 support, create a dual-architecture GKE standard cluster with amd64
+    and arm64 node pools, and build and publish multi-architecture images to Artifact Registry
+    using Docker Buildx. It focuses on tools and technologies such as Kubernetes, GKE, Skaffold,
+    and Cloud Build, Linux environments, Arm platforms including Neoverse, and cloud platforms
+    such as Google Cloud. The main steps cover Explore the benefits of migrating microservices
+    to Arm on GKE, Set up your environment, Create build-ready Dockerfiles for both architectures,
+    Build and deploy multi-architecture images on GKE, and Prepare manifests and deploy on GKE.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will prepare Dockerfiles for multi-architecture builds by adding arm64 support, create
+      a dual-architecture GKE standard cluster with amd64 and arm64 node pools, and build and
+      publish multi-architecture images to Artifact Registry using Docker Buildx. Learn how to
+      create dual-architecture GKE clusters with arm64 and amd64 node pools, build multi-architecture
+      Docker images, and migrate services to Google Axion processors.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for cloud, platform, and site reliability engineers who operate
+      Kubernetes on Google Cloud and need to build multi-architecture images and migrate services
+      from x86 to Arm using Google Axion processors.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud account](https://console.cloud.google.com/)
+      with billing enabled; A local Linux or macOS computer with Docker, Kubernetes CLI (kubectl),
+      Google Cloud CLI (gcloud), and Git installed, or access to Google Cloud Shell; Basic familiarity
+      with Docker, Kubernetes, and gcloud.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Kubernetes, GKE, Skaffold, and Cloud Build, Linux
+      environments, Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Explore the benefits of migrating microservices to
+      Arm on GKE, Set up your environment, Create build-ready Dockerfiles for both architectures,
+      Build and deploy multi-architecture images on GKE, and Prepare manifests and deploy on GKE.
+# END generated_summary_faq
+
 author: 
    - Rani Chowdary Mandepudi
 
@@ -58,3 +107,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
index 07e7f1153b..c07245efa8 100644
--- a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
@@ -19,6 +19,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 50715640292b60ea4216ee2211140c4212592a27356d628d945e83d9deb7fdcc
+  summary: >-
+    Learn how to add Arm-based Google Axion nodes to an existing x86 GKE cluster and rebuild applications
+    for multi-architecture support. It is designed for software developers who are looking to
+    migrate their existing x86 containerized applications to Arm. By the end, you will be able
+    to add Arm-based nodes (Google Axion) to an existing x86-based GKE cluster, rebuild an x86-based
+    application to make it multi-arch and run on Arm, and learn how to add taints and tolerations
+    to GKE clusters to schedule application pods on architecture specific nodes. It focuses on
+    tools and technologies such as Kubernetes and Runbook, Linux environments, Arm platforms including
+    Neoverse, and cloud platforms such as Google Cloud. The main steps cover Build and deploy
+    a multi-arch application on GKE.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will add Arm-based nodes (Google Axion) to an existing x86-based GKE cluster, rebuild
+      an x86-based application to make it multi-arch and run on Arm, and learn how to add taints
+      and tolerations to GKE clusters to schedule application pods on architecture specific nodes.
+      Learn how to add Arm-based Google Axion nodes to an existing x86 GKE cluster and rebuild
+      applications for multi-architecture support.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers who are looking to migrate their existing
+      x86 containerized applications to Arm.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud account](https://console.cloud.google.com/).
+      Create an account if needed.; A computer with [Google Cloud CLI](/install-guides/gcloud)
+      and [kubectl](/install-guides/kubectl/)installed.; An existing Google Kubernetes Engine
+      (GKE) cluster with x86-based nodes.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Kubernetes and Runbook, Linux environments, Arm
+      platforms such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Build and deploy a multi-arch application on GKE.
+# END generated_summary_faq
+
 author: Pranay Bakre
 
 ### Tags
@@ -60,3 +103,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/gke/_index.md b/content/learning-paths/servers-and-cloud-computing/gke/_index.md
index 3e283dabc2..587b4ebc77 100644
--- a/content/learning-paths/servers-and-cloud-computing/gke/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gke/_index.md
@@ -15,6 +15,43 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 7c3d37545c93db584d6e0ce88d8feaf0bf690e0c8786b664a6643be713d0336f
+  summary: >-
+    Learn how to automate the deployment of an Arm-based Google Kubernetes Engine cluster using
+    Terraform for container orchestration. It is designed for software developers who want to
+    deploy an Arm-based Kubernetes cluster using Google Kubernetes Engine (GKE). By the end, you
+    will be able to automate the deployment of an Arm-based GKE cluster using Terraform. It focuses
+    on tools and technologies such as Terraform and Kubernetes, Linux environments, Arm platforms
+    including Neoverse, and cloud platforms such as Google Cloud. The main steps cover Deploy
+    an Arm-based GKE Cluster using Terraform.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will automate the deployment of an Arm-based GKE cluster using Terraform. Learn how
+      to automate the deployment of an Arm-based Google Kubernetes Engine cluster using Terraform
+      for container orchestration.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers who want to deploy an Arm-based Kubernetes
+      cluster using Google Kubernetes Engine (GKE).
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Google Cloud account; A computer with
+      the following tools installed`:` Terraform, Google Cloud CLI (gcloud), Kubernetes CLI (kubectl).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Terraform and Kubernetes, Linux environments, Arm
+      platforms such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Deploy an Arm-based GKE Cluster using Terraform.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md b/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
index faede922a0..335f33935e 100644
--- a/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
@@ -17,6 +17,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 74952d68380c7540306543b0a7520f6960f673dd55ad5f164365fcfe1470380e
+  summary: >-
+    Rebuild and benchmark glibc with LSE atomics on Arm servers, then evaluate scalability using
+    MongoDB workloads and guidance on when LSE delivers a measurable uplift. It is designed for
+    software developers interested in learning how to improve the performance of their workloads
+    on Arm servers. By the end, you will be able to build and install glibc with LSE on an Arm
+    server, benchmark workload performance using glibc with LSE optimizations, and benchmark MongoDB
+    using glibc with LSE optimizations. It focuses on tools and technologies such as glibc, LSE,
+    MongoDB, and Runbook, Linux environments, and Arm platforms including Neoverse. The main steps
+    cover Build Glibc with LSE, Start MongoDB utilizing the newly built Glibc with LSE, Benchmark
+    MongoDB with YCSB, and Compare the results with LSE and NoLSE.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build and install glibc with LSE on an Arm server, benchmark workload performance
+      using glibc with LSE optimizations, and benchmark MongoDB using glibc with LSE optimizations.
+      Rebuild and benchmark glibc with LSE atomics on Arm servers, then evaluate scalability using
+      MongoDB workloads and guidance on when LSE delivers a measurable uplift.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers interested in learning how to improve
+      the performance of their workloads on Arm servers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm based instance from a cloud service
+      provider.; Review the learning path on [LSE](/learning-paths/servers-and-cloud-computing/lse/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including glibc, LSE, MongoDB, and Runbook, Linux environments,
+      and Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Build Glibc with LSE, Start MongoDB utilizing the
+      newly built Glibc with LSE, Benchmark MongoDB with YCSB, and Compare the results with LSE
+      and NoLSE.
+# END generated_summary_faq
+
 author: Ying Yu
 
 ### Tags
@@ -56,3 +98,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md b/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
index 92fc321e52..1a915544f8 100644
--- a/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
@@ -17,6 +17,52 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: aa78434138f08b424352302e92a1cd40d8297459bc65202715dcb41c40de6057
+  summary: >-
+    Learn how to provision Arm64 and x86_64 VM instances on Google Cloud, then install and use
+    Sweet and Benchstat to measure and compare Go application performance. It is designed for
+    This introductory topic is for developers who want to measure and compare the performance
+    of Go applications on Arm-based servers. By the end, you will be able to provision Arm64 and
+    x86_64 VM instances on Google Cloud, install Go, Sweet, and Benchstat on each VM instance,
+    and run benchmarks and use Benchstat to compare Go application performance across architectures.
+    It focuses on tools and technologies such as Go, Linux environments, Arm platforms including
+    Neoverse, and cloud platforms such as Google Cloud. The main steps cover Overview, Launch
+    an Arm-based c4a-standard-4 instance, Launch an Intel Emerald Rapids c4-standard-8 instance,
+    Install Go, Sweet, and Benchstat, and Benchmark types and metrics.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will provision Arm64 and x86_64 VM instances on Google Cloud, install Go, Sweet, and
+      Benchstat on each VM instance, and run benchmarks and use Benchstat to compare Go application
+      performance across architectures. Learn how to provision Arm64 and x86_64 VM instances on
+      Google Cloud, then install and use Sweet and Benchstat to measure and compare Go application
+      performance.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This introductory topic is for developers who want to measure and compare the performance
+      of Go applications on Arm-based servers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud account](https://console.cloud.google.com/).
+      This Learning Path can be run on any cloud provider or on-premises, but it focuses on Google
+      Cloud’s Axion Arm64-based instances.; A local machine with [Google Cloud CLI](/install-guides/gcloud/)
+      installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Go, Linux environments, Arm platforms such as Neoverse,
+      and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview, Launch an Arm-based c4a-standard-4 instance,
+      Launch an Intel Emerald Rapids c4-standard-8 instance, Install Go, Sweet, and Benchstat,
+      and Benchmark types and metrics.
+# END generated_summary_faq
+
 author: Geremy Cohen
 
 ### Tags
@@ -47,3 +93,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md
index 75eed034b2..b2060ad3cb 100644
--- a/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md
@@ -18,6 +18,57 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 46a0a3df799ac473f56d3820390af405b3301758cf15685a250a04c4854aba9e
+  summary: >-
+    Learn how to provision Azure Cobalt 100 Arm64 virtual machines and deploy Golang applications
+    with performance benchmarking on Arm architecture. It is designed for software developers,
+    DevOps engineers, and cloud architects looking to migrate their Golang (Go) applications from
+    x86_64 to high-performance Arm-based Azure Cobalt 100 virtual machines for improved cost efficiency
+    and performance. By the end, you will be able to provision an Azure Arm64 virtual machine
+    using the Azure portal, with Ubuntu Pro 24.04 LTS as the base image, deploy Golang on an Arm64-based
+    virtual machine running Ubuntu Pro 24.04 LTS, and perform Golang baseline testing and benchmarking
+    on both x86_64 and Arm64 virtual machines. It focuses on tools and technologies such as Golang,
+    Linux environments, Arm platforms including Neoverse, and cloud platforms such as Microsoft
+    Azure. The main steps cover Overview, Create an Azure Cobalt 100 Arm64 virtual machine for
+    Golang deployment, Install and configure Golang on Azure Cobalt 100 Arm64, Perform Golang
+    baseline testing and web server deployment on Azure Cobalt 100, and Run performance tests
+    using go test -bench.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will provision an Azure Arm64 virtual machine using the Azure portal, with Ubuntu Pro
+      24.04 LTS as the base image, deploy Golang on an Arm64-based virtual machine running Ubuntu
+      Pro 24.04 LTS, and perform Golang baseline testing and benchmarking on both x86_64 and Arm64
+      virtual machines. Learn how to provision Azure Cobalt 100 Arm64 virtual machines and deploy
+      Golang applications with performance benchmarking on Arm architecture.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers, DevOps engineers, and cloud architects
+      looking to migrate their Golang (Go) applications from x86_64 to high-performance Arm-based
+      Azure Cobalt 100 virtual machines for improved cost efficiency and performance.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Microsoft Azure](https://azure.microsoft.com/)
+      account with access to Azure Cobalt 100 Arm-based instances (Dpsv6-series); Basic familiarity
+      with the [Go programming language](https://go.dev/) and cloud deployment practices; Understanding
+      of Linux command line and virtual machine management.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Golang, Linux environments, Arm platforms such as
+      Neoverse, and cloud platforms such as Microsoft Azure.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview, Create an Azure Cobalt 100 Arm64 virtual
+      machine for Golang deployment, Install and configure Golang on Azure Cobalt 100 Arm64, Perform
+      Golang baseline testing and web server deployment on Azure Cobalt 100, and Run performance
+      tests using go test -bench.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
@@ -56,3 +107,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md
index 44f427f7d5..f5c759f084 100644
--- a/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md
@@ -25,6 +25,56 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 87e9d25d5fb1f45d126aa4ca2fa1e13d6a470f2bcdc70968aa4622790106e629
+  summary: >-
+    Learn how to install Helm on Google Cloud Axion C4A SUSE VMs and deploy applications like
+    NGINX, PostgreSQL, and Redis using Helm charts. It is designed for This is an introductory
+    topic intended for developers who want to get hands-on experience using Helm on Linux Arm64
+    systems, specifically Google Cloud C4A virtual machines powered by Axion processors. By the
+    end, you will be able to provision an Arm-based SUSE Linux Enterprise Server (SLES) virtual
+    machine on Google Cloud (C4A with Axion processors), install and configure Helm and kubectl
+    on a SUSE Arm64 (C4A) instance, and create and connect to a Google Kubernetes Engine (GKE)
+    cluster running on Arm-based nodes. It focuses on tools and technologies such as Helm, Kubernetes,
+    kubectl, GKE, and PostgreSQL, Linux environments, Arm platforms including Neoverse, and cloud
+    platforms such as Google Cloud. The main steps cover Get started with Helm on Google Axion
+    C4A (Arm-based), Create a Google Axion C4A virtual machine on Google Cloud, Install Helm,
+    Validate Helm workflows on a Google Axion C4A virtual machine, and Prepare a GKE cluster for
+    Helm deployments.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will provision an Arm-based SUSE Linux Enterprise Server (SLES) virtual machine on Google
+      Cloud (C4A with Axion processors), install and configure Helm and kubectl on a SUSE Arm64
+      (C4A) instance, and create and connect to a Google Kubernetes Engine (GKE) cluster running
+      on Arm-based nodes. Learn how to install Helm on Google Cloud Axion C4A SUSE VMs and deploy
+      applications like NGINX, PostgreSQL, and Redis using Helm charts.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic intended for developers who want to get hands-on experience
+      using Helm on Linux Arm64 systems, specifically Google Cloud C4A virtual machines powered
+      by Axion processors.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
+      account with billing enabled; Basic familiarity with [Kubernetes concepts](https://kubernetes.io/docs/concepts/);
+      Basic understanding of [Helm](https://helm.sh/docs/topics/architecture/) and Kubernetes
+      manifests; Familiarity with basic Linux command-line usage.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Helm, Kubernetes, kubectl, GKE, and PostgreSQL,
+      Linux environments, Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Get started with Helm on Google Axion C4A (Arm-based),
+      Create a Google Axion C4A virtual machine on Google Cloud, Install Helm, Validate Helm workflows
+      on a Google Axion C4A virtual machine, and Prepare a GKE cluster for Helm deployments.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/intro/_index.md b/content/learning-paths/servers-and-cloud-computing/intro/_index.md
index 2b067b887b..5de918d5aa 100644
--- a/content/learning-paths/servers-and-cloud-computing/intro/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/intro/_index.md
@@ -15,6 +15,42 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: d2786f42b505823253ae16c647ca2e03c154f371b7c326210fde8523b12a8188
+  summary: >-
+    Learn where Arm architecture is used in servers and cloud computing, and find Arm-based hardware
+    platforms for software development. It is designed for software developers working on server
+    and cloud applications who are new to the Arm architecture. By the end, you will be able to
+    identify where Arm architecture is used in servers and cloud computing and locate server and
+    cloud hardware for software development. It focuses on tools and technologies such as Runbook,
+    Linux environments, and Arm platforms including Neoverse. The main steps cover Arm in Servers
+    and Cloud Computing and Find Arm hardware.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will identify where Arm architecture is used in servers and cloud computing and locate
+      server and cloud hardware for software development. Learn where Arm architecture is used
+      in servers and cloud computing, and find Arm-based hardware platforms for software development.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers working on server and cloud applications
+      who are new to the Arm architecture.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: None.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Runbook, Linux environments, and Arm platforms such
+      as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Arm in Servers and Cloud Computing and Find Arm hardware.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
@@ -41,3 +77,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md b/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
index 9567886078..dd4bb07d32 100644
--- a/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
@@ -19,6 +19,47 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 6fc608d45c4fdf85a77bddd4f8c26b05a7950d1086e578a8be0dd637feeb5d79
+  summary: >-
+    Analyze and optimize interrupt request (IRQ) patterns on Arm Linux servers to improve network
+    workload performance through IRQ distribution strategies. It is designed for developers and
+    performance engineers who are interested in understanding how network interrupt patterns can
+    impact performance on cloud servers. By the end, you will be able to analyze the current interrupt
+    request (IRQ) layout on an Arm Linux system, experiment with different interrupt options and
+    patterns to improve performance, and configure optimal IRQ distribution strategies for your
+    workload. It focuses on Linux environments and Arm platforms including Neoverse and Cortex-A.
+    The main steps cover Understand and analyze network IRQ configuration, IRQ management patterns
+    for performance optimization, and Conclusion and recommendations.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will analyze the current interrupt request (IRQ) layout on an Arm Linux system, experiment
+      with different interrupt options and patterns to improve performance, and configure optimal
+      IRQ distribution strategies for your workload. Analyze and optimize interrupt request (IRQ)
+      patterns on Arm Linux servers to improve network workload performance through IRQ distribution
+      strategies.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers and performance engineers who are interested
+      in understanding how network interrupt patterns can impact performance on cloud servers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm computer running Linux; Some
+      familiarity with the Linux command line.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers Linux environments and Arm platforms such as Neoverse and Cortex-A.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Understand and analyze network IRQ configuration,
+      IRQ management patterns for performance optimization, and Conclusion and recommendations.
+# END generated_summary_faq
+
 author: Kiel Friedt
 
 ### Tags
@@ -61,3 +102,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md b/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
index cc0bc92e45..f75926caa0 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
@@ -19,6 +19,50 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: f57dd99bad280f64f53071373519e2d3ba8ae50b94fe1ad0a9cc40d02b458b9b
+  summary: >-
+    Monitor, interpret, and optimize Java Garbage Collector performance on Arm servers by comparing
+    different GCs and tuning parameters for your workload. It is designed for Java developers
+    aiming to optimize application performance on Arm-based servers, especially those migrating
+    applications from x86-based to Arm-based instances. By the end, you will be able to describe
+    the key differences between individual Java Garbage Collectors (GCs), monitor and interpret
+    Garbage Collector performance metrics, and adjust core parameters to optimize performance
+    for your specific workload. It focuses on tools and technologies such as Java and Runbook,
+    Linux environments, Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft
+    Azure, Google Cloud, and Oracle. The main steps cover Overview, Setup, Types of Garbage Collector,
+    Example Application, and Basic GC Tuning Options.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will describe the key differences between individual Java Garbage Collectors (GCs),
+      monitor and interpret Garbage Collector performance metrics, and adjust core parameters
+      to optimize performance for your specific workload. Monitor, interpret, and optimize Java
+      Garbage Collector performance on Arm servers by comparing different GCs and tuning parameters
+      for your workload.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is for Java developers aiming to optimize application performance on
+      Arm-based servers, especially those migrating applications from x86-based to Arm-based instances.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm-based instance from a cloud service
+      provider, or an on-premise Arm server.; Basic understanding of Java.; An [installation of
+      Java](/install-guides/java/) on your machine.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Java and Runbook, Linux environments, Arm platforms
+      such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview, Setup, Types of Garbage Collector, Example
+      Application, and Basic GC Tuning Options.
+# END generated_summary_faq
+
 author: Kieran Hejmadi
 
 ### Tags
@@ -57,3 +101,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
index 0aaa578d0c..3558244bcd 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
@@ -16,6 +16,50 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: e6f73fbca45a1be1644f79bbcfbaaec964e31f1aab236e9a872c72a6fd5e4b66
+  summary: >-
+    Deploy and optimize Java applications on Google Cloud Axion processors by testing JDK versions
+    and performance optimization flags. It is designed for software developers who want to learn
+    how to run their Java-based applications on Arm-based Google Axion processors in Google Cloud.
+    Most Java applications will run on Axion with no changes needed, but there are optimizations
+    that can help improve application performance on Axion. By the end, you will be able to create
+    an Arm-based VM instance with Google Axion CPU, deploy a Java application on Axion, and understand
+    Arm performance for different JDK versions. It focuses on tools and technologies such as Java,
+    Google Axion, and Runbook, Linux environments, Arm platforms including Neoverse V2, and cloud
+    platforms such as Google Cloud. The main steps cover Create an Arm-based VM instance with
+    Google Axion CPU, Install the JDK and build an application, and Test performance and optimize.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create an Arm-based VM instance with Google Axion CPU, deploy a Java application
+      on Axion, and understand Arm performance for different JDK versions. Deploy and optimize
+      Java applications on Google Cloud Axion processors by testing JDK versions and performance
+      optimization flags.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to learn how to run their
+      Java-based applications on Arm-based Google Axion processors in Google Cloud. Most Java
+      applications will run on Axion with no changes needed, but there are optimizations that
+      can help improve application performance on Axion.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud](https://cloud.google.com/)
+      account with access to Axion based instances (C4A).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Java, Google Axion, and Runbook, Linux environments,
+      Arm platforms such as Neoverse V2, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Create an Arm-based VM instance with Google Axion
+      CPU, Install the JDK and build an application, and Test performance and optimize.
+# END generated_summary_faq
+
 author: Joe Stech
 
 ### Tags
@@ -52,3 +96,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
index 89a9ecdf42..c11354af29 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
@@ -17,6 +17,51 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 58b0bb9f6e4190029d7edc65bdb04a597c7539de55a5e51ed59e88b174e527c3
+  summary: >-
+    Deploy Java on Azure Cobalt 100 Arm virtual machines and benchmark application performance
+    with JMH microbenchmarks. It is designed for This is an introductory topic about Java deployment
+    and benchmarking on Microsoft Azure Cobalt 100 Arm-based virtual machines. It is designed
+    for developers migrating Java applications from x86_64 to Arm architecture. By the end, you
+    will be able to provision an Azure Arm-based Cobalt 100 virtual machine using Azure console,
+    with Ubuntu Pro 24.04 LTS as the base image, deploy Java on the Azure Arm64 virtual machine,
+    and perform Java baseline testing and benchmarking on the Arm64 virtual machines. It focuses
+    on tools and technologies such as Java and JMH, Linux environments, Arm platforms including
+    Neoverse, and cloud platforms such as Microsoft Azure. The main steps cover Overview, Create
+    an Arm-based cloud virtual machine using Microsoft Cobalt 100 CPU, Install Java, Java Baseline
+    Testing, and FIXED, DO NOT MODIFY.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will provision an Azure Arm-based Cobalt 100 virtual machine using Azure console, with
+      Ubuntu Pro 24.04 LTS as the base image, deploy Java on the Azure Arm64 virtual machine,
+      and perform Java baseline testing and benchmarking on the Arm64 virtual machines. Deploy
+      Java on Azure Cobalt 100 Arm virtual machines and benchmark application performance with
+      JMH microbenchmarks.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic about Java deployment and benchmarking on Microsoft Azure
+      Cobalt 100 Arm-based virtual machines. It is designed for developers migrating Java applications
+      from x86_64 to Arm architecture.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Microsoft Azure](https://azure.microsoft.com/)
+      account with access to Cobalt 100 based instances (Dpsv6).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Java and JMH, Linux environments, Arm platforms
+      such as Neoverse, and cloud platforms such as Microsoft Azure.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview, Create an Arm-based cloud virtual machine
+      using Microsoft Cobalt 100 CPU, Install Java, Java Baseline Testing, and FIXED, DO NOT MODIFY.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
@@ -60,3 +105,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
index f39077837d..f5529f1062 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
@@ -17,6 +17,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 655180d91bb9b9cf571840b59d8943c1d2c73d8f8aea647db57dc2704ede3af8
+  summary: >-
+    Profile Java applications on Arm Neoverse servers using flame graphs generated with async-profiler
+    and Java agents to identify performance bottlenecks. It is designed for developers who want
+    to analyze the performance of Java applications on Arm Neoverse-based servers using flame
+    graphs. By the end, you will be able to set up a benchmarking environment using Tomcat and
+    wrk2, generate flame graphs using async-profiler, and generate flame graphs using a Java agent.
+    It focuses on tools and technologies such as OpenJDK 21, Apache Tomcat, async-profiler, FlameGraph,
+    and wrk2, Linux environments, and Arm platforms including Neoverse. The main steps cover Set
+    up Tomcat benchmark environment, Generate Java flame graphs using async-profiler, and Generate
+    Java flame graphs using a Java agent.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will set up a benchmarking environment using Tomcat and wrk2, generate flame graphs
+      using async-profiler, and generate flame graphs using a Java agent. Profile Java applications
+      on Arm Neoverse servers using flame graphs generated with async-profiler and Java agents
+      to identify performance bottlenecks.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers who want to analyze the performance of Java
+      applications on Arm Neoverse-based servers using flame graphs.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Access to both Arm-based and x86-based
+      computers running Ubuntu (you can use cloud-based server instances); Basic familiarity with
+      Java applications and performance profiling using flame graphs.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including OpenJDK 21, Apache Tomcat, async-profiler, FlameGraph,
+      and wrk2, Linux environments, and Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Set up Tomcat benchmark environment, Generate Java
+      flame graphs using async-profiler, and Generate Java flame graphs using a Java agent.
+# END generated_summary_faq
+
 author: 
   - Ying Yu
   - Martin Ma
@@ -51,3 +93,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md b/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
index f42c01c987..09a5f95c6a 100644
--- a/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
@@ -23,6 +23,57 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 525d893a796e8e4eb5cc7a9d5996bd7d55a35f8fe413f87b9a275a214d77626f
+  summary: >-
+    Deploy Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate installation,
+    and execute Arm-native CI/CD pipelines including Docker workflows. It is designed for software
+    developers deploying and optimizing Jenkins workloads on Arm Linux environments, specifically
+    on Microsoft Azure Cobalt 100 processors and Google Cloud C4A virtual machines powered by
+    Axion processors. By the end, you will be able to provision an Azure Arm64 virtual machine
+    using the Azure console with Ubuntu Pro 24.04 LTS, provision an Arm-based SUSE Linux virtual
+    machine on Google Cloud (C4A with Axion processors), and install Jenkins LTS with OpenJDK
+    17 on an Arm64 virtual machine. It focuses on tools and technologies such as Jenkins, OpenJDK
+    17, Docker, and Groovy (Jenkins Pipeline), Linux environments, Arm platforms including Neoverse,
+    and cloud platforms such as Microsoft Azure and Google Cloud. The main steps cover Technology
+    stack overview, Create an Arm-based virtual machine using Microsoft Cobalt 100, Create a firewall
+    rule on Azure, Install Jenkins on Azure Ubuntu Arm64 virtual machine, and Create a firewall
+    rule on GCP.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will provision an Azure Arm64 virtual machine using the Azure console with Ubuntu Pro
+      24.04 LTS, provision an Arm-based SUSE Linux virtual machine on Google Cloud (C4A with Axion
+      processors), and install Jenkins LTS with OpenJDK 17 on an Arm64 virtual machine. Deploy
+      Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate installation, and
+      execute Arm-native CI/CD pipelines including Docker workflows.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is for software developers deploying and optimizing Jenkins workloads
+      on Arm Linux environments, specifically on Microsoft Azure Cobalt 100 processors and Google
+      Cloud C4A virtual machines powered by Axion processors.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Microsoft Azure](https://azure.microsoft.com/)
+      account with access to Cobalt 100-based instances (Dpsv6); A [Google Cloud Platform](https://cloud.google.com/)
+      account with access to Arm-based virtual machine instances; Basic understanding of Linux
+      command line; Familiarity with CI/CD concepts and [Jenkins fundamentals](https://www.jenkins.io/doc/book/pipeline/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Jenkins, OpenJDK 17, Docker, and Groovy (Jenkins
+      Pipeline), Linux environments, Arm platforms such as Neoverse, and cloud platforms such
+      as Microsoft Azure and Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Technology stack overview, Create an Arm-based virtual
+      machine using Microsoft Cobalt 100, Create a firewall rule on Azure, Install Jenkins on
+      Azure Ubuntu Arm64 virtual machine, and Create a firewall rule on GCP.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ##### Tags
@@ -69,3 +120,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
index 6fd26f10e2..22d0c753cf 100644
--- a/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
@@ -19,6 +19,52 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: d510dd43a485e1c2fac404ef9a2e00b5be2449b99b1095c9a1841cd2fcba9b0e
+  summary: >-
+    Deploy Apache Kafka on Azure Cobalt 100 Arm virtual machines and benchmark message throughput
+    performance. It is designed for developers looking to migrate their Apache Kafka workloads
+    from x86_64 to Arm-based platforms, specifically on Microsoft Azure Cobalt 100 (arm64) virtual
+    machines. By the end, you will be able to provision an Azure Arm64 virtual machine using Azure
+    console, with Ubuntu Pro 24.04 LTS as the base image, deploy Kafka on an Ubuntu virtual machine,
+    and perform Kafka baseline testing and benchmarking on Arm64 virtual machines. It focuses
+    on tools and technologies such as Kafka, Linux environments, Arm platforms including Neoverse,
+    and cloud platforms such as Microsoft Azure. The main steps cover Overview, Create an Arm-based
+    cloud virtual machine using Microsoft Cobalt 100 CPU, Install Kafka, Run baseline testing
+    with Kafka on Azure Arm VM, and Benchmark with official Kafka tools.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will provision an Azure Arm64 virtual machine using Azure console, with Ubuntu Pro 24.04
+      LTS as the base image, deploy Kafka on an Ubuntu virtual machine, and perform Kafka baseline
+      testing and benchmarking on Arm64 virtual machines. Deploy Apache Kafka on Azure Cobalt
+      100 Arm virtual machines and benchmark message throughput performance.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for developers looking to migrate their Apache Kafka workloads
+      from x86_64 to Arm-based platforms, specifically on Microsoft Azure Cobalt 100 (arm64) virtual
+      machines.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Microsoft Azure](https://azure.microsoft.com/)
+      account with access to Cobalt 100 based instances (Dpsv6); Basic understanding of Linux
+      command line; Familiarity with the [Apache Kafka architecture](https://kafka.apache.org/)
+      and deployment practices on Arm64 platforms.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Kafka, Linux environments, Arm platforms such as
+      Neoverse, and cloud platforms such as Microsoft Azure.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview, Create an Arm-based cloud virtual machine
+      using Microsoft Cobalt 100 CPU, Install Kafka, Run baseline testing with Kafka on Azure
+      Arm VM, and Benchmark with official Kafka tools.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
@@ -57,3 +103,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/kafka/_index.md b/content/learning-paths/servers-and-cloud-computing/kafka/_index.md
index 41ff740f45..83c803fe50 100644
--- a/content/learning-paths/servers-and-cloud-computing/kafka/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kafka/_index.md
@@ -19,6 +19,47 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: b7f36df881c2c436143c1e5579d2c54cb5930f52998904098829c52fa7290f38
+  summary: >-
+    Deploy and configure a Kafka cluster with Zookeeper on Arm servers, test event streaming,
+    and automate deployment on AWS and Google Cloud. It is designed for software developers who
+    want to learn how to use Kafka and Zookeeper. By the end, you will be able to install Zookeeper
+    and Kafka, configure Zookeeper to work with Kafka, and test write/read events into the Kafka
+    cluster. It focuses on tools and technologies such as Kafka and ZooKeeper, Linux environments,
+    Arm platforms including Neoverse, and cloud platforms such as AWS and Google Cloud. The main
+    steps cover Introduction to Kafka and Zookeeper, Setup a 3 node Zookeeper Cluster, Set up
+    a 3 node Kafka Cluster, Verify that the Kafka Cluster is working, and Deploy Cluster Automatically
+    (AWS).
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install Zookeeper and Kafka, configure Zookeeper to work with Kafka, and test write/read
+      events into the Kafka cluster. Deploy and configure a Kafka cluster with Zookeeper on Arm
+      servers, test event streaming, and automate deployment on AWS and Google Cloud.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers who want to learn how to use Kafka and
+      Zookeeper.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Seven physical Arm machines or cloud
+      instances with either Ubuntu or Debian installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Kafka and ZooKeeper, Linux environments, Arm platforms
+      such as Neoverse, and cloud platforms such as AWS and Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Introduction to Kafka and Zookeeper, Setup a 3 node
+      Zookeeper Cluster, Set up a 3 node Kafka Cluster, Verify that the Kafka Cluster is working,
+      and Deploy Cluster Automatically (AWS).
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
@@ -61,3 +102,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md b/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
index ebd7e8749c..e9658e18a9 100644
--- a/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
@@ -19,6 +19,50 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 6ff33f6dfaf25e926a0ce8756b4e564e5aa37112e5425f9c3dce13f772b54145
+  summary: >-
+    Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing Kedify and
+    KEDA with Helm and testing autoscaling behavior. It is designed for developers running HTTP
+    workloads on Kubernetes who want to enable event-driven autoscaling with KEDA and Kedify.
+    By the end, you will be able to install Kedify (KEDA build, HTTP Scaler, and Kedify Agent)
+    with Helm, verify that Kedify and KEDA components are running in the cluster, and deploy a
+    sample HTTP application and test autoscaling behavior. It focuses on tools and technologies
+    such as Kubernetes, Helm, KEDA, and Kedify, Linux environments, Arm platforms including Neoverse,
+    and cloud platforms such as AWS, Microsoft Azure, and Google Cloud. The main steps cover Install
+    Kedify using Helm, Install an ingress controller, and Autoscale HTTP applications with Kedify
+    and Kubernetes Ingress.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install Kedify (KEDA build, HTTP Scaler, and Kedify Agent) with Helm, verify that
+      Kedify and KEDA components are running in the cluster, and deploy a sample HTTP application
+      and test autoscaling behavior. Enable event-driven autoscaling for HTTP workloads on Kubernetes
+      by installing Kedify and KEDA with Helm and testing autoscaling behavior.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers running HTTP workloads on Kubernetes who want
+      to enable event-driven autoscaling with KEDA and Kedify.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A running Kubernetes cluster (local
+      or cloud); Kubectl and Helm installed; Access to the Kedify Service dashboard to obtain
+      your Organization ID and API key (sign up at [Kedify dashboard](https://dashboard.kedify.io/)).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Kubernetes, Helm, KEDA, and Kedify, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, and Google
+      Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Install Kedify using Helm, Install an ingress controller,
+      and Autoscale HTTP applications with Kedify and Kubernetes Ingress.
+# END generated_summary_faq
+
 author: Zbynek Roubalik
 
 ### Tags
@@ -59,3 +103,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md b/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
index 755b247093..b704463512 100644
--- a/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
@@ -19,6 +19,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 830556dfd51aaa2dd95ee957e64306bab369297f7bc66325e7eb509f541f683c
+  summary: >-
+    Create, train, and evaluate a neural network model on Arm servers using Keras Core with TensorFlow,
+    PyTorch, and JAX backends. It is designed for engineers who want to create a neural network
+    model on Arm machines. By the end, you will be able to create a simple neural network model
+    using Keras Core, train and evaluate your neural network model with different backends, and
+    generate predictions with the trained model. It focuses on tools and technologies such as
+    Python, Keras, TensorFlow, PyTorch, and JAX, Linux environments, Arm platforms including Neoverse,
+    and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps
+    cover Overview of Keras Core, Install the required dependencies, and Run Keras Core.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create a simple neural network model using Keras Core, train and evaluate your
+      neural network model with different backends, and generate predictions with the trained
+      model. Create, train, and evaluate a neural network model on Arm servers using Keras Core
+      with TensorFlow, PyTorch, and JAX backends.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for engineers who want to create a neural network model on
+      Arm machines.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Basic Machine Learning knowledge.; An
+      [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service
+      provider, an on-premises Arm server, or a Linux virtual machine on your Arm device.; Familiarity
+      with SSH, the Linux command line, and basic system administration tasks.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Python, Keras, TensorFlow, PyTorch, and JAX, Linux
+      environments, Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft
+      Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview of Keras Core, Install the required dependencies,
+      and Run Keras Core.
+# END generated_summary_faq
+
 author: 
     - Diego Russo
     - Leandro Nunes
@@ -69,3 +112,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md b/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
index 8665eaa486..6b5a3c9506 100644
--- a/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
@@ -20,6 +20,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 004436cf14ff0056136889c58d676295c6dd2088f06f57f397a07ec9004e6b44
+  summary: >-
+    Compile and install custom Linux kernels on Arm cloud instances using TuxMake with configurations
+    for 64 KB page sizes and Fastpath testing. It is designed for software developers building
+    custom Linux kernels on Arm servers and cloud instances. By the end, you will be able to set
+    up a build environment for compiling Linux kernels on Arm cloud instances, build custom Linux
+    kernels with various configurations using TuxMake, and install and verify custom-built kernels.
+    It focuses on tools and technologies such as TuxMake, Linux environments, Arm platforms including
+    Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The
+    main steps cover Set up your Arm instance for kernel building, Build and install custom Linux
+    kernels, and Build kernels for Fastpath validation.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will set up a build environment for compiling Linux kernels on Arm cloud instances,
+      build custom Linux kernels with various configurations using TuxMake, and install and verify
+      custom-built kernels. Compile and install custom Linux kernels on Arm cloud instances using
+      TuxMake with configurations for 64 KB page sizes and Fastpath testing.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers building custom Linux kernels on Arm servers
+      and cloud instances.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm cloud instance with at least
+      24 vCPUs and 200 GB of free storage running Ubuntu 24.04 LTS; Understanding of kernel images
+      and modules; Familiarity with GRUB bootloader and initramfs.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including TuxMake, Linux environments, Arm platforms such
+      as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Set up your Arm instance for kernel building, Build
+      and install custom Linux kernels, and Build kernels for Fastpath validation.
+# END generated_summary_faq
+
 author: Geremy Cohen
 
 ### Tags
@@ -57,3 +99,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md b/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
index 3adc059fca..79c31167db 100644
--- a/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
@@ -18,6 +18,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 43e4e28b88b9721ca94457418f3b4887d0099017578a54da1db5fcdc11f4a122
+  summary: >-
+    Identify and migrate container images in a Kubernetes cluster to Arm-compatible versions using
+    KubeArchInspect reports. It is designed for software developers who want to ensure containers
+    running in a Kubernetes cluster support the Arm architecture. By the end, you will be able
+    to run KubeArchInspect to generate a report on the containers running in a Kubernetes cluster,
+    discover which images support the Arm architecture, and understand common reasons for an image
+    not supporting Arm. It focuses on tools and technologies such as Kubernetes and Runbook, Linux
+    environments, Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft
+    Azure, Google Cloud, and Oracle. The main steps cover Install KubeArchInspect, Run KubeArchInspect,
+    and Analyze the results.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will run KubeArchInspect to generate a report on the containers running in a Kubernetes
+      cluster, discover which images support the Arm architecture, and understand common reasons
+      for an image not supporting Arm. Identify and migrate container images in a Kubernetes cluster
+      to Arm-compatible versions using KubeArchInspect reports.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to ensure containers running
+      in a Kubernetes cluster support the Arm architecture.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A running Kubernetes cluster accessible
+      with `kubectl`.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Kubernetes and Runbook, Linux environments, Arm
+      platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud,
+      and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Install KubeArchInspect, Run KubeArchInspect, and
+      Analyze the results.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
@@ -63,3 +105,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md b/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
index c3937b1e2a..18cb6d0af4 100644
--- a/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
@@ -16,6 +16,43 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 22fa96e29143f2e0dc0c204919422914b3f70d63ddf833cf1067fbf67b525aa0
+  summary: >-
+    Deploy AWS Lambda functions on Graviton processors using Terraform for Python and Node.js
+    runtimes. It is designed for software developers who want to learn how to deploy Lambda functions
+    on AWS Graviton processors. By the end, you will be able to deploy Lambda functions on Graviton
+    processors using Terraform. It focuses on tools and technologies such as Terraform and AWS
+    Lambda, Linux environments, Arm platforms including Neoverse, and cloud platforms such as
+    AWS. The main steps cover Deploy Node.js Lambda functions on Graviton processors with Terraform
+    and Deploy Python Lambda functions on Graviton processors with Terraform.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will deploy Lambda functions on Graviton processors using Terraform. Deploy AWS Lambda
+      functions on Graviton processors using Terraform for Python and Node.js runtimes.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to learn how to deploy Lambda
+      functions on AWS Graviton processors.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A computer with [Terraform](/install-guides/terraform/)
+      and the [AWS CLI](/install-guides/aws-cli/) installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Terraform and AWS Lambda, Linux environments, Arm
+      platforms such as Neoverse, and cloud platforms such as AWS.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Deploy Node.js Lambda functions on Graviton processors
+      with Terraform and Deploy Python Lambda functions on Graviton processors with Terraform.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
@@ -55,3 +92,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md b/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
index 8ed9856b80..215d8078e6 100644
--- a/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
@@ -17,6 +17,45 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 66d13edff1371295f0e88a618e924ca67b85bcc8aa72034d1e582a0b267f0d05
+  summary: >-
+    Enable and measure libhugetlbfs performance improvements for MySQL and other workloads on
+    Arm Linux servers. It is designed for engineers looking for ways to increase performance on
+    Arm servers. By the end, you will be able to enable libhugetlbfs on an Arm server running
+    Linux and evaluate performance improvements for workloads such as MySQL. It focuses on tools
+    and technologies such as MySQL, GCC, and Runbook, Linux environments, Arm platforms including
+    Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The
+    main steps cover Enable libhugetlbfs and Enable libhugetlbfs on MySQL.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will enable libhugetlbfs on an Arm server running Linux and evaluate performance improvements
+      for workloads such as MySQL. Enable and measure libhugetlbfs performance improvements for
+      MySQL and other workloads on Arm Linux servers.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for engineers looking for ways to increase performance on Arm
+      servers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm server or virtual machine instance
+      from a cloud service provider with Ubuntu installed; Knowledge of how to build a MySQL server
+      and run the sysbench benchmark test.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including MySQL, GCC, and Runbook, Linux environments, Arm
+      platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud,
+      and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Enable libhugetlbfs and Enable libhugetlbfs on MySQL.
+# END generated_summary_faq
+
 author: Bolt Liu
 
 skilllevels: Advanced
@@ -56,3 +95,4 @@ weight: 1
 layout: learningpathall
 learning_path_main_page: 'yes'
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md b/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
index 9fe7bb869f..ef11793f78 100644
--- a/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
@@ -16,6 +16,47 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: d82aa4faeb599a3a1ac12d477204ebe0a4ddb99564bedced86ca0fc4851e17b9
+  summary: >-
+    Serve the llama.cpp chatbot through an OpenAI-compatible API, enabling existing OpenAI-style
+    clients and applications to run against a persistent Arm-hosted LLM. It is designed for developers
+    interested in running LLMs on Arm-based servers. By the end, you will be able to download
+    and build llama.cpp on your Arm server, download a pre-quantized Llama 3.1 model from Hugging
+    Face, and run the pre-quantized model on your Arm CPU and measure the performance. It focuses
+    on tools and technologies such as LLM, Generative AI, Python, Demo, and Hugging Face, Linux
+    environments, Arm platforms including Neoverse, and cloud platforms such as AWS. The main
+    steps cover Run a Large Language model (LLM) chatbot on Arm servers and Access the chatbot
+    using the OpenAI-compatible API.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will download and build llama.cpp on your Arm server, download a pre-quantized Llama
+      3.1 model from Hugging Face, and run the pre-quantized model on your Arm CPU and measure
+      the performance. Serve the llama.cpp chatbot through an OpenAI-compatible API, enabling
+      existing OpenAI-style clients and applications to run against a persistent Arm-hosted LLM.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers interested in running LLMs on Arm-based servers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An AWS Graviton4 r8g.16xlarge instance
+      to test Arm performance optimizations, or any [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/)
+      from a cloud service provider or an on-premise Arm server.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including LLM, Generative AI, Python, Demo, and Hugging Face,
+      Linux environments, Arm platforms such as Neoverse, and cloud platforms such as AWS.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Run a Large Language model (LLM) chatbot on Arm servers
+      and Access the chatbot using the OpenAI-compatible API.
+# END generated_summary_faq
+
 author:
     - Pareena Verma
     - Jason Andrews
@@ -63,3 +104,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md b/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
index 70de57361e..9673106b81 100644
--- a/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
@@ -21,6 +21,55 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 3e8307a5daf435c21f3cecff635ac51724a63ff9ea4307082d869601563b4204
+  summary: >-
+    Build a production-ready vision chatbot on Google Axion using Streamlit, PyTorch, and Hugging
+    Face Transformers with a quantized Llama 3.2-Vision model. It is designed for software developers
+    and ML engineers who are interested in deploying a production-ready vision chatbot for their
+    application with optimized performance on the Arm Architecture. By the end, you will be able
+    to build a frontend with Streamlit to input images and prompts, build the backend to download
+    a Llama 3.2-Vision model, quantize it, and run it using PyTorch and Hugging Face Transformers,
+    and monitor and analyze inference on Arm CPUs. It focuses on tools and technologies such as
+    Python, PyTorch, Streamlit, and Google Axion, Linux environments, Arm platforms including
+    Neoverse, and cloud platforms such as Google Cloud. The main steps cover Set up an LLM based-Vision
+    Chatbot, Deploy Vision Chatbot LLM backend server, Deploy Vision Chatbot LLM frontend server,
+    and Inference with Vision Chatbot.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build a frontend with Streamlit to input images and prompts, build the backend
+      to download a Llama 3.2-Vision model, quantize it, and run it using PyTorch and Hugging
+      Face Transformers, and monitor and analyze inference on Arm CPUs. Build a production-ready
+      vision chatbot on Google Axion using Streamlit, PyTorch, and Hugging Face Transformers with
+      a quantized Llama 3.2-Vision model.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is for software developers and ML engineers who are interested in deploying
+      a production-ready vision chatbot for their application with optimized performance on the
+      Arm Architecture.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Google Cloud Axion compute instance
+      or [any Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud
+      service provider with at least 32 cores.; Familiarity with REST APIs and web services.;
+      A basic understanding of Python and ML concepts.; A basic understanding of Streamlit.; A
+      basic understanding of LLM fundamentals.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Python, PyTorch, Streamlit, and Google Axion, Linux
+      environments, Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Set up an LLM based-Vision Chatbot, Deploy Vision
+      Chatbot LLM backend server, Deploy Vision Chatbot LLM frontend server, and Inference with
+      Vision Chatbot.
+# END generated_summary_faq
+
 author: Nobel Chowdary Mandepudi
 
 ### Tags
@@ -60,3 +109,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
index 4e28ca2a90..5739c9d5c3 100644
--- a/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
@@ -22,6 +22,53 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 36bf3c45f0b38350714ba41ea88c1551b33f6d299a65b3b0d6668fee2d88d835
+  summary: >-
+    Optimize llama.cpp on Arm CPUs by integrating Streamline Annotations to profile Prefill and
+    Decode stages, analyze operators, and evaluate multi-core execution. It is designed for software
+    developers, performance engineers, and AI practitioners who want to optimize llama.cpp performance
+    on Arm-based CPUs. By the end, you will be able to profile llama.cpp architecture and identify
+    the role of the Prefill and Decode stages, integrate Streamline Annotations into llama.cpp
+    for fine-grained performance insights, and capture and interpret profiling data with Streamline.
+    It focuses on tools and technologies such as Arm Streamline, CPP, llama.cpp, and Profiling,
+    Linux and Android environments, and Arm platforms including Cortex-A and Neoverse. The main
+    steps cover Overview, Explore llama.cpp architecture and the inference workflow, Integrate
+    Streamline Annotations into llama.cpp, Analyze token generation performance with Streamline
+    profiling, and Implement operator-level performance analysis with Annotation Channels.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will profile llama.cpp architecture and identify the role of the Prefill and Decode
+      stages, integrate Streamline Annotations into llama.cpp for fine-grained performance insights,
+      and capture and interpret profiling data with Streamline. Optimize llama.cpp on Arm CPUs
+      by integrating Streamline Annotations to profile Prefill and Decode stages, analyze operators,
+      and evaluate multi-core execution.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers, performance engineers, and AI practitioners
+      who want to optimize llama.cpp performance on Arm-based CPUs.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Basic understanding of llama.cpp; Understanding
+      of transformer models; Knowledge of Arm Streamline usage; An Arm Neoverse or Cortex-A hardware
+      platform running Linux or Android.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Arm Streamline, CPP, llama.cpp, and Profiling, Linux
+      and Android environments, and Arm platforms such as Cortex-A and Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview, Explore llama.cpp architecture and the inference
+      workflow, Integrate Streamline Annotations into llama.cpp, Analyze token generation performance
+      with Streamline profiling, and Implement operator-level performance analysis with Annotation
+      Channels.
+# END generated_summary_faq
+
 author: 
     - Zenon Zhilong Xiu
     - Odin Shen
@@ -69,3 +116,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/lse/_index.md b/content/learning-paths/servers-and-cloud-computing/lse/_index.md
index 2ba1f4db38..3a7be38cd2 100644
--- a/content/learning-paths/servers-and-cloud-computing/lse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/lse/_index.md
@@ -16,6 +16,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 9610291239b88c67e21055f94cd03fe7cf80f7d875d53ebe0d8de7837ce99fa7
+  summary: >-
+    Understand Large System Extensions (LSE) for Arm processors and verify whether applications
+    use LSE for improved atomic operation performance. It is designed for software developers
+    who want to learn about Large System Extensions and use them in an application. By the end,
+    you will be able to learn about Large System Extensions and find out if an application uses
+    Large System Extensions. It focuses on tools and technologies such as GCC and Runbook, Linux
+    environments, Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft
+    Azure, Google Cloud, and Oracle. The main steps cover Introduction to Large System Extensions
+    and Large System Extensions (LSE) Example.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will learn about Large System Extensions and find out if an application uses Large System
+      Extensions. Understand Large System Extensions (LSE) for Arm processors and verify whether
+      applications use LSE for improved atomic operation performance.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to learn about Large System
+      Extensions and use them in an application.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An [AWS account](/learning-paths/servers-and-cloud-computing/csp/aws/)
+      to access instance types with different AWS Graviton processors. If you don't have an AWS
+      account, you can substitute other Arm Linux computers.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including GCC and Runbook, Linux environments, Arm platforms
+      such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Introduction to Large System Extensions and Large
+      System Extensions (LSE) Example.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
@@ -57,3 +97,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md b/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
index b9061dd401..7eb79e111a 100644
--- a/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
@@ -19,6 +19,52 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: db4ce1c59ad10bd127b4990c82ce876311d7dc7e1ad5d39ec132daf82ceb8b79
+  summary: >-
+    Deploy MariaDB on Arm cloud instances across AWS, Azure, and Google Cloud using Docker, Amazon
+    RDS, and automation with Terraform and Ansible. It is designed for software developers who
+    want to deploy MariaDB on Arm servers. By the end, you will be able to deploy MariaDB on virtual
+    machines from different cloud service providers, deploy MariaDB using Docker, and deploy MariaDB
+    using Amazon RDS (Relational Database Service). It focuses on tools and technologies such
+    as Terraform, Ansible, MariaDB, Docker, and Runbook, Linux environments, Arm platforms including
+    Neoverse, and cloud platforms such as AWS, Microsoft Azure, and Google Cloud. The main steps
+    cover Install MariaDB on an AWS Arm based instance, Deploy MariaDB using RDS(AWS), Install
+    MariaDB on an Azure Arm based instance, Install MariaDB on a GCP Arm based instance, and Deploy
+    MariaDB via Docker.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will deploy MariaDB on virtual machines from different cloud service providers, deploy
+      MariaDB using Docker, and deploy MariaDB using Amazon RDS (Relational Database Service).
+      Deploy MariaDB on Arm cloud instances across AWS, Azure, and Google Cloud using Docker,
+      Amazon RDS, and automation with Terraform and Ansible.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to deploy MariaDB on Arm
+      servers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Cloud service provider accounts for
+      each service you want to use including AWS, Azure, and GCP; A local computer with [Docker](/install-guides/docker/),
+      [Terraform](/install-guides/terraform/), [AWS CLI](/install-guides/aws-cli/), [Azure CLI](/install-guides/azure-cli/),
+      [Google Cloud CLI](/install-guides/gcloud/), and [Ansible](/install-guides/ansible/) installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Terraform, Ansible, MariaDB, Docker, and Runbook,
+      Linux environments, Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft
+      Azure, and Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Install MariaDB on an AWS Arm based instance, Deploy
+      MariaDB using RDS(AWS), Install MariaDB on an Azure Arm based instance, Install MariaDB
+      on a GCP Arm based instance, and Deploy MariaDB via Docker.
+# END generated_summary_faq
+
 author: Jason Andrews
 ### Tags
 skilllevels: Introductory
@@ -64,3 +110,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/memcached/_index.md b/content/learning-paths/servers-and-cloud-computing/memcached/_index.md
index 012ffb4766..aeee2061d9 100644
--- a/content/learning-paths/servers-and-cloud-computing/memcached/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memcached/_index.md
@@ -16,6 +16,43 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: b42ca5cc8f4db6ba6019f3720a5ef46119aa403a2baaef5362e874f898ce0419
+  summary: >-
+    Install memcached on Arm cloud servers and benchmark in-memory key-value store performance
+    using open-source tools. It is designed for developers who want to use memcached as their
+    in-memory key-value store. By the end, you will be able to install and run memcached on your
+    Arm-based cloud server and use an open-source benchmark to test memcached performance. It
+    focuses on tools and technologies such as Runbook and Memcached, Linux environments, Arm platforms
+    including Neoverse, and cloud platforms such as AWS and Oracle. The main steps cover Run and
+    test Memcached on Arm servers.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install and run memcached on your Arm-based cloud server and use an open-source
+      benchmark to test memcached performance. Install memcached on Arm cloud servers and benchmark
+      in-memory key-value store performance using open-source tools.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers who want to use memcached as their in-memory
+      key-value store.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm based instance from an appropriate
+      cloud service provider.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Runbook and Memcached, Linux environments, Arm platforms
+      such as Neoverse, and cloud platforms such as AWS and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Run and test Memcached on Arm servers.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 test_images:
@@ -54,3 +91,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md b/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
index 6e2d63a46f..46ab4da8c7 100644
--- a/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
@@ -20,6 +20,56 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 4efe46aaf4580a6b8f263b69e8f072211bfd24fcf7f7a885689c927fc05a4c63
+  summary: >-
+    Deploy Memcached as a cache for MySQL and PostgreSQL on Arm servers. It is designed for developers
+    who want to use memcached as their in-memory key-value store. By the end, you will be able
+    to deploy memcached as a cache for MySQL on AWS, Azure and GCP Arm based Instance and deploy
+    memcached as a cache for PostgreSQL on AWS, Azure and GCP Arm based Instance. It focuses on
+    tools and technologies such as Memcached, SQL, MySQL, and PostgreSQL, Linux environments,
+    Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft Azure, and Google
+    Cloud. The main steps cover Deploy Memcached as a cache for MySQL on an AWS Arm based Instance,
+    Deploy Memcached as a cache for MySQL on an Azure Arm based Instance, Deploy Memcached as
+    a cache for MySQL on a Google Cloud Arm based Instance, Deploy Memcached as a cache for Postgres
+    on an AWS Arm based Instance, and Deploy Memcached as a cache for Postgres on an Azure Arm
+    based Instance.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will deploy memcached as a cache for MySQL on AWS, Azure and GCP Arm based Instance
+      and deploy memcached as a cache for PostgreSQL on AWS, Azure and GCP Arm based Instance.
+      Deploy Memcached as a cache for MySQL and PostgreSQL on Arm servers.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for developers who want to use memcached as their in-memory key-value
+      store.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Amazon Web Services (AWS) [account](https://aws.amazon.com/);
+      An Azure portal [account](https://azure.microsoft.com/en-in/get-started/azure-portal); A
+      Google Cloud [account](https://console.cloud.google.com/); A machine with [Terraform](/install-guides/terraform/),
+      [AWS CLI](/install-guides/aws-cli), [Google Cloud CLI](/install-guides/gcloud), [Azure CLI](/install-guides/azure-cli),
+      [AWS IAM authenticator](https://docs.aws.amazon.com/eks/latest/userguide/install-aws-iam-authenticator.html),
+      and [Ansible](/install-guides/ansible/) installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Memcached, SQL, MySQL, and PostgreSQL, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, and Google
+      Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Deploy Memcached as a cache for MySQL on an AWS Arm
+      based Instance, Deploy Memcached as a cache for MySQL on an Azure Arm based Instance, Deploy
+      Memcached as a cache for MySQL on a Google Cloud Arm based Instance, Deploy Memcached as
+      a cache for Postgres on an AWS Arm based Instance, and Deploy Memcached as a cache for Postgres
+      on an Azure Arm based Instance.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 
@@ -58,3 +108,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md b/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
index 14280f5e1b..ece9430094 100644
--- a/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
@@ -21,6 +21,56 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: d61c9ddcef1c7ffe12b3ee818852fb3f0a62a90cec451692c1186cf2c01de625
+  summary: >-
+    Use ASCT to measure cache latency, streaming bandwidth, and coherency latency on Arm Neoverse
+    systems, and compare results across Graviton generations. It is designed for software developers
+    and performance engineers who want to understand and characterize the CPU-side memory subsystem
+    of Arm Linux systems. By the end, you will be able to identify the core topology, cluster
+    layout, and cache hierarchy of an Arm Linux system using standard tools, measure cache and
+    memory latency using a pointer-chase benchmark, and measure single-core and multi-core streaming
+    bandwidth at each level of the memory hierarchy. It focuses on tools and technologies such
+    as ASCT and Perf, Linux environments, and Arm platforms including Neoverse. The main steps
+    cover Identify Arm CPU topology, cache hierarchy, and NUMA configuration, Analyze Arm cache
+    hierarchy and performance characteristics, Measure Arm cache and memory latency using ASCT
+    pointer chase, Measure Arm single-core memory bandwidth with ASCT, and Measure Arm multi-core
+    memory bandwidth and loaded latency with ASCT.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will identify the core topology, cluster layout, and cache hierarchy of an Arm Linux
+      system using standard tools, measure cache and memory latency using a pointer-chase benchmark,
+      and measure single-core and multi-core streaming bandwidth at each level of the memory hierarchy.
+      Use ASCT to measure cache latency, streaming bandwidth, and coherency latency on Arm Neoverse
+      systems, and compare results across Graviton generations.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers and performance engineers who want to
+      understand and characterize the CPU-side memory subsystem of Arm Linux systems.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Two or more Arm Linux systems with root
+      or sudo access. The examples use AWS Graviton2 and Graviton4 instances, but other systems
+      are possible; Arm System Characterization Tool (ASCT) installed on each system; A good understanding
+      of CPU memory subsystems, including cache hierarchies, cache lines, and DRAM in the memory
+      hierarchy.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including ASCT and Perf, Linux environments, and Arm platforms
+      such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Identify Arm CPU topology, cache hierarchy, and NUMA
+      configuration, Analyze Arm cache hierarchy and performance characteristics, Measure Arm
+      cache and memory latency using ASCT pointer chase, Measure Arm single-core memory bandwidth
+      with ASCT, and Measure Arm multi-core memory bandwidth and loaded latency with ASCT.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 skilllevels: Advanced
@@ -57,3 +107,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md b/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
index 2a62ac2cac..1352cb2e43 100644
--- a/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
@@ -21,6 +21,52 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 6aa61de638be961339d958345225d696ff2b83b8f9cab22bf956f9bf2d15c1aa
+  summary: >-
+    Test and validate thread synchronization approaches in the Arm memory model using Herd7, Litmus7,
+    and Arm hardware with assembly snippets. It is designed for developers seeking practical ways
+    to test thread synchronization approaches in the Arm memory model. By the end, you will be
+    able to test thread synchronization assembly snippets against the formal definition of the
+    Arm memory model, test thread synchronization assembly snippets on Arm hardware, and compare
+    the results of different thread synchronization approaches. It focuses on tools and technologies
+    such as Runbook, Herd7, Litmus7, and Arm ISA, Linux environments, and Arm platforms including
+    Neoverse. The main steps cover Thread Synchronization, Arm Memory Model, and Tools, Herd7
+    and Litmus7 Test Primer, Thread Synchronization Examples, and Additional Resources.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will test thread synchronization assembly snippets against the formal definition of
+      the Arm memory model, test thread synchronization assembly snippets on Arm hardware, and
+      compare the results of different thread synchronization approaches. Test and validate thread
+      synchronization approaches in the Arm memory model using Herd7, Litmus7, and Arm hardware
+      with assembly snippets.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for developers seeking practical ways to test thread synchronization
+      approaches in the Arm memory model.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An understanding of memory consistency
+      models (such as Sequential Consistency, Weak Ordering, Relaxed Consistency, and Processor
+      Consistency).; An understanding of thread synchronization.; Familiarity with Arm assembly
+      language, and the ability to find relevant information on Arm assembly instructions.; Familiarity
+      with general-purpose registers.; Familiarity with memory barriers, including Acquire-Release
+      Semantics.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Runbook, Herd7, Litmus7, and Arm ISA, Linux environments,
+      and Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Thread Synchronization, Arm Memory Model, and Tools,
+      Herd7 and Litmus7 Test Primer, Thread Synchronization Examples, and Additional Resources.
+# END generated_summary_faq
+
 author: Julio Suarez
 
 skilllevels: Advanced
@@ -57,3 +103,4 @@ weight: 1
 layout: learningpathall
 learning_path_main_page: 'yes'
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md b/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
index de38d50403..0e59850a39 100644
--- a/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
@@ -16,6 +16,51 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: a67d36fb650b77c170c15f193049490286a3f097801d0c79d701d3f5610fe1dc
+  summary: >-
+    Microbenchmark and tune network performance with iPerf3 and Linux traffic control walks you
+    through an end-to-end Arm software workflow. It is designed for performance engineers, Linux
+    system administrators, and application developers who want to microbenchmark, simulate, or
+    tune the networking performance of distributed systems. By the end, you will be able to run
+    accurate network microbenchmark tests using iPerf3, simulate real-world network conditions
+    using Linux Traffic Control (tc), and tune basic Linux kernel parameters to improve network
+    performance. It focuses on tools and technologies such as iPerf3, Linux environments, Arm
+    platforms including Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud,
+    and Oracle. The main steps cover Set up Arm-based Linux systems for network performance testing
+    with iPerf3, Microbenchmark the network connection, Simulate different network conditions,
+    and Tune kernel parameters.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will run accurate network microbenchmark tests using iPerf3, simulate real-world network
+      conditions using Linux Traffic Control (tc), and tune basic Linux kernel parameters to improve
+      network performance.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for performance engineers, Linux system administrators, and
+      application developers who want to microbenchmark, simulate, or tune the networking performance
+      of distributed systems.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Basic understanding of networking principles
+      such as Transmission Control Protocol/Internet Protocol (TCP/IP) and User Datagram Protocol
+      (UDP).; Access to two [Arm-based cloud instances](/learning-paths/servers-and-cloud-computing/csp/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including iPerf3, Linux environments, Arm platforms such as
+      Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Set up Arm-based Linux systems for network performance
+      testing with iPerf3, Microbenchmark the network connection, Simulate different network conditions,
+      and Tune kernel parameters.
+# END generated_summary_faq
+
 author: Kieran Hejmadi
 
 ### Tags
@@ -45,3 +90,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md b/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
index b0046f7d30..e036b9c491 100644
--- a/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
@@ -18,6 +18,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 56a38d1d742dd301d48e9ad61ff00e07048559f3f646815e22937113243adcdb
+  summary: >-
+    Scan source code for architecture-specific portability issues using migrate-ease to identify
+    and resolve AArch64 porting challenges before migration. It is designed for developers looking
+    to migrate applications to Arm-based servers using migrate-ease, a code analysis tool that
+    scans source code repositories to identify architecture-specific porting issues before migration.
+    By the end, you will be able to identify architecture-specific dependencies in your application's
+    source code, recognize common migration challenges and how to resolve them, and use migrate-ease
+    to detect and address AArch64 portability issues. It focuses on tools and technologies such
+    as Neon, SVE, Go, and Runbook, Linux environments, and Arm platforms including Neoverse. The
+    main steps cover Assessing your code for migration to Arm, Migrate-ease and supported programming
+    languages, Getting started with migrate-ease, and Try it out.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will identify architecture-specific dependencies in your application's source code,
+      recognize common migration challenges and how to resolve them, and use migrate-ease to detect
+      and address AArch64 portability issues. Scan source code for architecture-specific portability
+      issues using migrate-ease to identify and resolve AArch64 porting challenges before migration.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers looking to migrate applications to Arm-based
+      servers using migrate-ease, a code analysis tool that scans source code repositories to
+      identify architecture-specific porting issues before migration.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Access to an [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/)
+      for testing and validation.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Neon, SVE, Go, and Runbook, Linux environments,
+      and Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Assessing your code for migration to Arm, Migrate-ease
+      and supported programming languages, Getting started with migrate-ease, and Try it out.
+# END generated_summary_faq
+
 author: 
     - Odin Shen
     - Jun He
@@ -56,3 +99,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/migration/_index.md b/content/learning-paths/servers-and-cloud-computing/migration/_index.md
index b450d4c2fc..d600877f15 100644
--- a/content/learning-paths/servers-and-cloud-computing/migration/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/migration/_index.md
@@ -18,6 +18,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 6b2b985c39579c4d0855c8c28b610ba558e25b451a6b755475ff4393e2810670
+  summary: >-
+    Set up an Arm development environment, analyze dependencies, and understand common challenges
+    and scenarios for migrating applications to Arm servers. It is designed for software developers
+    looking to migrate applications to Arm servers. By the end, you will be able to set up an
+    Arm development machine, analyze application dependencies, and learn challenges and tips for
+    application migration. It focuses on tools and technologies such as Neon, SVE, Go, and Runbook,
+    Linux environments, Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft
+    Azure, Google Cloud, and Oracle. The main steps cover Migrating applications to Arm servers,
+    Migrating C/C++ applications, Migrating Java applications, Migrating Go applications, and
+    List of software products supporting Arm.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will set up an Arm development machine, analyze application dependencies, and learn
+      challenges and tips for application migration. Set up an Arm development environment, analyze
+      dependencies, and understand common challenges and scenarios for migrating applications
+      to Arm servers.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers looking to migrate applications to
+      Arm servers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/)
+      from a cloud service provider.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Neon, SVE, Go, and Runbook, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
+      Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Migrating applications to Arm servers, Migrating C/C++
+      applications, Migrating Java applications, Migrating Go applications, and List of software
+      products supporting Arm.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
@@ -67,3 +110,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
index 3bf52a4e73..b4a4577c6a 100644
--- a/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
@@ -18,6 +18,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 2eb252b19b7535fbb776a3153bfc9f70b6217088e5b4b55deb56d01393abaf3b
+  summary: >-
+    Build a Retrieval-Augmented Generation (RAG) application on Arm servers using Zilliz Cloud
+    for vector search and llama.cpp for LLM inference. It is designed for software developers
+    who want to create a Retrieval-Augmented Generation (RAG) application on Arm servers. By the
+    end, you will be able to create a simple RAG application using Zilliz Cloud and launch an
+    LLM service on Arm servers. It focuses on tools and technologies such as Python, Generative
+    AI, RAG, and Hugging Face, Linux environments, Arm platforms including Neoverse, and cloud
+    platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps cover Overview
+    and Install dependencies, Offline Data Loading, Launch the LLM Server, and Online RAG.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create a simple RAG application using Zilliz Cloud and launch an LLM service on
+      Arm servers. Build a Retrieval-Augmented Generation (RAG) application on Arm servers using
+      Zilliz Cloud for vector search and llama.cpp for LLM inference.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to create a Retrieval-Augmented
+      Generation (RAG) application on Arm servers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A basic understanding of a RAG pipeline.;
+      An AWS Graviton3 C7g.2xlarge instance, or any [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp)
+      from a cloud service provider or an on-premise Arm server.; A [Zilliz account](https://zilliz.com/cloud?utm_source=partner&utm_medium=referral&utm_campaign=2024-10-24_web_arm-dev-hub-data-loading_arm),
+      which you can sign up for with a free trial.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Python, Generative AI, RAG, and Hugging Face, Linux
+      environments, Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft
+      Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview and Install dependencies, Offline Data Loading,
+      Launch the LLM Server, and Online RAG.
+# END generated_summary_faq
+
 author: Chen Zhang
 
 ### Tags
@@ -62,3 +104,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
index 83f23fba17..f9c2553012 100644
--- a/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
@@ -20,6 +20,53 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 6c438573edec90b3aa4135ace38cd3ae604c58658c1949117ca80dbdd21394bd
+  summary: >-
+    Learn how to deploy and configure MinIO on an Azure Cobalt 100 virtual machine, benchmark
+    object storage throughput, and validate S3 compatibility using the boto3 Python SDK. It is
+    designed for developers, DevOps engineers, and platform engineers who want to deploy MinIO
+    object storage on Microsoft Azure Cobalt 100 virtual machines. By the end, you will be able
+    to provision an Azure Cobalt 100 virtual machine and deploy MinIO, benchmark MinIO storage
+    throughput for large object transfers, and validate S3 API compatibility using the boto3 Python
+    SDK. It focuses on tools and technologies such as MinIO, Python, and boto3, Linux environments,
+    Arm platforms including Neoverse, and cloud platforms such as Microsoft Azure. The main steps
+    cover Overview of Azure Cobalt 100 and MinIO, Create an Azure Cobalt 100 virtual machine,
+    Open MinIO ports in the Azure Network Security Group, Install and configure MinIO on Azure
+    Cobalt 100, and Benchmark MinIO storage performance on Azure Cobalt 100.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will provision an Azure Cobalt 100 virtual machine and deploy MinIO, benchmark MinIO
+      storage throughput for large object transfers, and validate S3 API compatibility using the
+      boto3 Python SDK. Learn how to deploy and configure MinIO on an Azure Cobalt 100 virtual
+      machine, benchmark object storage throughput, and validate S3 compatibility using the boto3
+      Python SDK.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers, DevOps engineers, and platform engineers who
+      want to deploy MinIO object storage on Microsoft Azure Cobalt 100 virtual machines.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Microsoft Azure account](https://azure.microsoft.com/)
+      with access to Cobalt 100-based instances (Dpsv6); Familiarity with SSH and remote server
+      access; Basic understanding of cloud storage concepts.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including MinIO, Python, and boto3, Linux environments, Arm
+      platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview of Azure Cobalt 100 and MinIO, Create an
+      Azure Cobalt 100 virtual machine, Open MinIO ports in the Azure Network Security Group,
+      Install and configure MinIO on Azure Cobalt 100, and Benchmark MinIO storage performance
+      on Azure Cobalt 100.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
@@ -63,3 +110,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md b/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
index cd163c3473..4ad049ea78 100644
--- a/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
@@ -17,6 +17,45 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 973ba6c1e00fc67e1702606412124d8172e071b9b677a1df53c3be66210bf704
+  summary: >-
+    Benchmark machine learning inference performance on Arm servers using TensorFlow and the MLPerf
+    Inference benchmark suite from MLCommons. It is designed for software developers interested
+    in benchmarking machine learning workloads on Arm servers. By the end, you will be able to
+    install and run TensorFlow on your Arm-based cloud server and use MLPerf Inference benchmark
+    suite, an open-sourced benchmark from MLCommons to test ML performance on your Arm server.
+    It focuses on tools and technologies such as TensorFlow and Runbook, Linux environments, Arm
+    platforms including Neoverse, and cloud platforms such as AWS and Oracle. The main steps cover
+    Measure ML Inference Performance on Arm servers.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install and run TensorFlow on your Arm-based cloud server and use MLPerf Inference
+      benchmark suite, an open-sourced benchmark from MLCommons to test ML performance on your
+      Arm server. Benchmark machine learning inference performance on Arm servers using TensorFlow
+      and the MLPerf Inference benchmark suite from MLCommons.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers interested in benchmarking machine
+      learning workloads on Arm servers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/)
+      from an appropriate cloud service provider or an on-premise Arm server.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including TensorFlow and Runbook, Linux environments, Arm
+      platforms such as Neoverse, and cloud platforms such as AWS and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Measure ML Inference Performance on Arm servers.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 test_images:
@@ -59,3 +98,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
index 2c9f76afe4..40825f4eaa 100644
--- a/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
@@ -18,6 +18,50 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: f270cfc90245c0090685fabf5cbebf62a714edbf34e1ea86c3df0bfbb4699ea4
+  summary: >-
+    Deploy MongoDB on Azure Cobalt 100 Arm virtual machines and benchmark database performance
+    using mongotop and mongostat monitoring tools. It is designed for software developers who
+    want to migrate MongoDB workloads to Arm-based platforms, with a focus on Microsoft Azure
+    Cobalt 100 Arm64 instances. By the end, you will be able to provision an Arm64-based Cobalt
+    100 virtual machine in Azure using Ubuntu Pro 24.04 LTS, deploy MongoDB on the Cobalt 100
+    instance, and run baseline tests and performance benchmarks on MongoDB in the Arm64 environment.
+    It focuses on tools and technologies such as MongoDB, mongotop, and mongostat, Linux environments,
+    Arm platforms including Neoverse, and cloud platforms such as Microsoft Azure. The main steps
+    cover What are Cobalt 100 and MongoDB?, Create an Arm-based cloud virtual machine using Cobalt
+    100, Install MongoDB and Mongosh, MongoDB Baseline Testing, and Monitor MongoDB with mongotop.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will provision an Arm64-based Cobalt 100 virtual machine in Azure using Ubuntu Pro 24.04
+      LTS, deploy MongoDB on the Cobalt 100 instance, and run baseline tests and performance benchmarks
+      on MongoDB in the Arm64 environment. Deploy MongoDB on Azure Cobalt 100 Arm virtual machines
+      and benchmark database performance using mongotop and mongostat monitoring tools.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to migrate MongoDB workloads
+      to Arm-based platforms, with a focus on Microsoft Azure Cobalt 100 Arm64 instances.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Microsoft Azure](https://azure.microsoft.com/)
+      account with access to Cobalt 100 (Dpsv6) instances; Familiarity with the [MongoDB architecture](https://www.mongodb.com/)
+      and deployment practices on Arm64 platforms.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including MongoDB, mongotop, and mongostat, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around What are Cobalt 100 and MongoDB?, Create an Arm-based
+      cloud virtual machine using Cobalt 100, Install MongoDB and Mongosh, MongoDB Baseline Testing,
+      and Monitor MongoDB with mongotop.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
@@ -58,3 +102,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
index be5b20e7b6..c9a72c8533 100644
--- a/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
@@ -17,6 +17,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: abbdde22271151fb1485c54bafe463e7797a0b913c7d4c99245d2914a3f9bb82
+  summary: >-
+    Deploy MongoDB on Google Cloud Axion C4A virtual machines and benchmark database performance
+    with Yahoo Cloud Serving Benchmark (YCSB). It is designed for This introductory topic is for
+    software developers who want to migrate MongoDB workloads from x86_64 to Arm-based platforms,
+    specifically on Google Axion-based C4A virtual machines. By the end, you will be able to create
+    an Arm virtual machine on Google Cloud (C4A Axion family), install and run MongoDB on the
+    Arm-based C4A instance, and benchmark MongoDB performance with Yahoo Cloud Serving Benchmark
+    (YCSB). It focuses on tools and technologies such as MongoDB and YCSB, Linux environments,
+    Arm platforms including Neoverse, and cloud platforms such as Google Cloud. The main steps
+    cover About Google Axion C4A series and MongoDB, Create Google Axion instance, Install MongoDB,
+    Baseline Testing, and MongoDB Benchmarking.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create an Arm virtual machine on Google Cloud (C4A Axion family), install and run
+      MongoDB on the Arm-based C4A instance, and benchmark MongoDB performance with Yahoo Cloud
+      Serving Benchmark (YCSB). Deploy MongoDB on Google Cloud Axion C4A virtual machines and
+      benchmark database performance with Yahoo Cloud Serving Benchmark (YCSB).
+  - question: Who is this Learning Path for?
+    answer: >-
+      This introductory topic is for software developers who want to migrate MongoDB workloads
+      from x86_64 to Arm-based platforms, specifically on Google Axion-based C4A virtual machines.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free?utm_source=google&hl=en)
+      account with billing enabled.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including MongoDB and YCSB, Linux environments, Arm platforms
+      such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around About Google Axion C4A series and MongoDB, Create
+      Google Axion instance, Install MongoDB, Baseline Testing, and MongoDB Benchmarking.
+# END generated_summary_faq
+
 author: Annie Tallund
 
 ##### Tags
@@ -57,3 +99,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # Indicates this should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md b/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
index 92fc2e608e..17edbf52c8 100644
--- a/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
@@ -3,6 +3,52 @@ title: Analyze the performance of MongoDB on Arm servers
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: b52a1b60a74e19f2a3b60b8a77199ad5369c1ccd3b4d5ce0252a169d9f6123fd
+  summary: >-
+    Install MongoDB on Arm servers and benchmark database performance using Yahoo Cloud Serving
+    Benchmark (YCSB) to compare against other architectures. It is designed for software developers
+    who want to learn how to deploy and measure MongoDB performance on Arm servers. By the end,
+    you will be able to install and run MongoDB on an Arm server, test MongoDB performance using
+    open-source tooling, and measure and compare the performance of MongoDB on Arm versus other
+    architectures with Yahoo Cloud Serving Benchmark (YCSB). It focuses on tools and technologies
+    such as MongoDB, GCC, and Runbook, Linux environments, Arm platforms including Neoverse, and
+    cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps cover
+    Install MongoDB on Arm, Creating MongoDB test scenarios, Benchmark MongoDB on Arm with Yahoo
+    Cloud Serving Benchmark (YCSB), Three node replica set testing with YCSB, and Alternative
+    performance testing of MongoDB.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install and run MongoDB on an Arm server, test MongoDB performance using open-source
+      tooling, and measure and compare the performance of MongoDB on Arm versus other architectures
+      with Yahoo Cloud Serving Benchmark (YCSB). Install MongoDB on Arm servers and benchmark
+      database performance using Yahoo Cloud Serving Benchmark (YCSB) to compare against other
+      architectures.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to learn how to deploy and
+      measure MongoDB performance on Arm servers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/)
+      from a cloud service provider.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including MongoDB, GCC, and Runbook, Linux environments, Arm
+      platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud,
+      and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Install MongoDB on Arm, Creating MongoDB test scenarios,
+      Benchmark MongoDB on Arm with Yahoo Cloud Serving Benchmark (YCSB), Three node replica set
+      testing with YCSB, and Alternative performance testing of MongoDB.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 minutes_to_complete: 30
@@ -62,3 +108,4 @@ further_reading:
 
 weight: 1
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/mpi/_index.md b/content/learning-paths/servers-and-cloud-computing/mpi/_index.md
index abf75d9c2b..05893a6eec 100644
--- a/content/learning-paths/servers-and-cloud-computing/mpi/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mpi/_index.md
@@ -19,6 +19,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 300794f4010658d945212c74c5257635d620cbb6230cac0de92bf23e4e9e29fe
+  summary: >-
+    Debug, profile, and optimize MPI parallel applications on Arm servers using Linaro Forge,
+    gdb, and Arm Performance Libraries. It is designed for HPC software developers writing MPI
+    applications. By the end, you will be able to debug and fix a parallel application, profile
+    and optimize your code, and use optimized routines for common math operations. It focuses
+    on tools and technologies such as Fortran, GCC, Linaro Forge, gdb, and mpi, Linux environments,
+    Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
+    Cloud, and Oracle. The main steps cover Before you start, Debug your application, and Optimize
+    your code.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will debug and fix a parallel application, profile and optimize your code, and use optimized
+      routines for common math operations. Debug, profile, and optimize MPI parallel applications
+      on Arm servers using Linaro Forge, gdb, and Arm Performance Libraries.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for HPC software developers writing MPI applications.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: General knowledge about distributed
+      parallelism (MPI); Some understanding of C, Python, and Linux commands; An Arm computer
+      running Linux. Cloud instances can be used, refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Fortran, GCC, Linaro Forge, gdb, and mpi, Linux
+      environments, Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft
+      Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Before you start, Debug your application, and Optimize
+      your code.
+# END generated_summary_faq
+
 author: Florent Lebeau
 
 ### Tags
@@ -57,3 +97,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md b/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md
index 80bc56b8b7..162ca66893 100644
--- a/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md
@@ -5,6 +5,48 @@ minutes_to_complete: 20
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: a10683fdd14359e93056dc4ba24581856833765c64915979d0466a06887e0755
+  summary: >-
+    Select and apply accuracy modes for vectorized math functions in Libamath to balance performance
+    and precision for your application. It is designed for developers who want to use the different
+    accuracy modes for vectorized math functions in Libamath, a component of Arm Performance Libraries.
+    By the end, you will be able to describe how accuracy is defined and measured in Libamath,
+    select an appropriate accuracy mode for your application, and use Libamath with different
+    vector accuracy modes in practice. It focuses on tools and technologies such as Arm Performance
+    Libraries, GCC, and Libamath, Linux environments, and Arm platforms including Neoverse. The
+    main steps cover Floating-point representation, Units in the last place (ULP), ULP error and
+    accuracy, Accuracy modes in Libamath, and Arm Performance Libraries example.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will describe how accuracy is defined and measured in Libamath, select an appropriate
+      accuracy mode for your application, and use Libamath with different vector accuracy modes
+      in practice. Select and apply accuracy modes for vectorized math functions in Libamath to
+      balance performance and precision for your application.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers who want to use the different accuracy modes
+      for vectorized math functions in Libamath, a component of Arm Performance Libraries.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm computer running Linux with [Arm
+      Performance Libraries](/install-guides/armpl/) version 25.04 or newer installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Arm Performance Libraries, GCC, and Libamath, Linux
+      environments, and Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Floating-point representation, Units in the last place
+      (ULP), ULP error and accuracy, Accuracy modes in Libamath, and Arm Performance Libraries
+      example.
+# END generated_summary_faq
+
 author: Joana Cruz
 
 who_is_this_for: This is an introductory topic for developers who want to use the different accuracy modes for vectorized math functions in Libamath, a component of Arm Performance Libraries.
@@ -57,3 +99,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md b/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md
index 185de52bb1..2f0a8957ff 100644
--- a/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md
@@ -20,6 +20,52 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: d765afadfe5155f0ecd5bd08373fa12464b2ee5ebb1f8c7831039eb07eba8831
+  summary: >-
+    Build a multi-architecture Kubernetes cluster running nginx on Azure AKS walks you through
+    an end-to-end Arm software workflow. It is designed for developers who want to deploy multi-architecture
+    Kubernetes workloads and compare nginx performance between x86 and Arm-based nodes in Azure
+    Kubernetes Service (AKS) clusters. By the end, you will be able to create a hybrid AKS cluster
+    with both x86 and Arm64 nodes, deploy nginx using multi-architecture container images across
+    different node types, and verify nginx deployment and functionality on each architecture.
+    It focuses on tools and technologies such as nginx, Web Server, Azure, and Kubernetes, Linux
+    environments, Arm platforms including Neoverse, and cloud platforms such as Microsoft Azure.
+    The main steps cover Start your journey with Arm and x86 nginx workloads on a single Kubernetes
+    cluster, Create the AKS cluster, Create the test utility, Deploy nginx on Intel x86, and Deploy
+    nginx on Arm.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create a hybrid AKS cluster with both x86 and Arm64 nodes, deploy nginx using multi-architecture
+      container images across different node types, and verify nginx deployment and functionality
+      on each architecture.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers who want to deploy multi-architecture Kubernetes
+      workloads and compare nginx performance between x86 and Arm-based nodes in Azure Kubernetes
+      Service (AKS) clusters.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An [Azure account](https://azure.microsoft.com/en-us/free/);
+      A local machine with [`jq`](https://jqlang.org/download/), [`curl`](https://curl.se/download.html),
+      [`wrk`](https://github.com/wg/wrk), [Azure CLI](/install-guides/azure-cli/), and [`kubectl`](/install-guides/kubectl/)
+      installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including nginx, Web Server, Azure, and Kubernetes, Linux
+      environments, Arm platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Start your journey with Arm and x86 nginx workloads
+      on a single Kubernetes cluster, Create the AKS cluster, Create the test utility, Deploy
+      nginx on Intel x86, and Deploy nginx on Arm.
+# END generated_summary_faq
+
 author:
     - Geremy Cohen
 
@@ -70,3 +116,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md b/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md
index b14c3b558d..aeb58159df 100644
--- a/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md
@@ -19,6 +19,52 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: cd31c217eaeab6faad263728c446ee188cd9d542904d87ae7bfd5120713dfaa4
+  summary: >-
+    Add Arm nodes to your GKE cluster using a multi-architecture Ollama container image walks
+    you through an end-to-end Arm software workflow. It is designed for developers who want to
+    compare the performance of amd64 and arm64 deployments by running inferences on a hybrid GKE
+    cluster using an Ollama multi-architecture container image. By the end, you will be able to
+    create a hybrid GKE cluster with amd64 and arm64 nodes, deploy Ollama services for amd64 and
+    arm64 architectures using a single multi-architecture container image, and validate deployments
+    by pinging, pulling models, and running inferences to compare architecture performance. It
+    focuses on tools and technologies such as LLM, Ollama, and Generative AI, Linux and macOS
+    environments, Arm platforms including Neoverse, and cloud platforms such as Google Cloud.
+    The main steps cover Create the GKE Cluster, Deploy Ollama amd64 to the cluster, Deploy Ollama
+    arm64 to the cluster, and Test functionality and performance.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create a hybrid GKE cluster with amd64 and arm64 nodes, deploy Ollama services
+      for amd64 and arm64 architectures using a single multi-architecture container image, and
+      validate deployments by pinging, pulling models, and running inferences to compare architecture
+      performance.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is for developers who want to compare the performance of amd64 and arm64
+      deployments by running inferences on a hybrid GKE cluster using an Ollama multi-architecture
+      container image.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud account](https://console.cloud.google.com/).;
+      A local machine with [Google Cloud CLI](/install-guides/gcloud/) and [kubectl](/install-guides/kubectl/)
+      installed.; The [GKE Cloud Plugin](https://cloud.google.com/kubernetes-engine/docs/how-to/cluster-access-for-kubectl#gcloud)
+      installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including LLM, Ollama, and Generative AI, Linux and macOS
+      environments, Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Create the GKE Cluster, Deploy Ollama amd64 to the
+      cluster, Deploy Ollama arm64 to the cluster, and Test functionality and performance.
+# END generated_summary_faq
+
 author:
     - Geremy Cohen
 
@@ -86,3 +132,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md
index 828ee780e0..dc9218db9f 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md
@@ -17,6 +17,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: b9bc1d1f965e4fdeeb9552d853330c201e00597829420c8a0397fa425c3231c4
+  summary: >-
+    Deploy MySQL on Microsoft Azure Cobalt 100 processors walks you through an end-to-end Arm
+    software workflow. It is designed for developers migrating MySQL applications from x86_64
+    to Arm. By the end, you will be able to provision an Azure Arm64 virtual machine using Azure
+    console, with Ubuntu Pro 24.04 LTS as the base image, deploy MySQL on the Ubuntu virtual machine,
+    and perform MySQL baseline testing and benchmarking on Arm64 virtual machines. It focuses
+    on tools and technologies such as MySQL, SQL, and Docker, Linux environments, Arm platforms
+    including Neoverse, and cloud platforms such as Microsoft Azure. The main steps cover Overview,
+    Create an Azure Cobalt 100 Arm64 virtual machine, Deploy MySQL on an Azure Arm64 virtual machine,
+    Validate MySQL functionality on Azure Arm64, and Benchmark MySQL with mysqlslap.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will provision an Azure Arm64 virtual machine using Azure console, with Ubuntu Pro 24.04
+      LTS as the base image, deploy MySQL on the Ubuntu virtual machine, and perform MySQL baseline
+      testing and benchmarking on Arm64 virtual machines.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers migrating MySQL applications from x86_64 to
+      Arm.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Microsoft Azure](https://azure.microsoft.com/)
+      account with access to Cobalt 100 based instances (Dpsv6); Familiarity with relational databases
+      and the basics of [MySQL](https://dev.mysql.com/doc/refman/8.0/en/introduction.html).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including MySQL, SQL, and Docker, Linux environments, Arm
+      platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview, Create an Azure Cobalt 100 Arm64 virtual
+      machine, Deploy MySQL on an Azure Arm64 virtual machine, Validate MySQL functionality on
+      Azure Arm64, and Benchmark MySQL with mysqlslap.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
@@ -60,3 +102,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql/_index.md
index 1278194647..fb4e38e229 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql/_index.md
@@ -15,6 +15,41 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: b9b2ed7f58611c9bc7e1867fe06700f3197eb095ddc62d91c00814021967cf72
+  summary: >-
+    Learn how to deploy MySQL walks you through an end-to-end Arm software workflow. It is designed
+    for software developers who want to deploy MySQL on Arm. By the end, you will be able to learn
+    about the various ways MySQL can be deployed and learn how to interact with a MySQL database
+    using a MySQL client CLI tool. It focuses on tools and technologies such as SQL and MySQL,
+    Linux environments, Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft
+    Azure, Google Cloud, and Oracle. The main steps cover Install, configure and check MySQL.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will learn about the various ways MySQL can be deployed and learn how to interact with
+      a MySQL database using a MySQL client CLI tool.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to deploy MySQL on Arm.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm based instance from a cloud service
+      provider, or an on-premise Arm server.; If you do not have an Arm node, the next section
+      discusses some options.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including SQL and MySQL, Linux environments, Arm platforms
+      such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Install, configure and check MySQL.
+# END generated_summary_faq
+
 author: Jason Andrews
 ### Tags
 skilllevels: Introductory
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md
index 46d7f60e3d..68b505698f 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md
@@ -15,6 +15,45 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: e473c0724855bbbc59481822130f7dc5e1684593527a6b5688939f84cd32963d
+  summary: >-
+    Benchmarking MySQL with Sysbench walks you through an end-to-end Arm software workflow. It
+    is designed for performance engineers who want to benchmark MySQL using Sysbench and optimize
+    performance on Arm Linux systems. By the end, you will be able to run Sysbench to benchmark
+    a MySQL database server and enable profile-guided optimization (PGO) for MySQL and examine
+    the performance improvements. It focuses on tools and technologies such as MySQL and Sysbench,
+    Linux environments, Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft
+    Azure, Google Cloud, and Oracle. The main steps cover Setup, configure, and run MySQL server,
+    Build and run Sysbench, and Enable profile-guided optimization for MySQL.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will run Sysbench to benchmark a MySQL database server and enable profile-guided optimization
+      (PGO) for MySQL and examine the performance improvements.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for performance engineers who want to benchmark MySQL using
+      Sysbench and optimize performance on Arm Linux systems.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Basic knowledge of [MySQL databases](https://www.mysql.com/);
+      Two Arm servers running Ubuntu 22.04, one for the MySQL server and the other for the Sysbench
+      client.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including MySQL and Sysbench, Linux environments, Arm platforms
+      such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Setup, configure, and run MySQL server, Build and
+      run Sysbench, and Enable profile-guided optimization for MySQL.
+# END generated_summary_faq
+
 author: Bolt Liu
 
 skilllevels: Introductory
@@ -58,3 +97,4 @@ weight: 1
 layout: learningpathall
 learning_path_main_page: 'yes'
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md
index 19d481d325..cde9ae8a3a 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md
@@ -13,6 +13,43 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: faa914d636e03296c6b65ba146745c543326955ec457577f047eec51aa6a3733
+  summary: >-
+    Learn how to Tune MySQL walks you through an end-to-end Arm software workflow. It is designed
+    for software developers and DevOps professionals interested in optimizing MySQL performance
+    on Arm-based VMs in the cloud. By the end, you will be able to tune MySQL to increase performance.
+    It focuses on tools and technologies such as SQL, MySQL, InnoDB, and Runbook, Linux environments,
+    Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
+    Cloud, and Oracle. The main steps cover About MySQL performance tuning, System, Kernel, Compiler,
+    and Libraries, and Tuning MySQL.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will tune MySQL to increase performance.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers and DevOps professionals interested in
+      optimizing MySQL performance on Arm-based VMs in the cloud.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Bare-metal or cloud [installation of
+      MySQL](/learning-paths/servers-and-cloud-computing/mysql/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including SQL, MySQL, InnoDB, and Runbook, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
+      Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around About MySQL performance tuning, System, Kernel, Compiler,
+      and Libraries, and Tuning MySQL.
+# END generated_summary_faq
+
 author: Julio Suarez
 
 skilllevels: Advanced
@@ -54,3 +91,4 @@ weight: 1
 layout: learningpathall
 learning_path_main_page: 'yes'
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md b/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md
index 97c135bb9a..16a3034ec7 100644
--- a/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md
@@ -21,6 +21,55 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 65336a8f8d1d11c4b127ea13982a581afffa94cbfd1af10a9e4df2becbc34b5a
+  summary: >-
+    Develop and Validate Firmware Pre-Silicon on Arm Neoverse CSS V3 walks you through an end-to-end
+    Arm software workflow. It is designed for This advanced topic is for firmware developers,
+    system architects, and silicon validation engineers working on Arm Neoverse CSS platforms
+    who require a pre-silicon workflow for the CSS-V3 reference design using Fixed Virtual Platforms
+    (FVPs). By the end, you will be able to explain the CSS-V3 architecture and the RD-V3 firmware
+    boot sequence (TF-A, RSE, SCP/MCP/LCP, UEFI/GRUB, Linux), set up a containerized build environment
+    and sync sources with a pinned manifest using repo, and build and boot the RD-V3 firmware
+    stack on FVP and map UART consoles to components. It focuses on tools and technologies such
+    as C, Docker, and FVP, Linux environments, Arm platforms including Neoverse, and cloud platforms
+    such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps cover Learn about the
+    Arm RD-V3 Platform, Understand the CSS-V3 boot flow and firmware stack, Build the RD-V3 Reference
+    Platform Software Stack, Simulate RD-V3 Boot Flow on Arm FVP, and Simulate Dual Chip RD-V3-R1
+    Platform.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will explain the CSS-V3 architecture and the RD-V3 firmware boot sequence (TF-A, RSE,
+      SCP/MCP/LCP, UEFI/GRUB, Linux), set up a containerized build environment and sync sources
+      with a pinned manifest using repo, and build and boot the RD-V3 firmware stack on FVP and
+      map UART consoles to components.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This advanced topic is for firmware developers, system architects, and silicon validation
+      engineers working on Arm Neoverse CSS platforms who require a pre-silicon workflow for the
+      CSS-V3 reference design using Fixed Virtual Platforms (FVPs).
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Access to an Arm Neoverse-based Linux
+      machine (cloud or local) with at least 80 GB of free storage; Familiarity with Linux command-line
+      tools and basic scripting; Understanding of firmware boot stages and SoC-level architecture;
+      Docker installed, or a GitHub Codespaces-compatible development environment.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including C, Docker, and FVP, Linux environments, Arm platforms
+      such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Learn about the Arm RD-V3 Platform, Understand the
+      CSS-V3 boot flow and firmware stack, Build the RD-V3 Reference Platform Software Stack,
+      Simulate RD-V3 Boot Flow on Arm FVP, and Simulate Dual Chip RD-V3-R1 Platform.
+# END generated_summary_faq
+
 author:
     - Odin Shen
     - Ann Cheng
@@ -63,3 +112,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md b/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md
index 004a0d0e21..3b450b5a3c 100644
--- a/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md
@@ -17,6 +17,51 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 5a5fd9b66a09de71009ccb098c7ef08a848463a8a7956643020ab1fb1db22fbb
+  summary: >-
+    Run a .NET Aspire application on Arm-based VMs on AWS and GCP walks you through an end-to-end
+    Arm software workflow. It is designed for software developers interested in learning how to
+    deploy .NET Aspire applications on Arm-based virtual machines (VMs) on Amazon Web Services
+    (AWS) and Google Cloud Platform (GCP). By the end, you will be able to demonstrate knowledge
+    and understanding of .NET Aspire developer tools, create a .NET Aspire application, and modify
+    code on a Windows on Arm development machine. It focuses on tools and technologies such as
+    .NET, C#, and Visual Studio Code, Windows and Linux environments, Arm platforms including
+    Neoverse, and cloud platforms such as AWS and Google Cloud. The main steps cover .NET Aspire,
+    Create a project and then an application, Run the application, Modify the Project, and Deploy
+    to AWS EC2.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will demonstrate knowledge and understanding of .NET Aspire developer tools, create
+      a .NET Aspire application, and modify code on a Windows on Arm development machine.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers interested in learning how to deploy
+      .NET Aspire applications on Arm-based virtual machines (VMs) on Amazon Web Services (AWS)
+      and Google Cloud Platform (GCP).
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows on Arm machine, for example
+      the Lenovo Thinkpad X13s running Windows 11 to build the .NET Aspire project.; An [Arm-based
+      instance](/learning-paths/servers-and-cloud-computing/csp/) from AWS or GCP.; Any code editor.
+      [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is
+      an example of a suitable editor.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including .NET, C#, and Visual Studio Code, Windows and Linux
+      environments, Arm platforms such as Neoverse, and cloud platforms such as AWS and Google
+      Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around .NET Aspire, Create a project and then an application,
+      Run the application, Modify the Project, and Deploy to AWS EC2.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
@@ -61,3 +106,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md
index 7409e44d5a..9694ca9fe6 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md
@@ -17,6 +17,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: e6f6f4d843b4974d1c1d67a35009b4428d08bb356d26c08a441f82726e002fb2
+  summary: >-
+    Deploy NGINX on Azure Cobalt 100 Arm-based virtual machines walks you through an end-to-end
+    Arm software workflow. It is designed for system administrators and developers who want to
+    learn how to deploy and benchmark NGINX on Microsoft Azure Cobalt 100 Arm-based instances.
+    By the end, you will be able to create an Arm64 virtual machine on Azure Cobalt 100 (Dpsv6)
+    using the Azure console with Ubuntu Pro 24.04 LTS as the base image, install and configure
+    the NGINX web server on the Azure Arm64 virtual machine, and configure and test a static website
+    with NGINX on the virtual machine. It focuses on tools and technologies such as NGINX and
+    ApacheBench, Linux environments, Arm platforms including Neoverse, and cloud platforms such
+    as Microsoft Azure. The main steps cover Overview of Azure Cobalt 100 and NGINX, Create an
+    Arm-based Azure VM with Cobalt 100, Install NGINX, NGINX Baseline Testing, and NGINX Benchmarking.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create an Arm64 virtual machine on Azure Cobalt 100 (Dpsv6) using the Azure console
+      with Ubuntu Pro 24.04 LTS as the base image, install and configure the NGINX web server
+      on the Azure Arm64 virtual machine, and configure and test a static website with NGINX on
+      the virtual machine.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for system administrators and developers who want to learn
+      how to deploy and benchmark NGINX on Microsoft Azure Cobalt 100 Arm-based instances.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Microsoft Azure](https://azure.microsoft.com/)
+      account with access to Cobalt 100 based instances (Dpsv6).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including NGINX and ApacheBench, Linux environments, Arm platforms
+      such as Neoverse, and cloud platforms such as Microsoft Azure.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview of Azure Cobalt 100 and NGINX, Create an
+      Arm-based Azure VM with Cobalt 100, Install NGINX, NGINX Baseline Testing, and NGINX Benchmarking.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
@@ -56,3 +98,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx/_index.md
index 8967d22424..fb48945ef5 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx/_index.md
@@ -17,6 +17,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: c5e077458808373c8ce9660235716b5bb55e4e7eb8b6300c162c041ef1c96cb0
+  summary: >-
+    Learn how to deploy Nginx walks you through an end-to-end Arm software workflow. It is designed
+    for engineers who want to use Nginx on Arm. By the end, you will be able to install and run
+    Nginx on Arm servers, set up Nginx as a web server, reverse proxy, or an API Gateway, and
+    verify Nginx is working correctly. It focuses on tools and technologies such as NGINX, Linux
+    environments, Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft
+    Azure, Google Cloud, and Oracle. The main steps cover Install Nginx using a package manager
+    and check the build configuration, Build Nginx from source, Setup a static file server, and
+    Setup a reverse proxy and API gateway.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install and run Nginx on Arm servers, set up Nginx as a web server, reverse proxy,
+      or an API Gateway, and verify Nginx is working correctly.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for engineers who want to use Nginx on Arm.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: To create a file server you will need
+      at least one [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from
+      a cloud service provider or one on-premises Arm server.; To create a reverse proxy or API
+      gateway you will need at least three Arm based instances from a cloud service provider or
+      at least three on-premises Arm servers.; Network settings (firewalls and security groups)
+      which allow communication on port 22 (SSH) and port 443 (HTTPS).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including NGINX, Linux environments, Arm platforms such as
+      Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Install Nginx using a package manager and check the
+      build configuration, Build Nginx from source, Setup a static file server, and Setup a reverse
+      proxy and API gateway.
+# END generated_summary_faq
+
 author: Julio Suarez
 
 ### Tags
@@ -60,3 +102,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
index b16ebaaecc..3bef5623e2 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
@@ -18,6 +18,44 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 10f13dee3459577fcadf5966cf80d990f3396321189f638432a3308699e773a8
+  summary: >-
+    Learn how to tune Nginx walks you through an end-to-end Arm software workflow. It is designed
+    for software developers who want to use Nginx on Arm. By the end, you will be able to describe
+    how kernel parameters can impact Nginx performance, describe how compilers and libraries can
+    impact Nginx performance, and tune a Nginx file server configuration file. It focuses on tools
+    and technologies such as NGINX and Runbook, Linux environments, Arm platforms including Neoverse,
+    and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps
+    cover Before and after tuning Nginx, Kernel, compiler, and libraries, Tune a static file server,
+    Tune a Reverse Proxy or API Gateway, and Test Optimizations.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will describe how kernel parameters can impact Nginx performance, describe how compilers
+      and libraries can impact Nginx performance, and tune a Nginx file server configuration file.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers who want to use Nginx on Arm.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A cloud or bare-metal installation of
+      a Nginx file server or load balancer.; If you do not already have a Nginx setup, a review
+      of [Learn how to deploy Nginx](/learning-paths/servers-and-cloud-computing/nginx/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including NGINX and Runbook, Linux environments, Arm platforms
+      such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Before and after tuning Nginx, Kernel, compiler, and
+      libraries, Tune a static file server, Tune a Reverse Proxy or API Gateway, and Test Optimizations.
+# END generated_summary_faq
+
 author: Julio Suarez
 
 ### Tags
@@ -55,3 +93,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md b/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md
index 7d8931b128..8d1125aefe 100644
--- a/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md
@@ -14,6 +14,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 81bdee16a18b09da91a7514eb70368771b251ed3f8ec657ec105ca10ecead038
+  summary: >-
+    Run a Natural Language Processing (NLP) model from Hugging Face on Arm servers walks you through
+    an end-to-end Arm software workflow. It is designed for software developers who want to learn
+    how to run a Natural Language Processing (NLP) model from Hugging Face using PyTorch on Arm
+    based servers. By the end, you will be able to deploy a PyTorch NLP model from Hugging Face
+    on an Arm AArch64 CPU and use the PyTorch profiler to analyze the execution time of the model.
+    It focuses on tools and technologies such as Python, PyTorch, and Hugging Face, Linux environments,
+    Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
+    Cloud, and Oracle. The main steps cover Run a Natural Language Processing (NLP) model from
+    Hugging Face on Arm servers.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will deploy a PyTorch NLP model from Hugging Face on an Arm AArch64 CPU and use the
+      PyTorch profiler to analyze the execution time of the model.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to learn how to run a Natural
+      Language Processing (NLP) model from Hugging Face using PyTorch on Arm based servers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/)
+      from a cloud service provider or an on-premise Arm server.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Python, PyTorch, and Hugging Face, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
+      Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Run a Natural Language Processing (NLP) model from
+      Hugging Face on Arm servers.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
@@ -59,3 +99,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md
index f9a639a71f..df00e60e53 100644
--- a/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md
@@ -21,6 +21,52 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 800eed835763098d4b369789d10de642bbdbaa390e8bfe0cb6ea2c4c78f7ecbd
+  summary: >-
+    Deploy Node.js on Google Cloud C4A Arm-based Axion VMs walks you through an end-to-end Arm
+    software workflow. It is designed for software developers migrating Node.js workloads from
+    x86_64 to Arm-based servers, specifically on Google Cloud C4A virtual machines built on Axion
+    processors. By the end, you will be able to provision an Arm-based SUSE Linux Enterprise Server
+    virtual machine on Google Cloud C4A instances with Axion processors, install and configure
+    Node.js on a SUSE Arm64 (C4A) instance, and validate Node.js functionality with baseline HTTP
+    server tests. It focuses on tools and technologies such as Node.js, npm, and Autocannon, Linux
+    environments, Arm platforms including Neoverse, and cloud platforms such as Google Cloud.
+    The main steps cover Getting started with Node.js on Google Axion C4A (Arm Neoverse-V2), Create
+    a Google Axion C4A Arm virtual machine on GCP, Install Node.js using Node Version Manager,
+    Validate Node.js baseline on Google Axion C4A Arm virtual machine, and Benchmark Node.js performance
+    with Autocannon.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will provision an Arm-based SUSE Linux Enterprise Server virtual machine on Google Cloud
+      C4A instances with Axion processors, install and configure Node.js on a SUSE Arm64 (C4A)
+      instance, and validate Node.js functionality with baseline HTTP server tests.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers migrating Node.js workloads from x86_64
+      to Arm-based servers, specifically on Google Cloud C4A virtual machines built on Axion processors.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
+      account with billing enabled; Familiarity with networking concepts and [Node.js event-driven
+      architecture](https://nodejs.org/en/docs/guides/event-loop-timers-and-nexttick).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Node.js, npm, and Autocannon, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Getting started with Node.js on Google Axion C4A (Arm
+      Neoverse-V2), Create a Google Axion C4A Arm virtual machine on GCP, Install Node.js using
+      Node Version Manager, Validate Node.js baseline on Google Axion C4A Arm virtual machine,
+      and Benchmark Node.js performance with Autocannon.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ##### Tags
@@ -63,3 +109,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md b/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md
index 1b631610ad..d95104ed43 100644
--- a/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md
@@ -14,6 +14,41 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:18Z'
+  generator: template
+  source_hash: 3ee5dd8d8edeb5dcb1e3be7bb09cdf0b1b2694f0e74e9eb3c6c2f17912399808
+  summary: >-
+    Deploy Arm Instances on Oracle Cloud Infrastructure (OCI) using Terraform walks you through
+    an end-to-end Arm software workflow. It is designed for software developers who are new to
+    deploying Arm instances on Oracle Cloud Infrastructure (OCI) using Terraform. By the end,
+    you will be able to automate Arm virtual machine creation on OCI using Terraform. It focuses
+    on tools and technologies such as Terraform, Linux environments, Arm platforms including Neoverse,
+    and cloud platforms such as Oracle. The main steps cover Automate OCI VM instance creation
+    using Terraform.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will automate Arm virtual machine creation on OCI using Terraform.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who are new to deploying Arm instances
+      on Oracle Cloud Infrastructure (OCI) using Terraform.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An OCI account; A computer with Terraform
+      installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Terraform, Linux environments, Arm platforms such
+      as Neoverse, and cloud platforms such as Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Automate OCI VM instance creation using Terraform.
+# END generated_summary_faq
+
 author: Frédéric -lefred- Descamps
 
 ### Tags
@@ -50,3 +85,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md
index 555831dea7..dd115aea2c 100644
--- a/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md
@@ -17,6 +17,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 84ba887426f3ca45b698c79028023d80cbf0a06e6bc2fb71fcbe924943078dd8
+  summary: >-
+    Deploy SqueezeNet 1.0 INT8 model with ONNX Runtime on Azure Cobalt 100 walks you through an
+    end-to-end Arm software workflow. It is designed for developers deploying ONNX-based applications
+    on Arm-based machines. By the end, you will be able to provision an Azure Arm64 virtual machine
+    using Azure console, with Ubuntu Pro 24.04 LTS as the base image and perform ONNX baseline
+    testing and benchmarking on Arm64 virtual machines. It focuses on tools and technologies such
+    as Python and ONNX Runtime, Linux environments, Arm platforms including Neoverse, and cloud
+    platforms such as Microsoft Azure. The main steps cover Overview, Create an Arm-based Azure
+    Cobalt 100 virtual machine, ONNX Installation, Baseline Testing, and Benchmark ONNX runtime
+    performance with onnxruntime_perf_test.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will provision an Azure Arm64 virtual machine using Azure console, with Ubuntu Pro 24.04
+      LTS as the base image and perform ONNX baseline testing and benchmarking on Arm64 virtual
+      machines.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is for developers deploying ONNX-based applications on Arm-based machines.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Microsoft Azure](https://azure.microsoft.com/)
+      account with access to Cobalt 100 based instances (Dpsv6); Basic understanding of Python
+      and machine learning concepts; Familiarity with [ONNX Runtime](https://onnxruntime.ai/docs/)
+      and Azure cloud services.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Python and ONNX Runtime, Linux environments, Arm
+      platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview, Create an Arm-based Azure Cobalt 100 virtual
+      machine, ONNX Installation, Baseline Testing, and Benchmark ONNX runtime performance with
+      onnxruntime_perf_test.
+# END generated_summary_faq
+
 author: Pareena Verma    
 
 ### Tags
@@ -60,3 +102,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/onnx/_index.md b/content/learning-paths/servers-and-cloud-computing/onnx/_index.md
index dd9c2c25b4..22af1cf047 100644
--- a/content/learning-paths/servers-and-cloud-computing/onnx/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/onnx/_index.md
@@ -18,6 +18,47 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: a9e547c4a9f99e1c7bfbfe582032ede11eb8ff5a74dbb7a93bada275ac435220
+  summary: >-
+    Deploy Phi-4-mini model with ONNX Runtime on Azure Cobalt 100 walks you through an end-to-end
+    Arm software workflow. It is designed for developers, ML engineers, and cloud practitioners
+    looking to deploy Microsoft's Phi Models on Arm-based servers using ONNX Runtime. By the end,
+    you will be able to quantize and run the Phi-4-mini model with ONNX Runtime on Azure and analyze
+    performance on Arm Neoverse N2 based Azure Cobalt 100 VMs. It focuses on tools and technologies
+    such as Python and ONNX Runtime, Linux environments, Arm platforms including Neoverse, and
+    cloud platforms such as Microsoft Azure. The main steps cover Build ONNX Runtime and set up
+    the Phi-4-mini Model, Run the Chatbot Server, and Interact with the Phi-4-mini Chatbot.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will quantize and run the Phi-4-mini model with ONNX Runtime on Azure and analyze performance
+      on Arm Neoverse N2 based Azure Cobalt 100 VMs.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for developers, ML engineers, and cloud practitioners looking
+      to deploy Microsoft's Phi Models on Arm-based servers using ONNX Runtime.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/)
+      from an appropriate cloud service provider. This Learning Path has been tested on an Azure
+      Cobalt 100 virtual machine.; Basic understanding of Python and machine learning concepts.;
+      Familiarity with ONNX Runtime and Azure cloud services.; Knowledge of Large Language Model
+      (LLM) fundamentals.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Python and ONNX Runtime, Linux environments, Arm
+      platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Build ONNX Runtime and set up the Phi-4-mini Model,
+      Run the Chatbot Server, and Interact with the Phi-4-mini Chatbot.
+# END generated_summary_faq
+
 author: Nobel Chowdary Mandepudi
 
 ### Tags
@@ -54,3 +95,4 @@ weight: 1                       # _index.md always has a weight of 1 to order co
 layout: "learningpathall"       # All files under learning paths use this wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md b/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md
index 4cd3f3b354..1f53b9c59d 100644
--- a/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md
@@ -20,6 +20,58 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: dec913a7a15e82ebf129e2ac64cba8d9140694840f8f9e817fb091b0c82c4cab
+  summary: >-
+    Simulate OpenBMC and UEFI pre-silicon on Neoverse RD-V3 walks you through an end-to-end Arm
+    software workflow. It is designed for This advanced topic is for firmware developers, platform
+    software engineers, and system integrators working on Arm Neoverse-based platforms. It is
+    especially useful for developers exploring pre-silicon development, testing, and integration
+    of Baseboard Management Controllers (BMC) with UEFI firmware. If you are building or validating
+    server-class reference platforms such as RD-V3, before hardware is available, this Learning
+    Path shows you how to simulate and debug the full boot path using Fixed Virtual Platforms
+    (FVPs). By the end, you will be able to understand the role of OpenBMC and UEFI in the Arm
+    server boot flow, simulate the firmware using the RD-V3 FVP, and build and launch OpenBMC
+    and UEFI images on the RD-V3 FVP. It focuses on tools and technologies such as C, Docker,
+    FVP, OpenBMC, and Yocto/BitBake, Linux environments, and Arm platforms including Neoverse.
+    The main steps cover What are OpenBMC and UEFI?, Set up the development environment for OpenBMC
+    and UEFI, Run OpenBMC and host UEFI simulation on RD-V3 FVP, Monitor and control the host
+    CPU using OpenBMC SOL and web UI, and Customize IPMI commands in OpenBMC.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand the role of OpenBMC and UEFI in the Arm server boot flow, simulate the
+      firmware using the RD-V3 FVP, and build and launch OpenBMC and UEFI images on the RD-V3
+      FVP.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This advanced topic is for firmware developers, platform software engineers, and system
+      integrators working on Arm Neoverse-based platforms. It is especially useful for developers
+      exploring pre-silicon development, testing, and integration of Baseboard Management Controllers
+      (BMC) with UEFI firmware. If you are building or validating server-class reference platforms
+      such as RD-V3, before hardware is available, this Learning Path shows you how to simulate
+      and debug the full boot path using Fixed Virtual Platforms (FVPs).
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm Neoverse-based Linux machine
+      (cloud or local) running Ubuntu 22.04 LTS; At least 80 GB free disk space and 48 GB RAM;
+      Working knowledge of Docker, Git, and common Linux terminal tools; Basic understanding of
+      the server firmware stack (such as UEFI, BMC, and TF-A).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including C, Docker, FVP, OpenBMC, and Yocto/BitBake, Linux
+      environments, and Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around What are OpenBMC and UEFI?, Set up the development
+      environment for OpenBMC and UEFI, Run OpenBMC and host UEFI simulation on RD-V3 FVP, Monitor
+      and control the host CPU using OpenBMC SOL and web UI, and Customize IPMI commands in OpenBMC.
+# END generated_summary_faq
+
 author:
   - Odin Shen
   - Ken Zhang
@@ -67,3 +119,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md b/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md
index feed3a1556..ef05ab5931 100644
--- a/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md
@@ -19,6 +19,53 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 778ac2a8b8ad9ffd665f6f0be545541090465f355094c354cc693e784d27559a
+  summary: >-
+    Learn how to profile an example C++ data-processing workload on Arm Linux with Arm Performix,
+    then accelerate random number generation using OpenRNG and Arm Performance Libraries. It is
+    designed for C++ developers who want to profile a data-processing workload on Arm Linux, identify
+    performance bottlenecks with Arm Performix, and accelerate random number generation using
+    OpenRNG and Arm Performance Libraries. By the end, you will be able to build and run a baseline
+    C++ data-processing workload on Arm Linux, use Arm Performix Code Hotspots to identify the
+    highest-impact optimization target, and accelerate random number generation by integrating
+    OpenRNG and Arm Performance Libraries. It focuses on tools and technologies such as CMake,
+    Arm Performix, OpenRNG, and Arm Performance Libraries, Linux environments, and Arm platforms
+    including Neoverse. The main steps cover Set up your environment, Run the baseline data-processing
+    example, Identify code hotspots with Arm Performix, Accelerate distribution generation with
+    OpenRNG, and Measure performance improvements with a microbenchmark.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build and run a baseline C++ data-processing workload on Arm Linux, use Arm Performix
+      Code Hotspots to identify the highest-impact optimization target, and accelerate random
+      number generation by integrating OpenRNG and Arm Performance Libraries. Learn how to profile
+      an example C++ data-processing workload on Arm Linux with Arm Performix, then accelerate
+      random number generation using OpenRNG and Arm Performance Libraries.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for C++ developers who want to profile a data-processing workload
+      on Arm Linux, identify performance bottlenecks with Arm Performix, and accelerate random
+      number generation using OpenRNG and Arm Performance Libraries.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm Linux (aarch64) server, such
+      as an AWS Graviton3 instance; Basic understanding of C++ and CMake.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including CMake, Arm Performix, OpenRNG, and Arm Performance
+      Libraries, Linux environments, and Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Set up your environment, Run the baseline data-processing
+      example, Identify code hotspots with Arm Performix, Accelerate distribution generation with
+      OpenRNG, and Measure performance improvements with a microbenchmark.
+# END generated_summary_faq
+
 author: Kieran Hejmadi
 
 ### Tags
@@ -53,4 +100,5 @@ further_reading:
 weight: 1                       # _index.md always has weight of 1 to order correctly
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
----
\ No newline at end of file
+---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/openshift/_index.md b/content/learning-paths/servers-and-cloud-computing/openshift/_index.md
index f1cd2e0679..95b82a0434 100644
--- a/content/learning-paths/servers-and-cloud-computing/openshift/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openshift/_index.md
@@ -16,6 +16,42 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 7972fb230273ab1809c6f0917c4bbc49934f495feb2649da665d67bf71a45a3f
+  summary: >-
+    Build multi-architecture applications with Red Hat OpenShift Pipelines on AWS walks you through
+    an end-to-end Arm software workflow. It is designed for OpenShift administrators who want
+    to migrate their applications to Arm. By the end, you will be able to migrate existing OpenShift
+    applications to Arm-based nodes. It focuses on tools and technologies such as Tekton and OpenShift,
+    Linux environments, Arm platforms including Neoverse, and cloud platforms such as AWS. The
+    main steps cover Migrate an x86 workload to Arm on AWS.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will migrate existing OpenShift applications to Arm-based nodes.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This topic is for OpenShift administrators who want to migrate their applications to Arm.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An AWS account with an OpenShift 4.18
+      cluster running x86 compute nodes; Red Hat OpenShift Pipelines (Tekton) Operator installed
+      in your cluster; Familiarity with the `oc` CLI, container fundamentals, and basic Tekton
+      concepts (Task, Pipeline, PipelineRun); Cluster access with cluster-admin or equivalent
+      permissions to configure nodes and pipelines.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Tekton and OpenShift, Linux environments, Arm platforms
+      such as Neoverse, and cloud platforms such as AWS.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Migrate an x86 workload to Arm on AWS.
+# END generated_summary_faq
+
 author: Jeff Young
 
 # Tags
@@ -59,3 +95,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md
index 71f165c9a4..3a2d9bc0b4 100644
--- a/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md
@@ -23,6 +23,57 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 42628afa923ce6e89693bdfc72469ac0803610c3decb6cd4f36bd1a003874d0d
+  summary: >-
+    Deploy OpenStack on Azure Cobalt 100 Arm64 virtual machines using DevStack for development
+    and Kolla-Ansible for containerized production deployments. It is designed for This learning
+    path is designed for developers, DevOps engineers, and platform engineers who want to deploy
+    and manage OpenStack on Arm-based cloud environments using Kolla-Ansible and DevStack. By
+    the end, you will be able to deploy OpenStack on Azure Cobalt 100 Arm64 virtual machines,
+    configure core OpenStack services (Keystone, Nova, Neutron, Glance, Cinder), and deploy containerized
+    OpenStack using Kolla-Ansible. It focuses on tools and technologies such as OpenStack, Kolla-Ansible,
+    DevStack, Python, and OpenStack CLI, Linux environments, Arm platforms including Neoverse,
+    and cloud platforms such as Microsoft Azure. The main steps cover Understand Azure Cobalt
+    100 and OpenStack, Create an Azure Cobalt 100 Arm64 virtual machine for DevStack, Deploy OpenStack
+    on an Azure Cobalt 100 Arm64 virtual machine using DevStack, Prepare Azure Arm64 virtual machine
+    for Kolla-Ansible, and Deploy OpenStack using Kolla-Ansible on an Azure Ubuntu Arm64 virtual
+    machine.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will deploy OpenStack on Azure Cobalt 100 Arm64 virtual machines, configure core OpenStack
+      services (Keystone, Nova, Neutron, Glance, Cinder), and deploy containerized OpenStack using
+      Kolla-Ansible. Deploy OpenStack on Azure Cobalt 100 Arm64 virtual machines using DevStack
+      for development and Kolla-Ansible for containerized production deployments.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This learning path is designed for developers, DevOps engineers, and platform engineers
+      who want to deploy and manage OpenStack on Arm-based cloud environments using Kolla-Ansible
+      and DevStack.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Microsoft Azure account](https://azure.microsoft.com/)
+      with access to Cobalt 100 based instances (Dpsv6); Basic knowledge of Linux command-line
+      operations; Familiarity with SSH and remote server access; Basic understanding of cloud
+      computing and virtualization concepts.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including OpenStack, Kolla-Ansible, DevStack, Python, and
+      OpenStack CLI, Linux environments, Arm platforms such as Neoverse, and cloud platforms such
+      as Microsoft Azure.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Understand Azure Cobalt 100 and OpenStack, Create
+      an Azure Cobalt 100 Arm64 virtual machine for DevStack, Deploy OpenStack on an Azure Cobalt
+      100 Arm64 virtual machine using DevStack, Prepare Azure Arm64 virtual machine for Kolla-Ansible,
+      and Deploy OpenStack using Kolla-Ansible on an Azure Ubuntu Arm64 virtual machine.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
@@ -68,3 +119,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md b/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md
index ea666bd900..04c058f563 100644
--- a/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md
@@ -18,6 +18,55 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: cb4227a3f8375d6ae63801891703d6e615bdc60a01fca099a2f76453775ef460
+  summary: >-
+    Deploy OpenTelemetry on Google Cloud C4A Axion processors walks you through an end-to-end
+    Arm software workflow. It is designed for DevOps engineers, platform engineers, and software
+    developers who want to deploy and observe a cloud-native microservice on Arm64-based Google
+    Cloud C4A Axion processors using OpenTelemetry. By the end, you will be able to deploy an
+    instrumented Python Flask microservice on Google Cloud C4A Axion processors, configure OpenTelemetry
+    Collector to process and route distributed traces and metrics, and integrate Prometheus and
+    Jaeger for comprehensive metrics collection and distributed tracing visualization. It focuses
+    on tools and technologies such as Flask, Docker, Prometheus, and Jaeger, Linux environments,
+    Arm platforms including Neoverse, and cloud platforms such as Google Cloud. The main steps
+    cover Get started with OpenTelemetry on Google Axion C4A, Create firewall rules on GCP for
+    Flask and observability components, Create a Google Axion C4A Arm virtual machine on GCP,
+    Set up OpenTelemetry environment and application on Arm64, and Deploy the OpenTelemetry observability
+    stack on Arm64.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will deploy an instrumented Python Flask microservice on Google Cloud C4A Axion processors,
+      configure OpenTelemetry Collector to process and route distributed traces and metrics, and
+      integrate Prometheus and Jaeger for comprehensive metrics collection and distributed tracing
+      visualization.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This learning path is for DevOps engineers, platform engineers, and software developers
+      who want to deploy and observe a cloud-native microservice on Arm64-based Google Cloud C4A
+      Axion processors using OpenTelemetry.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
+      account with billing enabled; Basic familiarity with Python and Flask; Basic understanding
+      of containers and Kubernetes concepts.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Flask, Docker, Prometheus, and Jaeger, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Get started with OpenTelemetry on Google Axion C4A,
+      Create firewall rules on GCP for Flask and observability components, Create a Google Axion
+      C4A Arm virtual machine on GCP, Set up OpenTelemetry environment and application on Arm64,
+      and Deploy the OpenTelemetry observability stack on Arm64.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ##### Tags
@@ -72,3 +121,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: yes
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/pac/_index.md b/content/learning-paths/servers-and-cloud-computing/pac/_index.md
index 75df90a3ca..abe55bc3de 100644
--- a/content/learning-paths/servers-and-cloud-computing/pac/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pac/_index.md
@@ -17,6 +17,51 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: fa699cafa5c0d998a63de306371bfbe47680c7453b28fc4b35d4fe2671902b23
+  summary: >-
+    Understand Arm Pointer Authentication walks you through an end-to-end Arm software workflow.
+    It is designed for software developers interested in understanding Arm Pointer Authentication.
+    By the end, you will be able to create a simple application on an Arm server with Pointer
+    Authentication, compile the application with and without Pointer Authentication to inspect
+    the instructions generated, and exploit the applications with and without Pointer Authentication
+    to demonstrate how Pointer Authentication instructions enhance security. It focuses on tools
+    and technologies such as Runbook, Linux environments, Arm platforms including Neoverse, and
+    cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps cover
+    Pointer Authentication on Arm, Example application, and Exploit applications built without
+    Pointer Authentication instructions.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create a simple application on an Arm server with Pointer Authentication, compile
+      the application with and without Pointer Authentication to inspect the instructions generated,
+      and exploit the applications with and without Pointer Authentication to demonstrate how
+      Pointer Authentication instructions enhance security.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers interested in understanding Arm Pointer
+      Authentication.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm based instance from a cloud service
+      provider, or an on-premise Arm server.; If needed, review [Get started with Arm-based cloud
+      instances](/learning-paths/servers-and-cloud-computing/csp/) to learn how to deploy Arm
+      in the cloud. These learning paths also point to more advanced learning paths that show
+      how to automate the deployment of Arm instances at different cloud providers.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Runbook, Linux environments, Arm platforms such
+      as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Pointer Authentication on Arm, Example application,
+      and Exploit applications built without Pointer Authentication instructions.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
@@ -63,3 +108,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md b/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md
index dc65cc1de5..3273649033 100644
--- a/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md
@@ -21,6 +21,58 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 0599665da13e9e1a8b017b0dac87c63f323ef769b1a5be62a60a32a36bc82696
+  summary: >-
+    Learn how to use an AI agent and the Performix tool through the Arm MCP Server to run the
+    Code Hotspots recipe on a C++ application, interpret flame graph results, and apply targeted
+    optimizations on Arm Neoverse. It is designed for developers who want to use AI-powered tools
+    to automate performance profiling and optimization of C++ applications on Arm Neoverse servers.
+    By the end, you will be able to describe how the Arm Performix tool in the Arm MCP Server
+    enables AI-driven profiling workflows, configure a GitHub Copilot prompt file to run the Code
+    Hotspots recipe on a remote Arm target, and use an AI agent to interpret flame graph results
+    and identify the hottest functions in a C++ application. It focuses on tools and technologies
+    such as Arm Performix, MCP, C++, and GitHub Copilot, Linux environments, and Arm platforms
+    including Neoverse. The main steps cover Understand AI-driven profiling with Arm Performix
+    MCP, Build the Mandelbrot example on Arm Neoverse, Run Code Hotspots with an AI agent, and
+    Optimize code with AI-driven profiling feedback.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will describe how the Arm Performix tool in the Arm MCP Server enables AI-driven profiling
+      workflows, configure a GitHub Copilot prompt file to run the Code Hotspots recipe on a remote
+      Arm target, and use an AI agent to interpret flame graph results and identify the hottest
+      functions in a C++ application. Learn how to use an AI agent and the Performix tool through
+      the Arm MCP Server to run the Code Hotspots recipe on a C++ application, interpret flame
+      graph results, and apply targeted optimizations on Arm Neoverse.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for developers who want to use AI-powered tools to automate performance
+      profiling and optimization of C++ applications on Arm Neoverse servers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Completion of the [Automate x86-to-Arm
+      application migration using Arm MCP Server](/learning-paths/servers-and-cloud-computing/arm-mcp-server/)
+      Learning Path, or equivalent familiarity with configuring the Arm MCP Server in an AI coding
+      assistant; Access to an Arm-based cloud instance running Linux, such as an AWS Graviton3
+      instance; Access to Arm Performix configured with the remote Arm target. See the [Arm Performix
+      install guide](/install-guides/performix/) for setup instructions; Basic understanding of
+      C++.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Arm Performix, MCP, C++, and GitHub Copilot, Linux
+      environments, and Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Understand AI-driven profiling with Arm Performix
+      MCP, Build the Mandelbrot example on Arm Neoverse, Run Code Hotspots with an AI agent, and
+      Optimize code with AI-driven profiling feedback.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
@@ -68,3 +120,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md b/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md
index 858febb90a..183718f7c0 100644
--- a/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md
@@ -19,6 +19,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: ec027f6022cc86a644a71a7d2016bf4601e2c4ac41570e861e9f124468b228ae
+  summary: >-
+    Optimize application performance using Arm Performix CPU microarchitecture analysis walks
+    you through an end-to-end Arm software workflow. It is designed for software developers who
+    want to learn performance analysis methodologies for Linux applications on Arm Neoverse-based
+    servers. By the end, you will be able to identify CPU pipeline bottlenecks using the Arm Performix
+    CPU Microarchitecture recipe, analyze instruction types and SIMD utilization using the Instruction
+    Mix recipe, and optimize application performance using vectorization and compiler flags. It
+    focuses on tools and technologies such as Arm Performix, C, and Runbook, Linux environments,
+    and Arm platforms including Neoverse. The main steps cover Set up the target environment and
+    compile the application, Identify application bottlenecks with the CPU Microarchitecture recipe,
+    and Analyze SIMD utilization with the Instruction Mix recipe.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will identify CPU pipeline bottlenecks using the Arm Performix CPU Microarchitecture
+      recipe, analyze instruction types and SIMD utilization using the Instruction Mix recipe,
+      and optimize application performance using vectorization and compiler flags.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to learn performance analysis
+      methodologies for Linux applications on Arm Neoverse-based servers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm Neoverse-based server running
+      Linux (bare-metal or cloud bare-metal instance preferred for access to hardware performance
+      counters); Familiarity with Linux command line; Basic understanding of CPU performance concepts.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Arm Performix, C, and Runbook, Linux environments,
+      and Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Set up the target environment and compile the application,
+      Identify application bottlenecks with the CPU Microarchitecture recipe, and Analyze SIMD
+      utilization with the Instruction Mix recipe.
+# END generated_summary_faq
+
 author:
 - Brendan Long
 - Kieran Hejmadi
@@ -57,3 +100,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md
index 2852738926..f2e05b060e 100644
--- a/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md
@@ -19,6 +19,50 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 719ec8966a776cc5ee97782b7ef7cc2b989a8616d14cb36b9847c9cbd2f16446
+  summary: >-
+    Deploy PHP on Google Cloud C4A Arm-based Axion VMs walks you through an end-to-end Arm software
+    workflow. It is designed for developers migrating Hypertext Preprocessor (PHP) workloads from
+    x86_64 to Arm-based servers, specifically on Google Cloud C4A virtual machines (VM) built
+    on Axion processors. By the end, you will be able to provision a SUSE Linux Enterprise Server
+    (SLES) virtual machine on a Google Cloud C4A Arm-based Axion virtual machine, install PHP
+    on a SUSE Arm64 C4A instance, and validate PHP functionality by running baseline HTTP server
+    tests. It focuses on tools and technologies such as PHP, Apache, and PHPBench, Linux environments,
+    Arm platforms including Neoverse, and cloud platforms such as Google Cloud. The main steps
+    cover Get started with PHP on Google Cloud Axion C4A Arm VMs, Provision a Google Axion C4A
+    Arm virtual machine on GCP, Install PHP, Validate PHP baseline on Google Cloud Axion C4A Arm
+    VM, and PHP Benchmarking.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will provision a SUSE Linux Enterprise Server (SLES) virtual machine on a Google Cloud
+      C4A Arm-based Axion virtual machine, install PHP on a SUSE Arm64 C4A instance, and validate
+      PHP functionality by running baseline HTTP server tests.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers migrating Hypertext Preprocessor (PHP) workloads
+      from x86_64 to Arm-based servers, specifically on Google Cloud C4A virtual machines (VM)
+      built on Axion processors.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
+      account with billing enabled; Basic familiarity with web servers and PHP scripting.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including PHP, Apache, and PHPBench, Linux environments, Arm
+      platforms such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Get started with PHP on Google Cloud Axion C4A Arm
+      VMs, Provision a Google Axion C4A Arm virtual machine on GCP, Install PHP, Validate PHP
+      baseline on Google Cloud Axion C4A Arm VM, and PHP Benchmarking.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ##### Tags
@@ -61,3 +105,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md b/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md
index 29f8c77eff..d3842163f6 100644
--- a/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md
@@ -19,6 +19,51 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 2fdb45e72a36eb114250486b88d603cc8687b9f6b44bb5bb656e25c37d9795a9
+  summary: >-
+    Optimize application performance with CPU affinity walks you through an end-to-end Arm software
+    workflow. It is designed for developers, performance engineers, and system administrators
+    looking to fine-tune the performance of their workload on many-core Arm-based systems. By
+    the end, you will be able to pin threads to specific CPU cores using taskset and source code
+    modifications, measure cache performance improvements from thread pinning using perf, and
+    evaluate performance trade-offs between throughput and latency consistency. It focuses on
+    tools and technologies such as C++, Python, taskset, perf, and Google Benchmark, Linux environments,
+    Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
+    Cloud, and Oracle. The main steps cover Understand thread pinning and CPU affinity, Create
+    a CPU-intensive program, Pin threads to cores with taskset, and Set CPU affinity in source
+    code.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will pin threads to specific CPU cores using taskset and source code modifications,
+      measure cache performance improvements from thread pinning using perf, and evaluate performance
+      trade-offs between throughput and latency consistency.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for developers, performance engineers, and system administrators
+      looking to fine-tune the performance of their workload on many-core Arm-based systems.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm Linux system with four or more
+      CPU cores; Experience with multi-threaded programming in C++ and Python; Understanding of
+      build systems and computer architecture concepts; Familiarity with Linux command-line tools.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including C++, Python, taskset, perf, and Google Benchmark,
+      Linux environments, Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft
+      Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Understand thread pinning and CPU affinity, Create
+      a CPU-intensive program, Pin threads to cores with taskset, and Set CPU affinity in source
+      code.
+# END generated_summary_faq
+
 author: Kieran Hejmadi
 
 ### Tags
@@ -70,3 +115,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md b/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md
index 0047348874..2fe306ee69 100644
--- a/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md
@@ -1,5 +1,4 @@
 ---
-
 title: Implement Code level Performance Analysis using the PMUv3 plugin 
 
 minutes_to_complete: 60
@@ -18,6 +17,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 3ec6ae40daff557dd05cd092ff7d6b5a63954a1618605030a443f56fe5ffe490
+  summary: >-
+    Implement Code level Performance Analysis using the PMUv3 plugin walks you through an end-to-end
+    Arm software workflow. It is designed for Engineers who want to carry out C/C++ performance
+    analysis by instrumenting code at the block level. By the end, you will be able to generate
+    a fine-grained, precise measurement of functions and other sections of code, instrument your
+    code to analyze a single section or multiple sections using the provided instrumentation scenarios,
+    and run and collect performance metrics and raw event values for any of the 15 event groups
+    (bundles) in a single run. It focuses on tools and technologies such as C, CPP, Python, and
+    Runbook, Linux environments, and Arm platforms including Neoverse. The main steps cover PMUv3
+    plugin features, Download and build the PMUv3 plugin, Instrument one section of code, Plot,
+    visualize, and analyze the results, and Instrument multiple sections of code.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will generate a fine-grained, precise measurement of functions and other sections of
+      code, instrument your code to analyze a single section or multiple sections using the provided
+      instrumentation scenarios, and run and collect performance metrics and raw event values
+      for any of the 15 event groups (bundles) in a single run.
+  - question: Who is this Learning Path for?
+    answer: >-
+      Engineers who want to carry out C/C++ performance analysis by instrumenting code at the
+      block level.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm-based computer running Linux.;
+      Some familiarity with Linux application performance analysis.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including C, CPP, Python, and Runbook, Linux environments,
+      and Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around PMUv3 plugin features, Download and build the PMUv3
+      plugin, Instrument one section of code, Plot, visualize, and analyze the results, and Instrument
+      multiple sections of code.
+# END generated_summary_faq
+
 author: Gayathri Narayana Yegna Narayanan
 
 ### Tags
@@ -56,3 +98,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md
index f9250dee6b..e5463602f9 100644
--- a/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md
@@ -20,6 +20,55 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 57827f45473867b3b5039a9cbca04d2c6d8a93e177ca0ab053999517389014b5
+  summary: >-
+    Deploy PostgreSQL on Azure Cobalt 100 Arm64 virtual machines, load a relational schema with
+    transactional data, and benchmark and optimize query performance using pgbench and pg_stat_statements.
+    It is designed for This learning path is designed for developers, DevOps engineers, and platform
+    engineers who want to deploy, manage, and optimize PostgreSQL databases on Arm-based cloud
+    infrastructure. By the end, you will be able to install and configure PostgreSQL on Azure
+    Cobalt 100 Arm64 virtual machines, deploy a relational database schema for transactional workloads,
+    and execute analytical SQL queries on operational data. It focuses on tools and technologies
+    such as PostgreSQL, SQL, and pgbench, Linux environments, Arm platforms including Neoverse,
+    and cloud platforms such as Microsoft Azure. The main steps cover Understand PostgreSQL on
+    Azure Cobalt 100, Create an Azure Cobalt 100 Arm64 virtual machine, Install and configure
+    PostgreSQL on Cobalt 100, Deploy a relational schema and run queries, and Benchmark and optimize
+    PostgreSQL on Cobalt 100.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install and configure PostgreSQL on Azure Cobalt 100 Arm64 virtual machines, deploy
+      a relational database schema for transactional workloads, and execute analytical SQL queries
+      on operational data. Deploy PostgreSQL on Azure Cobalt 100 Arm64 virtual machines, load
+      a relational schema with transactional data, and benchmark and optimize query performance
+      using pgbench and pg_stat_statements.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This learning path is designed for developers, DevOps engineers, and platform engineers
+      who want to deploy, manage, and optimize PostgreSQL databases on Arm-based cloud infrastructure.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Microsoft Azure account](https://azure.microsoft.com/)
+      with access to Cobalt 100 based instances (Dpsv6); Basic knowledge of Linux command-line
+      operations; Familiarity with SSH and remote server access; Basic understanding of databases
+      and SQL.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including PostgreSQL, SQL, and pgbench, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Understand PostgreSQL on Azure Cobalt 100, Create
+      an Azure Cobalt 100 Arm64 virtual machine, Install and configure PostgreSQL on Cobalt 100,
+      Deploy a relational schema and run queries, and Benchmark and optimize PostgreSQL on Cobalt
+      100.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 description: Deploy PostgreSQL on Azure Cobalt 100 Arm64 virtual machines, load a relational schema with transactional data, and benchmark and optimize query performance using pgbench and pg_stat_statements.
@@ -64,3 +113,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md b/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md
index 2e40332f04..01c658b5db 100644
--- a/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md
@@ -15,6 +15,42 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: a60cc2148a83f919467076e9d5a49fc4c9cec85670a919c7ba044cba72bfe219
+  summary: >-
+    Learn how to deploy PostgreSQL walks you through an end-to-end Arm software workflow. It is
+    designed for software developers who want to deploy PostgreSQL on Arm. By the end, you will
+    be able to learn about the various ways PostgreSQL can be deployed and learn how to interact
+    with a PostgreSQL database using the psql client tool. It focuses on tools and technologies
+    such as SQL and PostgreSQL, Linux environments, Arm platforms including Neoverse, and cloud
+    platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps cover How
+    do I install, configure, and check PostgreSQL?
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will learn about the various ways PostgreSQL can be deployed and learn how to interact
+      with a PostgreSQL database using the psql client tool.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to deploy PostgreSQL on Arm.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm based instance from a cloud service
+      provider, or an on-premise Arm server.; If you do not have an Arm node, the next section
+      discusses some options.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including SQL and PostgreSQL, Linux environments, Arm platforms
+      such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around How do I install, configure, and check PostgreSQL?
+# END generated_summary_faq
+
 author: Jason Andrews
 ### Tags
 skilllevels: Introductory
@@ -51,5 +87,3 @@ layout: "learningpathall"       # All files under learning paths have this same
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
 
-
-
diff --git a/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md
index a12eef6ec0..023ce4c754 100644
--- a/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md
@@ -13,6 +13,43 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: f30f7fb50000ae7ee28e46d7cbe4e73ba232c3daad2d559cfa41996d630cb936
+  summary: >-
+    Learn how to Tune PostgreSQL walks you through an end-to-end Arm software workflow. It is
+    designed for software developers and DevOps professionals interested in optimizing PostgreSQL
+    performance. By the end, you will be able to tune PostgreSQL to increase performance. It focuses
+    on tools and technologies such as SQL, PostgreSQL, HammerDB, and Runbook, Linux environments,
+    Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
+    Cloud, and Oracle. The main steps cover Before and after tuning PostgreSQL, System, Kernel,
+    compiler, and Libraries, Tuning PostgreSQL, and Testing PostgreSQL Tunings.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will tune PostgreSQL to increase performance.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers and DevOps professionals interested in
+      optimizing PostgreSQL performance.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Bare-metal or cloud [installation of
+      PostgreSQL](/learning-paths/servers-and-cloud-computing/postgresql/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including SQL, PostgreSQL, HammerDB, and Runbook, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
+      Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Before and after tuning PostgreSQL, System, Kernel,
+      compiler, and Libraries, Tuning PostgreSQL, and Testing PostgreSQL Tunings.
+# END generated_summary_faq
+
 author: Julio Suarez
 
 test_images:
@@ -55,3 +92,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md b/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
index 3c409a66d7..6d2d0a1918 100644
--- a/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
@@ -15,6 +15,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: ed85d6171f3c05bfdf1b396065fb0c73ce88724ff63fd1c3c8a93d4c27dd59f8
+  summary: >-
+    Run Process watch on your Arm machine walks you through an end-to-end Arm software workflow.
+    It is designed for software developers who want to build and run the Process Watch tool on
+    an Arm-based machine. By the end, you will be able to build and run the Process Watch tool
+    on your Arm machine, describe how Process Watch works, and check in real-time whether any
+    workloads are using specific Arm instructions or features. It focuses on tools and technologies
+    such as bpftool, libbpf, Capstone, C, and CPP, Linux environments, and Arm platforms including
+    Cortex-A and Neoverse. The main steps cover Install dependencies, Run Process Watch, Learn
+    how Process Watch works, and Using Process Watch.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build and run the Process Watch tool on your Arm machine, describe how Process
+      Watch works, and check in real-time whether any workloads are using specific Arm instructions
+      or features.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to build and run the Process
+      Watch tool on an Arm-based machine.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm-based system (bare metal server,
+      cloud instance, or developer board) running Linux with kernel version 5.8.0 or later.; Root
+      access, or the ability to run the sudo command.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including bpftool, libbpf, Capstone, C, and CPP, Linux environments,
+      and Arm platforms such as Cortex-A and Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Install dependencies, Run Process Watch, Learn how
+      Process Watch works, and Using Process Watch.
+# END generated_summary_faq
+
 author: Graham Woodward
 
 ### Tags
@@ -52,3 +92,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md b/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md
index a60f30dc1e..441e6dc683 100644
--- a/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md
@@ -14,6 +14,45 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: ef12378069d6f3538d8daefb5da7fc8e8ce4196277928d372745d12fde6e46a1
+  summary: >-
+    Profiling for Neoverse with Streamline CLI Tools walks you through an end-to-end Arm software
+    workflow. It is designed for This is an introductory guide for developers who want to measure
+    and optimize the performance of applications running on Arm Neoverse™-based servers. By the
+    end, you will be able to describe Arm's top-down profiling methodology and use Streamline
+    CLI tools to capture and analyze performance data from an application. It focuses on tools
+    and technologies such as Streamline CLI and Runbook, Linux environments, and Arm platforms
+    including Neoverse. The main steps cover System compatibility check, Performance analysis
+    concepts, Capture a performance profile, Example report, and Optimization checklist.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will describe Arm's top-down profiling methodology and use Streamline CLI tools to capture
+      and analyze performance data from an application.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory guide for developers who want to measure and optimize the performance
+      of applications running on Arm Neoverse™-based servers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm Neoverse-based (N1, N2 or V1)
+      computer running Linux. For your host OS, you can use Amazon Linux 2023 or newer, Debian
+      10 or newer, RHEL 8 or newer, or Ubuntu 20.04 or newer.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Streamline CLI and Runbook, Linux environments,
+      and Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around System compatibility check, Performance analysis concepts,
+      Capture a performance profile, Example report, and Optimization checklist.
+# END generated_summary_faq
+
 author: Julie Gaskin
 
 ### Tags
@@ -51,3 +90,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md
index 7da95247a3..364d2593e9 100644
--- a/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md
@@ -13,6 +13,51 @@ learning_objectives:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: aeb6a390b5134af2f851e36db17c5d3578d4697b3c372af86c6bd5176765e087
+  summary: >-
+    Deploy Puppet on Google Cloud C4A walks you through an end-to-end Arm software workflow. It
+    is designed for developers deploying and optimizing Puppet workloads on Arm Linux environments,
+    specifically using Google Cloud C4A virtual machines powered by Axion processors. By the end,
+    you will be able to provision an Arm-based SUSE SLES (SUSE Linux Enterprise Server) virtual
+    machine (VM) on Google Cloud C4A with Axion processors, install Puppet on a SUSE Arm64 C4A
+    instance, and verify Puppet by applying a test manifest and confirming successful resource
+    creation on Arm64. It focuses on tools and technologies such as Puppet, Ruby, Facter, and
+    Hiera, Linux environments, Arm platforms including Neoverse, and cloud platforms such as Google
+    Cloud. The main steps cover Get started with Arm-based Google Axion and Puppet, Create a Google
+    Axion C4A Arm virtual machine on GCP, Install Puppet on a GCP VM, Perform Puppet baseline
+    testing, and Benchmark Puppet.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will provision an Arm-based SUSE SLES (SUSE Linux Enterprise Server) virtual machine
+      (VM) on Google Cloud C4A with Axion processors, install Puppet on a SUSE Arm64 C4A instance,
+      and verify Puppet by applying a test manifest and confirming successful resource creation
+      on Arm64.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers deploying and optimizing Puppet workloads on
+      Arm Linux environments, specifically using Google Cloud C4A virtual machines powered by
+      Axion processors.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
+      account with billing enabled; Basic familiarity with [Puppet](https://www.puppet.com/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Puppet, Ruby, Facter, and Hiera, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Get started with Arm-based Google Axion and Puppet,
+      Create a Google Axion C4A Arm virtual machine on GCP, Install Puppet on a GCP VM, Perform
+      Puppet baseline testing, and Benchmark Puppet.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 prerequisites:
@@ -55,3 +100,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md b/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md
index b844a4bcaf..8888254834 100644
--- a/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md
@@ -17,6 +17,47 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 1c5be7bfc7785ae3b06c60210c06324f00aed7ba75145a0fa65425e6005d4f06
+  summary: >-
+    Run a Large Language Model (LLM) chatbot with PyTorch using KleidiAI on Arm servers walks
+    you through an end-to-end Arm software workflow. It is designed for software developers interested
+    in running LLMs using PyTorch on Arm-based servers. By the end, you will be able to download
+    the Meta Llama 3.1 model from the Meta Hugging Face repository, 4-bit quantize the model using
+    optimized INT4 KleidiAI Kernels for PyTorch, and run an LLM inference using PyTorch on an
+    Arm-based CPU. It focuses on tools and technologies such as LLM, Generative AI, Python, PyTorch,
+    and Hugging Face, Linux environments, Arm platforms including Neoverse, and cloud platforms
+    such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps cover Run a Large Language
+    model (LLM) chatbot on Arm servers and Chatbot with Streamlit Frontend.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will download the Meta Llama 3.1 model from the Meta Hugging Face repository, 4-bit
+      quantize the model using optimized INT4 KleidiAI Kernels for PyTorch, and run an LLM inference
+      using PyTorch on an Arm-based CPU.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers interested in running LLMs using PyTorch
+      on Arm-based servers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/)
+      with at least 16 CPUs from a cloud service provider or an on-premise Arm server.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including LLM, Generative AI, Python, PyTorch, and Hugging
+      Face, Linux environments, Arm platforms such as Neoverse, and cloud platforms such as AWS,
+      Microsoft Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Run a Large Language model (LLM) chatbot on Arm servers
+      and Chatbot with Streamlit Frontend.
+# END generated_summary_faq
+
 author:
     - Nikhil Gupta
     - Pareena Verma
@@ -67,3 +108,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md
index af5dfa24c4..bfd3f992bb 100644
--- a/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md
@@ -21,6 +21,54 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 8b180acd30b3b00484b6e7302b61fd8c3669ef83ff7fca41972d24c5df2682b7
+  summary: >-
+    Learn how to deploy Qdrant on Google Cloud C4A Axion processors, generate vector embeddings
+    with Sentence Transformers, and build a semantic search and chatbot retrieval system on Arm-based
+    infrastructure. It is designed for developers, data engineers, and platform engineers who
+    want to build semantic search systems and chatbot retrieval pipelines on Arm64-based Google
+    Cloud C4A Axion processors using the Qdrant vector database. By the end, you will be able
+    to deploy and run the Qdrant vector database on Google Cloud C4A Axion processors, generate
+    vector embeddings using transformer models, and store and index embeddings efficiently using
+    Qdrant. It focuses on tools and technologies such as Qdrant, Python, Sentence Transformers,
+    and Docker, Linux environments, Arm platforms including Neoverse, and cloud platforms such
+    as Google Cloud. The main steps cover Understand vector search with Qdrant on Google Axion,
+    Create a Google Axion C4A Arm virtual machine, Install and run Qdrant on Axion, Generate and
+    index vector embeddings, and Query vector embeddings with semantic search.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will deploy and run the Qdrant vector database on Google Cloud C4A Axion processors,
+      generate vector embeddings using transformer models, and store and index embeddings efficiently
+      using Qdrant. Learn how to deploy Qdrant on Google Cloud C4A Axion processors, generate
+      vector embeddings with Sentence Transformers, and build a semantic search and chatbot retrieval
+      system on Arm-based infrastructure.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers, data engineers, and platform engineers who
+      want to build semantic search systems and chatbot retrieval pipelines on Arm64-based Google
+      Cloud C4A Axion processors using the Qdrant vector database.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
+      account with billing enabled; Basic familiarity with Python; Basic understanding of machine
+      learning embeddings; Familiarity with Linux command-line operations.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Qdrant, Python, Sentence Transformers, and Docker,
+      Linux environments, Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Understand vector search with Qdrant on Google Axion,
+      Create a Google Axion C4A Arm virtual machine, Install and run Qdrant on Axion, Generate
+      and index vector embeddings, and Query vector embeddings with semantic search.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ##### Tags
@@ -70,3 +118,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: yes
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md
index a527948408..ac7d324570 100644
--- a/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md
@@ -21,6 +21,55 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: b4392f1cf38fa1f3b6c633c7ae1e8d30a51ea8d4e635287586b084cb0d8a556b
+  summary: >-
+    Deploy RabbitMQ on Arm64 Cloud Platforms (Azure and GCP) walks you through an end-to-end Arm
+    software workflow. It is designed for software engineers and platform engineers migrating
+    messaging and event-driven workloads from x86_64 to Arm-based servers, specifically on Microsoft
+    Azure Cobalt 100 Arm processors and Google Cloud C4A virtual machines powered by Axion processors.
+    By the end, you will be able to provision Arm-based Linux virtual machines on Google Cloud
+    (C4A with Axion processors) and Microsoft Azure (Cobalt 100), provision an Arm-based SUSE
+    SLES virtual machine on Google Cloud (C4A with Axion processors), and install and configure
+    RabbitMQ on Arm64 Linux (SUSE SLES on GCP and Ubuntu Pro 24.04 on Azure). It focuses on tools
+    and technologies such as RabbitMQ, Erlang, Python, and pika, Linux environments, Arm platforms
+    including Neoverse, and cloud platforms such as Microsoft Azure and Google Cloud. The main
+    steps cover Learn about Arm-based cloud platforms for RabbitMQ, Create an Azure Cobalt 100
+    virtual machine, Install RabbitMQ on Azure Cobalt 100, Validate RabbitMQ on Azure, and Create
+    a firewall rule for RabbitMQ.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will provision Arm-based Linux virtual machines on Google Cloud (C4A with Axion processors)
+      and Microsoft Azure (Cobalt 100), provision an Arm-based SUSE SLES virtual machine on Google
+      Cloud (C4A with Axion processors), and install and configure RabbitMQ on Arm64 Linux (SUSE
+      SLES on GCP and Ubuntu Pro 24.04 on Azure).
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software engineers and platform engineers migrating messaging
+      and event-driven workloads from x86_64 to Arm-based servers, specifically on Microsoft Azure
+      Cobalt 100 Arm processors and Google Cloud C4A virtual machines powered by Axion processors.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Microsoft Azure](https://azure.microsoft.com/)
+      account with access to Cobalt 100-based instances (Dpsv6).; A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
+      account with billing enabled; Basic understanding of message queues and messaging concepts
+      (publishers, consumers); Familiarity with Linux command-line operations.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including RabbitMQ, Erlang, Python, and pika, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as Microsoft Azure and Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Learn about Arm-based cloud platforms for RabbitMQ,
+      Create an Azure Cobalt 100 virtual machine, Install RabbitMQ on Azure Cobalt 100, Validate
+      RabbitMQ on Azure, and Create a firewall rule for RabbitMQ.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ##### Tags
@@ -70,3 +119,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/rag/_index.md b/content/learning-paths/servers-and-cloud-computing/rag/_index.md
index 5db432d5cf..1fbec981b1 100644
--- a/content/learning-paths/servers-and-cloud-computing/rag/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rag/_index.md
@@ -21,6 +21,52 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: d9435cc1dc1fe65432d83934e69993853dd3f294f66f98e9bcfb5f81e6ac6d5f
+  summary: >-
+    Deploy a RAG-based Chatbot with llama-cpp-python using KleidiAI on Google Axion processors
+    walks you through an end-to-end Arm software workflow. It is designed for software developers,
+    ML engineers, and those looking to deploy production-ready LLM chatbots with Retrieval Augmented
+    Generation (RAG) capabilities, knowledge base integration, and performance optimization for
+    Arm Architecture. By the end, you will be able to set up llama-cpp-python optimized for Arm
+    servers, implement RAG architecture using the Facebook AI Similarity Search (FAISS) vector
+    database, and optimize model performance through 4-bit quantization. It focuses on tools and
+    technologies such as Python, Streamlit, Google Axion, Demo, and Hugging Face, Linux environments,
+    Arm platforms including Neoverse, and cloud platforms such as Google Cloud. The main steps
+    cover Set up a RAG based LLM Chatbot, Deploy a RAG-based LLM backend server, Deploy RAG-based
+    LLM frontend server, and The RAG Chatbot and its Performance.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will set up llama-cpp-python optimized for Arm servers, implement RAG architecture using
+      the Facebook AI Similarity Search (FAISS) vector database, and optimize model performance
+      through 4-bit quantization.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is for software developers, ML engineers, and those looking to deploy
+      production-ready LLM chatbots with Retrieval Augmented Generation (RAG) capabilities, knowledge
+      base integration, and performance optimization for Arm Architecture.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Google Cloud Axion (or other Arm)
+      compute instance with at least 16 cores, 8GB of RAM, and 32GB disk space.; Basic understanding
+      of Python and ML concepts.; Familiarity with REST APIs and web services.; Basic knowledge
+      of vector databases.; Understanding of LLM fundamentals.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Python, Streamlit, Google Axion, Demo, and Hugging
+      Face, Linux environments, Arm platforms such as Neoverse, and cloud platforms such as Google
+      Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Set up a RAG based LLM Chatbot, Deploy a RAG-based
+      LLM backend server, Deploy RAG-based LLM frontend server, and The RAG Chatbot and its Performance.
+# END generated_summary_faq
+
 author: Nobel Chowdary Mandepudi
 
 ### Tags
@@ -61,3 +107,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/ran/_index.md b/content/learning-paths/servers-and-cloud-computing/ran/_index.md
index a726d206de..b0983c6728 100644
--- a/content/learning-paths/servers-and-cloud-computing/ran/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ran/_index.md
@@ -17,6 +17,41 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 2b329c566f7fea90010c52f1ce1cb11bccf9d32cf604aaa720bfca5ef1f85fae
+  summary: >-
+    Get started with the Arm 5G RAN Acceleration Library (ArmRAL) walks you through an end-to-end
+    Arm software workflow. It is designed for software developers new to the Arm RAN Acceleration
+    Library (ArmRAL). By the end, you will be able to build and install the Arm RAN Acceleration
+    Library and test the capabilities of your platform. It focuses on tools and technologies such
+    as ArmRAL, 5G, GCC, and Runbook, Linux environments, and Arm platforms including Neoverse.
+    The main steps cover Build and run ArmRAL.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build and install the Arm RAN Acceleration Library and test the capabilities of
+      your platform.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers new to the Arm RAN Acceleration Library
+      (ArmRAL).
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm computer running Linux. Cloud
+      instances can be used, refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including ArmRAL, 5G, GCC, and Runbook, Linux environments,
+      and Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Build and run ArmRAL.
+# END generated_summary_faq
+
 author: Ronan Synnott
 
 test_images:
@@ -62,3 +97,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md
index e98bce6a1a..6dc1a4b8ae 100644
--- a/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md
@@ -18,6 +18,54 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 0877f5f233ec8a50172f4bb9388ad38ae9b890a4d049679ca6ece083d760d872
+  summary: >-
+    Deploy and run distributed AI workloads using Ray on Google Cloud Axion C4A Arm-based VMs,
+    covering parallel tasks, hyperparameter tuning, and model serving with Ray Core, Train, Tune,
+    and Serve. It is designed for DevOps engineers, ML engineers, and software developers who
+    want to deploy and run distributed workloads using Ray on SUSE Linux Enterprise Server (SLES)
+    Arm64, execute parallel tasks, perform hyperparameter tuning, and serve models at scale. By
+    the end, you will be able to install and configure Ray on Google Cloud C4A Axion processors
+    for Arm64, run distributed tasks and parallel workloads using Ray Core, and perform distributed
+    training and hyperparameter tuning using Ray Train and Ray Tune. It focuses on tools and technologies
+    such as Ray, Python, and PyTorch, Linux environments, Arm platforms including Neoverse, and
+    cloud platforms such as Google Cloud. The main steps cover Get started with Ray on Google
+    Axion C4A, Create a firewall rule for Ray Dashboard and Serve, Create a Google Axion C4A Arm
+    virtual machine on GCP, Deploy Ray on GCP SUSE Arm64, and Run Distributed Workloads with Ray.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install and configure Ray on Google Cloud C4A Axion processors for Arm64, run distributed
+      tasks and parallel workloads using Ray Core, and perform distributed training and hyperparameter
+      tuning using Ray Train and Ray Tune. Deploy and run distributed AI workloads using Ray on
+      Google Cloud Axion C4A Arm-based VMs, covering parallel tasks, hyperparameter tuning, and
+      model serving with Ray Core, Train, Tune, and Serve.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for DevOps engineers, ML engineers, and software developers
+      who want to deploy and run distributed workloads using Ray on SUSE Linux Enterprise Server
+      (SLES) Arm64, execute parallel tasks, perform hyperparameter tuning, and serve models at
+      scale.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
+      account with billing enabled; Basic familiarity with Python and distributed systems concepts.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Ray, Python, and PyTorch, Linux environments, Arm
+      platforms such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Get started with Ray on Google Axion C4A, Create a
+      firewall rule for Ray Dashboard and Serve, Create a Google Axion C4A Arm virtual machine
+      on GCP, Deploy Ray on GCP SUSE Arm64, and Run Distributed Workloads with Ray.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ##### Tags
@@ -61,3 +109,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: yes
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md
index fc3ad585ed..ad68be6a4f 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md
@@ -20,6 +20,54 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 9b306bc318491834d0d74dd75221b24081acc20dac7993ccdbd1cb40ba6158ac
+  summary: >-
+    Learn how to install and configure Redis on an Azure Cobalt 100 Arm64 virtual machine, implement
+    real-time messaging with Pub/Sub and Streams, and benchmark throughput and latency on Arm
+    infrastructure. It is designed for developers, DevOps engineers, and platform engineers who
+    want to build real-time messaging systems and event-driven applications using Redis on Arm-based
+    cloud environments. By the end, you will be able to install and configure Redis on Azure Cobalt
+    100 Arm64 virtual machines, implement real-time messaging using Redis Pub/Sub, and build event-driven
+    pipelines using Redis Streams and consumer groups. It focuses on tools and technologies such
+    as Redis and Python, Linux environments, Arm platforms including Neoverse, and cloud platforms
+    such as Microsoft Azure. The main steps cover Understand Redis on Azure Cobalt 100, Create
+    an Azure Cobalt 100 Arm64 virtual machine, Install Redis and build messaging pipelines, and
+    Benchmark Redis performance on Cobalt 100.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install and configure Redis on Azure Cobalt 100 Arm64 virtual machines, implement
+      real-time messaging using Redis Pub/Sub, and build event-driven pipelines using Redis Streams
+      and consumer groups. Learn how to install and configure Redis on an Azure Cobalt 100 Arm64
+      virtual machine, implement real-time messaging with Pub/Sub and Streams, and benchmark throughput
+      and latency on Arm infrastructure.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers, DevOps engineers, and platform engineers who
+      want to build real-time messaging systems and event-driven applications using Redis on Arm-based
+      cloud environments.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Microsoft Azure account](https://azure.microsoft.com/)
+      with access to Cobalt 100 based instances (Dpsv6); Basic knowledge of Linux command-line
+      operations; Familiarity with SSH and remote server access; Basic understanding of databases,
+      caching, and messaging systems.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Redis and Python, Linux environments, Arm platforms
+      such as Neoverse, and cloud platforms such as Microsoft Azure.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Understand Redis on Azure Cobalt 100, Create an Azure
+      Cobalt 100 Arm64 virtual machine, Install Redis and build messaging pipelines, and Benchmark
+      Redis performance on Cobalt 100.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
@@ -63,3 +111,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md b/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md
index da3c4ad565..9f7e1d2128 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md
@@ -17,6 +17,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: eb008543ea4248b231be5e6345546164533edf2bc149613693095812bbbba3d9
+  summary: >-
+    Deploy Redis for data searching on Google Cloud C4A walks you through an end-to-end Arm software
+    workflow. It is designed for developers deploying and optimizing Redis-based data searching
+    workloads on Linux/Arm64 environments, specifically using Google Cloud C4A virtual machines
+    powered by Axion processors. By the end, you will be able to provision an Arm-based SUSE SLES
+    virtual machine on Google Cloud (C4A with Axion processors), install Redis on a SUSE Arm64
+    (C4A) instance, and verify Redis functionality by running the server and performing baseline
+    data insertion and retrieval tests on the Arm64 VM. It focuses on tools and technologies such
+    as Redis and redis-benchmark, Linux environments, Arm platforms including Neoverse, and cloud
+    platforms such as Google Cloud. The main steps cover Get started with Redis on Google Axion
+    C4A, Create a Compute Engine instance, Install Redis, Test Redis, and Benchmark Redis.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will provision an Arm-based SUSE SLES virtual machine on Google Cloud (C4A with Axion
+      processors), install Redis on a SUSE Arm64 (C4A) instance, and verify Redis functionality
+      by running the server and performing baseline data insertion and retrieval tests on the
+      Arm64 VM.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers deploying and optimizing Redis-based data searching
+      workloads on Linux/Arm64 environments, specifically using Google Cloud C4A virtual machines
+      powered by Axion processors.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
+      account with billing enabled; Basic familiarity with [Redis](https://redis.io/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Redis and redis-benchmark, Linux environments, Arm
+      platforms such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Get started with Redis on Google Axion C4A, Create
+      a Compute Engine instance, Install Redis, Test Redis, and Benchmark Redis.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ##### Tags
@@ -58,3 +101,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/redis/_index.md b/content/learning-paths/servers-and-cloud-computing/redis/_index.md
index 0284b323b9..b36db3b062 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis/_index.md
@@ -16,6 +16,44 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 7b9906a3c16ebd3c6b41618be7db76d7a97cc4b16c4da927257fb39a66753e12
+  summary: >-
+    Deploy Redis on Arm walks you through an end-to-end Arm software workflow. It is designed
+    for developers who want to deploy Redis on Arm based virtual machines. By the end, you will
+    be able to understand Redis deployment configurations and install and run Redis in a single-node
+    Arm based instance. It focuses on tools and technologies such as Redis and Runbook, Linux
+    environments, Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft
+    Azure, Google Cloud, and Oracle. The main steps cover Install, configure and connect to Redis
+    and Configure Redis single-node.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand Redis deployment configurations and install and run Redis in a single-node
+      Arm based instance.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers who want to deploy Redis on Arm based virtual
+      machines.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm based instance from a cloud service
+      provider, or an on-premise Arm server.; If you do not have an Arm node, the next section
+      discusses some options.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Redis and Runbook, Linux environments, Arm platforms
+      such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Install, configure and connect to Redis and Configure
+      Redis single-node.
+# END generated_summary_faq
+
 author: Elham Harirpoush
 ### Tags
 skilllevels: Introductory
diff --git a/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md b/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md
index 8c51e74518..d11d324f49 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md
@@ -17,6 +17,55 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 9146285feb38ee45171ac234f605eee08467bbf675a77df231a6dc63c38282f2
+  summary: >-
+    Deploy Redis as a cache for MySQL and PostgreSQL on Arm servers walks you through an end-to-end
+    Arm software workflow. It is designed for developers who want to deploy Redis as a cache on
+    Arm based virtual machines. By the end, you will be able to deploy Redis as a cache for MySQL
+    on AWS, Azure and GCP Arm based instance and deploy Redis as a cache for Postgres on AWS,
+    Azure and GCP Arm based instance. It focuses on tools and technologies such as Terraform,
+    Ansible, Redis, SQL, and MySQL, Linux environments, Arm platforms including Neoverse, and
+    cloud platforms such as AWS, Microsoft Azure, and Google Cloud. The main steps cover Deploy
+    Redis as a cache for MySQL on an AWS Arm based Instance, Deploy Redis as a cache for MySQL
+    on an Azure Arm based Instance, Deploy Redis as a cache for MySQL on a GCP Arm based Instance,
+    Deploy Redis as a cache for Postgres on an AWS Arm based Instance, and Deploy Redis as a cache
+    for Postgres on an Azure Arm based Instance.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will deploy Redis as a cache for MySQL on AWS, Azure and GCP Arm based instance and
+      deploy Redis as a cache for Postgres on AWS, Azure and GCP Arm based instance.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for developers who want to deploy Redis as a cache on Arm based
+      virtual machines.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Amazon Web Services (AWS) [account](https://aws.amazon.com/);
+      An Azure portal [account](https://azure.microsoft.com/en-in/get-started/azure-portal); A
+      Google Cloud [account](https://console.cloud.google.com/); A machine with [Terraform](/install-guides/terraform/),
+      [AWS CLI](/install-guides/aws-cli), [Google Cloud CLI](/install-guides/gcloud), [Azure CLI](/install-guides/azure-cli),
+      [AWS IAM authenticator](https://docs.aws.amazon.com/eks/latest/userguide/install-aws-iam-authenticator.html),
+      and [Ansible](/install-guides/ansible/) installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Terraform, Ansible, Redis, SQL, and MySQL, Linux
+      environments, Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft
+      Azure, and Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Deploy Redis as a cache for MySQL on an AWS Arm based
+      Instance, Deploy Redis as a cache for MySQL on an Azure Arm based Instance, Deploy Redis
+      as a cache for MySQL on a GCP Arm based Instance, Deploy Redis as a cache for Postgres on
+      an AWS Arm based Instance, and Deploy Redis as a cache for Postgres on an Azure Arm based
+      Instance.
+# END generated_summary_faq
+
 author: Jason Andrews
 ### Tags
 skilllevels: Advanced
@@ -52,3 +101,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md
index 0b9e2c12d4..f440f14ac7 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md
@@ -16,6 +16,45 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: a6ed103f2d5a00f4cb6c5df1c0812eb68bbad8746d3f351adc959c6880002bbc
+  summary: >-
+    Learn how to tune Redis walks you through an end-to-end Arm software workflow. It is designed
+    for software developers who want to deploy Redis on Arm-based servers and follow best practices
+    to get performance benefits. By the end, you will be able to learn about kernel parameters
+    that can impact Redis performance, learn about compiler and libraries that can impact Redis
+    performance, and tune a Redis configuration file for deployment. It focuses on tools and technologies
+    such as Redis and Runbook, Linux environments, Arm platforms including Neoverse, and cloud
+    platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps cover Kernel,
+    compiler, and OpenSSL settings and Tune Redis.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will learn about kernel parameters that can impact Redis performance, learn about compiler
+      and libraries that can impact Redis performance, and tune a Redis configuration file for
+      deployment.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers who want to deploy Redis on Arm-based
+      servers and follow best practices to get performance benefits.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Cloud or bare-metal installation of
+      an Redis file server; Review [Learn how to deploy Redis](/learning-paths/servers-and-cloud-computing/redis/)
+      if you do not already have Redis setup.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Redis and Runbook, Linux environments, Arm platforms
+      such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Kernel, compiler, and OpenSSL settings and Tune Redis.
+# END generated_summary_faq
+
 author: Elham Harirpoush
 
 ### Tags
@@ -49,3 +88,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md b/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md
index 48c4d115d7..d75c014130 100644
--- a/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md
@@ -18,6 +18,44 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: d060151c5f6ac7a743fb53cd3d2a10aa23f8f09245122a199a84ae17a8ab5ff9
+  summary: >-
+    Debug Neoverse N2 Reference Design with Arm Development Studio walks you through an end-to-end
+    Arm software workflow. It is designed for software developers who are interested in debugging
+    the Arm Neoverse N2 Reference Firmware Stack. By the end, you will be able to create a debug
+    connection, debug a System Control Processor (SCP), and debug Arm TF-A (Trusted Firmware-A).
+    It focuses on tools and technologies such as Arm Development Studio, Linux environments, and
+    Arm platforms including Neoverse. The main steps cover Set up your development environment,
+    Debugging SCP/LCP/RSE, Debugging BL1, Debugging BL31, and Debugging BL33 / UEFI.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create a debug connection, debug a System Control Processor (SCP), and debug Arm
+      TF-A (Trusted Firmware-A).
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers who are interested in debugging the Arm
+      Neoverse N2 Reference Firmware Stack.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Arm Development Studio, and a license
+      to use it.; An Arm Neoverse Reference Design (RD) Software Stack.; A Fixed Virtual Platform
+      (FVP).; A basic understanding of Neoverse Reference Design (RD) platform boot.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Arm Development Studio, Linux environments, and
+      Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Set up your development environment, Debugging SCP/LCP/RSE,
+      Debugging BL1, Debugging BL31, and Debugging BL33 / UEFI.
+# END generated_summary_faq
+
 author: Daniel Nguyen
 
 ### Tags
@@ -43,3 +81,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md b/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md
index 551ec005f9..27b5e3a798 100644
--- a/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md
@@ -17,6 +17,45 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 8f2515b7c416a821bd397a77297adc263397a8b3cb86e9bb34e646735a21f854
+  summary: >-
+    Get started with the Neoverse Reference Design software stack walks you through an end-to-end
+    Arm software workflow. It is designed for software developers interested in testing the Neoverse
+    Reference Design firmware stack. By the end, you will be able to set up your environment,
+    build the reference firmware stack, and test the reference firmware stack. It focuses on tools
+    and technologies such as Docker, FVP, Arm Development Studio, and Runbook, Linux environments,
+    and Arm platforms including Neoverse. The main steps cover Environment Setup, Build the software
+    stack, and Test With FVP.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will set up your environment, build the reference firmware stack, and test the reference
+      firmware stack.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers interested in testing the Neoverse
+      Reference Design firmware stack.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Some understanding of the [Reference
+      Design software stack architecture](https://neoverse-reference-design.docs.arm.com/en/latest/about/software_stack.html).;
+      Some understanding of the Linux command line.; Optionally a basic understanding of Docker
+      and containers.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Docker, FVP, Arm Development Studio, and Runbook,
+      Linux environments, and Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Environment Setup, Build the software stack, and Test
+      With FVP.
+# END generated_summary_faq
+
 author: 
     - Tom Pilar
     - Daniel Nguyen
@@ -49,3 +88,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md b/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md
index dfb6ca58f3..d9fd1ffc45 100644
--- a/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md
@@ -5,6 +5,50 @@ minutes_to_complete: 10
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: d643c9ef25aa5442aec65ac6a8f48264d4789f8e24ffd6270c2efacce48dd8fc
+  summary: >-
+    Enable reproducible math functions across vector extensions with Arm Performance Libraries
+    walks you through an end-to-end Arm software workflow. It is designed for developers who want
+    to produce reproducible code across vector extensions using math functions in Libamath, a
+    component of Arm Performance Libraries. By the end, you will be able to explain what numerical
+    reproducibility means in numerical software, describe generic applications of numerical reproducibility
+    in the industry, and describe how reproducibility is defined and implemented in Libamath.
+    It focuses on tools and technologies such as Arm Performance Libraries, GCC, LLVM, and Libamath,
+    Linux environments, and Arm platforms including Neoverse. The main steps cover Understand
+    numerical reproducibility in floating-point math, Explore where reproducibility is critical,
+    Enable reproducibility in Libamath, and Verify reproducible results across scalar, Neon, and
+    SVE.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will explain what numerical reproducibility means in numerical software, describe generic
+      applications of numerical reproducibility in the industry, and describe how reproducibility
+      is defined and implemented in Libamath.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers who want to produce reproducible code across
+      vector extensions using math functions in Libamath, a component of Arm Performance Libraries.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm computer running Linux with [Arm
+      Performance Libraries](/install-guides/armpl/) version 26.01 or newer installed; A C compiler
+      such as [GCC](/install-guides/gcc/native/) or Clang installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Arm Performance Libraries, GCC, LLVM, and Libamath,
+      Linux environments, and Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Understand numerical reproducibility in floating-point
+      math, Explore where reproducibility is critical, Enable reproducibility in Libamath, and
+      Verify reproducible results across scalar, Neon, and SVE.
+# END generated_summary_faq
+
 author: Joana Cruz
 
 who_is_this_for: This is an introductory topic for developers who want to produce reproducible code across vector extensions using math functions in Libamath, a component of Arm Performance Libraries. 
@@ -53,3 +97,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md b/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md
index c0f007d925..990a5b65b7 100644
--- a/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md
@@ -16,6 +16,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 180803cf9c6e34c0dda76cafdfcd0ce67edfaca4563f57ae0c36bfefd2199ae9
+  summary: >-
+    Learn how to create a virtual machine in a Realm using Arm Confidential Compute Architecture
+    (CCA) walks you through an end-to-end Arm software workflow. It is designed for software developers
+    who want to learn about Arm Confidential Compute Architecture (CCA). By the end, you will
+    be able to understand the reference software stack used in Arm CCA, build and run the software
+    stack on an Armv-A AEM Base FVP platform with support for RME extensions, and create a virtual
+    machine in a Realm running guest Linux. It focuses on tools and technologies such as GCC,
+    FVP, RME, CCA, and Runbook, Linux environments, and Arm platforms including Neoverse. The
+    main steps cover Build and run the Arm CCA stack on an Arm FVP.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand the reference software stack used in Arm CCA, build and run the software
+      stack on an Armv-A AEM Base FVP platform with support for RME extensions, and create a virtual
+      machine in a Realm running guest Linux.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to learn about Arm Confidential
+      Compute Architecture (CCA).
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An aarch64 or x86_64 computer running
+      Ubuntu 22.04. Cloud instances can be used, refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).;
+      If you use a client application to access your computer running Ubuntu, make sure that X11
+      forwarding is enabled.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including GCC, FVP, RME, CCA, and Runbook, Linux environments,
+      and Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Build and run the Arm CCA stack on an Arm FVP.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
@@ -54,3 +94,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md b/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md
index c81715936a..35c5a874f1 100644
--- a/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md
@@ -16,6 +16,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 27e17ea322cc7dc117f24d9d2007fbc0e6b498840b4097b859b1f376b8d8c9a6
+  summary: >-
+    Run an LLM chatbot with rtp-llm on Arm-based servers walks you through an end-to-end Arm software
+    workflow. It is designed for developers who are interested in running a Large Language Model
+    (LLM) with rtp-llm on Arm-based servers. By the end, you will be able to build rtp-llm on
+    an Arm-based server, download a Qwen model from Hugging Face, and run a Large Language Model
+    with rtp-llm. It focuses on tools and technologies such as LLM, Generative AI, Python, and
+    Hugging Face, Linux environments, Arm platforms including Neoverse, and cloud platforms such
+    as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps cover Background, Run an
+    LLM chatbot with rtp-llm on an Arm server, and Access the chatbot with rtp-llm using the OpenAI-compatible
+    API.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build rtp-llm on an Arm-based server, download a Qwen model from Hugging Face,
+      and run a Large Language Model with rtp-llm.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers who are interested in running a Large Language
+      Model (LLM) with rtp-llm on Arm-based servers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Any Arm Neoverse N2-based or Arm Neoverse
+      V2-based instance running Ubuntu 22.04 LTS from a cloud service provider or an on-premise
+      Arm server.; For the server, at least four cores and 16GB of RAM, with disk storage configured
+      up to at least 32 GB.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including LLM, Generative AI, Python, and Hugging Face, Linux
+      environments, Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft
+      Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Background, Run an LLM chatbot with rtp-llm on an
+      Arm server, and Access the chatbot with rtp-llm using the OpenAI-compatible API.
+# END generated_summary_faq
+
 author: Tianyu Li
 
 ### Tags
@@ -67,3 +109,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md b/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md
index c95dcada3f..e2ee758fbd 100644
--- a/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md
@@ -18,6 +18,51 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 092602df259461e2b22dbf0909a682fbacd59464eea38ab901f38cd0b8c6efd3
+  summary: >-
+    Deploy Ruby on Rails on Arm-based Google Cloud C4A virtual machines walks you through an end-to-end
+    Arm software workflow. It is designed for developers deploying and optimizing Ruby on Rails
+    workloads in Linux Arm64 environments, specifically using Google Cloud C4A virtual machines
+    powered by Axion processors. By the end, you will be able to provision an Arm-based SUSE SLES
+    (SUSE Linux Enterprise Server) virtual machine on Google Cloud (C4A with Axion processors),
+    install Ruby on Rails on a SUSE Arm64 (C4A) instance, and validate Ruby on Rails functionality
+    using PostgreSQL as the database. It focuses on tools and technologies such as Ruby, Rails,
+    and PostgreSQL, Linux environments, Arm platforms including Neoverse, and cloud platforms
+    such as Google Cloud. The main steps cover Get started with Ruby on Rails on Google Axion
+    C4A, Create a Google Axion C4A Arm virtual machine on GCP, Install Ruby on Rails on SUSE Linux,
+    Set up Ruby on Rails baseline testing, and Benchmark Ruby on Rails.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will provision an Arm-based SUSE SLES (SUSE Linux Enterprise Server) virtual machine
+      on Google Cloud (C4A with Axion processors), install Ruby on Rails on a SUSE Arm64 (C4A)
+      instance, and validate Ruby on Rails functionality using PostgreSQL as the database.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers deploying and optimizing Ruby on Rails workloads
+      in Linux Arm64 environments, specifically using Google Cloud C4A virtual machines powered
+      by Axion processors.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
+      account with billing enabled; Basic familiarity with Ruby programming, the Rails framework,
+      and the [PostgreSQL Relational Database](https://www.postgresql.org/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Ruby, Rails, and PostgreSQL, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Get started with Ruby on Rails on Google Axion C4A,
+      Create a Google Axion C4A Arm virtual machine on GCP, Install Ruby on Rails on SUSE Linux,
+      Set up Ruby on Rails baseline testing, and Benchmark Ruby on Rails.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
@@ -60,3 +105,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md
index b07eaa8e7b..79f98e80e1 100644
--- a/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md
@@ -19,6 +19,52 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: c3dd7a0ff9c645635e41b2d9110f4c52de627b752e33c3c6775e1d2e3ffc381d
+  summary: >-
+    Learn to deploy and benchmark Rust applications on Google Cloud C4A virtual machines powered
+    by Arm-based Axion processors. It is designed for developers deploying and optimizing Rust
+    workloads on Linux/Arm64 environments, specifically using Google Cloud C4A virtual machines
+    powered by Axion processors. By the end, you will be able to provision an Arm-based SUSE SLES
+    virtual machine on Google Cloud (C4A with Axion processors), install Rust and configure the
+    development environment on a SUSE Arm64 (C4A) instance, and verify Rust setup by compiling
+    and running a sample program to ensure toolchain functionality. It focuses on tools and technologies
+    such as Rust, Cargo, and Criterion, Linux environments, Arm platforms including Neoverse,
+    and cloud platforms such as Google Cloud. The main steps cover Get started with Rust on Google
+    Axion C4A (Arm Neoverse-V2), Create a Google Axion C4A Arm virtual machine on GCP, Perform
+    baseline testing, Benchmark Rust performance using Criterion, and FIXED, DO NOT MODIFY.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will provision an Arm-based SUSE SLES virtual machine on Google Cloud (C4A with Axion
+      processors), install Rust and configure the development environment on a SUSE Arm64 (C4A)
+      instance, and verify Rust setup by compiling and running a sample program to ensure toolchain
+      functionality. Learn to deploy and benchmark Rust applications on Google Cloud C4A virtual
+      machines powered by Arm-based Axion processors.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers deploying and optimizing Rust workloads on
+      Linux/Arm64 environments, specifically using Google Cloud C4A virtual machines powered by
+      Axion processors.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
+      account with billing enabled; Basic familiarity with [Rust](https://www.rust-lang.org/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Rust, Cargo, and Criterion, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Get started with Rust on Google Axion C4A (Arm Neoverse-V2),
+      Create a Google Axion C4A Arm virtual machine on GCP, Perform baseline testing, Benchmark
+      Rust performance using Criterion, and FIXED, DO NOT MODIFY.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ##### Tags
@@ -61,3 +107,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md b/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md
index 450c55d9bc..51003972ec 100644
--- a/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md
@@ -16,6 +16,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 3d9b35d09c97557dcde807bb83f994f8c3701c0d109c0a9bb388acc603d81b16
+  summary: >-
+    Perform Sentiment Analysis on X on Arm-based EKS clusters walks you through an end-to-end
+    Arm software workflow. It is designed for software developers who want to build an end-to-end
+    ML sentiment analysis solution on an Arm-based Amazon EKS cluster to analyze live posts on
+    X. By the end, you will be able to deploy a text classification model on Amazon EKS with Apache
+    Spark, use Elasticsearch and a Kibana dashboard to analyze the posts on X, and deploy Prometheus
+    and Grafana dashboards to monitor CPU and RAM usage of Kubernetes nodes. It focuses on tools
+    and technologies such as Kubernetes and AWS Elastic Kubernetes Service (EKS), Linux environments,
+    Arm platforms including Neoverse, and cloud platforms such as AWS. The main steps cover Overview,
+    Monitoring sentiment with Elasticsearch and Kibana, Set up Sentiment Analysis with Amazon
+    EKS, and Monitor the cluster with Prometheus and Grafana.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will deploy a text classification model on Amazon EKS with Apache Spark, use Elasticsearch
+      and a Kibana dashboard to analyze the posts on X, and deploy Prometheus and Grafana dashboards
+      to monitor CPU and RAM usage of Kubernetes nodes.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is for software developers who want to build an end-to-end ML sentiment
+      analysis solution on an Arm-based Amazon EKS cluster to analyze live posts on X .
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An AWS account.; A computer with Docker,
+      Terraform, the Amazon eksctl command-line interface, and kubectl installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Kubernetes and AWS Elastic Kubernetes Service (EKS),
+      Linux environments, Arm platforms such as Neoverse, and cloud platforms such as AWS.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview, Monitoring sentiment with Elasticsearch
+      and Kibana, Set up Sentiment Analysis with Amazon EKS, and Monitor the cluster with Prometheus
+      and Grafana.
+# END generated_summary_faq
+
 author: 
     - Pranay Bakre
     - Masoud Koleini
@@ -55,3 +97,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md
index 16983bae60..abfa015148 100644
--- a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md
@@ -15,6 +15,45 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 0a4dd65c6d0c7eb79479217b351e3d6c5b8917512d510283fd4d8387ff1339f5
+  summary: >-
+    Deploy AWS services using the Serverless Framework walks you through an end-to-end Arm software
+    workflow. It is designed for software developers interested in learning how to deploy AWS
+    cloud resources using the Serverless Framework. By the end, you will be able to learn how
+    to set up Serverless Framework for AWS and create a project and deploy AWS Lambda function.
+    It focuses on tools and technologies such as Node.js and Visual Studio Code, Windows environments,
+    Arm platforms including Neoverse, and cloud platforms such as AWS. The main steps cover Background,
+    Set up Serverless Framework for AWS, and Deploy AWS Lambda.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will learn how to set up Serverless Framework for AWS and create a project and deploy
+      AWS Lambda function.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This learning path is for software developers interested in learning how to deploy AWS cloud
+      resources using the Serverless Framework.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows on Arm computer such as the
+      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
+      Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user)
+      is suitable.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Node.js and Visual Studio Code, Windows environments,
+      Arm platforms such as Neoverse, and cloud platforms such as AWS.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Background, Set up Serverless Framework for AWS, and
+      Deploy AWS Lambda.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
@@ -56,3 +95,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md
index 6f3f209196..fdd2ffb358 100644
--- a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md
@@ -16,6 +16,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 2120b6bedf6ea5ae02d8b353a6485f307bcb39d0055c29d9e8a39df37a8b946e
+  summary: >-
+    Deploy and integrate AWS Lambda with DynamoDB using the Serverless Framework walks you through
+    an end-to-end Arm software workflow. It is designed for software developers interested in
+    learning how to deploy serverless applications using the Serverless Framework and Amazon Web
+    Services. It automates several manual deployment steps that developers typically need to perform
+    when deploying microservice-based or IoT applications. By the end, you will be able to create
+    a multi-resource Serverless Framework solution and automate deployment of AWS Lambda function
+    consuming data from DynamoDB. It focuses on tools and technologies such as Node.js and Visual
+    Studio Code, Linux, Windows, and macOS environments, Arm platforms including Neoverse, and
+    cloud platforms such as AWS. The main steps cover Objective, Service declaration, and Deployment.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create a multi-resource Serverless Framework solution and automate deployment of
+      AWS Lambda function consuming data from DynamoDB.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This learning path is for software developers interested in learning how to deploy serverless
+      applications using the Serverless Framework and Amazon Web Services. It automates several
+      manual deployment steps that developers typically need to perform when deploying microservice-based
+      or IoT applications.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows on Arm computer such as the
+      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
+      Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user)
+      is suitable.; Completion of [Deploy AWS services using the Serverless Framework](/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Node.js and Visual Studio Code, Linux, Windows,
+      and macOS environments, Arm platforms such as Neoverse, and cloud platforms such as AWS.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Objective, Service declaration, and Deployment.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
@@ -59,3 +101,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md
index b409d392d4..b1042bb88e 100644
--- a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md
@@ -16,6 +16,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: a31c6f9d674bf41cee16066708c884ca587289e181b7754ff75cdbd85bdb30e3
+  summary: >-
+    Deploy a static website to Amazon S3 and integrate with AWS Lambda and DynamoDB using the
+    Serverless Framework walks you through an end-to-end Arm software workflow. It is designed
+    for software developers interested in learning how to deploy serverless applications using
+    the Serverless Framework and Amazon Web Services. By the end, you will be able to create a
+    multi-resource Serverless Framework solution and automate deployment of a static website to
+    Amazon S3. It focuses on tools and technologies such as Node.js and Visual Studio Code, Linux,
+    Windows, and macOS environments, Arm platforms including Neoverse, and cloud platforms such
+    as AWS. The main steps cover Objective, Service declaration, Website, and Deployment.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will create a multi-resource Serverless Framework solution and automate deployment of
+      a static website to Amazon S3.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This learning path is for software developers interested in learning how to deploy serverless
+      applications using the Serverless Framework and Amazon Web Services.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A Windows on Arm computer such as the
+      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
+      Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user)
+      is suitable.; Completion of the Learning Path that shows you how to [Deploy AWS services
+      using the Serverless Framework](/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Node.js and Visual Studio Code, Linux, Windows,
+      and macOS environments, Arm platforms such as Neoverse, and cloud platforms such as AWS.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Objective, Service declaration, Website, and Deployment.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
@@ -58,3 +98,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/snappy/_index.md b/content/learning-paths/servers-and-cloud-computing/snappy/_index.md
index f7bb4289ab..0580c51974 100644
--- a/content/learning-paths/servers-and-cloud-computing/snappy/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/snappy/_index.md
@@ -17,6 +17,42 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 62edadd9a4163e020b55d33f541a13ceb604c3700ba56787b650563c446a3b0d
+  summary: >-
+    Measure performance of compression libraries on Arm servers walks you through an end-to-end
+    Arm software workflow. It is designed for software developers using compression libraries
+    on Arm servers. By the end, you will be able to install and run lzbench with snappy and zstd
+    and measure compression library performance running on 64-bit Arm AWS EC2 instance. It focuses
+    on tools and technologies such as snappy and Runbook, Linux environments, Arm platforms including
+    Neoverse, and cloud platforms such as AWS and Oracle. The main steps cover Install lzbench
+    and measure algorithm performance.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install and run lzbench with snappy and zstd and measure compression library performance
+      running on 64-bit Arm AWS EC2 instance.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers using compression libraries on Arm
+      servers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/)
+      from an appropriate cloud service provider.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including snappy and Runbook, Linux environments, Arm platforms
+      such as Neoverse, and cloud platforms such as AWS and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Install lzbench and measure algorithm performance.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 test_images:
@@ -54,3 +90,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md b/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md
index e432b93aff..1f8182ec19 100644
--- a/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md
@@ -17,6 +17,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 95da7df14cf36fe01e00868356cf117aa46007ac32e61b0df64d2eea28a5466e
+  summary: >-
+    Optimize the performance of Snort 3 using multithreading walks you through an end-to-end Arm
+    software workflow. It is designed for software developers familiar with Snort who want to
+    optimize performance by leveraging the benefits of multithreading. By the end, you will be
+    able to install Snort and dependencies, configure Snort Lua files to enable multithreading,
+    and use multithreading to process capture files and measure performance. It focuses on tools
+    and technologies such as AWS EC2, Snort3, Bash, and GCC, Linux environments, Arm platforms
+    including Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
+    The main steps cover Install Snort 3 and Dependencies and Test Snort 3 multithreading.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install Snort and dependencies, configure Snort Lua files to enable multithreading,
+      and use multithreading to process capture files and measure performance.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is for software developers familiar with Snort who want to optimize performance
+      by leveraging the benefits of multithreading.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm-based instance from a cloud provider,
+      or an Arm server running Ubuntu 20.04 or 22.04.; A basic understanding of Snort's operation
+      and configuration.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including AWS EC2, Snort3, Bash, and GCC, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
+      Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Install Snort 3 and Dependencies and Test Snort 3
+      multithreading.
+# END generated_summary_faq
+
 author: Preema Merlin Dsouza
 
 ### Tags
@@ -54,3 +94,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md
index f4fa3f808a..0b4d24d8c2 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md
@@ -18,6 +18,55 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 009f2219f1b949be39b4a1dd43a5e4c1ceb5bb273d9a11c3c155315160d593ac
+  summary: >-
+    Run Spark applications on Microsoft Azure Cobalt 100 processors walks you through an end-to-end
+    Arm software workflow. It is designed for This is an advanced topic that introduces Spark
+    deployment on Microsoft Azure Cobalt 100 (Arm-based) virtual machines. It is designed for
+    developers migrating Spark applications from x86_64 to Arm. By the end, you will be able to
+    provision an Azure Arm64 virtual machine using Azure console, learn how to create an Azure
+    Linux 3.0 Docker container, and deploy a Spark application inside an Azure Linux 3.0 Arm64-based
+    Docker container or an Azure Linux 3.0 custom-image based Azure virtual machine. It focuses
+    on tools and technologies such as Apache Spark, Python, and Docker, Linux environments, Arm
+    platforms including Neoverse, and cloud platforms such as Microsoft Azure. The main steps
+    cover Getting started with Microsoft Azure Cobalt 100, Azure Linux 3.0, and Apache Spark,
+    Create an Azure Cobalt 100 Arm64 virtual machine, Set up an Azure Linux 3.0 environment, Install
+    Apache Spark on Azure Cobalt 100 processors, and Validate Apache Spark on Azure Cobalt 100
+    Arm64 VMs.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will provision an Azure Arm64 virtual machine using Azure console, learn how to create
+      an Azure Linux 3.0 Docker container, and deploy a Spark application inside an Azure Linux
+      3.0 Arm64-based Docker container or an Azure Linux 3.0 custom-image based Azure virtual
+      machine.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic that introduces Spark deployment on Microsoft Azure Cobalt 100
+      (Arm-based) virtual machines. It is designed for developers migrating Spark applications
+      from x86_64 to Arm.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Microsoft Azure](https://azure.microsoft.com/)
+      account with access to Cobalt 100 based instances (Dpsv6); A machine with [Docker](/install-guides/docker/)
+      installed; Familiarity with distributed computing concepts and the [Apache Spark architecture](https://spark.apache.org/docs/latest/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Apache Spark, Python, and Docker, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Getting started with Microsoft Azure Cobalt 100, Azure
+      Linux 3.0, and Apache Spark, Create an Azure Cobalt 100 Arm64 virtual machine, Set up an
+      Azure Linux 3.0 environment, Install Apache Spark on Azure Cobalt 100 processors, and Validate
+      Apache Spark on Azure Cobalt 100 Arm64 VMs.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
@@ -65,3 +114,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md
index f3ef3beef3..a9b317c64d 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md
@@ -17,6 +17,55 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: a4ac73af7e8959a546a66ab776c0bca8b60957e6f1729aa00fd78783e6594c0b
+  summary: >-
+    Deploy Apache Spark on Google Axion processors walks you through an end-to-end Arm software
+    workflow. It is designed for This introductory topic is for software developers interested
+    in migrating their Apache Spark workloads from x86_64 platforms to Arm-based platforms, specifically
+    on Google Axion–based C4A virtual machines. By the end, you will be able to start an Arm virtual
+    machine on Google Cloud Platform (GCP) using the C4A Google Axion instance family with RHEL
+    9 as the base image, install and configure Apache Spark on Arm-based GCP C4A instances, and
+    validate Spark functionality through baseline testing. It focuses on tools and technologies
+    such as Apache Spark and Python, Linux environments, Arm platforms including Neoverse, and
+    cloud platforms such as Google Cloud. The main steps cover Getting started with Apache Spark
+    on Google Axion C4A (Arm Neoverse-V2), How to create a Google Axion C4A Arm virtual machine
+    on GCP, How to deploy Apache Spark on Google Axion C4A Arm virtual machines, Apache Spark
+    baseline testing on Google Axion C4A Arm VM, and Apache Spark performance benchmarks on Arm64
+    and x86_64 in Google Cloud.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will start an Arm virtual machine on Google Cloud Platform (GCP) using the C4A Google
+      Axion instance family with RHEL 9 as the base image, install and configure Apache Spark
+      on Arm-based GCP C4A instances, and validate Spark functionality through baseline testing.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This introductory topic is for software developers interested in migrating their Apache
+      Spark workloads from x86_64 platforms to Arm-based platforms, specifically on Google Axion–based
+      C4A virtual machines.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free?utm_source=google&hl=en)
+      account with billing enabled; Familiarity with distributed computing concepts and the [Apache
+      Spark architecture](https://spark.apache.org/docs/latest/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Apache Spark and Python, Linux environments, Arm
+      platforms such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Getting started with Apache Spark on Google Axion
+      C4A (Arm Neoverse-V2), How to create a Google Axion C4A Arm virtual machine on GCP, How
+      to deploy Apache Spark on Google Axion C4A Arm virtual machines, Apache Spark baseline testing
+      on Google Axion C4A Arm VM, and Apache Spark performance benchmarks on Arm64 and x86_64
+      in Google Cloud.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ##### Tags
@@ -59,4 +108,3 @@ layout: "learningpathall"       # All files under learning paths have this same
 learning_path_main_page: "yes"  # Indicates this should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
 
-
diff --git a/content/learning-paths/servers-and-cloud-computing/spark/_index.md b/content/learning-paths/servers-and-cloud-computing/spark/_index.md
index 1cb62dcd41..cd79287aab 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark/_index.md
@@ -15,6 +15,42 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 7a612e45aa64ac93fa9a1fe83da8a7c63ffbc3a4b159184b1a7b04fd46e8ee4b
+  summary: >-
+    Learn how to deploy Spark on AWS Graviton2 walks you through an end-to-end Arm software workflow.
+    It is designed for anyone who wants to deploy Spark on AWS Graviton2. By the end, you will
+    be able to automate Spark EC2 instance creation using Terraform and Ansible and deploy a single
+    instance of Spark on AWS Graviton2. It focuses on tools and technologies such as Terraform,
+    Linux environments, Arm platforms including Neoverse, and cloud platforms such as AWS. The
+    main steps cover Deploy a single node of Spark.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will automate Spark EC2 instance creation using Terraform and Ansible and deploy a single
+      instance of Spark on AWS Graviton2.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for anyone who wants to deploy Spark on AWS Graviton2.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Amazon Web Services (AWS) [account](https://aws.amazon.com/);
+      A machine with [Terraform](/install-guides/terraform/), [AWS CLI](/install-guides/aws-cli/),
+      [AWS IAM authenticator](https://docs.aws.amazon.com/eks/latest/userguide/install-aws-iam-authenticator.html),
+      and [Ansible](/install-guides/ansible/) installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Terraform, Linux environments, Arm platforms such
+      as Neoverse, and cloud platforms such as AWS.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Deploy a single node of Spark.
+# END generated_summary_faq
+
 author: Jason Andrews
 ### Tags
 skilllevels: Introductory
@@ -48,5 +84,3 @@ layout: "learningpathall"       # All files under learning paths have this same
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
 
-
-
diff --git a/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md b/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md
index e2db85b711..7d18abc413 100644
--- a/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md
@@ -16,6 +16,47 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: d3fa3b409ed7cf2ecb521fa4d19af8411718909881861ef3885214824031b541
+  summary: >-
+    Access running containers using Supervisor, SSH, and Remote.It walks you through an end-to-end
+    Arm software workflow. It is designed for software developers who want to learn how to run
+    multiple services in a container and access running containers using Supervisor, SSH, and
+    Remote.It during the debug and test phases of a project. By the end, you will be able to use
+    Supervisor to run multiple services in a container and access a container running in AWS Fargate
+    without changing the security group for debug and test. It focuses on tools and technologies
+    such as Docker, Remote.It, and Supervisor, Linux environments, Arm platforms including Neoverse
+    and Cortex-A, and cloud platforms such as AWS. The main steps cover Introduction to remote
+    container access, Install Supervisor, SSH, and Remote.It, and Access the container running
+    in AWS.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will use Supervisor to run multiple services in a container and access a container running
+      in AWS Fargate without changing the security group for debug and test.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to learn how to run multiple
+      services in a container and access running containers using Supervisor, SSH, and Remote.It
+      during the debug and test phases of a project.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm Linux computer running Docker;
+      An AWS account; A Remote.It account.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Docker, Remote.It, and Supervisor, Linux environments,
+      Arm platforms such as Neoverse and Cortex-A, and cloud platforms such as AWS.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Introduction to remote container access, Install Supervisor,
+      SSH, and Remote.It, and Access the container running in AWS.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
@@ -55,3 +96,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/sve/_index.md b/content/learning-paths/servers-and-cloud-computing/sve/_index.md
index 0a6f5500be..eb79c0d5f1 100644
--- a/content/learning-paths/servers-and-cloud-computing/sve/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sve/_index.md
@@ -16,6 +16,47 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 7b2074e39243a5cd0ccb6a01317c700a4797d8c66353f39aa4197237b6672027
+  summary: >-
+    Port Code to Arm Scalable Vector Extension (SVE) walks you through an end-to-end Arm software
+    workflow. It is designed for software developers using SIMD instructions for High-Performance
+    Computing, Machine Learning, Digital Signal Processing, Audio and Video Codec applications.
+    By the end, you will be able to understand the differences between SVE and Neon for vectorization,
+    compile code for SVE-capable Arm processors, and run SVE instructions on any Armv8-A processor.
+    It focuses on tools and technologies such as SVE, Neon, armie, GCC, and armclang, Linux environments,
+    Arm platforms including Neoverse and Cortex-A, and cloud platforms such as AWS, Microsoft
+    Azure, Google Cloud, and Oracle. The main steps cover From Arm Neon to SVE, Compile for SVE,
+    and Run SVE without capable hardware.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand the differences between SVE and Neon for vectorization, compile code
+      for SVE-capable Arm processors, and run SVE instructions on any Armv8-A processor.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers using SIMD instructions for High-Performance
+      Computing, Machine Learning, Digital Signal Processing, Audio and Video Codec applications.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: General knowledge about SIMD processing,
+      vectorization or Arm Neon.; An Arm computer running Linux. Cloud instances can be used,
+      refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including SVE, Neon, armie, GCC, and armclang, Linux environments,
+      Arm platforms such as Neoverse and Cortex-A, and cloud platforms such as AWS, Microsoft
+      Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around From Arm Neon to SVE, Compile for SVE, and Run SVE
+      without capable hardware.
+# END generated_summary_faq
+
 author: Florent Lebeau
 
 ### Tags
@@ -73,3 +114,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md b/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md
index 32cbc13934..d4c6a154de 100644
--- a/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md
@@ -18,6 +18,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: cc3f88ce181b1614f959497a24fafe736b26e55615792faab6b5dce29fac8d77
+  summary: >-
+    Accelerate search performance with SVE2 MATCH on Arm servers walks you through an end-to-end
+    Arm software workflow. It is designed for database developers, performance engineers, and
+    anyone optimizing data processing workloads on Arm-based cloud instances. By the end, you
+    will be able to understand the purpose and function of SVE2 MATCH instructions, implement
+    a search algorithm using both scalar and SVE2-based MATCH approaches, and benchmark and compare
+    performance between scalar and vectorized implementations. It focuses on tools and technologies
+    such as SVE2, Neon, and Runbook, Linux environments, Arm platforms including Neoverse, and
+    cloud platforms such as AWS, Microsoft Azure, and Google Cloud. The main steps cover Compare
+    search performance using scalar and SVE2 MATCH on Arm Servers.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand the purpose and function of SVE2 MATCH instructions, implement a search
+      algorithm using both scalar and SVE2-based MATCH approaches, and benchmark and compare performance
+      between scalar and vectorized implementations.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for database developers, performance engineers, and anyone
+      optimizing data processing workloads on Arm-based cloud instances.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Access to an [AWS Graviton4, Google
+      Axion, or Azure Cobalt 100 virtual machine](/learning-paths/servers-and-cloud-computing/csp/)
+      from a cloud service provider.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including SVE2, Neon, and Runbook, Linux environments, Arm
+      platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, and Google
+      Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Compare search performance using scalar and SVE2 MATCH
+      on Arm Servers.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 
@@ -53,3 +95,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md b/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md
index fcbec7b9f0..93de18b749 100644
--- a/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md
@@ -15,6 +15,47 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: d15c3083cb881838f6500eb56dfafb636d73bb282484a7f1b8f4a855dda37fb4
+  summary: >-
+    Get ready for performance analysis with Sysreport walks you through an end-to-end Arm software
+    workflow. It is designed for software developers who want to use the system capability reporting
+    tool, Sysreport, to understand and configure the performance features of their Arm Linux system.
+    By the end, you will be able to run Sysreport to get a quick report of the system configuration,
+    discover which performance analysis features are available and enabled, and make configuration
+    changes to improve performance information collection. It focuses on tools and technologies
+    such as Python and Runbook, Linux environments, Arm platforms including Cortex-A and Neoverse,
+    and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps
+    cover Before you begin, Run Sysreport, and Analyze the results.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will run Sysreport to get a quick report of the system configuration, discover which
+      performance analysis features are available and enabled, and make configuration changes
+      to improve performance information collection.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to use the system capability
+      reporting tool, Sysreport, to understand and configure the performance features of their
+      Arm Linux system.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm-based system (bare metal server,
+      cloud instance, developer board) running Linux.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Python and Runbook, Linux environments, Arm platforms
+      such as Cortex-A and Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
+      Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Before you begin, Run Sysreport, and Analyze the results.
+# END generated_summary_faq
+
 author: James Whitaker
 
 ### Tags
@@ -57,3 +98,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
index a35f6aae94..0f76e00850 100644
--- a/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
@@ -16,6 +16,51 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 2c20d30d25a0bb871bdb935d18f7ad8e0740139948133dbd728e10f0add0f94e
+  summary: >-
+    Deploy TensorFlow on Google Cloud C4A (Arm-based Axion VMs) walks you through an end-to-end
+    Arm software workflow. It is designed for software developers deploying and optimizing TensorFlow
+    workloads on Arm64 Linux environments, specifically using Google Cloud C4A virtual machines
+    powered by Axion processors. By the end, you will be able to provision an Arm-based SUSE Linux
+    Enterprise Server (SLES) virtual machine on Google Cloud (C4A with Axion processors), install
+    TensorFlow on a SUSE Arm64 (C4A) instance, and verify TensorFlow by running basic computation
+    and model training tests on Arm64. It focuses on tools and technologies such as TensorFlow,
+    Python, and Keras, Linux environments, Arm platforms including Neoverse, and cloud platforms
+    such as Google Cloud. The main steps cover Get started with TensorFlow on Google Axion C4A,
+    Create a Google Axion C4A Arm virtual machine on GCP, Install TensorFlow, Test TensorFlow
+    baseline performance on Google Axion C4A, and Benchmark TensorFlow model performance using
+    tf.keras.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will provision an Arm-based SUSE Linux Enterprise Server (SLES) virtual machine on Google
+      Cloud (C4A with Axion processors), install TensorFlow on a SUSE Arm64 (C4A) instance, and
+      verify TensorFlow by running basic computation and model training tests on Arm64.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers deploying and optimizing TensorFlow
+      workloads on Arm64 Linux environments, specifically using Google Cloud C4A virtual machines
+      powered by Axion processors.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
+      account with billing enabled; Basic familiarity with [TensorFlow](https://www.tensorflow.org/).
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including TensorFlow, Python, and Keras, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Get started with TensorFlow on Google Axion C4A, Create
+      a Google Axion C4A Arm virtual machine on GCP, Install TensorFlow, Test TensorFlow baseline
+      performance on Google Axion C4A, and Benchmark TensorFlow model performance using tf.keras.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ##### Tags
@@ -56,3 +101,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md b/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md
index f0171ea08b..75414e73a5 100644
--- a/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md
@@ -14,6 +14,44 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 0aaee75d902b938e63e37eca421dd28b91f583e784acdf3cccb1e20b8dda71bd
+  summary: >-
+    Run Text Classification with ThirdAI on Arm servers walks you through an end-to-end Arm software
+    workflow. It is designed for software developers who want to learn how to run text classification
+    tasks with ThirdAI on Arm servers. By the end, you will be able to train, evaluate, and deploy
+    a ThirdAI model and set up your Arm server for text classification tasks with ThirdAI. It
+    focuses on tools and technologies such as Python and ThirdAI, Linux environments, Arm platforms
+    including Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
+    The main steps cover Background and Overview of Learning Path, Train a model for text classification,
+    and Evaluate the model.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will train, evaluate, and deploy a ThirdAI model and set up your Arm server for text
+      classification tasks with ThirdAI.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is for software developers who want to learn how to run text classification tasks with
+      ThirdAI on Arm servers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/)
+      from a cloud service provider or an on-premise Arm server.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Python and ThirdAI, Linux environments, Arm platforms
+      such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Background and Overview of Learning Path, Train a
+      model for text classification, and Evaluate the model.
+# END generated_summary_faq
+
 author: ThirdAI
 
 ### Tags
@@ -51,3 +89,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md
index 069c857033..f1c03a60e5 100644
--- a/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md
@@ -17,6 +17,53 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: edf5bebb0440c110723c87ca27e5f4b21e4da588e4208b5391f7091ae93cb73d
+  summary: >-
+    Deploy a live sensor dashboard with TimescaleDB and Grafana on Google Cloud C4A walks you
+    through an end-to-end Arm software workflow. It is designed for DevOps engineers, database
+    engineers, and software developers who want to deploy and operate TimescaleDB on SUSE Linux
+    Enterprise Server (SLES) Arm64, ingest live time-series sensor data, and visualize it in Grafana.
+    By the end, you will be able to install and configure TimescaleDB on Google Cloud C4A Axion
+    processors by building from source for Arm64, create a real-time sensor data ingestion pipeline
+    using Python with hypertables, continuous aggregates, and retention policies, and build a
+    live sensor dashboard with Grafana that automatically refreshes to display time-series data.
+    It focuses on tools and technologies such as TimescaleDB, PostgreSQL, Python, Grafana, and
+    psycopg2, Linux environments, Arm platforms including Neoverse, and cloud platforms such as
+    Google Cloud. The main steps cover Get started with TimescaleDB on Google Axion C4A, Create
+    a firewall rule for Grafana/TimescaleDB, Create a Google Axion C4A Arm virtual machine on
+    GCP, Set up TimescaleDB on Arm64, and Ingest real-time sensor data on Arm64.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install and configure TimescaleDB on Google Cloud C4A Axion processors by building
+      from source for Arm64, create a real-time sensor data ingestion pipeline using Python with
+      hypertables, continuous aggregates, and retention policies, and build a live sensor dashboard
+      with Grafana that automatically refreshes to display time-series data.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for DevOps engineers, database engineers, and software developers
+      who want to deploy and operate TimescaleDB on SUSE Linux Enterprise Server (SLES) Arm64,
+      ingest live time-series sensor data, and visualize it in Grafana.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
+      account with billing enabled; Basic familiarity with SQL, Python, and Grafana.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including TimescaleDB, PostgreSQL, Python, Grafana, and psycopg2,
+      Linux environments, Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Get started with TimescaleDB on Google Axion C4A,
+      Create a firewall rule for Grafana/TimescaleDB, Create a Google Axion C4A Arm virtual machine
+      on GCP, Set up TimescaleDB on Arm64, and Ingest real-time sensor data on Arm64.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ##### Tags
@@ -68,4 +115,3 @@ layout: "learningpathall"
 learning_path_main_page: yes
 ---
 
-
diff --git a/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md b/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md
index 1463568ac9..c4271d97a0 100644
--- a/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md
@@ -16,6 +16,47 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: e6a652fd0b32796433a380012a79def743bc5452a52ffae785007977a2a8e3e0
+  summary: >-
+    Learn the Arm Neoverse N1 performance analysis methodology walks you through an end-to-end
+    Arm software workflow. It is designed for software developers who want to learn about performance
+    analysis methodology for Linux applications running on Arm Neoverse. By the end, you will
+    be able to understand sampling and counting for performance analysis, learn commonly used
+    hardware metrics, and analyze a sample application using the Arm Telemetry Solution and Linux
+    Perf. It focuses on tools and technologies such as perf, Telemetry, and Runbook, Linux environments,
+    and Arm platforms including Neoverse. The main steps cover Introduction to performance analysis,
+    Build an example application, Gather performance metrics, and Optimize the application.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand sampling and counting for performance analysis, learn commonly used
+      hardware metrics, and analyze a sample application using the Arm Telemetry Solution and
+      Linux Perf.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to learn about performance
+      analysis methodology for Linux applications running on Arm Neoverse.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm Neoverse N1 computer running
+      Linux. A bare metal or cloud metal instance is best because they expose more counters. You
+      can use a virtual machine (VM), but it may offer fewer counters and some commands might
+      not succeed. These instructions have been tested on the `a1.metal` instance type.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including perf, Telemetry, and Runbook, Linux environments,
+      and Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Introduction to performance analysis, Build an example
+      application, Gather performance metrics, and Optimize the application.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
@@ -57,3 +98,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md b/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md
index ace2a9eea9..7c7d9f4aca 100644
--- a/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md
@@ -15,6 +15,48 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: d25121278f9dd40e2fd19d988e35866c6bfa70cfd0b93e2ec4506c8ad8a26d88
+  summary: >-
+    Measure and accelerate PyTorch Inference on Arm servers walks you through an end-to-end Arm
+    software workflow. It is designed for software developers who want to learn how to measure
+    and accelerate the performance of Natural Language Processing (NLP), vision and recommender
+    PyTorch models on Arm-based servers. By the end, you will be able to download and install
+    the PyTorch Benchmarks suite, evaluate PyTorch model inference performance on an Arm-based
+    server using the PyTorch Benchmark suite, and compare the model inference performance using
+    eager mode and `torch.compile` mode in PyTorch. It focuses on tools and technologies such
+    as Python and PyTorch, Linux environments, Arm platforms including Neoverse, and cloud platforms
+    such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps cover Measure and accelerate
+    the inference performance of PyTorch models on Arm servers.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will download and install the PyTorch Benchmarks suite, evaluate PyTorch model inference
+      performance on an Arm-based server using the PyTorch Benchmark suite, and compare the model
+      inference performance using eager mode and `torch.compile` mode in PyTorch.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to learn how to measure and
+      accelerate the performance of Natural Language Processing (NLP), vision and recommender
+      PyTorch models on Arm-based servers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/)
+      from a cloud service provider or an on-premise Arm server.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Python and PyTorch, Linux environments, Arm platforms
+      such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Measure and accelerate the inference performance of
+      PyTorch models on Arm servers.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
@@ -59,3 +101,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md
index 83f8348def..272251c2f2 100644
--- a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md
@@ -15,6 +15,43 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 523ff99ccb8d13543f64839fa21040191d2a9ff7ce7c78fa590e8775fac754a6
+  summary: >-
+    Learn about Neoverse Non-cache PMU events using C and Assembly Language walks you through
+    an end-to-end Arm software workflow. It is designed for software and hardware engineers to
+    learn about why common non-cache PMU events occur. By the end, you will be able to describe
+    common non-cache PMU events and understand why specific code triggers specific PMU events
+    on the Neoverse N2 Core. It focuses on tools and technologies such as C, Assembly, and Runbook,
+    Linux environments, and Arm platforms including Neoverse. The main steps cover Introduction
+    to the PMU, Topdown Methodology L1 Events, TLB Events, and Operation Mix Events.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will describe common non-cache PMU events and understand why specific code triggers
+      specific PMU events on the Neoverse N2 Core.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software and hardware engineers to learn about why common
+      non-cache PMU events occur.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Some familiarity with performance analysis.;
+      The ability to read Arm assembly code.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including C, Assembly, and Runbook, Linux environments, and
+      Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Introduction to the PMU, Topdown Methodology L1 Events,
+      TLB Events, and Operation Mix Events.
+# END generated_summary_faq
+
 author: Johanna Skinnider
 
 ### Tags
@@ -54,3 +91,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md
index 37f9f656e7..f0b30e4aec 100644
--- a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md
@@ -16,6 +16,44 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 1b309980bef983966d948036e309e132b56f767161967187e72c6a9f81f17dce
+  summary: >-
+    Learn about Neoverse Cache PMU Events using C and Assembly Language walks you through an end-to-end
+    Arm software workflow. It is designed for software and hardware engineers who want to learn
+    about the causes of common Neoverse cache Performance Monitoring Unit (PMU) events. By the
+    end, you will be able to describe common cache PMU events, describe why some code triggers
+    PMU events on the Neoverse N2 core, and describe the events triggered during common scenarios.
+    It focuses on tools and technologies such as C, Assembly, and Runbook, Linux environments,
+    and Arm platforms including Neoverse. The main steps cover Introduction to the PMU, L1 Data
+    Cache Events, L1 Instruction Cache Events, L2 Unified Cache Events, and LL Cache Events.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will describe common cache PMU events, describe why some code triggers PMU events on
+      the Neoverse N2 core, and describe the events triggered during common scenarios.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software and hardware engineers who want to learn about the
+      causes of common Neoverse cache Performance Monitoring Unit (PMU) events.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Knowledge of performance analysis.;
+      The ability to read Arm assembly code.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including C, Assembly, and Runbook, Linux environments, and
+      Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Introduction to the PMU, L1 Data Cache Events, L1
+      Instruction Cache Events, L2 Unified Cache Events, and LL Cache Events.
+# END generated_summary_faq
+
 author: Johanna Skinnider
 
 ### Tags
@@ -55,3 +93,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md b/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md
index 295df8896a..1e7ac23920 100644
--- a/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md
@@ -19,6 +19,50 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 13bba1dee1c948f8b751a71479a5106014a2c21dab6ce3968a08bed9e3363de1
+  summary: >-
+    Scan multi-architecture containers with Trivy on Azure Cobalt 100 walks you through an end-to-end
+    Arm software workflow. It is designed for developers and DevOps engineers who want to integrate
+    security scanning into CI/CD pipelines for multi-architecture container images. By the end,
+    you will be able to build and scan multi-architecture container images using Trivy on Azure
+    Cobalt 100, configure self-hosted GitHub Actions Arm runners for CI/CD pipelines, and enforce
+    security gates in CI pipelines based on vulnerability severity. It focuses on tools and technologies
+    such as Trivy, Docker, GitHub Actions, and YAML, Linux environments, Arm platforms including
+    Neoverse, and cloud platforms such as Microsoft Azure. The main steps cover Learn Azure Cobalt
+    100 Arm64 and Use Trivy for Security Scanning, Create an Azure Cobalt 100 Arm64 virtual machine,
+    and Build and scan multi-architecture container images with Trivy.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build and scan multi-architecture container images using Trivy on Azure Cobalt
+      100, configure self-hosted GitHub Actions Arm runners for CI/CD pipelines, and enforce security
+      gates in CI pipelines based on vulnerability severity.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers and DevOps engineers who want to integrate
+      security scanning into CI/CD pipelines for multi-architecture container images.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Microsoft Azure account](https://azure.microsoft.com/)
+      with access to Cobalt 100 based instances (Dpsv6); Docker installed and basic knowledge
+      of containerization; Familiarity with CI/CD concepts; Basic knowledge of Linux command-line
+      operations; Familiarity with GitHub Actions runners.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Trivy, Docker, GitHub Actions, and YAML, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Learn Azure Cobalt 100 Arm64 and Use Trivy for Security
+      Scanning, Create an Azure Cobalt 100 Arm64 virtual machine, and Build and scan multi-architecture
+      container images with Trivy.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
@@ -67,3 +111,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md b/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md
index 9a2ce9023e..042fc6150c 100644
--- a/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md
@@ -19,6 +19,47 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 9f2b3f6c5e76ecb754de5c0fd84278ff71f71c5c7906acdc06911f2feff1f5fd
+  summary: >-
+    Tune network workloads on Arm-based bare-metal instances walks you through an end-to-end Arm
+    software workflow. It is designed for engineers who want to tune the performance of network
+    workloads on Arm Neoverse-based bare-metal instances. By the end, you will be able to set
+    up Apache Tomcat and wrk2 to benchmark HTTP on an Arm Neoverse bare‑metal host, establish
+    a reproducible baseline baseline (file‑descriptor limits, logging, thread counts, fixed core
+    set), and tune NIC queue count to match available cores and measure impact. It focuses on
+    tools and technologies such as Apache Tomcat, wrk2, and OpenJDK 21, Linux environments, and
+    Arm platforms including Neoverse. The main steps cover Set up Tomcat, Establish baseline performance,
+    Tune performance with NIC queue counts, NUMA-based tuning, and IOMMU-based tuning.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will set up Apache Tomcat and wrk2 to benchmark HTTP on an Arm Neoverse bare‑metal host,
+      establish a reproducible baseline baseline (file‑descriptor limits, logging, thread counts,
+      fixed core set), and tune NIC queue count to match available cores and measure impact.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for engineers who want to tune the performance of network workloads
+      on Arm Neoverse-based bare-metal instances.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm Neoverse-based bare-metal server
+      running Ubuntu 24.04 to run Apache Tomcat; Access to an x86_64 bare-metal server running
+      Ubuntu 24.04 to run `wrk2`; Basic familiarity with Java applications.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Apache Tomcat, wrk2, and OpenJDK 21, Linux environments,
+      and Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Set up Tomcat, Establish baseline performance, Tune
+      performance with NIC queue counts, NUMA-based tuning, and IOMMU-based tuning.
+# END generated_summary_faq
+
 author: Ying Yu, Ker Liu, Rui Chang
 
 ### Tags
@@ -50,3 +91,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md
index 10ec4db9b2..942091a3ef 100644
--- a/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md
@@ -18,6 +18,52 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: ee805f703acd45612c9a94e60c6322866dc1c8ff25d48de2fb4cd9067948c93e
+  summary: >-
+    Deploy TypeScript on Google Cloud C4A virtual machines walks you through an end-to-end Arm
+    software workflow. It is designed for developers deploying and optimizing TypeScript workloads
+    on Arm64 Linux environments, specifically using Google Cloud C4A virtual machines powered
+    by Axion processors. By the end, you will be able to provision an Arm-based SUSE Linux Enterprise
+    Server (SLES) virtual machine (VM) on Google Cloud, install TypeScript on a SUSE Arm64 C4A
+    instance, and validate TypeScript functionality by creating, compiling, and running a simple
+    TypeScript script on a Arm64 VM. It focuses on tools and technologies such as TypeScript,
+    node.js, and npm, Linux environments, Arm platforms including Neoverse, and cloud platforms
+    such as Google Cloud. The main steps cover Get started with TypeScript on Google Axion C4A
+    instances, Create a Google Axion C4A Arm virtual machine on GCP, Install TypeScript, Establish
+    a TypeScript performance baseline, and Benchmark TypeScript performance.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will provision an Arm-based SUSE Linux Enterprise Server (SLES) virtual machine (VM)
+      on Google Cloud, install TypeScript on a SUSE Arm64 C4A instance, and validate TypeScript
+      functionality by creating, compiling, and running a simple TypeScript script on a Arm64
+      VM.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers deploying and optimizing TypeScript workloads
+      on Arm64 Linux environments, specifically using Google Cloud C4A virtual machines powered
+      by Axion processors.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
+      account with billing enabled; Basic familiarity with [TypeScript](https://www.typescriptlang.org/)
+      and Node.js runtime environment.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including TypeScript, node.js, and npm, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Get started with TypeScript on Google Axion C4A instances,
+      Create a Google Axion C4A Arm virtual machine on GCP, Install TypeScript, Establish a TypeScript
+      performance baseline, and Benchmark TypeScript performance.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ##### Tags
@@ -60,3 +106,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md b/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md
index 1f7051acff..f29b64d3cd 100644
--- a/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md
@@ -15,6 +15,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 035bd363fc8ae772acf52d7e392f2db27087267706aeec86b4098c497e5fe91d
+  summary: >-
+    Migrate applications that leverage performance libraries walks you through an end-to-end Arm
+    software workflow. It is designed for both C and C++ developers who want to migrate applications
+    that rely on optimized performance libraries from x86 to Arm Architecture. By the end, you
+    will be able to describe the differences between standard and performance libraries, incorporate
+    optimized libraries, and port a basic application from x86 to AArch64. It focuses on tools
+    and technologies such as Arm Compiler for Linux, CPP, and Runbook, Linux environments, Arm
+    platforms including Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud,
+    and Oracle. The main steps cover Introduction to Libraries, Set up your environment, Use an
+    optimized math library, and Moving from x86 to AArch64.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will describe the differences between standard and performance libraries, incorporate
+      optimized libraries, and port a basic application from x86 to AArch64.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is for both C and C++ developers who want to migrate applications that
+      rely on optimized performance libraries from x86 to Arm Architecture.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Access to both an Arm and an x86-based
+      cloud instance.; Intermediate understanding of C++, compilers, and Linux.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Arm Compiler for Linux, CPP, and Runbook, Linux
+      environments, Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft
+      Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Introduction to Libraries, Set up your environment,
+      Use an optimized math library, and Moving from x86 to AArch64.
+# END generated_summary_faq
+
 author: Kieran Hejmadi
 
 ### Tags
@@ -50,3 +90,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md b/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md
index fb6e85873f..e41f748b27 100644
--- a/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md
@@ -16,6 +16,44 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: beb244c7766c474b47942b86f1cdd0d124c1cccb74dbe40f0b6c0917d0b3d31a
+  summary: >-
+    Install Vectorscan (Hyperscan on Arm) and use it with Snort 3 walks you through an end-to-end
+    Arm software workflow. It is designed for software developers using Hyperscan who want to
+    migrate to Arm. By the end, you will be able to install and run Vectorscan on an Arm-based
+    instance, install and run Snort 3 on your instance, and run Snort 3 with Vectorscan on capture
+    files and and measure performance. It focuses on tools and technologies such as Vectorscan,
+    Linux environments, Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft
+    Azure, Google Cloud, and Oracle. The main steps cover Run Vectorscan on Arm and Install Snort3
+    and run it with Vectorscan on Arm.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install and run Vectorscan on an Arm-based instance, install and run Snort 3 on
+      your instance, and run Snort 3 with Vectorscan on capture files and and measure performance.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers using Hyperscan who want to migrate
+      to Arm.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/)
+      from a cloud service provider or an Arm server with Ubuntu 20.04 or Ubuntu 22.04 installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Vectorscan, Linux environments, Arm platforms such
+      as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Run Vectorscan on Arm and Install Snort3 and run it
+      with Vectorscan on Arm.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md b/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
index f84ff7b4d4..d3fde326df 100644
--- a/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
@@ -19,6 +19,54 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 487e9829c6e08798ad01be7a01f192e10e99cb06b71108575f1919c134eece02
+  summary: >-
+    Run vLLM inference with INT4 quantization on Arm servers walks you through an end-to-end Arm
+    software workflow. It is designed for developers interested in building and optimizing vLLM
+    for Arm-based servers. This Learning Path shows you how to quantize large language models
+    (LLMs) to INT4, serve them using an OpenAI-compatible API, and benchmark model accuracy with
+    the LM Evaluation Harness. By the end, you will be able to build an optimized vLLM for aarch64
+    with oneDNN and the Arm Compute Library (ACL), set up all runtime dependencies including PyTorch,
+    llmcompressor, and Arm-optimized libraries, and quantize an LLM (DeepSeek‑V2‑Lite) to 4-bit
+    integer (INT4) precision. It focuses on tools and technologies such as vLLM, LM Evaluation
+    Harness, LLM, Generative AI, and Python, Linux environments, Arm platforms including Neoverse,
+    and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps
+    cover Build and validate vLLM for inference, Quantize an LLM to INT4, Serve high throughput
+    inference with vLLM, and Evaluate accuracy with LM Evaluation Harness.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build an optimized vLLM for aarch64 with oneDNN and the Arm Compute Library (ACL),
+      set up all runtime dependencies including PyTorch, llmcompressor, and Arm-optimized libraries,
+      and quantize an LLM (DeepSeek‑V2‑Lite) to 4-bit integer (INT4) precision.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers interested in building and optimizing vLLM
+      for Arm-based servers. This Learning Path shows you how to quantize large language models
+      (LLMs) to INT4, serve them using an OpenAI-compatible API, and benchmark model accuracy
+      with the LM Evaluation Harness.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm-based Linux server (Ubuntu 22.04+
+      recommended) with a minimum of 32 vCPUs, 64 GB RAM, and 64 GB free disk space; Python 3.12
+      and basic familiarity with Hugging Face Transformers and quantization.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including vLLM, LM Evaluation Harness, LLM, Generative AI,
+      and Python, Linux environments, Arm platforms such as Neoverse, and cloud platforms such
+      as AWS, Microsoft Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Build and validate vLLM for inference, Quantize an
+      LLM to INT4, Serve high throughput inference with vLLM, and Evaluate accuracy with LM Evaluation
+      Harness.
+# END generated_summary_faq
+
 author:
    - Nikhil Gupta
 
@@ -73,3 +121,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/vllm/_index.md b/content/learning-paths/servers-and-cloud-computing/vllm/_index.md
index bb9edd18b6..44caa39e35 100644
--- a/content/learning-paths/servers-and-cloud-computing/vllm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vllm/_index.md
@@ -16,6 +16,46 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: db5987ec7987375d9b51de843b0e1faf0e61731b14c5a563d19aaad26f50f6bb
+  summary: >-
+    Build and Run vLLM on Arm Servers walks you through an end-to-end Arm software workflow. It
+    is designed for software developers and AI engineers interested in learning how to use the
+    vLLM library on Arm servers. By the end, you will be able to build vLLM from source on an
+    Arm server, download a Qwen LLM from Hugging Face, and run local batch inference using vLLM.
+    It focuses on tools and technologies such as vLLM, LLM, Generative AI, Python, and Hugging
+    Face, Linux environments, Arm platforms including Neoverse, and cloud platforms such as AWS,
+    Microsoft Azure, Google Cloud, and Oracle. The main steps cover Build a vLLM from Source Code,
+    Run batch inference using vLLM, and Run an OpenAI-compatible server.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build vLLM from source on an Arm server, download a Qwen LLM from Hugging Face,
+      and run local batch inference using vLLM.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers and AI engineers interested in learning
+      how to use the vLLM library on Arm servers.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/)
+      from a cloud service provider, or a local Arm Linux computer with at least 8 CPUs and 16
+      GB RAM.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including vLLM, LLM, Generative AI, Python, and Hugging Face,
+      Linux environments, Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft
+      Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Build a vLLM from Source Code, Run batch inference
+      using vLLM, and Run an OpenAI-compatible server.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
@@ -59,3 +99,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md b/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
index bd6523437a..ad3351f205 100644
--- a/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
@@ -3,6 +3,45 @@ title: Run the vvenc H.266 encoder on Arm servers
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 4ed20056b2d82bd2c9ab1afe3d74657df9ac09808592d843c1b143626a8668ac
+  summary: >-
+    Run the vvenc H.266 encoder on Arm servers walks you through an end-to-end Arm software workflow.
+    It is designed for software developers who want to build and run the VVenC® (Fraunhofer Versatile
+    Video Encoder) H.266 project on Arm servers and measure the performance. By the end, you will
+    be able to build the VVenC® H.266 encoder project on an Arm-based server and run vvenc on
+    an Arm-based server to encode a real 1080p video file and measure the performance. It focuses
+    on tools and technologies such as vvenc, Linux environments, Arm platforms including Neoverse,
+    and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps
+    cover Build and run the H.266 VVenC encoder on Arm servers.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build the VVenC® H.266 encoder project on an Arm-based server and run vvenc on
+      an Arm-based server to encode a real 1080p video file and measure the performance.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to build and run the VVenC®
+      (Fraunhofer Versatile Video Encoder) H.266 project on Arm servers and measure the performance.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm Linux system or an [Arm-based
+      instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider.
+      This Learning Path has been tested on an Arm Neoverse N2-based Alibaba cloud ECS instance(g8y),
+      running Ubuntu 22.04.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including vvenc, Linux environments, Arm platforms such as
+      Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Build and run the H.266 VVenC encoder on Arm servers.
+# END generated_summary_faq
+
 author: Willen Yang
 
 minutes_to_complete: 20
@@ -54,5 +93,6 @@ further_reading:
 
 weight: 1
 layout: learningpathall
-learning_path_main_page: "yes" 
+learning_path_main_page: "yes"
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/whisper/_index.md b/content/learning-paths/servers-and-cloud-computing/whisper/_index.md
index 0e5867246d..bd5476f752 100644
--- a/content/learning-paths/servers-and-cloud-computing/whisper/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/whisper/_index.md
@@ -20,6 +20,49 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: e130630b5ae4626641779d0b66d3c1c132b90899cd3d6db07a46df8957c4da38
+  summary: >-
+    Accelerate Whisper on Arm with Hugging Face Transformers walks you through an end-to-end Arm
+    software workflow. It is designed for software developers familiar with basic machine learning
+    concepts and looking to run the OpenAI Whisper Automatic Speech Recognition (ASR) model efficiently,
+    using an Arm-based cloud instance. By the end, you will be able to install the dependencies
+    for the Whisper ASR Model, run the Whisper model using Hugging Face Transformers, and enable
+    performance-enhancing features for running the model on Arm CPUs. It focuses on tools and
+    technologies such as Python, Whisper, Demo, and Hugging Face, Linux environments, Arm platforms
+    including Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
+    The main steps cover Set up the Whisper Model and Run the Whisper Model.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install the dependencies for the Whisper ASR Model, run the Whisper model using
+      Hugging Face Transformers, and enable performance-enhancing features for running the model
+      on Arm CPUs.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is for software developers familiar with basic machine learning concepts
+      and looking to run the OpenAI Whisper Automatic Speech Recognition (ASR) model efficiently,
+      using an Arm-based cloud instance.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An [Arm-based compute instance](/learning-paths/servers-and-cloud-computing/intro/)
+      running Ubuntu with 32 cores, 8GB of RAM, and 32GB of disk space.; Basic knowledge of Python.;
+      Familiarity with machine learning concepts.; Familiarity with the fundamentals of the Whisper
+      ASR Model.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Python, Whisper, Demo, and Hugging Face, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
+      Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Set up the Whisper Model and Run the Whisper Model.
+# END generated_summary_faq
+
 author: Nobel Chowdary Mandepudi
 
 ### Tags
@@ -55,3 +98,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md b/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
index 0ff40028ea..6a284565fd 100644
--- a/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
@@ -9,6 +9,40 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 46f2645fb6ef298508af93569554315cfa2564be9891acabf1c874c94e52d70d
+  summary: >-
+    Deploy MySQL and WordPress on an always free tier Arm shape walks you through an end-to-end
+    Arm software workflow. It is designed for developers who want to install WordPress on Oracle
+    Cloud Infrastructure (OCI) using always free tier. By the end, you will be able to install
+    MySQL and WordPress on an Arm server running in OCI. It focuses on tools and technologies
+    such as MySQL and WordPress, Linux environments, Arm platforms including Neoverse, and cloud
+    platforms such as Oracle. The main steps cover Install WordPress and MySQL on an OCI Arm server.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install MySQL and WordPress on an Arm server running in OCI.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers who want to install WordPress on Oracle Cloud
+      Infrastructure (OCI) using always free tier.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An OCI account; An Arm compute instance
+      deployed on OCI with Oracle Linux.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including MySQL and WordPress, Linux environments, Arm platforms
+      such as Neoverse, and cloud platforms such as Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Install WordPress and MySQL on an OCI Arm server.
+# END generated_summary_faq
+
 author: Frédéric -lefred- Descamps
 
 who_is_this_for: This is an introductory topic for developers who want to install WordPress on Oracle Cloud Infrastructure (OCI) using always free tier.
@@ -51,3 +85,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/zlib/_index.md b/content/learning-paths/servers-and-cloud-computing/zlib/_index.md
index e9b43205f0..fb580a2164 100644
--- a/content/learning-paths/servers-and-cloud-computing/zlib/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/zlib/_index.md
@@ -17,6 +17,51 @@ prerequisites:
 
 generate_summary_faq: true
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v1
+  generated_at: '2026-04-30T18:58:19Z'
+  generator: template
+  source_hash: 8916f83f621505dc5406f14e70d04e702ea304950e34b4b76dc1bbc987bcc4e3
+  summary: >-
+    Learn how to build and use zlib-ng on Arm servers, using its Neon SIMD and ARMv8 CRC32 optimizations
+    to improve compression performance compared to the system default zlib. It is designed for
+    software developers who want to improve data compression performance on Arm servers by replacing
+    the default zlib with zlib-ng, an actively maintained fork that includes Neon SIMD and ARMv8
+    CRC32 optimizations. By the end, you will be able to build zlib-ng in zlib-compatible mode
+    on an Arm server, run example applications using zlib-ng as a drop-in replacement, and measure
+    and analyze performance improvements with zlib-ng. It focuses on tools and technologies such
+    as zlib, Linux environments, and Arm platforms including Neoverse. The main steps cover Build
+    and install zlib-ng on Arm servers, Improve Python application performance using zlib-ng,
+    and Use perf to analyze zlib-ng performance.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build zlib-ng in zlib-compatible mode on an Arm server, run example applications
+      using zlib-ng as a drop-in replacement, and measure and analyze performance improvements
+      with zlib-ng. Learn how to build and use zlib-ng on Arm servers, using its Neon SIMD and
+      ARMv8 CRC32 optimizations to improve compression performance compared to the system default
+      zlib.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to improve data compression
+      performance on Arm servers by replacing the default zlib with zlib-ng, an actively maintained
+      fork that includes Neon SIMD and ARMv8 CRC32 optimizations.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm Linux computer or an [Arm based
+      instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider
+      running Ubuntu 22.04 or Ubuntu 24.04.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including zlib, Linux environments, and Arm platforms such
+      as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Build and install zlib-ng on Arm servers, Improve
+      Python application performance using zlib-ng, and Use perf to analyze zlib-ng performance.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 test_images:
@@ -50,3 +95,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/reports/generated-summary-faq/latest-run.yml b/reports/generated-summary-faq/latest-run.yml
index ed6a25239a..a2ab511171 100644
--- a/reports/generated-summary-faq/latest-run.yml
+++ b/reports/generated-summary-faq/latest-run.yml
@@ -1,21 +1,16320 @@
 latest_run:
-  timestamp: ""
-  mode: ""
+  timestamp: '2026-04-30T18:58:19Z'
+  mode: write
   require_enable_flag: true
-  path_filter: ""
+  path_filter: ''
   limit: 0
-  run_url: ""
-  git_ref: ""
-  git_sha: ""
-  actor: ""
+  run_url: https://github.com/chrismoroney/arm-learning-paths/actions/runs/25183771014
+  git_ref: STESOL-345
+  git_sha: 89f5a746a3191977f5bd001f449a4f577acfb914
+  actor: chrismoroney
   template_version: summary-faq-v1
   totals:
-    processed: 0
-    added: 0
+    processed: 407
+    added: 407
     updated: 0
     unchanged: 0
     skipped: 0
     errors: 0
     removed: 0
-  paths: []
-history: []
+  paths:
+  - path: content/learning-paths/automotive/openadkit1_container/_index.md
+    status: added
+    changed_on_disk: true
+    summary_changed: true
+    faq_changed: true
+    faq_changes:
+      before_count: 0
+      after_count: 5
+      added_questions:
+      - What will you accomplish in this Learning Path?
+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to deploy and run containerized autonomous driving simulations using Autoware
+      Open AD Kit on Arm Neoverse with Docker, demonstrating SOAFEE-based Shift-Left development workflows.
+      It is desi...
+    source_hash: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
+  - path: content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
+    status: added
+    changed_on_disk: true
+    summary_changed: true
+    faq_changed: true
+    faq_changes:
+      before_count: 0
+      after_count: 5
+      added_questions:
+      - What will you accomplish in this Learning Path?
+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to implement functional safety isolation for autonomous driving systems
+      on Arm Neoverse using DDS-based communication, containerized deployment, and ISO 26262 compliance
+      principles. It is de...
+    source_hash: 92a6dac2b1674a44cd623a0c8c3189b38438124a6067ed7ca776999ff1d8b5bf
+  - path: content/learning-paths/automotive/system76-auto/_index.md
+    status: added
+    changed_on_disk: true
+    summary_changed: true
+    faq_changed: true
+    faq_changes:
+      before_count: 0
+      after_count: 5
+      added_questions:
+      - What will you accomplish in this Learning Path?
+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to build and run the Arm Automotive Solutions Software Reference Stack
+      locally on the System76 Thelio Astra desktop using Multipass virtualization and Yocto build tools.
+      It is designed for a...
+    source_hash: 2b758d2dcf28a683ab164e28578a736d6d730b81dfbc01a6765619052fcdebd0
+  - path: content/learning-paths/automotive/zenacssdebug/_index.md
+    status: added
+    changed_on_disk: true
+    summary_changed: true
+    faq_changed: true
+    faq_changes:
+      before_count: 0
+      after_count: 5
+      added_questions:
+      - What will you accomplish in this Learning Path?
+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to debug the Arm Zena CSS Reference Software Stack using Arm Development
+      Studio on a Fixed Virtual Platform, covering RSE, Safety Island, and Linux kernel debugging workflows.
+      It is designed...
+    source_hash: 8dccdbdd4d9727f3466987ce918d9df0ed9d4d4f28cd613162bacab01d9c18f3
+  - path: content/learning-paths/cross-platform/_example-learning-path/_index.md
+    status: added
+    changed_on_disk: true
+    summary_changed: true
+    faq_changed: true
+    faq_changes:
+      before_count: 0
+      after_count: 5
+      added_questions:
+      - What will you accomplish in this Learning Path?
+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn what type of content belongs in a Learning Path and how to format it. It is
+      designed for content creators and software developers who want to share Arm related information
+      as a step-by-step guid...
+    source_hash: a58d5eb4c4256f3e4f4374344ef0e73836e8b51ac7223b0a5238b22137ec0819
+  - path: content/learning-paths/cross-platform/adler32/_index.md
+    status: added
+    changed_on_disk: true
+    summary_changed: true
+    faq_changed: true
+    faq_changes:
+      before_count: 0
+      after_count: 5
+      added_questions:
+      - What will you accomplish in this Learning Path?
+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to use GitHub Copilot to write Neon intrinsics that accelerate the Adler32
+      checksum algorithm on Arm platforms, achieving significant performance improvements over standard
+      C implementations...
+    source_hash: 37b19106fba70423ba8c58f82097d9251f0ae208e882c852f9c4531afcebb46c
+  - path: content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md
+    status: added
+    changed_on_disk: true
+    summary_changed: true
+    faq_changed: true
+    faq_changes:
+      before_count: 0
+      after_count: 5
+      added_questions:
+      - What will you accomplish in this Learning Path?
+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to automate integration testing of MCP servers using Testcontainers and
+      PyTest, with hands-on examples and GitHub Actions CI/CD configuration. It is designed for software
+      developers and QA e...
+    source_hash: 597877722e5f277e5558e29ecd33878ac2262b70a84a9769325b3c3b0ce06e58
+  - path: content/learning-paths/cross-platform/avh_cicd/_index.md
+    status: added
+    changed_on_disk: true
+    summary_changed: true
+    faq_changed: true
+    faq_changes:
+      before_count: 0
+      after_count: 5
+      added_questions:
+      - What will you accomplish in this Learning Path?
+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to integrate Arm Virtual Hardware into a GitHub Actions CI/CD workflow
+      for automated embedded software testing and validation. It is designed for embedded software developers
+      new to Arm Virt...
+    source_hash: b6a7439a701f6ae09ce8c61f02150a61fa6ecc1151050e903492e16794999676
+  - path: content/learning-paths/cross-platform/avh_cicd2/_index.md
+    status: added
+    changed_on_disk: true
+    summary_changed: true
+    faq_changed: true
+    faq_changes:
+      before_count: 0
+      after_count: 5
+      added_questions:
+      - What will you accomplish in this Learning Path?
+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to integrate Arm Virtual Hardware with AWS and GitHub Actions for automated
+      CI/CD workflows, including CloudFormation setup and test automation. It is designed for DevOps integrating
+      AVH int...
+    source_hash: 251b6edf6a016f9fc73eb35216f56b2001465fbca8253bd7b6e6f9f4afcd25e6
+  - path: content/learning-paths/cross-platform/cca_rme/_index.md
+    status: added
+    changed_on_disk: true
+    summary_changed: true
+    faq_changed: true
+    faq_changes:
+      before_count: 0
+      after_count: 5
+      added_questions:
+      - What will you accomplish in this Learning Path?
+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to use Arm Development Studio to explore Realm Management Extension (RME)
+      and Arm Confidential Compute Architecture (CCA) through a bare-metal example running on the Arm
+      Architecture Envelop...
+    source_hash: a1685fb43efecb9690d03c7f8ee64ab20ae8aece91190e2223705106568b1038
+  - path: content/learning-paths/cross-platform/cpp-loop-size-context/_index.md
+    status: added
+    changed_on_disk: true
+    summary_changed: true
+    faq_changed: true
+    faq_changes:
+      before_count: 0
+      after_count: 5
+      added_questions:
+      - What will you accomplish in this Learning Path?
+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to optimize C++ loop performance on Arm by providing boundary information
+      to the compiler, enabling SIMD vectorization and reducing runtime through compile-time context.
+      It is designed for C...
+    source_hash: a639e60da034fd04e891c9236e9fe5dab47cca87f6174828275ef5a76c43c32e
+  - path: content/learning-paths/cross-platform/docker/_index.md
+    status: added
+    changed_on_disk: true
+    summary_changed: true
+    faq_changed: true
+    faq_changes:
+      before_count: 0
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+    summary_preview: Learn how to build, run, and share multi-architecture Docker images for Arm and x86
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+    summary_preview: Learn how to build multi-architecture Docker images for Arm and x86 using Docker
+      Build Cloud, with GitHub Actions automation for faster builds without emulation. It is designed
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+    summary_preview: Learn how to implement a dynamic memory allocator in C, understanding heap management
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+    summary_preview: Learn how to use the Eigen linear algebra library on Arm systems with ASIMD and SVE
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+    summary_preview: Learn how to deploy ERNIE-4.5 Mixture of Experts models on Armv9 devices using llama.cpp,
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+      designed for ...
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+    summary_preview: Learn how Arm and x86 floating-point implementations follow IEEE 754 standards, identify
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+    summary_preview: Learn how to optimize C/C++ applications using function multiversioning on Arm64
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+    summary_preview: Learn how to use GitHub Actions with Arm-hosted runners to build multi-architecture
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+    summary_preview: Learn how to build a GitLab CI/CD pipeline using Google Axion-based self-hosted runners.
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+    summary_preview: Learn how to build GitLab CI/CD pipelines using GitLab-hosted Arm64 runners to containerize
+      C applications, store images in GitLab Container Registry, and deploy on Arm infrastructure. It
+      is designed ...
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+    summary_preview: Learn how to identify and fix potential problems with integer and floating-point
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+    summary_preview: Learn how to port architecture-specific intrinsics to Arm processors. It is designed
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+    summary_preview: Learn how to run custom software benchmarks on IP Explorer simulation platforms and
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+    summary_preview: Learn how to use KleidiAI micro-kernels to accelerate AI inference performance through
+      optimized matrix multiplication on Arm processors with architecture features like i8mm. It is designed
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+    summary_preview: Learn how to optimize C/C++ code using compiler autovectorization techniques including
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+    summary_preview: Learn how to develop and test a modern C++ library using CMake, GoogleTest, and matrix
+      processing as a practical example on Arm platforms. It is designed for developers who want to learn
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+    summary_preview: Learn how to use llvm-mca with Compiler Explorer to analyze Arm assembly performance,
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+    summary_preview: Learn how to deploy a Model Context Protocol server on Raspberry Pi 5 and use the
+      OpenAI Agent SDK to create AI agents with custom tools for local inference. It is designed for LLM
+      and IoT developers ...
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+    summary_preview: Learn how to reduce memory latency impact in applications using cache alignment and
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+    summary_preview: Learn how to build MNN on an Armv9 system, run text, vision, and audio prompts with
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+    summary_preview: Learn how to implement and optimize matrix multiplication using Arm's Scalable Matrix
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+    summary_preview: Learn how to create and train a PyTorch neural network for MNIST digit classification,
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+    summary_preview: Learn how to install and configure Remote.It for secure remote device access using
+      SSH and other services, with proxy and peer-to-peer connection options. It is designed for software
+      developers who wa...
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+    summary_preview: Learn how to use the C99 restrict keyword to indicate non-overlapping memory regions
+      and enable better compiler optimizations for vectorization on Arm platforms. It is designed for
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+    summary_preview: Learn how to build an embedded Rust application for Arm processors, run it on a Fixed
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+    summary_preview: Learn how to use SIMD.info to port SIMD intrinsics across Arm architectures, including
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+    summary_preview: Learn how to write high-performance SIMD code using the SIMD Loops project, with
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+    summary_preview: Learn how to write SIMD code in Rust on Arm platforms using Neon intrinsics, portable
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+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to profile and optimize ExecuTorch models using SME2 acceleration on Arm
+      platforms, including operator-level analysis and performance bottleneck identification. It is designed
+      for developers...
+    source_hash: 36f7dfe13c8c940abbaad2b61620b3b1c6d97f580de15e573b45ef7760753797
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+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
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+    summary_preview: Learn how to deploy Tinkerblox UltraEdge HPC-I for edge AI and mixed workloads on
+      Arm platforms, including installation and configuration on Debian, Ubuntu, and Yocto systems. It
+      is designed for busin...
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+      - How is the Learning Path structured?
+      removed_questions: []
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+    summary_preview: Learn how to compare Arm Neoverse and Intel x86 top-down performance analysis methodologies
+      using PMU counters, Linux Perf, and topdown-tool to identify bottlenecks across architectures. It
+      is designe...
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+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
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+    summary_preview: Learn how to migrate x86-64 SIMD code to Arm64 by mapping Intel SSE/AVX to Arm Neon,
+      SVE, and SME, with code examples and migration strategies using autovectorization or intrinsics.
+      It is designed for...
+    source_hash: 26450ae17f7ed4242c52456c2780ffce5fad36b56dfc4a8482e8f236f855e134
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+      removed_questions: []
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+    summary_preview: Learn how to optimize SIMD performance on Arm by restructuring data layouts from
+      Array-of-Structures to Structure-of-Arrays, with practical examples using Neon and SVE intrinsics.
+      It is designed for C...
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+  - path: content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
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+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
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+    summary_preview: Learn how to sample and profile CPU instructions using WindowsPerf with Arm Statistical
+      Profiling Extension (SPE) on Windows on Arm, demonstrated with CPython workload analysis. It is
+      designed for dev...
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+  - path: content/learning-paths/cross-platform/woa_azure/_index.md
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+      - Which tools, languages, or platforms does it cover?
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+      removed_questions: []
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+    summary_preview: Learn how to create and connect to a Windows on Arm virtual machine in Microsoft
+      Azure using the Azure Marketplace and RDP. It is designed for software developers interested using
+      Windows on Arm in th...
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+  - path: content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
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+      removed_questions: []
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+    summary_preview: Learn how to build and deploy distributed Zenoh systems on Arm devices like Raspberry
+      Pi, using pub/sub, storage, and queryable models for scalable robotics and IoT applications. It
+      is designed for ro...
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+  - path: content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
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+      removed_questions: []
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+    summary_preview: Learn how to design and integrate a custom AXI-Lite peripheral with a Cortex-A9 processor
+      on the Zybo Z7-10 board using Vivado, configuring GPIOs to control LEDs based on switch inputs.
+      It is designed...
+    source_hash: d8c48c293b2d316d5e68e18ab9e81157ad114a64cf2efa6951374a55d25238d6
+  - path: content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
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+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit and
+      run inference on the Ethos-U85 NPU. It is designed for embedded developers who want to deploy a
+      neural network model t...
+    source_hash: 8585bb59efa67b3a92314e4382339fc5c0a14bb458931c0d98f2748379003857
+  - path: content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
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+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
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+    summary_preview: Learn how to build a motion-detection device with Raspberry Pi Pico (RP2040 Cortex-M0+)
+      using Arduino IDE, PIR sensors, and interrupt-driven programming on baremetal. It is designed for
+      software devel...
+    source_hash: 3ddb97eb97a4c7ede7951410086198ee793a9d452a79b607f211873971bd375d
+  - path: content/learning-paths/embedded-and-microcontrollers/armds/_index.md
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+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
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+    summary_preview: Learn how to import and build example projects in Arm Development Studio and debug
+      embedded applications using Fixed Virtual Platforms (FVPs) or hardware with DSTREAM debug probes.
+      It is designed for ...
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+  - path: content/learning-paths/embedded-and-microcontrollers/asm/_index.md
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+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
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+    summary_preview: Learn how to write mixed C and assembly programs for Cortex-M microcontrollers using
+      Keil MDK, following Arm Procedure Call Standard conventions. It is designed for software developers
+      who are interes...
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+  - path: content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
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+      removed_questions: []
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+    summary_preview: Learn how to create a custom Balena OS image, run it on Arm Virtual Hardware, and
+      deploy IoT applications to a virtual Raspberry Pi 4 device. It is designed for embedded software
+      developers interested...
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+  - path: content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
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+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
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+    summary_preview: Learn how to set up AWS IoT Greengrass Core on Arm Virtual Hardware and deploy AWS
+      Greengrass components to a virtual Raspberry Pi 4 device. It is designed for embedded software developers
+      interested ...
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+  - path: content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
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+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
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+    summary_preview: Learn how to build Matter reference examples on Arm Virtual Hardware, demonstrate
+      device communication, and automate testing with GitHub Actions CI/CD workflows. It is designed for
+      embedded software d...
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+    summary_preview: Learn how to export and compile a PaddleOCR text recognition model using TVMC and
+      deploy it on the Arm Corstone-300 FVP with Cortex-M55 processors. It is designed for software developers
+      interested in...
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+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
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+      removed_questions: []
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+    summary_preview: Learn how to create and integrate a virtual LED peripheral using the Virtual IO interface
+      of Arm Virtual Hardware to simulate real-world peripherals. It is designed for software developers
+      new to Arm ...
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+  - path: content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
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+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
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+    summary_preview: Learn how to build a complete IoT solution in Azure that streams, stores, monitors,
+      aggregates, and visualizes telemetry data from Arm devices using IoT Hub, Stream Analytics, Cosmos
+      DB, and Azure Fun...
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+      - Which tools, languages, or platforms does it cover?
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+    summary_preview: Learn how to create, build, and run a bare-metal embedded application for Armv8-A
+      processors using Arm Compiler for Embedded and Fixed Virtual Platforms, including basic exception
+      handling. It is desi...
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+  - path: content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
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+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
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+      removed_questions: []
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+    summary_preview: Learn how to deploy containerized embedded applications and firmware onto an Arm
+      Cortex-M core from a Cortex-A core using containerd, K3s, and the hybrid-runtime on Arm Virtual
+      Hardware. It is designe...
+    source_hash: 3394bf994039e441d57dced2abb64d725077d4672e0e68dc71f1de50e58f0408
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+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
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+    summary_preview: Learn how to create, build, and debug an RTX5 RTOS-based application using Keil μVision
+      with CMSIS-RTOS2 API and Event Recorder for embedded Cortex-M development. It is designed for software
+      developer...
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+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
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+    summary_preview: Learn how to create, configure, and debug an RTX5 RTOS application using Keil Studio
+      for VS Code with CMSIS-RTOS2 API for embedded Cortex-M development. It is designed for software
+      developers new to R...
+    source_hash: f4d1ab3448590c5654e2479937c9eec3e378d05229b29a45b8675139f40ac177
+  - path: content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
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+    summary_preview: Learn how to prototype and port DSP algorithms using the CMSIS-DSP Python package,
+      mapping Python code to efficient C implementations for embedded Cortex-M and Cortex-A platforms.
+      It is designed for d...
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+  - path: content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
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+      removed_questions: []
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+    summary_preview: Learn how to implement context switching operations on Arm Cortex-M processors using
+      the Memory Protection Unit and SysTick exception in a bare-metal environment. It is designed for
+      software developer...
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+  - path: content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
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+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
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+      removed_questions: []
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+    summary_preview: Learn how to set up and use code coverage in Keil MDK with FVPs to verify that your
+      embedded application tests execute all code paths. It is designed for embedded software developers
+      new to the code-c...
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+  - path: content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
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+      removed_questions: []
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+    summary_preview: Device-to-Device communication with Device Connect walks you through an end-to-end
+      Arm software workflow. It is designed for developers wiring up heterogeneous edge fleets, where
+      devices need a shared...
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+      - What do you need before you start?
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+      removed_questions: []
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+    summary_preview: Learn how to connect AI agents to Arm-based edge devices using Device Connect for
+      structured device access and Strands for agent orchestration, with examples for both simulated and
+      physical robots. It...
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+    summary_preview: Learn how to create a Dockerfile, build a Docker image with Arm Compiler for Embedded
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+      for embedded s...
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+    summary_preview: Learn how to collect and preprocess audio data using Edge Impulse, train an audio
+      classification model, and deploy it to the Arduino Nano RP2040 to control LEDs based on voice commands.
+      It is designed...
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+    summary_preview: Develop a image classification neural network model and deploy it on an STM32 B-L475E-IOT01A2
+      board. It is designed for embedded software developers interested in building neural network models
+      for mi...
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+    summary_preview: Learn where Arm architecture is used in microcontrollers and discover microcontroller
+      hardware options for software development on Arm Cortex-M processors. It is designed for software
+      developers worki...
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+    summary_preview: Learn what differentiates TinyML from other AI domains, explore Arm-based edge devices
+      for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed Virtual
+      Platform. It is ...
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+    summary_preview: Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300 virtual
+      hardware to understand complete IoT software stack construction. It is designed for embedded software
+      developers ...
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+    summary_preview: Learn how to set up a Jetson Orin Nano with a MIPI CSI-2 camera and perform real-time
+      object detection from live video and image files using DetectNet and TensorRT. It is designed for
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+    summary_preview: Learn how to import, build, and debug your first Keil Studio Cloud project. It is
+      designed for embedded software developers new to Keil Studio Cloud. By the end, you will be able
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+    summary_preview: Learn how to boot and configure the NXP FRDM i.MX 93 Arm board with Linux, create
+      a user with sudo access, connect to WiFi using ConnMan, and transfer files over the network. It
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+    summary_preview: Learn how to boot a Linux software stack on Arm Fixed Virtual Platforms (FVPs), then
+      debug Trusted Firmware-A and the Linux kernel using Arm Development Studio. It is designed for developers
+      who want ...
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+    summary_preview: Learn how to install the Python version of llama.cpp on a Raspberry Pi 5, download
+      an LLM from Hugging Face, assess memory and performance, and run the model using Python bindings.
+      It is designed for ...
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+    summary_preview: Learn the software migration methodology for porting Linux workloads from x86_64
+      to aarch64, including using Arm compilers, porting compiler intrinsics, and deploying applications
+      in containers. It is...
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+    summary_preview: Learn how to build examples from the Machine Learning Evaluation Kit (MLEK) and run
+      them on the Arm Ecosystem FVP for machine learning application development on microcontrollers.
+      It is designed for e...
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+    summary_preview: Learn how to understand and select physical and virtual hardware targets for ML application
+      development with Cortex-M and Ethos-U, identify software tools, and find example applications. It
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+    summary_preview: Learn how to create debug configurations for virtual platforms and development boards
+      in Arm Development Studio, including setting up connections for Fast Models and DSTREAM debug probes.
+      It is design...
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+    summary_preview: Learn how to bring up ExecuTorch executor_runner firmware on the NXP FRDM i.MX 93
+      Cortex-M33 using Linux RemoteProc, compile .pte models for Ethos-U65, and run inference with NPU
+      acceleration. It is d...
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+    summary_preview: Learn how to migrate a CMSIS v5-based CMSIS-Pack with device support to CMSIS v6
+      and update example projects for compatibility with the new CMSIS version. It is designed for maintainers
+      of CMSIS-Packs...
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+      removed_questions: []
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+    summary_preview: Learn how to migrate CMSIS v5 projects to CMSIS v6 by identifying supported toolchains,
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+      for embedded de...
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+    summary_preview: Learn how to run large language models locally on the Raspberry Pi 5 using Ollama,
+      control GPIO-connected devices, and deploy a privacy-first web-based smart home assistant without
+      cloud services. It ...
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+    summary_preview: Learn how to build a voice-controlled bot on a Raspberry Pi that listens for a wake
+      word, converts speech to text using Google Speech Recognition, sends requests to ChatGPT's API,
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+    summary_preview: Learn how to build and run multiple software examples on the Raspberry Pi 4, including
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+    summary_preview: Learn how to compile the Llama 3 large language model using ExecuTorch, deploy it
+      to a Raspberry Pi 5, and understand techniques for running LLMs in embedded environments. It is
+      designed for anyone in...
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+    summary_preview: Learn how to reduce compile time for embedded Linux projects by installing a Raspberry
+      Pi OS file system on an Arm server, building the MXNet machine learning framework, and transferring
+      it to a Raspb...
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+    summary_preview: Setup tools and start programming with Raspberry Pi Pico. It is designed for embedded
+      software developers new to Raspberry Pi Pico. By the end, you will be able to install the Raspberry
+      Pi Pico SDK, r...
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+    summary_preview: Learn how to profile Linux kernel modules using Arm Streamline to identify performance
+      bottlenecks, analyze both out-of-tree and in-tree modules, and use Statistical Profiling Extension
+      (SPE) for deep...
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+    summary_preview: Build a letter recognition neural network model using TensorFlow and deploy it on
+      an STM32 B-L475E-IOT01A2 board. It is designed for software developers interested in building network
+      models for micro...
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+    summary_preview: Learn how to build and run the reference Trusted Firmware-M tests and example application
+      on Arm Fixed Virtual Platforms for secure microcontroller development. It is designed for software
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+    summary_preview: Learn how to train a CNN image classification model using PyTorch, convert it to
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+    summary_preview: Learn how to install Keil MDK Tools, run a TrustZone hello world example on the NXP
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+    summary_preview: Learn how to acquire and print materials, assemble a universal SBC rack mount system
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+    summary_preview: Learn how to debug microcontrollers using µVision with basic run/stop debug, advanced
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+    summary_preview: Learn how to import, convert, and build uvprojx-based projects to csolution format
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+    summary_preview: Learn how to install vcpkg, initialize it, create vcpkg-configuration.json files,
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+    summary_preview: Learn how to identify Arm-based targets for TinyML, install Fixed Virtual Platforms,
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+    summary_preview: Introduction to building a minimal Yocto Linux image and running it on 64-bit Qemu
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+    summary_preview: Learn how to run a YOLO object detection model on the Himax WiseEye2 module, build
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+    summary_preview: Learn how to build and run Zephyr RTOS applications on the Arm Corstone-300 Fixed
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+    summary_preview: Learn how to install Workbench for Zephyr extension in VS Code, set up the complete
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+    summary_preview: Learn how to create and run Ubuntu containers on ChromeOS Crostini using LXC with
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+    summary_preview: Learn how to build and deploy high-fidelity robotic simulations and reinforcement
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+    summary_preview: Learn how to build and optimize quantized LLMs using llama.cpp on NVIDIA DGX Spark
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+    summary_preview: Learn how to build a Retrieval-Augmented Generation (RAG) pipeline on NVIDIA DGX
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+    summary_preview: Learn how to build an offline voice assistant combining speech-to-text via faster-whisper
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+    summary_preview: Learn how to run pre-trained AI models locally using Docker Model Runner and build
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+    summary_preview: Learn how to develop and build cross-platform desktop applications using the Electron
+      Framework on Windows on Arm devices. It is designed for developers who want to learn how to develop
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+    summary_preview: Learn how to automate Windows application builds on Arm architecture using GitHub
+      Arm-hosted runners and GitHub Actions workflows. It is designed for This introductory tutorial is
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+    summary_preview: Learn how to create and manage Arm-based Linux virtual machines using Hyper-V on
+      Windows on Arm devices. It is designed for software developers who want to use Linux virtual machines
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+    summary_preview: Learn where the Arm architecture is used in desktop and laptop computers and find
+      hardware for software development on Arm platforms. It is designed for developers working on laptops
+      and desktops and ...
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+    summary_preview: Learn how to build, test, and verify KleidiCV with Scalable Matrix Extensions (SME)
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+      who want t...
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+    summary_preview: Learn how to configure the LLVM toolchain with Visual Studio to build native Windows
+      on Arm applications using the open-source PuTTY project. It is designed for software developers
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+    summary_preview: Learn how to apply Arm Memory Tagging Extension (MTE) to protect dynamic memory allocations
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+    summary_preview: Learn how to install and configure Arch Linux for Arm with the i3 window manager
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+    summary_preview: Learn how to fine-tune large language models using PyTorch and Hugging Face on NVIDIA
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+    summary_preview: Learn how to use each of the tools supplied with Arm Performance Studio (formerly
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+    summary_preview: Learn how to capture frame data from Android applications and analyze performance
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+    summary_preview: Learn how to build an Android chatbot app using Arm's AI Chat library to run GGUF
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+    summary_preview: Learn how to build real-time image processing pipelines using Halide on Android,
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+    summary_preview: Learn how to create and configure an Android project with OpenCV support to process
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+    summary_preview: Learn how to implement face detection on Android devices using OpenCV, camera frame
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+    summary_preview: Get started with Scalable Vector Extension 2 (SVE2) on Android walks you through
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+    summary_preview: Learn how to optimize hardware ray tracing with Lumen on Android devices powered
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+    summary_preview: Learn how to build ONNX Runtime and the generate() API for Android to run a Phi-3
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+    summary_preview: Learn how to build a hands-free selfie Android application using MediaPipe multimodal
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+    summary_preview: Learn how to build an Android chat application with Llama models using ExecuTorch,
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+    summary_preview: Learn how to build a customer support chatbot for Android using Llama 3.2, ExecuTorch,
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+    summary_preview: Learn how to detect and debug memory safety bugs in Android applications using Arm
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+    summary_preview: Get started with Arm Accuracy Super Resolution (Arm ASR) walks you through an end-to-end
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+    summary_preview: Get started with Unity on Android walks you through an end-to-end Arm software workflow.
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+    summary_preview: Profile Android game performance in Godot with Arm Performance Studio walks you through
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+    summary_preview: How to Enable Hardware Ray Tracing on Lumen for Android Devices walks you through
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+    summary_preview: Get started with Arm hardware walks you through an end-to-end Arm software workflow.
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+    summary_preview: Learn how to run LLM inference on Android devices using MediaPipe with KleidiAI-enhanced
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+      who want to adjust ...
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+    summary_preview: Learn how to benchmark KleidiAI micro-kernels in ExecuTorch using SME/SME2 instructions
+      on Arm64 platforms with ETDump profiling and analysis. It is designed for developers, performance
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+    summary_preview: Learn how to fine-tune and evaluate Neural Super Sampling (NSS) models using PyTorch
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+    summary_preview: Learn how to run example C programs on AArch64 Linux to gain an introductory understanding
+      of the Arm Memory Tagging Extension (MTE). It is designed for developers who want to gain some experience
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+    summary_preview: Learn how to enable Arm Memory Tagging Extension (MTE) on a Google Pixel 8 smartphone,
+      trigger memory bug crashes, and interpret bug reports. It is designed for developers interested
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+    summary_preview: Learn how to capture high-quality frame datasets from Unreal Engine 5.5 gameplay
+      for training and evaluating neural graphics models like Neural Super Sampling. It is designed for
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+    summary_preview: Learn how to configure ML Extensions for Vulkan emulation and enable Neural Super
+      Sampling (NSS) in Unreal Engine for real-time upscaling. It is designed for developers experimenting
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+    summary_preview: Learn how to build, optimize, and deploy machine learning models using ONNX Runtime
+      on Arm64 platforms, including Raspberry Pi, cloud instances, and Android devices. It is designed
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+    summary_preview: Learn how to optimize vertex representations and analyze Vertex Memory Efficiency
+      using Arm Frame Advisor for improved GPU performance on Android. It is designed for Android graphics
+      application devel...
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+    summary_preview: Learn how to build llama.cpp with KleidiAI and SME2 support to profile and accelerate
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+    summary_preview: Learn how to build ONNX Runtime with KleidiAI and SME2 support for Android and profile
+      ONNX model performance to compare acceleration improvements. It is designed for software developers,
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+    summary_preview: Learn how to profile ML model execution times and application performance on Arm
+      Android devices using Arm Performance Studio and Android Studio Profiler. It is designed for software
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+    summary_preview: Learn how to deploy Unity applications to Android, profile code running on Arm devices,
+      and analyze performance data for optimization. It is designed for Unity developers wanting to analyze
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+    summary_preview: Learn how to apply post-training quantization to PyTorch models using TorchAO and
+      export INT8 models to .vgf format with the ExecuTorch Arm backend. It is designed for ML developers
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+    summary_preview: Learn how to use the Vulkan ray tracing API to implement realistic shadows, reflections,
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+      rendering a...
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+    summary_preview: Learn how to use Frame Advisor's Render Graph view to identify and resolve graphics
+      performance issues in Android applications. It is designed for Mobile application developers who
+      wish to improve gra...
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+    summary_preview: Learn how to convert and deploy the Stable Audio Open Small text-to-audio model to
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+      to deploy the ...
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+    summary_preview: Learn how to convert the Stable Audio Open Small model to ExecuTorch format and build
+      an audio generation application for Android or macOS. It is designed for developers who want to
+      deploy the Stable ...
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+    summary_preview: Learn how to build and install a Unity game on an Orange Pi 5 single-board computer
+      running Droid OS. It is designed for software developers who want to build and run a Unity game
+      on an Arm-based sing...
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+    summary_preview: Learn how to install Arm integration packages in Unity to view GPU metrics in Unity
+      Profiler and annotate games with markers for Arm Performance Studio. It is designed for Unity developers
+      who are tar...
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+    summary_preview: Learn how to use Arm Neon intrinsics in Unity C# scripts to optimize code on Android
+      and collect performance data using Unity Profiler. It is designed for Developers interested in leveraging
+      the Unity...
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+    summary_preview: Learn how to integrate Unity's Machine Learning Agents toolkit into games deployable
+      to Arm-powered Android devices. It is designed for Developers interested in leveraging the Unity
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+    summary_preview: Learn how to download, convert, and deploy Vision Transformers using the Mobile Neural
+      Network framework on Android with KleidiAI micro-kernels for optimized performance. It is designed
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+    summary_preview: Learn how to build and optimize a multimodal Voice Assistant application on Android
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+      a multimoda...
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+    summary_preview: Build an end-to-end, on-device voice assistant that understands both speech and emotion
+      using Whisper, HuBERT, ONNX Runtime, and a local LLM with llama.cpp on Arm. It is designed for developers,
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+    summary_preview: Learn how to set up ML Emulation Layers for Vulkan, run sample applications using
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+    summary_preview: Learn how to build and deploy an AI agent application on Arm servers using llama.cpp
+      and llama-cpp-agent with KleidiAI optimization for efficient LLM inference and function calling.
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+    summary_preview: Learn how to automate the deployment of an Arm-based Kubernetes cluster on Azure
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+    summary_preview: Learn how to deploy Apache Arrow and Arrow Flight on Google Cloud Axion C4A processors
+      for high-throughput columnar data processing and low-latency data transport with MinIO integration.
+      It is designe...
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+    summary_preview: Learn how to build llama.cpp, quantize the Arcee AFM-4.5B model, and run optimized
+      inference on AWS Graviton4 instances with perplexity-based quality evaluation. It is designed for
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+    summary_preview: Learn how to build llama.cpp, quantize the Arcee AFM-4.5B model, and run optimized
+      inference on Google Cloud Axion instances with perplexity-based quality evaluation. It is designed
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+    summary_preview: Learn how to deploy and manage applications on Google Cloud GKE Arm64 clusters using
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+      for developers an...
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+    summary_preview: Learn how to write correct concurrent C++ code when porting applications from x86
+      to Arm by understanding memory ordering differences and using best practices to avoid race conditions.
+      It is designed ...
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+    summary_preview: Learn how to automate x86-to-Arm application migration using the Arm MCP Server,
+      with AI-assisted compatibility checks, C++ code refactoring, and Docker-based validation on Arm
+      cloud platforms. It is ...
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+    summary_preview: Learn how to migrate C applications between Arm platforms using Kiro's AI-assisted
+      tooling to identify hardware dependencies and implement abstraction layers for cross-platform compatibility.
+      It is de...
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+    summary_preview: Learn how to install and configure a Linux kernel with 64K page size support on Arm
+      systems to improve memory efficiency and performance for memory-intensive workloads. It is designed
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+    summary_preview: Learn how to access and use Arm hardware performance counters and the system counter
+      from user space using PAPI, perf_event_open, and assembly code for performance instrumentation.
+      It is designed for ...
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+    summary_preview: Learn how to package multi-architecture container applications and deploy them on
+      AWS Fargate with Graviton processors using the AWS Copilot CLI. It is designed for software developers
+      who want to lea...
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+    summary_preview: Learn how to automate the creation and deployment of AWS Graviton instances using
+      Terraform with jump server access for secure infrastructure management. It is designed for software
+      developers who are...
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+    summary_preview: Learn how to create and deploy Azure Resource Manager templates to provision Arm64-based
+      Cobalt 100 virtual machines on Azure using the Azure CLI. It is designed for developers and DevOps
+      engineers wh...
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+    summary_preview: Learn how to configure a self-hosted GitHub runner on Azure Cobalt 100, create an
+      AKS cluster with Terraform, and deploy a .NET application using GitHub Actions CI/CD. It is designed
+      for software deve...
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+    summary_preview: Learn how to automate the creation of Azure Arm virtual machines using Terraform.
+      It is designed for software developers who are new to deploying Arm instances on Azure using Terraform.
+      By the end, yo...
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+    summary_preview: Learn how to create a custom Azure Linux 3.0 VM image using QEMU, upload it to Azure
+      Shared Image Gallery, and deploy it on Arm-based Cobalt 100 processors. It is designed for developers
+      who want to r...
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+    summary_preview: Learn how to deploy and accelerate PyTorch NLP sentiment analysis models from Hugging
+      Face on Arm servers with BFloat16 fast math kernel optimization on Graviton3 processors. It is designed
+      for softwa...
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+  - path: content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
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+    summary_preview: Learn how to implement and benchmark bitmap scanning operations for database workloads
+      using scalar, Neon, and SVE instructions on Arm-based cloud instances. It is designed for database
+      developers, pe...
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+    summary_preview: Learn how to build, profile, and optimize Arm executables using BOLT post-link binary
+      optimization to improve application performance through code layout improvements. It is designed
+      for software deve...
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+    summary_preview: Learn how to identify optimization candidates and apply LLVM BOLT post-link optimization
+      to AArch64 binaries using BRBE, SPE, instrumentation, and PMU profiling techniques. It is designed
+      for develope...
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+    summary_preview: Learn how to optimize Arm application binaries and shared libraries using BOLT profile
+      instrumentation, merge multiple profiles for improved coverage, and integrate optimized libraries.
+      It is designed...
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+  - path: content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
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+    summary_preview: Learn how to configure Buildkite agents on Google Axion C4A VMs to build and publish
+      multi-architecture Docker images using Docker Buildx for Arm and x86 platforms. It is designed for
+      developers who w...
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+      - Which tools, languages, or platforms does it cover?
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+      removed_questions: []
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+    summary_preview: Learn how to install and configure Apache Cassandra on Google Cloud Axion C4A Arm64
+      instances and benchmark read/write performance using cassandra-stress. It is designed for software
+      developers migrat...
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+      - Which tools, languages, or platforms does it cover?
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+      removed_questions: []
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+    summary_preview: Learn how to run the Arm CCA reference software stack on an FVP with RME support,
+      create a Realm virtual machine, and obtain attestation tokens for confidential computing. It is
+      designed for software ...
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+    summary_preview: Learn how Arm CCA Realms interact with I/O devices using VirtIO paravirtualization,
+      SWIOTLB bounce buffers, and PCIe-TDISP secure device attach mechanisms with attestation. It is designed
+      for develope...
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+      removed_questions: []
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+    summary_preview: Learn how to deploy a CCA realm on an FVP with RME support and connect it with attestation
+      services to create an end-to-end confidential computing workflow. It is designed for software developers
+      who ...
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+    summary_preview: Learn how to deploy Confidential Containers from encrypted images inside Arm CCA
+      Realms using Trustee services for attestation-based authorization on an FVP with RME support. It
+      is designed for develo...
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+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
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+      removed_questions: []
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+    summary_preview: Learn how to deploy a CCA realm workload on an FVP and connect it with Trustee services
+      to enable attestation-based confidential data processing. It is designed for software developers
+      who want to run...
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+    summary_preview: Learn how to inspect and verify Arm CCA attestation tokens using command-line tools
+      and the open-source Veraison attestation verification service. It is designed for developers who
+      would like to learn...
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+    summary_preview: Learn how to deploy a scalable Arm CCA attestation verifier service on AWS using
+      Veraison components with platform endorsement provisioning. It is designed for developers familiar
+      with CCA attestation...
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+    summary_preview: Learn how to set up CircleCI self-hosted machine runners on Google Cloud Axion C4A
+      SUSE VMs and execute Arm-native CI/CD workflows using custom resource classes. It is designed for
+      developers and DevO...
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+    summary_preview: Learn how to install and configure CircleCI self-hosted machine runners on AWS Graviton
+      Arm64 instances to execute CI/CD workflows natively on Arm. It is designed for developers and DevOps
+      engineers w...
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+    summary_preview: Learn how to install and run Clair on Arm servers using combined and distributed
+      deployment models to scan container images and generate vulnerability reports. It is designed for
+      software developers i...
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+    summary_preview: Learn how to install ClickHouse on Arm-based cloud instances and measure database
+      performance using ClickBench to determine appropriate instance configurations. It is designed for
+      software developers ...
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+    summary_preview: Learn how to deploy ClickHouse on Google Cloud Axion C4A processors and build a streaming
+      ETL pipeline using Apache Beam, Dataflow, and Pub/Sub for real-time analytics. It is designed for
+      developers d...
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+    summary_preview: Learn how to deploy an Arm-based Cobalt 100 virtual machine on Azure, connect via
+      SSH, and configure network security group rules for external connectivity. It is designed for developers
+      and DevOps en...
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+    summary_preview: Learn how to automate Docker image creation for Arm using AWS CodeBuild with GitHub
+      integration and run the images on any Arm system with Docker installed. It is designed for software
+      developers inter...
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+    summary_preview: Learn how to build and run the x265 H.265 codec on Arm servers with performance benchmarking
+      across various video resolutions and encoding presets. It is designed for software developers who
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+    summary_preview: Learn how to build and run the AV1 and VP9 video codecs on Arm Linux systems with
+      performance benchmarking across various resolutions and encoding configurations. It is designed
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+    summary_preview: Learn how to install and configure Couchbase on Google Cloud Axion C4A Arm64 instances
+      and benchmark read/write performance using YCSB workloads. It is designed for developers deploying
+      Couchbase work...
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+    summary_preview: Learn how to apply g++ compiler optimization techniques and flags to improve C++
+      application performance on Arm systems with hands-on examples. It is designed for beginner C++ developers
+      who are looki...
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+    summary_preview: Learn how to apply profile-guided optimization to C++ applications on Arm systems
+      and measure performance improvements using Google Benchmark. It is designed for Developers looking
+      to optimize C++ per...
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+    summary_preview: Learn how to profile and identify CPU hotspots in C++ applications on Arm Neoverse
+      using Arm Performix flame graphs to guide optimization. It is designed for software developers and
+      performance engine...
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+    summary_preview: Learn how to start an Arm-based virtual machine instance from major cloud service
+      providers and verify the Arm architecture is being used. It is designed for software developers
+      who are new to Arm-bas...
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+    summary_preview: Learn how to deploy and run the DeepSeek-R1 language model on Arm servers using llama.cpp
+      with quantization for efficient CPU inference. It is designed for developers who want to run DeepSeek-R1
+      on Ar...
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+    summary_preview: Learn how to use fio to microbenchmark storage performance on Arm systems and monitor
+      storage using iostat, iotop, and pidstat to identify bottlenecks. It is designed for developers
+      looking to optimiz...
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+    summary_preview: Run distributed LLM inference with llama.cpp across multiple AWS Graviton4 instances,
+      covering multi-node setup, coordination, and performance trade-offs. It is designed for This introductory
+      topic is...
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+    summary_preview: Learn how to create a simple Django web application and deploy it on Arm machines
+      using Nginx and PostgreSQL. It is designed for engineers who want to deploy a Django based application
+      on Arm machines...
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+    summary_preview: Learn how to deploy a production-grade Django REST API on Google Kubernetes Engine
+      with Arm64 Axion node pools integrated with Google Cloud managed data services. It is designed for
+      DevOps engineers a...
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+    summary_preview: Learn how to build and benchmark the Deep Learning Recommendation Model using PyTorch
+      and MLPerf on Arm Neoverse V2 processors. It is designed for software developers who want to set
+      up a pipeline in ...
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+    summary_preview: Learn how to use the Docker MCP Toolkit with the Arm MCP Server and GitHub Copilot
+      to automate container and code migration from x86 to Arm64. Through a hands-on example, migrate
+      a legacy C++ applicat...
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+    summary_preview: Learn how to build and run an OrchardCore CMS .NET application on Azure Cobalt 100
+      processors, covering AnyCPU configuration and shared C library integration. It is designed for .NET
+      developers who wa...
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+    summary_preview: Learn how to deploy Dynatrace OneAgent on Azure Cobalt 100 Arm64 virtual machines
+      and configure ActiveGate for secure infrastructure and application monitoring. It is designed for
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+    summary_preview: Learn how to create an AWS ECS cluster with Fargate and AWS Graviton processors,
+      then create and run containerized tasks on Arm infrastructure. It is designed for developers who
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+    summary_preview: Learn how to provision an Amazon EKS cluster on Arm-based Graviton instances and
+      deploy a WordPress application with MySQL database. It is designed for software developers new to
+      Kubernetes on AWS who...
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+    summary_preview: Learn how to use docker buildx and docker manifest to build and deploy multi-architecture
+      container images with x86/amd64 and arm64 support on Amazon EKS. It is designed for software developers
+      who wa...
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+      removed_questions: []
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+    summary_preview: Learn how to build, install, and run Envoy proxy on Arm servers and configure it
+      as a web server for traffic management. It is designed for engineers who want to use Envoy on Arm.
+      By the end, you will...
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+    summary_preview: Learn how to install and configure Envoy proxy on Google Cloud Axion C4A Arm64 instances
+      and benchmark HTTP proxy performance with load testing. It is designed for This introductory topic
+      for software...
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+    summary_preview: Learn how to optimize Envoy proxy performance on Arm servers using Transparent Huge
+      Pages and Profile-Guided Optimization techniques. It is designed for software developers who want
+      to use Envoy on Ar...
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+    summary_preview: Learn how stack buffer overflow exploits work on AArch64 by analyzing stack frame
+      layouts, redirecting control flow, and understanding defense mechanisms. It is designed for software
+      developers intere...
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+    summary_preview: Learn how to identify and fix false sharing issues using Perf C2C cache line analysis
+      and Arm Statistical Profiling Extension on Arm-based cloud systems. It is designed for performance-oriented
+      develo...
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+    summary_preview: Learn how to build custom Linux kernels using tuxmake and Fastpath, then benchmark
+      and compare kernel versions on Arm-based EC2 instances. It is designed for software developers and
+      performance engine...
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+    summary_preview: Learn how to implement exponential functions using Arm SVE intrinsics with the FEXPA
+      instruction for hardware-accelerated computations on Neoverse processors. It is designed for developers
+      interested ...
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+    summary_preview: Learn how to install and run Apache Flink on Arm servers and benchmark stream processing
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+    summary_preview: Learn how to install and configure Apache Flink on Google Cloud Axion C4A Arm64 instances
+      and benchmark stream processing performance with Nexmark. It is designed for developers deploying
+      and optimizi...
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+    summary_preview: Learn how to build scalable machine learning workflow pipelines on Google Cloud C4A
+      Axion processors using Flyte for workflow orchestration and gRPC for distributed service communication.
+      It is design...
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+  - path: content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
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+    summary_preview: Learn how to create an Arm64 Azure VM, install .NET SDK, containerize .NET applications,
+      and push Docker images to Azure Container Registry. It is designed for software developers interested
+      in learni...
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+    summary_preview: Learn how to create and run Docker containers on Azure Container Instances for Arm64-based
+      containerized application deployment. It is designed for developers interested in learning how to
+      create and ...
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+    summary_preview: Learn how to create an Azure Kubernetes Service cluster with Arm64 virtual machines
+      and deploy a containerized application to AKS. It is designed for This learning path is dedicated
+      to developers inte...
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+    summary_preview: Learn how to automate Azure resource deployment using Infrastructure as Code with
+      Pulumi to provision Azure Container Instances for containerized applications. It is designed for
+      developers interested...
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+    summary_preview: Learn how to deploy the ModelScope FunASR Chinese automatic speech recognition model
+      on Arm-based servers with real-time transcription and sentiment analysis. It is designed for developers
+      interested ...
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+  - path: content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
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+      removed_questions: []
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+    summary_preview: Learn how to install and configure Gardener Kubernetes management platform on Google
+      Cloud Axion C4A SUSE Arm64 instances and deploy workload clusters. It is designed for software developers
+      deploying...
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+  - path: content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
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+    summary_preview: Learn how to apply link-time optimization with the GCC toolchain to improve application
+      performance by optimizing across compilation units. It is designed for developers who want to improve
+      applicatio...
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+    summary_preview: Learn how to automate the creation of Arm virtual machines on Google Cloud Platform
+      using Terraform with jump server access configuration. It is designed for anyone new to using Arm
+      virtual machines i...
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+    summary_preview: Run Geekbench on Arm systems to benchmark CPU performance, interpret the results,
+      and compare different Arm configurations. It is designed for software developers interested in comparing
+      the performan...
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+  - path: content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
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+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
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+    summary_preview: Learn how to set up Arm-hosted GitHub runners and train PyTorch ML models using the
+      German Traffic Sign Recognition Benchmark dataset with automated workflows. It is designed for software
+      developers i...
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+      - Who is this Learning Path for?
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+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
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+    summary_preview: Learn how to install RunsOn self-hosted runner manager in your AWS account to execute
+      GitHub Actions workflows on Arm runners. It is designed for developers who want to use Arm runners
+      offered by AWS ...
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+      - Who is this Learning Path for?
+      - What do you need before you start?
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+    summary_preview: Learn how to provision a Google Axion C4A Arm virtual machine and set up a GitHub
+      Actions self-hosted runner for CI/CD workflows. It is designed for developers who want to deploy
+      a GitHub Actions self...
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
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+    summary_preview: Learn how to automate the deployment of an Arm-based Google Kubernetes Engine cluster
+      using Terraform for container orchestration. It is designed for software developers who want to
+      deploy an Arm-base...
+    source_hash: 7c3d37545c93db584d6e0ce88d8feaf0bf690e0c8786b664a6643be713d0336f
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+      - Who is this Learning Path for?
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+      - How is the Learning Path structured?
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+    summary_preview: Learn how to add Arm-based Google Axion nodes to an existing x86 GKE cluster and
+      rebuild applications for multi-architecture support. It is designed for software developers who
+      are looking to migrate ...
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
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+    summary_preview: Learn how to create dual-architecture GKE clusters with arm64 and amd64 node pools,
+      build multi-architecture Docker images, and migrate services to Google Axion processors. It is designed
+      for cloud, p...
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+    summary_preview: Rebuild and benchmark glibc with LSE atomics on Arm servers, then evaluate scalability
+      using MongoDB workloads and guidance on when LSE delivers a measurable uplift. It is designed for
+      software develo...
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+    summary_preview: Learn how to provision Arm64 and x86_64 VM instances on Google Cloud, then install
+      and use Sweet and Benchstat to measure and compare Go application performance. It is designed for
+      This introductory t...
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+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to provision Azure Cobalt 100 Arm64 virtual machines and deploy Golang
+      applications with performance benchmarking on Arm architecture. It is designed for software developers,
+      DevOps engineer...
+    source_hash: 46a0a3df799ac473f56d3820390af405b3301758cf15685a250a04c4854aba9e
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+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to install Helm on Google Cloud Axion C4A SUSE VMs and deploy applications
+      like NGINX, PostgreSQL, and Redis using Helm charts. It is designed for This is an introductory
+      topic intended for ...
+    source_hash: 87e9d25d5fb1f45d126aa4ca2fa1e13d6a470f2bcdc70968aa4622790106e629
+  - path: content/learning-paths/servers-and-cloud-computing/intro/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn where Arm architecture is used in servers and cloud computing, and find Arm-based
+      hardware platforms for software development. It is designed for software developers working on server
+      and cloud ...
+    source_hash: d2786f42b505823253ae16c647ca2e03c154f371b7c326210fde8523b12a8188
+  - path: content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Analyze and optimize interrupt request (IRQ) patterns on Arm Linux servers to improve
+      network workload performance through IRQ distribution strategies. It is designed for developers
+      and performance en...
+    source_hash: 6fc608d45c4fdf85a77bddd4f8c26b05a7950d1086e578a8be0dd637feeb5d79
+  - path: content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
+    status: added
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Monitor, interpret, and optimize Java Garbage Collector performance on Arm servers
+      by comparing different GCs and tuning parameters for your workload. It is designed for Java developers
+      aiming to opti...
+    source_hash: f57dd99bad280f64f53071373519e2d3ba8ae50b94fe1ad0a9cc40d02b458b9b
+  - path: content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
+    status: added
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Deploy and optimize Java applications on Google Cloud Axion processors by testing
+      JDK versions and performance optimization flags. It is designed for software developers who want
+      to learn how to run t...
+    source_hash: e6f73fbca45a1be1644f79bbcfbaaec964e31f1aab236e9a872c72a6fd5e4b66
+  - path: content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
+    status: added
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+    faq_changes:
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Deploy Java on Azure Cobalt 100 Arm virtual machines and benchmark application performance
+      with JMH microbenchmarks. It is designed for This is an introductory topic about Java deployment
+      and benchmar...
+    source_hash: 58b0bb9f6e4190029d7edc65bdb04a597c7539de55a5e51ed59e88b174e527c3
+  - path: content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
+    status: added
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Profile Java applications on Arm Neoverse servers using flame graphs generated with
+      async-profiler and Java agents to identify performance bottlenecks. It is designed for developers
+      who want to analyz...
+    source_hash: 655180d91bb9b9cf571840b59d8943c1d2c73d8f8aea647db57dc2704ede3af8
+  - path: content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
+    status: added
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Deploy Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate installation,
+      and execute Arm-native CI/CD pipelines including Docker workflows. It is designed for software developers
+      d...
+    source_hash: 525d893a796e8e4eb5cc7a9d5996bd7d55a35f8fe413f87b9a275a214d77626f
+  - path: content/learning-paths/servers-and-cloud-computing/kafka/_index.md
+    status: added
+    changed_on_disk: true
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+    faq_changed: true
+    faq_changes:
+      before_count: 0
+      after_count: 5
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Deploy and configure a Kafka cluster with Zookeeper on Arm servers, test event streaming,
+      and automate deployment on AWS and Google Cloud. It is designed for software developers who want
+      to learn how ...
+    source_hash: b7f36df881c2c436143c1e5579d2c54cb5930f52998904098829c52fa7290f38
+  - path: content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
+    status: added
+    changed_on_disk: true
+    summary_changed: true
+    faq_changed: true
+    faq_changes:
+      before_count: 0
+      after_count: 5
+      added_questions:
+      - What will you accomplish in this Learning Path?
+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Deploy Apache Kafka on Azure Cobalt 100 Arm virtual machines and benchmark message
+      throughput performance. It is designed for developers looking to migrate their Apache Kafka workloads
+      from x86_64 to ...
+    source_hash: d510dd43a485e1c2fac404ef9a2e00b5be2449b99b1095c9a1841cd2fcba9b0e
+  - path: content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
+    status: added
+    changed_on_disk: true
+    summary_changed: true
+    faq_changed: true
+    faq_changes:
+      before_count: 0
+      after_count: 5
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing Kedify
+      and KEDA with Helm and testing autoscaling behavior. It is designed for developers running HTTP
+      workloads on Kuber...
+    source_hash: 6ff33f6dfaf25e926a0ce8756b4e564e5aa37112e5425f9c3dce13f772b54145
+  - path: content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
+    status: added
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+      after_count: 5
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Create, train, and evaluate a neural network model on Arm servers using Keras Core
+      with TensorFlow, PyTorch, and JAX backends. It is designed for engineers who want to create a neural
+      network model on...
+    source_hash: 830556dfd51aaa2dd95ee957e64306bab369297f7bc66325e7eb509f541f683c
+  - path: content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
+    status: added
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+    faq_changes:
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+      after_count: 5
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Compile and install custom Linux kernels on Arm cloud instances using TuxMake with
+      configurations for 64 KB page sizes and Fastpath testing. It is designed for software developers
+      building custom Linu...
+    source_hash: 004436cf14ff0056136889c58d676295c6dd2088f06f57f397a07ec9004e6b44
+  - path: content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
+    status: added
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Identify and migrate container images in a Kubernetes cluster to Arm-compatible versions
+      using KubeArchInspect reports. It is designed for software developers who want to ensure containers
+      running in ...
+    source_hash: 43e4e28b88b9721ca94457418f3b4887d0099017578a54da1db5fcdc11f4a122
+  - path: content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
+    status: added
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Deploy AWS Lambda functions on Graviton processors using Terraform for Python and
+      Node.js runtimes. It is designed for software developers who want to learn how to deploy Lambda
+      functions on AWS Gravi...
+    source_hash: 22fa96e29143f2e0dc0c204919422914b3f70d63ddf833cf1067fbf67b525aa0
+  - path: content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Enable and measure libhugetlbfs performance improvements for MySQL and other workloads
+      on Arm Linux servers. It is designed for engineers looking for ways to increase performance on Arm
+      servers. By th...
+    source_hash: 66d13edff1371295f0e88a618e924ca67b85bcc8aa72034d1e582a0b267f0d05
+  - path: content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
+    status: added
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Serve the llama.cpp chatbot through an OpenAI-compatible API, enabling existing OpenAI-style
+      clients and applications to run against a persistent Arm-hosted LLM. It is designed for developers
+      interest...
+    source_hash: d82aa4faeb599a3a1ac12d477204ebe0a4ddb99564bedced86ca0fc4851e17b9
+  - path: content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Build a production-ready vision chatbot on Google Axion using Streamlit, PyTorch,
+      and Hugging Face Transformers with a quantized Llama 3.2-Vision model. It is designed for software
+      developers and ML e...
+    source_hash: 3e8307a5daf435c21f3cecff635ac51724a63ff9ea4307082d869601563b4204
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Optimize llama.cpp on Arm CPUs by integrating Streamline Annotations to profile Prefill
+      and Decode stages, analyze operators, and evaluate multi-core execution. It is designed for software
+      developers,...
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+  - path: content/learning-paths/servers-and-cloud-computing/lse/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Understand Large System Extensions (LSE) for Arm processors and verify whether applications
+      use LSE for improved atomic operation performance. It is designed for software developers who want
+      to learn ...
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+  - path: content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
+    status: added
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Deploy MariaDB on Arm cloud instances across AWS, Azure, and Google Cloud using Docker,
+      Amazon RDS, and automation with Terraform and Ansible. It is designed for software developers who
+      want to deploy...
+    source_hash: db4ce1c59ad10bd127b4990c82ce876311d7dc7e1ad5d39ec132daf82ceb8b79
+  - path: content/learning-paths/servers-and-cloud-computing/memcached/_index.md
+    status: added
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
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+    summary_preview: Install memcached on Arm cloud servers and benchmark in-memory key-value store performance
+      using open-source tools. It is designed for developers who want to use memcached as their in-memory
+      key-value...
+    source_hash: b42ca5cc8f4db6ba6019f3720a5ef46119aa403a2baaef5362e874f898ce0419
+  - path: content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
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+    summary_preview: Deploy Memcached as a cache for MySQL and PostgreSQL on Arm servers. It is designed
+      for developers who want to use memcached as their in-memory key-value store. By the end, you will
+      be able to deploy ...
+    source_hash: 4efe46aaf4580a6b8f263b69e8f072211bfd24fcf7f7a885689c927fc05a4c63
+  - path: content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Use ASCT to measure cache latency, streaming bandwidth, and coherency latency on
+      Arm Neoverse systems, and compare results across Graviton generations. It is designed for software
+      developers and perfo...
+    source_hash: d61c9ddcef1c7ffe12b3ee818852fb3f0a62a90cec451692c1186cf2c01de625
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
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+      removed_questions: []
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+    summary_preview: Test and validate thread synchronization approaches in the Arm memory model using
+      Herd7, Litmus7, and Arm hardware with assembly snippets. It is designed for developers seeking practical
+      ways to test ...
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+  - path: content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
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+      removed_questions: []
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+    summary_preview: Microbenchmark and tune network performance with iPerf3 and Linux traffic control
+      walks you through an end-to-end Arm software workflow. It is designed for performance engineers,
+      Linux system administ...
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+  - path: content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
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+      removed_questions: []
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+    summary_preview: Scan source code for architecture-specific portability issues using migrate-ease
+      to identify and resolve AArch64 porting challenges before migration. It is designed for developers
+      looking to migrate a...
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+    summary_preview: Set up an Arm development environment, analyze dependencies, and understand common
+      challenges and scenarios for migrating applications to Arm servers. It is designed for software
+      developers looking to...
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+    summary_preview: Build a Retrieval-Augmented Generation (RAG) application on Arm servers using Zilliz
+      Cloud for vector search and llama.cpp for LLM inference. It is designed for software developers
+      who want to create ...
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+    summary_preview: Learn how to deploy and configure MinIO on an Azure Cobalt 100 virtual machine, benchmark
+      object storage throughput, and validate S3 compatibility using the boto3 Python SDK. It is designed
+      for develo...
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+    summary_preview: Benchmark machine learning inference performance on Arm servers using TensorFlow
+      and the MLPerf Inference benchmark suite from MLCommons. It is designed for software developers
+      interested in benchmark...
+    source_hash: 973ba6c1e00fc67e1702606412124d8172e071b9b677a1df53c3be66210bf704
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+    summary_preview: Install MongoDB on Arm servers and benchmark database performance using Yahoo Cloud
+      Serving Benchmark (YCSB) to compare against other architectures. It is designed for software developers
+      who want to ...
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+    summary_preview: Deploy MongoDB on Azure Cobalt 100 Arm virtual machines and benchmark database performance
+      using mongotop and mongostat monitoring tools. It is designed for software developers who want to
+      migrate Mon...
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+    summary_preview: Deploy MongoDB on Google Cloud Axion C4A virtual machines and benchmark database
+      performance with Yahoo Cloud Serving Benchmark (YCSB). It is designed for This introductory topic
+      is for software devel...
+    source_hash: abbdde22271151fb1485c54bafe463e7797a0b913c7d4c99245d2914a3f9bb82
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+    summary_preview: Debug, profile, and optimize MPI parallel applications on Arm servers using Linaro
+      Forge, gdb, and Arm Performance Libraries. It is designed for HPC software developers writing MPI
+      applications. By th...
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+    summary_preview: Select and apply accuracy modes for vectorized math functions in Libamath to balance
+      performance and precision for your application. It is designed for developers who want to use the
+      different accurac...
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+    summary_preview: Build a multi-architecture Kubernetes cluster running nginx on Azure AKS walks you
+      through an end-to-end Arm software workflow. It is designed for developers who want to deploy multi-architecture
+      Kube...
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+    summary_preview: Add Arm nodes to your GKE cluster using a multi-architecture Ollama container image
+      walks you through an end-to-end Arm software workflow. It is designed for developers who want to
+      compare the perform...
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+    summary_preview: Learn how to deploy MySQL walks you through an end-to-end Arm software workflow.
+      It is designed for software developers who want to deploy MySQL on Arm. By the end, you will be
+      able to learn about the...
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+    summary_preview: Deploy MySQL on Microsoft Azure Cobalt 100 processors walks you through an end-to-end
+      Arm software workflow. It is designed for developers migrating MySQL applications from x86_64 to
+      Arm. By the end, ...
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+    summary_preview: Benchmarking MySQL with Sysbench walks you through an end-to-end Arm software workflow.
+      It is designed for performance engineers who want to benchmark MySQL using Sysbench and optimize
+      performance on ...
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+    summary_preview: Learn how to Tune MySQL walks you through an end-to-end Arm software workflow. It
+      is designed for software developers and DevOps professionals interested in optimizing MySQL performance
+      on Arm-based V...
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+    summary_preview: Develop and Validate Firmware Pre-Silicon on Arm Neoverse CSS V3 walks you through
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+    summary_preview: Run a .NET Aspire application on Arm-based VMs on AWS and GCP walks you through an
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+      how to deploy .NET As...
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+    summary_preview: Learn how to deploy Nginx walks you through an end-to-end Arm software workflow.
+      It is designed for engineers who want to use Nginx on Arm. By the end, you will be able to install
+      and run Nginx on Arm...
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+    summary_preview: Deploy NGINX on Azure Cobalt 100 Arm-based virtual machines walks you through an
+      end-to-end Arm software workflow. It is designed for system administrators and developers who want
+      to learn how to depl...
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+    summary_preview: Learn how to tune Nginx walks you through an end-to-end Arm software workflow. It
+      is designed for software developers who want to use Nginx on Arm. By the end, you will be able to
+      describe how kernel ...
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+    summary_preview: Run a Natural Language Processing (NLP) model from Hugging Face on Arm servers walks
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+      to learn how to ru...
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+    summary_preview: Deploy Node.js on Google Cloud C4A Arm-based Axion VMs walks you through an end-to-end
+      Arm software workflow. It is designed for software developers migrating Node.js workloads from x86_64
+      to Arm-base...
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+    summary_preview: Deploy Arm Instances on Oracle Cloud Infrastructure (OCI) using Terraform walks you
+      through an end-to-end Arm software workflow. It is designed for software developers who are new
+      to deploying Arm ins...
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+    summary_preview: Deploy Phi-4-mini model with ONNX Runtime on Azure Cobalt 100 walks you through an
+      end-to-end Arm software workflow. It is designed for developers, ML engineers, and cloud practitioners
+      looking to dep...
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+    summary_preview: Deploy SqueezeNet 1.0 INT8 model with ONNX Runtime on Azure Cobalt 100 walks you
+      through an end-to-end Arm software workflow. It is designed for developers deploying ONNX-based
+      applications on Arm-bas...
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+    summary_preview: Simulate OpenBMC and UEFI pre-silicon on Neoverse RD-V3 walks you through an end-to-end
+      Arm software workflow. It is designed for This advanced topic is for firmware developers, platform
+      software engi...
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+    summary_preview: Learn how to profile an example C++ data-processing workload on Arm Linux with Arm
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+    summary_preview: Build multi-architecture applications with Red Hat OpenShift Pipelines on AWS walks
+      you through an end-to-end Arm software workflow. It is designed for OpenShift administrators who
+      want to migrate the...
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+    summary_preview: Deploy OpenStack on Azure Cobalt 100 Arm64 virtual machines using DevStack for development
+      and Kolla-Ansible for containerized production deployments. It is designed for This learning path
+      is designed...
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+    summary_preview: Deploy OpenTelemetry on Google Cloud C4A Axion processors walks you through an end-to-end
+      Arm software workflow. It is designed for DevOps engineers, platform engineers, and software developers
+      who wa...
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+    summary_preview: Understand Arm Pointer Authentication walks you through an end-to-end Arm software
+      workflow. It is designed for software developers interested in understanding Arm Pointer Authentication.
+      By the end, ...
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+    summary_preview: Learn how to use an AI agent and the Performix tool through the Arm MCP Server to
+      run the Code Hotspots recipe on a C++ application, interpret flame graph results, and apply targeted
+      optimizations on ...
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+    summary_preview: Optimize application performance using Arm Performix CPU microarchitecture analysis
+      walks you through an end-to-end Arm software workflow. It is designed for software developers who
+      want to learn perf...
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+    summary_preview: Deploy PHP on Google Cloud C4A Arm-based Axion VMs walks you through an end-to-end
+      Arm software workflow. It is designed for developers migrating Hypertext Preprocessor (PHP) workloads
+      from x86_64 to ...
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+    summary_preview: Optimize application performance with CPU affinity walks you through an end-to-end
+      Arm software workflow. It is designed for developers, performance engineers, and system administrators
+      looking to fin...
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+    summary_preview: Implement Code level Performance Analysis using the PMUv3 plugin walks you through
+      an end-to-end Arm software workflow. It is designed for Engineers who want to carry out C/C++ performance
+      analysis by...
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+    summary_preview: Learn how to deploy PostgreSQL walks you through an end-to-end Arm software workflow.
+      It is designed for software developers who want to deploy PostgreSQL on Arm. By the end, you will
+      be able to learn...
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+    summary_preview: Deploy PostgreSQL on Azure Cobalt 100 Arm64 virtual machines, load a relational schema
+      with transactional data, and benchmark and optimize query performance using pgbench and pg_stat_statements.
+      It is...
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+    summary_preview: Learn how to Tune PostgreSQL walks you through an end-to-end Arm software workflow.
+      It is designed for software developers and DevOps professionals interested in optimizing PostgreSQL
+      performance. By ...
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+    summary_preview: Run Process watch on your Arm machine walks you through an end-to-end Arm software
+      workflow. It is designed for software developers who want to build and run the Process Watch tool
+      on an Arm-based mac...
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+    summary_preview: Profiling for Neoverse with Streamline CLI Tools walks you through an end-to-end
+      Arm software workflow. It is designed for This is an introductory guide for developers who want
+      to measure and optimize...
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+    summary_preview: Deploy Puppet on Google Cloud C4A walks you through an end-to-end Arm software workflow.
+      It is designed for developers deploying and optimizing Puppet workloads on Arm Linux environments,
+      specifically...
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+    summary_preview: Run a Large Language Model (LLM) chatbot with PyTorch using KleidiAI on Arm servers
+      walks you through an end-to-end Arm software workflow. It is designed for software developers interested
+      in running ...
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+    summary_preview: Learn how to deploy Qdrant on Google Cloud C4A Axion processors, generate vector
+      embeddings with Sentence Transformers, and build a semantic search and chatbot retrieval system
+      on Arm-based infrastruc...
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+    summary_preview: Deploy RabbitMQ on Arm64 Cloud Platforms (Azure and GCP) walks you through an end-to-end
+      Arm software workflow. It is designed for software engineers and platform engineers migrating messaging
+      and eve...
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+    summary_preview: Deploy a RAG-based Chatbot with llama-cpp-python using KleidiAI on Google Axion processors
+      walks you through an end-to-end Arm software workflow. It is designed for software developers, ML
+      engineers, ...
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+    summary_preview: Get started with the Arm 5G RAN Acceleration Library (ArmRAL) walks you through an
+      end-to-end Arm software workflow. It is designed for software developers new to the Arm RAN Acceleration
+      Library (Arm...
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+    summary_preview: Deploy and run distributed AI workloads using Ray on Google Cloud Axion C4A Arm-based
+      VMs, covering parallel tasks, hyperparameter tuning, and model serving with Ray Core, Train, Tune,
+      and Serve. It i...
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+    summary_preview: Deploy Redis on Arm walks you through an end-to-end Arm software workflow. It is
+      designed for developers who want to deploy Redis on Arm based virtual machines. By the end, you
+      will be able to underst...
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+    summary_preview: Learn how to install and configure Redis on an Azure Cobalt 100 Arm64 virtual machine,
+      implement real-time messaging with Pub/Sub and Streams, and benchmark throughput and latency on
+      Arm infrastructur...
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+    summary_preview: Deploy Redis for data searching on Google Cloud C4A walks you through an end-to-end
+      Arm software workflow. It is designed for developers deploying and optimizing Redis-based data searching
+      workloads o...
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+    summary_preview: Deploy Redis as a cache for MySQL and PostgreSQL on Arm servers walks you through
+      an end-to-end Arm software workflow. It is designed for developers who want to deploy Redis as a
+      cache on Arm based vi...
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+    summary_preview: Learn how to tune Redis walks you through an end-to-end Arm software workflow. It
+      is designed for software developers who want to deploy Redis on Arm-based servers and follow best
+      practices to get per...
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+    summary_preview: Debug Neoverse N2 Reference Design with Arm Development Studio walks you through
+      an end-to-end Arm software workflow. It is designed for software developers who are interested in
+      debugging the Arm Neo...
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+    summary_preview: Get started with the Neoverse Reference Design software stack walks you through an
+      end-to-end Arm software workflow. It is designed for software developers interested in testing the
+      Neoverse Reference...
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+    summary_preview: Build and Run vLLM on Arm Servers walks you through an end-to-end Arm software workflow.
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+    summary_preview: Run the vvenc H.266 encoder on Arm servers walks you through an end-to-end Arm software
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+    summary_preview: Learn how to use GitHub Copilot to write Neon intrinsics that accelerate the Adler32
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+    summary_preview: Learn how to use Arm Development Studio to explore Realm Management Extension (RME)
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+    summary_preview: Learn how to build a GitLab CI/CD pipeline using Google Axion-based self-hosted runners.
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+    summary_preview: Learn how to build GitLab CI/CD pipelines using GitLab-hosted Arm64 runners to containerize
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+    summary_preview: Learn how to identify and fix potential problems with integer and floating-point
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+    summary_preview: Learn how to port architecture-specific intrinsics to Arm processors. It is designed
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+    summary_preview: Learn how to run custom software benchmarks on IP Explorer simulation platforms and
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+    summary_preview: Learn how to use KleidiAI micro-kernels to accelerate AI inference performance through
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+    summary_preview: Learn how to optimize C/C++ code using compiler autovectorization techniques including
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+    summary_preview: Learn how to develop and test a modern C++ library using CMake, GoogleTest, and matrix
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+    summary_preview: Learn how to use llvm-mca with Compiler Explorer to analyze Arm assembly performance,
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+    summary_preview: Learn how to deploy a Model Context Protocol server on Raspberry Pi 5 and use the
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+    summary_preview: Learn how to reduce memory latency impact in applications using cache alignment and
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+    summary_preview: Learn how to build MNN on an Armv9 system, run text, vision, and audio prompts with
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+    summary_preview: Learn how to implement and optimize matrix multiplication using Arm's Scalable Matrix
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+    summary_preview: Learn how to create and train a PyTorch neural network for MNIST digit classification,
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+    summary_preview: Learn how to install and configure Remote.It for secure remote device access using
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+    summary_preview: Learn how to use the C99 restrict keyword to indicate non-overlapping memory regions
+      and enable better compiler optimizations for vectorization on Arm platforms. It is designed for
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+    summary_preview: Learn how to build an embedded Rust application for Arm processors, run it on a Fixed
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+    summary_preview: Learn how to use SIMD.info to port SIMD intrinsics across Arm architectures, including
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+    summary_preview: Learn how to write high-performance SIMD code using the SIMD Loops project, with
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+    summary_preview: Learn how to write SIMD code in Rust on Arm platforms using Neon intrinsics, portable
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+    summary_preview: Learn how to profile and optimize ExecuTorch models using SME2 acceleration on Arm
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+    summary_preview: Learn how to deploy Tinkerblox UltraEdge HPC-I for edge AI and mixed workloads on
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+    summary_preview: Learn how to compare Arm Neoverse and Intel x86 top-down performance analysis methodologies
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+    summary_preview: Learn how to migrate x86-64 SIMD code to Arm64 by mapping Intel SSE/AVX to Arm Neon,
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+    summary_preview: Learn how to optimize SIMD performance on Arm by restructuring data layouts from
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+    summary_preview: Learn how to create and connect to a Windows on Arm virtual machine in Microsoft
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+    summary_preview: Learn how to build and deploy distributed Zenoh systems on Arm devices like Raspberry
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+    summary_preview: Learn how to design and integrate a custom AXI-Lite peripheral with a Cortex-A9 processor
+      on the Zybo Z7-10 board using Vivado, configuring GPIOs to control LEDs based on switch inputs.
+      It is designed...
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+    summary_preview: Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit and
+      run inference on the Ethos-U85 NPU. It is designed for embedded developers who want to deploy a
+      neural network model t...
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+    summary_preview: Learn how to build a motion-detection device with Raspberry Pi Pico (RP2040 Cortex-M0+)
+      using Arduino IDE, PIR sensors, and interrupt-driven programming on baremetal. It is designed for
+      software devel...
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+    summary_preview: Learn how to import and build example projects in Arm Development Studio and debug
+      embedded applications using Fixed Virtual Platforms (FVPs) or hardware with DSTREAM debug probes.
+      It is designed for ...
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+    summary_preview: Learn how to write mixed C and assembly programs for Cortex-M microcontrollers using
+      Keil MDK, following Arm Procedure Call Standard conventions. It is designed for software developers
+      who are interes...
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+    summary_preview: Learn how to create a custom Balena OS image, run it on Arm Virtual Hardware, and
+      deploy IoT applications to a virtual Raspberry Pi 4 device. It is designed for embedded software
+      developers interested...
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+    summary_preview: Learn how to set up AWS IoT Greengrass Core on Arm Virtual Hardware and deploy AWS
+      Greengrass components to a virtual Raspberry Pi 4 device. It is designed for embedded software developers
+      interested ...
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+    summary_preview: Learn how to build Matter reference examples on Arm Virtual Hardware, demonstrate
+      device communication, and automate testing with GitHub Actions CI/CD workflows. It is designed for
+      embedded software d...
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+    summary_preview: Learn how to export and compile a PaddleOCR text recognition model using TVMC and
+      deploy it on the Arm Corstone-300 FVP with Cortex-M55 processors. It is designed for software developers
+      interested in...
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+    summary_preview: Learn how to create and integrate a virtual LED peripheral using the Virtual IO interface
+      of Arm Virtual Hardware to simulate real-world peripherals. It is designed for software developers
+      new to Arm ...
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+    summary_preview: Learn how to build a complete IoT solution in Azure that streams, stores, monitors,
+      aggregates, and visualizes telemetry data from Arm devices using IoT Hub, Stream Analytics, Cosmos
+      DB, and Azure Fun...
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+    summary_preview: Learn how to create, build, and run a bare-metal embedded application for Armv8-A
+      processors using Arm Compiler for Embedded and Fixed Virtual Platforms, including basic exception
+      handling. It is desi...
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+    summary_preview: Learn how to deploy containerized embedded applications and firmware onto an Arm
+      Cortex-M core from a Cortex-A core using containerd, K3s, and the hybrid-runtime on Arm Virtual
+      Hardware. It is designe...
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+    summary_preview: Learn how to create, build, and debug an RTX5 RTOS-based application using Keil μVision
+      with CMSIS-RTOS2 API and Event Recorder for embedded Cortex-M development. It is designed for software
+      developer...
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+    summary_preview: Learn how to create, configure, and debug an RTX5 RTOS application using Keil Studio
+      for VS Code with CMSIS-RTOS2 API for embedded Cortex-M development. It is designed for software
+      developers new to R...
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+    summary_preview: Learn how to prototype and port DSP algorithms using the CMSIS-DSP Python package,
+      mapping Python code to efficient C implementations for embedded Cortex-M and Cortex-A platforms.
+      It is designed for d...
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+    summary_preview: Learn how to implement context switching operations on Arm Cortex-M processors using
+      the Memory Protection Unit and SysTick exception in a bare-metal environment. It is designed for
+      software developer...
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+    summary_preview: Learn how to set up and use code coverage in Keil MDK with FVPs to verify that your
+      embedded application tests execute all code paths. It is designed for embedded software developers
+      new to the code-c...
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+    summary_preview: Device-to-Device communication with Device Connect walks you through an end-to-end
+      Arm software workflow. It is designed for developers wiring up heterogeneous edge fleets, where
+      devices need a shared...
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+    summary_preview: Learn how to connect AI agents to Arm-based edge devices using Device Connect for
+      structured device access and Strands for agent orchestration, with examples for both simulated and
+      physical robots. It...
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+    summary_preview: Learn how to create a Dockerfile, build a Docker image with Arm Compiler for Embedded
+      and Fixed Virtual Platforms, and test the containerized Arm development environment. It is designed
+      for embedded s...
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+    summary_preview: Learn how to collect and preprocess audio data using Edge Impulse, train an audio
+      classification model, and deploy it to the Arduino Nano RP2040 to control LEDs based on voice commands.
+      It is designed...
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+      - Which tools, languages, or platforms does it cover?
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+    summary_preview: Develop a image classification neural network model and deploy it on an STM32 B-L475E-IOT01A2
+      board. It is designed for embedded software developers interested in building neural network models
+      for mi...
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+    summary_preview: Learn where Arm architecture is used in microcontrollers and discover microcontroller
+      hardware options for software development on Arm Cortex-M processors. It is designed for software
+      developers worki...
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+    summary_preview: Learn what differentiates TinyML from other AI domains, explore Arm-based edge devices
+      for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed Virtual
+      Platform. It is ...
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+    summary_preview: Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300 virtual
+      hardware to understand complete IoT software stack construction. It is designed for embedded software
+      developers ...
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+    summary_preview: Learn how to set up a Jetson Orin Nano with a MIPI CSI-2 camera and perform real-time
+      object detection from live video and image files using DetectNet and TensorRT. It is designed for
+      developers inter...
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+  - path: content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
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+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
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+    summary_preview: Learn how to import, build, and debug your first Keil Studio Cloud project. It is
+      designed for embedded software developers new to Keil Studio Cloud. By the end, you will be able
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+    summary_preview: Learn how to boot and configure the NXP FRDM i.MX 93 Arm board with Linux, create
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+    summary_preview: Learn how to boot a Linux software stack on Arm Fixed Virtual Platforms (FVPs), then
+      debug Trusted Firmware-A and the Linux kernel using Arm Development Studio. It is designed for developers
+      who want ...
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+    summary_preview: Learn how to install the Python version of llama.cpp on a Raspberry Pi 5, download
+      an LLM from Hugging Face, assess memory and performance, and run the model using Python bindings.
+      It is designed for ...
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+    summary_preview: Learn the software migration methodology for porting Linux workloads from x86_64
+      to aarch64, including using Arm compilers, porting compiler intrinsics, and deploying applications
+      in containers. It is...
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+    summary_preview: Learn how to build examples from the Machine Learning Evaluation Kit (MLEK) and run
+      them on the Arm Ecosystem FVP for machine learning application development on microcontrollers.
+      It is designed for e...
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+    summary_preview: Learn how to understand and select physical and virtual hardware targets for ML application
+      development with Cortex-M and Ethos-U, identify software tools, and find example applications. It
+      is designe...
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+    summary_preview: Learn how to create debug configurations for virtual platforms and development boards
+      in Arm Development Studio, including setting up connections for Fast Models and DSTREAM debug probes.
+      It is design...
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+    summary_preview: Learn how to bring up ExecuTorch executor_runner firmware on the NXP FRDM i.MX 93
+      Cortex-M33 using Linux RemoteProc, compile .pte models for Ethos-U65, and run inference with NPU
+      acceleration. It is d...
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+    summary_preview: Learn how to migrate a CMSIS v5-based CMSIS-Pack with device support to CMSIS v6
+      and update example projects for compatibility with the new CMSIS version. It is designed for maintainers
+      of CMSIS-Packs...
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+    summary_preview: Learn how to migrate CMSIS v5 projects to CMSIS v6 by identifying supported toolchains,
+      installing required CMSIS-Packs, and selecting the necessary software components. It is designed
+      for embedded de...
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+    summary_preview: Learn how to run large language models locally on the Raspberry Pi 5 using Ollama,
+      control GPIO-connected devices, and deploy a privacy-first web-based smart home assistant without
+      cloud services. It ...
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+    summary_preview: Learn how to build a voice-controlled bot on a Raspberry Pi that listens for a wake
+      word, converts speech to text using Google Speech Recognition, sends requests to ChatGPT's API,
+      and plays audio resp...
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+    summary_preview: Learn how to build and run multiple software examples on the Raspberry Pi 4, including
+      TensorFlow and Docker applications, and compare its performance to Arm cloud servers. It is designed
+      for software...
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+    summary_preview: Learn how to compile the Llama 3 large language model using ExecuTorch, deploy it
+      to a Raspberry Pi 5, and understand techniques for running LLMs in embedded environments. It is
+      designed for anyone in...
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+    summary_preview: Learn how to reduce compile time for embedded Linux projects by installing a Raspberry
+      Pi OS file system on an Arm server, building the MXNet machine learning framework, and transferring
+      it to a Raspb...
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+    summary_preview: Setup tools and start programming with Raspberry Pi Pico. It is designed for embedded
+      software developers new to Raspberry Pi Pico. By the end, you will be able to install the Raspberry
+      Pi Pico SDK, r...
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+    summary_preview: Learn how to profile Linux kernel modules using Arm Streamline to identify performance
+      bottlenecks, analyze both out-of-tree and in-tree modules, and use Statistical Profiling Extension
+      (SPE) for deep...
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+    summary_preview: Build a letter recognition neural network model using TensorFlow and deploy it on
+      an STM32 B-L475E-IOT01A2 board. It is designed for software developers interested in building network
+      models for micro...
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+    summary_preview: Learn how to build and run the reference Trusted Firmware-M tests and example application
+      on Arm Fixed Virtual Platforms for secure microcontroller development. It is designed for software
+      developers ...
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+    summary_preview: Learn how to train a CNN image classification model using PyTorch, convert it to
+      ExecuTorch format, and run it as an interactive mini-game on Arm-based edge devices. It is designed
+      for machine learnin...
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+    summary_preview: Learn how to install Keil MDK Tools, run a TrustZone hello world example on the NXP
+      LPCXpresso55S69 board, and understand security state switching and secure function calls. It is
+      designed for softwar...
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+    summary_preview: Learn how to acquire and print materials, assemble a universal SBC rack mount system
+      in a 4U chassis, and install single board computers in the racks using 3D-printed parts. It is designed
+      for softwar...
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+    summary_preview: Learn how to debug microcontrollers using µVision with basic run/stop debug, advanced
+      techniques using Event Recorder and Serial Wire Viewer, ETM Trace for performance analysis, and
+      power measurement ...
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+    summary_preview: Learn how to import, convert, and build uvprojx-based projects to csolution format
+      using Keil Studio, µVision, and command-line tools for CMSIS-Toolbox compatibility. It is designed
+      for This is a topi...
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+    summary_preview: Learn how to install vcpkg, initialize it, create vcpkg-configuration.json files,
+      use vcpkg for tool management, activate tool licensing, and remove vcpkg for reproducible command-line
+      tool installati...
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+    summary_preview: Learn how to identify Arm-based targets for TinyML, install Fixed Virtual Platforms,
+      deploy ExecuTorch models on Corstone-320 FVP, and visualize model execution using the FVP graphical
+      interface. It i...
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+    summary_preview: Introduction to building a minimal Yocto Linux image and running it on 64-bit Qemu
+      Arm target. It is designed for software developers interested in learning the basics of building
+      Yocto Linux for embe...
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+    summary_preview: Learn how to build and run Zephyr RTOS applications on the Arm Corstone-300 Fixed
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+    summary_preview: Learn how to build and optimize quantized LLMs using llama.cpp on NVIDIA DGX Spark
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+    summary_preview: Learn how to build a Retrieval-Augmented Generation (RAG) pipeline on NVIDIA DGX
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+    summary_preview: Learn how to build an offline voice assistant combining speech-to-text via faster-whisper
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+    summary_preview: Learn how to run pre-trained AI models locally using Docker Model Runner and build
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+    summary_preview: Learn how to develop and build cross-platform desktop applications using the Electron
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+    summary_preview: Learn how to automate Windows application builds on Arm architecture using GitHub
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+    summary_preview: Learn how to create and manage Arm-based Linux virtual machines using Hyper-V on
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+    summary_preview: Learn where the Arm architecture is used in desktop and laptop computers and find
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+    summary_preview: Learn how to configure the LLVM toolchain with Visual Studio to build native Windows
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+    summary_preview: Learn how to apply Arm Memory Tagging Extension (MTE) to protect dynamic memory allocations
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+    summary_preview: Learn how to install and configure Arch Linux for Arm with the i3 window manager
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+    summary_preview: Learn how to create a CI/CD pipeline in GitHub using self-hosted Arm64 runners to
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+    summary_preview: Learn how to measure application resource usage, benchmark video encoding tasks,
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+    summary_preview: Learn how to automate Windows on Arm VM creation on Arm Linux systems using QEMU,
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+    summary_preview: Learn how to build native Arm applications and migrate x86/x64 applications to Arm
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+    summary_preview: Learn how to port Qt-based Python desktop applications with C/C++ dependencies to
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+    summary_preview: Learn how to create a static website hosted on Amazon S3 that interacts with AWS
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+    summary_preview: Learn how to create and build Chromium Embedded Framework desktop applications using
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+    summary_preview: Learn how to build and run a .NET 6 Windows Presentation Foundation (WPF) application
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+    summary_preview: Learn how to build ONNX Runtime with the Generate() API and run Phi-3 model inference
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+    summary_preview: Learn how to apply Profile-Guided Optimization (PGO) to build performance-tuned C++
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+    summary_preview: Learn how to build Python applications on Windows on Arm and leverage native Arm64
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+    summary_preview: Learn how to configure Windows Sandbox as a self-hosted GitHub Actions runner to
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+    summary_preview: Learn how to create C/C++ Win32 DLLs and port them to Arm64 for use in Windows console
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+    summary_preview: Learn how to create and build Windows Presentation Foundation (WPF) applications
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+    summary_preview: Get started with GitHub CI/CD development flow on a Windows on Arm machine (or virtual
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+      on Arm machines....
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+    summary_preview: Learn how to install WindowsPerf on Windows on Arm machines and generate sample performance
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+    summary_preview: Learn how to install and use the WindowsPerf Visual Studio extension to generate
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+    summary_preview: Learn how to use WindowsPerf for performance sampling on Windows on Arm, build CPython
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+    summary_preview: Learn how to import WindowsPerf data in Windows Performance Analyzer (WPA) and visualize
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+    summary_preview: Learn how to configure and run WSL with Linux distributions, graphical applications,
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+    summary_preview: Learn how to enable and verify Arm Fixed Rate Compression in Vulkan applications
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+    summary_preview: Learn how to build and optimize AI-powered camera pipeline applications on Arm Linux
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+    summary_preview: Learn how to use each of the tools supplied with Arm Performance Studio (formerly
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+    summary_preview: Learn how to capture frame data from Android applications and analyze performance
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+    summary_preview: Learn how to build an Android chatbot app using Arm's AI Chat library to run GGUF
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+    summary_preview: Learn how to implement face detection on Android devices using OpenCV, camera frame
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+    summary_preview: Learn how to accelerate OpenCV-based Android applications using KleidiCV for enhanced
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+    summary_preview: Get started with Scalable Vector Extension 2 (SVE2) on Android walks you through
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+    summary_preview: Learn how to integrate Dawn WebGPU in an Android application, render 3D objects,
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+    summary_preview: Learn how to optimize hardware ray tracing with Lumen on Android devices powered
+      by Arm Mali GPUs to maximize performance. It is designed for Unreal Engine developers interested
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+    summary_preview: Learn how to build ONNX Runtime and the generate() API for Android to run a Phi-3
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+    summary_preview: Learn how to build a hands-free selfie Android application using MediaPipe multimodal
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+    summary_preview: Learn how to build an Android chat application with Llama models using ExecuTorch,
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+    summary_preview: Learn how to build a customer support chatbot for Android using Llama 3.2, ExecuTorch,
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+    summary_preview: Learn how to detect and debug memory safety bugs in Android applications using Arm
+      Memory Tagging Extension (MTE) on a Google Pixel 8 smartphone. It is designed for developers interested
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+    summary_preview: Get started with Arm Accuracy Super Resolution (Arm ASR) walks you through an end-to-end
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+    summary_preview: Get started with Unity on Android walks you through an end-to-end Arm software workflow.
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+    summary_preview: Profile Android game performance in Godot with Arm Performance Studio walks you through
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+    summary_preview: How to Enable Hardware Ray Tracing on Lumen for Android Devices walks you through
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+    summary_preview: Get started with Arm hardware walks you through an end-to-end Arm software workflow.
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+    summary_preview: Understand KleidiAI SME2 matmul microkernels walks you through an end-to-end Arm
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+    summary_preview: Learn how to run LLM inference on Android devices using MediaPipe with KleidiAI-enhanced
+      Arm i8mm features to benchmark the Gemma 2B model. It is designed for Android developers who want
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+    summary_preview: Learn how to build the libGPUInfo library using Android NDK and query configuration
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+    summary_preview: Learn how to accelerate LiteRT model inference on Android using KleidiAI with SME2
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+    summary_preview: Learn how to benchmark KleidiAI micro-kernels in ExecuTorch using SME/SME2 instructions
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+    summary_preview: Learn how to fine-tune and evaluate Neural Super Sampling (NSS) models using PyTorch
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+    summary_preview: Learn how to run example C programs on AArch64 Linux to gain an introductory understanding
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+    summary_preview: Learn how to configure ML Extensions for Vulkan emulation and enable Neural Super
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+    summary_preview: Learn how to build llama.cpp with KleidiAI and SME2 support to profile and accelerate
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+    summary_preview: Learn how to build ONNX Runtime with KleidiAI and SME2 support for Android and profile
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+    summary_preview: Learn how to profile ML model execution times and application performance on Arm
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+    summary_preview: Learn how to deploy Unity applications to Android, profile code running on Arm devices,
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+    summary_preview: Learn how to apply post-training quantization to PyTorch models using TorchAO and
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+    summary_preview: Learn how to use the Vulkan ray tracing API to implement realistic shadows, reflections,
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+    summary_preview: Learn how to convert the Stable Audio Open Small model to ExecuTorch format and build
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+    summary_preview: Learn how to install Arm integration packages in Unity to view GPU metrics in Unity
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+    summary_preview: Learn how to use Arm Neon intrinsics in Unity C# scripts to optimize code on Android
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+    summary_preview: Learn how to integrate Unity's Machine Learning Agents toolkit into games deployable
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+    summary_preview: Learn how to download, convert, and deploy Vision Transformers using the Mobile Neural
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+    summary_preview: Learn how to build and optimize a multimodal Voice Assistant application on Android
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+    summary_preview: Build an end-to-end, on-device voice assistant that understands both speech and emotion
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+    summary_preview: Learn how to set up ML Emulation Layers for Vulkan, run sample applications using
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+    summary_preview: Learn how to build and deploy an AI agent application on Arm servers using llama.cpp
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+    summary_preview: Learn how to automate the deployment of an Arm-based Kubernetes cluster on Azure
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+    summary_preview: Learn how to deploy Apache Arrow and Arrow Flight on Google Cloud Axion C4A processors
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+    summary_preview: Learn how to build llama.cpp, quantize the Arcee AFM-4.5B model, and run optimized
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+    summary_preview: Learn how to build llama.cpp, quantize the Arcee AFM-4.5B model, and run optimized
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+    summary_preview: Learn how to deploy and manage applications on Google Cloud GKE Arm64 clusters using
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+    summary_preview: Learn how to automate x86-to-Arm application migration using the Arm MCP Server,
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+    summary_preview: Learn how to migrate C applications between Arm platforms using Kiro's AI-assisted
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+    summary_preview: Learn how to install and configure a Linux kernel with 64K page size support on Arm
+      systems to improve memory efficiency and performance for memory-intensive workloads. It is designed
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+    summary_preview: Learn how to access and use Arm hardware performance counters and the system counter
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+      It is designed for ...
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+    summary_preview: Learn how to package multi-architecture container applications and deploy them on
+      AWS Fargate with Graviton processors using the AWS Copilot CLI. It is designed for software developers
+      who want to lea...
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+    summary_preview: Learn how to automate the creation and deployment of AWS Graviton instances using
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+    summary_preview: Learn how to create and deploy Azure Resource Manager templates to provision Arm64-based
+      Cobalt 100 virtual machines on Azure using the Azure CLI. It is designed for developers and DevOps
+      engineers wh...
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+    summary_preview: Learn how to configure a self-hosted GitHub runner on Azure Cobalt 100, create an
+      AKS cluster with Terraform, and deploy a .NET application using GitHub Actions CI/CD. It is designed
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+    summary_preview: Learn how to automate the creation of Azure Arm virtual machines using Terraform.
+      It is designed for software developers who are new to deploying Arm instances on Azure using Terraform.
+      By the end, yo...
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+    summary_preview: Learn how to create a custom Azure Linux 3.0 VM image using QEMU, upload it to Azure
+      Shared Image Gallery, and deploy it on Arm-based Cobalt 100 processors. It is designed for developers
+      who want to r...
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+    summary_preview: Learn how to deploy and accelerate PyTorch NLP sentiment analysis models from Hugging
+      Face on Arm servers with BFloat16 fast math kernel optimization on Graviton3 processors. It is designed
+      for softwa...
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+    summary_preview: Learn how to implement and benchmark bitmap scanning operations for database workloads
+      using scalar, Neon, and SVE instructions on Arm-based cloud instances. It is designed for database
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+    summary_preview: Learn how to build, profile, and optimize Arm executables using BOLT post-link binary
+      optimization to improve application performance through code layout improvements. It is designed
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+    summary_preview: Learn how to identify optimization candidates and apply LLVM BOLT post-link optimization
+      to AArch64 binaries using BRBE, SPE, instrumentation, and PMU profiling techniques. It is designed
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+    summary_preview: Learn how to optimize Arm application binaries and shared libraries using BOLT profile
+      instrumentation, merge multiple profiles for improved coverage, and integrate optimized libraries.
+      It is designed...
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+    summary_preview: Learn how to configure Buildkite agents on Google Axion C4A VMs to build and publish
+      multi-architecture Docker images using Docker Buildx for Arm and x86 platforms. It is designed for
+      developers who w...
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+    summary_preview: Learn how to install and configure Apache Cassandra on Google Cloud Axion C4A Arm64
+      instances and benchmark read/write performance using cassandra-stress. It is designed for software
+      developers migrat...
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+    summary_preview: Learn how to run the Arm CCA reference software stack on an FVP with RME support,
+      create a Realm virtual machine, and obtain attestation tokens for confidential computing. It is
+      designed for software ...
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+    summary_preview: Learn how Arm CCA Realms interact with I/O devices using VirtIO paravirtualization,
+      SWIOTLB bounce buffers, and PCIe-TDISP secure device attach mechanisms with attestation. It is designed
+      for develope...
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+    summary_preview: Learn how to deploy a CCA realm on an FVP with RME support and connect it with attestation
+      services to create an end-to-end confidential computing workflow. It is designed for software developers
+      who ...
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+    summary_preview: Learn how to deploy Confidential Containers from encrypted images inside Arm CCA
+      Realms using Trustee services for attestation-based authorization on an FVP with RME support. It
+      is designed for develo...
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+    summary_preview: Learn how to deploy a CCA realm workload on an FVP and connect it with Trustee services
+      to enable attestation-based confidential data processing. It is designed for software developers
+      who want to run...
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+    summary_preview: Learn how to inspect and verify Arm CCA attestation tokens using command-line tools
+      and the open-source Veraison attestation verification service. It is designed for developers who
+      would like to learn...
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+    summary_preview: Learn how to deploy a scalable Arm CCA attestation verifier service on AWS using
+      Veraison components with platform endorsement provisioning. It is designed for developers familiar
+      with CCA attestation...
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+    summary_preview: Learn how to set up CircleCI self-hosted machine runners on Google Cloud Axion C4A
+      SUSE VMs and execute Arm-native CI/CD workflows using custom resource classes. It is designed for
+      developers and DevO...
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+    summary_preview: Learn how to install and configure CircleCI self-hosted machine runners on AWS Graviton
+      Arm64 instances to execute CI/CD workflows natively on Arm. It is designed for developers and DevOps
+      engineers w...
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+    summary_preview: Learn how to install and run Clair on Arm servers using combined and distributed
+      deployment models to scan container images and generate vulnerability reports. It is designed for
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+    summary_preview: Learn how to install ClickHouse on Arm-based cloud instances and measure database
+      performance using ClickBench to determine appropriate instance configurations. It is designed for
+      software developers ...
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+    summary_preview: Learn how to deploy ClickHouse on Google Cloud Axion C4A processors and build a streaming
+      ETL pipeline using Apache Beam, Dataflow, and Pub/Sub for real-time analytics. It is designed for
+      developers d...
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+    summary_preview: Learn how to deploy an Arm-based Cobalt 100 virtual machine on Azure, connect via
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+      and DevOps en...
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+    summary_preview: Learn how to automate Docker image creation for Arm using AWS CodeBuild with GitHub
+      integration and run the images on any Arm system with Docker installed. It is designed for software
+      developers inter...
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+    summary_preview: Learn how to build and run the x265 H.265 codec on Arm servers with performance benchmarking
+      across various video resolutions and encoding presets. It is designed for software developers who
+      want to b...
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+    summary_preview: Learn how to build and run the AV1 and VP9 video codecs on Arm Linux systems with
+      performance benchmarking across various resolutions and encoding configurations. It is designed
+      for software developer...
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+    summary_preview: Learn how to install and configure Couchbase on Google Cloud Axion C4A Arm64 instances
+      and benchmark read/write performance using YCSB workloads. It is designed for developers deploying
+      Couchbase work...
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+    summary_preview: Learn how to apply g++ compiler optimization techniques and flags to improve C++
+      application performance on Arm systems with hands-on examples. It is designed for beginner C++ developers
+      who are looki...
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+    summary_preview: Learn how to apply profile-guided optimization to C++ applications on Arm systems
+      and measure performance improvements using Google Benchmark. It is designed for Developers looking
+      to optimize C++ per...
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+    summary_preview: Learn how to profile and identify CPU hotspots in C++ applications on Arm Neoverse
+      using Arm Performix flame graphs to guide optimization. It is designed for software developers and
+      performance engine...
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+    summary_preview: Learn how to start an Arm-based virtual machine instance from major cloud service
+      providers and verify the Arm architecture is being used. It is designed for software developers
+      who are new to Arm-bas...
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+    summary_preview: Learn how to deploy and run the DeepSeek-R1 language model on Arm servers using llama.cpp
+      with quantization for efficient CPU inference. It is designed for developers who want to run DeepSeek-R1
+      on Ar...
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+    summary_preview: Learn how to use fio to microbenchmark storage performance on Arm systems and monitor
+      storage using iostat, iotop, and pidstat to identify bottlenecks. It is designed for developers
+      looking to optimiz...
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+    summary_preview: Run distributed LLM inference with llama.cpp across multiple AWS Graviton4 instances,
+      covering multi-node setup, coordination, and performance trade-offs. It is designed for This introductory
+      topic is...
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+  - path: content/learning-paths/servers-and-cloud-computing/django/_index.md
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+    summary_preview: Learn how to create a simple Django web application and deploy it on Arm machines
+      using Nginx and PostgreSQL. It is designed for engineers who want to deploy a Django based application
+      on Arm machines...
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+    summary_preview: Learn how to deploy a production-grade Django REST API on Google Kubernetes Engine
+      with Arm64 Axion node pools integrated with Google Cloud managed data services. It is designed for
+      DevOps engineers a...
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+    summary_preview: Learn how to build and benchmark the Deep Learning Recommendation Model using PyTorch
+      and MLPerf on Arm Neoverse V2 processors. It is designed for software developers who want to set
+      up a pipeline in ...
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+    summary_preview: Learn how to use the Docker MCP Toolkit with the Arm MCP Server and GitHub Copilot
+      to automate container and code migration from x86 to Arm64. Through a hands-on example, migrate
+      a legacy C++ applicat...
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+      - Which tools, languages, or platforms does it cover?
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+    summary_preview: Learn how to build and run an OrchardCore CMS .NET application on Azure Cobalt 100
+      processors, covering AnyCPU configuration and shared C library integration. It is designed for .NET
+      developers who wa...
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+  - path: content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
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+      - Which tools, languages, or platforms does it cover?
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+      removed_questions: []
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+    summary_preview: Learn how to deploy Dynatrace OneAgent on Azure Cobalt 100 Arm64 virtual machines
+      and configure ActiveGate for secure infrastructure and application monitoring. It is designed for
+      developers, DevOps e...
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+    summary_preview: Learn how to create an AWS ECS cluster with Fargate and AWS Graviton processors,
+      then create and run containerized tasks on Arm infrastructure. It is designed for developers who
+      want to use AWS Gravit...
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+    summary_preview: Learn how to provision an Amazon EKS cluster on Arm-based Graviton instances and
+      deploy a WordPress application with MySQL database. It is designed for software developers new to
+      Kubernetes on AWS who...
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+    summary_preview: Learn how to use docker buildx and docker manifest to build and deploy multi-architecture
+      container images with x86/amd64 and arm64 support on Amazon EKS. It is designed for software developers
+      who wa...
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+  - path: content/learning-paths/servers-and-cloud-computing/envoy/_index.md
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+      - Which tools, languages, or platforms does it cover?
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+    summary_preview: Learn how to build, install, and run Envoy proxy on Arm servers and configure it
+      as a web server for traffic management. It is designed for engineers who want to use Envoy on Arm.
+      By the end, you will...
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+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
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+    summary_preview: Learn how to install and configure Envoy proxy on Google Cloud Axion C4A Arm64 instances
+      and benchmark HTTP proxy performance with load testing. It is designed for This introductory topic
+      for software...
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+    summary_preview: Learn how to optimize Envoy proxy performance on Arm servers using Transparent Huge
+      Pages and Profile-Guided Optimization techniques. It is designed for software developers who want
+      to use Envoy on Ar...
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+  - path: content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md
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+      - Which tools, languages, or platforms does it cover?
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+      removed_questions: []
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+    summary_preview: Learn how stack buffer overflow exploits work on AArch64 by analyzing stack frame
+      layouts, redirecting control flow, and understanding defense mechanisms. It is designed for software
+      developers intere...
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+  - path: content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
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+    summary_preview: Learn how to identify and fix false sharing issues using Perf C2C cache line analysis
+      and Arm Statistical Profiling Extension on Arm-based cloud systems. It is designed for performance-oriented
+      develo...
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+    summary_preview: Learn how to build custom Linux kernels using tuxmake and Fastpath, then benchmark
+      and compare kernel versions on Arm-based EC2 instances. It is designed for software developers and
+      performance engine...
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+  - path: content/learning-paths/servers-and-cloud-computing/fexpa/_index.md
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+      - What do you need before you start?
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+    summary_preview: Learn how to implement exponential functions using Arm SVE intrinsics with the FEXPA
+      instruction for hardware-accelerated computations on Neoverse processors. It is designed for developers
+      interested ...
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+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
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+    summary_preview: Learn how to install and run Apache Flink on Arm servers and benchmark stream processing
+      performance using the Nexmark benchmark suite. It is designed for software developers using Flink
+      as their stre...
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+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
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+      updated_questions: []
+    summary_preview: Learn how to install and configure Apache Flink on Google Cloud Axion C4A Arm64 instances
+      and benchmark stream processing performance with Nexmark. It is designed for developers deploying
+      and optimizi...
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+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
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+      updated_questions: []
+    summary_preview: Learn how to build scalable machine learning workflow pipelines on Google Cloud C4A
+      Axion processors using Flyte for workflow orchestration and gRPC for distributed service communication.
+      It is design...
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+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
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+    summary_preview: Learn how to create an Arm64 Azure VM, install .NET SDK, containerize .NET applications,
+      and push Docker images to Azure Container Registry. It is designed for software developers interested
+      in learni...
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+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
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+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to create and run Docker containers on Azure Container Instances for Arm64-based
+      containerized application deployment. It is designed for developers interested in learning how to
+      create and ...
+    source_hash: 3823ad3df8ae868acfd59b5b5b541240624572fe739aaf2275185d5cd3578032
+  - path: content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to create an Azure Kubernetes Service cluster with Arm64 virtual machines
+      and deploy a containerized application to AKS. It is designed for This learning path is dedicated
+      to developers inte...
+    source_hash: a3347a62c3322d88b80fc7848fa5ee1d92ee86333c2a21891b958286f8b70935
+  - path: content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to automate Azure resource deployment using Infrastructure as Code with
+      Pulumi to provision Azure Container Instances for containerized applications. It is designed for
+      developers interested...
+    source_hash: 1510c787979257e191196e13999e98c20ca24d0857f72075649c28520c74c576
+  - path: content/learning-paths/servers-and-cloud-computing/funasr/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to deploy the ModelScope FunASR Chinese automatic speech recognition model
+      on Arm-based servers with real-time transcription and sentiment analysis. It is designed for developers
+      interested ...
+    source_hash: 410ec28d257ce7aa1308f11a741aa87baea80daf1acb113c4149761144269022
+  - path: content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to install and configure Gardener Kubernetes management platform on Google
+      Cloud Axion C4A SUSE Arm64 instances and deploy workload clusters. It is designed for software developers
+      deploying...
+    source_hash: 85d3d64e0eb0c3cfa0eee29cf1e4199049a6dcbf69634e578dad2b5443ca2b7f
+  - path: content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to apply link-time optimization with the GCC toolchain to improve application
+      performance by optimizing across compilation units. It is designed for developers who want to improve
+      applicatio...
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+  - path: content/learning-paths/servers-and-cloud-computing/gcp/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to automate the creation of Arm virtual machines on Google Cloud Platform
+      using Terraform with jump server access configuration. It is designed for anyone new to using Arm
+      virtual machines i...
+    source_hash: 24e2ffcf9b7cda919e6e864f18bb9424c0c328242c92f491c1b284e317ed7e1c
+  - path: content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Run Geekbench on Arm systems to benchmark CPU performance, interpret the results,
+      and compare different Arm configurations. It is designed for software developers interested in comparing
+      the performan...
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+  - path: content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to set up Arm-hosted GitHub runners and train PyTorch ML models using the
+      German Traffic Sign Recognition Benchmark dataset with automated workflows. It is designed for software
+      developers i...
+    source_hash: 642b67e7ca3717f499ab73efedd924eeab29a0055fad81c2d60fda993dcea195
+  - path: content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to install RunsOn self-hosted runner manager in your AWS account to execute
+      GitHub Actions workflows on Arm runners. It is designed for developers who want to use Arm runners
+      offered by AWS ...
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+  - path: content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to provision a Google Axion C4A Arm virtual machine and set up a GitHub
+      Actions self-hosted runner for CI/CD workflows. It is designed for developers who want to deploy
+      a GitHub Actions self...
+    source_hash: d5df467355a7792f7a04eafc4a6bbd2410353aca000cd2b1aec74a7ed93a9270
+  - path: content/learning-paths/servers-and-cloud-computing/gke/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to automate the deployment of an Arm-based Google Kubernetes Engine cluster
+      using Terraform for container orchestration. It is designed for software developers who want to
+      deploy an Arm-base...
+    source_hash: 7c3d37545c93db584d6e0ce88d8feaf0bf690e0c8786b664a6643be713d0336f
+  - path: content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to add Arm-based Google Axion nodes to an existing x86 GKE cluster and
+      rebuild applications for multi-architecture support. It is designed for software developers who
+      are looking to migrate ...
+    source_hash: 50715640292b60ea4216ee2211140c4212592a27356d628d945e83d9deb7fdcc
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to create dual-architecture GKE clusters with arm64 and amd64 node pools,
+      build multi-architecture Docker images, and migrate services to Google Axion processors. It is designed
+      for cloud, p...
+    source_hash: cf981c67553824c7c57b6dda9c2953ac80926750676ac101e939f6bbff655ab5
+  - path: content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Rebuild and benchmark glibc with LSE atomics on Arm servers, then evaluate scalability
+      using MongoDB workloads and guidance on when LSE delivers a measurable uplift. It is designed for
+      software develo...
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+  - path: content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to provision Arm64 and x86_64 VM instances on Google Cloud, then install
+      and use Sweet and Benchstat to measure and compare Go application performance. It is designed for
+      This introductory t...
+    source_hash: aa78434138f08b424352302e92a1cd40d8297459bc65202715dcb41c40de6057
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to provision Azure Cobalt 100 Arm64 virtual machines and deploy Golang
+      applications with performance benchmarking on Arm architecture. It is designed for software developers,
+      DevOps engineer...
+    source_hash: 46a0a3df799ac473f56d3820390af405b3301758cf15685a250a04c4854aba9e
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
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+    summary_preview: Learn how to install Helm on Google Cloud Axion C4A SUSE VMs and deploy applications
+      like NGINX, PostgreSQL, and Redis using Helm charts. It is designed for This is an introductory
+      topic intended for ...
+    source_hash: 87e9d25d5fb1f45d126aa4ca2fa1e13d6a470f2bcdc70968aa4622790106e629
+  - path: content/learning-paths/servers-and-cloud-computing/intro/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn where Arm architecture is used in servers and cloud computing, and find Arm-based
+      hardware platforms for software development. It is designed for software developers working on server
+      and cloud ...
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Analyze and optimize interrupt request (IRQ) patterns on Arm Linux servers to improve
+      network workload performance through IRQ distribution strategies. It is designed for developers
+      and performance en...
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+  - path: content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Monitor, interpret, and optimize Java Garbage Collector performance on Arm servers
+      by comparing different GCs and tuning parameters for your workload. It is designed for Java developers
+      aiming to opti...
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+  - path: content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
+    status: added
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+    faq_changes:
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Deploy and optimize Java applications on Google Cloud Axion processors by testing
+      JDK versions and performance optimization flags. It is designed for software developers who want
+      to learn how to run t...
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+  - path: content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
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+    faq_changes:
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Deploy Java on Azure Cobalt 100 Arm virtual machines and benchmark application performance
+      with JMH microbenchmarks. It is designed for This is an introductory topic about Java deployment
+      and benchmar...
+    source_hash: 58b0bb9f6e4190029d7edc65bdb04a597c7539de55a5e51ed59e88b174e527c3
+  - path: content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
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+    faq_changes:
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Profile Java applications on Arm Neoverse servers using flame graphs generated with
+      async-profiler and Java agents to identify performance bottlenecks. It is designed for developers
+      who want to analyz...
+    source_hash: 655180d91bb9b9cf571840b59d8943c1d2c73d8f8aea647db57dc2704ede3af8
+  - path: content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Deploy Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate installation,
+      and execute Arm-native CI/CD pipelines including Docker workflows. It is designed for software developers
+      d...
+    source_hash: 525d893a796e8e4eb5cc7a9d5996bd7d55a35f8fe413f87b9a275a214d77626f
+  - path: content/learning-paths/servers-and-cloud-computing/kafka/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Deploy and configure a Kafka cluster with Zookeeper on Arm servers, test event streaming,
+      and automate deployment on AWS and Google Cloud. It is designed for software developers who want
+      to learn how ...
+    source_hash: b7f36df881c2c436143c1e5579d2c54cb5930f52998904098829c52fa7290f38
+  - path: content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Deploy Apache Kafka on Azure Cobalt 100 Arm virtual machines and benchmark message
+      throughput performance. It is designed for developers looking to migrate their Apache Kafka workloads
+      from x86_64 to ...
+    source_hash: d510dd43a485e1c2fac404ef9a2e00b5be2449b99b1095c9a1841cd2fcba9b0e
+  - path: content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing Kedify
+      and KEDA with Helm and testing autoscaling behavior. It is designed for developers running HTTP
+      workloads on Kuber...
+    source_hash: 6ff33f6dfaf25e926a0ce8756b4e564e5aa37112e5425f9c3dce13f772b54145
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Create, train, and evaluate a neural network model on Arm servers using Keras Core
+      with TensorFlow, PyTorch, and JAX backends. It is designed for engineers who want to create a neural
+      network model on...
+    source_hash: 830556dfd51aaa2dd95ee957e64306bab369297f7bc66325e7eb509f541f683c
+  - path: content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Compile and install custom Linux kernels on Arm cloud instances using TuxMake with
+      configurations for 64 KB page sizes and Fastpath testing. It is designed for software developers
+      building custom Linu...
+    source_hash: 004436cf14ff0056136889c58d676295c6dd2088f06f57f397a07ec9004e6b44
+  - path: content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Identify and migrate container images in a Kubernetes cluster to Arm-compatible versions
+      using KubeArchInspect reports. It is designed for software developers who want to ensure containers
+      running in ...
+    source_hash: 43e4e28b88b9721ca94457418f3b4887d0099017578a54da1db5fcdc11f4a122
+  - path: content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
+    status: added
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Deploy AWS Lambda functions on Graviton processors using Terraform for Python and
+      Node.js runtimes. It is designed for software developers who want to learn how to deploy Lambda
+      functions on AWS Gravi...
+    source_hash: 22fa96e29143f2e0dc0c204919422914b3f70d63ddf833cf1067fbf67b525aa0
+  - path: content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Enable and measure libhugetlbfs performance improvements for MySQL and other workloads
+      on Arm Linux servers. It is designed for engineers looking for ways to increase performance on Arm
+      servers. By th...
+    source_hash: 66d13edff1371295f0e88a618e924ca67b85bcc8aa72034d1e582a0b267f0d05
+  - path: content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
+    status: added
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
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+    summary_preview: Serve the llama.cpp chatbot through an OpenAI-compatible API, enabling existing OpenAI-style
+      clients and applications to run against a persistent Arm-hosted LLM. It is designed for developers
+      interest...
+    source_hash: d82aa4faeb599a3a1ac12d477204ebe0a4ddb99564bedced86ca0fc4851e17b9
+  - path: content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
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+    summary_preview: Build a production-ready vision chatbot on Google Axion using Streamlit, PyTorch,
+      and Hugging Face Transformers with a quantized Llama 3.2-Vision model. It is designed for software
+      developers and ML e...
+    source_hash: 3e8307a5daf435c21f3cecff635ac51724a63ff9ea4307082d869601563b4204
+  - path: content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Optimize llama.cpp on Arm CPUs by integrating Streamline Annotations to profile Prefill
+      and Decode stages, analyze operators, and evaluate multi-core execution. It is designed for software
+      developers,...
+    source_hash: 36bf3c45f0b38350714ba41ea88c1551b33f6d299a65b3b0d6668fee2d88d835
+  - path: content/learning-paths/servers-and-cloud-computing/lse/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
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+    summary_preview: Understand Large System Extensions (LSE) for Arm processors and verify whether applications
+      use LSE for improved atomic operation performance. It is designed for software developers who want
+      to learn ...
+    source_hash: 9610291239b88c67e21055f94cd03fe7cf80f7d875d53ebe0d8de7837ce99fa7
+  - path: content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
+    status: added
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Deploy MariaDB on Arm cloud instances across AWS, Azure, and Google Cloud using Docker,
+      Amazon RDS, and automation with Terraform and Ansible. It is designed for software developers who
+      want to deploy...
+    source_hash: db4ce1c59ad10bd127b4990c82ce876311d7dc7e1ad5d39ec132daf82ceb8b79
+  - path: content/learning-paths/servers-and-cloud-computing/memcached/_index.md
+    status: added
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+    faq_changes:
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Install memcached on Arm cloud servers and benchmark in-memory key-value store performance
+      using open-source tools. It is designed for developers who want to use memcached as their in-memory
+      key-value...
+    source_hash: b42ca5cc8f4db6ba6019f3720a5ef46119aa403a2baaef5362e874f898ce0419
+  - path: content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
+    status: added
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Deploy Memcached as a cache for MySQL and PostgreSQL on Arm servers. It is designed
+      for developers who want to use memcached as their in-memory key-value store. By the end, you will
+      be able to deploy ...
+    source_hash: 4efe46aaf4580a6b8f263b69e8f072211bfd24fcf7f7a885689c927fc05a4c63
+  - path: content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
+    status: added
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Use ASCT to measure cache latency, streaming bandwidth, and coherency latency on
+      Arm Neoverse systems, and compare results across Graviton generations. It is designed for software
+      developers and perfo...
+    source_hash: d61c9ddcef1c7ffe12b3ee818852fb3f0a62a90cec451692c1186cf2c01de625
+  - path: content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
+    status: added
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
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+    summary_preview: Test and validate thread synchronization approaches in the Arm memory model using
+      Herd7, Litmus7, and Arm hardware with assembly snippets. It is designed for developers seeking practical
+      ways to test ...
+    source_hash: 6aa61de638be961339d958345225d696ff2b83b8f9cab22bf956f9bf2d15c1aa
+  - path: content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
+    status: added
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
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+    summary_preview: Microbenchmark and tune network performance with iPerf3 and Linux traffic control
+      walks you through an end-to-end Arm software workflow. It is designed for performance engineers,
+      Linux system administ...
+    source_hash: a67d36fb650b77c170c15f193049490286a3f097801d0c79d701d3f5610fe1dc
+  - path: content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
+    status: added
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Scan source code for architecture-specific portability issues using migrate-ease
+      to identify and resolve AArch64 porting challenges before migration. It is designed for developers
+      looking to migrate a...
+    source_hash: 56a38d1d742dd301d48e9ad61ff00e07048559f3f646815e22937113243adcdb
+  - path: content/learning-paths/servers-and-cloud-computing/migration/_index.md
+    status: added
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
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+    summary_preview: Set up an Arm development environment, analyze dependencies, and understand common
+      challenges and scenarios for migrating applications to Arm servers. It is designed for software
+      developers looking to...
+    source_hash: 6b2b985c39579c4d0855c8c28b610ba558e25b451a6b755475ff4393e2810670
+  - path: content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
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+    summary_preview: Build a Retrieval-Augmented Generation (RAG) application on Arm servers using Zilliz
+      Cloud for vector search and llama.cpp for LLM inference. It is designed for software developers
+      who want to create ...
+    source_hash: 2eb252b19b7535fbb776a3153bfc9f70b6217088e5b4b55deb56d01393abaf3b
+  - path: content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
+    status: added
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
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+    summary_preview: Learn how to deploy and configure MinIO on an Azure Cobalt 100 virtual machine, benchmark
+      object storage throughput, and validate S3 compatibility using the boto3 Python SDK. It is designed
+      for develo...
+    source_hash: 6c438573edec90b3aa4135ace38cd3ae604c58658c1949117ca80dbdd21394bd
+  - path: content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
+    status: added
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
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+    summary_preview: Benchmark machine learning inference performance on Arm servers using TensorFlow
+      and the MLPerf Inference benchmark suite from MLCommons. It is designed for software developers
+      interested in benchmark...
+    source_hash: 973ba6c1e00fc67e1702606412124d8172e071b9b677a1df53c3be66210bf704
+  - path: content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Install MongoDB on Arm servers and benchmark database performance using Yahoo Cloud
+      Serving Benchmark (YCSB) to compare against other architectures. It is designed for software developers
+      who want to ...
+    source_hash: b52a1b60a74e19f2a3b60b8a77199ad5369c1ccd3b4d5ce0252a169d9f6123fd
+  - path: content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
+    status: added
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Deploy MongoDB on Azure Cobalt 100 Arm virtual machines and benchmark database performance
+      using mongotop and mongostat monitoring tools. It is designed for software developers who want to
+      migrate Mon...
+    source_hash: f270cfc90245c0090685fabf5cbebf62a714edbf34e1ea86c3df0bfbb4699ea4
+  - path: content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
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+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Deploy MongoDB on Google Cloud Axion C4A virtual machines and benchmark database
+      performance with Yahoo Cloud Serving Benchmark (YCSB). It is designed for This introductory topic
+      is for software devel...
+    source_hash: abbdde22271151fb1485c54bafe463e7797a0b913c7d4c99245d2914a3f9bb82
+  - path: content/learning-paths/servers-and-cloud-computing/mpi/_index.md
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+    summary_preview: Debug, profile, and optimize MPI parallel applications on Arm servers using Linaro
+      Forge, gdb, and Arm Performance Libraries. It is designed for HPC software developers writing MPI
+      applications. By th...
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+    summary_preview: Select and apply accuracy modes for vectorized math functions in Libamath to balance
+      performance and precision for your application. It is designed for developers who want to use the
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+    summary_preview: Build a multi-architecture Kubernetes cluster running nginx on Azure AKS walks you
+      through an end-to-end Arm software workflow. It is designed for developers who want to deploy multi-architecture
+      Kube...
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+    summary_preview: Add Arm nodes to your GKE cluster using a multi-architecture Ollama container image
+      walks you through an end-to-end Arm software workflow. It is designed for developers who want to
+      compare the perform...
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+    summary_preview: Learn how to deploy MySQL walks you through an end-to-end Arm software workflow.
+      It is designed for software developers who want to deploy MySQL on Arm. By the end, you will be
+      able to learn about the...
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+    summary_preview: Deploy MySQL on Microsoft Azure Cobalt 100 processors walks you through an end-to-end
+      Arm software workflow. It is designed for developers migrating MySQL applications from x86_64 to
+      Arm. By the end, ...
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+    summary_preview: Benchmarking MySQL with Sysbench walks you through an end-to-end Arm software workflow.
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+    summary_preview: Learn how to Tune MySQL walks you through an end-to-end Arm software workflow. It
+      is designed for software developers and DevOps professionals interested in optimizing MySQL performance
+      on Arm-based V...
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+    summary_preview: Develop and Validate Firmware Pre-Silicon on Arm Neoverse CSS V3 walks you through
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+    summary_preview: Run a .NET Aspire application on Arm-based VMs on AWS and GCP walks you through an
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+      how to deploy .NET As...
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+    summary_preview: Learn how to deploy Nginx walks you through an end-to-end Arm software workflow.
+      It is designed for engineers who want to use Nginx on Arm. By the end, you will be able to install
+      and run Nginx on Arm...
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+    summary_preview: Deploy NGINX on Azure Cobalt 100 Arm-based virtual machines walks you through an
+      end-to-end Arm software workflow. It is designed for system administrators and developers who want
+      to learn how to depl...
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+    summary_preview: Learn how to tune Nginx walks you through an end-to-end Arm software workflow. It
+      is designed for software developers who want to use Nginx on Arm. By the end, you will be able to
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+    summary_preview: Run a Natural Language Processing (NLP) model from Hugging Face on Arm servers walks
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+    summary_preview: Deploy Node.js on Google Cloud C4A Arm-based Axion VMs walks you through an end-to-end
+      Arm software workflow. It is designed for software developers migrating Node.js workloads from x86_64
+      to Arm-base...
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+    summary_preview: Deploy Arm Instances on Oracle Cloud Infrastructure (OCI) using Terraform walks you
+      through an end-to-end Arm software workflow. It is designed for software developers who are new
+      to deploying Arm ins...
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+    summary_preview: Deploy Phi-4-mini model with ONNX Runtime on Azure Cobalt 100 walks you through an
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+      looking to dep...
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+    summary_preview: Deploy SqueezeNet 1.0 INT8 model with ONNX Runtime on Azure Cobalt 100 walks you
+      through an end-to-end Arm software workflow. It is designed for developers deploying ONNX-based
+      applications on Arm-bas...
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+    summary_preview: Simulate OpenBMC and UEFI pre-silicon on Neoverse RD-V3 walks you through an end-to-end
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+      software engi...
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+    summary_preview: Learn how to profile an example C++ data-processing workload on Arm Linux with Arm
+      Performix, then accelerate random number generation using OpenRNG and Arm Performance Libraries.
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+    summary_preview: Build multi-architecture applications with Red Hat OpenShift Pipelines on AWS walks
+      you through an end-to-end Arm software workflow. It is designed for OpenShift administrators who
+      want to migrate the...
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+    summary_preview: Deploy OpenStack on Azure Cobalt 100 Arm64 virtual machines using DevStack for development
+      and Kolla-Ansible for containerized production deployments. It is designed for This learning path
+      is designed...
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+    summary_preview: Deploy OpenTelemetry on Google Cloud C4A Axion processors walks you through an end-to-end
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+      who wa...
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+    summary_preview: Understand Arm Pointer Authentication walks you through an end-to-end Arm software
+      workflow. It is designed for software developers interested in understanding Arm Pointer Authentication.
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+    summary_preview: Learn how to use an AI agent and the Performix tool through the Arm MCP Server to
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+      optimizations on ...
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+    summary_preview: Optimize application performance using Arm Performix CPU microarchitecture analysis
+      walks you through an end-to-end Arm software workflow. It is designed for software developers who
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+    summary_preview: Deploy PHP on Google Cloud C4A Arm-based Axion VMs walks you through an end-to-end
+      Arm software workflow. It is designed for developers migrating Hypertext Preprocessor (PHP) workloads
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+    summary_preview: Optimize application performance with CPU affinity walks you through an end-to-end
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+    summary_preview: Deploy PostgreSQL on Azure Cobalt 100 Arm64 virtual machines, load a relational schema
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+    summary_preview: Run Process watch on your Arm machine walks you through an end-to-end Arm software
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+    summary_preview: Profiling for Neoverse with Streamline CLI Tools walks you through an end-to-end
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+    summary_preview: Deploy Puppet on Google Cloud C4A walks you through an end-to-end Arm software workflow.
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+    summary_preview: Run a Large Language Model (LLM) chatbot with PyTorch using KleidiAI on Arm servers
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+    summary_preview: Learn how to deploy Qdrant on Google Cloud C4A Axion processors, generate vector
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+    summary_preview: Deploy RabbitMQ on Arm64 Cloud Platforms (Azure and GCP) walks you through an end-to-end
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+      and eve...
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+    summary_preview: Deploy a RAG-based Chatbot with llama-cpp-python using KleidiAI on Google Axion processors
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+    summary_preview: Get started with the Arm 5G RAN Acceleration Library (ArmRAL) walks you through an
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+    summary_preview: Deploy and run distributed AI workloads using Ray on Google Cloud Axion C4A Arm-based
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+    summary_preview: Deploy Redis on Arm walks you through an end-to-end Arm software workflow. It is
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+    summary_preview: Learn how to install and configure Redis on an Azure Cobalt 100 Arm64 virtual machine,
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+    summary_preview: Deploy Redis for data searching on Google Cloud C4A walks you through an end-to-end
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+    summary_preview: Deploy Redis as a cache for MySQL and PostgreSQL on Arm servers walks you through
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+    summary_preview: Learn how to tune Redis walks you through an end-to-end Arm software workflow. It
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+    summary_preview: Debug Neoverse N2 Reference Design with Arm Development Studio walks you through
+      an end-to-end Arm software workflow. It is designed for software developers who are interested in
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+    summary_preview: Get started with the Neoverse Reference Design software stack walks you through an
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+    summary_preview: Enable reproducible math functions across vector extensions with Arm Performance
+      Libraries walks you through an end-to-end Arm software workflow. It is designed for developers who
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+    summary_preview: Learn how to create a virtual machine in a Realm using Arm Confidential Compute Architecture
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+    summary_preview: Run an LLM chatbot with rtp-llm on Arm-based servers walks you through an end-to-end
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+    summary_preview: Scan multi-architecture containers with Trivy on Azure Cobalt 100 walks you through
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+    summary_preview: Tune network workloads on Arm-based bare-metal instances walks you through an end-to-end
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+    summary_preview: Install Vectorscan (Hyperscan on Arm) and use it with Snort 3 walks you through an
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+    summary_preview: Build and Run vLLM on Arm Servers walks you through an end-to-end Arm software workflow.
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+      end-to-end Arm software workflow. It is designed for developers who want to install WordPress on
+      Oracle Cloud Infrastru...
+    source_hash: 46f2645fb6ef298508af93569554315cfa2564be9891acabf1c874c94e52d70d
+  - path: content/learning-paths/servers-and-cloud-computing/zlib/_index.md
+    status: added
+    changed_on_disk: true
+    summary_changed: true
+    faq_changed: true
+    faq_changes:
+      before_count: 0
+      after_count: 5
+      added_questions:
+      - What will you accomplish in this Learning Path?
+      - Who is this Learning Path for?
+      - What do you need before you start?
+      - Which tools, languages, or platforms does it cover?
+      - How is the Learning Path structured?
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to build and use zlib-ng on Arm servers, using its Neon SIMD and ARMv8
+      CRC32 optimizations to improve compression performance compared to the system default zlib. It is
+      designed for software...
+    source_hash: 8916f83f621505dc5406f14e70d04e702ea304950e34b4b76dc1bbc987bcc4e3

From cf77fea87e93cdeb334775992441bbd851b8f0df Mon Sep 17 00:00:00 2001
From: GitHub Actions Summary FAQ Bot <>
Date: Wed, 6 May 2026 17:18:02 +0000
Subject: [PATCH 03/23] Generate Learning Path summary and FAQ content

---
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 .../supervisord/_index.md                     |     2 +-
 .../servers-and-cloud-computing/sve/_index.md |     2 +-
 .../sve2-match/_index.md                      |     2 +-
 .../sysreport/_index.md                       |     2 +-
 .../tensorflow-gcp/_index.md                  |     2 +-
 .../thirdai-sentiment-analysis/_index.md      |     2 +-
 .../timescaledb-on-gcp/_index.md              |     2 +-
 .../top-down-n1/_index.md                     |     2 +-
 .../torchbench/_index.md                      |     2 +-
 .../triggering-pmu-events-2/_index.md         |     2 +-
 .../triggering-pmu-events/_index.md           |     2 +-
 .../trivy-on-gcpp/_index.md                   |     2 +-
 .../_index.md                                 |     2 +-
 .../typescript-on-gcp/_index.md               |     2 +-
 .../_index.md                                 |     2 +-
 .../vectorscan/_index.md                      |     2 +-
 .../vllm-acceleration/_index.md               |     2 +-
 .../vllm/_index.md                            |     2 +-
 .../vvenc/_index.md                           |     2 +-
 .../whisper/_index.md                         |     2 +-
 .../wordpress/_index.md                       |     2 +-
 .../zlib/_index.md                            |     2 +-
 reports/generated-summary-faq/latest-run.yml  | 12241 +++++++++++-----
 408 files changed, 8572 insertions(+), 4483 deletions(-)

diff --git a/content/learning-paths/automotive/openadkit1_container/_index.md b/content/learning-paths/automotive/openadkit1_container/_index.md
index e8cb96684c..df00c9a9ee 100644
--- a/content/learning-paths/automotive/openadkit1_container/_index.md
+++ b/content/learning-paths/automotive/openadkit1_container/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:53Z'
   generator: template
   source_hash: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
   summary: >-
diff --git a/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md b/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
index ac9fc9ca3b..f573ef004e 100644
--- a/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
+++ b/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:53Z'
   generator: template
   source_hash: 92a6dac2b1674a44cd623a0c8c3189b38438124a6067ed7ca776999ff1d8b5bf
   summary: >-
diff --git a/content/learning-paths/automotive/system76-auto/_index.md b/content/learning-paths/automotive/system76-auto/_index.md
index e42ec5ff8a..36ec8bae76 100644
--- a/content/learning-paths/automotive/system76-auto/_index.md
+++ b/content/learning-paths/automotive/system76-auto/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:53Z'
   generator: template
   source_hash: 2b758d2dcf28a683ab164e28578a736d6d730b81dfbc01a6765619052fcdebd0
   summary: >-
diff --git a/content/learning-paths/automotive/zenacssdebug/_index.md b/content/learning-paths/automotive/zenacssdebug/_index.md
index 07c5167009..59ca332e20 100644
--- a/content/learning-paths/automotive/zenacssdebug/_index.md
+++ b/content/learning-paths/automotive/zenacssdebug/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:53Z'
   generator: template
   source_hash: 8dccdbdd4d9727f3466987ce918d9df0ed9d4d4f28cd613162bacab01d9c18f3
   summary: >-
diff --git a/content/learning-paths/cross-platform/_example-learning-path/_index.md b/content/learning-paths/cross-platform/_example-learning-path/_index.md
index 1f6ea18b81..e7a1cb6faa 100644
--- a/content/learning-paths/cross-platform/_example-learning-path/_index.md
+++ b/content/learning-paths/cross-platform/_example-learning-path/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:53Z'
   generator: template
   source_hash: a58d5eb4c4256f3e4f4374344ef0e73836e8b51ac7223b0a5238b22137ec0819
   summary: >-
diff --git a/content/learning-paths/cross-platform/adler32/_index.md b/content/learning-paths/cross-platform/adler32/_index.md
index a4a5d82b8f..6ba56df7df 100644
--- a/content/learning-paths/cross-platform/adler32/_index.md
+++ b/content/learning-paths/cross-platform/adler32/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:53Z'
   generator: template
   source_hash: 37b19106fba70423ba8c58f82097d9251f0ae208e882c852f9c4531afcebb46c
   summary: >-
diff --git a/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md b/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md
index 13392da4fe..fb91678b01 100644
--- a/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md
+++ b/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:53Z'
   generator: template
   source_hash: 597877722e5f277e5558e29ecd33878ac2262b70a84a9769325b3c3b0ce06e58
   summary: >-
diff --git a/content/learning-paths/cross-platform/avh_cicd/_index.md b/content/learning-paths/cross-platform/avh_cicd/_index.md
index 5f7cd642d1..eb2eefba2d 100644
--- a/content/learning-paths/cross-platform/avh_cicd/_index.md
+++ b/content/learning-paths/cross-platform/avh_cicd/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:53Z'
   generator: template
   source_hash: b6a7439a701f6ae09ce8c61f02150a61fa6ecc1151050e903492e16794999676
   summary: >-
diff --git a/content/learning-paths/cross-platform/avh_cicd2/_index.md b/content/learning-paths/cross-platform/avh_cicd2/_index.md
index 713cb7c343..380af7e824 100644
--- a/content/learning-paths/cross-platform/avh_cicd2/_index.md
+++ b/content/learning-paths/cross-platform/avh_cicd2/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:53Z'
   generator: template
   source_hash: 251b6edf6a016f9fc73eb35216f56b2001465fbca8253bd7b6e6f9f4afcd25e6
   summary: >-
diff --git a/content/learning-paths/cross-platform/cca_rme/_index.md b/content/learning-paths/cross-platform/cca_rme/_index.md
index 110bfa417d..c685b0dde1 100644
--- a/content/learning-paths/cross-platform/cca_rme/_index.md
+++ b/content/learning-paths/cross-platform/cca_rme/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:53Z'
   generator: template
   source_hash: a1685fb43efecb9690d03c7f8ee64ab20ae8aece91190e2223705106568b1038
   summary: >-
diff --git a/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md b/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md
index b319d83984..2f307b3778 100644
--- a/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md
+++ b/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:53Z'
   generator: template
   source_hash: a639e60da034fd04e891c9236e9fe5dab47cca87f6174828275ef5a76c43c32e
   summary: >-
diff --git a/content/learning-paths/cross-platform/docker-build-cloud/_index.md b/content/learning-paths/cross-platform/docker-build-cloud/_index.md
index 4c027811d0..e9cd8f1a76 100644
--- a/content/learning-paths/cross-platform/docker-build-cloud/_index.md
+++ b/content/learning-paths/cross-platform/docker-build-cloud/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:53Z'
   generator: template
   source_hash: 9a94219b689b8e22a175c6067c6bb6d139d6ad22f3aac77c78b6b38970d5a5eb
   summary: >-
diff --git a/content/learning-paths/cross-platform/docker/_index.md b/content/learning-paths/cross-platform/docker/_index.md
index cf5bf3d1b1..2ea689adf5 100644
--- a/content/learning-paths/cross-platform/docker/_index.md
+++ b/content/learning-paths/cross-platform/docker/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:53Z'
   generator: template
   source_hash: 058f779bc7dd9faef294a57f4524bf525046d757e21a287744825fb9b290935e
   summary: >-
diff --git a/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md b/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md
index 2dfbc8e773..952dea3222 100644
--- a/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md
+++ b/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:53Z'
   generator: template
   source_hash: c59308676849aea9b7cfce8c48c7d6e1ca072ef6fb38fb193a34aa5b2597b9b9
   summary: >-
diff --git a/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md b/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md
index 859d00ad26..17c51160ae 100644
--- a/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md
+++ b/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:53Z'
   generator: template
   source_hash: 39c5e146afbfc74c3076d2cf3f1a67ddc0e4715f39b2cff1ccf76b288415bdc0
   summary: >-
diff --git a/content/learning-paths/cross-platform/ernie_moe_v9/_index.md b/content/learning-paths/cross-platform/ernie_moe_v9/_index.md
index 116f1d39dc..4777c47490 100644
--- a/content/learning-paths/cross-platform/ernie_moe_v9/_index.md
+++ b/content/learning-paths/cross-platform/ernie_moe_v9/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:53Z'
   generator: template
   source_hash: e835a550c955b13805c7859ff64e3b8d7fdee68222569225c53a0f15db72f046
   summary: >-
diff --git a/content/learning-paths/cross-platform/floating-point-behavior/_index.md b/content/learning-paths/cross-platform/floating-point-behavior/_index.md
index 0ed2cb95f5..f4f7501317 100644
--- a/content/learning-paths/cross-platform/floating-point-behavior/_index.md
+++ b/content/learning-paths/cross-platform/floating-point-behavior/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:53Z'
   generator: template
   source_hash: 6427d4466fcd34f7275d16820cbfa2afa3815e1f0306c02e5011b2a4a6afa984
   summary: >-
diff --git a/content/learning-paths/cross-platform/function-multiversioning/_index.md b/content/learning-paths/cross-platform/function-multiversioning/_index.md
index c91dafe330..170fe71c07 100644
--- a/content/learning-paths/cross-platform/function-multiversioning/_index.md
+++ b/content/learning-paths/cross-platform/function-multiversioning/_index.md
@@ -26,7 +26,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:53Z'
   generator: template
   source_hash: 1c1d745bd1af535315d5edd1b2b9214c96419d46abde3af8fa75bdde299bb65d
   summary: >-
diff --git a/content/learning-paths/cross-platform/github-arm-runners/_index.md b/content/learning-paths/cross-platform/github-arm-runners/_index.md
index 05e8037ae9..853c001f92 100644
--- a/content/learning-paths/cross-platform/github-arm-runners/_index.md
+++ b/content/learning-paths/cross-platform/github-arm-runners/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:53Z'
   generator: template
   source_hash: 3557d534c51f81839cc353ebbd600ec588a60197d2c27c9a58e97c25017d07e4
   summary: >-
diff --git a/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md b/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md
index 2180d3d3a5..3f3146b4c7 100644
--- a/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md
+++ b/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:53Z'
   generator: template
   source_hash: a6ad703d9d894da06ed4aa3725fcc9006d70050cef3f0a3ef9a758bcf4523289
   summary: >-
diff --git a/content/learning-paths/cross-platform/gitlab/_index.md b/content/learning-paths/cross-platform/gitlab/_index.md
index e39fd0c541..93659cb840 100644
--- a/content/learning-paths/cross-platform/gitlab/_index.md
+++ b/content/learning-paths/cross-platform/gitlab/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:53Z'
   generator: template
   source_hash: af9eb1c426e1844e2fd20215b7012d95c1efaf737f1d7b5be828931528342316
   summary: >-
diff --git a/content/learning-paths/cross-platform/integer-vs-floats/_index.md b/content/learning-paths/cross-platform/integer-vs-floats/_index.md
index 59bdf34566..038fd6e0ad 100644
--- a/content/learning-paths/cross-platform/integer-vs-floats/_index.md
+++ b/content/learning-paths/cross-platform/integer-vs-floats/_index.md
@@ -18,7 +18,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:53Z'
   generator: template
   source_hash: 1895767d551ba0fa249c5002d3e2e471acacdec06ddb7c2f5314a2fa0df94f2e
   summary: >-
diff --git a/content/learning-paths/cross-platform/intrinsics/_index.md b/content/learning-paths/cross-platform/intrinsics/_index.md
index 17e66128eb..7386883676 100644
--- a/content/learning-paths/cross-platform/intrinsics/_index.md
+++ b/content/learning-paths/cross-platform/intrinsics/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:53Z'
   generator: template
   source_hash: ecf51d9a9085f95dedda9c0cfbfa4d6350d0f68d81b61f9461bc51070abd0b69
   summary: >-
diff --git a/content/learning-paths/cross-platform/ipexplorer/_index.md b/content/learning-paths/cross-platform/ipexplorer/_index.md
index 4b18e42b17..5010828c6c 100644
--- a/content/learning-paths/cross-platform/ipexplorer/_index.md
+++ b/content/learning-paths/cross-platform/ipexplorer/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:53Z'
   generator: template
   source_hash: 47cd598f6e33c12d3729c319a1d6a7afcd748de7acd6faea639f2e8600a085ed
   summary: >-
diff --git a/content/learning-paths/cross-platform/kleidiai-explainer/_index.md b/content/learning-paths/cross-platform/kleidiai-explainer/_index.md
index 76839a6f88..f5bc22f0dd 100644
--- a/content/learning-paths/cross-platform/kleidiai-explainer/_index.md
+++ b/content/learning-paths/cross-platform/kleidiai-explainer/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:53Z'
   generator: template
   source_hash: 907255b3c2c1086b421abe4a1e378b68533de4524a4f4dd81138545a2ff1a5b5
   summary: >-
diff --git a/content/learning-paths/cross-platform/loop-reflowing/_index.md b/content/learning-paths/cross-platform/loop-reflowing/_index.md
index d28f826d17..4e84bf220c 100644
--- a/content/learning-paths/cross-platform/loop-reflowing/_index.md
+++ b/content/learning-paths/cross-platform/loop-reflowing/_index.md
@@ -17,7 +17,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 4218b8b0c1dee3862bb9765a83dfed0cb38555d1da7425e791c5c16b17f98c21
   summary: >-
diff --git a/content/learning-paths/cross-platform/matrix/_index.md b/content/learning-paths/cross-platform/matrix/_index.md
index 569e5fe2eb..0f5f300750 100644
--- a/content/learning-paths/cross-platform/matrix/_index.md
+++ b/content/learning-paths/cross-platform/matrix/_index.md
@@ -24,7 +24,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 5611b6d1eebbea167d1860e3ac7f4f7910584561cbb18c3ed696aa27215edce9
   summary: >-
diff --git a/content/learning-paths/cross-platform/mca-godbolt/_index.md b/content/learning-paths/cross-platform/mca-godbolt/_index.md
index f0aa724a83..cf962ce34f 100644
--- a/content/learning-paths/cross-platform/mca-godbolt/_index.md
+++ b/content/learning-paths/cross-platform/mca-godbolt/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: c86322d497541344f92907315793ab990669aa08bef994b0ce9ff1e32a5ba055
   summary: >-
diff --git a/content/learning-paths/cross-platform/mcp-ai-agent/_index.md b/content/learning-paths/cross-platform/mcp-ai-agent/_index.md
index c393e958b5..45f66ff7d4 100644
--- a/content/learning-paths/cross-platform/mcp-ai-agent/_index.md
+++ b/content/learning-paths/cross-platform/mcp-ai-agent/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 39d489081bfa22125a4046c17d5c00a32c0d7b298cba7b6a12373cf3cf6bac04
   summary: >-
diff --git a/content/learning-paths/cross-platform/memory-latency/_index.md b/content/learning-paths/cross-platform/memory-latency/_index.md
index 802e4e3831..bcffc6beae 100644
--- a/content/learning-paths/cross-platform/memory-latency/_index.md
+++ b/content/learning-paths/cross-platform/memory-latency/_index.md
@@ -18,7 +18,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 72e5ffe5091311850d17f30ed3ab4bd7487cbbd862d0d8751cc73bd91fff00e2
   summary: >-
diff --git a/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md b/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md
index e9fa29d886..eda97eca97 100644
--- a/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md
+++ b/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: bf3183bd62b590d4930f0bcf9c9dc836bbc5206a991be7bec5e18e9c20942352
   summary: >-
diff --git a/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md b/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md
index c40c97b4fb..92ed13a6e2 100644
--- a/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md
+++ b/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md
@@ -29,7 +29,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: eb01b77f36323331c080615edcbddbf8cb56cf005f2249f1ea309ab1dbec8616
   summary: >-
diff --git a/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md b/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md
index ac5de0604b..986d3b13e4 100644
--- a/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md
+++ b/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md
@@ -27,7 +27,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 1251465f13b67ee80292b66ef22069a1b6cc2ca67bb602cdd5e393a278576ec8
   summary: >-
diff --git a/content/learning-paths/cross-platform/remoteit/_index.md b/content/learning-paths/cross-platform/remoteit/_index.md
index c2e51be9a7..e859568df9 100644
--- a/content/learning-paths/cross-platform/remoteit/_index.md
+++ b/content/learning-paths/cross-platform/remoteit/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 927cfebb8ebf9595922dad115c9a8d10900e43c4f80e73e6102a71e3e4ca2da1
   summary: >-
diff --git a/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md b/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
index 6a8b527cfa..98c4e81747 100644
--- a/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
+++ b/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
@@ -18,7 +18,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 9825df99004e981fadce5884b40b2152f4e43064cbba2cd2129fd90006090678
   summary: >-
diff --git a/content/learning-paths/cross-platform/rust_armds/_index.md b/content/learning-paths/cross-platform/rust_armds/_index.md
index 819f2acf70..c127cc8748 100644
--- a/content/learning-paths/cross-platform/rust_armds/_index.md
+++ b/content/learning-paths/cross-platform/rust_armds/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: f237cd239e5886b21bd5bee88dcd9f95d88eda9a5c2eea363cc9db252e0c6e9f
   summary: >-
diff --git a/content/learning-paths/cross-platform/simd-info-demo/_index.md b/content/learning-paths/cross-platform/simd-info-demo/_index.md
index ae0582f89d..14d616b9de 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/_index.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 7a40efa6d83b4629888f9622260e6e9aa9192db836b203fa4bf388cbe636b7e6
   summary: >-
diff --git a/content/learning-paths/cross-platform/simd-loops/_index.md b/content/learning-paths/cross-platform/simd-loops/_index.md
index 528806a548..e868eab11b 100644
--- a/content/learning-paths/cross-platform/simd-loops/_index.md
+++ b/content/learning-paths/cross-platform/simd-loops/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: b1c43e1bf971db4582ca358c98dab2c7e6e047d6c79bfcc0db148bc575f33679
   summary: >-
diff --git a/content/learning-paths/cross-platform/simd-on-rust/_index.md b/content/learning-paths/cross-platform/simd-on-rust/_index.md
index 1a97bf3820..0a1c8a6886 100644
--- a/content/learning-paths/cross-platform/simd-on-rust/_index.md
+++ b/content/learning-paths/cross-platform/simd-on-rust/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: a051c519c1a4969f30d5a81e46823e77f0c15f163011b3a38f4c05104d853249
   summary: >-
diff --git a/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md b/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md
index 0b6dd600ed..f54c1c255f 100644
--- a/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md
+++ b/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md
@@ -25,7 +25,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 36f7dfe13c8c940abbaad2b61620b3b1c6d97f580de15e573b45ef7760753797
   summary: >-
diff --git a/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md b/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md
index 1593e53000..b02c067bcc 100644
--- a/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md
+++ b/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md
@@ -24,7 +24,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 09142517ecc64fba821062e1af4db3ac9d72f9adcd3f10c12d6fd5a4cf3daaf4
   summary: >-
diff --git a/content/learning-paths/cross-platform/topdown-compare/_index.md b/content/learning-paths/cross-platform/topdown-compare/_index.md
index 3a4132280d..25c2cfe798 100644
--- a/content/learning-paths/cross-platform/topdown-compare/_index.md
+++ b/content/learning-paths/cross-platform/topdown-compare/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 5f4b05e3d962476c67c4a4400c8fa71f04412f3f0354d5c3123f38af57791304
   summary: >-
diff --git a/content/learning-paths/cross-platform/vectorization-comparison/_index.md b/content/learning-paths/cross-platform/vectorization-comparison/_index.md
index f27210ac12..4156137de2 100644
--- a/content/learning-paths/cross-platform/vectorization-comparison/_index.md
+++ b/content/learning-paths/cross-platform/vectorization-comparison/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 26450ae17f7ed4242c52456c2780ffce5fad36b56dfc4a8482e8f236f855e134
   summary: >-
diff --git a/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md b/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md
index cbf784bf84..29c1a4ab92 100644
--- a/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md
+++ b/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md
@@ -18,7 +18,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 14a22194d3fc73ebebb62db9c7cfbeabe0bd9acdaf340b2df52072be65d98655
   summary: >-
diff --git a/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md b/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
index 9d4f7f31ee..834bce2289 100644
--- a/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
+++ b/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
@@ -25,7 +25,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: bf887ef2481b51cf6c32e49e3eb1d5577346b7b9b1e4d6a604c559597b5306f0
   summary: >-
diff --git a/content/learning-paths/cross-platform/woa_azure/_index.md b/content/learning-paths/cross-platform/woa_azure/_index.md
index 93b9b8e62f..f2a3c78113 100644
--- a/content/learning-paths/cross-platform/woa_azure/_index.md
+++ b/content/learning-paths/cross-platform/woa_azure/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 367566dfec0a76685b1504c2c1a996e50c55e67b2f4c5515057c0f0f1384ef98
   summary: >-
diff --git a/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md b/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
index 141695a40e..710b8c5e4a 100644
--- a/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
+++ b/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: a56dce27c6a846bcf35b6e9505142f1445b22ff71530f1189700049d7b14e994
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md b/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
index 8116740d6b..fa6e3cfec5 100644
--- a/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: d8c48c293b2d316d5e68e18ab9e81157ad114a64cf2efa6951374a55d25238d6
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md b/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
index 9d066b9ec5..efdf57210b 100644
--- a/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
@@ -25,7 +25,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 8585bb59efa67b3a92314e4382339fc5c0a14bb458931c0d98f2748379003857
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md b/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
index 8e45fd48a4..011317698f 100644
--- a/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
@@ -25,7 +25,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 3ddb97eb97a4c7ede7951410086198ee793a9d452a79b607f211873971bd375d
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/armds/_index.md b/content/learning-paths/embedded-and-microcontrollers/armds/_index.md
index d371f70cc5..68e3ae35c1 100644
--- a/content/learning-paths/embedded-and-microcontrollers/armds/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/armds/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 0103f51d42c230dbe75ff5b78ac15a33dfd2f2c0f4906fb665a8dd681512d2e1
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/asm/_index.md b/content/learning-paths/embedded-and-microcontrollers/asm/_index.md
index 6cae2fa488..2cf94cf02e 100644
--- a/content/learning-paths/embedded-and-microcontrollers/asm/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/asm/_index.md
@@ -9,7 +9,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 24245102b7b6cefd0d4f67fbfaff1fb27c913de33665929ec3cb15293a1b3b51
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
index b845c0441d..f5ad5e5ad3 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 983afd3fcc565266c8f12588086e36d736540e23d0e748c94b5d2a45ab53d6ab
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
index 0b1a61fa69..596f053e39 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 85845fd4a47960bca053aed8d87c1d1442a7f5de0be5caff349fc92c6980b07e
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
index 368b1c3313..3f1ca802b6 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: bd39d50c93219201ead5de5da627447a91a82ea9c65e232350facd0c3516bedc
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
index 330eefb1b2..7410b3e1d5 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 398077be1406d0787b0a5d8dc254472da0d125b51b0907769cd4a3516ce9111b
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
index e2d5710eaa..d562684722 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: aaff31b8917320c53825f3e7410b703bc7c7e273b1963fd80727b6b874f50566
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md b/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
index f76ab44deb..8d61f9cbc9 100644
--- a/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
@@ -25,7 +25,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 0e653bdbf5e5b08a6a00ba68e5ed215a0082e22c634d7d632d088851d48bb01c
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md b/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md
index 42b46506ba..c6bc8e4133 100644
--- a/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 6521f17d1a10ab8871d13917923f7f64320f69301b4c0c5f5b528163d3cb43c6
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md b/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
index 1f285b270f..e9050fe221 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 3394bf994039e441d57dced2abb64d725077d4672e0e68dc71f1de50e58f0408
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md
index bb84798e4a..874f5109c8 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: d8c73d658fa7a8051131276b8567cbd7376aac3b8f6e87c8ecdf224443c3ef99
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md
index c946a635e9..f034b8aca4 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: f4d1ab3448590c5654e2479937c9eec3e378d05229b29a45b8675139f40ac177
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md b/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
index 10553de353..3ae69d6d85 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
@@ -24,7 +24,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 69526ee8b840c8f5d77d49349d2f0cc7ff4594fec5e4311984ef72a0672d3462
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md b/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
index fb5aa6aea1..0e778370b7 100644
--- a/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 0b7a5895a87de83903c223215845b6c840602663838c0682f46e50d139d69c59
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md b/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
index ce90159dcb..1e658161a8 100644
--- a/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: f989929b11c48df42c2701dc71516cec0b8637b4c639c3dbbf6ac5e7440c8a56
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md b/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
index c78af8ce06..dc4538f417 100644
--- a/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 9d9e4c170fa318a9875c888c2c812ceb27c59f56c4b0dd7e0ae87dd31bb5783c
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md b/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md
index d106f39422..3b2f0c8799 100644
--- a/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: c31d398d3ce965a4193fca45cd025182d788a2fc603fbe8c828dfbd5a1c599ba
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/docker/_index.md b/content/learning-paths/embedded-and-microcontrollers/docker/_index.md
index 45a814a875..4473728661 100644
--- a/content/learning-paths/embedded-and-microcontrollers/docker/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/docker/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: a4b522c46c68d3c6f5c4af7c76b00c7bde48bb638147c201376bc19a4de79cca
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/edge/_index.md b/content/learning-paths/embedded-and-microcontrollers/edge/_index.md
index aa2f5574f7..d35a9ef6a8 100644
--- a/content/learning-paths/embedded-and-microcontrollers/edge/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/edge/_index.md
@@ -24,7 +24,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 8a7ec29d10a89649662ebb0fa4f14712984786902b6e7343a195abb1f3cbc00b
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md b/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md
index b7e053b84e..23fdca112a 100644
--- a/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 66024a2e3da90dcda298bf66b5de07952ed1e355d22172bc2812c46ad4fcda7f
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/intro/_index.md b/content/learning-paths/embedded-and-microcontrollers/intro/_index.md
index be48880b6e..f7c2bc7900 100644
--- a/content/learning-paths/embedded-and-microcontrollers/intro/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/intro/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: ac69e979101f6befe55abee1429299c163c70b9a886d87a8f64779babd9fe5af
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md b/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
index 553fbdf988..84a689c5e0 100644
--- a/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: aa49e4a1651367965e79d36c5f6af16e9b341c392d48cf051f24cbb21070e972
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md b/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
index 386986d2ed..9125f1d456 100644
--- a/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 11fc64eb1a0595b1d8e625e465be9fa87a4de04f59492004204ccbe61a92a5b7
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md b/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
index 8578f43fd0..ad421fffde 100644
--- a/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:15Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: fdf582f1b54f372768a2c896e1da152c50d22c3bd238848253cdbe18cc68222d
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md b/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
index a30174efd1..7d89958a47 100644
--- a/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: f3a01adf18ad93027b6ab61ceb5b0c470e6b5c298f9d4f944989a96ff64eec81
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md b/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
index 2d67caba71..8284468b46 100644
--- a/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
@@ -25,7 +25,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 567387e8e513458a360f74c14d3f4d02c4af392bc573620e605c93976e4d8b4f
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md b/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md
index b3bf07f2a0..bb0a46e0c9 100644
--- a/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 5943d817827bef274f175f7c14249cad769b2160094b3c65d7476aca6cbf0a1d
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md b/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md
index 8e1a9d37bb..44fd4899ea 100644
--- a/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: aa6252610c0603acfdfc8d4555bb7da93cbdd5b9d92ba140c5dc5f63d23e2b07
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/migration/_index.md b/content/learning-paths/embedded-and-microcontrollers/migration/_index.md
index e999cff896..c74fbf76fd 100644
--- a/content/learning-paths/embedded-and-microcontrollers/migration/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/migration/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 81a15ff0d5579be9529a8c9c444be57521ebc48bbf5234f042f5a47a8a709b5c
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md b/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md
index 688e1fd653..628112452c 100644
--- a/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: acf43df3c6f344b55bb413b7483d60e26d0e137489ac148bca64500e443140ab
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md b/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md
index 9563a14295..b7e13edda1 100644
--- a/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md
@@ -13,7 +13,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 0cfaa21be515b980097232ed38092b80d046df308120887e5503513a3384a1d7
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md b/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md
index a1225d1907..59d6afc885 100644
--- a/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: d2c403c7fd1001dbd985697c9eb018951a955358c2be39c727183a87a3dfdf51
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md b/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
index 6197856db8..a019b62b17 100644
--- a/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: be2b3571d4d2ed75a16bc97589abc22c970d83be868b7e94bb60962a4ba853da
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md b/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
index 67809f20b6..9014a88e6a 100644
--- a/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 8039812773c6c1ac45cceb4039cee801e627d67871b625283c1bb5b3fa2960b3
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md b/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md
index f6afaa0d75..6d980eced5 100644
--- a/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 7a192506fc8a15423d1257fe92e7655053e38be85aad8310dae29602e483fbba
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md b/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md
index 53b67bc5e5..20a3691de7 100644
--- a/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md
@@ -24,7 +24,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: a0145c77b3d4a8bb25a32c62adaa3ad378e65fccbe6db88d9a46a569897d238a
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md b/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md
index f0aa241dc8..21c1b35860 100644
--- a/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md
@@ -25,7 +25,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: ed2034f5c95c4148fca355f6d545219c320f2e73267a0725934c792ebc4c0c59
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
index 4b43ebd63b..d91e014500 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
@@ -25,7 +25,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 9cd2c3082c4874dc596e1b7b59c3dc2143aaf1ce3e66948ab8b6af190ffa3776
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
index 142e21db4e..577b8a2a18 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 17721158fe10e97314af364f138c32b7bcf53fdc88fe11fffeb5765f11e26b79
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md
index d72b350b0f..6cd1b41998 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 5e5fad1563b67ed38d1cf3399d8e0d62162e76cae8d6848c3be7638f67cd037c
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
index 4eef1751b3..2f0012bbc8 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: d9fe3cfc8f7a7092f40786763fc28e371697e4b57dba99b2c9b191dae4273911
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md b/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
index 3cadd093f8..874d9e8d93 100644
--- a/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
@@ -25,7 +25,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:54Z'
   generator: template
   source_hash: 0b8de63a15d3d77b9c9972103b1317d6c48907875ac625c3d1c1b8db5360879a
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md b/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
index d47b6de88b..b874ab7526 100644
--- a/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: b5714494f00fff407c39e6f2f7bf37de94cde61d7569ba147412fec1b2f537c2
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md b/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md
index 3f36b46b8d..6eeb9d7390 100644
--- a/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 8d8f9df559ff1b1570dc98baf640fc2d4c4d225d69f22529e1881b62d4017752
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md b/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md
index 4f87e24738..f9f0674e5d 100644
--- a/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md
@@ -24,7 +24,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 20c500fd5174c9901058676d4619981ee1c8a8cf8e331affea8c0292112651c9
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md b/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md
index 979e77534b..da86f18a77 100644
--- a/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 6c81f3c9595df1128ca6f4f89446f093826d2242fa789ffa2d37465854be1f8e
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md b/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
index 69e6e99f05..619eb6d053 100644
--- a/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
@@ -30,7 +30,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 57e05139cdea1de2f4a4ce239d701f0cef634b6ac11fd437cba47cc071e14098
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md b/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
index 14a43e49b3..01b541e85a 100644
--- a/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
@@ -12,7 +12,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 27ced3e9f69dbe51e75dcf13999ae1745a994137f31c86d6f17d4de341c7fa2a
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md b/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
index c9766cc9a1..6b648c05af 100644
--- a/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 179b0318bbef9cef31ca6bb82de853597a609f61d6868aaaa85cfb40a27a051a
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md b/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
index ba0f4e2e52..b350046d45 100644
--- a/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
@@ -24,7 +24,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 0c3422e1cd571e6abff676c28ec32c2ec69a626a406167f9166e57daa6829252
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md b/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md
index c0c44a9f70..583fb5f069 100644
--- a/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: dcdbc4cecc376ed4c0df9377d13cb4fe792ad7c68acfe0610d75cf41898804ff
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md b/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md
index f6121490d0..39dc028643 100644
--- a/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 3bcfc51edbab56e3c8416f27045a61b069333e6f0cd130e8689b9a4d9fde1dd6
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md b/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md
index 0f81c01c19..db69f9ffcd 100644
--- a/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md
@@ -25,7 +25,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: b0cb917bdcfb601c054e6cac93b2d04aca3ffe84b5a1963288fd7b89dd9bad3b
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md b/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md
index cedd1359fb..f7e0a4369c 100644
--- a/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 89f599ab33721a2d578d4fc39e505b5aed8d930aabac71f22d1ba28bfc4a5cf8
   summary: >-
diff --git a/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md b/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md
index f193703313..20ffd1c113 100644
--- a/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md
@@ -25,7 +25,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 808f1c99409f9ca3bb72419eaf950bc097f1c7af6c2dcade6385be97fdbbf713
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md b/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md
index 7c05084cbf..740a8b9df4 100644
--- a/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md
+++ b/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 137e974aee0ccba78e3375a1c8179af392e54a0304cfecdcd53cf4ac5c38917b
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md
index 6a43695829..179e33d515 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md
@@ -24,7 +24,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: eda533c727ea7094202e8784bf1ed2240cfea2573cefc73aca1a86797840778c
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md
index 86ed79bfa5..d4a2813615 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md
@@ -26,7 +26,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: ada333cc887badfd57815708ef93e172543da74f2c995b46a916817917e92394
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md
index 6bccdd15dd..f238c5f447 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: facf9c504a3caceb62ef898a04a6760e8aafeb7e802e5727cb80d3a7e7e344d0
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md
index 32f275f4e8..263bf4a63d 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 4ccde526ec4dd9fc18672e162e067108c7251161c1da0375a0bb0374a2f3a4ea
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/docker-models/_index.md b/content/learning-paths/laptops-and-desktops/docker-models/_index.md
index c6bb2909aa..86c3935ad2 100644
--- a/content/learning-paths/laptops-and-desktops/docker-models/_index.md
+++ b/content/learning-paths/laptops-and-desktops/docker-models/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: eae0a23635e7a025e1a73baaf5ccbd01f2c031ec76725c68893ca02190e36deb
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/electron/_index.md b/content/learning-paths/laptops-and-desktops/electron/_index.md
index 4814ba1eab..82675e3cbc 100644
--- a/content/learning-paths/laptops-and-desktops/electron/_index.md
+++ b/content/learning-paths/laptops-and-desktops/electron/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 8491a5e83e9e6436721f6078085e9b367121d19fdf228634dba859b3e1a0802a
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md b/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md
index 20e92d83af..8befe1e520 100644
--- a/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md
+++ b/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 7e108d774eb0fd0b2b72fa8b5d65e1efec0ff4ecf3d80d4e511e82cdf3862284
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/hyper-v/_index.md b/content/learning-paths/laptops-and-desktops/hyper-v/_index.md
index 1a09ea6de1..947f0565fb 100644
--- a/content/learning-paths/laptops-and-desktops/hyper-v/_index.md
+++ b/content/learning-paths/laptops-and-desktops/hyper-v/_index.md
@@ -18,7 +18,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 829130636ec6969f791826ef731b38f7bb87c025d910218822a113ecdef62306
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/intro/_index.md b/content/learning-paths/laptops-and-desktops/intro/_index.md
index 7bead73e66..5c191a8ad9 100644
--- a/content/learning-paths/laptops-and-desktops/intro/_index.md
+++ b/content/learning-paths/laptops-and-desktops/intro/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 5a5e4437a7e71182ede45ee091ae49117446fd1c34b02be92df75a41f4fd1f0d
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md b/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md
index 433f3a7025..65470ecc0e 100644
--- a/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md
+++ b/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 3e93048b46c24514a89ccc2f277b53111a419c049b53662e1461be38a22e83f7
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md b/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md
index cf3114d47c..834ab6ac29 100644
--- a/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md
+++ b/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 631134875aac73e168b60b86d1ca7b4e898196f98cb2825a4238f62129d2d862
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md b/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md
index e3a38ab113..57c7433bfa 100644
--- a/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md
+++ b/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 87d4afe4ce7f0cef113cd61fd712fde073cca0eaafbe86a2066b76a117328d11
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md b/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
index 3e287ebf5b..284937a929 100644
--- a/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
+++ b/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: e1befed4cafab0eaee29a31c2f259a6a90a14d9f8230c570b6c10a9840b761d5
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md b/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
index 4a7076f778..9d163ee963 100644
--- a/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
+++ b/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: aa2a78baf3e52172e37506c3f75254968d775b4eb516f9696a0a6998aba50e97
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md b/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
index a7c8ac56bd..3dfee9e29c 100644
--- a/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
+++ b/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: a851b2f81b6aac54aef84acf7537a1cc6b99f66ce31ffd26631d6a966401e4ed
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/_index.md b/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
index fd6ca8af2d..b55c82f3ec 100644
--- a/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: d24bf30aef690451942789bb66df63b7a3483ecc3cd2c4b3e60ce15fad90cb91
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md b/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md
index 5ce63e889d..9ab292dd32 100644
--- a/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: fc0d79605640cc8e7a36070a044323d6e278335484949921d0aa4e9cd163d4bd
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md b/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
index f13f6f3e32..66e74ea4f9 100644
--- a/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 915f6eb5e95bd42ed09b727b4599855d965125e67a8761fbd48a124e9e74b1bc
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md b/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
index c10d4af748..341493a9d1 100644
--- a/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 4069c5bce1ce4b689a7a67d740fc077dc55c9b0bfbab392f5984ba0bdd9e59c3
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md b/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
index 6d34a4b1c4..ebe1b4bd9d 100644
--- a/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 3b3ecd451ed8b634c7fbe194248cdf1d33432633efbcbef5de713495041ff425
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md b/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md
index 9a2b4c2e2a..6856cb4f05 100644
--- a/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 63389742eced4df89f85bdf56a01e489e52a9702d446b557f8f55312f9d31f20
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md b/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
index 9401fca61b..f5c5cb4953 100644
--- a/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: e60ce02e9d1872da06bc5bfeb18c7ec65f2bc17defb2b75292f930fb3ad41711
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
index e88e8b3f2c..4e1de16446 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: cddfb7b83e82f0daa513558b1e7ee09b55c63e2ff95675d67be7d4408d391aa4
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md
index 5da7ef0b27..643b9aaa33 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: f25597c6c9e69e09e9a86fa7c02d0ace9c347f293a21a11c00a6d3498300052c
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md
index 9fd53c4cc6..98656b350a 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: f685f45be9e5fc05b278e28590f9d421a920d43cc36ccb572545de4eaf4a799a
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md
index 31c04522d7..065d78ae2f 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: f5a93346a0fd55659b7c0a6df501db97742ec71755b6443051b654f3ce871cdf
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md
index 5ddbb77140..88884bcdb8 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 32186a4879e98aa113f461d2a2c705dee099404ed2020ef6fdb981a28bb0c0c3
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/win_cef/_index.md b/content/learning-paths/laptops-and-desktops/win_cef/_index.md
index b78abd0050..dd52088591 100644
--- a/content/learning-paths/laptops-and-desktops/win_cef/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_cef/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 5df8cc6d859c505ad79f8297056b06233956c92203a6d2352d9be8e498a42547
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/win_forms/_index.md b/content/learning-paths/laptops-and-desktops/win_forms/_index.md
index 0d094690dd..3687254f92 100644
--- a/content/learning-paths/laptops-and-desktops/win_forms/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_forms/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: d43413097704af29b9233dfe33fb675ffd5c6d5a172154d6a7542e77d6625c00
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/win_net/_index.md b/content/learning-paths/laptops-and-desktops/win_net/_index.md
index f5a1c0372b..eabe3d0bc9 100644
--- a/content/learning-paths/laptops-and-desktops/win_net/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_net/_index.md
@@ -18,7 +18,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 9cc8f77f98bc8f4afb7b77344e42615e420be421f85583f9fc4f5ec76516e6eb
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/win_net8/_index.md b/content/learning-paths/laptops-and-desktops/win_net8/_index.md
index 76c4bf8ad6..9f3de8745d 100644
--- a/content/learning-paths/laptops-and-desktops/win_net8/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_net8/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 218fd5b33e104360d5de9f632f9338e2f24041ea70f7a01fad0af6be2a11619c
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md b/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md
index 9b1a2448d7..0dab7649b4 100644
--- a/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 037509ebca3a7c5a58bef247532919cd8196c7993e946ce519585cdc82073daa
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md b/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md
index a7dc42b292..2892a01e8d 100644
--- a/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md
@@ -18,7 +18,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 606702e7afc9a29976d027eceab021570cf3f91ec408ef2dd2051df0b05d9bda
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md b/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md
index 437309a084..0f7160d75a 100644
--- a/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: b975cc83674410ec50ca49a31744eee4ac5a63c994dd28e46ed84f7afda0568d
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/win_python/_index.md b/content/learning-paths/laptops-and-desktops/win_python/_index.md
index f8b81c71ef..ed5057edfc 100644
--- a/content/learning-paths/laptops-and-desktops/win_python/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_python/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: d953214fe33ad89a683f79e7c29ce9348e5169e6529b808804329cb35060b431
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md b/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md
index 15d5353822..ff75321394 100644
--- a/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 055e3a3d8c0dc717e2246e3e8e7ebb00c7d2cea3ff9faabf7125ca1ebfff7a31
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md b/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md
index 907b3c9a62..33c84dafd2 100644
--- a/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: cacf4c6fc3cd3c2a9ab194f7a04981fd4820fca5482317a06c6b9fa02a1c9da2
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/win_winui3/_index.md b/content/learning-paths/laptops-and-desktops/win_winui3/_index.md
index 9abbcf0a8b..3a73676572 100644
--- a/content/learning-paths/laptops-and-desktops/win_winui3/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_winui3/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 383dcf17bce86b24a2d9c8d0982fa6e9ddf954955c16b420463ada951753dbfa
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/win_wpf/_index.md b/content/learning-paths/laptops-and-desktops/win_wpf/_index.md
index 9042d6f240..bacf4b9c7a 100644
--- a/content/learning-paths/laptops-and-desktops/win_wpf/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_wpf/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: cc1a494e7b03672122ff84073b677a93659eca7df601b3f77c7e7fd536cc1af9
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md b/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md
index de33462585..17a9854ac8 100644
--- a/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 16b7f8c67050ea336402791c0ecca2c688d5da9373c571986e12dcf646c8093c
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md b/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md
index 87acc6bc0b..89b8e4fff3 100644
--- a/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: f058f628cefb7766ef0728e594c9ef5b57bde97c1850395786d54cc591271503
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md b/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md
index 4bf5ea82c6..f176a1da2b 100644
--- a/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 828e4022f387698d2736d94010de5d93efa9f38ffd3a2e4f15252410e9ccedb2
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md
index a3c6719af5..4c06d06a74 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 1dd709b14537e06c80b9cdee58729cfa7066f44f0de4ceabe5450b33288731aa
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf/_index.md
index 98693e403e..cfe1d5ed0f 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 61a6390d42ce6bfc37a3bb981710dc23b8566070733f7979f9ec37bc63ab7d78
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md
index 757e79fd8a..bf7ee132ec 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: e23de84e7e05d771072ab799f5cc802476fd5e3c23da41a202c9c50fe9980e25
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
index 0f3a1280cc..01cde51739 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 1fd4d85855933a5dfc5edf5ae3abf5f4388d94c9ee69aff12b77eb766e88301f
   summary: >-
diff --git a/content/learning-paths/laptops-and-desktops/wsl2/_index.md b/content/learning-paths/laptops-and-desktops/wsl2/_index.md
index 0ae90fcaf9..d256e5a551 100644
--- a/content/learning-paths/laptops-and-desktops/wsl2/_index.md
+++ b/content/learning-paths/laptops-and-desktops/wsl2/_index.md
@@ -24,7 +24,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 03d816ff419e6e5b1533f95e9d4ffa087b9c0c9aefeee112f67b70223c8aedc3
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md b/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md
index 5e3d7c5cdd..d0908769f3 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: e4512ad20b372f616409199c51a38d95cb116ec6cf49d9004348d6bb8ee4014d
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md b/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md
index 7e225b2c7b..1e928c0319 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: dee181248ecc8cb40e3ad76642fdb216cbf1e7610dde2f2605ba04f111b8926a
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md b/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
index b87cd6d6ae..35918e7f98 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
@@ -25,7 +25,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 3d1a743c1b3ee617f52191fc9c9fd33a9d44454ac079f00b6f532e2865103377
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md b/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md
index 3ad8f3be24..b2d86f8bea 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: d5406f24ab38522b21e348ed3829ede7b1bcdce28e81ec4fddebbec280d2cb89
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md
index ddd2f1e8b7..27aa009b54 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: e63ac917c218aa5e5981f96a9821567d0f8ba9761d5ce6e4c9d7b6dea7ed5c5f
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
index 9974cf9c5a..85281d1929 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: fa04ff97605ae93b17c056c397c37f6fc17cf6f4853bfabe374610ede002e3df
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md
index 37eb76fa2f..7693135e8b 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 2137cec46fe8d57f11e9e4011306a140bba7d8a5b2326a746193c1794a148f75
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md
index c6782e9721..e0438e8bb4 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 3a120620bbc56e796aa45fbe01aa1455fbee085a1a4cf0d055416b7e95e72d0d
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
index 9d758ab205..0ad94857fa 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: 8e05fb124ad18194fba2ed83adae3e52673b2fad41af998ca8d0aa8cb9785f5e
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md
index 4d64fd8b8d..cb5b17230a 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:55Z'
   generator: template
   source_hash: be91053c5abcdd669ff8da7e66b2f717c4adaac27b3fb44ea073b69a68d2dba7
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
index f2f8dbf0e9..0caac6d57b 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
@@ -30,7 +30,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 8f58429f12e40b53e8a2e0d7eb260f22fbb7ac869ca64554a906ddcd28c9fd75
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md b/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
index 2360a3899e..bec0dda0ec 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:16Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: ef70ed2089132df657bd1ebfb9a66a39934d8a118b1e73974148ce11c104fef5
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md b/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md
index 1ee843e955..46835c102f 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: deeb745d72457138cb81252c421205cd8dfb43b5e29ceee3a15c5842cc90cc33
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md b/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md
index bed45cbf8b..2845f97b98 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md
@@ -25,7 +25,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 13ff69755e51c6d7c355616f95703b3f2cefaedcf1f8dbeab31fe2e3db9aec74
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md b/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md
index 7288ca6232..54e8498534 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md
@@ -27,7 +27,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 688b5b6eddc0480fa732308802d225696371c22a468bb6b140c6b74908222bbc
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md b/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md
index db98e92f5b..74c8f3c385 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md
@@ -27,7 +27,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 9ccf2cdd6406694d85c553204479d7ff1f688512056a3c76d4c85266cdc418b1
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md b/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md
index 189cbd8fd4..0fec8fd3e4 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md
@@ -24,7 +24,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 95d4fb855552939d1a0e9cccfe46333692ea291ee85dd08b816321db29ceebdc
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md
index ddac2ee308..8d5a989fe2 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: b194edd6ff4ffc5e39e7daa995271d2ed81ec31c8e88ddf1b23a54eb06dd1d06
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md
index 758015958d..269562961a 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 23df12c0e165a4120fa25b5573437121bc7af141376758ccd051ddac14e180fb
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md
index 000ae5e6f3..eaae93c1e3 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md
@@ -18,7 +18,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 44e7b5fe3fda52267dc1957319439895293276602b1f533de5665adaf6f7cafe
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md b/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md
index 8a583d095c..68e005c8bf 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 3f35558e0399cb4c851b120eaa2217a63c48336cbba96d23500d1b751e4aec63
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md b/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md
index 6cbb5e3e51..b16ab2fbf1 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md
@@ -16,7 +16,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: ab171b1ff6cfed7bce1cb8e50d4d9aeb4bee1972e8a409203cb438f94086a320
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md b/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md
index 1936aaaae9..fa5d78177e 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 5a94138f74e7b2266c35dbd555f9966ca0ff29fdbd1842d4724e0da0cd4e46db
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md b/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md
index 800bd80944..ee801911e1 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 0e51db9ceb25c31c42608f3c6744a4fa8f9d995805ed44a7d7fc83324889ea12
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md b/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md
index e2d3e88877..34383f55c8 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 68cdc7315795b6ffa794c0a1fd4cf325ab39ebb65e23efd27b7760ece76a2ec9
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md b/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md
index c12d891d5c..34a13f2a76 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: a744c8118a5a3cb97eee7bee271f768bdd71b4286e723c38a7b4ff6cd9e08d18
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md b/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md
index 5213a1d272..999f44ec7e 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: b55d902389806f5e84e674e5ba1f1f2d9e38af370c289ad344eff2dad3475df1
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md b/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md
index 0206cde4c9..cddfc409e6 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: e145caae5b20f7165251d88e334ba22d90c8b35270902778a2b6fe0ed0b250f6
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md b/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md
index 33eaeea436..c9a3ceb74f 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md
@@ -17,7 +17,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: a25cb73b70716e397d9477f8ab9729f4a094143d276e4de68cf8c33b273bb1ff
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md b/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md
index 36515c8bfc..3d18a29dc0 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: b6a9aa558ec5062c829d61b28fb92e25d5517249c011eb29f03cc36775b6f9af
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md b/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md
index f9f0974756..a42eba6662 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 5ca059af36f0ba375701e76ce82b7803f01ae6beab313336b333bfbdebaa3b1a
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md b/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md
index 6f5c2a460d..aff083f364 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md
@@ -25,7 +25,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 7ffbac59b340f3ef5119d2f5ba4a786fd15d521bb294f3a4213b083c9b4ce23d
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md b/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md
index 843074124f..fdfc98dfd7 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md
@@ -24,7 +24,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: aba1cea365c0c61e84fb545ada15a64ed52c099f1252dc6312f88ee82385d2d0
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md b/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md
index 18763a5704..b5e4d9e86e 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 586a505543df4237c943ea47210d735fafe7d5ed2c6ad18b1b99227987ef9b15
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md
index 2993ea9919..bedbecff2d 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 4962524245ec5e5e07d1207537208a22c2e9569f252f36d758c4f828d2104272
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md
index d71100089d..8ddb1e4129 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 1645a1c65527da010edd6fefddad3c865eac0f9cad417ba59709a57bb9dc847a
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md b/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md
index 77b490794a..03faf33390 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 01138f6538c655f866fb034a983461b2ac9b793142775465233b2ec8ec18d0c5
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md b/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md
index 09678b96f1..3c7bf0a42b 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 9950a58160736e0c60ad80ea602262347e2ba8a37a00e29f25dc96709026ea1f
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md b/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md
index 8c8b96ce1f..cb3322f400 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 56d9d0fe9606e42281a2d2be52992c7a4b6846b208376c92e7fe3094647d1c70
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md b/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md
index ea991a8190..c5ca6580fa 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 78f862a7c46fd4591fec45f91d040eb3f1093291c0a7328dddd2203dad72db3f
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md b/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md
index 28f9f553d2..69438da455 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: e2f6f3005c119e6f6fe97612d4e2600849106f244bce729d56bfeeedb30637c2
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md
index c15b3a2c48..48085c467d 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 388cab0dcb284c29fe2ad82f2b2934d9243c6356b2885d26db0a97c1a1d4d393
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md
index 751ec8d814..03686faf1d 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 7970846a399ddcdb488d90bf19cce3c6b74df6348e88914aed83661b2597fe7b
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md b/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md
index 3f4baba38a..9462764ad6 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: dea8c3e15cca703f075c8fa9ba3daf873a90e3eb2ee9975dd05b973dad744b54
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md b/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md
index afa59f4522..ab25feb38f 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 4a990e957658b03bac9306d5f8e6955734cc1d3b063f43c38ac525ed1bf95b79
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md b/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md
index f6381afe92..577e7589c1 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: f17ab3c4216495b88cee75a81612c11a4727d874114f914edf97c467f0d1c125
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md b/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md
index a0ed7ee4f3..4fdb2d328c 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 9cd19738cbe9cde618125b309bd4f2c57ad1799e807251fdaed09707bea2781d
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md b/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md
index d805106b93..e32d7c22bc 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: e7d95c8e7210be2fe4520fe94ccbaa3e437cd0edfa5caca549afaee22fb8b377
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md b/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md
index 35ad4e9e71..b988cdbb6d 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 192f5919261cfef88c107576dc444d2db3a07fbad98100d9c758bb75670a5a00
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md b/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md
index 6954a70392..ba9faa6ff8 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md
@@ -24,7 +24,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 2d8fca8838e759c3098358f131fb4b0884b0d80d5fdb2fd68719bd09fa4c3d32
   summary: >-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md
index 9164837502..fa776fee1c 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md
@@ -24,7 +24,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: af4f1c8964e0970c187643bcd0330a63a6ce66c38a2e6b4d2fc17857a12a3d7f
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md b/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
index 624d2c9acd..02bee1b6d2 100644
--- a/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 275878b5aa4c27dee394da12ad8b80e4c0d0235b3ddce66b1fe3f0e056ef3da9
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/aks/_index.md b/content/learning-paths/servers-and-cloud-computing/aks/_index.md
index a70afdc69b..f88a53359a 100644
--- a/content/learning-paths/servers-and-cloud-computing/aks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/aks/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 01c5cc8930650a8d81d67d4c48b62e0f9d7b1ebfc2d09a552e11046fb982fc52
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md b/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
index 06e3da7c21..fbaa94a9f8 100644
--- a/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
@@ -24,7 +24,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 90b638385267e085ea07a70a1c23f16578aa3caaeabeca1f651bcdcac5bf38fb
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
index 9ef5a60845..77bbc6458c 100644
--- a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 964c1e6a87810dca686a7b3218433ce09d31bd92882db10af355e8c50bb6091c
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
index 94ebd43bdb..b5ffb45089 100644
--- a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: b2f60d2c31ef380e6e6ef3028671ad9242dd51c98f4a192f2da32bb05b94e185
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
index 5996cefbb4..afff69463c 100644
--- a/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
@@ -26,7 +26,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: c6106f21859f5a8e04bbd579db47c7177bb5371d4c7f306054e56e81ed9e89a1
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md b/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
index af9ee6a2e1..9f94a09843 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 3a4bc8b2ab548be507b6d528844b0593b27f2dab3ab3c9649e807abdf4887ce0
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md b/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
index fc6668ef53..11af5c031a 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: b870ac5160d35ddb51955ca8379493e172787ced8125cde8ae79f7700e653a87
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md b/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
index 9e5ee18884..c9bc2fc4c0 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
@@ -24,7 +24,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 49b3f2b1464efb20f0c8fbcff46e9f30910d856567ca01c01dc348aa1c5740d1
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md b/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
index 57433583ce..aa6068f84d 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 67004eb9a75926144eb6f75124104972b3cf20a57a22b698c9359d20db3eca02
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md b/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
index 06a09f11ec..bc590a56c3 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md b/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
index 80254cc904..9e01f90f27 100644
--- a/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: ced3882cf8be11a55304d2697f450a2c862a81d87317ab42298148755e9f272c
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md b/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
index c373e2ea71..56bc7c9916 100644
--- a/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 087a4d56914e5b53cec5ad17e8d682e72d04ba6b394237c081efe4a3f939e04e
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
index be536fe154..611c0adabe 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 1682c0527bb49c417263286e2aba7c82fb9a27fa315ecc6b9ca10978b69cc236
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
index 3cb1dddf85..3097e1f7f8 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: b5bd3abde8d9dd3146e0f11f4819ac510417ad7f707b4d0970c02fd63801b358
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
index 4b7d26c192..453d0b3531 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 6713af3d70887933cf54c7fa36bdc88d71a51fa34fb29a4ce90636ff1de624c7
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
index cc8cfbe57a..e81f4c0fe1 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
@@ -25,7 +25,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 413467e581e3561425229d0f6118975dbb596d6a9494544a54f0b9ab22c1b89d
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md b/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
index 28a8cbd868..162da767bb 100644
--- a/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: a4cf1d9161b3a32e29694415762eda419752e1c3144662d5e131b6553f0a58e3
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md b/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
index 7ce905a37e..1db0995be7 100644
--- a/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 1701b37580fe5d012a5e6fd322307742656a748dfb766fd48914011167386e95
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md b/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
index e0e11c3537..c3703edb20 100644
--- a/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
@@ -28,7 +28,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 8654b656131bf1d529e11d85f874f7a81c01f7207340d2a606b6fd2d80bfad04
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md b/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
index bd81ea77f3..a119cacb5a 100644
--- a/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 84a8b96fe7df302e0a2a6e4645bbb6170b45a3e0b55e0ea3682ec47663d34819
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/bolt/_index.md b/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
index 93b64ab511..c982ad01c0 100644
--- a/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
index 1d4325a890..b033f64032 100644
--- a/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
@@ -24,7 +24,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 4bda206717eef380430009f859826d9bcf820442d13492cd3c22a114561e2917
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
index 80f89ca3e9..b16ca1ff84 100644
--- a/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: b8268d4df3b62aeb9943b750b436a936134d47745fa71db6cc08db8c84eb37be
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
index 2485389627..5886240101 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:56Z'
   generator: template
   source_hash: 3947ebbac742399e70001e41ac5face92819bed0deb55aba3e2414f98432023e
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
index 192f0a002d..28adffb245 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
@@ -24,7 +24,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: e21f51feb101ad90245ecceab95fe13e5eb61958faf24dff818eddd11f484b9a
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
index d3af0335a3..54522034ed 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: b032debbdfe82cbd017812cf671907520b146fd8684afce0c45c91e7f2287e18
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
index 9611ff919d..db40aa5437 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 649957b07b2bde05bc11047bd12bfc4f697c1a55eef1c3a25b5fafc95cda893d
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
index 13ffa361be..581af229d4 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 563456f5d151c7ef59bf458f6ac971c321077475a0a78d3b5a3885b97157ba9e
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
index 95d6082bcf..6c00409252 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 55b8032ceaf735d53b1157102a3a1da5aa5cb686e9ec51cf4896ded66d9bf263
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md
index afece7f2a8..217a3c547f 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md
@@ -24,7 +24,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 9e6dee3aef79c1c65cc1a1f4fe3528b9c5542d8046f0b059ef703079ef964d77
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
index 6567b60236..92fdf8c1f0 100644
--- a/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
@@ -28,7 +28,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: ec9cdca7aa9a5670f54ea3646f965149460d8e66d9d5d904e1b6dcf09867df39
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md b/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md
index 196517ec1f..de5ae1f186 100644
--- a/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: e04982fd668765fa2ab25f943f020b8d2b4a5e9b5ff3a51b7431cc4d93730180
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/clair/_index.md b/content/learning-paths/servers-and-cloud-computing/clair/_index.md
index 59e3be4177..aec656f33a 100644
--- a/content/learning-paths/servers-and-cloud-computing/clair/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/clair/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: e742eb44fa108bfcc4ee5a241414e6aa05e1fc0e1cceb589fb78a080f59b0d38
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
index bbf05c8dd2..0f03a83ad2 100644
--- a/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
@@ -26,7 +26,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: e77cd1373236f39f360afe6844d642fde290960ccdad26544ff1e3342f2a980c
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md b/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
index 44fdeefa0d..db8eae99f0 100644
--- a/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
@@ -18,7 +18,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 0d1390198cf2f0e87f509c5269fc2405cffcb2e57311024150d47e60a3273178
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
index 693732d3ba..1503fa453b 100644
--- a/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: e4eb84292a669a8d73bff4b63d2fe3f70382dd873d023fc8ff24db5ad6178ab8
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md b/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
index c6c2d78799..d98c1d2ca2 100644
--- a/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: e7ea48aaea0e25f624ea1d77c6252b0e537fdaeca3aacca560d7bc9d103bbbb3
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/codec/_index.md b/content/learning-paths/servers-and-cloud-computing/codec/_index.md
index 917de74f49..ced7f9ef21 100644
--- a/content/learning-paths/servers-and-cloud-computing/codec/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/codec/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 908a750f2a891a0c4c9d1183c2130ac5ac0fdcfb4a45185cef6ed6da47c9aaa9
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/codec1/_index.md b/content/learning-paths/servers-and-cloud-computing/codec1/_index.md
index c8a05e7325..0c3f54164a 100644
--- a/content/learning-paths/servers-and-cloud-computing/codec1/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/codec1/_index.md
@@ -7,7 +7,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: c0643a788cdb0b3e33fe645fbb61d99a1899806e3ee197541c1eb8134b2876c1
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
index 5330d0b111..ca3a8ea2f8 100644
--- a/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 0a7d76b8c944a50073c7524813acf83948155c7c61d9c92f9202eae1192c9600
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md b/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
index cc51e5439b..b8675f0eec 100644
--- a/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 22f845b8ea4dbb9ffc63fafb76c17c79aedde14a28417d49e1ab0833bbbc1eba
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md b/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md
index 83a57fe7eb..c419882aba 100644
--- a/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 4e7de348514d0b5a742fa47bb85a2a5814fa9bd587478e7ef08365d16273f6bf
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md b/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
index 35403e8bdd..3d98cc20ca 100644
--- a/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 58dba071f70b4f85b87b3bd27b3a7ba3ff985f80079a42e05b797a998aeaf104
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/csp/_index.md b/content/learning-paths/servers-and-cloud-computing/csp/_index.md
index 299f11f452..e1b93d65f0 100644
--- a/content/learning-paths/servers-and-cloud-computing/csp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/csp/_index.md
@@ -7,7 +7,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 9e94b69eabf35677c48db812bb85ea9cef184efc078a826b96954f540a45e915
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md b/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
index 0fa004a725..bb0de74320 100644
--- a/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:17Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: f600fcba0adea12ff1b8b092e75de553577d940dc3bf632e9a247cec22d364a4
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md b/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md
index 571b1ecf75..a0e61e25a5 100644
--- a/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: a1ec216948e7cfd4fc52815196bb3b99ab4e76c9c756aa9e9a8e3216ef5e7ce4
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md
index ad3edba10c..23baa202be 100644
--- a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 6ac0c7cf1ab4b3efa680acbf1e349b858bee5dc7992baf6689e1f293a83060a4
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md
index 11a116ef68..423eace708 100644
--- a/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md
@@ -26,7 +26,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 6675df4c91126b157dcbf39c96a773130f1a95e2f5680913979da96f6f6c97cd
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/django/_index.md b/content/learning-paths/servers-and-cloud-computing/django/_index.md
index f263f06717..63376e7602 100644
--- a/content/learning-paths/servers-and-cloud-computing/django/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/django/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md b/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
index b9973b6bd0..b529709b0c 100644
--- a/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 82716c2de19d18a154c85d03b7f2ec01839284262914f7bb3ad04b18a105379d
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md b/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
index 170352aac4..c2e86acd04 100644
--- a/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
@@ -28,7 +28,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 80785022032bf4e3c65da682e698940a212ee1ee77386698889a9fafbed9f823
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md b/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md
index 39efd68537..4924046b74 100644
--- a/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md
@@ -25,7 +25,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 08d8f0c86625ef41476d3a8b24bad9b0a0820797022ef847bf9bb17a976726a7
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
index 16dce84efa..0a275c1abe 100644
--- a/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 4ef29931bf19dc95bb586440796381725c271ef1b953dcf46d00ad9617eabbb1
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/ecs/_index.md b/content/learning-paths/servers-and-cloud-computing/ecs/_index.md
index f797106bfd..4330350ee0 100644
--- a/content/learning-paths/servers-and-cloud-computing/ecs/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ecs/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: ef5f9e7c8844b20b9044b43f4758bc1d74374521093d7738a7f8832d21f1dcac
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md b/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
index 2554ffa89a..6971489da3 100644
--- a/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: e32bdb090c422d1fb5bb1f9bd3af56c55fdf8989e6a7fe6a101e90dcb6f3eadd
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/eks/_index.md b/content/learning-paths/servers-and-cloud-computing/eks/_index.md
index 95de9c32c4..151678bad9 100644
--- a/content/learning-paths/servers-and-cloud-computing/eks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/eks/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 4f1c448eef66300e024bda27c420f9746047c0c4b76e8556d3d8693382206055
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md
index 3d15a290fd..3292f5cf6d 100644
--- a/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: f36b1e9a45b6ec29d8041b70997550d917322cf9cdd456db26083169d21175ed
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/envoy/_index.md b/content/learning-paths/servers-and-cloud-computing/envoy/_index.md
index f0f26d737b..fa5bb837f5 100644
--- a/content/learning-paths/servers-and-cloud-computing/envoy/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/envoy/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: d492b6dce11b7d8f6591ff3b3ce9aa2c382a6a7a749add228c3ac1f6ae57e218
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md
index 70c8dcf565..052fa1f275 100644
--- a/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: cbbcc8fa33f864e422f8db7d58fba32c26f6070be2846b246837c63ccf37e1c7
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md b/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md
index 40f9756f3f..dc6912d86a 100644
--- a/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md
@@ -24,7 +24,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 02eedcebf59a8ab506d4ebbf5bbe20ead367cf3c0700950db5a9059d2f671853
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md b/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
index ff8b206746..890b7084ea 100644
--- a/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: dde32f5d14a877313a8b0aafec58acb09cd1e77f4fc9138b9e1bd6d9fedf250a
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md b/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md
index bab988463d..07df987cad 100644
--- a/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 978d3da8668e38758c138313c31809f3de5a8cefd1b7c2b47a536c0ee364b692
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md b/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md
index 287f57db61..8d53c74587 100644
--- a/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: a67c552b76df6323eccc331c9146d0fcbdb8ad222fe81dbf2e1c2339c7609b61
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md
index 6239e32a75..cd2527e09c 100644
--- a/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: adcf2a1b8a4a77e5834e14a40e46e27b0cbe5e440fbf732a4366ce486d7fafb7
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/flink/_index.md b/content/learning-paths/servers-and-cloud-computing/flink/_index.md
index 0ee57822f5..268c8a7ad6 100644
--- a/content/learning-paths/servers-and-cloud-computing/flink/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/flink/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 974d71b1ee968e3aeabe900bfdd52ae4fbfdd0ca7dea420e6c1fc01f5475e8c1
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md b/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md
index a79e99dd06..cad2e25b6e 100644
--- a/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md
@@ -24,7 +24,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 85359b9210812675169f149c86bf11a1736ab838c011d18e876b246463d297ee
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
index 2702cb90d4..8899a5d8f9 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
@@ -26,7 +26,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 94866800acca2c5f9cd89f76f972af1a343aad5777b6844d49fac09eb764f580
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md
index dd26a59f14..d3a4090dc7 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 3823ad3df8ae868acfd59b5b5b541240624572fe739aaf2275185d5cd3578032
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
index 7c5e0a221c..7a1a427f18 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: a3347a62c3322d88b80fc7848fa5ee1d92ee86333c2a21891b958286f8b70935
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md
index 01bda1de59..6f9a63827b 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 1510c787979257e191196e13999e98c20ca24d0857f72075649c28520c74c576
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/funasr/_index.md b/content/learning-paths/servers-and-cloud-computing/funasr/_index.md
index 22830b32ef..49121c2bc6 100644
--- a/content/learning-paths/servers-and-cloud-computing/funasr/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/funasr/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 410ec28d257ce7aa1308f11a741aa87baea80daf1acb113c4149761144269022
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
index 70d1a30155..0cf86125e8 100644
--- a/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 85d3d64e0eb0c3cfa0eee29cf1e4199049a6dcbf69634e578dad2b5443ca2b7f
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md b/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
index f5d119c4f2..a2bc36f05b 100644
--- a/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 2e53b87d4dc7a7d1984e3bbe035038a60e619aea2f5793cb3082f3b59084bbf9
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/gcp/_index.md
index 81f449a9f5..4b185c9d2a 100644
--- a/content/learning-paths/servers-and-cloud-computing/gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gcp/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 24e2ffcf9b7cda919e6e864f18bb9424c0c328242c92f491c1b284e317ed7e1c
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md b/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
index d6455a8c61..4fc4516274 100644
--- a/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
@@ -18,7 +18,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 85a0a44bde6f217bdfc7fd0660ed6a86279b24c7ede302307ef6efb2626d83d9
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md b/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
index f814610a15..a97ace140c 100644
--- a/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 642b67e7ca3717f499ab73efedd924eeab29a0055fad81c2d60fda993dcea195
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md b/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
index 30489a3b3b..2697f67e62 100644
--- a/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: cc72ef1fe1fde9f4f9ea0769c0e04d749731b8073b2efc0e65d7f608665abc2d
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md b/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md
index 45e4604de1..059959169d 100644
--- a/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: d5df467355a7792f7a04eafc4a6bbd2410353aca000cd2b1aec74a7ed93a9270
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
index af07db6fa0..b33b30e42d 100644
--- a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: cf981c67553824c7c57b6dda9c2953ac80926750676ac101e939f6bbff655ab5
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
index c07245efa8..2a6e7a5cf2 100644
--- a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 50715640292b60ea4216ee2211140c4212592a27356d628d945e83d9deb7fdcc
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/gke/_index.md b/content/learning-paths/servers-and-cloud-computing/gke/_index.md
index 587b4ebc77..f0a5d330a0 100644
--- a/content/learning-paths/servers-and-cloud-computing/gke/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gke/_index.md
@@ -18,7 +18,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 7c3d37545c93db584d6e0ce88d8feaf0bf690e0c8786b664a6643be713d0336f
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md b/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
index 335f33935e..8828d973ae 100644
--- a/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 74952d68380c7540306543b0a7520f6960f673dd55ad5f164365fcfe1470380e
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md b/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
index 1a915544f8..c4390186ea 100644
--- a/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: aa78434138f08b424352302e92a1cd40d8297459bc65202715dcb41c40de6057
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md
index b2060ad3cb..c0584fc6e1 100644
--- a/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 46a0a3df799ac473f56d3820390af405b3301758cf15685a250a04c4854aba9e
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md
index f5c759f084..29ebca76b2 100644
--- a/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md
@@ -28,7 +28,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 87e9d25d5fb1f45d126aa4ca2fa1e13d6a470f2bcdc70968aa4622790106e629
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/intro/_index.md b/content/learning-paths/servers-and-cloud-computing/intro/_index.md
index 5de918d5aa..ded73d65f0 100644
--- a/content/learning-paths/servers-and-cloud-computing/intro/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/intro/_index.md
@@ -18,7 +18,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: d2786f42b505823253ae16c647ca2e03c154f371b7c326210fde8523b12a8188
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md b/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
index dd4bb07d32..8083b2284e 100644
--- a/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 6fc608d45c4fdf85a77bddd4f8c26b05a7950d1086e578a8be0dd637feeb5d79
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md b/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
index f75926caa0..8e7fc43985 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: f57dd99bad280f64f53071373519e2d3ba8ae50b94fe1ad0a9cc40d02b458b9b
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
index 3558244bcd..c7cc8f1029 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: e6f73fbca45a1be1644f79bbcfbaaec964e31f1aab236e9a872c72a6fd5e4b66
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
index c11354af29..bd4a2fd5d0 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 58b0bb9f6e4190029d7edc65bdb04a597c7539de55a5e51ed59e88b174e527c3
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
index f5529f1062..54c92e0f1c 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 655180d91bb9b9cf571840b59d8943c1d2c73d8f8aea647db57dc2704ede3af8
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md b/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
index 09a5f95c6a..d34d9768d0 100644
--- a/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
@@ -26,7 +26,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:57Z'
   generator: template
   source_hash: 525d893a796e8e4eb5cc7a9d5996bd7d55a35f8fe413f87b9a275a214d77626f
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
index 22d0c753cf..642b54b094 100644
--- a/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: d510dd43a485e1c2fac404ef9a2e00b5be2449b99b1095c9a1841cd2fcba9b0e
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/kafka/_index.md b/content/learning-paths/servers-and-cloud-computing/kafka/_index.md
index 83c803fe50..fe41155b05 100644
--- a/content/learning-paths/servers-and-cloud-computing/kafka/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kafka/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: b7f36df881c2c436143c1e5579d2c54cb5930f52998904098829c52fa7290f38
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md b/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
index e9658e18a9..90d67f8298 100644
--- a/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 6ff33f6dfaf25e926a0ce8756b4e564e5aa37112e5425f9c3dce13f772b54145
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md b/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
index b704463512..9a090ba96a 100644
--- a/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 830556dfd51aaa2dd95ee957e64306bab369297f7bc66325e7eb509f541f683c
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md b/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
index 6b5a3c9506..50a823dd84 100644
--- a/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 004436cf14ff0056136889c58d676295c6dd2088f06f57f397a07ec9004e6b44
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md b/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
index 79c31167db..4ead9f2004 100644
--- a/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 43e4e28b88b9721ca94457418f3b4887d0099017578a54da1db5fcdc11f4a122
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md b/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
index 18cb6d0af4..138a192eb2 100644
--- a/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 22fa96e29143f2e0dc0c204919422914b3f70d63ddf833cf1067fbf67b525aa0
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md b/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
index 215d8078e6..f2e718aaf0 100644
--- a/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 66d13edff1371295f0e88a618e924ca67b85bcc8aa72034d1e582a0b267f0d05
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md b/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
index ef11793f78..c486a10f69 100644
--- a/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: d82aa4faeb599a3a1ac12d477204ebe0a4ddb99564bedced86ca0fc4851e17b9
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md b/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
index 9673106b81..2f53e5ddf6 100644
--- a/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
@@ -24,7 +24,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 3e8307a5daf435c21f3cecff635ac51724a63ff9ea4307082d869601563b4204
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
index 5739c9d5c3..6ab83998d9 100644
--- a/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
@@ -25,7 +25,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 36bf3c45f0b38350714ba41ea88c1551b33f6d299a65b3b0d6668fee2d88d835
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/lse/_index.md b/content/learning-paths/servers-and-cloud-computing/lse/_index.md
index 3a7be38cd2..e2a1ef4dc6 100644
--- a/content/learning-paths/servers-and-cloud-computing/lse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/lse/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 9610291239b88c67e21055f94cd03fe7cf80f7d875d53ebe0d8de7837ce99fa7
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md b/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
index 7eb79e111a..5e83569d5c 100644
--- a/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: db4ce1c59ad10bd127b4990c82ce876311d7dc7e1ad5d39ec132daf82ceb8b79
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/memcached/_index.md b/content/learning-paths/servers-and-cloud-computing/memcached/_index.md
index aeee2061d9..2f60bf9fe2 100644
--- a/content/learning-paths/servers-and-cloud-computing/memcached/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memcached/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: b42ca5cc8f4db6ba6019f3720a5ef46119aa403a2baaef5362e874f898ce0419
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md b/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
index 46ab4da8c7..ef6f95731d 100644
--- a/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 4efe46aaf4580a6b8f263b69e8f072211bfd24fcf7f7a885689c927fc05a4c63
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md b/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
index ece9430094..5acd301cdc 100644
--- a/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
@@ -24,7 +24,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: d61c9ddcef1c7ffe12b3ee818852fb3f0a62a90cec451692c1186cf2c01de625
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md b/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
index 1352cb2e43..21c888487a 100644
--- a/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
@@ -24,7 +24,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 6aa61de638be961339d958345225d696ff2b83b8f9cab22bf956f9bf2d15c1aa
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md b/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
index 0e59850a39..e6dc211e2e 100644
--- a/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: a67d36fb650b77c170c15f193049490286a3f097801d0c79d701d3f5610fe1dc
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md b/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
index e036b9c491..02f7c111f6 100644
--- a/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 56a38d1d742dd301d48e9ad61ff00e07048559f3f646815e22937113243adcdb
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/migration/_index.md b/content/learning-paths/servers-and-cloud-computing/migration/_index.md
index d600877f15..42cf30144e 100644
--- a/content/learning-paths/servers-and-cloud-computing/migration/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/migration/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 6b2b985c39579c4d0855c8c28b610ba558e25b451a6b755475ff4393e2810670
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
index b4a4577c6a..b59da90a05 100644
--- a/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 2eb252b19b7535fbb776a3153bfc9f70b6217088e5b4b55deb56d01393abaf3b
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
index f9c2553012..d742cea942 100644
--- a/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 6c438573edec90b3aa4135ace38cd3ae604c58658c1949117ca80dbdd21394bd
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md b/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
index 4ad049ea78..1bcda4403d 100644
--- a/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 973ba6c1e00fc67e1702606412124d8172e071b9b677a1df53c3be66210bf704
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
index 40825f4eaa..a8e3e948c7 100644
--- a/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: f270cfc90245c0090685fabf5cbebf62a714edbf34e1ea86c3df0bfbb4699ea4
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
index c9a72c8533..7ea3d39843 100644
--- a/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: abbdde22271151fb1485c54bafe463e7797a0b913c7d4c99245d2914a3f9bb82
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md b/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
index 17edbf52c8..23d588cf50 100644
--- a/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
@@ -6,7 +6,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: b52a1b60a74e19f2a3b60b8a77199ad5369c1ccd3b4d5ce0252a169d9f6123fd
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/mpi/_index.md b/content/learning-paths/servers-and-cloud-computing/mpi/_index.md
index 05893a6eec..beb3899ab3 100644
--- a/content/learning-paths/servers-and-cloud-computing/mpi/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mpi/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 300794f4010658d945212c74c5257635d620cbb6230cac0de92bf23e4e9e29fe
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md b/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md
index 162ca66893..9dc70e65e7 100644
--- a/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md
@@ -8,7 +8,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: a10683fdd14359e93056dc4ba24581856833765c64915979d0466a06887e0755
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md b/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md
index 2f0a8957ff..cc3ba57d95 100644
--- a/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: d765afadfe5155f0ecd5bd08373fa12464b2ee5ebb1f8c7831039eb07eba8831
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md b/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md
index aeb58159df..33245ca9d0 100644
--- a/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: cd31c217eaeab6faad263728c446ee188cd9d542904d87ae7bfd5120713dfaa4
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md
index dc9218db9f..f58c1d675b 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: b9bc1d1f965e4fdeeb9552d853330c201e00597829420c8a0397fa425c3231c4
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql/_index.md
index fb4e38e229..3f542e6662 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql/_index.md
@@ -18,7 +18,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: b9b2ed7f58611c9bc7e1867fe06700f3197eb095ddc62d91c00814021967cf72
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md
index 68b505698f..6c8b4f431e 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md
@@ -18,7 +18,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: e473c0724855bbbc59481822130f7dc5e1684593527a6b5688939f84cd32963d
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md
index cde9ae8a3a..d874efcb6c 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md
@@ -16,7 +16,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: faa914d636e03296c6b65ba146745c543326955ec457577f047eec51aa6a3733
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md b/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md
index 16a3034ec7..daab27afa8 100644
--- a/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md
@@ -24,7 +24,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 65336a8f8d1d11c4b127ea13982a581afffa94cbfd1af10a9e4df2becbc34b5a
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md b/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md
index 3b450b5a3c..9a8c86bca3 100644
--- a/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 5a5fd9b66a09de71009ccb098c7ef08a848463a8a7956643020ab1fb1db22fbb
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md
index 9694ca9fe6..4c779f150a 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: e6f6f4d843b4974d1c1d67a35009b4428d08bb356d26c08a441f82726e002fb2
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx/_index.md
index fb48945ef5..e661d82c3f 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: c5e077458808373c8ce9660235716b5bb55e4e7eb8b6300c162c041ef1c96cb0
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
index 3bef5623e2..34d1a3a222 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 10f13dee3459577fcadf5966cf80d990f3396321189f638432a3308699e773a8
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md b/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md
index 8d1125aefe..13a496fb54 100644
--- a/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md
@@ -17,7 +17,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 81bdee16a18b09da91a7514eb70368771b251ed3f8ec657ec105ca10ecead038
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md
index df00e60e53..8048b6ad4d 100644
--- a/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md
@@ -24,7 +24,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 800eed835763098d4b369789d10de642bbdbaa390e8bfe0cb6ea2c4c78f7ecbd
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md b/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md
index d95104ed43..fa3fff0af0 100644
--- a/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md
@@ -17,7 +17,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:18Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 3ee5dd8d8edeb5dcb1e3be7bb09cdf0b1b2694f0e74e9eb3c6c2f17912399808
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md
index dd115aea2c..6eabf7db3c 100644
--- a/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 84ba887426f3ca45b698c79028023d80cbf0a06e6bc2fb71fcbe924943078dd8
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/onnx/_index.md b/content/learning-paths/servers-and-cloud-computing/onnx/_index.md
index 22af1cf047..47c53771d2 100644
--- a/content/learning-paths/servers-and-cloud-computing/onnx/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/onnx/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: a9e547c4a9f99e1c7bfbfe582032ede11eb8ff5a74dbb7a93bada275ac435220
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md b/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md
index 1f53b9c59d..7b4fc907bc 100644
--- a/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: dec913a7a15e82ebf129e2ac64cba8d9140694840f8f9e817fb091b0c82c4cab
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md b/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md
index ef05ab5931..839ab15f4a 100644
--- a/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 778ac2a8b8ad9ffd665f6f0be545541090465f355094c354cc693e784d27559a
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/openshift/_index.md b/content/learning-paths/servers-and-cloud-computing/openshift/_index.md
index 95b82a0434..e3f36f67ff 100644
--- a/content/learning-paths/servers-and-cloud-computing/openshift/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openshift/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 7972fb230273ab1809c6f0917c4bbc49934f495feb2649da665d67bf71a45a3f
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md
index 3a2d9bc0b4..783b79a783 100644
--- a/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md
@@ -26,7 +26,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 42628afa923ce6e89693bdfc72469ac0803610c3decb6cd4f36bd1a003874d0d
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md b/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md
index 04c058f563..7c3d3d06d9 100644
--- a/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: cb4227a3f8375d6ae63801891703d6e615bdc60a01fca099a2f76453775ef460
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/pac/_index.md b/content/learning-paths/servers-and-cloud-computing/pac/_index.md
index abe55bc3de..fe54514efa 100644
--- a/content/learning-paths/servers-and-cloud-computing/pac/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pac/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: fa699cafa5c0d998a63de306371bfbe47680c7453b28fc4b35d4fe2671902b23
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md b/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md
index 3273649033..a889873905 100644
--- a/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md
@@ -24,7 +24,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 0599665da13e9e1a8b017b0dac87c63f323ef769b1a5be62a60a32a36bc82696
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md b/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md
index 183718f7c0..8253d39d45 100644
--- a/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: ec027f6022cc86a644a71a7d2016bf4601e2c4ac41570e861e9f124468b228ae
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md
index f2e05b060e..e765c47f8a 100644
--- a/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 719ec8966a776cc5ee97782b7ef7cc2b989a8616d14cb36b9847c9cbd2f16446
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md b/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md
index d3842163f6..88e963729f 100644
--- a/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 2fdb45e72a36eb114250486b88d603cc8687b9f6b44bb5bb656e25c37d9795a9
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md b/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md
index 2fe306ee69..09286bccb3 100644
--- a/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 3ec6ae40daff557dd05cd092ff7d6b5a63954a1618605030a443f56fe5ffe490
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md
index e5463602f9..16679b2d80 100644
--- a/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 57827f45473867b3b5039a9cbca04d2c6d8a93e177ca0ab053999517389014b5
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md b/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md
index 01c658b5db..3d211a45a3 100644
--- a/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md
@@ -18,7 +18,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: a60cc2148a83f919467076e9d5a49fc4c9cec85670a919c7ba044cba72bfe219
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md
index 023ce4c754..57732a99ac 100644
--- a/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md
@@ -16,7 +16,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: f30f7fb50000ae7ee28e46d7cbe4e73ba232c3daad2d559cfa41996d630cb936
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md b/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
index 6d2d0a1918..cf7c6dd8bd 100644
--- a/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
@@ -18,7 +18,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: ed85d6171f3c05bfdf1b396065fb0c73ce88724ff63fd1c3c8a93d4c27dd59f8
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md b/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md
index 441e6dc683..4410205633 100644
--- a/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md
@@ -17,7 +17,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: ef12378069d6f3538d8daefb5da7fc8e8ce4196277928d372745d12fde6e46a1
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md
index 364d2593e9..d34a639f15 100644
--- a/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md
@@ -16,7 +16,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: aeb6a390b5134af2f851e36db17c5d3578d4697b3c372af86c6bd5176765e087
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md b/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md
index 8888254834..b9ee9ae74b 100644
--- a/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 1c5be7bfc7785ae3b06c60210c06324f00aed7ba75145a0fa65425e6005d4f06
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md
index bfd3f992bb..5bfa3ecb8d 100644
--- a/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md
@@ -24,7 +24,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 8b180acd30b3b00484b6e7302b61fd8c3669ef83ff7fca41972d24c5df2682b7
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md
index ac7d324570..35214ae109 100644
--- a/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md
@@ -24,7 +24,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: b4392f1cf38fa1f3b6c633c7ae1e8d30a51ea8d4e635287586b084cb0d8a556b
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/rag/_index.md b/content/learning-paths/servers-and-cloud-computing/rag/_index.md
index 1fbec981b1..e422d3c4e7 100644
--- a/content/learning-paths/servers-and-cloud-computing/rag/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rag/_index.md
@@ -24,7 +24,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: d9435cc1dc1fe65432d83934e69993853dd3f294f66f98e9bcfb5f81e6ac6d5f
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/ran/_index.md b/content/learning-paths/servers-and-cloud-computing/ran/_index.md
index b0983c6728..71a42b1555 100644
--- a/content/learning-paths/servers-and-cloud-computing/ran/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ran/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 2b329c566f7fea90010c52f1ce1cb11bccf9d32cf604aaa720bfca5ef1f85fae
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md
index 6dc1a4b8ae..8a7ccd68c2 100644
--- a/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 0877f5f233ec8a50172f4bb9388ad38ae9b890a4d049679ca6ece083d760d872
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md
index ad68be6a4f..c20d10f784 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: 9b306bc318491834d0d74dd75221b24081acc20dac7993ccdbd1cb40ba6158ac
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md b/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md
index 9f7e1d2128..1921df927c 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: eb008543ea4248b231be5e6345546164533edf2bc149613693095812bbbba3d9
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/redis/_index.md b/content/learning-paths/servers-and-cloud-computing/redis/_index.md
index b36db3b062..722d6f0c5c 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:58Z'
   generator: template
   source_hash: 7b9906a3c16ebd3c6b41618be7db76d7a97cc4b16c4da927257fb39a66753e12
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md b/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md
index d11d324f49..fb292d1154 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: 9146285feb38ee45171ac234f605eee08467bbf675a77df231a6dc63c38282f2
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md
index f440f14ac7..0d7ea4437e 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: a6ed103f2d5a00f4cb6c5df1c0812eb68bbad8746d3f351adc959c6880002bbc
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md b/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md
index d75c014130..45079083d5 100644
--- a/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: d060151c5f6ac7a743fb53cd3d2a10aa23f8f09245122a199a84ae17a8ab5ff9
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md b/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md
index 27b5e3a798..2d0095fcc8 100644
--- a/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: 8f2515b7c416a821bd397a77297adc263397a8b3cb86e9bb34e646735a21f854
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md b/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md
index d9fd1ffc45..fa8cdfe6ff 100644
--- a/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md
@@ -8,7 +8,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: d643c9ef25aa5442aec65ac6a8f48264d4789f8e24ffd6270c2efacce48dd8fc
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md b/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md
index 990a5b65b7..2ce65741ac 100644
--- a/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: 180803cf9c6e34c0dda76cafdfcd0ce67edfaca4563f57ae0c36bfefd2199ae9
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md b/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md
index 35c5a874f1..bab1a90e4f 100644
--- a/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: 27e17ea322cc7dc117f24d9d2007fbc0e6b498840b4097b859b1f376b8d8c9a6
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md b/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md
index e2ee758fbd..3e0a00fb55 100644
--- a/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: 092602df259461e2b22dbf0909a682fbacd59464eea38ab901f38cd0b8c6efd3
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md
index 79f98e80e1..1f73aa4eda 100644
--- a/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: c3dd7a0ff9c645635e41b2d9110f4c52de627b752e33c3c6775e1d2e3ffc381d
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md b/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md
index 51003972ec..09d149e45f 100644
--- a/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: 3d9b35d09c97557dcde807bb83f994f8c3701c0d109c0a9bb388acc603d81b16
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md
index abfa015148..cbb60670dc 100644
--- a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md
@@ -18,7 +18,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: 0a4dd65c6d0c7eb79479217b351e3d6c5b8917512d510283fd4d8387ff1339f5
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md
index fdd2ffb358..412a7a9aad 100644
--- a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: 2120b6bedf6ea5ae02d8b353a6485f307bcb39d0055c29d9e8a39df37a8b946e
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md
index b1042bb88e..9d2ae2e5b2 100644
--- a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: a31c6f9d674bf41cee16066708c884ca587289e181b7754ff75cdbd85bdb30e3
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/snappy/_index.md b/content/learning-paths/servers-and-cloud-computing/snappy/_index.md
index 0580c51974..ebee97ad23 100644
--- a/content/learning-paths/servers-and-cloud-computing/snappy/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/snappy/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: 62edadd9a4163e020b55d33f541a13ceb604c3700ba56787b650563c446a3b0d
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md b/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md
index 1f8182ec19..c0a9f2be79 100644
--- a/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: 95da7df14cf36fe01e00868356cf117aa46007ac32e61b0df64d2eea28a5466e
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md
index 0b4d24d8c2..d4f2bdd4e3 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: 009f2219f1b949be39b4a1dd43a5e4c1ceb5bb273d9a11c3c155315160d593ac
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md
index a9b317c64d..ad2ccca984 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: a4ac73af7e8959a546a66ab776c0bca8b60957e6f1729aa00fd78783e6594c0b
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/spark/_index.md b/content/learning-paths/servers-and-cloud-computing/spark/_index.md
index cd79287aab..89cdbdd862 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark/_index.md
@@ -18,7 +18,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: 7a612e45aa64ac93fa9a1fe83da8a7c63ffbc3a4b159184b1a7b04fd46e8ee4b
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md b/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md
index 7d18abc413..1081569b81 100644
--- a/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: d3fa3b409ed7cf2ecb521fa4d19af8411718909881861ef3885214824031b541
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/sve/_index.md b/content/learning-paths/servers-and-cloud-computing/sve/_index.md
index eb79c0d5f1..4d6179a76a 100644
--- a/content/learning-paths/servers-and-cloud-computing/sve/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sve/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: 7b2074e39243a5cd0ccb6a01317c700a4797d8c66353f39aa4197237b6672027
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md b/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md
index d4c6a154de..1d290ac587 100644
--- a/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: cc3f88ce181b1614f959497a24fafe736b26e55615792faab6b5dce29fac8d77
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md b/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md
index 93de18b749..928e31ef8d 100644
--- a/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md
@@ -18,7 +18,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: d15c3083cb881838f6500eb56dfafb636d73bb282484a7f1b8f4a855dda37fb4
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
index 0f76e00850..14f027cd9a 100644
--- a/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: 2c20d30d25a0bb871bdb935d18f7ad8e0740139948133dbd728e10f0add0f94e
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md b/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md
index 75414e73a5..21617dea23 100644
--- a/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md
@@ -17,7 +17,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: 0aaee75d902b938e63e37eca421dd28b91f583e784acdf3cccb1e20b8dda71bd
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md
index f1c03a60e5..aaf214cf37 100644
--- a/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: edf5bebb0440c110723c87ca27e5f4b21e4da588e4208b5391f7091ae93cb73d
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md b/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md
index c4271d97a0..e785a4631b 100644
--- a/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: e6a652fd0b32796433a380012a79def743bc5452a52ffae785007977a2a8e3e0
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md b/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md
index 7c7d9f4aca..8158475877 100644
--- a/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md
@@ -18,7 +18,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: d25121278f9dd40e2fd19d988e35866c6bfa70cfd0b93e2ec4506c8ad8a26d88
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md
index 272251c2f2..9e0cde01ed 100644
--- a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md
@@ -18,7 +18,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: 523ff99ccb8d13543f64839fa21040191d2a9ff7ce7c78fa590e8775fac754a6
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md
index f0b30e4aec..d4d5b436f6 100644
--- a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: 1b309980bef983966d948036e309e132b56f767161967187e72c6a9f81f17dce
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md b/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md
index 1e7ac23920..56050c584d 100644
--- a/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: 13bba1dee1c948f8b751a71479a5106014a2c21dab6ce3968a08bed9e3363de1
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md b/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md
index 042fc6150c..5205ed4fd8 100644
--- a/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: 9f2b3f6c5e76ecb754de5c0fd84278ff71f71c5c7906acdc06911f2feff1f5fd
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md
index 942091a3ef..3808ab2591 100644
--- a/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md
@@ -21,7 +21,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: ee805f703acd45612c9a94e60c6322866dc1c8ff25d48de2fb4cd9067948c93e
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md b/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md
index f29b64d3cd..a868ba51c1 100644
--- a/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md
@@ -18,7 +18,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: 035bd363fc8ae772acf52d7e392f2db27087267706aeec86b4098c497e5fe91d
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md b/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md
index e41f748b27..9bb95ea96b 100644
--- a/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: beb244c7766c474b47942b86f1cdd0d124c1cccb74dbe40f0b6c0917d0b3d31a
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md b/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
index d3fde326df..19730ce3ef 100644
--- a/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
@@ -22,7 +22,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: 487e9829c6e08798ad01be7a01f192e10e99cb06b71108575f1919c134eece02
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/vllm/_index.md b/content/learning-paths/servers-and-cloud-computing/vllm/_index.md
index 44caa39e35..292529edbf 100644
--- a/content/learning-paths/servers-and-cloud-computing/vllm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vllm/_index.md
@@ -19,7 +19,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: db5987ec7987375d9b51de843b0e1faf0e61731b14c5a563d19aaad26f50f6bb
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md b/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
index ad3351f205..2f6c7b821d 100644
--- a/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
@@ -6,7 +6,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: 4ed20056b2d82bd2c9ab1afe3d74657df9ac09808592d843c1b143626a8668ac
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/whisper/_index.md b/content/learning-paths/servers-and-cloud-computing/whisper/_index.md
index bd5476f752..7afac593e7 100644
--- a/content/learning-paths/servers-and-cloud-computing/whisper/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/whisper/_index.md
@@ -23,7 +23,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: e130630b5ae4626641779d0b66d3c1c132b90899cd3d6db07a46df8957c4da38
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md b/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
index 6a284565fd..7874886fc1 100644
--- a/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
@@ -12,7 +12,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: 46f2645fb6ef298508af93569554315cfa2564be9891acabf1c874c94e52d70d
   summary: >-
diff --git a/content/learning-paths/servers-and-cloud-computing/zlib/_index.md b/content/learning-paths/servers-and-cloud-computing/zlib/_index.md
index fb580a2164..798081d3d3 100644
--- a/content/learning-paths/servers-and-cloud-computing/zlib/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/zlib/_index.md
@@ -20,7 +20,7 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
-  generated_at: '2026-04-30T18:58:19Z'
+  generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: 8916f83f621505dc5406f14e70d04e702ea304950e34b4b76dc1bbc987bcc4e3
   summary: >-
diff --git a/reports/generated-summary-faq/latest-run.yml b/reports/generated-summary-faq/latest-run.yml
index a2ab511171..fb7c2386a3 100644
--- a/reports/generated-summary-faq/latest-run.yml
+++ b/reports/generated-summary-faq/latest-run.yml
@@ -1,37 +1,32 @@
 latest_run:
-  timestamp: '2026-04-30T18:58:19Z'
+  timestamp: '2026-05-06T17:17:59Z'
   mode: write
   require_enable_flag: true
   path_filter: ''
   limit: 0
-  run_url: https://github.com/chrismoroney/arm-learning-paths/actions/runs/25183771014
+  run_url: https://github.com/chrismoroney/arm-learning-paths/actions/runs/25450203813
   git_ref: STESOL-345
-  git_sha: 89f5a746a3191977f5bd001f449a4f577acfb914
+  git_sha: 4c5f94dd928413a40b0c163d7103c241b18b5f99
   actor: chrismoroney
   template_version: summary-faq-v1
   totals:
     processed: 407
-    added: 407
+    added: 0
     updated: 0
-    unchanged: 0
+    unchanged: 407
     skipped: 0
     errors: 0
     removed: 0
   paths:
   - path: content/learning-paths/automotive/openadkit1_container/_index.md
-    status: added
+    status: unchanged
     changed_on_disk: true
-    summary_changed: true
-    faq_changed: true
+    summary_changed: false
+    faq_changed: false
     faq_changes:
-      before_count: 0
+      before_count: 5
       after_count: 5
-      added_questions:
-      - What will you accomplish in this Learning Path?
-      - Who is this Learning Path for?
-      - What do you need before you start?
-      - Which tools, languages, or platforms does it cover?
-      - How is the Learning Path structured?
+      added_questions: []
       removed_questions: []
       updated_questions: []
     summary_preview: Learn how to deploy and run containerized autonomous driving simulations using Autoware
@@ -39,19 +34,14 @@ latest_run:
       It is desi...
     source_hash: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
   - path: content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
-    status: added
+    status: unchanged
     changed_on_disk: true
-    summary_changed: true
-    faq_changed: true
+    summary_changed: false
+    faq_changed: false
     faq_changes:
-      before_count: 0
+      before_count: 5
       after_count: 5
-      added_questions:
-      - What will you accomplish in this Learning Path?
-      - Who is this Learning Path for?
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     summary_preview: Learn how to debug the Arm Zena CSS Reference Software Stack using Arm Development
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     summary_preview: Learn how to use Arm Development Studio to explore Realm Management Extension (RME)
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     summary_preview: Learn the software migration methodology for porting Linux workloads from x86_64
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     summary_preview: Learn how to build examples from the Machine Learning Evaluation Kit (MLEK) and run
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     summary_preview: Learn how to understand and select physical and virtual hardware targets for ML application
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     summary_preview: Learn how to create debug configurations for virtual platforms and development boards
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     summary_preview: Learn how to bring up ExecuTorch executor_runner firmware on the NXP FRDM i.MX 93
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     summary_preview: Learn how to migrate a CMSIS v5-based CMSIS-Pack with device support to CMSIS v6
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     summary_preview: Learn how to migrate CMSIS v5 projects to CMSIS v6 by identifying supported toolchains,
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     summary_preview: Learn how to run large language models locally on the Raspberry Pi 5 using Ollama,
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     summary_preview: Learn how to build a voice-controlled bot on a Raspberry Pi that listens for a wake
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     summary_preview: Learn how to build and run multiple software examples on the Raspberry Pi 4, including
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     summary_preview: Learn how to compile the Llama 3 large language model using ExecuTorch, deploy it
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     summary_preview: Learn how to reduce compile time for embedded Linux projects by installing a Raspberry
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     summary_preview: Setup tools and start programming with Raspberry Pi Pico. It is designed for embedded
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     summary_preview: Learn how to profile Linux kernel modules using Arm Streamline to identify performance
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     summary_preview: Build a letter recognition neural network model using TensorFlow and deploy it on
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     summary_preview: Learn how to build and run the reference Trusted Firmware-M tests and example application
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     summary_preview: Learn how to train a CNN image classification model using PyTorch, convert it to
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     summary_preview: Learn how to install Keil MDK Tools, run a TrustZone hello world example on the NXP
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     summary_preview: Learn how to acquire and print materials, assemble a universal SBC rack mount system
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     summary_preview: Learn how to debug microcontrollers using µVision with basic run/stop debug, advanced
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     summary_preview: Learn how to import, convert, and build uvprojx-based projects to csolution format
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     summary_preview: Learn how to install vcpkg, initialize it, create vcpkg-configuration.json files,
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     summary_preview: Learn how to identify Arm-based targets for TinyML, install Fixed Virtual Platforms,
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     summary_preview: Introduction to building a minimal Yocto Linux image and running it on 64-bit Qemu
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     summary_preview: Learn how to run a YOLO object detection model on the Himax WiseEye2 module, build
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     summary_preview: Learn how to create and build Windows Forms applications and measure code execution
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     summary_preview: Learn how to build, run, and benchmark .NET 8 Console applications to measure performance
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     summary_preview: Learn how to configure Windows Sandbox as a self-hosted GitHub Actions runner to
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     summary_preview: Learn how to use each of the tools supplied with Arm Performance Studio (formerly
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     summary_preview: Learn how to build real-time image processing pipelines using Halide on Android,
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     summary_preview: Learn how to optimize hardware ray tracing with Lumen on Android devices powered
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     summary_preview: Learn how to build ONNX Runtime and the generate() API for Android to run a Phi-3
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     summary_preview: Learn how to build, optimize, and deploy machine learning models using ONNX Runtime
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     summary_preview: Learn how to deploy Apache Arrow and Arrow Flight on Google Cloud Axion C4A processors
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     summary_preview: Learn how to access and use Arm hardware performance counters and the system counter
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     summary_preview: Learn how to create and deploy Azure Resource Manager templates to provision Arm64-based
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     summary_preview: Learn how to configure a self-hosted GitHub runner on Azure Cobalt 100, create an
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     summary_preview: Learn how to automate the creation of Azure Arm virtual machines using Terraform.
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     summary_preview: Learn how to build, profile, and optimize Arm executables using BOLT post-link binary
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     summary_preview: Learn how to run the Arm CCA reference software stack on an FVP with RME support,
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     summary_preview: Learn how Arm CCA Realms interact with I/O devices using VirtIO paravirtualization,
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     summary_preview: Learn how to deploy a CCA realm on an FVP with RME support and connect it with attestation
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     summary_preview: Learn how to deploy Confidential Containers from encrypted images inside Arm CCA
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     summary_preview: Learn how to deploy a CCA realm workload on an FVP and connect it with Trustee services
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     summary_preview: Learn how to inspect and verify Arm CCA attestation tokens using command-line tools
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     summary_preview: Learn how to deploy a scalable Arm CCA attestation verifier service on AWS using
@@ -4719,19 +3544,14 @@ latest_run:
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     summary_preview: Learn how to set up CircleCI self-hosted machine runners on Google Cloud Axion C4A
@@ -4739,19 +3559,14 @@ latest_run:
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     summary_preview: Learn how to install and configure CircleCI self-hosted machine runners on AWS Graviton
@@ -4759,19 +3574,14 @@ latest_run:
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     summary_preview: Learn how to install and run Clair on Arm servers using combined and distributed
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     summary_preview: Learn how to install ClickHouse on Arm-based cloud instances and measure database
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     summary_preview: Learn how to deploy ClickHouse on Google Cloud Axion C4A processors and build a streaming
@@ -4819,19 +3619,14 @@ latest_run:
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   - path: content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
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     summary_preview: Learn how to deploy an Arm-based Cobalt 100 virtual machine on Azure, connect via
@@ -4839,19 +3634,14 @@ latest_run:
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   - path: content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
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     summary_preview: Learn how to automate Docker image creation for Arm using AWS CodeBuild with GitHub
@@ -4859,19 +3649,14 @@ latest_run:
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     summary_preview: Learn how to build and run the x265 H.265 codec on Arm servers with performance benchmarking
@@ -4879,19 +3664,14 @@ latest_run:
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     summary_preview: Learn how to build and run the AV1 and VP9 video codecs on Arm Linux systems with
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   - path: content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
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     summary_preview: Learn how to install and configure Couchbase on Google Cloud Axion C4A Arm64 instances
@@ -4919,19 +3694,14 @@ latest_run:
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   - path: content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
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     summary_preview: Learn how to apply g++ compiler optimization techniques and flags to improve C++
@@ -4939,19 +3709,14 @@ latest_run:
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     summary_preview: Learn how to apply profile-guided optimization to C++ applications on Arm systems
@@ -4959,19 +3724,14 @@ latest_run:
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   - path: content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
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     summary_preview: Learn how to profile and identify CPU hotspots in C++ applications on Arm Neoverse
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     summary_preview: Learn how to start an Arm-based virtual machine instance from major cloud service
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     summary_preview: Learn how to deploy and run the DeepSeek-R1 language model on Arm servers using llama.cpp
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     summary_preview: Learn how to use fio to microbenchmark storage performance on Arm systems and monitor
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     summary_preview: Run distributed LLM inference with llama.cpp across multiple AWS Graviton4 instances,
@@ -5059,19 +3799,14 @@ latest_run:
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     summary_preview: Learn how to create a simple Django web application and deploy it on Arm machines
@@ -5079,19 +3814,14 @@ latest_run:
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     summary_preview: Learn how to deploy a production-grade Django REST API on Google Kubernetes Engine
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     summary_preview: Learn how to build and benchmark the Deep Learning Recommendation Model using PyTorch
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     summary_preview: Learn how to use the Docker MCP Toolkit with the Arm MCP Server and GitHub Copilot
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     summary_preview: Learn how to build and run an OrchardCore CMS .NET application on Azure Cobalt 100
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     summary_preview: Learn how to deploy Dynatrace OneAgent on Azure Cobalt 100 Arm64 virtual machines
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     summary_preview: Learn how to create an AWS ECS cluster with Fargate and AWS Graviton processors,
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     summary_preview: Learn how to provision an Amazon EKS cluster on Arm-based Graviton instances and
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     summary_preview: Learn how to use docker buildx and docker manifest to build and deploy multi-architecture
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     summary_preview: Learn how to build, install, and run Envoy proxy on Arm servers and configure it
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     summary_preview: Learn how to install and configure Envoy proxy on Google Cloud Axion C4A Arm64 instances
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     summary_preview: Learn how to optimize Envoy proxy performance on Arm servers using Transparent Huge
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     summary_preview: Learn how stack buffer overflow exploits work on AArch64 by analyzing stack frame
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     summary_preview: Learn how to identify and fix false sharing issues using Perf C2C cache line analysis
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     summary_preview: Learn how to build custom Linux kernels using tuxmake and Fastpath, then benchmark
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     summary_preview: Learn how to implement exponential functions using Arm SVE intrinsics with the FEXPA
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     summary_preview: Learn how to install and run Apache Flink on Arm servers and benchmark stream processing
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     summary_preview: Learn how to install and configure Apache Flink on Google Cloud Axion C4A Arm64 instances
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     summary_preview: Learn how to build scalable machine learning workflow pipelines on Google Cloud C4A
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     summary_preview: Learn how to create an Arm64 Azure VM, install .NET SDK, containerize .NET applications,
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     summary_preview: Learn how to create and run Docker containers on Azure Container Instances for Arm64-based
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     summary_preview: Learn how to create an Azure Kubernetes Service cluster with Arm64 virtual machines
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     summary_preview: Learn how to automate Azure resource deployment using Infrastructure as Code with
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     summary_preview: Learn how to deploy the ModelScope FunASR Chinese automatic speech recognition model
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     summary_preview: Learn how to install and configure Gardener Kubernetes management platform on Google
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     summary_preview: Learn how to apply link-time optimization with the GCC toolchain to improve application
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     summary_preview: Learn how to automate the creation of Arm virtual machines on Google Cloud Platform
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     summary_preview: Run Geekbench on Arm systems to benchmark CPU performance, interpret the results,
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     summary_preview: Learn how to set up Arm-hosted GitHub runners and train PyTorch ML models using the
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     summary_preview: Learn how to install RunsOn self-hosted runner manager in your AWS account to execute
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     summary_preview: Learn how to provision a Google Axion C4A Arm virtual machine and set up a GitHub
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     summary_preview: Learn how to automate the deployment of an Arm-based Google Kubernetes Engine cluster
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     summary_preview: Learn how to add Arm-based Google Axion nodes to an existing x86 GKE cluster and
@@ -5719,19 +4294,14 @@ latest_run:
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     summary_preview: Learn how to create dual-architecture GKE clusters with arm64 and amd64 node pools,
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     summary_preview: Rebuild and benchmark glibc with LSE atomics on Arm servers, then evaluate scalability
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     summary_preview: Learn how to provision Arm64 and x86_64 VM instances on Google Cloud, then install
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     summary_preview: Learn how to provision Azure Cobalt 100 Arm64 virtual machines and deploy Golang
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     summary_preview: Learn how to install Helm on Google Cloud Axion C4A SUSE VMs and deploy applications
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     summary_preview: Learn where Arm architecture is used in servers and cloud computing, and find Arm-based
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     summary_preview: Analyze and optimize interrupt request (IRQ) patterns on Arm Linux servers to improve
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     summary_preview: Monitor, interpret, and optimize Java Garbage Collector performance on Arm servers
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     summary_preview: Deploy and optimize Java applications on Google Cloud Axion processors by testing
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     summary_preview: Deploy Java on Azure Cobalt 100 Arm virtual machines and benchmark application performance
@@ -5919,19 +4444,14 @@ latest_run:
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     summary_preview: Profile Java applications on Arm Neoverse servers using flame graphs generated with
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   - path: content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
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     summary_preview: Deploy Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate installation,
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     summary_preview: Deploy and configure a Kafka cluster with Zookeeper on Arm servers, test event streaming,
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     summary_preview: Deploy Apache Kafka on Azure Cobalt 100 Arm virtual machines and benchmark message
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     summary_preview: Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing Kedify
@@ -6019,19 +4519,14 @@ latest_run:
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     summary_preview: Create, train, and evaluate a neural network model on Arm servers using Keras Core
@@ -6039,19 +4534,14 @@ latest_run:
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     summary_preview: Compile and install custom Linux kernels on Arm cloud instances using TuxMake with
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     summary_preview: Identify and migrate container images in a Kubernetes cluster to Arm-compatible versions
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     summary_preview: Deploy AWS Lambda functions on Graviton processors using Terraform for Python and
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     summary_preview: Enable and measure libhugetlbfs performance improvements for MySQL and other workloads
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     summary_preview: Serve the llama.cpp chatbot through an OpenAI-compatible API, enabling existing OpenAI-style
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     summary_preview: Build a production-ready vision chatbot on Google Axion using Streamlit, PyTorch,
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     summary_preview: Optimize llama.cpp on Arm CPUs by integrating Streamline Annotations to profile Prefill
@@ -6179,19 +4639,14 @@ latest_run:
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     summary_preview: Understand Large System Extensions (LSE) for Arm processors and verify whether applications
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     summary_preview: Deploy MariaDB on Arm cloud instances across AWS, Azure, and Google Cloud using Docker,
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     summary_preview: Install memcached on Arm cloud servers and benchmark in-memory key-value store performance
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     summary_preview: Deploy Memcached as a cache for MySQL and PostgreSQL on Arm servers. It is designed
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     summary_preview: Use ASCT to measure cache latency, streaming bandwidth, and coherency latency on
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     summary_preview: Test and validate thread synchronization approaches in the Arm memory model using
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     summary_preview: Microbenchmark and tune network performance with iPerf3 and Linux traffic control
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     summary_preview: Scan source code for architecture-specific portability issues using migrate-ease
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     summary_preview: Set up an Arm development environment, analyze dependencies, and understand common
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     summary_preview: Build a Retrieval-Augmented Generation (RAG) application on Arm servers using Zilliz
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     summary_preview: Learn how to deploy and configure MinIO on an Azure Cobalt 100 virtual machine, benchmark
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     summary_preview: Benchmark machine learning inference performance on Arm servers using TensorFlow
@@ -6419,19 +4819,14 @@ latest_run:
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     summary_preview: Install MongoDB on Arm servers and benchmark database performance using Yahoo Cloud
@@ -6439,19 +4834,14 @@ latest_run:
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     summary_preview: Deploy MongoDB on Azure Cobalt 100 Arm virtual machines and benchmark database performance
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     summary_preview: Deploy MongoDB on Google Cloud Axion C4A virtual machines and benchmark database
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     summary_preview: Debug, profile, and optimize MPI parallel applications on Arm servers using Linaro
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     summary_preview: Select and apply accuracy modes for vectorized math functions in Libamath to balance
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     summary_preview: Build a multi-architecture Kubernetes cluster running nginx on Azure AKS walks you
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     summary_preview: Add Arm nodes to your GKE cluster using a multi-architecture Ollama container image
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     summary_preview: Learn how to deploy MySQL walks you through an end-to-end Arm software workflow.
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     summary_preview: Deploy MySQL on Microsoft Azure Cobalt 100 processors walks you through an end-to-end
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     summary_preview: Develop and Validate Firmware Pre-Silicon on Arm Neoverse CSS V3 walks you through
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     summary_preview: Run a .NET Aspire application on Arm-based VMs on AWS and GCP walks you through an
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     summary_preview: Deploy NGINX on Azure Cobalt 100 Arm-based virtual machines walks you through an
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     summary_preview: Deploy Arm Instances on Oracle Cloud Infrastructure (OCI) using Terraform walks you
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     summary_preview: Deploy Phi-4-mini model with ONNX Runtime on Azure Cobalt 100 walks you through an
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     summary_preview: Deploy SqueezeNet 1.0 INT8 model with ONNX Runtime on Azure Cobalt 100 walks you
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     summary_preview: Deploy OpenStack on Azure Cobalt 100 Arm64 virtual machines using DevStack for development
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     summary_preview: Deploy OpenTelemetry on Google Cloud C4A Axion processors walks you through an end-to-end
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     summary_preview: Understand Arm Pointer Authentication walks you through an end-to-end Arm software
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     summary_preview: Learn how to use an AI agent and the Performix tool through the Arm MCP Server to
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     summary_preview: Optimize application performance using Arm Performix CPU microarchitecture analysis
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     summary_preview: Deploy PHP on Google Cloud C4A Arm-based Axion VMs walks you through an end-to-end
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     summary_preview: Optimize application performance with CPU affinity walks you through an end-to-end
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     summary_preview: Implement Code level Performance Analysis using the PMUv3 plugin walks you through
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     summary_preview: Learn how to deploy PostgreSQL walks you through an end-to-end Arm software workflow.
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     summary_preview: Learn how to Tune PostgreSQL walks you through an end-to-end Arm software workflow.
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     summary_preview: Run Process watch on your Arm machine walks you through an end-to-end Arm software
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     summary_preview: Profiling for Neoverse with Streamline CLI Tools walks you through an end-to-end
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     summary_preview: Deploy Puppet on Google Cloud C4A walks you through an end-to-end Arm software workflow.
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     summary_preview: Run a Large Language Model (LLM) chatbot with PyTorch using KleidiAI on Arm servers
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     summary_preview: Learn how to deploy Qdrant on Google Cloud C4A Axion processors, generate vector
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     summary_preview: Deploy RabbitMQ on Arm64 Cloud Platforms (Azure and GCP) walks you through an end-to-end
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     summary_preview: Deploy a RAG-based Chatbot with llama-cpp-python using KleidiAI on Google Axion processors
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     summary_preview: Get started with the Arm 5G RAN Acceleration Library (ArmRAL) walks you through an
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     summary_preview: Deploy and run distributed AI workloads using Ray on Google Cloud Axion C4A Arm-based
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     summary_preview: Deploy Redis on Arm walks you through an end-to-end Arm software workflow. It is
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     summary_preview: Learn how to install and configure Redis on an Azure Cobalt 100 Arm64 virtual machine,
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     summary_preview: Deploy Redis for data searching on Google Cloud C4A walks you through an end-to-end
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     summary_preview: Deploy Redis as a cache for MySQL and PostgreSQL on Arm servers walks you through
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     summary_preview: Learn how to tune Redis walks you through an end-to-end Arm software workflow. It
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     summary_preview: Debug Neoverse N2 Reference Design with Arm Development Studio walks you through
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     summary_preview: Get started with the Neoverse Reference Design software stack walks you through an
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     summary_preview: Enable reproducible math functions across vector extensions with Arm Performance
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     summary_preview: Learn how to create a virtual machine in a Realm using Arm Confidential Compute Architecture
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     summary_preview: Run an LLM chatbot with rtp-llm on Arm-based servers walks you through an end-to-end
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     summary_preview: Deploy Ruby on Rails on Arm-based Google Cloud C4A virtual machines walks you through
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     summary_preview: Learn to deploy and benchmark Rust applications on Google Cloud C4A virtual machines
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     summary_preview: Perform Sentiment Analysis on X on Arm-based EKS clusters walks you through an end-to-end
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     summary_preview: Deploy AWS services using the Serverless Framework walks you through an end-to-end
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     summary_preview: Deploy and integrate AWS Lambda with DynamoDB using the Serverless Framework walks
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+    summary_preview: Install Vectorscan (Hyperscan on Arm) and use it with Snort 3 walks you through an
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+    summary_preview: Run the vvenc H.266 encoder on Arm servers walks you through an end-to-end Arm software
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+    summary_preview: Learn how to automate integration testing of MCP servers using Testcontainers and
+      PyTest, with hands-on examples and GitHub Actions CI/CD configuration. It is designed for software
+      developers and QA e...
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+    summary_preview: Learn how to integrate Arm Virtual Hardware into a GitHub Actions CI/CD workflow
+      for automated embedded software testing and validation. It is designed for embedded software developers
+      new to Arm Virt...
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+    summary_preview: Learn how to integrate Arm Virtual Hardware with AWS and GitHub Actions for automated
+      CI/CD workflows, including CloudFormation setup and test automation. It is designed for DevOps integrating
+      AVH int...
+    source_hash: 251b6edf6a016f9fc73eb35216f56b2001465fbca8253bd7b6e6f9f4afcd25e6
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+    summary_preview: Learn how to use Arm Development Studio to explore Realm Management Extension (RME)
+      and Arm Confidential Compute Architecture (CCA) through a bare-metal example running on the Arm
+      Architecture Envelop...
+    source_hash: a1685fb43efecb9690d03c7f8ee64ab20ae8aece91190e2223705106568b1038
+  - path: content/learning-paths/cross-platform/cpp-loop-size-context/_index.md
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+    summary_preview: Learn how to optimize C++ loop performance on Arm by providing boundary information
+      to the compiler, enabling SIMD vectorization and reducing runtime through compile-time context.
+      It is designed for C...
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+  - path: content/learning-paths/cross-platform/docker/_index.md
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+    summary_preview: Learn how to build, run, and share multi-architecture Docker images for Arm and x86
+      platforms using buildx, manifest, and remote builders. It is designed for software developers who
+      want to learn abou...
+    source_hash: 058f779bc7dd9faef294a57f4524bf525046d757e21a287744825fb9b290935e
+  - path: content/learning-paths/cross-platform/docker-build-cloud/_index.md
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+    summary_preview: Learn how to build multi-architecture Docker images for Arm and x86 using Docker
+      Build Cloud, with GitHub Actions automation for faster builds without emulation. It is designed
+      for software developers...
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+    summary_preview: Learn how to implement a dynamic memory allocator in C, understanding heap management
+      and how malloc and free work under the hood with practical examples. It is designed for software
+      developers learni...
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+  - path: content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md
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+    summary_preview: Learn how to use the Eigen linear algebra library on Arm systems with ASIMD and SVE
+      vectorization, including building TensorFlow with SVE support for optimized performance. It is designed
+      for C/C++ de...
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+  - path: content/learning-paths/cross-platform/ernie_moe_v9/_index.md
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+    summary_preview: Learn how to deploy ERNIE-4.5 Mixture of Experts models on Armv9 devices using llama.cpp,
+      compare PT and Thinking variants, and measure Armv9-specific hardware optimization impact. It is
+      designed for ...
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+    summary_preview: Learn how Arm and x86 floating-point implementations follow IEEE 754 standards, identify
+      rare undefined behavior differences, and write portable code across architectures. It is designed
+      for This is a...
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+  - path: content/learning-paths/cross-platform/function-multiversioning/_index.md
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+    summary_preview: Learn how to optimize C/C++ applications using function multiversioning on Arm64
+      targets with GCC or LLVM, enabling automatic runtime selection of hardware-optimized function versions.
+      It is designed ...
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+  - path: content/learning-paths/cross-platform/github-arm-runners/_index.md
+    status: unchanged
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+    summary_preview: Learn how to use GitHub Actions with Arm-hosted runners to build multi-architecture
+      container images for arm64 and amd64 platforms and automate deployment to Docker Hub. It is designed
+      for software de...
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+  - path: content/learning-paths/cross-platform/gitlab/_index.md
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+    summary_preview: Learn how to build a GitLab CI/CD pipeline using Google Axion-based self-hosted runners.
+      It is designed for DevOps professionals who are looking to build a CI/CD pipeline with GitLab on
+      Google Axion b...
+    source_hash: af9eb1c426e1844e2fd20215b7012d95c1efaf737f1d7b5be828931528342316
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+    summary_preview: Learn how to build GitLab CI/CD pipelines using GitLab-hosted Arm64 runners to containerize
+      C applications, store images in GitLab Container Registry, and deploy on Arm infrastructure. It
+      is designed ...
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+    summary_preview: Learn how to identify and fix potential problems with integer and floating-point
+      conversions in C/C++ code on Arm, including explicit conversions, implicit conversions, and type
+      demotion issues. It is...
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+    summary_preview: Learn how to port architecture-specific intrinsics to Arm processors. It is designed
+      for software developers interested in porting architecture specific intrinsics to Arm processors.
+      By the end, you w...
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+  - path: content/learning-paths/cross-platform/ipexplorer/_index.md
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+    summary_preview: Learn how to run custom software benchmarks on IP Explorer simulation platforms and
+      compare performance across Arm Cortex-M processors using cycle count analysis. It is designed for
+      IP Explorer users ...
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+  - path: content/learning-paths/cross-platform/kleidiai-explainer/_index.md
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+    summary_preview: Learn how to use KleidiAI micro-kernels to accelerate AI inference performance through
+      optimized matrix multiplication on Arm processors with architecture features like i8mm. It is designed
+      for develo...
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+  - path: content/learning-paths/cross-platform/loop-reflowing/_index.md
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+    summary_preview: Learn how to optimize C/C++ code using compiler autovectorization techniques including
+      loop modifications, restrict qualifiers, and conditional handling for Arm processors. It is designed
+      for C/C++ de...
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+    summary_preview: Learn how to develop and test a modern C++ library using CMake, GoogleTest, and matrix
+      processing as a practical example on Arm platforms. It is designed for developers who want to learn
+      how to develo...
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+  - path: content/learning-paths/cross-platform/mca-godbolt/_index.md
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+    summary_preview: Learn how to use llvm-mca with Compiler Explorer to analyze Arm assembly performance,
+      estimate hardware resource pressure, and diagnose performance issues. It is designed for developers
+      who want to di...
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+    summary_preview: Learn how to deploy a Model Context Protocol server on Raspberry Pi 5 and use the
+      OpenAI Agent SDK to create AI agents with custom tools for local inference. It is designed for LLM
+      and IoT developers ...
+    source_hash: 39d489081bfa22125a4046c17d5c00a32c0d7b298cba7b6a12373cf3cf6bac04
+  - path: content/learning-paths/cross-platform/memory-latency/_index.md
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+    summary_preview: Learn how to reduce memory latency impact in applications using cache alignment and
+      prefetching techniques on Arm processors for improved performance. It is designed for Arm developers
+      who want to lea...
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+  - path: content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md
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+    summary_preview: Learn how to build MNN on an Armv9 system, run text, vision, and audio prompts with
+      a multimodal Omni model, and combine image and audio inputs into a single-shot retail restock ticket
+      workflow. It is...
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+    summary_preview: Learn how to implement and optimize matrix multiplication using Arm's Scalable Matrix
+      Extension 2 (SME2) with assembly and intrinsics, including benchmarking and validation on Arm hardware.
+      It is desi...
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+    summary_preview: Learn how to create and train a PyTorch neural network for MNIST digit classification,
+      optimize it with quantization and fusing, and deploy it in an Android application with performance
+      measurement. I...
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+    summary_preview: Learn how to install and configure Remote.It for secure remote device access using
+      SSH and other services, with proxy and peer-to-peer connection options. It is designed for software
+      developers who wa...
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+  - path: content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
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+    summary_preview: Learn how to use the C99 restrict keyword to indicate non-overlapping memory regions
+      and enable better compiler optimizations for vectorization on Arm platforms. It is designed for
+      C developers who ar...
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+  - path: content/learning-paths/cross-platform/rust_armds/_index.md
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+    summary_preview: Learn how to build an embedded Rust application for Arm processors, run it on a Fixed
+      Virtual Platform, and debug it using Arm Development Studio. It is designed for embedded application
+      developers to...
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+  - path: content/learning-paths/cross-platform/simd-info-demo/_index.md
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+    summary_preview: Learn how to use SIMD.info to port SIMD intrinsics across Arm architectures, including
+      navigation, search, and comparison features for finding equivalent instructions. It is designed
+      for software deve...
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+    summary_preview: Learn how to write high-performance SIMD code using the SIMD Loops project, with
+      hands-on examples demonstrating SVE, SVE2, and SME2 features on Arm processors. It is designed for
+      software developers ...
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+    summary_preview: Learn how to write SIMD code in Rust on Arm platforms using Neon intrinsics, portable
+      SIMD abstractions, and optimize performance with architecture-specific instructions. It is designed
+      for software d...
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+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to profile and optimize ExecuTorch models using SME2 acceleration on Arm
+      platforms, including operator-level analysis and performance bottleneck identification. It is designed
+      for developers...
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+  - path: content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md
+    status: unchanged
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+    summary_preview: Learn how to deploy Tinkerblox UltraEdge HPC-I for edge AI and mixed workloads on
+      Arm platforms, including installation and configuration on Debian, Ubuntu, and Yocto systems. It
+      is designed for busin...
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+  - path: content/learning-paths/cross-platform/topdown-compare/_index.md
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+    summary_preview: Learn how to compare Arm Neoverse and Intel x86 top-down performance analysis methodologies
+      using PMU counters, Linux Perf, and topdown-tool to identify bottlenecks across architectures. It
+      is designe...
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+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to migrate x86-64 SIMD code to Arm64 by mapping Intel SSE/AVX to Arm Neon,
+      SVE, and SME, with code examples and migration strategies using autovectorization or intrinsics.
+      It is designed for...
+    source_hash: 26450ae17f7ed4242c52456c2780ffce5fad36b56dfc4a8482e8f236f855e134
+  - path: content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md
+    status: unchanged
+    changed_on_disk: true
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+    faq_changes:
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+      updated_questions: []
+    summary_preview: Learn how to optimize SIMD performance on Arm by restructuring data layouts from
+      Array-of-Structures to Structure-of-Arrays, with practical examples using Neon and SVE intrinsics.
+      It is designed for C...
+    source_hash: 14a22194d3fc73ebebb62db9c7cfbeabe0bd9acdaf340b2df52072be65d98655
+  - path: content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
+    status: unchanged
+    changed_on_disk: true
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+    faq_changes:
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+    summary_preview: Learn how to sample and profile CPU instructions using WindowsPerf with Arm Statistical
+      Profiling Extension (SPE) on Windows on Arm, demonstrated with CPython workload analysis. It is
+      designed for dev...
+    source_hash: bf887ef2481b51cf6c32e49e3eb1d5577346b7b9b1e4d6a604c559597b5306f0
+  - path: content/learning-paths/cross-platform/woa_azure/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
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+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to create and connect to a Windows on Arm virtual machine in Microsoft
+      Azure using the Azure Marketplace and RDP. It is designed for software developers interested using
+      Windows on Arm in th...
+    source_hash: 367566dfec0a76685b1504c2c1a996e50c55e67b2f4c5515057c0f0f1384ef98
+  - path: content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
+    status: unchanged
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+    summary_changed: false
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+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to build and deploy distributed Zenoh systems on Arm devices like Raspberry
+      Pi, using pub/sub, storage, and queryable models for scalable robotics and IoT applications. It
+      is designed for ro...
+    source_hash: a56dce27c6a846bcf35b6e9505142f1445b22ff71530f1189700049d7b14e994
+  - path: content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
+    status: unchanged
+    changed_on_disk: true
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+    faq_changed: false
+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to design and integrate a custom AXI-Lite peripheral with a Cortex-A9 processor
+      on the Zybo Z7-10 board using Vivado, configuring GPIOs to control LEDs based on switch inputs.
+      It is designed...
+    source_hash: d8c48c293b2d316d5e68e18ab9e81157ad114a64cf2efa6951374a55d25238d6
+  - path: content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
+      after_count: 5
+      added_questions: []
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit and
+      run inference on the Ethos-U85 NPU. It is designed for embedded developers who want to deploy a
+      neural network model t...
+    source_hash: 8585bb59efa67b3a92314e4382339fc5c0a14bb458931c0d98f2748379003857
+  - path: content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
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+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to build a motion-detection device with Raspberry Pi Pico (RP2040 Cortex-M0+)
+      using Arduino IDE, PIR sensors, and interrupt-driven programming on baremetal. It is designed for
+      software devel...
+    source_hash: 3ddb97eb97a4c7ede7951410086198ee793a9d452a79b607f211873971bd375d
+  - path: content/learning-paths/embedded-and-microcontrollers/armds/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
+      after_count: 5
+      added_questions: []
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to import and build example projects in Arm Development Studio and debug
+      embedded applications using Fixed Virtual Platforms (FVPs) or hardware with DSTREAM debug probes.
+      It is designed for ...
+    source_hash: 0103f51d42c230dbe75ff5b78ac15a33dfd2f2c0f4906fb665a8dd681512d2e1
+  - path: content/learning-paths/embedded-and-microcontrollers/asm/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
+      after_count: 5
+      added_questions: []
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to write mixed C and assembly programs for Cortex-M microcontrollers using
+      Keil MDK, following Arm Procedure Call Standard conventions. It is designed for software developers
+      who are interes...
+    source_hash: 24245102b7b6cefd0d4f67fbfaff1fb27c913de33665929ec3cb15293a1b3b51
+  - path: content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
+      after_count: 5
+      added_questions: []
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to create a custom Balena OS image, run it on Arm Virtual Hardware, and
+      deploy IoT applications to a virtual Raspberry Pi 4 device. It is designed for embedded software
+      developers interested...
+    source_hash: 983afd3fcc565266c8f12588086e36d736540e23d0e748c94b5d2a45ab53d6ab
+  - path: content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
+      after_count: 5
+      added_questions: []
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to set up AWS IoT Greengrass Core on Arm Virtual Hardware and deploy AWS
+      Greengrass components to a virtual Raspberry Pi 4 device. It is designed for embedded software developers
+      interested ...
+    source_hash: 85845fd4a47960bca053aed8d87c1d1442a7f5de0be5caff349fc92c6980b07e
+  - path: content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
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+      added_questions: []
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+      updated_questions: []
+    summary_preview: Learn how to build Matter reference examples on Arm Virtual Hardware, demonstrate
+      device communication, and automate testing with GitHub Actions CI/CD workflows. It is designed for
+      embedded software d...
+    source_hash: bd39d50c93219201ead5de5da627447a91a82ea9c65e232350facd0c3516bedc
+  - path: content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
+    status: unchanged
+    changed_on_disk: true
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+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to export and compile a PaddleOCR text recognition model using TVMC and
+      deploy it on the Arm Corstone-300 FVP with Cortex-M55 processors. It is designed for software developers
+      interested in...
+    source_hash: 398077be1406d0787b0a5d8dc254472da0d125b51b0907769cd4a3516ce9111b
+  - path: content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
+      after_count: 5
+      added_questions: []
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to create and integrate a virtual LED peripheral using the Virtual IO interface
+      of Arm Virtual Hardware to simulate real-world peripherals. It is designed for software developers
+      new to Arm ...
+    source_hash: aaff31b8917320c53825f3e7410b703bc7c7e273b1963fd80727b6b874f50566
+  - path: content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
+      after_count: 5
+      added_questions: []
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to build a complete IoT solution in Azure that streams, stores, monitors,
+      aggregates, and visualizes telemetry data from Arm devices using IoT Hub, Stream Analytics, Cosmos
+      DB, and Azure Fun...
+    source_hash: 0e653bdbf5e5b08a6a00ba68e5ed215a0082e22c634d7d632d088851d48bb01c
+  - path: content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
+      after_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to create, build, and run a bare-metal embedded application for Armv8-A
+      processors using Arm Compiler for Embedded and Fixed Virtual Platforms, including basic exception
+      handling. It is desi...
+    source_hash: 6521f17d1a10ab8871d13917923f7f64320f69301b4c0c5f5b528163d3cb43c6
+  - path: content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
+      after_count: 5
+      added_questions: []
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+      updated_questions: []
+    summary_preview: Learn how to deploy containerized embedded applications and firmware onto an Arm
+      Cortex-M core from a Cortex-A core using containerd, K3s, and the hybrid-runtime on Arm Virtual
+      Hardware. It is designe...
+    source_hash: 3394bf994039e441d57dced2abb64d725077d4672e0e68dc71f1de50e58f0408
+  - path: content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
+      after_count: 5
+      added_questions: []
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to create, build, and debug an RTX5 RTOS-based application using Keil μVision
+      with CMSIS-RTOS2 API and Event Recorder for embedded Cortex-M development. It is designed for software
+      developer...
+    source_hash: d8c73d658fa7a8051131276b8567cbd7376aac3b8f6e87c8ecdf224443c3ef99
+  - path: content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
+      after_count: 5
+      added_questions: []
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to create, configure, and debug an RTX5 RTOS application using Keil Studio
+      for VS Code with CMSIS-RTOS2 API for embedded Cortex-M development. It is designed for software
+      developers new to R...
+    source_hash: f4d1ab3448590c5654e2479937c9eec3e378d05229b29a45b8675139f40ac177
+  - path: content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
+      after_count: 5
+      added_questions: []
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to prototype and port DSP algorithms using the CMSIS-DSP Python package,
+      mapping Python code to efficient C implementations for embedded Cortex-M and Cortex-A platforms.
+      It is designed for d...
+    source_hash: 69526ee8b840c8f5d77d49349d2f0cc7ff4594fec5e4311984ef72a0672d3462
+  - path: content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
+      after_count: 5
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+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to implement context switching operations on Arm Cortex-M processors using
+      the Memory Protection Unit and SysTick exception in a bare-metal environment. It is designed for
+      software developer...
+    source_hash: 0b7a5895a87de83903c223215845b6c840602663838c0682f46e50d139d69c59
+  - path: content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
+      after_count: 5
+      added_questions: []
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to set up and use code coverage in Keil MDK with FVPs to verify that your
+      embedded application tests execute all code paths. It is designed for embedded software developers
+      new to the code-c...
+    source_hash: f989929b11c48df42c2701dc71516cec0b8637b4c639c3dbbf6ac5e7440c8a56
+  - path: content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
+      after_count: 5
+      added_questions: []
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Device-to-Device communication with Device Connect walks you through an end-to-end
+      Arm software workflow. It is designed for developers wiring up heterogeneous edge fleets, where
+      devices need a shared...
+    source_hash: 9d9e4c170fa318a9875c888c2c812ceb27c59f56c4b0dd7e0ae87dd31bb5783c
+  - path: content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to connect AI agents to Arm-based edge devices using Device Connect for
+      structured device access and Strands for agent orchestration, with examples for both simulated and
+      physical robots. It...
+    source_hash: c31d398d3ce965a4193fca45cd025182d788a2fc603fbe8c828dfbd5a1c599ba
+  - path: content/learning-paths/embedded-and-microcontrollers/docker/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
+      after_count: 5
+      added_questions: []
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to create a Dockerfile, build a Docker image with Arm Compiler for Embedded
+      and Fixed Virtual Platforms, and test the containerized Arm development environment. It is designed
+      for embedded s...
+    source_hash: a4b522c46c68d3c6f5c4af7c76b00c7bde48bb638147c201376bc19a4de79cca
+  - path: content/learning-paths/embedded-and-microcontrollers/edge/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
+      after_count: 5
+      added_questions: []
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to collect and preprocess audio data using Edge Impulse, train an audio
+      classification model, and deploy it to the Arduino Nano RP2040 to control LEDs based on voice commands.
+      It is designed...
+    source_hash: 8a7ec29d10a89649662ebb0fa4f14712984786902b6e7343a195abb1f3cbc00b
+  - path: content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
+      after_count: 5
+      added_questions: []
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Develop a image classification neural network model and deploy it on an STM32 B-L475E-IOT01A2
+      board. It is designed for embedded software developers interested in building neural network models
+      for mi...
+    source_hash: 66024a2e3da90dcda298bf66b5de07952ed1e355d22172bc2812c46ad4fcda7f
+  - path: content/learning-paths/embedded-and-microcontrollers/intro/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
+      after_count: 5
+      added_questions: []
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn where Arm architecture is used in microcontrollers and discover microcontroller
+      hardware options for software development on Arm Cortex-M processors. It is designed for software
+      developers worki...
+    source_hash: ac69e979101f6befe55abee1429299c163c70b9a886d87a8f64779babd9fe5af
+  - path: content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
+      after_count: 5
+      added_questions: []
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn what differentiates TinyML from other AI domains, explore Arm-based edge devices
+      for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed Virtual
+      Platform. It is ...
+    source_hash: aa49e4a1651367965e79d36c5f6af16e9b341c392d48cf051f24cbb21070e972
+  - path: content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
+      after_count: 5
+      added_questions: []
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300 virtual
+      hardware to understand complete IoT software stack construction. It is designed for embedded software
+      developers ...
+    source_hash: 11fc64eb1a0595b1d8e625e465be9fa87a4de04f59492004204ccbe61a92a5b7
+  - path: content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
+      after_count: 5
+      added_questions: []
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to set up a Jetson Orin Nano with a MIPI CSI-2 camera and perform real-time
+      object detection from live video and image files using DetectNet and TensorRT. It is designed for
+      developers inter...
+    source_hash: fdf582f1b54f372768a2c896e1da152c50d22c3bd238848253cdbe18cc68222d
+  - path: content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
+      after_count: 5
+      added_questions: []
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to import, build, and debug your first Keil Studio Cloud project. It is
+      designed for embedded software developers new to Keil Studio Cloud. By the end, you will be able
+      to import and build a...
+    source_hash: f3a01adf18ad93027b6ab61ceb5b0c470e6b5c298f9d4f944989a96ff64eec81
+  - path: content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
+      after_count: 5
+      added_questions: []
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to boot and configure the NXP FRDM i.MX 93 Arm board with Linux, create
+      a user with sudo access, connect to WiFi using ConnMan, and transfer files over the network. It
+      is designed for embedd...
+    source_hash: 567387e8e513458a360f74c14d3f4d02c4af392bc573620e605c93976e4d8b4f
+  - path: content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
+      after_count: 5
+      added_questions: []
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to boot a Linux software stack on Arm Fixed Virtual Platforms (FVPs), then
+      debug Trusted Firmware-A and the Linux kernel using Arm Development Studio. It is designed for developers
+      who want ...
+    source_hash: 5943d817827bef274f175f7c14249cad769b2160094b3c65d7476aca6cbf0a1d
+  - path: content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
+      after_count: 5
+      added_questions: []
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to install the Python version of llama.cpp on a Raspberry Pi 5, download
+      an LLM from Hugging Face, assess memory and performance, and run the model using Python bindings.
+      It is designed for ...
+    source_hash: aa6252610c0603acfdfc8d4555bb7da93cbdd5b9d92ba140c5dc5f63d23e2b07
+  - path: content/learning-paths/embedded-and-microcontrollers/migration/_index.md
+    status: unchanged
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+    summary_preview: Learn the software migration methodology for porting Linux workloads from x86_64
+      to aarch64, including using Arm compilers, porting compiler intrinsics, and deploying applications
+      in containers. It is...
+    source_hash: 81a15ff0d5579be9529a8c9c444be57521ebc48bbf5234f042f5a47a8a709b5c
+  - path: content/learning-paths/embedded-and-microcontrollers/mlek/_index.md
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+    faq_changes:
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+    summary_preview: Learn how to build examples from the Machine Learning Evaluation Kit (MLEK) and run
+      them on the Arm Ecosystem FVP for machine learning application development on microcontrollers.
+      It is designed for e...
+    source_hash: acf43df3c6f344b55bb413b7483d60e26d0e137489ac148bca64500e443140ab
+  - path: content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+    summary_preview: Learn how to understand and select physical and virtual hardware targets for ML application
+      development with Cortex-M and Ethos-U, identify software tools, and find example applications. It
+      is designe...
+    source_hash: 0cfaa21be515b980097232ed38092b80d046df308120887e5503513a3384a1d7
+  - path: content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to create debug configurations for virtual platforms and development boards
+      in Arm Development Studio, including setting up connections for Fast Models and DSTREAM debug probes.
+      It is design...
+    source_hash: d2c403c7fd1001dbd985697c9eb018951a955358c2be39c727183a87a3dfdf51
+  - path: content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to bring up ExecuTorch executor_runner firmware on the NXP FRDM i.MX 93
+      Cortex-M33 using Linux RemoteProc, compile .pte models for Ethos-U65, and run inference with NPU
+      acceleration. It is d...
+    source_hash: be2b3571d4d2ed75a16bc97589abc22c970d83be868b7e94bb60962a4ba853da
+  - path: content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
+    status: unchanged
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+    faq_changes:
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+      updated_questions: []
+    summary_preview: Learn how to migrate a CMSIS v5-based CMSIS-Pack with device support to CMSIS v6
+      and update example projects for compatibility with the new CMSIS version. It is designed for maintainers
+      of CMSIS-Packs...
+    source_hash: 8039812773c6c1ac45cceb4039cee801e627d67871b625283c1bb5b3fa2960b3
+  - path: content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to migrate CMSIS v5 projects to CMSIS v6 by identifying supported toolchains,
+      installing required CMSIS-Packs, and selecting the necessary software components. It is designed
+      for embedded de...
+    source_hash: 7a192506fc8a15423d1257fe92e7655053e38be85aad8310dae29602e483fbba
+  - path: content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md
+    status: unchanged
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+    summary_preview: Learn how to run large language models locally on the Raspberry Pi 5 using Ollama,
+      control GPIO-connected devices, and deploy a privacy-first web-based smart home assistant without
+      cloud services. It ...
+    source_hash: a0145c77b3d4a8bb25a32c62adaa3ad378e65fccbe6db88d9a46a569897d238a
+  - path: content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md
+    status: unchanged
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+    summary_preview: Learn how to build a voice-controlled bot on a Raspberry Pi that listens for a wake
+      word, converts speech to text using Google Speech Recognition, sends requests to ChatGPT's API,
+      and plays audio resp...
+    source_hash: ed2034f5c95c4148fca355f6d545219c320f2e73267a0725934c792ebc4c0c59
+  - path: content/learning-paths/embedded-and-microcontrollers/rpi/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to build and run multiple software examples on the Raspberry Pi 4, including
+      TensorFlow and Docker applications, and compare its performance to Arm cloud servers. It is designed
+      for software...
+    source_hash: 5e5fad1563b67ed38d1cf3399d8e0d62162e76cae8d6848c3be7638f67cd037c
+  - path: content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to compile the Llama 3 large language model using ExecuTorch, deploy it
+      to a Raspberry Pi 5, and understand techniques for running LLMs in embedded environments. It is
+      designed for anyone in...
+    source_hash: 9cd2c3082c4874dc596e1b7b59c3dc2143aaf1ce3e66948ab8b6af190ffa3776
+  - path: content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to reduce compile time for embedded Linux projects by installing a Raspberry
+      Pi OS file system on an Arm server, building the MXNet machine learning framework, and transferring
+      it to a Raspb...
+    source_hash: 17721158fe10e97314af364f138c32b7bcf53fdc88fe11fffeb5765f11e26b79
+  - path: content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Setup tools and start programming with Raspberry Pi Pico. It is designed for embedded
+      software developers new to Raspberry Pi Pico. By the end, you will be able to install the Raspberry
+      Pi Pico SDK, r...
+    source_hash: d9fe3cfc8f7a7092f40786763fc28e371697e4b57dba99b2c9b191dae4273911
+  - path: content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to profile Linux kernel modules using Arm Streamline to identify performance
+      bottlenecks, analyze both out-of-tree and in-tree modules, and use Statistical Profiling Extension
+      (SPE) for deep...
+    source_hash: 0b8de63a15d3d77b9c9972103b1317d6c48907875ac625c3d1c1b8db5360879a
+  - path: content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Build a letter recognition neural network model using TensorFlow and deploy it on
+      an STM32 B-L475E-IOT01A2 board. It is designed for software developers interested in building network
+      models for micro...
+    source_hash: b5714494f00fff407c39e6f2f7bf37de94cde61d7569ba147412fec1b2f537c2
+  - path: content/learning-paths/embedded-and-microcontrollers/tfm/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to build and run the reference Trusted Firmware-M tests and example application
+      on Arm Fixed Virtual Platforms for secure microcontroller development. It is designed for software
+      developers ...
+    source_hash: 8d8f9df559ff1b1570dc98baf640fc2d4c4d225d69f22529e1881b62d4017752
+  - path: content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md
+    status: unchanged
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+    faq_changes:
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+      updated_questions: []
+    summary_preview: Learn how to train a CNN image classification model using PyTorch, convert it to
+      ExecuTorch format, and run it as an interactive mini-game on Arm-based edge devices. It is designed
+      for machine learnin...
+    source_hash: 20c500fd5174c9901058676d4619981ee1c8a8cf8e331affea8c0292112651c9
+  - path: content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to install Keil MDK Tools, run a TrustZone hello world example on the NXP
+      LPCXpresso55S69 board, and understand security state switching and secure function calls. It is
+      designed for softwar...
+    source_hash: 6c81f3c9595df1128ca6f4f89446f093826d2242fa789ffa2d37465854be1f8e
+  - path: content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to acquire and print materials, assemble a universal SBC rack mount system
+      in a 4U chassis, and install single board computers in the racks using 3D-printed parts. It is designed
+      for softwar...
+    source_hash: 57e05139cdea1de2f4a4ce239d701f0cef634b6ac11fd437cba47cc071e14098
+  - path: content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+    summary_preview: Learn how to debug microcontrollers using µVision with basic run/stop debug, advanced
+      techniques using Event Recorder and Serial Wire Viewer, ETM Trace for performance analysis, and
+      power measurement ...
+    source_hash: 27ced3e9f69dbe51e75dcf13999ae1745a994137f31c86d6f17d4de341c7fa2a
+  - path: content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to import, convert, and build uvprojx-based projects to csolution format
+      using Keil Studio, µVision, and command-line tools for CMSIS-Toolbox compatibility. It is designed
+      for This is a topi...
+    source_hash: 179b0318bbef9cef31ca6bb82de853597a609f61d6868aaaa85cfb40a27a051a
+  - path: content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to install vcpkg, initialize it, create vcpkg-configuration.json files,
+      use vcpkg for tool management, activate tool licensing, and remove vcpkg for reproducible command-line
+      tool installati...
+    source_hash: 0c3422e1cd571e6abff676c28ec32c2ec69a626a406167f9166e57daa6829252
+  - path: content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+    summary_preview: Learn how to identify Arm-based targets for TinyML, install Fixed Virtual Platforms,
+      deploy ExecuTorch models on Corstone-320 FVP, and visualize model execution using the FVP graphical
+      interface. It i...
+    source_hash: dcdbc4cecc376ed4c0df9377d13cb4fe792ad7c68acfe0610d75cf41898804ff
+  - path: content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Introduction to building a minimal Yocto Linux image and running it on 64-bit Qemu
+      Arm target. It is designed for software developers interested in learning the basics of building
+      Yocto Linux for embe...
+    source_hash: 3bcfc51edbab56e3c8416f27045a61b069333e6f0cd130e8689b9a4d9fde1dd6
+  - path: content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to run a YOLO object detection model on the Himax WiseEye2 module, build
+      the Himax SDK, update firmware, and connect to the Grove Vision AI module for computer vision applications.
+      It is des...
+    source_hash: b0cb917bdcfb601c054e6cac93b2d04aca3ffe84b5a1963288fd7b89dd9bad3b
+  - path: content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md
+    status: unchanged
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+    faq_changes:
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+      updated_questions: []
+    summary_preview: Learn how to build and run Zephyr RTOS applications on the Arm Corstone-300 Fixed
+      Virtual Platform using Arm Virtual Hardware. It is designed for software developers getting started
+      with the Zephyr RT...
+    source_hash: 89f599ab33721a2d578d4fc39e505b5aed8d930aabac71f22d1ba28bfc4a5cf8
+  - path: content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to install Workbench for Zephyr extension in VS Code, set up the complete
+      Zephyr development environment, create and build Zephyr applications, debug embedded systems, and
+      perform memory usa...
+    source_hash: 808f1c99409f9ca3bb72419eaf950bc097f1c7af6c2dcade6385be97fdbbf713
+  - path: content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to create and run Ubuntu containers on ChromeOS Crostini using LXC with
+      file sharing and GUI application support on Arm-based Chromebooks. It is designed for software developers
+      who want to ...
+    source_hash: 137e974aee0ccba78e3375a1c8179af392e54a0304cfecdcd53cf4ac5c38917b
+  - path: content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md
+    status: unchanged
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+    summary_preview: Learn how to build and deploy high-fidelity robotic simulations and reinforcement
+      learning pipelines using Isaac Sim and Isaac Lab on Arm-based NVIDIA DGX Spark with Grace-Blackwell
+      architecture. It i...
+    source_hash: eda533c727ea7094202e8784bf1ed2240cfea2573cefc73aca1a86797840778c
+  - path: content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to build and optimize quantized LLMs using llama.cpp on NVIDIA DGX Spark
+      with Grace-Blackwell architecture, leveraging Armv9 SIMD acceleration. It is designed for AI practitioners,
+      performan...
+    source_hash: ada333cc887badfd57815708ef93e172543da74f2c995b46a916817917e92394
+  - path: content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to build a Retrieval-Augmented Generation (RAG) pipeline on NVIDIA DGX
+      Spark combining Arm Grace CPU orchestration with Blackwell GPU-accelerated inference using llama.cpp.
+      It is designed fo...
+    source_hash: facf9c504a3caceb62ef898a04a6760e8aafeb7e802e5727cb80d3a7e7e344d0
+  - path: content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to build an offline voice assistant combining speech-to-text via faster-whisper
+      and text generation via vLLM on Arm-based DGX Spark for privacy-focused deployments. It is designed
+      for develo...
+    source_hash: 4ccde526ec4dd9fc18672e162e067108c7251161c1da0375a0bb0374a2f3a4ea
+  - path: content/learning-paths/laptops-and-desktops/docker-models/_index.md
+    status: unchanged
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+    faq_changed: false
+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to run pre-trained AI models locally using Docker Model Runner and build
+      containerized applications integrating large language models. It is designed for software developers
+      and AI enthusias...
+    source_hash: eae0a23635e7a025e1a73baaf5ccbd01f2c031ec76725c68893ca02190e36deb
+  - path: content/learning-paths/laptops-and-desktops/electron/_index.md
+    status: unchanged
+    changed_on_disk: true
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+    faq_changes:
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+    summary_preview: Learn how to develop and build cross-platform desktop applications using the Electron
+      Framework on Windows on Arm devices. It is designed for developers who want to learn how to develop
+      cross-platform...
+    source_hash: 8491a5e83e9e6436721f6078085e9b367121d19fdf228634dba859b3e1a0802a
+  - path: content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to automate Windows application builds on Arm architecture using GitHub
+      Arm-hosted runners and GitHub Actions workflows. It is designed for This introductory tutorial is
+      for software develop...
+    source_hash: 7e108d774eb0fd0b2b72fa8b5d65e1efec0ff4ecf3d80d4e511e82cdf3862284
+  - path: content/learning-paths/laptops-and-desktops/hyper-v/_index.md
+    status: unchanged
+    changed_on_disk: true
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+    faq_changes:
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+      updated_questions: []
+    summary_preview: Learn how to create and manage Arm-based Linux virtual machines using Hyper-V on
+      Windows on Arm devices. It is designed for software developers who want to use Linux virtual machines
+      with Windows on A...
+    source_hash: 829130636ec6969f791826ef731b38f7bb87c025d910218822a113ecdef62306
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+    summary_preview: Learn where the Arm architecture is used in desktop and laptop computers and find
+      hardware for software development on Arm platforms. It is designed for developers working on laptops
+      and desktops and ...
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+    summary_preview: Learn how to build, test, and verify KleidiCV with Scalable Matrix Extensions (SME)
+      on Apple Silicon Macs for accelerated computer vision performance. It is designed for software developers
+      who want t...
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+    summary_preview: Learn how to configure the LLVM toolchain with Visual Studio to build native Windows
+      on Arm applications using the open-source PuTTY project. It is designed for software developers
+      doing native develo...
+    source_hash: 631134875aac73e168b60b86d1ca7b4e898196f98cb2825a4238f62129d2d862
+  - path: content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md
+    status: unchanged
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+    summary_preview: Learn how to apply Arm Memory Tagging Extension (MTE) to protect dynamic memory allocations
+      and prevent common memory use errors. It is designed for software developers who want to learn how
+      to use th...
+    source_hash: 87d4afe4ce7f0cef113cd61fd712fde073cca0eaafbe86a2066b76a117328d11
+  - path: content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
+    status: unchanged
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+    summary_preview: Learn how to install and configure Arch Linux for Arm with the i3 window manager
+      and Neovim editor on the Pinebook Pro laptop. It is designed for developers who want to use the
+      Pinebook Pro as an Arm ...
+    source_hash: e1befed4cafab0eaee29a31c2f259a6a90a14d9f8230c570b6c10a9840b761d5
+  - path: content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
+    status: unchanged
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+    summary_preview: Learn how to fine-tune large language models using PyTorch and Hugging Face on NVIDIA
+      DGX Spark to improve domain-specific accuracy. It is designed for AI developers and ML engineers
+      who want to fine-...
+    source_hash: aa2a78baf3e52172e37506c3f75254968d775b4eb516f9696a0a6998aba50e97
+  - path: content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
+    status: unchanged
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+    summary_preview: Learn how to create a CI/CD pipeline in GitHub using self-hosted Arm64 runners to
+      build and push Docker images to DockerHub. It is designed for software developers and IT practitioners
+      who want to lea...
+    source_hash: a851b2f81b6aac54aef84acf7537a1cc6b99f66ce31ffd26631d6a966401e4ed
+  - path: content/learning-paths/laptops-and-desktops/win-opencv/_index.md
+    status: unchanged
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+      updated_questions: []
+    summary_preview: Learn how to build the OpenCV library for Windows on Arm devices and develop computer
+      vision applications using OpenCV. It is designed for software developers who want to build and develop
+      application...
+    source_hash: d24bf30aef690451942789bb66df63b7a3483ecc3cd2c4b3e60ce15fad90cb91
+  - path: content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to measure application resource usage, benchmark video encoding tasks,
+      and monitor CPU, memory, and power consumption on Windows on Arm using FFmpeg and PowerShell. It
+      is designed for develo...
+    source_hash: fc0d79605640cc8e7a36070a044323d6e278335484949921d0aa4e9cd163d4bd
+  - path: content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to automate Windows on Arm VM creation on Arm Linux systems using QEMU,
+      KVM, and Bash scripts for development and testing. It is designed for developers and system administrators
+      who want to...
+    source_hash: 915f6eb5e95bd42ed09b727b4599855d965125e67a8761fbd48a124e9e74b1bc
+  - path: content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
+    status: unchanged
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+    faq_changes:
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+      updated_questions: []
+    summary_preview: Learn how to build native Arm applications and migrate x86/x64 applications to Arm
+      using Arm64EC on Windows on Arm devices. It is designed for software developers who want to use
+      Arm64EC with Windows ...
+    source_hash: 4069c5bce1ce4b689a7a67d740fc077dc55c9b0bfbab392f5984ba0bdd9e59c3
+  - path: content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
+    status: unchanged
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+    summary_preview: Learn how to port Qt-based Python desktop applications with C/C++ dependencies to
+      Arm64 using Arm64EC on Windows on Arm. It is designed for developers who want to learn how to port
+      their applications ...
+    source_hash: 3b3ecd451ed8b634c7fbe194248cdf1d33432633efbcbef5de713495041ff425
+  - path: content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md
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+    summary_preview: Learn how to build and run Qt-based desktop applications on Windows on Arm and investigate
+      native Arm64 performance improvements. It is designed for software developers who want to use the
+      native perf...
+    source_hash: 63389742eced4df89f85bdf56a01e489e52a9702d446b557f8f55312f9d31f20
+  - path: content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
+    status: unchanged
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+    summary_preview: Learn how to build and run an ASP.NET Core 8 web server application with Web API
+      and dependency injection services on Windows on Arm. It is designed for developers who are interested
+      in building a web...
+    source_hash: e60ce02e9d1872da06bc5bfeb18c7ec65f2bc17defb2b75292f930fb3ad41711
+  - path: content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
+    status: unchanged
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+    summary_preview: Learn how to create Node.js IoT applications that stream sensor data from Windows
+      on Arm devices to AWS IoT Core using MQTT. It is designed for developers who want to learn how to
+      create IoT applicati...
+    source_hash: cddfb7b83e82f0daa513558b1e7ee09b55c63e2ff95675d67be7d4408d391aa4
+  - path: content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md
+    status: unchanged
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+    summary_preview: Learn how to configure AWS IoT Core rules to parse MQTT messages and store IoT data
+      in Amazon DynamoDB from Windows on Arm devices. It is designed for developers who are interested
+      in using Amazon Dyn...
+    source_hash: f25597c6c9e69e09e9a86fa7c02d0ace9c347f293a21a11c00a6d3498300052c
+  - path: content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md
+    status: unchanged
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+    summary_preview: Learn how to process IoT data using AWS Lambda functions triggered by AWS IoT Core
+      messages from Windows on Arm devices. It is designed for developers who are interested in using
+      AWS Lambda for proces...
+    source_hash: f685f45be9e5fc05b278e28590f9d421a920d43cc36ccb572545de4eaf4a799a
+  - path: content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md
+    status: unchanged
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+    summary_preview: Learn how to implement AWS Lambda functions that process and aggregate IoT data stored
+      in DynamoDB tables from Windows on Arm devices. It is designed for developers who are interested
+      in using AWS Lam...
+    source_hash: f5a93346a0fd55659b7c0a6df501db97742ec71755b6443051b654f3ce871cdf
+  - path: content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md
+    status: unchanged
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+    summary_preview: Learn how to create a static website hosted on Amazon S3 that interacts with AWS
+      Lambda functions to display IoT data from Windows on Arm devices. It is designed for developers
+      who are interested in u...
+    source_hash: 32186a4879e98aa113f461d2a2c705dee099404ed2020ef6fdb981a28bb0c0c3
+  - path: content/learning-paths/laptops-and-desktops/win_cef/_index.md
+    status: unchanged
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+    summary_preview: Learn how to create and build Chromium Embedded Framework desktop applications using
+      CMake and web technologies on Windows on Arm. It is designed for developers who want to learn how
+      to use web techno...
+    source_hash: 5df8cc6d859c505ad79f8297056b06233956c92203a6d2352d9be8e498a42547
+  - path: content/learning-paths/laptops-and-desktops/win_forms/_index.md
+    status: unchanged
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+    summary_preview: Learn how to create and build Windows Forms applications and measure code execution
+      performance on Arm64. It is designed for developers who want to learn how to create Windows Forms
+      applications on Wi...
+    source_hash: d43413097704af29b9233dfe33fb675ffd5c6d5a172154d6a7542e77d6625c00
+  - path: content/learning-paths/laptops-and-desktops/win_net/_index.md
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+    summary_preview: Learn how to build and run a .NET 6 Windows Presentation Foundation (WPF) application
+      on Windows on Arm machines. It is designed for software developers doing native development on Windows
+      on Arm comp...
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+  - path: content/learning-paths/laptops-and-desktops/win_net8/_index.md
+    status: unchanged
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+    summary_preview: Learn how to build, run, and benchmark .NET 8 Console applications to measure performance
+      on Windows on Arm devices. It is designed for developers who want to benchmark the performance of
+      the .NET 8 a...
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+  - path: content/learning-paths/laptops-and-desktops/win_net_maui/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to create and build cross-platform .NET MAUI applications and measure code
+      execution performance uplift on Arm64. It is designed for developers who want to learn how to create
+      cross-platform...
+    source_hash: 037509ebca3a7c5a58bef247532919cd8196c7993e946ce519585cdc82073daa
+  - path: content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to build ONNX Runtime with the Generate() API and run Phi-3 model inference
+      with KleidiAI acceleration on Windows on Arm. It is designed for developers looking to build ONNX
+      Runtime for Wind...
+    source_hash: 606702e7afc9a29976d027eceab021570cf3f91ec408ef2dd2051df0b05d9bda
+  - path: content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to apply Profile-Guided Optimization (PGO) to build performance-tuned C++
+      binaries and measure improvements using Google Benchmark on Windows on Arm. It is designed for software
+      developers w...
+    source_hash: b975cc83674410ec50ca49a31744eee4ac5a63c994dd28e46ed84f7afda0568d
+  - path: content/learning-paths/laptops-and-desktops/win_python/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to build Python applications on Windows on Arm and leverage native Arm64
+      performance for platform-dependent packages. It is designed for developers who are interested in
+      building Python appl...
+    source_hash: d953214fe33ad89a683f79e7c29ce9348e5169e6529b808804329cb35060b431
+  - path: content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to configure Windows Sandbox as a self-hosted GitHub Actions runner to
+      build and run .NET 8 WPF applications in CI/CD workflows. It is designed for software developers
+      who are developing app...
+    source_hash: 055e3a3d8c0dc717e2246e3e8e7ebb00c7d2cea3ff9faabf7125ca1ebfff7a31
+  - path: content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md
+    status: unchanged
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+    summary_preview: Learn how to create C/C++ Win32 DLLs and port them to Arm64 for use in Windows console
+      applications. It is designed for developers who want to learn how to port their Win32 applications
+      to Arm64. By t...
+    source_hash: cacf4c6fc3cd3c2a9ab194f7a04981fd4820fca5482317a06c6b9fa02a1c9da2
+  - path: content/learning-paths/laptops-and-desktops/win_winui3/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to create and build Windows UI Library (WinUI) applications and measure
+      code execution performance on Arm64. It is designed for developers who want to learn how to create
+      cross-platform appl...
+    source_hash: 383dcf17bce86b24a2d9c8d0982fa6e9ddf954955c16b420463ada951753dbfa
+  - path: content/learning-paths/laptops-and-desktops/win_wpf/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to create and build Windows Presentation Foundation (WPF) applications
+      and measure code execution performance uplift on Arm64. It is designed for developers who want to
+      learn how to create d...
+    source_hash: cc1a494e7b03672122ff84073b677a93659eca7df601b3f77c7e7fd536cc1af9
+  - path: content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to create and build Xamarin Forms applications using the MVVM pattern and
+      measure code execution performance uplift on Arm64. It is designed for developers who want to learn
+      how to create cr...
+    source_hash: 16b7f8c67050ea336402791c0ecca2c688d5da9373c571986e12dcf646c8093c
+  - path: content/learning-paths/laptops-and-desktops/windows_armpl/_index.md
+    status: unchanged
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+    summary_preview: Learn how to develop Windows on Arm applications using Visual Studio and optimize
+      performance with Arm Performance Libraries. It is designed for software developers who want to improve
+      the performance...
+    source_hash: f058f628cefb7766ef0728e594c9ef5b57bde97c1850395786d54cc591271503
+  - path: content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Get started with GitHub CI/CD development flow on a Windows on Arm machine (or virtual
+      machine). It is designed for software developers interested in running their CI flows on Windows
+      on Arm machines....
+    source_hash: 828e4022f387698d2736d94010de5d93efa9f38ffd3a2e4f15252410e9ccedb2
+  - path: content/learning-paths/laptops-and-desktops/windowsperf/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to install WindowsPerf on Windows on Arm machines and generate sample performance
+      reports for CPU profiling. It is designed for software developers working on laptops and desktops
+      and new to...
+    source_hash: 61a6390d42ce6bfc37a3bb981710dc23b8566070733f7979f9ec37bc63ab7d78
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+    status: unchanged
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+    summary_preview: Learn how to install and use the WindowsPerf Visual Studio extension to generate
+      counting and sampling reports and analyze performance data in Windows Performance Analyzer. It is
+      designed for software...
+    source_hash: 1dd709b14537e06c80b9cdee58729cfa7066f44f0de4ceabe5450b33288731aa
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+    summary_preview: Learn how to use WindowsPerf for performance sampling on Windows on Arm, build CPython
+      from sources, and analyze native workload performance. It is designed for developers keen to understand
+      sampling ...
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+    summary_preview: Learn how to import WindowsPerf data in Windows Performance Analyzer (WPA) and visualize
+      timeline and telemetry data using the WPA plugin. It is designed for software developers interested
+      in using th...
+    source_hash: 1fd4d85855933a5dfc5edf5ae3abf5f4388d94c9ee69aff12b77eb766e88301f
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+    summary_preview: Learn how to configure and run WSL with Linux distributions, graphical applications,
+      remote desktop, and development tools on Windows on Arm computers. It is designed for Software developers
+      with Wind...
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+  - path: content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md
+    status: unchanged
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+    summary_preview: Learn how to enable and verify Arm Fixed Rate Compression in Vulkan applications
+      on Android devices to reduce memory footprint and bandwidth. It is designed for Software developers
+      of Android applicat...
+    source_hash: e4512ad20b372f616409199c51a38d95cb116ec6cf49d9004348d6bb8ee4014d
+  - path: content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md
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+    summary_preview: Learn how to build and optimize AI-powered camera pipeline applications on Arm Linux
+      using KleidiAI, KleidiCV, and SME2 to accelerate denoising, background blur, and low-light effects.
+      It is designed ...
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+  - path: content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
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+    summary_preview: Learn how to use each of the tools supplied with Arm Performance Studio (formerly
+      known as Arm Mobile Studio). It is designed for Android application and games developers new to
+      Arm Performance Studio...
+    source_hash: 3d1a743c1b3ee617f52191fc9c9fd33a9d44454ac079f00b6f532e2865103377
+  - path: content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md
+    status: unchanged
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+    summary_preview: Learn how to capture frame data from Android applications and analyze performance
+      inefficiencies using Frame Advisor in Arm Performance Studio. It is designed for Android application
+      developers who wa...
+    source_hash: d5406f24ab38522b21e348ed3829ede7b1bcdce28e81ec4fddebbec280d2cb89
+  - path: content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md
+    status: unchanged
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+    summary_preview: Learn how to build an Android chatbot app using Arm's AI Chat library to run GGUF
+      models on-device with optimized performance on Arm CPUs. It is designed for developers who want
+      to add a local, on-dev...
+    source_hash: e63ac917c218aa5e5981f96a9821567d0f8ba9761d5ce6e4c9d7b6dea7ed5c5f
+  - path: content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
+    status: unchanged
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+    summary_preview: Learn how to build real-time image processing pipelines using Halide on Android,
+      combining operations for improved performance in Kotlin applications. It is designed for developers
+      interested in learn...
+    source_hash: fa04ff97605ae93b17c056c397c37f6fc17cf6f4853bfabe374610ede002e3df
+  - path: content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md
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+    summary_preview: Learn how to create and configure an Android project with OpenCV support to process
+      camera images for computer vision applications. It is designed for developers who are interested
+      in creating Compute...
+    source_hash: 2137cec46fe8d57f11e9e4011306a140bba7d8a5b2326a746193c1794a148f75
+  - path: content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md
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+    summary_preview: Learn how to implement face detection on Android devices using OpenCV, camera frame
+      retrieval, and Haar cascade classifiers. It is designed for developers who are interested in creating
+      Computer Visio...
+    source_hash: 3a120620bbc56e796aa45fbe01aa1455fbee085a1a4cf0d055416b7e95e72d0d
+  - path: content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
+    status: unchanged
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+    summary_preview: Learn how to accelerate OpenCV-based Android applications using KleidiCV for enhanced
+      computer vision performance. It is designed for developers who are interested in creating Computer
+      Vision applicat...
+    source_hash: 8e05fb124ad18194fba2ed83adae3e52673b2fad41af998ca8d0aa8cb9785f5e
+  - path: content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md
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+    summary_preview: Get started with Scalable Vector Extension 2 (SVE2) on Android walks you through
+      an end-to-end Arm software workflow. It is designed for software developers interested in learning
+      how to use the Scala...
+    source_hash: be91053c5abcdd669ff8da7e66b2f717c4adaac27b3fb44ea073b69a68d2dba7
+  - path: content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to integrate Dawn WebGPU in an Android application, render 3D objects,
+      and profile the application using Streamline. It is designed for developers who are building GPU-based
+      Android applicat...
+    source_hash: 8f58429f12e40b53e8a2e0d7eb260f22fbb7ac869ca64554a906ddcd28c9fd75
+  - path: content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
+    status: unchanged
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+    summary_preview: Learn how to optimize hardware ray tracing with Lumen on Android devices powered
+      by Arm Mali GPUs to maximize performance. It is designed for Unreal Engine developers interested
+      in optimizing hardware...
+    source_hash: ef70ed2089132df657bd1ebfb9a66a39934d8a118b1e73974148ce11c104fef5
+  - path: content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md
+    status: unchanged
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+    summary_preview: Learn how to build ONNX Runtime and the generate() API for Android to run a Phi-3
+      model on Arm-based smartphones. It is designed for software developers interested in learning how
+      to build an Android ...
+    source_hash: deeb745d72457138cb81252c421205cd8dfb43b5e29ceee3a15c5842cc90cc33
+  - path: content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md
+    status: unchanged
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+    summary_preview: Learn how to build a hands-free selfie Android application using MediaPipe multimodal
+      AI, Kotlin flows, CameraX, and MVVM architecture. It is designed for mobile application developers
+      interested in l...
+    source_hash: 13ff69755e51c6d7c355616f95703b3f2cefaedcf1f8dbeab31fe2e3db9aec74
+  - path: content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md
+    status: unchanged
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+    summary_preview: Learn how to build an Android chat application with Llama models using ExecuTorch,
+      XNNPACK, and KleidiAI for accelerated performance on Arm smartphones. It is designed for software
+      developers interest...
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+  - path: content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md
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+    summary_preview: Learn how to build a customer support chatbot for Android using Llama 3.2, ExecuTorch,
+      and KleidiAI to run on-device inference on Arm platforms. It is designed for software developers
+      interested in bu...
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+  - path: content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md
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+    summary_preview: Learn how to detect and debug memory safety bugs in Android applications using Arm
+      Memory Tagging Extension (MTE) on a Google Pixel 8 smartphone. It is designed for developers interested
+      in learning h...
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+  - path: content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md
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+    summary_preview: Get started with Arm Accuracy Super Resolution (Arm ASR) walks you through an end-to-end
+      Arm software workflow. It is designed for mobile, gaming, and graphics developers who want to install
+      and confi...
+    source_hash: b194edd6ff4ffc5e39e7daa995271d2ed81ec31c8e88ddf1b23a54eb06dd1d06
+  - path: content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md
+    status: unchanged
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+    summary_preview: Get started with Unity on Android walks you through an end-to-end Arm software workflow.
+      It is designed for Unity developers who want to target Android devices. By the end, you will be
+      able to set up ...
+    source_hash: 23df12c0e165a4120fa25b5573437121bc7af141376758ccd051ddac14e180fb
+  - path: content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Profile Android game performance in Godot with Arm Performance Studio walks you through
+      an end-to-end Arm software workflow. It is designed for Godot developers targeting Android devices
+      who want to o...
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+  - path: content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: How to Enable Hardware Ray Tracing on Lumen for Android Devices walks you through
+      an end-to-end Arm software workflow. It is designed for Unreal Engine developers interested in using
+      hardware ray trac...
+    source_hash: 3f35558e0399cb4c851b120eaa2217a63c48336cbba96d23500d1b751e4aec63
+  - path: content/learning-paths/mobile-graphics-and-gaming/intro/_index.md
+    status: unchanged
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+    summary_preview: Get started with Arm hardware walks you through an end-to-end Arm software workflow.
+      It is designed for Developers new to the Arm architecture and looking for mobile hardware. By the
+      end, you will be ...
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+  - path: content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md
+    status: unchanged
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+    summary_preview: Understand KleidiAI SME2 matmul microkernels walks you through an end-to-end Arm
+      software workflow. It is designed for software developers, performance engineers, and AI practitioners.
+      By the end, you...
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+  - path: content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to run LLM inference on Android devices using MediaPipe with KleidiAI-enhanced
+      Arm i8mm features to benchmark the Gemma 2B model. It is designed for Android developers who want
+      to efficientl...
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+  - path: content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md
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+    summary_preview: Learn how to build the libGPUInfo library using Android NDK and query configuration
+      details of Arm Mali or Immortalis GPUs on Android devices. It is designed for Android developers
+      who want to adjust ...
+    source_hash: 68cdc7315795b6ffa794c0a1fd4cf325ab39ebb65e23efd27b7760ece76a2ec9
+  - path: content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md
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+    faq_changes:
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+    summary_preview: Learn how to accelerate LiteRT model inference on Android using KleidiAI with SME2
+      instructions and validate performance with the benchmark tool. It is designed for developers looking
+      to leverage Arm'...
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+  - path: content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to benchmark KleidiAI micro-kernels in ExecuTorch using SME/SME2 instructions
+      on Arm64 platforms with ETDump profiling and analysis. It is designed for developers, performance
+      engineers, and...
+    source_hash: b55d902389806f5e84e674e5ba1f1f2d9e38af370c289ad344eff2dad3475df1
+  - path: content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to fine-tune and evaluate Neural Super Sampling (NSS) models using PyTorch
+      and Arm's Model Gym API with hardware-aware optimization. It is designed for developers exploring
+      neural graphics a...
+    source_hash: e145caae5b20f7165251d88e334ba22d90c8b35270902778a2b6fe0ed0b250f6
+  - path: content/learning-paths/mobile-graphics-and-gaming/mte/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to run example C programs on AArch64 Linux to gain an introductory understanding
+      of the Arm Memory Tagging Extension (MTE). It is designed for developers who want to gain some experience
+      wit...
+    source_hash: a25cb73b70716e397d9477f8ab9729f4a094143d276e4de68cf8c33b273bb1ff
+  - path: content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to enable Arm Memory Tagging Extension (MTE) on a Google Pixel 8 smartphone,
+      trigger memory bug crashes, and interpret bug reports. It is designed for developers interested
+      in learning how t...
+    source_hash: b6a9aa558ec5062c829d61b28fb92e25d5517249c011eb29f03cc36775b6f9af
+  - path: content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to capture high-quality frame datasets from Unreal Engine 5.5 gameplay
+      for training and evaluating neural graphics models like Neural Super Sampling. It is designed for
+      Unreal Engine develop...
+    source_hash: 5ca059af36f0ba375701e76ce82b7803f01ae6beab313336b333bfbdebaa3b1a
+  - path: content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md
+    status: unchanged
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+    faq_changes:
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+      updated_questions: []
+    summary_preview: Learn how to configure ML Extensions for Vulkan emulation and enable Neural Super
+      Sampling (NSS) in Unreal Engine for real-time upscaling. It is designed for developers experimenting
+      with neural graph...
+    source_hash: 7ffbac59b340f3ef5119d2f5ba4a786fd15d521bb294f3a4213b083c9b4ce23d
+  - path: content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md
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+    faq_changes:
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+    summary_preview: Learn how to build, optimize, and deploy machine learning models using ONNX Runtime
+      on Arm64 platforms, including Raspberry Pi, cloud instances, and Android devices. It is designed
+      for developers who ...
+    source_hash: aba1cea365c0c61e84fb545ada15a64ed52c099f1252dc6312f88ee82385d2d0
+  - path: content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md
+    status: unchanged
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+    summary_preview: Learn how to optimize vertex representations and analyze Vertex Memory Efficiency
+      using Arm Frame Advisor for improved GPU performance on Android. It is designed for Android graphics
+      application devel...
+    source_hash: 586a505543df4237c943ea47210d735fafe7d5ed2c6ad18b1b99227987ef9b15
+  - path: content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md
+    status: unchanged
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+    faq_changes:
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+      updated_questions: []
+    summary_preview: Learn how to build llama.cpp with KleidiAI and SME2 support to profile and accelerate
+      LLM inference performance on Android devices. It is designed for software developers, performance
+      engineers, and A...
+    source_hash: 4962524245ec5e5e07d1207537208a22c2e9569f252f36d758c4f828d2104272
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+    status: unchanged
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+    faq_changes:
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+      updated_questions: []
+    summary_preview: Learn how to build ONNX Runtime with KleidiAI and SME2 support for Android and profile
+      ONNX model performance to compare acceleration improvements. It is designed for software developers,
+      performance ...
+    source_hash: 1645a1c65527da010edd6fefddad3c865eac0f9cad417ba59709a57bb9dc847a
+  - path: content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to profile ML model execution times and application performance on Arm
+      Android devices using Arm Performance Studio and Android Studio Profiler. It is designed for software
+      developers who wa...
+    source_hash: 01138f6538c655f866fb034a983461b2ac9b793142775465233b2ec8ec18d0c5
+  - path: content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md
+    status: unchanged
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+      updated_questions: []
+    summary_preview: Learn how to deploy Unity applications to Android, profile code running on Arm devices,
+      and analyze performance data for optimization. It is designed for Unity developers wanting to analyze
+      the perfor...
+    source_hash: 9950a58160736e0c60ad80ea602262347e2ba8a37a00e29f25dc96709026ea1f
+  - path: content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to apply post-training quantization to PyTorch models using TorchAO and
+      export INT8 models to .vgf format with the ExecuTorch Arm backend. It is designed for ML developers
+      who want to reduce...
+    source_hash: 56d9d0fe9606e42281a2d2be52992c7a4b6846b208376c92e7fe3094647d1c70
+  - path: content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to use the Vulkan ray tracing API to implement realistic shadows, reflections,
+      and refractions in Android applications. It is designed for Vulkan developers who are familiar with
+      rendering a...
+    source_hash: 78f862a7c46fd4591fec45f91d040eb3f1093291c0a7328dddd2203dad72db3f
+  - path: content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to use Frame Advisor's Render Graph view to identify and resolve graphics
+      performance issues in Android applications. It is designed for Mobile application developers who
+      wish to improve gra...
+    source_hash: e2f6f3005c119e6f6fe97612d4e2600849106f244bce729d56bfeeedb30637c2
+  - path: content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md
+    status: unchanged
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+    faq_changes:
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+      updated_questions: []
+    summary_preview: Learn how to convert and deploy the Stable Audio Open Small text-to-audio model to
+      LiteRT format for audio generation on Android devices and macOS. It is designed for developers looking
+      to deploy the ...
+    source_hash: 388cab0dcb284c29fe2ad82f2b2934d9243c6356b2885d26db0a97c1a1d4d393
+  - path: content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to convert the Stable Audio Open Small model to ExecuTorch format and build
+      an audio generation application for Android or macOS. It is designed for developers who want to
+      deploy the Stable ...
+    source_hash: 7970846a399ddcdb488d90bf19cce3c6b74df6348e88914aed83661b2597fe7b
+  - path: content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md
+    status: unchanged
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+    faq_changes:
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+      updated_questions: []
+    summary_preview: Learn how to build and install a Unity game on an Orange Pi 5 single-board computer
+      running Droid OS. It is designed for software developers who want to build and run a Unity game
+      on an Arm-based sing...
+    source_hash: dea8c3e15cca703f075c8fa9ba3daf873a90e3eb2ee9975dd05b973dad744b54
+  - path: content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md
+    status: unchanged
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+    summary_preview: Learn how to install Arm integration packages in Unity to view GPU metrics in Unity
+      Profiler and annotate games with markers for Arm Performance Studio. It is designed for Unity developers
+      who are tar...
+    source_hash: 4a990e957658b03bac9306d5f8e6955734cc1d3b063f43c38ac525ed1bf95b79
+  - path: content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md
+    status: unchanged
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+    faq_changes:
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+      updated_questions: []
+    summary_preview: Learn how to use Arm Neon intrinsics in Unity C# scripts to optimize code on Android
+      and collect performance data using Unity Profiler. It is designed for Developers interested in leveraging
+      the Unity...
+    source_hash: f17ab3c4216495b88cee75a81612c11a4727d874114f914edf97c467f0d1c125
+  - path: content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to integrate Unity's Machine Learning Agents toolkit into games deployable
+      to Arm-powered Android devices. It is designed for Developers interested in leveraging the Unity
+      Machine Learning A...
+    source_hash: 9cd19738cbe9cde618125b309bd4f2c57ad1799e807251fdaed09707bea2781d
+  - path: content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to download, convert, and deploy Vision Transformers using the Mobile Neural
+      Network framework on Android with KleidiAI micro-kernels for optimized performance. It is designed
+      for developers...
+    source_hash: e7d95c8e7210be2fe4520fe94ccbaa3e437cd0edfa5caca549afaee22fb8b377
+  - path: content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to build and optimize a multimodal Voice Assistant application on Android
+      using KleidiAI and SME2 for accelerated performance. It is designed for developers who want to implement
+      a multimoda...
+    source_hash: 192f5919261cfef88c107576dc444d2db3a07fbad98100d9c758bb75670a5a00
+  - path: content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Build an end-to-end, on-device voice assistant that understands both speech and emotion
+      using Whisper, HuBERT, ONNX Runtime, and a local LLM with llama.cpp on Arm. It is designed for developers,
+      ML pr...
+    source_hash: 2d8fca8838e759c3098358f131fb4b0884b0d80d5fdb2fd68719bd09fa4c3d32
+  - path: content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to set up ML Emulation Layers for Vulkan, run sample applications using
+      ML extensions, and debug the flow with RenderDoc. It is designed for engine developers interested
+      in learning about ne...
+    source_hash: af4f1c8964e0970c187643bcd0330a63a6ce66c38a2e6b4d2fc17857a12a3d7f
+  - path: content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to build and deploy an AI agent application on Arm servers using llama.cpp
+      and llama-cpp-agent with KleidiAI optimization for efficient LLM inference and function calling.
+      It is designed for...
+    source_hash: 275878b5aa4c27dee394da12ad8b80e4c0d0235b3ddce66b1fe3f0e056ef3da9
+  - path: content/learning-paths/servers-and-cloud-computing/aks/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+    summary_preview: Learn how to automate the deployment of an Arm-based Kubernetes cluster on Azure
+      AKS using Terraform and deploy a sample WordPress application as a workload. It is designed for
+      software developers who...
+    source_hash: 01c5cc8930650a8d81d67d4c48b62e0f9d7b1ebfc2d09a552e11046fb982fc52
+  - path: content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to deploy Apache Arrow and Arrow Flight on Google Cloud Axion C4A processors
+      for high-throughput columnar data processing and low-latency data transport with MinIO integration.
+      It is designe...
+    source_hash: 90b638385267e085ea07a70a1c23f16578aa3caaeabeca1f651bcdcac5bf38fb
+  - path: content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+    summary_preview: Learn how to build llama.cpp, quantize the Arcee AFM-4.5B model, and run optimized
+      inference on AWS Graviton4 instances with perplexity-based quality evaluation. It is designed for
+      developers and ML e...
+    source_hash: 964c1e6a87810dca686a7b3218433ce09d31bd92882db10af355e8c50bb6091c
+  - path: content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+    summary_preview: Learn how to build llama.cpp, quantize the Arcee AFM-4.5B model, and run optimized
+      inference on Google Cloud Axion instances with perplexity-based quality evaluation. It is designed
+      for developers and...
+    source_hash: b2f60d2c31ef380e6e6ef3028671ad9242dd51c98f4a192f2da32bb05b94e185
+  - path: content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to deploy and manage applications on Google Cloud GKE Arm64 clusters using
+      Argo CD GitOps workflows with automated sync and self-healing on Axion processors. It is designed
+      for developers an...
+    source_hash: c6106f21859f5a8e04bbd579db47c7177bb5371d4c7f306054e56e81ed9e89a1
+  - path: content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to write correct concurrent C++ code when porting applications from x86
+      to Arm by understanding memory ordering differences and using best practices to avoid race conditions.
+      It is designed ...
+    source_hash: 3a4bc8b2ab548be507b6d528844b0593b27f2dab3ab3c9649e807abdf4887ce0
+  - path: content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to automate x86-to-Arm application migration using the Arm MCP Server,
+      with AI-assisted compatibility checks, C++ code refactoring, and Docker-based validation on Arm
+      cloud platforms. It is ...
+    source_hash: b870ac5160d35ddb51955ca8379493e172787ced8125cde8ae79f7700e653a87
+  - path: content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
+    status: unchanged
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+    summary_preview: Learn how to migrate C applications between Arm platforms using Kiro's AI-assisted
+      tooling to identify hardware dependencies and implement abstraction layers for cross-platform compatibility.
+      It is de...
+    source_hash: 49b3f2b1464efb20f0c8fbcff46e9f30910d856567ca01c01dc348aa1c5740d1
+  - path: content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to install and configure a Linux kernel with 64K page size support on Arm
+      systems to improve memory efficiency and performance for memory-intensive workloads. It is designed
+      for developers w...
+    source_hash: 67004eb9a75926144eb6f75124104972b3cf20a57a22b698c9359d20db3eca02
+  - path: content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
+    status: unchanged
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+    summary_preview: Learn how to access and use Arm hardware performance counters and the system counter
+      from user space using PAPI, perf_event_open, and assembly code for performance instrumentation.
+      It is designed for ...
+    source_hash: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
+  - path: content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to package multi-architecture container applications and deploy them on
+      AWS Fargate with Graviton processors using the AWS Copilot CLI. It is designed for software developers
+      who want to lea...
+    source_hash: ced3882cf8be11a55304d2697f450a2c862a81d87317ab42298148755e9f272c
+  - path: content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to automate the creation and deployment of AWS Graviton instances using
+      Terraform with jump server access for secure infrastructure management. It is designed for software
+      developers who are...
+    source_hash: 087a4d56914e5b53cec5ad17e8d682e72d04ba6b394237c081efe4a3f939e04e
+  - path: content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to create and deploy Azure Resource Manager templates to provision Arm64-based
+      Cobalt 100 virtual machines on Azure using the Azure CLI. It is designed for developers and DevOps
+      engineers wh...
+    source_hash: 1682c0527bb49c417263286e2aba7c82fb9a27fa315ecc6b9ca10978b69cc236
+  - path: content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
+    status: unchanged
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+    summary_preview: Learn how to configure a self-hosted GitHub runner on Azure Cobalt 100, create an
+      AKS cluster with Terraform, and deploy a .NET application using GitHub Actions CI/CD. It is designed
+      for software deve...
+    source_hash: b5bd3abde8d9dd3146e0f11f4819ac510417ad7f707b4d0970c02fd63801b358
+  - path: content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+    summary_preview: Learn how to automate the creation of Azure Arm virtual machines using Terraform.
+      It is designed for software developers who are new to deploying Arm instances on Azure using Terraform.
+      By the end, yo...
+    source_hash: 6713af3d70887933cf54c7fa36bdc88d71a51fa34fb29a4ce90636ff1de624c7
+  - path: content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
+    status: unchanged
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+      updated_questions: []
+    summary_preview: Learn how to create a custom Azure Linux 3.0 VM image using QEMU, upload it to Azure
+      Shared Image Gallery, and deploy it on Arm-based Cobalt 100 processors. It is designed for developers
+      who want to r...
+    source_hash: 413467e581e3561425229d0f6118975dbb596d6a9494544a54f0b9ab22c1b89d
+  - path: content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
+    status: unchanged
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+    summary_preview: Learn how to deploy and accelerate PyTorch NLP sentiment analysis models from Hugging
+      Face on Arm servers with BFloat16 fast math kernel optimization on Graviton3 processors. It is designed
+      for softwa...
+    source_hash: a4cf1d9161b3a32e29694415762eda419752e1c3144662d5e131b6553f0a58e3
+  - path: content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
+      after_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to implement and benchmark bitmap scanning operations for database workloads
+      using scalar, Neon, and SVE instructions on Arm-based cloud instances. It is designed for database
+      developers, pe...
+    source_hash: 1701b37580fe5d012a5e6fd322307742656a748dfb766fd48914011167386e95
+  - path: content/learning-paths/servers-and-cloud-computing/bolt/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to build, profile, and optimize Arm executables using BOLT post-link binary
+      optimization to improve application performance through code layout improvements. It is designed
+      for software deve...
+    source_hash: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
+  - path: content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to identify optimization candidates and apply LLVM BOLT post-link optimization
+      to AArch64 binaries using BRBE, SPE, instrumentation, and PMU profiling techniques. It is designed
+      for develope...
+    source_hash: 8654b656131bf1d529e11d85f874f7a81c01f7207340d2a606b6fd2d80bfad04
+  - path: content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to optimize Arm application binaries and shared libraries using BOLT profile
+      instrumentation, merge multiple profiles for improved coverage, and integrate optimized libraries.
+      It is designed...
+    source_hash: 84a8b96fe7df302e0a2a6e4645bbb6170b45a3e0b55e0ea3682ec47663d34819
+  - path: content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
+    status: unchanged
+    changed_on_disk: true
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+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to configure Buildkite agents on Google Axion C4A VMs to build and publish
+      multi-architecture Docker images using Docker Buildx for Arm and x86 platforms. It is designed for
+      developers who w...
+    source_hash: 4bda206717eef380430009f859826d9bcf820442d13492cd3c22a114561e2917
+  - path: content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
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+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to install and configure Apache Cassandra on Google Cloud Axion C4A Arm64
+      instances and benchmark read/write performance using cassandra-stress. It is designed for software
+      developers migrat...
+    source_hash: b8268d4df3b62aeb9943b750b436a936134d47745fa71db6cc08db8c84eb37be
+  - path: content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to run the Arm CCA reference software stack on an FVP with RME support,
+      create a Realm virtual machine, and obtain attestation tokens for confidential computing. It is
+      designed for software ...
+    source_hash: 3947ebbac742399e70001e41ac5face92819bed0deb55aba3e2414f98432023e
+  - path: content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
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+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how Arm CCA Realms interact with I/O devices using VirtIO paravirtualization,
+      SWIOTLB bounce buffers, and PCIe-TDISP secure device attach mechanisms with attestation. It is designed
+      for develope...
+    source_hash: e21f51feb101ad90245ecceab95fe13e5eb61958faf24dff818eddd11f484b9a
+  - path: content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to deploy a CCA realm on an FVP with RME support and connect it with attestation
+      services to create an end-to-end confidential computing workflow. It is designed for software developers
+      who ...
+    source_hash: b032debbdfe82cbd017812cf671907520b146fd8684afce0c45c91e7f2287e18
+  - path: content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to deploy Confidential Containers from encrypted images inside Arm CCA
+      Realms using Trustee services for attestation-based authorization on an FVP with RME support. It
+      is designed for develo...
+    source_hash: 649957b07b2bde05bc11047bd12bfc4f697c1a55eef1c3a25b5fafc95cda893d
+  - path: content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
+      after_count: 5
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+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to deploy a CCA realm workload on an FVP and connect it with Trustee services
+      to enable attestation-based confidential data processing. It is designed for software developers
+      who want to run...
+    source_hash: 563456f5d151c7ef59bf458f6ac971c321077475a0a78d3b5a3885b97157ba9e
+  - path: content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md
+    status: unchanged
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+    faq_changed: false
+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to inspect and verify Arm CCA attestation tokens using command-line tools
+      and the open-source Veraison attestation verification service. It is designed for developers who
+      would like to learn...
+    source_hash: 9e6dee3aef79c1c65cc1a1f4fe3528b9c5542d8046f0b059ef703079ef964d77
+  - path: content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
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+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to deploy a scalable Arm CCA attestation verifier service on AWS using
+      Veraison components with platform endorsement provisioning. It is designed for developers familiar
+      with CCA attestation...
+    source_hash: 55b8032ceaf735d53b1157102a3a1da5aa5cb686e9ec51cf4896ded66d9bf263
+  - path: content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to set up CircleCI self-hosted machine runners on Google Cloud Axion C4A
+      SUSE VMs and execute Arm-native CI/CD workflows using custom resource classes. It is designed for
+      developers and DevO...
+    source_hash: ec9cdca7aa9a5670f54ea3646f965149460d8e66d9d5d904e1b6dcf09867df39
+  - path: content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md
+    status: unchanged
+    changed_on_disk: true
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+    faq_changed: false
+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to install and configure CircleCI self-hosted machine runners on AWS Graviton
+      Arm64 instances to execute CI/CD workflows natively on Arm. It is designed for developers and DevOps
+      engineers w...
+    source_hash: e04982fd668765fa2ab25f943f020b8d2b4a5e9b5ff3a51b7431cc4d93730180
+  - path: content/learning-paths/servers-and-cloud-computing/clair/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to install and run Clair on Arm servers using combined and distributed
+      deployment models to scan container images and generate vulnerability reports. It is designed for
+      software developers i...
+    source_hash: e742eb44fa108bfcc4ee5a241414e6aa05e1fc0e1cceb589fb78a080f59b0d38
+  - path: content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to install ClickHouse on Arm-based cloud instances and measure database
+      performance using ClickBench to determine appropriate instance configurations. It is designed for
+      software developers ...
+    source_hash: 0d1390198cf2f0e87f509c5269fc2405cffcb2e57311024150d47e60a3273178
+  - path: content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to deploy ClickHouse on Google Cloud Axion C4A processors and build a streaming
+      ETL pipeline using Apache Beam, Dataflow, and Pub/Sub for real-time analytics. It is designed for
+      developers d...
+    source_hash: e77cd1373236f39f360afe6844d642fde290960ccdad26544ff1e3342f2a980c
+  - path: content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to deploy an Arm-based Cobalt 100 virtual machine on Azure, connect via
+      SSH, and configure network security group rules for external connectivity. It is designed for developers
+      and DevOps en...
+    source_hash: e4eb84292a669a8d73bff4b63d2fe3f70382dd873d023fc8ff24db5ad6178ab8
+  - path: content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to automate Docker image creation for Arm using AWS CodeBuild with GitHub
+      integration and run the images on any Arm system with Docker installed. It is designed for software
+      developers inter...
+    source_hash: e7ea48aaea0e25f624ea1d77c6252b0e537fdaeca3aacca560d7bc9d103bbbb3
+  - path: content/learning-paths/servers-and-cloud-computing/codec/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to build and run the x265 H.265 codec on Arm servers with performance benchmarking
+      across various video resolutions and encoding presets. It is designed for software developers who
+      want to b...
+    source_hash: 908a750f2a891a0c4c9d1183c2130ac5ac0fdcfb4a45185cef6ed6da47c9aaa9
+  - path: content/learning-paths/servers-and-cloud-computing/codec1/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
+      after_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to build and run the AV1 and VP9 video codecs on Arm Linux systems with
+      performance benchmarking across various resolutions and encoding configurations. It is designed
+      for software developer...
+    source_hash: c0643a788cdb0b3e33fe645fbb61d99a1899806e3ee197541c1eb8134b2876c1
+  - path: content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
+      after_count: 5
+      added_questions: []
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to install and configure Couchbase on Google Cloud Axion C4A Arm64 instances
+      and benchmark read/write performance using YCSB workloads. It is designed for developers deploying
+      Couchbase work...
+    source_hash: 0a7d76b8c944a50073c7524813acf83948155c7c61d9c92f9202eae1192c9600
+  - path: content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to apply g++ compiler optimization techniques and flags to improve C++
+      application performance on Arm systems with hands-on examples. It is designed for beginner C++ developers
+      who are looki...
+    source_hash: 22f845b8ea4dbb9ffc63fafb76c17c79aedde14a28417d49e1ab0833bbbc1eba
+  - path: content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to apply profile-guided optimization to C++ applications on Arm systems
+      and measure performance improvements using Google Benchmark. It is designed for Developers looking
+      to optimize C++ per...
+    source_hash: 4e7de348514d0b5a742fa47bb85a2a5814fa9bd587478e7ef08365d16273f6bf
+  - path: content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
+      after_count: 5
+      added_questions: []
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to profile and identify CPU hotspots in C++ applications on Arm Neoverse
+      using Arm Performix flame graphs to guide optimization. It is designed for software developers and
+      performance engine...
+    source_hash: 58dba071f70b4f85b87b3bd27b3a7ba3ff985f80079a42e05b797a998aeaf104
+  - path: content/learning-paths/servers-and-cloud-computing/csp/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
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+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to start an Arm-based virtual machine instance from major cloud service
+      providers and verify the Arm architecture is being used. It is designed for software developers
+      who are new to Arm-bas...
+    source_hash: 9e94b69eabf35677c48db812bb85ea9cef184efc078a826b96954f540a45e915
+  - path: content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to deploy and run the DeepSeek-R1 language model on Arm servers using llama.cpp
+      with quantization for efficient CPU inference. It is designed for developers who want to run DeepSeek-R1
+      on Ar...
+    source_hash: f600fcba0adea12ff1b8b092e75de553577d940dc3bf632e9a247cec22d364a4
+  - path: content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to use fio to microbenchmark storage performance on Arm systems and monitor
+      storage using iostat, iotop, and pidstat to identify bottlenecks. It is designed for developers
+      looking to optimiz...
+    source_hash: a1ec216948e7cfd4fc52815196bb3b99ab4e76c9c756aa9e9a8e3216ef5e7ce4
+  - path: content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
+      after_count: 5
+      added_questions: []
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Run distributed LLM inference with llama.cpp across multiple AWS Graviton4 instances,
+      covering multi-node setup, coordination, and performance trade-offs. It is designed for This introductory
+      topic is...
+    source_hash: 6ac0c7cf1ab4b3efa680acbf1e349b858bee5dc7992baf6689e1f293a83060a4
+  - path: content/learning-paths/servers-and-cloud-computing/django/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to create a simple Django web application and deploy it on Arm machines
+      using Nginx and PostgreSQL. It is designed for engineers who want to deploy a Django based application
+      on Arm machines...
+    source_hash: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
+  - path: content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to deploy a production-grade Django REST API on Google Kubernetes Engine
+      with Arm64 Axion node pools integrated with Google Cloud managed data services. It is designed for
+      DevOps engineers a...
+    source_hash: 6675df4c91126b157dcbf39c96a773130f1a95e2f5680913979da96f6f6c97cd
+  - path: content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
+      after_count: 5
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+      updated_questions: []
+    summary_preview: Learn how to build and benchmark the Deep Learning Recommendation Model using PyTorch
+      and MLPerf on Arm Neoverse V2 processors. It is designed for software developers who want to set
+      up a pipeline in ...
+    source_hash: 82716c2de19d18a154c85d03b7f2ec01839284262914f7bb3ad04b18a105379d
+  - path: content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
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+      added_questions: []
+      removed_questions: []
+      updated_questions: []
+    summary_preview: Learn how to use the Docker MCP Toolkit with the Arm MCP Server and GitHub Copilot
+      to automate container and code migration from x86 to Arm64. Through a hands-on example, migrate
+      a legacy C++ applicat...
+    source_hash: 80785022032bf4e3c65da682e698940a212ee1ee77386698889a9fafbed9f823
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+    summary_preview: Learn how to build and run an OrchardCore CMS .NET application on Azure Cobalt 100
+      processors, covering AnyCPU configuration and shared C library integration. It is designed for .NET
+      developers who wa...
+    source_hash: 08d8f0c86625ef41476d3a8b24bad9b0a0820797022ef847bf9bb17a976726a7
+  - path: content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
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+    summary_preview: Learn how to deploy Dynatrace OneAgent on Azure Cobalt 100 Arm64 virtual machines
+      and configure ActiveGate for secure infrastructure and application monitoring. It is designed for
+      developers, DevOps e...
+    source_hash: 4ef29931bf19dc95bb586440796381725c271ef1b953dcf46d00ad9617eabbb1
+  - path: content/learning-paths/servers-and-cloud-computing/ecs/_index.md
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+    summary_preview: Learn how to create an AWS ECS cluster with Fargate and AWS Graviton processors,
+      then create and run containerized tasks on Arm infrastructure. It is designed for developers who
+      want to use AWS Gravit...
+    source_hash: ef5f9e7c8844b20b9044b43f4758bc1d74374521093d7738a7f8832d21f1dcac
+  - path: content/learning-paths/servers-and-cloud-computing/eks/_index.md
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+    summary_preview: Learn how to provision an Amazon EKS cluster on Arm-based Graviton instances and
+      deploy a WordPress application with MySQL database. It is designed for software developers new to
+      Kubernetes on AWS who...
+    source_hash: 4f1c448eef66300e024bda27c420f9746047c0c4b76e8556d3d8693382206055
+  - path: content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
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+    summary_preview: Learn how to use docker buildx and docker manifest to build and deploy multi-architecture
+      container images with x86/amd64 and arm64 support on Amazon EKS. It is designed for software developers
+      who wa...
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+  - path: content/learning-paths/servers-and-cloud-computing/envoy/_index.md
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+    summary_preview: Learn how to build, install, and run Envoy proxy on Arm servers and configure it
+      as a web server for traffic management. It is designed for engineers who want to use Envoy on Arm.
+      By the end, you will...
+    source_hash: d492b6dce11b7d8f6591ff3b3ce9aa2c382a6a7a749add228c3ac1f6ae57e218
+  - path: content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md
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+    summary_preview: Learn how to install and configure Envoy proxy on Google Cloud Axion C4A Arm64 instances
+      and benchmark HTTP proxy performance with load testing. It is designed for This introductory topic
+      for software...
+    source_hash: f36b1e9a45b6ec29d8041b70997550d917322cf9cdd456db26083169d21175ed
+  - path: content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md
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+    summary_preview: Learn how to optimize Envoy proxy performance on Arm servers using Transparent Huge
+      Pages and Profile-Guided Optimization techniques. It is designed for software developers who want
+      to use Envoy on Ar...
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+  - path: content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md
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+    summary_preview: Learn how stack buffer overflow exploits work on AArch64 by analyzing stack frame
+      layouts, redirecting control flow, and understanding defense mechanisms. It is designed for software
+      developers intere...
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+  - path: content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
+    status: unchanged
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+    summary_preview: Learn how to identify and fix false sharing issues using Perf C2C cache line analysis
+      and Arm Statistical Profiling Extension on Arm-based cloud systems. It is designed for performance-oriented
+      develo...
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+  - path: content/learning-paths/servers-and-cloud-computing/fastpath/_index.md
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+    summary_preview: Learn how to build custom Linux kernels using tuxmake and Fastpath, then benchmark
+      and compare kernel versions on Arm-based EC2 instances. It is designed for software developers and
+      performance engine...
+    source_hash: 978d3da8668e38758c138313c31809f3de5a8cefd1b7c2b47a536c0ee364b692
+  - path: content/learning-paths/servers-and-cloud-computing/fexpa/_index.md
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+    summary_preview: Learn how to implement exponential functions using Arm SVE intrinsics with the FEXPA
+      instruction for hardware-accelerated computations on Neoverse processors. It is designed for developers
+      interested ...
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+  - path: content/learning-paths/servers-and-cloud-computing/flink/_index.md
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+    summary_preview: Learn how to install and run Apache Flink on Arm servers and benchmark stream processing
+      performance using the Nexmark benchmark suite. It is designed for software developers using Flink
+      as their stre...
+    source_hash: 974d71b1ee968e3aeabe900bfdd52ae4fbfdd0ca7dea420e6c1fc01f5475e8c1
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+    summary_preview: Learn how to install and configure Apache Flink on Google Cloud Axion C4A Arm64 instances
+      and benchmark stream processing performance with Nexmark. It is designed for developers deploying
+      and optimizi...
+    source_hash: adcf2a1b8a4a77e5834e14a40e46e27b0cbe5e440fbf732a4366ce486d7fafb7
+  - path: content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md
+    status: unchanged
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+    summary_preview: Learn how to build scalable machine learning workflow pipelines on Google Cloud C4A
+      Axion processors using Flyte for workflow orchestration and gRPC for distributed service communication.
+      It is design...
+    source_hash: 85359b9210812675169f149c86bf11a1736ab838c011d18e876b246463d297ee
+  - path: content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
+    status: unchanged
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+    summary_preview: Learn how to create an Arm64 Azure VM, install .NET SDK, containerize .NET applications,
+      and push Docker images to Azure Container Registry. It is designed for software developers interested
+      in learni...
+    source_hash: 94866800acca2c5f9cd89f76f972af1a343aad5777b6844d49fac09eb764f580
+  - path: content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md
+    status: unchanged
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+    summary_preview: Learn how to create and run Docker containers on Azure Container Instances for Arm64-based
+      containerized application deployment. It is designed for developers interested in learning how to
+      create and ...
+    source_hash: 3823ad3df8ae868acfd59b5b5b541240624572fe739aaf2275185d5cd3578032
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+    summary_preview: Learn how to create an Azure Kubernetes Service cluster with Arm64 virtual machines
+      and deploy a containerized application to AKS. It is designed for This learning path is dedicated
+      to developers inte...
+    source_hash: a3347a62c3322d88b80fc7848fa5ee1d92ee86333c2a21891b958286f8b70935
+  - path: content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md
+    status: unchanged
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+    summary_preview: Learn how to automate Azure resource deployment using Infrastructure as Code with
+      Pulumi to provision Azure Container Instances for containerized applications. It is designed for
+      developers interested...
+    source_hash: 1510c787979257e191196e13999e98c20ca24d0857f72075649c28520c74c576
+  - path: content/learning-paths/servers-and-cloud-computing/funasr/_index.md
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+    summary_preview: Learn how to deploy the ModelScope FunASR Chinese automatic speech recognition model
+      on Arm-based servers with real-time transcription and sentiment analysis. It is designed for developers
+      interested ...
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+  - path: content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
+    status: unchanged
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+    summary_preview: Learn how to install and configure Gardener Kubernetes management platform on Google
+      Cloud Axion C4A SUSE Arm64 instances and deploy workload clusters. It is designed for software developers
+      deploying...
+    source_hash: 85d3d64e0eb0c3cfa0eee29cf1e4199049a6dcbf69634e578dad2b5443ca2b7f
+  - path: content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
+    status: unchanged
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+    summary_preview: Learn how to apply link-time optimization with the GCC toolchain to improve application
+      performance by optimizing across compilation units. It is designed for developers who want to improve
+      applicatio...
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+  - path: content/learning-paths/servers-and-cloud-computing/gcp/_index.md
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+    summary_preview: Learn how to automate the creation of Arm virtual machines on Google Cloud Platform
+      using Terraform with jump server access configuration. It is designed for anyone new to using Arm
+      virtual machines i...
+    source_hash: 24e2ffcf9b7cda919e6e864f18bb9424c0c328242c92f491c1b284e317ed7e1c
+  - path: content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
+    status: unchanged
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+    summary_preview: Run Geekbench on Arm systems to benchmark CPU performance, interpret the results,
+      and compare different Arm configurations. It is designed for software developers interested in comparing
+      the performan...
+    source_hash: 85a0a44bde6f217bdfc7fd0660ed6a86279b24c7ede302307ef6efb2626d83d9
+  - path: content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
+    status: unchanged
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+    summary_preview: Learn how to set up Arm-hosted GitHub runners and train PyTorch ML models using the
+      German Traffic Sign Recognition Benchmark dataset with automated workflows. It is designed for software
+      developers i...
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+  - path: content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Learn how to install RunsOn self-hosted runner manager in your AWS account to execute
+      GitHub Actions workflows on Arm runners. It is designed for developers who want to use Arm runners
+      offered by AWS ...
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+    status: unchanged
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+    summary_preview: Learn how to provision a Google Axion C4A Arm virtual machine and set up a GitHub
+      Actions self-hosted runner for CI/CD workflows. It is designed for developers who want to deploy
+      a GitHub Actions self...
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+  - path: content/learning-paths/servers-and-cloud-computing/gke/_index.md
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+    faq_changes:
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+    summary_preview: Learn how to automate the deployment of an Arm-based Google Kubernetes Engine cluster
+      using Terraform for container orchestration. It is designed for software developers who want to
+      deploy an Arm-base...
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+  - path: content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
+    status: unchanged
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+    summary_preview: Learn how to add Arm-based Google Axion nodes to an existing x86 GKE cluster and
+      rebuild applications for multi-architecture support. It is designed for software developers who
+      are looking to migrate ...
+    source_hash: 50715640292b60ea4216ee2211140c4212592a27356d628d945e83d9deb7fdcc
+  - path: content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
+    status: unchanged
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+    summary_preview: Learn how to create dual-architecture GKE clusters with arm64 and amd64 node pools,
+      build multi-architecture Docker images, and migrate services to Google Axion processors. It is designed
+      for cloud, p...
+    source_hash: cf981c67553824c7c57b6dda9c2953ac80926750676ac101e939f6bbff655ab5
+  - path: content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Rebuild and benchmark glibc with LSE atomics on Arm servers, then evaluate scalability
+      using MongoDB workloads and guidance on when LSE delivers a measurable uplift. It is designed for
+      software develo...
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+  - path: content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
+    status: unchanged
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+    summary_preview: Learn how to provision Arm64 and x86_64 VM instances on Google Cloud, then install
+      and use Sweet and Benchstat to measure and compare Go application performance. It is designed for
+      This introductory t...
+    source_hash: aa78434138f08b424352302e92a1cd40d8297459bc65202715dcb41c40de6057
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+    status: unchanged
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+    summary_preview: Learn how to provision Azure Cobalt 100 Arm64 virtual machines and deploy Golang
+      applications with performance benchmarking on Arm architecture. It is designed for software developers,
+      DevOps engineer...
+    source_hash: 46a0a3df799ac473f56d3820390af405b3301758cf15685a250a04c4854aba9e
+  - path: content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md
+    status: unchanged
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+    summary_preview: Learn how to install Helm on Google Cloud Axion C4A SUSE VMs and deploy applications
+      like NGINX, PostgreSQL, and Redis using Helm charts. It is designed for This is an introductory
+      topic intended for ...
+    source_hash: 87e9d25d5fb1f45d126aa4ca2fa1e13d6a470f2bcdc70968aa4622790106e629
+  - path: content/learning-paths/servers-and-cloud-computing/intro/_index.md
+    status: unchanged
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+    summary_preview: Learn where Arm architecture is used in servers and cloud computing, and find Arm-based
+      hardware platforms for software development. It is designed for software developers working on server
+      and cloud ...
+    source_hash: d2786f42b505823253ae16c647ca2e03c154f371b7c326210fde8523b12a8188
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+    summary_preview: Analyze and optimize interrupt request (IRQ) patterns on Arm Linux servers to improve
+      network workload performance through IRQ distribution strategies. It is designed for developers
+      and performance en...
+    source_hash: 6fc608d45c4fdf85a77bddd4f8c26b05a7950d1086e578a8be0dd637feeb5d79
+  - path: content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
+    status: unchanged
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+      updated_questions: []
+    summary_preview: Monitor, interpret, and optimize Java Garbage Collector performance on Arm servers
+      by comparing different GCs and tuning parameters for your workload. It is designed for Java developers
+      aiming to opti...
+    source_hash: f57dd99bad280f64f53071373519e2d3ba8ae50b94fe1ad0a9cc40d02b458b9b
+  - path: content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Deploy and optimize Java applications on Google Cloud Axion processors by testing
+      JDK versions and performance optimization flags. It is designed for software developers who want
+      to learn how to run t...
+    source_hash: e6f73fbca45a1be1644f79bbcfbaaec964e31f1aab236e9a872c72a6fd5e4b66
+  - path: content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Deploy Java on Azure Cobalt 100 Arm virtual machines and benchmark application performance
+      with JMH microbenchmarks. It is designed for This is an introductory topic about Java deployment
+      and benchmar...
+    source_hash: 58b0bb9f6e4190029d7edc65bdb04a597c7539de55a5e51ed59e88b174e527c3
+  - path: content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
+    status: unchanged
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+    faq_changes:
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+      updated_questions: []
+    summary_preview: Profile Java applications on Arm Neoverse servers using flame graphs generated with
+      async-profiler and Java agents to identify performance bottlenecks. It is designed for developers
+      who want to analyz...
+    source_hash: 655180d91bb9b9cf571840b59d8943c1d2c73d8f8aea647db57dc2704ede3af8
+  - path: content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Deploy Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate installation,
+      and execute Arm-native CI/CD pipelines including Docker workflows. It is designed for software developers
+      d...
+    source_hash: 525d893a796e8e4eb5cc7a9d5996bd7d55a35f8fe413f87b9a275a214d77626f
+  - path: content/learning-paths/servers-and-cloud-computing/kafka/_index.md
+    status: unchanged
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+    faq_changes:
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+      updated_questions: []
+    summary_preview: Deploy and configure a Kafka cluster with Zookeeper on Arm servers, test event streaming,
+      and automate deployment on AWS and Google Cloud. It is designed for software developers who want
+      to learn how ...
+    source_hash: b7f36df881c2c436143c1e5579d2c54cb5930f52998904098829c52fa7290f38
+  - path: content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
+    status: unchanged
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+    faq_changed: false
+    faq_changes:
+      before_count: 5
+      after_count: 5
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+      updated_questions: []
+    summary_preview: Deploy Apache Kafka on Azure Cobalt 100 Arm virtual machines and benchmark message
+      throughput performance. It is designed for developers looking to migrate their Apache Kafka workloads
+      from x86_64 to ...
+    source_hash: d510dd43a485e1c2fac404ef9a2e00b5be2449b99b1095c9a1841cd2fcba9b0e
+  - path: content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing Kedify
+      and KEDA with Helm and testing autoscaling behavior. It is designed for developers running HTTP
+      workloads on Kuber...
+    source_hash: 6ff33f6dfaf25e926a0ce8756b4e564e5aa37112e5425f9c3dce13f772b54145
+  - path: content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+    summary_preview: Create, train, and evaluate a neural network model on Arm servers using Keras Core
+      with TensorFlow, PyTorch, and JAX backends. It is designed for engineers who want to create a neural
+      network model on...
+    source_hash: 830556dfd51aaa2dd95ee957e64306bab369297f7bc66325e7eb509f541f683c
+  - path: content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+    summary_preview: Compile and install custom Linux kernels on Arm cloud instances using TuxMake with
+      configurations for 64 KB page sizes and Fastpath testing. It is designed for software developers
+      building custom Linu...
+    source_hash: 004436cf14ff0056136889c58d676295c6dd2088f06f57f397a07ec9004e6b44
+  - path: content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Identify and migrate container images in a Kubernetes cluster to Arm-compatible versions
+      using KubeArchInspect reports. It is designed for software developers who want to ensure containers
+      running in ...
+    source_hash: 43e4e28b88b9721ca94457418f3b4887d0099017578a54da1db5fcdc11f4a122
+  - path: content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Deploy AWS Lambda functions on Graviton processors using Terraform for Python and
+      Node.js runtimes. It is designed for software developers who want to learn how to deploy Lambda
+      functions on AWS Gravi...
+    source_hash: 22fa96e29143f2e0dc0c204919422914b3f70d63ddf833cf1067fbf67b525aa0
+  - path: content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
+    status: unchanged
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+    summary_changed: false
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+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Enable and measure libhugetlbfs performance improvements for MySQL and other workloads
+      on Arm Linux servers. It is designed for engineers looking for ways to increase performance on Arm
+      servers. By th...
+    source_hash: 66d13edff1371295f0e88a618e924ca67b85bcc8aa72034d1e582a0b267f0d05
+  - path: content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+    summary_preview: Serve the llama.cpp chatbot through an OpenAI-compatible API, enabling existing OpenAI-style
+      clients and applications to run against a persistent Arm-hosted LLM. It is designed for developers
+      interest...
+    source_hash: d82aa4faeb599a3a1ac12d477204ebe0a4ddb99564bedced86ca0fc4851e17b9
+  - path: content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+    summary_preview: Build a production-ready vision chatbot on Google Axion using Streamlit, PyTorch,
+      and Hugging Face Transformers with a quantized Llama 3.2-Vision model. It is designed for software
+      developers and ML e...
+    source_hash: 3e8307a5daf435c21f3cecff635ac51724a63ff9ea4307082d869601563b4204
+  - path: content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+    summary_preview: Optimize llama.cpp on Arm CPUs by integrating Streamline Annotations to profile Prefill
+      and Decode stages, analyze operators, and evaluate multi-core execution. It is designed for software
+      developers,...
+    source_hash: 36bf3c45f0b38350714ba41ea88c1551b33f6d299a65b3b0d6668fee2d88d835
+  - path: content/learning-paths/servers-and-cloud-computing/lse/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Understand Large System Extensions (LSE) for Arm processors and verify whether applications
+      use LSE for improved atomic operation performance. It is designed for software developers who want
+      to learn ...
+    source_hash: 9610291239b88c67e21055f94cd03fe7cf80f7d875d53ebe0d8de7837ce99fa7
+  - path: content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+    summary_preview: Deploy MariaDB on Arm cloud instances across AWS, Azure, and Google Cloud using Docker,
+      Amazon RDS, and automation with Terraform and Ansible. It is designed for software developers who
+      want to deploy...
+    source_hash: db4ce1c59ad10bd127b4990c82ce876311d7dc7e1ad5d39ec132daf82ceb8b79
+  - path: content/learning-paths/servers-and-cloud-computing/memcached/_index.md
+    status: unchanged
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+    summary_changed: false
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+    faq_changes:
+      before_count: 5
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+    summary_preview: Install memcached on Arm cloud servers and benchmark in-memory key-value store performance
+      using open-source tools. It is designed for developers who want to use memcached as their in-memory
+      key-value...
+    source_hash: b42ca5cc8f4db6ba6019f3720a5ef46119aa403a2baaef5362e874f898ce0419
+  - path: content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Deploy Memcached as a cache for MySQL and PostgreSQL on Arm servers. It is designed
+      for developers who want to use memcached as their in-memory key-value store. By the end, you will
+      be able to deploy ...
+    source_hash: 4efe46aaf4580a6b8f263b69e8f072211bfd24fcf7f7a885689c927fc05a4c63
+  - path: content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
+    status: unchanged
+    changed_on_disk: true
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+    faq_changed: false
+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Use ASCT to measure cache latency, streaming bandwidth, and coherency latency on
+      Arm Neoverse systems, and compare results across Graviton generations. It is designed for software
+      developers and perfo...
+    source_hash: d61c9ddcef1c7ffe12b3ee818852fb3f0a62a90cec451692c1186cf2c01de625
+  - path: content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Test and validate thread synchronization approaches in the Arm memory model using
+      Herd7, Litmus7, and Arm hardware with assembly snippets. It is designed for developers seeking practical
+      ways to test ...
+    source_hash: 6aa61de638be961339d958345225d696ff2b83b8f9cab22bf956f9bf2d15c1aa
+  - path: content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
+    status: unchanged
+    changed_on_disk: true
+    summary_changed: false
+    faq_changed: false
+    faq_changes:
+      before_count: 5
+      after_count: 5
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+      updated_questions: []
+    summary_preview: Microbenchmark and tune network performance with iPerf3 and Linux traffic control
+      walks you through an end-to-end Arm software workflow. It is designed for performance engineers,
+      Linux system administ...
+    source_hash: a67d36fb650b77c170c15f193049490286a3f097801d0c79d701d3f5610fe1dc
+  - path: content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
+    status: unchanged
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+    faq_changed: false
+    faq_changes:
+      before_count: 5
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+    summary_preview: Scan source code for architecture-specific portability issues using migrate-ease
+      to identify and resolve AArch64 porting challenges before migration. It is designed for developers
+      looking to migrate a...
+    source_hash: 56a38d1d742dd301d48e9ad61ff00e07048559f3f646815e22937113243adcdb
+  - path: content/learning-paths/servers-and-cloud-computing/migration/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Set up an Arm development environment, analyze dependencies, and understand common
+      challenges and scenarios for migrating applications to Arm servers. It is designed for software
+      developers looking to...
+    source_hash: 6b2b985c39579c4d0855c8c28b610ba558e25b451a6b755475ff4393e2810670
+  - path: content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
+    status: unchanged
+    changed_on_disk: true
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+    faq_changed: false
+    faq_changes:
+      before_count: 5
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+      updated_questions: []
+    summary_preview: Build a Retrieval-Augmented Generation (RAG) application on Arm servers using Zilliz
+      Cloud for vector search and llama.cpp for LLM inference. It is designed for software developers
+      who want to create ...
+    source_hash: 2eb252b19b7535fbb776a3153bfc9f70b6217088e5b4b55deb56d01393abaf3b
+  - path: content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
+    status: unchanged
+    changed_on_disk: true
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+    faq_changed: false
+    faq_changes:
+      before_count: 5
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+    summary_preview: Learn how to deploy and configure MinIO on an Azure Cobalt 100 virtual machine, benchmark
+      object storage throughput, and validate S3 compatibility using the boto3 Python SDK. It is designed
+      for develo...
+    source_hash: 6c438573edec90b3aa4135ace38cd3ae604c58658c1949117ca80dbdd21394bd
+  - path: content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
+    status: unchanged
+    changed_on_disk: true
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+    faq_changes:
+      before_count: 5
+      after_count: 5
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+      updated_questions: []
+    summary_preview: Benchmark machine learning inference performance on Arm servers using TensorFlow
+      and the MLPerf Inference benchmark suite from MLCommons. It is designed for software developers
+      interested in benchmark...
+    source_hash: 973ba6c1e00fc67e1702606412124d8172e071b9b677a1df53c3be66210bf704
+  - path: content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
+    status: unchanged
+    changed_on_disk: true
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+    faq_changes:
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+    summary_preview: Install MongoDB on Arm servers and benchmark database performance using Yahoo Cloud
+      Serving Benchmark (YCSB) to compare against other architectures. It is designed for software developers
+      who want to ...
+    source_hash: b52a1b60a74e19f2a3b60b8a77199ad5369c1ccd3b4d5ce0252a169d9f6123fd
+  - path: content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+    summary_preview: Deploy MongoDB on Azure Cobalt 100 Arm virtual machines and benchmark database performance
+      using mongotop and mongostat monitoring tools. It is designed for software developers who want to
+      migrate Mon...
+    source_hash: f270cfc90245c0090685fabf5cbebf62a714edbf34e1ea86c3df0bfbb4699ea4
+  - path: content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Deploy MongoDB on Google Cloud Axion C4A virtual machines and benchmark database
+      performance with Yahoo Cloud Serving Benchmark (YCSB). It is designed for This introductory topic
+      is for software devel...
+    source_hash: abbdde22271151fb1485c54bafe463e7797a0b913c7d4c99245d2914a3f9bb82
+  - path: content/learning-paths/servers-and-cloud-computing/mpi/_index.md
+    status: unchanged
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+    summary_preview: Debug, profile, and optimize MPI parallel applications on Arm servers using Linaro
+      Forge, gdb, and Arm Performance Libraries. It is designed for HPC software developers writing MPI
+      applications. By th...
+    source_hash: 300794f4010658d945212c74c5257635d620cbb6230cac0de92bf23e4e9e29fe
+  - path: content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Select and apply accuracy modes for vectorized math functions in Libamath to balance
+      performance and precision for your application. It is designed for developers who want to use the
+      different accurac...
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+  - path: content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md
+    status: unchanged
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+    faq_changes:
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+    summary_preview: Build a multi-architecture Kubernetes cluster running nginx on Azure AKS walks you
+      through an end-to-end Arm software workflow. It is designed for developers who want to deploy multi-architecture
+      Kube...
+    source_hash: d765afadfe5155f0ecd5bd08373fa12464b2ee5ebb1f8c7831039eb07eba8831
+  - path: content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md
+    status: unchanged
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+    faq_changes:
+      before_count: 5
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+    summary_preview: Add Arm nodes to your GKE cluster using a multi-architecture Ollama container image
+      walks you through an end-to-end Arm software workflow. It is designed for developers who want to
+      compare the perform...
+    source_hash: cd31c217eaeab6faad263728c446ee188cd9d542904d87ae7bfd5120713dfaa4
+  - path: content/learning-paths/servers-and-cloud-computing/mysql/_index.md
+    status: unchanged
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+    summary_preview: Learn how to deploy MySQL walks you through an end-to-end Arm software workflow.
+      It is designed for software developers who want to deploy MySQL on Arm. By the end, you will be
+      able to learn about the...
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+    summary_preview: Deploy MySQL on Microsoft Azure Cobalt 100 processors walks you through an end-to-end
+      Arm software workflow. It is designed for developers migrating MySQL applications from x86_64 to
+      Arm. By the end, ...
+    source_hash: b9bc1d1f965e4fdeeb9552d853330c201e00597829420c8a0397fa425c3231c4
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+    summary_preview: Benchmarking MySQL with Sysbench walks you through an end-to-end Arm software workflow.
+      It is designed for performance engineers who want to benchmark MySQL using Sysbench and optimize
+      performance on ...
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+    summary_preview: Learn how to Tune MySQL walks you through an end-to-end Arm software workflow. It
+      is designed for software developers and DevOps professionals interested in optimizing MySQL performance
+      on Arm-based V...
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+    summary_preview: Develop and Validate Firmware Pre-Silicon on Arm Neoverse CSS V3 walks you through
+      an end-to-end Arm software workflow. It is designed for This advanced topic is for firmware developers,
+      system archit...
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+    summary_preview: Run a .NET Aspire application on Arm-based VMs on AWS and GCP walks you through an
+      end-to-end Arm software workflow. It is designed for software developers interested in learning
+      how to deploy .NET As...
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+  - path: content/learning-paths/servers-and-cloud-computing/nginx/_index.md
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+    summary_preview: Learn how to deploy Nginx walks you through an end-to-end Arm software workflow.
+      It is designed for engineers who want to use Nginx on Arm. By the end, you will be able to install
+      and run Nginx on Arm...
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+    summary_preview: Deploy NGINX on Azure Cobalt 100 Arm-based virtual machines walks you through an
+      end-to-end Arm software workflow. It is designed for system administrators and developers who want
+      to learn how to depl...
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+    summary_preview: Learn how to tune Nginx walks you through an end-to-end Arm software workflow. It
+      is designed for software developers who want to use Nginx on Arm. By the end, you will be able to
+      describe how kernel ...
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+  - path: content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md
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+    summary_preview: Run a Natural Language Processing (NLP) model from Hugging Face on Arm servers walks
+      you through an end-to-end Arm software workflow. It is designed for software developers who want
+      to learn how to ru...
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+    faq_changes:
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+    summary_preview: Deploy Node.js on Google Cloud C4A Arm-based Axion VMs walks you through an end-to-end
+      Arm software workflow. It is designed for software developers migrating Node.js workloads from x86_64
+      to Arm-base...
+    source_hash: 800eed835763098d4b369789d10de642bbdbaa390e8bfe0cb6ea2c4c78f7ecbd
+  - path: content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md
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+    faq_changes:
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+    summary_preview: Deploy Arm Instances on Oracle Cloud Infrastructure (OCI) using Terraform walks you
+      through an end-to-end Arm software workflow. It is designed for software developers who are new
+      to deploying Arm ins...
+    source_hash: 3ee5dd8d8edeb5dcb1e3be7bb09cdf0b1b2694f0e74e9eb3c6c2f17912399808
+  - path: content/learning-paths/servers-and-cloud-computing/onnx/_index.md
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+    faq_changes:
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+    summary_preview: Deploy Phi-4-mini model with ONNX Runtime on Azure Cobalt 100 walks you through an
+      end-to-end Arm software workflow. It is designed for developers, ML engineers, and cloud practitioners
+      looking to dep...
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+    faq_changes:
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+    summary_preview: Deploy SqueezeNet 1.0 INT8 model with ONNX Runtime on Azure Cobalt 100 walks you
+      through an end-to-end Arm software workflow. It is designed for developers deploying ONNX-based
+      applications on Arm-bas...
+    source_hash: 84ba887426f3ca45b698c79028023d80cbf0a06e6bc2fb71fcbe924943078dd8
+  - path: content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md
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+    summary_preview: Simulate OpenBMC and UEFI pre-silicon on Neoverse RD-V3 walks you through an end-to-end
+      Arm software workflow. It is designed for This advanced topic is for firmware developers, platform
+      software engi...
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+  - path: content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md
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+    summary_preview: Learn how to profile an example C++ data-processing workload on Arm Linux with Arm
+      Performix, then accelerate random number generation using OpenRNG and Arm Performance Libraries.
+      It is designed for C...
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+  - path: content/learning-paths/servers-and-cloud-computing/openshift/_index.md
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+    summary_preview: Build multi-architecture applications with Red Hat OpenShift Pipelines on AWS walks
+      you through an end-to-end Arm software workflow. It is designed for OpenShift administrators who
+      want to migrate the...
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+    summary_preview: Deploy OpenStack on Azure Cobalt 100 Arm64 virtual machines using DevStack for development
+      and Kolla-Ansible for containerized production deployments. It is designed for This learning path
+      is designed...
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+    summary_preview: Deploy OpenTelemetry on Google Cloud C4A Axion processors walks you through an end-to-end
+      Arm software workflow. It is designed for DevOps engineers, platform engineers, and software developers
+      who wa...
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+    summary_preview: Understand Arm Pointer Authentication walks you through an end-to-end Arm software
+      workflow. It is designed for software developers interested in understanding Arm Pointer Authentication.
+      By the end, ...
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+    summary_preview: Learn how to use an AI agent and the Performix tool through the Arm MCP Server to
+      run the Code Hotspots recipe on a C++ application, interpret flame graph results, and apply targeted
+      optimizations on ...
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+    summary_preview: Optimize application performance using Arm Performix CPU microarchitecture analysis
+      walks you through an end-to-end Arm software workflow. It is designed for software developers who
+      want to learn perf...
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+    summary_preview: Deploy PHP on Google Cloud C4A Arm-based Axion VMs walks you through an end-to-end
+      Arm software workflow. It is designed for developers migrating Hypertext Preprocessor (PHP) workloads
+      from x86_64 to ...
+    source_hash: 719ec8966a776cc5ee97782b7ef7cc2b989a8616d14cb36b9847c9cbd2f16446
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+    summary_preview: Optimize application performance with CPU affinity walks you through an end-to-end
+      Arm software workflow. It is designed for developers, performance engineers, and system administrators
+      looking to fin...
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+    summary_preview: Implement Code level Performance Analysis using the PMUv3 plugin walks you through
+      an end-to-end Arm software workflow. It is designed for Engineers who want to carry out C/C++ performance
+      analysis by...
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+    summary_preview: Learn how to deploy PostgreSQL walks you through an end-to-end Arm software workflow.
+      It is designed for software developers who want to deploy PostgreSQL on Arm. By the end, you will
+      be able to learn...
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+    summary_preview: Deploy PostgreSQL on Azure Cobalt 100 Arm64 virtual machines, load a relational schema
+      with transactional data, and benchmark and optimize query performance using pgbench and pg_stat_statements.
+      It is...
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+    summary_preview: Learn how to Tune PostgreSQL walks you through an end-to-end Arm software workflow.
+      It is designed for software developers and DevOps professionals interested in optimizing PostgreSQL
+      performance. By ...
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+    summary_preview: Run Process watch on your Arm machine walks you through an end-to-end Arm software
+      workflow. It is designed for software developers who want to build and run the Process Watch tool
+      on an Arm-based mac...
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+  - path: content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md
+    status: unchanged
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+    summary_preview: Profiling for Neoverse with Streamline CLI Tools walks you through an end-to-end
+      Arm software workflow. It is designed for This is an introductory guide for developers who want
+      to measure and optimize...
+    source_hash: ef12378069d6f3538d8daefb5da7fc8e8ce4196277928d372745d12fde6e46a1
+  - path: content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md
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+    summary_preview: Deploy Puppet on Google Cloud C4A walks you through an end-to-end Arm software workflow.
+      It is designed for developers deploying and optimizing Puppet workloads on Arm Linux environments,
+      specifically...
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+  - path: content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md
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+    summary_preview: Run a Large Language Model (LLM) chatbot with PyTorch using KleidiAI on Arm servers
+      walks you through an end-to-end Arm software workflow. It is designed for software developers interested
+      in running ...
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+  - path: content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md
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+    summary_preview: Learn how to deploy Qdrant on Google Cloud C4A Axion processors, generate vector
+      embeddings with Sentence Transformers, and build a semantic search and chatbot retrieval system
+      on Arm-based infrastruc...
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+  - path: content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md
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+    summary_preview: Deploy RabbitMQ on Arm64 Cloud Platforms (Azure and GCP) walks you through an end-to-end
+      Arm software workflow. It is designed for software engineers and platform engineers migrating messaging
+      and eve...
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+  - path: content/learning-paths/servers-and-cloud-computing/rag/_index.md
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+    summary_preview: Deploy a RAG-based Chatbot with llama-cpp-python using KleidiAI on Google Axion processors
+      walks you through an end-to-end Arm software workflow. It is designed for software developers, ML
+      engineers, ...
+    source_hash: d9435cc1dc1fe65432d83934e69993853dd3f294f66f98e9bcfb5f81e6ac6d5f
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+    summary_preview: Get started with the Arm 5G RAN Acceleration Library (ArmRAL) walks you through an
+      end-to-end Arm software workflow. It is designed for software developers new to the Arm RAN Acceleration
+      Library (Arm...
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     summary_preview: Run Spark applications on Microsoft Azure Cobalt 100 processors walks you through
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     summary_preview: Access running containers using Supervisor, SSH, and Remote.It walks you through
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     summary_preview: Port Code to Arm Scalable Vector Extension (SVE) walks you through an end-to-end
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-      - What do you need before you start?
-      - Which tools, languages, or platforms does it cover?
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     summary_preview: Accelerate search performance with SVE2 MATCH on Arm servers walks you through an
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     summary_preview: Get ready for performance analysis with Sysreport walks you through an end-to-end
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-      - What will you accomplish in this Learning Path?
-      - Who is this Learning Path for?
-      - What do you need before you start?
-      - Which tools, languages, or platforms does it cover?
-      - How is the Learning Path structured?
+      added_questions: []
       removed_questions: []
       updated_questions: []
     summary_preview: Measure and accelerate PyTorch Inference on Arm servers walks you through an end-to-end
@@ -7899,19 +12054,14 @@ latest_run:
       accelerate th...
     source_hash: d25121278f9dd40e2fd19d988e35866c6bfa70cfd0b93e2ec4506c8ad8a26d88
   - path: content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md
-    status: added
+    status: unchanged
     changed_on_disk: true
-    summary_changed: true
-    faq_changed: true
+    summary_changed: false
+    faq_changed: false
     faq_changes:
-      before_count: 0
+      before_count: 5
       after_count: 5
-      added_questions:
-      - What will you accomplish in this Learning Path?
-      - Who is this Learning Path for?
-      - What do you need before you start?
-      - Which tools, languages, or platforms does it cover?
-      - How is the Learning Path structured?
+      added_questions: []
       removed_questions: []
       updated_questions: []
     summary_preview: Learn about Neoverse Cache PMU Events using C and Assembly Language walks you through
@@ -7919,19 +12069,14 @@ latest_run:
       to learn about th...
     source_hash: 1b309980bef983966d948036e309e132b56f767161967187e72c6a9f81f17dce
   - path: content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md
-    status: added
+    status: unchanged
     changed_on_disk: true
-    summary_changed: true
-    faq_changed: true
+    summary_changed: false
+    faq_changed: false
     faq_changes:
-      before_count: 0
+      before_count: 5
       after_count: 5
-      added_questions:
-      - What will you accomplish in this Learning Path?
-      - Who is this Learning Path for?
-      - What do you need before you start?
-      - Which tools, languages, or platforms does it cover?
-      - How is the Learning Path structured?
+      added_questions: []
       removed_questions: []
       updated_questions: []
     summary_preview: Learn about Neoverse Non-cache PMU events using C and Assembly Language walks you
@@ -7939,19 +12084,14 @@ latest_run:
       to learn about why com...
     source_hash: 523ff99ccb8d13543f64839fa21040191d2a9ff7ce7c78fa590e8775fac754a6
   - path: content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md
-    status: added
+    status: unchanged
     changed_on_disk: true
-    summary_changed: true
-    faq_changed: true
+    summary_changed: false
+    faq_changed: false
     faq_changes:
-      before_count: 0
+      before_count: 5
       after_count: 5
-      added_questions:
-      - What will you accomplish in this Learning Path?
-      - Who is this Learning Path for?
-      - What do you need before you start?
-      - Which tools, languages, or platforms does it cover?
-      - How is the Learning Path structured?
+      added_questions: []
       removed_questions: []
       updated_questions: []
     summary_preview: Scan multi-architecture containers with Trivy on Azure Cobalt 100 walks you through
@@ -7959,19 +12099,14 @@ latest_run:
       to integrate securi...
     source_hash: 13bba1dee1c948f8b751a71479a5106014a2c21dab6ce3968a08bed9e3363de1
   - path: content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md
-    status: added
+    status: unchanged
     changed_on_disk: true
-    summary_changed: true
-    faq_changed: true
+    summary_changed: false
+    faq_changed: false
     faq_changes:
-      before_count: 0
+      before_count: 5
       after_count: 5
-      added_questions:
-      - What will you accomplish in this Learning Path?
-      - Who is this Learning Path for?
-      - What do you need before you start?
-      - Which tools, languages, or platforms does it cover?
-      - How is the Learning Path structured?
+      added_questions: []
       removed_questions: []
       updated_questions: []
     summary_preview: Tune network workloads on Arm-based bare-metal instances walks you through an end-to-end
@@ -7979,19 +12114,14 @@ latest_run:
       workloads on Ar...
     source_hash: 9f2b3f6c5e76ecb754de5c0fd84278ff71f71c5c7906acdc06911f2feff1f5fd
   - path: content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md
-    status: added
+    status: unchanged
     changed_on_disk: true
-    summary_changed: true
-    faq_changed: true
+    summary_changed: false
+    faq_changed: false
     faq_changes:
-      before_count: 0
+      before_count: 5
       after_count: 5
-      added_questions:
-      - What will you accomplish in this Learning Path?
-      - Who is this Learning Path for?
-      - What do you need before you start?
-      - Which tools, languages, or platforms does it cover?
-      - How is the Learning Path structured?
+      added_questions: []
       removed_questions: []
       updated_questions: []
     summary_preview: Deploy TypeScript on Google Cloud C4A virtual machines walks you through an end-to-end
@@ -7999,19 +12129,14 @@ latest_run:
       on Arm64 Linux...
     source_hash: ee805f703acd45612c9a94e60c6322866dc1c8ff25d48de2fb4cd9067948c93e
   - path: content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md
-    status: added
+    status: unchanged
     changed_on_disk: true
-    summary_changed: true
-    faq_changed: true
+    summary_changed: false
+    faq_changed: false
     faq_changes:
-      before_count: 0
+      before_count: 5
       after_count: 5
-      added_questions:
-      - What will you accomplish in this Learning Path?
-      - Who is this Learning Path for?
-      - What do you need before you start?
-      - Which tools, languages, or platforms does it cover?
-      - How is the Learning Path structured?
+      added_questions: []
       removed_questions: []
       updated_questions: []
     summary_preview: Migrate applications that leverage performance libraries walks you through an end-to-end
@@ -8019,19 +12144,14 @@ latest_run:
       that rely ...
     source_hash: 035bd363fc8ae772acf52d7e392f2db27087267706aeec86b4098c497e5fe91d
   - path: content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md
-    status: added
+    status: unchanged
     changed_on_disk: true
-    summary_changed: true
-    faq_changed: true
+    summary_changed: false
+    faq_changed: false
     faq_changes:
-      before_count: 0
+      before_count: 5
       after_count: 5
-      added_questions:
-      - What will you accomplish in this Learning Path?
-      - Who is this Learning Path for?
-      - What do you need before you start?
-      - Which tools, languages, or platforms does it cover?
-      - How is the Learning Path structured?
+      added_questions: []
       removed_questions: []
       updated_questions: []
     summary_preview: Install Vectorscan (Hyperscan on Arm) and use it with Snort 3 walks you through an
@@ -8039,19 +12159,14 @@ latest_run:
       to migrate to Arm. ...
     source_hash: beb244c7766c474b47942b86f1cdd0d124c1cccb74dbe40f0b6c0917d0b3d31a
   - path: content/learning-paths/servers-and-cloud-computing/vllm/_index.md
-    status: added
+    status: unchanged
     changed_on_disk: true
-    summary_changed: true
-    faq_changed: true
+    summary_changed: false
+    faq_changed: false
     faq_changes:
-      before_count: 0
+      before_count: 5
       after_count: 5
-      added_questions:
-      - What will you accomplish in this Learning Path?
-      - Who is this Learning Path for?
-      - What do you need before you start?
-      - Which tools, languages, or platforms does it cover?
-      - How is the Learning Path structured?
+      added_questions: []
       removed_questions: []
       updated_questions: []
     summary_preview: Build and Run vLLM on Arm Servers walks you through an end-to-end Arm software workflow.
@@ -8059,19 +12174,14 @@ latest_run:
       library on A...
     source_hash: db5987ec7987375d9b51de843b0e1faf0e61731b14c5a563d19aaad26f50f6bb
   - path: content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
-    status: added
+    status: unchanged
     changed_on_disk: true
-    summary_changed: true
-    faq_changed: true
+    summary_changed: false
+    faq_changed: false
     faq_changes:
-      before_count: 0
+      before_count: 5
       after_count: 5
-      added_questions:
-      - What will you accomplish in this Learning Path?
-      - Who is this Learning Path for?
-      - What do you need before you start?
-      - Which tools, languages, or platforms does it cover?
-      - How is the Learning Path structured?
+      added_questions: []
       removed_questions: []
       updated_questions: []
     summary_preview: Run vLLM inference with INT4 quantization on Arm servers walks you through an end-to-end
@@ -8079,19 +12189,14 @@ latest_run:
       for Arm-based s...
     source_hash: 487e9829c6e08798ad01be7a01f192e10e99cb06b71108575f1919c134eece02
   - path: content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
-    status: added
+    status: unchanged
     changed_on_disk: true
-    summary_changed: true
-    faq_changed: true
+    summary_changed: false
+    faq_changed: false
     faq_changes:
-      before_count: 0
+      before_count: 5
       after_count: 5
-      added_questions:
-      - What will you accomplish in this Learning Path?
-      - Who is this Learning Path for?
-      - What do you need before you start?
-      - Which tools, languages, or platforms does it cover?
-      - How is the Learning Path structured?
+      added_questions: []
       removed_questions: []
       updated_questions: []
     summary_preview: Run the vvenc H.266 encoder on Arm servers walks you through an end-to-end Arm software
@@ -8099,19 +12204,14 @@ latest_run:
       Versatile Vide...
     source_hash: 4ed20056b2d82bd2c9ab1afe3d74657df9ac09808592d843c1b143626a8668ac
   - path: content/learning-paths/servers-and-cloud-computing/whisper/_index.md
-    status: added
+    status: unchanged
     changed_on_disk: true
-    summary_changed: true
-    faq_changed: true
+    summary_changed: false
+    faq_changed: false
     faq_changes:
-      before_count: 0
+      before_count: 5
       after_count: 5
-      added_questions:
-      - What will you accomplish in this Learning Path?
-      - Who is this Learning Path for?
-      - What do you need before you start?
-      - Which tools, languages, or platforms does it cover?
-      - How is the Learning Path structured?
+      added_questions: []
       removed_questions: []
       updated_questions: []
     summary_preview: Accelerate Whisper on Arm with Hugging Face Transformers walks you through an end-to-end
@@ -8119,19 +12219,14 @@ latest_run:
       concepts and...
     source_hash: e130630b5ae4626641779d0b66d3c1c132b90899cd3d6db07a46df8957c4da38
   - path: content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
-    status: added
+    status: unchanged
     changed_on_disk: true
-    summary_changed: true
-    faq_changed: true
+    summary_changed: false
+    faq_changed: false
     faq_changes:
-      before_count: 0
+      before_count: 5
       after_count: 5
-      added_questions:
-      - What will you accomplish in this Learning Path?
-      - Who is this Learning Path for?
-      - What do you need before you start?
-      - Which tools, languages, or platforms does it cover?
-      - How is the Learning Path structured?
+      added_questions: []
       removed_questions: []
       updated_questions: []
     summary_preview: Deploy MySQL and WordPress on an always free tier Arm shape walks you through an
@@ -8139,26 +12234,20 @@ latest_run:
       Oracle Cloud Infrastru...
     source_hash: 46f2645fb6ef298508af93569554315cfa2564be9891acabf1c874c94e52d70d
   - path: content/learning-paths/servers-and-cloud-computing/zlib/_index.md
-    status: added
+    status: unchanged
     changed_on_disk: true
-    summary_changed: true
-    faq_changed: true
+    summary_changed: false
+    faq_changed: false
     faq_changes:
-      before_count: 0
+      before_count: 5
       after_count: 5
-      added_questions:
-      - What will you accomplish in this Learning Path?
-      - Who is this Learning Path for?
-      - What do you need before you start?
-      - Which tools, languages, or platforms does it cover?
-      - How is the Learning Path structured?
+      added_questions: []
       removed_questions: []
       updated_questions: []
     summary_preview: Learn how to build and use zlib-ng on Arm servers, using its Neon SIMD and ARMv8
       CRC32 optimizations to improve compression performance compared to the system default zlib. It is
       designed for software...
     source_hash: 8916f83f621505dc5406f14e70d04e702ea304950e34b4b76dc1bbc987bcc4e3
-history:
 - timestamp: '2026-04-30T18:58:19Z'
   mode: write
   require_enable_flag: true

From c9c4fdb3b9ac4d10e42706e5cf13a96da575319c Mon Sep 17 00:00:00 2001
From: Christopher Moroney <christopher.moroney1@gmail.com>
Date: Wed, 6 May 2026 11:41:42 -0700
Subject: [PATCH 04/23] updates to configuration for faq

---
 .github/workflows/generate-summary-faq.yml   |    81 +-
 archetypes/learning-path/_index.md           |     6 +
 reports/generated-summary-faq/latest-run.yml | 20436 +----------------
 tools/generate_summary_faq.py                |   601 +-
 4 files changed, 610 insertions(+), 20514 deletions(-)

diff --git a/.github/workflows/generate-summary-faq.yml b/.github/workflows/generate-summary-faq.yml
index 9273ccadd2..ed1b38510c 100644
--- a/.github/workflows/generate-summary-faq.yml
+++ b/.github/workflows/generate-summary-faq.yml
@@ -112,6 +112,8 @@ jobs:
           import yaml
 
           report_path = pathlib.Path("reports/generated-summary-faq/latest-run.yml")
+          print("# Generate summary/FAQ report")
+          print("")
           print("Generated summary/FAQ report: `reports/generated-summary-faq/latest-run.yml`")
           print("")
 
@@ -122,16 +124,75 @@ jobs:
           report = yaml.safe_load(report_path.read_text(encoding="utf-8")) or {}
           latest = report.get("latest_run", {})
           totals = latest.get("totals", {})
-          print("## Run totals")
-          print("")
-          print(f"- Processed: {totals.get('processed', 0)}")
-          print(f"- Added: {totals.get('added', 0)}")
-          print(f"- Updated: {totals.get('updated', 0)}")
-          print(f"- Unchanged: {totals.get('unchanged', 0)}")
-          print(f"- Errors: {totals.get('errors', 0)}")
+          section_totals = latest.get("section_totals", {})
+          reason_totals = latest.get("reason_totals", {})
+          paths = latest.get("paths", [])
+
+          def metric_row(label, value):
+              return f"| {label} | {value} |"
+
+          print("## Run overview")
           print("")
-          print("## Paths")
+          print("| Metric | Count |")
+          print("| --- | ---: |")
+          print(metric_row("Processed", totals.get("processed", 0)))
+          print(metric_row("Added", totals.get("added", 0)))
+          print(metric_row("Updated", totals.get("updated", 0)))
+          print(metric_row("Drift detected", totals.get("drift_detected", 0)))
+          print(metric_row("Unchanged", totals.get("unchanged", 0)))
+          print(metric_row("Errors", totals.get("errors", 0)))
+          print(metric_row("Summary changed", totals.get("summary_changed", 0)))
+          print(metric_row("FAQs changed", totals.get("faq_changed", 0)))
+          print(metric_row("Rerun flags reset", totals.get("rerun_flags_reset", 0)))
           print("")
-          for entry in latest.get("paths", []):
-              print(f"- {entry.get('status', 'unknown')}: {entry.get('path', '')}")
+
+          for section_name, title in (("summary", "Summary actions"), ("faqs", "FAQ actions")):
+              actions = section_totals.get(section_name, {})
+              print(f"## {title}")
+              print("")
+              print("| Action | Count |")
+              print("| --- | ---: |")
+              for action in (
+                  "created",
+                  "repaired_missing",
+                  "rerun_requested",
+                  "drift_detected_preserved",
+                  "unchanged",
+              ):
+                  print(metric_row(action, actions.get(action, 0)))
+              print("")
+
+          nonzero_reasons = [(reason, count) for reason, count in reason_totals.items() if count]
+          if nonzero_reasons:
+              print("## Change reasons")
+              print("")
+              print("| Reason | Count |")
+              print("| --- | ---: |")
+              for reason, count in nonzero_reasons:
+                  print(metric_row(reason, count))
+              print("")
+
+          interesting_paths = [
+              entry for entry in paths
+              if entry.get("status") != "unchanged" or entry.get("change_reasons")
+          ]
+
+          if interesting_paths:
+              print("## Path details")
+              print("")
+              print("| Path | Status | Summary | FAQs | Reasons |")
+              print("| --- | --- | --- | --- | --- |")
+              for entry in interesting_paths[:50]:
+                  summary_action = entry.get("summary", {}).get("action", "")
+                  faq_action = entry.get("faqs", {}).get("action", "")
+                  reasons = ", ".join(entry.get("change_reasons", [])) or "none"
+                  print(
+                      f"| `{entry.get('path', '')}` | {entry.get('status', 'unknown')} | "
+                      f"{summary_action} | {faq_action} | {reasons} |"
+                  )
+              if len(interesting_paths) > 50:
+                  print("")
+                  print(f"_Showing the first 50 path rows out of {len(interesting_paths)}._")
+          else:
+              print("All processed Learning Paths were fully unchanged.")
           PY
diff --git a/archetypes/learning-path/_index.md b/archetypes/learning-path/_index.md
index cf781f3648..ae51757566 100644
--- a/archetypes/learning-path/_index.md
+++ b/archetypes/learning-path/_index.md
@@ -17,6 +17,12 @@ prerequisites:
 # generated summary/FAQ GitHub Action.
 generate_summary_faq: false
 
+# Optional one-shot controls: set either field to true to regenerate just that
+# generated section the next time the summary/FAQ workflow runs. The workflow
+# resets them to false after a successful write.
+rerun_summary: false
+rerun_faqs: false
+
 author: PLACEHOLDER NAME
 
 ### Tags
diff --git a/reports/generated-summary-faq/latest-run.yml b/reports/generated-summary-faq/latest-run.yml
index fb7c2386a3..ed45e96bb7 100644
--- a/reports/generated-summary-faq/latest-run.yml
+++ b/reports/generated-summary-faq/latest-run.yml
@@ -1,20409 +1,47 @@
 latest_run:
-  timestamp: '2026-05-06T17:17:59Z'
-  mode: write
+  timestamp: ""
+  mode: ""
   require_enable_flag: true
-  path_filter: ''
+  path_filter: ""
   limit: 0
-  run_url: https://github.com/chrismoroney/arm-learning-paths/actions/runs/25450203813
-  git_ref: STESOL-345
-  git_sha: 4c5f94dd928413a40b0c163d7103c241b18b5f99
-  actor: chrismoroney
-  template_version: summary-faq-v1
+  run_url: ""
+  git_ref: ""
+  git_sha: ""
+  actor: ""
+  template_version: summary-faq-v2
   totals:
-    processed: 407
+    processed: 0
     added: 0
     updated: 0
-    unchanged: 407
-    skipped: 0
-    errors: 0
-    removed: 0
-  paths:
-  - path: content/learning-paths/automotive/openadkit1_container/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
-      after_count: 5
-      added_questions: []
-      removed_questions: []
-      updated_questions: []
-    summary_preview: Learn how to deploy and run containerized autonomous driving simulations using Autoware
-      Open AD Kit on Arm Neoverse with Docker, demonstrating SOAFEE-based Shift-Left development workflows.
-      It is desi...
-    source_hash: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
-  - path: content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
-      after_count: 5
-      added_questions: []
-      removed_questions: []
-      updated_questions: []
-    summary_preview: Learn how to implement functional safety isolation for autonomous driving systems
-      on Arm Neoverse using DDS-based communication, containerized deployment, and ISO 26262 compliance
-      principles. It is de...
-    source_hash: 92a6dac2b1674a44cd623a0c8c3189b38438124a6067ed7ca776999ff1d8b5bf
-  - path: content/learning-paths/automotive/system76-auto/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
-      after_count: 5
-      added_questions: []
-      removed_questions: []
-      updated_questions: []
-    summary_preview: Learn how to build and run the Arm Automotive Solutions Software Reference Stack
-      locally on the System76 Thelio Astra desktop using Multipass virtualization and Yocto build tools.
-      It is designed for a...
-    source_hash: 2b758d2dcf28a683ab164e28578a736d6d730b81dfbc01a6765619052fcdebd0
-  - path: content/learning-paths/automotive/zenacssdebug/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
-      after_count: 5
-      added_questions: []
-      removed_questions: []
-      updated_questions: []
-    summary_preview: Learn how to debug the Arm Zena CSS Reference Software Stack using Arm Development
-      Studio on a Fixed Virtual Platform, covering RSE, Safety Island, and Linux kernel debugging workflows.
-      It is designed...
-    source_hash: 8dccdbdd4d9727f3466987ce918d9df0ed9d4d4f28cd613162bacab01d9c18f3
-  - path: content/learning-paths/cross-platform/_example-learning-path/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
-      after_count: 5
-      added_questions: []
-      removed_questions: []
-      updated_questions: []
-    summary_preview: Learn what type of content belongs in a Learning Path and how to format it. It is
-      designed for content creators and software developers who want to share Arm related information
-      as a step-by-step guid...
-    source_hash: a58d5eb4c4256f3e4f4374344ef0e73836e8b51ac7223b0a5238b22137ec0819
-  - path: content/learning-paths/cross-platform/adler32/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
-      after_count: 5
-      added_questions: []
-      removed_questions: []
-      updated_questions: []
-    summary_preview: Learn how to use GitHub Copilot to write Neon intrinsics that accelerate the Adler32
-      checksum algorithm on Arm platforms, achieving significant performance improvements over standard
-      C implementations...
-    source_hash: 37b19106fba70423ba8c58f82097d9251f0ae208e882c852f9c4531afcebb46c
-  - path: content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
-      after_count: 5
-      added_questions: []
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-    summary_preview: Learn how to automate integration testing of MCP servers using Testcontainers and
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-      developers and QA e...
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-    summary_preview: Learn how to integrate Arm Virtual Hardware into a GitHub Actions CI/CD workflow
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-    summary_preview: Learn how to integrate Arm Virtual Hardware with AWS and GitHub Actions for automated
-      CI/CD workflows, including CloudFormation setup and test automation. It is designed for DevOps integrating
-      AVH int...
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-    summary_preview: Learn how to use Arm Development Studio to explore Realm Management Extension (RME)
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-      Architecture Envelop...
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-    summary_preview: Learn how to optimize C++ loop performance on Arm by providing boundary information
-      to the compiler, enabling SIMD vectorization and reducing runtime through compile-time context.
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-  - path: content/learning-paths/cross-platform/docker/_index.md
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-    summary_preview: Learn how to build, run, and share multi-architecture Docker images for Arm and x86
-      platforms using buildx, manifest, and remote builders. It is designed for software developers who
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-    summary_preview: Learn how to build multi-architecture Docker images for Arm and x86 using Docker
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-    summary_preview: Learn how to implement a dynamic memory allocator in C, understanding heap management
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-  - path: content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md
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-    summary_preview: Learn how to use the Eigen linear algebra library on Arm systems with ASIMD and SVE
-      vectorization, including building TensorFlow with SVE support for optimized performance. It is designed
-      for C/C++ de...
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-  - path: content/learning-paths/cross-platform/ernie_moe_v9/_index.md
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-    summary_preview: Learn how to deploy ERNIE-4.5 Mixture of Experts models on Armv9 devices using llama.cpp,
-      compare PT and Thinking variants, and measure Armv9-specific hardware optimization impact. It is
-      designed for ...
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-    summary_preview: Learn how Arm and x86 floating-point implementations follow IEEE 754 standards, identify
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-    summary_preview: Learn how to optimize C/C++ applications using function multiversioning on Arm64
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-      It is designed ...
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-  - path: content/learning-paths/cross-platform/github-arm-runners/_index.md
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-    summary_preview: Learn how to use GitHub Actions with Arm-hosted runners to build multi-architecture
-      container images for arm64 and amd64 platforms and automate deployment to Docker Hub. It is designed
-      for software de...
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-  - path: content/learning-paths/cross-platform/gitlab/_index.md
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-    summary_preview: Learn how to build a GitLab CI/CD pipeline using Google Axion-based self-hosted runners.
-      It is designed for DevOps professionals who are looking to build a CI/CD pipeline with GitLab on
-      Google Axion b...
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-  - path: content/learning-paths/cross-platform/gitlab-managed-runners/_index.md
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-    summary_preview: Learn how to build GitLab CI/CD pipelines using GitLab-hosted Arm64 runners to containerize
-      C applications, store images in GitLab Container Registry, and deploy on Arm infrastructure. It
-      is designed ...
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-  - path: content/learning-paths/cross-platform/integer-vs-floats/_index.md
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-    summary_preview: Learn how to identify and fix potential problems with integer and floating-point
-      conversions in C/C++ code on Arm, including explicit conversions, implicit conversions, and type
-      demotion issues. It is...
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-  - path: content/learning-paths/cross-platform/intrinsics/_index.md
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-    summary_preview: Learn how to port architecture-specific intrinsics to Arm processors. It is designed
-      for software developers interested in porting architecture specific intrinsics to Arm processors.
-      By the end, you w...
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-  - path: content/learning-paths/cross-platform/ipexplorer/_index.md
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-    summary_preview: Learn how to run custom software benchmarks on IP Explorer simulation platforms and
-      compare performance across Arm Cortex-M processors using cycle count analysis. It is designed for
-      IP Explorer users ...
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-  - path: content/learning-paths/cross-platform/kleidiai-explainer/_index.md
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-    summary_preview: Learn how to use KleidiAI micro-kernels to accelerate AI inference performance through
-      optimized matrix multiplication on Arm processors with architecture features like i8mm. It is designed
-      for develo...
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-  - path: content/learning-paths/cross-platform/loop-reflowing/_index.md
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-    summary_preview: Learn how to optimize C/C++ code using compiler autovectorization techniques including
-      loop modifications, restrict qualifiers, and conditional handling for Arm processors. It is designed
-      for C/C++ de...
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-  - path: content/learning-paths/cross-platform/matrix/_index.md
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-    summary_preview: Learn how to develop and test a modern C++ library using CMake, GoogleTest, and matrix
-      processing as a practical example on Arm platforms. It is designed for developers who want to learn
-      how to develo...
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-  - path: content/learning-paths/cross-platform/mca-godbolt/_index.md
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-    summary_preview: Learn how to use llvm-mca with Compiler Explorer to analyze Arm assembly performance,
-      estimate hardware resource pressure, and diagnose performance issues. It is designed for developers
-      who want to di...
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-  - path: content/learning-paths/cross-platform/mcp-ai-agent/_index.md
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-    summary_preview: Learn how to deploy a Model Context Protocol server on Raspberry Pi 5 and use the
-      OpenAI Agent SDK to create AI agents with custom tools for local inference. It is designed for LLM
-      and IoT developers ...
-    source_hash: 39d489081bfa22125a4046c17d5c00a32c0d7b298cba7b6a12373cf3cf6bac04
-  - path: content/learning-paths/cross-platform/memory-latency/_index.md
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-    summary_preview: Learn how to reduce memory latency impact in applications using cache alignment and
-      prefetching techniques on Arm processors for improved performance. It is designed for Arm developers
-      who want to lea...
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-  - path: content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md
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-    summary_preview: Learn how to build MNN on an Armv9 system, run text, vision, and audio prompts with
-      a multimodal Omni model, and combine image and audio inputs into a single-shot retail restock ticket
-      workflow. It is...
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-  - path: content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md
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-    summary_preview: Learn how to implement and optimize matrix multiplication using Arm's Scalable Matrix
-      Extension 2 (SME2) with assembly and intrinsics, including benchmarking and validation on Arm hardware.
-      It is desi...
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-  - path: content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md
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-    summary_preview: Learn how to create and train a PyTorch neural network for MNIST digit classification,
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-      measurement. I...
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-  - path: content/learning-paths/cross-platform/remoteit/_index.md
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-    summary_preview: Learn how to install and configure Remote.It for secure remote device access using
-      SSH and other services, with proxy and peer-to-peer connection options. It is designed for software
-      developers who wa...
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-  - path: content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
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-    summary_preview: Learn how to use the C99 restrict keyword to indicate non-overlapping memory regions
-      and enable better compiler optimizations for vectorization on Arm platforms. It is designed for
-      C developers who ar...
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-  - path: content/learning-paths/cross-platform/rust_armds/_index.md
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-    summary_preview: Learn how to build an embedded Rust application for Arm processors, run it on a Fixed
-      Virtual Platform, and debug it using Arm Development Studio. It is designed for embedded application
-      developers to...
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-  - path: content/learning-paths/cross-platform/simd-info-demo/_index.md
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-    summary_preview: Learn how to use SIMD.info to port SIMD intrinsics across Arm architectures, including
-      navigation, search, and comparison features for finding equivalent instructions. It is designed
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-    summary_preview: Learn how to write high-performance SIMD code using the SIMD Loops project, with
-      hands-on examples demonstrating SVE, SVE2, and SME2 features on Arm processors. It is designed for
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-  - path: content/learning-paths/cross-platform/simd-on-rust/_index.md
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-    summary_preview: Learn how to write SIMD code in Rust on Arm platforms using Neon intrinsics, portable
-      SIMD abstractions, and optimize performance with architecture-specific instructions. It is designed
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-  - path: content/learning-paths/cross-platform/sme-executorch-profiling/_index.md
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-    faq_changes:
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-    summary_preview: Learn how to profile and optimize ExecuTorch models using SME2 acceleration on Arm
-      platforms, including operator-level analysis and performance bottleneck identification. It is designed
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-  - path: content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md
-    status: unchanged
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-    summary_preview: Learn how to deploy Tinkerblox UltraEdge HPC-I for edge AI and mixed workloads on
-      Arm platforms, including installation and configuration on Debian, Ubuntu, and Yocto systems. It
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-  - path: content/learning-paths/cross-platform/topdown-compare/_index.md
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-    summary_preview: Learn how to compare Arm Neoverse and Intel x86 top-down performance analysis methodologies
-      using PMU counters, Linux Perf, and topdown-tool to identify bottlenecks across architectures. It
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-  - path: content/learning-paths/cross-platform/vectorization-comparison/_index.md
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-    summary_preview: Learn how to migrate x86-64 SIMD code to Arm64 by mapping Intel SSE/AVX to Arm Neon,
-      SVE, and SME, with code examples and migration strategies using autovectorization or intrinsics.
-      It is designed for...
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-    summary_preview: Learn how to optimize SIMD performance on Arm by restructuring data layouts from
-      Array-of-Structures to Structure-of-Arrays, with practical examples using Neon and SVE intrinsics.
-      It is designed for C...
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-  - path: content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
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-    summary_preview: Learn how to sample and profile CPU instructions using WindowsPerf with Arm Statistical
-      Profiling Extension (SPE) on Windows on Arm, demonstrated with CPython workload analysis. It is
-      designed for dev...
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-  - path: content/learning-paths/cross-platform/woa_azure/_index.md
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-    summary_preview: Learn how to create and connect to a Windows on Arm virtual machine in Microsoft
-      Azure using the Azure Marketplace and RDP. It is designed for software developers interested using
-      Windows on Arm in th...
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-  - path: content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to build and deploy distributed Zenoh systems on Arm devices like Raspberry
-      Pi, using pub/sub, storage, and queryable models for scalable robotics and IoT applications. It
-      is designed for ro...
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-  - path: content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
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-    summary_preview: Learn how to design and integrate a custom AXI-Lite peripheral with a Cortex-A9 processor
-      on the Zybo Z7-10 board using Vivado, configuring GPIOs to control LEDs based on switch inputs.
-      It is designed...
-    source_hash: d8c48c293b2d316d5e68e18ab9e81157ad114a64cf2efa6951374a55d25238d6
-  - path: content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
-    status: unchanged
-    changed_on_disk: true
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-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit and
-      run inference on the Ethos-U85 NPU. It is designed for embedded developers who want to deploy a
-      neural network model t...
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-  - path: content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
-    status: unchanged
-    changed_on_disk: true
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to build a motion-detection device with Raspberry Pi Pico (RP2040 Cortex-M0+)
-      using Arduino IDE, PIR sensors, and interrupt-driven programming on baremetal. It is designed for
-      software devel...
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-  - path: content/learning-paths/embedded-and-microcontrollers/armds/_index.md
-    status: unchanged
-    changed_on_disk: true
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to import and build example projects in Arm Development Studio and debug
-      embedded applications using Fixed Virtual Platforms (FVPs) or hardware with DSTREAM debug probes.
-      It is designed for ...
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-  - path: content/learning-paths/embedded-and-microcontrollers/asm/_index.md
-    status: unchanged
-    changed_on_disk: true
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-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to write mixed C and assembly programs for Cortex-M microcontrollers using
-      Keil MDK, following Arm Procedure Call Standard conventions. It is designed for software developers
-      who are interes...
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-  - path: content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to create a custom Balena OS image, run it on Arm Virtual Hardware, and
-      deploy IoT applications to a virtual Raspberry Pi 4 device. It is designed for embedded software
-      developers interested...
-    source_hash: 983afd3fcc565266c8f12588086e36d736540e23d0e748c94b5d2a45ab53d6ab
-  - path: content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
-    status: unchanged
-    changed_on_disk: true
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to set up AWS IoT Greengrass Core on Arm Virtual Hardware and deploy AWS
-      Greengrass components to a virtual Raspberry Pi 4 device. It is designed for embedded software developers
-      interested ...
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-  - path: content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to build Matter reference examples on Arm Virtual Hardware, demonstrate
-      device communication, and automate testing with GitHub Actions CI/CD workflows. It is designed for
-      embedded software d...
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-  - path: content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
-    status: unchanged
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-    faq_changes:
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-    summary_preview: Learn how to export and compile a PaddleOCR text recognition model using TVMC and
-      deploy it on the Arm Corstone-300 FVP with Cortex-M55 processors. It is designed for software developers
-      interested in...
-    source_hash: 398077be1406d0787b0a5d8dc254472da0d125b51b0907769cd4a3516ce9111b
-  - path: content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to create and integrate a virtual LED peripheral using the Virtual IO interface
-      of Arm Virtual Hardware to simulate real-world peripherals. It is designed for software developers
-      new to Arm ...
-    source_hash: aaff31b8917320c53825f3e7410b703bc7c7e273b1963fd80727b6b874f50566
-  - path: content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to build a complete IoT solution in Azure that streams, stores, monitors,
-      aggregates, and visualizes telemetry data from Arm devices using IoT Hub, Stream Analytics, Cosmos
-      DB, and Azure Fun...
-    source_hash: 0e653bdbf5e5b08a6a00ba68e5ed215a0082e22c634d7d632d088851d48bb01c
-  - path: content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md
-    status: unchanged
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-    faq_changes:
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-    summary_preview: Learn how to create, build, and run a bare-metal embedded application for Armv8-A
-      processors using Arm Compiler for Embedded and Fixed Virtual Platforms, including basic exception
-      handling. It is desi...
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-  - path: content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to deploy containerized embedded applications and firmware onto an Arm
-      Cortex-M core from a Cortex-A core using containerd, K3s, and the hybrid-runtime on Arm Virtual
-      Hardware. It is designe...
-    source_hash: 3394bf994039e441d57dced2abb64d725077d4672e0e68dc71f1de50e58f0408
-  - path: content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to create, build, and debug an RTX5 RTOS-based application using Keil μVision
-      with CMSIS-RTOS2 API and Event Recorder for embedded Cortex-M development. It is designed for software
-      developer...
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-  - path: content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md
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-    faq_changes:
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-    summary_preview: Learn how to create, configure, and debug an RTX5 RTOS application using Keil Studio
-      for VS Code with CMSIS-RTOS2 API for embedded Cortex-M development. It is designed for software
-      developers new to R...
-    source_hash: f4d1ab3448590c5654e2479937c9eec3e378d05229b29a45b8675139f40ac177
-  - path: content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
-    status: unchanged
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-    faq_changes:
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-    summary_preview: Learn how to prototype and port DSP algorithms using the CMSIS-DSP Python package,
-      mapping Python code to efficient C implementations for embedded Cortex-M and Cortex-A platforms.
-      It is designed for d...
-    source_hash: 69526ee8b840c8f5d77d49349d2f0cc7ff4594fec5e4311984ef72a0672d3462
-  - path: content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to implement context switching operations on Arm Cortex-M processors using
-      the Memory Protection Unit and SysTick exception in a bare-metal environment. It is designed for
-      software developer...
-    source_hash: 0b7a5895a87de83903c223215845b6c840602663838c0682f46e50d139d69c59
-  - path: content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to set up and use code coverage in Keil MDK with FVPs to verify that your
-      embedded application tests execute all code paths. It is designed for embedded software developers
-      new to the code-c...
-    source_hash: f989929b11c48df42c2701dc71516cec0b8637b4c639c3dbbf6ac5e7440c8a56
-  - path: content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Device-to-Device communication with Device Connect walks you through an end-to-end
-      Arm software workflow. It is designed for developers wiring up heterogeneous edge fleets, where
-      devices need a shared...
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-  - path: content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to connect AI agents to Arm-based edge devices using Device Connect for
-      structured device access and Strands for agent orchestration, with examples for both simulated and
-      physical robots. It...
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-  - path: content/learning-paths/embedded-and-microcontrollers/docker/_index.md
-    status: unchanged
-    changed_on_disk: true
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-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to create a Dockerfile, build a Docker image with Arm Compiler for Embedded
-      and Fixed Virtual Platforms, and test the containerized Arm development environment. It is designed
-      for embedded s...
-    source_hash: a4b522c46c68d3c6f5c4af7c76b00c7bde48bb638147c201376bc19a4de79cca
-  - path: content/learning-paths/embedded-and-microcontrollers/edge/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to collect and preprocess audio data using Edge Impulse, train an audio
-      classification model, and deploy it to the Arduino Nano RP2040 to control LEDs based on voice commands.
-      It is designed...
-    source_hash: 8a7ec29d10a89649662ebb0fa4f14712984786902b6e7343a195abb1f3cbc00b
-  - path: content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Develop a image classification neural network model and deploy it on an STM32 B-L475E-IOT01A2
-      board. It is designed for embedded software developers interested in building neural network models
-      for mi...
-    source_hash: 66024a2e3da90dcda298bf66b5de07952ed1e355d22172bc2812c46ad4fcda7f
-  - path: content/learning-paths/embedded-and-microcontrollers/intro/_index.md
-    status: unchanged
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-    summary_preview: Learn where Arm architecture is used in microcontrollers and discover microcontroller
-      hardware options for software development on Arm Cortex-M processors. It is designed for software
-      developers worki...
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-  - path: content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
-    status: unchanged
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-    faq_changes:
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-    summary_preview: Learn what differentiates TinyML from other AI domains, explore Arm-based edge devices
-      for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed Virtual
-      Platform. It is ...
-    source_hash: aa49e4a1651367965e79d36c5f6af16e9b341c392d48cf051f24cbb21070e972
-  - path: content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
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-    faq_changes:
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-    summary_preview: Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300 virtual
-      hardware to understand complete IoT software stack construction. It is designed for embedded software
-      developers ...
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-  - path: content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to set up a Jetson Orin Nano with a MIPI CSI-2 camera and perform real-time
-      object detection from live video and image files using DetectNet and TensorRT. It is designed for
-      developers inter...
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-  - path: content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
-    status: unchanged
-    changed_on_disk: true
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-    faq_changed: false
-    faq_changes:
-      before_count: 5
-      after_count: 5
-      added_questions: []
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-      updated_questions: []
-    summary_preview: Learn how to import, build, and debug your first Keil Studio Cloud project. It is
-      designed for embedded software developers new to Keil Studio Cloud. By the end, you will be able
-      to import and build a...
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-  - path: content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to boot and configure the NXP FRDM i.MX 93 Arm board with Linux, create
-      a user with sudo access, connect to WiFi using ConnMan, and transfer files over the network. It
-      is designed for embedd...
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-  - path: content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to boot a Linux software stack on Arm Fixed Virtual Platforms (FVPs), then
-      debug Trusted Firmware-A and the Linux kernel using Arm Development Studio. It is designed for developers
-      who want ...
-    source_hash: 5943d817827bef274f175f7c14249cad769b2160094b3c65d7476aca6cbf0a1d
-  - path: content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to install the Python version of llama.cpp on a Raspberry Pi 5, download
-      an LLM from Hugging Face, assess memory and performance, and run the model using Python bindings.
-      It is designed for ...
-    source_hash: aa6252610c0603acfdfc8d4555bb7da93cbdd5b9d92ba140c5dc5f63d23e2b07
-  - path: content/learning-paths/embedded-and-microcontrollers/migration/_index.md
-    status: unchanged
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-    faq_changes:
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-    summary_preview: Learn the software migration methodology for porting Linux workloads from x86_64
-      to aarch64, including using Arm compilers, porting compiler intrinsics, and deploying applications
-      in containers. It is...
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-    summary_preview: Learn how to build examples from the Machine Learning Evaluation Kit (MLEK) and run
-      them on the Arm Ecosystem FVP for machine learning application development on microcontrollers.
-      It is designed for e...
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-    summary_preview: Learn how to understand and select physical and virtual hardware targets for ML application
-      development with Cortex-M and Ethos-U, identify software tools, and find example applications. It
-      is designe...
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-    summary_preview: Learn how to create debug configurations for virtual platforms and development boards
-      in Arm Development Studio, including setting up connections for Fast Models and DSTREAM debug probes.
-      It is design...
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-    summary_preview: Learn how to bring up ExecuTorch executor_runner firmware on the NXP FRDM i.MX 93
-      Cortex-M33 using Linux RemoteProc, compile .pte models for Ethos-U65, and run inference with NPU
-      acceleration. It is d...
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-    summary_preview: Learn how to migrate a CMSIS v5-based CMSIS-Pack with device support to CMSIS v6
-      and update example projects for compatibility with the new CMSIS version. It is designed for maintainers
-      of CMSIS-Packs...
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-    summary_preview: Learn how to migrate CMSIS v5 projects to CMSIS v6 by identifying supported toolchains,
-      installing required CMSIS-Packs, and selecting the necessary software components. It is designed
-      for embedded de...
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-    summary_preview: Learn how to run large language models locally on the Raspberry Pi 5 using Ollama,
-      control GPIO-connected devices, and deploy a privacy-first web-based smart home assistant without
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-    summary_preview: Learn how to build a voice-controlled bot on a Raspberry Pi that listens for a wake
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-      and plays audio resp...
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-    summary_preview: Learn how to build and run multiple software examples on the Raspberry Pi 4, including
-      TensorFlow and Docker applications, and compare its performance to Arm cloud servers. It is designed
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-    summary_preview: Learn how to compile the Llama 3 large language model using ExecuTorch, deploy it
-      to a Raspberry Pi 5, and understand techniques for running LLMs in embedded environments. It is
-      designed for anyone in...
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-    summary_preview: Learn how to reduce compile time for embedded Linux projects by installing a Raspberry
-      Pi OS file system on an Arm server, building the MXNet machine learning framework, and transferring
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-    summary_preview: Setup tools and start programming with Raspberry Pi Pico. It is designed for embedded
-      software developers new to Raspberry Pi Pico. By the end, you will be able to install the Raspberry
-      Pi Pico SDK, r...
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-  - path: content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
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-    summary_preview: Learn how to profile Linux kernel modules using Arm Streamline to identify performance
-      bottlenecks, analyze both out-of-tree and in-tree modules, and use Statistical Profiling Extension
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-    summary_preview: Build a letter recognition neural network model using TensorFlow and deploy it on
-      an STM32 B-L475E-IOT01A2 board. It is designed for software developers interested in building network
-      models for micro...
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-    summary_preview: Learn how to build and run the reference Trusted Firmware-M tests and example application
-      on Arm Fixed Virtual Platforms for secure microcontroller development. It is designed for software
-      developers ...
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-    summary_preview: Learn how to train a CNN image classification model using PyTorch, convert it to
-      ExecuTorch format, and run it as an interactive mini-game on Arm-based edge devices. It is designed
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-    summary_preview: Learn how to install Keil MDK Tools, run a TrustZone hello world example on the NXP
-      LPCXpresso55S69 board, and understand security state switching and secure function calls. It is
-      designed for softwar...
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-    summary_preview: Learn how to acquire and print materials, assemble a universal SBC rack mount system
-      in a 4U chassis, and install single board computers in the racks using 3D-printed parts. It is designed
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-    summary_preview: Learn how to debug microcontrollers using µVision with basic run/stop debug, advanced
-      techniques using Event Recorder and Serial Wire Viewer, ETM Trace for performance analysis, and
-      power measurement ...
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-  - path: content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
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-    summary_preview: Learn how to import, convert, and build uvprojx-based projects to csolution format
-      using Keil Studio, µVision, and command-line tools for CMSIS-Toolbox compatibility. It is designed
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-  - path: content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
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-    summary_preview: Learn how to install vcpkg, initialize it, create vcpkg-configuration.json files,
-      use vcpkg for tool management, activate tool licensing, and remove vcpkg for reproducible command-line
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-    summary_preview: Learn how to identify Arm-based targets for TinyML, install Fixed Virtual Platforms,
-      deploy ExecuTorch models on Corstone-320 FVP, and visualize model execution using the FVP graphical
-      interface. It i...
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-  - path: content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md
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-    summary_preview: Introduction to building a minimal Yocto Linux image and running it on 64-bit Qemu
-      Arm target. It is designed for software developers interested in learning the basics of building
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-  - path: content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md
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-    summary_preview: Learn how to run a YOLO object detection model on the Himax WiseEye2 module, build
-      the Himax SDK, update firmware, and connect to the Grove Vision AI module for computer vision applications.
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-  - path: content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md
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-    summary_preview: Learn how to build and run Zephyr RTOS applications on the Arm Corstone-300 Fixed
-      Virtual Platform using Arm Virtual Hardware. It is designed for software developers getting started
-      with the Zephyr RT...
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-    summary_preview: Learn how to install Workbench for Zephyr extension in VS Code, set up the complete
-      Zephyr development environment, create and build Zephyr applications, debug embedded systems, and
-      perform memory usa...
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-    summary_preview: Learn how to create and run Ubuntu containers on ChromeOS Crostini using LXC with
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-    summary_preview: Learn how to build and deploy high-fidelity robotic simulations and reinforcement
-      learning pipelines using Isaac Sim and Isaac Lab on Arm-based NVIDIA DGX Spark with Grace-Blackwell
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-    summary_preview: Learn how to build and optimize quantized LLMs using llama.cpp on NVIDIA DGX Spark
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-    summary_preview: Learn how to build a Retrieval-Augmented Generation (RAG) pipeline on NVIDIA DGX
-      Spark combining Arm Grace CPU orchestration with Blackwell GPU-accelerated inference using llama.cpp.
-      It is designed fo...
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-    summary_preview: Learn how to build an offline voice assistant combining speech-to-text via faster-whisper
-      and text generation via vLLM on Arm-based DGX Spark for privacy-focused deployments. It is designed
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-    summary_preview: Learn how to run pre-trained AI models locally using Docker Model Runner and build
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-    summary_preview: Learn how to develop and build cross-platform desktop applications using the Electron
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-    summary_preview: Learn where the Arm architecture is used in desktop and laptop computers and find
-      hardware for software development on Arm platforms. It is designed for developers working on laptops
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-    summary_preview: Learn how to build, test, and verify KleidiCV with Scalable Matrix Extensions (SME)
-      on Apple Silicon Macs for accelerated computer vision performance. It is designed for software developers
-      who want t...
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-    summary_preview: Learn how to configure the LLVM toolchain with Visual Studio to build native Windows
-      on Arm applications using the open-source PuTTY project. It is designed for software developers
-      doing native develo...
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-    summary_preview: Learn how to apply Arm Memory Tagging Extension (MTE) to protect dynamic memory allocations
-      and prevent common memory use errors. It is designed for software developers who want to learn how
-      to use th...
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-  - path: content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
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-    summary_preview: Learn how to install and configure Arch Linux for Arm with the i3 window manager
-      and Neovim editor on the Pinebook Pro laptop. It is designed for developers who want to use the
-      Pinebook Pro as an Arm ...
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-  - path: content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
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-    summary_preview: Learn how to fine-tune large language models using PyTorch and Hugging Face on NVIDIA
-      DGX Spark to improve domain-specific accuracy. It is designed for AI developers and ML engineers
-      who want to fine-...
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-  - path: content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
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-    summary_preview: Learn how to create a CI/CD pipeline in GitHub using self-hosted Arm64 runners to
-      build and push Docker images to DockerHub. It is designed for software developers and IT practitioners
-      who want to lea...
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-  - path: content/learning-paths/laptops-and-desktops/win-opencv/_index.md
-    status: unchanged
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-    summary_preview: Learn how to build the OpenCV library for Windows on Arm devices and develop computer
-      vision applications using OpenCV. It is designed for software developers who want to build and develop
-      application...
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-    status: unchanged
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-    faq_changes:
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-    summary_preview: Learn how to measure application resource usage, benchmark video encoding tasks,
-      and monitor CPU, memory, and power consumption on Windows on Arm using FFmpeg and PowerShell. It
-      is designed for develo...
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-  - path: content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to automate Windows on Arm VM creation on Arm Linux systems using QEMU,
-      KVM, and Bash scripts for development and testing. It is designed for developers and system administrators
-      who want to...
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-  - path: content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
-    status: unchanged
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-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to build native Arm applications and migrate x86/x64 applications to Arm
-      using Arm64EC on Windows on Arm devices. It is designed for software developers who want to use
-      Arm64EC with Windows ...
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-  - path: content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
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-    summary_preview: Learn how to port Qt-based Python desktop applications with C/C++ dependencies to
-      Arm64 using Arm64EC on Windows on Arm. It is designed for developers who want to learn how to port
-      their applications ...
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-    summary_preview: Learn how to build and run Qt-based desktop applications on Windows on Arm and investigate
-      native Arm64 performance improvements. It is designed for software developers who want to use the
-      native perf...
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-  - path: content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
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-    summary_preview: Learn how to build and run an ASP.NET Core 8 web server application with Web API
-      and dependency injection services on Windows on Arm. It is designed for developers who are interested
-      in building a web...
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-  - path: content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
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-    summary_preview: Learn how to create Node.js IoT applications that stream sensor data from Windows
-      on Arm devices to AWS IoT Core using MQTT. It is designed for developers who want to learn how to
-      create IoT applicati...
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-  - path: content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md
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-    summary_preview: Learn how to configure AWS IoT Core rules to parse MQTT messages and store IoT data
-      in Amazon DynamoDB from Windows on Arm devices. It is designed for developers who are interested
-      in using Amazon Dyn...
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-  - path: content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md
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-    summary_preview: Learn how to process IoT data using AWS Lambda functions triggered by AWS IoT Core
-      messages from Windows on Arm devices. It is designed for developers who are interested in using
-      AWS Lambda for proces...
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-  - path: content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md
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-    summary_preview: Learn how to implement AWS Lambda functions that process and aggregate IoT data stored
-      in DynamoDB tables from Windows on Arm devices. It is designed for developers who are interested
-      in using AWS Lam...
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-  - path: content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md
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-    summary_preview: Learn how to create a static website hosted on Amazon S3 that interacts with AWS
-      Lambda functions to display IoT data from Windows on Arm devices. It is designed for developers
-      who are interested in u...
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-  - path: content/learning-paths/laptops-and-desktops/win_cef/_index.md
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-    summary_preview: Learn how to create and build Chromium Embedded Framework desktop applications using
-      CMake and web technologies on Windows on Arm. It is designed for developers who want to learn how
-      to use web techno...
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-  - path: content/learning-paths/laptops-and-desktops/win_forms/_index.md
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-    summary_preview: Learn how to create and build Windows Forms applications and measure code execution
-      performance on Arm64. It is designed for developers who want to learn how to create Windows Forms
-      applications on Wi...
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-    summary_preview: Learn how to build and run a .NET 6 Windows Presentation Foundation (WPF) application
-      on Windows on Arm machines. It is designed for software developers doing native development on Windows
-      on Arm comp...
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-  - path: content/learning-paths/laptops-and-desktops/win_net8/_index.md
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-    summary_preview: Learn how to build, run, and benchmark .NET 8 Console applications to measure performance
-      on Windows on Arm devices. It is designed for developers who want to benchmark the performance of
-      the .NET 8 a...
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-  - path: content/learning-paths/laptops-and-desktops/win_net_maui/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to create and build cross-platform .NET MAUI applications and measure code
-      execution performance uplift on Arm64. It is designed for developers who want to learn how to create
-      cross-platform...
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-  - path: content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md
-    status: unchanged
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-    faq_changed: false
-    faq_changes:
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-    summary_preview: Learn how to build ONNX Runtime with the Generate() API and run Phi-3 model inference
-      with KleidiAI acceleration on Windows on Arm. It is designed for developers looking to build ONNX
-      Runtime for Wind...
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-  - path: content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to apply Profile-Guided Optimization (PGO) to build performance-tuned C++
-      binaries and measure improvements using Google Benchmark on Windows on Arm. It is designed for software
-      developers w...
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-  - path: content/learning-paths/laptops-and-desktops/win_python/_index.md
-    status: unchanged
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-    faq_changes:
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-    summary_preview: Learn how to build Python applications on Windows on Arm and leverage native Arm64
-      performance for platform-dependent packages. It is designed for developers who are interested in
-      building Python appl...
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-  - path: content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md
-    status: unchanged
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-    faq_changes:
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-    summary_preview: Learn how to configure Windows Sandbox as a self-hosted GitHub Actions runner to
-      build and run .NET 8 WPF applications in CI/CD workflows. It is designed for software developers
-      who are developing app...
-    source_hash: 055e3a3d8c0dc717e2246e3e8e7ebb00c7d2cea3ff9faabf7125ca1ebfff7a31
-  - path: content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md
-    status: unchanged
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-    faq_changes:
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-    summary_preview: Learn how to create C/C++ Win32 DLLs and port them to Arm64 for use in Windows console
-      applications. It is designed for developers who want to learn how to port their Win32 applications
-      to Arm64. By t...
-    source_hash: cacf4c6fc3cd3c2a9ab194f7a04981fd4820fca5482317a06c6b9fa02a1c9da2
-  - path: content/learning-paths/laptops-and-desktops/win_winui3/_index.md
-    status: unchanged
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-    faq_changes:
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-    summary_preview: Learn how to create and build Windows UI Library (WinUI) applications and measure
-      code execution performance on Arm64. It is designed for developers who want to learn how to create
-      cross-platform appl...
-    source_hash: 383dcf17bce86b24a2d9c8d0982fa6e9ddf954955c16b420463ada951753dbfa
-  - path: content/learning-paths/laptops-and-desktops/win_wpf/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to create and build Windows Presentation Foundation (WPF) applications
-      and measure code execution performance uplift on Arm64. It is designed for developers who want to
-      learn how to create d...
-    source_hash: cc1a494e7b03672122ff84073b677a93659eca7df601b3f77c7e7fd536cc1af9
-  - path: content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to create and build Xamarin Forms applications using the MVVM pattern and
-      measure code execution performance uplift on Arm64. It is designed for developers who want to learn
-      how to create cr...
-    source_hash: 16b7f8c67050ea336402791c0ecca2c688d5da9373c571986e12dcf646c8093c
-  - path: content/learning-paths/laptops-and-desktops/windows_armpl/_index.md
-    status: unchanged
-    changed_on_disk: true
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to develop Windows on Arm applications using Visual Studio and optimize
-      performance with Arm Performance Libraries. It is designed for software developers who want to improve
-      the performance...
-    source_hash: f058f628cefb7766ef0728e594c9ef5b57bde97c1850395786d54cc591271503
-  - path: content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md
-    status: unchanged
-    changed_on_disk: true
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Get started with GitHub CI/CD development flow on a Windows on Arm machine (or virtual
-      machine). It is designed for software developers interested in running their CI flows on Windows
-      on Arm machines....
-    source_hash: 828e4022f387698d2736d94010de5d93efa9f38ffd3a2e4f15252410e9ccedb2
-  - path: content/learning-paths/laptops-and-desktops/windowsperf/_index.md
-    status: unchanged
-    changed_on_disk: true
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-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to install WindowsPerf on Windows on Arm machines and generate sample performance
-      reports for CPU profiling. It is designed for software developers working on laptops and desktops
-      and new to...
-    source_hash: 61a6390d42ce6bfc37a3bb981710dc23b8566070733f7979f9ec37bc63ab7d78
-  - path: content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md
-    status: unchanged
-    changed_on_disk: true
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-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to install and use the WindowsPerf Visual Studio extension to generate
-      counting and sampling reports and analyze performance data in Windows Performance Analyzer. It is
-      designed for software...
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-    summary_preview: Learn how to use WindowsPerf for performance sampling on Windows on Arm, build CPython
-      from sources, and analyze native workload performance. It is designed for developers keen to understand
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-    summary_preview: Learn how to import WindowsPerf data in Windows Performance Analyzer (WPA) and visualize
-      timeline and telemetry data using the WPA plugin. It is designed for software developers interested
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-    summary_preview: Learn how to configure and run WSL with Linux distributions, graphical applications,
-      remote desktop, and development tools on Windows on Arm computers. It is designed for Software developers
-      with Wind...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md
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-    summary_preview: Learn how to enable and verify Arm Fixed Rate Compression in Vulkan applications
-      on Android devices to reduce memory footprint and bandwidth. It is designed for Software developers
-      of Android applicat...
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-    summary_preview: Learn how to build and optimize AI-powered camera pipeline applications on Arm Linux
-      using KleidiAI, KleidiCV, and SME2 to accelerate denoising, background blur, and low-light effects.
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-  - path: content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
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-    summary_preview: Learn how to use each of the tools supplied with Arm Performance Studio (formerly
-      known as Arm Mobile Studio). It is designed for Android application and games developers new to
-      Arm Performance Studio...
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-    summary_preview: Learn how to capture frame data from Android applications and analyze performance
-      inefficiencies using Frame Advisor in Arm Performance Studio. It is designed for Android application
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-    summary_preview: Learn how to build an Android chatbot app using Arm's AI Chat library to run GGUF
-      models on-device with optimized performance on Arm CPUs. It is designed for developers who want
-      to add a local, on-dev...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
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-    summary_preview: Learn how to build real-time image processing pipelines using Halide on Android,
-      combining operations for improved performance in Kotlin applications. It is designed for developers
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-  - path: content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md
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-    summary_preview: Learn how to create and configure an Android project with OpenCV support to process
-      camera images for computer vision applications. It is designed for developers who are interested
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-    summary_preview: Learn how to implement face detection on Android devices using OpenCV, camera frame
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-  - path: content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
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-    summary_preview: Learn how to accelerate OpenCV-based Android applications using KleidiCV for enhanced
-      computer vision performance. It is designed for developers who are interested in creating Computer
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-  - path: content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md
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-    summary_preview: Get started with Scalable Vector Extension 2 (SVE2) on Android walks you through
-      an end-to-end Arm software workflow. It is designed for software developers interested in learning
-      how to use the Scala...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
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-    faq_changes:
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-    summary_preview: Learn how to integrate Dawn WebGPU in an Android application, render 3D objects,
-      and profile the application using Streamline. It is designed for developers who are building GPU-based
-      Android applicat...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
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-    summary_preview: Learn how to optimize hardware ray tracing with Lumen on Android devices powered
-      by Arm Mali GPUs to maximize performance. It is designed for Unreal Engine developers interested
-      in optimizing hardware...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md
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-    summary_preview: Learn how to build ONNX Runtime and the generate() API for Android to run a Phi-3
-      model on Arm-based smartphones. It is designed for software developers interested in learning how
-      to build an Android ...
-    source_hash: deeb745d72457138cb81252c421205cd8dfb43b5e29ceee3a15c5842cc90cc33
-  - path: content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md
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-    summary_preview: Learn how to build a hands-free selfie Android application using MediaPipe multimodal
-      AI, Kotlin flows, CameraX, and MVVM architecture. It is designed for mobile application developers
-      interested in l...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md
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-    summary_preview: Learn how to build an Android chat application with Llama models using ExecuTorch,
-      XNNPACK, and KleidiAI for accelerated performance on Arm smartphones. It is designed for software
-      developers interest...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md
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-    summary_preview: Learn how to build a customer support chatbot for Android using Llama 3.2, ExecuTorch,
-      and KleidiAI to run on-device inference on Arm platforms. It is designed for software developers
-      interested in bu...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md
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-    summary_preview: Learn how to detect and debug memory safety bugs in Android applications using Arm
-      Memory Tagging Extension (MTE) on a Google Pixel 8 smartphone. It is designed for developers interested
-      in learning h...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md
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-    summary_preview: Get started with Arm Accuracy Super Resolution (Arm ASR) walks you through an end-to-end
-      Arm software workflow. It is designed for mobile, gaming, and graphics developers who want to install
-      and confi...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md
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-    summary_preview: Get started with Unity on Android walks you through an end-to-end Arm software workflow.
-      It is designed for Unity developers who want to target Android devices. By the end, you will be
-      able to set up ...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md
-    status: unchanged
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-    summary_preview: Profile Android game performance in Godot with Arm Performance Studio walks you through
-      an end-to-end Arm software workflow. It is designed for Godot developers targeting Android devices
-      who want to o...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md
-    status: unchanged
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-    faq_changes:
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-    summary_preview: How to Enable Hardware Ray Tracing on Lumen for Android Devices walks you through
-      an end-to-end Arm software workflow. It is designed for Unreal Engine developers interested in using
-      hardware ray trac...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/intro/_index.md
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-    summary_preview: Get started with Arm hardware walks you through an end-to-end Arm software workflow.
-      It is designed for Developers new to the Arm architecture and looking for mobile hardware. By the
-      end, you will be ...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md
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-    summary_preview: Understand KleidiAI SME2 matmul microkernels walks you through an end-to-end Arm
-      software workflow. It is designed for software developers, performance engineers, and AI practitioners.
-      By the end, you...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md
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-    faq_changes:
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-    summary_preview: Learn how to run LLM inference on Android devices using MediaPipe with KleidiAI-enhanced
-      Arm i8mm features to benchmark the Gemma 2B model. It is designed for Android developers who want
-      to efficientl...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md
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-    summary_preview: Learn how to build the libGPUInfo library using Android NDK and query configuration
-      details of Arm Mali or Immortalis GPUs on Android devices. It is designed for Android developers
-      who want to adjust ...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md
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-    faq_changes:
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-    summary_preview: Learn how to accelerate LiteRT model inference on Android using KleidiAI with SME2
-      instructions and validate performance with the benchmark tool. It is designed for developers looking
-      to leverage Arm'...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md
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-    faq_changes:
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-    summary_preview: Learn how to benchmark KleidiAI micro-kernels in ExecuTorch using SME/SME2 instructions
-      on Arm64 platforms with ETDump profiling and analysis. It is designed for developers, performance
-      engineers, and...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md
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-    faq_changes:
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-    summary_preview: Learn how to fine-tune and evaluate Neural Super Sampling (NSS) models using PyTorch
-      and Arm's Model Gym API with hardware-aware optimization. It is designed for developers exploring
-      neural graphics a...
-    source_hash: e145caae5b20f7165251d88e334ba22d90c8b35270902778a2b6fe0ed0b250f6
-  - path: content/learning-paths/mobile-graphics-and-gaming/mte/_index.md
-    status: unchanged
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-    faq_changes:
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-    summary_preview: Learn how to run example C programs on AArch64 Linux to gain an introductory understanding
-      of the Arm Memory Tagging Extension (MTE). It is designed for developers who want to gain some experience
-      wit...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md
-    status: unchanged
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-    faq_changes:
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-    summary_preview: Learn how to enable Arm Memory Tagging Extension (MTE) on a Google Pixel 8 smartphone,
-      trigger memory bug crashes, and interpret bug reports. It is designed for developers interested
-      in learning how t...
-    source_hash: b6a9aa558ec5062c829d61b28fb92e25d5517249c011eb29f03cc36775b6f9af
-  - path: content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to capture high-quality frame datasets from Unreal Engine 5.5 gameplay
-      for training and evaluating neural graphics models like Neural Super Sampling. It is designed for
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-    source_hash: 5ca059af36f0ba375701e76ce82b7803f01ae6beab313336b333bfbdebaa3b1a
-  - path: content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md
-    status: unchanged
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-    faq_changes:
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-    summary_preview: Learn how to configure ML Extensions for Vulkan emulation and enable Neural Super
-      Sampling (NSS) in Unreal Engine for real-time upscaling. It is designed for developers experimenting
-      with neural graph...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md
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-    summary_preview: Learn how to build, optimize, and deploy machine learning models using ONNX Runtime
-      on Arm64 platforms, including Raspberry Pi, cloud instances, and Android devices. It is designed
-      for developers who ...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md
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-    summary_preview: Learn how to optimize vertex representations and analyze Vertex Memory Efficiency
-      using Arm Frame Advisor for improved GPU performance on Android. It is designed for Android graphics
-      application devel...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md
-    status: unchanged
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-    faq_changes:
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-      updated_questions: []
-    summary_preview: Learn how to build llama.cpp with KleidiAI and SME2 support to profile and accelerate
-      LLM inference performance on Android devices. It is designed for software developers, performance
-      engineers, and A...
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-    faq_changes:
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-      updated_questions: []
-    summary_preview: Learn how to build ONNX Runtime with KleidiAI and SME2 support for Android and profile
-      ONNX model performance to compare acceleration improvements. It is designed for software developers,
-      performance ...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md
-    status: unchanged
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-    faq_changes:
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-    summary_preview: Learn how to profile ML model execution times and application performance on Arm
-      Android devices using Arm Performance Studio and Android Studio Profiler. It is designed for software
-      developers who wa...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md
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-    summary_preview: Learn how to deploy Unity applications to Android, profile code running on Arm devices,
-      and analyze performance data for optimization. It is designed for Unity developers wanting to analyze
-      the perfor...
-    source_hash: 9950a58160736e0c60ad80ea602262347e2ba8a37a00e29f25dc96709026ea1f
-  - path: content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md
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-    summary_preview: Learn how to apply post-training quantization to PyTorch models using TorchAO and
-      export INT8 models to .vgf format with the ExecuTorch Arm backend. It is designed for ML developers
-      who want to reduce...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md
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-    faq_changes:
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-    summary_preview: Learn how to use the Vulkan ray tracing API to implement realistic shadows, reflections,
-      and refractions in Android applications. It is designed for Vulkan developers who are familiar with
-      rendering a...
-    source_hash: 78f862a7c46fd4591fec45f91d040eb3f1093291c0a7328dddd2203dad72db3f
-  - path: content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to use Frame Advisor's Render Graph view to identify and resolve graphics
-      performance issues in Android applications. It is designed for Mobile application developers who
-      wish to improve gra...
-    source_hash: e2f6f3005c119e6f6fe97612d4e2600849106f244bce729d56bfeeedb30637c2
-  - path: content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md
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-    faq_changes:
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-    summary_preview: Learn how to convert and deploy the Stable Audio Open Small text-to-audio model to
-      LiteRT format for audio generation on Android devices and macOS. It is designed for developers looking
-      to deploy the ...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md
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-    faq_changes:
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-      updated_questions: []
-    summary_preview: Learn how to convert the Stable Audio Open Small model to ExecuTorch format and build
-      an audio generation application for Android or macOS. It is designed for developers who want to
-      deploy the Stable ...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md
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-    summary_preview: Learn how to build and install a Unity game on an Orange Pi 5 single-board computer
-      running Droid OS. It is designed for software developers who want to build and run a Unity game
-      on an Arm-based sing...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md
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-    summary_preview: Learn how to install Arm integration packages in Unity to view GPU metrics in Unity
-      Profiler and annotate games with markers for Arm Performance Studio. It is designed for Unity developers
-      who are tar...
-    source_hash: 4a990e957658b03bac9306d5f8e6955734cc1d3b063f43c38ac525ed1bf95b79
-  - path: content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md
-    status: unchanged
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-    faq_changes:
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-    summary_preview: Learn how to use Arm Neon intrinsics in Unity C# scripts to optimize code on Android
-      and collect performance data using Unity Profiler. It is designed for Developers interested in leveraging
-      the Unity...
-    source_hash: f17ab3c4216495b88cee75a81612c11a4727d874114f914edf97c467f0d1c125
-  - path: content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to integrate Unity's Machine Learning Agents toolkit into games deployable
-      to Arm-powered Android devices. It is designed for Developers interested in leveraging the Unity
-      Machine Learning A...
-    source_hash: 9cd19738cbe9cde618125b309bd4f2c57ad1799e807251fdaed09707bea2781d
-  - path: content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to download, convert, and deploy Vision Transformers using the Mobile Neural
-      Network framework on Android with KleidiAI micro-kernels for optimized performance. It is designed
-      for developers...
-    source_hash: e7d95c8e7210be2fe4520fe94ccbaa3e437cd0edfa5caca549afaee22fb8b377
-  - path: content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to build and optimize a multimodal Voice Assistant application on Android
-      using KleidiAI and SME2 for accelerated performance. It is designed for developers who want to implement
-      a multimoda...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md
-    status: unchanged
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-    faq_changes:
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-    summary_preview: Build an end-to-end, on-device voice assistant that understands both speech and emotion
-      using Whisper, HuBERT, ONNX Runtime, and a local LLM with llama.cpp on Arm. It is designed for developers,
-      ML pr...
-    source_hash: 2d8fca8838e759c3098358f131fb4b0884b0d80d5fdb2fd68719bd09fa4c3d32
-  - path: content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md
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-    faq_changes:
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-    summary_preview: Learn how to set up ML Emulation Layers for Vulkan, run sample applications using
-      ML extensions, and debug the flow with RenderDoc. It is designed for engine developers interested
-      in learning about ne...
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-  - path: content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
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-    summary_preview: Learn how to build and deploy an AI agent application on Arm servers using llama.cpp
-      and llama-cpp-agent with KleidiAI optimization for efficient LLM inference and function calling.
-      It is designed for...
-    source_hash: 275878b5aa4c27dee394da12ad8b80e4c0d0235b3ddce66b1fe3f0e056ef3da9
-  - path: content/learning-paths/servers-and-cloud-computing/aks/_index.md
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-    faq_changes:
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-    summary_preview: Learn how to automate the deployment of an Arm-based Kubernetes cluster on Azure
-      AKS using Terraform and deploy a sample WordPress application as a workload. It is designed for
-      software developers who...
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-  - path: content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
-    status: unchanged
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-    faq_changes:
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-    summary_preview: Learn how to deploy Apache Arrow and Arrow Flight on Google Cloud Axion C4A processors
-      for high-throughput columnar data processing and low-latency data transport with MinIO integration.
-      It is designe...
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-  - path: content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to build llama.cpp, quantize the Arcee AFM-4.5B model, and run optimized
-      inference on AWS Graviton4 instances with perplexity-based quality evaluation. It is designed for
-      developers and ML e...
-    source_hash: 964c1e6a87810dca686a7b3218433ce09d31bd92882db10af355e8c50bb6091c
-  - path: content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to build llama.cpp, quantize the Arcee AFM-4.5B model, and run optimized
-      inference on Google Cloud Axion instances with perplexity-based quality evaluation. It is designed
-      for developers and...
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-  - path: content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to deploy and manage applications on Google Cloud GKE Arm64 clusters using
-      Argo CD GitOps workflows with automated sync and self-healing on Axion processors. It is designed
-      for developers an...
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-  - path: content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to write correct concurrent C++ code when porting applications from x86
-      to Arm by understanding memory ordering differences and using best practices to avoid race conditions.
-      It is designed ...
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-  - path: content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
-    status: unchanged
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-    summary_preview: Learn how to automate x86-to-Arm application migration using the Arm MCP Server,
-      with AI-assisted compatibility checks, C++ code refactoring, and Docker-based validation on Arm
-      cloud platforms. It is ...
-    source_hash: b870ac5160d35ddb51955ca8379493e172787ced8125cde8ae79f7700e653a87
-  - path: content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
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-    summary_preview: Learn how to migrate C applications between Arm platforms using Kiro's AI-assisted
-      tooling to identify hardware dependencies and implement abstraction layers for cross-platform compatibility.
-      It is de...
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-  - path: content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
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-    summary_preview: Learn how to install and configure a Linux kernel with 64K page size support on Arm
-      systems to improve memory efficiency and performance for memory-intensive workloads. It is designed
-      for developers w...
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-  - path: content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
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-    summary_preview: Learn how to access and use Arm hardware performance counters and the system counter
-      from user space using PAPI, perf_event_open, and assembly code for performance instrumentation.
-      It is designed for ...
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-  - path: content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
-    status: unchanged
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-    faq_changes:
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-    summary_preview: Learn how to package multi-architecture container applications and deploy them on
-      AWS Fargate with Graviton processors using the AWS Copilot CLI. It is designed for software developers
-      who want to lea...
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-  - path: content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to automate the creation and deployment of AWS Graviton instances using
-      Terraform with jump server access for secure infrastructure management. It is designed for software
-      developers who are...
-    source_hash: 087a4d56914e5b53cec5ad17e8d682e72d04ba6b394237c081efe4a3f939e04e
-  - path: content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
-    status: unchanged
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-    summary_preview: Learn how to create and deploy Azure Resource Manager templates to provision Arm64-based
-      Cobalt 100 virtual machines on Azure using the Azure CLI. It is designed for developers and DevOps
-      engineers wh...
-    source_hash: 1682c0527bb49c417263286e2aba7c82fb9a27fa315ecc6b9ca10978b69cc236
-  - path: content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
-    status: unchanged
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-    summary_preview: Learn how to configure a self-hosted GitHub runner on Azure Cobalt 100, create an
-      AKS cluster with Terraform, and deploy a .NET application using GitHub Actions CI/CD. It is designed
-      for software deve...
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-  - path: content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
-    status: unchanged
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-    summary_preview: Learn how to automate the creation of Azure Arm virtual machines using Terraform.
-      It is designed for software developers who are new to deploying Arm instances on Azure using Terraform.
-      By the end, yo...
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-  - path: content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
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-    summary_preview: Learn how to create a custom Azure Linux 3.0 VM image using QEMU, upload it to Azure
-      Shared Image Gallery, and deploy it on Arm-based Cobalt 100 processors. It is designed for developers
-      who want to r...
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-  - path: content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
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-    summary_preview: Learn how to deploy and accelerate PyTorch NLP sentiment analysis models from Hugging
-      Face on Arm servers with BFloat16 fast math kernel optimization on Graviton3 processors. It is designed
-      for softwa...
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-  - path: content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to implement and benchmark bitmap scanning operations for database workloads
-      using scalar, Neon, and SVE instructions on Arm-based cloud instances. It is designed for database
-      developers, pe...
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-  - path: content/learning-paths/servers-and-cloud-computing/bolt/_index.md
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to build, profile, and optimize Arm executables using BOLT post-link binary
-      optimization to improve application performance through code layout improvements. It is designed
-      for software deve...
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-  - path: content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to identify optimization candidates and apply LLVM BOLT post-link optimization
-      to AArch64 binaries using BRBE, SPE, instrumentation, and PMU profiling techniques. It is designed
-      for develope...
-    source_hash: 8654b656131bf1d529e11d85f874f7a81c01f7207340d2a606b6fd2d80bfad04
-  - path: content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
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-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to optimize Arm application binaries and shared libraries using BOLT profile
-      instrumentation, merge multiple profiles for improved coverage, and integrate optimized libraries.
-      It is designed...
-    source_hash: 84a8b96fe7df302e0a2a6e4645bbb6170b45a3e0b55e0ea3682ec47663d34819
-  - path: content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to configure Buildkite agents on Google Axion C4A VMs to build and publish
-      multi-architecture Docker images using Docker Buildx for Arm and x86 platforms. It is designed for
-      developers who w...
-    source_hash: 4bda206717eef380430009f859826d9bcf820442d13492cd3c22a114561e2917
-  - path: content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to install and configure Apache Cassandra on Google Cloud Axion C4A Arm64
-      instances and benchmark read/write performance using cassandra-stress. It is designed for software
-      developers migrat...
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-  - path: content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
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-      before_count: 5
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-    summary_preview: Learn how to run the Arm CCA reference software stack on an FVP with RME support,
-      create a Realm virtual machine, and obtain attestation tokens for confidential computing. It is
-      designed for software ...
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-  - path: content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how Arm CCA Realms interact with I/O devices using VirtIO paravirtualization,
-      SWIOTLB bounce buffers, and PCIe-TDISP secure device attach mechanisms with attestation. It is designed
-      for develope...
-    source_hash: e21f51feb101ad90245ecceab95fe13e5eb61958faf24dff818eddd11f484b9a
-  - path: content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to deploy a CCA realm on an FVP with RME support and connect it with attestation
-      services to create an end-to-end confidential computing workflow. It is designed for software developers
-      who ...
-    source_hash: b032debbdfe82cbd017812cf671907520b146fd8684afce0c45c91e7f2287e18
-  - path: content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
-    status: unchanged
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-    faq_changes:
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-    summary_preview: Learn how to deploy Confidential Containers from encrypted images inside Arm CCA
-      Realms using Trustee services for attestation-based authorization on an FVP with RME support. It
-      is designed for develo...
-    source_hash: 649957b07b2bde05bc11047bd12bfc4f697c1a55eef1c3a25b5fafc95cda893d
-  - path: content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to deploy a CCA realm workload on an FVP and connect it with Trustee services
-      to enable attestation-based confidential data processing. It is designed for software developers
-      who want to run...
-    source_hash: 563456f5d151c7ef59bf458f6ac971c321077475a0a78d3b5a3885b97157ba9e
-  - path: content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md
-    status: unchanged
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-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to inspect and verify Arm CCA attestation tokens using command-line tools
-      and the open-source Veraison attestation verification service. It is designed for developers who
-      would like to learn...
-    source_hash: 9e6dee3aef79c1c65cc1a1f4fe3528b9c5542d8046f0b059ef703079ef964d77
-  - path: content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to deploy a scalable Arm CCA attestation verifier service on AWS using
-      Veraison components with platform endorsement provisioning. It is designed for developers familiar
-      with CCA attestation...
-    source_hash: 55b8032ceaf735d53b1157102a3a1da5aa5cb686e9ec51cf4896ded66d9bf263
-  - path: content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to set up CircleCI self-hosted machine runners on Google Cloud Axion C4A
-      SUSE VMs and execute Arm-native CI/CD workflows using custom resource classes. It is designed for
-      developers and DevO...
-    source_hash: ec9cdca7aa9a5670f54ea3646f965149460d8e66d9d5d904e1b6dcf09867df39
-  - path: content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to install and configure CircleCI self-hosted machine runners on AWS Graviton
-      Arm64 instances to execute CI/CD workflows natively on Arm. It is designed for developers and DevOps
-      engineers w...
-    source_hash: e04982fd668765fa2ab25f943f020b8d2b4a5e9b5ff3a51b7431cc4d93730180
-  - path: content/learning-paths/servers-and-cloud-computing/clair/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to install and run Clair on Arm servers using combined and distributed
-      deployment models to scan container images and generate vulnerability reports. It is designed for
-      software developers i...
-    source_hash: e742eb44fa108bfcc4ee5a241414e6aa05e1fc0e1cceb589fb78a080f59b0d38
-  - path: content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to install ClickHouse on Arm-based cloud instances and measure database
-      performance using ClickBench to determine appropriate instance configurations. It is designed for
-      software developers ...
-    source_hash: 0d1390198cf2f0e87f509c5269fc2405cffcb2e57311024150d47e60a3273178
-  - path: content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to deploy ClickHouse on Google Cloud Axion C4A processors and build a streaming
-      ETL pipeline using Apache Beam, Dataflow, and Pub/Sub for real-time analytics. It is designed for
-      developers d...
-    source_hash: e77cd1373236f39f360afe6844d642fde290960ccdad26544ff1e3342f2a980c
-  - path: content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
-    status: unchanged
-    changed_on_disk: true
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-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to deploy an Arm-based Cobalt 100 virtual machine on Azure, connect via
-      SSH, and configure network security group rules for external connectivity. It is designed for developers
-      and DevOps en...
-    source_hash: e4eb84292a669a8d73bff4b63d2fe3f70382dd873d023fc8ff24db5ad6178ab8
-  - path: content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to automate Docker image creation for Arm using AWS CodeBuild with GitHub
-      integration and run the images on any Arm system with Docker installed. It is designed for software
-      developers inter...
-    source_hash: e7ea48aaea0e25f624ea1d77c6252b0e537fdaeca3aacca560d7bc9d103bbbb3
-  - path: content/learning-paths/servers-and-cloud-computing/codec/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to build and run the x265 H.265 codec on Arm servers with performance benchmarking
-      across various video resolutions and encoding presets. It is designed for software developers who
-      want to b...
-    source_hash: 908a750f2a891a0c4c9d1183c2130ac5ac0fdcfb4a45185cef6ed6da47c9aaa9
-  - path: content/learning-paths/servers-and-cloud-computing/codec1/_index.md
-    status: unchanged
-    changed_on_disk: true
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-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to build and run the AV1 and VP9 video codecs on Arm Linux systems with
-      performance benchmarking across various resolutions and encoding configurations. It is designed
-      for software developer...
-    source_hash: c0643a788cdb0b3e33fe645fbb61d99a1899806e3ee197541c1eb8134b2876c1
-  - path: content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to install and configure Couchbase on Google Cloud Axion C4A Arm64 instances
-      and benchmark read/write performance using YCSB workloads. It is designed for developers deploying
-      Couchbase work...
-    source_hash: 0a7d76b8c944a50073c7524813acf83948155c7c61d9c92f9202eae1192c9600
-  - path: content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to apply g++ compiler optimization techniques and flags to improve C++
-      application performance on Arm systems with hands-on examples. It is designed for beginner C++ developers
-      who are looki...
-    source_hash: 22f845b8ea4dbb9ffc63fafb76c17c79aedde14a28417d49e1ab0833bbbc1eba
-  - path: content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to apply profile-guided optimization to C++ applications on Arm systems
-      and measure performance improvements using Google Benchmark. It is designed for Developers looking
-      to optimize C++ per...
-    source_hash: 4e7de348514d0b5a742fa47bb85a2a5814fa9bd587478e7ef08365d16273f6bf
-  - path: content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to profile and identify CPU hotspots in C++ applications on Arm Neoverse
-      using Arm Performix flame graphs to guide optimization. It is designed for software developers and
-      performance engine...
-    source_hash: 58dba071f70b4f85b87b3bd27b3a7ba3ff985f80079a42e05b797a998aeaf104
-  - path: content/learning-paths/servers-and-cloud-computing/csp/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to start an Arm-based virtual machine instance from major cloud service
-      providers and verify the Arm architecture is being used. It is designed for software developers
-      who are new to Arm-bas...
-    source_hash: 9e94b69eabf35677c48db812bb85ea9cef184efc078a826b96954f540a45e915
-  - path: content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
-    status: unchanged
-    changed_on_disk: true
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-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to deploy and run the DeepSeek-R1 language model on Arm servers using llama.cpp
-      with quantization for efficient CPU inference. It is designed for developers who want to run DeepSeek-R1
-      on Ar...
-    source_hash: f600fcba0adea12ff1b8b092e75de553577d940dc3bf632e9a247cec22d364a4
-  - path: content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to use fio to microbenchmark storage performance on Arm systems and monitor
-      storage using iostat, iotop, and pidstat to identify bottlenecks. It is designed for developers
-      looking to optimiz...
-    source_hash: a1ec216948e7cfd4fc52815196bb3b99ab4e76c9c756aa9e9a8e3216ef5e7ce4
-  - path: content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md
-    status: unchanged
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-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Run distributed LLM inference with llama.cpp across multiple AWS Graviton4 instances,
-      covering multi-node setup, coordination, and performance trade-offs. It is designed for This introductory
-      topic is...
-    source_hash: 6ac0c7cf1ab4b3efa680acbf1e349b858bee5dc7992baf6689e1f293a83060a4
-  - path: content/learning-paths/servers-and-cloud-computing/django/_index.md
-    status: unchanged
-    changed_on_disk: true
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-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to create a simple Django web application and deploy it on Arm machines
-      using Nginx and PostgreSQL. It is designed for engineers who want to deploy a Django based application
-      on Arm machines...
-    source_hash: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
-  - path: content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md
-    status: unchanged
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-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to deploy a production-grade Django REST API on Google Kubernetes Engine
-      with Arm64 Axion node pools integrated with Google Cloud managed data services. It is designed for
-      DevOps engineers a...
-    source_hash: 6675df4c91126b157dcbf39c96a773130f1a95e2f5680913979da96f6f6c97cd
-  - path: content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to build and benchmark the Deep Learning Recommendation Model using PyTorch
-      and MLPerf on Arm Neoverse V2 processors. It is designed for software developers who want to set
-      up a pipeline in ...
-    source_hash: 82716c2de19d18a154c85d03b7f2ec01839284262914f7bb3ad04b18a105379d
-  - path: content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to use the Docker MCP Toolkit with the Arm MCP Server and GitHub Copilot
-      to automate container and code migration from x86 to Arm64. Through a hands-on example, migrate
-      a legacy C++ applicat...
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-    summary_preview: Learn how to build and run an OrchardCore CMS .NET application on Azure Cobalt 100
-      processors, covering AnyCPU configuration and shared C library integration. It is designed for .NET
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-    summary_preview: Learn how to deploy Dynatrace OneAgent on Azure Cobalt 100 Arm64 virtual machines
-      and configure ActiveGate for secure infrastructure and application monitoring. It is designed for
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-    summary_preview: Learn how to create an AWS ECS cluster with Fargate and AWS Graviton processors,
-      then create and run containerized tasks on Arm infrastructure. It is designed for developers who
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-    summary_preview: Learn how to provision an Amazon EKS cluster on Arm-based Graviton instances and
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-    summary_preview: Learn how to use docker buildx and docker manifest to build and deploy multi-architecture
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-    summary_preview: Learn how to build, install, and run Envoy proxy on Arm servers and configure it
-      as a web server for traffic management. It is designed for engineers who want to use Envoy on Arm.
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-    summary_preview: Learn how to install and configure Envoy proxy on Google Cloud Axion C4A Arm64 instances
-      and benchmark HTTP proxy performance with load testing. It is designed for This introductory topic
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-    summary_preview: Learn how to optimize Envoy proxy performance on Arm servers using Transparent Huge
-      Pages and Profile-Guided Optimization techniques. It is designed for software developers who want
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-    summary_preview: Learn how stack buffer overflow exploits work on AArch64 by analyzing stack frame
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-    summary_preview: Learn how to identify and fix false sharing issues using Perf C2C cache line analysis
-      and Arm Statistical Profiling Extension on Arm-based cloud systems. It is designed for performance-oriented
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-    summary_preview: Learn how to build custom Linux kernels using tuxmake and Fastpath, then benchmark
-      and compare kernel versions on Arm-based EC2 instances. It is designed for software developers and
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-    summary_preview: Learn how to implement exponential functions using Arm SVE intrinsics with the FEXPA
-      instruction for hardware-accelerated computations on Neoverse processors. It is designed for developers
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-    summary_preview: Learn how to install and run Apache Flink on Arm servers and benchmark stream processing
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-    summary_preview: Learn how to install and configure Apache Flink on Google Cloud Axion C4A Arm64 instances
-      and benchmark stream processing performance with Nexmark. It is designed for developers deploying
-      and optimizi...
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-    summary_preview: Learn how to build scalable machine learning workflow pipelines on Google Cloud C4A
-      Axion processors using Flyte for workflow orchestration and gRPC for distributed service communication.
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-  - path: content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
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-    summary_preview: Learn how to create an Arm64 Azure VM, install .NET SDK, containerize .NET applications,
-      and push Docker images to Azure Container Registry. It is designed for software developers interested
-      in learni...
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-    summary_preview: Learn how to create and run Docker containers on Azure Container Instances for Arm64-based
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-      create and ...
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-    summary_preview: Learn how to create an Azure Kubernetes Service cluster with Arm64 virtual machines
-      and deploy a containerized application to AKS. It is designed for This learning path is dedicated
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-    summary_preview: Learn how to automate Azure resource deployment using Infrastructure as Code with
-      Pulumi to provision Azure Container Instances for containerized applications. It is designed for
-      developers interested...
-    source_hash: 1510c787979257e191196e13999e98c20ca24d0857f72075649c28520c74c576
-  - path: content/learning-paths/servers-and-cloud-computing/funasr/_index.md
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-    summary_preview: Learn how to deploy the ModelScope FunASR Chinese automatic speech recognition model
-      on Arm-based servers with real-time transcription and sentiment analysis. It is designed for developers
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-    source_hash: 410ec28d257ce7aa1308f11a741aa87baea80daf1acb113c4149761144269022
-  - path: content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
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-    summary_preview: Learn how to install and configure Gardener Kubernetes management platform on Google
-      Cloud Axion C4A SUSE Arm64 instances and deploy workload clusters. It is designed for software developers
-      deploying...
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-  - path: content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
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-    summary_preview: Learn how to apply link-time optimization with the GCC toolchain to improve application
-      performance by optimizing across compilation units. It is designed for developers who want to improve
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-  - path: content/learning-paths/servers-and-cloud-computing/gcp/_index.md
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-    summary_preview: Learn how to automate the creation of Arm virtual machines on Google Cloud Platform
-      using Terraform with jump server access configuration. It is designed for anyone new to using Arm
-      virtual machines i...
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-  - path: content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
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-    summary_preview: Run Geekbench on Arm systems to benchmark CPU performance, interpret the results,
-      and compare different Arm configurations. It is designed for software developers interested in comparing
-      the performan...
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-  - path: content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
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-    summary_preview: Learn how to set up Arm-hosted GitHub runners and train PyTorch ML models using the
-      German Traffic Sign Recognition Benchmark dataset with automated workflows. It is designed for software
-      developers i...
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-  - path: content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
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-    summary_preview: Learn how to install RunsOn self-hosted runner manager in your AWS account to execute
-      GitHub Actions workflows on Arm runners. It is designed for developers who want to use Arm runners
-      offered by AWS ...
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-    summary_preview: Learn how to provision a Google Axion C4A Arm virtual machine and set up a GitHub
-      Actions self-hosted runner for CI/CD workflows. It is designed for developers who want to deploy
-      a GitHub Actions self...
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-  - path: content/learning-paths/servers-and-cloud-computing/gke/_index.md
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-    summary_preview: Learn how to automate the deployment of an Arm-based Google Kubernetes Engine cluster
-      using Terraform for container orchestration. It is designed for software developers who want to
-      deploy an Arm-base...
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-  - path: content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
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-    summary_preview: Learn how to add Arm-based Google Axion nodes to an existing x86 GKE cluster and
-      rebuild applications for multi-architecture support. It is designed for software developers who
-      are looking to migrate ...
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-  - path: content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
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-    summary_preview: Learn how to create dual-architecture GKE clusters with arm64 and amd64 node pools,
-      build multi-architecture Docker images, and migrate services to Google Axion processors. It is designed
-      for cloud, p...
-    source_hash: cf981c67553824c7c57b6dda9c2953ac80926750676ac101e939f6bbff655ab5
-  - path: content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
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-    summary_preview: Rebuild and benchmark glibc with LSE atomics on Arm servers, then evaluate scalability
-      using MongoDB workloads and guidance on when LSE delivers a measurable uplift. It is designed for
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-    source_hash: 74952d68380c7540306543b0a7520f6960f673dd55ad5f164365fcfe1470380e
-  - path: content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
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-    summary_preview: Learn how to provision Arm64 and x86_64 VM instances on Google Cloud, then install
-      and use Sweet and Benchstat to measure and compare Go application performance. It is designed for
-      This introductory t...
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-  - path: content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md
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-    summary_preview: Learn how to provision Azure Cobalt 100 Arm64 virtual machines and deploy Golang
-      applications with performance benchmarking on Arm architecture. It is designed for software developers,
-      DevOps engineer...
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-  - path: content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md
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-    summary_preview: Learn how to install Helm on Google Cloud Axion C4A SUSE VMs and deploy applications
-      like NGINX, PostgreSQL, and Redis using Helm charts. It is designed for This is an introductory
-      topic intended for ...
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-  - path: content/learning-paths/servers-and-cloud-computing/intro/_index.md
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-    summary_preview: Learn where Arm architecture is used in servers and cloud computing, and find Arm-based
-      hardware platforms for software development. It is designed for software developers working on server
-      and cloud ...
-    source_hash: d2786f42b505823253ae16c647ca2e03c154f371b7c326210fde8523b12a8188
-  - path: content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
-    status: unchanged
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-    faq_changes:
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-    summary_preview: Analyze and optimize interrupt request (IRQ) patterns on Arm Linux servers to improve
-      network workload performance through IRQ distribution strategies. It is designed for developers
-      and performance en...
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-    summary_preview: Monitor, interpret, and optimize Java Garbage Collector performance on Arm servers
-      by comparing different GCs and tuning parameters for your workload. It is designed for Java developers
-      aiming to opti...
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-    summary_preview: Deploy and optimize Java applications on Google Cloud Axion processors by testing
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-    summary_preview: Deploy Java on Azure Cobalt 100 Arm virtual machines and benchmark application performance
-      with JMH microbenchmarks. It is designed for This is an introductory topic about Java deployment
-      and benchmar...
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-    summary_preview: Profile Java applications on Arm Neoverse servers using flame graphs generated with
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-      who want to analyz...
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-    summary_preview: Deploy Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate installation,
-      and execute Arm-native CI/CD pipelines including Docker workflows. It is designed for software developers
-      d...
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-    summary_preview: Deploy and configure a Kafka cluster with Zookeeper on Arm servers, test event streaming,
-      and automate deployment on AWS and Google Cloud. It is designed for software developers who want
-      to learn how ...
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-    summary_preview: Deploy Apache Kafka on Azure Cobalt 100 Arm virtual machines and benchmark message
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-      from x86_64 to ...
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-    summary_preview: Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing Kedify
-      and KEDA with Helm and testing autoscaling behavior. It is designed for developers running HTTP
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-    summary_preview: Create, train, and evaluate a neural network model on Arm servers using Keras Core
-      with TensorFlow, PyTorch, and JAX backends. It is designed for engineers who want to create a neural
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-    summary_preview: Compile and install custom Linux kernels on Arm cloud instances using TuxMake with
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-      building custom Linu...
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-    summary_preview: Identify and migrate container images in a Kubernetes cluster to Arm-compatible versions
-      using KubeArchInspect reports. It is designed for software developers who want to ensure containers
-      running in ...
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-    summary_preview: Deploy AWS Lambda functions on Graviton processors using Terraform for Python and
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-      functions on AWS Gravi...
-    source_hash: 22fa96e29143f2e0dc0c204919422914b3f70d63ddf833cf1067fbf67b525aa0
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-    summary_preview: Enable and measure libhugetlbfs performance improvements for MySQL and other workloads
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-    summary_preview: Serve the llama.cpp chatbot through an OpenAI-compatible API, enabling existing OpenAI-style
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-    summary_preview: Build a production-ready vision chatbot on Google Axion using Streamlit, PyTorch,
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-      developers and ML e...
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-    summary_preview: Optimize llama.cpp on Arm CPUs by integrating Streamline Annotations to profile Prefill
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-    summary_preview: Understand Large System Extensions (LSE) for Arm processors and verify whether applications
-      use LSE for improved atomic operation performance. It is designed for software developers who want
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-    summary_preview: Deploy MariaDB on Arm cloud instances across AWS, Azure, and Google Cloud using Docker,
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-    summary_preview: Install memcached on Arm cloud servers and benchmark in-memory key-value store performance
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-  - path: content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
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-    summary_preview: Deploy Memcached as a cache for MySQL and PostgreSQL on Arm servers. It is designed
-      for developers who want to use memcached as their in-memory key-value store. By the end, you will
-      be able to deploy ...
-    source_hash: 4efe46aaf4580a6b8f263b69e8f072211bfd24fcf7f7a885689c927fc05a4c63
-  - path: content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
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-    faq_changes:
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-    summary_preview: Use ASCT to measure cache latency, streaming bandwidth, and coherency latency on
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-  - path: content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
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-    summary_preview: Test and validate thread synchronization approaches in the Arm memory model using
-      Herd7, Litmus7, and Arm hardware with assembly snippets. It is designed for developers seeking practical
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-  - path: content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
-    status: unchanged
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-    faq_changes:
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-    summary_preview: Microbenchmark and tune network performance with iPerf3 and Linux traffic control
-      walks you through an end-to-end Arm software workflow. It is designed for performance engineers,
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-    source_hash: a67d36fb650b77c170c15f193049490286a3f097801d0c79d701d3f5610fe1dc
-  - path: content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
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-    faq_changes:
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-    summary_preview: Scan source code for architecture-specific portability issues using migrate-ease
-      to identify and resolve AArch64 porting challenges before migration. It is designed for developers
-      looking to migrate a...
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-  - path: content/learning-paths/servers-and-cloud-computing/migration/_index.md
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-    summary_preview: Set up an Arm development environment, analyze dependencies, and understand common
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-  - path: content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
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-    summary_preview: Build a Retrieval-Augmented Generation (RAG) application on Arm servers using Zilliz
-      Cloud for vector search and llama.cpp for LLM inference. It is designed for software developers
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-    source_hash: 2eb252b19b7535fbb776a3153bfc9f70b6217088e5b4b55deb56d01393abaf3b
-  - path: content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
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-    summary_preview: Learn how to deploy and configure MinIO on an Azure Cobalt 100 virtual machine, benchmark
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-  - path: content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
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-    summary_preview: Benchmark machine learning inference performance on Arm servers using TensorFlow
-      and the MLPerf Inference benchmark suite from MLCommons. It is designed for software developers
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-    source_hash: 973ba6c1e00fc67e1702606412124d8172e071b9b677a1df53c3be66210bf704
-  - path: content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
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-    summary_preview: Install MongoDB on Arm servers and benchmark database performance using Yahoo Cloud
-      Serving Benchmark (YCSB) to compare against other architectures. It is designed for software developers
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-  - path: content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
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-    summary_preview: Deploy MongoDB on Azure Cobalt 100 Arm virtual machines and benchmark database performance
-      using mongotop and mongostat monitoring tools. It is designed for software developers who want to
-      migrate Mon...
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-  - path: content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
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-    summary_preview: Deploy MongoDB on Google Cloud Axion C4A virtual machines and benchmark database
-      performance with Yahoo Cloud Serving Benchmark (YCSB). It is designed for This introductory topic
-      is for software devel...
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-    summary_preview: Debug, profile, and optimize MPI parallel applications on Arm servers using Linaro
-      Forge, gdb, and Arm Performance Libraries. It is designed for HPC software developers writing MPI
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-    summary_preview: Select and apply accuracy modes for vectorized math functions in Libamath to balance
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-    summary_preview: Build a multi-architecture Kubernetes cluster running nginx on Azure AKS walks you
-      through an end-to-end Arm software workflow. It is designed for developers who want to deploy multi-architecture
-      Kube...
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-    summary_preview: Add Arm nodes to your GKE cluster using a multi-architecture Ollama container image
-      walks you through an end-to-end Arm software workflow. It is designed for developers who want to
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-    summary_preview: Deploy a RAG-based Chatbot with llama-cpp-python using KleidiAI on Google Axion processors
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-    summary_preview: Get started with the Arm 5G RAN Acceleration Library (ArmRAL) walks you through an
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-      updated_questions: []
-    summary_preview: Learn how to develop and test a modern C++ library using CMake, GoogleTest, and matrix
-      processing as a practical example on Arm platforms. It is designed for developers who want to learn
-      how to develo...
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-  - path: content/learning-paths/cross-platform/mca-godbolt/_index.md
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-    faq_changes:
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-    summary_preview: Learn how to use llvm-mca with Compiler Explorer to analyze Arm assembly performance,
-      estimate hardware resource pressure, and diagnose performance issues. It is designed for developers
-      who want to di...
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-  - path: content/learning-paths/cross-platform/mcp-ai-agent/_index.md
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-      before_count: 5
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-    summary_preview: Learn how to deploy a Model Context Protocol server on Raspberry Pi 5 and use the
-      OpenAI Agent SDK to create AI agents with custom tools for local inference. It is designed for LLM
-      and IoT developers ...
-    source_hash: 39d489081bfa22125a4046c17d5c00a32c0d7b298cba7b6a12373cf3cf6bac04
-  - path: content/learning-paths/cross-platform/memory-latency/_index.md
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-    summary_preview: Learn how to reduce memory latency impact in applications using cache alignment and
-      prefetching techniques on Arm processors for improved performance. It is designed for Arm developers
-      who want to lea...
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-  - path: content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md
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-      before_count: 5
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-    summary_preview: Learn how to build MNN on an Armv9 system, run text, vision, and audio prompts with
-      a multimodal Omni model, and combine image and audio inputs into a single-shot retail restock ticket
-      workflow. It is...
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-  - path: content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md
-    status: unchanged
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-    summary_preview: Learn how to implement and optimize matrix multiplication using Arm's Scalable Matrix
-      Extension 2 (SME2) with assembly and intrinsics, including benchmarking and validation on Arm hardware.
-      It is desi...
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-  - path: content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to create and train a PyTorch neural network for MNIST digit classification,
-      optimize it with quantization and fusing, and deploy it in an Android application with performance
-      measurement. I...
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-  - path: content/learning-paths/cross-platform/remoteit/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to install and configure Remote.It for secure remote device access using
-      SSH and other services, with proxy and peer-to-peer connection options. It is designed for software
-      developers who wa...
-    source_hash: 927cfebb8ebf9595922dad115c9a8d10900e43c4f80e73e6102a71e3e4ca2da1
-  - path: content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to use the C99 restrict keyword to indicate non-overlapping memory regions
-      and enable better compiler optimizations for vectorization on Arm platforms. It is designed for
-      C developers who ar...
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-  - path: content/learning-paths/cross-platform/rust_armds/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to build an embedded Rust application for Arm processors, run it on a Fixed
-      Virtual Platform, and debug it using Arm Development Studio. It is designed for embedded application
-      developers to...
-    source_hash: f237cd239e5886b21bd5bee88dcd9f95d88eda9a5c2eea363cc9db252e0c6e9f
-  - path: content/learning-paths/cross-platform/simd-info-demo/_index.md
-    status: unchanged
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-    faq_changes:
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-    summary_preview: Learn how to use SIMD.info to port SIMD intrinsics across Arm architectures, including
-      navigation, search, and comparison features for finding equivalent instructions. It is designed
-      for software deve...
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-  - path: content/learning-paths/cross-platform/simd-loops/_index.md
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-    summary_preview: Learn how to write high-performance SIMD code using the SIMD Loops project, with
-      hands-on examples demonstrating SVE, SVE2, and SME2 features on Arm processors. It is designed for
-      software developers ...
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-  - path: content/learning-paths/cross-platform/simd-on-rust/_index.md
-    status: unchanged
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-    summary_preview: Learn how to write SIMD code in Rust on Arm platforms using Neon intrinsics, portable
-      SIMD abstractions, and optimize performance with architecture-specific instructions. It is designed
-      for software d...
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-  - path: content/learning-paths/cross-platform/sme-executorch-profiling/_index.md
-    status: unchanged
-    changed_on_disk: true
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-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to profile and optimize ExecuTorch models using SME2 acceleration on Arm
-      platforms, including operator-level analysis and performance bottleneck identification. It is designed
-      for developers...
-    source_hash: 36f7dfe13c8c940abbaad2b61620b3b1c6d97f580de15e573b45ef7760753797
-  - path: content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to deploy Tinkerblox UltraEdge HPC-I for edge AI and mixed workloads on
-      Arm platforms, including installation and configuration on Debian, Ubuntu, and Yocto systems. It
-      is designed for busin...
-    source_hash: 09142517ecc64fba821062e1af4db3ac9d72f9adcd3f10c12d6fd5a4cf3daaf4
-  - path: content/learning-paths/cross-platform/topdown-compare/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to compare Arm Neoverse and Intel x86 top-down performance analysis methodologies
-      using PMU counters, Linux Perf, and topdown-tool to identify bottlenecks across architectures. It
-      is designe...
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-  - path: content/learning-paths/cross-platform/vectorization-comparison/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to migrate x86-64 SIMD code to Arm64 by mapping Intel SSE/AVX to Arm Neon,
-      SVE, and SME, with code examples and migration strategies using autovectorization or intrinsics.
-      It is designed for...
-    source_hash: 26450ae17f7ed4242c52456c2780ffce5fad36b56dfc4a8482e8f236f855e134
-  - path: content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to optimize SIMD performance on Arm by restructuring data layouts from
-      Array-of-Structures to Structure-of-Arrays, with practical examples using Neon and SVE intrinsics.
-      It is designed for C...
-    source_hash: 14a22194d3fc73ebebb62db9c7cfbeabe0bd9acdaf340b2df52072be65d98655
-  - path: content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to sample and profile CPU instructions using WindowsPerf with Arm Statistical
-      Profiling Extension (SPE) on Windows on Arm, demonstrated with CPython workload analysis. It is
-      designed for dev...
-    source_hash: bf887ef2481b51cf6c32e49e3eb1d5577346b7b9b1e4d6a604c559597b5306f0
-  - path: content/learning-paths/cross-platform/woa_azure/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to create and connect to a Windows on Arm virtual machine in Microsoft
-      Azure using the Azure Marketplace and RDP. It is designed for software developers interested using
-      Windows on Arm in th...
-    source_hash: 367566dfec0a76685b1504c2c1a996e50c55e67b2f4c5515057c0f0f1384ef98
-  - path: content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to build and deploy distributed Zenoh systems on Arm devices like Raspberry
-      Pi, using pub/sub, storage, and queryable models for scalable robotics and IoT applications. It
-      is designed for ro...
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-  - path: content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to design and integrate a custom AXI-Lite peripheral with a Cortex-A9 processor
-      on the Zybo Z7-10 board using Vivado, configuring GPIOs to control LEDs based on switch inputs.
-      It is designed...
-    source_hash: d8c48c293b2d316d5e68e18ab9e81157ad114a64cf2efa6951374a55d25238d6
-  - path: content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit and
-      run inference on the Ethos-U85 NPU. It is designed for embedded developers who want to deploy a
-      neural network model t...
-    source_hash: 8585bb59efa67b3a92314e4382339fc5c0a14bb458931c0d98f2748379003857
-  - path: content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
-    status: unchanged
-    changed_on_disk: true
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-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to build a motion-detection device with Raspberry Pi Pico (RP2040 Cortex-M0+)
-      using Arduino IDE, PIR sensors, and interrupt-driven programming on baremetal. It is designed for
-      software devel...
-    source_hash: 3ddb97eb97a4c7ede7951410086198ee793a9d452a79b607f211873971bd375d
-  - path: content/learning-paths/embedded-and-microcontrollers/armds/_index.md
-    status: unchanged
-    changed_on_disk: true
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-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to import and build example projects in Arm Development Studio and debug
-      embedded applications using Fixed Virtual Platforms (FVPs) or hardware with DSTREAM debug probes.
-      It is designed for ...
-    source_hash: 0103f51d42c230dbe75ff5b78ac15a33dfd2f2c0f4906fb665a8dd681512d2e1
-  - path: content/learning-paths/embedded-and-microcontrollers/asm/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to write mixed C and assembly programs for Cortex-M microcontrollers using
-      Keil MDK, following Arm Procedure Call Standard conventions. It is designed for software developers
-      who are interes...
-    source_hash: 24245102b7b6cefd0d4f67fbfaff1fb27c913de33665929ec3cb15293a1b3b51
-  - path: content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
-    status: unchanged
-    changed_on_disk: true
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-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to create a custom Balena OS image, run it on Arm Virtual Hardware, and
-      deploy IoT applications to a virtual Raspberry Pi 4 device. It is designed for embedded software
-      developers interested...
-    source_hash: 983afd3fcc565266c8f12588086e36d736540e23d0e748c94b5d2a45ab53d6ab
-  - path: content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to set up AWS IoT Greengrass Core on Arm Virtual Hardware and deploy AWS
-      Greengrass components to a virtual Raspberry Pi 4 device. It is designed for embedded software developers
-      interested ...
-    source_hash: 85845fd4a47960bca053aed8d87c1d1442a7f5de0be5caff349fc92c6980b07e
-  - path: content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
-    status: unchanged
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-    faq_changes:
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-    summary_preview: Learn how to build Matter reference examples on Arm Virtual Hardware, demonstrate
-      device communication, and automate testing with GitHub Actions CI/CD workflows. It is designed for
-      embedded software d...
-    source_hash: bd39d50c93219201ead5de5da627447a91a82ea9c65e232350facd0c3516bedc
-  - path: content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
-    status: unchanged
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-    faq_changes:
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-    summary_preview: Learn how to export and compile a PaddleOCR text recognition model using TVMC and
-      deploy it on the Arm Corstone-300 FVP with Cortex-M55 processors. It is designed for software developers
-      interested in...
-    source_hash: 398077be1406d0787b0a5d8dc254472da0d125b51b0907769cd4a3516ce9111b
-  - path: content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to create and integrate a virtual LED peripheral using the Virtual IO interface
-      of Arm Virtual Hardware to simulate real-world peripherals. It is designed for software developers
-      new to Arm ...
-    source_hash: aaff31b8917320c53825f3e7410b703bc7c7e273b1963fd80727b6b874f50566
-  - path: content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
-    status: unchanged
-    changed_on_disk: true
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-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to build a complete IoT solution in Azure that streams, stores, monitors,
-      aggregates, and visualizes telemetry data from Arm devices using IoT Hub, Stream Analytics, Cosmos
-      DB, and Azure Fun...
-    source_hash: 0e653bdbf5e5b08a6a00ba68e5ed215a0082e22c634d7d632d088851d48bb01c
-  - path: content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to create, build, and run a bare-metal embedded application for Armv8-A
-      processors using Arm Compiler for Embedded and Fixed Virtual Platforms, including basic exception
-      handling. It is desi...
-    source_hash: 6521f17d1a10ab8871d13917923f7f64320f69301b4c0c5f5b528163d3cb43c6
-  - path: content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to deploy containerized embedded applications and firmware onto an Arm
-      Cortex-M core from a Cortex-A core using containerd, K3s, and the hybrid-runtime on Arm Virtual
-      Hardware. It is designe...
-    source_hash: 3394bf994039e441d57dced2abb64d725077d4672e0e68dc71f1de50e58f0408
-  - path: content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md
-    status: unchanged
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-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to create, build, and debug an RTX5 RTOS-based application using Keil μVision
-      with CMSIS-RTOS2 API and Event Recorder for embedded Cortex-M development. It is designed for software
-      developer...
-    source_hash: d8c73d658fa7a8051131276b8567cbd7376aac3b8f6e87c8ecdf224443c3ef99
-  - path: content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to create, configure, and debug an RTX5 RTOS application using Keil Studio
-      for VS Code with CMSIS-RTOS2 API for embedded Cortex-M development. It is designed for software
-      developers new to R...
-    source_hash: f4d1ab3448590c5654e2479937c9eec3e378d05229b29a45b8675139f40ac177
-  - path: content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
-    status: unchanged
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-    summary_preview: Learn how to prototype and port DSP algorithms using the CMSIS-DSP Python package,
-      mapping Python code to efficient C implementations for embedded Cortex-M and Cortex-A platforms.
-      It is designed for d...
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-    summary_preview: Learn how to implement context switching operations on Arm Cortex-M processors using
-      the Memory Protection Unit and SysTick exception in a bare-metal environment. It is designed for
-      software developer...
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-  - path: content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
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-    summary_preview: Learn how to set up and use code coverage in Keil MDK with FVPs to verify that your
-      embedded application tests execute all code paths. It is designed for embedded software developers
-      new to the code-c...
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-    summary_preview: Device-to-Device communication with Device Connect walks you through an end-to-end
-      Arm software workflow. It is designed for developers wiring up heterogeneous edge fleets, where
-      devices need a shared...
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-    summary_preview: Learn how to connect AI agents to Arm-based edge devices using Device Connect for
-      structured device access and Strands for agent orchestration, with examples for both simulated and
-      physical robots. It...
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-  - path: content/learning-paths/embedded-and-microcontrollers/docker/_index.md
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-    summary_preview: Learn how to create a Dockerfile, build a Docker image with Arm Compiler for Embedded
-      and Fixed Virtual Platforms, and test the containerized Arm development environment. It is designed
-      for embedded s...
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-  - path: content/learning-paths/embedded-and-microcontrollers/edge/_index.md
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-    summary_preview: Learn how to collect and preprocess audio data using Edge Impulse, train an audio
-      classification model, and deploy it to the Arduino Nano RP2040 to control LEDs based on voice commands.
-      It is designed...
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-    summary_preview: Develop a image classification neural network model and deploy it on an STM32 B-L475E-IOT01A2
-      board. It is designed for embedded software developers interested in building neural network models
-      for mi...
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-  - path: content/learning-paths/embedded-and-microcontrollers/intro/_index.md
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-    summary_preview: Learn where Arm architecture is used in microcontrollers and discover microcontroller
-      hardware options for software development on Arm Cortex-M processors. It is designed for software
-      developers worki...
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-    summary_preview: Learn what differentiates TinyML from other AI domains, explore Arm-based edge devices
-      for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed Virtual
-      Platform. It is ...
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-    summary_preview: Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300 virtual
-      hardware to understand complete IoT software stack construction. It is designed for embedded software
-      developers ...
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-    summary_preview: Learn how to set up a Jetson Orin Nano with a MIPI CSI-2 camera and perform real-time
-      object detection from live video and image files using DetectNet and TensorRT. It is designed for
-      developers inter...
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-  - path: content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
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-    faq_changes:
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-    summary_preview: Learn how to import, build, and debug your first Keil Studio Cloud project. It is
-      designed for embedded software developers new to Keil Studio Cloud. By the end, you will be able
-      to import and build a...
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-    summary_preview: Learn how to boot and configure the NXP FRDM i.MX 93 Arm board with Linux, create
-      a user with sudo access, connect to WiFi using ConnMan, and transfer files over the network. It
-      is designed for embedd...
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-    summary_preview: Learn how to boot a Linux software stack on Arm Fixed Virtual Platforms (FVPs), then
-      debug Trusted Firmware-A and the Linux kernel using Arm Development Studio. It is designed for developers
-      who want ...
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-    summary_preview: Learn how to install the Python version of llama.cpp on a Raspberry Pi 5, download
-      an LLM from Hugging Face, assess memory and performance, and run the model using Python bindings.
-      It is designed for ...
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-  - path: content/learning-paths/embedded-and-microcontrollers/migration/_index.md
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-    summary_preview: Learn the software migration methodology for porting Linux workloads from x86_64
-      to aarch64, including using Arm compilers, porting compiler intrinsics, and deploying applications
-      in containers. It is...
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-    summary_preview: Learn how to build examples from the Machine Learning Evaluation Kit (MLEK) and run
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-      It is designed for e...
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-    summary_preview: Learn how to understand and select physical and virtual hardware targets for ML application
-      development with Cortex-M and Ethos-U, identify software tools, and find example applications. It
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-    summary_preview: Learn how to create debug configurations for virtual platforms and development boards
-      in Arm Development Studio, including setting up connections for Fast Models and DSTREAM debug probes.
-      It is design...
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-  - path: content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
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-    summary_preview: Learn how to bring up ExecuTorch executor_runner firmware on the NXP FRDM i.MX 93
-      Cortex-M33 using Linux RemoteProc, compile .pte models for Ethos-U65, and run inference with NPU
-      acceleration. It is d...
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-  - path: content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
-    status: unchanged
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-    summary_preview: Learn how to migrate a CMSIS v5-based CMSIS-Pack with device support to CMSIS v6
-      and update example projects for compatibility with the new CMSIS version. It is designed for maintainers
-      of CMSIS-Packs...
-    source_hash: 8039812773c6c1ac45cceb4039cee801e627d67871b625283c1bb5b3fa2960b3
-  - path: content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md
-    status: unchanged
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-    faq_changes:
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-    summary_preview: Learn how to migrate CMSIS v5 projects to CMSIS v6 by identifying supported toolchains,
-      installing required CMSIS-Packs, and selecting the necessary software components. It is designed
-      for embedded de...
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-  - path: content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md
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-    summary_preview: Learn how to run large language models locally on the Raspberry Pi 5 using Ollama,
-      control GPIO-connected devices, and deploy a privacy-first web-based smart home assistant without
-      cloud services. It ...
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-  - path: content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md
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-    summary_preview: Learn how to build a voice-controlled bot on a Raspberry Pi that listens for a wake
-      word, converts speech to text using Google Speech Recognition, sends requests to ChatGPT's API,
-      and plays audio resp...
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-  - path: content/learning-paths/embedded-and-microcontrollers/rpi/_index.md
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-    summary_preview: Learn how to build and run multiple software examples on the Raspberry Pi 4, including
-      TensorFlow and Docker applications, and compare its performance to Arm cloud servers. It is designed
-      for software...
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-  - path: content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
-    status: unchanged
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-    faq_changes:
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-    summary_preview: Learn how to compile the Llama 3 large language model using ExecuTorch, deploy it
-      to a Raspberry Pi 5, and understand techniques for running LLMs in embedded environments. It is
-      designed for anyone in...
-    source_hash: 9cd2c3082c4874dc596e1b7b59c3dc2143aaf1ce3e66948ab8b6af190ffa3776
-  - path: content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
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-    summary_preview: Learn how to reduce compile time for embedded Linux projects by installing a Raspberry
-      Pi OS file system on an Arm server, building the MXNet machine learning framework, and transferring
-      it to a Raspb...
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-  - path: content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
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-    summary_preview: Setup tools and start programming with Raspberry Pi Pico. It is designed for embedded
-      software developers new to Raspberry Pi Pico. By the end, you will be able to install the Raspberry
-      Pi Pico SDK, r...
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-  - path: content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
-    status: unchanged
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-    summary_preview: Learn how to profile Linux kernel modules using Arm Streamline to identify performance
-      bottlenecks, analyze both out-of-tree and in-tree modules, and use Statistical Profiling Extension
-      (SPE) for deep...
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-  - path: content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Build a letter recognition neural network model using TensorFlow and deploy it on
-      an STM32 B-L475E-IOT01A2 board. It is designed for software developers interested in building network
-      models for micro...
-    source_hash: b5714494f00fff407c39e6f2f7bf37de94cde61d7569ba147412fec1b2f537c2
-  - path: content/learning-paths/embedded-and-microcontrollers/tfm/_index.md
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-    summary_preview: Learn how to build and run the reference Trusted Firmware-M tests and example application
-      on Arm Fixed Virtual Platforms for secure microcontroller development. It is designed for software
-      developers ...
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-  - path: content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to train a CNN image classification model using PyTorch, convert it to
-      ExecuTorch format, and run it as an interactive mini-game on Arm-based edge devices. It is designed
-      for machine learnin...
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-  - path: content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to install Keil MDK Tools, run a TrustZone hello world example on the NXP
-      LPCXpresso55S69 board, and understand security state switching and secure function calls. It is
-      designed for softwar...
-    source_hash: 6c81f3c9595df1128ca6f4f89446f093826d2242fa789ffa2d37465854be1f8e
-  - path: content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to acquire and print materials, assemble a universal SBC rack mount system
-      in a 4U chassis, and install single board computers in the racks using 3D-printed parts. It is designed
-      for softwar...
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-  - path: content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
-    status: unchanged
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-    faq_changes:
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-    summary_preview: Learn how to debug microcontrollers using µVision with basic run/stop debug, advanced
-      techniques using Event Recorder and Serial Wire Viewer, ETM Trace for performance analysis, and
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-  - path: content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
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-    summary_preview: Learn how to import, convert, and build uvprojx-based projects to csolution format
-      using Keil Studio, µVision, and command-line tools for CMSIS-Toolbox compatibility. It is designed
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-    summary_preview: Learn how to install vcpkg, initialize it, create vcpkg-configuration.json files,
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-      tool installati...
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-    summary_preview: Learn how to identify Arm-based targets for TinyML, install Fixed Virtual Platforms,
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-    summary_preview: Introduction to building a minimal Yocto Linux image and running it on 64-bit Qemu
-      Arm target. It is designed for software developers interested in learning the basics of building
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-    summary_preview: Learn how to run a YOLO object detection model on the Himax WiseEye2 module, build
-      the Himax SDK, update firmware, and connect to the Grove Vision AI module for computer vision applications.
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-    summary_preview: Learn how to build and run Zephyr RTOS applications on the Arm Corstone-300 Fixed
-      Virtual Platform using Arm Virtual Hardware. It is designed for software developers getting started
-      with the Zephyr RT...
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-    summary_preview: Learn how to install Workbench for Zephyr extension in VS Code, set up the complete
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-      perform memory usa...
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-    summary_preview: Learn how to create and run Ubuntu containers on ChromeOS Crostini using LXC with
-      file sharing and GUI application support on Arm-based Chromebooks. It is designed for software developers
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-    summary_preview: Learn how to build and deploy high-fidelity robotic simulations and reinforcement
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-      architecture. It i...
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-    summary_preview: Learn how to build and optimize quantized LLMs using llama.cpp on NVIDIA DGX Spark
-      with Grace-Blackwell architecture, leveraging Armv9 SIMD acceleration. It is designed for AI practitioners,
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-    summary_preview: Learn how to build a Retrieval-Augmented Generation (RAG) pipeline on NVIDIA DGX
-      Spark combining Arm Grace CPU orchestration with Blackwell GPU-accelerated inference using llama.cpp.
-      It is designed fo...
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-    summary_preview: Learn how to build an offline voice assistant combining speech-to-text via faster-whisper
-      and text generation via vLLM on Arm-based DGX Spark for privacy-focused deployments. It is designed
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-  - path: content/learning-paths/laptops-and-desktops/docker-models/_index.md
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-    summary_preview: Learn how to run pre-trained AI models locally using Docker Model Runner and build
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-    summary_preview: Learn how to develop and build cross-platform desktop applications using the Electron
-      Framework on Windows on Arm devices. It is designed for developers who want to learn how to develop
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-    summary_preview: Learn how to automate Windows application builds on Arm architecture using GitHub
-      Arm-hosted runners and GitHub Actions workflows. It is designed for This introductory tutorial is
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-  - path: content/learning-paths/laptops-and-desktops/hyper-v/_index.md
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-    summary_preview: Learn how to create and manage Arm-based Linux virtual machines using Hyper-V on
-      Windows on Arm devices. It is designed for software developers who want to use Linux virtual machines
-      with Windows on A...
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-  - path: content/learning-paths/laptops-and-desktops/intro/_index.md
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-    summary_preview: Learn where the Arm architecture is used in desktop and laptop computers and find
-      hardware for software development on Arm platforms. It is designed for developers working on laptops
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-  - path: content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md
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-    summary_preview: Learn how to build, test, and verify KleidiCV with Scalable Matrix Extensions (SME)
-      on Apple Silicon Macs for accelerated computer vision performance. It is designed for software developers
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-    summary_preview: Learn how to configure the LLVM toolchain with Visual Studio to build native Windows
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-      doing native develo...
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-    summary_preview: Learn how to apply Arm Memory Tagging Extension (MTE) to protect dynamic memory allocations
-      and prevent common memory use errors. It is designed for software developers who want to learn how
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-  - path: content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
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-    summary_preview: Learn how to install and configure Arch Linux for Arm with the i3 window manager
-      and Neovim editor on the Pinebook Pro laptop. It is designed for developers who want to use the
-      Pinebook Pro as an Arm ...
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-    summary_preview: Learn how to fine-tune large language models using PyTorch and Hugging Face on NVIDIA
-      DGX Spark to improve domain-specific accuracy. It is designed for AI developers and ML engineers
-      who want to fine-...
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-  - path: content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
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-    summary_preview: Learn how to create a CI/CD pipeline in GitHub using self-hosted Arm64 runners to
-      build and push Docker images to DockerHub. It is designed for software developers and IT practitioners
-      who want to lea...
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-  - path: content/learning-paths/laptops-and-desktops/win-opencv/_index.md
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-    summary_preview: Learn how to build the OpenCV library for Windows on Arm devices and develop computer
-      vision applications using OpenCV. It is designed for software developers who want to build and develop
-      application...
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-    summary_preview: Learn how to measure application resource usage, benchmark video encoding tasks,
-      and monitor CPU, memory, and power consumption on Windows on Arm using FFmpeg and PowerShell. It
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-  - path: content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
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-    summary_preview: Learn how to automate Windows on Arm VM creation on Arm Linux systems using QEMU,
-      KVM, and Bash scripts for development and testing. It is designed for developers and system administrators
-      who want to...
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-  - path: content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
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-    summary_preview: Learn how to build native Arm applications and migrate x86/x64 applications to Arm
-      using Arm64EC on Windows on Arm devices. It is designed for software developers who want to use
-      Arm64EC with Windows ...
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-    summary_preview: Learn how to port Qt-based Python desktop applications with C/C++ dependencies to
-      Arm64 using Arm64EC on Windows on Arm. It is designed for developers who want to learn how to port
-      their applications ...
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-    summary_preview: Learn how to build and run Qt-based desktop applications on Windows on Arm and investigate
-      native Arm64 performance improvements. It is designed for software developers who want to use the
-      native perf...
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-  - path: content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
-    status: unchanged
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-    faq_changes:
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-    summary_preview: Learn how to build and run an ASP.NET Core 8 web server application with Web API
-      and dependency injection services on Windows on Arm. It is designed for developers who are interested
-      in building a web...
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-  - path: content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
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-    summary_preview: Learn how to create Node.js IoT applications that stream sensor data from Windows
-      on Arm devices to AWS IoT Core using MQTT. It is designed for developers who want to learn how to
-      create IoT applicati...
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-  - path: content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to configure AWS IoT Core rules to parse MQTT messages and store IoT data
-      in Amazon DynamoDB from Windows on Arm devices. It is designed for developers who are interested
-      in using Amazon Dyn...
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-  - path: content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md
-    status: unchanged
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-    faq_changes:
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-    summary_preview: Learn how to process IoT data using AWS Lambda functions triggered by AWS IoT Core
-      messages from Windows on Arm devices. It is designed for developers who are interested in using
-      AWS Lambda for proces...
-    source_hash: f685f45be9e5fc05b278e28590f9d421a920d43cc36ccb572545de4eaf4a799a
-  - path: content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md
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-      before_count: 5
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-    summary_preview: Learn how to implement AWS Lambda functions that process and aggregate IoT data stored
-      in DynamoDB tables from Windows on Arm devices. It is designed for developers who are interested
-      in using AWS Lam...
-    source_hash: f5a93346a0fd55659b7c0a6df501db97742ec71755b6443051b654f3ce871cdf
-  - path: content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to create a static website hosted on Amazon S3 that interacts with AWS
-      Lambda functions to display IoT data from Windows on Arm devices. It is designed for developers
-      who are interested in u...
-    source_hash: 32186a4879e98aa113f461d2a2c705dee099404ed2020ef6fdb981a28bb0c0c3
-  - path: content/learning-paths/laptops-and-desktops/win_cef/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to create and build Chromium Embedded Framework desktop applications using
-      CMake and web technologies on Windows on Arm. It is designed for developers who want to learn how
-      to use web techno...
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-    summary_preview: Learn how to create and build Windows Forms applications and measure code execution
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-    summary_preview: Learn how to build and run a .NET 6 Windows Presentation Foundation (WPF) application
-      on Windows on Arm machines. It is designed for software developers doing native development on Windows
-      on Arm comp...
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-    summary_preview: Learn how to build, run, and benchmark .NET 8 Console applications to measure performance
-      on Windows on Arm devices. It is designed for developers who want to benchmark the performance of
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-    summary_preview: Learn how to create and build cross-platform .NET MAUI applications and measure code
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-      cross-platform...
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-  - path: content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md
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-    summary_preview: Learn how to build ONNX Runtime with the Generate() API and run Phi-3 model inference
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-      Runtime for Wind...
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-    summary_preview: Learn how to apply Profile-Guided Optimization (PGO) to build performance-tuned C++
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-  - path: content/learning-paths/laptops-and-desktops/win_python/_index.md
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-    summary_preview: Learn how to build Python applications on Windows on Arm and leverage native Arm64
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-    summary_preview: Learn how to configure Windows Sandbox as a self-hosted GitHub Actions runner to
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-    summary_preview: Learn how to create C/C++ Win32 DLLs and port them to Arm64 for use in Windows console
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-      to Arm64. By t...
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-    summary_preview: Learn how to create and build Windows UI Library (WinUI) applications and measure
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-  - path: content/learning-paths/laptops-and-desktops/win_wpf/_index.md
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-    summary_preview: Learn how to create and build Windows Presentation Foundation (WPF) applications
-      and measure code execution performance uplift on Arm64. It is designed for developers who want to
-      learn how to create d...
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-    summary_preview: Learn how to create and build Xamarin Forms applications using the MVVM pattern and
-      measure code execution performance uplift on Arm64. It is designed for developers who want to learn
-      how to create cr...
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-  - path: content/learning-paths/laptops-and-desktops/windows_armpl/_index.md
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-    summary_preview: Learn how to develop Windows on Arm applications using Visual Studio and optimize
-      performance with Arm Performance Libraries. It is designed for software developers who want to improve
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-  - path: content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md
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-    summary_preview: Get started with GitHub CI/CD development flow on a Windows on Arm machine (or virtual
-      machine). It is designed for software developers interested in running their CI flows on Windows
-      on Arm machines....
-    source_hash: 828e4022f387698d2736d94010de5d93efa9f38ffd3a2e4f15252410e9ccedb2
-  - path: content/learning-paths/laptops-and-desktops/windowsperf/_index.md
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-    summary_preview: Learn how to install WindowsPerf on Windows on Arm machines and generate sample performance
-      reports for CPU profiling. It is designed for software developers working on laptops and desktops
-      and new to...
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-  - path: content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md
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-    summary_preview: Learn how to install and use the WindowsPerf Visual Studio extension to generate
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-  - path: content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md
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-    summary_preview: Learn how to use WindowsPerf for performance sampling on Windows on Arm, build CPython
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-  - path: content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
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-    summary_preview: Learn how to import WindowsPerf data in Windows Performance Analyzer (WPA) and visualize
-      timeline and telemetry data using the WPA plugin. It is designed for software developers interested
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-  - path: content/learning-paths/laptops-and-desktops/wsl2/_index.md
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-    summary_preview: Learn how to configure and run WSL with Linux distributions, graphical applications,
-      remote desktop, and development tools on Windows on Arm computers. It is designed for Software developers
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-  - path: content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md
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-    summary_preview: Learn how to enable and verify Arm Fixed Rate Compression in Vulkan applications
-      on Android devices to reduce memory footprint and bandwidth. It is designed for Software developers
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-  - path: content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md
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-    summary_preview: Learn how to build and optimize AI-powered camera pipeline applications on Arm Linux
-      using KleidiAI, KleidiCV, and SME2 to accelerate denoising, background blur, and low-light effects.
-      It is designed ...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
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-    summary_preview: Learn how to use each of the tools supplied with Arm Performance Studio (formerly
-      known as Arm Mobile Studio). It is designed for Android application and games developers new to
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-  - path: content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md
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-    summary_preview: Learn how to capture frame data from Android applications and analyze performance
-      inefficiencies using Frame Advisor in Arm Performance Studio. It is designed for Android application
-      developers who wa...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md
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-    summary_preview: Learn how to build an Android chatbot app using Arm's AI Chat library to run GGUF
-      models on-device with optimized performance on Arm CPUs. It is designed for developers who want
-      to add a local, on-dev...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
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-    summary_preview: Learn how to build real-time image processing pipelines using Halide on Android,
-      combining operations for improved performance in Kotlin applications. It is designed for developers
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-    summary_preview: Learn how to create and configure an Android project with OpenCV support to process
-      camera images for computer vision applications. It is designed for developers who are interested
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-    summary_preview: Learn how to implement face detection on Android devices using OpenCV, camera frame
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-      Computer Visio...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
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-    summary_preview: Learn how to accelerate OpenCV-based Android applications using KleidiCV for enhanced
-      computer vision performance. It is designed for developers who are interested in creating Computer
-      Vision applicat...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md
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-    summary_preview: Get started with Scalable Vector Extension 2 (SVE2) on Android walks you through
-      an end-to-end Arm software workflow. It is designed for software developers interested in learning
-      how to use the Scala...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
-    status: unchanged
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-    summary_preview: Learn how to integrate Dawn WebGPU in an Android application, render 3D objects,
-      and profile the application using Streamline. It is designed for developers who are building GPU-based
-      Android applicat...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
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-    summary_preview: Learn how to optimize hardware ray tracing with Lumen on Android devices powered
-      by Arm Mali GPUs to maximize performance. It is designed for Unreal Engine developers interested
-      in optimizing hardware...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md
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-    summary_preview: Learn how to build ONNX Runtime and the generate() API for Android to run a Phi-3
-      model on Arm-based smartphones. It is designed for software developers interested in learning how
-      to build an Android ...
-    source_hash: deeb745d72457138cb81252c421205cd8dfb43b5e29ceee3a15c5842cc90cc33
-  - path: content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md
-    status: unchanged
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-    summary_preview: Learn how to build a hands-free selfie Android application using MediaPipe multimodal
-      AI, Kotlin flows, CameraX, and MVVM architecture. It is designed for mobile application developers
-      interested in l...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md
-    status: unchanged
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-    summary_preview: Learn how to build an Android chat application with Llama models using ExecuTorch,
-      XNNPACK, and KleidiAI for accelerated performance on Arm smartphones. It is designed for software
-      developers interest...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md
-    status: unchanged
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-    summary_preview: Learn how to build a customer support chatbot for Android using Llama 3.2, ExecuTorch,
-      and KleidiAI to run on-device inference on Arm platforms. It is designed for software developers
-      interested in bu...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md
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-    summary_preview: Learn how to detect and debug memory safety bugs in Android applications using Arm
-      Memory Tagging Extension (MTE) on a Google Pixel 8 smartphone. It is designed for developers interested
-      in learning h...
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-    summary_preview: Get started with Arm Accuracy Super Resolution (Arm ASR) walks you through an end-to-end
-      Arm software workflow. It is designed for mobile, gaming, and graphics developers who want to install
-      and confi...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md
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-    summary_preview: Get started with Unity on Android walks you through an end-to-end Arm software workflow.
-      It is designed for Unity developers who want to target Android devices. By the end, you will be
-      able to set up ...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md
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-    summary_preview: Profile Android game performance in Godot with Arm Performance Studio walks you through
-      an end-to-end Arm software workflow. It is designed for Godot developers targeting Android devices
-      who want to o...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md
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-    summary_preview: How to Enable Hardware Ray Tracing on Lumen for Android Devices walks you through
-      an end-to-end Arm software workflow. It is designed for Unreal Engine developers interested in using
-      hardware ray trac...
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-    summary_preview: Get started with Arm hardware walks you through an end-to-end Arm software workflow.
-      It is designed for Developers new to the Arm architecture and looking for mobile hardware. By the
-      end, you will be ...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md
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-    summary_preview: Understand KleidiAI SME2 matmul microkernels walks you through an end-to-end Arm
-      software workflow. It is designed for software developers, performance engineers, and AI practitioners.
-      By the end, you...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md
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-    summary_preview: Learn how to run LLM inference on Android devices using MediaPipe with KleidiAI-enhanced
-      Arm i8mm features to benchmark the Gemma 2B model. It is designed for Android developers who want
-      to efficientl...
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-    summary_preview: Learn how to build the libGPUInfo library using Android NDK and query configuration
-      details of Arm Mali or Immortalis GPUs on Android devices. It is designed for Android developers
-      who want to adjust ...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md
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-    summary_preview: Learn how to accelerate LiteRT model inference on Android using KleidiAI with SME2
-      instructions and validate performance with the benchmark tool. It is designed for developers looking
-      to leverage Arm'...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md
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-    summary_preview: Learn how to benchmark KleidiAI micro-kernels in ExecuTorch using SME/SME2 instructions
-      on Arm64 platforms with ETDump profiling and analysis. It is designed for developers, performance
-      engineers, and...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md
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-    summary_preview: Learn how to fine-tune and evaluate Neural Super Sampling (NSS) models using PyTorch
-      and Arm's Model Gym API with hardware-aware optimization. It is designed for developers exploring
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-  - path: content/learning-paths/mobile-graphics-and-gaming/mte/_index.md
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-    summary_preview: Learn how to run example C programs on AArch64 Linux to gain an introductory understanding
-      of the Arm Memory Tagging Extension (MTE). It is designed for developers who want to gain some experience
-      wit...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md
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-    summary_preview: Learn how to enable Arm Memory Tagging Extension (MTE) on a Google Pixel 8 smartphone,
-      trigger memory bug crashes, and interpret bug reports. It is designed for developers interested
-      in learning how t...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md
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-    summary_preview: Learn how to capture high-quality frame datasets from Unreal Engine 5.5 gameplay
-      for training and evaluating neural graphics models like Neural Super Sampling. It is designed for
-      Unreal Engine develop...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md
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-    faq_changes:
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-    summary_preview: Learn how to configure ML Extensions for Vulkan emulation and enable Neural Super
-      Sampling (NSS) in Unreal Engine for real-time upscaling. It is designed for developers experimenting
-      with neural graph...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md
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-    summary_preview: Learn how to build, optimize, and deploy machine learning models using ONNX Runtime
-      on Arm64 platforms, including Raspberry Pi, cloud instances, and Android devices. It is designed
-      for developers who ...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md
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-    summary_preview: Learn how to optimize vertex representations and analyze Vertex Memory Efficiency
-      using Arm Frame Advisor for improved GPU performance on Android. It is designed for Android graphics
-      application devel...
-    source_hash: 586a505543df4237c943ea47210d735fafe7d5ed2c6ad18b1b99227987ef9b15
-  - path: content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md
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-    summary_preview: Learn how to build llama.cpp with KleidiAI and SME2 support to profile and accelerate
-      LLM inference performance on Android devices. It is designed for software developers, performance
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-    summary_preview: Learn how to build ONNX Runtime with KleidiAI and SME2 support for Android and profile
-      ONNX model performance to compare acceleration improvements. It is designed for software developers,
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-  - path: content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md
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-    summary_preview: Learn how to profile ML model execution times and application performance on Arm
-      Android devices using Arm Performance Studio and Android Studio Profiler. It is designed for software
-      developers who wa...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md
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-    summary_preview: Learn how to deploy Unity applications to Android, profile code running on Arm devices,
-      and analyze performance data for optimization. It is designed for Unity developers wanting to analyze
-      the perfor...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md
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-    summary_preview: Learn how to apply post-training quantization to PyTorch models using TorchAO and
-      export INT8 models to .vgf format with the ExecuTorch Arm backend. It is designed for ML developers
-      who want to reduce...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md
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-    summary_preview: Learn how to use the Vulkan ray tracing API to implement realistic shadows, reflections,
-      and refractions in Android applications. It is designed for Vulkan developers who are familiar with
-      rendering a...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md
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-    faq_changes:
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-    summary_preview: Learn how to use Frame Advisor's Render Graph view to identify and resolve graphics
-      performance issues in Android applications. It is designed for Mobile application developers who
-      wish to improve gra...
-    source_hash: e2f6f3005c119e6f6fe97612d4e2600849106f244bce729d56bfeeedb30637c2
-  - path: content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md
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-    faq_changes:
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-    summary_preview: Learn how to convert and deploy the Stable Audio Open Small text-to-audio model to
-      LiteRT format for audio generation on Android devices and macOS. It is designed for developers looking
-      to deploy the ...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md
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-    faq_changes:
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-    summary_preview: Learn how to convert the Stable Audio Open Small model to ExecuTorch format and build
-      an audio generation application for Android or macOS. It is designed for developers who want to
-      deploy the Stable ...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md
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-    summary_preview: Learn how to build and install a Unity game on an Orange Pi 5 single-board computer
-      running Droid OS. It is designed for software developers who want to build and run a Unity game
-      on an Arm-based sing...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md
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-    summary_preview: Learn how to install Arm integration packages in Unity to view GPU metrics in Unity
-      Profiler and annotate games with markers for Arm Performance Studio. It is designed for Unity developers
-      who are tar...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md
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-    summary_preview: Learn how to use Arm Neon intrinsics in Unity C# scripts to optimize code on Android
-      and collect performance data using Unity Profiler. It is designed for Developers interested in leveraging
-      the Unity...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md
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-    summary_preview: Learn how to integrate Unity's Machine Learning Agents toolkit into games deployable
-      to Arm-powered Android devices. It is designed for Developers interested in leveraging the Unity
-      Machine Learning A...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md
-    status: unchanged
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-    faq_changes:
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-    summary_preview: Learn how to download, convert, and deploy Vision Transformers using the Mobile Neural
-      Network framework on Android with KleidiAI micro-kernels for optimized performance. It is designed
-      for developers...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md
-    status: unchanged
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-    faq_changes:
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-    summary_preview: Learn how to build and optimize a multimodal Voice Assistant application on Android
-      using KleidiAI and SME2 for accelerated performance. It is designed for developers who want to implement
-      a multimoda...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md
-    status: unchanged
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-    summary_preview: Build an end-to-end, on-device voice assistant that understands both speech and emotion
-      using Whisper, HuBERT, ONNX Runtime, and a local LLM with llama.cpp on Arm. It is designed for developers,
-      ML pr...
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-  - path: content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md
-    status: unchanged
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-    faq_changes:
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-    summary_preview: Learn how to set up ML Emulation Layers for Vulkan, run sample applications using
-      ML extensions, and debug the flow with RenderDoc. It is designed for engine developers interested
-      in learning about ne...
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-  - path: content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
-    status: unchanged
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-    summary_preview: Learn how to build and deploy an AI agent application on Arm servers using llama.cpp
-      and llama-cpp-agent with KleidiAI optimization for efficient LLM inference and function calling.
-      It is designed for...
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-  - path: content/learning-paths/servers-and-cloud-computing/aks/_index.md
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-    summary_preview: Learn how to automate the deployment of an Arm-based Kubernetes cluster on Azure
-      AKS using Terraform and deploy a sample WordPress application as a workload. It is designed for
-      software developers who...
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-  - path: content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to deploy Apache Arrow and Arrow Flight on Google Cloud Axion C4A processors
-      for high-throughput columnar data processing and low-latency data transport with MinIO integration.
-      It is designe...
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-  - path: content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to build llama.cpp, quantize the Arcee AFM-4.5B model, and run optimized
-      inference on AWS Graviton4 instances with perplexity-based quality evaluation. It is designed for
-      developers and ML e...
-    source_hash: 964c1e6a87810dca686a7b3218433ce09d31bd92882db10af355e8c50bb6091c
-  - path: content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to build llama.cpp, quantize the Arcee AFM-4.5B model, and run optimized
-      inference on Google Cloud Axion instances with perplexity-based quality evaluation. It is designed
-      for developers and...
-    source_hash: b2f60d2c31ef380e6e6ef3028671ad9242dd51c98f4a192f2da32bb05b94e185
-  - path: content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to deploy and manage applications on Google Cloud GKE Arm64 clusters using
-      Argo CD GitOps workflows with automated sync and self-healing on Axion processors. It is designed
-      for developers an...
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-  - path: content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to write correct concurrent C++ code when porting applications from x86
-      to Arm by understanding memory ordering differences and using best practices to avoid race conditions.
-      It is designed ...
-    source_hash: 3a4bc8b2ab548be507b6d528844b0593b27f2dab3ab3c9649e807abdf4887ce0
-  - path: content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to automate x86-to-Arm application migration using the Arm MCP Server,
-      with AI-assisted compatibility checks, C++ code refactoring, and Docker-based validation on Arm
-      cloud platforms. It is ...
-    source_hash: b870ac5160d35ddb51955ca8379493e172787ced8125cde8ae79f7700e653a87
-  - path: content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to migrate C applications between Arm platforms using Kiro's AI-assisted
-      tooling to identify hardware dependencies and implement abstraction layers for cross-platform compatibility.
-      It is de...
-    source_hash: 49b3f2b1464efb20f0c8fbcff46e9f30910d856567ca01c01dc348aa1c5740d1
-  - path: content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to install and configure a Linux kernel with 64K page size support on Arm
-      systems to improve memory efficiency and performance for memory-intensive workloads. It is designed
-      for developers w...
-    source_hash: 67004eb9a75926144eb6f75124104972b3cf20a57a22b698c9359d20db3eca02
-  - path: content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to access and use Arm hardware performance counters and the system counter
-      from user space using PAPI, perf_event_open, and assembly code for performance instrumentation.
-      It is designed for ...
-    source_hash: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
-  - path: content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
-    status: unchanged
-    changed_on_disk: true
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-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to package multi-architecture container applications and deploy them on
-      AWS Fargate with Graviton processors using the AWS Copilot CLI. It is designed for software developers
-      who want to lea...
-    source_hash: ced3882cf8be11a55304d2697f450a2c862a81d87317ab42298148755e9f272c
-  - path: content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to automate the creation and deployment of AWS Graviton instances using
-      Terraform with jump server access for secure infrastructure management. It is designed for software
-      developers who are...
-    source_hash: 087a4d56914e5b53cec5ad17e8d682e72d04ba6b394237c081efe4a3f939e04e
-  - path: content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
-    status: unchanged
-    changed_on_disk: true
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to create and deploy Azure Resource Manager templates to provision Arm64-based
-      Cobalt 100 virtual machines on Azure using the Azure CLI. It is designed for developers and DevOps
-      engineers wh...
-    source_hash: 1682c0527bb49c417263286e2aba7c82fb9a27fa315ecc6b9ca10978b69cc236
-  - path: content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to configure a self-hosted GitHub runner on Azure Cobalt 100, create an
-      AKS cluster with Terraform, and deploy a .NET application using GitHub Actions CI/CD. It is designed
-      for software deve...
-    source_hash: b5bd3abde8d9dd3146e0f11f4819ac510417ad7f707b4d0970c02fd63801b358
-  - path: content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
-    status: unchanged
-    changed_on_disk: true
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-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to automate the creation of Azure Arm virtual machines using Terraform.
-      It is designed for software developers who are new to deploying Arm instances on Azure using Terraform.
-      By the end, yo...
-    source_hash: 6713af3d70887933cf54c7fa36bdc88d71a51fa34fb29a4ce90636ff1de624c7
-  - path: content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to create a custom Azure Linux 3.0 VM image using QEMU, upload it to Azure
-      Shared Image Gallery, and deploy it on Arm-based Cobalt 100 processors. It is designed for developers
-      who want to r...
-    source_hash: 413467e581e3561425229d0f6118975dbb596d6a9494544a54f0b9ab22c1b89d
-  - path: content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
-    status: unchanged
-    changed_on_disk: true
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-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to deploy and accelerate PyTorch NLP sentiment analysis models from Hugging
-      Face on Arm servers with BFloat16 fast math kernel optimization on Graviton3 processors. It is designed
-      for softwa...
-    source_hash: a4cf1d9161b3a32e29694415762eda419752e1c3144662d5e131b6553f0a58e3
-  - path: content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to implement and benchmark bitmap scanning operations for database workloads
-      using scalar, Neon, and SVE instructions on Arm-based cloud instances. It is designed for database
-      developers, pe...
-    source_hash: 1701b37580fe5d012a5e6fd322307742656a748dfb766fd48914011167386e95
-  - path: content/learning-paths/servers-and-cloud-computing/bolt/_index.md
-    status: unchanged
-    changed_on_disk: true
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-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to build, profile, and optimize Arm executables using BOLT post-link binary
-      optimization to improve application performance through code layout improvements. It is designed
-      for software deve...
-    source_hash: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
-  - path: content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
-    status: unchanged
-    changed_on_disk: true
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-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to identify optimization candidates and apply LLVM BOLT post-link optimization
-      to AArch64 binaries using BRBE, SPE, instrumentation, and PMU profiling techniques. It is designed
-      for develope...
-    source_hash: 8654b656131bf1d529e11d85f874f7a81c01f7207340d2a606b6fd2d80bfad04
-  - path: content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
-    status: unchanged
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-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to optimize Arm application binaries and shared libraries using BOLT profile
-      instrumentation, merge multiple profiles for improved coverage, and integrate optimized libraries.
-      It is designed...
-    source_hash: 84a8b96fe7df302e0a2a6e4645bbb6170b45a3e0b55e0ea3682ec47663d34819
-  - path: content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to configure Buildkite agents on Google Axion C4A VMs to build and publish
-      multi-architecture Docker images using Docker Buildx for Arm and x86 platforms. It is designed for
-      developers who w...
-    source_hash: 4bda206717eef380430009f859826d9bcf820442d13492cd3c22a114561e2917
-  - path: content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to install and configure Apache Cassandra on Google Cloud Axion C4A Arm64
-      instances and benchmark read/write performance using cassandra-stress. It is designed for software
-      developers migrat...
-    source_hash: b8268d4df3b62aeb9943b750b436a936134d47745fa71db6cc08db8c84eb37be
-  - path: content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-      removed_questions: []
-      updated_questions: []
-    summary_preview: Learn how to run the Arm CCA reference software stack on an FVP with RME support,
-      create a Realm virtual machine, and obtain attestation tokens for confidential computing. It is
-      designed for software ...
-    source_hash: 3947ebbac742399e70001e41ac5face92819bed0deb55aba3e2414f98432023e
-  - path: content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
-      after_count: 5
-      added_questions: []
-      removed_questions: []
-      updated_questions: []
-    summary_preview: Learn how Arm CCA Realms interact with I/O devices using VirtIO paravirtualization,
-      SWIOTLB bounce buffers, and PCIe-TDISP secure device attach mechanisms with attestation. It is designed
-      for develope...
-    source_hash: e21f51feb101ad90245ecceab95fe13e5eb61958faf24dff818eddd11f484b9a
-  - path: content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-      removed_questions: []
-      updated_questions: []
-    summary_preview: Learn how to deploy a CCA realm on an FVP with RME support and connect it with attestation
-      services to create an end-to-end confidential computing workflow. It is designed for software developers
-      who ...
-    source_hash: b032debbdfe82cbd017812cf671907520b146fd8684afce0c45c91e7f2287e18
-  - path: content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
-      after_count: 5
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-      removed_questions: []
-      updated_questions: []
-    summary_preview: Learn how to deploy Confidential Containers from encrypted images inside Arm CCA
-      Realms using Trustee services for attestation-based authorization on an FVP with RME support. It
-      is designed for develo...
-    source_hash: 649957b07b2bde05bc11047bd12bfc4f697c1a55eef1c3a25b5fafc95cda893d
-  - path: content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
-      after_count: 5
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-      removed_questions: []
-      updated_questions: []
-    summary_preview: Learn how to deploy a CCA realm workload on an FVP and connect it with Trustee services
-      to enable attestation-based confidential data processing. It is designed for software developers
-      who want to run...
-    source_hash: 563456f5d151c7ef59bf458f6ac971c321077475a0a78d3b5a3885b97157ba9e
-  - path: content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
-      after_count: 5
-      added_questions: []
-      removed_questions: []
-      updated_questions: []
-    summary_preview: Learn how to inspect and verify Arm CCA attestation tokens using command-line tools
-      and the open-source Veraison attestation verification service. It is designed for developers who
-      would like to learn...
-    source_hash: 9e6dee3aef79c1c65cc1a1f4fe3528b9c5542d8046f0b059ef703079ef964d77
-  - path: content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
-      after_count: 5
-      added_questions: []
-      removed_questions: []
-      updated_questions: []
-    summary_preview: Learn how to deploy a scalable Arm CCA attestation verifier service on AWS using
-      Veraison components with platform endorsement provisioning. It is designed for developers familiar
-      with CCA attestation...
-    source_hash: 55b8032ceaf735d53b1157102a3a1da5aa5cb686e9ec51cf4896ded66d9bf263
-  - path: content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
-      after_count: 5
-      added_questions: []
-      removed_questions: []
-      updated_questions: []
-    summary_preview: Learn how to set up CircleCI self-hosted machine runners on Google Cloud Axion C4A
-      SUSE VMs and execute Arm-native CI/CD workflows using custom resource classes. It is designed for
-      developers and DevO...
-    source_hash: ec9cdca7aa9a5670f54ea3646f965149460d8e66d9d5d904e1b6dcf09867df39
-  - path: content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
-      after_count: 5
-      added_questions: []
-      removed_questions: []
-      updated_questions: []
-    summary_preview: Learn how to install and configure CircleCI self-hosted machine runners on AWS Graviton
-      Arm64 instances to execute CI/CD workflows natively on Arm. It is designed for developers and DevOps
-      engineers w...
-    source_hash: e04982fd668765fa2ab25f943f020b8d2b4a5e9b5ff3a51b7431cc4d93730180
-  - path: content/learning-paths/servers-and-cloud-computing/clair/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
-      after_count: 5
-      added_questions: []
-      removed_questions: []
-      updated_questions: []
-    summary_preview: Learn how to install and run Clair on Arm servers using combined and distributed
-      deployment models to scan container images and generate vulnerability reports. It is designed for
-      software developers i...
-    source_hash: e742eb44fa108bfcc4ee5a241414e6aa05e1fc0e1cceb589fb78a080f59b0d38
-  - path: content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
-      after_count: 5
-      added_questions: []
-      removed_questions: []
-      updated_questions: []
-    summary_preview: Learn how to install ClickHouse on Arm-based cloud instances and measure database
-      performance using ClickBench to determine appropriate instance configurations. It is designed for
-      software developers ...
-    source_hash: 0d1390198cf2f0e87f509c5269fc2405cffcb2e57311024150d47e60a3273178
-  - path: content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
-      after_count: 5
-      added_questions: []
-      removed_questions: []
-      updated_questions: []
-    summary_preview: Learn how to deploy ClickHouse on Google Cloud Axion C4A processors and build a streaming
-      ETL pipeline using Apache Beam, Dataflow, and Pub/Sub for real-time analytics. It is designed for
-      developers d...
-    source_hash: e77cd1373236f39f360afe6844d642fde290960ccdad26544ff1e3342f2a980c
-  - path: content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
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-    faq_changes:
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-    summary_preview: Learn how to build and run the x265 H.265 codec on Arm servers with performance benchmarking
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-    summary_preview: Learn how to build and run the AV1 and VP9 video codecs on Arm Linux systems with
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-    summary_preview: Learn how to install and configure Couchbase on Google Cloud Axion C4A Arm64 instances
-      and benchmark read/write performance using YCSB workloads. It is designed for developers deploying
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-    summary_preview: Learn how to apply profile-guided optimization to C++ applications on Arm systems
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-    summary_preview: Learn how to profile and identify CPU hotspots in C++ applications on Arm Neoverse
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-    summary_preview: Learn how to start an Arm-based virtual machine instance from major cloud service
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-    summary_preview: Learn how to deploy and run the DeepSeek-R1 language model on Arm servers using llama.cpp
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-    summary_preview: Learn how to use fio to microbenchmark storage performance on Arm systems and monitor
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-    summary_preview: Run distributed LLM inference with llama.cpp across multiple AWS Graviton4 instances,
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-    summary_preview: Learn how to create a simple Django web application and deploy it on Arm machines
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-    summary_preview: Learn how to deploy a production-grade Django REST API on Google Kubernetes Engine
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-    summary_preview: Learn how to build and benchmark the Deep Learning Recommendation Model using PyTorch
-      and MLPerf on Arm Neoverse V2 processors. It is designed for software developers who want to set
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-    summary_preview: Learn how to use the Docker MCP Toolkit with the Arm MCP Server and GitHub Copilot
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-    summary_preview: Learn how to build and run an OrchardCore CMS .NET application on Azure Cobalt 100
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-    summary_preview: Learn how to deploy Dynatrace OneAgent on Azure Cobalt 100 Arm64 virtual machines
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-    summary_preview: Learn how to create an AWS ECS cluster with Fargate and AWS Graviton processors,
-      then create and run containerized tasks on Arm infrastructure. It is designed for developers who
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-    summary_preview: Learn how to provision an Amazon EKS cluster on Arm-based Graviton instances and
-      deploy a WordPress application with MySQL database. It is designed for software developers new to
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-    summary_preview: Learn how to use docker buildx and docker manifest to build and deploy multi-architecture
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-  - path: content/learning-paths/servers-and-cloud-computing/envoy/_index.md
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-    summary_preview: Learn how to build, install, and run Envoy proxy on Arm servers and configure it
-      as a web server for traffic management. It is designed for engineers who want to use Envoy on Arm.
-      By the end, you will...
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-    summary_preview: Learn how to install and configure Envoy proxy on Google Cloud Axion C4A Arm64 instances
-      and benchmark HTTP proxy performance with load testing. It is designed for This introductory topic
-      for software...
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-    summary_preview: Learn how to optimize Envoy proxy performance on Arm servers using Transparent Huge
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-    summary_preview: Learn how stack buffer overflow exploits work on AArch64 by analyzing stack frame
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-    summary_preview: Learn how to identify and fix false sharing issues using Perf C2C cache line analysis
-      and Arm Statistical Profiling Extension on Arm-based cloud systems. It is designed for performance-oriented
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-    summary_preview: Learn how to build custom Linux kernels using tuxmake and Fastpath, then benchmark
-      and compare kernel versions on Arm-based EC2 instances. It is designed for software developers and
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-    summary_preview: Learn how to implement exponential functions using Arm SVE intrinsics with the FEXPA
-      instruction for hardware-accelerated computations on Neoverse processors. It is designed for developers
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-    summary_preview: Learn how to install and run Apache Flink on Arm servers and benchmark stream processing
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-    summary_preview: Learn how to install and configure Apache Flink on Google Cloud Axion C4A Arm64 instances
-      and benchmark stream processing performance with Nexmark. It is designed for developers deploying
-      and optimizi...
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-    summary_preview: Learn how to build scalable machine learning workflow pipelines on Google Cloud C4A
-      Axion processors using Flyte for workflow orchestration and gRPC for distributed service communication.
-      It is design...
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-    summary_preview: Learn how to create an Arm64 Azure VM, install .NET SDK, containerize .NET applications,
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-  - path: content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md
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-    summary_preview: Learn how to create and run Docker containers on Azure Container Instances for Arm64-based
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-    summary_preview: Learn how to create an Azure Kubernetes Service cluster with Arm64 virtual machines
-      and deploy a containerized application to AKS. It is designed for This learning path is dedicated
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-    summary_preview: Learn how to automate Azure resource deployment using Infrastructure as Code with
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-    summary_preview: Learn how to install and configure Gardener Kubernetes management platform on Google
-      Cloud Axion C4A SUSE Arm64 instances and deploy workload clusters. It is designed for software developers
-      deploying...
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-  - path: content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
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-    summary_preview: Learn how to apply link-time optimization with the GCC toolchain to improve application
-      performance by optimizing across compilation units. It is designed for developers who want to improve
-      applicatio...
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-  - path: content/learning-paths/servers-and-cloud-computing/gcp/_index.md
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-    faq_changes:
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-    summary_preview: Learn how to automate the creation of Arm virtual machines on Google Cloud Platform
-      using Terraform with jump server access configuration. It is designed for anyone new to using Arm
-      virtual machines i...
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-  - path: content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
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-    faq_changes:
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-    summary_preview: Run Geekbench on Arm systems to benchmark CPU performance, interpret the results,
-      and compare different Arm configurations. It is designed for software developers interested in comparing
-      the performan...
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-  - path: content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
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-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to set up Arm-hosted GitHub runners and train PyTorch ML models using the
-      German Traffic Sign Recognition Benchmark dataset with automated workflows. It is designed for software
-      developers i...
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-  - path: content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
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-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Learn how to install RunsOn self-hosted runner manager in your AWS account to execute
-      GitHub Actions workflows on Arm runners. It is designed for developers who want to use Arm runners
-      offered by AWS ...
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-    faq_changes:
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-    summary_preview: Learn how to provision a Google Axion C4A Arm virtual machine and set up a GitHub
-      Actions self-hosted runner for CI/CD workflows. It is designed for developers who want to deploy
-      a GitHub Actions self...
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-  - path: content/learning-paths/servers-and-cloud-computing/gke/_index.md
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to automate the deployment of an Arm-based Google Kubernetes Engine cluster
-      using Terraform for container orchestration. It is designed for software developers who want to
-      deploy an Arm-base...
-    source_hash: 7c3d37545c93db584d6e0ce88d8feaf0bf690e0c8786b664a6643be713d0336f
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-      before_count: 5
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-    summary_preview: Learn how to add Arm-based Google Axion nodes to an existing x86 GKE cluster and
-      rebuild applications for multi-architecture support. It is designed for software developers who
-      are looking to migrate ...
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-  - path: content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to create dual-architecture GKE clusters with arm64 and amd64 node pools,
-      build multi-architecture Docker images, and migrate services to Google Axion processors. It is designed
-      for cloud, p...
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-  - path: content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Rebuild and benchmark glibc with LSE atomics on Arm servers, then evaluate scalability
-      using MongoDB workloads and guidance on when LSE delivers a measurable uplift. It is designed for
-      software develo...
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-  - path: content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Learn how to provision Arm64 and x86_64 VM instances on Google Cloud, then install
-      and use Sweet and Benchstat to measure and compare Go application performance. It is designed for
-      This introductory t...
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-    summary_preview: Learn how to provision Azure Cobalt 100 Arm64 virtual machines and deploy Golang
-      applications with performance benchmarking on Arm architecture. It is designed for software developers,
-      DevOps engineer...
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-    summary_preview: Learn how to install Helm on Google Cloud Axion C4A SUSE VMs and deploy applications
-      like NGINX, PostgreSQL, and Redis using Helm charts. It is designed for This is an introductory
-      topic intended for ...
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-  - path: content/learning-paths/servers-and-cloud-computing/intro/_index.md
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-    summary_preview: Learn where Arm architecture is used in servers and cloud computing, and find Arm-based
-      hardware platforms for software development. It is designed for software developers working on server
-      and cloud ...
-    source_hash: d2786f42b505823253ae16c647ca2e03c154f371b7c326210fde8523b12a8188
-  - path: content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Analyze and optimize interrupt request (IRQ) patterns on Arm Linux servers to improve
-      network workload performance through IRQ distribution strategies. It is designed for developers
-      and performance en...
-    source_hash: 6fc608d45c4fdf85a77bddd4f8c26b05a7950d1086e578a8be0dd637feeb5d79
-  - path: content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Monitor, interpret, and optimize Java Garbage Collector performance on Arm servers
-      by comparing different GCs and tuning parameters for your workload. It is designed for Java developers
-      aiming to opti...
-    source_hash: f57dd99bad280f64f53071373519e2d3ba8ae50b94fe1ad0a9cc40d02b458b9b
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-    faq_changes:
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-    summary_preview: Deploy and optimize Java applications on Google Cloud Axion processors by testing
-      JDK versions and performance optimization flags. It is designed for software developers who want
-      to learn how to run t...
-    source_hash: e6f73fbca45a1be1644f79bbcfbaaec964e31f1aab236e9a872c72a6fd5e4b66
-  - path: content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
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-    summary_preview: Deploy Java on Azure Cobalt 100 Arm virtual machines and benchmark application performance
-      with JMH microbenchmarks. It is designed for This is an introductory topic about Java deployment
-      and benchmar...
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-  - path: content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
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-    summary_preview: Profile Java applications on Arm Neoverse servers using flame graphs generated with
-      async-profiler and Java agents to identify performance bottlenecks. It is designed for developers
-      who want to analyz...
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-  - path: content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Deploy Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate installation,
-      and execute Arm-native CI/CD pipelines including Docker workflows. It is designed for software developers
-      d...
-    source_hash: 525d893a796e8e4eb5cc7a9d5996bd7d55a35f8fe413f87b9a275a214d77626f
-  - path: content/learning-paths/servers-and-cloud-computing/kafka/_index.md
-    status: unchanged
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-    faq_changes:
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-    summary_preview: Deploy and configure a Kafka cluster with Zookeeper on Arm servers, test event streaming,
-      and automate deployment on AWS and Google Cloud. It is designed for software developers who want
-      to learn how ...
-    source_hash: b7f36df881c2c436143c1e5579d2c54cb5930f52998904098829c52fa7290f38
-  - path: content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Deploy Apache Kafka on Azure Cobalt 100 Arm virtual machines and benchmark message
-      throughput performance. It is designed for developers looking to migrate their Apache Kafka workloads
-      from x86_64 to ...
-    source_hash: d510dd43a485e1c2fac404ef9a2e00b5be2449b99b1095c9a1841cd2fcba9b0e
-  - path: content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-    summary_preview: Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing Kedify
-      and KEDA with Helm and testing autoscaling behavior. It is designed for developers running HTTP
-      workloads on Kuber...
-    source_hash: 6ff33f6dfaf25e926a0ce8756b4e564e5aa37112e5425f9c3dce13f772b54145
-  - path: content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Create, train, and evaluate a neural network model on Arm servers using Keras Core
-      with TensorFlow, PyTorch, and JAX backends. It is designed for engineers who want to create a neural
-      network model on...
-    source_hash: 830556dfd51aaa2dd95ee957e64306bab369297f7bc66325e7eb509f541f683c
-  - path: content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
-    status: unchanged
-    changed_on_disk: true
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-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-    summary_preview: Compile and install custom Linux kernels on Arm cloud instances using TuxMake with
-      configurations for 64 KB page sizes and Fastpath testing. It is designed for software developers
-      building custom Linu...
-    source_hash: 004436cf14ff0056136889c58d676295c6dd2088f06f57f397a07ec9004e6b44
-  - path: content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
-    status: unchanged
-    changed_on_disk: true
-    summary_changed: false
-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Identify and migrate container images in a Kubernetes cluster to Arm-compatible versions
-      using KubeArchInspect reports. It is designed for software developers who want to ensure containers
-      running in ...
-    source_hash: 43e4e28b88b9721ca94457418f3b4887d0099017578a54da1db5fcdc11f4a122
-  - path: content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
-    status: unchanged
-    changed_on_disk: true
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-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Deploy AWS Lambda functions on Graviton processors using Terraform for Python and
-      Node.js runtimes. It is designed for software developers who want to learn how to deploy Lambda
-      functions on AWS Gravi...
-    source_hash: 22fa96e29143f2e0dc0c204919422914b3f70d63ddf833cf1067fbf67b525aa0
-  - path: content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Enable and measure libhugetlbfs performance improvements for MySQL and other workloads
-      on Arm Linux servers. It is designed for engineers looking for ways to increase performance on Arm
-      servers. By th...
-    source_hash: 66d13edff1371295f0e88a618e924ca67b85bcc8aa72034d1e582a0b267f0d05
-  - path: content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
-    status: unchanged
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-    faq_changes:
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-    summary_preview: Serve the llama.cpp chatbot through an OpenAI-compatible API, enabling existing OpenAI-style
-      clients and applications to run against a persistent Arm-hosted LLM. It is designed for developers
-      interest...
-    source_hash: d82aa4faeb599a3a1ac12d477204ebe0a4ddb99564bedced86ca0fc4851e17b9
-  - path: content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Build a production-ready vision chatbot on Google Axion using Streamlit, PyTorch,
-      and Hugging Face Transformers with a quantized Llama 3.2-Vision model. It is designed for software
-      developers and ML e...
-    source_hash: 3e8307a5daf435c21f3cecff635ac51724a63ff9ea4307082d869601563b4204
-  - path: content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Optimize llama.cpp on Arm CPUs by integrating Streamline Annotations to profile Prefill
-      and Decode stages, analyze operators, and evaluate multi-core execution. It is designed for software
-      developers,...
-    source_hash: 36bf3c45f0b38350714ba41ea88c1551b33f6d299a65b3b0d6668fee2d88d835
-  - path: content/learning-paths/servers-and-cloud-computing/lse/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Understand Large System Extensions (LSE) for Arm processors and verify whether applications
-      use LSE for improved atomic operation performance. It is designed for software developers who want
-      to learn ...
-    source_hash: 9610291239b88c67e21055f94cd03fe7cf80f7d875d53ebe0d8de7837ce99fa7
-  - path: content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
-    status: unchanged
-    changed_on_disk: true
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-    faq_changed: false
-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Deploy MariaDB on Arm cloud instances across AWS, Azure, and Google Cloud using Docker,
-      Amazon RDS, and automation with Terraform and Ansible. It is designed for software developers who
-      want to deploy...
-    source_hash: db4ce1c59ad10bd127b4990c82ce876311d7dc7e1ad5d39ec132daf82ceb8b79
-  - path: content/learning-paths/servers-and-cloud-computing/memcached/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-    summary_preview: Install memcached on Arm cloud servers and benchmark in-memory key-value store performance
-      using open-source tools. It is designed for developers who want to use memcached as their in-memory
-      key-value...
-    source_hash: b42ca5cc8f4db6ba6019f3720a5ef46119aa403a2baaef5362e874f898ce0419
-  - path: content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
-    status: unchanged
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-    faq_changes:
-      before_count: 5
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-      updated_questions: []
-    summary_preview: Deploy Memcached as a cache for MySQL and PostgreSQL on Arm servers. It is designed
-      for developers who want to use memcached as their in-memory key-value store. By the end, you will
-      be able to deploy ...
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-    summary_preview: Deploy Node.js on Google Cloud C4A Arm-based Axion VMs walks you through an end-to-end
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-    summary_preview: Deploy Phi-4-mini model with ONNX Runtime on Azure Cobalt 100 walks you through an
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-    summary_preview: Deploy SqueezeNet 1.0 INT8 model with ONNX Runtime on Azure Cobalt 100 walks you
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-    summary_preview: Simulate OpenBMC and UEFI pre-silicon on Neoverse RD-V3 walks you through an end-to-end
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-    summary_preview: Build multi-architecture applications with Red Hat OpenShift Pipelines on AWS walks
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-    summary_preview: Deploy OpenStack on Azure Cobalt 100 Arm64 virtual machines using DevStack for development
-      and Kolla-Ansible for containerized production deployments. It is designed for This learning path
-      is designed...
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-    summary_preview: Deploy OpenTelemetry on Google Cloud C4A Axion processors walks you through an end-to-end
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-    summary_preview: Learn how to create and integrate a virtual LED peripheral using the Virtual IO interface
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-    summary_preview: Learn how to build a complete IoT solution in Azure that streams, stores, monitors,
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-    summary_preview: Learn how to install and configure Couchbase on Google Cloud Axion C4A Arm64 instances
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-    summary_preview: Learn how to install Helm on Google Cloud Axion C4A SUSE VMs and deploy applications
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-    summary_preview: Learn where Arm architecture is used in servers and cloud computing, and find Arm-based
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-    summary_preview: Deploy and optimize Java applications on Google Cloud Axion processors by testing
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-    summary_preview: Profile Java applications on Arm Neoverse servers using flame graphs generated with
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-    summary_preview: Deploy Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate installation,
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-    summary_preview: Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing Kedify
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-    summary_preview: Create, train, and evaluate a neural network model on Arm servers using Keras Core
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-    summary_preview: Compile and install custom Linux kernels on Arm cloud instances using TuxMake with
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-    summary_preview: Identify and migrate container images in a Kubernetes cluster to Arm-compatible versions
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-    summary_preview: Deploy AWS Lambda functions on Graviton processors using Terraform for Python and
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-    summary_preview: Optimize llama.cpp on Arm CPUs by integrating Streamline Annotations to profile Prefill
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-    summary_preview: Deploy MariaDB on Arm cloud instances across AWS, Azure, and Google Cloud using Docker,
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-      - Who is this Learning Path for?
-      - What do you need before you start?
-      - Which tools, languages, or platforms does it cover?
-      - How is the Learning Path structured?
-      removed_questions: []
-      updated_questions: []
-    summary_preview: Learn about Neoverse Non-cache PMU events using C and Assembly Language walks you
-      through an end-to-end Arm software workflow. It is designed for software and hardware engineers
-      to learn about why com...
-    source_hash: 523ff99ccb8d13543f64839fa21040191d2a9ff7ce7c78fa590e8775fac754a6
-  - path: content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md
-    status: added
-    changed_on_disk: true
-    summary_changed: true
-    faq_changed: true
-    faq_changes:
-      before_count: 0
-      after_count: 5
-      added_questions:
-      - What will you accomplish in this Learning Path?
-      - Who is this Learning Path for?
-      - What do you need before you start?
-      - Which tools, languages, or platforms does it cover?
-      - How is the Learning Path structured?
-      removed_questions: []
-      updated_questions: []
-    summary_preview: Scan multi-architecture containers with Trivy on Azure Cobalt 100 walks you through
-      an end-to-end Arm software workflow. It is designed for developers and DevOps engineers who want
-      to integrate securi...
-    source_hash: 13bba1dee1c948f8b751a71479a5106014a2c21dab6ce3968a08bed9e3363de1
-  - path: content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md
-    status: added
-    changed_on_disk: true
-    summary_changed: true
-    faq_changed: true
-    faq_changes:
-      before_count: 0
-      after_count: 5
-      added_questions:
-      - What will you accomplish in this Learning Path?
-      - Who is this Learning Path for?
-      - What do you need before you start?
-      - Which tools, languages, or platforms does it cover?
-      - How is the Learning Path structured?
-      removed_questions: []
-      updated_questions: []
-    summary_preview: Tune network workloads on Arm-based bare-metal instances walks you through an end-to-end
-      Arm software workflow. It is designed for engineers who want to tune the performance of network
-      workloads on Ar...
-    source_hash: 9f2b3f6c5e76ecb754de5c0fd84278ff71f71c5c7906acdc06911f2feff1f5fd
-  - path: content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md
-    status: added
-    changed_on_disk: true
-    summary_changed: true
-    faq_changed: true
-    faq_changes:
-      before_count: 0
-      after_count: 5
-      added_questions:
-      - What will you accomplish in this Learning Path?
-      - Who is this Learning Path for?
-      - What do you need before you start?
-      - Which tools, languages, or platforms does it cover?
-      - How is the Learning Path structured?
-      removed_questions: []
-      updated_questions: []
-    summary_preview: Deploy TypeScript on Google Cloud C4A virtual machines walks you through an end-to-end
-      Arm software workflow. It is designed for developers deploying and optimizing TypeScript workloads
-      on Arm64 Linux...
-    source_hash: ee805f703acd45612c9a94e60c6322866dc1c8ff25d48de2fb4cd9067948c93e
-  - path: content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md
-    status: added
-    changed_on_disk: true
-    summary_changed: true
-    faq_changed: true
-    faq_changes:
-      before_count: 0
-      after_count: 5
-      added_questions:
-      - What will you accomplish in this Learning Path?
-      - Who is this Learning Path for?
-      - What do you need before you start?
-      - Which tools, languages, or platforms does it cover?
-      - How is the Learning Path structured?
-      removed_questions: []
-      updated_questions: []
-    summary_preview: Migrate applications that leverage performance libraries walks you through an end-to-end
-      Arm software workflow. It is designed for both C and C++ developers who want to migrate applications
-      that rely ...
-    source_hash: 035bd363fc8ae772acf52d7e392f2db27087267706aeec86b4098c497e5fe91d
-  - path: content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md
-    status: added
-    changed_on_disk: true
-    summary_changed: true
-    faq_changed: true
-    faq_changes:
-      before_count: 0
-      after_count: 5
-      added_questions:
-      - What will you accomplish in this Learning Path?
-      - Who is this Learning Path for?
-      - What do you need before you start?
-      - Which tools, languages, or platforms does it cover?
-      - How is the Learning Path structured?
-      removed_questions: []
-      updated_questions: []
-    summary_preview: Install Vectorscan (Hyperscan on Arm) and use it with Snort 3 walks you through an
-      end-to-end Arm software workflow. It is designed for software developers using Hyperscan who want
-      to migrate to Arm. ...
-    source_hash: beb244c7766c474b47942b86f1cdd0d124c1cccb74dbe40f0b6c0917d0b3d31a
-  - path: content/learning-paths/servers-and-cloud-computing/vllm/_index.md
-    status: added
-    changed_on_disk: true
-    summary_changed: true
-    faq_changed: true
-    faq_changes:
-      before_count: 0
-      after_count: 5
-      added_questions:
-      - What will you accomplish in this Learning Path?
-      - Who is this Learning Path for?
-      - What do you need before you start?
-      - Which tools, languages, or platforms does it cover?
-      - How is the Learning Path structured?
-      removed_questions: []
-      updated_questions: []
-    summary_preview: Build and Run vLLM on Arm Servers walks you through an end-to-end Arm software workflow.
-      It is designed for software developers and AI engineers interested in learning how to use the vLLM
-      library on A...
-    source_hash: db5987ec7987375d9b51de843b0e1faf0e61731b14c5a563d19aaad26f50f6bb
-  - path: content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
-    status: added
-    changed_on_disk: true
-    summary_changed: true
-    faq_changed: true
-    faq_changes:
-      before_count: 0
-      after_count: 5
-      added_questions:
-      - What will you accomplish in this Learning Path?
-      - Who is this Learning Path for?
-      - What do you need before you start?
-      - Which tools, languages, or platforms does it cover?
-      - How is the Learning Path structured?
-      removed_questions: []
-      updated_questions: []
-    summary_preview: Run vLLM inference with INT4 quantization on Arm servers walks you through an end-to-end
-      Arm software workflow. It is designed for developers interested in building and optimizing vLLM
-      for Arm-based s...
-    source_hash: 487e9829c6e08798ad01be7a01f192e10e99cb06b71108575f1919c134eece02
-  - path: content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
-    status: added
-    changed_on_disk: true
-    summary_changed: true
-    faq_changed: true
-    faq_changes:
-      before_count: 0
-      after_count: 5
-      added_questions:
-      - What will you accomplish in this Learning Path?
-      - Who is this Learning Path for?
-      - What do you need before you start?
-      - Which tools, languages, or platforms does it cover?
-      - How is the Learning Path structured?
-      removed_questions: []
-      updated_questions: []
-    summary_preview: Run the vvenc H.266 encoder on Arm servers walks you through an end-to-end Arm software
-      workflow. It is designed for software developers who want to build and run the VVenC® (Fraunhofer
-      Versatile Vide...
-    source_hash: 4ed20056b2d82bd2c9ab1afe3d74657df9ac09808592d843c1b143626a8668ac
-  - path: content/learning-paths/servers-and-cloud-computing/whisper/_index.md
-    status: added
-    changed_on_disk: true
-    summary_changed: true
-    faq_changed: true
-    faq_changes:
-      before_count: 0
-      after_count: 5
-      added_questions:
-      - What will you accomplish in this Learning Path?
-      - Who is this Learning Path for?
-      - What do you need before you start?
-      - Which tools, languages, or platforms does it cover?
-      - How is the Learning Path structured?
-      removed_questions: []
-      updated_questions: []
-    summary_preview: Accelerate Whisper on Arm with Hugging Face Transformers walks you through an end-to-end
-      Arm software workflow. It is designed for software developers familiar with basic machine learning
-      concepts and...
-    source_hash: e130630b5ae4626641779d0b66d3c1c132b90899cd3d6db07a46df8957c4da38
-  - path: content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
-    status: added
-    changed_on_disk: true
-    summary_changed: true
-    faq_changed: true
-    faq_changes:
-      before_count: 0
-      after_count: 5
-      added_questions:
-      - What will you accomplish in this Learning Path?
-      - Who is this Learning Path for?
-      - What do you need before you start?
-      - Which tools, languages, or platforms does it cover?
-      - How is the Learning Path structured?
-      removed_questions: []
-      updated_questions: []
-    summary_preview: Deploy MySQL and WordPress on an always free tier Arm shape walks you through an
-      end-to-end Arm software workflow. It is designed for developers who want to install WordPress on
-      Oracle Cloud Infrastru...
-    source_hash: 46f2645fb6ef298508af93569554315cfa2564be9891acabf1c874c94e52d70d
-  - path: content/learning-paths/servers-and-cloud-computing/zlib/_index.md
-    status: added
-    changed_on_disk: true
-    summary_changed: true
-    faq_changed: true
-    faq_changes:
-      before_count: 0
-      after_count: 5
-      added_questions:
-      - What will you accomplish in this Learning Path?
-      - Who is this Learning Path for?
-      - What do you need before you start?
-      - Which tools, languages, or platforms does it cover?
-      - How is the Learning Path structured?
-      removed_questions: []
-      updated_questions: []
-    summary_preview: Learn how to build and use zlib-ng on Arm servers, using its Neon SIMD and ARMv8
-      CRC32 optimizations to improve compression performance compared to the system default zlib. It is
-      designed for software...
-    source_hash: 8916f83f621505dc5406f14e70d04e702ea304950e34b4b76dc1bbc987bcc4e3
+    summary_changed: 0
+    faq_changed: 0
+    rerun_flags_reset: 0
+  section_totals:
+    summary:
+      created: 0
+      repaired_missing: 0
+      rerun_requested: 0
+      drift_detected_preserved: 0
+      unchanged: 0
+    faqs:
+      created: 0
+      repaired_missing: 0
+      rerun_requested: 0
+      drift_detected_preserved: 0
+      unchanged: 0
+  reason_totals:
+    initial_generation: 0
+    missing_summary: 0
+    missing_faqs: 0
+    rerun_summary: 0
+    rerun_faqs: 0
+    summary_drift_detected: 0
+    faq_drift_detected: 0
+    rerun_flags_reset: 0
+  paths: []
+history: []
diff --git a/tools/generate_summary_faq.py b/tools/generate_summary_faq.py
index 971854b690..a064c9f8be 100644
--- a/tools/generate_summary_faq.py
+++ b/tools/generate_summary_faq.py
@@ -3,24 +3,31 @@
 """
 Generate summary and FAQ content for Learning Path _index.md files.
 
-This script is intentionally template-driven for the first iteration so it can
-run in CI without external AI dependencies. It:
+This script is intentionally template-driven so it can run in CI without
+external AI dependencies. It:
 
 - selects eligible Learning Paths using a front-matter flag or explicit paths
-- generates a managed `generated_summary_faq` front-matter block
-- updates `_index.md` files in place when requested
-- writes a central run report with per-path change details
+- manages a `generated_summary_faq` front-matter block
+- supports one-shot `rerun_summary` / `rerun_faqs` controls
+- auto-repairs missing generated summary/FAQ sections
+- reports section-level changes, drift, and reasons in a central YAML file
 
 Managed front-matter contract:
 
     generate_summary_faq: true
+    rerun_summary: false
+    rerun_faqs: false
 
     # START generated_summary_faq
     generated_summary_faq:
-      template_version: summary-faq-v1
-      generated_at: "2026-04-30T19:40:00Z"
+      template_version: summary-faq-v2
+      generated_at: "2026-05-06T19:40:00Z"
       generator: template
       source_hash: "..."
+      summary_generated_at: "2026-05-06T19:40:00Z"
+      summary_source_hash: "..."
+      faq_generated_at: "2026-05-06T19:40:00Z"
+      faq_source_hash: "..."
       summary: >-
         ...
       faqs:
@@ -36,7 +43,6 @@
 import copy
 import hashlib
 import json
-import os
 import re
 import sys
 from dataclasses import dataclass
@@ -52,12 +58,41 @@
 DEFAULT_REPORT_PATH = REPO_ROOT / "reports" / "generated-summary-faq" / "latest-run.yml"
 
 ENABLE_FLAG = "generate_summary_faq"
+RERUN_SUMMARY_FLAG = "rerun_summary"
+RERUN_FAQS_FLAG = "rerun_faqs"
+
 GENERATED_KEY = "generated_summary_faq"
 MANAGED_START = "# START generated_summary_faq"
 MANAGED_END = "# END generated_summary_faq"
-TEMPLATE_VERSION = "summary-faq-v1"
+
+TEMPLATE_VERSION = "summary-faq-v2"
 DEFAULT_HISTORY_LIMIT = 20
 
+SUMMARY_SOURCE_HASH_KEY = "summary_source_hash"
+SUMMARY_GENERATED_AT_KEY = "summary_generated_at"
+FAQ_SOURCE_HASH_KEY = "faq_source_hash"
+FAQ_GENERATED_AT_KEY = "faq_generated_at"
+
+SUMMARY_ACTIONS = (
+    "created",
+    "repaired_missing",
+    "rerun_requested",
+    "drift_detected_preserved",
+    "unchanged",
+)
+FAQ_ACTIONS = SUMMARY_ACTIONS
+REASON_ORDER = (
+    "initial_generation",
+    "missing_summary",
+    "missing_faqs",
+    "rerun_summary",
+    "rerun_faqs",
+    "summary_drift_detected",
+    "faq_drift_detected",
+    "rerun_flags_reset",
+)
+CHANGE_ACTIONS = {"created", "repaired_missing", "rerun_requested"}
+
 
 class BlockString(str):
     """Marker type so YAML emits readable folded blocks for long prose."""
@@ -159,6 +194,10 @@ def parse_args() -> argparse.Namespace:
     return args
 
 
+def current_timestamp() -> str:
+    return datetime.now(timezone.utc).replace(microsecond=0).isoformat().replace("+00:00", "Z")
+
+
 def read_markdown_document(path: Path, require_front_matter: bool = True) -> MarkdownDocument:
     raw_text = path.read_text(encoding="utf-8")
     match = re.match(r"\A---\s*\n(.*?)\n---\s*\n?(.*)\Z", raw_text, re.DOTALL)
@@ -224,12 +263,20 @@ def discover_learning_path_indexes() -> List[Path]:
     return [path for path in indexes if path.is_file()]
 
 
+def as_bool(value: Any) -> bool:
+    if isinstance(value, bool):
+        return value
+    if isinstance(value, str):
+        return value.strip().lower() in {"1", "true", "yes", "on"}
+    return bool(value)
+
+
 def has_enable_flag(doc: MarkdownDocument) -> bool:
-    return bool(doc.metadata.get(ENABLE_FLAG))
+    return as_bool(doc.metadata.get(ENABLE_FLAG))
 
 
 def is_draft(doc: MarkdownDocument) -> bool:
-    return bool(doc.metadata.get("draft", False))
+    return as_bool(doc.metadata.get("draft", False))
 
 
 def load_steps(index_path: Path) -> List[StepPage]:
@@ -277,6 +324,13 @@ def strip_markdown_links(text: str) -> str:
     return compact_whitespace(text)
 
 
+def preview_text(value: str, limit: int = 200) -> str:
+    preview = compact_whitespace(strip_markdown_links(str(value or "")))
+    if len(preview) > limit:
+        preview = preview[:limit] + "..."
+    return preview
+
+
 def normalize_audience(value: str) -> str:
     cleaned = compact_whitespace(value)
     patterns = [
@@ -361,7 +415,7 @@ def build_step_sentence(steps: Sequence[StepPage]) -> str:
         step.title
         for step in steps
         if step.path.name not in {"_index.md", "_next-steps.md", "_review.md", "_demo.md"}
-        and not step.metadata.get("hide_from_navpane", False)
+        and not as_bool(step.metadata.get("hide_from_navpane", False))
         and step.title
     ]
     if not visible_titles:
@@ -417,7 +471,7 @@ def build_faqs(metadata: Dict[str, Any], steps: Sequence[StepPage]) -> List[Dict
         step.title
         for step in steps
         if step.path.name not in {"_index.md", "_next-steps.md", "_review.md", "_demo.md"}
-        and not step.metadata.get("hide_from_navpane", False)
+        and not as_bool(step.metadata.get("hide_from_navpane", False))
         and step.title
     ]
 
@@ -503,30 +557,115 @@ def build_source_hash(metadata: Dict[str, Any], steps: Sequence[StepPage]) -> st
     return hashlib.sha256(payload.encode("utf-8")).hexdigest()
 
 
-def build_generated_block(metadata: Dict[str, Any], steps: Sequence[StepPage]) -> Dict[str, Any]:
-    generated_at = datetime.now(timezone.utc).replace(microsecond=0).isoformat().replace("+00:00", "Z")
-    summary = build_summary(metadata, steps)
-    faqs = build_faqs(metadata, steps)
+def extract_existing_summary(existing_generated: Dict[str, Any] | None) -> str:
+    if not isinstance(existing_generated, dict):
+        return ""
+    value = existing_generated.get("summary", "")
+    if value is None:
+        return ""
+    return str(value).strip()
+
+
+def extract_existing_faqs(existing_generated: Dict[str, Any] | None) -> List[Dict[str, str]]:
+    if not isinstance(existing_generated, dict):
+        return []
+
+    raw_faqs = existing_generated.get("faqs")
+    if not isinstance(raw_faqs, list):
+        return []
+
+    normalized: List[Dict[str, str]] = []
+    for raw_faq in raw_faqs:
+        if not isinstance(raw_faq, dict):
+            continue
+        question = str(raw_faq.get("question", "")).strip()
+        answer = str(raw_faq.get("answer", "")).strip()
+        if question and answer:
+            normalized.append({"question": question, "answer": answer})
+    return normalized
+
+
+def summaries_differ(existing: str, new: str) -> bool:
+    return compact_whitespace(existing) != compact_whitespace(new)
+
 
-    wrapped_faqs = []
+def faq_mapping(faqs: Sequence[Dict[str, Any]]) -> Dict[str, str]:
+    mapping: Dict[str, str] = {}
     for faq in faqs:
-        wrapped_faqs.append(
-            {
-                "question": faq["question"],
-                "answer": BlockString(faq["answer"]),
-            }
-        )
+        if not isinstance(faq, dict):
+            continue
+        question = compact_whitespace(str(faq.get("question", "")))
+        answer = compact_whitespace(str(faq.get("answer", "")))
+        if question:
+            mapping[question] = answer
+    return mapping
+
+
+def classify_faq_changes(existing_faqs: Sequence[Dict[str, Any]], new_faqs: Sequence[Dict[str, Any]]) -> Dict[str, Any]:
+    existing_by_question = faq_mapping(existing_faqs)
+    new_by_question = faq_mapping(new_faqs)
+
+    added_questions = [question for question in new_by_question if question not in existing_by_question]
+    removed_questions = [question for question in existing_by_question if question not in new_by_question]
+    updated_questions = [
+        question
+        for question in new_by_question
+        if question in existing_by_question and new_by_question[question] != existing_by_question[question]
+    ]
 
     return {
-        "template_version": TEMPLATE_VERSION,
-        "generated_at": generated_at,
-        "generator": "template",
-        "source_hash": build_source_hash(metadata, steps),
-        "summary": BlockString(summary),
-        "faqs": wrapped_faqs,
+        "before_count": len(existing_by_question),
+        "after_count": len(new_by_question),
+        "added_questions": added_questions,
+        "removed_questions": removed_questions,
+        "updated_questions": updated_questions,
     }
 
 
+def faq_differences_exist(change_details: Dict[str, Any]) -> bool:
+    return bool(
+        change_details["added_questions"]
+        or change_details["removed_questions"]
+        or change_details["updated_questions"]
+        or change_details["before_count"] != change_details["after_count"]
+    )
+
+
+def get_existing_section_source_hash(existing_generated: Dict[str, Any] | None, section: str) -> str:
+    if not isinstance(existing_generated, dict):
+        return ""
+
+    key = SUMMARY_SOURCE_HASH_KEY if section == "summary" else FAQ_SOURCE_HASH_KEY
+    value = compact_whitespace(str(existing_generated.get(key, "")))
+    if value:
+        return value
+
+    return compact_whitespace(str(existing_generated.get("source_hash", "")))
+
+
+def get_existing_section_generated_at(existing_generated: Dict[str, Any] | None, section: str) -> str:
+    if not isinstance(existing_generated, dict):
+        return ""
+
+    key = SUMMARY_GENERATED_AT_KEY if section == "summary" else FAQ_GENERATED_AT_KEY
+    value = compact_whitespace(str(existing_generated.get(key, "")))
+    if value:
+        return value
+
+    return compact_whitespace(str(existing_generated.get("generated_at", "")))
+
+
+def normalize_faqs_for_output(faqs: Sequence[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    wrapped_faqs: List[Dict[str, Any]] = []
+    for faq in faqs:
+        question = str(faq.get("question", "")).strip()
+        answer = str(faq.get("answer", "")).strip()
+        if not question or not answer:
+            continue
+        wrapped_faqs.append({"question": question, "answer": BlockString(answer)})
+    return wrapped_faqs
+
+
 def make_managed_yaml_block(generated_block: Dict[str, Any]) -> str:
     serializable = {GENERATED_KEY: copy.deepcopy(generated_block)}
     yaml_block = yaml.dump(
@@ -570,82 +709,129 @@ def insert_or_replace_managed_block(front_matter_text: str, generated_block: Dic
     return (front_matter_text.rstrip() + "\n\n" + managed_block).rstrip()
 
 
+def replace_top_level_scalar_line(front_matter_text: str, key: str, new_value: str) -> str:
+    pattern = re.compile(rf"(?m)^{re.escape(key)}:\s*.*$")
+    if not pattern.search(front_matter_text):
+        return front_matter_text
+    return pattern.sub(f"{key}: {new_value}", front_matter_text, count=1)
+
+
 def rebuild_markdown(doc: MarkdownDocument, updated_front_matter_text: str) -> str:
     content = doc.content.lstrip("\n")
     return f"---\n{updated_front_matter_text.rstrip()}\n---\n\n{content}"
 
 
-def classify_faq_changes(existing: Dict[str, Any], new: Dict[str, Any]) -> Dict[str, Any]:
-    existing_faqs = existing.get("faqs") or []
-    new_faqs = new.get("faqs") or []
-
-    existing_by_question = {
-        faq.get("question"): compact_whitespace(str(faq.get("answer", "")))
-        for faq in existing_faqs
-        if isinstance(faq, dict) and faq.get("question")
-    }
-    new_by_question = {
-        faq.get("question"): compact_whitespace(str(faq.get("answer", "")))
-        for faq in new_faqs
-        if isinstance(faq, dict) and faq.get("question")
-    }
-
-    added_questions = [question for question in new_by_question if question not in existing_by_question]
-    removed_questions = [question for question in existing_by_question if question not in new_by_question]
-    updated_questions = [
-        question
-        for question in new_by_question
-        if question in existing_by_question and new_by_question[question] != existing_by_question[question]
-    ]
+def report_path_for_output(path: Path) -> str:
+    try:
+        return str(path.relative_to(REPO_ROOT))
+    except ValueError:
+        return str(path)
 
-    return {
-        "before_count": len(existing_by_question),
-        "after_count": len(new_by_question),
-        "added_questions": added_questions,
-        "removed_questions": removed_questions,
-        "updated_questions": updated_questions,
-    }
 
+def build_section_output_metadata(
+    existing_generated: Dict[str, Any] | None,
+    current_source_hash: str,
+    generated_at: str,
+    section: str,
+    action: str,
+    section_matches_current: bool,
+) -> Dict[str, str]:
+    existing_source_hash = get_existing_section_source_hash(existing_generated, section)
+    existing_generated_at = get_existing_section_generated_at(existing_generated, section)
 
-def classify_change(existing: Dict[str, Any] | None, new: Dict[str, Any]) -> Dict[str, Any]:
-    if existing is None:
-        faq_changes = classify_faq_changes({}, new)
+    if action in CHANGE_ACTIONS:
         return {
-            "status": "added",
-            "summary_changed": True,
-            "faq_changed": bool(new.get("faqs")),
-            "faq_changes": faq_changes,
+            "source_hash": current_source_hash,
+            "generated_at": generated_at,
         }
 
-    existing_for_compare = copy.deepcopy(existing)
-    new_for_compare = copy.deepcopy(new)
+    if existing_source_hash:
+        source_hash = existing_source_hash
+    elif section_matches_current:
+        source_hash = current_source_hash
+    else:
+        source_hash = ""
 
-    existing_for_compare.pop("generated_at", None)
-    new_for_compare.pop("generated_at", None)
+    if existing_generated_at:
+        section_generated_at = existing_generated_at
+    elif section_matches_current:
+        section_generated_at = generated_at
+    else:
+        section_generated_at = ""
 
-    summary_changed = compact_whitespace(str(existing_for_compare.get("summary", ""))) != compact_whitespace(
-        str(new_for_compare.get("summary", ""))
-    )
+    return {
+        "source_hash": source_hash,
+        "generated_at": section_generated_at,
+    }
 
-    faq_changes = classify_faq_changes(existing_for_compare, new_for_compare)
-    faq_changed = bool(
-        faq_changes["added_questions"] or faq_changes["removed_questions"] or faq_changes["updated_questions"]
+
+def build_updated_generated_block(
+    existing_generated: Dict[str, Any] | None,
+    summary_after: str,
+    faqs_after: Sequence[Dict[str, Any]],
+    desired_summary: str,
+    desired_faqs: Sequence[Dict[str, Any]],
+    current_source_hash: str,
+    generated_at: str,
+    summary_action: str,
+    faq_action: str,
+) -> Dict[str, Any]:
+    summary_matches_current = not summaries_differ(summary_after, desired_summary)
+    faqs_match_current = not faq_differences_exist(classify_faq_changes(faqs_after, desired_faqs))
+
+    summary_meta = build_section_output_metadata(
+        existing_generated=existing_generated,
+        current_source_hash=current_source_hash,
+        generated_at=generated_at,
+        section="summary",
+        action=summary_action,
+        section_matches_current=summary_matches_current,
+    )
+    faq_meta = build_section_output_metadata(
+        existing_generated=existing_generated,
+        current_source_hash=current_source_hash,
+        generated_at=generated_at,
+        section="faqs",
+        action=faq_action,
+        section_matches_current=faqs_match_current,
     )
 
-    status = "updated" if existing_for_compare != new_for_compare else "unchanged"
+    if summary_matches_current and faqs_match_current:
+        top_level_source_hash = current_source_hash
+    else:
+        top_level_source_hash = compact_whitespace(str((existing_generated or {}).get("source_hash", ""))) or current_source_hash
+
     return {
-        "status": status,
-        "summary_changed": summary_changed,
-        "faq_changed": faq_changed,
-        "faq_changes": faq_changes,
+        "template_version": TEMPLATE_VERSION,
+        "generated_at": generated_at,
+        "generator": "template",
+        "source_hash": top_level_source_hash,
+        SUMMARY_GENERATED_AT_KEY: summary_meta["generated_at"],
+        SUMMARY_SOURCE_HASH_KEY: summary_meta["source_hash"],
+        FAQ_GENERATED_AT_KEY: faq_meta["generated_at"],
+        FAQ_SOURCE_HASH_KEY: faq_meta["source_hash"],
+        "summary": BlockString(summary_after),
+        "faqs": normalize_faqs_for_output(faqs_after),
     }
 
 
-def report_path_for_output(path: Path) -> str:
-    try:
-        return str(path.relative_to(REPO_ROOT))
-    except ValueError:
-        return str(path)
+def build_result_status(
+    existing_generated: Dict[str, Any] | None,
+    changed_on_disk: bool,
+    summary_drift_detected: bool,
+    faq_drift_detected: bool,
+) -> str:
+    if existing_generated is None and changed_on_disk:
+        return "added"
+    if changed_on_disk:
+        return "updated"
+    if summary_drift_detected or faq_drift_detected:
+        return "drift_detected"
+    return "unchanged"
+
+
+def zeroed_action_counts(actions: Sequence[str]) -> Dict[str, int]:
+    return {action: 0 for action in actions}
 
 
 def build_run_report(
@@ -655,16 +841,54 @@ def build_run_report(
 ) -> Dict[str, Any]:
     totals = {
         "processed": len(processed_paths),
-        "added": sum(1 for result in per_path_results if result["status"] == "added"),
-        "updated": sum(1 for result in per_path_results if result["status"] == "updated"),
-        "unchanged": sum(1 for result in per_path_results if result["status"] == "unchanged"),
-        "skipped": sum(1 for result in per_path_results if result["status"] == "skipped"),
-        "errors": sum(1 for result in per_path_results if result["status"] == "error"),
+        "added": 0,
+        "updated": 0,
+        "unchanged": 0,
+        "drift_detected": 0,
+        "skipped": 0,
+        "errors": 0,
         "removed": 0,
+        "summary_changed": 0,
+        "faq_changed": 0,
+        "rerun_flags_reset": 0,
     }
+    section_totals = {
+        "summary": zeroed_action_counts(SUMMARY_ACTIONS),
+        "faqs": zeroed_action_counts(FAQ_ACTIONS),
+    }
+    reason_totals = {reason: 0 for reason in REASON_ORDER}
+
+    for result in per_path_results:
+        status = result.get("status", "error")
+        totals_key = "errors" if status == "error" else status
+        if totals_key in totals:
+            totals[totals_key] += 1
+
+        summary_result = result.get("summary", {})
+        if summary_result.get("changed"):
+            totals["summary_changed"] += 1
+        summary_action = summary_result.get("action")
+        if summary_action in section_totals["summary"]:
+            section_totals["summary"][summary_action] += 1
+
+        faq_result = result.get("faqs", {})
+        if faq_result.get("changed"):
+            totals["faq_changed"] += 1
+        faq_action = faq_result.get("action")
+        if faq_action in section_totals["faqs"]:
+            section_totals["faqs"][faq_action] += 1
+
+        rerun_flags_reset = result.get("rerun_flags_reset", [])
+        if rerun_flags_reset:
+            totals["rerun_flags_reset"] += 1
+
+        for reason in result.get("change_reasons", []):
+            if reason not in reason_totals:
+                reason_totals[reason] = 0
+            reason_totals[reason] += 1
 
     return {
-        "timestamp": datetime.now(timezone.utc).replace(microsecond=0).isoformat().replace("+00:00", "Z"),
+        "timestamp": current_timestamp(),
         "mode": "write" if args.write else "dry-run",
         "require_enable_flag": not args.allow_unflagged,
         "path_filter": args.path_filter or "",
@@ -675,6 +899,8 @@ def build_run_report(
         "actor": args.actor or "",
         "template_version": TEMPLATE_VERSION,
         "totals": totals,
+        "section_totals": section_totals,
+        "reason_totals": reason_totals,
         "paths": per_path_results,
     }
 
@@ -713,11 +939,33 @@ def print_result_summary(run_report: Dict[str, Any]) -> None:
     totals = run_report["totals"]
     print(
         "Processed {processed} Learning Paths: "
-        "{added} added, {updated} updated, {unchanged} unchanged, {errors} errors.".format(**totals)
+        "{added} added, {updated} updated, {drift_detected} drift detected, "
+        "{unchanged} unchanged, {errors} errors.".format(**totals)
+    )
+
+    summary_actions = run_report["section_totals"]["summary"]
+    faq_actions = run_report["section_totals"]["faqs"]
+    print(
+        "Summary actions: "
+        f"{summary_actions['created']} created, "
+        f"{summary_actions['repaired_missing']} repaired_missing, "
+        f"{summary_actions['rerun_requested']} rerun_requested, "
+        f"{summary_actions['drift_detected_preserved']} drift_detected_preserved."
+    )
+    print(
+        "FAQ actions: "
+        f"{faq_actions['created']} created, "
+        f"{faq_actions['repaired_missing']} repaired_missing, "
+        f"{faq_actions['rerun_requested']} rerun_requested, "
+        f"{faq_actions['drift_detected_preserved']} drift_detected_preserved."
     )
+
     for result in run_report["paths"]:
         status = result["status"]
-        print(f"- {status.upper():9s} {result['path']}")
+        summary_action = result.get("summary", {}).get("action", "")
+        faq_action = result.get("faqs", {}).get("action", "")
+        reasons = ", ".join(result.get("change_reasons", [])) or "none"
+        print(f"- {status.upper():14s} {result['path']} | summary={summary_action} | faqs={faq_action} | reasons={reasons}")
 
 
 def select_learning_paths(args: argparse.Namespace) -> List[Path]:
@@ -743,32 +991,175 @@ def process_learning_path(index_path: Path, args: argparse.Namespace) -> Dict[st
     try:
         doc = read_markdown_document(index_path)
         steps = load_steps(index_path)
+
         existing_generated = doc.metadata.get(GENERATED_KEY)
         if existing_generated is not None and not isinstance(existing_generated, dict):
             raise ValueError(f"{GENERATED_KEY} in {index_path} must be a mapping when present.")
 
-        new_generated = build_generated_block(doc.metadata, steps)
-        change = classify_change(existing_generated, new_generated)
+        rerun_summary_requested = as_bool(doc.metadata.get(RERUN_SUMMARY_FLAG))
+        rerun_faqs_requested = as_bool(doc.metadata.get(RERUN_FAQS_FLAG))
+
+        generated_at = current_timestamp()
+        current_source_hash = build_source_hash(doc.metadata, steps)
+        desired_summary = build_summary(doc.metadata, steps)
+        desired_faqs = build_faqs(doc.metadata, steps)
+
+        existing_summary = extract_existing_summary(existing_generated)
+        existing_faqs = extract_existing_faqs(existing_generated)
+
+        summary_missing_before = existing_generated is not None and not compact_whitespace(existing_summary)
+        faqs_missing_before = existing_generated is not None and not existing_faqs
+
+        change_reasons: List[str] = []
+        if existing_generated is None:
+            change_reasons.append("initial_generation")
+
+        if summary_missing_before:
+            change_reasons.append("missing_summary")
+        if faqs_missing_before:
+            change_reasons.append("missing_faqs")
+
+        if rerun_summary_requested:
+            change_reasons.append("rerun_summary")
+        if rerun_faqs_requested:
+            change_reasons.append("rerun_faqs")
+
+        if existing_generated is None:
+            summary_action = "created"
+            summary_after = desired_summary
+        elif summary_missing_before:
+            summary_action = "repaired_missing"
+            summary_after = desired_summary
+        elif rerun_summary_requested:
+            summary_action = "rerun_requested"
+            summary_after = desired_summary
+        else:
+            summary_after = existing_summary
+            if summaries_differ(existing_summary, desired_summary):
+                summary_action = "drift_detected_preserved"
+                change_reasons.append("summary_drift_detected")
+            else:
+                summary_action = "unchanged"
+
+        if existing_generated is None:
+            faq_action = "created"
+            faqs_after = desired_faqs
+        elif faqs_missing_before:
+            faq_action = "repaired_missing"
+            faqs_after = desired_faqs
+        elif rerun_faqs_requested:
+            faq_action = "rerun_requested"
+            faqs_after = desired_faqs
+        else:
+            faqs_after = existing_faqs
+            if faq_differences_exist(classify_faq_changes(existing_faqs, desired_faqs)):
+                faq_action = "drift_detected_preserved"
+                change_reasons.append("faq_drift_detected")
+            else:
+                faq_action = "unchanged"
+
+        summary_changed = summaries_differ(existing_summary, summary_after)
+        faq_change_details = classify_faq_changes(existing_faqs, faqs_after)
+        faq_changed = faq_differences_exist(faq_change_details)
+
+        summary_drift_detected = summary_action == "drift_detected_preserved"
+        faq_generated_diff = classify_faq_changes(existing_faqs, desired_faqs)
+        faq_drift_detected = faq_action == "drift_detected_preserved"
+
+        managed_block_updated = existing_generated is None or summary_action in CHANGE_ACTIONS or faq_action in CHANGE_ACTIONS
+        rerun_flags_reset = []
+        if rerun_summary_requested:
+            rerun_flags_reset.append(RERUN_SUMMARY_FLAG)
+        if rerun_faqs_requested:
+            rerun_flags_reset.append(RERUN_FAQS_FLAG)
+        if rerun_flags_reset:
+            change_reasons.append("rerun_flags_reset")
+
+        updated_front_matter = doc.front_matter_text
+        summary_source_hash_after = get_existing_section_source_hash(existing_generated, "summary")
+        summary_generated_at_after = get_existing_section_generated_at(existing_generated, "summary")
+        faq_source_hash_after = get_existing_section_source_hash(existing_generated, "faqs")
+        faq_generated_at_after = get_existing_section_generated_at(existing_generated, "faqs")
+        template_version_after = compact_whitespace(str((existing_generated or {}).get("template_version", "")))
+
+        if managed_block_updated:
+            updated_generated = build_updated_generated_block(
+                existing_generated=existing_generated,
+                summary_after=summary_after,
+                faqs_after=faqs_after,
+                desired_summary=desired_summary,
+                desired_faqs=desired_faqs,
+                current_source_hash=current_source_hash,
+                generated_at=generated_at,
+                summary_action=summary_action,
+                faq_action=faq_action,
+            )
+            updated_front_matter = insert_or_replace_managed_block(updated_front_matter, updated_generated)
+            summary_source_hash_after = compact_whitespace(str(updated_generated.get(SUMMARY_SOURCE_HASH_KEY, "")))
+            summary_generated_at_after = compact_whitespace(str(updated_generated.get(SUMMARY_GENERATED_AT_KEY, "")))
+            faq_source_hash_after = compact_whitespace(str(updated_generated.get(FAQ_SOURCE_HASH_KEY, "")))
+            faq_generated_at_after = compact_whitespace(str(updated_generated.get(FAQ_GENERATED_AT_KEY, "")))
+            template_version_after = compact_whitespace(str(updated_generated.get("template_version", "")))
+
+        if rerun_summary_requested:
+            updated_front_matter = replace_top_level_scalar_line(updated_front_matter, RERUN_SUMMARY_FLAG, "false")
+        if rerun_faqs_requested:
+            updated_front_matter = replace_top_level_scalar_line(updated_front_matter, RERUN_FAQS_FLAG, "false")
 
-        updated_front_matter = insert_or_replace_managed_block(doc.front_matter_text, new_generated)
         updated_markdown = rebuild_markdown(doc, updated_front_matter)
         changed_on_disk = updated_markdown != doc.raw_text
 
         if args.write and changed_on_disk:
             index_path.write_text(updated_markdown, encoding="utf-8")
 
-        preview_summary = compact_whitespace(strip_markdown_links(str(new_generated.get("summary", ""))))
-        preview_summary = preview_summary[:200] + ("..." if len(preview_summary) > 200 else "")
+        result_status = build_result_status(
+            existing_generated=existing_generated,
+            changed_on_disk=changed_on_disk,
+            summary_drift_detected=summary_drift_detected,
+            faq_drift_detected=faq_drift_detected,
+        )
 
         return {
             "path": report_path_for_output(index_path),
-            "status": change["status"] if changed_on_disk else "unchanged",
+            "status": result_status,
             "changed_on_disk": changed_on_disk,
-            "summary_changed": change["summary_changed"],
-            "faq_changed": change["faq_changed"],
-            "faq_changes": change["faq_changes"],
-            "summary_preview": preview_summary,
-            "source_hash": new_generated["source_hash"],
+            "managed_block_updated": managed_block_updated,
+            "rerun_flags_reset": rerun_flags_reset,
+            "change_reasons": change_reasons,
+            "template_version_before": compact_whitespace(str((existing_generated or {}).get("template_version", ""))),
+            "template_version_after": template_version_after,
+            "summary": {
+                "action": summary_action,
+                "missing_before": summary_missing_before,
+                "rerun_requested": rerun_summary_requested,
+                "changed": summary_changed,
+                "drift_detected": summary_drift_detected,
+                "source_hash_before": get_existing_section_source_hash(existing_generated, "summary"),
+                "source_hash_after": summary_source_hash_after,
+                "current_source_hash": current_source_hash,
+                "generated_at_before": get_existing_section_generated_at(existing_generated, "summary"),
+                "generated_at_after": summary_generated_at_after,
+                "preview_before": preview_text(existing_summary),
+                "preview_after": preview_text(summary_after),
+                "preview_generated": preview_text(desired_summary),
+            },
+            "faqs": {
+                "action": faq_action,
+                "missing_before": faqs_missing_before,
+                "rerun_requested": rerun_faqs_requested,
+                "changed": faq_changed,
+                "drift_detected": faq_drift_detected,
+                "source_hash_before": get_existing_section_source_hash(existing_generated, "faqs"),
+                "source_hash_after": faq_source_hash_after,
+                "current_source_hash": current_source_hash,
+                "generated_at_before": get_existing_section_generated_at(existing_generated, "faqs"),
+                "generated_at_after": faq_generated_at_after,
+                "before_count": len(existing_faqs),
+                "after_count": len(faqs_after),
+                "generated_count": len(desired_faqs),
+                "change_details": faq_change_details,
+                "generated_diff": faq_generated_diff,
+            },
         }
     except Exception as exc:
         return {

From ca1a951c407273b9870b93413b2394817294dee2 Mon Sep 17 00:00:00 2001
From: GitHub Actions Summary FAQ Bot <>
Date: Wed, 6 May 2026 18:45:10 +0000
Subject: [PATCH 05/23] Generate Learning Path summary and FAQ content

---
 reports/generated-summary-faq/latest-run.yml | 44027 ++++++++++++++++-
 1 file changed, 44014 insertions(+), 13 deletions(-)

diff --git a/reports/generated-summary-faq/latest-run.yml b/reports/generated-summary-faq/latest-run.yml
index ed45e96bb7..4ddbd31f5d 100644
--- a/reports/generated-summary-faq/latest-run.yml
+++ b/reports/generated-summary-faq/latest-run.yml
@@ -1,19 +1,19 @@
 latest_run:
-  timestamp: ""
-  mode: ""
+  timestamp: '2026-05-06T18:45:07Z'
+  mode: write
   require_enable_flag: true
-  path_filter: ""
+  path_filter: ''
   limit: 0
-  run_url: ""
-  git_ref: ""
-  git_sha: ""
-  actor: ""
+  run_url: https://github.com/chrismoroney/arm-learning-paths/actions/runs/25454338790
+  git_ref: STESOL-345
+  git_sha: c9c4fdb3b9ac4d10e42706e5cf13a96da575319c
+  actor: chrismoroney
   template_version: summary-faq-v2
   totals:
-    processed: 0
+    processed: 407
     added: 0
     updated: 0
-    unchanged: 0
+    unchanged: 407
     drift_detected: 0
     skipped: 0
     errors: 0
@@ -27,13 +27,13 @@ latest_run:
       repaired_missing: 0
       rerun_requested: 0
       drift_detected_preserved: 0
-      unchanged: 0
+      unchanged: 407
     faqs:
       created: 0
       repaired_missing: 0
       rerun_requested: 0
       drift_detected_preserved: 0
-      unchanged: 0
+      unchanged: 407
   reason_totals:
     initial_generation: 0
     missing_summary: 0
@@ -43,5 +43,44006 @@ latest_run:
     summary_drift_detected: 0
     faq_drift_detected: 0
     rerun_flags_reset: 0
-  paths: []
-history: []
+  paths:
+  - path: content/learning-paths/automotive/openadkit1_container/_index.md
+    status: unchanged
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons: []
+    template_version_before: summary-faq-v1
+    template_version_after: summary-faq-v1
+    summary:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
+      source_hash_after: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
+      current_source_hash: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
+      generated_at_before: '2026-05-06T17:17:53Z'
+      generated_at_after: '2026-05-06T17:17:53Z'
+      preview_before: Learn how to deploy and run containerized autonomous driving simulations using Autoware
+        Open AD Kit on Arm Neoverse with Docker, demonstrating SOAFEE-based Shift-Left development workflows.
+        It is desi...
+      preview_after: Learn how to deploy and run containerized autonomous driving simulations using Autoware
+        Open AD Kit on Arm Neoverse with Docker, demonstrating SOAFEE-based Shift-Left development workflows.
+        It is desi...
+      preview_generated: Learn how to deploy and run containerized autonomous driving simulations using
+        Autoware Open AD Kit on Arm Neoverse with Docker, demonstrating SOAFEE-based Shift-Left development
+        workflows. It is desi...
+    faqs:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
+      source_hash_after: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
+      current_source_hash: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
+      generated_at_before: '2026-05-06T17:17:53Z'
+      generated_at_after: '2026-05-06T17:17:53Z'
+      before_count: 5
+      after_count: 5
+      generated_count: 5
+      change_details:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+      generated_diff:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+  - path: content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
+    status: unchanged
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons: []
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+        is designed for ro...
+      preview_generated: Learn how to build and deploy distributed Zenoh systems on Arm devices like Raspberry
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+      preview_after: Learn how to design and integrate a custom AXI-Lite peripheral with a Cortex-A9 processor
+        on the Zybo Z7-10 board using Vivado, configuring GPIOs to control LEDs based on switch inputs.
+        It is designed...
+      preview_generated: Learn how to design and integrate a custom AXI-Lite peripheral with a Cortex-A9
+        processor on the Zybo Z7-10 board using Vivado, configuring GPIOs to control LEDs based on switch
+        inputs. It is designed...
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+      preview_before: Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit and
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+        a neural network model t...
+      preview_after: Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit and
+        run inference on the Ethos-U85 NPU. It is designed for embedded developers who want to deploy
+        a neural network model t...
+      preview_generated: Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit
+        and run inference on the Ethos-U85 NPU. It is designed for embedded developers who want to deploy
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+        using Arduino IDE, PIR sensors, and interrupt-driven programming on baremetal. It is designed
+        for software devel...
+      preview_generated: Learn how to build a motion-detection device with Raspberry Pi Pico (RP2040 Cortex-M0+)
+        using Arduino IDE, PIR sensors, and interrupt-driven programming on baremetal. It is designed
+        for software devel...
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+        It is designed for ...
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+        embedded applications using Fixed Virtual Platforms (FVPs) or hardware with DSTREAM debug probes.
+        It is designed for ...
+      preview_generated: Learn how to import and build example projects in Arm Development Studio and
+        debug embedded applications using Fixed Virtual Platforms (FVPs) or hardware with DSTREAM debug
+        probes. It is designed for ...
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+      preview_before: Learn how to write mixed C and assembly programs for Cortex-M microcontrollers using
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+        who are interes...
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+        who are interes...
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+        using Keil MDK, following Arm Procedure Call Standard conventions. It is designed for software
+        developers who are interes...
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+  - path: content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
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+      preview_before: Learn how to create a custom Balena OS image, run it on Arm Virtual Hardware, and
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+        deploy IoT applications to a virtual Raspberry Pi 4 device. It is designed for embedded software
+        developers interested...
+      preview_generated: Learn how to create a custom Balena OS image, run it on Arm Virtual Hardware,
+        and deploy IoT applications to a virtual Raspberry Pi 4 device. It is designed for embedded software
+        developers interested...
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+  - path: content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
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+      preview_before: Learn how to set up AWS IoT Greengrass Core on Arm Virtual Hardware and deploy AWS
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+      preview_after: Learn how to set up AWS IoT Greengrass Core on Arm Virtual Hardware and deploy AWS
+        Greengrass components to a virtual Raspberry Pi 4 device. It is designed for embedded software
+        developers interested ...
+      preview_generated: Learn how to set up AWS IoT Greengrass Core on Arm Virtual Hardware and deploy
+        AWS Greengrass components to a virtual Raspberry Pi 4 device. It is designed for embedded software
+        developers interested ...
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+  - path: content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
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+      preview_before: Learn how to build Matter reference examples on Arm Virtual Hardware, demonstrate
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+        DB, and Azure Fun...
+      preview_generated: Learn how to build a complete IoT solution in Azure that streams, stores, monitors,
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+        DB, and Azure Fun...
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+        processors using Arm Compiler for Embedded and Fixed Virtual Platforms, including basic exception
+        handling. It is desi...
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+      preview_before: Device-to-Device communication with Device Connect walks you through an end-to-end
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+        for structured device access and Strands for agent orchestration, with examples for both simulated
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+        commands. It is designed...
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+      preview_before: Learn what differentiates TinyML from other AI domains, explore Arm-based edge devices
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+      preview_generated: Learn what differentiates TinyML from other AI domains, explore Arm-based edge
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+      preview_after: Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300 virtual
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+      preview_generated: Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300
+        virtual hardware to understand complete IoT software stack construction. It is designed for embedded
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+      preview_before: Learn how to set up a Jetson Orin Nano with a MIPI CSI-2 camera and perform real-time
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+      preview_generated: Learn how to set up a Jetson Orin Nano with a MIPI CSI-2 camera and perform real-time
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+      preview_before: Learn how to import, build, and debug your first Keil Studio Cloud project. It is
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+        to import and build a...
+      preview_generated: Learn how to import, build, and debug your first Keil Studio Cloud project. It
+        is designed for embedded software developers new to Keil Studio Cloud. By the end, you will be
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+    status: unchanged
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+        is designed for embedd...
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+        is designed for embedd...
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+      preview_generated: Learn how to boot a Linux software stack on Arm Fixed Virtual Platforms (FVPs),
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+      preview_generated: Learn how to install the Python version of llama.cpp on a Raspberry Pi 5, download
+        an LLM from Hugging Face, assess memory and performance, and run the model using Python bindings.
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+      preview_before: Learn how to build examples from the Machine Learning Evaluation Kit (MLEK) and
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+        It is designed for e...
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+      preview_before: Learn how to understand and select physical and virtual hardware targets for ML
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+      preview_generated: Learn how to create debug configurations for virtual platforms and development
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+      preview_generated: Learn how to bring up ExecuTorch executor_runner firmware on the NXP FRDM i.MX
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+        NPU acceleration. It is d...
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+      preview_before: Learn how to migrate a CMSIS v5-based CMSIS-Pack with device support to CMSIS v6
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+      preview_after: Learn how to migrate a CMSIS v5-based CMSIS-Pack with device support to CMSIS v6
+        and update example projects for compatibility with the new CMSIS version. It is designed for maintainers
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+      preview_generated: Learn how to migrate a CMSIS v5-based CMSIS-Pack with device support to CMSIS
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+      preview_before: Learn how to migrate CMSIS v5 projects to CMSIS v6 by identifying supported toolchains,
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+        installing required CMSIS-Packs, and selecting the necessary software components. It is designed
+        for embedded de...
+      preview_generated: Learn how to migrate CMSIS v5 projects to CMSIS v6 by identifying supported toolchains,
+        installing required CMSIS-Packs, and selecting the necessary software components. It is designed
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+      preview_before: Learn how to run large language models locally on the Raspberry Pi 5 using Ollama,
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+        cloud services. It ...
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+        control GPIO-connected devices, and deploy a privacy-first web-based smart home assistant without
+        cloud services. It ...
+      preview_generated: Learn how to run large language models locally on the Raspberry Pi 5 using Ollama,
+        control GPIO-connected devices, and deploy a privacy-first web-based smart home assistant without
+        cloud services. It ...
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+      preview_before: Learn how to build a voice-controlled bot on a Raspberry Pi that listens for a wake
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+        and plays audio resp...
+      preview_after: Learn how to build a voice-controlled bot on a Raspberry Pi that listens for a wake
+        word, converts speech to text using Google Speech Recognition, sends requests to ChatGPT's API,
+        and plays audio resp...
+      preview_generated: Learn how to build a voice-controlled bot on a Raspberry Pi that listens for
+        a wake word, converts speech to text using Google Speech Recognition, sends requests to ChatGPT's
+        API, and plays audio resp...
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+        for software...
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+      preview_before: Learn how to compile the Llama 3 large language model using ExecuTorch, deploy it
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+      preview_after: Learn how to compile the Llama 3 large language model using ExecuTorch, deploy it
+        to a Raspberry Pi 5, and understand techniques for running LLMs in embedded environments. It is
+        designed for anyone in...
+      preview_generated: Learn how to compile the Llama 3 large language model using ExecuTorch, deploy
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+      preview_before: Learn how to reduce compile time for embedded Linux projects by installing a Raspberry
+        Pi OS file system on an Arm server, building the MXNet machine learning framework, and transferring
+        it to a Raspb...
+      preview_after: Learn how to reduce compile time for embedded Linux projects by installing a Raspberry
+        Pi OS file system on an Arm server, building the MXNet machine learning framework, and transferring
+        it to a Raspb...
+      preview_generated: Learn how to reduce compile time for embedded Linux projects by installing a
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+        transferring it to a Raspb...
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+        developers ...
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+        designed for machine learnin...
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+      preview_before: Learn how to install Keil MDK Tools, run a TrustZone hello world example on the
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+  - path: content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
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+        in a 4U chassis, and install single board computers in the racks using 3D-printed parts. It is
+        designed for softwar...
+      preview_generated: Learn how to acquire and print materials, assemble a universal SBC rack mount
+        system in a 4U chassis, and install single board computers in the racks using 3D-printed parts.
+        It is designed for softwar...
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+  - path: content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
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+      preview_before: Learn how to debug microcontrollers using µVision with basic run/stop debug, advanced
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+        techniques using Event Recorder and Serial Wire Viewer, ETM Trace for performance analysis, and
+        power measurement ...
+      preview_generated: Learn how to debug microcontrollers using µVision with basic run/stop debug,
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+  - path: content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
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+      preview_before: Learn how to import, convert, and build uvprojx-based projects to csolution format
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+        who want to lea...
+      preview_generated: Learn how to package multi-architecture container applications and deploy them
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+        engineers wh...
+      preview_generated: Learn how to create and deploy Azure Resource Manager templates to provision
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+        and DevOps engineers wh...
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+        It is designed for software developers who are new to deploying Arm instances on Azure using Terraform.
+        By the end, yo...
+      preview_generated: Learn how to automate the creation of Azure Arm virtual machines using Terraform.
+        It is designed for software developers who are new to deploying Arm instances on Azure using Terraform.
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+      preview_generated: Learn how to create a custom Azure Linux 3.0 VM image using QEMU, upload it to
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+        developers, pe...
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+        It is designed...
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+        profile instrumentation, merge multiple profiles for improved coverage, and integrate optimized
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+        instances and benchmark read/write performance using cassandra-stress. It is designed for software
+        developers migrat...
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+        software developers migrat...
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+        create a Realm virtual machine, and obtain attestation tokens for confidential computing. It is
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+      preview_generated: Learn how Arm CCA Realms interact with I/O devices using VirtIO paravirtualization,
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+        Realms using Trustee services for attestation-based authorization on an FVP with RME support.
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+      preview_generated: Learn how to deploy Confidential Containers from encrypted images inside Arm
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+      preview_generated: Learn how to install and run Clair on Arm servers using combined and distributed
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+      preview_generated: Learn how to deploy Dynatrace OneAgent on Azure Cobalt 100 Arm64 virtual machines
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+      preview_generated: Learn how to create an AWS ECS cluster with Fargate and AWS Graviton processors,
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+      preview_generated: Learn how to create dual-architecture GKE clusters with arm64 and amd64 node
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+      preview_generated: Learn how to provision Arm64 and x86_64 VM instances on Google Cloud, then install
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+      preview_generated: Monitor, interpret, and optimize Java Garbage Collector performance on Arm servers
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+        JDK versions and performance optimization flags. It is designed for software developers who want
+        to learn how to run t...
+      preview_generated: Deploy and optimize Java applications on Google Cloud Axion processors by testing
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+      preview_before: Deploy Java on Azure Cobalt 100 Arm virtual machines and benchmark application performance
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+        and benchmar...
+      preview_after: Deploy Java on Azure Cobalt 100 Arm virtual machines and benchmark application performance
+        with JMH microbenchmarks. It is designed for This is an introductory topic about Java deployment
+        and benchmar...
+      preview_generated: Deploy Java on Azure Cobalt 100 Arm virtual machines and benchmark application
+        performance with JMH microbenchmarks. It is designed for This is an introductory topic about Java
+        deployment and benchmar...
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+      preview_before: Profile Java applications on Arm Neoverse servers using flame graphs generated with
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+      preview_after: Profile Java applications on Arm Neoverse servers using flame graphs generated with
+        async-profiler and Java agents to identify performance bottlenecks. It is designed for developers
+        who want to analyz...
+      preview_generated: Profile Java applications on Arm Neoverse servers using flame graphs generated
+        with async-profiler and Java agents to identify performance bottlenecks. It is designed for developers
+        who want to analyz...
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+  - path: content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
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+      preview_before: Deploy Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate installation,
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+      preview_after: Deploy Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate installation,
+        and execute Arm-native CI/CD pipelines including Docker workflows. It is designed for software
+        developers d...
+      preview_generated: Deploy Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate
+        installation, and execute Arm-native CI/CD pipelines including Docker workflows. It is designed
+        for software developers d...
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+  - path: content/learning-paths/servers-and-cloud-computing/kafka/_index.md
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+      preview_before: Deploy and configure a Kafka cluster with Zookeeper on Arm servers, test event streaming,
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+      preview_after: Deploy and configure a Kafka cluster with Zookeeper on Arm servers, test event streaming,
+        and automate deployment on AWS and Google Cloud. It is designed for software developers who want
+        to learn how ...
+      preview_generated: Deploy and configure a Kafka cluster with Zookeeper on Arm servers, test event
+        streaming, and automate deployment on AWS and Google Cloud. It is designed for software developers
+        who want to learn how ...
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+  - path: content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
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+      preview_before: Deploy Apache Kafka on Azure Cobalt 100 Arm virtual machines and benchmark message
+        throughput performance. It is designed for developers looking to migrate their Apache Kafka workloads
+        from x86_64 to ...
+      preview_after: Deploy Apache Kafka on Azure Cobalt 100 Arm virtual machines and benchmark message
+        throughput performance. It is designed for developers looking to migrate their Apache Kafka workloads
+        from x86_64 to ...
+      preview_generated: Deploy Apache Kafka on Azure Cobalt 100 Arm virtual machines and benchmark message
+        throughput performance. It is designed for developers looking to migrate their Apache Kafka workloads
+        from x86_64 to ...
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+      preview_before: Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing Kedify
+        and KEDA with Helm and testing autoscaling behavior. It is designed for developers running HTTP
+        workloads on Kuber...
+      preview_after: Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing Kedify
+        and KEDA with Helm and testing autoscaling behavior. It is designed for developers running HTTP
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+      preview_generated: Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing
+        Kedify and KEDA with Helm and testing autoscaling behavior. It is designed for developers running
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+      preview_before: Create, train, and evaluate a neural network model on Arm servers using Keras Core
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+      preview_after: Create, train, and evaluate a neural network model on Arm servers using Keras Core
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+      preview_generated: Create, train, and evaluate a neural network model on Arm servers using Keras
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+  - path: content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
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+      preview_after: Compile and install custom Linux kernels on Arm cloud instances using TuxMake with
+        configurations for 64 KB page sizes and Fastpath testing. It is designed for software developers
+        building custom Linu...
+      preview_generated: Compile and install custom Linux kernels on Arm cloud instances using TuxMake
+        with configurations for 64 KB page sizes and Fastpath testing. It is designed for software developers
+        building custom Linu...
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+  - path: content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
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+        who want to deploy...
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+        who want to deploy...
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+      preview_generated: Benchmark machine learning inference performance on Arm servers using TensorFlow
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+        is designed...
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+        who wa...
+      preview_generated: Deploy OpenTelemetry on Google Cloud C4A Axion processors walks you through an
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+      preview_generated: Understand Arm Pointer Authentication walks you through an end-to-end Arm software
+        workflow. It is designed for software developers interested in understanding Arm Pointer Authentication.
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+        Windows on Arm in th...
+      preview_after: Learn how to create and connect to a Windows on Arm virtual machine in Microsoft
+        Azure using the Azure Marketplace and RDP. It is designed for software developers interested using
+        Windows on Arm in th...
+      preview_generated: Learn how to create and connect to a Windows on Arm virtual machine in Microsoft
+        Azure using the Azure Marketplace and RDP. It is designed for software developers interested using
+        Windows on Arm in th...
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+      preview_before: Learn how to build and deploy distributed Zenoh systems on Arm devices like Raspberry
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+        is designed for ro...
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+        Pi, using pub/sub, storage, and queryable models for scalable robotics and IoT applications. It
+        is designed for ro...
+      preview_generated: Learn how to build and deploy distributed Zenoh systems on Arm devices like Raspberry
+        Pi, using pub/sub, storage, and queryable models for scalable robotics and IoT applications. It
+        is designed for ro...
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+      preview_before: Learn how to design and integrate a custom AXI-Lite peripheral with a Cortex-A9
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+        inputs. It is designed...
+      preview_after: Learn how to design and integrate a custom AXI-Lite peripheral with a Cortex-A9 processor
+        on the Zybo Z7-10 board using Vivado, configuring GPIOs to control LEDs based on switch inputs.
+        It is designed...
+      preview_generated: Learn how to design and integrate a custom AXI-Lite peripheral with a Cortex-A9
+        processor on the Zybo Z7-10 board using Vivado, configuring GPIOs to control LEDs based on switch
+        inputs. It is designed...
+    faqs:
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+  - path: content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
+    status: unchanged
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+      preview_before: Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit and
+        run inference on the Ethos-U85 NPU. It is designed for embedded developers who want to deploy
+        a neural network model t...
+      preview_after: Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit and
+        run inference on the Ethos-U85 NPU. It is designed for embedded developers who want to deploy
+        a neural network model t...
+      preview_generated: Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit
+        and run inference on the Ethos-U85 NPU. It is designed for embedded developers who want to deploy
+        a neural network model t...
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+  - path: content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
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+      preview_before: Learn how to build a motion-detection device with Raspberry Pi Pico (RP2040 Cortex-M0+)
+        using Arduino IDE, PIR sensors, and interrupt-driven programming on baremetal. It is designed
+        for software devel...
+      preview_after: Learn how to build a motion-detection device with Raspberry Pi Pico (RP2040 Cortex-M0+)
+        using Arduino IDE, PIR sensors, and interrupt-driven programming on baremetal. It is designed
+        for software devel...
+      preview_generated: Learn how to build a motion-detection device with Raspberry Pi Pico (RP2040 Cortex-M0+)
+        using Arduino IDE, PIR sensors, and interrupt-driven programming on baremetal. It is designed
+        for software devel...
+    faqs:
+      action: unchanged
+      missing_before: false
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+      changed: false
+      drift_detected: false
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+  - path: content/learning-paths/embedded-and-microcontrollers/armds/_index.md
+    status: unchanged
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+    template_version_before: summary-faq-v1
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+      preview_before: Learn how to import and build example projects in Arm Development Studio and debug
+        embedded applications using Fixed Virtual Platforms (FVPs) or hardware with DSTREAM debug probes.
+        It is designed for ...
+      preview_after: Learn how to import and build example projects in Arm Development Studio and debug
+        embedded applications using Fixed Virtual Platforms (FVPs) or hardware with DSTREAM debug probes.
+        It is designed for ...
+      preview_generated: Learn how to import and build example projects in Arm Development Studio and
+        debug embedded applications using Fixed Virtual Platforms (FVPs) or hardware with DSTREAM debug
+        probes. It is designed for ...
+    faqs:
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+  - path: content/learning-paths/embedded-and-microcontrollers/asm/_index.md
+    status: unchanged
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+      generated_at_after: '2026-05-06T17:17:54Z'
+      preview_before: Learn how to write mixed C and assembly programs for Cortex-M microcontrollers using
+        Keil MDK, following Arm Procedure Call Standard conventions. It is designed for software developers
+        who are interes...
+      preview_after: Learn how to write mixed C and assembly programs for Cortex-M microcontrollers using
+        Keil MDK, following Arm Procedure Call Standard conventions. It is designed for software developers
+        who are interes...
+      preview_generated: Learn how to write mixed C and assembly programs for Cortex-M microcontrollers
+        using Keil MDK, following Arm Procedure Call Standard conventions. It is designed for software
+        developers who are interes...
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+  - path: content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
+    status: unchanged
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+      preview_before: Learn how to create a custom Balena OS image, run it on Arm Virtual Hardware, and
+        deploy IoT applications to a virtual Raspberry Pi 4 device. It is designed for embedded software
+        developers interested...
+      preview_after: Learn how to create a custom Balena OS image, run it on Arm Virtual Hardware, and
+        deploy IoT applications to a virtual Raspberry Pi 4 device. It is designed for embedded software
+        developers interested...
+      preview_generated: Learn how to create a custom Balena OS image, run it on Arm Virtual Hardware,
+        and deploy IoT applications to a virtual Raspberry Pi 4 device. It is designed for embedded software
+        developers interested...
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+  - path: content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
+    status: unchanged
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+      preview_before: Learn how to set up AWS IoT Greengrass Core on Arm Virtual Hardware and deploy AWS
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+        developers interested ...
+      preview_after: Learn how to set up AWS IoT Greengrass Core on Arm Virtual Hardware and deploy AWS
+        Greengrass components to a virtual Raspberry Pi 4 device. It is designed for embedded software
+        developers interested ...
+      preview_generated: Learn how to set up AWS IoT Greengrass Core on Arm Virtual Hardware and deploy
+        AWS Greengrass components to a virtual Raspberry Pi 4 device. It is designed for embedded software
+        developers interested ...
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+        for structured device access and Strands for agent orchestration, with examples for both simulated
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+        hardware options for software development on Arm Cortex-M processors. It is designed for software
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+      preview_before: Learn what differentiates TinyML from other AI domains, explore Arm-based edge devices
+        for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed Virtual
+        Platform. It is ...
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+        for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed Virtual
+        Platform. It is ...
+      preview_generated: Learn what differentiates TinyML from other AI domains, explore Arm-based edge
+        devices for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed
+        Virtual Platform. It is ...
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+        It is designe...
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+      preview_before: Learn how to bring up ExecuTorch executor_runner firmware on the NXP FRDM i.MX 93
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+        acceleration. It is d...
+      preview_generated: Learn how to bring up ExecuTorch executor_runner firmware on the NXP FRDM i.MX
+        93 Cortex-M33 using Linux RemoteProc, compile .pte models for Ethos-U65, and run inference with
+        NPU acceleration. It is d...
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+      preview_before: Learn how to migrate a CMSIS v5-based CMSIS-Pack with device support to CMSIS v6
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+      preview_after: Learn how to migrate a CMSIS v5-based CMSIS-Pack with device support to CMSIS v6
+        and update example projects for compatibility with the new CMSIS version. It is designed for maintainers
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+      preview_generated: Learn how to migrate a CMSIS v5-based CMSIS-Pack with device support to CMSIS
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+      preview_before: Learn how to migrate CMSIS v5 projects to CMSIS v6 by identifying supported toolchains,
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+        for embedded de...
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+      preview_before: Learn how to run large language models locally on the Raspberry Pi 5 using Ollama,
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+        cloud services. It ...
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+        control GPIO-connected devices, and deploy a privacy-first web-based smart home assistant without
+        cloud services. It ...
+      preview_generated: Learn how to run large language models locally on the Raspberry Pi 5 using Ollama,
+        control GPIO-connected devices, and deploy a privacy-first web-based smart home assistant without
+        cloud services. It ...
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+      preview_before: Learn how to build a voice-controlled bot on a Raspberry Pi that listens for a wake
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+        and plays audio resp...
+      preview_after: Learn how to build a voice-controlled bot on a Raspberry Pi that listens for a wake
+        word, converts speech to text using Google Speech Recognition, sends requests to ChatGPT's API,
+        and plays audio resp...
+      preview_generated: Learn how to build a voice-controlled bot on a Raspberry Pi that listens for
+        a wake word, converts speech to text using Google Speech Recognition, sends requests to ChatGPT's
+        API, and plays audio resp...
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+        for software...
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+      preview_before: Learn how to compile the Llama 3 large language model using ExecuTorch, deploy it
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+      preview_after: Learn how to compile the Llama 3 large language model using ExecuTorch, deploy it
+        to a Raspberry Pi 5, and understand techniques for running LLMs in embedded environments. It is
+        designed for anyone in...
+      preview_generated: Learn how to compile the Llama 3 large language model using ExecuTorch, deploy
+        it to a Raspberry Pi 5, and understand techniques for running LLMs in embedded environments. It
+        is designed for anyone in...
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+        software developers new to Raspberry Pi Pico. By the end, you will be able to install the Raspberry
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+        bottlenecks, analyze both out-of-tree and in-tree modules, and use Statistical Profiling Extension
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+        network models for micro...
+      preview_generated: Build a letter recognition neural network model using TensorFlow and deploy it
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+        on Arm Fixed Virtual Platforms for secure microcontroller development. It is designed for software
+        developers ...
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+        application on Arm Fixed Virtual Platforms for secure microcontroller development. It is designed
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+        for machine learnin...
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+        to ExecuTorch format, and run it as an interactive mini-game on Arm-based edge devices. It is
+        designed for machine learnin...
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+      preview_before: Learn how to install Keil MDK Tools, run a TrustZone hello world example on the
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+        It is designed for softwar...
+      preview_after: Learn how to install Keil MDK Tools, run a TrustZone hello world example on the NXP
+        LPCXpresso55S69 board, and understand security state switching and secure function calls. It is
+        designed for softwar...
+      preview_generated: Learn how to install Keil MDK Tools, run a TrustZone hello world example on the
+        NXP LPCXpresso55S69 board, and understand security state switching and secure function calls.
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+  - path: content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
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+      preview_before: Learn how to acquire and print materials, assemble a universal SBC rack mount system
+        in a 4U chassis, and install single board computers in the racks using 3D-printed parts. It is
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+        in a 4U chassis, and install single board computers in the racks using 3D-printed parts. It is
+        designed for softwar...
+      preview_generated: Learn how to acquire and print materials, assemble a universal SBC rack mount
+        system in a 4U chassis, and install single board computers in the racks using 3D-printed parts.
+        It is designed for softwar...
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+  - path: content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
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+      preview_before: Learn how to debug microcontrollers using µVision with basic run/stop debug, advanced
+        techniques using Event Recorder and Serial Wire Viewer, ETM Trace for performance analysis, and
+        power measurement ...
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+        techniques using Event Recorder and Serial Wire Viewer, ETM Trace for performance analysis, and
+        power measurement ...
+      preview_generated: Learn how to debug microcontrollers using µVision with basic run/stop debug,
+        advanced techniques using Event Recorder and Serial Wire Viewer, ETM Trace for performance analysis,
+        and power measurement ...
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+      preview_generated: Learn how to import, convert, and build uvprojx-based projects to csolution format
+        using Keil Studio, µVision, and command-line tools for CMSIS-Toolbox compatibility. It is designed
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+        for developers w...
+      preview_generated: Learn how to install and configure a Linux kernel with 64K page size support
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+        It is designed for ...
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+        who want to lea...
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+        engineers wh...
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+      preview_generated: Learn how to automate the creation of Azure Arm virtual machines using Terraform.
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+      preview_generated: Learn how to deploy Confidential Containers from encrypted images inside Arm
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+      preview_generated: Learn how to build and run an OrchardCore CMS .NET application on Azure Cobalt
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+      preview_generated: Learn how to create an AWS ECS cluster with Fargate and AWS Graviton processors,
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+      preview_generated: Learn how to provision an Amazon EKS cluster on Arm-based Graviton instances
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+        who wa...
+      preview_generated: Learn how to use docker buildx and docker manifest to build and deploy multi-architecture
+        container images with x86/amd64 and arm64 support on Amazon EKS. It is designed for software developers
+        who wa...
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+        and benchmark HTTP proxy performance with load testing. It is designed for This introductory topic
+        for software...
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+        instances and benchmark HTTP proxy performance with load testing. It is designed for This introductory
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+      preview_generated: Learn how to optimize Envoy proxy performance on Arm servers using Transparent
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+      preview_before: Learn how to identify and fix false sharing issues using Perf C2C cache line analysis
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+        JDK versions and performance optimization flags. It is designed for software developers who want
+        to learn how to run t...
+      preview_after: Deploy and optimize Java applications on Google Cloud Axion processors by testing
+        JDK versions and performance optimization flags. It is designed for software developers who want
+        to learn how to run t...
+      preview_generated: Deploy and optimize Java applications on Google Cloud Axion processors by testing
+        JDK versions and performance optimization flags. It is designed for software developers who want
+        to learn how to run t...
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+      preview_before: Deploy Java on Azure Cobalt 100 Arm virtual machines and benchmark application performance
+        with JMH microbenchmarks. It is designed for This is an introductory topic about Java deployment
+        and benchmar...
+      preview_after: Deploy Java on Azure Cobalt 100 Arm virtual machines and benchmark application performance
+        with JMH microbenchmarks. It is designed for This is an introductory topic about Java deployment
+        and benchmar...
+      preview_generated: Deploy Java on Azure Cobalt 100 Arm virtual machines and benchmark application
+        performance with JMH microbenchmarks. It is designed for This is an introductory topic about Java
+        deployment and benchmar...
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+      preview_before: Profile Java applications on Arm Neoverse servers using flame graphs generated with
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+      preview_after: Profile Java applications on Arm Neoverse servers using flame graphs generated with
+        async-profiler and Java agents to identify performance bottlenecks. It is designed for developers
+        who want to analyz...
+      preview_generated: Profile Java applications on Arm Neoverse servers using flame graphs generated
+        with async-profiler and Java agents to identify performance bottlenecks. It is designed for developers
+        who want to analyz...
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+  - path: content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
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+      preview_before: Deploy Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate installation,
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+      preview_after: Deploy Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate installation,
+        and execute Arm-native CI/CD pipelines including Docker workflows. It is designed for software
+        developers d...
+      preview_generated: Deploy Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate
+        installation, and execute Arm-native CI/CD pipelines including Docker workflows. It is designed
+        for software developers d...
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+  - path: content/learning-paths/servers-and-cloud-computing/kafka/_index.md
+    status: unchanged
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+      preview_before: Deploy and configure a Kafka cluster with Zookeeper on Arm servers, test event streaming,
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+      preview_after: Deploy and configure a Kafka cluster with Zookeeper on Arm servers, test event streaming,
+        and automate deployment on AWS and Google Cloud. It is designed for software developers who want
+        to learn how ...
+      preview_generated: Deploy and configure a Kafka cluster with Zookeeper on Arm servers, test event
+        streaming, and automate deployment on AWS and Google Cloud. It is designed for software developers
+        who want to learn how ...
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+  - path: content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
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+      preview_before: Deploy Apache Kafka on Azure Cobalt 100 Arm virtual machines and benchmark message
+        throughput performance. It is designed for developers looking to migrate their Apache Kafka workloads
+        from x86_64 to ...
+      preview_after: Deploy Apache Kafka on Azure Cobalt 100 Arm virtual machines and benchmark message
+        throughput performance. It is designed for developers looking to migrate their Apache Kafka workloads
+        from x86_64 to ...
+      preview_generated: Deploy Apache Kafka on Azure Cobalt 100 Arm virtual machines and benchmark message
+        throughput performance. It is designed for developers looking to migrate their Apache Kafka workloads
+        from x86_64 to ...
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+  - path: content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
+    status: unchanged
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+      preview_before: Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing Kedify
+        and KEDA with Helm and testing autoscaling behavior. It is designed for developers running HTTP
+        workloads on Kuber...
+      preview_after: Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing Kedify
+        and KEDA with Helm and testing autoscaling behavior. It is designed for developers running HTTP
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+      preview_generated: Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing
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+  - path: content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
+    status: unchanged
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+      preview_before: Create, train, and evaluate a neural network model on Arm servers using Keras Core
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+      preview_after: Create, train, and evaluate a neural network model on Arm servers using Keras Core
+        with TensorFlow, PyTorch, and JAX backends. It is designed for engineers who want to create a
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+      preview_generated: Create, train, and evaluate a neural network model on Arm servers using Keras
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+  - path: content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
+    status: unchanged
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+      preview_before: Compile and install custom Linux kernels on Arm cloud instances using TuxMake with
+        configurations for 64 KB page sizes and Fastpath testing. It is designed for software developers
+        building custom Linu...
+      preview_after: Compile and install custom Linux kernels on Arm cloud instances using TuxMake with
+        configurations for 64 KB page sizes and Fastpath testing. It is designed for software developers
+        building custom Linu...
+      preview_generated: Compile and install custom Linux kernels on Arm cloud instances using TuxMake
+        with configurations for 64 KB page sizes and Fastpath testing. It is designed for software developers
+        building custom Linu...
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+      preview_generated: Benchmark machine learning inference performance on Arm servers using TensorFlow
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+        looking to dep...
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+        looking to dep...
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+        an end-to-end Arm software workflow. It is designed for developers, ML engineers, and cloud practitioners
+        looking to dep...
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+        applications on Arm-bas...
+      preview_after: Deploy SqueezeNet 1.0 INT8 model with ONNX Runtime on Azure Cobalt 100 walks you
+        through an end-to-end Arm software workflow. It is designed for developers deploying ONNX-based
+        applications on Arm-bas...
+      preview_generated: Deploy SqueezeNet 1.0 INT8 model with ONNX Runtime on Azure Cobalt 100 walks
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+        applications on Arm-bas...
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+  - path: content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md
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+        software engi...
+      preview_after: Simulate OpenBMC and UEFI pre-silicon on Neoverse RD-V3 walks you through an end-to-end
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+        software engi...
+      preview_generated: Simulate OpenBMC and UEFI pre-silicon on Neoverse RD-V3 walks you through an
+        end-to-end Arm software workflow. It is designed for This advanced topic is for firmware developers,
+        platform software engi...
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+  - path: content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md
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+        Performix, then accelerate random number generation using OpenRNG and Arm Performance Libraries.
+        It is designed for C...
+      preview_generated: Learn how to profile an example C++ data-processing workload on Arm Linux with
+        Arm Performix, then accelerate random number generation using OpenRNG and Arm Performance Libraries.
+        It is designed for C...
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+  - path: content/learning-paths/servers-and-cloud-computing/openshift/_index.md
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+        you through an end-to-end Arm software workflow. It is designed for OpenShift administrators who
+        want to migrate the...
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+        you through an end-to-end Arm software workflow. It is designed for OpenShift administrators who
+        want to migrate the...
+      preview_generated: Build multi-architecture applications with Red Hat OpenShift Pipelines on AWS
+        walks you through an end-to-end Arm software workflow. It is designed for OpenShift administrators
+        who want to migrate the...
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+      preview_before: Deploy OpenStack on Azure Cobalt 100 Arm64 virtual machines using DevStack for development
+        and Kolla-Ansible for containerized production deployments. It is designed for This learning path
+        is designed...
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+        and Kolla-Ansible for containerized production deployments. It is designed for This learning path
+        is designed...
+      preview_generated: Deploy OpenStack on Azure Cobalt 100 Arm64 virtual machines using DevStack for
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+        learning path is designed...
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+  - path: content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md
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+      preview_before: Deploy OpenTelemetry on Google Cloud C4A Axion processors walks you through an end-to-end
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+        who wa...
+      preview_after: Deploy OpenTelemetry on Google Cloud C4A Axion processors walks you through an end-to-end
+        Arm software workflow. It is designed for DevOps engineers, platform engineers, and software developers
+        who wa...
+      preview_generated: Deploy OpenTelemetry on Google Cloud C4A Axion processors walks you through an
+        end-to-end Arm software workflow. It is designed for DevOps engineers, platform engineers, and
+        software developers who wa...
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+  - path: content/learning-paths/servers-and-cloud-computing/pac/_index.md
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+      preview_before: Run the vvenc H.266 encoder on Arm servers walks you through an end-to-end Arm software
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+      preview_generated: Accelerate Whisper on Arm with Hugging Face Transformers walks you through an
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+        learning concepts and...
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+  - path: content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
+    status: unchanged
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+      generated_at_before: '2026-05-06T17:17:59Z'
+      generated_at_after: '2026-05-06T17:17:59Z'
+      preview_before: Deploy MySQL and WordPress on an always free tier Arm shape walks you through an
+        end-to-end Arm software workflow. It is designed for developers who want to install WordPress
+        on Oracle Cloud Infrastru...
+      preview_after: Deploy MySQL and WordPress on an always free tier Arm shape walks you through an
+        end-to-end Arm software workflow. It is designed for developers who want to install WordPress
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+      preview_generated: Deploy MySQL and WordPress on an always free tier Arm shape walks you through
+        an end-to-end Arm software workflow. It is designed for developers who want to install WordPress
+        on Oracle Cloud Infrastru...
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+  - path: content/learning-paths/servers-and-cloud-computing/zlib/_index.md
+    status: unchanged
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+      generated_at_before: '2026-05-06T17:17:59Z'
+      generated_at_after: '2026-05-06T17:17:59Z'
+      preview_before: Learn how to build and use zlib-ng on Arm servers, using its Neon SIMD and ARMv8
+        CRC32 optimizations to improve compression performance compared to the system default zlib. It
+        is designed for software...
+      preview_after: Learn how to build and use zlib-ng on Arm servers, using its Neon SIMD and ARMv8
+        CRC32 optimizations to improve compression performance compared to the system default zlib. It
+        is designed for software...
+      preview_generated: Learn how to build and use zlib-ng on Arm servers, using its Neon SIMD and ARMv8
+        CRC32 optimizations to improve compression performance compared to the system default zlib. It
+        is designed for software...
+    faqs:
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From c8dfa7c2a900d34d3ce54247db7861750f9b8298 Mon Sep 17 00:00:00 2001
From: Christopher Moroney <christopher.moroney1@gmail.com>
Date: Wed, 6 May 2026 11:50:06 -0700
Subject: [PATCH 06/23] test new flags and config

---
 .../learning-paths/servers-and-cloud-computing/bolt/_index.md  | 3 ++-
 .../servers-and-cloud-computing/django/_index.md               | 3 ++-
 .../servers-and-cloud-computing/nginx_tune/_index.md           | 3 ++-
 .../servers-and-cloud-computing/tensorflow-gcp/_index.md       | 3 ++-
 4 files changed, 8 insertions(+), 4 deletions(-)

diff --git a/content/learning-paths/servers-and-cloud-computing/bolt/_index.md b/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
index c982ad01c0..4d8da0b2ed 100644
--- a/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
@@ -16,6 +16,8 @@ prerequisites:
     - (Optional) A second, more powerful Linux system to build the software executable and run BOLT.
 
 generate_summary_faq: true
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
@@ -96,4 +98,3 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
-
diff --git a/content/learning-paths/servers-and-cloud-computing/django/_index.md b/content/learning-paths/servers-and-cloud-computing/django/_index.md
index 63376e7602..6bf8976122 100644
--- a/content/learning-paths/servers-and-cloud-computing/django/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/django/_index.md
@@ -18,6 +18,8 @@ prerequisites:
     - To install both [Nginx](/learning-paths/servers-and-cloud-computing/nginx/) and [PostgreSQL](/learning-paths/servers-and-cloud-computing/postgresql/)
 
 generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
@@ -105,4 +107,3 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
-
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
index 34d1a3a222..63f74fc3c4 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
@@ -17,6 +17,8 @@ prerequisites:
     - If you do not already have a Nginx setup, a review of [Learn how to deploy Nginx](/learning-paths/servers-and-cloud-computing/nginx/).
 
 generate_summary_faq: true
+rerun_summary: true
+rerun_faqs: false
 
 # START generated_summary_faq
 generated_summary_faq:
@@ -93,4 +95,3 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
-
diff --git a/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
index 14f027cd9a..2a427fbf13 100644
--- a/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
@@ -15,6 +15,8 @@ prerequisites:
   - Basic familiarity with [TensorFlow](https://www.tensorflow.org/)
 
 generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 # START generated_summary_faq
 generated_summary_faq:
@@ -101,4 +103,3 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
-

From c723aed9efa9129f33294ea88a6540645c16c7ba Mon Sep 17 00:00:00 2001
From: GitHub Actions Summary FAQ Bot <>
Date: Wed, 6 May 2026 18:52:22 +0000
Subject: [PATCH 07/23] Generate Learning Path summary and FAQ content

---
 .../bolt/_index.md                            |    13 +-
 .../django/_index.md                          |    11 +-
 .../nginx_tune/_index.md                      |    11 +-
 .../tensorflow-gcp/_index.md                  |     1 +
 reports/generated-summary-faq/latest-run.yml  | 22135 +++++++++++++++-
 5 files changed, 22116 insertions(+), 55 deletions(-)

diff --git a/content/learning-paths/servers-and-cloud-computing/bolt/_index.md b/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
index 4d8da0b2ed..16862039d9 100644
--- a/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
@@ -16,15 +16,19 @@ prerequisites:
     - (Optional) A second, more powerful Linux system to build the software executable and run BOLT.
 
 generate_summary_faq: true
-rerun_summary: true
-rerun_faqs: true
+rerun_summary: false
+rerun_faqs: false
 
 # START generated_summary_faq
 generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
+  template_version: summary-faq-v2
+  generated_at: '2026-05-06T18:52:13Z'
   generator: template
   source_hash: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
+  summary_generated_at: '2026-05-06T18:52:13Z'
+  summary_source_hash: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
+  faq_generated_at: '2026-05-06T18:52:13Z'
+  faq_source_hash: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
   summary: >-
     Learn how to build, profile, and optimize Arm executables using BOLT post-link binary optimization
     to improve application performance through code layout improvements. It is designed for software
@@ -98,3 +102,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/django/_index.md b/content/learning-paths/servers-and-cloud-computing/django/_index.md
index 6bf8976122..2f7839c214 100644
--- a/content/learning-paths/servers-and-cloud-computing/django/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/django/_index.md
@@ -19,14 +19,18 @@ prerequisites:
 
 generate_summary_faq: true
 rerun_summary: false
-rerun_faqs: true
+rerun_faqs: false
 
 # START generated_summary_faq
 generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
+  template_version: summary-faq-v2
+  generated_at: '2026-05-06T18:52:13Z'
   generator: template
   source_hash: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
+  summary_generated_at: '2026-05-06T17:17:57Z'
+  summary_source_hash: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
+  faq_generated_at: '2026-05-06T18:52:13Z'
+  faq_source_hash: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
   summary: >-
     Learn how to create a simple Django web application and deploy it on Arm machines using Nginx
     and PostgreSQL. It is designed for engineers who want to deploy a Django based application
@@ -107,3 +111,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
index 63f74fc3c4..161be7dc82 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
@@ -17,15 +17,19 @@ prerequisites:
     - If you do not already have a Nginx setup, a review of [Learn how to deploy Nginx](/learning-paths/servers-and-cloud-computing/nginx/).
 
 generate_summary_faq: true
-rerun_summary: true
+rerun_summary: false
 rerun_faqs: false
 
 # START generated_summary_faq
 generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
+  template_version: summary-faq-v2
+  generated_at: '2026-05-06T18:52:14Z'
   generator: template
   source_hash: 10f13dee3459577fcadf5966cf80d990f3396321189f638432a3308699e773a8
+  summary_generated_at: '2026-05-06T18:52:14Z'
+  summary_source_hash: 10f13dee3459577fcadf5966cf80d990f3396321189f638432a3308699e773a8
+  faq_generated_at: '2026-05-06T17:17:58Z'
+  faq_source_hash: 10f13dee3459577fcadf5966cf80d990f3396321189f638432a3308699e773a8
   summary: >-
     Learn how to tune Nginx walks you through an end-to-end Arm software workflow. It is designed
     for software developers who want to use Nginx on Arm. By the end, you will be able to describe
@@ -95,3 +99,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
index 2a427fbf13..9e3a48a6b0 100644
--- a/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
@@ -103,3 +103,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/reports/generated-summary-faq/latest-run.yml b/reports/generated-summary-faq/latest-run.yml
index 4ddbd31f5d..8753b5adca 100644
--- a/reports/generated-summary-faq/latest-run.yml
+++ b/reports/generated-summary-faq/latest-run.yml
@@ -1,48 +1,48 @@
 latest_run:
-  timestamp: '2026-05-06T18:45:07Z'
+  timestamp: '2026-05-06T18:52:15Z'
   mode: write
   require_enable_flag: true
   path_filter: ''
   limit: 0
-  run_url: https://github.com/chrismoroney/arm-learning-paths/actions/runs/25454338790
+  run_url: https://github.com/chrismoroney/arm-learning-paths/actions/runs/25454686544
   git_ref: STESOL-345
-  git_sha: c9c4fdb3b9ac4d10e42706e5cf13a96da575319c
+  git_sha: d9b0c1ddc48efd31a96a1eb7400b86c6776f0f88
   actor: chrismoroney
   template_version: summary-faq-v2
   totals:
     processed: 407
     added: 0
-    updated: 0
-    unchanged: 407
+    updated: 4
+    unchanged: 403
     drift_detected: 0
     skipped: 0
     errors: 0
     removed: 0
     summary_changed: 0
     faq_changed: 0
-    rerun_flags_reset: 0
+    rerun_flags_reset: 3
   section_totals:
     summary:
       created: 0
       repaired_missing: 0
-      rerun_requested: 0
+      rerun_requested: 2
       drift_detected_preserved: 0
-      unchanged: 407
+      unchanged: 405
     faqs:
       created: 0
       repaired_missing: 0
-      rerun_requested: 0
+      rerun_requested: 2
       drift_detected_preserved: 0
-      unchanged: 407
+      unchanged: 405
   reason_totals:
     initial_generation: 0
     missing_summary: 0
     missing_faqs: 0
-    rerun_summary: 0
-    rerun_faqs: 0
+    rerun_summary: 2
+    rerun_faqs: 2
     summary_drift_detected: 0
     faq_drift_detected: 0
-    rerun_flags_reset: 0
+    rerun_flags_reset: 3
   paths:
   - path: content/learning-paths/automotive/openadkit1_container/_index.md
     status: unchanged
@@ -12087,24 +12087,29 @@ latest_run:
         removed_questions: []
         updated_questions: []
   - path: content/learning-paths/servers-and-cloud-computing/bolt/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    rerun_flags_reset:
+    - rerun_summary
+    - rerun_faqs
+    change_reasons:
+    - rerun_summary
+    - rerun_faqs
+    - rerun_flags_reset
     template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    template_version_after: summary-faq-v2
     summary:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
+      rerun_requested: true
       changed: false
       drift_detected: false
       source_hash_before: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
       source_hash_after: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
       current_source_hash: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
       generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_after: '2026-05-06T18:52:13Z'
       preview_before: Learn how to build, profile, and optimize Arm executables using BOLT post-link binary
         optimization to improve application performance through code layout improvements. It is designed
         for software deve...
@@ -12115,16 +12120,16 @@ latest_run:
         binary optimization to improve application performance through code layout improvements. It is
         designed for software deve...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
+      rerun_requested: true
       changed: false
       drift_detected: false
       source_hash_before: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
       source_hash_after: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
       current_source_hash: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
       generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_after: '2026-05-06T18:52:13Z'
       before_count: 5
       after_count: 5
       generated_count: 5
@@ -13653,13 +13658,16 @@ latest_run:
         removed_questions: []
         updated_questions: []
   - path: content/learning-paths/servers-and-cloud-computing/django/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
     template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    template_version_after: summary-faq-v2
     summary:
       action: unchanged
       missing_before: false
@@ -13681,16 +13689,16 @@ latest_run:
         using Nginx and PostgreSQL. It is designed for engineers who want to deploy a Django based application
         on Arm machines...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
+      rerun_requested: true
       changed: false
       drift_detected: false
       source_hash_before: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
       source_hash_after: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
       current_source_hash: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
       generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_after: '2026-05-06T18:52:13Z'
       before_count: 5
       after_count: 5
       generated_count: 5
@@ -18135,24 +18143,27 @@ latest_run:
         removed_questions: []
         updated_questions: []
   - path: content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    rerun_flags_reset:
+    - rerun_summary
+    change_reasons:
+    - rerun_summary
+    - rerun_flags_reset
     template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    template_version_after: summary-faq-v2
     summary:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
+      rerun_requested: true
       changed: false
       drift_detected: false
       source_hash_before: 10f13dee3459577fcadf5966cf80d990f3396321189f638432a3308699e773a8
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       current_source_hash: 10f13dee3459577fcadf5966cf80d990f3396321189f638432a3308699e773a8
       generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_after: '2026-05-06T18:52:14Z'
       preview_before: Learn how to tune Nginx walks you through an end-to-end Arm software workflow. It
         is designed for software developers who want to use Nginx on Arm. By the end, you will be able
         to describe how kernel ...
@@ -21051,8 +21062,8 @@ latest_run:
         removed_questions: []
         updated_questions: []
   - path: content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
-    status: unchanged
-    changed_on_disk: false
+    status: updated
+    changed_on_disk: true
     managed_block_updated: false
     rerun_flags_reset: []
     change_reasons: []
@@ -22023,6 +22034,22040 @@ latest_run:
         removed_questions: []
         updated_questions: []
 history:
+- timestamp: '2026-05-06T18:52:15Z'
+  mode: write
+  require_enable_flag: true
+  path_filter: ''
+  limit: 0
+  run_url: https://github.com/chrismoroney/arm-learning-paths/actions/runs/25454686544
+  git_ref: STESOL-345
+  git_sha: d9b0c1ddc48efd31a96a1eb7400b86c6776f0f88
+  actor: chrismoroney
+  template_version: summary-faq-v2
+  totals:
+    processed: 407
+    added: 0
+    updated: 4
+    unchanged: 403
+    drift_detected: 0
+    skipped: 0
+    errors: 0
+    removed: 0
+    summary_changed: 0
+    faq_changed: 0
+    rerun_flags_reset: 3
+  section_totals:
+    summary:
+      created: 0
+      repaired_missing: 0
+      rerun_requested: 2
+      drift_detected_preserved: 0
+      unchanged: 405
+    faqs:
+      created: 0
+      repaired_missing: 0
+      rerun_requested: 2
+      drift_detected_preserved: 0
+      unchanged: 405
+  reason_totals:
+    initial_generation: 0
+    missing_summary: 0
+    missing_faqs: 0
+    rerun_summary: 2
+    rerun_faqs: 2
+    summary_drift_detected: 0
+    faq_drift_detected: 0
+    rerun_flags_reset: 3
+  paths:
+  - path: content/learning-paths/automotive/openadkit1_container/_index.md
+    status: unchanged
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons: []
+    template_version_before: summary-faq-v1
+    template_version_after: summary-faq-v1
+    summary:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
+      source_hash_after: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
+      current_source_hash: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
+      generated_at_before: '2026-05-06T17:17:53Z'
+      generated_at_after: '2026-05-06T17:17:53Z'
+      preview_before: Learn how to deploy and run containerized autonomous driving simulations using Autoware
+        Open AD Kit on Arm Neoverse with Docker, demonstrating SOAFEE-based Shift-Left development workflows.
+        It is desi...
+      preview_after: Learn how to deploy and run containerized autonomous driving simulations using Autoware
+        Open AD Kit on Arm Neoverse with Docker, demonstrating SOAFEE-based Shift-Left development workflows.
+        It is desi...
+      preview_generated: Learn how to deploy and run containerized autonomous driving simulations using
+        Autoware Open AD Kit on Arm Neoverse with Docker, demonstrating SOAFEE-based Shift-Left development
+        workflows. It is desi...
+    faqs:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
+      source_hash_after: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
+      current_source_hash: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
+      generated_at_before: '2026-05-06T17:17:53Z'
+      generated_at_after: '2026-05-06T17:17:53Z'
+      before_count: 5
+      after_count: 5
+      generated_count: 5
+      change_details:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+      generated_diff:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+  - path: content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
+    status: unchanged
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons: []
+    template_version_before: summary-faq-v1
+    template_version_after: summary-faq-v1
+    summary:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: 92a6dac2b1674a44cd623a0c8c3189b38438124a6067ed7ca776999ff1d8b5bf
+      source_hash_after: 92a6dac2b1674a44cd623a0c8c3189b38438124a6067ed7ca776999ff1d8b5bf
+      current_source_hash: 92a6dac2b1674a44cd623a0c8c3189b38438124a6067ed7ca776999ff1d8b5bf
+      generated_at_before: '2026-05-06T17:17:53Z'
+      generated_at_after: '2026-05-06T17:17:53Z'
+      preview_before: Learn how to implement functional safety isolation for autonomous driving systems
+        on Arm Neoverse using DDS-based communication, containerized deployment, and ISO 26262 compliance
+        principles. It is de...
+      preview_after: Learn how to implement functional safety isolation for autonomous driving systems
+        on Arm Neoverse using DDS-based communication, containerized deployment, and ISO 26262 compliance
+        principles. It is de...
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+      preview_generated: Learn how to build and deploy distributed Zenoh systems on Arm devices like Raspberry
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+      preview_after: Learn how to design and integrate a custom AXI-Lite peripheral with a Cortex-A9 processor
+        on the Zybo Z7-10 board using Vivado, configuring GPIOs to control LEDs based on switch inputs.
+        It is designed...
+      preview_generated: Learn how to design and integrate a custom AXI-Lite peripheral with a Cortex-A9
+        processor on the Zybo Z7-10 board using Vivado, configuring GPIOs to control LEDs based on switch
+        inputs. It is designed...
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+      preview_before: Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit and
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+        a neural network model t...
+      preview_after: Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit and
+        run inference on the Ethos-U85 NPU. It is designed for embedded developers who want to deploy
+        a neural network model t...
+      preview_generated: Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit
+        and run inference on the Ethos-U85 NPU. It is designed for embedded developers who want to deploy
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+        using Arduino IDE, PIR sensors, and interrupt-driven programming on baremetal. It is designed
+        for software devel...
+      preview_generated: Learn how to build a motion-detection device with Raspberry Pi Pico (RP2040 Cortex-M0+)
+        using Arduino IDE, PIR sensors, and interrupt-driven programming on baremetal. It is designed
+        for software devel...
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+      preview_before: Learn how to import and build example projects in Arm Development Studio and debug
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+        It is designed for ...
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+        embedded applications using Fixed Virtual Platforms (FVPs) or hardware with DSTREAM debug probes.
+        It is designed for ...
+      preview_generated: Learn how to import and build example projects in Arm Development Studio and
+        debug embedded applications using Fixed Virtual Platforms (FVPs) or hardware with DSTREAM debug
+        probes. It is designed for ...
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+      preview_before: Learn how to write mixed C and assembly programs for Cortex-M microcontrollers using
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+        Keil MDK, following Arm Procedure Call Standard conventions. It is designed for software developers
+        who are interes...
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+        using Keil MDK, following Arm Procedure Call Standard conventions. It is designed for software
+        developers who are interes...
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+  - path: content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
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+      preview_before: Learn how to create a custom Balena OS image, run it on Arm Virtual Hardware, and
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+        deploy IoT applications to a virtual Raspberry Pi 4 device. It is designed for embedded software
+        developers interested...
+      preview_generated: Learn how to create a custom Balena OS image, run it on Arm Virtual Hardware,
+        and deploy IoT applications to a virtual Raspberry Pi 4 device. It is designed for embedded software
+        developers interested...
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+  - path: content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
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+      preview_before: Learn how to set up AWS IoT Greengrass Core on Arm Virtual Hardware and deploy AWS
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+        developers interested ...
+      preview_after: Learn how to set up AWS IoT Greengrass Core on Arm Virtual Hardware and deploy AWS
+        Greengrass components to a virtual Raspberry Pi 4 device. It is designed for embedded software
+        developers interested ...
+      preview_generated: Learn how to set up AWS IoT Greengrass Core on Arm Virtual Hardware and deploy
+        AWS Greengrass components to a virtual Raspberry Pi 4 device. It is designed for embedded software
+        developers interested ...
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+  - path: content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
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+      preview_before: Learn how to build Matter reference examples on Arm Virtual Hardware, demonstrate
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+      preview_after: Learn how to build Matter reference examples on Arm Virtual Hardware, demonstrate
+        device communication, and automate testing with GitHub Actions CI/CD workflows. It is designed
+        for embedded software d...
+      preview_generated: Learn how to build Matter reference examples on Arm Virtual Hardware, demonstrate
+        device communication, and automate testing with GitHub Actions CI/CD workflows. It is designed
+        for embedded software d...
+    faqs:
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+      preview_generated: Learn how to prototype and port DSP algorithms using the CMSIS-DSP Python package,
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+      preview_before: Learn how to implement context switching operations on Arm Cortex-M processors using
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+      preview_before: Device-to-Device communication with Device Connect walks you through an end-to-end
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+      preview_before: Learn how to connect AI agents to Arm-based edge devices using Device Connect for
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+        and physical robots. It...
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+        for structured device access and Strands for agent orchestration, with examples for both simulated
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+      preview_before: Learn how to create a Dockerfile, build a Docker image with Arm Compiler for Embedded
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+        commands. It is designed...
+      preview_generated: Learn how to collect and preprocess audio data using Edge Impulse, train an audio
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+        commands. It is designed...
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+        developers worki...
+      preview_generated: Learn where Arm architecture is used in microcontrollers and discover microcontroller
+        hardware options for software development on Arm Cortex-M processors. It is designed for software
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+      preview_before: Learn what differentiates TinyML from other AI domains, explore Arm-based edge devices
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+        for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed Virtual
+        Platform. It is ...
+      preview_generated: Learn what differentiates TinyML from other AI domains, explore Arm-based edge
+        devices for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed
+        Virtual Platform. It is ...
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+  - path: content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
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+      preview_generated: Learn how to create debug configurations for virtual platforms and development
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+      preview_before: Learn how to bring up ExecuTorch executor_runner firmware on the NXP FRDM i.MX 93
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+      preview_after: Learn how to bring up ExecuTorch executor_runner firmware on the NXP FRDM i.MX 93
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+      preview_generated: Learn how to bring up ExecuTorch executor_runner firmware on the NXP FRDM i.MX
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+      preview_before: Learn how to migrate a CMSIS v5-based CMSIS-Pack with device support to CMSIS v6
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+      preview_generated: Learn how to migrate CMSIS v5 projects to CMSIS v6 by identifying supported toolchains,
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+        control GPIO-connected devices, and deploy a privacy-first web-based smart home assistant without
+        cloud services. It ...
+      preview_generated: Learn how to run large language models locally on the Raspberry Pi 5 using Ollama,
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+      preview_before: Learn how to build a voice-controlled bot on a Raspberry Pi that listens for a wake
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+        and plays audio resp...
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+        word, converts speech to text using Google Speech Recognition, sends requests to ChatGPT's API,
+        and plays audio resp...
+      preview_generated: Learn how to build a voice-controlled bot on a Raspberry Pi that listens for
+        a wake word, converts speech to text using Google Speech Recognition, sends requests to ChatGPT's
+        API, and plays audio resp...
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+      preview_generated: Learn how to compile the Llama 3 large language model using ExecuTorch, deploy
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+      preview_before: Learn how to reduce compile time for embedded Linux projects by installing a Raspberry
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+        it to a Raspb...
+      preview_after: Learn how to reduce compile time for embedded Linux projects by installing a Raspberry
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+        it to a Raspb...
+      preview_generated: Learn how to reduce compile time for embedded Linux projects by installing a
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+      preview_generated: Build a letter recognition neural network model using TensorFlow and deploy it
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+        developers ...
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+        designed for machine learnin...
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+      preview_before: Learn how to install Keil MDK Tools, run a TrustZone hello world example on the
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+        designed for softwar...
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+        It is designed for softwar...
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+      preview_before: Learn how to acquire and print materials, assemble a universal SBC rack mount system
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+        in a 4U chassis, and install single board computers in the racks using 3D-printed parts. It is
+        designed for softwar...
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+        system in a 4U chassis, and install single board computers in the racks using 3D-printed parts.
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+  - path: content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
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+      preview_before: Learn how to debug microcontrollers using µVision with basic run/stop debug, advanced
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+        techniques using Event Recorder and Serial Wire Viewer, ETM Trace for performance analysis, and
+        power measurement ...
+      preview_generated: Learn how to debug microcontrollers using µVision with basic run/stop debug,
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+  - path: content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
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+      preview_before: Learn how to import, convert, and build uvprojx-based projects to csolution format
+        using Keil Studio, µVision, and command-line tools for CMSIS-Toolbox compatibility. It is designed
+        for This is a topi...
+      preview_after: Learn how to import, convert, and build uvprojx-based projects to csolution format
+        using Keil Studio, µVision, and command-line tools for CMSIS-Toolbox compatibility. It is designed
+        for This is a topi...
+      preview_generated: Learn how to import, convert, and build uvprojx-based projects to csolution format
+        using Keil Studio, µVision, and command-line tools for CMSIS-Toolbox compatibility. It is designed
+        for This is a topi...
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+        from user space using PAPI, perf_event_open, and assembly code for performance instrumentation.
+        It is designed for ...
+      preview_generated: Learn how to access and use Arm hardware performance counters and the system
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+        who want to lea...
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+        who want to lea...
+      preview_generated: Learn how to package multi-architecture container applications and deploy them
+        on AWS Fargate with Graviton processors using the AWS Copilot CLI. It is designed for software
+        developers who want to lea...
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+      preview_generated: Learn how to automate the creation and deployment of AWS Graviton instances using
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+        engineers wh...
+      preview_generated: Learn how to create and deploy Azure Resource Manager templates to provision
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+        and DevOps engineers wh...
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+        It is designed for software developers who are new to deploying Arm instances on Azure using Terraform.
+        By the end, yo...
+      preview_generated: Learn how to automate the creation of Azure Arm virtual machines using Terraform.
+        It is designed for software developers who are new to deploying Arm instances on Azure using Terraform.
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+      preview_before: Learn how to implement and benchmark bitmap scanning operations for database workloads
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+      preview_generated: Learn how to implement and benchmark bitmap scanning operations for database
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+      preview_generated: Learn how to deploy Confidential Containers from encrypted images inside Arm
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+        who want to run...
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+      preview_generated: Learn how to install and run Clair on Arm servers using combined and distributed
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+      preview_generated: Learn how to create an AWS ECS cluster with Fargate and AWS Graviton processors,
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+        and benchmar...
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+        with JMH microbenchmarks. It is designed for This is an introductory topic about Java deployment
+        and benchmar...
+      preview_generated: Deploy Java on Azure Cobalt 100 Arm virtual machines and benchmark application
+        performance with JMH microbenchmarks. It is designed for This is an introductory topic about Java
+        deployment and benchmar...
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+      preview_before: Profile Java applications on Arm Neoverse servers using flame graphs generated with
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+      preview_after: Profile Java applications on Arm Neoverse servers using flame graphs generated with
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+        who want to analyz...
+      preview_generated: Profile Java applications on Arm Neoverse servers using flame graphs generated
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+      preview_after: Deploy Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate installation,
+        and execute Arm-native CI/CD pipelines including Docker workflows. It is designed for software
+        developers d...
+      preview_generated: Deploy Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate
+        installation, and execute Arm-native CI/CD pipelines including Docker workflows. It is designed
+        for software developers d...
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+      preview_before: Deploy and configure a Kafka cluster with Zookeeper on Arm servers, test event streaming,
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+      preview_after: Deploy and configure a Kafka cluster with Zookeeper on Arm servers, test event streaming,
+        and automate deployment on AWS and Google Cloud. It is designed for software developers who want
+        to learn how ...
+      preview_generated: Deploy and configure a Kafka cluster with Zookeeper on Arm servers, test event
+        streaming, and automate deployment on AWS and Google Cloud. It is designed for software developers
+        who want to learn how ...
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+      preview_before: Deploy Apache Kafka on Azure Cobalt 100 Arm virtual machines and benchmark message
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+        from x86_64 to ...
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+        throughput performance. It is designed for developers looking to migrate their Apache Kafka workloads
+        from x86_64 to ...
+      preview_generated: Deploy Apache Kafka on Azure Cobalt 100 Arm virtual machines and benchmark message
+        throughput performance. It is designed for developers looking to migrate their Apache Kafka workloads
+        from x86_64 to ...
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+      preview_before: Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing Kedify
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+      preview_after: Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing Kedify
+        and KEDA with Helm and testing autoscaling behavior. It is designed for developers running HTTP
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+      preview_generated: Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing
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+      preview_after: Create, train, and evaluate a neural network model on Arm servers using Keras Core
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+      preview_generated: Create, train, and evaluate a neural network model on Arm servers using Keras
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+  - path: content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
+    status: unchanged
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+      preview_after: Compile and install custom Linux kernels on Arm cloud instances using TuxMake with
+        configurations for 64 KB page sizes and Fastpath testing. It is designed for software developers
+        building custom Linu...
+      preview_generated: Compile and install custom Linux kernels on Arm cloud instances using TuxMake
+        with configurations for 64 KB page sizes and Fastpath testing. It is designed for software developers
+        building custom Linu...
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+  - path: content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
+    status: unchanged
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+      preview_before: Identify and migrate container images in a Kubernetes cluster to Arm-compatible
+        versions using KubeArchInspect reports. It is designed for software developers who want to ensure
+        containers running in ...
+      preview_after: Identify and migrate container images in a Kubernetes cluster to Arm-compatible versions
+        using KubeArchInspect reports. It is designed for software developers who want to ensure containers
+        running in ...
+      preview_generated: Identify and migrate container images in a Kubernetes cluster to Arm-compatible
+        versions using KubeArchInspect reports. It is designed for software developers who want to ensure
+        containers running in ...
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+        interested in benchmark...
+      preview_generated: Benchmark machine learning inference performance on Arm servers using TensorFlow
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+      preview_before: Install MongoDB on Arm servers and benchmark database performance using Yahoo Cloud
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+        Serving Benchmark (YCSB) to compare against other architectures. It is designed for software developers
+        who want to ...
+      preview_generated: Install MongoDB on Arm servers and benchmark database performance using Yahoo
+        Cloud Serving Benchmark (YCSB) to compare against other architectures. It is designed for software
+        developers who want to ...
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+        is designed...
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+        is designed...
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+        who wa...
+      preview_generated: Deploy OpenTelemetry on Google Cloud C4A Axion processors walks you through an
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+      preview_generated: Understand Arm Pointer Authentication walks you through an end-to-end Arm software
+        workflow. It is designed for software developers interested in understanding Arm Pointer Authentication.
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 - timestamp: '2026-05-06T18:45:07Z'
   mode: write
   require_enable_flag: true

From fb9a9decd347924be14e4503865f03b0b2d990e4 Mon Sep 17 00:00:00 2001
From: Christopher Moroney <christopher.moroney1@gmail.com>
Date: Wed, 6 May 2026 12:03:25 -0700
Subject: [PATCH 08/23] consistency tag with all lps

---
 content/learning-paths/automotive/intro/_index.md               | 2 ++
 .../learning-paths/automotive/openadkit1_container/_index.md    | 2 ++
 .../automotive/openadkit2_safetyisolation/_index.md             | 2 ++
 content/learning-paths/automotive/system76-auto/_index.md       | 2 ++
 content/learning-paths/automotive/zenacssdebug/_index.md        | 2 ++
 .../cross-platform/_example-learning-path/_index.md             | 2 ++
 content/learning-paths/cross-platform/adler32/_index.md         | 2 ++
 .../cross-platform/automate-mcp-with-testcontainers/_index.md   | 2 ++
 content/learning-paths/cross-platform/avh_cicd/_index.md        | 2 ++
 content/learning-paths/cross-platform/avh_cicd2/_index.md       | 2 ++
 content/learning-paths/cross-platform/cca_rme/_index.md         | 2 ++
 .../cross-platform/cpp-loop-size-context/_index.md              | 2 ++
 .../learning-paths/cross-platform/docker-build-cloud/_index.md  | 2 ++
 content/learning-paths/cross-platform/docker/_index.md          | 2 ++
 .../cross-platform/dynamic-memory-allocator/_index.md           | 2 ++
 .../cross-platform/eigen-linear-algebra-on-arm/_index.md        | 2 ++
 content/learning-paths/cross-platform/ernie_moe_v9/_index.md    | 2 ++
 .../cross-platform/floating-point-behavior/_index.md            | 2 ++
 .../cross-platform/function-multiversioning/_index.md           | 2 ++
 .../learning-paths/cross-platform/github-arm-runners/_index.md  | 2 ++
 .../cross-platform/gitlab-managed-runners/_index.md             | 2 ++
 content/learning-paths/cross-platform/gitlab/_index.md          | 2 ++
 .../learning-paths/cross-platform/integer-vs-floats/_index.md   | 2 ++
 content/learning-paths/cross-platform/intrinsics/_index.md      | 2 ++
 content/learning-paths/cross-platform/ipexplorer/_index.md      | 2 ++
 .../learning-paths/cross-platform/kleidiai-explainer/_index.md  | 2 ++
 .../llm-fine-tuning-for-web-applications/_index.md              | 2 ++
 content/learning-paths/cross-platform/loop-reflowing/_index.md  | 2 ++
 content/learning-paths/cross-platform/matrix/_index.md          | 2 ++
 content/learning-paths/cross-platform/mca-godbolt/_index.md     | 2 ++
 content/learning-paths/cross-platform/mcp-ai-agent/_index.md    | 2 ++
 content/learning-paths/cross-platform/memory-latency/_index.md  | 2 ++
 .../learning-paths/cross-platform/multimodel_mnn_v9/_index.md   | 2 ++
 .../cross-platform/multiplying-matrices-with-sme2/_index.md     | 2 ++
 content/learning-paths/cross-platform/psa-tfm/_index.md         | 2 ++
 .../pytorch-digit-classification-arch-training/_index.md        | 2 ++
 content/learning-paths/cross-platform/remoteit/_index.md        | 2 ++
 .../cross-platform/restrict-keyword-c99/_index.md               | 2 ++
 content/learning-paths/cross-platform/rust_armds/_index.md      | 2 ++
 content/learning-paths/cross-platform/simd-info-demo/_index.md  | 2 ++
 content/learning-paths/cross-platform/simd-loops/_index.md      | 2 ++
 content/learning-paths/cross-platform/simd-on-rust/_index.md    | 2 ++
 .../cross-platform/sme-executorch-profiling/_index.md           | 2 ++
 .../cross-platform/tinkerblox_ultraedge/_index.md               | 2 ++
 content/learning-paths/cross-platform/topdown-compare/_index.md | 2 ++
 .../cross-platform/vectorization-comparison/_index.md           | 2 ++
 .../cross-platform/vectorization-friendly-data-layout/_index.md | 2 ++
 .../cross-platform/windowsperf_sampling_cpython_spe/_index.md   | 2 ++
 content/learning-paths/cross-platform/woa_azure/_index.md       | 2 ++
 .../cross-platform/zenoh-multinode-ros2/_index.md               | 2 ++
 .../embedded-and-microcontrollers/advanced_soc/_index.md        | 2 ++
 .../alif-image-classification/_index.md                         | 2 ++
 .../embedded-and-microcontrollers/arduino-pico/_index.md        | 2 ++
 .../embedded-and-microcontrollers/armds/_index.md               | 2 ++
 .../learning-paths/embedded-and-microcontrollers/asm/_index.md  | 2 ++
 .../embedded-and-microcontrollers/avh_balena/_index.md          | 2 ++
 .../embedded-and-microcontrollers/avh_greengrass/_index.md      | 2 ++
 .../embedded-and-microcontrollers/avh_matter/_index.md          | 2 ++
 .../embedded-and-microcontrollers/avh_ppocr/_index.md           | 2 ++
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 .../servers-and-cloud-computing/redis-data-searching/_index.md  | 2 ++
 .../learning-paths/servers-and-cloud-computing/redis/_index.md  | 2 ++
 .../servers-and-cloud-computing/redis_cache/_index.md           | 2 ++
 .../servers-and-cloud-computing/redis_tune/_index.md            | 2 ++
 .../servers-and-cloud-computing/refinfra-debug/_index.md        | 2 ++
 .../servers-and-cloud-computing/refinfra-quick-start/_index.md  | 2 ++
 .../servers-and-cloud-computing/reproducible-libamath/_index.md | 2 ++
 .../servers-and-cloud-computing/rme-cca-basics/_index.md        | 2 ++
 .../servers-and-cloud-computing/rtp-llm/_index.md               | 2 ++
 .../servers-and-cloud-computing/ruby-on-rails/_index.md         | 2 ++
 .../servers-and-cloud-computing/rust-on-gcp/_index.md           | 2 ++
 .../sentiment-analysis-eks/_index.md                            | 2 ++
 .../serverless-framework-aws-intro/_index.md                    | 2 ++
 .../serverless-framework-aws-lambda-dynamodb/_index.md          | 2 ++
 .../serverless-framework-aws-s3/_index.md                       | 2 ++
 .../learning-paths/servers-and-cloud-computing/snappy/_index.md | 2 ++
 .../servers-and-cloud-computing/snort3-multithreading/_index.md | 2 ++
 .../servers-and-cloud-computing/spark-on-azure/_index.md        | 2 ++
 .../servers-and-cloud-computing/spark-on-gcp/_index.md          | 2 ++
 .../servers-and-cloud-computing/spark-velox-cobalt/_index.md    | 2 ++
 .../learning-paths/servers-and-cloud-computing/spark/_index.md  | 2 ++
 .../servers-and-cloud-computing/supervisord/_index.md           | 2 ++
 .../learning-paths/servers-and-cloud-computing/sve/_index.md    | 2 ++
 .../servers-and-cloud-computing/sve2-match/_index.md            | 2 ++
 .../servers-and-cloud-computing/sysreport/_index.md             | 2 ++
 .../thirdai-sentiment-analysis/_index.md                        | 2 ++
 .../servers-and-cloud-computing/timescaledb-on-gcp/_index.md    | 2 ++
 .../servers-and-cloud-computing/top-down-n1/_index.md           | 2 ++
 .../servers-and-cloud-computing/torchbench/_index.md            | 2 ++
 .../triggering-pmu-events-2/_index.md                           | 2 ++
 .../servers-and-cloud-computing/triggering-pmu-events/_index.md | 2 ++
 .../servers-and-cloud-computing/trivy-on-gcpp/_index.md         | 2 ++
 .../tune-network-workloads-on-bare-metal/_index.md              | 2 ++
 .../servers-and-cloud-computing/typescript-on-gcp/_index.md     | 2 ++
 .../using-and-porting-performance-libs/_index.md                | 2 ++
 .../servers-and-cloud-computing/vLLM-quant/_index.md            | 2 ++
 .../servers-and-cloud-computing/vectorscan/_index.md            | 2 ++
 .../servers-and-cloud-computing/vllm-acceleration/_index.md     | 2 ++
 .../learning-paths/servers-and-cloud-computing/vllm/_index.md   | 2 ++
 .../learning-paths/servers-and-cloud-computing/vvenc/_index.md  | 2 ++
 .../servers-and-cloud-computing/whisper/_index.md               | 2 ++
 .../servers-and-cloud-computing/wordpress/_index.md             | 2 ++
 .../learning-paths/servers-and-cloud-computing/zlib/_index.md   | 2 ++
 414 files changed, 828 insertions(+)
 mode change 100644 => 100755 content/learning-paths/mobile-graphics-and-gaming/gemma4-kleidiai-sme2/_index.md

diff --git a/content/learning-paths/automotive/intro/_index.md b/content/learning-paths/automotive/intro/_index.md
index acf3ebbbe1..89c60b3067 100644
--- a/content/learning-paths/automotive/intro/_index.md
+++ b/content/learning-paths/automotive/intro/_index.md
@@ -18,6 +18,8 @@ cascade:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/automotive/openadkit1_container/_index.md b/content/learning-paths/automotive/openadkit1_container/_index.md
index df00c9a9ee..12376755e1 100644
--- a/content/learning-paths/automotive/openadkit1_container/_index.md
+++ b/content/learning-paths/automotive/openadkit1_container/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md b/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
index f573ef004e..9acb9d4436 100644
--- a/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
+++ b/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/automotive/system76-auto/_index.md b/content/learning-paths/automotive/system76-auto/_index.md
index 36ec8bae76..c3adfaf5c4 100644
--- a/content/learning-paths/automotive/system76-auto/_index.md
+++ b/content/learning-paths/automotive/system76-auto/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/automotive/zenacssdebug/_index.md b/content/learning-paths/automotive/zenacssdebug/_index.md
index 59ca332e20..5932c99b1a 100644
--- a/content/learning-paths/automotive/zenacssdebug/_index.md
+++ b/content/learning-paths/automotive/zenacssdebug/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/_example-learning-path/_index.md b/content/learning-paths/cross-platform/_example-learning-path/_index.md
index e7a1cb6faa..1f32ed6bdc 100644
--- a/content/learning-paths/cross-platform/_example-learning-path/_index.md
+++ b/content/learning-paths/cross-platform/_example-learning-path/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/adler32/_index.md b/content/learning-paths/cross-platform/adler32/_index.md
index 6ba56df7df..ed59df0b6e 100644
--- a/content/learning-paths/cross-platform/adler32/_index.md
+++ b/content/learning-paths/cross-platform/adler32/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md b/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md
index fb91678b01..5c5f2c89e9 100644
--- a/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md
+++ b/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/avh_cicd/_index.md b/content/learning-paths/cross-platform/avh_cicd/_index.md
index eb2eefba2d..633d150618 100644
--- a/content/learning-paths/cross-platform/avh_cicd/_index.md
+++ b/content/learning-paths/cross-platform/avh_cicd/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/avh_cicd2/_index.md b/content/learning-paths/cross-platform/avh_cicd2/_index.md
index 380af7e824..ce6fc0de25 100644
--- a/content/learning-paths/cross-platform/avh_cicd2/_index.md
+++ b/content/learning-paths/cross-platform/avh_cicd2/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/cca_rme/_index.md b/content/learning-paths/cross-platform/cca_rme/_index.md
index c685b0dde1..813299db98 100644
--- a/content/learning-paths/cross-platform/cca_rme/_index.md
+++ b/content/learning-paths/cross-platform/cca_rme/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md b/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md
index 2f307b3778..01dfdc0691 100644
--- a/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md
+++ b/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/docker-build-cloud/_index.md b/content/learning-paths/cross-platform/docker-build-cloud/_index.md
index e9cd8f1a76..3a3261f34a 100644
--- a/content/learning-paths/cross-platform/docker-build-cloud/_index.md
+++ b/content/learning-paths/cross-platform/docker-build-cloud/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/docker/_index.md b/content/learning-paths/cross-platform/docker/_index.md
index 2ea689adf5..01722652ed 100644
--- a/content/learning-paths/cross-platform/docker/_index.md
+++ b/content/learning-paths/cross-platform/docker/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md b/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md
index 952dea3222..fc9a856b22 100644
--- a/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md
+++ b/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md b/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md
index 17c51160ae..15a4031100 100644
--- a/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md
+++ b/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/ernie_moe_v9/_index.md b/content/learning-paths/cross-platform/ernie_moe_v9/_index.md
index 4777c47490..c5fd3c8860 100644
--- a/content/learning-paths/cross-platform/ernie_moe_v9/_index.md
+++ b/content/learning-paths/cross-platform/ernie_moe_v9/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/floating-point-behavior/_index.md b/content/learning-paths/cross-platform/floating-point-behavior/_index.md
index f4f7501317..aca2855fdf 100644
--- a/content/learning-paths/cross-platform/floating-point-behavior/_index.md
+++ b/content/learning-paths/cross-platform/floating-point-behavior/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/function-multiversioning/_index.md b/content/learning-paths/cross-platform/function-multiversioning/_index.md
index 170fe71c07..a64cf8c3d3 100644
--- a/content/learning-paths/cross-platform/function-multiversioning/_index.md
+++ b/content/learning-paths/cross-platform/function-multiversioning/_index.md
@@ -23,6 +23,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/github-arm-runners/_index.md b/content/learning-paths/cross-platform/github-arm-runners/_index.md
index 853c001f92..a6e585c661 100644
--- a/content/learning-paths/cross-platform/github-arm-runners/_index.md
+++ b/content/learning-paths/cross-platform/github-arm-runners/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md b/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md
index 3f3146b4c7..ea9d184f16 100644
--- a/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md
+++ b/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/gitlab/_index.md b/content/learning-paths/cross-platform/gitlab/_index.md
index 93659cb840..ec380b1568 100644
--- a/content/learning-paths/cross-platform/gitlab/_index.md
+++ b/content/learning-paths/cross-platform/gitlab/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/integer-vs-floats/_index.md b/content/learning-paths/cross-platform/integer-vs-floats/_index.md
index 038fd6e0ad..f6393038a0 100644
--- a/content/learning-paths/cross-platform/integer-vs-floats/_index.md
+++ b/content/learning-paths/cross-platform/integer-vs-floats/_index.md
@@ -15,6 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/intrinsics/_index.md b/content/learning-paths/cross-platform/intrinsics/_index.md
index 7386883676..0b092148ff 100644
--- a/content/learning-paths/cross-platform/intrinsics/_index.md
+++ b/content/learning-paths/cross-platform/intrinsics/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/ipexplorer/_index.md b/content/learning-paths/cross-platform/ipexplorer/_index.md
index 5010828c6c..a620d0db72 100644
--- a/content/learning-paths/cross-platform/ipexplorer/_index.md
+++ b/content/learning-paths/cross-platform/ipexplorer/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/kleidiai-explainer/_index.md b/content/learning-paths/cross-platform/kleidiai-explainer/_index.md
index f5bc22f0dd..0d9cec787b 100644
--- a/content/learning-paths/cross-platform/kleidiai-explainer/_index.md
+++ b/content/learning-paths/cross-platform/kleidiai-explainer/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/llm-fine-tuning-for-web-applications/_index.md b/content/learning-paths/cross-platform/llm-fine-tuning-for-web-applications/_index.md
index 4aad6e5871..5be707837c 100644
--- a/content/learning-paths/cross-platform/llm-fine-tuning-for-web-applications/_index.md
+++ b/content/learning-paths/cross-platform/llm-fine-tuning-for-web-applications/_index.md
@@ -29,6 +29,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 author: Parichay Das
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/loop-reflowing/_index.md b/content/learning-paths/cross-platform/loop-reflowing/_index.md
index 4e84bf220c..ae79dc2d75 100644
--- a/content/learning-paths/cross-platform/loop-reflowing/_index.md
+++ b/content/learning-paths/cross-platform/loop-reflowing/_index.md
@@ -14,6 +14,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/matrix/_index.md b/content/learning-paths/cross-platform/matrix/_index.md
index 0f5f300750..5831b7c0a9 100644
--- a/content/learning-paths/cross-platform/matrix/_index.md
+++ b/content/learning-paths/cross-platform/matrix/_index.md
@@ -21,6 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/mca-godbolt/_index.md b/content/learning-paths/cross-platform/mca-godbolt/_index.md
index cf962ce34f..ac8d08ec18 100644
--- a/content/learning-paths/cross-platform/mca-godbolt/_index.md
+++ b/content/learning-paths/cross-platform/mca-godbolt/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/mcp-ai-agent/_index.md b/content/learning-paths/cross-platform/mcp-ai-agent/_index.md
index 45f66ff7d4..b77ae889a2 100644
--- a/content/learning-paths/cross-platform/mcp-ai-agent/_index.md
+++ b/content/learning-paths/cross-platform/mcp-ai-agent/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/memory-latency/_index.md b/content/learning-paths/cross-platform/memory-latency/_index.md
index bcffc6beae..6e1de49094 100644
--- a/content/learning-paths/cross-platform/memory-latency/_index.md
+++ b/content/learning-paths/cross-platform/memory-latency/_index.md
@@ -15,6 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md b/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md
index eda97eca97..9f11138627 100644
--- a/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md
+++ b/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md b/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md
index 92ed13a6e2..5c319452a2 100644
--- a/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md
+++ b/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md
@@ -26,6 +26,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/psa-tfm/_index.md b/content/learning-paths/cross-platform/psa-tfm/_index.md
index e0d6476541..05d9d60c28 100644
--- a/content/learning-paths/cross-platform/psa-tfm/_index.md
+++ b/content/learning-paths/cross-platform/psa-tfm/_index.md
@@ -21,6 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 author: Ronan Synnott
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md b/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md
index 986d3b13e4..890e40e725 100644
--- a/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md
+++ b/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md
@@ -24,6 +24,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/remoteit/_index.md b/content/learning-paths/cross-platform/remoteit/_index.md
index e859568df9..a96dab5688 100644
--- a/content/learning-paths/cross-platform/remoteit/_index.md
+++ b/content/learning-paths/cross-platform/remoteit/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md b/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
index 98c4e81747..44e744cb29 100644
--- a/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
+++ b/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
@@ -15,6 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/rust_armds/_index.md b/content/learning-paths/cross-platform/rust_armds/_index.md
index c127cc8748..4e71235d14 100644
--- a/content/learning-paths/cross-platform/rust_armds/_index.md
+++ b/content/learning-paths/cross-platform/rust_armds/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/simd-info-demo/_index.md b/content/learning-paths/cross-platform/simd-info-demo/_index.md
index 14d616b9de..bed3c137ff 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/_index.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/simd-loops/_index.md b/content/learning-paths/cross-platform/simd-loops/_index.md
index e868eab11b..53a46c61ce 100644
--- a/content/learning-paths/cross-platform/simd-loops/_index.md
+++ b/content/learning-paths/cross-platform/simd-loops/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/simd-on-rust/_index.md b/content/learning-paths/cross-platform/simd-on-rust/_index.md
index 0a1c8a6886..04e6d31743 100644
--- a/content/learning-paths/cross-platform/simd-on-rust/_index.md
+++ b/content/learning-paths/cross-platform/simd-on-rust/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md b/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md
index f54c1c255f..0e9c7d19d9 100644
--- a/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md
+++ b/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md
@@ -22,6 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md b/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md
index b02c067bcc..17dcc1a4fe 100644
--- a/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md
+++ b/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md
@@ -21,6 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/topdown-compare/_index.md b/content/learning-paths/cross-platform/topdown-compare/_index.md
index 25c2cfe798..5dd4f2510b 100644
--- a/content/learning-paths/cross-platform/topdown-compare/_index.md
+++ b/content/learning-paths/cross-platform/topdown-compare/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/vectorization-comparison/_index.md b/content/learning-paths/cross-platform/vectorization-comparison/_index.md
index 4156137de2..5408a18456 100644
--- a/content/learning-paths/cross-platform/vectorization-comparison/_index.md
+++ b/content/learning-paths/cross-platform/vectorization-comparison/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md b/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md
index 29c1a4ab92..afa60a7831 100644
--- a/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md
+++ b/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md
@@ -15,6 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md b/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
index 834bce2289..3eb50d1638 100644
--- a/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
+++ b/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
@@ -22,6 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/woa_azure/_index.md b/content/learning-paths/cross-platform/woa_azure/_index.md
index f2a3c78113..315c4a0039 100644
--- a/content/learning-paths/cross-platform/woa_azure/_index.md
+++ b/content/learning-paths/cross-platform/woa_azure/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md b/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
index 710b8c5e4a..c1874f5517 100644
--- a/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
+++ b/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md b/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
index fa6e3cfec5..21c95cbdf0 100644
--- a/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md b/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
index efdf57210b..07cd885aa0 100644
--- a/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
@@ -22,6 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md b/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
index 011317698f..a3ee1d48bb 100644
--- a/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
@@ -22,6 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/armds/_index.md b/content/learning-paths/embedded-and-microcontrollers/armds/_index.md
index 68e3ae35c1..3b555bab3c 100644
--- a/content/learning-paths/embedded-and-microcontrollers/armds/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/armds/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/asm/_index.md b/content/learning-paths/embedded-and-microcontrollers/asm/_index.md
index 2cf94cf02e..5e7271adde 100644
--- a/content/learning-paths/embedded-and-microcontrollers/asm/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/asm/_index.md
@@ -6,6 +6,8 @@ description: Learn how to write mixed C and assembly programs for Cortex-M micro
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
index f5ad5e5ad3..4e1b75d69b 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
index 596f053e39..d9012994e9 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
index 3f1ca802b6..c7adf5115f 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
index 7410b3e1d5..aab8916015 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
index d562684722..58add017fd 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md b/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
index 8d61f9cbc9..77fa289d93 100644
--- a/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
@@ -22,6 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md b/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md
index c6bc8e4133..a51ad99c3c 100644
--- a/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md b/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
index e9050fe221..44cb3d177e 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md
index 874f5109c8..0675f2aef6 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md
index f034b8aca4..b0199b372c 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md b/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
index 3ae69d6d85..e82a36ec16 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
@@ -21,6 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md b/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
index 0e778370b7..1a248b0a12 100644
--- a/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md b/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
index 1e658161a8..9e98fa37f3 100644
--- a/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md b/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
index dc4538f417..05f49dad41 100644
--- a/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md b/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md
index 3b2f0c8799..a4ecc9372c 100644
--- a/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/docker/_index.md b/content/learning-paths/embedded-and-microcontrollers/docker/_index.md
index 4473728661..e66768bfd5 100644
--- a/content/learning-paths/embedded-and-microcontrollers/docker/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/docker/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/edge/_index.md b/content/learning-paths/embedded-and-microcontrollers/edge/_index.md
index d35a9ef6a8..8241aa6a79 100644
--- a/content/learning-paths/embedded-and-microcontrollers/edge/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/edge/_index.md
@@ -21,6 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/_index.md b/content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/_index.md
index 748e84fe0e..0947547f7c 100644
--- a/content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/_index.md
@@ -26,6 +26,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 author: Doug Anson
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md b/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md
index 23fdca112a..740ae28f03 100644
--- a/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/intro/_index.md b/content/learning-paths/embedded-and-microcontrollers/intro/_index.md
index f7c2bc7900..e7ac4baca1 100644
--- a/content/learning-paths/embedded-and-microcontrollers/intro/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/intro/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md b/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
index 84a689c5e0..21e2e19f1c 100644
--- a/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md b/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
index 9125f1d456..a1b4af43ad 100644
--- a/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md b/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
index ad421fffde..5b6d4a4e0b 100644
--- a/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md b/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
index 7d89958a47..0b5d786586 100644
--- a/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md b/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
index 8284468b46..5675b121c3 100644
--- a/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
@@ -22,6 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md b/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md
index bb0a46e0c9..3682848ba3 100644
--- a/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md b/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md
index 44fd4899ea..c245945cee 100644
--- a/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/migration/_index.md b/content/learning-paths/embedded-and-microcontrollers/migration/_index.md
index c74fbf76fd..ae13c5e8ef 100644
--- a/content/learning-paths/embedded-and-microcontrollers/migration/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/migration/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md b/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md
index 628112452c..c67807075e 100644
--- a/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md b/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md
index b7e13edda1..22021ed7da 100644
--- a/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md
@@ -10,6 +10,8 @@ armips:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md b/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md
index 59d6afc885..c21eccfe27 100644
--- a/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md b/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
index a019b62b17..12baf516f4 100644
--- a/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md b/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
index 9014a88e6a..df0bfc7b50 100644
--- a/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md b/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md
index 6d980eced5..c217a6ffce 100644
--- a/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md b/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md
index 20a3691de7..f3a7f9361e 100644
--- a/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md
@@ -21,6 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md b/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md
index 21c1b35860..052fa884b4 100644
--- a/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md
@@ -22,6 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
index d91e014500..9135444236 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
@@ -22,6 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
index 577b8a2a18..26ae753359 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md
index 6cd1b41998..500fc5a342 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
index 2f0012bbc8..ae2695ba3e 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md b/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
index 874d9e8d93..b3c0352488 100644
--- a/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
@@ -22,6 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md b/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
index b874ab7526..ce15489463 100644
--- a/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md b/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md
index 6eeb9d7390..9bed20cc42 100644
--- a/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md b/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md
index f9f0674e5d..39220ce6a7 100644
--- a/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md
@@ -21,6 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md b/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md
index da86f18a77..3995a9fffc 100644
--- a/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md b/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
index 619eb6d053..3c2d5e6adb 100644
--- a/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
@@ -27,6 +27,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md b/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
index 01b541e85a..9474d078c5 100644
--- a/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
@@ -9,6 +9,8 @@ minutes_to_complete: 90
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md b/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
index 6b648c05af..b689b9b66f 100644
--- a/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md b/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
index b350046d45..94af8c2d46 100644
--- a/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
@@ -21,6 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md b/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md
index 583fb5f069..90970d04ba 100644
--- a/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md b/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md
index 39dc028643..236e3bd23a 100644
--- a/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md b/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md
index db69f9ffcd..a8eaa3368b 100644
--- a/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md
@@ -22,6 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md b/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md
index f7e0a4369c..347ff6295c 100644
--- a/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md b/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md
index 20ffd1c113..caece5bafe 100644
--- a/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md
@@ -22,6 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md b/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md
index 740a8b9df4..6f3eab5405 100644
--- a/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md
+++ b/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md
index 179e33d515..500b104490 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md
@@ -21,6 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md
index d4a2813615..148ad041fe 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md
@@ -23,6 +23,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md
index f238c5f447..4d4d8478f0 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md
index 263bf4a63d..9b276ec5e0 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/docker-models/_index.md b/content/learning-paths/laptops-and-desktops/docker-models/_index.md
index 86c3935ad2..fa1d3e7913 100644
--- a/content/learning-paths/laptops-and-desktops/docker-models/_index.md
+++ b/content/learning-paths/laptops-and-desktops/docker-models/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/electron/_index.md b/content/learning-paths/laptops-and-desktops/electron/_index.md
index 82675e3cbc..c88a29930d 100644
--- a/content/learning-paths/laptops-and-desktops/electron/_index.md
+++ b/content/learning-paths/laptops-and-desktops/electron/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md b/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md
index 8befe1e520..62d8c8e808 100644
--- a/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md
+++ b/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/hyper-v/_index.md b/content/learning-paths/laptops-and-desktops/hyper-v/_index.md
index 947f0565fb..869e9d0bfb 100644
--- a/content/learning-paths/laptops-and-desktops/hyper-v/_index.md
+++ b/content/learning-paths/laptops-and-desktops/hyper-v/_index.md
@@ -15,6 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/intro/_index.md b/content/learning-paths/laptops-and-desktops/intro/_index.md
index 5c191a8ad9..a1044532af 100644
--- a/content/learning-paths/laptops-and-desktops/intro/_index.md
+++ b/content/learning-paths/laptops-and-desktops/intro/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md b/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md
index 65470ecc0e..54c1ce1604 100644
--- a/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md
+++ b/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md b/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md
index 834ab6ac29..3062f413fb 100644
--- a/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md
+++ b/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md b/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md
index 57c7433bfa..df844aef86 100644
--- a/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md
+++ b/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md b/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
index 284937a929..2a513b7ca7 100644
--- a/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
+++ b/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md b/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
index 9d163ee963..cc466e9c9b 100644
--- a/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
+++ b/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md b/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
index 3dfee9e29c..7c704dabab 100644
--- a/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
+++ b/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/_index.md b/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
index b55c82f3ec..87888d23a5 100644
--- a/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md b/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md
index 9ab292dd32..74d98834b2 100644
--- a/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md b/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
index 66e74ea4f9..707d19c927 100644
--- a/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md b/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
index 341493a9d1..acde952664 100644
--- a/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md b/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
index ebe1b4bd9d..44abfcf25a 100644
--- a/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md b/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md
index 6856cb4f05..b28a5bad1b 100644
--- a/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md b/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
index f5c5cb4953..133e7d1a92 100644
--- a/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
index 4e1de16446..c12f1108cd 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md
index 643b9aaa33..b67d933f75 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md
index 98656b350a..08f9f7cf12 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md
index 065d78ae2f..20c798d50c 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md
index 88884bcdb8..6e9277a053 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_cef/_index.md b/content/learning-paths/laptops-and-desktops/win_cef/_index.md
index dd52088591..45ccfbed96 100644
--- a/content/learning-paths/laptops-and-desktops/win_cef/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_cef/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_forms/_index.md b/content/learning-paths/laptops-and-desktops/win_forms/_index.md
index 3687254f92..61b9bf2e66 100644
--- a/content/learning-paths/laptops-and-desktops/win_forms/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_forms/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_net/_index.md b/content/learning-paths/laptops-and-desktops/win_net/_index.md
index eabe3d0bc9..9dd9481e81 100644
--- a/content/learning-paths/laptops-and-desktops/win_net/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_net/_index.md
@@ -15,6 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_net8/_index.md b/content/learning-paths/laptops-and-desktops/win_net8/_index.md
index 9f3de8745d..c591476be3 100644
--- a/content/learning-paths/laptops-and-desktops/win_net8/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_net8/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md b/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md
index 0dab7649b4..04bbcfcffe 100644
--- a/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md b/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md
index 2892a01e8d..665aa52242 100644
--- a/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md
@@ -15,6 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md b/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md
index 0f7160d75a..37913c345d 100644
--- a/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_python/_index.md b/content/learning-paths/laptops-and-desktops/win_python/_index.md
index ed5057edfc..d8ecd05167 100644
--- a/content/learning-paths/laptops-and-desktops/win_python/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_python/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_python_onnx/_index.md b/content/learning-paths/laptops-and-desktops/win_python_onnx/_index.md
index 9fe0e22375..29d95ae54e 100644
--- a/content/learning-paths/laptops-and-desktops/win_python_onnx/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_python_onnx/_index.md
@@ -21,6 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md b/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md
index ff75321394..83171f2ecc 100644
--- a/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md b/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md
index 33c84dafd2..963a60fcaa 100644
--- a/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_winui3/_index.md b/content/learning-paths/laptops-and-desktops/win_winui3/_index.md
index 3a73676572..b81e1894eb 100644
--- a/content/learning-paths/laptops-and-desktops/win_winui3/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_winui3/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_wpf/_index.md b/content/learning-paths/laptops-and-desktops/win_wpf/_index.md
index bacf4b9c7a..33a4955a57 100644
--- a/content/learning-paths/laptops-and-desktops/win_wpf/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_wpf/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md b/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md
index 17a9854ac8..f47f879d85 100644
--- a/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md b/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md
index 89b8e4fff3..2d7a57306c 100644
--- a/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md b/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md
index f176a1da2b..2d043e25ba 100644
--- a/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md
index 4c06d06a74..200ca6e74f 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf/_index.md
index cfe1d5ed0f..6c3ccc4e33 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md
index bf7ee132ec..0ae8e52814 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
index 01cde51739..9f0f963ec9 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/wsl2/_index.md b/content/learning-paths/laptops-and-desktops/wsl2/_index.md
index d256e5a551..5cec7b4dc8 100644
--- a/content/learning-paths/laptops-and-desktops/wsl2/_index.md
+++ b/content/learning-paths/laptops-and-desktops/wsl2/_index.md
@@ -21,6 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md b/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md
index d0908769f3..145b0bff0d 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md b/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md
index 1e928c0319..9133c8dcb2 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md b/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
index 35918e7f98..2b4b59db18 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
@@ -22,6 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md b/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md
index b2d86f8bea..aa21f63344 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md
index 27aa009b54..bf273a9c6c 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
index 85281d1929..12c9675202 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_neon/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_neon/_index.md
index c5b7565460..4c1ccd84e6 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_neon/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_neon/_index.md
@@ -22,6 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md
index 7693135e8b..ce30889bb0 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md
index e0438e8bb4..c78727a38a 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
index 0ad94857fa..16c580ccee 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md
index cb5b17230a..5a6b7634f6 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
index 0caac6d57b..bbef6eb701 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
@@ -27,6 +27,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md b/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
index bec0dda0ec..aed31a02ca 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md b/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md
index 46835c102f..ee103065fb 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md b/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md
index 2845f97b98..c107cb9cae 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md
@@ -22,6 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md b/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md
index 54e8498534..2ae9b79520 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md
@@ -24,6 +24,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md b/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md
index 74c8f3c385..fb66b6e200 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md
@@ -24,6 +24,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md b/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md
index 0fec8fd3e4..753fe65570 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md
@@ -21,6 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/gemma4-kleidiai-sme2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/gemma4-kleidiai-sme2/_index.md
old mode 100644
new mode 100755
index 9e2feca315..166a901961
--- a/content/learning-paths/mobile-graphics-and-gaming/gemma4-kleidiai-sme2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/gemma4-kleidiai-sme2/_index.md
@@ -21,6 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 author: Annie Tallund
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md
index 8d5a989fe2..1f6b35f250 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md
index 269562961a..57e9396a83 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md
index eaae93c1e3..db0ebaf80a 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md
@@ -15,6 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md b/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md
index 68e005c8bf..e368f7a719 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md b/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md
index b16ab2fbf1..c5dd8d5485 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md
@@ -13,6 +13,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md b/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md
index fa5d78177e..8d1edc08a5 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md b/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md
index ee801911e1..b7b1f8fb48 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md b/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md
index 34383f55c8..d2c6983c60 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md b/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md
index 34a13f2a76..e8cf03c77d 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md b/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md
index 999f44ec7e..ac6f97f630 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md b/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md
index cddfc409e6..a26ea72861 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md b/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md
index c9a3ceb74f..74971ec5fb 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md
@@ -14,6 +14,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md b/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md
index 3d18a29dc0..b614ba4fa4 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md b/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md
index a42eba6662..5fe3aa867a 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md b/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md
index aff083f364..d3fcbe88ea 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md
@@ -22,6 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md b/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md
index fdfc98dfd7..07d118c3dd 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md
@@ -21,6 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md b/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md
index b5e4d9e86e..142f373d45 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md
index bedbecff2d..74a2987ee8 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md
index 8ddb1e4129..66e1cb4048 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md b/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md
index 03faf33390..6c5a030600 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md b/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md
index 3c7bf0a42b..2c3cec4435 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md b/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md
index cb3322f400..d6e72f6388 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md b/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md
index c5ca6580fa..d2709d2b8b 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md b/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md
index 69438da455..fcf6324146 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md
index 48085c467d..9011cfc392 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md
index 03686faf1d..ec4f625ec0 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md b/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md
index 9462764ad6..eaf571df08 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md b/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md
index ab25feb38f..3a9aa7d3b1 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md b/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md
index 577e7589c1..cc5bf6ea51 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md b/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md
index 4fdb2d328c..d47220768d 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md b/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md
index e32d7c22bc..f2d149b26b 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md b/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md
index b988cdbb6d..a02454b8c7 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md b/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md
index ba9faa6ff8..ac9aadc96f 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md
@@ -21,6 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md
index fa776fee1c..e5f3b86fcb 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md
@@ -21,6 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md b/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
index 02bee1b6d2..f29ad74618 100644
--- a/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/aks/_index.md b/content/learning-paths/servers-and-cloud-computing/aks/_index.md
index f88a53359a..5de9a3240d 100644
--- a/content/learning-paths/servers-and-cloud-computing/aks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/aks/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md b/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
index fbaa94a9f8..67465db3ef 100644
--- a/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
@@ -21,6 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
index 77bbc6458c..e637f92ad9 100644
--- a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
index b5ffb45089..5bb07cf40a 100644
--- a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
index afff69463c..b35eef1ff4 100644
--- a/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
@@ -23,6 +23,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md b/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
index 9f94a09843..13624ff5a8 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md b/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
index 11af5c031a..bb9925c8b6 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md b/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
index c9bc2fc4c0..9030af24aa 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
@@ -21,6 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md b/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
index aa6068f84d..a5e86f26c1 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md b/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
index bc590a56c3..ce5ae6b291 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md b/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
index 9e01f90f27..3b6134ff17 100644
--- a/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md b/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
index 56bc7c9916..814c22a2c1 100644
--- a/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
index 611c0adabe..147a978a1c 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
index 3097e1f7f8..5e9531ce41 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
index 453d0b3531..20a4780e8e 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
index e81f4c0fe1..a424d89d39 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
@@ -22,6 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md b/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
index 162da767bb..5283c9ca29 100644
--- a/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md b/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
index 1db0995be7..2f0f56b24c 100644
--- a/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md b/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
index c3703edb20..d94f9c4fdf 100644
--- a/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
@@ -25,6 +25,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md b/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
index a119cacb5a..3992a9178a 100644
--- a/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
index b033f64032..ba36d3dce2 100644
--- a/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
@@ -21,6 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
index b16ca1ff84..a0668e722a 100644
--- a/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
index 5886240101..398f1f3023 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
index 28adffb245..96d9c75271 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
@@ -21,6 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
index 54522034ed..273e3b63d5 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
index db40aa5437..88bda0253b 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
index 581af229d4..7020a64806 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
index 6c00409252..874e2102bb 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md
index 217a3c547f..ed904f0fbe 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md
@@ -21,6 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
index 92fdf8c1f0..d0d935aa81 100644
--- a/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
@@ -25,6 +25,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md b/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md
index de5ae1f186..3601f35efc 100644
--- a/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/clair/_index.md b/content/learning-paths/servers-and-cloud-computing/clair/_index.md
index aec656f33a..deacc3c664 100644
--- a/content/learning-paths/servers-and-cloud-computing/clair/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/clair/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
index 0f03a83ad2..b76d2d25d0 100644
--- a/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
@@ -23,6 +23,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md b/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
index db8eae99f0..5d6b808e07 100644
--- a/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
@@ -15,6 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
index 1503fa453b..a10f8cc607 100644
--- a/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md b/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
index d98c1d2ca2..57c84a5af8 100644
--- a/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/codec/_index.md b/content/learning-paths/servers-and-cloud-computing/codec/_index.md
index ced7f9ef21..2eeaedec8a 100644
--- a/content/learning-paths/servers-and-cloud-computing/codec/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/codec/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/codec1/_index.md b/content/learning-paths/servers-and-cloud-computing/codec1/_index.md
index 0c3f54164a..388412574d 100644
--- a/content/learning-paths/servers-and-cloud-computing/codec1/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/codec1/_index.md
@@ -4,6 +4,8 @@ description: Learn how to build and run the AV1 and VP9 video codecs on Arm Linu
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
index ca3a8ea2f8..ed17dc845d 100644
--- a/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md b/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
index b8675f0eec..610e6e1b97 100644
--- a/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md b/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md
index c419882aba..051944f9e9 100644
--- a/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md b/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
index 3d98cc20ca..7f9f744f4a 100644
--- a/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/csp/_index.md b/content/learning-paths/servers-and-cloud-computing/csp/_index.md
index e1b93d65f0..2d9d19c349 100644
--- a/content/learning-paths/servers-and-cloud-computing/csp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/csp/_index.md
@@ -4,6 +4,8 @@ description: Learn how to start an Arm-based virtual machine instance from major
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md b/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
index bb0de74320..7e23900f7f 100644
--- a/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md b/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md
index a0e61e25a5..96f322c93d 100644
--- a/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md
index 23baa202be..aa0d7ce216 100644
--- a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md
index 423eace708..6d1a4e7a3d 100644
--- a/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md
@@ -23,6 +23,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md b/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
index b529709b0c..85210eefb4 100644
--- a/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md b/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
index c2e86acd04..6cbe32ce5b 100644
--- a/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
@@ -25,6 +25,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md b/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md
index 4924046b74..338318270b 100644
--- a/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md
@@ -22,6 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
index 0a275c1abe..a1b67d67a2 100644
--- a/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/ecs/_index.md b/content/learning-paths/servers-and-cloud-computing/ecs/_index.md
index 4330350ee0..10346af14c 100644
--- a/content/learning-paths/servers-and-cloud-computing/ecs/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ecs/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md b/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
index 6971489da3..94d76afda6 100644
--- a/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/eks/_index.md b/content/learning-paths/servers-and-cloud-computing/eks/_index.md
index 151678bad9..4445af6e72 100644
--- a/content/learning-paths/servers-and-cloud-computing/eks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/eks/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md
index 3292f5cf6d..3db7ef52fb 100644
--- a/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/envoy/_index.md b/content/learning-paths/servers-and-cloud-computing/envoy/_index.md
index fa5bb837f5..1ad73408ad 100644
--- a/content/learning-paths/servers-and-cloud-computing/envoy/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/envoy/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md
index 052fa1f275..398925d448 100644
--- a/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md b/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md
index dc6912d86a..5a56cd993b 100644
--- a/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md
@@ -21,6 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md b/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
index 890b7084ea..c010dd1a3b 100644
--- a/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md b/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md
index 07df987cad..e02aec0326 100644
--- a/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md b/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md
index 8d53c74587..aa042d7297 100644
--- a/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md
index cd2527e09c..b51b3772b9 100644
--- a/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/flink/_index.md b/content/learning-paths/servers-and-cloud-computing/flink/_index.md
index 268c8a7ad6..057a6b7783 100644
--- a/content/learning-paths/servers-and-cloud-computing/flink/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/flink/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md b/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md
index cad2e25b6e..0929929cad 100644
--- a/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md
@@ -21,6 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
index 8899a5d8f9..f1ba801c97 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
@@ -23,6 +23,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md
index d3a4090dc7..cd010418c1 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
index 7a1a427f18..79c9c6f647 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md
index 6f9a63827b..c6c71fd0aa 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/funasr/_index.md b/content/learning-paths/servers-and-cloud-computing/funasr/_index.md
index 49121c2bc6..c21cee3be4 100644
--- a/content/learning-paths/servers-and-cloud-computing/funasr/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/funasr/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
index 0cf86125e8..131ddbc380 100644
--- a/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md b/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
index a2bc36f05b..e264303b70 100644
--- a/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/gcp/_index.md
index 4b185c9d2a..a711c975db 100644
--- a/content/learning-paths/servers-and-cloud-computing/gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gcp/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md b/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
index 4fc4516274..0acc50bf74 100644
--- a/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
@@ -15,6 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md b/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
index a97ace140c..517c61364b 100644
--- a/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md b/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
index 2697f67e62..fe5818f61d 100644
--- a/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md b/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md
index 059959169d..d4239e8fdd 100644
--- a/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
index b33b30e42d..ba97c2588f 100644
--- a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
index 2a6e7a5cf2..99458bcecd 100644
--- a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/gke/_index.md b/content/learning-paths/servers-and-cloud-computing/gke/_index.md
index f0a5d330a0..e6da81b3d3 100644
--- a/content/learning-paths/servers-and-cloud-computing/gke/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gke/_index.md
@@ -15,6 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md b/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
index 8828d973ae..55246db96e 100644
--- a/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md b/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
index c4390186ea..1e1c7b2b3c 100644
--- a/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md
index c0584fc6e1..989757db53 100644
--- a/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md
index 29ebca76b2..9607de2ed2 100644
--- a/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md
@@ -25,6 +25,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/intro/_index.md b/content/learning-paths/servers-and-cloud-computing/intro/_index.md
index ded73d65f0..605eacb71d 100644
--- a/content/learning-paths/servers-and-cloud-computing/intro/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/intro/_index.md
@@ -15,6 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md b/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
index 8083b2284e..fb436a7715 100644
--- a/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md b/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
index 8e7fc43985..c3e1c470a8 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
index c7cc8f1029..cfb2a8997e 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
index bd4a2fd5d0..e4d7115ca6 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
index 54c92e0f1c..ade81d1695 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md b/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
index d34d9768d0..1f50cc2ad5 100644
--- a/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
@@ -23,6 +23,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
index 642b54b094..fc63356c50 100644
--- a/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/kafka/_index.md b/content/learning-paths/servers-and-cloud-computing/kafka/_index.md
index fe41155b05..ead5c8e1c0 100644
--- a/content/learning-paths/servers-and-cloud-computing/kafka/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kafka/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md b/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
index 90d67f8298..406ffb1989 100644
--- a/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md b/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
index 9a090ba96a..dc2e368182 100644
--- a/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md b/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
index 50a823dd84..b7817d04e0 100644
--- a/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md b/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
index 4ead9f2004..d845138766 100644
--- a/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md b/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
index 138a192eb2..5307413245 100644
--- a/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md b/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
index f2e718aaf0..83e64f157d 100644
--- a/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md b/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
index c486a10f69..216f6e1c21 100644
--- a/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md b/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
index 2f53e5ddf6..cd4fcf3a47 100644
--- a/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
@@ -21,6 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
index 6ab83998d9..9c78aca168 100644
--- a/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
@@ -22,6 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/lse/_index.md b/content/learning-paths/servers-and-cloud-computing/lse/_index.md
index e2a1ef4dc6..f1fee8e657 100644
--- a/content/learning-paths/servers-and-cloud-computing/lse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/lse/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md b/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
index 5e83569d5c..b5ee73097e 100644
--- a/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/memcached/_index.md b/content/learning-paths/servers-and-cloud-computing/memcached/_index.md
index 2f60bf9fe2..62241a8487 100644
--- a/content/learning-paths/servers-and-cloud-computing/memcached/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memcached/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md b/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
index ef6f95731d..fb52dfce2b 100644
--- a/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md b/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
index 5acd301cdc..f388af7947 100644
--- a/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
@@ -21,6 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md b/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
index 21c888487a..1be004fc72 100644
--- a/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
@@ -21,6 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md b/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
index e6dc211e2e..768394cc38 100644
--- a/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md b/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
index 02f7c111f6..8c165932c1 100644
--- a/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/migration/_index.md b/content/learning-paths/servers-and-cloud-computing/migration/_index.md
index 42cf30144e..5ba69147f1 100644
--- a/content/learning-paths/servers-and-cloud-computing/migration/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/migration/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
index b59da90a05..bdfc4534ea 100644
--- a/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
index d742cea942..bba1d8680d 100644
--- a/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md b/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
index 1bcda4403d..55b4caa315 100644
--- a/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
index a8e3e948c7..395462fe50 100644
--- a/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
index 7ea3d39843..2b107a712f 100644
--- a/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md b/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
index 23d588cf50..d88c85b452 100644
--- a/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
@@ -3,6 +3,8 @@ title: Analyze the performance of MongoDB on Arm servers
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/mpi/_index.md b/content/learning-paths/servers-and-cloud-computing/mpi/_index.md
index beb3899ab3..10610f20e3 100644
--- a/content/learning-paths/servers-and-cloud-computing/mpi/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mpi/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md b/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md
index 9dc70e65e7..ad7b4fe440 100644
--- a/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md
@@ -5,6 +5,8 @@ minutes_to_complete: 20
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md b/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md
index cc3ba57d95..d847e70ce6 100644
--- a/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md b/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md
index 33245ca9d0..d0e7437afe 100644
--- a/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md
index f58c1d675b..5588bd0ac0 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql-lns-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql-lns-azure/_index.md
index e6f7caf151..0437fa6694 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql-lns-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql-lns-azure/_index.md
@@ -23,6 +23,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 author: Doug Anson
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql/_index.md
index 3f542e6662..3b366f819a 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql/_index.md
@@ -15,6 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md
index 6c8b4f431e..f915217214 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md
@@ -15,6 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md
index d874efcb6c..59f72c94c9 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md
@@ -13,6 +13,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md b/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md
index daab27afa8..84a1f6e7d5 100644
--- a/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md
@@ -21,6 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md b/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md
index 9a8c86bca3..f317f68604 100644
--- a/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md
index 4c779f150a..66aa2879e7 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx/_index.md
index e661d82c3f..aed423e9db 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md b/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md
index 13a496fb54..7efef9dd73 100644
--- a/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md
@@ -14,6 +14,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md
index 8048b6ad4d..c0058e2a86 100644
--- a/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md
@@ -21,6 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md b/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md
index fa3fff0af0..8e688e9e1d 100644
--- a/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md
@@ -14,6 +14,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md
index 6eabf7db3c..0dba2cb183 100644
--- a/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/onnx/_index.md b/content/learning-paths/servers-and-cloud-computing/onnx/_index.md
index 47c53771d2..f61e437ca8 100644
--- a/content/learning-paths/servers-and-cloud-computing/onnx/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/onnx/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md b/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md
index 7b4fc907bc..6608779392 100644
--- a/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md b/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md
index 839ab15f4a..1596ad4463 100644
--- a/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/openshift/_index.md b/content/learning-paths/servers-and-cloud-computing/openshift/_index.md
index e3f36f67ff..7c984ed6bc 100644
--- a/content/learning-paths/servers-and-cloud-computing/openshift/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openshift/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md
index 783b79a783..cc5932f442 100644
--- a/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md
@@ -23,6 +23,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md b/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md
index 7c3d3d06d9..061a543fda 100644
--- a/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/pac/_index.md b/content/learning-paths/servers-and-cloud-computing/pac/_index.md
index fe54514efa..ddadeff8f8 100644
--- a/content/learning-paths/servers-and-cloud-computing/pac/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pac/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md b/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md
index a889873905..51ea9418a1 100644
--- a/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md
@@ -21,6 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md b/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md
index 8253d39d45..68dd13b32b 100644
--- a/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/performix-system-characterization/_index.md b/content/learning-paths/servers-and-cloud-computing/performix-system-characterization/_index.md
index 51d9c8985b..774d35bf8e 100644
--- a/content/learning-paths/servers-and-cloud-computing/performix-system-characterization/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/performix-system-characterization/_index.md
@@ -21,6 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 author:
 - Brendan Long
 - David Wong
diff --git a/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md
index e765c47f8a..cfa9a30185 100644
--- a/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md b/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md
index 88e963729f..ea83c82287 100644
--- a/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md b/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md
index 09286bccb3..09a15edb03 100644
--- a/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md
index 16679b2d80..237710f94f 100644
--- a/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md b/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md
index 3d211a45a3..104d12078a 100644
--- a/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md
@@ -15,6 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md
index 57732a99ac..d0eb47d1d9 100644
--- a/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md
@@ -13,6 +13,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md b/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
index cf7c6dd8bd..0bb6cbd4ab 100644
--- a/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
@@ -15,6 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md b/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md
index 4410205633..aced00b904 100644
--- a/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md
@@ -14,6 +14,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md
index d34a639f15..cb66eeab16 100644
--- a/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md
@@ -13,6 +13,8 @@ learning_objectives:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md b/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md
index b9ee9ae74b..ab965d17e6 100644
--- a/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md
index 5bfa3ecb8d..e96357a490 100644
--- a/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md
@@ -21,6 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md
index 35214ae109..2b2c960b47 100644
--- a/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md
@@ -21,6 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/rag/_index.md b/content/learning-paths/servers-and-cloud-computing/rag/_index.md
index e422d3c4e7..e7bdb03ced 100644
--- a/content/learning-paths/servers-and-cloud-computing/rag/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rag/_index.md
@@ -21,6 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/ran/_index.md b/content/learning-paths/servers-and-cloud-computing/ran/_index.md
index 71a42b1555..d9123b17b5 100644
--- a/content/learning-paths/servers-and-cloud-computing/ran/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ran/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md
index 8a7ccd68c2..c1d1e2205f 100644
--- a/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md
index c20d10f784..96c676bcd5 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md b/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md
index 1921df927c..896e074b73 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/redis/_index.md b/content/learning-paths/servers-and-cloud-computing/redis/_index.md
index 722d6f0c5c..1a17b59d84 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md b/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md
index fb292d1154..0d9f36b81b 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md
index 0d7ea4437e..3f1589824f 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md b/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md
index 45079083d5..4445a938fb 100644
--- a/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md b/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md
index 2d0095fcc8..202cdd9269 100644
--- a/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md b/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md
index fa8cdfe6ff..d44db8afb3 100644
--- a/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md
@@ -5,6 +5,8 @@ minutes_to_complete: 10
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md b/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md
index 2ce65741ac..4b6310b05d 100644
--- a/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md b/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md
index bab1a90e4f..873a701235 100644
--- a/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md b/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md
index 3e0a00fb55..72e1db98ba 100644
--- a/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md
index 1f73aa4eda..8ceeea6933 100644
--- a/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md b/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md
index 09d149e45f..981b50220e 100644
--- a/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md
index cbb60670dc..f0c577c0c5 100644
--- a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md
@@ -15,6 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md
index 412a7a9aad..56d7963576 100644
--- a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md
index 9d2ae2e5b2..1506dcd7bc 100644
--- a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/snappy/_index.md b/content/learning-paths/servers-and-cloud-computing/snappy/_index.md
index ebee97ad23..b3154b8b80 100644
--- a/content/learning-paths/servers-and-cloud-computing/snappy/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/snappy/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md b/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md
index c0a9f2be79..18296858b6 100644
--- a/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md
index d4f2bdd4e3..fa9c352a86 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md
index ad2ccca984..54c201811c 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/spark-velox-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/spark-velox-cobalt/_index.md
index 591b2d5163..8e59bd11a5 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark-velox-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark-velox-cobalt/_index.md
@@ -24,6 +24,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/spark/_index.md b/content/learning-paths/servers-and-cloud-computing/spark/_index.md
index 89cdbdd862..cea2b9d293 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark/_index.md
@@ -15,6 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md b/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md
index 1081569b81..ce20081b89 100644
--- a/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/sve/_index.md b/content/learning-paths/servers-and-cloud-computing/sve/_index.md
index 4d6179a76a..33e140e480 100644
--- a/content/learning-paths/servers-and-cloud-computing/sve/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sve/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md b/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md
index 1d290ac587..ea58ebb840 100644
--- a/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md b/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md
index 928e31ef8d..a45ac74d74 100644
--- a/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md
@@ -15,6 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md b/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md
index 21617dea23..6a30f1edd5 100644
--- a/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md
@@ -14,6 +14,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md
index aaf214cf37..7bbda69de0 100644
--- a/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md b/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md
index e785a4631b..e5f6852b33 100644
--- a/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md b/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md
index 8158475877..2179a39b9a 100644
--- a/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md
@@ -15,6 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md
index 9e0cde01ed..6958a7696b 100644
--- a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md
@@ -15,6 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md
index d4d5b436f6..05959d376f 100644
--- a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md b/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md
index 56050c584d..b75f9d5502 100644
--- a/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md b/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md
index 5205ed4fd8..98c8593b4f 100644
--- a/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md
index 3808ab2591..a470e6df32 100644
--- a/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md
@@ -18,6 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md b/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md
index a868ba51c1..2e0aaef437 100644
--- a/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md
@@ -15,6 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/vLLM-quant/_index.md b/content/learning-paths/servers-and-cloud-computing/vLLM-quant/_index.md
index 8fe82c690c..b454c45d65 100644
--- a/content/learning-paths/servers-and-cloud-computing/vLLM-quant/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vLLM-quant/_index.md
@@ -24,6 +24,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 author: 
    - Rani Chowdary Mandepudi
    - Phalani Paladugu
diff --git a/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md b/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md
index 9bb95ea96b..d3cc08b454 100644
--- a/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md b/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
index 19730ce3ef..c84d01bb6e 100644
--- a/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
@@ -19,6 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/vllm/_index.md b/content/learning-paths/servers-and-cloud-computing/vllm/_index.md
index 292529edbf..d51019c7c2 100644
--- a/content/learning-paths/servers-and-cloud-computing/vllm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vllm/_index.md
@@ -16,6 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md b/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
index 2f6c7b821d..46a79fb50e 100644
--- a/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
@@ -3,6 +3,8 @@ title: Run the vvenc H.266 encoder on Arm servers
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/whisper/_index.md b/content/learning-paths/servers-and-cloud-computing/whisper/_index.md
index 7afac593e7..b68a0a1ed3 100644
--- a/content/learning-paths/servers-and-cloud-computing/whisper/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/whisper/_index.md
@@ -20,6 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md b/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
index 7874886fc1..e21b61ea3d 100644
--- a/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
@@ -9,6 +9,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/zlib/_index.md b/content/learning-paths/servers-and-cloud-computing/zlib/_index.md
index 798081d3d3..7059a614bc 100644
--- a/content/learning-paths/servers-and-cloud-computing/zlib/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/zlib/_index.md
@@ -17,6 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
+# rerun_summary: false
+# rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1

From cf5467eb8ba400f347026eefcd342a96c2cc529b Mon Sep 17 00:00:00 2001
From: GitHub Actions Summary FAQ Bot <>
Date: Wed, 6 May 2026 19:05:17 +0000
Subject: [PATCH 09/23] Generate Learning Path summary and FAQ content

---
 reports/generated-summary-faq/latest-run.yml | 22124 ++++++++++++++++-
 1 file changed, 22068 insertions(+), 56 deletions(-)

diff --git a/reports/generated-summary-faq/latest-run.yml b/reports/generated-summary-faq/latest-run.yml
index 8753b5adca..eff3abfa64 100644
--- a/reports/generated-summary-faq/latest-run.yml
+++ b/reports/generated-summary-faq/latest-run.yml
@@ -1,48 +1,48 @@
 latest_run:
-  timestamp: '2026-05-06T18:52:15Z'
+  timestamp: '2026-05-06T19:05:07Z'
   mode: write
   require_enable_flag: true
   path_filter: ''
   limit: 0
-  run_url: https://github.com/chrismoroney/arm-learning-paths/actions/runs/25454686544
+  run_url: https://github.com/chrismoroney/arm-learning-paths/actions/runs/25455312233
   git_ref: STESOL-345
-  git_sha: d9b0c1ddc48efd31a96a1eb7400b86c6776f0f88
+  git_sha: fb9a9decd347924be14e4503865f03b0b2d990e4
   actor: chrismoroney
   template_version: summary-faq-v2
   totals:
     processed: 407
     added: 0
-    updated: 4
-    unchanged: 403
+    updated: 0
+    unchanged: 407
     drift_detected: 0
     skipped: 0
     errors: 0
     removed: 0
     summary_changed: 0
     faq_changed: 0
-    rerun_flags_reset: 3
+    rerun_flags_reset: 0
   section_totals:
     summary:
       created: 0
       repaired_missing: 0
-      rerun_requested: 2
+      rerun_requested: 0
       drift_detected_preserved: 0
-      unchanged: 405
+      unchanged: 407
     faqs:
       created: 0
       repaired_missing: 0
-      rerun_requested: 2
+      rerun_requested: 0
       drift_detected_preserved: 0
-      unchanged: 405
+      unchanged: 407
   reason_totals:
     initial_generation: 0
     missing_summary: 0
     missing_faqs: 0
-    rerun_summary: 2
-    rerun_faqs: 2
+    rerun_summary: 0
+    rerun_faqs: 0
     summary_drift_detected: 0
     faq_drift_detected: 0
-    rerun_flags_reset: 3
+    rerun_flags_reset: 0
   paths:
   - path: content/learning-paths/automotive/openadkit1_container/_index.md
     status: unchanged
@@ -12087,28 +12087,23 @@ latest_run:
         removed_questions: []
         updated_questions: []
   - path: content/learning-paths/servers-and-cloud-computing/bolt/_index.md
-    status: updated
-    changed_on_disk: true
-    managed_block_updated: true
-    rerun_flags_reset:
-    - rerun_summary
-    - rerun_faqs
-    change_reasons:
-    - rerun_summary
-    - rerun_faqs
-    - rerun_flags_reset
-    template_version_before: summary-faq-v1
+    status: unchanged
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons: []
+    template_version_before: summary-faq-v2
     template_version_after: summary-faq-v2
     summary:
-      action: rerun_requested
+      action: unchanged
       missing_before: false
-      rerun_requested: true
+      rerun_requested: false
       changed: false
       drift_detected: false
       source_hash_before: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
       source_hash_after: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
       current_source_hash: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
-      generated_at_before: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-05-06T18:52:13Z'
       generated_at_after: '2026-05-06T18:52:13Z'
       preview_before: Learn how to build, profile, and optimize Arm executables using BOLT post-link binary
         optimization to improve application performance through code layout improvements. It is designed
@@ -12120,15 +12115,15 @@ latest_run:
         binary optimization to improve application performance through code layout improvements. It is
         designed for software deve...
     faqs:
-      action: rerun_requested
+      action: unchanged
       missing_before: false
-      rerun_requested: true
+      rerun_requested: false
       changed: false
       drift_detected: false
       source_hash_before: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
       source_hash_after: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
       current_source_hash: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
-      generated_at_before: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-05-06T18:52:13Z'
       generated_at_after: '2026-05-06T18:52:13Z'
       before_count: 5
       after_count: 5
@@ -13658,15 +13653,12 @@ latest_run:
         removed_questions: []
         updated_questions: []
   - path: content/learning-paths/servers-and-cloud-computing/django/_index.md
-    status: updated
-    changed_on_disk: true
-    managed_block_updated: true
-    rerun_flags_reset:
-    - rerun_faqs
-    change_reasons:
-    - rerun_faqs
-    - rerun_flags_reset
-    template_version_before: summary-faq-v1
+    status: unchanged
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons: []
+    template_version_before: summary-faq-v2
     template_version_after: summary-faq-v2
     summary:
       action: unchanged
@@ -13689,15 +13681,15 @@ latest_run:
         using Nginx and PostgreSQL. It is designed for engineers who want to deploy a Django based application
         on Arm machines...
     faqs:
-      action: rerun_requested
+      action: unchanged
       missing_before: false
-      rerun_requested: true
+      rerun_requested: false
       changed: false
       drift_detected: false
       source_hash_before: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
       source_hash_after: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
       current_source_hash: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
-      generated_at_before: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-05-06T18:52:13Z'
       generated_at_after: '2026-05-06T18:52:13Z'
       before_count: 5
       after_count: 5
@@ -18143,26 +18135,23 @@ latest_run:
         removed_questions: []
         updated_questions: []
   - path: content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
-    status: updated
-    changed_on_disk: true
-    managed_block_updated: true
-    rerun_flags_reset:
-    - rerun_summary
-    change_reasons:
-    - rerun_summary
-    - rerun_flags_reset
-    template_version_before: summary-faq-v1
+    status: unchanged
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons: []
+    template_version_before: summary-faq-v2
     template_version_after: summary-faq-v2
     summary:
-      action: rerun_requested
+      action: unchanged
       missing_before: false
-      rerun_requested: true
+      rerun_requested: false
       changed: false
       drift_detected: false
       source_hash_before: 10f13dee3459577fcadf5966cf80d990f3396321189f638432a3308699e773a8
       source_hash_after: 10f13dee3459577fcadf5966cf80d990f3396321189f638432a3308699e773a8
       current_source_hash: 10f13dee3459577fcadf5966cf80d990f3396321189f638432a3308699e773a8
-      generated_at_before: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-05-06T18:52:14Z'
       generated_at_after: '2026-05-06T18:52:14Z'
       preview_before: Learn how to tune Nginx walks you through an end-to-end Arm software workflow. It
         is designed for software developers who want to use Nginx on Arm. By the end, you will be able
@@ -21062,8 +21051,8 @@ latest_run:
         removed_questions: []
         updated_questions: []
   - path: content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
-    status: updated
-    changed_on_disk: true
+    status: unchanged
+    changed_on_disk: false
     managed_block_updated: false
     rerun_flags_reset: []
     change_reasons: []
@@ -22034,6 +22023,22029 @@ latest_run:
         removed_questions: []
         updated_questions: []
 history:
+- timestamp: '2026-05-06T19:05:07Z'
+  mode: write
+  require_enable_flag: true
+  path_filter: ''
+  limit: 0
+  run_url: https://github.com/chrismoroney/arm-learning-paths/actions/runs/25455312233
+  git_ref: STESOL-345
+  git_sha: fb9a9decd347924be14e4503865f03b0b2d990e4
+  actor: chrismoroney
+  template_version: summary-faq-v2
+  totals:
+    processed: 407
+    added: 0
+    updated: 0
+    unchanged: 407
+    drift_detected: 0
+    skipped: 0
+    errors: 0
+    removed: 0
+    summary_changed: 0
+    faq_changed: 0
+    rerun_flags_reset: 0
+  section_totals:
+    summary:
+      created: 0
+      repaired_missing: 0
+      rerun_requested: 0
+      drift_detected_preserved: 0
+      unchanged: 407
+    faqs:
+      created: 0
+      repaired_missing: 0
+      rerun_requested: 0
+      drift_detected_preserved: 0
+      unchanged: 407
+  reason_totals:
+    initial_generation: 0
+    missing_summary: 0
+    missing_faqs: 0
+    rerun_summary: 0
+    rerun_faqs: 0
+    summary_drift_detected: 0
+    faq_drift_detected: 0
+    rerun_flags_reset: 0
+  paths:
+  - path: content/learning-paths/automotive/openadkit1_container/_index.md
+    status: unchanged
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons: []
+    template_version_before: summary-faq-v1
+    template_version_after: summary-faq-v1
+    summary:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
+      source_hash_after: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
+      current_source_hash: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
+      generated_at_before: '2026-05-06T17:17:53Z'
+      generated_at_after: '2026-05-06T17:17:53Z'
+      preview_before: Learn how to deploy and run containerized autonomous driving simulations using Autoware
+        Open AD Kit on Arm Neoverse with Docker, demonstrating SOAFEE-based Shift-Left development workflows.
+        It is desi...
+      preview_after: Learn how to deploy and run containerized autonomous driving simulations using Autoware
+        Open AD Kit on Arm Neoverse with Docker, demonstrating SOAFEE-based Shift-Left development workflows.
+        It is desi...
+      preview_generated: Learn how to deploy and run containerized autonomous driving simulations using
+        Autoware Open AD Kit on Arm Neoverse with Docker, demonstrating SOAFEE-based Shift-Left development
+        workflows. It is desi...
+    faqs:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
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+      current_source_hash: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
+      generated_at_before: '2026-05-06T17:17:53Z'
+      generated_at_after: '2026-05-06T17:17:53Z'
+      before_count: 5
+      after_count: 5
+      generated_count: 5
+      change_details:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+      generated_diff:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+  - path: content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
+    status: unchanged
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons: []
+    template_version_before: summary-faq-v1
+    template_version_after: summary-faq-v1
+    summary:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: 92a6dac2b1674a44cd623a0c8c3189b38438124a6067ed7ca776999ff1d8b5bf
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+      current_source_hash: 92a6dac2b1674a44cd623a0c8c3189b38438124a6067ed7ca776999ff1d8b5bf
+      generated_at_before: '2026-05-06T17:17:53Z'
+      generated_at_after: '2026-05-06T17:17:53Z'
+      preview_before: Learn how to implement functional safety isolation for autonomous driving systems
+        on Arm Neoverse using DDS-based communication, containerized deployment, and ISO 26262 compliance
+        principles. It is de...
+      preview_after: Learn how to implement functional safety isolation for autonomous driving systems
+        on Arm Neoverse using DDS-based communication, containerized deployment, and ISO 26262 compliance
+        principles. It is de...
+      preview_generated: Learn how to implement functional safety isolation for autonomous driving systems
+        on Arm Neoverse using DDS-based communication, containerized deployment, and ISO 26262 compliance
+        principles. It is de...
+    faqs:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: 92a6dac2b1674a44cd623a0c8c3189b38438124a6067ed7ca776999ff1d8b5bf
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+      current_source_hash: 92a6dac2b1674a44cd623a0c8c3189b38438124a6067ed7ca776999ff1d8b5bf
+      generated_at_before: '2026-05-06T17:17:53Z'
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+      before_count: 5
+      after_count: 5
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+      change_details:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+      generated_diff:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+  - path: content/learning-paths/automotive/system76-auto/_index.md
+    status: unchanged
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons: []
+    template_version_before: summary-faq-v1
+    template_version_after: summary-faq-v1
+    summary:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: 2b758d2dcf28a683ab164e28578a736d6d730b81dfbc01a6765619052fcdebd0
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+      current_source_hash: 2b758d2dcf28a683ab164e28578a736d6d730b81dfbc01a6765619052fcdebd0
+      generated_at_before: '2026-05-06T17:17:53Z'
+      generated_at_after: '2026-05-06T17:17:53Z'
+      preview_before: Learn how to build and run the Arm Automotive Solutions Software Reference Stack
+        locally on the System76 Thelio Astra desktop using Multipass virtualization and Yocto build tools.
+        It is designed for a...
+      preview_after: Learn how to build and run the Arm Automotive Solutions Software Reference Stack
+        locally on the System76 Thelio Astra desktop using Multipass virtualization and Yocto build tools.
+        It is designed for a...
+      preview_generated: Learn how to build and run the Arm Automotive Solutions Software Reference Stack
+        locally on the System76 Thelio Astra desktop using Multipass virtualization and Yocto build tools.
+        It is designed for a...
+    faqs:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: 2b758d2dcf28a683ab164e28578a736d6d730b81dfbc01a6765619052fcdebd0
+      source_hash_after: 2b758d2dcf28a683ab164e28578a736d6d730b81dfbc01a6765619052fcdebd0
+      current_source_hash: 2b758d2dcf28a683ab164e28578a736d6d730b81dfbc01a6765619052fcdebd0
+      generated_at_before: '2026-05-06T17:17:53Z'
+      generated_at_after: '2026-05-06T17:17:53Z'
+      before_count: 5
+      after_count: 5
+      generated_count: 5
+      change_details:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+      generated_diff:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+  - path: content/learning-paths/automotive/zenacssdebug/_index.md
+    status: unchanged
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons: []
+    template_version_before: summary-faq-v1
+    template_version_after: summary-faq-v1
+    summary:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
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+        It is designe...
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+        using PMU counters, Linux Perf, and topdown-tool to identify bottlenecks across architectures.
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+      preview_after: Learn how to migrate x86-64 SIMD code to Arm64 by mapping Intel SSE/AVX to Arm Neon,
+        SVE, and SME, with code examples and migration strategies using autovectorization or intrinsics.
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+      preview_generated: Learn how to migrate x86-64 SIMD code to Arm64 by mapping Intel SSE/AVX to Arm
+        Neon, SVE, and SME, with code examples and migration strategies using autovectorization or intrinsics.
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+      preview_before: Learn how to sample and profile CPU instructions using WindowsPerf with Arm Statistical
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+        Profiling Extension (SPE) on Windows on Arm, demonstrated with CPython workload analysis. It is
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+      preview_generated: Learn how to sample and profile CPU instructions using WindowsPerf with Arm Statistical
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+      preview_after: Learn how to create and connect to a Windows on Arm virtual machine in Microsoft
+        Azure using the Azure Marketplace and RDP. It is designed for software developers interested using
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+      preview_generated: Learn how to create and connect to a Windows on Arm virtual machine in Microsoft
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+      preview_before: Learn how to build and deploy distributed Zenoh systems on Arm devices like Raspberry
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+        is designed for ro...
+      preview_generated: Learn how to build and deploy distributed Zenoh systems on Arm devices like Raspberry
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+      preview_before: Learn how to design and integrate a custom AXI-Lite peripheral with a Cortex-A9
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+        inputs. It is designed...
+      preview_after: Learn how to design and integrate a custom AXI-Lite peripheral with a Cortex-A9 processor
+        on the Zybo Z7-10 board using Vivado, configuring GPIOs to control LEDs based on switch inputs.
+        It is designed...
+      preview_generated: Learn how to design and integrate a custom AXI-Lite peripheral with a Cortex-A9
+        processor on the Zybo Z7-10 board using Vivado, configuring GPIOs to control LEDs based on switch
+        inputs. It is designed...
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+        DB, and Azure Fun...
+      preview_generated: Learn how to build a complete IoT solution in Azure that streams, stores, monitors,
+        aggregates, and visualizes telemetry data from Arm devices using IoT Hub, Stream Analytics, Cosmos
+        DB, and Azure Fun...
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+        handling. It is desi...
+      preview_generated: Learn how to create, build, and run a bare-metal embedded application for Armv8-A
+        processors using Arm Compiler for Embedded and Fixed Virtual Platforms, including basic exception
+        handling. It is desi...
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+      preview_generated: Learn how to deploy containerized embedded applications and firmware onto an
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+      preview_generated: Learn how to create, build, and debug an RTX5 RTOS-based application using Keil
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+      preview_generated: Learn how to create, configure, and debug an RTX5 RTOS application using Keil
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+      preview_generated: Learn how to prototype and port DSP algorithms using the CMSIS-DSP Python package,
+        mapping Python code to efficient C implementations for embedded Cortex-M and Cortex-A platforms.
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+        for embedded s...
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+        commands. It is designed...
+      preview_generated: Learn how to collect and preprocess audio data using Edge Impulse, train an audio
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+      preview_before: Develop a image classification neural network model and deploy it on an STM32 B-L475E-IOT01A2
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+        for mi...
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+      preview_before: Learn where Arm architecture is used in microcontrollers and discover microcontroller
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+        hardware options for software development on Arm Cortex-M processors. It is designed for software
+        developers worki...
+      preview_generated: Learn where Arm architecture is used in microcontrollers and discover microcontroller
+        hardware options for software development on Arm Cortex-M processors. It is designed for software
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+      preview_before: Learn what differentiates TinyML from other AI domains, explore Arm-based edge devices
+        for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed Virtual
+        Platform. It is ...
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+        for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed Virtual
+        Platform. It is ...
+      preview_generated: Learn what differentiates TinyML from other AI domains, explore Arm-based edge
+        devices for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed
+        Virtual Platform. It is ...
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+  - path: content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
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+      preview_before: Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300 virtual
+        hardware to understand complete IoT software stack construction. It is designed for embedded software
+        developers ...
+      preview_after: Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300 virtual
+        hardware to understand complete IoT software stack construction. It is designed for embedded software
+        developers ...
+      preview_generated: Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300
+        virtual hardware to understand complete IoT software stack construction. It is designed for embedded
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+      preview_before: Learn how to set up a Jetson Orin Nano with a MIPI CSI-2 camera and perform real-time
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+      preview_after: Learn how to set up a Jetson Orin Nano with a MIPI CSI-2 camera and perform real-time
+        object detection from live video and image files using DetectNet and TensorRT. It is designed
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+      preview_generated: Learn how to set up a Jetson Orin Nano with a MIPI CSI-2 camera and perform real-time
+        object detection from live video and image files using DetectNet and TensorRT. It is designed
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+        It is designed for e...
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+      preview_before: Learn how to create debug configurations for virtual platforms and development boards
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+        and update example projects for compatibility with the new CMSIS version. It is designed for maintainers
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+      preview_generated: Learn how to migrate a CMSIS v5-based CMSIS-Pack with device support to CMSIS
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+      preview_generated: Learn how to run large language models locally on the Raspberry Pi 5 using Ollama,
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+        and plays audio resp...
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+        and plays audio resp...
+      preview_generated: Learn how to build a voice-controlled bot on a Raspberry Pi that listens for
+        a wake word, converts speech to text using Google Speech Recognition, sends requests to ChatGPT's
+        API, and plays audio resp...
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+        for software...
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+        designed for anyone in...
+      preview_generated: Learn how to compile the Llama 3 large language model using ExecuTorch, deploy
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+        it to a Raspb...
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+        it to a Raspb...
+      preview_generated: Learn how to reduce compile time for embedded Linux projects by installing a
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+      preview_before: Setup tools and start programming with Raspberry Pi Pico. It is designed for embedded
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+        software developers new to Raspberry Pi Pico. By the end, you will be able to install the Raspberry
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+  - path: content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
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+      preview_before: Learn how to profile Linux kernel modules using Arm Streamline to identify performance
+        bottlenecks, analyze both out-of-tree and in-tree modules, and use Statistical Profiling Extension
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+        bottlenecks, analyze both out-of-tree and in-tree modules, and use Statistical Profiling Extension
+        (SPE) for deep...
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+      preview_generated: Learn how to install Keil MDK Tools, run a TrustZone hello world example on the
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+        designed for softwar...
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+        It is designed for softwar...
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+      preview_generated: Learn how to debug microcontrollers using µVision with basic run/stop debug,
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+      preview_before: Learn how to import, convert, and build uvprojx-based projects to csolution format
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+        for This is a topi...
+      preview_after: Learn how to import, convert, and build uvprojx-based projects to csolution format
+        using Keil Studio, µVision, and command-line tools for CMSIS-Toolbox compatibility. It is designed
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+      preview_generated: Learn how to import, convert, and build uvprojx-based projects to csolution format
+        using Keil Studio, µVision, and command-line tools for CMSIS-Toolbox compatibility. It is designed
+        for This is a topi...
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+      preview_before: Learn how to identify Arm-based targets for TinyML, install Fixed Virtual Platforms,
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+        who want to lea...
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+        who want to lea...
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+        engineers wh...
+      preview_generated: Learn how to create and deploy Azure Resource Manager templates to provision
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+        By the end, yo...
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+        By the end, yo...
+      preview_generated: Learn how to automate the creation of Azure Arm virtual machines using Terraform.
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+        who want to r...
+      preview_generated: Learn how to create a custom Azure Linux 3.0 VM image using QEMU, upload it to
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+        using scalar, Neon, and SVE instructions on Arm-based cloud instances. It is designed for database
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+        developers, pe...
+      preview_generated: Learn how to implement and benchmark bitmap scanning operations for database
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+      preview_before: Learn how to build, profile, and optimize Arm executables using BOLT post-link binary
+        optimization to improve application performance through code layout improvements. It is designed
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+        optimization to improve application performance through code layout improvements. It is designed
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+      preview_generated: Learn how to build, profile, and optimize Arm executables using BOLT post-link
+        binary optimization to improve application performance through code layout improvements. It is
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+        Realms using Trustee services for attestation-based authorization on an FVP with RME support.
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+      preview_generated: Learn how to deploy Confidential Containers from encrypted images inside Arm
+        CCA Realms using Trustee services for attestation-based authorization on an FVP with RME support.
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+      preview_generated: Learn how to install and run Clair on Arm servers using combined and distributed
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+        performance using ClickBench to determine appropriate instance configurations. It is designed
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+        offered by AWS ...
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+        offered by AWS ...
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+        using Terraform for container orchestration. It is designed for software developers who want to
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+        using Terraform for container orchestration. It is designed for software developers who want to
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+        and rebuild applications for multi-architecture support. It is designed for software developers
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+        designed for cloud, p...
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+        build multi-architecture Docker images, and migrate services to Google Axion processors. It is
+        designed for cloud, p...
+      preview_generated: Learn how to create dual-architecture GKE clusters with arm64 and amd64 node
+        pools, build multi-architecture Docker images, and migrate services to Google Axion processors.
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+      preview_before: Learn how to provision Arm64 and x86_64 VM instances on Google Cloud, then install
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+        and use Sweet and Benchstat to measure and compare Go application performance. It is designed
+        for This introductory t...
+      preview_generated: Learn how to provision Arm64 and x86_64 VM instances on Google Cloud, then install
+        and use Sweet and Benchstat to measure and compare Go application performance. It is designed
+        for This introductory t...
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+      preview_generated: Learn how to provision Azure Cobalt 100 Arm64 virtual machines and deploy Golang
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+      preview_generated: Learn how to install Helm on Google Cloud Axion C4A SUSE VMs and deploy applications
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+      preview_generated: Learn where Arm architecture is used in servers and cloud computing, and find
+        Arm-based hardware platforms for software development. It is designed for software developers
+        working on server and cloud ...
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+  - path: content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
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+      preview_generated: Analyze and optimize interrupt request (IRQ) patterns on Arm Linux servers to
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+        aiming to opti...
+      preview_generated: Monitor, interpret, and optimize Java Garbage Collector performance on Arm servers
+        by comparing different GCs and tuning parameters for your workload. It is designed for Java developers
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+        JDK versions and performance optimization flags. It is designed for software developers who want
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+      preview_generated: Deploy and optimize Java applications on Google Cloud Axion processors by testing
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+        and benchmar...
+      preview_generated: Deploy Java on Azure Cobalt 100 Arm virtual machines and benchmark application
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+        deployment and benchmar...
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+        who want to analyz...
+      preview_generated: Profile Java applications on Arm Neoverse servers using flame graphs generated
+        with async-profiler and Java agents to identify performance bottlenecks. It is designed for developers
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+      preview_before: Deploy Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate installation,
+        and execute Arm-native CI/CD pipelines including Docker workflows. It is designed for software
+        developers d...
+      preview_after: Deploy Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate installation,
+        and execute Arm-native CI/CD pipelines including Docker workflows. It is designed for software
+        developers d...
+      preview_generated: Deploy Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate
+        installation, and execute Arm-native CI/CD pipelines including Docker workflows. It is designed
+        for software developers d...
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+  - path: content/learning-paths/servers-and-cloud-computing/kafka/_index.md
+    status: unchanged
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+      preview_before: Deploy and configure a Kafka cluster with Zookeeper on Arm servers, test event streaming,
+        and automate deployment on AWS and Google Cloud. It is designed for software developers who want
+        to learn how ...
+      preview_after: Deploy and configure a Kafka cluster with Zookeeper on Arm servers, test event streaming,
+        and automate deployment on AWS and Google Cloud. It is designed for software developers who want
+        to learn how ...
+      preview_generated: Deploy and configure a Kafka cluster with Zookeeper on Arm servers, test event
+        streaming, and automate deployment on AWS and Google Cloud. It is designed for software developers
+        who want to learn how ...
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+  - path: content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
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+      preview_before: Deploy Apache Kafka on Azure Cobalt 100 Arm virtual machines and benchmark message
+        throughput performance. It is designed for developers looking to migrate their Apache Kafka workloads
+        from x86_64 to ...
+      preview_after: Deploy Apache Kafka on Azure Cobalt 100 Arm virtual machines and benchmark message
+        throughput performance. It is designed for developers looking to migrate their Apache Kafka workloads
+        from x86_64 to ...
+      preview_generated: Deploy Apache Kafka on Azure Cobalt 100 Arm virtual machines and benchmark message
+        throughput performance. It is designed for developers looking to migrate their Apache Kafka workloads
+        from x86_64 to ...
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+  - path: content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
+    status: unchanged
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+    rerun_flags_reset: []
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+      preview_before: Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing Kedify
+        and KEDA with Helm and testing autoscaling behavior. It is designed for developers running HTTP
+        workloads on Kuber...
+      preview_after: Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing Kedify
+        and KEDA with Helm and testing autoscaling behavior. It is designed for developers running HTTP
+        workloads on Kuber...
+      preview_generated: Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing
+        Kedify and KEDA with Helm and testing autoscaling behavior. It is designed for developers running
+        HTTP workloads on Kuber...
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+  - path: content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
+    status: unchanged
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+      preview_before: Create, train, and evaluate a neural network model on Arm servers using Keras Core
+        with TensorFlow, PyTorch, and JAX backends. It is designed for engineers who want to create a
+        neural network model on...
+      preview_after: Create, train, and evaluate a neural network model on Arm servers using Keras Core
+        with TensorFlow, PyTorch, and JAX backends. It is designed for engineers who want to create a
+        neural network model on...
+      preview_generated: Create, train, and evaluate a neural network model on Arm servers using Keras
+        Core with TensorFlow, PyTorch, and JAX backends. It is designed for engineers who want to create
+        a neural network model on...
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+  - path: content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
+    status: unchanged
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+      preview_before: Compile and install custom Linux kernels on Arm cloud instances using TuxMake with
+        configurations for 64 KB page sizes and Fastpath testing. It is designed for software developers
+        building custom Linu...
+      preview_after: Compile and install custom Linux kernels on Arm cloud instances using TuxMake with
+        configurations for 64 KB page sizes and Fastpath testing. It is designed for software developers
+        building custom Linu...
+      preview_generated: Compile and install custom Linux kernels on Arm cloud instances using TuxMake
+        with configurations for 64 KB page sizes and Fastpath testing. It is designed for software developers
+        building custom Linu...
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+  - path: content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
+    status: unchanged
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+      preview_before: Identify and migrate container images in a Kubernetes cluster to Arm-compatible
+        versions using KubeArchInspect reports. It is designed for software developers who want to ensure
+        containers running in ...
+      preview_after: Identify and migrate container images in a Kubernetes cluster to Arm-compatible versions
+        using KubeArchInspect reports. It is designed for software developers who want to ensure containers
+        running in ...
+      preview_generated: Identify and migrate container images in a Kubernetes cluster to Arm-compatible
+        versions using KubeArchInspect reports. It is designed for software developers who want to ensure
+        containers running in ...
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+  - path: content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
+    status: unchanged
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+      preview_before: Deploy AWS Lambda functions on Graviton processors using Terraform for Python and
+        Node.js runtimes. It is designed for software developers who want to learn how to deploy Lambda
+        functions on AWS Gravi...
+      preview_after: Deploy AWS Lambda functions on Graviton processors using Terraform for Python and
+        Node.js runtimes. It is designed for software developers who want to learn how to deploy Lambda
+        functions on AWS Gravi...
+      preview_generated: Deploy AWS Lambda functions on Graviton processors using Terraform for Python
+        and Node.js runtimes. It is designed for software developers who want to learn how to deploy Lambda
+        functions on AWS Gravi...
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+  - path: content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
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+        who want to deploy...
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+        and the MLPerf Inference benchmark suite from MLCommons. It is designed for software developers
+        interested in benchmark...
+      preview_generated: Benchmark machine learning inference performance on Arm servers using TensorFlow
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+      preview_before: Install MongoDB on Arm servers and benchmark database performance using Yahoo Cloud
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+        Serving Benchmark (YCSB) to compare against other architectures. It is designed for software developers
+        who want to ...
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+        Cloud Serving Benchmark (YCSB) to compare against other architectures. It is designed for software
+        developers who want to ...
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+        using mongotop and mongostat monitoring tools. It is designed for software developers who want
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+      preview_generated: Deploy MongoDB on Azure Cobalt 100 Arm virtual machines and benchmark database
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+        for Arm-based s...
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+        Arm software workflow. It is designed for developers interested in building and optimizing vLLM
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+      preview_generated: Run vLLM inference with INT4 quantization on Arm servers walks you through an
+        end-to-end Arm software workflow. It is designed for developers interested in building and optimizing
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+  - path: content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
+    status: unchanged
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+    rerun_flags_reset: []
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+    template_version_before: summary-faq-v1
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+    summary:
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+      drift_detected: false
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+      generated_at_before: '2026-05-06T17:17:59Z'
+      generated_at_after: '2026-05-06T17:17:59Z'
+      preview_before: Run the vvenc H.266 encoder on Arm servers walks you through an end-to-end Arm software
+        workflow. It is designed for software developers who want to build and run the VVenC® (Fraunhofer
+        Versatile Vide...
+      preview_after: Run the vvenc H.266 encoder on Arm servers walks you through an end-to-end Arm software
+        workflow. It is designed for software developers who want to build and run the VVenC® (Fraunhofer
+        Versatile Vide...
+      preview_generated: Run the vvenc H.266 encoder on Arm servers walks you through an end-to-end Arm
+        software workflow. It is designed for software developers who want to build and run the VVenC®
+        (Fraunhofer Versatile Vide...
+    faqs:
+      action: unchanged
+      missing_before: false
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+      changed: false
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+  - path: content/learning-paths/servers-and-cloud-computing/whisper/_index.md
+    status: unchanged
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons: []
+    template_version_before: summary-faq-v1
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+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: e130630b5ae4626641779d0b66d3c1c132b90899cd3d6db07a46df8957c4da38
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+      generated_at_before: '2026-05-06T17:17:59Z'
+      generated_at_after: '2026-05-06T17:17:59Z'
+      preview_before: Accelerate Whisper on Arm with Hugging Face Transformers walks you through an end-to-end
+        Arm software workflow. It is designed for software developers familiar with basic machine learning
+        concepts and...
+      preview_after: Accelerate Whisper on Arm with Hugging Face Transformers walks you through an end-to-end
+        Arm software workflow. It is designed for software developers familiar with basic machine learning
+        concepts and...
+      preview_generated: Accelerate Whisper on Arm with Hugging Face Transformers walks you through an
+        end-to-end Arm software workflow. It is designed for software developers familiar with basic machine
+        learning concepts and...
+    faqs:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
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+  - path: content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
+    status: unchanged
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons: []
+    template_version_before: summary-faq-v1
+    template_version_after: summary-faq-v1
+    summary:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: 46f2645fb6ef298508af93569554315cfa2564be9891acabf1c874c94e52d70d
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+      current_source_hash: 46f2645fb6ef298508af93569554315cfa2564be9891acabf1c874c94e52d70d
+      generated_at_before: '2026-05-06T17:17:59Z'
+      generated_at_after: '2026-05-06T17:17:59Z'
+      preview_before: Deploy MySQL and WordPress on an always free tier Arm shape walks you through an
+        end-to-end Arm software workflow. It is designed for developers who want to install WordPress
+        on Oracle Cloud Infrastru...
+      preview_after: Deploy MySQL and WordPress on an always free tier Arm shape walks you through an
+        end-to-end Arm software workflow. It is designed for developers who want to install WordPress
+        on Oracle Cloud Infrastru...
+      preview_generated: Deploy MySQL and WordPress on an always free tier Arm shape walks you through
+        an end-to-end Arm software workflow. It is designed for developers who want to install WordPress
+        on Oracle Cloud Infrastru...
+    faqs:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: 46f2645fb6ef298508af93569554315cfa2564be9891acabf1c874c94e52d70d
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+      current_source_hash: 46f2645fb6ef298508af93569554315cfa2564be9891acabf1c874c94e52d70d
+      generated_at_before: '2026-05-06T17:17:59Z'
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+      before_count: 5
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+      change_details:
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+        added_questions: []
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+        updated_questions: []
+      generated_diff:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/zlib/_index.md
+    status: unchanged
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons: []
+    template_version_before: summary-faq-v1
+    template_version_after: summary-faq-v1
+    summary:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: 8916f83f621505dc5406f14e70d04e702ea304950e34b4b76dc1bbc987bcc4e3
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+      current_source_hash: 8916f83f621505dc5406f14e70d04e702ea304950e34b4b76dc1bbc987bcc4e3
+      generated_at_before: '2026-05-06T17:17:59Z'
+      generated_at_after: '2026-05-06T17:17:59Z'
+      preview_before: Learn how to build and use zlib-ng on Arm servers, using its Neon SIMD and ARMv8
+        CRC32 optimizations to improve compression performance compared to the system default zlib. It
+        is designed for software...
+      preview_after: Learn how to build and use zlib-ng on Arm servers, using its Neon SIMD and ARMv8
+        CRC32 optimizations to improve compression performance compared to the system default zlib. It
+        is designed for software...
+      preview_generated: Learn how to build and use zlib-ng on Arm servers, using its Neon SIMD and ARMv8
+        CRC32 optimizations to improve compression performance compared to the system default zlib. It
+        is designed for software...
+    faqs:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: 8916f83f621505dc5406f14e70d04e702ea304950e34b4b76dc1bbc987bcc4e3
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+      current_source_hash: 8916f83f621505dc5406f14e70d04e702ea304950e34b4b76dc1bbc987bcc4e3
+      generated_at_before: '2026-05-06T17:17:59Z'
+      generated_at_after: '2026-05-06T17:17:59Z'
+      before_count: 5
+      after_count: 5
+      generated_count: 5
+      change_details:
+        before_count: 5
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+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+      generated_diff:
+        before_count: 5
+        after_count: 5
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+        removed_questions: []
+        updated_questions: []
 - timestamp: '2026-05-06T18:52:15Z'
   mode: write
   require_enable_flag: true

From 11b692bcb6927f6450b29a4415afff5a725148a1 Mon Sep 17 00:00:00 2001
From: Christopher Moroney <christopher.moroney1@gmail.com>
Date: Fri, 8 May 2026 09:29:31 -0700
Subject: [PATCH 10/23] test with other metrics

---
 .../arm_pmu/_index.md                         | 43 -------------------
 .../bolt/_index.md                            |  5 +--
 .../django/_index.md                          |  5 +--
 .../microbenchmark-network-iperf3/_index.md   | 19 ++------
 .../nginx_tune/_index.md                      |  5 +--
 .../processwatch/_index.md                    | 24 -----------
 .../tensorflow-gcp/_index.md                  | 18 +-------
 7 files changed, 12 insertions(+), 107 deletions(-)

diff --git a/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md b/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
index ce5ae6b291..cd8864ebb4 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
@@ -18,48 +18,6 @@ generate_summary_faq: true
 
 # rerun_summary: false
 # rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
-  summary: >-
-    Learn how to access and use Arm hardware performance counters and the system counter from
-    user space using PAPI, perf_event_open, and assembly code for performance instrumentation.
-    It is designed for software developers who want to instrument hardware event counters or the
-    system counter in software applications. By the end, you will be able to understand different
-    options for accessing counters from user space, use the system counter to measure time in
-    code, and use PAPI to instrument event counters in code. It focuses on tools and technologies
-    such as PAPI, perf, Assembly, GCC, and Runbook, Linux environments, and Arm platforms including
-    Neoverse. The main steps cover Counter access options, Use a system counter, Use PAPI for
-    counting, and Use perf_event_open for counting.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand different options for accessing counters from user space, use the system
-      counter to measure time in code, and use PAPI to instrument event counters in code. Learn
-      how to access and use Arm hardware performance counters and the system counter from user
-      space using PAPI, perf_event_open, and assembly code for performance instrumentation.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers who want to instrument hardware event
-      counters or the system counter in software applications.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm computer running Linux. A bare
-      metal or cloud metal instance is best because they expose more counters. You can use a virtual
-      machine (VM), but fewer counters may be available. These instructions have been tested on
-      the `a1.metal` instance type.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including PAPI, perf, Assembly, GCC, and Runbook, Linux environments,
-      and Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Counter access options, Use a system counter, Use
-      PAPI for counting, and Use perf_event_open for counting.
-# END generated_summary_faq
 
 author: Julio Suarez
 
@@ -99,4 +57,3 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
-
diff --git a/content/learning-paths/servers-and-cloud-computing/bolt/_index.md b/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
index 16862039d9..caac7926d2 100644
--- a/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
@@ -16,8 +16,8 @@ prerequisites:
     - (Optional) A second, more powerful Linux system to build the software executable and run BOLT.
 
 generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
@@ -102,4 +102,3 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
-
diff --git a/content/learning-paths/servers-and-cloud-computing/django/_index.md b/content/learning-paths/servers-and-cloud-computing/django/_index.md
index 2f7839c214..1dcd192f1c 100644
--- a/content/learning-paths/servers-and-cloud-computing/django/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/django/_index.md
@@ -18,8 +18,8 @@ prerequisites:
     - To install both [Nginx](/learning-paths/servers-and-cloud-computing/nginx/) and [PostgreSQL](/learning-paths/servers-and-cloud-computing/postgresql/)
 
 generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
+# rerun_summary: false
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
@@ -111,4 +111,3 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
-
diff --git a/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md b/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
index 768394cc38..9cd7b75714 100644
--- a/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
@@ -25,23 +25,13 @@ generated_summary_faq:
   generator: template
   source_hash: a67d36fb650b77c170c15f193049490286a3f097801d0c79d701d3f5610fe1dc
   summary: >-
-    Microbenchmark and tune network performance with iPerf3 and Linux traffic control walks you
-    through an end-to-end Arm software workflow. It is designed for performance engineers, Linux
-    system administrators, and application developers who want to microbenchmark, simulate, or
-    tune the networking performance of distributed systems. By the end, you will be able to run
-    accurate network microbenchmark tests using iPerf3, simulate real-world network conditions
-    using Linux Traffic Control (tc), and tune basic Linux kernel parameters to improve network
-    performance. It focuses on tools and technologies such as iPerf3, Linux environments, Arm
-    platforms including Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud,
-    and Oracle. The main steps cover Set up Arm-based Linux systems for network performance testing
-    with iPerf3, Microbenchmark the network connection, Simulate different network conditions,
-    and Tune kernel parameters.
+    This branch-only testing summary is intentionally out of sync with the current Learning Path
+    source content so the workflow report records preserved summary drift for this LP.
   faqs:
   - question: What will you accomplish in this Learning Path?
     answer: >-
-      You will run accurate network microbenchmark tests using iPerf3, simulate real-world network
-      conditions using Linux Traffic Control (tc), and tune basic Linux kernel parameters to improve
-      network performance.
+      This branch-only testing answer is intentionally stale so the workflow report records preserved
+      FAQ drift for this LP.
   - question: Who is this Learning Path for?
     answer: >-
       This is an introductory topic for performance engineers, Linux system administrators, and
@@ -92,4 +82,3 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
-
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
index 161be7dc82..2c18b0f96e 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
@@ -17,8 +17,8 @@ prerequisites:
     - If you do not already have a Nginx setup, a review of [Learn how to deploy Nginx](/learning-paths/servers-and-cloud-computing/nginx/).
 
 generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+# rerun_faqs: false
 
 # START generated_summary_faq
 generated_summary_faq:
@@ -99,4 +99,3 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
-
diff --git a/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md b/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
index 0bb6cbd4ab..638f5a66ba 100644
--- a/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
@@ -32,29 +32,6 @@ generated_summary_faq:
     such as bpftool, libbpf, Capstone, C, and CPP, Linux environments, and Arm platforms including
     Cortex-A and Neoverse. The main steps cover Install dependencies, Run Process Watch, Learn
     how Process Watch works, and Using Process Watch.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build and run the Process Watch tool on your Arm machine, describe how Process
-      Watch works, and check in real-time whether any workloads are using specific Arm instructions
-      or features.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to build and run the Process
-      Watch tool on an Arm-based machine.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm-based system (bare metal server,
-      cloud instance, or developer board) running Linux with kernel version 5.8.0 or later.; Root
-      access, or the ability to run the sudo command.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including bpftool, libbpf, Capstone, C, and CPP, Linux environments,
-      and Arm platforms such as Cortex-A and Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Install dependencies, Run Process Watch, Learn how
-      Process Watch works, and Using Process Watch.
 # END generated_summary_faq
 
 author: Graham Woodward
@@ -94,4 +71,3 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
-
diff --git a/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
index 9e3a48a6b0..2b7f1a8da1 100644
--- a/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
@@ -15,8 +15,8 @@ prerequisites:
   - Basic familiarity with [TensorFlow](https://www.tensorflow.org/)
 
 generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
+# rerun_summary: false
+# rerun_faqs: false
 
 # START generated_summary_faq
 generated_summary_faq:
@@ -24,19 +24,6 @@ generated_summary_faq:
   generated_at: '2026-05-06T17:17:59Z'
   generator: template
   source_hash: 2c20d30d25a0bb871bdb935d18f7ad8e0740139948133dbd728e10f0add0f94e
-  summary: >-
-    Deploy TensorFlow on Google Cloud C4A (Arm-based Axion VMs) walks you through an end-to-end
-    Arm software workflow. It is designed for software developers deploying and optimizing TensorFlow
-    workloads on Arm64 Linux environments, specifically using Google Cloud C4A virtual machines
-    powered by Axion processors. By the end, you will be able to provision an Arm-based SUSE Linux
-    Enterprise Server (SLES) virtual machine on Google Cloud (C4A with Axion processors), install
-    TensorFlow on a SUSE Arm64 (C4A) instance, and verify TensorFlow by running basic computation
-    and model training tests on Arm64. It focuses on tools and technologies such as TensorFlow,
-    Python, and Keras, Linux environments, Arm platforms including Neoverse, and cloud platforms
-    such as Google Cloud. The main steps cover Get started with TensorFlow on Google Axion C4A,
-    Create a Google Axion C4A Arm virtual machine on GCP, Install TensorFlow, Test TensorFlow
-    baseline performance on Google Axion C4A, and Benchmark TensorFlow model performance using
-    tf.keras.
   faqs:
   - question: What will you accomplish in this Learning Path?
     answer: >-
@@ -103,4 +90,3 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
-

From e937ddc5f98d8e28315b92dec5fd36b737fa945b Mon Sep 17 00:00:00 2001
From: GitHub Actions Summary FAQ Bot <>
Date: Fri, 8 May 2026 16:31:45 +0000
Subject: [PATCH 11/23] Generate Learning Path summary and FAQ content

---
 .../arm_pmu/_index.md                         |    48 +
 .../bolt/_index.md                            |    11 +-
 .../django/_index.md                          |     7 +-
 .../microbenchmark-network-iperf3/_index.md   |     1 +
 .../nginx_tune/_index.md                      |     7 +-
 .../processwatch/_index.md                    |    32 +-
 .../tensorflow-gcp/_index.md                  |    22 +-
 reports/generated-summary-faq/latest-run.yml  | 22321 +++++++++++++++-
 8 files changed, 22316 insertions(+), 133 deletions(-)

diff --git a/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md b/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
index cd8864ebb4..b08f11193d 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
@@ -19,6 +19,53 @@ generate_summary_faq: true
 # rerun_summary: false
 # rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v2
+  generated_at: '2026-05-08T16:31:30Z'
+  generator: template
+  source_hash: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
+  summary_generated_at: '2026-05-08T16:31:30Z'
+  summary_source_hash: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
+  faq_generated_at: '2026-05-08T16:31:30Z'
+  faq_source_hash: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
+  summary: >-
+    Learn how to access and use Arm hardware performance counters and the system counter from
+    user space using PAPI, perf_event_open, and assembly code for performance instrumentation.
+    It is designed for software developers who want to instrument hardware event counters or the
+    system counter in software applications. By the end, you will be able to understand different
+    options for accessing counters from user space, use the system counter to measure time in
+    code, and use PAPI to instrument event counters in code. It focuses on tools and technologies
+    such as PAPI, perf, Assembly, GCC, and Runbook, Linux environments, and Arm platforms including
+    Neoverse. The main steps cover Counter access options, Use a system counter, Use PAPI for
+    counting, and Use perf_event_open for counting.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand different options for accessing counters from user space, use the system
+      counter to measure time in code, and use PAPI to instrument event counters in code. Learn
+      how to access and use Arm hardware performance counters and the system counter from user
+      space using PAPI, perf_event_open, and assembly code for performance instrumentation.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers who want to instrument hardware event
+      counters or the system counter in software applications.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm computer running Linux. A bare
+      metal or cloud metal instance is best because they expose more counters. You can use a virtual
+      machine (VM), but fewer counters may be available. These instructions have been tested on
+      the `a1.metal` instance type.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including PAPI, perf, Assembly, GCC, and Runbook, Linux environments,
+      and Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Counter access options, Use a system counter, Use
+      PAPI for counting, and Use perf_event_open for counting.
+# END generated_summary_faq
+
 author: Julio Suarez
 
 ### Tags
@@ -57,3 +104,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/bolt/_index.md b/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
index caac7926d2..59dcf48db9 100644
--- a/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
@@ -16,18 +16,18 @@ prerequisites:
     - (Optional) A second, more powerful Linux system to build the software executable and run BOLT.
 
 generate_summary_faq: true
-rerun_summary: true
-rerun_faqs: true
+rerun_summary: false
+rerun_faqs: false
 
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v2
-  generated_at: '2026-05-06T18:52:13Z'
+  generated_at: '2026-05-08T16:31:30Z'
   generator: template
   source_hash: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
-  summary_generated_at: '2026-05-06T18:52:13Z'
+  summary_generated_at: '2026-05-08T16:31:30Z'
   summary_source_hash: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
-  faq_generated_at: '2026-05-06T18:52:13Z'
+  faq_generated_at: '2026-05-08T16:31:30Z'
   faq_source_hash: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
   summary: >-
     Learn how to build, profile, and optimize Arm executables using BOLT post-link binary optimization
@@ -102,3 +102,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/django/_index.md b/content/learning-paths/servers-and-cloud-computing/django/_index.md
index 1dcd192f1c..518dbe2190 100644
--- a/content/learning-paths/servers-and-cloud-computing/django/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/django/_index.md
@@ -19,17 +19,17 @@ prerequisites:
 
 generate_summary_faq: true
 # rerun_summary: false
-rerun_faqs: true
+rerun_faqs: false
 
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v2
-  generated_at: '2026-05-06T18:52:13Z'
+  generated_at: '2026-05-08T16:31:30Z'
   generator: template
   source_hash: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
   summary_generated_at: '2026-05-06T17:17:57Z'
   summary_source_hash: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
-  faq_generated_at: '2026-05-06T18:52:13Z'
+  faq_generated_at: '2026-05-08T16:31:30Z'
   faq_source_hash: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
   summary: >-
     Learn how to create a simple Django web application and deploy it on Arm machines using Nginx
@@ -111,3 +111,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md b/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
index 9cd7b75714..74152c8107 100644
--- a/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
@@ -82,3 +82,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
index 2c18b0f96e..13d70aa1de 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
@@ -17,16 +17,16 @@ prerequisites:
     - If you do not already have a Nginx setup, a review of [Learn how to deploy Nginx](/learning-paths/servers-and-cloud-computing/nginx/).
 
 generate_summary_faq: true
-rerun_summary: true
+rerun_summary: false
 # rerun_faqs: false
 
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v2
-  generated_at: '2026-05-06T18:52:14Z'
+  generated_at: '2026-05-08T16:31:32Z'
   generator: template
   source_hash: 10f13dee3459577fcadf5966cf80d990f3396321189f638432a3308699e773a8
-  summary_generated_at: '2026-05-06T18:52:14Z'
+  summary_generated_at: '2026-05-08T16:31:32Z'
   summary_source_hash: 10f13dee3459577fcadf5966cf80d990f3396321189f638432a3308699e773a8
   faq_generated_at: '2026-05-06T17:17:58Z'
   faq_source_hash: 10f13dee3459577fcadf5966cf80d990f3396321189f638432a3308699e773a8
@@ -99,3 +99,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md b/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
index 638f5a66ba..541099f8b2 100644
--- a/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
@@ -19,10 +19,14 @@ generate_summary_faq: true
 # rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
+  template_version: summary-faq-v2
+  generated_at: '2026-05-08T16:31:32Z'
   generator: template
   source_hash: ed85d6171f3c05bfdf1b396065fb0c73ce88724ff63fd1c3c8a93d4c27dd59f8
+  summary_generated_at: '2026-05-06T17:17:58Z'
+  summary_source_hash: ed85d6171f3c05bfdf1b396065fb0c73ce88724ff63fd1c3c8a93d4c27dd59f8
+  faq_generated_at: '2026-05-08T16:31:32Z'
+  faq_source_hash: ed85d6171f3c05bfdf1b396065fb0c73ce88724ff63fd1c3c8a93d4c27dd59f8
   summary: >-
     Run Process watch on your Arm machine walks you through an end-to-end Arm software workflow.
     It is designed for software developers who want to build and run the Process Watch tool on
@@ -32,6 +36,29 @@ generated_summary_faq:
     such as bpftool, libbpf, Capstone, C, and CPP, Linux environments, and Arm platforms including
     Cortex-A and Neoverse. The main steps cover Install dependencies, Run Process Watch, Learn
     how Process Watch works, and Using Process Watch.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build and run the Process Watch tool on your Arm machine, describe how Process
+      Watch works, and check in real-time whether any workloads are using specific Arm instructions
+      or features.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to build and run the Process
+      Watch tool on an Arm-based machine.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm-based system (bare metal server,
+      cloud instance, or developer board) running Linux with kernel version 5.8.0 or later.; Root
+      access, or the ability to run the sudo command.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including bpftool, libbpf, Capstone, C, and CPP, Linux environments,
+      and Arm platforms such as Cortex-A and Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Install dependencies, Run Process Watch, Learn how
+      Process Watch works, and Using Process Watch.
 # END generated_summary_faq
 
 author: Graham Woodward
@@ -71,3 +98,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
index 2b7f1a8da1..b7278443ea 100644
--- a/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
@@ -20,10 +20,27 @@ generate_summary_faq: true
 
 # START generated_summary_faq
 generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
+  template_version: summary-faq-v2
+  generated_at: '2026-05-08T16:31:32Z'
   generator: template
   source_hash: 2c20d30d25a0bb871bdb935d18f7ad8e0740139948133dbd728e10f0add0f94e
+  summary_generated_at: '2026-05-08T16:31:32Z'
+  summary_source_hash: 2c20d30d25a0bb871bdb935d18f7ad8e0740139948133dbd728e10f0add0f94e
+  faq_generated_at: '2026-05-06T17:17:59Z'
+  faq_source_hash: 2c20d30d25a0bb871bdb935d18f7ad8e0740139948133dbd728e10f0add0f94e
+  summary: >-
+    Deploy TensorFlow on Google Cloud C4A (Arm-based Axion VMs) walks you through an end-to-end
+    Arm software workflow. It is designed for software developers deploying and optimizing TensorFlow
+    workloads on Arm64 Linux environments, specifically using Google Cloud C4A virtual machines
+    powered by Axion processors. By the end, you will be able to provision an Arm-based SUSE Linux
+    Enterprise Server (SLES) virtual machine on Google Cloud (C4A with Axion processors), install
+    TensorFlow on a SUSE Arm64 (C4A) instance, and verify TensorFlow by running basic computation
+    and model training tests on Arm64. It focuses on tools and technologies such as TensorFlow,
+    Python, and Keras, Linux environments, Arm platforms including Neoverse, and cloud platforms
+    such as Google Cloud. The main steps cover Get started with TensorFlow on Google Axion C4A,
+    Create a Google Axion C4A Arm virtual machine on GCP, Install TensorFlow, Test TensorFlow
+    baseline performance on Google Axion C4A, and Benchmark TensorFlow model performance using
+    tf.keras.
   faqs:
   - question: What will you accomplish in this Learning Path?
     answer: >-
@@ -90,3 +107,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/reports/generated-summary-faq/latest-run.yml b/reports/generated-summary-faq/latest-run.yml
index eff3abfa64..302b875e0d 100644
--- a/reports/generated-summary-faq/latest-run.yml
+++ b/reports/generated-summary-faq/latest-run.yml
@@ -1,48 +1,48 @@
 latest_run:
-  timestamp: '2026-05-06T19:05:07Z'
+  timestamp: '2026-05-08T16:31:32Z'
   mode: write
   require_enable_flag: true
   path_filter: ''
   limit: 0
-  run_url: https://github.com/chrismoroney/arm-learning-paths/actions/runs/25455312233
+  run_url: https://github.com/chrismoroney/arm-learning-paths/actions/runs/25567077610
   git_ref: STESOL-345
-  git_sha: fb9a9decd347924be14e4503865f03b0b2d990e4
+  git_sha: 11b692bcb6927f6450b29a4415afff5a725148a1
   actor: chrismoroney
   template_version: summary-faq-v2
   totals:
     processed: 407
-    added: 0
-    updated: 0
-    unchanged: 407
+    added: 1
+    updated: 6
+    unchanged: 400
     drift_detected: 0
     skipped: 0
     errors: 0
     removed: 0
-    summary_changed: 0
-    faq_changed: 0
-    rerun_flags_reset: 0
+    summary_changed: 2
+    faq_changed: 2
+    rerun_flags_reset: 3
   section_totals:
     summary:
-      created: 0
-      repaired_missing: 0
-      rerun_requested: 0
-      drift_detected_preserved: 0
-      unchanged: 407
+      created: 1
+      repaired_missing: 1
+      rerun_requested: 2
+      drift_detected_preserved: 1
+      unchanged: 402
     faqs:
-      created: 0
-      repaired_missing: 0
-      rerun_requested: 0
-      drift_detected_preserved: 0
-      unchanged: 407
+      created: 1
+      repaired_missing: 1
+      rerun_requested: 2
+      drift_detected_preserved: 1
+      unchanged: 402
   reason_totals:
-    initial_generation: 0
-    missing_summary: 0
-    missing_faqs: 0
-    rerun_summary: 0
-    rerun_faqs: 0
-    summary_drift_detected: 0
-    faq_drift_detected: 0
-    rerun_flags_reset: 0
+    initial_generation: 1
+    missing_summary: 1
+    missing_faqs: 1
+    rerun_summary: 2
+    rerun_faqs: 2
+    summary_drift_detected: 1
+    faq_drift_detected: 1
+    rerun_flags_reset: 3
   paths:
   - path: content/learning-paths/automotive/openadkit1_container/_index.md
     status: unchanged
@@ -11601,27 +11601,26 @@ latest_run:
         removed_questions: []
         updated_questions: []
   - path: content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
+    status: added
+    changed_on_disk: true
+    managed_block_updated: true
     rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    change_reasons:
+    - initial_generation
+    template_version_before: ''
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       preview_after: Learn how to access and use Arm hardware performance counters and the system counter
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         It is designed for ...
@@ -11629,29 +11628,39 @@ latest_run:
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         It is designed for ...
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+        - Which tools, languages, or platforms does it cover?
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+        - Which tools, languages, or platforms does it cover?
+        - How is the Learning Path structured?
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   - path: content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
@@ -12087,24 +12096,29 @@ latest_run:
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   - path: content/learning-paths/servers-and-cloud-computing/bolt/_index.md
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       preview_before: Learn how to build, profile, and optimize Arm executables using BOLT post-link binary
         optimization to improve application performance through code layout improvements. It is designed
         for software deve...
@@ -12115,16 +12129,16 @@ latest_run:
         binary optimization to improve application performance through code layout improvements. It is
         designed for software deve...
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@@ -13681,16 +13698,16 @@ latest_run:
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         on Arm machines...
     faqs:
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   - path: content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
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-      preview_after: Microbenchmark and tune network performance with iPerf3 and Linux traffic control
-        walks you through an end-to-end Arm software workflow. It is designed for performance engineers,
-        Linux system administ...
+      preview_before: This branch-only testing summary is intentionally out of sync with the current Learning
+        Path source content so the workflow report records preserved summary drift for this LP.
+      preview_after: This branch-only testing summary is intentionally out of sync with the current Learning
+        Path source content so the workflow report records preserved summary drift for this LP.
       preview_generated: Microbenchmark and tune network performance with iPerf3 and Linux traffic control
         walks you through an end-to-end Arm software workflow. It is designed for performance engineers,
         Linux system administ...
     faqs:
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   - path: content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
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         on an Arm-based mac...
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-      preview_before: Deploy TensorFlow on Google Cloud C4A (Arm-based Axion VMs) walks you through an
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       preview_after: Deploy TensorFlow on Google Cloud C4A (Arm-based Axion VMs) walks you through an
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@@ -22023,6 +22054,22060 @@ latest_run:
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+      preview_after: Learn how to design and integrate a custom AXI-Lite peripheral with a Cortex-A9 processor
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+      preview_generated: Learn how to design and integrate a custom AXI-Lite peripheral with a Cortex-A9
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+      preview_after: Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit and
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+        a neural network model t...
+      preview_generated: Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit
+        and run inference on the Ethos-U85 NPU. It is designed for embedded developers who want to deploy
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+        using Arduino IDE, PIR sensors, and interrupt-driven programming on baremetal. It is designed
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+        using Arduino IDE, PIR sensors, and interrupt-driven programming on baremetal. It is designed
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+        It is designed for ...
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+        embedded applications using Fixed Virtual Platforms (FVPs) or hardware with DSTREAM debug probes.
+        It is designed for ...
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+        debug embedded applications using Fixed Virtual Platforms (FVPs) or hardware with DSTREAM debug
+        probes. It is designed for ...
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+      preview_before: Learn how to write mixed C and assembly programs for Cortex-M microcontrollers using
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+        who are interes...
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+        who are interes...
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+        using Keil MDK, following Arm Procedure Call Standard conventions. It is designed for software
+        developers who are interes...
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+  - path: content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
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+      preview_before: Learn how to create a custom Balena OS image, run it on Arm Virtual Hardware, and
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+        deploy IoT applications to a virtual Raspberry Pi 4 device. It is designed for embedded software
+        developers interested...
+      preview_generated: Learn how to create a custom Balena OS image, run it on Arm Virtual Hardware,
+        and deploy IoT applications to a virtual Raspberry Pi 4 device. It is designed for embedded software
+        developers interested...
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+  - path: content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
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+      preview_before: Learn how to set up AWS IoT Greengrass Core on Arm Virtual Hardware and deploy AWS
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+        developers interested ...
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+        developers interested ...
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+      preview_generated: Learn how to build Matter reference examples on Arm Virtual Hardware, demonstrate
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+        DB, and Azure Fun...
+      preview_generated: Learn how to build a complete IoT solution in Azure that streams, stores, monitors,
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+        DB, and Azure Fun...
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+        handling. It is desi...
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+      preview_generated: Learn how to set up and use code coverage in Keil MDK with FVPs to verify that
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+      preview_before: Device-to-Device communication with Device Connect walks you through an end-to-end
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+        Arm software workflow. It is designed for developers wiring up heterogeneous edge fleets, where
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+      preview_generated: Device-to-Device communication with Device Connect walks you through an end-to-end
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+        and physical robots. It...
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+        for structured device access and Strands for agent orchestration, with examples for both simulated
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+        commands. It is designed...
+      preview_generated: Learn how to collect and preprocess audio data using Edge Impulse, train an audio
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+        commands. It is designed...
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+      preview_before: Develop a image classification neural network model and deploy it on an STM32 B-L475E-IOT01A2
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+        board. It is designed for embedded software developers interested in building neural network models
+        for mi...
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+      preview_before: Learn what differentiates TinyML from other AI domains, explore Arm-based edge devices
+        for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed Virtual
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+        for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed Virtual
+        Platform. It is ...
+      preview_generated: Learn what differentiates TinyML from other AI domains, explore Arm-based edge
+        devices for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed
+        Virtual Platform. It is ...
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+  - path: content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
+    status: unchanged
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+      preview_before: Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300 virtual
+        hardware to understand complete IoT software stack construction. It is designed for embedded software
+        developers ...
+      preview_after: Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300 virtual
+        hardware to understand complete IoT software stack construction. It is designed for embedded software
+        developers ...
+      preview_generated: Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300
+        virtual hardware to understand complete IoT software stack construction. It is designed for embedded
+        software developers ...
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+      preview_before: Learn how to understand and select physical and virtual hardware targets for ML
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+      preview_before: Learn how to create debug configurations for virtual platforms and development boards
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+        and plays audio resp...
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+      preview_before: Setup tools and start programming with Raspberry Pi Pico. It is designed for embedded
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+      preview_generated: Build a letter recognition neural network model using TensorFlow and deploy it
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+        designed for machine learnin...
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+      preview_before: Learn how to install Keil MDK Tools, run a TrustZone hello world example on the
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+        It is designed for softwar...
+      preview_after: Learn how to install Keil MDK Tools, run a TrustZone hello world example on the NXP
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+  - path: content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
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+        in a 4U chassis, and install single board computers in the racks using 3D-printed parts. It is
+        designed for softwar...
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+        system in a 4U chassis, and install single board computers in the racks using 3D-printed parts.
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+  - path: content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
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+      preview_before: Learn how to debug microcontrollers using µVision with basic run/stop debug, advanced
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+        power measurement ...
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+        and power measurement ...
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+      preview_before: Learn how to import, convert, and build uvprojx-based projects to csolution format
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+        for This is a topi...
+      preview_after: Learn how to import, convert, and build uvprojx-based projects to csolution format
+        using Keil Studio, µVision, and command-line tools for CMSIS-Toolbox compatibility. It is designed
+        for This is a topi...
+      preview_generated: Learn how to import, convert, and build uvprojx-based projects to csolution format
+        using Keil Studio, µVision, and command-line tools for CMSIS-Toolbox compatibility. It is designed
+        for This is a topi...
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+      preview_generated: Learn how to install and run Clair on Arm servers using combined and distributed
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+        who wa...
+      preview_generated: Learn how to use docker buildx and docker manifest to build and deploy multi-architecture
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+        and benchmark HTTP proxy performance with load testing. It is designed for This introductory topic
+        for software...
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+      preview_before: Learn how to implement exponential functions using Arm SVE intrinsics with the FEXPA
+        instruction for hardware-accelerated computations on Neoverse processors. It is designed for developers
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+      preview_generated: Learn how to provision Arm64 and x86_64 VM instances on Google Cloud, then install
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+      preview_generated: Learn where Arm architecture is used in servers and cloud computing, and find
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+        and benchmar...
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+      preview_generated: Profile Java applications on Arm Neoverse servers using flame graphs generated
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+        developers d...
+      preview_generated: Deploy Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate
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+      preview_after: Deploy and configure a Kafka cluster with Zookeeper on Arm servers, test event streaming,
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+        to learn how ...
+      preview_generated: Deploy and configure a Kafka cluster with Zookeeper on Arm servers, test event
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+        who want to learn how ...
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+        throughput performance. It is designed for developers looking to migrate their Apache Kafka workloads
+        from x86_64 to ...
+      preview_generated: Deploy Apache Kafka on Azure Cobalt 100 Arm virtual machines and benchmark message
+        throughput performance. It is designed for developers looking to migrate their Apache Kafka workloads
+        from x86_64 to ...
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+      preview_before: Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing Kedify
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+      preview_generated: Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing
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+      preview_generated: Compile and install custom Linux kernels on Arm cloud instances using TuxMake
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+      preview_after: Identify and migrate container images in a Kubernetes cluster to Arm-compatible versions
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+      preview_generated: Identify and migrate container images in a Kubernetes cluster to Arm-compatible
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+        developers and perfo...
+      preview_generated: Use ASCT to measure cache latency, streaming bandwidth, and coherency latency
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+      preview_generated: Test and validate thread synchronization approaches in the Arm memory model using
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+        to identify and resolve AArch64 porting challenges before migration. It is designed for developers
+        looking to migrate a...
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+      preview_before: Learn how to deploy and configure MinIO on an Azure Cobalt 100 virtual machine,
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+      preview_before: Benchmark machine learning inference performance on Arm servers using TensorFlow
+        and the MLPerf Inference benchmark suite from MLCommons. It is designed for software developers
+        interested in benchmark...
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+        and the MLPerf Inference benchmark suite from MLCommons. It is designed for software developers
+        interested in benchmark...
+      preview_generated: Benchmark machine learning inference performance on Arm servers using TensorFlow
+        and the MLPerf Inference benchmark suite from MLCommons. It is designed for software developers
+        interested in benchmark...
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+  - path: content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
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+      preview_before: Install MongoDB on Arm servers and benchmark database performance using Yahoo Cloud
+        Serving Benchmark (YCSB) to compare against other architectures. It is designed for software developers
+        who want to ...
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+        Serving Benchmark (YCSB) to compare against other architectures. It is designed for software developers
+        who want to ...
+      preview_generated: Install MongoDB on Arm servers and benchmark database performance using Yahoo
+        Cloud Serving Benchmark (YCSB) to compare against other architectures. It is designed for software
+        developers who want to ...
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+      preview_generated: Deploy SqueezeNet 1.0 INT8 model with ONNX Runtime on Azure Cobalt 100 walks
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+      preview_generated: Build multi-architecture applications with Red Hat OpenShift Pipelines on AWS
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+        is designed...
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+        and Kolla-Ansible for containerized production deployments. It is designed for This learning path
+        is designed...
+      preview_generated: Deploy OpenStack on Azure Cobalt 100 Arm64 virtual machines using DevStack for
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+        learning path is designed...
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+        Arm software workflow. It is designed for DevOps engineers, platform engineers, and software developers
+        who wa...
+      preview_generated: Deploy OpenTelemetry on Google Cloud C4A Axion processors walks you through an
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+      preview_generated: Understand Arm Pointer Authentication walks you through an end-to-end Arm software
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+    summary:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
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+      generated_at_before: '2026-05-06T17:17:59Z'
+      generated_at_after: '2026-05-06T17:17:59Z'
+      preview_before: Install Vectorscan (Hyperscan on Arm) and use it with Snort 3 walks you through
+        an end-to-end Arm software workflow. It is designed for software developers using Hyperscan who
+        want to migrate to Arm. ...
+      preview_after: Install Vectorscan (Hyperscan on Arm) and use it with Snort 3 walks you through an
+        end-to-end Arm software workflow. It is designed for software developers using Hyperscan who want
+        to migrate to Arm. ...
+      preview_generated: Install Vectorscan (Hyperscan on Arm) and use it with Snort 3 walks you through
+        an end-to-end Arm software workflow. It is designed for software developers using Hyperscan who
+        want to migrate to Arm. ...
+    faqs:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: beb244c7766c474b47942b86f1cdd0d124c1cccb74dbe40f0b6c0917d0b3d31a
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+      before_count: 5
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+      change_details:
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+        removed_questions: []
+        updated_questions: []
+      generated_diff:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/vllm/_index.md
+    status: unchanged
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons: []
+    template_version_before: summary-faq-v1
+    template_version_after: summary-faq-v1
+    summary:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: db5987ec7987375d9b51de843b0e1faf0e61731b14c5a563d19aaad26f50f6bb
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+      generated_at_before: '2026-05-06T17:17:59Z'
+      generated_at_after: '2026-05-06T17:17:59Z'
+      preview_before: Build and Run vLLM on Arm Servers walks you through an end-to-end Arm software workflow.
+        It is designed for software developers and AI engineers interested in learning how to use the
+        vLLM library on A...
+      preview_after: Build and Run vLLM on Arm Servers walks you through an end-to-end Arm software workflow.
+        It is designed for software developers and AI engineers interested in learning how to use the
+        vLLM library on A...
+      preview_generated: Build and Run vLLM on Arm Servers walks you through an end-to-end Arm software
+        workflow. It is designed for software developers and AI engineers interested in learning how to
+        use the vLLM library on A...
+    faqs:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: db5987ec7987375d9b51de843b0e1faf0e61731b14c5a563d19aaad26f50f6bb
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+      generated_at_after: '2026-05-06T17:17:59Z'
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+      change_details:
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+        added_questions: []
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+        updated_questions: []
+      generated_diff:
+        before_count: 5
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+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
+    status: unchanged
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons: []
+    template_version_before: summary-faq-v1
+    template_version_after: summary-faq-v1
+    summary:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: 487e9829c6e08798ad01be7a01f192e10e99cb06b71108575f1919c134eece02
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+      generated_at_before: '2026-05-06T17:17:59Z'
+      generated_at_after: '2026-05-06T17:17:59Z'
+      preview_before: Run vLLM inference with INT4 quantization on Arm servers walks you through an end-to-end
+        Arm software workflow. It is designed for developers interested in building and optimizing vLLM
+        for Arm-based s...
+      preview_after: Run vLLM inference with INT4 quantization on Arm servers walks you through an end-to-end
+        Arm software workflow. It is designed for developers interested in building and optimizing vLLM
+        for Arm-based s...
+      preview_generated: Run vLLM inference with INT4 quantization on Arm servers walks you through an
+        end-to-end Arm software workflow. It is designed for developers interested in building and optimizing
+        vLLM for Arm-based s...
+    faqs:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: 487e9829c6e08798ad01be7a01f192e10e99cb06b71108575f1919c134eece02
+      source_hash_after: 487e9829c6e08798ad01be7a01f192e10e99cb06b71108575f1919c134eece02
+      current_source_hash: 487e9829c6e08798ad01be7a01f192e10e99cb06b71108575f1919c134eece02
+      generated_at_before: '2026-05-06T17:17:59Z'
+      generated_at_after: '2026-05-06T17:17:59Z'
+      before_count: 5
+      after_count: 5
+      generated_count: 5
+      change_details:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+      generated_diff:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
+    status: unchanged
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons: []
+    template_version_before: summary-faq-v1
+    template_version_after: summary-faq-v1
+    summary:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: 4ed20056b2d82bd2c9ab1afe3d74657df9ac09808592d843c1b143626a8668ac
+      source_hash_after: 4ed20056b2d82bd2c9ab1afe3d74657df9ac09808592d843c1b143626a8668ac
+      current_source_hash: 4ed20056b2d82bd2c9ab1afe3d74657df9ac09808592d843c1b143626a8668ac
+      generated_at_before: '2026-05-06T17:17:59Z'
+      generated_at_after: '2026-05-06T17:17:59Z'
+      preview_before: Run the vvenc H.266 encoder on Arm servers walks you through an end-to-end Arm software
+        workflow. It is designed for software developers who want to build and run the VVenC® (Fraunhofer
+        Versatile Vide...
+      preview_after: Run the vvenc H.266 encoder on Arm servers walks you through an end-to-end Arm software
+        workflow. It is designed for software developers who want to build and run the VVenC® (Fraunhofer
+        Versatile Vide...
+      preview_generated: Run the vvenc H.266 encoder on Arm servers walks you through an end-to-end Arm
+        software workflow. It is designed for software developers who want to build and run the VVenC®
+        (Fraunhofer Versatile Vide...
+    faqs:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: 4ed20056b2d82bd2c9ab1afe3d74657df9ac09808592d843c1b143626a8668ac
+      source_hash_after: 4ed20056b2d82bd2c9ab1afe3d74657df9ac09808592d843c1b143626a8668ac
+      current_source_hash: 4ed20056b2d82bd2c9ab1afe3d74657df9ac09808592d843c1b143626a8668ac
+      generated_at_before: '2026-05-06T17:17:59Z'
+      generated_at_after: '2026-05-06T17:17:59Z'
+      before_count: 5
+      after_count: 5
+      generated_count: 5
+      change_details:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+      generated_diff:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/whisper/_index.md
+    status: unchanged
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons: []
+    template_version_before: summary-faq-v1
+    template_version_after: summary-faq-v1
+    summary:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: e130630b5ae4626641779d0b66d3c1c132b90899cd3d6db07a46df8957c4da38
+      source_hash_after: e130630b5ae4626641779d0b66d3c1c132b90899cd3d6db07a46df8957c4da38
+      current_source_hash: e130630b5ae4626641779d0b66d3c1c132b90899cd3d6db07a46df8957c4da38
+      generated_at_before: '2026-05-06T17:17:59Z'
+      generated_at_after: '2026-05-06T17:17:59Z'
+      preview_before: Accelerate Whisper on Arm with Hugging Face Transformers walks you through an end-to-end
+        Arm software workflow. It is designed for software developers familiar with basic machine learning
+        concepts and...
+      preview_after: Accelerate Whisper on Arm with Hugging Face Transformers walks you through an end-to-end
+        Arm software workflow. It is designed for software developers familiar with basic machine learning
+        concepts and...
+      preview_generated: Accelerate Whisper on Arm with Hugging Face Transformers walks you through an
+        end-to-end Arm software workflow. It is designed for software developers familiar with basic machine
+        learning concepts and...
+    faqs:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: e130630b5ae4626641779d0b66d3c1c132b90899cd3d6db07a46df8957c4da38
+      source_hash_after: e130630b5ae4626641779d0b66d3c1c132b90899cd3d6db07a46df8957c4da38
+      current_source_hash: e130630b5ae4626641779d0b66d3c1c132b90899cd3d6db07a46df8957c4da38
+      generated_at_before: '2026-05-06T17:17:59Z'
+      generated_at_after: '2026-05-06T17:17:59Z'
+      before_count: 5
+      after_count: 5
+      generated_count: 5
+      change_details:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+      generated_diff:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
+    status: unchanged
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons: []
+    template_version_before: summary-faq-v1
+    template_version_after: summary-faq-v1
+    summary:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: 46f2645fb6ef298508af93569554315cfa2564be9891acabf1c874c94e52d70d
+      source_hash_after: 46f2645fb6ef298508af93569554315cfa2564be9891acabf1c874c94e52d70d
+      current_source_hash: 46f2645fb6ef298508af93569554315cfa2564be9891acabf1c874c94e52d70d
+      generated_at_before: '2026-05-06T17:17:59Z'
+      generated_at_after: '2026-05-06T17:17:59Z'
+      preview_before: Deploy MySQL and WordPress on an always free tier Arm shape walks you through an
+        end-to-end Arm software workflow. It is designed for developers who want to install WordPress
+        on Oracle Cloud Infrastru...
+      preview_after: Deploy MySQL and WordPress on an always free tier Arm shape walks you through an
+        end-to-end Arm software workflow. It is designed for developers who want to install WordPress
+        on Oracle Cloud Infrastru...
+      preview_generated: Deploy MySQL and WordPress on an always free tier Arm shape walks you through
+        an end-to-end Arm software workflow. It is designed for developers who want to install WordPress
+        on Oracle Cloud Infrastru...
+    faqs:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: 46f2645fb6ef298508af93569554315cfa2564be9891acabf1c874c94e52d70d
+      source_hash_after: 46f2645fb6ef298508af93569554315cfa2564be9891acabf1c874c94e52d70d
+      current_source_hash: 46f2645fb6ef298508af93569554315cfa2564be9891acabf1c874c94e52d70d
+      generated_at_before: '2026-05-06T17:17:59Z'
+      generated_at_after: '2026-05-06T17:17:59Z'
+      before_count: 5
+      after_count: 5
+      generated_count: 5
+      change_details:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+      generated_diff:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/zlib/_index.md
+    status: unchanged
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons: []
+    template_version_before: summary-faq-v1
+    template_version_after: summary-faq-v1
+    summary:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: 8916f83f621505dc5406f14e70d04e702ea304950e34b4b76dc1bbc987bcc4e3
+      source_hash_after: 8916f83f621505dc5406f14e70d04e702ea304950e34b4b76dc1bbc987bcc4e3
+      current_source_hash: 8916f83f621505dc5406f14e70d04e702ea304950e34b4b76dc1bbc987bcc4e3
+      generated_at_before: '2026-05-06T17:17:59Z'
+      generated_at_after: '2026-05-06T17:17:59Z'
+      preview_before: Learn how to build and use zlib-ng on Arm servers, using its Neon SIMD and ARMv8
+        CRC32 optimizations to improve compression performance compared to the system default zlib. It
+        is designed for software...
+      preview_after: Learn how to build and use zlib-ng on Arm servers, using its Neon SIMD and ARMv8
+        CRC32 optimizations to improve compression performance compared to the system default zlib. It
+        is designed for software...
+      preview_generated: Learn how to build and use zlib-ng on Arm servers, using its Neon SIMD and ARMv8
+        CRC32 optimizations to improve compression performance compared to the system default zlib. It
+        is designed for software...
+    faqs:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: 8916f83f621505dc5406f14e70d04e702ea304950e34b4b76dc1bbc987bcc4e3
+      source_hash_after: 8916f83f621505dc5406f14e70d04e702ea304950e34b4b76dc1bbc987bcc4e3
+      current_source_hash: 8916f83f621505dc5406f14e70d04e702ea304950e34b4b76dc1bbc987bcc4e3
+      generated_at_before: '2026-05-06T17:17:59Z'
+      generated_at_after: '2026-05-06T17:17:59Z'
+      before_count: 5
+      after_count: 5
+      generated_count: 5
+      change_details:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+      generated_diff:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
 - timestamp: '2026-05-06T19:05:07Z'
   mode: write
   require_enable_flag: true

From a102be7c3271ce56120c193bfc2c779705fbecfb Mon Sep 17 00:00:00 2001
From: Christopher Moroney <christopher.moroney1@gmail.com>
Date: Fri, 8 May 2026 11:08:14 -0700
Subject: [PATCH 12/23] test another run

---
 .github/workflows/generate-summary-faq.yml    |     1 +
 .../arm_pmu/_index.md                         |    48 -
 .../bolt/_index.md                            |     5 +-
 .../django/_index.md                          |     3 +-
 .../nginx_tune/_index.md                      |     3 +-
 .../processwatch/_index.md                    |    24 -
 .../tensorflow-gcp/_index.md                  |    14 -
 reports/generated-summary-faq/latest-run.yml  | 44133 +++++++++++++++-
 tools/generate_summary_faq.py                 |     5 +
 9 files changed, 44117 insertions(+), 119 deletions(-)

diff --git a/.github/workflows/generate-summary-faq.yml b/.github/workflows/generate-summary-faq.yml
index ed1b38510c..b3195751f0 100644
--- a/.github/workflows/generate-summary-faq.yml
+++ b/.github/workflows/generate-summary-faq.yml
@@ -139,6 +139,7 @@ jobs:
           print(metric_row("Added", totals.get("added", 0)))
           print(metric_row("Updated", totals.get("updated", 0)))
           print(metric_row("Drift detected", totals.get("drift_detected", 0)))
+          print(metric_row("Paths with drift", totals.get("paths_with_drift", 0)))
           print(metric_row("Unchanged", totals.get("unchanged", 0)))
           print(metric_row("Errors", totals.get("errors", 0)))
           print(metric_row("Summary changed", totals.get("summary_changed", 0)))
diff --git a/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md b/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
index b08f11193d..cd8864ebb4 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
@@ -19,53 +19,6 @@ generate_summary_faq: true
 # rerun_summary: false
 # rerun_faqs: false
 
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v2
-  generated_at: '2026-05-08T16:31:30Z'
-  generator: template
-  source_hash: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
-  summary_generated_at: '2026-05-08T16:31:30Z'
-  summary_source_hash: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
-  faq_generated_at: '2026-05-08T16:31:30Z'
-  faq_source_hash: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
-  summary: >-
-    Learn how to access and use Arm hardware performance counters and the system counter from
-    user space using PAPI, perf_event_open, and assembly code for performance instrumentation.
-    It is designed for software developers who want to instrument hardware event counters or the
-    system counter in software applications. By the end, you will be able to understand different
-    options for accessing counters from user space, use the system counter to measure time in
-    code, and use PAPI to instrument event counters in code. It focuses on tools and technologies
-    such as PAPI, perf, Assembly, GCC, and Runbook, Linux environments, and Arm platforms including
-    Neoverse. The main steps cover Counter access options, Use a system counter, Use PAPI for
-    counting, and Use perf_event_open for counting.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand different options for accessing counters from user space, use the system
-      counter to measure time in code, and use PAPI to instrument event counters in code. Learn
-      how to access and use Arm hardware performance counters and the system counter from user
-      space using PAPI, perf_event_open, and assembly code for performance instrumentation.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers who want to instrument hardware event
-      counters or the system counter in software applications.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm computer running Linux. A bare
-      metal or cloud metal instance is best because they expose more counters. You can use a virtual
-      machine (VM), but fewer counters may be available. These instructions have been tested on
-      the `a1.metal` instance type.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including PAPI, perf, Assembly, GCC, and Runbook, Linux environments,
-      and Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Counter access options, Use a system counter, Use
-      PAPI for counting, and Use perf_event_open for counting.
-# END generated_summary_faq
-
 author: Julio Suarez
 
 ### Tags
@@ -104,4 +57,3 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
-
diff --git a/content/learning-paths/servers-and-cloud-computing/bolt/_index.md b/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
index 59dcf48db9..5fe9cbf948 100644
--- a/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
@@ -16,8 +16,8 @@ prerequisites:
     - (Optional) A second, more powerful Linux system to build the software executable and run BOLT.
 
 generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
@@ -102,4 +102,3 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
-
diff --git a/content/learning-paths/servers-and-cloud-computing/django/_index.md b/content/learning-paths/servers-and-cloud-computing/django/_index.md
index 518dbe2190..fdb9673228 100644
--- a/content/learning-paths/servers-and-cloud-computing/django/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/django/_index.md
@@ -19,7 +19,7 @@ prerequisites:
 
 generate_summary_faq: true
 # rerun_summary: false
-rerun_faqs: false
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
@@ -111,4 +111,3 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
-
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
index 13d70aa1de..71c2147094 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
@@ -17,7 +17,7 @@ prerequisites:
     - If you do not already have a Nginx setup, a review of [Learn how to deploy Nginx](/learning-paths/servers-and-cloud-computing/nginx/).
 
 generate_summary_faq: true
-rerun_summary: false
+rerun_summary: true
 # rerun_faqs: false
 
 # START generated_summary_faq
@@ -99,4 +99,3 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
-
diff --git a/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md b/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
index 541099f8b2..594279eba5 100644
--- a/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
@@ -36,29 +36,6 @@ generated_summary_faq:
     such as bpftool, libbpf, Capstone, C, and CPP, Linux environments, and Arm platforms including
     Cortex-A and Neoverse. The main steps cover Install dependencies, Run Process Watch, Learn
     how Process Watch works, and Using Process Watch.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build and run the Process Watch tool on your Arm machine, describe how Process
-      Watch works, and check in real-time whether any workloads are using specific Arm instructions
-      or features.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to build and run the Process
-      Watch tool on an Arm-based machine.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm-based system (bare metal server,
-      cloud instance, or developer board) running Linux with kernel version 5.8.0 or later.; Root
-      access, or the ability to run the sudo command.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including bpftool, libbpf, Capstone, C, and CPP, Linux environments,
-      and Arm platforms such as Cortex-A and Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Install dependencies, Run Process Watch, Learn how
-      Process Watch works, and Using Process Watch.
 # END generated_summary_faq
 
 author: Graham Woodward
@@ -98,4 +75,3 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
-
diff --git a/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
index b7278443ea..7529711128 100644
--- a/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
@@ -28,19 +28,6 @@ generated_summary_faq:
   summary_source_hash: 2c20d30d25a0bb871bdb935d18f7ad8e0740139948133dbd728e10f0add0f94e
   faq_generated_at: '2026-05-06T17:17:59Z'
   faq_source_hash: 2c20d30d25a0bb871bdb935d18f7ad8e0740139948133dbd728e10f0add0f94e
-  summary: >-
-    Deploy TensorFlow on Google Cloud C4A (Arm-based Axion VMs) walks you through an end-to-end
-    Arm software workflow. It is designed for software developers deploying and optimizing TensorFlow
-    workloads on Arm64 Linux environments, specifically using Google Cloud C4A virtual machines
-    powered by Axion processors. By the end, you will be able to provision an Arm-based SUSE Linux
-    Enterprise Server (SLES) virtual machine on Google Cloud (C4A with Axion processors), install
-    TensorFlow on a SUSE Arm64 (C4A) instance, and verify TensorFlow by running basic computation
-    and model training tests on Arm64. It focuses on tools and technologies such as TensorFlow,
-    Python, and Keras, Linux environments, Arm platforms including Neoverse, and cloud platforms
-    such as Google Cloud. The main steps cover Get started with TensorFlow on Google Axion C4A,
-    Create a Google Axion C4A Arm virtual machine on GCP, Install TensorFlow, Test TensorFlow
-    baseline performance on Google Axion C4A, and Benchmark TensorFlow model performance using
-    tf.keras.
   faqs:
   - question: What will you accomplish in this Learning Path?
     answer: >-
@@ -107,4 +94,3 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
-
diff --git a/reports/generated-summary-faq/latest-run.yml b/reports/generated-summary-faq/latest-run.yml
index 302b875e0d..479d3278f4 100644
--- a/reports/generated-summary-faq/latest-run.yml
+++ b/reports/generated-summary-faq/latest-run.yml
@@ -1,20 +1,21 @@
 latest_run:
-  timestamp: '2026-05-08T16:31:32Z'
-  mode: write
+  timestamp: '2026-05-08T17:55:58Z'
+  mode: dry-run
   require_enable_flag: true
   path_filter: ''
   limit: 0
-  run_url: https://github.com/chrismoroney/arm-learning-paths/actions/runs/25567077610
-  git_ref: STESOL-345
-  git_sha: 11b692bcb6927f6450b29a4415afff5a725148a1
-  actor: chrismoroney
+  run_url: ''
+  git_ref: ''
+  git_sha: ''
+  actor: ''
   template_version: summary-faq-v2
   totals:
     processed: 407
     added: 1
-    updated: 6
+    updated: 5
     unchanged: 400
-    drift_detected: 0
+    drift_detected: 1
+    paths_with_drift: 1
     skipped: 0
     errors: 0
     removed: 0
@@ -11619,7 +11620,7 @@ latest_run:
       source_hash_after: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
       current_source_hash: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
       generated_at_before: ''
-      generated_at_after: '2026-05-08T16:31:30Z'
+      generated_at_after: '2026-05-08T17:55:57Z'
       preview_before: ''
       preview_after: Learn how to access and use Arm hardware performance counters and the system counter
         from user space using PAPI, perf_event_open, and assembly code for performance instrumentation.
@@ -11637,7 +11638,7 @@ latest_run:
       source_hash_after: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
       current_source_hash: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
       generated_at_before: ''
-      generated_at_after: '2026-05-08T16:31:30Z'
+      generated_at_after: '2026-05-08T17:55:57Z'
       before_count: 0
       after_count: 5
       generated_count: 5
@@ -12117,8 +12118,8 @@ latest_run:
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       current_source_hash: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
-      generated_at_before: '2026-05-06T18:52:13Z'
-      generated_at_after: '2026-05-08T16:31:30Z'
+      generated_at_before: '2026-05-08T16:31:30Z'
+      generated_at_after: '2026-05-08T17:55:57Z'
       preview_before: Learn how to build, profile, and optimize Arm executables using BOLT post-link binary
         optimization to improve application performance through code layout improvements. It is designed
         for software deve...
@@ -12137,8 +12138,8 @@ latest_run:
       source_hash_before: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
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-      generated_at_before: '2026-05-06T18:52:13Z'
-      generated_at_after: '2026-05-08T16:31:30Z'
+      generated_at_before: '2026-05-08T16:31:30Z'
+      generated_at_after: '2026-05-08T17:55:57Z'
       before_count: 5
       after_count: 5
       generated_count: 5
@@ -13706,8 +13707,8 @@ latest_run:
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       current_source_hash: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
-      generated_at_before: '2026-05-06T18:52:13Z'
-      generated_at_after: '2026-05-08T16:31:30Z'
+      generated_at_before: '2026-05-08T16:31:30Z'
+      generated_at_after: '2026-05-08T17:55:57Z'
       before_count: 5
       after_count: 5
       generated_count: 5
@@ -17018,8 +17019,8 @@ latest_run:
         removed_questions: []
         updated_questions: []
   - path: content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
-    status: updated
-    changed_on_disk: true
+    status: drift_detected
+    changed_on_disk: false
     managed_block_updated: false
     rerun_flags_reset: []
     change_reasons:
@@ -18172,8 +18173,8 @@ latest_run:
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       current_source_hash: 10f13dee3459577fcadf5966cf80d990f3396321189f638432a3308699e773a8
-      generated_at_before: '2026-05-06T18:52:14Z'
-      generated_at_after: '2026-05-08T16:31:32Z'
+      generated_at_before: '2026-05-08T16:31:32Z'
+      generated_at_after: '2026-05-08T17:55:57Z'
       preview_before: Learn how to tune Nginx walks you through an end-to-end Arm software workflow. It
         is designed for software developers who want to use Nginx on Arm. By the end, you will be able
         to describe how kernel ...
@@ -19242,7 +19243,7 @@ latest_run:
     rerun_flags_reset: []
     change_reasons:
     - missing_faqs
-    template_version_before: summary-faq-v1
+    template_version_before: summary-faq-v2
     template_version_after: summary-faq-v2
     summary:
       action: unchanged
@@ -19273,8 +19274,8 @@ latest_run:
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       current_source_hash: ed85d6171f3c05bfdf1b396065fb0c73ce88724ff63fd1c3c8a93d4c27dd59f8
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-08T16:31:32Z'
+      generated_at_before: '2026-05-08T16:31:32Z'
+      generated_at_after: '2026-05-08T17:55:57Z'
       before_count: 0
       after_count: 5
       generated_count: 5
@@ -21089,7 +21090,7 @@ latest_run:
     rerun_flags_reset: []
     change_reasons:
     - missing_summary
-    template_version_before: summary-faq-v1
+    template_version_before: summary-faq-v2
     template_version_after: summary-faq-v2
     summary:
       action: repaired_missing
@@ -21100,8 +21101,8 @@ latest_run:
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       current_source_hash: 2c20d30d25a0bb871bdb935d18f7ad8e0740139948133dbd728e10f0add0f94e
-      generated_at_before: '2026-05-06T17:17:59Z'
-      generated_at_after: '2026-05-08T16:31:32Z'
+      generated_at_before: '2026-05-08T16:31:32Z'
+      generated_at_after: '2026-05-08T17:55:58Z'
       preview_before: ''
       preview_after: Deploy TensorFlow on Google Cloud C4A (Arm-based Axion VMs) walks you through an
         end-to-end Arm software workflow. It is designed for software developers deploying and optimizing
@@ -22054,6 +22055,44086 @@ latest_run:
         removed_questions: []
         updated_questions: []
 history:
+- timestamp: '2026-05-08T17:55:58Z'
+  mode: dry-run
+  require_enable_flag: true
+  path_filter: ''
+  limit: 0
+  run_url: ''
+  git_ref: ''
+  git_sha: ''
+  actor: ''
+  template_version: summary-faq-v2
+  totals:
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+    updated: 5
+    unchanged: 400
+    drift_detected: 1
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+    removed: 0
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+      drift_detected_preserved: 1
+      unchanged: 402
+    faqs:
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+  paths:
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+      generated_at_before: '2026-05-06T17:17:53Z'
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+      preview_before: Learn how to deploy and run containerized autonomous driving simulations using Autoware
+        Open AD Kit on Arm Neoverse with Docker, demonstrating SOAFEE-based Shift-Left development workflows.
+        It is desi...
+      preview_after: Learn how to deploy and run containerized autonomous driving simulations using Autoware
+        Open AD Kit on Arm Neoverse with Docker, demonstrating SOAFEE-based Shift-Left development workflows.
+        It is desi...
+      preview_generated: Learn how to deploy and run containerized autonomous driving simulations using
+        Autoware Open AD Kit on Arm Neoverse with Docker, demonstrating SOAFEE-based Shift-Left development
+        workflows. It is desi...
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+  - path: content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
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+      preview_before: Learn how to implement functional safety isolation for autonomous driving systems
+        on Arm Neoverse using DDS-based communication, containerized deployment, and ISO 26262 compliance
+        principles. It is de...
+      preview_after: Learn how to implement functional safety isolation for autonomous driving systems
+        on Arm Neoverse using DDS-based communication, containerized deployment, and ISO 26262 compliance
+        principles. It is de...
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+        on Arm Neoverse using DDS-based communication, containerized deployment, and ISO 26262 compliance
+        principles. It is de...
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+  - path: content/learning-paths/automotive/system76-auto/_index.md
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+      preview_before: Learn how to build and run the Arm Automotive Solutions Software Reference Stack
+        locally on the System76 Thelio Astra desktop using Multipass virtualization and Yocto build tools.
+        It is designed for a...
+      preview_after: Learn how to build and run the Arm Automotive Solutions Software Reference Stack
+        locally on the System76 Thelio Astra desktop using Multipass virtualization and Yocto build tools.
+        It is designed for a...
+      preview_generated: Learn how to build and run the Arm Automotive Solutions Software Reference Stack
+        locally on the System76 Thelio Astra desktop using Multipass virtualization and Yocto build tools.
+        It is designed for a...
+    faqs:
+      action: unchanged
+      missing_before: false
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+      preview_after: Learn how to design and integrate a custom AXI-Lite peripheral with a Cortex-A9 processor
+        on the Zybo Z7-10 board using Vivado, configuring GPIOs to control LEDs based on switch inputs.
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+      preview_generated: Learn how to design and integrate a custom AXI-Lite peripheral with a Cortex-A9
+        processor on the Zybo Z7-10 board using Vivado, configuring GPIOs to control LEDs based on switch
+        inputs. It is designed...
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+      preview_before: Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit and
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+      preview_after: Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit and
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+        a neural network model t...
+      preview_generated: Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit
+        and run inference on the Ethos-U85 NPU. It is designed for embedded developers who want to deploy
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+        using Arduino IDE, PIR sensors, and interrupt-driven programming on baremetal. It is designed
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+        It is designed for ...
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+        embedded applications using Fixed Virtual Platforms (FVPs) or hardware with DSTREAM debug probes.
+        It is designed for ...
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+        debug embedded applications using Fixed Virtual Platforms (FVPs) or hardware with DSTREAM debug
+        probes. It is designed for ...
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+      preview_before: Learn how to write mixed C and assembly programs for Cortex-M microcontrollers using
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+        who are interes...
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+        using Keil MDK, following Arm Procedure Call Standard conventions. It is designed for software
+        developers who are interes...
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+  - path: content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
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+      preview_before: Learn how to create a custom Balena OS image, run it on Arm Virtual Hardware, and
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+        developers interested...
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+        deploy IoT applications to a virtual Raspberry Pi 4 device. It is designed for embedded software
+        developers interested...
+      preview_generated: Learn how to create a custom Balena OS image, run it on Arm Virtual Hardware,
+        and deploy IoT applications to a virtual Raspberry Pi 4 device. It is designed for embedded software
+        developers interested...
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+  - path: content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
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+      preview_before: Learn how to set up AWS IoT Greengrass Core on Arm Virtual Hardware and deploy AWS
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+      preview_after: Learn how to set up AWS IoT Greengrass Core on Arm Virtual Hardware and deploy AWS
+        Greengrass components to a virtual Raspberry Pi 4 device. It is designed for embedded software
+        developers interested ...
+      preview_generated: Learn how to set up AWS IoT Greengrass Core on Arm Virtual Hardware and deploy
+        AWS Greengrass components to a virtual Raspberry Pi 4 device. It is designed for embedded software
+        developers interested ...
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+  - path: content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
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+      preview_before: Learn how to build Matter reference examples on Arm Virtual Hardware, demonstrate
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+        for embedded software d...
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+  - path: content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
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+      preview_before: Learn how to export and compile a PaddleOCR text recognition model using TVMC and
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+        developers interested in...
+      preview_after: Learn how to export and compile a PaddleOCR text recognition model using TVMC and
+        deploy it on the Arm Corstone-300 FVP with Cortex-M55 processors. It is designed for software
+        developers interested in...
+      preview_generated: Learn how to export and compile a PaddleOCR text recognition model using TVMC
+        and deploy it on the Arm Corstone-300 FVP with Cortex-M55 processors. It is designed for software
+        developers interested in...
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+      preview_generated: Learn how to build a complete IoT solution in Azure that streams, stores, monitors,
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+      preview_before: Device-to-Device communication with Device Connect walks you through an end-to-end
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+        Arm software workflow. It is designed for developers wiring up heterogeneous edge fleets, where
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+      preview_before: Learn how to connect AI agents to Arm-based edge devices using Device Connect for
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+        and physical robots. It...
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+        for structured device access and Strands for agent orchestration, with examples for both simulated
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+      preview_before: Learn how to create a Dockerfile, build a Docker image with Arm Compiler for Embedded
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+        for embedded s...
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+        and Fixed Virtual Platforms, and test the containerized Arm development environment. It is designed
+        for embedded s...
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+        Embedded and Fixed Virtual Platforms, and test the containerized Arm development environment.
+        It is designed for embedded s...
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+      preview_before: Learn how to collect and preprocess audio data using Edge Impulse, train an audio
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+        commands. It is designed...
+      preview_after: Learn how to collect and preprocess audio data using Edge Impulse, train an audio
+        classification model, and deploy it to the Arduino Nano RP2040 to control LEDs based on voice
+        commands. It is designed...
+      preview_generated: Learn how to collect and preprocess audio data using Edge Impulse, train an audio
+        classification model, and deploy it to the Arduino Nano RP2040 to control LEDs based on voice
+        commands. It is designed...
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+  - path: content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md
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+      preview_before: Develop a image classification neural network model and deploy it on an STM32 B-L475E-IOT01A2
+        board. It is designed for embedded software developers interested in building neural network models
+        for mi...
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+        board. It is designed for embedded software developers interested in building neural network models
+        for mi...
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+  - path: content/learning-paths/embedded-and-microcontrollers/intro/_index.md
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+      preview_before: Learn where Arm architecture is used in microcontrollers and discover microcontroller
+        hardware options for software development on Arm Cortex-M processors. It is designed for software
+        developers worki...
+      preview_after: Learn where Arm architecture is used in microcontrollers and discover microcontroller
+        hardware options for software development on Arm Cortex-M processors. It is designed for software
+        developers worki...
+      preview_generated: Learn where Arm architecture is used in microcontrollers and discover microcontroller
+        hardware options for software development on Arm Cortex-M processors. It is designed for software
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+  - path: content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
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+      preview_before: Learn what differentiates TinyML from other AI domains, explore Arm-based edge devices
+        for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed Virtual
+        Platform. It is ...
+      preview_after: Learn what differentiates TinyML from other AI domains, explore Arm-based edge devices
+        for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed Virtual
+        Platform. It is ...
+      preview_generated: Learn what differentiates TinyML from other AI domains, explore Arm-based edge
+        devices for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed
+        Virtual Platform. It is ...
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+  - path: content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
+    status: unchanged
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+      generated_at_after: '2026-05-06T17:17:54Z'
+      preview_before: Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300 virtual
+        hardware to understand complete IoT software stack construction. It is designed for embedded software
+        developers ...
+      preview_after: Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300 virtual
+        hardware to understand complete IoT software stack construction. It is designed for embedded software
+        developers ...
+      preview_generated: Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300
+        virtual hardware to understand complete IoT software stack construction. It is designed for embedded
+        software developers ...
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+  - path: content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
+    status: unchanged
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+      preview_before: Learn how to set up a Jetson Orin Nano with a MIPI CSI-2 camera and perform real-time
+        object detection from live video and image files using DetectNet and TensorRT. It is designed
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+      preview_before: Learn how to create debug configurations for virtual platforms and development boards
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+        and plays audio resp...
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+        and plays audio resp...
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+        (SPE) for deep...
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+        bottlenecks, analyze both out-of-tree and in-tree modules, and use Statistical Profiling Extension
+        (SPE) for deep...
+      preview_generated: Learn how to profile Linux kernel modules using Arm Streamline to identify performance
+        bottlenecks, analyze both out-of-tree and in-tree modules, and use Statistical Profiling Extension
+        (SPE) for deep...
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+      preview_before: Build a letter recognition neural network model using TensorFlow and deploy it on
+        an STM32 B-L475E-IOT01A2 board. It is designed for software developers interested in building
+        network models for micro...
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+        an STM32 B-L475E-IOT01A2 board. It is designed for software developers interested in building
+        network models for micro...
+      preview_generated: Build a letter recognition neural network model using TensorFlow and deploy it
+        on an STM32 B-L475E-IOT01A2 board. It is designed for software developers interested in building
+        network models for micro...
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+  - path: content/learning-paths/embedded-and-microcontrollers/tfm/_index.md
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+      preview_before: Learn how to build and run the reference Trusted Firmware-M tests and example application
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+      preview_after: Learn how to build and run the reference Trusted Firmware-M tests and example application
+        on Arm Fixed Virtual Platforms for secure microcontroller development. It is designed for software
+        developers ...
+      preview_generated: Learn how to build and run the reference Trusted Firmware-M tests and example
+        application on Arm Fixed Virtual Platforms for secure microcontroller development. It is designed
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+  - path: content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md
+    status: unchanged
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+        for machine learnin...
+      preview_after: Learn how to train a CNN image classification model using PyTorch, convert it to
+        ExecuTorch format, and run it as an interactive mini-game on Arm-based edge devices. It is designed
+        for machine learnin...
+      preview_generated: Learn how to train a CNN image classification model using PyTorch, convert it
+        to ExecuTorch format, and run it as an interactive mini-game on Arm-based edge devices. It is
+        designed for machine learnin...
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+  - path: content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md
+    status: unchanged
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+      preview_before: Learn how to install Keil MDK Tools, run a TrustZone hello world example on the
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+        It is designed for softwar...
+      preview_after: Learn how to install Keil MDK Tools, run a TrustZone hello world example on the NXP
+        LPCXpresso55S69 board, and understand security state switching and secure function calls. It is
+        designed for softwar...
+      preview_generated: Learn how to install Keil MDK Tools, run a TrustZone hello world example on the
+        NXP LPCXpresso55S69 board, and understand security state switching and secure function calls.
+        It is designed for softwar...
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+  - path: content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
+    status: unchanged
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+      preview_before: Learn how to acquire and print materials, assemble a universal SBC rack mount system
+        in a 4U chassis, and install single board computers in the racks using 3D-printed parts. It is
+        designed for softwar...
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+        in a 4U chassis, and install single board computers in the racks using 3D-printed parts. It is
+        designed for softwar...
+      preview_generated: Learn how to acquire and print materials, assemble a universal SBC rack mount
+        system in a 4U chassis, and install single board computers in the racks using 3D-printed parts.
+        It is designed for softwar...
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+  - path: content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
+    status: unchanged
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+      preview_before: Learn how to debug microcontrollers using µVision with basic run/stop debug, advanced
+        techniques using Event Recorder and Serial Wire Viewer, ETM Trace for performance analysis, and
+        power measurement ...
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+        techniques using Event Recorder and Serial Wire Viewer, ETM Trace for performance analysis, and
+        power measurement ...
+      preview_generated: Learn how to debug microcontrollers using µVision with basic run/stop debug,
+        advanced techniques using Event Recorder and Serial Wire Viewer, ETM Trace for performance analysis,
+        and power measurement ...
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+  - path: content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
+    status: unchanged
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+      preview_before: Learn how to import, convert, and build uvprojx-based projects to csolution format
+        using Keil Studio, µVision, and command-line tools for CMSIS-Toolbox compatibility. It is designed
+        for This is a topi...
+      preview_after: Learn how to import, convert, and build uvprojx-based projects to csolution format
+        using Keil Studio, µVision, and command-line tools for CMSIS-Toolbox compatibility. It is designed
+        for This is a topi...
+      preview_generated: Learn how to import, convert, and build uvprojx-based projects to csolution format
+        using Keil Studio, µVision, and command-line tools for CMSIS-Toolbox compatibility. It is designed
+        for This is a topi...
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+      missing_before: false
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+      source_hash_before: 179b0318bbef9cef31ca6bb82de853597a609f61d6868aaaa85cfb40a27a051a
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+  - path: content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
+    status: unchanged
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+      preview_before: Learn how to install vcpkg, initialize it, create vcpkg-configuration.json files,
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+        tool installati...
+      preview_after: Learn how to install vcpkg, initialize it, create vcpkg-configuration.json files,
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+        tool installati...
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+        use vcpkg for tool management, activate tool licensing, and remove vcpkg for reproducible command-line
+        tool installati...
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+        who want to lea...
+      preview_generated: Learn how to package multi-architecture container applications and deploy them
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+        engineers wh...
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+      preview_generated: Learn how to automate the creation of Azure Arm virtual machines using Terraform.
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+      preview_generated: Learn how to install and run Clair on Arm servers using combined and distributed
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+        performance using ClickBench to determine appropriate instance configurations. It is designed
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+      preview_generated: Learn how to install ClickHouse on Arm-based cloud instances and measure database
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+      preview_generated: Learn how to deploy an Arm-based Cobalt 100 virtual machine on Azure, connect
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+      preview_before: Learn how to provision Arm64 and x86_64 VM instances on Google Cloud, then install
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+        for This introductory t...
+      preview_generated: Learn how to provision Arm64 and x86_64 VM instances on Google Cloud, then install
+        and use Sweet and Benchstat to measure and compare Go application performance. It is designed
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+      preview_generated: Learn where Arm architecture is used in servers and cloud computing, and find
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+      preview_generated: Analyze and optimize interrupt request (IRQ) patterns on Arm Linux servers to
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+      preview_generated: Monitor, interpret, and optimize Java Garbage Collector performance on Arm servers
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+      preview_after: Profile Java applications on Arm Neoverse servers using flame graphs generated with
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+        who want to analyz...
+      preview_generated: Profile Java applications on Arm Neoverse servers using flame graphs generated
+        with async-profiler and Java agents to identify performance bottlenecks. It is designed for developers
+        who want to analyz...
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+      preview_before: Deploy Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate installation,
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+      preview_after: Deploy Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate installation,
+        and execute Arm-native CI/CD pipelines including Docker workflows. It is designed for software
+        developers d...
+      preview_generated: Deploy Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate
+        installation, and execute Arm-native CI/CD pipelines including Docker workflows. It is designed
+        for software developers d...
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+  - path: content/learning-paths/servers-and-cloud-computing/kafka/_index.md
+    status: unchanged
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+      preview_before: Deploy and configure a Kafka cluster with Zookeeper on Arm servers, test event streaming,
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+      preview_after: Deploy and configure a Kafka cluster with Zookeeper on Arm servers, test event streaming,
+        and automate deployment on AWS and Google Cloud. It is designed for software developers who want
+        to learn how ...
+      preview_generated: Deploy and configure a Kafka cluster with Zookeeper on Arm servers, test event
+        streaming, and automate deployment on AWS and Google Cloud. It is designed for software developers
+        who want to learn how ...
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+  - path: content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
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+      preview_before: Deploy Apache Kafka on Azure Cobalt 100 Arm virtual machines and benchmark message
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+        from x86_64 to ...
+      preview_after: Deploy Apache Kafka on Azure Cobalt 100 Arm virtual machines and benchmark message
+        throughput performance. It is designed for developers looking to migrate their Apache Kafka workloads
+        from x86_64 to ...
+      preview_generated: Deploy Apache Kafka on Azure Cobalt 100 Arm virtual machines and benchmark message
+        throughput performance. It is designed for developers looking to migrate their Apache Kafka workloads
+        from x86_64 to ...
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+      preview_before: Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing Kedify
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+      preview_after: Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing Kedify
+        and KEDA with Helm and testing autoscaling behavior. It is designed for developers running HTTP
+        workloads on Kuber...
+      preview_generated: Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing
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+      preview_before: Create, train, and evaluate a neural network model on Arm servers using Keras Core
+        with TensorFlow, PyTorch, and JAX backends. It is designed for engineers who want to create a
+        neural network model on...
+      preview_after: Create, train, and evaluate a neural network model on Arm servers using Keras Core
+        with TensorFlow, PyTorch, and JAX backends. It is designed for engineers who want to create a
+        neural network model on...
+      preview_generated: Create, train, and evaluate a neural network model on Arm servers using Keras
+        Core with TensorFlow, PyTorch, and JAX backends. It is designed for engineers who want to create
+        a neural network model on...
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+  - path: content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
+    status: unchanged
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+      preview_before: Compile and install custom Linux kernels on Arm cloud instances using TuxMake with
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+        building custom Linu...
+      preview_after: Compile and install custom Linux kernels on Arm cloud instances using TuxMake with
+        configurations for 64 KB page sizes and Fastpath testing. It is designed for software developers
+        building custom Linu...
+      preview_generated: Compile and install custom Linux kernels on Arm cloud instances using TuxMake
+        with configurations for 64 KB page sizes and Fastpath testing. It is designed for software developers
+        building custom Linu...
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+  - path: content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
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+      preview_before: Identify and migrate container images in a Kubernetes cluster to Arm-compatible
+        versions using KubeArchInspect reports. It is designed for software developers who want to ensure
+        containers running in ...
+      preview_after: Identify and migrate container images in a Kubernetes cluster to Arm-compatible versions
+        using KubeArchInspect reports. It is designed for software developers who want to ensure containers
+        running in ...
+      preview_generated: Identify and migrate container images in a Kubernetes cluster to Arm-compatible
+        versions using KubeArchInspect reports. It is designed for software developers who want to ensure
+        containers running in ...
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+  - path: content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
+    status: unchanged
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+      preview_before: Deploy AWS Lambda functions on Graviton processors using Terraform for Python and
+        Node.js runtimes. It is designed for software developers who want to learn how to deploy Lambda
+        functions on AWS Gravi...
+      preview_after: Deploy AWS Lambda functions on Graviton processors using Terraform for Python and
+        Node.js runtimes. It is designed for software developers who want to learn how to deploy Lambda
+        functions on AWS Gravi...
+      preview_generated: Deploy AWS Lambda functions on Graviton processors using Terraform for Python
+        and Node.js runtimes. It is designed for software developers who want to learn how to deploy Lambda
+        functions on AWS Gravi...
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+        developers,...
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+        Amazon RDS, and automation with Terraform and Ansible. It is designed for software developers
+        who want to deploy...
+      preview_generated: Deploy MariaDB on Arm cloud instances across AWS, Azure, and Google Cloud using
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+        who want to deploy...
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+      preview_generated: Use ASCT to measure cache latency, streaming bandwidth, and coherency latency
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+      preview_generated: Test and validate thread synchronization approaches in the Arm memory model using
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+      preview_before: Benchmark machine learning inference performance on Arm servers using TensorFlow
+        and the MLPerf Inference benchmark suite from MLCommons. It is designed for software developers
+        interested in benchmark...
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+        and the MLPerf Inference benchmark suite from MLCommons. It is designed for software developers
+        interested in benchmark...
+      preview_generated: Benchmark machine learning inference performance on Arm servers using TensorFlow
+        and the MLPerf Inference benchmark suite from MLCommons. It is designed for software developers
+        interested in benchmark...
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+  - path: content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
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+      preview_before: Install MongoDB on Arm servers and benchmark database performance using Yahoo Cloud
+        Serving Benchmark (YCSB) to compare against other architectures. It is designed for software developers
+        who want to ...
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+        Serving Benchmark (YCSB) to compare against other architectures. It is designed for software developers
+        who want to ...
+      preview_generated: Install MongoDB on Arm servers and benchmark database performance using Yahoo
+        Cloud Serving Benchmark (YCSB) to compare against other architectures. It is designed for software
+        developers who want to ...
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+      preview_before: Deploy MongoDB on Azure Cobalt 100 Arm virtual machines and benchmark database performance
+        using mongotop and mongostat monitoring tools. It is designed for software developers who want
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+      preview_after: Deploy MongoDB on Azure Cobalt 100 Arm virtual machines and benchmark database performance
+        using mongotop and mongostat monitoring tools. It is designed for software developers who want
+        to migrate Mon...
+      preview_generated: Deploy MongoDB on Azure Cobalt 100 Arm virtual machines and benchmark database
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+        who want to migrate Mon...
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+      preview_generated: Learn how to build and deploy distributed Zenoh systems on Arm devices like Raspberry
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+      preview_after: Learn how to design and integrate a custom AXI-Lite peripheral with a Cortex-A9 processor
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+      preview_generated: Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit
+        and run inference on the Ethos-U85 NPU. It is designed for embedded developers who want to deploy
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+        using Arduino IDE, PIR sensors, and interrupt-driven programming on baremetal. It is designed
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+        It is designed for ...
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+        embedded applications using Fixed Virtual Platforms (FVPs) or hardware with DSTREAM debug probes.
+        It is designed for ...
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+        debug embedded applications using Fixed Virtual Platforms (FVPs) or hardware with DSTREAM debug
+        probes. It is designed for ...
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+      preview_before: Learn how to write mixed C and assembly programs for Cortex-M microcontrollers using
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+        who are interes...
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+        who are interes...
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+        using Keil MDK, following Arm Procedure Call Standard conventions. It is designed for software
+        developers who are interes...
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+  - path: content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
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+      preview_before: Learn how to create a custom Balena OS image, run it on Arm Virtual Hardware, and
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+        deploy IoT applications to a virtual Raspberry Pi 4 device. It is designed for embedded software
+        developers interested...
+      preview_generated: Learn how to create a custom Balena OS image, run it on Arm Virtual Hardware,
+        and deploy IoT applications to a virtual Raspberry Pi 4 device. It is designed for embedded software
+        developers interested...
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+  - path: content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
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+      preview_before: Learn how to set up AWS IoT Greengrass Core on Arm Virtual Hardware and deploy AWS
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+        developers interested ...
+      preview_generated: Learn how to set up AWS IoT Greengrass Core on Arm Virtual Hardware and deploy
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+        developers interested ...
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+  - path: content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
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+      preview_before: Learn how to build Matter reference examples on Arm Virtual Hardware, demonstrate
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+      preview_generated: Learn how to build Matter reference examples on Arm Virtual Hardware, demonstrate
+        device communication, and automate testing with GitHub Actions CI/CD workflows. It is designed
+        for embedded software d...
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+  - path: content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
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+        DB, and Azure Fun...
+      preview_generated: Learn how to build a complete IoT solution in Azure that streams, stores, monitors,
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+        DB, and Azure Fun...
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+      preview_generated: Learn how to create, build, and run a bare-metal embedded application for Armv8-A
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+        handling. It is desi...
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+      preview_generated: Learn how to set up and use code coverage in Keil MDK with FVPs to verify that
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+      preview_before: Device-to-Device communication with Device Connect walks you through an end-to-end
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+        Arm software workflow. It is designed for developers wiring up heterogeneous edge fleets, where
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+      preview_generated: Device-to-Device communication with Device Connect walks you through an end-to-end
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+        and physical robots. It...
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+        for structured device access and Strands for agent orchestration, with examples for both simulated
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+        commands. It is designed...
+      preview_generated: Learn how to collect and preprocess audio data using Edge Impulse, train an audio
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+        commands. It is designed...
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+      preview_before: Develop a image classification neural network model and deploy it on an STM32 B-L475E-IOT01A2
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+        board. It is designed for embedded software developers interested in building neural network models
+        for mi...
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+      preview_before: Learn what differentiates TinyML from other AI domains, explore Arm-based edge devices
+        for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed Virtual
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+        for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed Virtual
+        Platform. It is ...
+      preview_generated: Learn what differentiates TinyML from other AI domains, explore Arm-based edge
+        devices for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed
+        Virtual Platform. It is ...
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+  - path: content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
+    status: unchanged
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+      preview_before: Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300 virtual
+        hardware to understand complete IoT software stack construction. It is designed for embedded software
+        developers ...
+      preview_after: Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300 virtual
+        hardware to understand complete IoT software stack construction. It is designed for embedded software
+        developers ...
+      preview_generated: Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300
+        virtual hardware to understand complete IoT software stack construction. It is designed for embedded
+        software developers ...
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+      preview_before: Learn how to understand and select physical and virtual hardware targets for ML
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+      preview_before: Learn how to create debug configurations for virtual platforms and development boards
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+        and plays audio resp...
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+      preview_before: Setup tools and start programming with Raspberry Pi Pico. It is designed for embedded
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+      preview_generated: Build a letter recognition neural network model using TensorFlow and deploy it
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+        designed for machine learnin...
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+      preview_before: Learn how to install Keil MDK Tools, run a TrustZone hello world example on the
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+        It is designed for softwar...
+      preview_after: Learn how to install Keil MDK Tools, run a TrustZone hello world example on the NXP
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+  - path: content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
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+        in a 4U chassis, and install single board computers in the racks using 3D-printed parts. It is
+        designed for softwar...
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+        system in a 4U chassis, and install single board computers in the racks using 3D-printed parts.
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+  - path: content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
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+      preview_before: Learn how to debug microcontrollers using µVision with basic run/stop debug, advanced
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+        power measurement ...
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+        and power measurement ...
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+      preview_before: Learn how to import, convert, and build uvprojx-based projects to csolution format
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+        for This is a topi...
+      preview_after: Learn how to import, convert, and build uvprojx-based projects to csolution format
+        using Keil Studio, µVision, and command-line tools for CMSIS-Toolbox compatibility. It is designed
+        for This is a topi...
+      preview_generated: Learn how to import, convert, and build uvprojx-based projects to csolution format
+        using Keil Studio, µVision, and command-line tools for CMSIS-Toolbox compatibility. It is designed
+        for This is a topi...
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+        who want to lea...
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+        on AWS Fargate with Graviton processors using the AWS Copilot CLI. It is designed for software
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+        engineers wh...
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+        It is designed for software developers who are new to deploying Arm instances on Azure using Terraform.
+        By the end, yo...
+      preview_generated: Learn how to automate the creation of Azure Arm virtual machines using Terraform.
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+        who want to r...
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+        who want to r...
+      preview_generated: Learn how to create a custom Azure Linux 3.0 VM image using QEMU, upload it to
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+        developers, pe...
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+        Realms using Trustee services for attestation-based authorization on an FVP with RME support.
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+      preview_generated: Learn how to deploy Confidential Containers from encrypted images inside Arm
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+      preview_generated: Learn how to install and run Clair on Arm servers using combined and distributed
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+      preview_generated: Learn how to use docker buildx and docker manifest to build and deploy multi-architecture
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+        for software...
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+      preview_before: Learn how to implement exponential functions using Arm SVE intrinsics with the FEXPA
+        instruction for hardware-accelerated computations on Neoverse processors. It is designed for developers
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+      preview_generated: Learn how to provision Arm64 and x86_64 VM instances on Google Cloud, then install
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+      preview_generated: Learn where Arm architecture is used in servers and cloud computing, and find
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+        and benchmar...
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+      preview_generated: Profile Java applications on Arm Neoverse servers using flame graphs generated
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+        developers d...
+      preview_generated: Deploy Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate
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+      preview_after: Deploy and configure a Kafka cluster with Zookeeper on Arm servers, test event streaming,
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+        to learn how ...
+      preview_generated: Deploy and configure a Kafka cluster with Zookeeper on Arm servers, test event
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+        who want to learn how ...
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+        throughput performance. It is designed for developers looking to migrate their Apache Kafka workloads
+        from x86_64 to ...
+      preview_generated: Deploy Apache Kafka on Azure Cobalt 100 Arm virtual machines and benchmark message
+        throughput performance. It is designed for developers looking to migrate their Apache Kafka workloads
+        from x86_64 to ...
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+      preview_before: Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing Kedify
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+      preview_generated: Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing
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+      preview_generated: Compile and install custom Linux kernels on Arm cloud instances using TuxMake
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+      preview_after: Identify and migrate container images in a Kubernetes cluster to Arm-compatible versions
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+      preview_generated: Identify and migrate container images in a Kubernetes cluster to Arm-compatible
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+        Arm Neoverse systems, and compare results across Graviton generations. It is designed for software
+        developers and perfo...
+      preview_generated: Use ASCT to measure cache latency, streaming bandwidth, and coherency latency
+        on Arm Neoverse systems, and compare results across Graviton generations. It is designed for software
+        developers and perfo...
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+        Herd7, Litmus7, and Arm hardware with assembly snippets. It is designed for developers seeking
+        practical ways to test ...
+      preview_generated: Test and validate thread synchronization approaches in the Arm memory model using
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+      preview_after: This branch-only testing summary is intentionally out of sync with the current Learning
+        Path source content so the workflow report records preserved summary drift for this LP.
+      preview_generated: Microbenchmark and tune network performance with iPerf3 and Linux traffic control
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+        - What will you accomplish in this Learning Path?
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+        to identify and resolve AArch64 porting challenges before migration. It is designed for developers
+        looking to migrate a...
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+        who want to create ...
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+        Cloud for vector search and llama.cpp for LLM inference. It is designed for software developers
+        who want to create ...
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+        who want to create ...
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+      preview_before: Learn how to deploy and configure MinIO on an Azure Cobalt 100 virtual machine,
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+      preview_before: Benchmark machine learning inference performance on Arm servers using TensorFlow
+        and the MLPerf Inference benchmark suite from MLCommons. It is designed for software developers
+        interested in benchmark...
+      preview_after: Benchmark machine learning inference performance on Arm servers using TensorFlow
+        and the MLPerf Inference benchmark suite from MLCommons. It is designed for software developers
+        interested in benchmark...
+      preview_generated: Benchmark machine learning inference performance on Arm servers using TensorFlow
+        and the MLPerf Inference benchmark suite from MLCommons. It is designed for software developers
+        interested in benchmark...
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+  - path: content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
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+      preview_before: Install MongoDB on Arm servers and benchmark database performance using Yahoo Cloud
+        Serving Benchmark (YCSB) to compare against other architectures. It is designed for software developers
+        who want to ...
+      preview_after: Install MongoDB on Arm servers and benchmark database performance using Yahoo Cloud
+        Serving Benchmark (YCSB) to compare against other architectures. It is designed for software developers
+        who want to ...
+      preview_generated: Install MongoDB on Arm servers and benchmark database performance using Yahoo
+        Cloud Serving Benchmark (YCSB) to compare against other architectures. It is designed for software
+        developers who want to ...
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+      preview_generated: Deploy SqueezeNet 1.0 INT8 model with ONNX Runtime on Azure Cobalt 100 walks
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+      preview_generated: Build multi-architecture applications with Red Hat OpenShift Pipelines on AWS
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+        is designed...
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+        and Kolla-Ansible for containerized production deployments. It is designed for This learning path
+        is designed...
+      preview_generated: Deploy OpenStack on Azure Cobalt 100 Arm64 virtual machines using DevStack for
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+        learning path is designed...
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+        Arm software workflow. It is designed for DevOps engineers, platform engineers, and software developers
+        who wa...
+      preview_generated: Deploy OpenTelemetry on Google Cloud C4A Axion processors walks you through an
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+      preview_generated: Understand Arm Pointer Authentication walks you through an end-to-end Arm software
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+    summary:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
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+      generated_at_before: '2026-05-06T17:17:59Z'
+      generated_at_after: '2026-05-06T17:17:59Z'
+      preview_before: Install Vectorscan (Hyperscan on Arm) and use it with Snort 3 walks you through
+        an end-to-end Arm software workflow. It is designed for software developers using Hyperscan who
+        want to migrate to Arm. ...
+      preview_after: Install Vectorscan (Hyperscan on Arm) and use it with Snort 3 walks you through an
+        end-to-end Arm software workflow. It is designed for software developers using Hyperscan who want
+        to migrate to Arm. ...
+      preview_generated: Install Vectorscan (Hyperscan on Arm) and use it with Snort 3 walks you through
+        an end-to-end Arm software workflow. It is designed for software developers using Hyperscan who
+        want to migrate to Arm. ...
+    faqs:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: beb244c7766c474b47942b86f1cdd0d124c1cccb74dbe40f0b6c0917d0b3d31a
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+      current_source_hash: beb244c7766c474b47942b86f1cdd0d124c1cccb74dbe40f0b6c0917d0b3d31a
+      generated_at_before: '2026-05-06T17:17:59Z'
+      generated_at_after: '2026-05-06T17:17:59Z'
+      before_count: 5
+      after_count: 5
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+      change_details:
+        before_count: 5
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+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+      generated_diff:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/vllm/_index.md
+    status: unchanged
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons: []
+    template_version_before: summary-faq-v1
+    template_version_after: summary-faq-v1
+    summary:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: db5987ec7987375d9b51de843b0e1faf0e61731b14c5a563d19aaad26f50f6bb
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+      current_source_hash: db5987ec7987375d9b51de843b0e1faf0e61731b14c5a563d19aaad26f50f6bb
+      generated_at_before: '2026-05-06T17:17:59Z'
+      generated_at_after: '2026-05-06T17:17:59Z'
+      preview_before: Build and Run vLLM on Arm Servers walks you through an end-to-end Arm software workflow.
+        It is designed for software developers and AI engineers interested in learning how to use the
+        vLLM library on A...
+      preview_after: Build and Run vLLM on Arm Servers walks you through an end-to-end Arm software workflow.
+        It is designed for software developers and AI engineers interested in learning how to use the
+        vLLM library on A...
+      preview_generated: Build and Run vLLM on Arm Servers walks you through an end-to-end Arm software
+        workflow. It is designed for software developers and AI engineers interested in learning how to
+        use the vLLM library on A...
+    faqs:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: db5987ec7987375d9b51de843b0e1faf0e61731b14c5a563d19aaad26f50f6bb
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+      current_source_hash: db5987ec7987375d9b51de843b0e1faf0e61731b14c5a563d19aaad26f50f6bb
+      generated_at_before: '2026-05-06T17:17:59Z'
+      generated_at_after: '2026-05-06T17:17:59Z'
+      before_count: 5
+      after_count: 5
+      generated_count: 5
+      change_details:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+      generated_diff:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
+    status: unchanged
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons: []
+    template_version_before: summary-faq-v1
+    template_version_after: summary-faq-v1
+    summary:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: 487e9829c6e08798ad01be7a01f192e10e99cb06b71108575f1919c134eece02
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+      current_source_hash: 487e9829c6e08798ad01be7a01f192e10e99cb06b71108575f1919c134eece02
+      generated_at_before: '2026-05-06T17:17:59Z'
+      generated_at_after: '2026-05-06T17:17:59Z'
+      preview_before: Run vLLM inference with INT4 quantization on Arm servers walks you through an end-to-end
+        Arm software workflow. It is designed for developers interested in building and optimizing vLLM
+        for Arm-based s...
+      preview_after: Run vLLM inference with INT4 quantization on Arm servers walks you through an end-to-end
+        Arm software workflow. It is designed for developers interested in building and optimizing vLLM
+        for Arm-based s...
+      preview_generated: Run vLLM inference with INT4 quantization on Arm servers walks you through an
+        end-to-end Arm software workflow. It is designed for developers interested in building and optimizing
+        vLLM for Arm-based s...
+    faqs:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: 487e9829c6e08798ad01be7a01f192e10e99cb06b71108575f1919c134eece02
+      source_hash_after: 487e9829c6e08798ad01be7a01f192e10e99cb06b71108575f1919c134eece02
+      current_source_hash: 487e9829c6e08798ad01be7a01f192e10e99cb06b71108575f1919c134eece02
+      generated_at_before: '2026-05-06T17:17:59Z'
+      generated_at_after: '2026-05-06T17:17:59Z'
+      before_count: 5
+      after_count: 5
+      generated_count: 5
+      change_details:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+      generated_diff:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
+    status: unchanged
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons: []
+    template_version_before: summary-faq-v1
+    template_version_after: summary-faq-v1
+    summary:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: 4ed20056b2d82bd2c9ab1afe3d74657df9ac09808592d843c1b143626a8668ac
+      source_hash_after: 4ed20056b2d82bd2c9ab1afe3d74657df9ac09808592d843c1b143626a8668ac
+      current_source_hash: 4ed20056b2d82bd2c9ab1afe3d74657df9ac09808592d843c1b143626a8668ac
+      generated_at_before: '2026-05-06T17:17:59Z'
+      generated_at_after: '2026-05-06T17:17:59Z'
+      preview_before: Run the vvenc H.266 encoder on Arm servers walks you through an end-to-end Arm software
+        workflow. It is designed for software developers who want to build and run the VVenC® (Fraunhofer
+        Versatile Vide...
+      preview_after: Run the vvenc H.266 encoder on Arm servers walks you through an end-to-end Arm software
+        workflow. It is designed for software developers who want to build and run the VVenC® (Fraunhofer
+        Versatile Vide...
+      preview_generated: Run the vvenc H.266 encoder on Arm servers walks you through an end-to-end Arm
+        software workflow. It is designed for software developers who want to build and run the VVenC®
+        (Fraunhofer Versatile Vide...
+    faqs:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: 4ed20056b2d82bd2c9ab1afe3d74657df9ac09808592d843c1b143626a8668ac
+      source_hash_after: 4ed20056b2d82bd2c9ab1afe3d74657df9ac09808592d843c1b143626a8668ac
+      current_source_hash: 4ed20056b2d82bd2c9ab1afe3d74657df9ac09808592d843c1b143626a8668ac
+      generated_at_before: '2026-05-06T17:17:59Z'
+      generated_at_after: '2026-05-06T17:17:59Z'
+      before_count: 5
+      after_count: 5
+      generated_count: 5
+      change_details:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+      generated_diff:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/whisper/_index.md
+    status: unchanged
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons: []
+    template_version_before: summary-faq-v1
+    template_version_after: summary-faq-v1
+    summary:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: e130630b5ae4626641779d0b66d3c1c132b90899cd3d6db07a46df8957c4da38
+      source_hash_after: e130630b5ae4626641779d0b66d3c1c132b90899cd3d6db07a46df8957c4da38
+      current_source_hash: e130630b5ae4626641779d0b66d3c1c132b90899cd3d6db07a46df8957c4da38
+      generated_at_before: '2026-05-06T17:17:59Z'
+      generated_at_after: '2026-05-06T17:17:59Z'
+      preview_before: Accelerate Whisper on Arm with Hugging Face Transformers walks you through an end-to-end
+        Arm software workflow. It is designed for software developers familiar with basic machine learning
+        concepts and...
+      preview_after: Accelerate Whisper on Arm with Hugging Face Transformers walks you through an end-to-end
+        Arm software workflow. It is designed for software developers familiar with basic machine learning
+        concepts and...
+      preview_generated: Accelerate Whisper on Arm with Hugging Face Transformers walks you through an
+        end-to-end Arm software workflow. It is designed for software developers familiar with basic machine
+        learning concepts and...
+    faqs:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: e130630b5ae4626641779d0b66d3c1c132b90899cd3d6db07a46df8957c4da38
+      source_hash_after: e130630b5ae4626641779d0b66d3c1c132b90899cd3d6db07a46df8957c4da38
+      current_source_hash: e130630b5ae4626641779d0b66d3c1c132b90899cd3d6db07a46df8957c4da38
+      generated_at_before: '2026-05-06T17:17:59Z'
+      generated_at_after: '2026-05-06T17:17:59Z'
+      before_count: 5
+      after_count: 5
+      generated_count: 5
+      change_details:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+      generated_diff:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
+    status: unchanged
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons: []
+    template_version_before: summary-faq-v1
+    template_version_after: summary-faq-v1
+    summary:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: 46f2645fb6ef298508af93569554315cfa2564be9891acabf1c874c94e52d70d
+      source_hash_after: 46f2645fb6ef298508af93569554315cfa2564be9891acabf1c874c94e52d70d
+      current_source_hash: 46f2645fb6ef298508af93569554315cfa2564be9891acabf1c874c94e52d70d
+      generated_at_before: '2026-05-06T17:17:59Z'
+      generated_at_after: '2026-05-06T17:17:59Z'
+      preview_before: Deploy MySQL and WordPress on an always free tier Arm shape walks you through an
+        end-to-end Arm software workflow. It is designed for developers who want to install WordPress
+        on Oracle Cloud Infrastru...
+      preview_after: Deploy MySQL and WordPress on an always free tier Arm shape walks you through an
+        end-to-end Arm software workflow. It is designed for developers who want to install WordPress
+        on Oracle Cloud Infrastru...
+      preview_generated: Deploy MySQL and WordPress on an always free tier Arm shape walks you through
+        an end-to-end Arm software workflow. It is designed for developers who want to install WordPress
+        on Oracle Cloud Infrastru...
+    faqs:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: 46f2645fb6ef298508af93569554315cfa2564be9891acabf1c874c94e52d70d
+      source_hash_after: 46f2645fb6ef298508af93569554315cfa2564be9891acabf1c874c94e52d70d
+      current_source_hash: 46f2645fb6ef298508af93569554315cfa2564be9891acabf1c874c94e52d70d
+      generated_at_before: '2026-05-06T17:17:59Z'
+      generated_at_after: '2026-05-06T17:17:59Z'
+      before_count: 5
+      after_count: 5
+      generated_count: 5
+      change_details:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+      generated_diff:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/zlib/_index.md
+    status: unchanged
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons: []
+    template_version_before: summary-faq-v1
+    template_version_after: summary-faq-v1
+    summary:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: 8916f83f621505dc5406f14e70d04e702ea304950e34b4b76dc1bbc987bcc4e3
+      source_hash_after: 8916f83f621505dc5406f14e70d04e702ea304950e34b4b76dc1bbc987bcc4e3
+      current_source_hash: 8916f83f621505dc5406f14e70d04e702ea304950e34b4b76dc1bbc987bcc4e3
+      generated_at_before: '2026-05-06T17:17:59Z'
+      generated_at_after: '2026-05-06T17:17:59Z'
+      preview_before: Learn how to build and use zlib-ng on Arm servers, using its Neon SIMD and ARMv8
+        CRC32 optimizations to improve compression performance compared to the system default zlib. It
+        is designed for software...
+      preview_after: Learn how to build and use zlib-ng on Arm servers, using its Neon SIMD and ARMv8
+        CRC32 optimizations to improve compression performance compared to the system default zlib. It
+        is designed for software...
+      preview_generated: Learn how to build and use zlib-ng on Arm servers, using its Neon SIMD and ARMv8
+        CRC32 optimizations to improve compression performance compared to the system default zlib. It
+        is designed for software...
+    faqs:
+      action: unchanged
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: false
+      source_hash_before: 8916f83f621505dc5406f14e70d04e702ea304950e34b4b76dc1bbc987bcc4e3
+      source_hash_after: 8916f83f621505dc5406f14e70d04e702ea304950e34b4b76dc1bbc987bcc4e3
+      current_source_hash: 8916f83f621505dc5406f14e70d04e702ea304950e34b4b76dc1bbc987bcc4e3
+      generated_at_before: '2026-05-06T17:17:59Z'
+      generated_at_after: '2026-05-06T17:17:59Z'
+      before_count: 5
+      after_count: 5
+      generated_count: 5
+      change_details:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+      generated_diff:
+        before_count: 5
+        after_count: 5
+        added_questions: []
+        removed_questions: []
+        updated_questions: []
 - timestamp: '2026-05-08T16:31:32Z'
   mode: write
   require_enable_flag: true
diff --git a/tools/generate_summary_faq.py b/tools/generate_summary_faq.py
index a064c9f8be..e82bba484d 100644
--- a/tools/generate_summary_faq.py
+++ b/tools/generate_summary_faq.py
@@ -845,6 +845,7 @@ def build_run_report(
         "updated": 0,
         "unchanged": 0,
         "drift_detected": 0,
+        "paths_with_drift": 0,
         "skipped": 0,
         "errors": 0,
         "removed": 0,
@@ -878,6 +879,9 @@ def build_run_report(
         if faq_action in section_totals["faqs"]:
             section_totals["faqs"][faq_action] += 1
 
+        if summary_result.get("drift_detected") or faq_result.get("drift_detected"):
+            totals["paths_with_drift"] += 1
+
         rerun_flags_reset = result.get("rerun_flags_reset", [])
         if rerun_flags_reset:
             totals["rerun_flags_reset"] += 1
@@ -940,6 +944,7 @@ def print_result_summary(run_report: Dict[str, Any]) -> None:
     print(
         "Processed {processed} Learning Paths: "
         "{added} added, {updated} updated, {drift_detected} drift detected, "
+        "{paths_with_drift} paths with drift, "
         "{unchanged} unchanged, {errors} errors.".format(**totals)
     )
 

From bf35e1bbf55c9aef4e25f9a98ff45ad5afbf4fe4 Mon Sep 17 00:00:00 2001
From: GitHub Actions Summary FAQ Bot <>
Date: Fri, 8 May 2026 18:10:24 +0000
Subject: [PATCH 13/23] Generate Learning Path summary and FAQ content

---
 .../arm_pmu/_index.md                         |    48 +
 .../bolt/_index.md                            |    11 +-
 .../django/_index.md                          |     7 +-
 .../nginx_tune/_index.md                      |     7 +-
 .../processwatch/_index.md                    |    28 +-
 .../tensorflow-gcp/_index.md                  |    18 +-
 reports/generated-summary-faq/latest-run.yml  | 22083 +++++++++++++++-
 7 files changed, 22173 insertions(+), 29 deletions(-)

diff --git a/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md b/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
index cd8864ebb4..d2053c6a16 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
@@ -19,6 +19,53 @@ generate_summary_faq: true
 # rerun_summary: false
 # rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v2
+  generated_at: '2026-05-08T18:10:01Z'
+  generator: template
+  source_hash: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
+  summary_generated_at: '2026-05-08T18:10:01Z'
+  summary_source_hash: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
+  faq_generated_at: '2026-05-08T18:10:01Z'
+  faq_source_hash: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
+  summary: >-
+    Learn how to access and use Arm hardware performance counters and the system counter from
+    user space using PAPI, perf_event_open, and assembly code for performance instrumentation.
+    It is designed for software developers who want to instrument hardware event counters or the
+    system counter in software applications. By the end, you will be able to understand different
+    options for accessing counters from user space, use the system counter to measure time in
+    code, and use PAPI to instrument event counters in code. It focuses on tools and technologies
+    such as PAPI, perf, Assembly, GCC, and Runbook, Linux environments, and Arm platforms including
+    Neoverse. The main steps cover Counter access options, Use a system counter, Use PAPI for
+    counting, and Use perf_event_open for counting.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will understand different options for accessing counters from user space, use the system
+      counter to measure time in code, and use PAPI to instrument event counters in code. Learn
+      how to access and use Arm hardware performance counters and the system counter from user
+      space using PAPI, perf_event_open, and assembly code for performance instrumentation.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers who want to instrument hardware event
+      counters or the system counter in software applications.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm computer running Linux. A bare
+      metal or cloud metal instance is best because they expose more counters. You can use a virtual
+      machine (VM), but fewer counters may be available. These instructions have been tested on
+      the `a1.metal` instance type.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including PAPI, perf, Assembly, GCC, and Runbook, Linux environments,
+      and Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Counter access options, Use a system counter, Use
+      PAPI for counting, and Use perf_event_open for counting.
+# END generated_summary_faq
+
 author: Julio Suarez
 
 ### Tags
@@ -57,3 +104,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/bolt/_index.md b/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
index 5fe9cbf948..0a6cbb0c8d 100644
--- a/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
@@ -16,18 +16,18 @@ prerequisites:
     - (Optional) A second, more powerful Linux system to build the software executable and run BOLT.
 
 generate_summary_faq: true
-rerun_summary: true
-rerun_faqs: true
+rerun_summary: false
+rerun_faqs: false
 
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v2
-  generated_at: '2026-05-08T16:31:30Z'
+  generated_at: '2026-05-08T18:10:01Z'
   generator: template
   source_hash: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
-  summary_generated_at: '2026-05-08T16:31:30Z'
+  summary_generated_at: '2026-05-08T18:10:01Z'
   summary_source_hash: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
-  faq_generated_at: '2026-05-08T16:31:30Z'
+  faq_generated_at: '2026-05-08T18:10:01Z'
   faq_source_hash: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
   summary: >-
     Learn how to build, profile, and optimize Arm executables using BOLT post-link binary optimization
@@ -102,3 +102,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/django/_index.md b/content/learning-paths/servers-and-cloud-computing/django/_index.md
index fdb9673228..bc6bad6204 100644
--- a/content/learning-paths/servers-and-cloud-computing/django/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/django/_index.md
@@ -19,17 +19,17 @@ prerequisites:
 
 generate_summary_faq: true
 # rerun_summary: false
-rerun_faqs: true
+rerun_faqs: false
 
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v2
-  generated_at: '2026-05-08T16:31:30Z'
+  generated_at: '2026-05-08T18:10:02Z'
   generator: template
   source_hash: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
   summary_generated_at: '2026-05-06T17:17:57Z'
   summary_source_hash: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
-  faq_generated_at: '2026-05-08T16:31:30Z'
+  faq_generated_at: '2026-05-08T18:10:02Z'
   faq_source_hash: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
   summary: >-
     Learn how to create a simple Django web application and deploy it on Arm machines using Nginx
@@ -111,3 +111,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
index 71c2147094..fe7e406114 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
@@ -17,16 +17,16 @@ prerequisites:
     - If you do not already have a Nginx setup, a review of [Learn how to deploy Nginx](/learning-paths/servers-and-cloud-computing/nginx/).
 
 generate_summary_faq: true
-rerun_summary: true
+rerun_summary: false
 # rerun_faqs: false
 
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v2
-  generated_at: '2026-05-08T16:31:32Z'
+  generated_at: '2026-05-08T18:10:03Z'
   generator: template
   source_hash: 10f13dee3459577fcadf5966cf80d990f3396321189f638432a3308699e773a8
-  summary_generated_at: '2026-05-08T16:31:32Z'
+  summary_generated_at: '2026-05-08T18:10:03Z'
   summary_source_hash: 10f13dee3459577fcadf5966cf80d990f3396321189f638432a3308699e773a8
   faq_generated_at: '2026-05-06T17:17:58Z'
   faq_source_hash: 10f13dee3459577fcadf5966cf80d990f3396321189f638432a3308699e773a8
@@ -99,3 +99,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md b/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
index 594279eba5..28df3c6fc0 100644
--- a/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
@@ -20,12 +20,12 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v2
-  generated_at: '2026-05-08T16:31:32Z'
+  generated_at: '2026-05-08T18:10:03Z'
   generator: template
   source_hash: ed85d6171f3c05bfdf1b396065fb0c73ce88724ff63fd1c3c8a93d4c27dd59f8
   summary_generated_at: '2026-05-06T17:17:58Z'
   summary_source_hash: ed85d6171f3c05bfdf1b396065fb0c73ce88724ff63fd1c3c8a93d4c27dd59f8
-  faq_generated_at: '2026-05-08T16:31:32Z'
+  faq_generated_at: '2026-05-08T18:10:03Z'
   faq_source_hash: ed85d6171f3c05bfdf1b396065fb0c73ce88724ff63fd1c3c8a93d4c27dd59f8
   summary: >-
     Run Process watch on your Arm machine walks you through an end-to-end Arm software workflow.
@@ -36,6 +36,29 @@ generated_summary_faq:
     such as bpftool, libbpf, Capstone, C, and CPP, Linux environments, and Arm platforms including
     Cortex-A and Neoverse. The main steps cover Install dependencies, Run Process Watch, Learn
     how Process Watch works, and Using Process Watch.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will build and run the Process Watch tool on your Arm machine, describe how Process
+      Watch works, and check in real-time whether any workloads are using specific Arm instructions
+      or features.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers who want to build and run the Process
+      Watch tool on an Arm-based machine.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Arm-based system (bare metal server,
+      cloud instance, or developer board) running Linux with kernel version 5.8.0 or later.; Root
+      access, or the ability to run the sudo command.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including bpftool, libbpf, Capstone, C, and CPP, Linux environments,
+      and Arm platforms such as Cortex-A and Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Install dependencies, Run Process Watch, Learn how
+      Process Watch works, and Using Process Watch.
 # END generated_summary_faq
 
 author: Graham Woodward
@@ -75,3 +98,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+
diff --git a/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
index 7529711128..7e11ea4b7d 100644
--- a/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
@@ -21,13 +21,26 @@ generate_summary_faq: true
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v2
-  generated_at: '2026-05-08T16:31:32Z'
+  generated_at: '2026-05-08T18:10:04Z'
   generator: template
   source_hash: 2c20d30d25a0bb871bdb935d18f7ad8e0740139948133dbd728e10f0add0f94e
-  summary_generated_at: '2026-05-08T16:31:32Z'
+  summary_generated_at: '2026-05-08T18:10:04Z'
   summary_source_hash: 2c20d30d25a0bb871bdb935d18f7ad8e0740139948133dbd728e10f0add0f94e
   faq_generated_at: '2026-05-06T17:17:59Z'
   faq_source_hash: 2c20d30d25a0bb871bdb935d18f7ad8e0740139948133dbd728e10f0add0f94e
+  summary: >-
+    Deploy TensorFlow on Google Cloud C4A (Arm-based Axion VMs) walks you through an end-to-end
+    Arm software workflow. It is designed for software developers deploying and optimizing TensorFlow
+    workloads on Arm64 Linux environments, specifically using Google Cloud C4A virtual machines
+    powered by Axion processors. By the end, you will be able to provision an Arm-based SUSE Linux
+    Enterprise Server (SLES) virtual machine on Google Cloud (C4A with Axion processors), install
+    TensorFlow on a SUSE Arm64 (C4A) instance, and verify TensorFlow by running basic computation
+    and model training tests on Arm64. It focuses on tools and technologies such as TensorFlow,
+    Python, and Keras, Linux environments, Arm platforms including Neoverse, and cloud platforms
+    such as Google Cloud. The main steps cover Get started with TensorFlow on Google Axion C4A,
+    Create a Google Axion C4A Arm virtual machine on GCP, Install TensorFlow, Test TensorFlow
+    baseline performance on Google Axion C4A, and Benchmark TensorFlow model performance using
+    tf.keras.
   faqs:
   - question: What will you accomplish in this Learning Path?
     answer: >-
@@ -94,3 +107,4 @@ weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
 ---
+
diff --git a/reports/generated-summary-faq/latest-run.yml b/reports/generated-summary-faq/latest-run.yml
index 479d3278f4..4ac4d8e2bf 100644
--- a/reports/generated-summary-faq/latest-run.yml
+++ b/reports/generated-summary-faq/latest-run.yml
@@ -1,13 +1,13 @@
 latest_run:
-  timestamp: '2026-05-08T17:55:58Z'
-  mode: dry-run
+  timestamp: '2026-05-08T18:10:04Z'
+  mode: write
   require_enable_flag: true
   path_filter: ''
   limit: 0
-  run_url: ''
-  git_ref: ''
-  git_sha: ''
-  actor: ''
+  run_url: https://github.com/chrismoroney/arm-learning-paths/actions/runs/25571591409
+  git_ref: STESOL-345
+  git_sha: a102be7c3271ce56120c193bfc2c779705fbecfb
+  actor: chrismoroney
   template_version: summary-faq-v2
   totals:
     processed: 407
@@ -11620,7 +11620,7 @@ latest_run:
       source_hash_after: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
       current_source_hash: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
       generated_at_before: ''
-      generated_at_after: '2026-05-08T17:55:57Z'
+      generated_at_after: '2026-05-08T18:10:01Z'
       preview_before: ''
       preview_after: Learn how to access and use Arm hardware performance counters and the system counter
         from user space using PAPI, perf_event_open, and assembly code for performance instrumentation.
@@ -11638,7 +11638,7 @@ latest_run:
       source_hash_after: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
       current_source_hash: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
       generated_at_before: ''
-      generated_at_after: '2026-05-08T17:55:57Z'
+      generated_at_after: '2026-05-08T18:10:01Z'
       before_count: 0
       after_count: 5
       generated_count: 5
@@ -12119,7 +12119,7 @@ latest_run:
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       current_source_hash: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
       generated_at_before: '2026-05-08T16:31:30Z'
-      generated_at_after: '2026-05-08T17:55:57Z'
+      generated_at_after: '2026-05-08T18:10:01Z'
       preview_before: Learn how to build, profile, and optimize Arm executables using BOLT post-link binary
         optimization to improve application performance through code layout improvements. It is designed
         for software deve...
@@ -12139,7 +12139,7 @@ latest_run:
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       generated_at_before: '2026-05-08T16:31:30Z'
-      generated_at_after: '2026-05-08T17:55:57Z'
+      generated_at_after: '2026-05-08T18:10:01Z'
       before_count: 5
       after_count: 5
       generated_count: 5
@@ -13708,7 +13708,7 @@ latest_run:
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       generated_at_before: '2026-05-08T16:31:30Z'
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+      generated_at_after: '2026-05-08T18:10:02Z'
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@@ -18174,7 +18174,7 @@ latest_run:
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       generated_at_before: '2026-05-08T16:31:32Z'
-      generated_at_after: '2026-05-08T17:55:57Z'
+      generated_at_after: '2026-05-08T18:10:03Z'
       preview_before: Learn how to tune Nginx walks you through an end-to-end Arm software workflow. It
         is designed for software developers who want to use Nginx on Arm. By the end, you will be able
         to describe how kernel ...
@@ -19275,7 +19275,7 @@ latest_run:
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+      generated_at_after: '2026-05-08T18:10:03Z'
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@@ -21102,7 +21102,7 @@ latest_run:
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-      generated_at_after: '2026-05-08T17:55:58Z'
+      generated_at_after: '2026-05-08T18:10:04Z'
       preview_before: ''
       preview_after: Deploy TensorFlow on Google Cloud C4A (Arm-based Axion VMs) walks you through an
         end-to-end Arm software workflow. It is designed for software developers deploying and optimizing
@@ -22055,6 +22055,22061 @@ latest_run:
         removed_questions: []
         updated_questions: []
 history:
+- timestamp: '2026-05-08T18:10:04Z'
+  mode: write
+  require_enable_flag: true
+  path_filter: ''
+  limit: 0
+  run_url: https://github.com/chrismoroney/arm-learning-paths/actions/runs/25571591409
+  git_ref: STESOL-345
+  git_sha: a102be7c3271ce56120c193bfc2c779705fbecfb
+  actor: chrismoroney
+  template_version: summary-faq-v2
+  totals:
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+    added: 1
+    updated: 5
+    unchanged: 400
+    drift_detected: 1
+    paths_with_drift: 1
+    skipped: 0
+    errors: 0
+    removed: 0
+    summary_changed: 2
+    faq_changed: 2
+    rerun_flags_reset: 3
+  section_totals:
+    summary:
+      created: 1
+      repaired_missing: 1
+      rerun_requested: 2
+      drift_detected_preserved: 1
+      unchanged: 402
+    faqs:
+      created: 1
+      repaired_missing: 1
+      rerun_requested: 2
+      drift_detected_preserved: 1
+      unchanged: 402
+  reason_totals:
+    initial_generation: 1
+    missing_summary: 1
+    missing_faqs: 1
+    rerun_summary: 2
+    rerun_faqs: 2
+    summary_drift_detected: 1
+    faq_drift_detected: 1
+    rerun_flags_reset: 3
+  paths:
+  - path: content/learning-paths/automotive/openadkit1_container/_index.md
+    status: unchanged
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons: []
+    template_version_before: summary-faq-v1
+    template_version_after: summary-faq-v1
+    summary:
+      action: unchanged
+      missing_before: false
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+      drift_detected: false
+      source_hash_before: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
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+      generated_at_before: '2026-05-06T17:17:53Z'
+      generated_at_after: '2026-05-06T17:17:53Z'
+      preview_before: Learn how to deploy and run containerized autonomous driving simulations using Autoware
+        Open AD Kit on Arm Neoverse with Docker, demonstrating SOAFEE-based Shift-Left development workflows.
+        It is desi...
+      preview_after: Learn how to deploy and run containerized autonomous driving simulations using Autoware
+        Open AD Kit on Arm Neoverse with Docker, demonstrating SOAFEE-based Shift-Left development workflows.
+        It is desi...
+      preview_generated: Learn how to deploy and run containerized autonomous driving simulations using
+        Autoware Open AD Kit on Arm Neoverse with Docker, demonstrating SOAFEE-based Shift-Left development
+        workflows. It is desi...
+    faqs:
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+      source_hash_before: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
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+      change_details:
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+      generated_diff:
+        before_count: 5
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+        added_questions: []
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+  - path: content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
+    status: unchanged
+    changed_on_disk: false
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+    rerun_flags_reset: []
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+    template_version_before: summary-faq-v1
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+      generated_at_before: '2026-05-06T17:17:53Z'
+      generated_at_after: '2026-05-06T17:17:53Z'
+      preview_before: Learn how to implement functional safety isolation for autonomous driving systems
+        on Arm Neoverse using DDS-based communication, containerized deployment, and ISO 26262 compliance
+        principles. It is de...
+      preview_after: Learn how to implement functional safety isolation for autonomous driving systems
+        on Arm Neoverse using DDS-based communication, containerized deployment, and ISO 26262 compliance
+        principles. It is de...
+      preview_generated: Learn how to implement functional safety isolation for autonomous driving systems
+        on Arm Neoverse using DDS-based communication, containerized deployment, and ISO 26262 compliance
+        principles. It is de...
+    faqs:
+      action: unchanged
+      missing_before: false
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+        Pi, using pub/sub, storage, and queryable models for scalable robotics and IoT applications. It
+        is designed for ro...
+      preview_generated: Learn how to build and deploy distributed Zenoh systems on Arm devices like Raspberry
+        Pi, using pub/sub, storage, and queryable models for scalable robotics and IoT applications. It
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+      preview_before: Learn how to design and integrate a custom AXI-Lite peripheral with a Cortex-A9
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+        inputs. It is designed...
+      preview_after: Learn how to design and integrate a custom AXI-Lite peripheral with a Cortex-A9 processor
+        on the Zybo Z7-10 board using Vivado, configuring GPIOs to control LEDs based on switch inputs.
+        It is designed...
+      preview_generated: Learn how to design and integrate a custom AXI-Lite peripheral with a Cortex-A9
+        processor on the Zybo Z7-10 board using Vivado, configuring GPIOs to control LEDs based on switch
+        inputs. It is designed...
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+      preview_before: Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit and
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+        a neural network model t...
+      preview_after: Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit and
+        run inference on the Ethos-U85 NPU. It is designed for embedded developers who want to deploy
+        a neural network model t...
+      preview_generated: Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit
+        and run inference on the Ethos-U85 NPU. It is designed for embedded developers who want to deploy
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+        using Arduino IDE, PIR sensors, and interrupt-driven programming on baremetal. It is designed
+        for software devel...
+      preview_generated: Learn how to build a motion-detection device with Raspberry Pi Pico (RP2040 Cortex-M0+)
+        using Arduino IDE, PIR sensors, and interrupt-driven programming on baremetal. It is designed
+        for software devel...
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+  - path: content/learning-paths/embedded-and-microcontrollers/armds/_index.md
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+        It is designed for ...
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+        embedded applications using Fixed Virtual Platforms (FVPs) or hardware with DSTREAM debug probes.
+        It is designed for ...
+      preview_generated: Learn how to import and build example projects in Arm Development Studio and
+        debug embedded applications using Fixed Virtual Platforms (FVPs) or hardware with DSTREAM debug
+        probes. It is designed for ...
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+      preview_before: Learn how to write mixed C and assembly programs for Cortex-M microcontrollers using
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+        who are interes...
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+        Keil MDK, following Arm Procedure Call Standard conventions. It is designed for software developers
+        who are interes...
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+        using Keil MDK, following Arm Procedure Call Standard conventions. It is designed for software
+        developers who are interes...
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+  - path: content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
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+      preview_before: Learn how to create a custom Balena OS image, run it on Arm Virtual Hardware, and
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+        developers interested...
+      preview_after: Learn how to create a custom Balena OS image, run it on Arm Virtual Hardware, and
+        deploy IoT applications to a virtual Raspberry Pi 4 device. It is designed for embedded software
+        developers interested...
+      preview_generated: Learn how to create a custom Balena OS image, run it on Arm Virtual Hardware,
+        and deploy IoT applications to a virtual Raspberry Pi 4 device. It is designed for embedded software
+        developers interested...
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+  - path: content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
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+      preview_before: Learn how to set up AWS IoT Greengrass Core on Arm Virtual Hardware and deploy AWS
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+        developers interested ...
+      preview_after: Learn how to set up AWS IoT Greengrass Core on Arm Virtual Hardware and deploy AWS
+        Greengrass components to a virtual Raspberry Pi 4 device. It is designed for embedded software
+        developers interested ...
+      preview_generated: Learn how to set up AWS IoT Greengrass Core on Arm Virtual Hardware and deploy
+        AWS Greengrass components to a virtual Raspberry Pi 4 device. It is designed for embedded software
+        developers interested ...
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+  - path: content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
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+      preview_before: Learn how to build Matter reference examples on Arm Virtual Hardware, demonstrate
+        device communication, and automate testing with GitHub Actions CI/CD workflows. It is designed
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+      preview_after: Learn how to build Matter reference examples on Arm Virtual Hardware, demonstrate
+        device communication, and automate testing with GitHub Actions CI/CD workflows. It is designed
+        for embedded software d...
+      preview_generated: Learn how to build Matter reference examples on Arm Virtual Hardware, demonstrate
+        device communication, and automate testing with GitHub Actions CI/CD workflows. It is designed
+        for embedded software d...
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+        DB, and Azure Fun...
+      preview_generated: Learn how to build a complete IoT solution in Azure that streams, stores, monitors,
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+        DB, and Azure Fun...
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+        handling. It is desi...
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+        It is designed for d...
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+      preview_before: Learn how to implement context switching operations on Arm Cortex-M processors using
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+        using the Memory Protection Unit and SysTick exception in a bare-metal environment. It is designed
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+        for structured device access and Strands for agent orchestration, with examples for both simulated
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+      preview_before: Learn how to create a Dockerfile, build a Docker image with Arm Compiler for Embedded
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+        for embedded s...
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+        It is designed for embedded s...
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+        commands. It is designed...
+      preview_generated: Learn how to collect and preprocess audio data using Edge Impulse, train an audio
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+        commands. It is designed...
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+      preview_before: Develop a image classification neural network model and deploy it on an STM32 B-L475E-IOT01A2
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+        board. It is designed for embedded software developers interested in building neural network models
+        for mi...
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+        developers worki...
+      preview_generated: Learn where Arm architecture is used in microcontrollers and discover microcontroller
+        hardware options for software development on Arm Cortex-M processors. It is designed for software
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+      preview_before: Learn what differentiates TinyML from other AI domains, explore Arm-based edge devices
+        for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed Virtual
+        Platform. It is ...
+      preview_after: Learn what differentiates TinyML from other AI domains, explore Arm-based edge devices
+        for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed Virtual
+        Platform. It is ...
+      preview_generated: Learn what differentiates TinyML from other AI domains, explore Arm-based edge
+        devices for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed
+        Virtual Platform. It is ...
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+  - path: content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
+    status: unchanged
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+      preview_before: Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300 virtual
+        hardware to understand complete IoT software stack construction. It is designed for embedded software
+        developers ...
+      preview_after: Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300 virtual
+        hardware to understand complete IoT software stack construction. It is designed for embedded software
+        developers ...
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+      preview_generated: Learn how to create debug configurations for virtual platforms and development
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+      preview_generated: Learn how to bring up ExecuTorch executor_runner firmware on the NXP FRDM i.MX
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+      preview_before: Learn how to migrate a CMSIS v5-based CMSIS-Pack with device support to CMSIS v6
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+      preview_generated: Learn how to migrate a CMSIS v5-based CMSIS-Pack with device support to CMSIS
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+      preview_generated: Learn how to migrate CMSIS v5 projects to CMSIS v6 by identifying supported toolchains,
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+        control GPIO-connected devices, and deploy a privacy-first web-based smart home assistant without
+        cloud services. It ...
+      preview_generated: Learn how to run large language models locally on the Raspberry Pi 5 using Ollama,
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+        cloud services. It ...
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+      preview_before: Learn how to build a voice-controlled bot on a Raspberry Pi that listens for a wake
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+        and plays audio resp...
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+        word, converts speech to text using Google Speech Recognition, sends requests to ChatGPT's API,
+        and plays audio resp...
+      preview_generated: Learn how to build a voice-controlled bot on a Raspberry Pi that listens for
+        a wake word, converts speech to text using Google Speech Recognition, sends requests to ChatGPT's
+        API, and plays audio resp...
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+        for software...
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+      preview_before: Learn how to compile the Llama 3 large language model using ExecuTorch, deploy it
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+        to a Raspberry Pi 5, and understand techniques for running LLMs in embedded environments. It is
+        designed for anyone in...
+      preview_generated: Learn how to compile the Llama 3 large language model using ExecuTorch, deploy
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+      preview_before: Learn how to reduce compile time for embedded Linux projects by installing a Raspberry
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+        it to a Raspb...
+      preview_after: Learn how to reduce compile time for embedded Linux projects by installing a Raspberry
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+        it to a Raspb...
+      preview_generated: Learn how to reduce compile time for embedded Linux projects by installing a
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+      preview_generated: Build a letter recognition neural network model using TensorFlow and deploy it
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+        developers ...
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+        designed for machine learnin...
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+      preview_before: Learn how to install Keil MDK Tools, run a TrustZone hello world example on the
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+        in a 4U chassis, and install single board computers in the racks using 3D-printed parts. It is
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+  - path: content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
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+      preview_before: Learn how to debug microcontrollers using µVision with basic run/stop debug, advanced
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+  - path: content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
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+      preview_before: Learn how to import, convert, and build uvprojx-based projects to csolution format
+        using Keil Studio, µVision, and command-line tools for CMSIS-Toolbox compatibility. It is designed
+        for This is a topi...
+      preview_after: Learn how to import, convert, and build uvprojx-based projects to csolution format
+        using Keil Studio, µVision, and command-line tools for CMSIS-Toolbox compatibility. It is designed
+        for This is a topi...
+      preview_generated: Learn how to import, convert, and build uvprojx-based projects to csolution format
+        using Keil Studio, µVision, and command-line tools for CMSIS-Toolbox compatibility. It is designed
+        for This is a topi...
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+        Realms using Trustee services for attestation-based authorization on an FVP with RME support.
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+      preview_generated: Learn how to deploy Confidential Containers from encrypted images inside Arm
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+        who want to run...
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+      preview_generated: Learn how to install and run Clair on Arm servers using combined and distributed
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+        and benchmar...
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+        with JMH microbenchmarks. It is designed for This is an introductory topic about Java deployment
+        and benchmar...
+      preview_generated: Deploy Java on Azure Cobalt 100 Arm virtual machines and benchmark application
+        performance with JMH microbenchmarks. It is designed for This is an introductory topic about Java
+        deployment and benchmar...
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+      preview_before: Profile Java applications on Arm Neoverse servers using flame graphs generated with
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+      preview_after: Profile Java applications on Arm Neoverse servers using flame graphs generated with
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+        who want to analyz...
+      preview_generated: Profile Java applications on Arm Neoverse servers using flame graphs generated
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+      preview_after: Deploy Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate installation,
+        and execute Arm-native CI/CD pipelines including Docker workflows. It is designed for software
+        developers d...
+      preview_generated: Deploy Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate
+        installation, and execute Arm-native CI/CD pipelines including Docker workflows. It is designed
+        for software developers d...
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+      preview_before: Deploy and configure a Kafka cluster with Zookeeper on Arm servers, test event streaming,
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+      preview_after: Deploy and configure a Kafka cluster with Zookeeper on Arm servers, test event streaming,
+        and automate deployment on AWS and Google Cloud. It is designed for software developers who want
+        to learn how ...
+      preview_generated: Deploy and configure a Kafka cluster with Zookeeper on Arm servers, test event
+        streaming, and automate deployment on AWS and Google Cloud. It is designed for software developers
+        who want to learn how ...
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+      preview_before: Deploy Apache Kafka on Azure Cobalt 100 Arm virtual machines and benchmark message
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+        from x86_64 to ...
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+        throughput performance. It is designed for developers looking to migrate their Apache Kafka workloads
+        from x86_64 to ...
+      preview_generated: Deploy Apache Kafka on Azure Cobalt 100 Arm virtual machines and benchmark message
+        throughput performance. It is designed for developers looking to migrate their Apache Kafka workloads
+        from x86_64 to ...
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+      preview_before: Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing Kedify
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+      preview_after: Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing Kedify
+        and KEDA with Helm and testing autoscaling behavior. It is designed for developers running HTTP
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+      preview_generated: Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing
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+      preview_after: Create, train, and evaluate a neural network model on Arm servers using Keras Core
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+      preview_generated: Create, train, and evaluate a neural network model on Arm servers using Keras
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+  - path: content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
+    status: unchanged
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+      preview_after: Compile and install custom Linux kernels on Arm cloud instances using TuxMake with
+        configurations for 64 KB page sizes and Fastpath testing. It is designed for software developers
+        building custom Linu...
+      preview_generated: Compile and install custom Linux kernels on Arm cloud instances using TuxMake
+        with configurations for 64 KB page sizes and Fastpath testing. It is designed for software developers
+        building custom Linu...
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+  - path: content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
+    status: unchanged
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+      preview_before: Identify and migrate container images in a Kubernetes cluster to Arm-compatible
+        versions using KubeArchInspect reports. It is designed for software developers who want to ensure
+        containers running in ...
+      preview_after: Identify and migrate container images in a Kubernetes cluster to Arm-compatible versions
+        using KubeArchInspect reports. It is designed for software developers who want to ensure containers
+        running in ...
+      preview_generated: Identify and migrate container images in a Kubernetes cluster to Arm-compatible
+        versions using KubeArchInspect reports. It is designed for software developers who want to ensure
+        containers running in ...
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+        developers and perfo...
+      preview_after: Use ASCT to measure cache latency, streaming bandwidth, and coherency latency on
+        Arm Neoverse systems, and compare results across Graviton generations. It is designed for software
+        developers and perfo...
+      preview_generated: Use ASCT to measure cache latency, streaming bandwidth, and coherency latency
+        on Arm Neoverse systems, and compare results across Graviton generations. It is designed for software
+        developers and perfo...
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+      preview_before: Test and validate thread synchronization approaches in the Arm memory model using
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+      preview_after: Test and validate thread synchronization approaches in the Arm memory model using
+        Herd7, Litmus7, and Arm hardware with assembly snippets. It is designed for developers seeking
+        practical ways to test ...
+      preview_generated: Test and validate thread synchronization approaches in the Arm memory model using
+        Herd7, Litmus7, and Arm hardware with assembly snippets. It is designed for developers seeking
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+      preview_generated: Microbenchmark and tune network performance with iPerf3 and Linux traffic control
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+        - What will you accomplish in this Learning Path?
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+      preview_before: Scan source code for architecture-specific portability issues using migrate-ease
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+        to identify and resolve AArch64 porting challenges before migration. It is designed for developers
+        looking to migrate a...
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+        to identify and resolve AArch64 porting challenges before migration. It is designed for developers
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+      preview_before: Build a Retrieval-Augmented Generation (RAG) application on Arm servers using Zilliz
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+        who want to create ...
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+        Cloud for vector search and llama.cpp for LLM inference. It is designed for software developers
+        who want to create ...
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+        who want to create ...
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+      preview_before: Learn how to deploy and configure MinIO on an Azure Cobalt 100 virtual machine,
+        benchmark object storage throughput, and validate S3 compatibility using the boto3 Python SDK.
+        It is designed for develo...
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+        object storage throughput, and validate S3 compatibility using the boto3 Python SDK. It is designed
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+        benchmark object storage throughput, and validate S3 compatibility using the boto3 Python SDK.
+        It is designed for develo...
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+  - path: content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
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+      preview_before: Benchmark machine learning inference performance on Arm servers using TensorFlow
+        and the MLPerf Inference benchmark suite from MLCommons. It is designed for software developers
+        interested in benchmark...
+      preview_after: Benchmark machine learning inference performance on Arm servers using TensorFlow
+        and the MLPerf Inference benchmark suite from MLCommons. It is designed for software developers
+        interested in benchmark...
+      preview_generated: Benchmark machine learning inference performance on Arm servers using TensorFlow
+        and the MLPerf Inference benchmark suite from MLCommons. It is designed for software developers
+        interested in benchmark...
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+  - path: content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
+    status: unchanged
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+      preview_before: Install MongoDB on Arm servers and benchmark database performance using Yahoo Cloud
+        Serving Benchmark (YCSB) to compare against other architectures. It is designed for software developers
+        who want to ...
+      preview_after: Install MongoDB on Arm servers and benchmark database performance using Yahoo Cloud
+        Serving Benchmark (YCSB) to compare against other architectures. It is designed for software developers
+        who want to ...
+      preview_generated: Install MongoDB on Arm servers and benchmark database performance using Yahoo
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 - timestamp: '2026-05-08T17:55:58Z'
   mode: dry-run
   require_enable_flag: true

From f79ee7e5f40a1d01f4a801847647eec92d7f41cc Mon Sep 17 00:00:00 2001
From: Christopher Moroney <christopher.moroney1@gmail.com>
Date: Tue, 12 May 2026 11:15:44 -0700
Subject: [PATCH 14/23] another dry run - more stats to capture

---
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 .../openadkit2_safetyisolation/_index.md      |     4 +-
 .../automotive/system76-auto/_index.md        |     4 +-
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 .../cross-platform/adler32/_index.md          |     4 +-
 .../_index.md                                 |     4 +-
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 .../cross-platform/cca_rme/_index.md          |     4 +-
 .../cpp-loop-size-context/_index.md           |     4 +-
 .../docker-build-cloud/_index.md              |     4 +-
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 .../dynamic-memory-allocator/_index.md        |     4 +-
 .../eigen-linear-algebra-on-arm/_index.md     |     4 +-
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 .../_index.md                                 |     4 +-
 .../cross-platform/loop-reflowing/_index.md   |     4 +-
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 .../cross-platform/mca-godbolt/_index.md      |     4 +-
 .../cross-platform/mcp-ai-agent/_index.md     |     4 +-
 .../cross-platform/memory-latency/_index.md   |     4 +-
 .../multimodel_mnn_v9/_index.md               |     4 +-
 .../multiplying-matrices-with-sme2/_index.md  |     4 +-
 .../cross-platform/psa-tfm/_index.md          |     4 +-
 .../_index.md                                 |     4 +-
 .../cross-platform/remoteit/_index.md         |     4 +-
 .../restrict-keyword-c99/_index.md            |     4 +-
 .../cross-platform/rust_armds/_index.md       |     4 +-
 .../cross-platform/simd-info-demo/_index.md   |     4 +-
 .../cross-platform/simd-loops/_index.md       |     4 +-
 .../cross-platform/simd-on-rust/_index.md     |     4 +-
 .../sme-executorch-profiling/_index.md        |     4 +-
 .../tinkerblox_ultraedge/_index.md            |     4 +-
 .../cross-platform/topdown-compare/_index.md  |     4 +-
 .../vectorization-comparison/_index.md        |     4 +-
 .../_index.md                                 |     4 +-
 .../_index.md                                 |     4 +-
 .../cross-platform/woa_azure/_index.md        |     4 +-
 .../zenoh-multinode-ros2/_index.md            |     4 +-
 .../advanced_soc/_index.md                    |     4 +-
 .../alif-image-classification/_index.md       |     4 +-
 .../arduino-pico/_index.md                    |     4 +-
 .../armds/_index.md                           |     4 +-
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 reports/generated-summary-faq/latest-run.yml  | 22895 +++++++++++++++-
 418 files changed, 23367 insertions(+), 1637 deletions(-)

diff --git a/content/learning-paths/automotive/intro/_index.md b/content/learning-paths/automotive/intro/_index.md
index 89c60b3067..ad69253a86 100644
--- a/content/learning-paths/automotive/intro/_index.md
+++ b/content/learning-paths/automotive/intro/_index.md
@@ -18,8 +18,8 @@ cascade:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/automotive/openadkit1_container/_index.md b/content/learning-paths/automotive/openadkit1_container/_index.md
index 12376755e1..d2af4d057e 100644
--- a/content/learning-paths/automotive/openadkit1_container/_index.md
+++ b/content/learning-paths/automotive/openadkit1_container/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md b/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
index 9acb9d4436..3e59b216d8 100644
--- a/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
+++ b/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/automotive/system76-auto/_index.md b/content/learning-paths/automotive/system76-auto/_index.md
index c3adfaf5c4..bb2cf31f77 100644
--- a/content/learning-paths/automotive/system76-auto/_index.md
+++ b/content/learning-paths/automotive/system76-auto/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/automotive/zenacssdebug/_index.md b/content/learning-paths/automotive/zenacssdebug/_index.md
index 5932c99b1a..b7f49da42c 100644
--- a/content/learning-paths/automotive/zenacssdebug/_index.md
+++ b/content/learning-paths/automotive/zenacssdebug/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/_example-learning-path/_index.md b/content/learning-paths/cross-platform/_example-learning-path/_index.md
index 1f32ed6bdc..123e3988a1 100644
--- a/content/learning-paths/cross-platform/_example-learning-path/_index.md
+++ b/content/learning-paths/cross-platform/_example-learning-path/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/adler32/_index.md b/content/learning-paths/cross-platform/adler32/_index.md
index ed59df0b6e..478ac2b93a 100644
--- a/content/learning-paths/cross-platform/adler32/_index.md
+++ b/content/learning-paths/cross-platform/adler32/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md b/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md
index 5c5f2c89e9..4d4c999afd 100644
--- a/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md
+++ b/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/avh_cicd/_index.md b/content/learning-paths/cross-platform/avh_cicd/_index.md
index 633d150618..6367153d95 100644
--- a/content/learning-paths/cross-platform/avh_cicd/_index.md
+++ b/content/learning-paths/cross-platform/avh_cicd/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/avh_cicd2/_index.md b/content/learning-paths/cross-platform/avh_cicd2/_index.md
index ce6fc0de25..0a70d6a147 100644
--- a/content/learning-paths/cross-platform/avh_cicd2/_index.md
+++ b/content/learning-paths/cross-platform/avh_cicd2/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/cca_rme/_index.md b/content/learning-paths/cross-platform/cca_rme/_index.md
index 813299db98..973dba7462 100644
--- a/content/learning-paths/cross-platform/cca_rme/_index.md
+++ b/content/learning-paths/cross-platform/cca_rme/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md b/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md
index 01dfdc0691..2128260c50 100644
--- a/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md
+++ b/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/docker-build-cloud/_index.md b/content/learning-paths/cross-platform/docker-build-cloud/_index.md
index 3a3261f34a..d961430ba8 100644
--- a/content/learning-paths/cross-platform/docker-build-cloud/_index.md
+++ b/content/learning-paths/cross-platform/docker-build-cloud/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/docker/_index.md b/content/learning-paths/cross-platform/docker/_index.md
index 01722652ed..b2696c7f3c 100644
--- a/content/learning-paths/cross-platform/docker/_index.md
+++ b/content/learning-paths/cross-platform/docker/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md b/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md
index fc9a856b22..c7bc4142cb 100644
--- a/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md
+++ b/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md b/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md
index 15a4031100..bab37a3dbf 100644
--- a/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md
+++ b/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/ernie_moe_v9/_index.md b/content/learning-paths/cross-platform/ernie_moe_v9/_index.md
index c5fd3c8860..0fd72dc933 100644
--- a/content/learning-paths/cross-platform/ernie_moe_v9/_index.md
+++ b/content/learning-paths/cross-platform/ernie_moe_v9/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/floating-point-behavior/_index.md b/content/learning-paths/cross-platform/floating-point-behavior/_index.md
index aca2855fdf..237dac2a69 100644
--- a/content/learning-paths/cross-platform/floating-point-behavior/_index.md
+++ b/content/learning-paths/cross-platform/floating-point-behavior/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/function-multiversioning/_index.md b/content/learning-paths/cross-platform/function-multiversioning/_index.md
index a64cf8c3d3..1aaeba9249 100644
--- a/content/learning-paths/cross-platform/function-multiversioning/_index.md
+++ b/content/learning-paths/cross-platform/function-multiversioning/_index.md
@@ -23,8 +23,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/github-arm-runners/_index.md b/content/learning-paths/cross-platform/github-arm-runners/_index.md
index a6e585c661..ea3efb87e2 100644
--- a/content/learning-paths/cross-platform/github-arm-runners/_index.md
+++ b/content/learning-paths/cross-platform/github-arm-runners/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md b/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md
index ea9d184f16..78e3f6e6e1 100644
--- a/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md
+++ b/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/gitlab/_index.md b/content/learning-paths/cross-platform/gitlab/_index.md
index ec380b1568..e76930ec2d 100644
--- a/content/learning-paths/cross-platform/gitlab/_index.md
+++ b/content/learning-paths/cross-platform/gitlab/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/integer-vs-floats/_index.md b/content/learning-paths/cross-platform/integer-vs-floats/_index.md
index f6393038a0..af6d4d64a7 100644
--- a/content/learning-paths/cross-platform/integer-vs-floats/_index.md
+++ b/content/learning-paths/cross-platform/integer-vs-floats/_index.md
@@ -15,8 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/intrinsics/_index.md b/content/learning-paths/cross-platform/intrinsics/_index.md
index 0b092148ff..3289ee0b5a 100644
--- a/content/learning-paths/cross-platform/intrinsics/_index.md
+++ b/content/learning-paths/cross-platform/intrinsics/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/ipexplorer/_index.md b/content/learning-paths/cross-platform/ipexplorer/_index.md
index a620d0db72..ae1966a35f 100644
--- a/content/learning-paths/cross-platform/ipexplorer/_index.md
+++ b/content/learning-paths/cross-platform/ipexplorer/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/kleidiai-explainer/_index.md b/content/learning-paths/cross-platform/kleidiai-explainer/_index.md
index 0d9cec787b..735f0293da 100644
--- a/content/learning-paths/cross-platform/kleidiai-explainer/_index.md
+++ b/content/learning-paths/cross-platform/kleidiai-explainer/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/llm-fine-tuning-for-web-applications/_index.md b/content/learning-paths/cross-platform/llm-fine-tuning-for-web-applications/_index.md
index 5be707837c..cc483dc8bc 100644
--- a/content/learning-paths/cross-platform/llm-fine-tuning-for-web-applications/_index.md
+++ b/content/learning-paths/cross-platform/llm-fine-tuning-for-web-applications/_index.md
@@ -29,8 +29,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 author: Parichay Das
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/loop-reflowing/_index.md b/content/learning-paths/cross-platform/loop-reflowing/_index.md
index ae79dc2d75..eea90d8a0a 100644
--- a/content/learning-paths/cross-platform/loop-reflowing/_index.md
+++ b/content/learning-paths/cross-platform/loop-reflowing/_index.md
@@ -14,8 +14,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/matrix/_index.md b/content/learning-paths/cross-platform/matrix/_index.md
index 5831b7c0a9..f5a5248862 100644
--- a/content/learning-paths/cross-platform/matrix/_index.md
+++ b/content/learning-paths/cross-platform/matrix/_index.md
@@ -21,8 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/mca-godbolt/_index.md b/content/learning-paths/cross-platform/mca-godbolt/_index.md
index ac8d08ec18..0d888dd7af 100644
--- a/content/learning-paths/cross-platform/mca-godbolt/_index.md
+++ b/content/learning-paths/cross-platform/mca-godbolt/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/mcp-ai-agent/_index.md b/content/learning-paths/cross-platform/mcp-ai-agent/_index.md
index b77ae889a2..2437b5b230 100644
--- a/content/learning-paths/cross-platform/mcp-ai-agent/_index.md
+++ b/content/learning-paths/cross-platform/mcp-ai-agent/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/memory-latency/_index.md b/content/learning-paths/cross-platform/memory-latency/_index.md
index 6e1de49094..0f0fd68184 100644
--- a/content/learning-paths/cross-platform/memory-latency/_index.md
+++ b/content/learning-paths/cross-platform/memory-latency/_index.md
@@ -15,8 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md b/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md
index 9f11138627..0494748119 100644
--- a/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md
+++ b/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md b/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md
index 5c319452a2..650ce7c3c0 100644
--- a/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md
+++ b/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md
@@ -26,8 +26,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/psa-tfm/_index.md b/content/learning-paths/cross-platform/psa-tfm/_index.md
index 05d9d60c28..b0cccb49f4 100644
--- a/content/learning-paths/cross-platform/psa-tfm/_index.md
+++ b/content/learning-paths/cross-platform/psa-tfm/_index.md
@@ -21,8 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 author: Ronan Synnott
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md b/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md
index 890e40e725..3672a4e806 100644
--- a/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md
+++ b/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md
@@ -24,8 +24,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/remoteit/_index.md b/content/learning-paths/cross-platform/remoteit/_index.md
index a96dab5688..ce789f72f5 100644
--- a/content/learning-paths/cross-platform/remoteit/_index.md
+++ b/content/learning-paths/cross-platform/remoteit/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md b/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
index 44e744cb29..b3e7acbb8b 100644
--- a/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
+++ b/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
@@ -15,8 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/rust_armds/_index.md b/content/learning-paths/cross-platform/rust_armds/_index.md
index 4e71235d14..98712c7968 100644
--- a/content/learning-paths/cross-platform/rust_armds/_index.md
+++ b/content/learning-paths/cross-platform/rust_armds/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/simd-info-demo/_index.md b/content/learning-paths/cross-platform/simd-info-demo/_index.md
index bed3c137ff..826273b033 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/_index.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/simd-loops/_index.md b/content/learning-paths/cross-platform/simd-loops/_index.md
index 53a46c61ce..4af0091924 100644
--- a/content/learning-paths/cross-platform/simd-loops/_index.md
+++ b/content/learning-paths/cross-platform/simd-loops/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/simd-on-rust/_index.md b/content/learning-paths/cross-platform/simd-on-rust/_index.md
index 04e6d31743..97930e3717 100644
--- a/content/learning-paths/cross-platform/simd-on-rust/_index.md
+++ b/content/learning-paths/cross-platform/simd-on-rust/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md b/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md
index 0e9c7d19d9..0d4f4d23e9 100644
--- a/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md
+++ b/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md
@@ -22,8 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md b/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md
index 17dcc1a4fe..f9e72f7db2 100644
--- a/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md
+++ b/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md
@@ -21,8 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/topdown-compare/_index.md b/content/learning-paths/cross-platform/topdown-compare/_index.md
index 5dd4f2510b..951f24a600 100644
--- a/content/learning-paths/cross-platform/topdown-compare/_index.md
+++ b/content/learning-paths/cross-platform/topdown-compare/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/vectorization-comparison/_index.md b/content/learning-paths/cross-platform/vectorization-comparison/_index.md
index 5408a18456..a0cc7f74fa 100644
--- a/content/learning-paths/cross-platform/vectorization-comparison/_index.md
+++ b/content/learning-paths/cross-platform/vectorization-comparison/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md b/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md
index afa60a7831..a4f4325474 100644
--- a/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md
+++ b/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md
@@ -15,8 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md b/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
index 3eb50d1638..7d9e54ca9d 100644
--- a/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
+++ b/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
@@ -22,8 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/woa_azure/_index.md b/content/learning-paths/cross-platform/woa_azure/_index.md
index 315c4a0039..0408a20f28 100644
--- a/content/learning-paths/cross-platform/woa_azure/_index.md
+++ b/content/learning-paths/cross-platform/woa_azure/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md b/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
index c1874f5517..76d79ebe51 100644
--- a/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
+++ b/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md b/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
index 21c95cbdf0..fc1ed70ab0 100644
--- a/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md b/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
index 07cd885aa0..a7486cf3f0 100644
--- a/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
@@ -22,8 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md b/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
index a3ee1d48bb..809dd31563 100644
--- a/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
@@ -22,8 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/armds/_index.md b/content/learning-paths/embedded-and-microcontrollers/armds/_index.md
index 3b555bab3c..fd47030083 100644
--- a/content/learning-paths/embedded-and-microcontrollers/armds/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/armds/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/asm/_index.md b/content/learning-paths/embedded-and-microcontrollers/asm/_index.md
index 5e7271adde..6a541e946c 100644
--- a/content/learning-paths/embedded-and-microcontrollers/asm/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/asm/_index.md
@@ -6,8 +6,8 @@ description: Learn how to write mixed C and assembly programs for Cortex-M micro
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
index 4e1b75d69b..da0fbac19d 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
index d9012994e9..bb02c2cdf2 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
index c7adf5115f..738f2cd217 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
index aab8916015..c9cd78b4a2 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
index 58add017fd..7941d570af 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md b/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
index 77fa289d93..075ea65dee 100644
--- a/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
@@ -22,8 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md b/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md
index a51ad99c3c..f4ef8f4bac 100644
--- a/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md b/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
index 44cb3d177e..a15dac6ef8 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md
index 0675f2aef6..bb1804510a 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md
index b0199b372c..e2bd4ae554 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md b/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
index e82a36ec16..6097ef64ea 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
@@ -21,8 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md b/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
index 1a248b0a12..d14f7ff0be 100644
--- a/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md b/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
index 9e98fa37f3..86ada0ec39 100644
--- a/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md b/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
index 05f49dad41..e7dfa3a961 100644
--- a/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md b/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md
index a4ecc9372c..410034e3db 100644
--- a/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/docker/_index.md b/content/learning-paths/embedded-and-microcontrollers/docker/_index.md
index e66768bfd5..938643bcf9 100644
--- a/content/learning-paths/embedded-and-microcontrollers/docker/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/docker/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/edge/_index.md b/content/learning-paths/embedded-and-microcontrollers/edge/_index.md
index 8241aa6a79..a07c307dff 100644
--- a/content/learning-paths/embedded-and-microcontrollers/edge/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/edge/_index.md
@@ -21,8 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/_index.md b/content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/_index.md
index 0947547f7c..932265347c 100644
--- a/content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/_index.md
@@ -26,8 +26,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 author: Doug Anson
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md b/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md
index 740ae28f03..471d3b5b5e 100644
--- a/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/intro/_index.md b/content/learning-paths/embedded-and-microcontrollers/intro/_index.md
index e7ac4baca1..838c68092f 100644
--- a/content/learning-paths/embedded-and-microcontrollers/intro/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/intro/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md b/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
index 21e2e19f1c..addf5de247 100644
--- a/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md b/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
index a1b4af43ad..81fde20d5f 100644
--- a/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md b/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
index 5b6d4a4e0b..13a1900b2e 100644
--- a/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md b/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
index 0b5d786586..300e948f34 100644
--- a/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md b/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
index 5675b121c3..7ca5d81190 100644
--- a/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
@@ -22,8 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md b/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md
index 3682848ba3..fb91a5b843 100644
--- a/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md b/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md
index c245945cee..6ebe1afeef 100644
--- a/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/migration/_index.md b/content/learning-paths/embedded-and-microcontrollers/migration/_index.md
index ae13c5e8ef..08cbd2cf49 100644
--- a/content/learning-paths/embedded-and-microcontrollers/migration/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/migration/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md b/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md
index c67807075e..65cdc955da 100644
--- a/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md b/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md
index 22021ed7da..8f7aef60ec 100644
--- a/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md
@@ -10,8 +10,8 @@ armips:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md b/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md
index c21eccfe27..5add99f81c 100644
--- a/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md b/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
index 12baf516f4..aa1755cc39 100644
--- a/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md b/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
index df0bfc7b50..9b8c912292 100644
--- a/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md b/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md
index c217a6ffce..e4ca311701 100644
--- a/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md b/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md
index f3a7f9361e..22764fda2a 100644
--- a/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md
@@ -21,8 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md b/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md
index 052fa884b4..658614debc 100644
--- a/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md
@@ -22,8 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
index 9135444236..4ca5e89463 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
@@ -22,8 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
index 26ae753359..9729d2f492 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md
index 500fc5a342..981a531b5f 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
index ae2695ba3e..ad9533fb6b 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md b/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
index b3c0352488..aa4dea1167 100644
--- a/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
@@ -22,8 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md b/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
index ce15489463..24ea361380 100644
--- a/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md b/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md
index 9bed20cc42..a2efe17b73 100644
--- a/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md b/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md
index 39220ce6a7..d52fb0b173 100644
--- a/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md
@@ -21,8 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md b/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md
index 3995a9fffc..493694d31c 100644
--- a/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md b/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
index 3c2d5e6adb..e364fbc9b8 100644
--- a/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
@@ -27,8 +27,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md b/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
index 9474d078c5..8a225d1da4 100644
--- a/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
@@ -9,8 +9,8 @@ minutes_to_complete: 90
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md b/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
index b689b9b66f..f46fa7a587 100644
--- a/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md b/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
index 94af8c2d46..80924a7475 100644
--- a/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
@@ -21,8 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md b/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md
index 90970d04ba..aaa2d462d0 100644
--- a/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md b/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md
index 236e3bd23a..f518e939d0 100644
--- a/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md b/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md
index a8eaa3368b..605d4ef693 100644
--- a/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md
@@ -22,8 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md b/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md
index 347ff6295c..c327c3894f 100644
--- a/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md b/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md
index caece5bafe..2e2a45bebb 100644
--- a/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md
@@ -22,8 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md b/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md
index 6f3eab5405..2165838530 100644
--- a/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md
+++ b/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md
index 500b104490..97d017fdc3 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md
@@ -21,8 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md
index 148ad041fe..70a484d291 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md
@@ -23,8 +23,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md
index 4d4d8478f0..27558707c8 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md
index 9b276ec5e0..8496fafe2e 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/docker-models/_index.md b/content/learning-paths/laptops-and-desktops/docker-models/_index.md
index fa1d3e7913..c3aa0d5f7d 100644
--- a/content/learning-paths/laptops-and-desktops/docker-models/_index.md
+++ b/content/learning-paths/laptops-and-desktops/docker-models/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/electron/_index.md b/content/learning-paths/laptops-and-desktops/electron/_index.md
index c88a29930d..58f9bd7fa7 100644
--- a/content/learning-paths/laptops-and-desktops/electron/_index.md
+++ b/content/learning-paths/laptops-and-desktops/electron/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md b/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md
index 62d8c8e808..2f6ff31ed2 100644
--- a/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md
+++ b/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/hyper-v/_index.md b/content/learning-paths/laptops-and-desktops/hyper-v/_index.md
index 869e9d0bfb..dd71fe252b 100644
--- a/content/learning-paths/laptops-and-desktops/hyper-v/_index.md
+++ b/content/learning-paths/laptops-and-desktops/hyper-v/_index.md
@@ -15,8 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/intro/_index.md b/content/learning-paths/laptops-and-desktops/intro/_index.md
index a1044532af..015261bba2 100644
--- a/content/learning-paths/laptops-and-desktops/intro/_index.md
+++ b/content/learning-paths/laptops-and-desktops/intro/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md b/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md
index 54c1ce1604..f53a23df15 100644
--- a/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md
+++ b/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md b/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md
index 3062f413fb..9d92abf146 100644
--- a/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md
+++ b/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md b/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md
index df844aef86..a9ed037a28 100644
--- a/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md
+++ b/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md b/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
index 2a513b7ca7..95d457f1fa 100644
--- a/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
+++ b/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md b/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
index cc466e9c9b..a70570fd8e 100644
--- a/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
+++ b/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md b/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
index 7c704dabab..fbded961b9 100644
--- a/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
+++ b/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/_index.md b/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
index 87888d23a5..6d44b2a15d 100644
--- a/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md b/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md
index 74d98834b2..373bdaa31a 100644
--- a/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md b/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
index 707d19c927..c25a035a60 100644
--- a/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md b/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
index acde952664..6a463a9b3a 100644
--- a/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md b/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
index 44abfcf25a..7de787ea56 100644
--- a/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md b/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md
index b28a5bad1b..15cdc97f11 100644
--- a/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md b/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
index 133e7d1a92..20bcaf0236 100644
--- a/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
index c12f1108cd..e92b2e38b6 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md
index b67d933f75..205e69979d 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md
index 08f9f7cf12..b9c43e3ed4 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md
index 20c798d50c..7eb228b02b 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md
index 6e9277a053..485483380a 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_cef/_index.md b/content/learning-paths/laptops-and-desktops/win_cef/_index.md
index 45ccfbed96..0794ac8073 100644
--- a/content/learning-paths/laptops-and-desktops/win_cef/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_cef/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_forms/_index.md b/content/learning-paths/laptops-and-desktops/win_forms/_index.md
index 61b9bf2e66..997dcc9d70 100644
--- a/content/learning-paths/laptops-and-desktops/win_forms/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_forms/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_net/_index.md b/content/learning-paths/laptops-and-desktops/win_net/_index.md
index 9dd9481e81..fc23cc243d 100644
--- a/content/learning-paths/laptops-and-desktops/win_net/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_net/_index.md
@@ -15,8 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_net8/_index.md b/content/learning-paths/laptops-and-desktops/win_net8/_index.md
index c591476be3..2eb23bfa4f 100644
--- a/content/learning-paths/laptops-and-desktops/win_net8/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_net8/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md b/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md
index 04bbcfcffe..f195d2b507 100644
--- a/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md b/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md
index 665aa52242..085e6db215 100644
--- a/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md
@@ -15,8 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md b/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md
index 37913c345d..c93a0ebe9e 100644
--- a/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_python/_index.md b/content/learning-paths/laptops-and-desktops/win_python/_index.md
index d8ecd05167..9bdd7e7365 100644
--- a/content/learning-paths/laptops-and-desktops/win_python/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_python/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_python_onnx/_index.md b/content/learning-paths/laptops-and-desktops/win_python_onnx/_index.md
index 29d95ae54e..7b101afe13 100644
--- a/content/learning-paths/laptops-and-desktops/win_python_onnx/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_python_onnx/_index.md
@@ -21,8 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md b/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md
index 83171f2ecc..c6f6437688 100644
--- a/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md b/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md
index 963a60fcaa..fe8cfb0856 100644
--- a/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_winui3/_index.md b/content/learning-paths/laptops-and-desktops/win_winui3/_index.md
index b81e1894eb..7ead1dfdb5 100644
--- a/content/learning-paths/laptops-and-desktops/win_winui3/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_winui3/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_wpf/_index.md b/content/learning-paths/laptops-and-desktops/win_wpf/_index.md
index 33a4955a57..2fb9b4f463 100644
--- a/content/learning-paths/laptops-and-desktops/win_wpf/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_wpf/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md b/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md
index f47f879d85..4f4bc6b072 100644
--- a/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md b/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md
index 2d7a57306c..2b1250a95c 100644
--- a/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md b/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md
index 2d043e25ba..503fed7be1 100644
--- a/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md
index 200ca6e74f..454858e4fd 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf/_index.md
index 6c3ccc4e33..ef363abeda 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md
index 0ae8e52814..dac3c04261 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
index 9f0f963ec9..996b840137 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/laptops-and-desktops/wsl2/_index.md b/content/learning-paths/laptops-and-desktops/wsl2/_index.md
index 5cec7b4dc8..bf36e86248 100644
--- a/content/learning-paths/laptops-and-desktops/wsl2/_index.md
+++ b/content/learning-paths/laptops-and-desktops/wsl2/_index.md
@@ -21,8 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md b/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md
index 145b0bff0d..6dda13f235 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md b/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md
index 9133c8dcb2..d5645b0504 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md b/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
index 2b4b59db18..d11d9ccdf1 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
@@ -22,8 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md b/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md
index aa21f63344..756ce990cb 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md
index bf273a9c6c..f800e90c0a 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
index 12c9675202..5e9fee2f0c 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_neon/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_neon/_index.md
index 4c1ccd84e6..96247658a5 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_neon/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_neon/_index.md
@@ -22,8 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md
index ce30889bb0..19a67b4c26 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md
index c78727a38a..66a732a021 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
index 16c580ccee..7610da1658 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md
index 5a6b7634f6..ae068545ff 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
index bbef6eb701..6e07a8ecfc 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
@@ -27,8 +27,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md b/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
index aed31a02ca..482375a63b 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md b/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md
index ee103065fb..98d1739e3a 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md b/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md
index c107cb9cae..24674975ff 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md
@@ -22,8 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md b/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md
index 2ae9b79520..3cdb10cdeb 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md
@@ -24,8 +24,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md b/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md
index fb66b6e200..f8148b6921 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md
@@ -24,8 +24,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md b/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md
index 753fe65570..c8be0a262c 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md
@@ -21,8 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/gemma4-kleidiai-sme2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/gemma4-kleidiai-sme2/_index.md
index 166a901961..0556545fb3 100755
--- a/content/learning-paths/mobile-graphics-and-gaming/gemma4-kleidiai-sme2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/gemma4-kleidiai-sme2/_index.md
@@ -21,8 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 author: Annie Tallund
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md
index 1f6b35f250..a51b1c5d64 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md
index 57e9396a83..92a4fb1ad1 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md
index db0ebaf80a..40602be9e3 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md
@@ -15,8 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md b/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md
index e368f7a719..d46ebcc9ca 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md b/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md
index c5dd8d5485..55853978b5 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md
@@ -13,8 +13,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md b/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md
index 8d1edc08a5..0ce125b7f2 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md b/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md
index b7b1f8fb48..71a37cdfe2 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md b/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md
index d2c6983c60..a33bc0d80c 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md b/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md
index e8cf03c77d..666350f0c9 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md b/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md
index ac6f97f630..fc0da0a7fe 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md b/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md
index a26ea72861..9b22f00225 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md b/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md
index 74971ec5fb..2ec57d7ef1 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md
@@ -14,8 +14,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md b/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md
index b614ba4fa4..52f54fb893 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md b/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md
index 5fe3aa867a..4c895cc514 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md b/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md
index d3fcbe88ea..5375d9c093 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md
@@ -22,8 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md b/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md
index 07d118c3dd..9949a0590a 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md
@@ -21,8 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md b/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md
index 142f373d45..9a4eea0265 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md
index 74a2987ee8..87dd4776da 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md
index 66e1cb4048..977cb4ad6b 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md b/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md
index 6c5a030600..5930c7fc83 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md b/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md
index 2c3cec4435..ea27c6fd37 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md b/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md
index d6e72f6388..5cbfc92883 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md b/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md
index d2709d2b8b..af4605f932 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md b/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md
index fcf6324146..5d27793ae6 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md
index 9011cfc392..df42872803 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md
index ec4f625ec0..90f62c62b2 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md b/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md
index eaf571df08..66ea500d78 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md b/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md
index 3a9aa7d3b1..56c5302e5b 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md b/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md
index cc5bf6ea51..2e331a4304 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md b/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md
index d47220768d..0719c29ace 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md b/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md
index f2d149b26b..98201178fe 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md b/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md
index a02454b8c7..b5de5220cc 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md b/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md
index ac9aadc96f..4a727e246b 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md
@@ -21,8 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md
index e5f3b86fcb..0b48eeed99 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md
@@ -21,8 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md b/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
index f29ad74618..df5c156cab 100644
--- a/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
@@ -19,51 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 275878b5aa4c27dee394da12ad8b80e4c0d0235b3ddce66b1fe3f0e056ef3da9
-  summary: >-
-    Learn how to build and deploy an AI agent application on Arm servers using llama.cpp and llama-cpp-agent
-    with KleidiAI optimization for efficient LLM inference and function calling. It is designed
-    for software developers and ML engineers looking to deploy an optimized AI agent application.
-    By the end, you will be able to set up llama-cpp-python optimized for Arm servers, run optimized
-    Large Language Models (LLMs), and create custom functions for LLMs. It focuses on tools and
-    technologies such as Python, AWS Graviton, and AI, Linux environments, Arm platforms including
-    Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The
-    main steps cover Introduction to AI Agents and Agent Use Cases, Set Up Your Local Environment
-    to Run an AI Application, AI Agent Application, and Explore and Test Your AI Agent.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will set up llama-cpp-python optimized for Arm servers, run optimized Large Language
-      Models (LLMs), and create custom functions for LLMs. Learn how to build and deploy an AI
-      agent application on Arm servers using llama.cpp and llama-cpp-agent with KleidiAI optimization
-      for efficient LLM inference and function calling.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers and ML engineers looking to deploy
-      an optimized AI agent application.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/)
-      from a cloud service provider or an on-premise Arm server.; Basic understanding of Python
-      and prompt engineering.; Understanding of LLM fundamentals.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Python, AWS Graviton, and AI, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
-      Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Introduction to AI Agents and Agent Use Cases, Set
-      Up Your Local Environment to Run an AI Application, AI Agent Application, and Explore and
-      Test Your AI Agent.
-# END generated_summary_faq
+rerun_summary: false
+rerun_faqs: false
 
 author: Andrew Choi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/aks/_index.md b/content/learning-paths/servers-and-cloud-computing/aks/_index.md
index 5de9a3240d..d3960b3ca8 100644
--- a/content/learning-paths/servers-and-cloud-computing/aks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/aks/_index.md
@@ -17,47 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 01c5cc8930650a8d81d67d4c48b62e0f9d7b1ebfc2d09a552e11046fb982fc52
-  summary: >-
-    Learn how to automate the deployment of an Arm-based Kubernetes cluster on Azure AKS using
-    Terraform and deploy a sample WordPress application as a workload. It is designed for software
-    developers who want to deploy an Arm-based Kubernetes cluster using Azure Kubernetes Service
-    (AKS). By the end, you will be able to automate the deployment of an Arm-based AKS cluster
-    using Terraform and install Wordpress on AKS as an example workload. It focuses on tools and
-    technologies such as Terraform, Kubernetes, WordPress, and MySQL, Linux environments, Arm
-    platforms including Neoverse, and cloud platforms such as Microsoft Azure. The main steps
-    cover Deploy an Arm-based AKS Cluster using Terraform and Deploy a WordPress Example.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will automate the deployment of an Arm-based AKS cluster using Terraform and install
-      Wordpress on AKS as an example workload. Learn how to automate the deployment of an Arm-based
-      Kubernetes cluster on Azure AKS using Terraform and deploy a sample WordPress application
-      as a workload.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers who want to deploy an Arm-based Kubernetes
-      cluster using Azure Kubernetes Service (AKS).
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Azure account; A machine with [Terraform](/install-guides/terraform/),
-      [Azure CLI](/install-guides/azure-cli), and [Kubectl](/install-guides/kubectl/) installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Terraform, Kubernetes, WordPress, and MySQL, Linux
-      environments, Arm platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Deploy an Arm-based AKS Cluster using Terraform and
-      Deploy a WordPress Example.
-# END generated_summary_faq
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md b/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
index 67465db3ef..c39d3643ff 100644
--- a/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
@@ -21,57 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 90b638385267e085ea07a70a1c23f16578aa3caaeabeca1f651bcdcac5bf38fb
-  summary: >-
-    Learn how to deploy Apache Arrow and Arrow Flight on Google Cloud Axion C4A processors for
-    high-throughput columnar data processing and low-latency data transport with MinIO integration.
-    It is designed for data engineers, platform engineers, and developers who aim to build high-performance
-    analytics pipelines on Arm64-based Google Cloud C4A Axion processors using Apache Arrow and
-    Arrow Flight. By the end, you will be able to deploy Apache Arrow–based data processing workloads
-    on Google Cloud C4A Axion processors, set up and run an Arrow Flight server for high-throughput,
-    low-latency data transport, and read and write columnar data formats such as Parquet and ORC
-    using Apache Arrow. It focuses on tools and technologies such as Apache Arrow, Arrow Flight,
-    Python, and MinIO, Linux environments, Arm platforms including Neoverse, and cloud platforms
-    such as Google Cloud. The main steps cover Get started with Apache Arrow and Arrow Flight
-    on Google Axion C4A, Create firewall rules on GCP for Apache Arrow, MinIO, and Arrow Flight,
-    Create a Google Axion C4A arm64 virtual machine on GCP, Set up Apache Arrow and MinIO on arm64,
-    and Analyze columnar data with Apache Arrow on arm64.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will deploy Apache Arrow–based data processing workloads on Google Cloud C4A Axion processors,
-      set up and run an Arrow Flight server for high-throughput, low-latency data transport, and
-      read and write columnar data formats such as Parquet and ORC using Apache Arrow. Learn how
-      to deploy Apache Arrow and Arrow Flight on Google Cloud Axion C4A processors for high-throughput
-      columnar data processing and low-latency data transport with MinIO integration.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for data engineers, platform engineers, and developers who
-      aim to build high-performance analytics pipelines on Arm64-based Google Cloud C4A Axion
-      processors using Apache Arrow and Arrow Flight.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
-      account with billing enabled; Basic familiarity with Python; Basic understanding of data
-      formats such as Parquet or ORC; Familiarity with Linux command-line operations.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Apache Arrow, Arrow Flight, Python, and MinIO, Linux
-      environments, Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Get started with Apache Arrow and Arrow Flight on
-      Google Axion C4A, Create firewall rules on GCP for Apache Arrow, MinIO, and Arrow Flight,
-      Create a Google Axion C4A arm64 virtual machine on GCP, Set up Apache Arrow and MinIO on
-      arm64, and Analyze columnar data with Apache Arrow on arm64.
-# END generated_summary_faq
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
index e637f92ad9..598f66c460 100644
--- a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
@@ -19,49 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 964c1e6a87810dca686a7b3218433ce09d31bd92882db10af355e8c50bb6091c
-  summary: >-
-    Learn how to build llama.cpp, quantize the Arcee AFM-4.5B model, and run optimized inference
-    on AWS Graviton4 instances with perplexity-based quality evaluation. It is designed for developers
-    and ML engineers who want to deploy Arcee's AFM-4.5B small language model on AWS Graviton4
-    instances using Llama.cpp. By the end, you will be able to launch an Arm-based EC2 instance
-    on AWS Graviton4, build and install Llama.cpp from source, and download and quantize the AFM-4.5B
-    model from Hugging Face. It focuses on tools and technologies such as AWS, Hugging Face, Python,
-    and Llama.cpp, Linux environments, Arm platforms including Neoverse, and cloud platforms such
-    as AWS. The main steps cover Overview, Provision your Graviton4 environment, Configure your
-    Graviton4 environment, Build Llama.cpp, and Install Python dependencies.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will launch an Arm-based EC2 instance on AWS Graviton4, build and install Llama.cpp
-      from source, and download and quantize the AFM-4.5B model from Hugging Face. Learn how to
-      build llama.cpp, quantize the Arcee AFM-4.5B model, and run optimized inference on AWS Graviton4
-      instances with perplexity-based quality evaluation.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is for developers and ML engineers who want to deploy Arcee's AFM-4.5B
-      small language model on AWS Graviton4 instances using Llama.cpp.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An [AWS account](https://aws.amazon.com/)
-      with permission to launch Graviton4 (`c8g.4xlarge` or larger) instances; Basic familiarity
-      with Linux and SSH.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including AWS, Hugging Face, Python, and Llama.cpp, Linux
-      environments, Arm platforms such as Neoverse, and cloud platforms such as AWS.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview, Provision your Graviton4 environment, Configure
-      your Graviton4 environment, Build Llama.cpp, and Install Python dependencies.
-# END generated_summary_faq
+rerun_summary: false
+rerun_faqs: false
 
 author: Julien Simon
 
diff --git a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
index 5bb07cf40a..1169586f94 100644
--- a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
@@ -19,53 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: b2f60d2c31ef380e6e6ef3028671ad9242dd51c98f4a192f2da32bb05b94e185
-  summary: >-
-    Learn how to build llama.cpp, quantize the Arcee AFM-4.5B model, and run optimized inference
-    on Google Cloud Axion instances with perplexity-based quality evaluation. It is designed for
-    developers and ML engineers who want to deploy Arcee's AFM-4.5B small language model on Google
-    Cloud Axion instances using Llama.cpp. By the end, you will be able to launch an Arm-based
-    Compute Engine instance on Google Cloud Axion, build and install Llama.cpp from source, and
-    download and quantize the AFM-4.5B model from Hugging Face. It focuses on tools and technologies
-    such as Google Cloud, Hugging Face, Python, and Llama.cpp, Linux environments, Arm platforms
-    including Neoverse, and cloud platforms such as Google Cloud. The main steps cover AFM-4.5B
-    deployment on Google Cloud Axion with Llama.cpp, Provision a Google Cloud Axion Arm64 environment,
-    Configure your Google Cloud Axion Arm64 environment, Build Llama.cpp on Google Cloud Axion
-    Arm64, and Install Python dependencies for Llama.cpp.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will launch an Arm-based Compute Engine instance on Google Cloud Axion, build and install
-      Llama.cpp from source, and download and quantize the AFM-4.5B model from Hugging Face. Learn
-      how to build llama.cpp, quantize the Arcee AFM-4.5B model, and run optimized inference on
-      Google Cloud Axion instances with perplexity-based quality evaluation.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is for developers and ML engineers who want to deploy Arcee's AFM-4.5B
-      small language model on Google Cloud Axion instances using Llama.cpp.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud account](https://console.cloud.google.com/)
-      with permission to launch Axion (`c4a-standard-16` or larger) instances; Basic familiarity
-      with Linux and SSH.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Google Cloud, Hugging Face, Python, and Llama.cpp,
-      Linux environments, Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around AFM-4.5B deployment on Google Cloud Axion with Llama.cpp,
-      Provision a Google Cloud Axion Arm64 environment, Configure your Google Cloud Axion Arm64
-      environment, Build Llama.cpp on Google Cloud Axion Arm64, and Install Python dependencies
-      for Llama.cpp.
-# END generated_summary_faq
+rerun_summary: false
+rerun_faqs: false
 
 author: Julien Simon
 
diff --git a/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
index b35eef1ff4..2077b677f2 100644
--- a/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
@@ -23,58 +23,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: c6106f21859f5a8e04bbd579db47c7177bb5371d4c7f306054e56e81ed9e89a1
-  summary: >-
-    Learn how to deploy and manage applications on Google Cloud GKE Arm64 clusters using Argo
-    CD GitOps workflows with automated sync and self-healing on Axion processors. It is designed
-    for developers and platform engineers who want hands-on experience implementing GitOps using
-    Argo CD on Arm64-based Google Kubernetes Engine (GKE) clusters running on Google Axion (C4A)
-    processors. By the end, you will be able to provision an Arm-based SUSE Linux Enterprise Server
-    (SLES) virtual machine on Google Cloud (C4A with Axion processors), create and connect to
-    a Google Kubernetes Engine (GKE) cluster running on Arm64 (Axion) nodes, and install and validate
-    Argo CD on an Arm-based GKE cluster. It focuses on tools and technologies such as Argo CD,
-    Kubernetes, kubectl, GKE, and Git, Linux environments, Arm platforms including Neoverse, and
-    cloud platforms such as Google Cloud. The main steps cover Get started with Argo CD on Google
-    Axion C4A (Arm-based), Create a Google Axion C4A virtual machine on Google Cloud, Prepare
-    a GKE cluster for Argo CD deployments, Install and access Argo CD on Arm64 GKE, and Deploy
-    applications using GitOps with Argo CD.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will provision an Arm-based SUSE Linux Enterprise Server (SLES) virtual machine on Google
-      Cloud (C4A with Axion processors), create and connect to a Google Kubernetes Engine (GKE)
-      cluster running on Arm64 (Axion) nodes, and install and validate Argo CD on an Arm-based
-      GKE cluster. Learn how to deploy and manage applications on Google Cloud GKE Arm64 clusters
-      using Argo CD GitOps workflows with automated sync and self-healing on Axion processors.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers and platform engineers who want hands-on experience
-      implementing GitOps using Argo CD on Arm64-based Google Kubernetes Engine (GKE) clusters
-      running on Google Axion (C4A) processors.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
-      account with billing enabled; Basic familiarity with [Kubernetes concepts](https://kubernetes.io/docs/concepts/);
-      Basic understanding of Git and GitHub workflows; Familiarity with basic Linux command-line
-      usage.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Argo CD, Kubernetes, kubectl, GKE, and Git, Linux
-      environments, Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Get started with Argo CD on Google Axion C4A (Arm-based),
-      Create a Google Axion C4A virtual machine on Google Cloud, Prepare a GKE cluster for Argo
-      CD deployments, Install and access Argo CD on Arm64 GKE, and Deploy applications using GitOps
-      with Argo CD.
-# END generated_summary_faq
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md b/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
index 13624ff5a8..de7c0bf45d 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
@@ -17,49 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 3a4bc8b2ab548be507b6d528844b0593b27f2dab3ab3c9649e807abdf4887ce0
-  summary: >-
-    Learn how to write correct concurrent C++ code when porting applications from x86 to Arm by
-    understanding memory ordering differences and using best practices to avoid race conditions.
-    It is designed for C++ developers porting applications from x86 to Arm and optimizing performance.
-    By the end, you will be able to describe at a high level what a memory model does, and the
-    types of memory ordering, describe the differences between the Arm and x86 memory model, and
-    employ best practices for writing C++ on Arm to avoid race conditions. It focuses on tools
-    and technologies such as CPP, TSan, and Runbook, Linux environments, and Arm platforms including
-    Neoverse. The main steps cover Introduction to C++ Memory Models, The C++ Memory Model and
-    Atomics, Walk through a Race condition example, and Detecting race conditions.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will describe at a high level what a memory model does, and the types of memory ordering,
-      describe the differences between the Arm and x86 memory model, and employ best practices
-      for writing C++ on Arm to avoid race conditions. Learn how to write correct concurrent C++
-      code when porting applications from x86 to Arm by understanding memory ordering differences
-      and using best practices to avoid race conditions.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for C++ developers porting applications from x86 to Arm and optimizing
-      performance.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Access to an x86 and an Arm cloud instance
-      (virtual machine).; Proficiency in C++ programming.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including CPP, TSan, and Runbook, Linux environments, and
-      Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Introduction to C++ Memory Models, The C++ Memory
-      Model and Atomics, Walk through a Race condition example, and Detecting race conditions.
-# END generated_summary_faq
+rerun_summary: false
+rerun_faqs: false
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md b/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
index bb9925c8b6..88453261c2 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
@@ -20,53 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: b870ac5160d35ddb51955ca8379493e172787ced8125cde8ae79f7700e653a87
-  summary: >-
-    Learn how to automate x86-to-Arm application migration using the Arm MCP Server, with AI-assisted
-    compatibility checks, C++ code refactoring, and Docker-based validation on Arm cloud platforms.
-    It is designed for developers who want to use AI-powered tools to migrate x86 applications
-    to Arm-based cloud instances. By the end, you will be able to explain how the Arm MCP Server
-    enables AI-driven x86-to-Arm migration workflows, use AI-assisted checks to inspect Docker
-    images for Arm compatibility, and set up and use the Arm Cloud Migration Agent in GitHub Copilot
-    to automate x86-to-Arm code migration. It focuses on tools and technologies such as MCP, Docker,
-    CPP, and GitHub Copilot, Linux environments, and Arm platforms including Neoverse. The main
-    steps cover Understand the Arm MCP Server for AI-driven x86-to-Arm migration, Verify Docker
-    image compatibility with Arm using AI, Arm Cloud Migration Agent in GitHub Copilot, and Configure
-    other AI agents to automate Arm migration workflows.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will explain how the Arm MCP Server enables AI-driven x86-to-Arm migration workflows,
-      use AI-assisted checks to inspect Docker images for Arm compatibility, and set up and use
-      the Arm Cloud Migration Agent in GitHub Copilot to automate x86-to-Arm code migration. Learn
-      how to automate x86-to-Arm application migration using the Arm MCP Server, with AI-assisted
-      compatibility checks, C++ code refactoring, and Docker-based validation on Arm cloud platforms.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for developers who want to use AI-powered tools to migrate x86
-      applications to Arm-based cloud instances.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An AI-powered IDE such as VS Code, Copilot
-      in VS Code, Kiro (IDE or CLI) or Codex; Basic familiarity with Docker and C/C++ development;
-      Access to an Arm-based cloud instance or local Arm computer running Linux or macOS.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including MCP, Docker, CPP, and GitHub Copilot, Linux environments,
-      and Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Understand the Arm MCP Server for AI-driven x86-to-Arm
-      migration, Verify Docker image compatibility with Arm using AI, Arm Cloud Migration Agent
-      in GitHub Copilot, and Configure other AI agents to automate Arm migration workflows.
-# END generated_summary_faq
+rerun_summary: false
+rerun_faqs: false
 
 author: Joe Stech
 
diff --git a/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md b/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
index 9030af24aa..d66560842d 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
@@ -21,59 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 49b3f2b1464efb20f0c8fbcff46e9f30910d856567ca01c01dc348aa1c5740d1
-  summary: >-
-    Learn how to migrate C applications between Arm platforms using Kiro's AI-assisted tooling
-    to identify hardware dependencies and implement abstraction layers for cross-platform compatibility.
-    It is designed for experienced developers who need to migrate applications between Arm-based
-    platforms using AI-assisted tooling. You will work through a structured, repeatable migration
-    workflow using Kiro Arm SoC Migration Power, moving an application from AWS Graviton3 (Neoverse)
-    to Raspberry Pi 5 (Cortex-A). The techniques apply broadly to cloud-to-edge and cross-architecture
-    migrations across the Arm ecosystem. By the end, you will be able to install and configure
-    Kiro Arm SoC Migration Power, apply a structured migration workflow across Arm platforms,
-    and identify platform-specific and hardware-dependent code using AI-guided analysis. It focuses
-    on tools and technologies such as Kiro, AWS EC2, GCC, C, and CMake, Linux environments, Arm
-    platforms including Neoverse and Cortex-A, and cloud platforms such as AWS, Microsoft Azure,
-    Google Cloud, and Oracle. The main steps cover Install Arm SoC Migration Power, Develop on
-    source platform, Migrate using Arm SoC Migration Power, and Validate migration with testing.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install and configure Kiro Arm SoC Migration Power, apply a structured migration
-      workflow across Arm platforms, and identify platform-specific and hardware-dependent code
-      using AI-guided analysis. Learn how to migrate C applications between Arm platforms using
-      Kiro's AI-assisted tooling to identify hardware dependencies and implement abstraction layers
-      for cross-platform compatibility.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for experienced developers who need to migrate applications between
-      Arm-based platforms using AI-assisted tooling. You will work through a structured, repeatable
-      migration workflow using Kiro Arm SoC Migration Power, moving an application from AWS Graviton3
-      (Neoverse) to Raspberry Pi 5 (Cortex-A). The techniques apply broadly to cloud-to-edge and
-      cross-architecture migrations across the Arm ecosystem.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Access to both source and target Arm
-      platforms (for example, AWS Graviton3 and Raspberry Pi 5); Working knowledge of C programming;
-      Familiarity with Linux development environments and basic embedded or cloud deployment concepts;
-      Experience building applications with GCC and CMake.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Kiro, AWS EC2, GCC, C, and CMake, Linux environments,
-      Arm platforms such as Neoverse and Cortex-A, and cloud platforms such as AWS, Microsoft
-      Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Install Arm SoC Migration Power, Develop on source
-      platform, Migrate using Arm SoC Migration Power, and Validate migration with testing.
-# END generated_summary_faq
+rerun_summary: false
+rerun_faqs: false
 
 author: Daniel Schleicher
 
diff --git a/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md b/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
index a5e86f26c1..018176c114 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
@@ -19,49 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 67004eb9a75926144eb6f75124104972b3cf20a57a22b698c9359d20db3eca02
-  summary: >-
-    Learn how to install and configure a Linux kernel with 64K page size support on Arm systems
-    to improve memory efficiency and performance for memory-intensive workloads. It is designed
-    for developers who want to modify the Linux kernel page size on Arm-based systems to improve
-    performance for memory-intensive workloads. By the end, you will be able to explain the differences
-    in page size configuration between Arm64 and x86 architectures, understand how page size affects
-    memory efficiency and system performance, and check the current memory page size on an Arm-based
-    Linux system. It focuses on tools and technologies such as bash, Linux environments, and Arm
-    platforms including Neoverse. The main steps cover Overview, Change page size on Ubuntu, Change
-    page size on Debian, and Change page size on CentOS.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will explain the differences in page size configuration between Arm64 and x86 architectures,
-      understand how page size affects memory efficiency and system performance, and check the
-      current memory page size on an Arm-based Linux system. Learn how to install and configure
-      a Linux kernel with 64K page size support on Arm systems to improve memory efficiency and
-      performance for memory-intensive workloads.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers who want to modify the Linux kernel page size
-      on Arm-based systems to improve performance for memory-intensive workloads.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Access to an Arm-based Linux system
-      running Ubuntu, Debian, or CentOS.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including bash, Linux environments, and Arm platforms such
-      as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview, Change page size on Ubuntu, Change page
-      size on Debian, and Change page size on CentOS.
-# END generated_summary_faq
+rerun_summary: false
+rerun_faqs: false
 
 author: Geremy Cohen
 
diff --git a/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md b/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
index d2053c6a16..f643feed3a 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md b/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
index 3b6134ff17..41c1eef932 100644
--- a/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md b/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
index 814c22a2c1..78511f0e12 100644
--- a/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
index 147a978a1c..20477665db 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
index 5e9531ce41..f20928060c 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
index 20a4780e8e..4028d5cbae 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
index a424d89d39..88e33bb768 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
@@ -22,8 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md b/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
index 5283c9ca29..cc6f7b5204 100644
--- a/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md b/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
index 2f0f56b24c..1b206a68ce 100644
--- a/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md b/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
index d94f9c4fdf..bab292ec40 100644
--- a/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
@@ -25,8 +25,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md b/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
index 3992a9178a..79e89fa11c 100644
--- a/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
index ba36d3dce2..5353639d2a 100644
--- a/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
@@ -21,8 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
index a0668e722a..5b45682d82 100644
--- a/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
index 398f1f3023..51289ba3ba 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
index 96d9c75271..37aa1f5f1c 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
@@ -21,8 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
index 273e3b63d5..6065163e6c 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
index 88bda0253b..b3c0b4ae11 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
index 7020a64806..f676911f4f 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
index 874e2102bb..bb2f435bb5 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md
index ed904f0fbe..52bab2273f 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md
@@ -21,8 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
index d0d935aa81..f393ed4373 100644
--- a/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
@@ -25,8 +25,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md b/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md
index 3601f35efc..7c432a63d2 100644
--- a/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/clair/_index.md b/content/learning-paths/servers-and-cloud-computing/clair/_index.md
index deacc3c664..5930133365 100644
--- a/content/learning-paths/servers-and-cloud-computing/clair/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/clair/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
index b76d2d25d0..b5669c10d4 100644
--- a/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
@@ -23,8 +23,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md b/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
index 5d6b808e07..6a06f37c4d 100644
--- a/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
@@ -15,8 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
index a10f8cc607..99bd73c35d 100644
--- a/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md b/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
index 57c84a5af8..7d8540703b 100644
--- a/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/codec/_index.md b/content/learning-paths/servers-and-cloud-computing/codec/_index.md
index 2eeaedec8a..42febb3a63 100644
--- a/content/learning-paths/servers-and-cloud-computing/codec/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/codec/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/codec1/_index.md b/content/learning-paths/servers-and-cloud-computing/codec1/_index.md
index 388412574d..84c94b91a4 100644
--- a/content/learning-paths/servers-and-cloud-computing/codec1/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/codec1/_index.md
@@ -4,8 +4,8 @@ description: Learn how to build and run the AV1 and VP9 video codecs on Arm Linu
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
index ed17dc845d..660e6bb2df 100644
--- a/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md b/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
index 610e6e1b97..13cd53b25d 100644
--- a/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md b/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md
index 051944f9e9..4ce1b42f5e 100644
--- a/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md b/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
index 7f9f744f4a..1e44547dae 100644
--- a/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/csp/_index.md b/content/learning-paths/servers-and-cloud-computing/csp/_index.md
index 2d9d19c349..048a5982ab 100644
--- a/content/learning-paths/servers-and-cloud-computing/csp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/csp/_index.md
@@ -4,8 +4,8 @@ description: Learn how to start an Arm-based virtual machine instance from major
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md b/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
index 7e23900f7f..d171d85d18 100644
--- a/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md b/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md
index 96f322c93d..e0a35c0451 100644
--- a/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md
index aa0d7ce216..b750027384 100644
--- a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md
index 6d1a4e7a3d..c631a54661 100644
--- a/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md
@@ -23,8 +23,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/django/_index.md b/content/learning-paths/servers-and-cloud-computing/django/_index.md
index bc6bad6204..c1561c4e80 100644
--- a/content/learning-paths/servers-and-cloud-computing/django/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/django/_index.md
@@ -18,7 +18,7 @@ prerequisites:
     - To install both [Nginx](/learning-paths/servers-and-cloud-computing/nginx/) and [PostgreSQL](/learning-paths/servers-and-cloud-computing/postgresql/)
 
 generate_summary_faq: true
-# rerun_summary: false
+rerun_summary: false
 rerun_faqs: false
 
 # START generated_summary_faq
diff --git a/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md b/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
index 85210eefb4..4860a0d382 100644
--- a/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md b/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
index 6cbe32ce5b..f769b225fd 100644
--- a/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
@@ -25,8 +25,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md b/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md
index 338318270b..cc75f96ded 100644
--- a/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md
@@ -22,8 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
index a1b67d67a2..d691009614 100644
--- a/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/ecs/_index.md b/content/learning-paths/servers-and-cloud-computing/ecs/_index.md
index 10346af14c..c964ebdcc2 100644
--- a/content/learning-paths/servers-and-cloud-computing/ecs/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ecs/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md b/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
index 94d76afda6..98645ef410 100644
--- a/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/eks/_index.md b/content/learning-paths/servers-and-cloud-computing/eks/_index.md
index 4445af6e72..96a317469d 100644
--- a/content/learning-paths/servers-and-cloud-computing/eks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/eks/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md
index 3db7ef52fb..346eb19737 100644
--- a/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/envoy/_index.md b/content/learning-paths/servers-and-cloud-computing/envoy/_index.md
index 1ad73408ad..771e3512ed 100644
--- a/content/learning-paths/servers-and-cloud-computing/envoy/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/envoy/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md
index 398925d448..2e8d3189f4 100644
--- a/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md b/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md
index 5a56cd993b..b3176576b0 100644
--- a/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md
@@ -21,8 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md b/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
index c010dd1a3b..1dc00ae8e6 100644
--- a/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md b/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md
index e02aec0326..68a0d01738 100644
--- a/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md b/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md
index aa042d7297..d0563cffd8 100644
--- a/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md
index b51b3772b9..547daeab61 100644
--- a/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/flink/_index.md b/content/learning-paths/servers-and-cloud-computing/flink/_index.md
index 057a6b7783..c3fe9a4cbe 100644
--- a/content/learning-paths/servers-and-cloud-computing/flink/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/flink/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md b/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md
index 0929929cad..fcfd48e252 100644
--- a/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md
@@ -21,8 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
index f1ba801c97..ce64de7a86 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
@@ -23,8 +23,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md
index cd010418c1..40ce3f9083 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
index 79c9c6f647..b638a9556a 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md
index c6c71fd0aa..eb411b3a49 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/funasr/_index.md b/content/learning-paths/servers-and-cloud-computing/funasr/_index.md
index c21cee3be4..12d454a58c 100644
--- a/content/learning-paths/servers-and-cloud-computing/funasr/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/funasr/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
index 131ddbc380..c2c9e68d4d 100644
--- a/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md b/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
index e264303b70..c1a677b168 100644
--- a/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/gcp/_index.md
index a711c975db..ffe39addef 100644
--- a/content/learning-paths/servers-and-cloud-computing/gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gcp/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md b/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
index 0acc50bf74..c28512c376 100644
--- a/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
@@ -15,8 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md b/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
index 517c61364b..9f685b78b1 100644
--- a/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md b/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
index fe5818f61d..3e78307b98 100644
--- a/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md b/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md
index d4239e8fdd..0e263f2ee1 100644
--- a/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
index ba97c2588f..e420f14d10 100644
--- a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
index 99458bcecd..19a13fa267 100644
--- a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/gke/_index.md b/content/learning-paths/servers-and-cloud-computing/gke/_index.md
index e6da81b3d3..6967d32458 100644
--- a/content/learning-paths/servers-and-cloud-computing/gke/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gke/_index.md
@@ -15,8 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md b/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
index 55246db96e..0fad6ac841 100644
--- a/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md b/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
index 1e1c7b2b3c..5c13cacf00 100644
--- a/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md
index 989757db53..b4392a7e25 100644
--- a/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md
index 9607de2ed2..b4fd31d70f 100644
--- a/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md
@@ -25,8 +25,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/intro/_index.md b/content/learning-paths/servers-and-cloud-computing/intro/_index.md
index 605eacb71d..7a61f69f5d 100644
--- a/content/learning-paths/servers-and-cloud-computing/intro/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/intro/_index.md
@@ -15,8 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md b/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
index fb436a7715..5efa1e7eb1 100644
--- a/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md b/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
index c3e1c470a8..cc042f5a09 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
index cfb2a8997e..4938bf789a 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
index e4d7115ca6..40f68e6181 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
index ade81d1695..6c3006b81e 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md b/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
index 1f50cc2ad5..64d6516760 100644
--- a/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
@@ -23,8 +23,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
index fc63356c50..d3b085919c 100644
--- a/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/kafka/_index.md b/content/learning-paths/servers-and-cloud-computing/kafka/_index.md
index ead5c8e1c0..6e230348f8 100644
--- a/content/learning-paths/servers-and-cloud-computing/kafka/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kafka/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md b/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
index 406ffb1989..c00df49175 100644
--- a/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md b/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
index dc2e368182..836ef574f1 100644
--- a/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md b/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
index b7817d04e0..4a8e453715 100644
--- a/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md b/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
index d845138766..921b134036 100644
--- a/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md b/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
index 5307413245..baaa6debad 100644
--- a/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md b/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
index 83e64f157d..bf2eac0448 100644
--- a/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md b/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
index 216f6e1c21..5920ca76a0 100644
--- a/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md b/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
index cd4fcf3a47..b5253c7d47 100644
--- a/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
@@ -21,8 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
index 9c78aca168..aa06fb5c7a 100644
--- a/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
@@ -22,8 +22,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/lse/_index.md b/content/learning-paths/servers-and-cloud-computing/lse/_index.md
index f1fee8e657..8bfce7cead 100644
--- a/content/learning-paths/servers-and-cloud-computing/lse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/lse/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md b/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
index b5ee73097e..73c4f5a370 100644
--- a/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/memcached/_index.md b/content/learning-paths/servers-and-cloud-computing/memcached/_index.md
index 62241a8487..e346728998 100644
--- a/content/learning-paths/servers-and-cloud-computing/memcached/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memcached/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md b/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
index fb52dfce2b..d27d8d5361 100644
--- a/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md b/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
index f388af7947..dec4ca386b 100644
--- a/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
@@ -21,8 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md b/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
index 1be004fc72..8a1131a83d 100644
--- a/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
@@ -21,8 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md b/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
index 74152c8107..e9fa872160 100644
--- a/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md b/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
index 8c165932c1..5d169ce8ad 100644
--- a/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/migration/_index.md b/content/learning-paths/servers-and-cloud-computing/migration/_index.md
index 5ba69147f1..37b96a6cc1 100644
--- a/content/learning-paths/servers-and-cloud-computing/migration/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/migration/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
index bdfc4534ea..3c094beff7 100644
--- a/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
index bba1d8680d..b9b5ca55f5 100644
--- a/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md b/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
index 55b4caa315..db4ff79737 100644
--- a/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
index 395462fe50..5965a078af 100644
--- a/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
index 2b107a712f..539682646f 100644
--- a/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md b/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
index d88c85b452..2a487d736e 100644
--- a/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
@@ -3,8 +3,8 @@ title: Analyze the performance of MongoDB on Arm servers
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/mpi/_index.md b/content/learning-paths/servers-and-cloud-computing/mpi/_index.md
index 10610f20e3..5133e1bb06 100644
--- a/content/learning-paths/servers-and-cloud-computing/mpi/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mpi/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md b/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md
index ad7b4fe440..e93ca8e3e4 100644
--- a/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md
@@ -5,8 +5,8 @@ minutes_to_complete: 20
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md b/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md
index d847e70ce6..9ab33a7f3b 100644
--- a/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md b/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md
index d0e7437afe..2f7a128e5b 100644
--- a/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md
index 5588bd0ac0..8f4ed98ce9 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql-lns-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql-lns-azure/_index.md
index 0437fa6694..de5b2ede22 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql-lns-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql-lns-azure/_index.md
@@ -23,8 +23,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 author: Doug Anson
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql/_index.md
index 3b366f819a..ef38e5a7ae 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql/_index.md
@@ -15,8 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md
index f915217214..37b4830705 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md
@@ -15,8 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md
index 59f72c94c9..71be8b4837 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md
@@ -13,8 +13,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md b/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md
index 84a1f6e7d5..73133e66ec 100644
--- a/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md
@@ -21,8 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md b/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md
index f317f68604..c24a6998e9 100644
--- a/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md
index 66aa2879e7..67ac84b30e 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx/_index.md
index aed423e9db..af974d9735 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
index fe7e406114..c734ea7ed1 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
@@ -18,7 +18,7 @@ prerequisites:
 
 generate_summary_faq: true
 rerun_summary: false
-# rerun_faqs: false
+rerun_faqs: false
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md b/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md
index 7efef9dd73..e84f2fc7ac 100644
--- a/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md
@@ -14,8 +14,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md
index c0058e2a86..8079ff23f1 100644
--- a/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md
@@ -21,8 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md b/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md
index 8e688e9e1d..5efbc685b4 100644
--- a/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md
@@ -14,8 +14,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md
index 0dba2cb183..1db4e0c58e 100644
--- a/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/onnx/_index.md b/content/learning-paths/servers-and-cloud-computing/onnx/_index.md
index f61e437ca8..8da50339b8 100644
--- a/content/learning-paths/servers-and-cloud-computing/onnx/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/onnx/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md b/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md
index 6608779392..5923a080b1 100644
--- a/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md b/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md
index 1596ad4463..f4142e51dc 100644
--- a/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/openshift/_index.md b/content/learning-paths/servers-and-cloud-computing/openshift/_index.md
index 7c984ed6bc..11486ff589 100644
--- a/content/learning-paths/servers-and-cloud-computing/openshift/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openshift/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md
index cc5932f442..98bc221b9e 100644
--- a/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md
@@ -23,8 +23,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md b/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md
index 061a543fda..80d81ad85c 100644
--- a/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/pac/_index.md b/content/learning-paths/servers-and-cloud-computing/pac/_index.md
index ddadeff8f8..1debef89e1 100644
--- a/content/learning-paths/servers-and-cloud-computing/pac/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pac/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md b/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md
index 51ea9418a1..d90ee2c215 100644
--- a/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md
@@ -21,8 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md b/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md
index 68dd13b32b..4fd38dc4a5 100644
--- a/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/performix-system-characterization/_index.md b/content/learning-paths/servers-and-cloud-computing/performix-system-characterization/_index.md
index 774d35bf8e..05cb2a6884 100644
--- a/content/learning-paths/servers-and-cloud-computing/performix-system-characterization/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/performix-system-characterization/_index.md
@@ -21,8 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 author:
 - Brendan Long
 - David Wong
diff --git a/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md
index cfa9a30185..3029dbaba4 100644
--- a/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md b/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md
index ea83c82287..9b85b168f4 100644
--- a/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md b/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md
index 09a15edb03..40c8ccda42 100644
--- a/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md
index 237710f94f..181c649156 100644
--- a/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md b/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md
index 104d12078a..1cd3ac0c14 100644
--- a/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md
@@ -15,8 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md
index d0eb47d1d9..1463d54cae 100644
--- a/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md
@@ -13,8 +13,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md b/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
index 28df3c6fc0..2d388188cc 100644
--- a/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
@@ -15,8 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v2
diff --git a/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md b/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md
index aced00b904..f7cfa357a1 100644
--- a/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md
@@ -14,8 +14,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md
index cb66eeab16..1dabbe8d6d 100644
--- a/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md
@@ -13,8 +13,8 @@ learning_objectives:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md b/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md
index ab965d17e6..bc3fa8cdb3 100644
--- a/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md
index e96357a490..b012c7d702 100644
--- a/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md
@@ -21,8 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md
index 2b2c960b47..9a5fb3aa11 100644
--- a/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md
@@ -21,8 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/rag/_index.md b/content/learning-paths/servers-and-cloud-computing/rag/_index.md
index e7bdb03ced..8eeac1174a 100644
--- a/content/learning-paths/servers-and-cloud-computing/rag/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rag/_index.md
@@ -21,8 +21,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/ran/_index.md b/content/learning-paths/servers-and-cloud-computing/ran/_index.md
index d9123b17b5..d509825c79 100644
--- a/content/learning-paths/servers-and-cloud-computing/ran/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ran/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md
index c1d1e2205f..fe44f7e4f4 100644
--- a/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md
index 96c676bcd5..936da54e41 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md b/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md
index 896e074b73..63b1b12400 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/redis/_index.md b/content/learning-paths/servers-and-cloud-computing/redis/_index.md
index 1a17b59d84..e49b427322 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md b/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md
index 0d9f36b81b..9c1c87227f 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md
index 3f1589824f..7ec56bf112 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md b/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md
index 4445a938fb..1155363120 100644
--- a/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md b/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md
index 202cdd9269..4a07b1c854 100644
--- a/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md b/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md
index d44db8afb3..f736df6fbf 100644
--- a/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md
@@ -5,8 +5,8 @@ minutes_to_complete: 10
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md b/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md
index 4b6310b05d..0dbc068ba7 100644
--- a/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md b/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md
index 873a701235..d6ebf147fa 100644
--- a/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md b/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md
index 72e1db98ba..9d76d963a8 100644
--- a/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md
index 8ceeea6933..74e5c94c10 100644
--- a/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md b/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md
index 981b50220e..0a6967c31c 100644
--- a/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md
index f0c577c0c5..d1886a5d84 100644
--- a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md
@@ -15,8 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md
index 56d7963576..3a50c79857 100644
--- a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md
index 1506dcd7bc..d9825df66f 100644
--- a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/snappy/_index.md b/content/learning-paths/servers-and-cloud-computing/snappy/_index.md
index b3154b8b80..618b203a7d 100644
--- a/content/learning-paths/servers-and-cloud-computing/snappy/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/snappy/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md b/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md
index 18296858b6..6cff8986bb 100644
--- a/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md
index fa9c352a86..b2edfbf2a7 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md
index 54c201811c..299cbbc61f 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/spark-velox-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/spark-velox-cobalt/_index.md
index 8e59bd11a5..358eb5e9eb 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark-velox-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark-velox-cobalt/_index.md
@@ -24,8 +24,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/spark/_index.md b/content/learning-paths/servers-and-cloud-computing/spark/_index.md
index cea2b9d293..a818f586c4 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark/_index.md
@@ -15,8 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md b/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md
index ce20081b89..9fe62a9cbb 100644
--- a/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/sve/_index.md b/content/learning-paths/servers-and-cloud-computing/sve/_index.md
index 33e140e480..393e9c34a7 100644
--- a/content/learning-paths/servers-and-cloud-computing/sve/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sve/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md b/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md
index ea58ebb840..fd44bbf71c 100644
--- a/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md b/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md
index a45ac74d74..1b424d85f3 100644
--- a/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md
@@ -15,8 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
index 7e11ea4b7d..60e5fc767e 100644
--- a/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
@@ -15,8 +15,8 @@ prerequisites:
   - Basic familiarity with [TensorFlow](https://www.tensorflow.org/)
 
 generate_summary_faq: true
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md b/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md
index 6a30f1edd5..760100199a 100644
--- a/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md
@@ -14,8 +14,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md
index 7bbda69de0..d108189428 100644
--- a/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md b/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md
index e5f6852b33..a60f31e57e 100644
--- a/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md b/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md
index 2179a39b9a..80e75a8451 100644
--- a/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md
@@ -15,8 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md
index 6958a7696b..95dc898eb3 100644
--- a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md
@@ -15,8 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md
index 05959d376f..459e47f382 100644
--- a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md b/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md
index b75f9d5502..bfc8483859 100644
--- a/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md b/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md
index 98c8593b4f..db71146b46 100644
--- a/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md
index a470e6df32..e6462054eb 100644
--- a/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md
@@ -18,8 +18,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md b/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md
index 2e0aaef437..d52aa13a72 100644
--- a/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md
@@ -15,8 +15,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/vLLM-quant/_index.md b/content/learning-paths/servers-and-cloud-computing/vLLM-quant/_index.md
index b454c45d65..260f44aaf1 100644
--- a/content/learning-paths/servers-and-cloud-computing/vLLM-quant/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vLLM-quant/_index.md
@@ -24,8 +24,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 author: 
    - Rani Chowdary Mandepudi
    - Phalani Paladugu
diff --git a/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md b/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md
index d3cc08b454..322342e812 100644
--- a/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md b/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
index c84d01bb6e..dbccf97839 100644
--- a/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
@@ -19,8 +19,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/vllm/_index.md b/content/learning-paths/servers-and-cloud-computing/vllm/_index.md
index d51019c7c2..15fb05a0f8 100644
--- a/content/learning-paths/servers-and-cloud-computing/vllm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vllm/_index.md
@@ -16,8 +16,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md b/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
index 46a79fb50e..02bdbab923 100644
--- a/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
@@ -3,8 +3,8 @@ title: Run the vvenc H.266 encoder on Arm servers
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/whisper/_index.md b/content/learning-paths/servers-and-cloud-computing/whisper/_index.md
index b68a0a1ed3..c8d80e3796 100644
--- a/content/learning-paths/servers-and-cloud-computing/whisper/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/whisper/_index.md
@@ -20,8 +20,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md b/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
index e21b61ea3d..93ce4adde8 100644
--- a/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
@@ -9,8 +9,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/content/learning-paths/servers-and-cloud-computing/zlib/_index.md b/content/learning-paths/servers-and-cloud-computing/zlib/_index.md
index 7059a614bc..d7eda3ec69 100644
--- a/content/learning-paths/servers-and-cloud-computing/zlib/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/zlib/_index.md
@@ -17,8 +17,8 @@ prerequisites:
 
 generate_summary_faq: true
 
-# rerun_summary: false
-# rerun_faqs: false
+rerun_summary: false
+rerun_faqs: false
 # START generated_summary_faq
 generated_summary_faq:
   template_version: summary-faq-v1
diff --git a/reports/generated-summary-faq/latest-run.yml b/reports/generated-summary-faq/latest-run.yml
index 4ac4d8e2bf..ce23f4ae32 100644
--- a/reports/generated-summary-faq/latest-run.yml
+++ b/reports/generated-summary-faq/latest-run.yml
@@ -1,49 +1,49 @@
 latest_run:
-  timestamp: '2026-05-08T18:10:04Z'
-  mode: write
+  timestamp: '2026-05-12T18:09:37Z'
+  mode: dry-run
   require_enable_flag: true
   path_filter: ''
   limit: 0
-  run_url: https://github.com/chrismoroney/arm-learning-paths/actions/runs/25571591409
-  git_ref: STESOL-345
-  git_sha: a102be7c3271ce56120c193bfc2c779705fbecfb
-  actor: chrismoroney
+  run_url: ''
+  git_ref: ''
+  git_sha: ''
+  actor: ''
   template_version: summary-faq-v2
   totals:
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     removed: 0
-    summary_changed: 2
-    faq_changed: 2
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+    summary_changed: 10
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   section_totals:
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   reason_totals:
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   paths:
   - path: content/learning-paths/automotive/openadkit1_container/_index.md
     status: unchanged
@@ -11062,27 +11062,26 @@ latest_run:
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         updated_questions: []
   - path: content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
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-        and llama-cpp-agent with KleidiAI optimization for efficient LLM inference and function calling.
-        It is designed for...
+      generated_at_before: ''
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+      preview_before: ''
       preview_after: Learn how to build and deploy an AI agent application on Arm servers using llama.cpp
         and llama-cpp-agent with KleidiAI optimization for efficient LLM inference and function calling.
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@@ -11090,53 +11089,62 @@ latest_run:
         and llama-cpp-agent with KleidiAI optimization for efficient LLM inference and function calling.
         It is designed for...
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@@ -11144,53 +11152,62 @@ latest_run:
         AKS using Terraform and deploy a sample WordPress application as a workload. It is designed for
         software developers who...
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+        - How is the Learning Path structured?
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         for high-throughput columnar data processing and low-latency data transport with MinIO integration.
         It is designe...
@@ -11198,53 +11215,62 @@ latest_run:
         for high-throughput columnar data processing and low-latency data transport with MinIO integration.
         It is designe...
     faqs:
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@@ -11252,53 +11278,62 @@ latest_run:
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+        - What do you need before you start?
+        - Which tools, languages, or platforms does it cover?
+        - How is the Learning Path structured?
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+        - What do you need before you start?
+        - Which tools, languages, or platforms does it cover?
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+        - What do you need before you start?
+        - Which tools, languages, or platforms does it cover?
+        - How is the Learning Path structured?
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+      preview_before: ''
       preview_after: Learn how to deploy and manage applications on Google Cloud GKE Arm64 clusters using
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@@ -11360,53 +11404,62 @@ latest_run:
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+        who want to r...
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+        It is designed...
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+        profile instrumentation, merge multiple profiles for improved coverage, and integrate optimized
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+        Realms using Trustee services for attestation-based authorization on an FVP with RME support.
+        It is designed for develo...
+      preview_generated: Learn how to deploy Confidential Containers from encrypted images inside Arm
+        CCA Realms using Trustee services for attestation-based authorization on an FVP with RME support.
+        It is designed for develo...
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+      preview_generated: Learn how to deploy a scalable Arm CCA attestation verifier service on AWS using
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+      preview_before: Learn how to provision Arm64 and x86_64 VM instances on Google Cloud, then install
+        and use Sweet and Benchstat to measure and compare Go application performance. It is designed
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+        for This introductory t...
+      preview_generated: Learn how to provision Arm64 and x86_64 VM instances on Google Cloud, then install
+        and use Sweet and Benchstat to measure and compare Go application performance. It is designed
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+        DevOps engineer...
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+      preview_generated: Learn where Arm architecture is used in servers and cloud computing, and find
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+      preview_generated: Analyze and optimize interrupt request (IRQ) patterns on Arm Linux servers to
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+        aiming to opti...
+      preview_generated: Monitor, interpret, and optimize Java Garbage Collector performance on Arm servers
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+        aiming to opti...
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+      preview_generated: Deploy and optimize Java applications on Google Cloud Axion processors by testing
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+        and benchmar...
+      preview_generated: Deploy Java on Azure Cobalt 100 Arm virtual machines and benchmark application
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+        deployment and benchmar...
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+      preview_generated: Profile Java applications on Arm Neoverse servers using flame graphs generated
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+      preview_generated: Deploy Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate
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+      preview_generated: Identify and migrate container images in a Kubernetes cluster to Arm-compatible
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+      preview_generated: Understand Large System Extensions (LSE) for Arm processors and verify whether
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+        Amazon RDS, and automation with Terraform and Ansible. It is designed for software developers
+        who want to deploy...
+      preview_generated: Deploy MariaDB on Arm cloud instances across AWS, Azure, and Google Cloud using
+        Docker, Amazon RDS, and automation with Terraform and Ansible. It is designed for software developers
+        who want to deploy...
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+      preview_after: Deploy Memcached as a cache for MySQL and PostgreSQL on Arm servers. It is designed
+        for developers who want to use memcached as their in-memory key-value store. By the end, you will
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+      preview_generated: Deploy Memcached as a cache for MySQL and PostgreSQL on Arm servers. It is designed
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+        developers and perfo...
+      preview_generated: Use ASCT to measure cache latency, streaming bandwidth, and coherency latency
+        on Arm Neoverse systems, and compare results across Graviton generations. It is designed for software
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+        Herd7, Litmus7, and Arm hardware with assembly snippets. It is designed for developers seeking
+        practical ways to test ...
+      preview_generated: Test and validate thread synchronization approaches in the Arm memory model using
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+  - path: content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
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+        Path source content so the workflow report records preserved summary drift for this LP.
+      preview_generated: Microbenchmark and tune network performance with iPerf3 and Linux traffic control
+        walks you through an end-to-end Arm software workflow. It is designed for performance engineers,
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+        - What will you accomplish in this Learning Path?
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+        and the MLPerf Inference benchmark suite from MLCommons. It is designed for software developers
+        interested in benchmark...
+      preview_generated: Benchmark machine learning inference performance on Arm servers using TensorFlow
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+        Serving Benchmark (YCSB) to compare against other architectures. It is designed for software developers
+        who want to ...
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+        walks you through an end-to-end Arm software workflow. It is designed for software developers
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+        Arm software workflow. It is designed for software developers migrating Node.js workloads from
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+      preview_generated: Deploy Node.js on Google Cloud C4A Arm-based Axion VMs walks you through an end-to-end
+        Arm software workflow. It is designed for software developers migrating Node.js workloads from
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+        through an end-to-end Arm software workflow. It is designed for software developers who are new
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+        end-to-end Arm software workflow. It is designed for developers, ML engineers, and cloud practitioners
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+      preview_generated: Deploy SqueezeNet 1.0 INT8 model with ONNX Runtime on Azure Cobalt 100 walks
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+      preview_generated: Learn how to deploy Tinkerblox UltraEdge HPC-I for edge AI and mixed workloads
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+      preview_before: Learn how to compare Arm Neoverse and Intel x86 top-down performance analysis methodologies
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+        It is designe...
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+        using PMU counters, Linux Perf, and topdown-tool to identify bottlenecks across architectures.
+        It is designe...
+      preview_generated: Learn how to compare Arm Neoverse and Intel x86 top-down performance analysis
+        methodologies using PMU counters, Linux Perf, and topdown-tool to identify bottlenecks across
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+      preview_after: Learn how to migrate x86-64 SIMD code to Arm64 by mapping Intel SSE/AVX to Arm Neon,
+        SVE, and SME, with code examples and migration strategies using autovectorization or intrinsics.
+        It is designed for...
+      preview_generated: Learn how to migrate x86-64 SIMD code to Arm64 by mapping Intel SSE/AVX to Arm
+        Neon, SVE, and SME, with code examples and migration strategies using autovectorization or intrinsics.
+        It is designed for...
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+      preview_before: Learn how to optimize SIMD performance on Arm by restructuring data layouts from
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+      preview_before: Learn how to sample and profile CPU instructions using WindowsPerf with Arm Statistical
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+        Profiling Extension (SPE) on Windows on Arm, demonstrated with CPython workload analysis. It is
+        designed for dev...
+      preview_generated: Learn how to sample and profile CPU instructions using WindowsPerf with Arm Statistical
+        Profiling Extension (SPE) on Windows on Arm, demonstrated with CPython workload analysis. It is
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+      preview_before: Learn how to create and connect to a Windows on Arm virtual machine in Microsoft
+        Azure using the Azure Marketplace and RDP. It is designed for software developers interested using
+        Windows on Arm in th...
+      preview_after: Learn how to create and connect to a Windows on Arm virtual machine in Microsoft
+        Azure using the Azure Marketplace and RDP. It is designed for software developers interested using
+        Windows on Arm in th...
+      preview_generated: Learn how to create and connect to a Windows on Arm virtual machine in Microsoft
+        Azure using the Azure Marketplace and RDP. It is designed for software developers interested using
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+      preview_before: Learn how to build and deploy distributed Zenoh systems on Arm devices like Raspberry
+        Pi, using pub/sub, storage, and queryable models for scalable robotics and IoT applications. It
+        is designed for ro...
+      preview_after: Learn how to build and deploy distributed Zenoh systems on Arm devices like Raspberry
+        Pi, using pub/sub, storage, and queryable models for scalable robotics and IoT applications. It
+        is designed for ro...
+      preview_generated: Learn how to build and deploy distributed Zenoh systems on Arm devices like Raspberry
+        Pi, using pub/sub, storage, and queryable models for scalable robotics and IoT applications. It
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+  - path: content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
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+      preview_before: Learn how to design and integrate a custom AXI-Lite peripheral with a Cortex-A9
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+        inputs. It is designed...
+      preview_after: Learn how to design and integrate a custom AXI-Lite peripheral with a Cortex-A9 processor
+        on the Zybo Z7-10 board using Vivado, configuring GPIOs to control LEDs based on switch inputs.
+        It is designed...
+      preview_generated: Learn how to design and integrate a custom AXI-Lite peripheral with a Cortex-A9
+        processor on the Zybo Z7-10 board using Vivado, configuring GPIOs to control LEDs based on switch
+        inputs. It is designed...
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+  - path: content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
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+      preview_before: Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit and
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+        a neural network model t...
+      preview_after: Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit and
+        run inference on the Ethos-U85 NPU. It is designed for embedded developers who want to deploy
+        a neural network model t...
+      preview_generated: Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit
+        and run inference on the Ethos-U85 NPU. It is designed for embedded developers who want to deploy
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+        deploy IoT applications to a virtual Raspberry Pi 4 device. It is designed for embedded software
+        developers interested...
+      preview_generated: Learn how to create a custom Balena OS image, run it on Arm Virtual Hardware,
+        and deploy IoT applications to a virtual Raspberry Pi 4 device. It is designed for embedded software
+        developers interested...
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+        developers interested ...
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+  - path: content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
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+      preview_before: Learn how to create and integrate a virtual LED peripheral using the Virtual IO
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+        of Arm Virtual Hardware to simulate real-world peripherals. It is designed for software developers
+        new to Arm ...
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+        developers new to Arm ...
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+  - path: content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
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+      preview_before: Learn how to build a complete IoT solution in Azure that streams, stores, monitors,
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+        DB, and Azure Fun...
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+        aggregates, and visualizes telemetry data from Arm devices using IoT Hub, Stream Analytics, Cosmos
+        DB, and Azure Fun...
+      preview_generated: Learn how to build a complete IoT solution in Azure that streams, stores, monitors,
+        aggregates, and visualizes telemetry data from Arm devices using IoT Hub, Stream Analytics, Cosmos
+        DB, and Azure Fun...
+    faqs:
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+      preview_generated: Learn how to connect AI agents to Arm-based edge devices using Device Connect
+        for structured device access and Strands for agent orchestration, with examples for both simulated
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+      preview_before: Learn how to create a Dockerfile, build a Docker image with Arm Compiler for Embedded
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+        for embedded s...
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+        and Fixed Virtual Platforms, and test the containerized Arm development environment. It is designed
+        for embedded s...
+      preview_generated: Learn how to create a Dockerfile, build a Docker image with Arm Compiler for
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+        It is designed for embedded s...
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+        commands. It is designed...
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+        commands. It is designed...
+      preview_generated: Learn how to collect and preprocess audio data using Edge Impulse, train an audio
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+      preview_before: Develop a image classification neural network model and deploy it on an STM32 B-L475E-IOT01A2
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+        for mi...
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+        board. It is designed for embedded software developers interested in building neural network models
+        for mi...
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+      preview_before: Learn where Arm architecture is used in microcontrollers and discover microcontroller
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+        developers worki...
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+        hardware options for software development on Arm Cortex-M processors. It is designed for software
+        developers worki...
+      preview_generated: Learn where Arm architecture is used in microcontrollers and discover microcontroller
+        hardware options for software development on Arm Cortex-M processors. It is designed for software
+        developers worki...
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+      preview_before: Learn what differentiates TinyML from other AI domains, explore Arm-based edge devices
+        for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed Virtual
+        Platform. It is ...
+      preview_after: Learn what differentiates TinyML from other AI domains, explore Arm-based edge devices
+        for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed Virtual
+        Platform. It is ...
+      preview_generated: Learn what differentiates TinyML from other AI domains, explore Arm-based edge
+        devices for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed
+        Virtual Platform. It is ...
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+  - path: content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
+    status: unchanged
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+      preview_before: Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300 virtual
+        hardware to understand complete IoT software stack construction. It is designed for embedded software
+        developers ...
+      preview_after: Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300 virtual
+        hardware to understand complete IoT software stack construction. It is designed for embedded software
+        developers ...
+      preview_generated: Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300
+        virtual hardware to understand complete IoT software stack construction. It is designed for embedded
+        software developers ...
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+  - path: content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
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+      preview_before: Learn how to set up a Jetson Orin Nano with a MIPI CSI-2 camera and perform real-time
+        object detection from live video and image files using DetectNet and TensorRT. It is designed
+        for developers inter...
+      preview_after: Learn how to set up a Jetson Orin Nano with a MIPI CSI-2 camera and perform real-time
+        object detection from live video and image files using DetectNet and TensorRT. It is designed
+        for developers inter...
+      preview_generated: Learn how to set up a Jetson Orin Nano with a MIPI CSI-2 camera and perform real-time
+        object detection from live video and image files using DetectNet and TensorRT. It is designed
+        for developers inter...
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+  - path: content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
+    status: unchanged
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+      generated_at_after: '2026-05-06T17:17:54Z'
+      preview_before: Learn how to import, build, and debug your first Keil Studio Cloud project. It is
+        designed for embedded software developers new to Keil Studio Cloud. By the end, you will be able
+        to import and build a...
+      preview_after: Learn how to import, build, and debug your first Keil Studio Cloud project. It is
+        designed for embedded software developers new to Keil Studio Cloud. By the end, you will be able
+        to import and build a...
+      preview_generated: Learn how to import, build, and debug your first Keil Studio Cloud project. It
+        is designed for embedded software developers new to Keil Studio Cloud. By the end, you will be
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+  - path: content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
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+      preview_after: Learn how to migrate CMSIS v5 projects to CMSIS v6 by identifying supported toolchains,
+        installing required CMSIS-Packs, and selecting the necessary software components. It is designed
+        for embedded de...
+      preview_generated: Learn how to migrate CMSIS v5 projects to CMSIS v6 by identifying supported toolchains,
+        installing required CMSIS-Packs, and selecting the necessary software components. It is designed
+        for embedded de...
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+      preview_before: Learn how to run large language models locally on the Raspberry Pi 5 using Ollama,
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+        cloud services. It ...
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+        control GPIO-connected devices, and deploy a privacy-first web-based smart home assistant without
+        cloud services. It ...
+      preview_generated: Learn how to run large language models locally on the Raspberry Pi 5 using Ollama,
+        control GPIO-connected devices, and deploy a privacy-first web-based smart home assistant without
+        cloud services. It ...
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+        and plays audio resp...
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+        and plays audio resp...
+      preview_generated: Learn how to build a voice-controlled bot on a Raspberry Pi that listens for
+        a wake word, converts speech to text using Google Speech Recognition, sends requests to ChatGPT's
+        API, and plays audio resp...
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+        for software...
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+  - path: content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
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+      preview_before: Learn how to compile the Llama 3 large language model using ExecuTorch, deploy it
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+        designed for anyone in...
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+        to a Raspberry Pi 5, and understand techniques for running LLMs in embedded environments. It is
+        designed for anyone in...
+      preview_generated: Learn how to compile the Llama 3 large language model using ExecuTorch, deploy
+        it to a Raspberry Pi 5, and understand techniques for running LLMs in embedded environments. It
+        is designed for anyone in...
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+  - path: content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
+    status: unchanged
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+      preview_before: Learn how to reduce compile time for embedded Linux projects by installing a Raspberry
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+        it to a Raspb...
+      preview_after: Learn how to reduce compile time for embedded Linux projects by installing a Raspberry
+        Pi OS file system on an Arm server, building the MXNet machine learning framework, and transferring
+        it to a Raspb...
+      preview_generated: Learn how to reduce compile time for embedded Linux projects by installing a
+        Raspberry Pi OS file system on an Arm server, building the MXNet machine learning framework, and
+        transferring it to a Raspb...
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+  - path: content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
+    status: unchanged
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+      preview_before: Setup tools and start programming with Raspberry Pi Pico. It is designed for embedded
+        software developers new to Raspberry Pi Pico. By the end, you will be able to install the Raspberry
+        Pi Pico SDK, r...
+      preview_after: Setup tools and start programming with Raspberry Pi Pico. It is designed for embedded
+        software developers new to Raspberry Pi Pico. By the end, you will be able to install the Raspberry
+        Pi Pico SDK, r...
+      preview_generated: Setup tools and start programming with Raspberry Pi Pico. It is designed for
+        embedded software developers new to Raspberry Pi Pico. By the end, you will be able to install
+        the Raspberry Pi Pico SDK, r...
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+  - path: content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
+    status: unchanged
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+      preview_before: Learn how to profile Linux kernel modules using Arm Streamline to identify performance
+        bottlenecks, analyze both out-of-tree and in-tree modules, and use Statistical Profiling Extension
+        (SPE) for deep...
+      preview_after: Learn how to profile Linux kernel modules using Arm Streamline to identify performance
+        bottlenecks, analyze both out-of-tree and in-tree modules, and use Statistical Profiling Extension
+        (SPE) for deep...
+      preview_generated: Learn how to profile Linux kernel modules using Arm Streamline to identify performance
+        bottlenecks, analyze both out-of-tree and in-tree modules, and use Statistical Profiling Extension
+        (SPE) for deep...
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+  - path: content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
+    status: unchanged
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+      preview_before: Build a letter recognition neural network model using TensorFlow and deploy it on
+        an STM32 B-L475E-IOT01A2 board. It is designed for software developers interested in building
+        network models for micro...
+      preview_after: Build a letter recognition neural network model using TensorFlow and deploy it on
+        an STM32 B-L475E-IOT01A2 board. It is designed for software developers interested in building
+        network models for micro...
+      preview_generated: Build a letter recognition neural network model using TensorFlow and deploy it
+        on an STM32 B-L475E-IOT01A2 board. It is designed for software developers interested in building
+        network models for micro...
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+        designed for softwar...
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+        for This is a topi...
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+      preview_before: Introduction to building a minimal Yocto Linux image and running it on 64-bit Qemu
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+        Arm target. It is designed for software developers interested in learning the basics of building
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-    - rerun_flags_reset
+    status: unchanged
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons: []
     template_version_before: summary-faq-v2
     template_version_after: summary-faq-v2
     summary:
@@ -13699,15 +35888,15 @@ latest_run:
         using Nginx and PostgreSQL. It is designed for engineers who want to deploy a Django based application
         on Arm machines...
     faqs:
-      action: rerun_requested
+      action: unchanged
       missing_before: false
-      rerun_requested: true
+      rerun_requested: false
       changed: false
       drift_detected: false
       source_hash_before: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
       source_hash_after: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
       current_source_hash: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
-      generated_at_before: '2026-05-08T16:31:30Z'
+      generated_at_before: '2026-05-08T18:10:02Z'
       generated_at_after: '2026-05-08T18:10:02Z'
       before_count: 5
       after_count: 5
@@ -18154,26 +40343,23 @@ latest_run:
         removed_questions: []
         updated_questions: []
   - path: content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
-    status: updated
-    changed_on_disk: true
-    managed_block_updated: true
-    rerun_flags_reset:
-    - rerun_summary
-    change_reasons:
-    - rerun_summary
-    - rerun_flags_reset
+    status: unchanged
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons: []
     template_version_before: summary-faq-v2
     template_version_after: summary-faq-v2
     summary:
-      action: rerun_requested
+      action: unchanged
       missing_before: false
-      rerun_requested: true
+      rerun_requested: false
       changed: false
       drift_detected: false
       source_hash_before: 10f13dee3459577fcadf5966cf80d990f3396321189f638432a3308699e773a8
       source_hash_after: 10f13dee3459577fcadf5966cf80d990f3396321189f638432a3308699e773a8
       current_source_hash: 10f13dee3459577fcadf5966cf80d990f3396321189f638432a3308699e773a8
-      generated_at_before: '2026-05-08T16:31:32Z'
+      generated_at_before: '2026-05-08T18:10:03Z'
       generated_at_after: '2026-05-08T18:10:03Z'
       preview_before: Learn how to tune Nginx walks you through an end-to-end Arm software workflow. It
         is designed for software developers who want to use Nginx on Arm. By the end, you will be able
@@ -19237,12 +41423,11 @@ latest_run:
         removed_questions: []
         updated_questions: []
   - path: content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
-    status: updated
-    changed_on_disk: true
-    managed_block_updated: true
+    status: unchanged
+    changed_on_disk: false
+    managed_block_updated: false
     rerun_flags_reset: []
-    change_reasons:
-    - missing_faqs
+    change_reasons: []
     template_version_before: summary-faq-v2
     template_version_after: summary-faq-v2
     summary:
@@ -19266,39 +41451,29 @@ latest_run:
         workflow. It is designed for software developers who want to build and run the Process Watch tool
         on an Arm-based mac...
     faqs:
-      action: repaired_missing
-      missing_before: true
+      action: unchanged
+      missing_before: false
       rerun_requested: false
-      changed: true
+      changed: false
       drift_detected: false
       source_hash_before: ed85d6171f3c05bfdf1b396065fb0c73ce88724ff63fd1c3c8a93d4c27dd59f8
       source_hash_after: ed85d6171f3c05bfdf1b396065fb0c73ce88724ff63fd1c3c8a93d4c27dd59f8
       current_source_hash: ed85d6171f3c05bfdf1b396065fb0c73ce88724ff63fd1c3c8a93d4c27dd59f8
-      generated_at_before: '2026-05-08T16:31:32Z'
+      generated_at_before: '2026-05-08T18:10:03Z'
       generated_at_after: '2026-05-08T18:10:03Z'
-      before_count: 0
+      before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
-        before_count: 0
+        before_count: 5
         after_count: 5
-        added_questions:
-        - What will you accomplish in this Learning Path?
-        - Who is this Learning Path for?
-        - What do you need before you start?
-        - Which tools, languages, or platforms does it cover?
-        - How is the Learning Path structured?
+        added_questions: []
         removed_questions: []
         updated_questions: []
       generated_diff:
-        before_count: 0
+        before_count: 5
         after_count: 5
-        added_questions:
-        - What will you accomplish in this Learning Path?
-        - Who is this Learning Path for?
-        - What do you need before you start?
-        - Which tools, languages, or platforms does it cover?
-        - How is the Learning Path structured?
+        added_questions: []
         removed_questions: []
         updated_questions: []
   - path: content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md
@@ -21084,26 +43259,27 @@ latest_run:
         removed_questions: []
         updated_questions: []
   - path: content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
-    status: updated
-    changed_on_disk: true
-    managed_block_updated: true
+    status: unchanged
+    changed_on_disk: false
+    managed_block_updated: false
     rerun_flags_reset: []
-    change_reasons:
-    - missing_summary
+    change_reasons: []
     template_version_before: summary-faq-v2
     template_version_after: summary-faq-v2
     summary:
-      action: repaired_missing
-      missing_before: true
+      action: unchanged
+      missing_before: false
       rerun_requested: false
-      changed: true
+      changed: false
       drift_detected: false
       source_hash_before: 2c20d30d25a0bb871bdb935d18f7ad8e0740139948133dbd728e10f0add0f94e
       source_hash_after: 2c20d30d25a0bb871bdb935d18f7ad8e0740139948133dbd728e10f0add0f94e
       current_source_hash: 2c20d30d25a0bb871bdb935d18f7ad8e0740139948133dbd728e10f0add0f94e
-      generated_at_before: '2026-05-08T16:31:32Z'
+      generated_at_before: '2026-05-08T18:10:04Z'
       generated_at_after: '2026-05-08T18:10:04Z'
-      preview_before: ''
+      preview_before: Deploy TensorFlow on Google Cloud C4A (Arm-based Axion VMs) walks you through an
+        end-to-end Arm software workflow. It is designed for software developers deploying and optimizing
+        TensorFlow workloads ...
       preview_after: Deploy TensorFlow on Google Cloud C4A (Arm-based Axion VMs) walks you through an
         end-to-end Arm software workflow. It is designed for software developers deploying and optimizing
         TensorFlow workloads ...
@@ -22054,7 +44230,6 @@ latest_run:
         added_questions: []
         removed_questions: []
         updated_questions: []
-history:
 - timestamp: '2026-05-08T18:10:04Z'
   mode: write
   require_enable_flag: true

From 9e87669bc8772781982bf95fa0c2ce5d355371a4 Mon Sep 17 00:00:00 2001
From: GitHub Actions Summary FAQ Bot <>
Date: Tue, 12 May 2026 18:20:51 +0000
Subject: [PATCH 15/23] Generate Learning Path summary and FAQ content

---
 .../ai-agent-on-cpu/_index.md                 |    48 +
 .../servers-and-cloud-computing/aks/_index.md |    44 +
 .../apache_arrow_and_flight/_index.md         |    54 +
 .../arcee-foundation-model-on-aws/_index.md   |    46 +
 .../arcee-foundation-model-on-gcp/_index.md   |    50 +
 .../argo-cd-gcp/_index.md                     |    55 +
 .../arm-cpp-memory-model/_index.md            |    46 +
 .../arm-mcp-server/_index.md                  |    50 +
 .../arm-soc-migration-learning-path/_index.md |    56 +
 .../arm_linux_page_size/_index.md             |    46 +
 reports/generated-summary-faq/latest-run.yml  | 22167 +++++++++++++++-
 11 files changed, 22636 insertions(+), 26 deletions(-)

diff --git a/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md b/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
index df5c156cab..e16c252ea3 100644
--- a/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
@@ -22,6 +22,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v2
+  generated_at: '2026-05-12T18:20:22Z'
+  generator: template
+  source_hash: 275878b5aa4c27dee394da12ad8b80e4c0d0235b3ddce66b1fe3f0e056ef3da9
+  summary_generated_at: '2026-05-12T18:20:22Z'
+  summary_source_hash: 275878b5aa4c27dee394da12ad8b80e4c0d0235b3ddce66b1fe3f0e056ef3da9
+  faq_generated_at: '2026-05-12T18:20:22Z'
+  faq_source_hash: 275878b5aa4c27dee394da12ad8b80e4c0d0235b3ddce66b1fe3f0e056ef3da9
+  summary: >-
+    Learn how to build and deploy an AI agent application on Arm servers using llama.cpp and llama-cpp-agent
+    with KleidiAI optimization for efficient LLM inference and function calling. It is designed
+    for software developers and ML engineers looking to deploy an optimized AI agent application.
+    By the end, you will be able to set up llama-cpp-python optimized for Arm servers, run optimized
+    Large Language Models (LLMs), and create custom functions for LLMs. It focuses on tools and
+    technologies such as Python, AWS Graviton, and AI, Linux environments, Arm platforms including
+    Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The
+    main steps cover Introduction to AI Agents and Agent Use Cases, Set Up Your Local Environment
+    to Run an AI Application, AI Agent Application, and Explore and Test Your AI Agent.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will set up llama-cpp-python optimized for Arm servers, run optimized Large Language
+      Models (LLMs), and create custom functions for LLMs. Learn how to build and deploy an AI
+      agent application on Arm servers using llama.cpp and llama-cpp-agent with KleidiAI optimization
+      for efficient LLM inference and function calling.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for software developers and ML engineers looking to deploy
+      an optimized AI agent application.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/)
+      from a cloud service provider or an on-premise Arm server.; Basic understanding of Python
+      and prompt engineering.; Understanding of LLM fundamentals.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Python, AWS Graviton, and AI, Linux environments,
+      Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
+      Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Introduction to AI Agents and Agent Use Cases, Set
+      Up Your Local Environment to Run an AI Application, AI Agent Application, and Explore and
+      Test Your AI Agent.
+# END generated_summary_faq
+
 author: Andrew Choi
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/aks/_index.md b/content/learning-paths/servers-and-cloud-computing/aks/_index.md
index d3960b3ca8..0fd1303021 100644
--- a/content/learning-paths/servers-and-cloud-computing/aks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/aks/_index.md
@@ -20,6 +20,50 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v2
+  generated_at: '2026-05-12T18:20:22Z'
+  generator: template
+  source_hash: 01c5cc8930650a8d81d67d4c48b62e0f9d7b1ebfc2d09a552e11046fb982fc52
+  summary_generated_at: '2026-05-12T18:20:22Z'
+  summary_source_hash: 01c5cc8930650a8d81d67d4c48b62e0f9d7b1ebfc2d09a552e11046fb982fc52
+  faq_generated_at: '2026-05-12T18:20:22Z'
+  faq_source_hash: 01c5cc8930650a8d81d67d4c48b62e0f9d7b1ebfc2d09a552e11046fb982fc52
+  summary: >-
+    Learn how to automate the deployment of an Arm-based Kubernetes cluster on Azure AKS using
+    Terraform and deploy a sample WordPress application as a workload. It is designed for software
+    developers who want to deploy an Arm-based Kubernetes cluster using Azure Kubernetes Service
+    (AKS). By the end, you will be able to automate the deployment of an Arm-based AKS cluster
+    using Terraform and install Wordpress on AKS as an example workload. It focuses on tools and
+    technologies such as Terraform, Kubernetes, WordPress, and MySQL, Linux environments, Arm
+    platforms including Neoverse, and cloud platforms such as Microsoft Azure. The main steps
+    cover Deploy an Arm-based AKS Cluster using Terraform and Deploy a WordPress Example.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will automate the deployment of an Arm-based AKS cluster using Terraform and install
+      Wordpress on AKS as an example workload. Learn how to automate the deployment of an Arm-based
+      Kubernetes cluster on Azure AKS using Terraform and deploy a sample WordPress application
+      as a workload.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for software developers who want to deploy an Arm-based Kubernetes
+      cluster using Azure Kubernetes Service (AKS).
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An Azure account; A machine with [Terraform](/install-guides/terraform/),
+      [Azure CLI](/install-guides/azure-cli), and [Kubectl](/install-guides/kubectl/) installed.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Terraform, Kubernetes, WordPress, and MySQL, Linux
+      environments, Arm platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Deploy an Arm-based AKS Cluster using Terraform and
+      Deploy a WordPress Example.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md b/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
index c39d3643ff..9b654e442e 100644
--- a/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
@@ -24,6 +24,60 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v2
+  generated_at: '2026-05-12T18:20:22Z'
+  generator: template
+  source_hash: 90b638385267e085ea07a70a1c23f16578aa3caaeabeca1f651bcdcac5bf38fb
+  summary_generated_at: '2026-05-12T18:20:22Z'
+  summary_source_hash: 90b638385267e085ea07a70a1c23f16578aa3caaeabeca1f651bcdcac5bf38fb
+  faq_generated_at: '2026-05-12T18:20:22Z'
+  faq_source_hash: 90b638385267e085ea07a70a1c23f16578aa3caaeabeca1f651bcdcac5bf38fb
+  summary: >-
+    Learn how to deploy Apache Arrow and Arrow Flight on Google Cloud Axion C4A processors for
+    high-throughput columnar data processing and low-latency data transport with MinIO integration.
+    It is designed for data engineers, platform engineers, and developers who aim to build high-performance
+    analytics pipelines on Arm64-based Google Cloud C4A Axion processors using Apache Arrow and
+    Arrow Flight. By the end, you will be able to deploy Apache Arrow–based data processing workloads
+    on Google Cloud C4A Axion processors, set up and run an Arrow Flight server for high-throughput,
+    low-latency data transport, and read and write columnar data formats such as Parquet and ORC
+    using Apache Arrow. It focuses on tools and technologies such as Apache Arrow, Arrow Flight,
+    Python, and MinIO, Linux environments, Arm platforms including Neoverse, and cloud platforms
+    such as Google Cloud. The main steps cover Get started with Apache Arrow and Arrow Flight
+    on Google Axion C4A, Create firewall rules on GCP for Apache Arrow, MinIO, and Arrow Flight,
+    Create a Google Axion C4A arm64 virtual machine on GCP, Set up Apache Arrow and MinIO on arm64,
+    and Analyze columnar data with Apache Arrow on arm64.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will deploy Apache Arrow–based data processing workloads on Google Cloud C4A Axion processors,
+      set up and run an Arrow Flight server for high-throughput, low-latency data transport, and
+      read and write columnar data formats such as Parquet and ORC using Apache Arrow. Learn how
+      to deploy Apache Arrow and Arrow Flight on Google Cloud Axion C4A processors for high-throughput
+      columnar data processing and low-latency data transport with MinIO integration.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for data engineers, platform engineers, and developers who
+      aim to build high-performance analytics pipelines on Arm64-based Google Cloud C4A Axion
+      processors using Apache Arrow and Arrow Flight.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
+      account with billing enabled; Basic familiarity with Python; Basic understanding of data
+      formats such as Parquet or ORC; Familiarity with Linux command-line operations.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Apache Arrow, Arrow Flight, Python, and MinIO, Linux
+      environments, Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Get started with Apache Arrow and Arrow Flight on
+      Google Axion C4A, Create firewall rules on GCP for Apache Arrow, MinIO, and Arrow Flight,
+      Create a Google Axion C4A arm64 virtual machine on GCP, Set up Apache Arrow and MinIO on
+      arm64, and Analyze columnar data with Apache Arrow on arm64.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
index 598f66c460..8a392bff8a 100644
--- a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
@@ -22,6 +22,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v2
+  generated_at: '2026-05-12T18:20:22Z'
+  generator: template
+  source_hash: 964c1e6a87810dca686a7b3218433ce09d31bd92882db10af355e8c50bb6091c
+  summary_generated_at: '2026-05-12T18:20:22Z'
+  summary_source_hash: 964c1e6a87810dca686a7b3218433ce09d31bd92882db10af355e8c50bb6091c
+  faq_generated_at: '2026-05-12T18:20:22Z'
+  faq_source_hash: 964c1e6a87810dca686a7b3218433ce09d31bd92882db10af355e8c50bb6091c
+  summary: >-
+    Learn how to build llama.cpp, quantize the Arcee AFM-4.5B model, and run optimized inference
+    on AWS Graviton4 instances with perplexity-based quality evaluation. It is designed for developers
+    and ML engineers who want to deploy Arcee's AFM-4.5B small language model on AWS Graviton4
+    instances using Llama.cpp. By the end, you will be able to launch an Arm-based EC2 instance
+    on AWS Graviton4, build and install Llama.cpp from source, and download and quantize the AFM-4.5B
+    model from Hugging Face. It focuses on tools and technologies such as AWS, Hugging Face, Python,
+    and Llama.cpp, Linux environments, Arm platforms including Neoverse, and cloud platforms such
+    as AWS. The main steps cover Overview, Provision your Graviton4 environment, Configure your
+    Graviton4 environment, Build Llama.cpp, and Install Python dependencies.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will launch an Arm-based EC2 instance on AWS Graviton4, build and install Llama.cpp
+      from source, and download and quantize the AFM-4.5B model from Hugging Face. Learn how to
+      build llama.cpp, quantize the Arcee AFM-4.5B model, and run optimized inference on AWS Graviton4
+      instances with perplexity-based quality evaluation.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is for developers and ML engineers who want to deploy Arcee's AFM-4.5B
+      small language model on AWS Graviton4 instances using Llama.cpp.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An [AWS account](https://aws.amazon.com/)
+      with permission to launch Graviton4 (`c8g.4xlarge` or larger) instances; Basic familiarity
+      with Linux and SSH.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including AWS, Hugging Face, Python, and Llama.cpp, Linux
+      environments, Arm platforms such as Neoverse, and cloud platforms such as AWS.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview, Provision your Graviton4 environment, Configure
+      your Graviton4 environment, Build Llama.cpp, and Install Python dependencies.
+# END generated_summary_faq
+
 author: Julien Simon
 
 # Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
index 1169586f94..031b7b8c99 100644
--- a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
@@ -22,6 +22,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v2
+  generated_at: '2026-05-12T18:20:22Z'
+  generator: template
+  source_hash: b2f60d2c31ef380e6e6ef3028671ad9242dd51c98f4a192f2da32bb05b94e185
+  summary_generated_at: '2026-05-12T18:20:22Z'
+  summary_source_hash: b2f60d2c31ef380e6e6ef3028671ad9242dd51c98f4a192f2da32bb05b94e185
+  faq_generated_at: '2026-05-12T18:20:22Z'
+  faq_source_hash: b2f60d2c31ef380e6e6ef3028671ad9242dd51c98f4a192f2da32bb05b94e185
+  summary: >-
+    Learn how to build llama.cpp, quantize the Arcee AFM-4.5B model, and run optimized inference
+    on Google Cloud Axion instances with perplexity-based quality evaluation. It is designed for
+    developers and ML engineers who want to deploy Arcee's AFM-4.5B small language model on Google
+    Cloud Axion instances using Llama.cpp. By the end, you will be able to launch an Arm-based
+    Compute Engine instance on Google Cloud Axion, build and install Llama.cpp from source, and
+    download and quantize the AFM-4.5B model from Hugging Face. It focuses on tools and technologies
+    such as Google Cloud, Hugging Face, Python, and Llama.cpp, Linux environments, Arm platforms
+    including Neoverse, and cloud platforms such as Google Cloud. The main steps cover AFM-4.5B
+    deployment on Google Cloud Axion with Llama.cpp, Provision a Google Cloud Axion Arm64 environment,
+    Configure your Google Cloud Axion Arm64 environment, Build Llama.cpp on Google Cloud Axion
+    Arm64, and Install Python dependencies for Llama.cpp.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will launch an Arm-based Compute Engine instance on Google Cloud Axion, build and install
+      Llama.cpp from source, and download and quantize the AFM-4.5B model from Hugging Face. Learn
+      how to build llama.cpp, quantize the Arcee AFM-4.5B model, and run optimized inference on
+      Google Cloud Axion instances with perplexity-based quality evaluation.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This Learning Path is for developers and ML engineers who want to deploy Arcee's AFM-4.5B
+      small language model on Google Cloud Axion instances using Llama.cpp.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud account](https://console.cloud.google.com/)
+      with permission to launch Axion (`c4a-standard-16` or larger) instances; Basic familiarity
+      with Linux and SSH.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Google Cloud, Hugging Face, Python, and Llama.cpp,
+      Linux environments, Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around AFM-4.5B deployment on Google Cloud Axion with Llama.cpp,
+      Provision a Google Cloud Axion Arm64 environment, Configure your Google Cloud Axion Arm64
+      environment, Build Llama.cpp on Google Cloud Axion Arm64, and Install Python dependencies
+      for Llama.cpp.
+# END generated_summary_faq
+
 author: Julien Simon
 
 # Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
index 2077b677f2..83904bafcb 100644
--- a/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
@@ -26,6 +26,61 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v2
+  generated_at: '2026-05-12T18:20:22Z'
+  generator: template
+  source_hash: c6106f21859f5a8e04bbd579db47c7177bb5371d4c7f306054e56e81ed9e89a1
+  summary_generated_at: '2026-05-12T18:20:22Z'
+  summary_source_hash: c6106f21859f5a8e04bbd579db47c7177bb5371d4c7f306054e56e81ed9e89a1
+  faq_generated_at: '2026-05-12T18:20:22Z'
+  faq_source_hash: c6106f21859f5a8e04bbd579db47c7177bb5371d4c7f306054e56e81ed9e89a1
+  summary: >-
+    Learn how to deploy and manage applications on Google Cloud GKE Arm64 clusters using Argo
+    CD GitOps workflows with automated sync and self-healing on Axion processors. It is designed
+    for developers and platform engineers who want hands-on experience implementing GitOps using
+    Argo CD on Arm64-based Google Kubernetes Engine (GKE) clusters running on Google Axion (C4A)
+    processors. By the end, you will be able to provision an Arm-based SUSE Linux Enterprise Server
+    (SLES) virtual machine on Google Cloud (C4A with Axion processors), create and connect to
+    a Google Kubernetes Engine (GKE) cluster running on Arm64 (Axion) nodes, and install and validate
+    Argo CD on an Arm-based GKE cluster. It focuses on tools and technologies such as Argo CD,
+    Kubernetes, kubectl, GKE, and Git, Linux environments, Arm platforms including Neoverse, and
+    cloud platforms such as Google Cloud. The main steps cover Get started with Argo CD on Google
+    Axion C4A (Arm-based), Create a Google Axion C4A virtual machine on Google Cloud, Prepare
+    a GKE cluster for Argo CD deployments, Install and access Argo CD on Arm64 GKE, and Deploy
+    applications using GitOps with Argo CD.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will provision an Arm-based SUSE Linux Enterprise Server (SLES) virtual machine on Google
+      Cloud (C4A with Axion processors), create and connect to a Google Kubernetes Engine (GKE)
+      cluster running on Arm64 (Axion) nodes, and install and validate Argo CD on an Arm-based
+      GKE cluster. Learn how to deploy and manage applications on Google Cloud GKE Arm64 clusters
+      using Argo CD GitOps workflows with automated sync and self-healing on Axion processors.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers and platform engineers who want hands-on experience
+      implementing GitOps using Argo CD on Arm64-based Google Kubernetes Engine (GKE) clusters
+      running on Google Axion (C4A) processors.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
+      account with billing enabled; Basic familiarity with [Kubernetes concepts](https://kubernetes.io/docs/concepts/);
+      Basic understanding of Git and GitHub workflows; Familiarity with basic Linux command-line
+      usage.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Argo CD, Kubernetes, kubectl, GKE, and Git, Linux
+      environments, Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Get started with Argo CD on Google Axion C4A (Arm-based),
+      Create a Google Axion C4A virtual machine on Google Cloud, Prepare a GKE cluster for Argo
+      CD deployments, Install and access Argo CD on Arm64 GKE, and Deploy applications using GitOps
+      with Argo CD.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md b/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
index de7c0bf45d..b2fb23a1b5 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
@@ -20,6 +20,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v2
+  generated_at: '2026-05-12T18:20:22Z'
+  generator: template
+  source_hash: 3a4bc8b2ab548be507b6d528844b0593b27f2dab3ab3c9649e807abdf4887ce0
+  summary_generated_at: '2026-05-12T18:20:22Z'
+  summary_source_hash: 3a4bc8b2ab548be507b6d528844b0593b27f2dab3ab3c9649e807abdf4887ce0
+  faq_generated_at: '2026-05-12T18:20:22Z'
+  faq_source_hash: 3a4bc8b2ab548be507b6d528844b0593b27f2dab3ab3c9649e807abdf4887ce0
+  summary: >-
+    Learn how to write correct concurrent C++ code when porting applications from x86 to Arm by
+    understanding memory ordering differences and using best practices to avoid race conditions.
+    It is designed for C++ developers porting applications from x86 to Arm and optimizing performance.
+    By the end, you will be able to describe at a high level what a memory model does, and the
+    types of memory ordering, describe the differences between the Arm and x86 memory model, and
+    employ best practices for writing C++ on Arm to avoid race conditions. It focuses on tools
+    and technologies such as CPP, TSan, and Runbook, Linux environments, and Arm platforms including
+    Neoverse. The main steps cover Introduction to C++ Memory Models, The C++ Memory Model and
+    Atomics, Walk through a Race condition example, and Detecting race conditions.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will describe at a high level what a memory model does, and the types of memory ordering,
+      describe the differences between the Arm and x86 memory model, and employ best practices
+      for writing C++ on Arm to avoid race conditions. Learn how to write correct concurrent C++
+      code when porting applications from x86 to Arm by understanding memory ordering differences
+      and using best practices to avoid race conditions.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for C++ developers porting applications from x86 to Arm and optimizing
+      performance.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Access to an x86 and an Arm cloud instance
+      (virtual machine).; Proficiency in C++ programming.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including CPP, TSan, and Runbook, Linux environments, and
+      Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Introduction to C++ Memory Models, The C++ Memory
+      Model and Atomics, Walk through a Race condition example, and Detecting race conditions.
+# END generated_summary_faq
+
 author: Kieran Hejmadi
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md b/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
index 88453261c2..874faf87e3 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
@@ -23,6 +23,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v2
+  generated_at: '2026-05-12T18:20:22Z'
+  generator: template
+  source_hash: b870ac5160d35ddb51955ca8379493e172787ced8125cde8ae79f7700e653a87
+  summary_generated_at: '2026-05-12T18:20:22Z'
+  summary_source_hash: b870ac5160d35ddb51955ca8379493e172787ced8125cde8ae79f7700e653a87
+  faq_generated_at: '2026-05-12T18:20:22Z'
+  faq_source_hash: b870ac5160d35ddb51955ca8379493e172787ced8125cde8ae79f7700e653a87
+  summary: >-
+    Learn how to automate x86-to-Arm application migration using the Arm MCP Server, with AI-assisted
+    compatibility checks, C++ code refactoring, and Docker-based validation on Arm cloud platforms.
+    It is designed for developers who want to use AI-powered tools to migrate x86 applications
+    to Arm-based cloud instances. By the end, you will be able to explain how the Arm MCP Server
+    enables AI-driven x86-to-Arm migration workflows, use AI-assisted checks to inspect Docker
+    images for Arm compatibility, and set up and use the Arm Cloud Migration Agent in GitHub Copilot
+    to automate x86-to-Arm code migration. It focuses on tools and technologies such as MCP, Docker,
+    CPP, and GitHub Copilot, Linux environments, and Arm platforms including Neoverse. The main
+    steps cover Understand the Arm MCP Server for AI-driven x86-to-Arm migration, Verify Docker
+    image compatibility with Arm using AI, Arm Cloud Migration Agent in GitHub Copilot, and Configure
+    other AI agents to automate Arm migration workflows.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will explain how the Arm MCP Server enables AI-driven x86-to-Arm migration workflows,
+      use AI-assisted checks to inspect Docker images for Arm compatibility, and set up and use
+      the Arm Cloud Migration Agent in GitHub Copilot to automate x86-to-Arm code migration. Learn
+      how to automate x86-to-Arm application migration using the Arm MCP Server, with AI-assisted
+      compatibility checks, C++ code refactoring, and Docker-based validation on Arm cloud platforms.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for developers who want to use AI-powered tools to migrate x86
+      applications to Arm-based cloud instances.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: An AI-powered IDE such as VS Code, Copilot
+      in VS Code, Kiro (IDE or CLI) or Codex; Basic familiarity with Docker and C/C++ development;
+      Access to an Arm-based cloud instance or local Arm computer running Linux or macOS.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including MCP, Docker, CPP, and GitHub Copilot, Linux environments,
+      and Arm platforms such as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Understand the Arm MCP Server for AI-driven x86-to-Arm
+      migration, Verify Docker image compatibility with Arm using AI, Arm Cloud Migration Agent
+      in GitHub Copilot, and Configure other AI agents to automate Arm migration workflows.
+# END generated_summary_faq
+
 author: Joe Stech
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md b/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
index d66560842d..276a92445b 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
@@ -24,6 +24,62 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v2
+  generated_at: '2026-05-12T18:20:22Z'
+  generator: template
+  source_hash: 49b3f2b1464efb20f0c8fbcff46e9f30910d856567ca01c01dc348aa1c5740d1
+  summary_generated_at: '2026-05-12T18:20:22Z'
+  summary_source_hash: 49b3f2b1464efb20f0c8fbcff46e9f30910d856567ca01c01dc348aa1c5740d1
+  faq_generated_at: '2026-05-12T18:20:22Z'
+  faq_source_hash: 49b3f2b1464efb20f0c8fbcff46e9f30910d856567ca01c01dc348aa1c5740d1
+  summary: >-
+    Learn how to migrate C applications between Arm platforms using Kiro's AI-assisted tooling
+    to identify hardware dependencies and implement abstraction layers for cross-platform compatibility.
+    It is designed for experienced developers who need to migrate applications between Arm-based
+    platforms using AI-assisted tooling. You will work through a structured, repeatable migration
+    workflow using Kiro Arm SoC Migration Power, moving an application from AWS Graviton3 (Neoverse)
+    to Raspberry Pi 5 (Cortex-A). The techniques apply broadly to cloud-to-edge and cross-architecture
+    migrations across the Arm ecosystem. By the end, you will be able to install and configure
+    Kiro Arm SoC Migration Power, apply a structured migration workflow across Arm platforms,
+    and identify platform-specific and hardware-dependent code using AI-guided analysis. It focuses
+    on tools and technologies such as Kiro, AWS EC2, GCC, C, and CMake, Linux environments, Arm
+    platforms including Neoverse and Cortex-A, and cloud platforms such as AWS, Microsoft Azure,
+    Google Cloud, and Oracle. The main steps cover Install Arm SoC Migration Power, Develop on
+    source platform, Migrate using Arm SoC Migration Power, and Validate migration with testing.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will install and configure Kiro Arm SoC Migration Power, apply a structured migration
+      workflow across Arm platforms, and identify platform-specific and hardware-dependent code
+      using AI-guided analysis. Learn how to migrate C applications between Arm platforms using
+      Kiro's AI-assisted tooling to identify hardware dependencies and implement abstraction layers
+      for cross-platform compatibility.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an advanced topic for experienced developers who need to migrate applications between
+      Arm-based platforms using AI-assisted tooling. You will work through a structured, repeatable
+      migration workflow using Kiro Arm SoC Migration Power, moving an application from AWS Graviton3
+      (Neoverse) to Raspberry Pi 5 (Cortex-A). The techniques apply broadly to cloud-to-edge and
+      cross-architecture migrations across the Arm ecosystem.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Access to both source and target Arm
+      platforms (for example, AWS Graviton3 and Raspberry Pi 5); Working knowledge of C programming;
+      Familiarity with Linux development environments and basic embedded or cloud deployment concepts;
+      Experience building applications with GCC and CMake.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including Kiro, AWS EC2, GCC, C, and CMake, Linux environments,
+      Arm platforms such as Neoverse and Cortex-A, and cloud platforms such as AWS, Microsoft
+      Azure, Google Cloud, and Oracle.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Install Arm SoC Migration Power, Develop on source
+      platform, Migrate using Arm SoC Migration Power, and Validate migration with testing.
+# END generated_summary_faq
+
 author: Daniel Schleicher
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md b/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
index 018176c114..bab06b2e72 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
@@ -22,6 +22,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v2
+  generated_at: '2026-05-12T18:20:22Z'
+  generator: template
+  source_hash: 67004eb9a75926144eb6f75124104972b3cf20a57a22b698c9359d20db3eca02
+  summary_generated_at: '2026-05-12T18:20:22Z'
+  summary_source_hash: 67004eb9a75926144eb6f75124104972b3cf20a57a22b698c9359d20db3eca02
+  faq_generated_at: '2026-05-12T18:20:22Z'
+  faq_source_hash: 67004eb9a75926144eb6f75124104972b3cf20a57a22b698c9359d20db3eca02
+  summary: >-
+    Learn how to install and configure a Linux kernel with 64K page size support on Arm systems
+    to improve memory efficiency and performance for memory-intensive workloads. It is designed
+    for developers who want to modify the Linux kernel page size on Arm-based systems to improve
+    performance for memory-intensive workloads. By the end, you will be able to explain the differences
+    in page size configuration between Arm64 and x86 architectures, understand how page size affects
+    memory efficiency and system performance, and check the current memory page size on an Arm-based
+    Linux system. It focuses on tools and technologies such as bash, Linux environments, and Arm
+    platforms including Neoverse. The main steps cover Overview, Change page size on Ubuntu, Change
+    page size on Debian, and Change page size on CentOS.
+  faqs:
+  - question: What will you accomplish in this Learning Path?
+    answer: >-
+      You will explain the differences in page size configuration between Arm64 and x86 architectures,
+      understand how page size affects memory efficiency and system performance, and check the
+      current memory page size on an Arm-based Linux system. Learn how to install and configure
+      a Linux kernel with 64K page size support on Arm systems to improve memory efficiency and
+      performance for memory-intensive workloads.
+  - question: Who is this Learning Path for?
+    answer: >-
+      This is an introductory topic for developers who want to modify the Linux kernel page size
+      on Arm-based systems to improve performance for memory-intensive workloads.
+  - question: What do you need before you start?
+    answer: >-
+      Before you start, make sure you have the following: Access to an Arm-based Linux system
+      running Ubuntu, Debian, or CentOS.
+  - question: Which tools, languages, or platforms does it cover?
+    answer: >-
+      It covers tools and languages including bash, Linux environments, and Arm platforms such
+      as Neoverse.
+  - question: How is the Learning Path structured?
+    answer: >-
+      The Learning Path is organized around Overview, Change page size on Ubuntu, Change page
+      size on Debian, and Change page size on CentOS.
+# END generated_summary_faq
+
 author: Geremy Cohen
 
 ### Tags
diff --git a/reports/generated-summary-faq/latest-run.yml b/reports/generated-summary-faq/latest-run.yml
index ce23f4ae32..6ade0b66ab 100644
--- a/reports/generated-summary-faq/latest-run.yml
+++ b/reports/generated-summary-faq/latest-run.yml
@@ -1,13 +1,13 @@
 latest_run:
-  timestamp: '2026-05-12T18:09:37Z'
-  mode: dry-run
+  timestamp: '2026-05-12T18:20:25Z'
+  mode: write
   require_enable_flag: true
   path_filter: ''
   limit: 0
-  run_url: ''
-  git_ref: ''
-  git_sha: ''
-  actor: ''
+  run_url: https://github.com/chrismoroney/arm-learning-paths/actions/runs/25753610365
+  git_ref: STESOL-345
+  git_sha: f79ee7e5f40a1d01f4a801847647eec92d7f41cc
+  actor: chrismoroney
   template_version: summary-faq-v2
   totals:
     processed: 407
@@ -11080,7 +11080,7 @@ latest_run:
       source_hash_after: 275878b5aa4c27dee394da12ad8b80e4c0d0235b3ddce66b1fe3f0e056ef3da9
       current_source_hash: 275878b5aa4c27dee394da12ad8b80e4c0d0235b3ddce66b1fe3f0e056ef3da9
       generated_at_before: ''
-      generated_at_after: '2026-05-12T18:09:36Z'
+      generated_at_after: '2026-05-12T18:20:22Z'
       preview_before: ''
       preview_after: Learn how to build and deploy an AI agent application on Arm servers using llama.cpp
         and llama-cpp-agent with KleidiAI optimization for efficient LLM inference and function calling.
@@ -11098,7 +11098,7 @@ latest_run:
       source_hash_after: 275878b5aa4c27dee394da12ad8b80e4c0d0235b3ddce66b1fe3f0e056ef3da9
       current_source_hash: 275878b5aa4c27dee394da12ad8b80e4c0d0235b3ddce66b1fe3f0e056ef3da9
       generated_at_before: ''
-      generated_at_after: '2026-05-12T18:09:36Z'
+      generated_at_after: '2026-05-12T18:20:22Z'
       before_count: 0
       after_count: 5
       generated_count: 5
@@ -11143,7 +11143,7 @@ latest_run:
       source_hash_after: 01c5cc8930650a8d81d67d4c48b62e0f9d7b1ebfc2d09a552e11046fb982fc52
       current_source_hash: 01c5cc8930650a8d81d67d4c48b62e0f9d7b1ebfc2d09a552e11046fb982fc52
       generated_at_before: ''
-      generated_at_after: '2026-05-12T18:09:36Z'
+      generated_at_after: '2026-05-12T18:20:22Z'
       preview_before: ''
       preview_after: Learn how to automate the deployment of an Arm-based Kubernetes cluster on Azure
         AKS using Terraform and deploy a sample WordPress application as a workload. It is designed for
@@ -11161,7 +11161,7 @@ latest_run:
       source_hash_after: 01c5cc8930650a8d81d67d4c48b62e0f9d7b1ebfc2d09a552e11046fb982fc52
       current_source_hash: 01c5cc8930650a8d81d67d4c48b62e0f9d7b1ebfc2d09a552e11046fb982fc52
       generated_at_before: ''
-      generated_at_after: '2026-05-12T18:09:36Z'
+      generated_at_after: '2026-05-12T18:20:22Z'
       before_count: 0
       after_count: 5
       generated_count: 5
@@ -11206,7 +11206,7 @@ latest_run:
       source_hash_after: 90b638385267e085ea07a70a1c23f16578aa3caaeabeca1f651bcdcac5bf38fb
       current_source_hash: 90b638385267e085ea07a70a1c23f16578aa3caaeabeca1f651bcdcac5bf38fb
       generated_at_before: ''
-      generated_at_after: '2026-05-12T18:09:36Z'
+      generated_at_after: '2026-05-12T18:20:22Z'
       preview_before: ''
       preview_after: Learn how to deploy Apache Arrow and Arrow Flight on Google Cloud Axion C4A processors
         for high-throughput columnar data processing and low-latency data transport with MinIO integration.
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+      preview_before: Learn how to compare Arm Neoverse and Intel x86 top-down performance analysis methodologies
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+        It is designe...
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+        using PMU counters, Linux Perf, and topdown-tool to identify bottlenecks across architectures.
+        It is designe...
+      preview_generated: Learn how to compare Arm Neoverse and Intel x86 top-down performance analysis
+        methodologies using PMU counters, Linux Perf, and topdown-tool to identify bottlenecks across
+        architectures. It is designe...
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+      preview_after: Learn how to migrate x86-64 SIMD code to Arm64 by mapping Intel SSE/AVX to Arm Neon,
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+        It is designed for...
+      preview_generated: Learn how to migrate x86-64 SIMD code to Arm64 by mapping Intel SSE/AVX to Arm
+        Neon, SVE, and SME, with code examples and migration strategies using autovectorization or intrinsics.
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+        It is designed for C...
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+      preview_before: Learn how to sample and profile CPU instructions using WindowsPerf with Arm Statistical
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+        Profiling Extension (SPE) on Windows on Arm, demonstrated with CPython workload analysis. It is
+        designed for dev...
+      preview_generated: Learn how to sample and profile CPU instructions using WindowsPerf with Arm Statistical
+        Profiling Extension (SPE) on Windows on Arm, demonstrated with CPython workload analysis. It is
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+        Azure using the Azure Marketplace and RDP. It is designed for software developers interested using
+        Windows on Arm in th...
+      preview_after: Learn how to create and connect to a Windows on Arm virtual machine in Microsoft
+        Azure using the Azure Marketplace and RDP. It is designed for software developers interested using
+        Windows on Arm in th...
+      preview_generated: Learn how to create and connect to a Windows on Arm virtual machine in Microsoft
+        Azure using the Azure Marketplace and RDP. It is designed for software developers interested using
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+      preview_before: Learn how to build and deploy distributed Zenoh systems on Arm devices like Raspberry
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+        is designed for ro...
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+        Pi, using pub/sub, storage, and queryable models for scalable robotics and IoT applications. It
+        is designed for ro...
+      preview_generated: Learn how to build and deploy distributed Zenoh systems on Arm devices like Raspberry
+        Pi, using pub/sub, storage, and queryable models for scalable robotics and IoT applications. It
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+      preview_before: Learn how to design and integrate a custom AXI-Lite peripheral with a Cortex-A9
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+        inputs. It is designed...
+      preview_after: Learn how to design and integrate a custom AXI-Lite peripheral with a Cortex-A9 processor
+        on the Zybo Z7-10 board using Vivado, configuring GPIOs to control LEDs based on switch inputs.
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+      preview_generated: Learn how to design and integrate a custom AXI-Lite peripheral with a Cortex-A9
+        processor on the Zybo Z7-10 board using Vivado, configuring GPIOs to control LEDs based on switch
+        inputs. It is designed...
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+  - path: content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
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+      preview_before: Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit and
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+        a neural network model t...
+      preview_after: Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit and
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+      preview_generated: Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit
+        and run inference on the Ethos-U85 NPU. It is designed for embedded developers who want to deploy
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+        who are interes...
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+        developers interested...
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+        developers interested...
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+        developers interested ...
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+      preview_generated: Learn how to build Matter reference examples on Arm Virtual Hardware, demonstrate
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+        new to Arm ...
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+  - path: content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
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+        DB, and Azure Fun...
+      preview_generated: Learn how to build a complete IoT solution in Azure that streams, stores, monitors,
+        aggregates, and visualizes telemetry data from Arm devices using IoT Hub, Stream Analytics, Cosmos
+        DB, and Azure Fun...
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+        and Fixed Virtual Platforms, and test the containerized Arm development environment. It is designed
+        for embedded s...
+      preview_generated: Learn how to create a Dockerfile, build a Docker image with Arm Compiler for
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+        It is designed for embedded s...
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+        commands. It is designed...
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+        commands. It is designed...
+      preview_generated: Learn how to collect and preprocess audio data using Edge Impulse, train an audio
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+        commands. It is designed...
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+      preview_before: Develop a image classification neural network model and deploy it on an STM32 B-L475E-IOT01A2
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+        for mi...
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+        board. It is designed for embedded software developers interested in building neural network models
+        for mi...
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+        hardware options for software development on Arm Cortex-M processors. It is designed for software
+        developers worki...
+      preview_generated: Learn where Arm architecture is used in microcontrollers and discover microcontroller
+        hardware options for software development on Arm Cortex-M processors. It is designed for software
+        developers worki...
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+      preview_before: Learn what differentiates TinyML from other AI domains, explore Arm-based edge devices
+        for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed Virtual
+        Platform. It is ...
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+        for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed Virtual
+        Platform. It is ...
+      preview_generated: Learn what differentiates TinyML from other AI domains, explore Arm-based edge
+        devices for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed
+        Virtual Platform. It is ...
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+  - path: content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
+    status: unchanged
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+      preview_before: Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300 virtual
+        hardware to understand complete IoT software stack construction. It is designed for embedded software
+        developers ...
+      preview_after: Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300 virtual
+        hardware to understand complete IoT software stack construction. It is designed for embedded software
+        developers ...
+      preview_generated: Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300
+        virtual hardware to understand complete IoT software stack construction. It is designed for embedded
+        software developers ...
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+  - path: content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
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+      preview_before: Learn how to set up a Jetson Orin Nano with a MIPI CSI-2 camera and perform real-time
+        object detection from live video and image files using DetectNet and TensorRT. It is designed
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+      preview_after: Learn how to set up a Jetson Orin Nano with a MIPI CSI-2 camera and perform real-time
+        object detection from live video and image files using DetectNet and TensorRT. It is designed
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+      preview_generated: Learn how to set up a Jetson Orin Nano with a MIPI CSI-2 camera and perform real-time
+        object detection from live video and image files using DetectNet and TensorRT. It is designed
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+  - path: content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
+    status: unchanged
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+      generated_at_after: '2026-05-06T17:17:54Z'
+      preview_before: Learn how to import, build, and debug your first Keil Studio Cloud project. It is
+        designed for embedded software developers new to Keil Studio Cloud. By the end, you will be able
+        to import and build a...
+      preview_after: Learn how to import, build, and debug your first Keil Studio Cloud project. It is
+        designed for embedded software developers new to Keil Studio Cloud. By the end, you will be able
+        to import and build a...
+      preview_generated: Learn how to import, build, and debug your first Keil Studio Cloud project. It
+        is designed for embedded software developers new to Keil Studio Cloud. By the end, you will be
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+  - path: content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
+    status: unchanged
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+      preview_before: Learn how to boot and configure the NXP FRDM i.MX 93 Arm board with Linux, create
+        a user with sudo access, connect to WiFi using ConnMan, and transfer files over the network. It
+        is designed for embedd...
+      preview_after: Learn how to boot and configure the NXP FRDM i.MX 93 Arm board with Linux, create
+        a user with sudo access, connect to WiFi using ConnMan, and transfer files over the network. It
+        is designed for embedd...
+      preview_generated: Learn how to boot and configure the NXP FRDM i.MX 93 Arm board with Linux, create
+        a user with sudo access, connect to WiFi using ConnMan, and transfer files over the network. It
+        is designed for embedd...
+    faqs:
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+      preview_after: Learn how to migrate CMSIS v5 projects to CMSIS v6 by identifying supported toolchains,
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+      preview_generated: Learn how to run large language models locally on the Raspberry Pi 5 using Ollama,
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+        and plays audio resp...
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+        and plays audio resp...
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+        API, and plays audio resp...
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+      preview_generated: Learn how to compile the Llama 3 large language model using ExecuTorch, deploy
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+        it to a Raspb...
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+      preview_generated: Learn how to reduce compile time for embedded Linux projects by installing a
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+      preview_before: Setup tools and start programming with Raspberry Pi Pico. It is designed for embedded
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+        software developers new to Raspberry Pi Pico. By the end, you will be able to install the Raspberry
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+      preview_before: Learn how to profile Linux kernel modules using Arm Streamline to identify performance
+        bottlenecks, analyze both out-of-tree and in-tree modules, and use Statistical Profiling Extension
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+        bottlenecks, analyze both out-of-tree and in-tree modules, and use Statistical Profiling Extension
+        (SPE) for deep...
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+      preview_generated: Build a letter recognition neural network model using TensorFlow and deploy it
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+      preview_before: Learn how to provision Arm64 and x86_64 VM instances on Google Cloud, then install
+        and use Sweet and Benchstat to measure and compare Go application performance. It is designed
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+        for This introductory t...
+      preview_generated: Learn how to provision Arm64 and x86_64 VM instances on Google Cloud, then install
+        and use Sweet and Benchstat to measure and compare Go application performance. It is designed
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+        DevOps engineer...
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+      preview_generated: Learn where Arm architecture is used in servers and cloud computing, and find
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+      preview_generated: Analyze and optimize interrupt request (IRQ) patterns on Arm Linux servers to
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+        aiming to opti...
+      preview_generated: Monitor, interpret, and optimize Java Garbage Collector performance on Arm servers
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+        aiming to opti...
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+      preview_generated: Deploy and optimize Java applications on Google Cloud Axion processors by testing
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+        and benchmar...
+      preview_generated: Deploy Java on Azure Cobalt 100 Arm virtual machines and benchmark application
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+        deployment and benchmar...
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+      preview_generated: Profile Java applications on Arm Neoverse servers using flame graphs generated
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+      preview_generated: Deploy Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate
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+      preview_generated: Identify and migrate container images in a Kubernetes cluster to Arm-compatible
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+      preview_generated: Understand Large System Extensions (LSE) for Arm processors and verify whether
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+        Amazon RDS, and automation with Terraform and Ansible. It is designed for software developers
+        who want to deploy...
+      preview_generated: Deploy MariaDB on Arm cloud instances across AWS, Azure, and Google Cloud using
+        Docker, Amazon RDS, and automation with Terraform and Ansible. It is designed for software developers
+        who want to deploy...
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+      preview_after: Deploy Memcached as a cache for MySQL and PostgreSQL on Arm servers. It is designed
+        for developers who want to use memcached as their in-memory key-value store. By the end, you will
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+      preview_generated: Deploy Memcached as a cache for MySQL and PostgreSQL on Arm servers. It is designed
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+        developers and perfo...
+      preview_generated: Use ASCT to measure cache latency, streaming bandwidth, and coherency latency
+        on Arm Neoverse systems, and compare results across Graviton generations. It is designed for software
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+        Herd7, Litmus7, and Arm hardware with assembly snippets. It is designed for developers seeking
+        practical ways to test ...
+      preview_generated: Test and validate thread synchronization approaches in the Arm memory model using
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+  - path: content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
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+        Path source content so the workflow report records preserved summary drift for this LP.
+      preview_generated: Microbenchmark and tune network performance with iPerf3 and Linux traffic control
+        walks you through an end-to-end Arm software workflow. It is designed for performance engineers,
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+        - What will you accomplish in this Learning Path?
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+        and the MLPerf Inference benchmark suite from MLCommons. It is designed for software developers
+        interested in benchmark...
+      preview_generated: Benchmark machine learning inference performance on Arm servers using TensorFlow
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+        Serving Benchmark (YCSB) to compare against other architectures. It is designed for software developers
+        who want to ...
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+        walks you through an end-to-end Arm software workflow. It is designed for software developers
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+        Arm software workflow. It is designed for software developers migrating Node.js workloads from
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+      preview_generated: Deploy Node.js on Google Cloud C4A Arm-based Axion VMs walks you through an end-to-end
+        Arm software workflow. It is designed for software developers migrating Node.js workloads from
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+        through an end-to-end Arm software workflow. It is designed for software developers who are new
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+        end-to-end Arm software workflow. It is designed for developers, ML engineers, and cloud practitioners
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+      preview_generated: Deploy SqueezeNet 1.0 INT8 model with ONNX Runtime on Azure Cobalt 100 walks
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   mode: dry-run
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From 053e12a298ddc2a65685ec21374a58558ecb7ba2 Mon Sep 17 00:00:00 2001
From: Christopher Moroney <christopher.moroney1@gmail.com>
Date: Fri, 15 May 2026 15:01:49 -0700
Subject: [PATCH 16/23] prepare fresh run, use LLM run

---
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 .../arm-soc-migration-learning-path/_index.md |  55 --
 .../arm_linux_page_size/_index.md             |  45 --
 .../arm_pmu/_index.md                         |  46 --
 .../aws-copilot/_index.md                     |  40 +-
 .../aws-terraform/_index.md                   |  42 +-
 .../azure-arm-template/_index.md              |  46 +-
 .../azure-cobalt-cicd-aks/_index.md           |  44 +-
 .../azure-terraform/_index.md                 |  40 +-
 .../azure-vm/_index.md                        |  44 +-
 .../benchmark-nlp/_index.md                   |  47 +-
 .../bitmap_scan_sve2/_index.md                |  46 +-
 .../bolt-demo/_index.md                       |  49 +-
 .../bolt-merge/_index.md                      |  44 +-
 .../bolt/_index.md                            |  48 --
 .../buildkite-gcp/_index.md                   |  51 +-
 .../cassandra-on-gcp/_index.md                |  47 +-
 .../cca-container/_index.md                   |  43 +-
 .../cca-device-attach/_index.md               |  48 +-
 .../cca-essentials/_index.md                  |  49 +-
 .../cca-kata/_index.md                        |  47 +-
 .../cca-trustee/_index.md                     |  47 --
 .../cca-veraison-aws/_index.md                |  43 --
 .../cca-veraison/_index.md                    |  41 +-
 .../circleci-gcp/_index.md                    |  50 +-
 .../circleci-on-aws/_index.md                 |  46 +-
 .../clair/_index.md                           |  42 +-
 .../clickhouse-gcp/_index.md                  |  51 +-
 .../clickhouse/_index.md                      |  40 +-
 .../cobalt/_index.md                          |  43 +-
 .../codebuild/_index.md                       |  43 +-
 .../codec/_index.md                           |  40 +-
 .../codec1/_index.md                          |  38 +-
 .../couchbase-on-gcp/_index.md                |  46 +-
 .../cplusplus_compilers_flags/_index.md       |  38 --
 .../cpp-profile-guided-optimisation/_index.md |  41 +-
 .../cpu_hotspot_performix/_index.md           |  39 --
 .../servers-and-cloud-computing/csp/_index.md |  39 +-
 .../deepseek-cpu/_index.md                    |  42 +-
 .../disk-io-benchmark/_index.md               |  43 +-
 .../_index.md                                 |  45 +-
 .../django-on-gcp/_index.md                   |  51 +-
 .../django/_index.md                          |  46 --
 .../dlrm/_index.md                            |  41 --
 .../docker-mcp-toolkit/_index.md              |  54 +-
 .../dotnet-migration/_index.md                |  45 +-
 .../dynatrace-azure/_index.md                 |  50 +-
 .../servers-and-cloud-computing/ecs/_index.md |  41 +-
 .../eks-multi-arch/_index.md                  |  45 +-
 .../servers-and-cloud-computing/eks/_index.md |  38 +-
 .../envoy-gcp/_index.md                       |  46 +-
 .../envoy/_index.md                           |  38 +-
 .../envoy_tune/_index.md                      |  37 +-
 .../_index.md                                 |  44 +-
 .../false-sharing-arm-spe/_index.md           |  47 +-
 .../fastpath/_index.md                        |  44 +-
 .../fexpa/_index.md                           |  44 +-
 .../flink-on-gcp/_index.md                    |  47 +-
 .../flink/_index.md                           |  39 --
 .../flyte-with-grpc/_index.md                 |  48 +-
 .../from-iot-to-the-cloud-part1/_index.md     |  48 +-
 .../from-iot-to-the-cloud-part2/_index.md     |  44 +-
 .../from-iot-to-the-cloud-part3/_index.md     |  41 --
 .../from-iot-to-the-cloud-part4/_index.md     |  44 +-
 .../funasr/_index.md                          |  48 +-
 .../gardener-gcp/_index.md                    |  50 +-
 .../gcc-lto/_index.md                         |  39 +-
 .../servers-and-cloud-computing/gcp/_index.md |  43 +-
 .../geekbench/_index.md                       |  37 +-
 .../gh-runners/_index.md                      |  49 +-
 .../github-actions-runner/_index.md           |  39 +-
 .../github-on-arm/_index.md                   |  43 +-
 .../gke-multi-arch-axion/_index.md            |  49 +-
 .../gke-multi-arch/_index.md                  |  43 +-
 .../servers-and-cloud-computing/gke/_index.md |  37 +-
 .../glibc-with-lse/_index.md                  |  42 +-
 .../go-benchmarking-with-sweet/_index.md      |  46 +-
 .../golang-on-azure/_index.md                 |  51 +-
 .../helm-on-gcp/_index.md                     |  50 +-
 .../intro/_index.md                           |  36 +-
 .../irq-tuning-guide/_index.md                |  41 +-
 .../java-gc-tuning/_index.md                  |  44 +-
 .../java-on-axion/_index.md                   |  44 +-
 .../java-on-azure/_index.md                   |  45 +-
 .../java-perf-flamegraph/_index.md            |  42 +-
 .../jenkins/_index.md                         |  51 +-
 .../kafka-azure/_index.md                     |  46 +-
 .../kafka/_index.md                           |  41 +-
 .../kedify-http-autoscaling/_index.md         |  44 +-
 .../keras-core/_index.md                      |  43 +-
 .../kernel-build/_index.md                    |  42 +-
 .../kubearchinspect/_index.md                 |  42 +-
 .../lambda_functions/_index.md                |  37 +-
 .../libhugetlbfs/_index.md                    |  38 --
 .../llama-cpu/_index.md                       |  41 +-
 .../llama-vision/_index.md                    |  49 +-
 .../llama_cpp_streamline/_index.md            |  47 +-
 .../servers-and-cloud-computing/lse/_index.md |  40 +-
 .../mariadb/_index.md                         |  46 +-
 .../memcached/_index.md                       |  37 +-
 .../memcached_cache/_index.md                 |  50 +-
 .../memory-subsystem/_index.md                |  50 +-
 .../memory_consistency/_index.md              |  46 +-
 .../microbenchmark-network-iperf3/_index.md   |  35 +-
 .../migrate-ease/_index.md                    |  43 +-
 .../migration/_index.md                       |  43 +-
 .../milvus-rag/_index.md                      |  42 +-
 .../minio-cobalt/_index.md                    |  47 +-
 .../ml-perf/_index.md                         |  39 +-
 .../mongodb-on-azure/_index.md                |  44 +-
 .../mongodb-on-gcp/_index.md                  |  42 +-
 .../mongodb/_index.md                         |  46 +-
 .../servers-and-cloud-computing/mpi/_index.md |  40 +-
 .../multi-accuracy-libamath/_index.md         |  42 +-
 .../multiarch_nginx_on_aks/_index.md          |  46 +-
 .../multiarch_ollama_on_gke/_index.md         |  46 +-
 .../mysql-azure/_index.md                     |  42 +-
 .../mysql/_index.md                           |  35 +-
 .../mysql_benchmark/_index.md                 |  39 +-
 .../mysql_tune/_index.md                      |  37 +-
 .../neoverse-rdv3-swstack/_index.md           |  49 +-
 .../net-aspire/_index.md                      |  45 +-
 .../nginx-on-azure/_index.md                  |  42 +-
 .../nginx/_index.md                           |  42 +-
 .../nginx_tune/_index.md                      |  41 --
 .../nlp-hugging-face/_index.md                |  40 +-
 .../node-js-gcp/_index.md                     |  46 +-
 .../oci-terraform/_index.md                   |  35 +-
 .../onnx-on-azure/_index.md                   |  42 +-
 .../onnx/_index.md                            |  41 +-
 .../openbmc-rdv3/_index.md                    |  52 +-
 .../openrng-with-performix/_index.md          |  47 +-
 .../openshift/_index.md                       |  36 +-
 .../openstack-on-azure/_index.md              |  51 +-
 .../opentelemetry/_index.md                   |  49 +-
 .../servers-and-cloud-computing/pac/_index.md |  45 +-
 .../performix-mcp-agent/_index.md             |  52 +-
 .../performix-microarchitecture/_index.md     |  43 +-
 .../php-on-gcp/_index.md                      |  44 +-
 .../pinning-threads/_index.md                 |  45 +-
 .../pmuv3_plugin_learning_path/_index.md      |  43 +-
 .../postgresql-cobalt/_index.md               |  49 +-
 .../postgresql/_index.md                      |  36 +-
 .../postgresql_tune/_index.md                 |  37 +-
 .../processwatch/_index.md                    |  44 +-
 .../profiling-for-neoverse/_index.md          |  38 --
 .../puppet-on-gcp/_index.md                   |  45 +-
 .../pytorch-llama/_index.md                   |  41 +-
 .../qdrant-on-axion/_index.md                 |  48 +-
 .../rabbitmq-gcp/_index.md                    |  49 +-
 .../servers-and-cloud-computing/rag/_index.md |  46 +-
 .../servers-and-cloud-computing/ran/_index.md |  35 +-
 .../ray-on-axion/_index.md                    |  48 +-
 .../redis-cobalt/_index.md                    |  48 +-
 .../redis-data-searching/_index.md            |  43 +-
 .../redis/_index.md                           |  38 +-
 .../redis_cache/_index.md                     |  49 +-
 .../redis_tune/_index.md                      |  39 +-
 .../refinfra-debug/_index.md                  |  38 +-
 .../refinfra-quick-start/_index.md            |  39 +-
 .../reproducible-libamath/_index.md           |  44 +-
 .../rme-cca-basics/_index.md                  |  40 +-
 .../rtp-llm/_index.md                         |  42 +-
 .../ruby-on-rails/_index.md                   |  45 +-
 .../rust-on-gcp/_index.md                     |  46 +-
 .../sentiment-analysis-eks/_index.md          |  42 +-
 .../serverless-framework-aws-intro/_index.md  |  39 +-
 .../_index.md                                 |  42 +-
 .../serverless-framework-aws-s3/_index.md     |  39 --
 .../snappy/_index.md                          |  36 +-
 .../snort3-multithreading/_index.md           |  39 --
 .../spark-on-azure/_index.md                  |  49 +-
 .../spark-on-gcp/_index.md                    |  49 +-
 .../spark/_index.md                           |  36 +-
 .../supervisord/_index.md                     |  41 +-
 .../servers-and-cloud-computing/sve/_index.md |  41 +-
 .../sve2-match/_index.md                      |  42 +-
 .../sysreport/_index.md                       |  41 +-
 .../tensorflow-gcp/_index.md                  |  48 --
 .../thirdai-sentiment-analysis/_index.md      |  38 +-
 .../timescaledb-on-gcp/_index.md              |  46 --
 .../top-down-n1/_index.md                     |  40 --
 .../torchbench/_index.md                      |  42 +-
 .../triggering-pmu-events-2/_index.md         |  37 +-
 .../triggering-pmu-events/_index.md           |  38 +-
 .../trivy-on-gcpp/_index.md                   |  44 +-
 .../_index.md                                 |  41 +-
 .../typescript-on-gcp/_index.md               |  45 --
 .../_index.md                                 |  40 +-
 .../vectorscan/_index.md                      |  37 --
 .../vllm-acceleration/_index.md               |  48 +-
 .../vllm/_index.md                            |  40 +-
 .../vvenc/_index.md                           |  39 +-
 .../whisper/_index.md                         |  43 +-
 .../wordpress/_index.md                       |  34 +-
 .../zlib/_index.md                            |  45 +-
 .../test-helper-automotive.yml                | 612 ++++++++++++++++++
 .../learning-paths/generated-summary-faq.html |  85 ++-
 tools/generate_summary_faq.py                 | 449 ++++++++++++-
 tools/prompts/summary_faq_system.md           |  23 +
 tools/prompts/summary_faq_user.md             |  13 +
 tools/test_summary_faq_ai_local.sh            | 162 +++++
 416 files changed, 1758 insertions(+), 17178 deletions(-)
 create mode 100644 reports/generated-summary-faq/test-helper-automotive.yml
 create mode 100644 tools/prompts/summary_faq_system.md
 create mode 100644 tools/prompts/summary_faq_user.md
 create mode 100755 tools/test_summary_faq_ai_local.sh

diff --git a/.github/workflows/generate-summary-faq.yml b/.github/workflows/generate-summary-faq.yml
index b3195751f0..aa67357f54 100644
--- a/.github/workflows/generate-summary-faq.yml
+++ b/.github/workflows/generate-summary-faq.yml
@@ -17,6 +17,19 @@ on:
         required: true
         default: true
         type: boolean
+      generation_mode:
+        description: "Use ai for the Arm OpenAI proxy, or template for local deterministic fallback."
+        required: true
+        default: ai
+        type: choice
+        options:
+          - ai
+          - template
+      openai_model:
+        description: "Model or deployment name exposed by the configured OpenAI-compatible endpoint."
+        required: true
+        default: gpt-4.1-mini
+        type: string
       dry_run:
         description: "Generate the report without changing any _index.md files."
         required: true
@@ -53,6 +66,10 @@ jobs:
           INPUT_PATHS: ${{ inputs.paths }}
           INPUT_LIMIT: ${{ inputs.limit }}
           INPUT_REQUIRE_FLAG: ${{ inputs.require_flag }}
+          INPUT_GENERATION_MODE: ${{ inputs.generation_mode }}
+          INPUT_OPENAI_MODEL: ${{ inputs.openai_model }}
+          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+          OPENAI_BASE_URL: ${{ vars.OPENAI_BASE_URL || 'https://openai-api-proxy.geo.arm.com/api/providers/openai/v1/responses/' }}
           INPUT_DRY_RUN: ${{ inputs.dry_run }}
           REPORT_FILE: reports/generated-summary-faq/latest-run.yml
           RUN_URL: ${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}
@@ -64,6 +81,9 @@ jobs:
             python tools/generate_summary_faq.py
             --path-filter "$INPUT_PATHS"
             --limit "$INPUT_LIMIT"
+            --generation-mode "$INPUT_GENERATION_MODE"
+            --openai-base-url "$OPENAI_BASE_URL"
+            --openai-model "$INPUT_OPENAI_MODEL"
             --report-file "$REPORT_FILE"
             --run-url "$RUN_URL"
             --git-ref "$GIT_REF_NAME"
diff --git a/.gitignore b/.gitignore
index 261203bea1..312349ca93 100644
--- a/.gitignore
+++ b/.gitignore
@@ -27,3 +27,5 @@ tags
 
 # Generated spell check config
 .spellcheck-non-draft.yml
+reports/generated-summary-faq/local-test.yml
+reports/generated-summary-faq/*.txt
diff --git a/assets/css/content-pages.css b/assets/css/content-pages.css
index 6e95ef59d9..bd70a6f1b1 100644
--- a/assets/css/content-pages.css
+++ b/assets/css/content-pages.css
@@ -261,6 +261,73 @@ html[theme='dark'] .incorrect-explain {color: #e86868} /* 27% lighter than arm's
     color: var(--arm-green);
 }
 
+
+
+/* Generated AI-assisted summary/FAQ block
+*******************************************************************/
+.generated-summary-faq__label {
+    align-items: center;
+    display: flex;
+    gap: 8px;
+}
+
+.generated-summary-faq__badge {
+    border: 1px solid var(--arm-web-safe-blue);
+    color: var(--arm-web-safe-blue);
+    display: inline-block;
+    font-size: 0.8rem;
+    font-weight: 700;
+    line-height: 1;
+    padding: 5px 8px;
+    text-transform: uppercase;
+}
+
+html[theme='dark'] .generated-summary-faq__badge {
+    border-color: var(--arm-light-blue);
+    color: var(--arm-light-blue);
+}
+
+.generated-summary-faq__info {
+    align-items: center;
+    background: transparent;
+    border: 1px solid var(--arm-web-safe-blue);
+    color: var(--arm-web-safe-blue);
+    cursor: pointer;
+    display: inline-flex;
+    font-size: 0.8rem;
+    font-weight: 700;
+    height: 24px;
+    justify-content: center;
+    padding: 0;
+    width: 24px;
+}
+
+.generated-summary-faq__info:hover {
+    border-color: var(--arm-green);
+    color: var(--arm-green);
+}
+
+html[theme='dark'] .generated-summary-faq__info {
+    border-color: var(--arm-light-blue);
+    color: var(--arm-light-blue);
+}
+
+ads-expansion-panel.generated-summary-faq__panel {
+    --ads-expansion-panel-border-color: var(--arm-light-grey);
+    --ads-expansion-panel-content-background-color: transparent;
+    --ads-expansion-panel-toggle-background-color: transparent;
+    --ads-expansion-panel-toggle-background-color-hover: transparent;
+    --ads-expansion-panel-toggle-icon-color-hover: var(--arm-green);
+}
+
+html[theme='dark'] ads-expansion-panel.generated-summary-faq__panel {
+    --ads-expansion-panel-border-color: rgba(163, 168, 174, 0.25);
+    --ads-expansion-panel-content-background-color: transparent;
+    --ads-expansion-panel-toggle-background-color: transparent;
+    --ads-expansion-panel-toggle-background-color-hover: transparent;
+    --ads-expansion-panel-toggle-icon-color-hover: var(--arm-green);
+}
+
 .share-icon {
     width: 36px;
     height: 36px;
diff --git a/content/learning-paths/automotive/openadkit1_container/_index.md b/content/learning-paths/automotive/openadkit1_container/_index.md
index d2af4d057e..1d08a09b58 100644
--- a/content/learning-paths/automotive/openadkit1_container/_index.md
+++ b/content/learning-paths/automotive/openadkit1_container/_index.md
@@ -19,51 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:53Z'
-  generator: template
-  source_hash: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
-  summary: >-
-    Learn how to deploy and run containerized autonomous driving simulations using Autoware Open
-    AD Kit on Arm Neoverse with Docker, demonstrating SOAFEE-based Shift-Left development workflows.
-    It is designed for automotive developers, aimed at helping them accelerate autonomous driving
-    software development before automotive hardware is available. By the end, you will be able
-    to understand the SOAFEE architecture and its role in supporting Shift-Left software development
-    strategies to optimize the autonomous driving development process, use the Autoware Open AD
-    Kit simulation environment, and run containerized workloads on Arm Neoverse processors with
-    Docker, supporting execution on both cloud-based and on-premise servers. It focuses on tools
-    and technologies such as Python, Docker, and ROS 2, Linux environments, and Arm platforms
-    including Neoverse. The main steps cover About Software-Defined Vehicles and SOAFEE, Learn
-    about ROS 2 and Open AD Kit, Set up Open AD Kit, and Run the Open AD Kit demo.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand the SOAFEE architecture and its role in supporting Shift-Left software
-      development strategies to optimize the autonomous driving development process, use the Autoware
-      Open AD Kit simulation environment, and run containerized workloads on Arm Neoverse processors
-      with Docker, supporting execution on both cloud-based and on-premise servers. Learn how
-      to deploy and run containerized autonomous driving simulations using Autoware Open AD Kit
-      on Arm Neoverse with Docker, demonstrating SOAFEE-based Shift-Left development workflows.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for automotive developers, aimed at helping them accelerate
-      autonomous driving software development before automotive hardware is available.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm Neoverse cloud instance, or a
-      local Arm Neoverse Linux computer with at least 16 CPUs and 32GB of RAM; Familiarity with
-      Docker and Docker Compose.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Python, Docker, and ROS 2, Linux environments, and
-      Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around About Software-Defined Vehicles and SOAFEE, Learn
-      about ROS 2 and Open AD Kit, Set up Open AD Kit, and Run the Open AD Kit demo.
-# END generated_summary_faq
+
 
 author: Odin Shen
 
diff --git a/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md b/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
index 3e59b216d8..84f0614e79 100644
--- a/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
+++ b/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
@@ -20,60 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:53Z'
-  generator: template
-  source_hash: 92a6dac2b1674a44cd623a0c8c3189b38438124a6067ed7ca776999ff1d8b5bf
-  summary: >-
-    Learn how to implement functional safety isolation for autonomous driving systems on Arm Neoverse
-    using DDS-based communication, containerized deployment, and ISO 26262 compliance principles.
-    It is designed for automotive engineers developing safety-critical systems. You'll learn how
-    to accelerate ISO 26262-compliant development workflows using Arm-based cloud compute, containerized
-    simulation, and DDS-based communication. By the end, you will be able to apply functional
-    safety principles, including risk prevention, fault detection, and ASIL compliance, to build
-    robust, certifiable automotive systems, use DDS and a publish-subscribe architecture for low-latency,
-    scalable, and fault-tolerant communication in autonomous driving systems, and implement distributed
-    development by separating the simulation platform into independent, safety-isolated components.
-    It focuses on tools and technologies such as Python, Docker, ROS 2, and DDS, Linux environments,
-    and Arm platforms including Neoverse. The main steps cover Why functional safety matters in
-    software systems, Understand functional safety risks, Apply ISO 26262 and ASIL levels, Implement
-    safety-critical isolation using safety island architecture, and Functional safety for automotive
-    software development.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will apply functional safety principles, including risk prevention, fault detection,
-      and ASIL compliance, to build robust, certifiable automotive systems, use DDS and a publish-subscribe
-      architecture for low-latency, scalable, and fault-tolerant communication in autonomous driving
-      systems, and implement distributed development by separating the simulation platform into
-      independent, safety-isolated components. Learn how to implement functional safety isolation
-      for autonomous driving systems on Arm Neoverse using DDS-based communication, containerized
-      deployment, and ISO 26262 compliance principles.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is for automotive engineers developing safety-critical systems. You'll
-      learn how to accelerate ISO 26262-compliant development workflows using Arm-based cloud
-      compute, containerized simulation, and DDS-based communication.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Access to two Arm-based Neoverse cloud
-      instances, or a local Arm Neoverse Linux system with at least 16 CPUs and 32 GB of RAM;
-      Completion of the [Deploy Open AD Kit containerized autonomous driving simulation on Arm
-      Neoverse](/learning-paths/automotive/openadkit1_container/) Learning Path; Basic familiarity
-      with Docker.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Python, Docker, ROS 2, and DDS, Linux environments,
-      and Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Why functional safety matters in software systems,
-      Understand functional safety risks, Apply ISO 26262 and ASIL levels, Implement safety-critical
-      isolation using safety island architecture, and Functional safety for automotive software
-      development.
-# END generated_summary_faq
+
 
 author: 
     - Odin Shen
diff --git a/content/learning-paths/automotive/system76-auto/_index.md b/content/learning-paths/automotive/system76-auto/_index.md
index bb2cf31f77..825f615b05 100644
--- a/content/learning-paths/automotive/system76-auto/_index.md
+++ b/content/learning-paths/automotive/system76-auto/_index.md
@@ -18,48 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:53Z'
-  generator: template
-  source_hash: 2b758d2dcf28a683ab164e28578a736d6d730b81dfbc01a6765619052fcdebd0
-  summary: >-
-    Learn how to build and run the Arm Automotive Solutions Software Reference Stack locally on
-    the System76 Thelio Astra desktop using Multipass virtualization and Yocto build tools. It
-    is designed for automotive developers interested in local development using the System76 Thelio
-    Astra Linux desktop computer. By the end, you will be able to create an efficient automotive
-    development environment on the System76 Thelio Astra desktop and build and run the Arm Automotive
-    Solutions Software Reference Stack locally. It focuses on tools and technologies such as Multipass,
-    Yocto, Docker, and Git, Linux environments, and Arm platforms including Neoverse. The main
-    steps cover Thelio Astra, Set up an automotive development environment, Arm Automotive Solutions
-    Software Reference Stack, Build the Arm Automotive Solutions Software Reference Stack, and
-    Parsec.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create an efficient automotive development environment on the System76 Thelio Astra
-      desktop and build and run the Arm Automotive Solutions Software Reference Stack locally.
-      Learn how to build and run the Arm Automotive Solutions Software Reference Stack locally
-      on the System76 Thelio Astra desktop using Multipass virtualization and Yocto build tools.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for automotive developers interested in local development
-      using the System76 Thelio Astra Linux desktop computer.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A System76 Thelio Astra desktop computer
-      running Ubuntu 24.04.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Multipass, Yocto, Docker, and Git, Linux environments,
-      and Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Thelio Astra, Set up an automotive development environment,
-      Arm Automotive Solutions Software Reference Stack, Build the Arm Automotive Solutions Software
-      Reference Stack, and Parsec.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/automotive/zenacssdebug/_index.md b/content/learning-paths/automotive/zenacssdebug/_index.md
index b7f49da42c..2660064c61 100644
--- a/content/learning-paths/automotive/zenacssdebug/_index.md
+++ b/content/learning-paths/automotive/zenacssdebug/_index.md
@@ -22,52 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:53Z'
-  generator: template
-  source_hash: 8dccdbdd4d9727f3466987ce918d9df0ed9d4d4f28cd613162bacab01d9c18f3
-  summary: >-
-    Learn how to debug the Arm Zena CSS Reference Software Stack using Arm Development Studio
-    on a Fixed Virtual Platform, covering RSE, Safety Island, and Linux kernel debugging workflows.
-    It is designed for This introductory topic is for software developers who want to use Arm
-    Development Studio to explore and debug the Arm Zena Compute Subsystem (CSS) Reference Software
-    Stack on a Fixed Virtual Platform (FVP). By the end, you will be able to set up and save a
-    debug configuration for the Arm Zena CSS FVP, start Runtime Security Engine (RSE) debug at
-    reset and step through early boot, and attach to and debug Safety Island (SI) firmware. It
-    focuses on tools and technologies such as Arm Development Studio, Arm Zena CSS, and FVP, Linux
-    environments, and Arm platforms including Cortex-A and Cortex-R. The main steps cover Getting
-    started, Launch the FVP, Configure the model, Create debug connections, and Debug RSE from
-    reset.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will set up and save a debug configuration for the Arm Zena CSS FVP, start Runtime Security
-      Engine (RSE) debug at reset and step through early boot, and attach to and debug Safety
-      Island (SI) firmware. Learn how to debug the Arm Zena CSS Reference Software Stack using
-      Arm Development Studio on a Fixed Virtual Platform, covering RSE, Safety Island, and Linux
-      kernel debugging workflows.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This introductory topic is for software developers who want to use Arm Development Studio
-      to explore and debug the Arm Zena Compute Subsystem (CSS) Reference Software Stack on a
-      Fixed Virtual Platform (FVP).
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Ubuntu 22.04 host machine; Arm Development
-      Studio 2024.1 or later with a valid license - for support see the [Install Guide for Arm
-      DS](/install-guides/armds); Basic understanding of the Arm Zena CSS software stack, Armv8-A/Armv9-A
-      cores, and Linux.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Arm Development Studio, Arm Zena CSS, and FVP, Linux
-      environments, and Arm platforms such as Cortex-A and Cortex-R.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Getting started, Launch the FVP, Configure the model,
-      Create debug connections, and Debug RSE from reset.
-# END generated_summary_faq
+
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/cross-platform/_example-learning-path/_index.md b/content/learning-paths/cross-platform/_example-learning-path/_index.md
index 123e3988a1..b11e1f3b27 100644
--- a/content/learning-paths/cross-platform/_example-learning-path/_index.md
+++ b/content/learning-paths/cross-platform/_example-learning-path/_index.md
@@ -20,41 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:53Z'
-  generator: template
-  source_hash: a58d5eb4c4256f3e4f4374344ef0e73836e8b51ac7223b0a5238b22137ec0819
-  summary: >-
-    Learn what type of content belongs in a Learning Path and how to format it. It is designed
-    for content creators and software developers who want to share Arm related information as
-    a step-by-step guide called a Learning Path. By the end, you will be able to understand what
-    type of content belongs in a Learning Path, set up the required tools for Learning Path creation,
-    and write and format your own Learning Path using markdown. It focuses on tools and technologies
-    such as Hugo. The main steps cover Learning Path basics, Learning Path setup, Create a new
-    Learning Path, Modify Learning Path metadata, and Contribute.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand what type of content belongs in a Learning Path, set up the required
-      tools for Learning Path creation, and write and format your own Learning Path using markdown.
-      Learn what type of content belongs in a Learning Path and how to format it.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This topic is for content creators and software developers who want to share Arm related
-      information as a step-by-step guide called a Learning Path.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [GitHub](https://github.com/) account.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Hugo.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Learning Path basics, Learning Path setup, Create
-      a new Learning Path, Modify Learning Path metadata, and Contribute.
-# END generated_summary_faq
+
 
 author: Zach Lasiuk
 
diff --git a/content/learning-paths/cross-platform/adler32/_index.md b/content/learning-paths/cross-platform/adler32/_index.md
index 478ac2b93a..7541c39763 100644
--- a/content/learning-paths/cross-platform/adler32/_index.md
+++ b/content/learning-paths/cross-platform/adler32/_index.md
@@ -18,45 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:53Z'
-  generator: template
-  source_hash: 37b19106fba70423ba8c58f82097d9251f0ae208e882c852f9c4531afcebb46c
-  summary: >-
-    Learn how to use GitHub Copilot to write Neon intrinsics that accelerate the Adler32 checksum
-    algorithm on Arm platforms, achieving significant performance improvements over standard C
-    implementations. It is designed for C/C++ developers who are interested in using GitHub Copilot
-    to improve performance using Neon intrinsics. By the end, you will be able to use GitHub Copilot
-    to write Neon intrinsics that accelerate the Adler32 checksum algorithm. It focuses on tools
-    and technologies such as GCC and Runbook, Linux environments, and Arm platforms including
-    Neoverse and Cortex-A. The main steps cover About Neon and Adler32, Create a C Version of
-    Adler32, Create a test program, Create a Makefile, and Build and run the test program.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will use GitHub Copilot to write Neon intrinsics that accelerate the Adler32 checksum
-      algorithm. Learn how to use GitHub Copilot to write Neon intrinsics that accelerate the
-      Adler32 checksum algorithm on Arm platforms, achieving significant performance improvements
-      over standard C implementations.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for C/C++ developers who are interested in using GitHub Copilot
-      to improve performance using Neon intrinsics.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm computer running Linux with the
-      GNU compiler (gcc) installed.; Visual Studio Code with the GitHub Copilot extension installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including GCC and Runbook, Linux environments, and Arm platforms
-      such as Neoverse and Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around About Neon and Adler32, Create a C Version of Adler32,
-      Create a test program, Create a Makefile, and Build and run the test program.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md b/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md
index 4d4c999afd..f843ed4313 100644
--- a/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md
+++ b/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md
@@ -21,52 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:53Z'
-  generator: template
-  source_hash: 597877722e5f277e5558e29ecd33878ac2262b70a84a9769325b3c3b0ce06e58
-  summary: >-
-    Learn how to automate integration testing of MCP servers using Testcontainers and PyTest,
-    with hands-on examples and GitHub Actions CI/CD configuration. It is designed for software
-    developers and QA engineers who want to automate integration testing of Model Context Protocol
-    (MCP) servers using Testcontainers and PyTest. By the end, you will be able to set up Testcontainers
-    with PyTest for containerized testing of MCP servers, write and run integration tests that
-    validate MCP server functionality, and configure GitHub Actions to automate MCP server testing
-    in CI/CD pipelines. It focuses on tools and technologies such as Python, Pytest, Docker, GitHub
-    Actions, and Testcontainers, Linux, macOS, and Windows environments, and Arm platforms including
-    Neoverse and Cortex-A. The main steps cover Introduction to MCP server testing, Set up your
-    testing environment, Run a basic Testcontainers example, Write integration tests for MCP servers,
-    and Configure GitHub Actions for CI/CD.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will set up Testcontainers with PyTest for containerized testing of MCP servers, write
-      and run integration tests that validate MCP server functionality, and configure GitHub Actions
-      to automate MCP server testing in CI/CD pipelines. Learn how to automate integration testing
-      of MCP servers using Testcontainers and PyTest, with hands-on examples and GitHub Actions
-      CI/CD configuration.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers and QA engineers who want to automate
-      integration testing of Model Context Protocol (MCP) servers using Testcontainers and PyTest.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A computer with [Docker](/install-guides/docker/)
-      and Python 3.11 or later installed; Basic familiarity with Python, PyTest, and container
-      concepts; Familiarity with the [Model Context Protocol (MCP)](https://modelcontextprotocol.io/)
-      specification.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Python, Pytest, Docker, GitHub Actions, and Testcontainers,
-      Linux, macOS, and Windows environments, and Arm platforms such as Neoverse and Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Introduction to MCP server testing, Set up your testing
-      environment, Run a basic Testcontainers example, Write integration tests for MCP servers,
-      and Configure GitHub Actions for CI/CD.
-# END generated_summary_faq
+
 
 author: Neethu Elizabeth Simon
 
diff --git a/content/learning-paths/cross-platform/avh_cicd/_index.md b/content/learning-paths/cross-platform/avh_cicd/_index.md
index 6367153d95..66d7b7ce4d 100644
--- a/content/learning-paths/cross-platform/avh_cicd/_index.md
+++ b/content/learning-paths/cross-platform/avh_cicd/_index.md
@@ -18,43 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:53Z'
-  generator: template
-  source_hash: b6a7439a701f6ae09ce8c61f02150a61fa6ecc1151050e903492e16794999676
-  summary: >-
-    Learn how to integrate Arm Virtual Hardware into a GitHub Actions CI/CD workflow for automated
-    embedded software testing and validation. It is designed for embedded software developers
-    new to Arm Virtual Hardware and its features. By the end, you will be able to prepare a GitHub
-    repository and integrate AVH into a CI/CD flow with GitHub Actions. It focuses on tools and
-    technologies such as Arm Virtual Hardware and GitHub, Baremetal environments, and Arm platforms
-    including Cortex-M. The main steps cover Prepare GitHub repository for CI/CD development and
-    Self-hosted runner.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will prepare a GitHub repository and integrate AVH into a CI/CD flow with GitHub Actions.
-      Learn how to integrate Arm Virtual Hardware into a GitHub Actions CI/CD workflow for automated
-      embedded software testing and validation.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for embedded software developers new to Arm Virtual Hardware
-      and its features.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Some familiarity with CI/CD concepts
-      is assumed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Arm Virtual Hardware and GitHub, Baremetal environments,
-      and Arm platforms such as Cortex-M.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Prepare GitHub repository for CI/CD development and
-      Self-hosted runner.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/cross-platform/avh_cicd2/_index.md b/content/learning-paths/cross-platform/avh_cicd2/_index.md
index 0a70d6a147..4855850ffa 100644
--- a/content/learning-paths/cross-platform/avh_cicd2/_index.md
+++ b/content/learning-paths/cross-platform/avh_cicd2/_index.md
@@ -19,43 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:53Z'
-  generator: template
-  source_hash: 251b6edf6a016f9fc73eb35216f56b2001465fbca8253bd7b6e6f9f4afcd25e6
-  summary: >-
-    Learn how to integrate Arm Virtual Hardware with AWS and GitHub Actions for automated CI/CD
-    workflows, including CloudFormation setup and test automation. It is designed for DevOps integrating
-    AVH into their CI/CD flows. By the end, you will be able to prepare AWS account for GitHub
-    integration and integrate Arm Virtual Hardware into CI/CD flow with GitHub Actions. It focuses
-    on tools and technologies such as Arm Virtual Hardware and GitHub, Baremetal environments,
-    and Arm platforms including Cortex-M. The main steps cover Prepare AWS account for GitHub
-    integration and Automate build and validation example.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will prepare AWS account for GitHub integration and integrate Arm Virtual Hardware into
-      CI/CD flow with GitHub Actions. Learn how to integrate Arm Virtual Hardware with AWS and
-      GitHub Actions for automated CI/CD workflows, including CloudFormation setup and test automation.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for DevOps integrating AVH into their CI/CD flows.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: This learning path builds on [Integrate
-      Arm Virtual Hardware into CI/CD workflow 1](/learning-paths/cross-platform/avh_cicd/).;
-      Valid AWS and GitHub accounts are required.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Arm Virtual Hardware and GitHub, Baremetal environments,
-      and Arm platforms such as Cortex-M.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Prepare AWS account for GitHub integration and Automate
-      build and validation example.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/cross-platform/cca_rme/_index.md b/content/learning-paths/cross-platform/cca_rme/_index.md
index 973dba7462..b8a7a41474 100644
--- a/content/learning-paths/cross-platform/cca_rme/_index.md
+++ b/content/learning-paths/cross-platform/cca_rme/_index.md
@@ -19,47 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:53Z'
-  generator: template
-  source_hash: a1685fb43efecb9690d03c7f8ee64ab20ae8aece91190e2223705106568b1038
-  summary: >-
-    Learn how to use Arm Development Studio to explore Realm Management Extension (RME) and Arm
-    Confidential Compute Architecture (CCA) through a bare-metal example running on the Arm Architecture
-    Envelope Model. It is designed for developers interested in learning the concepts of Realm
-    Management Extension and the Arm Confidential Compute Architecture (CCA). By the end, you
-    will be able to understand the Arm Confidential Compute Architecture (CCA) and understand
-    a simple bare-metal example provided with Arm Development Studio. It focuses on tools and
-    technologies such as Trusted Firmware, Arm Development Studio, RME, CCA, and Runbook, Linux
-    and Android environments, and Arm platforms including Neoverse, Cortex-A, and Armv9-A. The
-    main steps cover Arm Confidential Compute Architecture and Bare-metal example.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand the Arm Confidential Compute Architecture (CCA) and understand a simple
-      bare-metal example provided with Arm Development Studio. Learn how to use Arm Development
-      Studio to explore Realm Management Extension (RME) and Arm Confidential Compute Architecture
-      (CCA) through a bare-metal example running on the Arm Architecture Envelope Model.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers interested in learning the concepts of Realm
-      Management Extension and the Arm Confidential Compute Architecture (CCA).
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Some understanding of the Arm architecture;
-      Arm Development Studio, 2023.0 or later.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Trusted Firmware, Arm Development Studio, RME, CCA,
-      and Runbook, Linux and Android environments, and Arm platforms such as Neoverse, Cortex-A,
-      and Armv9-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Arm Confidential Compute Architecture and Bare-metal
-      example.
-# END generated_summary_faq
+
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md b/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md
index 2128260c50..0c71b9ea48 100644
--- a/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md
+++ b/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md
@@ -20,48 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:53Z'
-  generator: template
-  source_hash: a639e60da034fd04e891c9236e9fe5dab47cca87f6174828275ef5a76c43c32e
-  summary: >-
-    Learn how to optimize C++ loop performance on Arm by providing boundary information to the
-    compiler, enabling SIMD vectorization and reducing runtime through compile-time context. It
-    is designed for C++ developers who want to improve the runtime of loops using existing knowledge
-    of the loop size. By the end, you will be able to learn how to communicate loop size constraints
-    to the compiler for better optimization, understand how providing compile-time context can
-    improve runtime performance, and implement techniques to express loop boundaries that enable
-    better code generation. It focuses on tools and technologies such as CPP and Runbook, Linux
-    environments, and Arm platforms including Neoverse and Cortex-A. The main steps cover Understand
-    developer knowledge for compiler optimizations, Baseline loop implementation, and Optimize
-    loops using boundary information.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will learn how to communicate loop size constraints to the compiler for better optimization,
-      understand how providing compile-time context can improve runtime performance, and implement
-      techniques to express loop boundaries that enable better code generation. Learn how to optimize
-      C++ loop performance on Arm by providing boundary information to the compiler, enabling
-      SIMD vectorization and reducing runtime through compile-time context.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for C++ developers who want to improve the runtime of loops
-      using existing knowledge of the loop size.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm computer running Linux. You can
-      also use a virtual machine from a [cloud service provider](/learning-paths/servers-and-cloud-computing/csp/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including CPP and Runbook, Linux environments, and Arm platforms
-      such as Neoverse and Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Understand developer knowledge for compiler optimizations,
-      Baseline loop implementation, and Optimize loops using boundary information.
-# END generated_summary_faq
+
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/cross-platform/docker-build-cloud/_index.md b/content/learning-paths/cross-platform/docker-build-cloud/_index.md
index d961430ba8..b78516eaf9 100644
--- a/content/learning-paths/cross-platform/docker-build-cloud/_index.md
+++ b/content/learning-paths/cross-platform/docker-build-cloud/_index.md
@@ -20,45 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:53Z'
-  generator: template
-  source_hash: 9a94219b689b8e22a175c6067c6bb6d139d6ad22f3aac77c78b6b38970d5a5eb
-  summary: >-
-    Learn how to build multi-architecture Docker images for Arm and x86 using Docker Build Cloud,
-    with GitHub Actions automation for faster builds without emulation. It is designed for software
-    developers who want to learn how to use Docker Build Cloud. By the end, you will be able to
-    build Arm images and multi-architecture images with Docker Build Cloud and use GitHub Actions
-    to automate image builds. It focuses on tools and technologies such as Docker, Linux environments,
-    and Arm platforms including Neoverse. The main steps cover Setup and build images with Docker
-    Build Cloud and Use GitHub Actions with Docker Build Cloud.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build Arm images and multi-architecture images with Docker Build Cloud and use
-      GitHub Actions to automate image builds. Learn how to build multi-architecture Docker images
-      for Arm and x86 using Docker Build Cloud, with GitHub Actions automation for faster builds
-      without emulation.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to learn how to use Docker
-      Build Cloud.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A computer with Docker installed. This
-      can be Windows, macOS, or Linux. Any architecture can be used.; A GitHub account; A Docker
-      Hub account.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Docker, Linux environments, and Arm platforms such
-      as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Setup and build images with Docker Build Cloud and
-      Use GitHub Actions with Docker Build Cloud.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/cross-platform/docker/_index.md b/content/learning-paths/cross-platform/docker/_index.md
index b2696c7f3c..502ba523e3 100644
--- a/content/learning-paths/cross-platform/docker/_index.md
+++ b/content/learning-paths/cross-platform/docker/_index.md
@@ -21,47 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:53Z'
-  generator: template
-  source_hash: 058f779bc7dd9faef294a57f4524bf525046d757e21a287744825fb9b290935e
-  summary: >-
-    Learn how to build, run, and share multi-architecture Docker images for Arm and x86 platforms
-    using buildx, manifest, and remote builders. It is designed for software developers who want
-    to learn about Docker for the Arm architecture. By the end, you will be able to build, run,
-    and share Docker images, perform multi-architecture builds using Docker buildx, and use a
-    remote server to build a Docker image for the Arm architecture. It focuses on tools and technologies
-    such as Docker, Linux environments, and Arm platforms including Neoverse and Cortex-A. The
-    main steps cover Build, run, and share a Docker image, Build multi-architecture images with
-    Docker buildx, Perform remote Docker builds on an Arm server, Use Docker manifest to create
-    multi-architecture images, and Check container images for multi-architecture support.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build, run, and share Docker images, perform multi-architecture builds using Docker
-      buildx, and use a remote server to build a Docker image for the Arm architecture. Learn
-      how to build, run, and share multi-architecture Docker images for Arm and x86 platforms
-      using buildx, manifest, and remote builders.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to learn about Docker for
-      the Arm architecture.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows, macOS, or Linux computer
-      with Docker installed, any architecture can be used; An Arm Linux server with Docker installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Docker, Linux environments, and Arm platforms such
-      as Neoverse and Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Build, run, and share a Docker image, Build multi-architecture
-      images with Docker buildx, Perform remote Docker builds on an Arm server, Use Docker manifest
-      to create multi-architecture images, and Check container images for multi-architecture support.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md b/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md
index c7bc4142cb..931f2b915a 100644
--- a/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md
+++ b/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md
@@ -20,48 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:53Z'
-  generator: template
-  source_hash: c59308676849aea9b7cfce8c48c7d6e1ca072ef6fb38fb193a34aa5b2597b9b9
-  summary: >-
-    Learn how to implement a dynamic memory allocator in C, understanding heap management and
-    how malloc and free work under the hood with practical examples. It is designed for software
-    developers learning about dynamic memory allocation for the first time, and who may have used
-    malloc and free in C programming. It also provides a starting point to explore more advanced
-    memory allocation topics. By the end, you will be able to explain how dynamic memory allocation
-    and the C heap works, write a simple dynamic memory allocator, and explain some of the risks
-    of heap allocation in general. It focuses on tools and technologies such as C and Runbook,
-    Linux environments, and Arm platforms including Cortex-A and Neoverse. The main steps cover
-    Dynamic memory allocation, Design a dynamic memory allocator, Implement a dynamic memory allocator,
-    and Memory allocation summary.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will explain how dynamic memory allocation and the C heap works, write a simple dynamic
-      memory allocator, and explain some of the risks of heap allocation in general. Learn how
-      to implement a dynamic memory allocator in C, understanding heap management and how malloc
-      and free work under the hood with practical examples.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers learning about dynamic memory allocation
-      for the first time, and who may have used malloc and free in C programming. It also provides
-      a starting point to explore more advanced memory allocation topics.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Familiarity with C programming, with
-      a good understanding of pointers.; A Linux machine to run the example code.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including C and Runbook, Linux environments, and Arm platforms
-      such as Cortex-A and Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Dynamic memory allocation, Design a dynamic memory
-      allocator, Implement a dynamic memory allocator, and Memory allocation summary.
-# END generated_summary_faq
+
 
 author: David Spickett
 
diff --git a/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md b/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md
index bab37a3dbf..7f1bcadeb4 100644
--- a/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md
+++ b/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md
@@ -18,43 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:53Z'
-  generator: template
-  source_hash: 39c5e146afbfc74c3076d2cf3f1a67ddc0e4715f39b2cff1ccf76b288415bdc0
-  summary: >-
-    Learn how to use the Eigen linear algebra library on Arm systems with ASIMD and SVE vectorization,
-    including building TensorFlow with SVE support for optimized performance. It is designed for
-    C/C++ developers who want to create high performance applications using the Eigen linear algebra
-    library. By the end, you will be able to describe how to use Eigen on Arm systems and build
-    TensorFlow with SVE on Arm systems. It focuses on tools and technologies such as GCC, Clang,
-    and Runbook, Linux environments, and Arm platforms including Cortex-A and Neoverse. The main
-    steps cover About Eigen, Eigen examples, Eigen on Arm, and Build and Run TensorFlow with SVE.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will describe how to use Eigen on Arm systems and build TensorFlow with SVE on Arm systems.
-      Learn how to use the Eigen linear algebra library on Arm systems with ASIMD and SVE vectorization,
-      including building TensorFlow with SVE support for optimized performance.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for C/C++ developers who want to create high performance applications
-      using the Eigen linear algebra library.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm-based computer running Linux
-      and a recent version of a C++ compiler (Clang or GCC).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including GCC, Clang, and Runbook, Linux environments, and
-      Arm platforms such as Cortex-A and Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around About Eigen, Eigen examples, Eigen on Arm, and Build
-      and Run TensorFlow with SVE.
-# END generated_summary_faq
+
 
 author: Konstantinos Margaritis
 
diff --git a/content/learning-paths/cross-platform/ernie_moe_v9/_index.md b/content/learning-paths/cross-platform/ernie_moe_v9/_index.md
index 0fd72dc933..28c5e9859b 100644
--- a/content/learning-paths/cross-platform/ernie_moe_v9/_index.md
+++ b/content/learning-paths/cross-platform/ernie_moe_v9/_index.md
@@ -19,54 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:53Z'
-  generator: template
-  source_hash: e835a550c955b13805c7859ff64e3b8d7fdee68222569225c53a0f15db72f046
-  summary: >-
-    Learn how to deploy ERNIE-4.5 Mixture of Experts models on Armv9 devices using llama.cpp,
-    compare PT and Thinking variants, and measure Armv9-specific hardware optimization impact.
-    It is designed for developers and engineers who want to deploy Mixture of Experts (MoE) models,
-    such as ERNIE 4.5, on edge devices. MoE architectures allow large LLMs with 21 billion or
-    more parameters to run with only a fraction of their weights active per inference, making
-    them ideal for resource constrained environments. By the end, you will be able to deploy MoE
-    models like ERNIE-4.5 on edge devices using llama.cpp, compare inference behavior between
-    ERNIE-4.5 PT and Thinking versions, and measure performance impact of Armv9-specific hardware
-    optimizations. It focuses on tools and technologies such as Python, CPP, Bash, and llama.cpp,
-    Linux environments, and Arm platforms including Cortex-A. The main steps cover Understand
-    Mixture of Experts architecture for edge deployment, Set up llama.cpp on an Armv9 development
-    board, Compare ERNIE model behavior and expert routing, and Optimize performance with Armv9
-    hardware features.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will deploy MoE models like ERNIE-4.5 on edge devices using llama.cpp, compare inference
-      behavior between ERNIE-4.5 PT and Thinking versions, and measure performance impact of Armv9-specific
-      hardware optimizations. Learn how to deploy ERNIE-4.5 Mixture of Experts models on Armv9
-      devices using llama.cpp, compare PT and Thinking variants, and measure Armv9-specific hardware
-      optimization impact.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for developers and engineers who want to deploy Mixture of Experts
-      (MoE) models, such as ERNIE 4.5, on edge devices. MoE architectures allow large LLMs with
-      21 billion or more parameters to run with only a fraction of their weights active per inference,
-      making them ideal for resource constrained environments.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Armv9 device with at least 32 GB
-      of available disk space, for example, Radxa Orion O6.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Python, CPP, Bash, and llama.cpp, Linux environments,
-      and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Understand Mixture of Experts architecture for edge
-      deployment, Set up llama.cpp on an Armv9 development board, Compare ERNIE model behavior
-      and expert routing, and Optimize performance with Armv9 hardware features.
-# END generated_summary_faq
+
 
 author: Odin Shen
 
diff --git a/content/learning-paths/cross-platform/floating-point-behavior/_index.md b/content/learning-paths/cross-platform/floating-point-behavior/_index.md
index 237dac2a69..cb08e9917e 100644
--- a/content/learning-paths/cross-platform/floating-point-behavior/_index.md
+++ b/content/learning-paths/cross-platform/floating-point-behavior/_index.md
@@ -20,52 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:53Z'
-  generator: template
-  source_hash: 6427d4466fcd34f7275d16820cbfa2afa3815e1f0306c02e5011b2a4a6afa984
-  summary: >-
-    Learn how Arm and x86 floating-point implementations follow IEEE 754 standards, identify rare
-    undefined behavior differences, and write portable code across architectures. It is designed
-    for This is a topic for developers who are porting applications from x86 to Arm and want to
-    understand floating-point behavior across these architectures. Both architectures provide
-    reliable and consistent floating-point computation following the IEEE 754 standard. By the
-    end, you will be able to understand that Arm and x86 produce identical results for all well-defined
-    floating-point operations, recognize that differences only occur in special undefined cases
-    permitted by IEEE 754, and learn to recognize floating-point differences and make your code
-    portable across architectures. It focuses on tools and technologies such as CPP, Linux environments,
-    and Arm platforms including Cortex-A and Neoverse. The main steps cover Floating-point representation,
-    Overflow in floating-point to integer conversion, and Precision and floating-point instruction
-    considerations.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand that Arm and x86 produce identical results for all well-defined floating-point
-      operations, recognize that differences only occur in special undefined cases permitted by
-      IEEE 754, and learn to recognize floating-point differences and make your code portable
-      across architectures. Learn how Arm and x86 floating-point implementations follow IEEE 754
-      standards, identify rare undefined behavior differences, and write portable code across
-      architectures.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is a topic for developers who are porting applications from x86 to Arm and want to
-      understand floating-point behavior across these architectures. Both architectures provide
-      reliable and consistent floating-point computation following the IEEE 754 standard.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Access to an x86 and an Arm Linux machine.;
-      Familiarity with floating-point numbers.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including CPP, Linux environments, and Arm platforms such
-      as Cortex-A and Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Floating-point representation, Overflow in floating-point
-      to integer conversion, and Precision and floating-point instruction considerations.
-# END generated_summary_faq
+
 
 author: 
     - Kieran Hejmadi
diff --git a/content/learning-paths/cross-platform/function-multiversioning/_index.md b/content/learning-paths/cross-platform/function-multiversioning/_index.md
index 1aaeba9249..45f76f01a4 100644
--- a/content/learning-paths/cross-platform/function-multiversioning/_index.md
+++ b/content/learning-paths/cross-platform/function-multiversioning/_index.md
@@ -25,52 +25,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:53Z'
-  generator: template
-  source_hash: 1c1d745bd1af535315d5edd1b2b9214c96419d46abde3af8fa75bdde299bb65d
-  summary: >-
-    Learn how to optimize C/C++ applications using function multiversioning on Arm64 targets with
-    GCC or LLVM, enabling automatic runtime selection of hardware-optimized function versions.
-    It is designed for developers interested in optimizing their C/C++ applications across Arm64
-    targets. By the end, you will be able to use hardware features to tune your applications at
-    function level, create multiple versions of C/C++ functions for the targets that you intend
-    to run applications on, and assist the compiler in generating optimal code for the targets,
-    or provide your own optimized versions at source level. It focuses on tools and technologies
-    such as C, CPP, and Runbook, Linux, Android, and macOS environments, and Arm platforms including
-    Cortex-A and Neoverse. The main steps cover About function multiversioning, Example 1 - code
-    generation, Example 2 - runtime using ACLE intrinsics, Example 3 - inline assembly at runtime,
-    and Compatibility with streaming mode.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will use hardware features to tune your applications at function level, create multiple
-      versions of C/C++ functions for the targets that you intend to run applications on, and
-      assist the compiler in generating optimal code for the targets, or provide your own optimized
-      versions at source level. Learn how to optimize C/C++ applications using function multiversioning
-      on Arm64 targets with GCC or LLVM, enabling automatic runtime selection of hardware-optimized
-      function versions.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for developers interested in optimizing their C/C++ applications
-      across Arm64 targets.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Basic knowledge of GNU function attributes.;
-      Familiarity with indirect functions (ifuncs).; Basic knowledge of loop vectorization.; Familiarity
-      with Arm assembly.; A LLVM 20 compiler with runtime library support or GCC 16.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including C, CPP, and Runbook, Linux, Android, and macOS environments,
-      and Arm platforms such as Cortex-A and Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around About function multiversioning, Example 1 - code generation,
-      Example 2 - runtime using ACLE intrinsics, Example 3 - inline assembly at runtime, and Compatibility
-      with streaming mode.
-# END generated_summary_faq
+
 
 author: Alexandros Lamprineas
 
diff --git a/content/learning-paths/cross-platform/github-arm-runners/_index.md b/content/learning-paths/cross-platform/github-arm-runners/_index.md
index ea3efb87e2..62a123fb24 100644
--- a/content/learning-paths/cross-platform/github-arm-runners/_index.md
+++ b/content/learning-paths/cross-platform/github-arm-runners/_index.md
@@ -19,47 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:53Z'
-  generator: template
-  source_hash: 3557d534c51f81839cc353ebbd600ec588a60197d2c27c9a58e97c25017d07e4
-  summary: >-
-    Learn how to use GitHub Actions with Arm-hosted runners to build multi-architecture container
-    images for arm64 and amd64 platforms and automate deployment to Docker Hub. It is designed
-    for software developers who want to learn how to use Arm-hosted runners for GitHub Actions
-    jobs. By the end, you will be able to build Arm images and multi-architecture images with
-    Arm-hosted runners and use GitHub Actions to automate image builds. It focuses on tools and
-    technologies such as GitHub, Docker, and Runbook, Linux environments, and Arm platforms including
-    Neoverse. The main steps cover Build options for multi-architecture container images, Arm-hosted
-    runners for public repositories, Create a new Arm-hosted runner for private repositories,
-    and Run GitHub Actions jobs on the Arm-hosted runner.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build Arm images and multi-architecture images with Arm-hosted runners and use
-      GitHub Actions to automate image builds. Learn how to use GitHub Actions with Arm-hosted
-      runners to build multi-architecture container images for arm64 and amd64 platforms and automate
-      deployment to Docker Hub.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to learn how to use Arm-hosted
-      runners for GitHub Actions jobs.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A GitHub account (a Team or Enterprise
-      Cloud plan is required for private repositories).; A Docker Hub account.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including GitHub, Docker, and Runbook, Linux environments,
-      and Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Build options for multi-architecture container images,
-      Arm-hosted runners for public repositories, Create a new Arm-hosted runner for private repositories,
-      and Run GitHub Actions jobs on the Arm-hosted runner.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md b/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md
index 78e3f6e6e1..00d7e8be99 100644
--- a/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md
+++ b/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md
@@ -22,48 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:53Z'
-  generator: template
-  source_hash: a6ad703d9d894da06ed4aa3725fcc9006d70050cef3f0a3ef9a758bcf4523289
-  summary: >-
-    Learn how to build GitLab CI/CD pipelines using GitLab-hosted Arm64 runners to containerize
-    C applications, store images in GitLab Container Registry, and deploy on Arm infrastructure.
-    It is designed for DevOps engineers who want to build CI/CD pipelines on Arm-based infrastructure
-    using GitLab-hosted runners. By the end, you will be able to create a GitLab project with
-    CI/CD configuration, configure pipeline stages to use Arm64 runners, and build and containerize
-    applications for Arm64 architecture. It focuses on tools and technologies such as GitLab,
-    Docker, and C, Linux environments, Arm platforms including Neoverse, and cloud platforms such
-    as Google Cloud. The main steps cover Build and use GitLab-hosted Arm runners for CI/CD, Create
-    a GitLab project, Build and configure your Arm64 CI/CD pipeline, and Run pipeline and verify
-    results.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create a GitLab project with CI/CD configuration, configure pipeline stages to
-      use Arm64 runners, and build and containerize applications for Arm64 architecture. Learn
-      how to build GitLab CI/CD pipelines using GitLab-hosted Arm64 runners to containerize C
-      applications, store images in GitLab Container Registry, and deploy on Arm infrastructure.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for DevOps engineers who want to build CI/CD pipelines on
-      Arm-based infrastructure using GitLab-hosted runners.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A GitLab account (free tier includes
-      Arm64 runner access).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including GitLab, Docker, and C, Linux environments, Arm platforms
-      such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Build and use GitLab-hosted Arm runners for CI/CD,
-      Create a GitLab project, Build and configure your Arm64 CI/CD pipeline, and Run pipeline
-      and verify results.
-# END generated_summary_faq
+
 
 author: Mohamed Ismail
 
diff --git a/content/learning-paths/cross-platform/gitlab/_index.md b/content/learning-paths/cross-platform/gitlab/_index.md
index e76930ec2d..e6630b70f9 100644
--- a/content/learning-paths/cross-platform/gitlab/_index.md
+++ b/content/learning-paths/cross-platform/gitlab/_index.md
@@ -22,47 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:53Z'
-  generator: template
-  source_hash: af9eb1c426e1844e2fd20215b7012d95c1efaf737f1d7b5be828931528342316
-  summary: >-
-    Learn how to build a GitLab CI/CD pipeline using Google Axion-based self-hosted runners. It
-    is designed for DevOps professionals who are looking to build a CI/CD pipeline with GitLab
-    on Google Axion based self-hosted GitLab runners. By the end, you will be able to create a
-    Google Axion based GitLab self-hosted runner, build a CI/CD pipeline with multi-architecture
-    support, and build multi-architecture docker images using native GitLab runners on x86 and
-    Arm. It focuses on tools and technologies such as Kubernetes, Docker, and GitLab, Linux environments,
-    Arm platforms including Neoverse, and cloud platforms such as Google Cloud. The main steps
-    cover Create a Google Axion-based GitLab self-hosted runner and Automate the build and deployment
-    of a multi-arch application with GitLab CI/CD.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create a Google Axion based GitLab self-hosted runner, build a CI/CD pipeline with
-      multi-architecture support, and build multi-architecture docker images using native GitLab
-      runners on x86 and Arm. Learn how to build a GitLab CI/CD pipeline using Google Axion-based
-      self-hosted runners.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for DevOps professionals who are looking to build a CI/CD pipeline
-      with GitLab on Google Axion based self-hosted GitLab runners.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud account](https://console.cloud.google.com/).
-      Create an account if needed.; A computer with [Google Cloud CLI](/install-guides/gcloud)
-      and [kubectl](/install-guides/kubectl/)installed.; A valid GitLab account.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Kubernetes, Docker, and GitLab, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Create a Google Axion-based GitLab self-hosted runner
-      and Automate the build and deployment of a multi-arch application with GitLab CI/CD.
-# END generated_summary_faq
+
 
 author: Pranay Bakre
 
diff --git a/content/learning-paths/cross-platform/integer-vs-floats/_index.md b/content/learning-paths/cross-platform/integer-vs-floats/_index.md
index af6d4d64a7..07cc35d957 100644
--- a/content/learning-paths/cross-platform/integer-vs-floats/_index.md
+++ b/content/learning-paths/cross-platform/integer-vs-floats/_index.md
@@ -17,47 +17,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:53Z'
-  generator: template
-  source_hash: 1895767d551ba0fa249c5002d3e2e471acacdec06ddb7c2f5314a2fa0df94f2e
-  summary: >-
-    Learn how to identify and fix potential problems with integer and floating-point conversions
-    in C/C++ code on Arm, including explicit conversions, implicit conversions, and type demotion
-    issues. It is designed for C/C++ developers who are interested in learning about the intricacies
-    of conversions between floating-point numbers and integers. By the end, you will be able to
-    learn how to identify and fix potential problems in integer/float conversions in C/C++ on
-    Arm. It focuses on tools and technologies such as GCC, Clang, and Runbook, Linux environments,
-    and Arm platforms including Aarch64, Armv8-a, and Armv9-a. The main steps cover An introduction
-    to integer and floating-point data types, Integer and floating-point conversions, More on
-    implicit conversions, and Data type demotions.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will learn how to identify and fix potential problems in integer/float conversions in
-      C/C++ on Arm. Learn how to identify and fix potential problems with integer and floating-point
-      conversions in C/C++ code on Arm, including explicit conversions, implicit conversions,
-      and type demotion issues.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for C/C++ developers who are interested in learning about the
-      intricacies of conversions between floating-point numbers and integers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm computer running Linux and a
-      recent version of a C++ compiler (Clang or GCC) installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including GCC, Clang, and Runbook, Linux environments, and
-      Arm platforms such as Aarch64, Armv8-a, and Armv9-a.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around An introduction to integer and floating-point data
-      types, Integer and floating-point conversions, More on implicit conversions, and Data type
-      demotions.
-# END generated_summary_faq
+
 
 author: Konstantinos Margaritis
 
diff --git a/content/learning-paths/cross-platform/intrinsics/_index.md b/content/learning-paths/cross-platform/intrinsics/_index.md
index 3289ee0b5a..83eaf56c0f 100644
--- a/content/learning-paths/cross-platform/intrinsics/_index.md
+++ b/content/learning-paths/cross-platform/intrinsics/_index.md
@@ -22,45 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:53Z'
-  generator: template
-  source_hash: ecf51d9a9085f95dedda9c0cfbfa4d6350d0f68d81b61f9461bc51070abd0b69
-  summary: >-
-    Learn how to port architecture-specific intrinsics to Arm processors. It is designed for software
-    developers interested in porting architecture specific intrinsics to Arm processors. By the
-    end, you will be able to describe what intrinsics are and how to find them in code and evaluate
-    options and use header-only libraries to port architecture-specific intrinsics to Arm. It
-    focuses on tools and technologies such as Neon, SVE, Intrinsics, and Runbook, Linux environments,
-    and Arm platforms including Neoverse and Cortex-A. The main steps cover Code Migration to
-    Arm, Use sse2neon to port code to Arm, Use SIMD Everywhere to port code to Arm, and Find intrinsics
-    in large code bases.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will describe what intrinsics are and how to find them in code and evaluate options
-      and use header-only libraries to port architecture-specific intrinsics to Arm. Learn how
-      to port architecture-specific intrinsics to Arm processors.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers interested in porting architecture specific
-      intrinsics to Arm processors.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Some understanding of SIMD concepts.;
-      An Arm based machine or [cloud instance](/learning-paths/servers-and-cloud-computing/csp/)
-      running Ubuntu Linux.; Optionally, an `x86_64` machine also running Ubuntu.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Neon, SVE, Intrinsics, and Runbook, Linux environments,
-      and Arm platforms such as Neoverse and Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Code Migration to Arm, Use sse2neon to port code to
-      Arm, Use SIMD Everywhere to port code to Arm, and Find intrinsics in large code bases.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/cross-platform/ipexplorer/_index.md b/content/learning-paths/cross-platform/ipexplorer/_index.md
index ae1966a35f..ea1c8c3e3d 100644
--- a/content/learning-paths/cross-platform/ipexplorer/_index.md
+++ b/content/learning-paths/cross-platform/ipexplorer/_index.md
@@ -18,45 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:53Z'
-  generator: template
-  source_hash: 47cd598f6e33c12d3729c319a1d6a7afcd748de7acd6faea639f2e8600a085ed
-  summary: >-
-    Learn how to run custom software benchmarks on IP Explorer simulation platforms and compare
-    performance across Arm Cortex-M processors using cycle count analysis. It is designed for
-    IP Explorer users using the software simulation platforms available. By the end, you will
-    be able to run a pre-installed example on IP Explorer simulation platform, create your own
-    example benchmark, and upload and run your benchmark. It focuses on tools and technologies
-    such as IP Explorer, Baremetal environments, and Arm platforms including Cortex-A, Cortex-R,
-    and Cortex-M. The main steps cover Run a pre-installed example and compare performance, Create
-    a custom example, and Upload and run your custom example.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will run a pre-installed example on IP Explorer simulation platform, create your own
-      example benchmark, and upload and run your benchmark. Learn how to run custom software benchmarks
-      on IP Explorer simulation platforms and compare performance across Arm Cortex-M processors
-      using cycle count analysis.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for IP Explorer users using the software simulation platforms
-      available.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm account that can access IP Explorer;
-      (Optional) A Linux machine with the desired compilers installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including IP Explorer, Baremetal environments, and Arm platforms
-      such as Cortex-A, Cortex-R, and Cortex-M.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Run a pre-installed example and compare performance,
-      Create a custom example, and Upload and run your custom example.
-# END generated_summary_faq
+
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/cross-platform/kleidiai-explainer/_index.md b/content/learning-paths/cross-platform/kleidiai-explainer/_index.md
index 735f0293da..6c7a3d9535 100644
--- a/content/learning-paths/cross-platform/kleidiai-explainer/_index.md
+++ b/content/learning-paths/cross-platform/kleidiai-explainer/_index.md
@@ -18,51 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:53Z'
-  generator: template
-  source_hash: 907255b3c2c1086b421abe4a1e378b68533de4524a4f4dd81138545a2ff1a5b5
-  summary: >-
-    Learn how to use KleidiAI micro-kernels to accelerate AI inference performance through optimized
-    matrix multiplication on Arm processors with architecture features like i8mm. It is designed
-    for developers who want to learn how to use KleidiAI to accelerate the execution of Generative
-    AI workloads on hardware. By the end, you will be able to describe how basic math operations
-    power Large Language Models, describe how the KleidiAI micro-kernels speed up Generative AI
-    inference performance, and run a basic C++ matrix multiplication example to showcase the speedup
-    that KleidiAI micro-kernels can deliver. It focuses on tools and technologies such as CPP,
-    Generative AI, Neon, and Runbook, Linux environments, and Arm platforms including Cortex-A
-    and Neoverse. The main steps cover KleidiAI and matrix multiplication, KleidiAI in a real
-    software stack, and Quantizing and packing micro-kernels.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will describe how basic math operations power Large Language Models, describe how the
-      KleidiAI micro-kernels speed up Generative AI inference performance, and run a basic C++
-      matrix multiplication example to showcase the speedup that KleidiAI micro-kernels can deliver.
-      Learn how to use KleidiAI micro-kernels to accelerate AI inference performance through optimized
-      matrix multiplication on Arm processors with architecture features like i8mm.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers who want to learn how to use KleidiAI to accelerate
-      the execution of Generative AI workloads on hardware.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm-based Linux machine that implements
-      the Int8 Matrix Multiplication (*i8mm*) architecture feature. The example in this Learning
-      Path is run on an AWS Graviton 3 instance. Instructions on setting up an Arm-based server
-      are [found here](/learning-paths/servers-and-cloud-computing/csp/aws/).; A basic understanding
-      of linear algebra terminology, such as dot product and matrix multiplication.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including CPP, Generative AI, Neon, and Runbook, Linux environments,
-      and Arm platforms such as Cortex-A and Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around KleidiAI and matrix multiplication, KleidiAI in a
-      real software stack, and Quantizing and packing micro-kernels.
-# END generated_summary_faq
+
 
 author: Zach Lasiuk
 ### Tags
diff --git a/content/learning-paths/cross-platform/loop-reflowing/_index.md b/content/learning-paths/cross-platform/loop-reflowing/_index.md
index eea90d8a0a..352d0939a6 100644
--- a/content/learning-paths/cross-platform/loop-reflowing/_index.md
+++ b/content/learning-paths/cross-platform/loop-reflowing/_index.md
@@ -16,45 +16,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 4218b8b0c1dee3862bb9765a83dfed0cb38555d1da7425e791c5c16b17f98c21
-  summary: >-
-    Learn how to optimize C/C++ code using compiler autovectorization techniques including loop
-    modifications, restrict qualifiers, and conditional handling for Arm processors. It is designed
-    for C/C++ developers who are interested in taking advantage of autovectorization in compilers.
-    By the end, you will be able to modify loops to take advantage of autovectorization in compilers.
-    It focuses on tools and technologies such as GCC, Clang, and Runbook, Linux environments,
-    and Arm platforms including Neoverse and Cortex-A. The main steps cover An introduction to
-    autovectorization, Autovectorization using the restrict keyword, Autovectorization limits,
-    Autovectorization and conditionals, and Autovectorization on Arm.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will modify loops to take advantage of autovectorization in compilers. Learn how to
-      optimize C/C++ code using compiler autovectorization techniques including loop modifications,
-      restrict qualifiers, and conditional handling for Arm processors.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for C/C++ developers who are interested in taking advantage of
-      autovectorization in compilers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm computer running Linux and a
-      recent version of Clang or the GNU compiler (gcc) installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including GCC, Clang, and Runbook, Linux environments, and
-      Arm platforms such as Neoverse and Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around An introduction to autovectorization, Autovectorization
-      using the restrict keyword, Autovectorization limits, Autovectorization and conditionals,
-      and Autovectorization on Arm.
-# END generated_summary_faq
+
 
 author: Konstantinos Margaritis
 
diff --git a/content/learning-paths/cross-platform/matrix/_index.md b/content/learning-paths/cross-platform/matrix/_index.md
index f5a5248862..739b76718e 100644
--- a/content/learning-paths/cross-platform/matrix/_index.md
+++ b/content/learning-paths/cross-platform/matrix/_index.md
@@ -23,47 +23,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 5611b6d1eebbea167d1860e3ac7f4f7910584561cbb18c3ed696aa27215edce9
-  summary: >-
-    Learn how to develop and test a modern C++ library using CMake, GoogleTest, and matrix processing
-    as a practical example on Arm platforms. It is designed for developers who want to learn how
-    to develop a library in modern C++ on Arm, using matrix processing as an example. By the end,
-    you will be able to develop a new C++ library and test a C++ library, ensuring it does not
-    regress functionally. It focuses on tools and technologies such as CPP, GCC, Clang, CMake,
-    and Google Test, Linux, macOS, and Windows environments, and Arm platforms including Neoverse
-    and Cortex-A. The main steps cover Laying the foundations, Test the library, Start coding,
-    and Implement matrix operations.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will develop a new C++ library and test a C++ library, ensuring it does not regress
-      functionally. Learn how to develop and test a modern C++ library using CMake, GoogleTest,
-      and matrix processing as a practical example on Arm platforms.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for developers who want to learn how to develop a library in modern
-      C++ on Arm, using matrix processing as an example.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm-based computer running Linux,
-      macOS, or Windows.; An intermediate understanding of C++ programming.; A suitable Integrated
-      Development Environment (IDE).; The [CMake](/install-guides/cmake/) build tool.; A C++ compiler
-      with C++17 support.; A build system [GNU Make](https://www.gnu.org/software/make/) or [Ninja](https://ninja-build.org/).;
-      A documentation generator [Doxygen](https://www.doxygen.nl/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including CPP, GCC, Clang, CMake, and Google Test, Linux,
-      macOS, and Windows environments, and Arm platforms such as Neoverse and Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Laying the foundations, Test the library, Start coding,
-      and Implement matrix operations.
-# END generated_summary_faq
+
 
 author: Arnaud de Grandmaison
 
diff --git a/content/learning-paths/cross-platform/mca-godbolt/_index.md b/content/learning-paths/cross-platform/mca-godbolt/_index.md
index 0d888dd7af..70d430268a 100644
--- a/content/learning-paths/cross-platform/mca-godbolt/_index.md
+++ b/content/learning-paths/cross-platform/mca-godbolt/_index.md
@@ -19,47 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: c86322d497541344f92907315793ab990669aa08bef994b0ce9ff1e32a5ba055
-  summary: >-
-    Learn how to use llvm-mca with Compiler Explorer to analyze Arm assembly performance, estimate
-    hardware resource pressure, and diagnose performance issues. It is designed for developers
-    who want to diagnose performance issues of Arm programs using LLVM Machine Code Analyzer (MCA)
-    and Compiler Explorer. By the end, you will be able to estimate the hardware resource pressure
-    and the number of cycles taken to execute your code snippet using llvm-mca, describe how this
-    estimate can help diagnose possible performance issues, and use Compiler Explorer to run llvm-mca.
-    It focuses on tools and technologies such as Assembly, llvm-mca, and Runbook, Linux, Windows,
-    and macOS environments, and Arm platforms including Cortex-A and Neoverse. The main steps
-    cover Background, Run MCA with Arm assembly, and Use MCA with Compiler Explorer.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will estimate the hardware resource pressure and the number of cycles taken to execute
-      your code snippet using llvm-mca, describe how this estimate can help diagnose possible
-      performance issues, and use Compiler Explorer to run llvm-mca. Learn how to use llvm-mca
-      with Compiler Explorer to analyze Arm assembly performance, estimate hardware resource pressure,
-      and diagnose performance issues.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers who want to diagnose performance issues of
-      Arm programs using LLVM Machine Code Analyzer (MCA) and Compiler Explorer.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Familiarity with Arm assembly.; LLVM
-      version 16 or newer, which includes support for Neoverse V2.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Assembly, llvm-mca, and Runbook, Linux, Windows,
-      and macOS environments, and Arm platforms such as Cortex-A and Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Background, Run MCA with Arm assembly, and Use MCA
-      with Compiler Explorer.
-# END generated_summary_faq
+
 
 author: Asher Dobrescu
 
diff --git a/content/learning-paths/cross-platform/mcp-ai-agent/_index.md b/content/learning-paths/cross-platform/mcp-ai-agent/_index.md
index 2437b5b230..5e51b448b3 100644
--- a/content/learning-paths/cross-platform/mcp-ai-agent/_index.md
+++ b/content/learning-paths/cross-platform/mcp-ai-agent/_index.md
@@ -22,55 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 39d489081bfa22125a4046c17d5c00a32c0d7b298cba7b6a12373cf3cf6bac04
-  summary: >-
-    Learn how to deploy a Model Context Protocol server on Raspberry Pi 5 and use the OpenAI Agent
-    SDK to create AI agents with custom tools for local inference. It is designed for LLM and
-    IoT developers who want to run and interact with AI agents on edge devices like the Raspberry
-    Pi 5. You'll learn how to deploy a lightweight Model Context Protocol (MCP) server and use
-    the OpenAI Agent SDK to create and register tools for intelligent local inference. By the
-    end, you will be able to deploy a lightweight Model Context Protocol (MCP) server on Raspberry
-    Pi 5 for local AI agent execution, use the OpenAI Agent SDK to interact with a local AI agent,
-    and design and register custom tools for the agent tasks. It focuses on tools and technologies
-    such as Python, AI, Raspberry Pi, and MCP, Linux environments, and Arm platforms including
-    Cortex-A. The main steps cover Introduction to Model Context Protocol (MCP) and Python uv
-    package for local AI agents, Set up an MCP server on Raspberry Pi 5, and Build and run an
-    AI agent on your development machine.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will deploy a lightweight Model Context Protocol (MCP) server on Raspberry Pi 5 for
-      local AI agent execution, use the OpenAI Agent SDK to interact with a local AI agent, and
-      design and register custom tools for the agent tasks. Learn how to deploy a Model Context
-      Protocol server on Raspberry Pi 5 and use the OpenAI Agent SDK to create AI agents with
-      custom tools for local inference.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is for LLM and IoT developers who want to run and interact with AI agents
-      on edge devices like the Raspberry Pi 5. You'll learn how to deploy a lightweight Model
-      Context Protocol (MCP) server and use the OpenAI Agent SDK to create and register tools
-      for intelligent local inference.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Raspberry Pi 5](https://www.raspberrypi.com/products/raspberry-pi-5/)
-      with a Linux-based OS installed.; Familiarity with Python programming and prompt engineering
-      techniques.; Basic understanding of Large Language Models (LLMs) and how they are used in
-      local inference.; Understanding of AI agents and the OpenAI Agent SDK (or similar frameworks).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Python, AI, Raspberry Pi, and MCP, Linux environments,
-      and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Introduction to Model Context Protocol (MCP) and Python
-      uv package for local AI agents, Set up an MCP server on Raspberry Pi 5, and Build and run
-      an AI agent on your development machine.
-# END generated_summary_faq
+
 
 author: Andrew Choi
 
diff --git a/content/learning-paths/cross-platform/memory-latency/_index.md b/content/learning-paths/cross-platform/memory-latency/_index.md
index 0f0fd68184..7cc4852065 100644
--- a/content/learning-paths/cross-platform/memory-latency/_index.md
+++ b/content/learning-paths/cross-platform/memory-latency/_index.md
@@ -17,46 +17,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 72e5ffe5091311850d17f30ed3ab4bd7487cbbd862d0d8751cc73bd91fff00e2
-  summary: >-
-    Learn how to reduce memory latency impact in applications using cache alignment and prefetching
-    techniques on Arm processors for improved performance. It is designed for Arm developers who
-    want to learn about memory latency and cache usage in application programming. By the end,
-    you will be able to explain the importance of memory latency and how to reduce its impact,
-    identify how cache alignment impacts performance, and use cache prefetching to improve performance.
-    It focuses on tools and technologies such as GCC, Clang, and Runbook, Linux environments,
-    and Arm platforms including Cortex-A and Neoverse. The main steps cover About memory latency,
-    How latency impacts performance - part 1, How latency impacts performance - part 2, Cache
-    alignment, and Cache prefetching.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will explain the importance of memory latency and how to reduce its impact, identify
-      how cache alignment impacts performance, and use cache prefetching to improve performance.
-      Learn how to reduce memory latency impact in applications using cache alignment and prefetching
-      techniques on Arm processors for improved performance.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for Arm developers who want to learn about memory latency
-      and cache usage in application programming.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm computer running Linux with recent
-      versions of Clang or GCC installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including GCC, Clang, and Runbook, Linux environments, and
-      Arm platforms such as Cortex-A and Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around About memory latency, How latency impacts performance
-      - part 1, How latency impacts performance - part 2, Cache alignment, and Cache prefetching.
-# END generated_summary_faq
+
 
 author: Konstantinos Margaritis
 
diff --git a/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md b/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md
index 0494748119..f2c4cf40c4 100644
--- a/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md
+++ b/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md
@@ -22,59 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: bf3183bd62b590d4930f0bcf9c9dc836bbc5206a991be7bec5e18e9c20942352
-  summary: >-
-    Learn how to build MNN on an Armv9 system, run text, vision, and audio prompts with a multimodal
-    Omni model, and combine image and audio inputs into a single-shot retail restock ticket workflow.
-    It is designed for developers and engineers who want to run multimodal image, audio, and text
-    models on Armv9 Linux systems using MNN as a portable, CPU-first inference runtime. It is
-    aimed at readers who are comfortable building software from source and want a reproducible
-    on-device workflow without quantization or heterogeneous scheduling. By the end, you will
-    be able to build MNN natively on an Armv9 Linux system for multimodal inference, verify a
-    CPU-only Omni model workflow with text, vision, and audio prompts, and create a reproducible
-    multimodal application flow that combines image and audio inputs into an actionable restock
-    ticket. It focuses on tools and technologies such as CMake, CPP, and Bash, Linux environments,
-    and Arm platforms including Cortex-A. The main steps cover Run multimodal inference with MNN
-    on Armv9, Build MNN and prepare an Omni model on Armv9, Validate text-only inference with
-    an Omni model on Armv9, Run a vision retail shelf audit with MNN Omni, and Convert spoken
-    restock notes into structured tickets with MNN Omni.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build MNN natively on an Armv9 Linux system for multimodal inference, verify a
-      CPU-only Omni model workflow with text, vision, and audio prompts, and create a reproducible
-      multimodal application flow that combines image and audio inputs into an actionable restock
-      ticket. Learn how to build MNN on an Armv9 system, run text, vision, and audio prompts with
-      a multimodal Omni model, and combine image and audio inputs into a single-shot retail restock
-      ticket workflow.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is for developers and engineers who want to run multimodal image, audio,
-      and text models on Armv9 Linux systems using MNN as a portable, CPU-first inference runtime.
-      It is aimed at readers who are comfortable building software from source and want a reproducible
-      on-device workflow without quantization or heterogeneous scheduling.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Armv9 Linux device with at least
-      32 GB of available disk space, for example a Radxa Orion O6; Familiarity with the Linux
-      command line, Git, and building C++ projects with CMake; Internet access to download source
-      code, model assets, and sample data.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including CMake, CPP, and Bash, Linux environments, and Arm
-      platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Run multimodal inference with MNN on Armv9, Build
-      MNN and prepare an Omni model on Armv9, Validate text-only inference with an Omni model
-      on Armv9, Run a vision retail shelf audit with MNN Omni, and Convert spoken restock notes
-      into structured tickets with MNN Omni.
-# END generated_summary_faq
+
 
 author: Odin Shen
 
diff --git a/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md b/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md
index 650ce7c3c0..4e2527ee78 100644
--- a/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md
+++ b/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md
@@ -28,57 +28,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: eb01b77f36323331c080615edcbddbf8cb56cf005f2249f1ea309ab1dbec8616
-  summary: >-
-    Learn how to implement and optimize matrix multiplication using Arm's Scalable Matrix Extension
-    2 (SME2) with assembly and intrinsics, including benchmarking and validation on Arm hardware.
-    It is designed for This Learning Path is an advanced topic for developers who want to accelerate
-    the performance of matrix multiplication using Arm's Scalable Matrix Extension Version 2 (SME2).
-    By the end, you will be able to implement a baseline matrix multiplication kernel in C without
-    SME2, use SME2 assembly instructions to accelerate matrix multiplication performance, and
-    use SME2 intrinsics to vectorize and optimize matrix multiplication. It focuses on tools and
-    technologies such as C, Clang, LLVM, and SME2, Linux, macOS, and Windows environments, and
-    Arm platforms including Arm C1. The main steps cover Overview, Set up your SME2 development
-    environment, Test your SME2 development environment, Streaming mode and ZA state in SME, and
-    Vanilla matrix multiplication.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will implement a baseline matrix multiplication kernel in C without SME2, use SME2 assembly
-      instructions to accelerate matrix multiplication performance, and use SME2 intrinsics to
-      vectorize and optimize matrix multiplication. Learn how to implement and optimize matrix
-      multiplication using Arm's Scalable Matrix Extension 2 (SME2) with assembly and intrinsics,
-      including benchmarking and validation on Arm hardware.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is an advanced topic for developers who want to accelerate the performance
-      of matrix multiplication using Arm's Scalable Matrix Extension Version 2 (SME2).
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Working knowledge of Arm’s SVE and SME2
-      instruction sets; Intermediate proficiency with the C programming language and the Armv9-A
-      assembly language; A computer running Linux, macOS, or Windows; Installations of Git, CMake
-      and Ninja for project setup; A platform that supports SME2 - see the list of [devices with
-      SME2 support](/learning-paths/cross-platform/multiplying-matrices-with-sme2/1-get-started/#devices)
-      or an emulator to run code with SME2 instructions; Installation of Docker for SME2 emulation
-      (if you don't have SME2 available); Installation of Android Development Studio and adb (if
-      you're targeting an Android phone with SME2 support); Compiler support for SME2 instructions
-      (for example, LLVM 18 or later with SME2 backend support).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including C, Clang, LLVM, and SME2, Linux, macOS, and Windows
-      environments, and Arm platforms such as Arm C1.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview, Set up your SME2 development environment,
-      Test your SME2 development environment, Streaming mode and ZA state in SME, and Vanilla
-      matrix multiplication.
-# END generated_summary_faq
+
 
 author: Arnaud de Grandmaison
 
diff --git a/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md b/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md
index 3672a4e806..c3b0c0ebbd 100644
--- a/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md
+++ b/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md
@@ -26,52 +26,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 1251465f13b67ee80292b66ef22069a1b6cc2ca67bb602cdd5e393a278576ec8
-  summary: >-
-    Learn how to create and train a PyTorch neural network for MNIST digit classification, optimize
-    it with quantization and fusing, and deploy it in an Android application with performance
-    measurement. It is designed for software developers interested in learning how to use PyTorch
-    to create and train a feedforward neural network for digit classification, and also software
-    developers interested in learning how to use and apply optimizations to the trained model
-    in an Android application. By the end, you will be able to prepare a PyTorch development environment,
-    download and prepare the MNIST dataset, and create and train a neural network architecture
-    using PyTorch. It focuses on tools and technologies such as Android Studio and Visual Studio
-    Code, Windows, Linux, and macOS environments, and Arm platforms including Cortex-A and Neoverse.
-    The main steps cover Prepare a PyTorch Development Environment, Create a PyTorch model for
-    MNIST, About PyTorch Model Training, Perform Training and Save the Model, and Deploy the Model
-    for Inference.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will prepare a PyTorch development environment, download and prepare the MNIST dataset,
-      and create and train a neural network architecture using PyTorch. Learn how to create and
-      train a PyTorch neural network for MNIST digit classification, optimize it with quantization
-      and fusing, and deploy it in an Android application with performance measurement.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers interested in learning how to use PyTorch
-      to create and train a feedforward neural network for digit classification, and also software
-      developers interested in learning how to use and apply optimizations to the trained model
-      in an Android application.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A machine that can run Python3, Visual
-      Studio Code, and Android Studio.; For the OS, you can use Windows, Linux, or macOS.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Android Studio and Visual Studio Code, Windows,
-      Linux, and macOS environments, and Arm platforms such as Cortex-A and Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Prepare a PyTorch Development Environment, Create
-      a PyTorch model for MNIST, About PyTorch Model Training, Perform Training and Save the Model,
-      and Deploy the Model for Inference.
-# END generated_summary_faq
+
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/cross-platform/remoteit/_index.md b/content/learning-paths/cross-platform/remoteit/_index.md
index ce789f72f5..7545103ab7 100644
--- a/content/learning-paths/cross-platform/remoteit/_index.md
+++ b/content/learning-paths/cross-platform/remoteit/_index.md
@@ -21,53 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 927cfebb8ebf9595922dad115c9a8d10900e43c4f80e73e6102a71e3e4ca2da1
-  summary: >-
-    Learn how to install and configure Remote.It for secure remote device access using SSH and
-    other services, with proxy and peer-to-peer connection options. It is designed for software
-    developers who want to use Remote.It to establish private network connections between users
-    and devices or devices to device. By the end, you will be able to install Remote.It on target
-    devices (devices you would like to access remotely), access your Remote.It enabled devices
-    from anywhere, and understand the different types of network connections (proxy vs. Peer to
-    peer). It focuses on tools and technologies such as Remote.It, Linux, Windows, and macOS environments,
-    and Arm platforms including Neoverse and Cortex-A. The main steps cover Remote.It Packages,
-    Installing the Remote.It Device Package, Remote.It CLI, and Connections.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install Remote.It on target devices (devices you would like to access remotely),
-      access your Remote.It enabled devices from anywhere, and understand the different types
-      of network connections (proxy vs. Peer to peer). Learn how to install and configure Remote.It
-      for secure remote device access using SSH and other services, with proxy and peer-to-peer
-      connection options.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to use Remote.It to establish
-      private network connections between users and devices or devices to device.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows, macOS, or Linux computer
-      which you will use to configure your devices as well as connect to your remote devices.;
-      A device/computer to which you would like remote access. A device can be a Windows, Mac,
-      or Linux computer including development kits such as Raspberry Pi or cloud-hosted such as
-      within Arm Virtual Hardware or within AWS. You will need a method to control this device
-      before Remote.It is deployed which can be local access or access via another remote connectivity
-      solution (Remote Desktop, VPN, etc.); Determine if your device that you would like to access
-      remotely also needs to make connections to other Remote.It devices.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Remote.It, Linux, Windows, and macOS environments,
-      and Arm platforms such as Neoverse and Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Remote.It Packages, Installing the Remote.It Device
-      Package, Remote.It CLI, and Connections.
-# END generated_summary_faq
+
 
 author: Brenda Strech
 
diff --git a/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md b/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
index b3e7acbb8b..1d572f2e5b 100644
--- a/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
+++ b/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
@@ -17,42 +17,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 9825df99004e981fadce5884b40b2152f4e43064cbba2cd2129fd90006090678
-  summary: >-
-    Learn how to use the C99 restrict keyword to indicate non-overlapping memory regions and enable
-    better compiler optimizations for vectorization on Arm platforms. It is designed for C developers
-    who are interested in software optimization. By the end, you will be able to learn the importance
-    of using the `restrict` keyword in C correctly. It focuses on tools and technologies such
-    as GCC, Clang, SVE2, and Runbook, Linux environments, and Arm platforms including Aarch64,
-    Armv8-a, and Armv9-a. The main steps cover What problem does restrict solve?, Another example
-    with SVE2, and When can you use restrict.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will learn the importance of using the `restrict` keyword in C correctly. Learn how
-      to use the C99 restrict keyword to indicate non-overlapping memory regions and enable better
-      compiler optimizations for vectorization on Arm platforms.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for C developers who are interested in software optimization.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm computer running Linux OS and
-      a recent version of compiler (Clang or GCC) installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including GCC, Clang, SVE2, and Runbook, Linux environments,
-      and Arm platforms such as Aarch64, Armv8-a, and Armv9-a.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around What problem does restrict solve?, Another example
-      with SVE2, and When can you use restrict.
-# END generated_summary_faq
+
 
 author: Konstantinos Margaritis
 
diff --git a/content/learning-paths/cross-platform/rust_armds/_index.md b/content/learning-paths/cross-platform/rust_armds/_index.md
index 98712c7968..386b528c6f 100644
--- a/content/learning-paths/cross-platform/rust_armds/_index.md
+++ b/content/learning-paths/cross-platform/rust_armds/_index.md
@@ -19,43 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: f237cd239e5886b21bd5bee88dcd9f95d88eda9a5c2eea363cc9db252e0c6e9f
-  summary: >-
-    Learn how to build an embedded Rust application for Arm processors, run it on a Fixed Virtual
-    Platform, and debug it using Arm Development Studio. It is designed for embedded application
-    developers to get started with Rust. By the end, you will be able to build an embedded application
-    in Rust, run the application on a Fixed Virtual Platform (FVP), and debug the application
-    with Arm Development Studio. It focuses on tools and technologies such as IP Explorer, Baremetal
-    environments, and Arm platforms including Cortex-A, Cortex-R, and Cortex-M. The main steps
-    cover Install tools and build an example and Run the example on FVP and debug with Arm Debugger.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build an embedded application in Rust, run the application on a Fixed Virtual Platform
-      (FVP), and debug the application with Arm Development Studio. Learn how to build an embedded
-      Rust application for Arm processors, run it on a Fixed Virtual Platform, and debug it using
-      Arm Development Studio.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for embedded application developers to get started with Rust.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An installation of Arm Development Studio.;
-      A basic understanding of Rust programming.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including IP Explorer, Baremetal environments, and Arm platforms
-      such as Cortex-A, Cortex-R, and Cortex-M.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Install tools and build an example and Run the example
-      on FVP and debug with Arm Debugger.
-# END generated_summary_faq
+
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/cross-platform/simd-info-demo/_index.md b/content/learning-paths/cross-platform/simd-info-demo/_index.md
index 826273b033..76ababaf4c 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/_index.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/_index.md
@@ -18,48 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 7a40efa6d83b4629888f9622260e6e9aa9192db836b203fa4bf388cbe636b7e6
-  summary: >-
-    Learn how to use SIMD.info to port SIMD intrinsics across Arm architectures, including navigation,
-    search, and comparison features for finding equivalent instructions. It is designed for software
-    developers who are interested in porting SIMD code across Arm platforms. By the end, you will
-    be able to describe how to use SIMD.info's tools and features, such as navigation, search,
-    and comparison, to simplify the process of finding equivalent SIMD intrinsics between architectures
-    to improve code portability. It focuses on tools and technologies such as GCC, Clang, Rust,
-    and Runbook, Linux environments, and Arm platforms including AArch64, Armv8-A, and Armv9-A.
-    The main steps cover Overview, SIMD.info Features, Example Program, Porting Process, and Code
-    Verification.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will describe how to use SIMD.info's tools and features, such as navigation, search,
-      and comparison, to simplify the process of finding equivalent SIMD intrinsics between architectures
-      to improve code portability. Learn how to use SIMD.info to port SIMD intrinsics across Arm
-      architectures, including navigation, search, and comparison features for finding equivalent
-      instructions.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is for software developers who are interested in porting SIMD code across
-      Arm platforms.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A basic understanding of SIMD.; Access
-      to an Arm platform with a SIMD-supported engine, installed with recent versions of a C compiler
-      such as Clang or GCC.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including GCC, Clang, Rust, and Runbook, Linux environments,
-      and Arm platforms such as AArch64, Armv8-A, and Armv9-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview, SIMD.info Features, Example Program, Porting
-      Process, and Code Verification.
-# END generated_summary_faq
+
 
 author: 
     - Georgios Mermigkis
diff --git a/content/learning-paths/cross-platform/simd-loops/_index.md b/content/learning-paths/cross-platform/simd-loops/_index.md
index 4af0091924..a784ea7d36 100644
--- a/content/learning-paths/cross-platform/simd-loops/_index.md
+++ b/content/learning-paths/cross-platform/simd-loops/_index.md
@@ -22,53 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: b1c43e1bf971db4582ca358c98dab2c7e6e047d6c79bfcc0db148bc575f33679
-  summary: >-
-    Learn how to write high-performance SIMD code using the SIMD Loops project, with hands-on
-    examples demonstrating SVE, SVE2, and SME2 features on Arm processors. It is designed for
-    software developers who want to learn how to use the full range of features available in SVE,
-    SVE2, and SME2 to improve software performance on Arm processors. By the end, you will be
-    able to improve SIMD code performance using Scalable Vector Extension (SVE) and Scalable Matrix
-    Extension (SME), describe what SIMD Loops contains and how kernels are organized across scalar,
-    Neon, SVE, SVE2, and SME2 variants, and build and run a selected kernel with the provided
-    runner and validate correctness against the C reference. It focuses on tools and technologies
-    such as C, CPP, GCC, Clang, and SME2, Linux and macOS environments, and Arm platforms including
-    Neoverse and Cortex-A. The main steps cover About Single Instruction, Multiple Data loops,
-    Using SIMD Loops, Code example, and Learning with SIMD Loops.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will improve SIMD code performance using Scalable Vector Extension (SVE) and Scalable
-      Matrix Extension (SME), describe what SIMD Loops contains and how kernels are organized
-      across scalar, Neon, SVE, SVE2, and SME2 variants, and build and run a selected kernel with
-      the provided runner and validate correctness against the C reference. Learn how to write
-      high-performance SIMD code using the SIMD Loops project, with hands-on examples demonstrating
-      SVE, SVE2, and SME2 features on Arm processors.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers who want to learn how to use the full
-      range of features available in SVE, SVE2, and SME2 to improve software performance on Arm
-      processors.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An AArch64 computer running Linux or
-      macOS. You can use cloud instances, refer to [Get started with Arm-based cloud instances](/learning-paths/servers-and-cloud-computing/csp/)
-      for a list of cloud service providers; Some familiarity with SIMD programming and Neon intrinsics;
-      Recent toolchains that support SVE/SME (GCC 13+ or Clang 16+ recommended).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including C, CPP, GCC, Clang, and SME2, Linux and macOS environments,
-      and Arm platforms such as Neoverse and Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around About Single Instruction, Multiple Data loops, Using
-      SIMD Loops, Code example, and Learning with SIMD Loops.
-# END generated_summary_faq
+
 
 author:
     - Alejandro Martinez Vicente
diff --git a/content/learning-paths/cross-platform/simd-on-rust/_index.md b/content/learning-paths/cross-platform/simd-on-rust/_index.md
index 97930e3717..ad254c97d0 100644
--- a/content/learning-paths/cross-platform/simd-on-rust/_index.md
+++ b/content/learning-paths/cross-platform/simd-on-rust/_index.md
@@ -20,47 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: a051c519c1a4969f30d5a81e46823e77f0c15f163011b3a38f4c05104d853249
-  summary: >-
-    Learn how to write SIMD code in Rust on Arm platforms using Neon intrinsics, portable SIMD
-    abstractions, and optimize performance with architecture-specific instructions. It is designed
-    for software developers who want to take advantage of SIMD code on Arm systems using Rust.
-    By the end, you will be able to write SIMD code with Rust using std::arch and Neon intrinsics
-    on Arm, use portable SIMD abstractions with std::simd for cross-platform code, and apply feature
-    detection and target attributes for architecture-specific optimizations. It focuses on tools
-    and technologies such as GCC, Clang, Rust, and Runbook, Linux environments, and Arm platforms
-    including Cortex-A and Neoverse. The main steps cover Introduction to Rust, Arm SIMD on Rust,
-    Inlining Intrinsics, Matrix transpose, and A more complicated example.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will write SIMD code with Rust using std::arch and Neon intrinsics on Arm, use portable
-      SIMD abstractions with std::simd for cross-platform code, and apply feature detection and
-      target attributes for architecture-specific optimizations. Learn how to write SIMD code
-      in Rust on Arm platforms using Neon intrinsics, portable SIMD abstractions, and optimize
-      performance with architecture-specific instructions.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers who want to take advantage of SIMD code
-      on Arm systems using Rust.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm-based computer with recent versions
-      of a C compiler (Clang or GCC) and a Rust compiler installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including GCC, Clang, Rust, and Runbook, Linux environments,
-      and Arm platforms such as Cortex-A and Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Introduction to Rust, Arm SIMD on Rust, Inlining Intrinsics,
-      Matrix transpose, and A more complicated example.
-# END generated_summary_faq
+
 
 author: Konstantinos Margaritis
 
diff --git a/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md b/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md
index 0d4f4d23e9..92f6efddc4 100644
--- a/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md
+++ b/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md
@@ -24,54 +24,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 36f7dfe13c8c940abbaad2b61620b3b1c6d97f580de15e573b45ef7760753797
-  summary: >-
-    Learn how to profile and optimize ExecuTorch models using SME2 acceleration on Arm platforms,
-    including operator-level analysis and performance bottleneck identification. It is designed
-    for developers and performance engineers who deploy ExecuTorch models on Arm devices and want
-    to understand and reduce inference latency. By the end, you will be able to understand how
-    SME2 acceleration changes the performance profile of ExecuTorch models by reducing compute-bound
-    bottlenecks, interpret operator-level and operator-category breakdowns (for example, convolution,
-    GEMM, data movement, and other operators), and identify which operators benefit most from
-    SME2 acceleration and which operators become the new performance bottlenecks. It focuses on
-    tools and technologies such as ExecuTorch, Python, CMake, and SME2, macOS and Android environments,
-    and Arm platforms including Cortex-A and Arm C1. The main steps cover Explore ExecuTorch profiling
-    with SME2, Set up the ExecuTorch profiling environment, Export PyTorch models and analyze
-    performance, and Automate profiling workflows with AI agents.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand how SME2 acceleration changes the performance profile of ExecuTorch
-      models by reducing compute-bound bottlenecks, interpret operator-level and operator-category
-      breakdowns (for example, convolution, GEMM, data movement, and other operators), and identify
-      which operators benefit most from SME2 acceleration and which operators become the new performance
-      bottlenecks. Learn how to profile and optimize ExecuTorch models using SME2 acceleration
-      on Arm platforms, including operator-level analysis and performance bottleneck identification.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for developers and performance engineers who deploy ExecuTorch
-      models on Arm devices and want to understand and reduce inference latency.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Apple Silicon macOS host with Python
-      3.9 or later and CMake 3.29 or later; Basic familiarity with ExecuTorch or PyTorch; Optionally,
-      an Android device with Armv9 and SME2 support for on-device testing (if used, configure
-      power management settings to ensure consistent performance measurements).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including ExecuTorch, Python, CMake, and SME2, macOS and Android
-      environments, and Arm platforms such as Cortex-A and Arm C1.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Explore ExecuTorch profiling with SME2, Set up the
-      ExecuTorch profiling environment, Export PyTorch models and analyze performance, and Automate
-      profiling workflows with AI agents.
-# END generated_summary_faq
+
 
 author: Jason Zhu, Tyler Mullenbach, Damien Dooley 
 
diff --git a/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md b/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md
index f9e72f7db2..a3d0643afa 100644
--- a/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md
+++ b/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md
@@ -23,59 +23,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 09142517ecc64fba821062e1af4db3ac9d72f9adcd3f10c12d6fd5a4cf3daaf4
-  summary: >-
-    Learn how to deploy Tinkerblox UltraEdge HPC-I for edge AI and mixed workloads on Arm platforms,
-    including installation and configuration on Debian, Ubuntu, and Yocto systems. It is designed
-    for business, R&D, and engineering teams seeking to optimize CPU and GPU infrastructure utilization
-    while reducing total cost of ownership on edge and constrained environments. It's ideal for
-    innovation and development teams building next-generation AI workloads using alternative runtime
-    environments and packaging technologies. By the end, you will be able to understand the layered
-    architecture of UltraEdge core, boost, and prime, build applications using the UltraEdge MicroStack,
-    and deploy the MicroPacs on Linux-based compute systems and scale to cloud or data-center
-    environments. It focuses on tools and technologies such as Tinkerblox, Linux and other environments,
-    Arm platforms including Neoverse, and cloud platforms such as Google Cloud. The main steps
-    cover Understand UltraEdge HPC-I architecture for edge AI and mixed workloads, Provision a
-    Google Axion C4A VM for Yocto image builds on Arm, Build and install Yocto images for NXP
-    S32G-VNP-GLDBOX3 with UltraEdge, Install UltraEdge on Debian and Ubuntu for Edge AI workloads,
-    and Run and manage UltraEdge HPC-I for AI and mixed workloads on Arm.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand the layered architecture of UltraEdge core, boost, and prime, build
-      applications using the UltraEdge MicroStack, and deploy the MicroPacs on Linux-based compute
-      systems and scale to cloud or data-center environments. Learn how to deploy Tinkerblox UltraEdge
-      HPC-I for edge AI and mixed workloads on Arm platforms, including installation and configuration
-      on Debian, Ubuntu, and Yocto systems.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for business, R&D, and engineering teams seeking to optimize CPU
-      and GPU infrastructure utilization while reducing total cost of ownership on edge and constrained
-      environments. It's ideal for innovation and development teams building next-generation AI
-      workloads using alternative runtime environments and packaging technologies.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Experience using Linux on embedded or
-      SBC platforms; Understanding of container runtimes (containerd) and CNI networking; Basic
-      knowledge of communication protocols (MQTT, HTTP, and others); Familiarity with edge-cloud
-      architectures and data-flow orchestration.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Tinkerblox, Linux and other environments, Arm platforms
-      such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Understand UltraEdge HPC-I architecture for edge AI
-      and mixed workloads, Provision a Google Axion C4A VM for Yocto image builds on Arm, Build
-      and install Yocto images for NXP S32G-VNP-GLDBOX3 with UltraEdge, Install UltraEdge on Debian
-      and Ubuntu for Edge AI workloads, and Run and manage UltraEdge HPC-I for AI and mixed workloads
-      on Arm.
-# END generated_summary_faq
+
 
 author: Tinkerblox
 
diff --git a/content/learning-paths/cross-platform/topdown-compare/_index.md b/content/learning-paths/cross-platform/topdown-compare/_index.md
index 951f24a600..b13dfbebb2 100644
--- a/content/learning-paths/cross-platform/topdown-compare/_index.md
+++ b/content/learning-paths/cross-platform/topdown-compare/_index.md
@@ -21,58 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 5f4b05e3d962476c67c4a4400c8fa71f04412f3f0354d5c3123f38af57791304
-  summary: >-
-    Learn how to compare Arm Neoverse and Intel x86 top-down performance analysis methodologies
-    using PMU counters, Linux Perf, and topdown-tool to identify bottlenecks across architectures.
-    It is designed for software developers and performance engineers who want to understand the
-    similarities and differences between Arm Neoverse and Intel x86 top-down performance analysis
-    using PMU counters, Linux Perf, and the topdown-tool. By the end, you will be able to compare
-    Intel x86 multi-level hierarchical methodology with Arm Neoverse micro-architecture exploration
-    methodology, execute performance analysis using Linux Perf on x86 and topdown-tool on Arm
-    systems, and analyze Backend Bound, Frontend Bound, Bad Speculation, and Retiring categories
-    across both architectures. It focuses on tools and technologies such as GCC, Clang, Perf,
-    and topdown-tool, Linux environments, and Arm platforms including Neoverse. The main steps
-    cover Analyze Intel x86 and Arm Neoverse top-down performance methodologies, Understand Intel
-    x86 multilevel hierarchical top-down analysis, Understand Arm Neoverse top-down analysis,
-    Evaluate cross-platform PMU counter differences, and Measure cross-platform performance with
-    topdown-tool and Perf PMU counters.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will compare Intel x86 multi-level hierarchical methodology with Arm Neoverse micro-architecture
-      exploration methodology, execute performance analysis using Linux Perf on x86 and topdown-tool
-      on Arm systems, and analyze Backend Bound, Frontend Bound, Bad Speculation, and Retiring
-      categories across both architectures. Learn how to compare Arm Neoverse and Intel x86 top-down
-      performance analysis methodologies using PMU counters, Linux Perf, and topdown-tool to identify
-      bottlenecks across architectures.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers and performance engineers who want to
-      understand the similarities and differences between Arm Neoverse and Intel x86 top-down
-      performance analysis using PMU counters, Linux Perf, and the topdown-tool.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Familiarity with performance analysis
-      on Linux systems using Perf and PMU counters; Access to Arm Neoverse V2 and Intel x86 Linux
-      systems to run the code example; Basic understanding of CPU pipeline concepts and performance
-      bottlenecks.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including GCC, Clang, Perf, and topdown-tool, Linux environments,
-      and Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Analyze Intel x86 and Arm Neoverse top-down performance
-      methodologies, Understand Intel x86 multilevel hierarchical top-down analysis, Understand
-      Arm Neoverse top-down analysis, Evaluate cross-platform PMU counter differences, and Measure
-      cross-platform performance with topdown-tool and Perf PMU counters.
-# END generated_summary_faq
+
 
 author:
     - Jason Andrews
diff --git a/content/learning-paths/cross-platform/vectorization-comparison/_index.md b/content/learning-paths/cross-platform/vectorization-comparison/_index.md
index a0cc7f74fa..8a503d001b 100644
--- a/content/learning-paths/cross-platform/vectorization-comparison/_index.md
+++ b/content/learning-paths/cross-platform/vectorization-comparison/_index.md
@@ -20,47 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 26450ae17f7ed4242c52456c2780ffce5fad36b56dfc4a8482e8f236f855e134
-  summary: >-
-    Learn how to migrate x86-64 SIMD code to Arm64 by mapping Intel SSE/AVX to Arm Neon, SVE,
-    and SME, with code examples and migration strategies using autovectorization or intrinsics.
-    It is designed for developers migrating vectorized (SIMD) code from x86-64 to Arm64. By the
-    end, you will be able to identify how Arm vector extensions including Neon, Scalable Vector
-    Extension (SVE), and Scalable Matrix Extension (SME) map to vector extensions from other architectures
-    and plan a migration strategy using autovectorization, intrinsics, or library substitution.
-    It focuses on tools and technologies such as GCC and Clang, Linux environments, and Arm platforms
-    including Neoverse. The main steps cover Migrate SIMD code to the Arm architecture and Explore
-    vector extension code examples.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will identify how Arm vector extensions including Neon, Scalable Vector Extension (SVE),
-      and Scalable Matrix Extension (SME) map to vector extensions from other architectures and
-      plan a migration strategy using autovectorization, intrinsics, or library substitution.
-      Learn how to migrate x86-64 SIMD code to Arm64 by mapping Intel SSE/AVX to Arm Neon, SVE,
-      and SME, with code examples and migration strategies using autovectorization or intrinsics.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for developers migrating vectorized (SIMD) code from x86-64 to
-      Arm64.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Familiarity with vector extensions,
-      SIMD programming, and compiler intrinsics; Access to Linux systems with Neon and SVE support.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including GCC and Clang, Linux environments, and Arm platforms
-      such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Migrate SIMD code to the Arm architecture and Explore
-      vector extension code examples.
-# END generated_summary_faq
+
 
 author:
     - Jason Andrews
diff --git a/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md b/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md
index a4f4325474..aad9435bb0 100644
--- a/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md
+++ b/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md
@@ -17,44 +17,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 14a22194d3fc73ebebb62db9c7cfbeabe0bd9acdaf340b2df52072be65d98655
-  summary: >-
-    Learn how to optimize SIMD performance on Arm by restructuring data layouts from Array-of-Structures
-    to Structure-of-Arrays, with practical examples using Neon and SVE intrinsics. It is designed
-    for C/C++ developers who are interested in improving the performance of SIMD code. By the
-    end, you will be able to comprehend the importance of data layout when writing SIMD code.
-    It focuses on tools and technologies such as GCC, Clang, and Runbook, Linux environments,
-    and Arm platforms including Neoverse and Cortex-A. The main steps cover What exactly is data
-    layout?, Improve data alignment, Increase complexity, Write hand optimized SIMD code, and
-    Structure of arrays.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will comprehend the importance of data layout when writing SIMD code. Learn how to optimize
-      SIMD performance on Arm by restructuring data layouts from Array-of-Structures to Structure-of-Arrays,
-      with practical examples using Neon and SVE intrinsics.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for C/C++ developers who are interested in improving the performance
-      of SIMD code.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm computer running Linux and a
-      recent version of Clang or the GNU compiler (gcc) installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including GCC, Clang, and Runbook, Linux environments, and
-      Arm platforms such as Neoverse and Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around What exactly is data layout?, Improve data alignment,
-      Increase complexity, Write hand optimized SIMD code, and Structure of arrays.
-# END generated_summary_faq
+
 
 author: Konstantinos Margaritis
 
diff --git a/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md b/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
index 7d9e54ca9d..586431d3a1 100644
--- a/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
+++ b/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
@@ -24,47 +24,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: bf887ef2481b51cf6c32e49e3eb1d5577346b7b9b1e4d6a604c559597b5306f0
-  summary: >-
-    Learn how to sample and profile CPU instructions using WindowsPerf with Arm Statistical Profiling
-    Extension (SPE) on Windows on Arm, demonstrated with CPython workload analysis. It is designed
-    for developers who would like to learn about sampling CPU instructions with WindowsPerf and
-    the Arm Statistical Profiling Extension (SPE). By the end, you will be able to use WindowsPerf
-    with a native Windows on Arm workload, describe the basic concepts of sampling with Arm SPE,
-    and explore the WindowsPerf command line. It focuses on tools and technologies such as WindowsPerf,
-    Python, and perf, Windows environments, and Arm platforms including Neoverse and Cortex-A.
-    The main steps cover Overview of Arm Statistical Profiling Extension, Setup, WindowsPerf Sample
-    using SPE, WindowsPerf Record using SPE, and Summary.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will use WindowsPerf with a native Windows on Arm workload, describe the basic concepts
-      of sampling with Arm SPE, and explore the WindowsPerf command line. Learn how to sample
-      and profile CPU instructions using WindowsPerf with Arm Statistical Profiling Extension
-      (SPE) on Windows on Arm, demonstrated with CPython workload analysis.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers who would like to learn about sampling CPU
-      instructions with WindowsPerf and the Arm Statistical Profiling Extension (SPE).
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows on Arm desktop or development
-      machine, with CPU support for SPE.; An installation of [WindowsPerf](/install-guides/wperf).;
-      An installation of [Visual Studio](/install-guides/vs-woa/).; An installation of [Git](/install-guides/git-woa/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including WindowsPerf, Python, and perf, Windows environments,
-      and Arm platforms such as Neoverse and Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview of Arm Statistical Profiling Extension, Setup,
-      WindowsPerf Sample using SPE, WindowsPerf Record using SPE, and Summary.
-# END generated_summary_faq
+
 
 author: Przemyslaw Wirkus
 
diff --git a/content/learning-paths/cross-platform/woa_azure/_index.md b/content/learning-paths/cross-platform/woa_azure/_index.md
index 0408a20f28..e63fc22cea 100644
--- a/content/learning-paths/cross-platform/woa_azure/_index.md
+++ b/content/learning-paths/cross-platform/woa_azure/_index.md
@@ -19,44 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 367566dfec0a76685b1504c2c1a996e50c55e67b2f4c5515057c0f0f1384ef98
-  summary: >-
-    Learn how to create and connect to a Windows on Arm virtual machine in Microsoft Azure using
-    the Azure Marketplace and RDP. It is designed for software developers interested using Windows
-    on Arm in the Azure cloud. By the end, you will be able to start a Windows on Arm virtual
-    machine in Azure cloud and discover all Arm-based image offerings in the Azure Image Marketplace.
-    It focuses on Windows environments, Arm platforms including Neoverse, and cloud platforms
-    such as Microsoft Azure. The main steps cover Create Windows on Arm virtual machine in Azure
-    cloud.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will start a Windows on Arm virtual machine in Azure cloud and discover all Arm-based
-      image offerings in the Azure Image Marketplace. Learn how to create and connect to a Windows
-      on Arm virtual machine in Microsoft Azure using the Azure Marketplace and RDP.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers interested using Windows on Arm in
-      the Azure cloud.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Azure Cloud account.; An RDP client
-      to connect to your Windows on Arm instance. For more info on RDP clients, see [Remote Desktop
-      clients for Remote Desktop Services and remote PCs](https://learn.microsoft.com/en-us/windows-server/remote/remote-desktop-services/clients/remote-desktop-clients)
-      to get started.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers Windows environments, Arm platforms such as Neoverse, and cloud platforms such
-      as Microsoft Azure.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Create Windows on Arm virtual machine in Azure cloud.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md b/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
index 76d79ebe51..c85fc13f92 100644
--- a/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
+++ b/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
@@ -21,58 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: a56dce27c6a846bcf35b6e9505142f1445b22ff71530f1189700049d7b14e994
-  summary: >-
-    Learn how to build and deploy distributed Zenoh systems on Arm devices like Raspberry Pi,
-    using pub/sub, storage, and queryable models for scalable robotics and IoT applications. It
-    is designed for robotics developers, industrial automation engineers, and IoT system architects
-    who are building distributed, scalable, and low-latency applications. Whether you're using
-    the Robot Operating System (ROS), developing autonomous systems, or designing multi-node communication
-    frameworks, you can use Eclipse Zenoh on Arm-based platforms, both in the cloud and on local
-    devices like Raspberry Pi. By the end, you will be able to understand Zenoh's architecture
-    and how it integrates pub/sub, storage, querying, and computation models, build and run Zenoh
-    examples on both Arm servers and Raspberry Pi, and set up and deploy a multi-node Zenoh system.
-    It focuses on tools and technologies such as ROS 2, C, Raspberry Pi, Zenoh, and Rust, Linux
-    environments, and Arm platforms including Cortex-A and Neoverse. The main steps cover Build
-    scalable communication systems with Eclipse Zenoh, Get started with Zenoh on Raspberry Pi
-    and Arm Linux, Containerize and deploy Zenoh across multiple Raspberry Pi devices, Run a simple
-    Zenoh pub/sub example, and Run a Zenoh storage and query example.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand Zenoh's architecture and how it integrates pub/sub, storage, querying,
-      and computation models, build and run Zenoh examples on both Arm servers and Raspberry Pi,
-      and set up and deploy a multi-node Zenoh system. Learn how to build and deploy distributed
-      Zenoh systems on Arm devices like Raspberry Pi, using pub/sub, storage, and queryable models
-      for scalable robotics and IoT applications.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is for robotics developers, industrial automation engineers, and IoT
-      system architects who are building distributed, scalable, and low-latency applications.
-      Whether you're using the Robot Operating System (ROS), developing autonomous systems, or
-      designing multi-node communication frameworks, you can use Eclipse Zenoh on Arm-based platforms,
-      both in the cloud and on local devices like Raspberry Pi.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: At least two local Cortex-A devices
-      running Linux, such as Raspberry Pi 4 or Pi 5. You can also use Arm servers or cloud instances;
-      Experience with ROS 2 applications.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including ROS 2, C, Raspberry Pi, Zenoh, and Rust, Linux environments,
-      and Arm platforms such as Cortex-A and Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Build scalable communication systems with Eclipse
-      Zenoh, Get started with Zenoh on Raspberry Pi and Arm Linux, Containerize and deploy Zenoh
-      across multiple Raspberry Pi devices, Run a simple Zenoh pub/sub example, and Run a Zenoh
-      storage and query example.
-# END generated_summary_faq
+
 
 author: 
     - Odin Shen
diff --git a/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md b/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
index fc1ed70ab0..8a0261f9c7 100644
--- a/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
@@ -21,49 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: d8c48c293b2d316d5e68e18ab9e81157ad114a64cf2efa6951374a55d25238d6
-  summary: >-
-    Learn how to design and integrate a custom AXI-Lite peripheral with a Cortex-A9 processor
-    on the Zybo Z7-10 board using Vivado, configuring GPIOs to control LEDs based on switch inputs.
-    It is designed for software developers interested in System on Chip Design. By the end, you
-    will be able to configure and integrate an AXI-Lite peripheral with a Cortex-A9 Processing
-    System, program the Cortex-A9 processor to read the state of switches and control the LEDs
-    using a C program, and demonstrate a basic functional system that lights up the LEDs based
-    on the status of the switches. It focuses on tools and technologies such as C, Baremetal environments,
-    and Arm platforms including Cortex-A. The main steps cover Setup a Workspace in Xilinx Vivado,
-    Create a custom AXI4 Peripheral, Connect AXI4 Peripheral to ZYNQ Processing System, and Generate
-    the bitstream and write your application using Vitis IDE.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will configure and integrate an AXI-Lite peripheral with a Cortex-A9 Processing System,
-      program the Cortex-A9 processor to read the state of switches and control the LEDs using
-      a C program, and demonstrate a basic functional system that lights up the LEDs based on
-      the status of the switches. Learn how to design and integrate a custom AXI-Lite peripheral
-      with a Cortex-A9 processor on the Zybo Z7-10 board using Vivado, configuring GPIOs to control
-      LEDs based on switch inputs.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers interested in System on Chip Design.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Some familiarity with Verilog; Basic
-      understanding of System on Chip design; A 'Zybo Z7-10' development board.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including C, Baremetal environments, and Arm platforms such
-      as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Setup a Workspace in Xilinx Vivado, Create a custom
-      AXI4 Peripheral, Connect AXI4 Peripheral to ZYNQ Processing System, and Generate the bitstream
-      and write your application using Vitis IDE.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md b/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
index a7486cf3f0..88f943f28d 100644
--- a/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
@@ -24,53 +24,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 8585bb59efa67b3a92314e4382339fc5c0a14bb458931c0d98f2748379003857
-  summary: >-
-    Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit and run inference
-    on the Ethos-U85 NPU. It is designed for embedded developers who want to deploy a neural network
-    model to an Arm Cortex-M55 microcontroller using ExecuTorch and an Ethos-U85 NPU. By the end,
-    you will be able to compile a MobileNetV2 model for the Ethos-U85 NPU using ExecuTorch's ahead-of-time
-    (AOT) compiler on an Arm-based cloud instance, build ExecuTorch static libraries for bare-metal
-    Cortex-M55 targets, and configure CMSIS project files, memory layout, and linker scripts for
-    an ML workload on the Alif Ensemble E8. It focuses on tools and technologies such as ExecuTorch,
-    PyTorch, GCC, CMSIS-Toolbox, and Python, Baremetal environments, and Arm platforms including
-    Cortex-M and Ethos-U. The main steps cover Set up the Alif Ensemble E8 DevKit, Compile the
-    model on an Arm cloud instance, Create the image classification firmware project, Add the
-    application code, and Configure memory layout and flash settings.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will compile a MobileNetV2 model for the Ethos-U85 NPU using ExecuTorch's ahead-of-time
-      (AOT) compiler on an Arm-based cloud instance, build ExecuTorch static libraries for bare-metal
-      Cortex-M55 targets, and configure CMSIS project files, memory layout, and linker scripts
-      for an ML workload on the Alif Ensemble E8. Deploy a MobileNetV2 image classification model
-      to an Alif Ensemble E8 DevKit and run inference on the Ethos-U85 NPU.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for embedded developers who want to deploy a neural network model
-      to an Arm Cortex-M55 microcontroller using ExecuTorch and an Ethos-U85 NPU.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Experience with C/C++ and embedded development
-      concepts; An [Alif Ensemble E8 DevKit](https://alifsemi.com/support/kits/ensemble-e8devkit/)
-      with a USB-C cable; A SEGGER J-Link debug probe (included in the DevKit); A development
-      machine running macOS on Apple Silicon with Visual Studio Code installed; An AWS account
-      or access to an Arm-based cloud instance for native Arm compilation.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including ExecuTorch, PyTorch, GCC, CMSIS-Toolbox, and Python,
-      Baremetal environments, and Arm platforms such as Cortex-M and Ethos-U.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Set up the Alif Ensemble E8 DevKit, Compile the model
-      on an Arm cloud instance, Create the image classification firmware project, Add the application
-      code, and Configure memory layout and flash settings.
-# END generated_summary_faq
+
 
 author: Gabriel Peterson
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md b/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
index 809dd31563..22546a9728 100644
--- a/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
@@ -24,47 +24,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 3ddb97eb97a4c7ede7951410086198ee793a9d452a79b607f211873971bd375d
-  summary: >-
-    Learn how to build a motion-detection device with Raspberry Pi Pico (RP2040 Cortex-M0+) using
-    Arduino IDE, PIR sensors, and interrupt-driven programming on baremetal. It is designed for
-    software developers interested in embedded programming. By the end, you will be able to understand
-    the basics of embedded programming, know the differences between embedded and application
-    development, and write a simple embedded application. It focuses on tools and technologies
-    such as Arduino, Baremetal environments, and Arm platforms including Cortex-M. The main steps
-    cover About Embedded Programming, Application Programming, Embedded Programming, Embedded
-    Programming on Arm, and Build a smart device prototype.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand the basics of embedded programming, know the differences between embedded
-      and application development, and write a simple embedded application. Learn how to build
-      a motion-detection device with Raspberry Pi Pico (RP2040 Cortex-M0+) using Arduino IDE,
-      PIR sensors, and interrupt-driven programming on baremetal.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers interested in embedded programming.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: The [Arduino IDE with the RP2040 board
-      support package](/install-guides/arduino-pico/) installed on your computer; A [Raspberry
-      Pi Pico](https://www.raspberrypi.com/products/raspberry-pi-pico/) board; A [PIR sensor](https://www.amazon.com/HiLetgo-HC-SR501-Infrared-Sensor-Arduino/dp/B07KZW86YR/ref=sr_1_3?keywords=pir+sensor&qid=1698432931&sr=8-3)
-      for detecting motion; A [peizo-electric buzzer](https://www.amazon.com/mxuteuk-Electronic-Computers-Printers-Components/dp/B07VK1GJ9X/ref=sr_1_4?crid=2FAXYI17HZKDB&keywords=piezo+buzzer&qid=1698432968&sprefix=peizo%2Caps%2C148&sr=8-4)
-      for signaling motion.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Arduino, Baremetal environments, and Arm platforms
-      such as Cortex-M.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around About Embedded Programming, Application Programming,
-      Embedded Programming, Embedded Programming on Arm, and Build a smart device prototype.
-# END generated_summary_faq
+
 
 author: Michael Hall
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/armds/_index.md b/content/learning-paths/embedded-and-microcontrollers/armds/_index.md
index fd47030083..ab1dc6d200 100644
--- a/content/learning-paths/embedded-and-microcontrollers/armds/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/armds/_index.md
@@ -18,47 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 0103f51d42c230dbe75ff5b78ac15a33dfd2f2c0f4906fb665a8dd681512d2e1
-  summary: >-
-    Learn how to import and build example projects in Arm Development Studio and debug embedded
-    applications using Fixed Virtual Platforms (FVPs) or hardware with DSTREAM debug probes. It
-    is designed for embedded software developers new to Arm Development Studio. By the end, you
-    will be able to import and build an example project, debug the example code running on a Fixed
-    Virtual Platform (FVP), and debug the example code running on a board with a DSTREAM debug
-    probe. It focuses on tools and technologies such as Arm Development Studio, Arm Compiler for
-    Embedded, Arm Fast Models, and DSTREAM, Baremetal environments, and Arm platforms including
-    Cortex-A, Cortex-R, Cortex-M, and Neoverse. The main steps cover Import and build example
-    project, Debug the example, and Other compilers and project types.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will import and build an example project, debug the example code running on a Fixed
-      Virtual Platform (FVP), and debug the example code running on a board with a DSTREAM debug
-      probe. Learn how to import and build example projects in Arm Development Studio and debug
-      embedded applications using Fixed Virtual Platforms (FVPs) or hardware with DSTREAM debug
-      probes.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for embedded software developers new to Arm Development Studio.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Some familiarity with embedded programming
-      is assumed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Arm Development Studio, Arm Compiler for Embedded,
-      Arm Fast Models, and DSTREAM, Baremetal environments, and Arm platforms such as Cortex-A,
-      Cortex-R, Cortex-M, and Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Import and build example project, Debug the example,
-      and Other compilers and project types.
-# END generated_summary_faq
+
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/asm/_index.md b/content/learning-paths/embedded-and-microcontrollers/asm/_index.md
index 6a541e946c..9654cf0bbb 100644
--- a/content/learning-paths/embedded-and-microcontrollers/asm/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/asm/_index.md
@@ -8,46 +8,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 24245102b7b6cefd0d4f67fbfaff1fb27c913de33665929ec3cb15293a1b3b51
-  summary: >-
-    Learn how to write mixed C and assembly programs for Cortex-M microcontrollers using Keil
-    MDK, following Arm Procedure Call Standard conventions. It is designed for software developers
-    who are interested in programming microcontrollers with C/Assembly. By the end, you will be
-    able to write a mixed C program and assembly language subroutines for the microcontroller,
-    call the subroutines written in assembly in a C function, and use Arm register calling conventions
-    when writing subroutines in assembly language. It focuses on tools and technologies such as
-    Keil MDK, Baremetal environments, and Arm platforms including Cortex-M. The main steps cover
-    Keil MDK versions, Setting up a Project in Keil Studio (VS Code), Setting up a Project in
-    Keil MDK (μVision), and Writing assembly functions.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will write a mixed C program and assembly language subroutines for the microcontroller,
-      call the subroutines written in assembly in a C function, and use Arm register calling conventions
-      when writing subroutines in assembly language. Learn how to write mixed C and assembly programs
-      for Cortex-M microcontrollers using Keil MDK, following Arm Procedure Call Standard conventions.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who are interested in programming
-      microcontrollers with C/Assembly.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Some familiarity with C/Assembly.; An
-      installation of Keil MDK.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Keil MDK, Baremetal environments, and Arm platforms
-      such as Cortex-M.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Keil MDK versions, Setting up a Project in Keil Studio
-      (VS Code), Setting up a Project in Keil MDK (μVision), and Writing assembly functions.
-# END generated_summary_faq
+
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
index da0fbac19d..e6f5bc7caf 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
@@ -22,44 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 983afd3fcc565266c8f12588086e36d736540e23d0e748c94b5d2a45ab53d6ab
-  summary: >-
-    Learn how to create a custom Balena OS image, run it on Arm Virtual Hardware, and deploy IoT
-    applications to a virtual Raspberry Pi 4 device. It is designed for embedded software developers
-    interested in Balena OS. By the end, you will be able to start a Raspberry Pi Arm Virtual
-    Hardware instance, create a Balena OS image for Arm Virtual Hardware, and deploy a pre-built
-    Balena Hub application. It focuses on tools and technologies such as Arm Virtual Hardware,
-    balenaCloud, Raspberry Pi, and BalenaOS, Linux environments, and Arm platforms including Cortex-A.
-    The main steps cover Prepare a custom Balena OS image, Install Balena OS on AVH, and Deploy
-    an application to your device.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will start a Raspberry Pi Arm Virtual Hardware instance, create a Balena OS image for
-      Arm Virtual Hardware, and deploy a pre-built Balena Hub application. Learn how to create
-      a custom Balena OS image, run it on Arm Virtual Hardware, and deploy IoT applications to
-      a virtual Raspberry Pi 4 device.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for embedded software developers interested in Balena OS.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Balena Cloud account; An Arm Virtual
-      Hardware account; A Linux machine with root access; Some familiarity with embedded Linux.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Arm Virtual Hardware, balenaCloud, Raspberry Pi,
-      and BalenaOS, Linux environments, and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Prepare a custom Balena OS image, Install Balena OS
-      on AVH, and Deploy an application to your device.
-# END generated_summary_faq
+
 
 author: Michael Hall
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
index bb02c2cdf2..5ee92c8e18 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
@@ -20,44 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 85845fd4a47960bca053aed8d87c1d1442a7f5de0be5caff349fc92c6980b07e
-  summary: >-
-    Learn how to set up AWS IoT Greengrass Core on Arm Virtual Hardware and deploy AWS Greengrass
-    components to a virtual Raspberry Pi 4 device. It is designed for embedded software developers
-    interested in AWS IoT Greengrass. By the end, you will be able to start a Raspberry Pi Arm
-    Virtual Hardware instance and deploy pre-built AWS IoT Greengrass components on Arm Virtual
-    Hardware. It focuses on tools and technologies such as Arm Virtual Hardware, AWS IoT Greengrass,
-    and Raspberry Pi, Linux environments, and Arm platforms including Cortex-A. The main steps
-    cover Set up your accounts and create a virtual device and Deploy an AWS IoT Greengrass component
-    to your device.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will start a Raspberry Pi Arm Virtual Hardware instance and deploy pre-built AWS IoT
-      Greengrass components on Arm Virtual Hardware. Learn how to set up AWS IoT Greengrass Core
-      on Arm Virtual Hardware and deploy AWS Greengrass components to a virtual Raspberry Pi 4
-      device.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for embedded software developers interested in AWS IoT Greengrass.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Amazon AWS account; An Arm Virtual
-      Hardware account; Some familiarity with embedded Linux.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Arm Virtual Hardware, AWS IoT Greengrass, and Raspberry
-      Pi, Linux environments, and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Set up your accounts and create a virtual device and
-      Deploy an AWS IoT Greengrass component to your device.
-# END generated_summary_faq
+
 
 author: Michael Hall
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
index 738f2cd217..7e83aa907d 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
@@ -20,46 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: bd39d50c93219201ead5de5da627447a91a82ea9c65e232350facd0c3516bedc
-  summary: >-
-    Learn how to build Matter reference examples on Arm Virtual Hardware, demonstrate device communication,
-    and automate testing with GitHub Actions CI/CD workflows. It is designed for embedded software
-    developers new to Arm Virtual Hardware. By the end, you will be able to instantiate Arm Virtual
-    Hardware instances, build and run Matter examples on Arm Virtual Hardware, and demonstrate
-    communication between two virtual hardware targets. It focuses on tools and technologies such
-    as Matter, Arm Virtual Hardware, and GitHub, Linux environments, and Arm platforms including
-    Cortex-A. The main steps cover Prepare AVH instances of Raspberry Pi 4, Build and run Matter
-    examples on Arm Virtual Hardware, Manage development in a CI/CD workflow with Self-Hosted
-    Runner, and Control Arm Virtual Hardware with API.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will instantiate Arm Virtual Hardware instances, build and run Matter examples on Arm
-      Virtual Hardware, and demonstrate communication between two virtual hardware targets. Learn
-      how to build Matter reference examples on Arm Virtual Hardware, demonstrate device communication,
-      and automate testing with GitHub Actions CI/CD workflows.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for embedded software developers new to Arm Virtual Hardware.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Some familiarity with embedded programming
-      is assumed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Matter, Arm Virtual Hardware, and GitHub, Linux
-      environments, and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Prepare AVH instances of Raspberry Pi 4, Build and
-      run Matter examples on Arm Virtual Hardware, Manage development in a CI/CD workflow with
-      Self-Hosted Runner, and Control Arm Virtual Hardware with API.
-# END generated_summary_faq
+
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
index c9cd78b4a2..07c76a1e34 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
@@ -21,46 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 398077be1406d0787b0a5d8dc254472da0d125b51b0907769cd4a3516ce9111b
-  summary: >-
-    Learn how to export and compile a PaddleOCR text recognition model using TVMC and deploy it
-    on the Arm Corstone-300 FVP with Cortex-M55 processors. It is designed for software developers
-    interested in using PaddlePaddle for Arm Cortex-M processors. By the end, you will be able
-    to export Paddle inference model, compile Paddle inference model with TVMC, and deploy on
-    the AVH Corstone-300 platform with Arm Cortex-M55. It focuses on tools and technologies such
-    as Arm Virtual Hardware, GCC, Paddle, and TVMC, Baremetal environments, and Arm platforms
-    including Cortex-M and Corstone. The main steps cover Overview of OCR and Deploy the PaddleOCR
-    model.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will export Paddle inference model, compile Paddle inference model with TVMC, and deploy
-      on the AVH Corstone-300 platform with Arm Cortex-M55. Learn how to export and compile a
-      PaddleOCR text recognition model using TVMC and deploy it on the Arm Corstone-300 FVP with
-      Cortex-M55 processors.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers interested in using PaddlePaddle for
-      Arm Cortex-M processors.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Some familiarity with embedded programming;
-      Some familiarity with AI/ML software development; An Amazon Web Services(AWS) [account](https://aws.amazon.com/)
-      to subscribe [Arm Virtual Hardware](https://aws.amazon.com/marketplace/pp/prodview-urbpq7yo5va7g)
-      Amazon Machine Image(AMI).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Arm Virtual Hardware, GCC, Paddle, and TVMC, Baremetal
-      environments, and Arm platforms such as Cortex-M and Corstone.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview of OCR and Deploy the PaddleOCR model.
-# END generated_summary_faq
+
 
 author: Liliya Wu
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
index 7941d570af..a7aa92aeec 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
@@ -18,41 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: aaff31b8917320c53825f3e7410b703bc7c7e273b1963fd80727b6b874f50566
-  summary: >-
-    Learn how to create and integrate a virtual LED peripheral using the Virtual IO interface
-    of Arm Virtual Hardware to simulate real-world peripherals. It is designed for software developers
-    new to Arm Virtual Hardware and its features. By the end, you will be able to create and integrate
-    an LED peripheral with the Virtual IO (VIO) interface of AVH. It focuses on tools and technologies
-    such as Arm Virtual Hardware, Baremetal environments, and Arm platforms including Cortex-M
-    and Corstone. The main steps cover Create a peripheral using Virtual Input/Output (VIO).
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create and integrate an LED peripheral with the Virtual IO (VIO) interface of AVH.
-      Learn how to create and integrate a virtual LED peripheral using the Virtual IO interface
-      of Arm Virtual Hardware to simulate real-world peripherals.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers new to Arm Virtual Hardware and its
-      features.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A valid [AWS](https://aws.amazon.com/)
-      account; Some familiarity with Python.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Arm Virtual Hardware, Baremetal environments, and
-      Arm platforms such as Cortex-M and Corstone.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Create a peripheral using Virtual Input/Output (VIO).
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md b/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
index 075ea65dee..2a41c67206 100644
--- a/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
@@ -24,52 +24,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 0e653bdbf5e5b08a6a00ba68e5ed215a0082e22c634d7d632d088851d48bb01c
-  summary: >-
-    Learn how to build a complete IoT solution in Azure that streams, stores, monitors, aggregates,
-    and visualizes telemetry data from Arm devices using IoT Hub, Stream Analytics, Cosmos DB,
-    and Azure Functions. It is designed for developers who want to build a comprehensive IoT solution
-    in Azure that streams, stores, monitors, aggregates, and visualizes telemetry data from Arm
-    IoT devices. By the end, you will be able to set up and configure Azure IoT Hub for device
-    communication, register an IoT device and stream telemetry data using the Azure IoT SDK, and
-    route IoT data to Azure services using Azure Stream Analytics. It focuses on tools and technologies
-    such as Python, Azure, and Visual Studio Code, Windows, Linux, and macOS environments, and
-    Arm platforms including Cortex-A. The main steps cover Overview, Create Azure IoT Hub, Build
-    a Python-based IoT telemetry simulator, Process IoT telemetry in real time with Azure Stream
-    Analytics, and Store data in Azure Cosmos DB with Azure Stream Analytics.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will set up and configure Azure IoT Hub for device communication, register an IoT device
-      and stream telemetry data using the Azure IoT SDK, and route IoT data to Azure services
-      using Azure Stream Analytics. Learn how to build a complete IoT solution in Azure that streams,
-      stores, monitors, aggregates, and visualizes telemetry data from Arm devices using IoT Hub,
-      Stream Analytics, Cosmos DB, and Azure Functions.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for developers who want to build a comprehensive IoT solution
-      in Azure that streams, stores, monitors, aggregates, and visualizes telemetry data from
-      Arm IoT devices.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A machine with Python 3 and Visual Studio
-      Code installed; An active Azure account with sufficient permissions to create resources
-      (such as IoT Hub, Functions, and Cosmos DB).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Python, Azure, and Visual Studio Code, Windows,
-      Linux, and macOS environments, and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview, Create Azure IoT Hub, Build a Python-based
-      IoT telemetry simulator, Process IoT telemetry in real time with Azure Stream Analytics,
-      and Store data in Azure Cosmos DB with Azure Stream Analytics.
-# END generated_summary_faq
+
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md b/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md
index f4ef8f4bac..87c3e071a1 100644
--- a/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md
@@ -21,48 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 6521f17d1a10ab8871d13917923f7f64320f69301b4c0c5f5b528163d3cb43c6
-  summary: >-
-    Learn how to create, build, and run a bare-metal embedded application for Armv8-A processors
-    using Arm Compiler for Embedded and Fixed Virtual Platforms, including basic exception handling.
-    It is designed for embedded software developers new to Armv8-A processors and/or the Arm Compiler
-    for Embedded. By the end, you will be able to create and build an example project, run example
-    on Fixed Virtual Platform (FVP), and understand basic boot code and other syntax. It focuses
-    on tools and technologies such as Arm Development Studio, Arm Compiler for Embedded, and Arm
-    Fast Models, Baremetal environments, and Arm platforms including Cortex-A. The main steps
-    cover Create and build a Hello World example project, Write a reset handler, Modify the example
-    to use the UART for printf output, Create event-driven application (1), and Create event-driven
-    application (2).
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create and build an example project, run example on Fixed Virtual Platform (FVP),
-      and understand basic boot code and other syntax. Learn how to create, build, and run a bare-metal
-      embedded application for Armv8-A processors using Arm Compiler for Embedded and Fixed Virtual
-      Platforms, including basic exception handling.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for embedded software developers new to Armv8-A processors
-      and/or the Arm Compiler for Embedded.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Some familiarity with embedded programming
-      is assumed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Arm Development Studio, Arm Compiler for Embedded,
-      and Arm Fast Models, Baremetal environments, and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Create and build a Hello World example project, Write
-      a reset handler, Modify the example to use the UART for printf output, Create event-driven
-      application (1), and Create event-driven application (2).
-# END generated_summary_faq
+
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md b/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
index a15dac6ef8..d8de531b83 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
@@ -21,50 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 3394bf994039e441d57dced2abb64d725077d4672e0e68dc71f1de50e58f0408
-  summary: >-
-    Learn how to deploy containerized embedded applications and firmware onto an Arm Cortex-M
-    core from a Cortex-A core using containerd, K3s, and the hybrid-runtime on Arm Virtual Hardware.
-    It is designed for developers interested in learning how to deploy software (embedded applications
-    and firmware) onto other processors in the system, using Linux running on the application
-    core. By the end, you will be able to deploy a containerized embedded application onto an
-    Arm Cortex-M core from an Arm Cortex-A core using containerd and K3s, build a firmware container
-    image, and build the hybrid-runtime components. It focuses on tools and technologies such
-    as Docker, Arm Virtual Hardware, K3s, and Containerd, Linux environments, and Arm platforms
-    including Cortex-M and Cortex-A. The main steps cover Motivation, Hybrid container runtime,
-    AVH device setup, Deploy firmware container using `containerd`, and Deploy SMARTER Demo using
-    K3s.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will deploy a containerized embedded application onto an Arm Cortex-M core from an Arm
-      Cortex-A core using containerd and K3s, build a firmware container image, and build the
-      hybrid-runtime components. Learn how to deploy containerized embedded applications and firmware
-      onto an Arm Cortex-M core from a Cortex-A core using containerd, K3s, and the hybrid-runtime
-      on Arm Virtual Hardware.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This learning path is for developers interested in learning how to deploy software (embedded
-      applications and firmware) onto other processors in the system, using Linux running on the
-      application core.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A valid account with [Arm Virtual Hardware](https://app.avh.arm.com/login);
-      An Arm Linux host machine (if you want to build your own runtime and container image).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Docker, Arm Virtual Hardware, K3s, and Containerd,
-      Linux environments, and Arm platforms such as Cortex-M and Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Motivation, Hybrid container runtime, AVH device setup,
-      Deploy firmware container using `containerd`, and Deploy SMARTER Demo using K3s.
-# END generated_summary_faq
+
 
 author: Basma El Gaabouri
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md
index bb1804510a..a9e7047df0 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md
@@ -18,43 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: d8c73d658fa7a8051131276b8567cbd7376aac3b8f6e87c8ecdf224443c3ef99
-  summary: >-
-    Learn how to create, build, and debug an RTX5 RTOS-based application using Keil μVision with
-    CMSIS-RTOS2 API and Event Recorder for embedded Cortex-M development. It is designed for software
-    developers new to RTOS development. By the end, you will be able to implement a basic RTOS-based
-    application. It focuses on tools and technologies such as Keil RTX RTOS, Keil MDK, and Arm
-    Development Studio, RTOS environments, and Arm platforms including Cortex-M. The main steps
-    cover Create and setup Keil MDK project, Initialize the operating system, Create RTOS threads,
-    Build and run the application, and Using Event Recorder.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will implement a basic RTOS-based application. Learn how to create, build, and debug
-      an RTX5 RTOS-based application using Keil μVision with CMSIS-RTOS2 API and Event Recorder
-      for embedded Cortex-M development.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers new to RTOS development.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An installation of [Arm Keil MDK](/install-guides/mdk)
-      or [Arm Development Studio](/install-guides/armds) (MDK recommended); Some familiarity with
-      CMSIS is assumed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Keil RTX RTOS, Keil MDK, and Arm Development Studio,
-      RTOS environments, and Arm platforms such as Cortex-M.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Create and setup Keil MDK project, Initialize the
-      operating system, Create RTOS threads, Build and run the application, and Using Event Recorder.
-# END generated_summary_faq
+
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md
index e2bd4ae554..ad350298d4 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md
@@ -20,48 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: f4d1ab3448590c5654e2479937c9eec3e378d05229b29a45b8675139f40ac177
-  summary: >-
-    Learn how to create, configure, and debug an RTX5 RTOS application using Keil Studio for VS
-    Code with CMSIS-RTOS2 API for embedded Cortex-M development. It is designed for software developers
-    new to RTOS development. By the end, you will be able to understand the basics of RTX-based
-    RTOS application development, configure and manage an RTOS project in Keil Studio, including
-    defining the memory map, selecting software components, and setting up debugging configurations
-    for Cortex-M processors, and create and manage multiple threads within an RTX5 RTOS application.
-    It focuses on tools and technologies such as Keil RTX RTOS, Keil MDK, and Arm Development
-    Studio, RTOS environments, and Arm platforms including Cortex-M. The main steps cover Create
-    csolution project, Initialize the Operating System, Create RTOS Threads, and Build and run
-    the application.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand the basics of RTX-based RTOS application development, configure and
-      manage an RTOS project in Keil Studio, including defining the memory map, selecting software
-      components, and setting up debugging configurations for Cortex-M processors, and create
-      and manage multiple threads within an RTX5 RTOS application. Learn how to create, configure,
-      and debug an RTX5 RTOS application using Keil Studio for VS Code with CMSIS-RTOS2 API for
-      embedded Cortex-M development.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers new to RTOS development.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Installation of [Arm Keil Studio for
-      VS Code](/install-guides/keilstudio_vs); Some familiarity with CMSIS is assumed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Keil RTX RTOS, Keil MDK, and Arm Development Studio,
-      RTOS environments, and Arm platforms such as Cortex-M.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Create csolution project, Initialize the Operating
-      System, Create RTOS Threads, and Build and run the application.
-# END generated_summary_faq
+
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md b/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
index 6097ef64ea..d30fce6604 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
@@ -23,48 +23,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 69526ee8b840c8f5d77d49349d2f0cc7ff4594fec5e4311984ef72a0672d3462
-  summary: >-
-    Learn how to prototype and port DSP algorithms using the CMSIS-DSP Python package, mapping
-    Python code to efficient C implementations for embedded Cortex-M and Cortex-A platforms. It
-    is designed for developers looking to integrate the CMSIS-DSP library into their applications
-    using Python. By the end, you will be able to use the CMSIS-DSP Python package to prototype
-    DSP algorithms, understand how the Python API maps to the C implementation, and build and
-    port a complex DSP application using CMSIS-DSP. It focuses on tools and technologies such
-    as CMSIS-DSP, Python, C, Jupyter Notebook, and NumPy, Linux, Windows, and macOS environments,
-    and Arm platforms including Cortex-M and Cortex-A. The main steps cover CMSIS-DSP Python package,
-    Set up environment, Load an audio file, Write a simple VAD, and Write a noise suppression
-    algorithm.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will use the CMSIS-DSP Python package to prototype DSP algorithms, understand how the
-      Python API maps to the C implementation, and build and port a complex DSP application using
-      CMSIS-DSP. Learn how to prototype and port DSP algorithms using the CMSIS-DSP Python package,
-      mapping Python code to efficient C implementations for embedded Cortex-M and Cortex-A platforms.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for developers looking to integrate the CMSIS-DSP library into
-      their applications using Python.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Familiarity with Python and digital
-      signal processing concepts.; Working knowledge of C.; Prior exposure to CMSIS-DSP.; Python
-      installed on your machine.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including CMSIS-DSP, Python, C, Jupyter Notebook, and NumPy,
-      Linux, Windows, and macOS environments, and Arm platforms such as Cortex-M and Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around CMSIS-DSP Python package, Set up environment, Load
-      an audio file, Write a simple VAD, and Write a noise suppression algorithm.
-# END generated_summary_faq
+
 
 author: Christophe Favergeon
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md b/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
index d14f7ff0be..db6fe71c57 100644
--- a/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
@@ -20,45 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 0b7a5895a87de83903c223215845b6c840602663838c0682f46e50d139d69c59
-  summary: >-
-    Learn how to implement context switching operations on Arm Cortex-M processors using the Memory
-    Protection Unit and SysTick exception in a bare-metal environment. It is designed for software
-    developers who would like to learn about context switching operations on Cortex-M processors
-    in a bare-metal environment. By the end, you will be able to understand the basics of context
-    switching, learn how to program the Memory Protection Unit (MPU), and learn how to use the
-    SysTick exception with context switching operations. It focuses on tools and technologies
-    such as CMSIS and Arm Development Studio, Baremetal environments, and Arm platforms including
-    Cortex-M. The main steps cover Example Arm DS project to demonstrate context switching operations.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand the basics of context switching, learn how to program the Memory Protection
-      Unit (MPU), and learn how to use the SysTick exception with context switching operations.
-      Learn how to implement context switching operations on Arm Cortex-M processors using the
-      Memory Protection Unit and SysTick exception in a bare-metal environment.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who would like to learn about context
-      switching operations on Cortex-M processors in a bare-metal environment.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Basic knowledge and familiarity with
-      Cortex-M processors.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including CMSIS and Arm Development Studio, Baremetal environments,
-      and Arm platforms such as Cortex-M.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Example Arm DS project to demonstrate context switching
-      operations.
-# END generated_summary_faq
+
 
 author: Uma Ramalingam
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md b/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
index 86ada0ec39..d2b5ac0825 100644
--- a/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
@@ -18,40 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: f989929b11c48df42c2701dc71516cec0b8637b4c639c3dbbf6ac5e7440c8a56
-  summary: >-
-    Learn how to set up and use code coverage in Keil MDK with FVPs to verify that your embedded
-    application tests execute all code paths. It is designed for embedded software developers
-    new to the code-coverage feature in Keil MDK. By the end, you will be able to set up project
-    execution on FVP and understand basics of the Code Coverage report. It focuses on tools and
-    technologies such as Keil MDK and FVP, Baremetal and RTOS environments, and Arm platforms
-    including Cortex-M. The main steps cover What is Code Coverage? and Set up Code Coverage.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will set up project execution on FVP and understand basics of the Code Coverage report.
-      Learn how to set up and use code coverage in Keil MDK with FVPs to verify that your embedded
-      application tests execute all code paths.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for embedded software developers new to the code-coverage
-      feature in Keil MDK.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Basic familiarity with Keil MDK.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Keil MDK and FVP, Baremetal and RTOS environments,
-      and Arm platforms such as Cortex-M.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around What is Code Coverage? and Set up Code Coverage.
-# END generated_summary_faq
+
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md b/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
index e7dfa3a961..b02cf4e982 100644
--- a/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
@@ -19,52 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 9d9e4c170fa318a9875c888c2c812ceb27c59f56c4b0dd7e0ae87dd31bb5783c
-  summary: >-
-    Device-to-Device communication with Device Connect walks you through an end-to-end Arm software
-    workflow. It is designed for developers wiring up heterogeneous edge fleets, where devices
-    need a shared way to find each other and a shared way to be controlled by agents. Device Connect
-    provides this communication protocol between agents and devices, and standardizes how devices
-    from different vendors advertise themselves and exchange structured messages, so both peer
-    devices and AI agents can discover and invoke them through the same driver model. You'll use
-    it to stand up peer-to-peer communication between two devices, with no broker or cloud service
-    in between. By the end, you will be able to understand Device Connect Edge SDK primitives,
-    set up a Python environment for Device Connect with no hardware required, and build two simulated
-    devices. It focuses on tools and technologies such as Python, Linux, macOS, and Windows environments,
-    and Arm platforms including Cortex-A. The main steps cover Why device-to-device at the edge,
-    Device Connect developer model, and Set up D2D communication between a sensor and a monitor.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand Device Connect Edge SDK primitives, set up a Python environment for
-      Device Connect with no hardware required, and build two simulated devices.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers wiring up heterogeneous edge fleets, where
-      devices need a shared way to find each other and a shared way to be controlled by agents.
-      Device Connect provides this communication protocol between agents and devices, and standardizes
-      how devices from different vendors advertise themselves and exchange structured messages,
-      so both peer devices and AI agents can discover and invoke them through the same driver
-      model. You'll use it to stand up peer-to-peer communication between two devices, with no
-      broker or cloud service in between.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Basic familiarity with Python and the
-      command line.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Python, Linux, macOS, and Windows environments,
-      and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Why device-to-device at the edge, Device Connect developer
-      model, and Set up D2D communication between a sensor and a monitor.
-# END generated_summary_faq
+
 
 author:
     - Kavya Sri Chennoju
diff --git a/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md b/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md
index 410034e3db..328f9398b8 100644
--- a/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md
@@ -21,56 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: c31d398d3ce965a4193fca45cd025182d788a2fc603fbe8c828dfbd5a1c599ba
-  summary: >-
-    Learn how to connect AI agents to Arm-based edge devices using Device Connect for structured
-    device access and Strands for agent orchestration, with examples for both simulated and physical
-    robots. It is designed for software developers who want to connect AI agents to edge devices.
-    You'll use Device Connect, Arm's platform for structured device access, and Strands, AWS's
-    open-source agent SDK. The examples cover both physical and simulated devices. By the end,
-    you will be able to understand how Device Connect and Strands work together to give AI agents
-    structured access to Arm-based edge devices, set up a Python environment with the Device Connect
-    SDK and agent tools installed from source, and start a simulated robot that registers itself
-    on the local network and is discovered automatically by an agent. It focuses on tools and
-    technologies such as Python, Docker, and strands-agents, Linux and macOS environments, and
-    Arm platforms including Cortex-A and Neoverse. The main steps cover Learn Device Connect and
-    Strands architecture for edge devices, Set up the Device Connect and Strands developer environment,
-    Run device discovery and agent control examples, and Run with full Device Connect infrastructure
-    (optional).
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand how Device Connect and Strands work together to give AI agents structured
-      access to Arm-based edge devices, set up a Python environment with the Device Connect SDK
-      and agent tools installed from source, and start a simulated robot that registers itself
-      on the local network and is discovered automatically by an agent. Learn how to connect AI
-      agents to Arm-based edge devices using Device Connect for structured device access and Strands
-      for agent orchestration, with examples for both simulated and physical robots.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to connect AI agents to edge
-      devices. You'll use Device Connect, Arm's platform for structured device access, and Strands,
-      AWS's open-source agent SDK. The examples cover both physical and simulated devices.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A development machine with git installed;
-      Basic familiarity with command-line tools; (Optional) A Raspberry Pi for testing a full
-      device-to-device (D2D) setup.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Python, Docker, and strands-agents, Linux and macOS
-      environments, and Arm platforms such as Cortex-A and Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Learn Device Connect and Strands architecture for
-      edge devices, Set up the Device Connect and Strands developer environment, Run device discovery
-      and agent control examples, and Run with full Device Connect infrastructure (optional).
-# END generated_summary_faq
+
 
 author: 
     - Annie Tallund
diff --git a/content/learning-paths/embedded-and-microcontrollers/docker/_index.md b/content/learning-paths/embedded-and-microcontrollers/docker/_index.md
index 938643bcf9..327977b314 100644
--- a/content/learning-paths/embedded-and-microcontrollers/docker/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/docker/_index.md
@@ -18,41 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: a4b522c46c68d3c6f5c4af7c76b00c7bde48bb638147c201376bc19a4de79cca
-  summary: >-
-    Learn how to create a Dockerfile, build a Docker image with Arm Compiler for Embedded and
-    Fixed Virtual Platforms, and test the containerized Arm development environment. It is designed
-    for embedded software developers new to Docker. By the end, you will be able to create and
-    understand a Dockerfile, build Docker image, and test the image. It focuses on tools and technologies
-    such as Docker, Arm Development Studio, Arm Compiler for Embedded, and Arm Fast Models, Baremetal
-    environments, and Arm platforms including Cortex-A, Cortex-R, Cortex-M, and Neoverse. The
-    main steps cover Create Dockerfile and build docker image.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create and understand a Dockerfile, build Docker image, and test the image. Learn
-      how to create a Dockerfile, build a Docker image with Arm Compiler for Embedded and Fixed
-      Virtual Platforms, and test the containerized Arm development environment.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for embedded software developers new to Docker.
-  - question: What do you need before you start?
-    answer: >-
-      There are no explicit prerequisites listed for this Learning Path.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Docker, Arm Development Studio, Arm Compiler for
-      Embedded, and Arm Fast Models, Baremetal environments, and Arm platforms such as Cortex-A,
-      Cortex-R, Cortex-M, and Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Create Dockerfile and build docker image.
-# END generated_summary_faq
+
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/edge/_index.md b/content/learning-paths/embedded-and-microcontrollers/edge/_index.md
index a07c307dff..b3b3399731 100644
--- a/content/learning-paths/embedded-and-microcontrollers/edge/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/edge/_index.md
@@ -23,51 +23,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 8a7ec29d10a89649662ebb0fa4f14712984786902b6e7343a195abb1f3cbc00b
-  summary: >-
-    Learn how to collect and preprocess audio data using Edge Impulse, train an audio classification
-    model, and deploy it to the Arduino Nano RP2040 to control LEDs based on voice commands. It
-    is designed for beginners in Edge AI and TinyML, including developers, engineers, hobbyists,
-    AI/ML enthusiasts, and researchers working with embedded AI and IoT. By the end, you will
-    be able to understand the basics of Edge AI and TinyML, collect and preprocess audio data
-    using Edge Impulse, and train and deploy an audio classification model on the Arduino Nano
-    RP2040. It focuses on tools and technologies such as Edge Impulse, tinyML, Edge AI, and Arduino,
-    Baremetal environments, and Arm platforms including Cortex-M. The main steps cover Overview,
-    Train and deploy a TinyML audio classifier with Edge Impulse, Board connection and IDE setup,
-    and Program your first TinyML device.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand the basics of Edge AI and TinyML, collect and preprocess audio data
-      using Edge Impulse, and train and deploy an audio classification model on the Arduino Nano
-      RP2040. Learn how to collect and preprocess audio data using Edge Impulse, train an audio
-      classification model, and deploy it to the Arduino Nano RP2040 to control LEDs based on
-      voice commands.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is for beginners in Edge AI and TinyML, including developers, engineers,
-      hobbyists, AI/ML enthusiasts, and researchers working with embedded AI and IoT.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Completion of [Embedded programming
-      with Arduino on the Raspberry Pi Pico](/learning-paths/embedded-and-microcontrollers/arduino-pico/)
-      if you're an absolute beginner.; An [Edge Impulse Studio](https://studio.edgeimpulse.com/signup)
-      account.; The [Arduino IDE](/install-guides/arduino-pico/) with the RP2040 board support
-      package installed on your computer.; An [Arduino Nano RP2040 Connect board](https://store.arduino.cc/products/arduino-nano-rp2040-connect-with-headers).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Edge Impulse, tinyML, Edge AI, and Arduino, Baremetal
-      environments, and Arm platforms such as Cortex-M.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview, Train and deploy a TinyML audio classifier
-      with Edge Impulse, Board connection and IDE setup, and Program your first TinyML device.
-# END generated_summary_faq
+
 
 author: Bright Edudzi Gershon Kordorwu
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md b/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md
index 471d3b5b5e..ffb43b34e6 100644
--- a/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md
@@ -20,45 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 66024a2e3da90dcda298bf66b5de07952ed1e355d22172bc2812c46ad4fcda7f
-  summary: >-
-    Develop a image classification neural network model and deploy it on an STM32 B-L475E-IOT01A2
-    board. It is designed for embedded software developers interested in building neural network
-    models for microcontrollers. By the end, you will be able to build a convolution neural network(CNN)
-    model for image classification and run the CNN model on an STM32 B-L475E-IOT01A2 board using
-    STM Cube AI. It focuses on tools and technologies such as TensorFlow and STM32, Baremetal
-    environments, and Arm platforms including Cortex-M. The main steps cover Prepare environment,
-    Build an image classification NN model trained with the CIFAR-10 dataset, Deploy the image
-    classification NN model on STM32, and Run the image classification NN model on STM32.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build a convolution neural network(CNN) model for image classification and run
-      the CNN model on an STM32 B-L475E-IOT01A2 board using STM Cube AI. Develop a image classification
-      neural network model and deploy it on an STM32 B-L475E-IOT01A2 board.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for embedded software developers interested in building neural
-      network models for microcontrollers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Familiarity with ML concepts; Familiarity
-      with C programming on microcontrollers; STM32 B-L475E-IOT01A2 board.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including TensorFlow and STM32, Baremetal environments, and
-      Arm platforms such as Cortex-M.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Prepare environment, Build an image classification
-      NN model trained with the CIFAR-10 dataset, Deploy the image classification NN model on
-      STM32, and Run the image classification NN model on STM32.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/intro/_index.md b/content/learning-paths/embedded-and-microcontrollers/intro/_index.md
index 838c68092f..1603c7b30f 100644
--- a/content/learning-paths/embedded-and-microcontrollers/intro/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/intro/_index.md
@@ -18,41 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: ac69e979101f6befe55abee1429299c163c70b9a886d87a8f64779babd9fe5af
-  summary: >-
-    Learn where Arm architecture is used in microcontrollers and discover microcontroller hardware
-    options for software development on Arm Cortex-M processors. It is designed for software developers
-    working on microcontroller applications and new to the Arm architecture. By the end, you will
-    be able to understand where the Arm architecture is used in microcontrollers and find microcontroller
-    hardware to use for software development. It focuses on Baremetal and RTOS environments and
-    Arm platforms including Cortex-M and Ethos-U. The main steps cover Arm in Microcontrollers,
-    Find Arm hardware, and Other learning resources.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand where the Arm architecture is used in microcontrollers and find microcontroller
-      hardware to use for software development. Learn where Arm architecture is used in microcontrollers
-      and discover microcontroller hardware options for software development on Arm Cortex-M processors.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers working on microcontroller applications
-      and new to the Arm architecture.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: None.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers Baremetal and RTOS environments and Arm platforms such as Cortex-M and Ethos-U.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Arm in Microcontrollers, Find Arm hardware, and Other
-      learning resources.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md b/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
index addf5de247..ab49177811 100644
--- a/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
@@ -22,48 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: aa49e4a1651367965e79d36c5f6af16e9b341c392d48cf051f24cbb21070e972
-  summary: >-
-    Learn what differentiates TinyML from other AI domains, explore Arm-based edge devices for
-    TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed Virtual
-    Platform. It is designed for developers and data scientists new to Tiny Machine Learning (TinyML)
-    who want to explore its potential using PyTorch and ExecuTorch. By the end, you will be able
-    to describe what differentiates TinyML from other AI domains, describe the benefits of deploying
-    AI models on Arm-based edge devices, and identify suitable Arm-based devices for TinyML applications.
-    It focuses on tools and technologies such as Arm Virtual Hardware, FVP, Python, PyTorch, and
-    ExecuTorch, Linux environments, and Arm platforms including Cortex-A, Cortex-M, and Ethos-U.
-    The main steps cover Overview, Install ExecuTorch, Set up the Corstone-320 FVP, and Build
-    a simple PyTorch model.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will describe what differentiates TinyML from other AI domains, describe the benefits
-      of deploying AI models on Arm-based edge devices, and identify suitable Arm-based devices
-      for TinyML applications. Learn what differentiates TinyML from other AI domains, explore
-      Arm-based edge devices for TinyML, and set up a development environment using ExecuTorch
-      and Corstone-320 Fixed Virtual Platform.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers and data scientists new to Tiny Machine Learning
-      (TinyML) who want to explore its potential using PyTorch and ExecuTorch.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Basic knowledge of Machine Learning
-      concepts; A Linux computer.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Arm Virtual Hardware, FVP, Python, PyTorch, and
-      ExecuTorch, Linux environments, and Arm platforms such as Cortex-A, Cortex-M, and Ethos-U.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview, Install ExecuTorch, Set up the Corstone-320
-      FVP, and Build a simple PyTorch model.
-# END generated_summary_faq
+
 
 author: Dominica Abena O. Amanfo
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md b/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
index 81fde20d5f..9abf0f8c90 100644
--- a/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
@@ -19,45 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 11fc64eb1a0595b1d8e625e465be9fa87a4de04f59492004204ccbe61a92a5b7
-  summary: >-
-    Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300 virtual hardware
-    to understand complete IoT software stack construction. It is designed for embedded software
-    developers interested in learning how a complete IoT software stack is constructed. By the
-    end, you will be able to build examples from Open-IoT-SDK and run the examples on Corstone-300
-    virtual hardware. It focuses on tools and technologies such as Arm Virtual Hardware, FVP,
-    and Arm Compiler for Embedded, Baremetal and RTOS environments, and Arm platforms including
-    Cortex-M, Ethos-U, and Corstone. The main steps cover Build and run Open-IoT-SDK examples
-    and Enable AWS connectivity.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build examples from Open-IoT-SDK and run the examples on Corstone-300 virtual hardware.
-      Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300 virtual hardware
-      to understand complete IoT software stack construction.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for embedded software developers interested in learning how
-      a complete IoT software stack is constructed.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Some familiarity with embedded programming;
-      An AWS account (required for Arm Virtual Hardware).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Arm Virtual Hardware, FVP, and Arm Compiler for
-      Embedded, Baremetal and RTOS environments, and Arm platforms such as Cortex-M, Ethos-U,
-      and Corstone.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Build and run Open-IoT-SDK examples and Enable AWS
-      connectivity.
-# END generated_summary_faq
+
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md b/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
index 13a1900b2e..38086bd277 100644
--- a/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
@@ -20,46 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: fdf582f1b54f372768a2c896e1da152c50d22c3bd238848253cdbe18cc68222d
-  summary: >-
-    Learn how to set up a Jetson Orin Nano with a MIPI CSI-2 camera and perform real-time object
-    detection from live video and image files using DetectNet and TensorRT. It is designed for
-    developers interested in integrating object detection into their applications. By the end,
-    you will be able to set up a Jetson Orin Nano with a MIPI CSI-2 camera for object detection
-    and detect objects from both live video and image files. It focuses on tools and technologies
-    such as DetectNet, TensorRT, and Docker, Linux environments, and Arm platforms including Cortex-A.
-    The main steps cover Set up your Jetson Orin Nano, Launch the image classification Docker
-    container, and Detect objects in video and images.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will set up a Jetson Orin Nano with a MIPI CSI-2 camera for object detection and detect
-      objects from both live video and image files. Learn how to set up a Jetson Orin Nano with
-      a MIPI CSI-2 camera and perform real-time object detection from live video and image files
-      using DetectNet and TensorRT.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers interested in integrating object detection
-      into their applications.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Jetson Orin Nano](https://developer.nvidia.com/embedded/learn/jetson-orin-nano-devkit-user-guide/index.html);
-      A microSD card (64GB UHS-1 or larger is recommended); A MIPI CSI-2 camera, with a 22 pin
-      connector on at least one end.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including DetectNet, TensorRT, and Docker, Linux environments,
-      and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Set up your Jetson Orin Nano, Launch the image classification
-      Docker container, and Detect objects in video and images.
-# END generated_summary_faq
+
 
 author: Gabriel Peterson
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md b/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
index 300e948f34..278f8b4545 100644
--- a/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
@@ -19,39 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: f3a01adf18ad93027b6ab61ceb5b0c470e6b5c298f9d4f944989a96ff64eec81
-  summary: >-
-    Learn how to import, build, and debug your first Keil Studio Cloud project. It is designed
-    for embedded software developers new to Keil Studio Cloud. By the end, you will be able to
-    import and build an example project and run the example on Arm Virtual Hardware. It focuses
-    on tools and technologies such as Arm Compiler for Embedded, Arm Virtual Hardware, and CMSIS,
-    Baremetal and RTOS environments, and Arm platforms including Cortex-M. The main steps cover
-    Work with an example project.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will import and build an example project and run the example on Arm Virtual Hardware.
-      Learn how to import, build, and debug your first Keil Studio Cloud project.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for embedded software developers new to Keil Studio Cloud.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Some familiarity with embedded programming
-      is assumed; An [Arm Account](https://developer.arm.com/register) is required.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Arm Compiler for Embedded, Arm Virtual Hardware,
-      and CMSIS, Baremetal and RTOS environments, and Arm platforms such as Cortex-M.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Work with an example project.
-# END generated_summary_faq
+
 
 author: Christopher Seidl 
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md b/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
index 7ca5d81190..3a15934426 100644
--- a/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
@@ -24,50 +24,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 567387e8e513458a360f74c14d3f4d02c4af392bc573620e605c93976e4d8b4f
-  summary: >-
-    Learn how to boot and configure the NXP FRDM i.MX 93 Arm board with Linux, create a user with
-    sudo access, connect to WiFi using ConnMan, and transfer files over the network. It is designed
-    for embedded developers and ML engineers who want to boot an NXP FRDM i.MX 93 board, connect
-    over serial, enable WiFi, and transfer files for on-device development on Arm. By the end,
-    you will be able to boot the NXP FRDM i.MX 93 board and log in to Linux over a serial console,
-    create a non-root Linux user with sudo access for development workflows, and connect the board
-    to WiFi using ConnMan. It focuses on tools and technologies such as Bash, systemd, picocom,
-    ConnMan, and OpenSSH, Linux and macOS environments, and Arm platforms including Cortex-A.
-    The main steps cover Set up the board, Set up a Linux user and connect to WiFi, Transfer files
-    to the board, and (Optional) Enable Persistent WiFi.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will boot the NXP FRDM i.MX 93 board and log in to Linux over a serial console, create
-      a non-root Linux user with sudo access for development workflows, and connect the board
-      to WiFi using ConnMan. Learn how to boot and configure the NXP FRDM i.MX 93 Arm board with
-      Linux, create a user with sudo access, connect to WiFi using ConnMan, and transfer files
-      over the network.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for embedded developers and ML engineers who want to boot
-      an NXP FRDM i.MX 93 board, connect over serial, enable WiFi, and transfer files for on-device
-      development on Arm.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An NXP [FRDM i.MX 93](https://www.nxp.com/design/design-center/development-boards-and-designs/frdm-i-mx-93-development-board:FRDM-IMX93)
-      board.; A computer running Linux or macOS.; A USB-C cable for the board's **DBG** serial
-      connection.; A USB-C power supply/cable for the board's **POWER** port.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Bash, systemd, picocom, ConnMan, and OpenSSH, Linux
-      and macOS environments, and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Set up the board, Set up a Linux user and connect
-      to WiFi, Transfer files to the board, and (Optional) Enable Persistent WiFi.
-# END generated_summary_faq
+
 
 author: Waheed Brown
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md b/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md
index fb91a5b843..28ba51f1c0 100644
--- a/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md
@@ -19,48 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 5943d817827bef274f175f7c14249cad769b2160094b3c65d7476aca6cbf0a1d
-  summary: >-
-    Learn how to boot a Linux software stack on Arm Fixed Virtual Platforms (FVPs), then debug
-    Trusted Firmware-A and the Linux kernel using Arm Development Studio. It is designed for developers
-    who want to run Linux on Arm Fixed Virtual Platforms (FVPs) and debug both Trusted Firmware-A
-    and the Linux kernel using Arm Development Studio. By the end, you will be able to boot and
-    run a Linux software stack on an Arm Fixed Virtual Platform (FVP) and debug Trusted Firmware-A
-    and the Linux kernel using Arm Development Studio. It focuses on tools and technologies such
-    as Arm Development Studio, C, and Assembly, Linux environments, and Arm platforms including
-    Cortex-A. The main steps cover Introduction to Arm Fixed Virtual Platforms (FVPs), Configure
-    Trusted Firmware-A build flags to include cpu_ops support, Modify the device tree for CPU
-    FVPs, Run the Linux software stack on an FVP, and Debug the software stack.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will boot and run a Linux software stack on an Arm Fixed Virtual Platform (FVP) and
-      debug Trusted Firmware-A and the Linux kernel using Arm Development Studio. Learn how to
-      boot a Linux software stack on Arm Fixed Virtual Platforms (FVPs), then debug Trusted Firmware-A
-      and the Linux kernel using Arm Development Studio.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This topic is for developers who want to run Linux on Arm Fixed Virtual Platforms (FVPs)
-      and debug both Trusted Firmware-A and the Linux kernel using Arm Development Studio.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Linux-based x86-64 host computer with
-      Arm Development Studio installed.; Basic understanding of Assembly and C programming.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Arm Development Studio, C, and Assembly, Linux environments,
-      and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Introduction to Arm Fixed Virtual Platforms (FVPs),
-      Configure Trusted Firmware-A build flags to include cpu_ops support, Modify the device tree
-      for CPU FVPs, Run the Linux software stack on an FVP, and Debug the software stack.
-# END generated_summary_faq
+
 
 author: Qixiang Xu
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md b/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md
index 6ebe1afeef..2ec9f1da56 100644
--- a/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md
@@ -20,45 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: aa6252610c0603acfdfc8d4555bb7da93cbdd5b9d92ba140c5dc5f63d23e2b07
-  summary: >-
-    Learn how to install the Python version of llama.cpp on a Raspberry Pi 5, download an LLM
-    from Hugging Face, assess memory and performance, and run the model using Python bindings.
-    It is designed for anyone interested in running a local Large Language Model on a Raspberry
-    Pi 5. By the end, you will be able to install the Python version of llama.cpp on your Raspberry
-    Pi 5, download an LLM from Hugging Face, and assess LLM memory size and performance. It focuses
-    on tools and technologies such as LLM, Generative AI, Raspberry Pi, Python, and Hugging Face,
-    Linux environments, and Arm platforms including Cortex-A. The main steps cover Run a Large
-    Language Model (LLM) chatbot on a Raspberry Pi 5.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install the Python version of llama.cpp on your Raspberry Pi 5, download an LLM
-      from Hugging Face, and assess LLM memory size and performance. Learn how to install the
-      Python version of llama.cpp on a Raspberry Pi 5, download an LLM from Hugging Face, assess
-      memory and performance, and run the model using Python bindings.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for anyone interested in running a local Large Language Model
-      on a Raspberry Pi 5.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Raspberry Pi 5 running Raspberry Pi
-      OS.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including LLM, Generative AI, Raspberry Pi, Python, and Hugging
-      Face, Linux environments, and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Run a Large Language Model (LLM) chatbot on a Raspberry
-      Pi 5.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/migration/_index.md b/content/learning-paths/embedded-and-microcontrollers/migration/_index.md
index 08cbd2cf49..623c215903 100644
--- a/content/learning-paths/embedded-and-microcontrollers/migration/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/migration/_index.md
@@ -21,47 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 81a15ff0d5579be9529a8c9c444be57521ebc48bbf5234f042f5a47a8a709b5c
-  summary: >-
-    Learn the software migration methodology for porting Linux workloads from x86_64 to aarch64,
-    including using Arm compilers, porting compiler intrinsics, and deploying applications in
-    containers. It is designed for embedded software developers looking at migrating Linux workloads
-    to aarch64. By the end, you will be able to understand software migration methodology, use
-    different Arm compilers and libraries, and port applications containing compiler intrinsics.
-    It focuses on tools and technologies such as GCC, Arm Compiler for Linux, Docker, and Neon,
-    Linux environments, and Arm platforms including Cortex-A and Neoverse. The main steps cover
-    Porting methodology, Porting analysis, Development environment, Application porting, and Run
-    and evaluate.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand software migration methodology, use different Arm compilers and libraries,
-      and port applications containing compiler intrinsics. Learn the software migration methodology
-      for porting Linux workloads from x86_64 to aarch64, including using Arm compilers, porting
-      compiler intrinsics, and deploying applications in containers.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for embedded software developers looking at migrating Linux workloads
-      to aarch64.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Introductory understanding of software
-      containers; Knowledge about building workflows; Access to an aarch64 or x86_64 machine running
-      Linux.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including GCC, Arm Compiler for Linux, Docker, and Neon, Linux
-      environments, and Arm platforms such as Cortex-A and Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Porting methodology, Porting analysis, Development
-      environment, Application porting, and Run and evaluate.
-# END generated_summary_faq
+
 
 author: Kasper Mecklenburg
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md b/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md
index 65cdc955da..0f4d85f759 100644
--- a/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md
@@ -19,45 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: acf43df3c6f344b55bb413b7483d60e26d0e137489ac148bca64500e443140ab
-  summary: >-
-    Learn how to build examples from the Machine Learning Evaluation Kit (MLEK) and run them on
-    the Arm Ecosystem FVP for machine learning application development on microcontrollers. It
-    is designed for embedded software developers interested in machine learning applications.
-    By the end, you will be able to build examples from Machine Learning Evaluation Kit (MLEK)
-    and run the examples on Arm Ecosystem FVP. It focuses on tools and technologies such as Arm
-    Virtual Hardware, FVP, GCC, and Arm Compiler for Embedded, Baremetal environments, and Arm
-    platforms including Cortex-M, Ethos-U, and Corstone. The main steps cover Build the ML Evaluation
-    Kit examples, Install Arm Ecosystem FVP, and Run the examples on the FVP.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build examples from Machine Learning Evaluation Kit (MLEK) and run the examples
-      on Arm Ecosystem FVP. Learn how to build examples from the Machine Learning Evaluation Kit
-      (MLEK) and run them on the Arm Ecosystem FVP for machine learning application development
-      on microcontrollers.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for embedded software developers interested in machine learning
-      applications.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Some familiarity with embedded programming;
-      A Linux host machine running Ubuntu.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Arm Virtual Hardware, FVP, GCC, and Arm Compiler
-      for Embedded, Baremetal environments, and Arm platforms such as Cortex-M, Ethos-U, and Corstone.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Build the ML Evaluation Kit examples, Install Arm
-      Ecosystem FVP, and Run the examples on the FVP.
-# END generated_summary_faq
+
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md b/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md
index 8f7aef60ec..1bc04d2527 100644
--- a/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md
@@ -12,49 +12,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 0cfaa21be515b980097232ed38092b80d046df308120887e5503513a3384a1d7
-  summary: >-
-    Learn how to understand and select physical and virtual hardware targets for ML application
-    development with Cortex-M and Ethos-U, identify software tools, and find example applications.
-    It is designed for embedded software developers interested in learning about machine learning.
-    By the end, you will be able to understand and select physical and virtual hardware targets
-    for ML application development with Cortex-M and Ethos-U, identify and install software tools
-    used for machine learning applications on microcontrollers, and find and learn from existing
-    example applications. It focuses on tools and technologies such as FVP, Arm Virtual Hardware,
-    GCC, Arm Compiler for Embedded, and MPS3, Baremetal environments, and Arm platforms including
-    Cortex-M, Ethos-U, and Corstone. The main steps cover Overview, Development platforms, and
-    Software development considerations.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand and select physical and virtual hardware targets for ML application
-      development with Cortex-M and Ethos-U, identify and install software tools used for machine
-      learning applications on microcontrollers, and find and learn from existing example applications.
-      Learn how to understand and select physical and virtual hardware targets for ML application
-      development with Cortex-M and Ethos-U, identify software tools, and find example applications.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for embedded software developers interested in learning about
-      machine learning.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Some familiarity with microcontroller
-      software development.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including FVP, Arm Virtual Hardware, GCC, Arm Compiler for
-      Embedded, and MPS3, Baremetal environments, and Arm platforms such as Cortex-M, Ethos-U,
-      and Corstone.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview, Development platforms, and Software development
-      considerations.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md b/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md
index 5add99f81c..b396ffa87d 100644
--- a/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md
@@ -18,43 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: d2c403c7fd1001dbd985697c9eb018951a955358c2be39c727183a87a3dfdf51
-  summary: >-
-    Learn how to create debug configurations for virtual platforms and development boards in Arm
-    Development Studio, including setting up connections for Fast Models and DSTREAM debug probes.
-    It is designed for embedded software developers new to Arm Development Studio. By the end,
-    you will be able to create a debug configuration for a virtual platform and create a debug
-    configuration for a development board. It focuses on tools and technologies such as Arm Development
-    Studio, Arm Fast Models, and DSTREAM, Baremetal environments, and Arm platforms including
-    Cortex-A, Cortex-R, Cortex-M, and Neoverse. The main steps cover Debug connection to Arm Fast
-    Models and Debug connection with Arm DSTREAM.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create a debug configuration for a virtual platform and create a debug configuration
-      for a development board. Learn how to create debug configurations for virtual platforms
-      and development boards in Arm Development Studio, including setting up connections for Fast
-      Models and DSTREAM debug probes.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for embedded software developers new to Arm Development Studio.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Some familiarity with embedded debug.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Arm Development Studio, Arm Fast Models, and DSTREAM,
-      Baremetal environments, and Arm platforms such as Cortex-A, Cortex-R, Cortex-M, and Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Debug connection to Arm Fast Models and Debug connection
-      with Arm DSTREAM.
-# END generated_summary_faq
+
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md b/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
index aa1755cc39..3fae9b8d9a 100644
--- a/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
@@ -22,56 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: be2b3571d4d2ed75a16bc97589abc22c970d83be868b7e94bb60962a4ba853da
-  summary: >-
-    Learn how to bring up ExecuTorch executor_runner firmware on the NXP FRDM i.MX 93 Cortex-M33
-    using Linux RemoteProc, compile .pte models for Ethos-U65, and run inference with NPU acceleration.
-    It is designed for developers and data scientists new to TinyML who want to observe ExecuTorch
-    performance on a physical device. By the end, you will be able to bring up a custom ExecuTorch
-    `executor_runner` firmware on the FRDM i.MX 93 Cortex-M33 using Linux RemoteProc, compile
-    an ExecuTorch `.pte` model for Ethos-U65 and run inference with NPU acceleration, and understand
-    how heterogeneous Arm systems split responsibilities across application cores, microcontrollers,
-    and NPUs. It focuses on tools and technologies such as Baremetal, Python, PyTorch, ExecuTorch,
-    and Arm Compute Library, Linux and macOS environments, and Arm platforms including Cortex-A,
-    Cortex-M, and Ethos-U. The main steps cover Understand ExecuTorch deployment on NXP with Ethos-U,
-    Boot the NXP FRDM i.MX 93 board, Set up the ExecuTorch build environment, Build and install
-    ExecuTorch, and Build ExecuTorch models for Ethos-U65.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will bring up a custom ExecuTorch `executor_runner` firmware on the FRDM i.MX 93 Cortex-M33
-      using Linux RemoteProc, compile an ExecuTorch `.pte` model for Ethos-U65 and run inference
-      with NPU acceleration, and understand how heterogeneous Arm systems split responsibilities
-      across application cores, microcontrollers, and NPUs. Learn how to bring up ExecuTorch executor_runner
-      firmware on the NXP FRDM i.MX 93 Cortex-M33 using Linux RemoteProc, compile .pte models
-      for Ethos-U65, and run inference with NPU acceleration.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers and data scientists new to TinyML who want
-      to observe ExecuTorch performance on a physical device.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An NXP [FRDM i.MX 93](https://www.nxp.com/design/design-center/development-boards-and-designs/frdm-i-mx-93-development-board:FRDM-IMX93)
-      development board; A USB Mini-B to USB Type-A cable, or a USB Mini-B to USB Type-C cable;
-      Completion of [Use Linux on an NXP FRDM i.MX 93 board](/learning-paths/embedded-and-microcontrollers/linux-nxp-board/)
-      (Linux setup, login access, and file transfer); Basic knowledge of Machine Learning concepts;
-      A host computer to compile ExecuTorch libraries.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Baremetal, Python, PyTorch, ExecuTorch, and Arm
-      Compute Library, Linux and macOS environments, and Arm platforms such as Cortex-A, Cortex-M,
-      and Ethos-U.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Understand ExecuTorch deployment on NXP with Ethos-U,
-      Boot the NXP FRDM i.MX 93 board, Set up the ExecuTorch build environment, Build and install
-      ExecuTorch, and Build ExecuTorch models for Ethos-U65.
-# END generated_summary_faq
+
 
 author: 
 - Waheed Brown
diff --git a/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md b/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
index 9b8c912292..cc086bf5d9 100644
--- a/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
@@ -19,40 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 8039812773c6c1ac45cceb4039cee801e627d67871b625283c1bb5b3fa2960b3
-  summary: >-
-    Learn how to migrate a CMSIS v5-based CMSIS-Pack with device support to CMSIS v6 and update
-    example projects for compatibility with the new CMSIS version. It is designed for maintainers
-    of CMSIS-Packs with device support. By the end, you will be able to migrate a CMSIS v5-based
-    CMSIS-Pack with device support to CMSIS v6 and update example projects. It focuses on tools
-    and technologies such as CMSIS and CMSIS-Toolbox, Baremetal and RTOS environments, and Arm
-    platforms including Cortex-M. The main steps cover Device support and Example projects.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will migrate a CMSIS v5-based CMSIS-Pack with device support to CMSIS v6 and update
-      example projects. Learn how to migrate a CMSIS v5-based CMSIS-Pack with device support to
-      CMSIS v6 and update example projects for compatibility with the new CMSIS version.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for maintainers of CMSIS-Packs with device support.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A good understanding of [CMSIS-Packs](https://open-cmsis-pack.github.io/Open-CMSIS-Pack-Spec/main/html/index.html).;
-      A CMSIS-Pack that contains device support and was created for CMSIS v5.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including CMSIS and CMSIS-Toolbox, Baremetal and RTOS environments,
-      and Arm platforms such as Cortex-M.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Device support and Example projects.
-# END generated_summary_faq
+
 
 author: Christopher Seidl
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md b/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md
index e4ca311701..9fbb8673c5 100644
--- a/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md
@@ -20,45 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 7a192506fc8a15423d1257fe92e7655053e38be85aad8310dae29602e483fbba
-  summary: >-
-    Learn how to migrate CMSIS v5 projects to CMSIS v6 by identifying supported toolchains, installing
-    required CMSIS-Packs, and selecting the necessary software components. It is designed for
-    embedded developers who want to migrate their projects to CMSIS v6. By the end, you will be
-    able to identify the supported toolchains, install the required CMSIS-Packs, and select the
-    software components needed to migrate your projects to CMSIS v6. It focuses on tools and technologies
-    such as CMSIS and CMSIS-Toolbox, Baremetal and RTOS environments, and Arm platforms including
-    Cortex-M. The main steps cover Supported toolchains, Required CMSIS-Packs, Device mapping,
-    Project format conversion, and Troubleshooting.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will identify the supported toolchains, install the required CMSIS-Packs, and select
-      the software components needed to migrate your projects to CMSIS v6. Learn how to migrate
-      CMSIS v5 projects to CMSIS v6 by identifying supported toolchains, installing required CMSIS-Packs,
-      and selecting the necessary software components.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for embedded developers who want to migrate their projects to
-      CMSIS v6.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A CMSIS v5 based project.; A basic understanding
-      of the CMSIS-Pack system.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including CMSIS and CMSIS-Toolbox, Baremetal and RTOS environments,
-      and Arm platforms such as Cortex-M.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Supported toolchains, Required CMSIS-Packs, Device
-      mapping, Project format conversion, and Troubleshooting.
-# END generated_summary_faq
+
 
 author: Christopher Seidl
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md b/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md
index 22764fda2a..b6fd139c65 100644
--- a/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md
@@ -23,59 +23,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: a0145c77b3d4a8bb25a32c62adaa3ad378e65fccbe6db88d9a46a569897d238a
-  summary: >-
-    Learn how to run large language models locally on the Raspberry Pi 5 using Ollama, control
-    GPIO-connected devices, and deploy a privacy-first web-based smart home assistant without
-    cloud services. It is designed for edge AI developers, Raspberry Pi hobbyists, and software
-    engineers who want to build privacy-first smart home assistants. You’ll learn how to run large
-    language models (LLMs) locally on the Raspberry Pi 5 using Ollama, control GPIO-connected
-    devices, and deploy a web-based assistant without relying on cloud services. By the end, you
-    will be able to understand how the Arm architecture enables efficient, private, and responsive
-    LLM inference, run a smart home assistant on Raspberry Pi 5 with local LLM integration, and
-    wire and control physical devices (for example, LEDs) using Raspberry Pi GPIO pins. It focuses
-    on tools and technologies such as Python, Ollama, FastAPI, and Raspberry Pi, Linux environments,
-    and Arm platforms including Cortex-A. The main steps cover Run LLMs locally on Raspberry Pi
-    5 for Edge AI, Set up software dependencies on Raspberry Pi 5 for Ollama and LLMs, Test Raspberry
-    Pi 5 GPIO pins for smart home devices, and Build and Run a Smart Home Assistant on Raspberry
-    Pi 5 with LLMs.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand how the Arm architecture enables efficient, private, and responsive
-      LLM inference, run a smart home assistant on Raspberry Pi 5 with local LLM integration,
-      and wire and control physical devices (for example, LEDs) using Raspberry Pi GPIO pins.
-      Learn how to run large language models locally on the Raspberry Pi 5 using Ollama, control
-      GPIO-connected devices, and deploy a privacy-first web-based smart home assistant without
-      cloud services.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for edge AI developers, Raspberry Pi hobbyists, and software
-      engineers who want to build privacy-first smart home assistants. You’ll learn how to run
-      large language models (LLMs) locally on the Raspberry Pi 5 using Ollama, control GPIO-connected
-      devices, and deploy a web-based assistant without relying on cloud services.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm-based single board computer (for
-      example, Raspberry Pi 5 running Raspberry Pi OS); Electronic components (breadboard, LEDs,
-      resistors, jumper wires) for GPIO testing; Familiarity with Python programming, Raspberry
-      Pi GPIO pinout, and basic electronics.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Python, Ollama, FastAPI, and Raspberry Pi, Linux
-      environments, and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Run LLMs locally on Raspberry Pi 5 for Edge AI, Set
-      up software dependencies on Raspberry Pi 5 for Ollama and LLMs, Test Raspberry Pi 5 GPIO
-      pins for smart home devices, and Build and Run a Smart Home Assistant on Raspberry Pi 5
-      with LLMs.
-# END generated_summary_faq
+
 
 author: Fidel Makatia Omusilibwa
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md b/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md
index 658614debc..228f705634 100644
--- a/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md
@@ -24,49 +24,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: ed2034f5c95c4148fca355f6d545219c320f2e73267a0725934c792ebc4c0c59
-  summary: >-
-    Learn how to build a voice-controlled bot on a Raspberry Pi that listens for a wake word,
-    converts speech to text using Google Speech Recognition, sends requests to ChatGPT's API,
-    and plays audio responses. It is designed for This is an introductory project for developers
-    interested in integrating a Chatbot (namely ChatGPT) into Raspberry Pi projects. By the end,
-    you will be able to run a bot on a Raspberry Pi that will listen to you and respond to what
-    you say, learn how to listen for a keyword and wake a program when the keyword is heard, and
-    convert speech from the microphone to text using Google Speech Recognition. It focuses on
-    tools and technologies such as ChatGPT, Porcupine, and Python, Linux environments, and Arm
-    platforms including Cortex-A. The main steps cover Initial setup, Configure and test audio,
-    Create the Python application, and Run and test the bot.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will run a bot on a Raspberry Pi that will listen to you and respond to what you say,
-      learn how to listen for a keyword and wake a program when the keyword is heard, and convert
-      speech from the microphone to text using Google Speech Recognition. Learn how to build a
-      voice-controlled bot on a Raspberry Pi that listens for a wake word, converts speech to
-      text using Google Speech Recognition, sends requests to ChatGPT's API, and plays audio responses.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory project for developers interested in integrating a Chatbot (namely
-      ChatGPT) into Raspberry Pi projects.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Raspberry Pi 4 or 5 (earlier models
-      may also work); A microSD card with at least 16GB of storage; A Linux compatible USB microphone
-      and USB speakers or a USB audio device with a microphone and speakers.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including ChatGPT, Porcupine, and Python, Linux environments,
-      and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Initial setup, Configure and test audio, Create the
-      Python application, and Run and test the bot.
-# END generated_summary_faq
+
 
 author: Gabriel Peterson
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
index 4ca5e89463..dc8dffae58 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
@@ -24,48 +24,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 9cd2c3082c4874dc596e1b7b59c3dc2143aaf1ce3e66948ab8b6af190ffa3776
-  summary: >-
-    Learn how to compile the Llama 3 large language model using ExecuTorch, deploy it to a Raspberry
-    Pi 5, and understand techniques for running LLMs in embedded environments. It is designed
-    for anyone interested in running the Llama 3 model on a Raspberry Pi 5, and learning about
-    techniques for running large language models (LLMs) in an embedded environment. By the end,
-    you will be able to use Docker to run Raspberry Pi OS on an Arm Linux server, compile a Large
-    Language Model (LLM) using ExecuTorch, and deploy the Llama 3 model on an edge device. It
-    focuses on tools and technologies such as LLM, Generative AI, Raspberry Pi, Hugging Face,
-    and ExecuTorch, Linux environments, and Arm platforms including Cortex-A. The main steps cover
-    Set up the development environment, Set up ExecuTorch, Set up Llama 3, and Run the model on
-    a Raspberry Pi 5.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will use Docker to run Raspberry Pi OS on an Arm Linux server, compile a Large Language
-      Model (LLM) using ExecuTorch, and deploy the Llama 3 model on an edge device. Learn how
-      to compile the Llama 3 large language model using ExecuTorch, deploy it to a Raspberry Pi
-      5, and understand techniques for running LLMs in embedded environments.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for anyone interested in running the Llama 3 model on a Raspberry
-      Pi 5, and learning about techniques for running large language models (LLMs) in an embedded
-      environment.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm Linux machine or an [Arm cloud
-      instance](/learning-paths/servers-and-cloud-computing/csp/).; A Raspberry Pi 5.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including LLM, Generative AI, Raspberry Pi, Hugging Face,
-      and ExecuTorch, Linux environments, and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Set up the development environment, Set up ExecuTorch,
-      Set up Llama 3, and Run the model on a Raspberry Pi 5.
-# END generated_summary_faq
+
 
 author: Annie Tallund
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
index 9729d2f492..04298b7825 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
@@ -22,47 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 17721158fe10e97314af364f138c32b7bcf53fdc88fe11fffeb5765f11e26b79
-  summary: >-
-    Learn how to reduce compile time for embedded Linux projects by installing a Raspberry Pi
-    OS file system on an Arm server, building the MXNet machine learning framework, and transferring
-    it to a Raspberry Pi. It is designed for software developers who want to reduce compile time
-    for embedded Linux software projects. By the end, you will be able to install a Raspberry
-    Pi OS file system on an Arm server, reduce compile time for a Linux application, the MXNet
-    machine learning framework, and transfer the compiled MXNet application to a Raspberry Pi
-    and test it. It focuses on tools and technologies such as Raspberry Pi and MXNet, Linux environments,
-    and Arm platforms including Neoverse and Cortex-A72. The main steps cover User change, User
-    change, and User change.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install a Raspberry Pi OS file system on an Arm server, reduce compile time for
-      a Linux application, the MXNet machine learning framework, and transfer the compiled MXNet
-      application to a Raspberry Pi and test it. Learn how to reduce compile time for embedded
-      Linux projects by installing a Raspberry Pi OS file system on an Arm server, building the
-      MXNet machine learning framework, and transferring it to a Raspberry Pi.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers who want to reduce compile time for embedded
-      Linux software projects.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm computer running Linux. Cloud
-      instances can be used, refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).;
-      A Raspberry Pi 3 or 4 board.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Raspberry Pi and MXNet, Linux environments, and
-      Arm platforms such as Neoverse and Cortex-A72.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around User change, User change, and User change.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md
index 981a531b5f..5ee8ae6702 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md
@@ -19,44 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 5e5fad1563b67ed38d1cf3399d8e0d62162e76cae8d6848c3be7638f67cd037c
-  summary: >-
-    Learn how to build and run multiple software examples on the Raspberry Pi 4, including TensorFlow
-    and Docker applications, and compare its performance to Arm cloud servers. It is designed
-    for software developers interested in the Raspberry Pi 4. By the end, you will be able to
-    build and run multiple software examples on the Raspberry Pi 4 and compare and contrast the
-    Raspberry Pi 4 to an Arm cloud server. It focuses on tools and technologies such as Raspberry
-    Pi, TensorFlow, and Docker, Linux environments, and Arm platforms including Cortex-A and Neoverse.
-    The main steps cover Introduction to the Raspberry Pi 4, Setup a Raspberry Pi 4 and an Arm
-    cloud instance, Identifying the hardware, Linux Kernel Compile, and TensorFlow.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build and run multiple software examples on the Raspberry Pi 4 and compare and
-      contrast the Raspberry Pi 4 to an Arm cloud server. Learn how to build and run multiple
-      software examples on the Raspberry Pi 4, including TensorFlow and Docker applications, and
-      compare its performance to Arm cloud servers.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers interested in the Raspberry Pi 4.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Raspberry Pi 4 board; An [Arm based
-      instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Raspberry Pi, TensorFlow, and Docker, Linux environments,
-      and Arm platforms such as Cortex-A and Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Introduction to the Raspberry Pi 4, Setup a Raspberry
-      Pi 4 and an Arm cloud instance, Identifying the hardware, Linux Kernel Compile, and TensorFlow.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
index ad9533fb6b..ed54b56838 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
@@ -21,42 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: d9fe3cfc8f7a7092f40786763fc28e371697e4b57dba99b2c9b191dae4273911
-  summary: >-
-    Setup tools and start programming with Raspberry Pi Pico. It is designed for embedded software
-    developers new to Raspberry Pi Pico. By the end, you will be able to install the Raspberry
-    Pi Pico SDK, run a hello world example, and measure application performance. It focuses on
-    tools and technologies such as Raspberry Pi, Baremetal environments, and Arm platforms including
-    Cortex-M. The main steps cover How do I install the Raspberry Pi Pico SDK?, How do I run Hello
-    World for Raspberry Pi Pico?, How do I measure RPi Pico Application Performance?, and How
-    do I debug RPi Pico applications?
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install the Raspberry Pi Pico SDK, run a hello world example, and measure application
-      performance. Setup tools and start programming with Raspberry Pi Pico.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for embedded software developers new to Raspberry Pi Pico.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Raspberry Pi Pico board.; Raspberry
-      Pi 3, 4, 400, or 5 as a development computer.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Raspberry Pi, Baremetal environments, and Arm platforms
-      such as Cortex-M.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around How do I install the Raspberry Pi Pico SDK?, How do
-      I run Hello World for Raspberry Pi Pico?, How do I measure RPi Pico Application Performance?,
-      and How do I debug RPi Pico applications?
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md b/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
index aa4dea1167..e547bcff7c 100644
--- a/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
@@ -24,52 +24,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:54Z'
-  generator: template
-  source_hash: 0b8de63a15d3d77b9c9972103b1317d6c48907875ac625c3d1c1b8db5360879a
-  summary: >-
-    Learn how to profile Linux kernel modules using Arm Streamline to identify performance bottlenecks,
-    analyze both out-of-tree and in-tree modules, and use Statistical Profiling Extension (SPE)
-    for deeper insights. It is designed for developers and performance engineers interested in
-    profiling Linux kernel performance. By the end, you will be able to understand why profiling
-    Linux kernel modules is important for performance and stability, set up and use Arm Streamline
-    to profile the Linux kernel, and profile both out-of-tree and in-tree kernel modules on Arm-based
-    systems. It focuses on tools and technologies such as Arm Streamline, Arm Performance Studio,
-    Linux kernel, and Performance analysis, Linux environments, and Arm platforms including Cortex-A.
-    The main steps cover Profile Linux kernel modules with Arm Streamline, Set up your environment,
-    Build the out-of-tree kernel module, Profile the out-of-tree kernel module, and Integrate
-    a custom character device driver into the Linux kernel.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand why profiling Linux kernel modules is important for performance and
-      stability, set up and use Arm Streamline to profile the Linux kernel, and profile both out-of-tree
-      and in-tree kernel modules on Arm-based systems. Learn how to profile Linux kernel modules
-      using Arm Streamline to identify performance bottlenecks, analyze both out-of-tree and in-tree
-      modules, and use Statistical Profiling Extension (SPE) for deeper insights.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for developers and performance engineers interested in profiling
-      Linux kernel performance.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Basic understanding of Linux kernel
-      development and module programming; Arm-based Linux target device (such as a Raspberry Pi,
-      BeagleBone, or similar board) with Secure Shell (SSH) access; A host machine that meets
-      [Buildroot system requirements](https://buildroot.org/downloads/manual/manual.html#requirement).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Arm Streamline, Arm Performance Studio, Linux kernel,
-      and Performance analysis, Linux environments, and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Profile Linux kernel modules with Arm Streamline,
-      Set up your environment, Build the out-of-tree kernel module, Profile the out-of-tree kernel
-      module, and Integrate a custom character device driver into the Linux kernel.
-# END generated_summary_faq
+
 
 author: Yahya Abouelseoud
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md b/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
index 24ea361380..98080ed5f5 100644
--- a/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
@@ -20,45 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: b5714494f00fff407c39e6f2f7bf37de94cde61d7569ba147412fec1b2f537c2
-  summary: >-
-    Build a letter recognition neural network model using TensorFlow and deploy it on an STM32
-    B-L475E-IOT01A2 board. It is designed for software developers interested in building network
-    models for microcontrollers. By the end, you will be able to build a letter recognition neural
-    network(NN) model using TensorFlow framework and run the NN model on an STM32 B-L475E-IOT01A2
-    board using STM32CubeAI. It focuses on tools and technologies such as TensorFlow and STM32,
-    Baremetal environments, and Arm platforms including Cortex-M. The main steps cover Prepare
-    development environment, Collect training data, Train the model, Feature extraction, and Run
-    the model on development board.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build a letter recognition neural network(NN) model using TensorFlow framework
-      and run the NN model on an STM32 B-L475E-IOT01A2 board using STM32CubeAI. Build a letter
-      recognition neural network model using TensorFlow and deploy it on an STM32 B-L475E-IOT01A2
-      board.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers interested in building network models
-      for microcontrollers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Familiarity with ML concepts; Familiarity
-      with C programming on microcontrollers; STM32 B-L475E-IOT01A2 board.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including TensorFlow and STM32, Baremetal environments, and
-      Arm platforms such as Cortex-M.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Prepare development environment, Collect training
-      data, Train the model, Feature extraction, and Run the model on development board.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md b/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md
index a2efe17b73..219e5a3c2f 100644
--- a/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md
@@ -20,41 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 8d8f9df559ff1b1570dc98baf640fc2d4c4d225d69f22529e1881b62d4017752
-  summary: >-
-    Learn how to build and run the reference Trusted Firmware-M tests and example application
-    on Arm Fixed Virtual Platforms for secure microcontroller development. It is designed for
-    software developers new to Trusted Firmware-M. By the end, you will be able to build and run
-    the reference TF-M tests and example application. It focuses on tools and technologies such
-    as Arm Virtual Hardware, FVP, TrustZone, and Trusted Firmware, Baremetal environments, and
-    Arm platforms including Cortex-M and Corstone. The main steps cover Build and run TF-M tests
-    and example application.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build and run the reference TF-M tests and example application. Learn how to build
-      and run the reference Trusted Firmware-M tests and example application on Arm Fixed Virtual
-      Platforms for secure microcontroller development.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers new to Trusted Firmware-M.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Some familiarity with embedded C programming;
-      A machine running Ubuntu Linux.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Arm Virtual Hardware, FVP, TrustZone, and Trusted
-      Firmware, Baremetal environments, and Arm platforms such as Cortex-M and Corstone.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Build and run TF-M tests and example application.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md b/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md
index d52fb0b173..d0c6c64bf1 100644
--- a/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md
@@ -23,50 +23,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 20c500fd5174c9901058676d4619981ee1c8a8cf8e331affea8c0292112651c9
-  summary: >-
-    Learn how to train a CNN image classification model using PyTorch, convert it to ExecuTorch
-    format, and run it as an interactive mini-game on Arm-based edge devices. It is designed for
-    machine learning developers who want to deploy TinyML models on Arm-based edge devices using
-    PyTorch and ExecuTorch. By the end, you will be able to train a small Convolutional Neural
-    Network (CNN) for image classification using PyTorch, use synthetic data generation for training
-    a model when real data is limited, and convert and optimize a PyTorch model to an ExecuTorch
-    program (`.pte`) for Arm-based devices. It focuses on tools and technologies such as tinyML,
-    Computer Vision, Edge AI, CNN, and PyTorch, Linux environments, and Arm platforms including
-    Cortex-M and Ethos-U. The main steps cover Set up your environment, Train and Test the rock-paper-scissors
-    Model, and Run the model on Corstone-320 FVP.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will train a small Convolutional Neural Network (CNN) for image classification using
-      PyTorch, use synthetic data generation for training a model when real data is limited, and
-      convert and optimize a PyTorch model to an ExecuTorch program (`.pte`) for Arm-based devices.
-      Learn how to train a CNN image classification model using PyTorch, convert it to ExecuTorch
-      format, and run it as an interactive mini-game on Arm-based edge devices.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for machine learning developers who want to deploy TinyML
-      models on Arm-based edge devices using PyTorch and ExecuTorch.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Basic understanding of machine learning
-      concepts; Familiarity with Python and the PyTorch library; Completion of the Learning Path
-      [Introduction to TinyML on Arm using PyTorch and ExecuTorch](/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/);
-      An x86 Linux host machine or VM running Ubuntu 22.04 or later.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including tinyML, Computer Vision, Edge AI, CNN, and PyTorch,
-      Linux environments, and Arm platforms such as Cortex-M and Ethos-U.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Set up your environment, Train and Test the rock-paper-scissors
-      Model, and Run the model on Corstone-320 FVP.
-# END generated_summary_faq
+
 
 author: Dominica Abena O. Amanfo
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md b/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md
index 493694d31c..3c6f1f95e6 100644
--- a/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md
@@ -22,43 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 6c81f3c9595df1128ca6f4f89446f093826d2242fa789ffa2d37465854be1f8e
-  summary: >-
-    Learn how to install Keil MDK Tools, run a TrustZone hello world example on the NXP LPCXpresso55S69
-    board, and understand security state switching and secure function calls. It is designed for
-    software developers new to using TrustZone. By the end, you will be able to install the Keil
-    MDK Tools, run a hello world TrustZone example, and understand switching of security states.
-    It focuses on tools and technologies such as TrustZone, Arm Compiler for Embedded, and Keil
-    MDK, Baremetal environments, and Arm platforms including Cortex-M. The main steps cover Run
-    TrustZone Hello World example and Breaking down the application.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install the Keil MDK Tools, run a hello world TrustZone example, and understand
-      switching of security states. Learn how to install Keil MDK Tools, run a TrustZone hello
-      world example on the NXP LPCXpresso55S69 board, and understand security state switching
-      and secure function calls.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers new to using TrustZone.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Familiar with C programming on microcontrollers;
-      Comfortable with Windows; NXP LPCXpresso55S69 board.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including TrustZone, Arm Compiler for Embedded, and Keil MDK,
-      Baremetal environments, and Arm platforms such as Cortex-M.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Run TrustZone Hello World example and Breaking down
-      the application.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md b/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
index e364fbc9b8..1138856ddf 100644
--- a/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
@@ -29,54 +29,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 57e05139cdea1de2f4a4ce239d701f0cef634b6ac11fd437cba47cc071e14098
-  summary: >-
-    Learn how to acquire and print materials, assemble a universal SBC rack mount system in a
-    4U chassis, and install single board computers in the racks using 3D-printed parts. It is
-    designed for software developers and hobbyists who want to build a rack mount system for housing
-    single board computers. By the end, you will be able to acquire and print the required materials,
-    assemble and install the universal SBC rack mount system in a 4U chassis, and install single
-    board computers in the racks. It focuses on tools and technologies such as Fusion 360, Linux
-    environments, and Arm platforms including Cortex-A. The main steps cover Print the Required
-    Parts, Assembly Instructions for the Chassis Bays, and Assembly Instructions for the Chassis
-    Bays.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will acquire and print the required materials, assemble and install the universal SBC
-      rack mount system in a 4U chassis, and install single board computers in the racks. Learn
-      how to acquire and print materials, assemble a universal SBC rack mount system in a 4U chassis,
-      and install single board computers in the racks using 3D-printed parts.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers and hobbyists who want to build a
-      rack mount system for housing single board computers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: 3D printer; Hack saw or chop saw to
-      cut threaded steel rods; 4U server chassis with the insides removed. For example, Rosewill
-      RSV-L4500 4U Industrial Rack-Mount Server Chassis; 8-32 stainless steel threaded rods at
-      least 405 mm long. 4 x 405 mm long rods are also required for each bay row. [Example part](https://www.mcmaster.com/98847A009/);
-      8-32 stainless steel hex nut. 8 per bay row. [Example part](https://www.mcmaster.com/91841A009/);
-      8-32 stainless steel wing nut. 8 per bay row. [Example part](https://www.mcmaster.com/92001A291/);
-      \#8 stainless steel washer. 8 per bay row. [Example part](https://www.mcmaster.com/90107A010/);
-      18-8 stainless steel socket head screw. 4 per card. [Example part](https://www.mcmaster.com/91292A016/);
-      18-8 stainless steel hex nut. 4 per card. [Example part](https://www.mcmaster.com/91828A113/);
-      PETG filament. Others can work, but PETG allows some flex without the risk of snapping.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Fusion 360, Linux environments, and Arm platforms
-      such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Print the Required Parts, Assembly Instructions for
-      the Chassis Bays, and Assembly Instructions for the Chassis Bays.
-# END generated_summary_faq
+
 
 author: Gabriel Peterson
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md b/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
index 8a225d1da4..50850b4111 100644
--- a/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
@@ -11,49 +11,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 27ced3e9f69dbe51e75dcf13999ae1745a994137f31c86d6f17d4de341c7fa2a
-  summary: >-
-    Learn how to debug microcontrollers using µVision with basic run/stop debug, advanced techniques
-    using Event Recorder and Serial Wire Viewer, ETM Trace for performance analysis, and power
-    measurement with ULINKplus. It is designed for software developers who want to debug microcontrollers
-    using µVision. By the end, you will be able to use basic run/stop debug, learn advanced debug
-    techniques using Event Recorder and Serial Wire Viewer, and learn to use ETM Trace for optimum
-    performance. It focuses on tools and technologies such as Keil MDK and FVP, RTOS and Baremetal
-    environments, and Arm platforms including Cortex-M. The main steps cover Use basic run/stop
-    debug, Debug using Event Recorder, Debug using Serial Wire Viewer, Advanced debug with ETM
-    trace, and Measure Power with ULINKplus.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will use basic run/stop debug, learn advanced debug techniques using Event Recorder
-      and Serial Wire Viewer, and learn to use ETM Trace for optimum performance. Learn how to
-      debug microcontrollers using µVision with basic run/stop debug, advanced techniques using
-      Event Recorder and Serial Wire Viewer, ETM Trace for performance analysis, and power measurement
-      with ULINKplus.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers who want to debug microcontrollers using
-      µVision.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Some familiarity with embedded programming
-      is assumed; An [Arm Account](https://developer.arm.com/register) is required; A Windows
-      machine; Installation of [Arm Keil MDK](/install-guides/mdk/) with an active MDK-Community
-      license; Installation of the [Corstone-300 Ecosystem FVP](/install-guides/fm_fvp/eco_fvp/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Keil MDK and FVP, RTOS and Baremetal environments,
-      and Arm platforms such as Cortex-M.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Use basic run/stop debug, Debug using Event Recorder,
-      Debug using Serial Wire Viewer, Advanced debug with ETM trace, and Measure Power with ULINKplus.
-# END generated_summary_faq
+
 
 author: Christopher Seidl
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md b/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
index f46fa7a587..e33a99a965 100644
--- a/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
@@ -22,47 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 179b0318bbef9cef31ca6bb82de853597a609f61d6868aaaa85cfb40a27a051a
-  summary: >-
-    Learn how to import, convert, and build uvprojx-based projects to csolution format using Keil
-    Studio, µVision, and command-line tools for CMSIS-Toolbox compatibility. It is designed for
-    This is a topic for users of µVision who want to migrate to the new project format (csolution)
-    required by CMSIS-Toolbox. By the end, you will be able to import, convert, and build uvprojx-based
-    projects in Keil Studio, convert uvprojx-based projects in µVision, and convert and build
-    uvprojx-based projects on the command line. It focuses on tools and technologies such as Keil
-    MDK and CMSIS-Toolbox, Windows, Linux, and macOS environments, and Arm platforms including
-    Cortex-M. The main steps cover Using Keil Studio, Using µVision, and Using the command line.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will import, convert, and build uvprojx-based projects in Keil Studio, convert uvprojx-based
-      projects in µVision, and convert and build uvprojx-based projects on the command line. Learn
-      how to import, convert, and build uvprojx-based projects to csolution format using Keil
-      Studio, µVision, and command-line tools for CMSIS-Toolbox compatibility.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is a topic for users of µVision who want to migrate to the new project format (csolution)
-      required by CMSIS-Toolbox.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Install [Keil Studio](/install-guides/keilstudio_vs/)
-      on your machine.; Install [µVision](/install-guides/mdk/) on your machine.; Install [uv2csolution](https://arm-software.github.io/MDK-Toolbox/01_installation/)
-      for the command line flow.; The &micro;Vision project must use Arm Compiler 6 as the default
-      toolchain. Arm Compiler 5 is not supported.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Keil MDK and CMSIS-Toolbox, Windows, Linux, and
-      macOS environments, and Arm platforms such as Cortex-M.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Using Keil Studio, Using µVision, and Using the command
-      line.
-# END generated_summary_faq
+
 
 author: Christopher Seidl
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md b/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
index 80924a7475..3bc2a5c68d 100644
--- a/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
@@ -23,46 +23,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 0c3422e1cd571e6abff676c28ec32c2ec69a626a406167f9166e57daa6829252
-  summary: >-
-    Learn how to install vcpkg, initialize it, create vcpkg-configuration.json files, use vcpkg
-    for tool management, activate tool licensing, and remove vcpkg for reproducible command-line
-    tool installations. It is designed for software developers who want to create reproducible
-    tool installations on the command line. By the end, you will be able to install vcpkg, initialize
-    vcpkg, and create a vcpkg-configuration.json file. It focuses on tools and technologies such
-    as vcpkg, Linux, Windows, and macOS environments, and Arm platforms including Cortex-M. The
-    main steps cover Install vcpkg, Initialize vcpkg, Create a vcpkg-configuration.json file,
-    Use vcpkg, and Activate a license.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install vcpkg, initialize vcpkg, and create a vcpkg-configuration.json file. Learn
-      how to install vcpkg, initialize it, create vcpkg-configuration.json files, use vcpkg for
-      tool management, activate tool licensing, and remove vcpkg for reproducible command-line
-      tool installations.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to create reproducible tool
-      installations on the command line.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A basic understanding of the [development
-      tools for Arm Cortex-M](https://developer.arm.com/Tools%20and%20Software/); Command line
-      access to your machine.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including vcpkg, Linux, Windows, and macOS environments, and
-      Arm platforms such as Cortex-M.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Install vcpkg, Initialize vcpkg, Create a vcpkg-configuration.json
-      file, Use vcpkg, and Activate a license.
-# END generated_summary_faq
+
 
 author: Christopher Seidl
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md b/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md
index aaa2d462d0..a4c78d454e 100644
--- a/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md
@@ -22,51 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: dcdbc4cecc376ed4c0df9377d13cb4fe792ad7c68acfe0610d75cf41898804ff
-  summary: >-
-    Learn how to identify Arm-based targets for TinyML, install Fixed Virtual Platforms, deploy
-    ExecuTorch models on Corstone-320 FVP, and visualize model execution using the FVP graphical
-    interface. It is designed for developers and data scientists who are new to TinyML and want
-    to visualize ExecuTorch model performance on virtual Arm hardware. By the end, you will be
-    able to identify Arm-based targets suitable for TinyML workloads, install and configure Fixed
-    Virtual Platforms (FVPs), and deploy a TinyML model using ExecuTorch on a Corstone-320 FVP.
-    It focuses on tools and technologies such as Arm Virtual Hardware, FVP, Python, PyTorch, and
-    ExecuTorch, Linux and macOS environments, and Arm platforms including Cortex-A, Cortex-M,
-    and Ethos-U. The main steps cover Overview, Understand the ExecuTorch workflow, Set up your
-    ExecuTorch environment, Set up the Corstone-320 Fixed Virtual Platform, and Deploy and run
-    Mobilenet V2 on the Corstone-320 FVP.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will identify Arm-based targets suitable for TinyML workloads, install and configure
-      Fixed Virtual Platforms (FVPs), and deploy a TinyML model using ExecuTorch on a Corstone-320
-      FVP. Learn how to identify Arm-based targets for TinyML, install Fixed Virtual Platforms,
-      deploy ExecuTorch models on Corstone-320 FVP, and visualize model execution using the FVP
-      graphical interface.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers and data scientists who are new to TinyML and
-      want to visualize ExecuTorch model performance on virtual Arm hardware.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Familiarity with basic machine learning
-      concepts; A Linux or macOS computer with Python 3 installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Arm Virtual Hardware, FVP, Python, PyTorch, and
-      ExecuTorch, Linux and macOS environments, and Arm platforms such as Cortex-A, Cortex-M,
-      and Ethos-U.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview, Understand the ExecuTorch workflow, Set
-      up your ExecuTorch environment, Set up the Corstone-320 Fixed Virtual Platform, and Deploy
-      and run Mobilenet V2 on the Corstone-320 FVP.
-# END generated_summary_faq
+
 
 author: Waheed Brown
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md b/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md
index f518e939d0..7762a1275e 100644
--- a/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md
@@ -19,41 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 3bcfc51edbab56e3c8416f27045a61b069333e6f0cd130e8689b9a4d9fde1dd6
-  summary: >-
-    Introduction to building a minimal Yocto Linux image and running it on 64-bit Qemu Arm target.
-    It is designed for software developers interested in learning the basics of building Yocto
-    Linux for embedded Arm targets. By the end, you will be able to build a minimal Yocto Linux
-    image for generic 64-bit Arm target and run the built Yocto image on Qemu. It focuses on tools
-    and technologies such as Yocto Project and QEMU, Linux environments, and Arm platforms including
-    Cortex-A. The main steps cover How do I get started with Yocto Linux on Qemu?
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build a minimal Yocto Linux image for generic 64-bit Arm target and run the built
-      Yocto image on Qemu. Introduction to building a minimal Yocto Linux image and running it
-      on 64-bit Qemu Arm target.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers interested in learning the basics
-      of building Yocto Linux for embedded Arm targets.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Some familiarity with embedded Linux.;
-      A linux machine running Ubuntu 22.04 with at least 60 GB of disk space.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Yocto Project and QEMU, Linux environments, and
-      Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around How do I get started with Yocto Linux on Qemu?
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md b/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md
index 605d4ef693..b0ab9b1ad6 100644
--- a/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md
@@ -24,50 +24,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: b0cb917bdcfb601c054e6cac93b2d04aca3ffe84b5a1963288fd7b89dd9bad3b
-  summary: >-
-    Learn how to run a YOLO object detection model on the Himax WiseEye2 module, build the Himax
-    SDK, update firmware, and connect to the Grove Vision AI module for computer vision applications.
-    It is designed for developers who would like to learn about how to run a computer vision application
-    on an embedded device from Himax. By the end, you will be able to run a You-Only-Look-Once
-    (YOLO) object detection model on a Himax WiseEye2 module, build the Himax Software Development
-    Kit (SDK) and generate a firmware image file, and update firmware on the Himax WiseEye2. It
-    focuses on tools and technologies such as Himax SDK, Python, and Hugging Face, Linux and macOS
-    environments, and Arm platforms including Cortex-M55 and Ethos-U55. The main steps cover Overview,
-    Set up the environment, Build the firmware, Flash firmware onto the microcontroller, and Run
-    additional models in the web toolkit.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will run a You-Only-Look-Once (YOLO) object detection model on a Himax WiseEye2 module,
-      build the Himax Software Development Kit (SDK) and generate a firmware image file, and update
-      firmware on the Himax WiseEye2. Learn how to run a YOLO object detection model on the Himax
-      WiseEye2 module, build the Himax SDK, update firmware, and connect to the Grove Vision AI
-      module for computer vision applications.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers who would like to learn about how to run a
-      computer vision application on an embedded device from Himax.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Seeed Grove Vision AI Module V2](https://www.seeedstudio.com/Grove-Vision-AI-Module-V2-p-5851.html)
-      development board.; An [OV5647-62 Camera Module](https://www.seeedstudio.com/OV5647-69-1-FOV-Camera-module-for-Raspberry-Pi-3B-4B-p-5484.html).;
-      A Flexible Printed Circuit (FPC) cable.; A USB-C cable.; An x86 Linux machine, or a Mac
-      running macOS.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Himax SDK, Python, and Hugging Face, Linux and macOS
-      environments, and Arm platforms such as Cortex-M55 and Ethos-U55.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview, Set up the environment, Build the firmware,
-      Flash firmware onto the microcontroller, and Run additional models in the web toolkit.
-# END generated_summary_faq
+
 
 author:
     - Chaodong Gong
diff --git a/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md b/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md
index c327c3894f..514959da2d 100644
--- a/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md
@@ -20,40 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 89f599ab33721a2d578d4fc39e505b5aed8d930aabac71f22d1ba28bfc4a5cf8
-  summary: >-
-    Learn how to build and run Zephyr RTOS applications on the Arm Corstone-300 Fixed Virtual
-    Platform using Arm Virtual Hardware. It is designed for software developers getting started
-    with the Zephyr RTOS. By the end, you will be able to build and run Zephyr applications on
-    the Corstone-300. It focuses on tools and technologies such as Zephyr, Arm Virtual Hardware,
-    and FVP, RTOS environments, and Arm platforms including Cortex-M. The main steps cover Build
-    and run Zephyr applications.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build and run Zephyr applications on the Corstone-300. Learn how to build and run
-      Zephyr RTOS applications on the Arm Corstone-300 Fixed Virtual Platform using Arm Virtual
-      Hardware.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers getting started with the Zephyr RTOS.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Some familiarity with embedded C programming;
-      A Linux machine running Ubuntu, or an AWS account to use [Arm Virtual Hardware](https://www.arm.com/products/development-tools/simulation/virtual-hardware).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Zephyr, Arm Virtual Hardware, and FVP, RTOS environments,
-      and Arm platforms such as Cortex-M.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Build and run Zephyr applications.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md b/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md
index 2e2a45bebb..d7c59de272 100644
--- a/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md
@@ -24,49 +24,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 808f1c99409f9ca3bb72419eaf950bc097f1c7af6c2dcade6385be97fdbbf713
-  summary: >-
-    Learn how to install Workbench for Zephyr extension in VS Code, set up the complete Zephyr
-    development environment, create and build Zephyr applications, debug embedded systems, and
-    perform memory usage analysis. It is designed for embedded developers targeting Arm-based
-    platforms with the Zephyr RTOS using the Workbench for Zephyr extension for VS Code. By the
-    end, you will be able to install and configure the Workbench for Zephyr extension in VS Code,
-    set up a complete Zephyr development environment including the SDK and toolchain, and create,
-    build, and debug Zephyr applications using hands-on examples. It focuses on tools and technologies
-    such as Zephyr and C, RTOS environments, and Arm platforms including Cortex-M. The main steps
-    cover Set up your environment, Build a Zephyr application with Zephyr workbench, and Analyze
-    and debug a Zephyr application.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install and configure the Workbench for Zephyr extension in VS Code, set up a complete
-      Zephyr development environment including the SDK and toolchain, and create, build, and debug
-      Zephyr applications using hands-on examples. Learn how to install Workbench for Zephyr extension
-      in VS Code, set up the complete Zephyr development environment, create and build Zephyr
-      applications, debug embedded systems, and perform memory usage analysis.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for embedded developers targeting Arm-based platforms with
-      the Zephyr RTOS using the Workbench for Zephyr extension for VS Code.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Basic familiarity with embedded C programming;
-      Visual Studio Code; A Cortex-M development board; Windows 10+ (64-bit), macOS with Homebrew,
-      or Linux (preferably Ubuntu 20.04+).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Zephyr and C, RTOS environments, and Arm platforms
-      such as Cortex-M.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Set up your environment, Build a Zephyr application
-      with Zephyr workbench, and Analyze and debug a Zephyr application.
-# END generated_summary_faq
+
 
 author: 
     - Ayoub Bourjilat
diff --git a/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md b/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md
index 2165838530..790c3afcb5 100644
--- a/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md
+++ b/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md
@@ -22,49 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 137e974aee0ccba78e3375a1c8179af392e54a0304cfecdcd53cf4ac5c38917b
-  summary: >-
-    Learn how to create and run Ubuntu containers on ChromeOS Crostini using LXC with file sharing
-    and GUI application support on Arm-based Chromebooks. It is designed for software developers
-    who want to install Ubuntu and other Linux distributions on their Arm-based Chromebook with
-    ChromeOS file sharing and GUI support. By the end, you will be able to create and run an Ubuntu
-    24.04 container on ChromeOS Crostini using LXC and Termina shell, set up ChromeOS integration
-    for file sharing and GUI applications, and manage LXC containers on ChromeOS. It focuses on
-    tools and technologies such as Ubuntu, ChromeOS environments, and Arm platforms including
-    Cortex-A. The main steps cover Create an Ubuntu 24.04 container on ChromeOS, Integrate ChromeOS
-    with Linux containers, Enable desktop applications, and Manage Linux containers with additional
-    commands.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create and run an Ubuntu 24.04 container on ChromeOS Crostini using LXC and Termina
-      shell, set up ChromeOS integration for file sharing and GUI applications, and manage LXC
-      containers on ChromeOS. Learn how to create and run Ubuntu containers on ChromeOS Crostini
-      using LXC with file sharing and GUI application support on Arm-based Chromebooks.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is for software developers who want to install Ubuntu and other Linux
-      distributions on their Arm-based Chromebook with ChromeOS file sharing and GUI support.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A ChromeOS device with the Linux development
-      environment enabled. The Lenovo Chromebook Plus 14 is recommended.; Basic knowledge of the
-      Linux command line.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Ubuntu, ChromeOS environments, and Arm platforms
-      such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Create an Ubuntu 24.04 container on ChromeOS, Integrate
-      ChromeOS with Linux containers, Enable desktop applications, and Manage Linux containers
-      with additional commands.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md
index 97d017fdc3..c599af362d 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md
@@ -23,57 +23,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: eda533c727ea7094202e8784bf1ed2240cfea2573cefc73aca1a86797840778c
-  summary: >-
-    Learn how to build and deploy high-fidelity robotic simulations and reinforcement learning
-    pipelines using Isaac Sim and Isaac Lab on Arm-based NVIDIA DGX Spark with Grace-Blackwell
-    architecture. It is designed for robotics developers, simulation engineers, and AI researchers
-    who want to run high-fidelity robotic simulations and reinforcement learning (RL) pipelines
-    using NVIDIA Isaac Sim and Isaac Lab on Arm-based NVIDIA DGX Spark system powered by the Grace–Blackwell
-    (GB10) architecture. By the end, you will be able to describe the roles of Isaac Sim and Isaac
-    Lab within a robotics simulation and RL pipeline, build and configure Isaac Sim and Isaac
-    Lab on an Arm-based DGX Spark system, and launch and control a robot simulation in Isaac Sim
-    using Python. It focuses on tools and technologies such as Python, Bash, IsaacSim, and IsaacLab,
-    Linux environments, and Arm platforms including Cortex-X and Cortex-A. The main steps cover
-    Explore Isaac Sim and Isaac Lab for robotic workflows on DGX Spark, Set up Isaac Sim and Isaac
-    Lab on DGX Spark, Run and Understand a Sample Robot Simulation with Isaac Sim, and Train a
-    Humanoid Locomotion Policy with Isaac Lab on DGX Spark.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will describe the roles of Isaac Sim and Isaac Lab within a robotics simulation and
-      RL pipeline, build and configure Isaac Sim and Isaac Lab on an Arm-based DGX Spark system,
-      and launch and control a robot simulation in Isaac Sim using Python. Learn how to build
-      and deploy high-fidelity robotic simulations and reinforcement learning pipelines using
-      Isaac Sim and Isaac Lab on Arm-based NVIDIA DGX Spark with Grace-Blackwell architecture.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for robotics developers, simulation engineers, and AI researchers
-      who want to run high-fidelity robotic simulations and reinforcement learning (RL) pipelines
-      using NVIDIA Isaac Sim and Isaac Lab on Arm-based NVIDIA DGX Spark system powered by the
-      Grace–Blackwell (GB10) architecture.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A NVIDIA DGX Spark system with at least
-      50 GB of free disk space; Familiarity with Linux command-line tools; Experience with Python
-      scripting and virtual environments; Basic understanding of reinforcement learning concepts
-      (rewards, policies, episodes).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Python, Bash, IsaacSim, and IsaacLab, Linux environments,
-      and Arm platforms such as Cortex-X and Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Explore Isaac Sim and Isaac Lab for robotic workflows
-      on DGX Spark, Set up Isaac Sim and Isaac Lab on DGX Spark, Run and Understand a Sample Robot
-      Simulation with Isaac Sim, and Train a Humanoid Locomotion Policy with Isaac Lab on DGX
-      Spark.
-# END generated_summary_faq
+
 
 author:
     - Johnny Nunez
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md
index 70a484d291..5e89b06086 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md
@@ -25,56 +25,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: ada333cc887badfd57815708ef93e172543da74f2c995b46a916817917e92394
-  summary: >-
-    Learn how to build and optimize quantized LLMs using llama.cpp on NVIDIA DGX Spark with Grace-Blackwell
-    architecture, leveraging Armv9 SIMD acceleration. It is designed for AI practitioners, performance
-    engineers, and system architects who want to learn how to deploy and optimize quantized large
-    language models (LLMs) on NVIDIA DGX Spark systems powered by the Grace-Blackwell (GB10) architecture.
-    By the end, you will be able to describe the Grace–Blackwell (GB10) architecture and its support
-    for efficient AI inference, build CUDA-enabled and CPU-only versions of llama.cpp for flexible
-    deployment, and validate the functionality of both builds on the DGX Spark platform. It focuses
-    on tools and technologies such as Python, C, Bash, and llama.cpp, Linux environments, and
-    Arm platforms including Cortex-A and Cortex-X. The main steps cover Explore Grace Blackwell
-    architecture for efficient quantized LLM inference, Verify your Grace Blackwell system readiness
-    for AI inference, Build the GPU version of llama.cpp on GB10, Build the CPU version of llama.cpp
-    on GB10, and Analyze CPU instruction mix using Process Watch.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will describe the Grace–Blackwell (GB10) architecture and its support for efficient
-      AI inference, build CUDA-enabled and CPU-only versions of llama.cpp for flexible deployment,
-      and validate the functionality of both builds on the DGX Spark platform. Learn how to build
-      and optimize quantized LLMs using llama.cpp on NVIDIA DGX Spark with Grace-Blackwell architecture,
-      leveraging Armv9 SIMD acceleration.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for AI practitioners, performance engineers, and system architects
-      who want to learn how to deploy and optimize quantized large language models (LLMs) on NVIDIA
-      DGX Spark systems powered by the Grace-Blackwell (GB10) architecture.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Access to an NVIDIA DGX Spark system
-      with at least 15 GB of available disk space; Familiarity with command-line interfaces and
-      basic Linux operations; Understanding of CUDA programming basics and GPU/CPU compute concepts;
-      Basic knowledge of quantized large language models (LLMs) and machine learning inference;
-      Experience building software from source using CMake and make.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Python, C, Bash, and llama.cpp, Linux environments,
-      and Arm platforms such as Cortex-A and Cortex-X.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Explore Grace Blackwell architecture for efficient
-      quantized LLM inference, Verify your Grace Blackwell system readiness for AI inference,
-      Build the GPU version of llama.cpp on GB10, Build the CPU version of llama.cpp on GB10,
-      and Analyze CPU instruction mix using Process Watch.
-# END generated_summary_faq
+
 
 author: Odin Shen
 
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md
index 27558707c8..c243177a2b 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md
@@ -20,56 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: facf9c504a3caceb62ef898a04a6760e8aafeb7e802e5727cb80d3a7e7e344d0
-  summary: >-
-    Learn how to build a Retrieval-Augmented Generation (RAG) pipeline on NVIDIA DGX Spark combining
-    Arm Grace CPU orchestration with Blackwell GPU-accelerated inference using llama.cpp. It is
-    designed for developers who want to build a Retrieval-Augmented Generation (RAG) pipeline
-    on the NVIDIA DGX Spark platform. You'll learn how Arm-based Grace CPUs handle document retrieval
-    and orchestration, while Blackwell GPUs speed up large language model inference using the
-    open-source llama.cpp REST server. This is a great fit if you're interested in combining Arm
-    CPU management with GPU-accelerated AI workloads. By the end, you will be able to describe
-    how a RAG system combines document retrieval and language model generation, deploy a hybrid
-    CPU-GPU RAG pipeline on the GB10 platform using open-source tools, and use the llama.cpp REST
-    Server for GPU-accelerated inference with CPU-managed retrieval. It focuses on tools and technologies
-    such as Python, llama.cpp, and Hugging Face, Linux environments, and Arm platforms including
-    Cortex-A. The main steps cover Explore building a RAG pipeline on Arm-based Grace–Blackwell
-    systems, Configure the RAG development environment and models, Add documents to the RAG vector
-    database, Build and run the RAG pipeline, and Monitor unified memory performance.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will describe how a RAG system combines document retrieval and language model generation,
-      deploy a hybrid CPU-GPU RAG pipeline on the GB10 platform using open-source tools, and use
-      the llama.cpp REST Server for GPU-accelerated inference with CPU-managed retrieval. Learn
-      how to build a Retrieval-Augmented Generation (RAG) pipeline on NVIDIA DGX Spark combining
-      Arm Grace CPU orchestration with Blackwell GPU-accelerated inference using llama.cpp.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for developers who want to build a Retrieval-Augmented Generation
-      (RAG) pipeline on the NVIDIA DGX Spark platform. You'll learn how Arm-based Grace CPUs handle
-      document retrieval and orchestration, while Blackwell GPUs speed up large language model
-      inference using the open-source llama.cpp REST server. This is a great fit if you're interested
-      in combining Arm CPU management with GPU-accelerated AI workloads.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An NVIDIA DGX Spark system with at least
-      15 GB of available disk space.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Python, llama.cpp, and Hugging Face, Linux environments,
-      and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Explore building a RAG pipeline on Arm-based Grace–Blackwell
-      systems, Configure the RAG development environment and models, Add documents to the RAG
-      vector database, Build and run the RAG pipeline, and Monitor unified memory performance.
-# END generated_summary_faq
+
 
 author: Odin Shen
 
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md
index 8496fafe2e..c453374dbb 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md
@@ -22,51 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 4ccde526ec4dd9fc18672e162e067108c7251161c1da0375a0bb0374a2f3a4ea
-  summary: >-
-    Learn how to build an offline voice assistant combining speech-to-text via faster-whisper
-    and text generation via vLLM on Arm-based DGX Spark for privacy-focused deployments. It is
-    designed for developers and ML engineers who want to build private, offline voice assistant
-    systems on Arm-based servers such as DGX Spark. By the end, you will be able to explain the
-    architecture of an offline voice chatbot pipeline combining speech-to-text (STT) and vLLM,
-    capture and segment real-time audio using PyAudio and Voice Activity Detection (VAD), and
-    transcribe speech using faster-whisper and generate replies using vLLM. It focuses on tools
-    and technologies such as Docker and Python, Linux environments, and Arm platforms including
-    Neoverse. The main steps cover Build an offline voice assistant with whisper and vLLM, Install
-    faster-whisper for local speech recognition, Build a real-time STT pipeline on CPU, Fine-tune
-    segmentation parameters, and Build a real-time offline voice chatbot using STT and vLLM.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will explain the architecture of an offline voice chatbot pipeline combining speech-to-text
-      (STT) and vLLM, capture and segment real-time audio using PyAudio and Voice Activity Detection
-      (VAD), and transcribe speech using faster-whisper and generate replies using vLLM. Learn
-      how to build an offline voice assistant combining speech-to-text via faster-whisper and
-      text generation via vLLM on Arm-based DGX Spark for privacy-focused deployments.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for developers and ML engineers who want to build private, offline
-      voice assistant systems on Arm-based servers such as DGX Spark.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An NVIDIA DGX Spark system with at least
-      15 GB of available disk space; A USB microphone for audio input.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Docker and Python, Linux environments, and Arm platforms
-      such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Build an offline voice assistant with whisper and
-      vLLM, Install faster-whisper for local speech recognition, Build a real-time STT pipeline
-      on CPU, Fine-tune segmentation parameters, and Build a real-time offline voice chatbot using
-      STT and vLLM.
-# END generated_summary_faq
+
 
 author: Odin Shen
 
diff --git a/content/learning-paths/laptops-and-desktops/docker-models/_index.md b/content/learning-paths/laptops-and-desktops/docker-models/_index.md
index c3aa0d5f7d..a40d34d255 100644
--- a/content/learning-paths/laptops-and-desktops/docker-models/_index.md
+++ b/content/learning-paths/laptops-and-desktops/docker-models/_index.md
@@ -20,46 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: eae0a23635e7a025e1a73baaf5ccbd01f2c031ec76725c68893ca02190e36deb
-  summary: >-
-    Learn how to run pre-trained AI models locally using Docker Model Runner and build containerized
-    applications integrating large language models. It is designed for software developers and
-    AI enthusiasts who want to run pre-trained AI models locally using Docker Model Runner. By
-    the end, you will be able to run AI models locally using Docker Model Runner and build containerized
-    applications that integrate Large Language Models (LLMs). It focuses on tools and technologies
-    such as Docker, Python, and LLM, Windows and macOS environments, and Arm platforms including
-    Neoverse and Cortex-A. The main steps cover Run AI models using Docker Model Runner and Run
-    a containerized AI chat app with Docker Compose.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will run AI models locally using Docker Model Runner and build containerized applications
-      that integrate Large Language Models (LLMs). Learn how to run pre-trained AI models locally
-      using Docker Model Runner and build containerized applications integrating large language
-      models.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is for software developers and AI enthusiasts who want to run pre-trained AI models
-      locally using Docker Model Runner.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Docker Desktop (version 4.40 or later)
-      installed on a system with at least 16GB of RAM (recommended).; Basic understanding of Docker
-      CLI and concepts.; Familiarity with LLM concepts.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Docker, Python, and LLM, Windows and macOS environments,
-      and Arm platforms such as Neoverse and Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Run AI models using Docker Model Runner and Run a
-      containerized AI chat app with Docker Compose.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/laptops-and-desktops/electron/_index.md b/content/learning-paths/laptops-and-desktops/electron/_index.md
index 58f9bd7fa7..b3b22e3b07 100644
--- a/content/learning-paths/laptops-and-desktops/electron/_index.md
+++ b/content/learning-paths/laptops-and-desktops/electron/_index.md
@@ -20,47 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 8491a5e83e9e6436721f6078085e9b367121d19fdf228634dba859b3e1a0802a
-  summary: >-
-    Learn how to develop and build cross-platform desktop applications using the Electron Framework
-    on Windows on Arm devices. It is designed for developers who want to learn how to develop
-    cross-platform desktop applications using the Electron Framework on Windows on Arm (WoA).
-    By the end, you will be able to implement a sample application using the electron framework
-    on a Windows on Arm machine and learn how to create a multi platform build of the application.
-    It focuses on tools and technologies such as JavaScript, HTML, and Visual Studio Code, Windows
-    environments, and Arm platforms including Cortex-A. The main steps cover Create the application
-    using the electron framework and Build the cross-platform application.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will implement a sample application using the electron framework on a Windows on Arm
-      machine and learn how to create a multi platform build of the application. Learn how to
-      develop and build cross-platform desktop applications using the Electron Framework on Windows
-      on Arm devices.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This learning path is for developers who want to learn how to develop cross-platform desktop
-      applications using the Electron Framework on Windows on Arm (WoA).
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows on Arm computer such as the
-      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
-      Node.js for Arm64. You can find the [Node.js installer](https://nodejs.org/dist/v20.10.0/node-v20.10.0-arm64.msi).;
-      Any code editor; we recommend using [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including JavaScript, HTML, and Visual Studio Code, Windows
-      environments, and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Create the application using the electron framework
-      and Build the cross-platform application.
-# END generated_summary_faq
+
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md b/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md
index 2f6ff31ed2..d0758d366c 100644
--- a/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md
+++ b/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md
@@ -20,46 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 7e108d774eb0fd0b2b72fa8b5d65e1efec0ff4ecf3d80d4e511e82cdf3862284
-  summary: >-
-    Learn how to automate Windows application builds on Arm architecture using GitHub Arm-hosted
-    runners and GitHub Actions workflows. It is designed for This introductory tutorial is for
-    software developers looking to automate Windows application builds on Arm architecture using
-    GitHub Actions. By the end, you will be able to describe GitHub Arm-hosted Windows runners,
-    configure workflows to run on Arm-hosted runners, and automate Windows application builds
-    with GitHub Actions. It focuses on tools and technologies such as GitHub, Visual Studio, MSBuild,
-    and Arm Performance Libraries, Windows environments, and Arm platforms including Cortex-A.
-    The main steps cover Introduction to GitHub Arm-hosted Runners and Automate the Build of Windows
-    Applications.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will describe GitHub Arm-hosted Windows runners, configure workflows to run on Arm-hosted
-      runners, and automate Windows application builds with GitHub Actions. Learn how to automate
-      Windows application builds on Arm architecture using GitHub Arm-hosted runners and GitHub
-      Actions workflows.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This introductory tutorial is for software developers looking to automate Windows application
-      builds on Arm architecture using GitHub Actions.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A GitHub account.; Familiarity with
-      GitHub Actions.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including GitHub, Visual Studio, MSBuild, and Arm Performance
-      Libraries, Windows environments, and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Introduction to GitHub Arm-hosted Runners and Automate
-      the Build of Windows Applications.
-# END generated_summary_faq
+
 
 author: 
     - Pareena Verma
diff --git a/content/learning-paths/laptops-and-desktops/hyper-v/_index.md b/content/learning-paths/laptops-and-desktops/hyper-v/_index.md
index dd71fe252b..39046c1fb8 100644
--- a/content/learning-paths/laptops-and-desktops/hyper-v/_index.md
+++ b/content/learning-paths/laptops-and-desktops/hyper-v/_index.md
@@ -17,40 +17,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 829130636ec6969f791826ef731b38f7bb87c025d910218822a113ecdef62306
-  summary: >-
-    Learn how to create and manage Arm-based Linux virtual machines using Hyper-V on Windows on
-    Arm devices. It is designed for software developers who want to use Linux virtual machines
-    with Windows on Arm devices. By the end, you will be able to create Arm-based Linux virtual
-    machines using Hyper-V. It focuses on tools and technologies such as Hyper-V, Windows and
-    Linux environments, and Arm platforms including Cortex-A. The main steps cover Create a Linux
-    virtual machine using Hyper-V.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create Arm-based Linux virtual machines using Hyper-V. Learn how to create and
-      manage Arm-based Linux virtual machines using Hyper-V on Windows on Arm devices.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to use Linux virtual machines
-      with Windows on Arm devices.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows on Arm computer such as the
-      Lenovo Thinkpad X13s running Windows 11 with [Hyper-V](/install-guides/hyper-v/) installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Hyper-V, Windows and Linux environments, and Arm
-      platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Create a Linux virtual machine using Hyper-V.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/laptops-and-desktops/intro/_index.md b/content/learning-paths/laptops-and-desktops/intro/_index.md
index 015261bba2..fe861fa54f 100644
--- a/content/learning-paths/laptops-and-desktops/intro/_index.md
+++ b/content/learning-paths/laptops-and-desktops/intro/_index.md
@@ -18,40 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 5a5e4437a7e71182ede45ee091ae49117446fd1c34b02be92df75a41f4fd1f0d
-  summary: >-
-    Learn where the Arm architecture is used in desktop and laptop computers and find hardware
-    for software development on Arm platforms. It is designed for developers working on laptops
-    and desktops and new to the Arm architecture. By the end, you will be able to understand where
-    the Arm architecture is used in desktop and laptop computers and find desktop and laptop hardware
-    to use for software development. It focuses on Linux, Windows, and ChromeOS environments.
-    The main steps cover Arm in Laptops and Desktops and Find Arm hardware.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand where the Arm architecture is used in desktop and laptop computers and
-      find desktop and laptop hardware to use for software development. Learn where the Arm architecture
-      is used in desktop and laptop computers and find hardware for software development on Arm
-      platforms.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers working on laptops and desktops and new to
-      the Arm architecture.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Nothing.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers Linux, Windows, and ChromeOS environments.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Arm in Laptops and Desktops and Find Arm hardware.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md b/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md
index f53a23df15..a5d535a521 100644
--- a/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md
+++ b/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md
@@ -21,45 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 3e93048b46c24514a89ccc2f277b53111a419c049b53662e1461be38a22e83f7
-  summary: >-
-    Learn how to build, test, and verify KleidiCV with Scalable Matrix Extensions (SME) on Apple
-    Silicon Macs for accelerated computer vision performance. It is designed for software developers
-    who want to build and test KleidiCV on macOS. By the end, you will be able to install and
-    compile KleidiCV on macOS, run KleidiCV example tests, and enable Scalable Matrix Extensions
-    (SME) and verify increased SME performance. It focuses on tools and technologies such as KleidiCV
-    and C, macOS environments, and Arm platforms including Cortex-A. The main steps cover Download
-    and build KleidiCV software and Test KleidiCV and verify SME backend support.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install and compile KleidiCV on macOS, run KleidiCV example tests, and enable Scalable
-      Matrix Extensions (SME) and verify increased SME performance. Learn how to build, test,
-      and verify KleidiCV with Scalable Matrix Extensions (SME) on Apple Silicon Macs for accelerated
-      computer vision performance.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to build and test KleidiCV
-      on macOS.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Mac with Apple Silicon (M4 generation
-      or newer); Xcode command line tools installed; Basic familiarity with using the Terminal
-      and command-line tools.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including KleidiCV and C, macOS environments, and Arm platforms
-      such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Download and build KleidiCV software and Test KleidiCV
-      and verify SME backend support.
-# END generated_summary_faq
+
 
 author: Jett Zhou
 
diff --git a/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md b/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md
index 9d92abf146..43334786e2 100644
--- a/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md
+++ b/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md
@@ -18,45 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 631134875aac73e168b60b86d1ca7b4e898196f98cb2825a4238f62129d2d862
-  summary: >-
-    Learn how to configure the LLVM toolchain with Visual Studio to build native Windows on Arm
-    applications using the open-source PuTTY project. It is designed for software developers doing
-    native development on Windows on Arm computers. By the end, you will be able to configure
-    the native LLVM toolchain with Visual Studio to compile for Windows on Arm and build open-source
-    PuTTY application for Windows on Arm using the native LLVM toolchain. It focuses on tools
-    and technologies such as LLVM and Visual Studio Code, Windows environments, and Arm platforms
-    including Cortex-A. The main steps cover Build a native windows application using LLVM for
-    Windows on Arm.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will configure the native LLVM toolchain with Visual Studio to compile for Windows on
-      Arm and build open-source PuTTY application for Windows on Arm using the native LLVM toolchain.
-      Learn how to configure the LLVM toolchain with Visual Studio to build native Windows on
-      Arm applications using the open-source PuTTY project.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers doing native development on Windows
-      on Arm computers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows on Arm computer such as the
-      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including LLVM and Visual Studio Code, Windows environments,
-      and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Build a native windows application using LLVM for
-      Windows on Arm.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md b/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md
index a9ed037a28..5bae458289 100644
--- a/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md
+++ b/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md
@@ -20,48 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 87d4afe4ce7f0cef113cd61fd712fde073cca0eaafbe86a2066b76a117328d11
-  summary: >-
-    Learn how to apply Arm Memory Tagging Extension (MTE) to protect dynamic memory allocations
-    and prevent common memory use errors. It is designed for software developers who want to learn
-    how to use the Memory Tagging Extension (MTE) to protect dynamic memory allocations. By the
-    end, you will be able to learn how to apply MTE to an existing memory allocator and understand
-    how MTE can prevent common memory use errors. It focuses on tools and technologies such as
-    MTE, Linux, and C, Linux environments, and Arm platforms including Cortex-A. The main steps
-    cover Why Use Memory Tagging?, Implement Memory Tagging for a Dynamic Memory Allocator, Memory
-    Tagging Changes, Preventing Mistakes By Using Memory Tagging, and Memory Tagged Allocation
-    Summary.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will learn how to apply MTE to an existing memory allocator and understand how MTE can
-      prevent common memory use errors. Learn how to apply Arm Memory Tagging Extension (MTE)
-      to protect dynamic memory allocations and prevent common memory use errors.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers who want to learn how to use the Memory
-      Tagging Extension (MTE) to protect dynamic memory allocations.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Linux computer.; Basic knowledge of
-      how MTE works. Refer to the [Learn about Memory Tagging Extension Learning Path](/learning-paths/mobile-graphics-and-gaming/mte/);
-      Knowledge of how a dynamic memory allocator can be implemented. Refer to [Write a Dynamic
-      Memory Allocator Learning Path](/learning-paths/cross-platform/dynamic-memory-allocator/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including MTE, Linux, and C, Linux environments, and Arm platforms
-      such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Why Use Memory Tagging?, Implement Memory Tagging
-      for a Dynamic Memory Allocator, Memory Tagging Changes, Preventing Mistakes By Using Memory
-      Tagging, and Memory Tagged Allocation Summary.
-# END generated_summary_faq
+
 
 author: David Spickett
 
diff --git a/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md b/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
index 95d457f1fa..3e96049087 100644
--- a/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
+++ b/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
@@ -20,44 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: e1befed4cafab0eaee29a31c2f259a6a90a14d9f8230c570b6c10a9840b761d5
-  summary: >-
-    Learn how to install and configure Arch Linux for Arm with the i3 window manager and Neovim
-    editor on the Pinebook Pro laptop. It is designed for developers who want to use the Pinebook
-    Pro as an Arm Linux development machine. By the end, you will be able to install and configure
-    Arch Linux for Arm, install and configure the i3 window manager, and install and configure
-    the Neovim editor. It focuses on tools and technologies such as i3, Alacritty, and Neovim,
-    Linux environments, and Arm platforms including Cortex-A72 and Cortex-A53. The main steps
-    cover How do I install Arch Linux?, How do you install the i3 Windows Manager?, and How do
-    I install and configure Neovim?
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install and configure Arch Linux for Arm, install and configure the i3 window manager,
-      and install and configure the Neovim editor. Learn how to install and configure Arch Linux
-      for Arm with the i3 window manager and Neovim editor on the Pinebook Pro laptop.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for developers who want to use the Pinebook Pro as an Arm Linux
-      development machine.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Pinebook Pro laptop; A microSD card
-      (8GB or greater; class 10 or faster).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including i3, Alacritty, and Neovim, Linux environments, and
-      Arm platforms such as Cortex-A72 and Cortex-A53.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around How do I install Arch Linux?, How do you install the
-      i3 Windows Manager?, and How do I install and configure Neovim?
-# END generated_summary_faq
+
 
 author: Gabriel Peterson
 
diff --git a/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md b/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
index a70570fd8e..02e132e286 100644
--- a/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
+++ b/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
@@ -21,47 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: aa2a78baf3e52172e37506c3f75254968d775b4eb516f9696a0a6998aba50e97
-  summary: >-
-    Learn how to fine-tune large language models using PyTorch and Hugging Face on NVIDIA DGX
-    Spark to improve domain-specific accuracy. It is designed for AI developers and ML engineers
-    who want to fine-tune large language models using PyTorch and Hugging Face on the NVIDIA DGX
-    Spark platform. By the end, you will be able to understand how fine-tuning teaches a model
-    domain-specific knowledge, prepare a custom JSONL dataset for supervised fine-tuning, and
-    fine-tune Llama 3.2 3B on Raspberry Pi datasheet content using PyTorch and Hugging Face. It
-    focuses on tools and technologies such as Python, PyTorch, Docker, and Hugging Face, Linux
-    environments, and Arm platforms including Cortex-A and Neoverse. The main steps cover Set
-    up your NVIDIA DGX Spark, Understand fine-tuning, Fine-tune a model with PyTorch and Hugging
-    Face, and Test your fine-tuned model with vLLM.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand how fine-tuning teaches a model domain-specific knowledge, prepare a
-      custom JSONL dataset for supervised fine-tuning, and fine-tune Llama 3.2 3B on Raspberry
-      Pi datasheet content using PyTorch and Hugging Face. Learn how to fine-tune large language
-      models using PyTorch and Hugging Face on NVIDIA DGX Spark to improve domain-specific accuracy.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for AI developers and ML engineers who want to fine-tune large
-      language models using PyTorch and Hugging Face on the NVIDIA DGX Spark platform.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Hugging Face account and access token;
-      NVIDIA DGX Spark workstation.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Python, PyTorch, Docker, and Hugging Face, Linux
-      environments, and Arm platforms such as Cortex-A and Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Set up your NVIDIA DGX Spark, Understand fine-tuning,
-      Fine-tune a model with PyTorch and Hugging Face, and Test your fine-tuned model with vLLM.
-# END generated_summary_faq
+
 
 author: Michael Hall
 
diff --git a/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md b/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
index fbded961b9..d7e33d8833 100644
--- a/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
+++ b/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
@@ -21,46 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: a851b2f81b6aac54aef84acf7537a1cc6b99f66ce31ffd26631d6a966401e4ed
-  summary: >-
-    Learn how to create a CI/CD pipeline in GitHub using self-hosted Arm64 runners to build and
-    push Docker images to DockerHub. It is designed for software developers and IT practitioners
-    who want to learn how to use GitHub Actions for CI/CD purposes. By the end, you will be able
-    to create a CI/CD pipeline in GitHub, use a self-hosted runner, and build and push the Docker
-    image to DockerHub. It focuses on tools and technologies such as .NET and Visual Studio Code,
-    Linux environments, and Arm platforms including Cortex-A. The main steps cover Background:
-    GitHub Actions and CI/CD, Further Context, Setting up the DockerHub Repository, Prepare GitHub
-    Repository, and Prepare the runner.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create a CI/CD pipeline in GitHub, use a self-hosted runner, and build and push
-      the Docker image to DockerHub. Learn how to create a CI/CD pipeline in GitHub using self-hosted
-      Arm64 runners to build and push Docker images to DockerHub.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is for software developers and IT practitioners who want to learn how
-      to use GitHub Actions for CI/CD purposes.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm64-powered machine, either virtual
-      or physical. This Learning Path demonstration uses an Arm64-powered VM with Ubuntu 22.04.;
-      A DockerHub account. You can [set up a free DockerHub account](https://hub.docker.com/signup).;
-      A GitHub account. You can [sign up for GitHub](https://github.com/signup).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including .NET and Visual Studio Code, Linux environments,
-      and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Background: GitHub Actions and CI/CD, Further Context,
-      Setting up the DockerHub Repository, Prepare GitHub Repository, and Prepare the runner.
-# END generated_summary_faq
+
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/_index.md b/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
index 6d44b2a15d..f925f06639 100644
--- a/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
@@ -18,43 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: d24bf30aef690451942789bb66df63b7a3483ecc3cd2c4b3e60ce15fad90cb91
-  summary: >-
-    Learn how to build the OpenCV library for Windows on Arm devices and develop computer vision
-    applications using OpenCV. It is designed for software developers who want to build and develop
-    applications on Windows on Arm devices using OpenCV. By the end, you will be able to build
-    the OpenCV library for Windows on Arm devices and develop applications using OpenCV. It focuses
-    on tools and technologies such as Visual Studio, Clang, OpenCV, and CPP, Windows environments,
-    and Arm platforms including Cortex-A. The main steps cover OpenCV and Compilers for Windows
-    on Arm, Setup, Build OpenCV Applications with Clang, and Build OpenCV Applications with MSVC.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build the OpenCV library for Windows on Arm devices and develop applications using
-      OpenCV. Learn how to build the OpenCV library for Windows on Arm devices and develop computer
-      vision applications using OpenCV.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers who want to build and develop applications
-      on Windows on Arm devices using OpenCV.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows on Arm machine such as the
-      Lenovo Thinkpad X13s, or an [Azure virtual machine](/learning-paths/cross-platform/woa_azure/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Visual Studio, Clang, OpenCV, and CPP, Windows environments,
-      and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around OpenCV and Compilers for Windows on Arm, Setup, Build
-      OpenCV Applications with Clang, and Build OpenCV Applications with MSVC.
-# END generated_summary_faq
+
 
 author: Koki Mitsunami
 
diff --git a/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md b/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md
index 373bdaa31a..4c246b932f 100644
--- a/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md
@@ -20,48 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: fc0d79605640cc8e7a36070a044323d6e278335484949921d0aa4e9cd163d4bd
-  summary: >-
-    Learn how to measure application resource usage, benchmark video encoding tasks, and monitor
-    CPU, memory, and power consumption on Windows on Arm using FFmpeg and PowerShell. It is designed
-    for developers who want to measure resource usage of applications on Windows on Arm devices
-    using FFmpeg. By the end, you will be able to measure application resource usage using FFmpeg
-    and PowerShell, benchmark a video encoding task, and monitor CPU, memory, and power consumption
-    during a video decode task. It focuses on tools and technologies such as FFmpeg and PowerShell,
-    Windows environments, and Arm platforms including Cortex-A. The main steps cover Set up FFmpeg
-    and encode a test video, Track system resource usage on Windows on Arm with PowerShell, and
-    Measure power usage on Windows on Arm with PowerShell.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will measure application resource usage using FFmpeg and PowerShell, benchmark a video
-      encoding task, and monitor CPU, memory, and power consumption during a video decode task.
-      Learn how to measure application resource usage, benchmark video encoding tasks, and monitor
-      CPU, memory, and power consumption on Windows on Arm using FFmpeg and PowerShell.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers who want to measure resource usage of applications
-      on Windows on Arm devices using FFmpeg.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows on Arm computer such as the
-      Lenovo Thinkpad X13s running Windows 11; A code editor such as [Visual Studio Code for Windows
-      on Arm](https://code.visualstudio.com/docs/?dv=win32arm64user).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including FFmpeg and PowerShell, Windows environments, and
-      Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Set up FFmpeg and encode a test video, Track system
-      resource usage on Windows on Arm with PowerShell, and Measure power usage on Windows on
-      Arm with PowerShell.
-# END generated_summary_faq
+
 
 author: Ruifeng Wang
 
diff --git a/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md b/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
index c25a035a60..d140b69879 100644
--- a/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
@@ -20,48 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 915f6eb5e95bd42ed09b727b4599855d965125e67a8761fbd48a124e9e74b1bc
-  summary: >-
-    Learn how to automate Windows on Arm VM creation on Arm Linux systems using QEMU, KVM, and
-    Bash scripts for development and testing. It is designed for developers and system administrators
-    who want to automate Windows on Arm virtual machine (VM) creation on Arm Linux systems using
-    QEMU and KVM. By the end, you will be able to understand the process of creating a Windows
-    on Arm virtual machine using Bash scripts, run scripts for VM creation and management, and
-    troubleshoot common VM setup and runtime issues. It focuses on tools and technologies such
-    as QEMU, KVM, Bash, and RDP, Linux and Windows environments, and Arm platforms including Neoverse
-    and Cortex-A. The main steps cover Check system requirements, Understand and customize Windows
-    on Arm VM automation scripts, Create a Windows on Arm virtual machine, and Run a Windows on
-    Arm virtual machine.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand the process of creating a Windows on Arm virtual machine using Bash
-      scripts, run scripts for VM creation and management, and troubleshoot common VM setup and
-      runtime issues. Learn how to automate Windows on Arm VM creation on Arm Linux systems using
-      QEMU, KVM, and Bash scripts for development and testing.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers and system administrators who want to automate
-      Windows on Arm virtual machine (VM) creation on Arm Linux systems using QEMU and KVM.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm Linux system with KVM support
-      and a minimum of 8GB RAM and 50GB free disk space.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including QEMU, KVM, Bash, and RDP, Linux and Windows environments,
-      and Arm platforms such as Neoverse and Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Check system requirements, Understand and customize
-      Windows on Arm VM automation scripts, Create a Windows on Arm virtual machine, and Run a
-      Windows on Arm virtual machine.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md b/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
index 6a463a9b3a..46b586e318 100644
--- a/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
@@ -18,43 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 4069c5bce1ce4b689a7a67d740fc077dc55c9b0bfbab392f5984ba0bdd9e59c3
-  summary: >-
-    Learn how to build native Arm applications and migrate x86/x64 applications to Arm using Arm64EC
-    on Windows on Arm devices. It is designed for software developers who want to use Arm64EC
-    with Windows on Arm devices. By the end, you will be able to build native Arm applications
-    and migrate x86 or x64 applications to Arm using Arm64EC and compare the performance of a
-    simple application using different build configurations. It focuses on tools and technologies
-    such as Arm64EC and Visual Studio, Windows environments, and Arm platforms including Cortex-A.
-    The main steps cover Build an application on Windows 11 using Arm64EC.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build native Arm applications and migrate x86 or x64 applications to Arm using
-      Arm64EC and compare the performance of a simple application using different build configurations.
-      Learn how to build native Arm applications and migrate x86/x64 applications to Arm using
-      Arm64EC on Windows on Arm devices.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to use Arm64EC with Windows
-      on Arm devices.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows on Arm computer such as the
-      Lenovo Thinkpad X13s running Windows 11.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Arm64EC and Visual Studio, Windows environments,
-      and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Build an application on Windows 11 using Arm64EC.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md b/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
index 7de787ea56..0e72738f25 100644
--- a/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
@@ -22,47 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 3b3ecd451ed8b634c7fbe194248cdf1d33432633efbcbef5de713495041ff425
-  summary: >-
-    Learn how to port Qt-based Python desktop applications with C/C++ dependencies to Arm64 using
-    Arm64EC on Windows on Arm. It is designed for developers who want to learn how to port their
-    applications to Arm64 using Arm64EC. By the end, you will be able to build a Qt-based Python
-    desktop application, create C/C++ dependencies and use them in the Qt-based Python app, and
-    learn how to port the C/C++ based dependencies to Arm64 using Arm64EC. It focuses on tools
-    and technologies such as C, CPP, and Qt, Windows environments, and Arm platforms including
-    Cortex-A. The main steps cover Application, Porting using CMake, and Porting using MSBuild.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build a Qt-based Python desktop application, create C/C++ dependencies and use
-      them in the Qt-based Python app, and learn how to port the C/C++ based dependencies to Arm64
-      using Arm64EC. Learn how to port Qt-based Python desktop applications with C/C++ dependencies
-      to Arm64 using Arm64EC on Windows on Arm.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers who want to learn how to port their applications
-      to Arm64 using Arm64EC.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows on Arm computer such as the
-      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
-      Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user)
-      is suitable.; Visual Studio 2022 with Arm build tools. [Refer to this guide for the installation
-      steps](https://developer.arm.com/documentation/102528/0100/Install-Visual-Studio).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including C, CPP, and Qt, Windows environments, and Arm platforms
-      such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Application, Porting using CMake, and Porting using
-      MSBuild.
-# END generated_summary_faq
+
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md b/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md
index 15cdc97f11..4ef101fa33 100644
--- a/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md
@@ -19,43 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 63389742eced4df89f85bdf56a01e489e52a9702d446b557f8f55312f9d31f20
-  summary: >-
-    Learn how to build and run Qt-based desktop applications on Windows on Arm and investigate
-    native Arm64 performance improvements. It is designed for software developers who want to
-    use the native performance of the Qt framework for building desktop applications on Windows
-    on Arm (WoA). By the end, you will be able to build and run a Qt-based desktop application
-    and investigate performance improvements gained by running on Arm64. It focuses on tools and
-    technologies such as C, CPP, and Qt, Windows environments, and Arm platforms including Cortex-A.
-    The main steps cover Qt application.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build and run a Qt-based desktop application and investigate performance improvements
-      gained by running on Arm64. Learn how to build and run Qt-based desktop applications on
-      Windows on Arm and investigate native Arm64 performance improvements.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to use the native performance
-      of the Qt framework for building desktop applications on Windows on Arm (WoA).
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows on Arm computer such as the
-      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
-      [Qt framework](https://www.qt.io/) or [Qt for Open Source Development](https://www.qt.io/download-open-source).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including C, CPP, and Qt, Windows environments, and Arm platforms
-      such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Qt application.
-# END generated_summary_faq
+
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md b/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
index 20bcaf0236..1a7f742f08 100644
--- a/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
@@ -21,45 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: e60ce02e9d1872da06bc5bfeb18c7ec65f2bc17defb2b75292f930fb3ad41711
-  summary: >-
-    Learn how to build and run an ASP.NET Core 8 web server application with Web API and dependency
-    injection services on Windows on Arm. It is designed for developers who are interested in
-    building a web server for a headless IoT applications. By the end, you will be able to build
-    and run an ASP.NET Core 8 application, create a Web API, and create and use services using
-    the dependency injection. It focuses on tools and technologies such as .NET and Visual Studio
-    Code, Windows environments, and Arm platforms including Cortex-A. The main steps cover Create
-    a ASP.NET Core Web API project and Build, run, and access the web server.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build and run an ASP.NET Core 8 application, create a Web API, and create and use
-      services using the dependency injection. Learn how to build and run an ASP.NET Core 8 web
-      server application with Web API and dependency injection services on Windows on Arm.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for developers who are interested in building a web server for
-      a headless IoT applications.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows on Arm computer such as the
-      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
-      .NET 8 SDK for [arm64](https://dotnet.microsoft.com/en-us/download/dotnet/thank-you/sdk-8.0.100-windows-arm64-installer).;
-      Any code editor, we recommend using [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including .NET and Visual Studio Code, Windows environments,
-      and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Create a ASP.NET Core Web API project and Build, run,
-      and access the web server.
-# END generated_summary_faq
+
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
index e92b2e38b6..9c649c7d48 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
@@ -20,46 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: cddfb7b83e82f0daa513558b1e7ee09b55c63e2ff95675d67be7d4408d391aa4
-  summary: >-
-    Learn how to create Node.js IoT applications that stream sensor data from Windows on Arm devices
-    to AWS IoT Core using MQTT. It is designed for developers who want to learn how to create
-    IoT applications using Windows on Arm and AWS IoT Core. By the end, you will be able to create
-    a Node.js that streams synthesized sensor data to AWS cloud, register a device in AWS IoT
-    Core, and send data from a device to AWS IoT Core. It focuses on tools and technologies such
-    as Node.js and Visual Studio, Windows environments, and Arm platforms including Cortex-A.
-    The main steps cover AWS IoT Core, Connect the emulator to AWS IoT Core and stream data, and
-    Testing the data stream.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create a Node.js that streams synthesized sensor data to AWS cloud, register a
-      device in AWS IoT Core, and send data from a device to AWS IoT Core. Learn how to create
-      Node.js IoT applications that stream sensor data from Windows on Arm devices to AWS IoT
-      Core using MQTT.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This learning path is for developers who want to learn how to create IoT applications using
-      Windows on Arm and AWS IoT Core.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows-on-Arm computer such as the
-      Lenovo Thinkpad X13s running Windows 11 or a Windows-on-Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
-      Any code editor. Visual Studio Code is suitable.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Node.js and Visual Studio, Windows environments,
-      and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around AWS IoT Core, Connect the emulator to AWS IoT Core
-      and stream data, and Testing the data stream.
-# END generated_summary_faq
+
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md
index 205e69979d..4dda9edfaa 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md
@@ -21,47 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: f25597c6c9e69e09e9a86fa7c02d0ace9c347f293a21a11c00a6d3498300052c
-  summary: >-
-    Learn how to configure AWS IoT Core rules to parse MQTT messages and store IoT data in Amazon
-    DynamoDB from Windows on Arm devices. It is designed for developers who are interested in
-    using Amazon DynamoDB as a database for storing data. By the end, you will be able to gain
-    familiarity with Amazon DynamoDB, be able to run the IoT application that streams data to
-    AWS IoT Core, and be able to create the rule that parses messages from AWS IoT Core and writes
-    them to DynamoDB. It focuses on tools and technologies such as .NET and Visual Studio Code,
-    Windows environments, and Arm platforms including Cortex-A. The main steps cover Background
-    and Create a ASP.NET Core Web API project.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will gain familiarity with Amazon DynamoDB, be able to run the IoT application that
-      streams data to AWS IoT Core, and be able to create the rule that parses messages from AWS
-      IoT Core and writes them to DynamoDB. Learn how to configure AWS IoT Core rules to parse
-      MQTT messages and store IoT data in Amazon DynamoDB from Windows on Arm devices.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for developers who are interested in using Amazon DynamoDB as
-      a database for storing data.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows on Arm computer such as the
-      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
-      Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user)
-      is suitable.; Completion of the [Create IoT applications with Windows on Arm and AWS IoT
-      Core](/learning-paths/laptops-and-desktops/win_aws_iot/) Learning Path.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including .NET and Visual Studio Code, Windows environments,
-      and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Background and Create a ASP.NET Core Web API project.
-# END generated_summary_faq
+
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md
index b9c43e3ed4..d8d9bf15d1 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md
@@ -22,48 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: f685f45be9e5fc05b278e28590f9d421a920d43cc36ccb572545de4eaf4a799a
-  summary: >-
-    Learn how to process IoT data using AWS Lambda functions triggered by AWS IoT Core messages
-    from Windows on Arm devices. It is designed for developers who are interested in using AWS
-    Lambda for processing data streamed by IoT applications and devices. By the end, you will
-    be able to describe how to use AWS Lambda for IoT applications running on Arm64, process data
-    from IoT devices, and describe the serverless compute services in AWS. It focuses on tools
-    and technologies such as .NET and Visual Studio Code, Windows environments, and Arm platforms
-    including Cortex-A. The main steps cover Overview of Learning Path, Background, Create a Rule,
-    Implement Lambda Function, and Summary.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will describe how to use AWS Lambda for IoT applications running on Arm64, process data
-      from IoT devices, and describe the serverless compute services in AWS. Learn how to process
-      IoT data using AWS Lambda functions triggered by AWS IoT Core messages from Windows on Arm
-      devices.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for developers who are interested in using AWS Lambda for processing
-      data streamed by IoT applications and devices.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows on Arm computer such as the
-      a Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
-      Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user)
-      is suitable.; Completion of the [Create IoT applications with Windows on Arm and AWS IoT
-      Core](/learning-paths/laptops-and-desktops/win_aws_iot/) Learning Path.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including .NET and Visual Studio Code, Windows environments,
-      and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview of Learning Path, Background, Create a Rule,
-      Implement Lambda Function, and Summary.
-# END generated_summary_faq
+
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md
index 7eb228b02b..617b86c4c7 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md
@@ -20,48 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: f5a93346a0fd55659b7c0a6df501db97742ec71755b6443051b654f3ce871cdf
-  summary: >-
-    Learn how to implement AWS Lambda functions that process and aggregate IoT data stored in
-    DynamoDB tables from Windows on Arm devices. It is designed for developers who are interested
-    in using AWS Lambda for processing data stored in DynamoDB. By the end, you will be able to
-    implement an AWS Lambda function that processes data stored in a DynamoDB table and learn
-    how to work with DynamoDB to scan and aggregate records. It focuses on tools and technologies
-    such as Node.js and Visual Studio Code, Windows environments, and Arm platforms including
-    Cortex-A. The main steps cover Background, Create the AWS Lambda Function, Implement the AWS
-    Lambda Function, and Test Lambda Function.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will implement an AWS Lambda function that processes data stored in a DynamoDB table
-      and learn how to work with DynamoDB to scan and aggregate records. Learn how to implement
-      AWS Lambda functions that process and aggregate IoT data stored in DynamoDB tables from
-      Windows on Arm devices.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for developers who are interested in using AWS Lambda for processing
-      data stored in DynamoDB.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows on Arm computer such as the
-      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
-      Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user)
-      is suitable.; Completion of the [Create IoT applications with Windows on Arm and AWS IoT
-      Core](/learning-paths/laptops-and-desktops/win_aws_iot/) Learning Path.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Node.js and Visual Studio Code, Windows environments,
-      and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Background, Create the AWS Lambda Function, Implement
-      the AWS Lambda Function, and Test Lambda Function.
-# END generated_summary_faq
+
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md
index 485483380a..db0d583c73 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md
@@ -20,46 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 32186a4879e98aa113f461d2a2c705dee099404ed2020ef6fdb981a28bb0c0c3
-  summary: >-
-    Learn how to create a static website hosted on Amazon S3 that interacts with AWS Lambda functions
-    to display IoT data from Windows on Arm devices. It is designed for developers who are interested
-    in using Amazon Web Services (AWS) S3 for hosting their IoT websites. By the end, you will
-    be able to gain familiarity with Amazon S3 and create a static website that interacts with
-    AWS Lambda. It focuses on tools and technologies such as Node.js and Visual Studio Code, Windows
-    environments, and Arm platforms including Cortex-A. The main steps cover Background, Static
-    website, Add AWS Lambda Endpoint, and Deploy website to Amazon S3.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will gain familiarity with Amazon S3 and create a static website that interacts with
-      AWS Lambda. Learn how to create a static website hosted on Amazon S3 that interacts with
-      AWS Lambda functions to display IoT data from Windows on Arm devices.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for developers who are interested in using Amazon Web Services
-      (AWS) S3 for hosting their IoT websites.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows on Arm computer such as the
-      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
-      Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user)
-      is suitable.; Completion of the [Use AWS Lambda for IoT applications](/learning-paths/laptops-and-desktops/win_aws_iot_lambda/)
-      Learning Path.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Node.js and Visual Studio Code, Windows environments,
-      and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Background, Static website, Add AWS Lambda Endpoint,
-      and Deploy website to Amazon S3.
-# END generated_summary_faq
+
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_cef/_index.md b/content/learning-paths/laptops-and-desktops/win_cef/_index.md
index 0794ac8073..cbb2154b6c 100644
--- a/content/learning-paths/laptops-and-desktops/win_cef/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_cef/_index.md
@@ -19,43 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 5df8cc6d859c505ad79f8297056b06233956c92203a6d2352d9be8e498a42547
-  summary: >-
-    Learn how to create and build Chromium Embedded Framework desktop applications using CMake
-    and web technologies on Windows on Arm. It is designed for developers who want to learn how
-    to use web technologies for developing Desktop apps on Windows on Arm (WoA). By the end, you
-    will be able to create and build a Chromium Embedded Framework project using CMake and modify
-    and style the application. It focuses on tools and technologies such as CPP, CMake, HTML,
-    JavaScript, and CSS, Windows environments, and Arm platforms including Cortex-A. The main
-    steps cover Create a Chromium Embedded Framework Project.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create and build a Chromium Embedded Framework project using CMake and modify and
-      style the application. Learn how to create and build Chromium Embedded Framework desktop
-      applications using CMake and web technologies on Windows on Arm.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This learning path is for developers who want to learn how to use web technologies for developing
-      Desktop apps on Windows on Arm (WoA).
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows on Arm computer such as the
-      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
-      Visual Studio 2022.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including CPP, CMake, HTML, JavaScript, and CSS, Windows environments,
-      and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Create a Chromium Embedded Framework Project.
-# END generated_summary_faq
+
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_forms/_index.md b/content/learning-paths/laptops-and-desktops/win_forms/_index.md
index 997dcc9d70..2cff72e0e9 100644
--- a/content/learning-paths/laptops-and-desktops/win_forms/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_forms/_index.md
@@ -19,44 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: d43413097704af29b9233dfe33fb675ffd5c6d5a172154d6a7542e77d6625c00
-  summary: >-
-    Learn how to create and build Windows Forms applications and measure code execution performance
-    on Arm64. It is designed for developers who want to learn how to create Windows Forms applications
-    on Windows on Arm (WoA). By the end, you will be able to create and build a Windows Forms
-    application and measure code execution performance on Arm64. It focuses on tools and technologies
-    such as Windows Forms, C#, and .NET, Windows environments, and Arm platforms including Cortex-A.
-    The main steps cover Create an application using Windows Forms and Compare the performance
-    results.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create and build a Windows Forms application and measure code execution performance
-      on Arm64. Learn how to create and build Windows Forms applications and measure code execution
-      performance on Arm64.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This learning path is for developers who want to learn how to create Windows Forms applications
-      on Windows on Arm (WoA).
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows on Arm computer such as the
-      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
-      Visual Studio 2022 with .NET Desktop Development workload.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Windows Forms, C#, and .NET, Windows environments,
-      and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Create an application using Windows Forms and Compare
-      the performance results.
-# END generated_summary_faq
+
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_net/_index.md b/content/learning-paths/laptops-and-desktops/win_net/_index.md
index fc23cc243d..8af0b81fed 100644
--- a/content/learning-paths/laptops-and-desktops/win_net/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_net/_index.md
@@ -17,43 +17,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 9cc8f77f98bc8f4afb7b77344e42615e420be421f85583f9fc4f5ec76516e6eb
-  summary: >-
-    Learn how to build and run a .NET 6 Windows Presentation Foundation (WPF) application on Windows
-    on Arm machines. It is designed for software developers doing native development on Windows
-    on Arm computers. By the end, you will be able to build and run a .NET 6 Windows Presentation
-    Foundation (WPF) application on a Windows on Arm machine. It focuses on tools and technologies
-    such as .NET and Visual Studio, Windows environments, and Arm platforms including Cortex-A.
-    The main steps cover Build a native windows application using .NET 6 framework for Windows
-    on Arm.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build and run a .NET 6 Windows Presentation Foundation (WPF) application on a Windows
-      on Arm machine. Learn how to build and run a .NET 6 Windows Presentation Foundation (WPF)
-      application on Windows on Arm machines.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers doing native development on Windows
-      on Arm computers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows on Arm computer such as the
-      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including .NET and Visual Studio, Windows environments, and
-      Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Build a native windows application using .NET 6 framework
-      for Windows on Arm.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/laptops-and-desktops/win_net8/_index.md b/content/learning-paths/laptops-and-desktops/win_net8/_index.md
index 2eb23bfa4f..1af7fca2ab 100644
--- a/content/learning-paths/laptops-and-desktops/win_net8/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_net8/_index.md
@@ -21,46 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 218fd5b33e104360d5de9f632f9338e2f24041ea70f7a01fad0af6be2a11619c
-  summary: >-
-    Learn how to build, run, and benchmark .NET 8 Console applications to measure performance
-    on Windows on Arm devices. It is designed for developers who want to benchmark the performance
-    of the .NET 8 applications on Windows on Arm (WoA). By the end, you will be able to build
-    and run .NET 8 Console Applications, benchmark .NET applications, and implement custom performance
-    benchmarks. It focuses on tools and technologies such as .NET, Visual Studio, and Visual Studio
-    Code, Windows environments, and Arm platforms including Cortex-A. The main steps cover Measuring
-    performance of the .NET apps and Custom benchmarks.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build and run .NET 8 Console Applications, benchmark .NET applications, and implement
-      custom performance benchmarks. Learn how to build, run, and benchmark .NET 8 Console applications
-      to measure performance on Windows on Arm devices.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This learning path is for developers who want to benchmark the performance of the .NET 8
-      applications on Windows on Arm (WoA).
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows on Arm computer such as the
-      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
-      .NET 8 SDK for [x64](https://dotnet.microsoft.com/en-us/download/dotnet/thank-you/sdk-8.0.100-windows-x64-installer)
-      and [arm64](https://dotnet.microsoft.com/en-us/download/dotnet/thank-you/sdk-8.0.100-windows-arm64-installer).;
-      Any code editor, we recommend using [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including .NET, Visual Studio, and Visual Studio Code, Windows
-      environments, and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Measuring performance of the .NET apps and Custom
-      benchmarks.
-# END generated_summary_faq
+
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md b/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md
index f195d2b507..8582af6b81 100644
--- a/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md
@@ -19,44 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 037509ebca3a7c5a58bef247532919cd8196c7993e946ce519585cdc82073daa
-  summary: >-
-    Learn how to create and build cross-platform .NET MAUI applications and measure code execution
-    performance uplift on Arm64. It is designed for developers who want to learn how to create
-    cross-platform applications with .NET MAUI and leverage performance improvements on Arm64.
-    By the end, you will be able to create and build a .NET MAUI application and measure code
-    execution performance uplift on Arm64. It focuses on tools and technologies such as .NET,
-    C#, and Visual Studio, Windows environments, and Arm platforms including Cortex-A. The main
-    steps cover Create a .NET MAUI Project and Implement the application.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create and build a .NET MAUI application and measure code execution performance
-      uplift on Arm64. Learn how to create and build cross-platform .NET MAUI applications and
-      measure code execution performance uplift on Arm64.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This learning path is for developers who want to learn how to create cross-platform applications
-      with .NET MAUI and leverage performance improvements on Arm64.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows on Arm computer such as the
-      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
-      Visual Studio 2022 with .NET Multi-platform App UI development and Universal Windows Platform
-      development installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including .NET, C#, and Visual Studio, Windows environments,
-      and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Create a .NET MAUI Project and Implement the application.
-# END generated_summary_faq
+
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md b/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md
index 085e6db215..f22c2b73ec 100644
--- a/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md
@@ -17,46 +17,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 606702e7afc9a29976d027eceab021570cf3f91ec408ef2dd2051df0b05d9bda
-  summary: >-
-    Learn how to build ONNX Runtime with the Generate() API and run Phi-3 model inference with
-    KleidiAI acceleration on Windows on Arm. It is designed for developers looking to build ONNX
-    Runtime for Windows on Arm (WoA) and leverage the Generate() API to run Phi-3 inference with
-    KleidiAI acceleration. By the end, you will be able to build ONNX Runtime and enable the Generate()
-    API for Windows on Arm and run inference with a Phi-3 model using ONNX Runtime with KleidiAI
-    acceleration. It focuses on tools and technologies such as Visual Studio, CPP, Python, Git,
-    and CMake, Windows environments, and Arm platforms including Cortex-A. The main steps cover
-    Set up your Environment, Build ONNX Runtime, Build ONNX Runtime Generate() API, and Run Phi3
-    Model.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build ONNX Runtime and enable the Generate() API for Windows on Arm and run inference
-      with a Phi-3 model using ONNX Runtime with KleidiAI acceleration. Learn how to build ONNX
-      Runtime with the Generate() API and run Phi-3 model inference with KleidiAI acceleration
-      on Windows on Arm.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for developers looking to build ONNX Runtime for Windows on Arm
-      (WoA) and leverage the Generate() API to run Phi-3 inference with KleidiAI acceleration.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows on Arm computer such as a
-      Lenovo Thinkpad X13 running Windows 11, or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Visual Studio, CPP, Python, Git, and CMake, Windows
-      environments, and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Set up your Environment, Build ONNX Runtime, Build
-      ONNX Runtime Generate() API, and Run Phi3 Model.
-# END generated_summary_faq
+
 
 author: Barbara Corriero
 
diff --git a/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md b/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md
index c93a0ebe9e..b2054b0f99 100644
--- a/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md
@@ -20,49 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: b975cc83674410ec50ca49a31744eee4ac5a63c994dd28e46ed84f7afda0568d
-  summary: >-
-    Learn how to apply Profile-Guided Optimization (PGO) to build performance-tuned C++ binaries
-    and measure improvements using Google Benchmark on Windows on Arm. It is designed for software
-    developers who want to optimize C++ application performance on Windows on Arm using Profile-Guided
-    Optimization (PGO). By the end, you will be able to microbenchmark a function using Google
-    Benchmark, apply profile-guided optimization to build performance-tuned binaries for Windows
-    on Arm, and measure and compare performance improvements from PGO-optimized builds. It focuses
-    on tools and technologies such as C, MSVC, Google Benchmark, and PGO, Windows environments,
-    and Arm platforms including Cortex-A. The main steps cover Understand Profile-Guided Optimization,
-    Understand Google Benchmark basics, Create a baseline benchmark, and Apply Profile-Guided
-    Optimization.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will microbenchmark a function using Google Benchmark, apply profile-guided optimization
-      to build performance-tuned binaries for Windows on Arm, and measure and compare performance
-      improvements from PGO-optimized builds. Learn how to apply Profile-Guided Optimization (PGO)
-      to build performance-tuned C++ binaries and measure improvements using Google Benchmark
-      on Windows on Arm.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to optimize C++ application
-      performance on Windows on Arm using Profile-Guided Optimization (PGO).
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Familiarity with C++ development and
-      compiling programs from the command line; A Windows on Arm machine with [Visual Studio](/install-guides/vs-woa/)
-      and the C++ desktop development tools installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including C, MSVC, Google Benchmark, and PGO, Windows environments,
-      and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Understand Profile-Guided Optimization, Understand
-      Google Benchmark basics, Create a baseline benchmark, and Apply Profile-Guided Optimization.
-# END generated_summary_faq
+
 
 author: Tom Dunkle
 
diff --git a/content/learning-paths/laptops-and-desktops/win_python/_index.md b/content/learning-paths/laptops-and-desktops/win_python/_index.md
index 9bdd7e7365..a38a94b4f8 100644
--- a/content/learning-paths/laptops-and-desktops/win_python/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_python/_index.md
@@ -20,45 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: d953214fe33ad89a683f79e7c29ce9348e5169e6529b808804329cb35060b431
-  summary: >-
-    Learn how to build Python applications on Windows on Arm and leverage native Arm64 performance
-    for platform-dependent packages. It is designed for developers who are interested in building
-    Python applications on Arm. By the end, you will be able to understand the platform-dependency
-    of Python packages and leverage native Arm64 for Python applications. It focuses on tools
-    and technologies such as Python and Visual Studio Code, Windows environments, and Arm platforms
-    including Cortex-A. The main steps cover Platform-specificity of the Python packages and Build
-    the application.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand the platform-dependency of Python packages and leverage native Arm64
-      for Python applications. Learn how to build Python applications on Windows on Arm and leverage
-      native Arm64 performance for platform-dependent packages.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers who are interested in building Python applications
-      on Arm.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows on Arm computer such as the
-      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
-      Any code editor, we recommend using [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user).;
-      Visual Studio 2022 with Arm build tools. [Refer to this guide for the installation steps](https://developer.arm.com/documentation/102528/0100/Install-Visual-Studio).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Python and Visual Studio Code, Windows environments,
-      and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Platform-specificity of the Python packages and Build
-      the application.
-# END generated_summary_faq
+
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md b/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md
index c6f6437688..957fa2ac1c 100644
--- a/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md
@@ -20,47 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 055e3a3d8c0dc717e2246e3e8e7ebb00c7d2cea3ff9faabf7125ca1ebfff7a31
-  summary: >-
-    Learn how to configure Windows Sandbox as a self-hosted GitHub Actions runner to build and
-    run .NET 8 WPF applications in CI/CD workflows. It is designed for software developers who
-    are developing applications on Windows on Arm computers. By the end, you will be able to configure
-    Windows Sandbox as a self-hosted GitHub Actions runner and build and run a .NET 8 Windows
-    Presentation Foundation (WPF) application using a self-hosted GitHub Actions runner in your
-    CI/CD workflow. It focuses on tools and technologies such as .NET, Visual Studio, and Windows
-    Sandbox, Windows environments, and Arm platforms including Cortex-A. The main steps cover
-    Configure Windows Sandbox as your GitHub Actions self-hosted Arm64 runner and Build and run
-    the .NET application using the GitHub Actions workflow.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will configure Windows Sandbox as a self-hosted GitHub Actions runner and build and
-      run a .NET 8 Windows Presentation Foundation (WPF) application using a self-hosted GitHub
-      Actions runner in your CI/CD workflow. Learn how to configure Windows Sandbox as a self-hosted
-      GitHub Actions runner to build and run .NET 8 WPF applications in CI/CD workflows.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who are developing applications on
-      Windows on Arm computers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows on Arm computer such as the
-      Lenovo Thinkpad X13s running Windows 11 Version 22H2 which has [Windows Sandbox enabled](/install-guides/windows-sandbox-woa).;
-      A valid [GitHub account](https://github.com/) to complete this Learning Path.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including .NET, Visual Studio, and Windows Sandbox, Windows
-      environments, and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Configure Windows Sandbox as your GitHub Actions self-hosted
-      Arm64 runner and Build and run the .NET application using the GitHub Actions workflow.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md b/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md
index fe8cfb0856..a0a4ec2eb1 100644
--- a/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md
@@ -21,42 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: cacf4c6fc3cd3c2a9ab194f7a04981fd4820fca5482317a06c6b9fa02a1c9da2
-  summary: >-
-    Learn how to create C/C++ Win32 DLLs and port them to Arm64 for use in Windows console applications.
-    It is designed for developers who want to learn how to port their Win32 applications to Arm64.
-    By the end, you will be able to create C/C++ Win32 DLL, use Win32 DLL in the Console App,
-    and learn how to port the C/C++ Win32 DLL to Arm64. It focuses on tools and technologies such
-    as C and CPP, Windows environments, and Arm platforms including Cortex-A. The main steps cover
-    Porting Win32 code to Arm64.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create C/C++ Win32 DLL, use Win32 DLL in the Console App, and learn how to port
-      the C/C++ Win32 DLL to Arm64. Learn how to create C/C++ Win32 DLLs and port them to Arm64
-      for use in Windows console applications.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers who want to learn how to port their Win32 applications
-      to Arm64.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows on Arm computer such as the
-      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
-      Refer to [Visual Studio 2022 with Arm build tools](/install-guides/vs-woa).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including C and CPP, Windows environments, and Arm platforms
-      such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Porting Win32 code to Arm64.
-# END generated_summary_faq
+
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_winui3/_index.md b/content/learning-paths/laptops-and-desktops/win_winui3/_index.md
index 7ead1dfdb5..844750a91e 100644
--- a/content/learning-paths/laptops-and-desktops/win_winui3/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_winui3/_index.md
@@ -19,45 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 383dcf17bce86b24a2d9c8d0982fa6e9ddf954955c16b420463ada951753dbfa
-  summary: >-
-    Learn how to create and build Windows UI Library (WinUI) applications and measure code execution
-    performance on Arm64. It is designed for developers who want to learn how to create cross-platform
-    applications and leverage performance improvements on Arm64. By the end, you will be able
-    to create and build a Windows UI Library (WinUI) application and measure code execution performance
-    on Arm64. It focuses on tools and technologies such as WinUI 3, C#, .NET, and Visual Studio,
-    Windows environments, and Arm platforms including Cortex-A. The main steps cover Creating
-    an application and Comparing the performance on various platforms.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create and build a Windows UI Library (WinUI) application and measure code execution
-      performance on Arm64. Learn how to create and build Windows UI Library (WinUI) applications
-      and measure code execution performance on Arm64.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This learning path is for developers who want to learn how to create cross-platform applications
-      and leverage performance improvements on Arm64.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows on Arm computer such as the
-      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
-      Visual Studio 2022 with .NET desktop development and Universal Windows Platform development
-      installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including WinUI 3, C#, .NET, and Visual Studio, Windows environments,
-      and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Creating an application and Comparing the performance
-      on various platforms.
-# END generated_summary_faq
+
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_wpf/_index.md b/content/learning-paths/laptops-and-desktops/win_wpf/_index.md
index 2fb9b4f463..cba4d7c225 100644
--- a/content/learning-paths/laptops-and-desktops/win_wpf/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_wpf/_index.md
@@ -19,45 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: cc1a494e7b03672122ff84073b677a93659eca7df601b3f77c7e7fd536cc1af9
-  summary: >-
-    Learn how to create and build Windows Presentation Foundation (WPF) applications and measure
-    code execution performance uplift on Arm64. It is designed for developers who want to learn
-    how to create desktop applications and leverage performance improvements on Arm64. By the
-    end, you will be able to create and build a Windows Presentation Foundation (WPF) application
-    and measure code execution performance uplift on Arm64. It focuses on tools and technologies
-    such as Windows Presentation Foundation, C#, .NET, and Visual Studio, Windows environments,
-    and Arm platforms including Cortex-A. The main steps cover Create an application using Windows
-    Presentation Foundation (WPF) and Run the application and compare execution times.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create and build a Windows Presentation Foundation (WPF) application and measure
-      code execution performance uplift on Arm64. Learn how to create and build Windows Presentation
-      Foundation (WPF) applications and measure code execution performance uplift on Arm64.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This learning path is for developers who want to learn how to create desktop applications
-      and leverage performance improvements on Arm64.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows on Arm computer such as the
-      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
-      Visual Studio 2022 with .NET desktop development installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Windows Presentation Foundation, C#, .NET, and Visual
-      Studio, Windows environments, and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Create an application using Windows Presentation Foundation
-      (WPF) and Run the application and compare execution times.
-# END generated_summary_faq
+
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md b/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md
index 4f4bc6b072..864c45d668 100644
--- a/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md
@@ -20,47 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 16b7f8c67050ea336402791c0ecca2c688d5da9373c571986e12dcf646c8093c
-  summary: >-
-    Learn how to create and build Xamarin Forms applications using the MVVM pattern and measure
-    code execution performance uplift on Arm64. It is designed for developers who want to learn
-    how to create cross-platform applications and leverage performance improvements on Arm64.
-    By the end, you will be able to create and build an Xamarin Forms application, measure code
-    execution performance uplift on Arm64, and learn how to use the Model-View-ViewModel (MVVM)
-    architectural pattern. It focuses on tools and technologies such as Xamarin Forms, C#, .NET,
-    and Visual Studio, Windows environments, and Arm platforms including Cortex-A. The main steps
-    cover Create an application using Xamarin Forms and Implement logic with Model View ViewModel.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create and build an Xamarin Forms application, measure code execution performance
-      uplift on Arm64, and learn how to use the Model-View-ViewModel (MVVM) architectural pattern.
-      Learn how to create and build Xamarin Forms applications using the MVVM pattern and measure
-      code execution performance uplift on Arm64.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This learning path is for developers who want to learn how to create cross-platform applications
-      and leverage performance improvements on Arm64.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows on Arm computer such as the
-      Lenovo Thinkpad X13s running Windows 11 or a a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
-      Visual Studio 2022 with .NET desktop development and Universal Windows Platform development
-      installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Xamarin Forms, C#, .NET, and Visual Studio, Windows
-      environments, and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Create an application using Xamarin Forms and Implement
-      logic with Model View ViewModel.
-# END generated_summary_faq
+
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md b/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md
index 2b1250a95c..f83a0f0d03 100644
--- a/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md
@@ -18,47 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: f058f628cefb7766ef0728e594c9ef5b57bde97c1850395786d54cc591271503
-  summary: >-
-    Learn how to develop Windows on Arm applications using Visual Studio and optimize performance
-    with Arm Performance Libraries. It is designed for software developers who want to improve
-    the performance of Windows on Arm applications using Arm Performance Libraries. By the end,
-    you will be able to develop a Windows on Arm application using Microsoft Visual Studio and
-    utilize Arm Performance Libraries to optimize the performance of an application. It focuses
-    on tools and technologies such as Visual Studio, C#, .NET, and Arm Performance Libraries,
-    Windows environments, and Arm platforms including Cortex-A. The main steps cover Before you
-    begin, Create and Run a Windows on Arm application, Git setup, Build and Profile an Application
-    with Spin the Cube and Visual Studio, and Use Arm Performance Libraries to Optimize Performance.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will develop a Windows on Arm application using Microsoft Visual Studio and utilize
-      Arm Performance Libraries to optimize the performance of an application. Learn how to develop
-      Windows on Arm applications using Visual Studio and optimize performance with Arm Performance
-      Libraries.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to improve the performance
-      of Windows on Arm applications using Arm Performance Libraries.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows on Arm computer such as the
-      Lenovo Thinkpad X13s running Windows 11.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Visual Studio, C#, .NET, and Arm Performance Libraries,
-      Windows environments, and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Before you begin, Create and Run a Windows on Arm
-      application, Git setup, Build and Profile an Application with Spin the Cube and Visual Studio,
-      and Use Arm Performance Libraries to Optimize Performance.
-# END generated_summary_faq
+
 
 author: Odin Shen
 
diff --git a/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md b/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md
index 503fed7be1..97128786e3 100644
--- a/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md
@@ -20,43 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 828e4022f387698d2736d94010de5d93efa9f38ffd3a2e4f15252410e9ccedb2
-  summary: >-
-    Get started with GitHub CI/CD development flow on a Windows on Arm machine (or virtual machine).
-    It is designed for software developers interested in running their CI flows on Windows on
-    Arm machines. By the end, you will be able to setup a CI/CD flow with GitHub Actions to use
-    Windows on Arm as the self-hosted runner host and run a simple GitHub Actions workflow. It
-    focuses on tools and technologies such as GitHub, Windows environments, and Arm platforms
-    including Neoverse. The main steps cover Setup GitHub Self-hosted Runner and Create and run
-    simple workflow.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will setup a CI/CD flow with GitHub Actions to use Windows on Arm as the self-hosted
-      runner host and run a simple GitHub Actions workflow. Get started with GitHub CI/CD development
-      flow on a Windows on Arm machine (or virtual machine).
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers interested in running their CI flows
-      on Windows on Arm machines.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Some familiarity with CI/CD concepts
-      is assumed; Valid GitHub account; Microsoft Azure account (if using virtual machine).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including GitHub, Windows environments, and Arm platforms
-      such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Setup GitHub Self-hosted Runner and Create and run
-      simple workflow.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md
index 454858e4fd..9145318eab 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md
@@ -21,47 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 1dd709b14537e06c80b9cdee58729cfa7066f44f0de4ceabe5450b33288731aa
-  summary: >-
-    Learn how to install and use the WindowsPerf Visual Studio extension to generate counting
-    and sampling reports and analyze performance data in Windows Performance Analyzer. It is designed
-    for software developers using Visual Studio on Windows on Arm who want to integrate WindowsPerf
-    into their development flow. By the end, you will be able to install and use the WindowsPerf
-    Visual Studio extension, generate a counting report and explore the data, and review the report
-    in Windows Performance Analyzer (WPA). It focuses on tools and technologies such as WindowsPerf,
-    perf, and Visual Studio, Windows environments, and Arm platforms including Cortex-A. The main
-    steps cover Configure your development tools, Use the counting feature, Use the sampling feature,
-    and The SPE Feature.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install and use the WindowsPerf Visual Studio extension, generate a counting report
-      and explore the data, and review the report in Windows Performance Analyzer (WPA). Learn
-      how to install and use the WindowsPerf Visual Studio extension to generate counting and
-      sampling reports and analyze performance data in Windows Performance Analyzer.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers using Visual Studio on Windows on
-      Arm who want to integrate WindowsPerf into their development flow.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A desktop or laptop running Windows
-      on Arm.; Visual Studio 2022 Community Edition, WindowsPerf, WindowsPerf Visual Studio extension,
-      and Windows Performance Analyzer (WPA) installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including WindowsPerf, perf, and Visual Studio, Windows environments,
-      and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Configure your development tools, Use the counting
-      feature, Use the sampling feature, and The SPE Feature.
-# END generated_summary_faq
+
 
 author: 
   - Nader Zouaoui
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf/_index.md
index ef363abeda..35d977371a 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf/_index.md
@@ -18,41 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 61a6390d42ce6bfc37a3bb981710dc23b8566070733f7979f9ec37bc63ab7d78
-  summary: >-
-    Learn how to install WindowsPerf on Windows on Arm machines and generate sample performance
-    reports for CPU profiling. It is designed for software developers working on laptops and desktops
-    and new to the Arm architecture. By the end, you will be able to install WindowsPerf on Windows
-    on Arm machine and generate a sample report. It focuses on tools and technologies such as
-    WindowsPerf, Windows environments, and Arm platforms including Cortex-A and Neoverse. The
-    main steps cover WindowsPerf and WindowsPerf cheat sheet.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install WindowsPerf on Windows on Arm machine and generate a sample report. Learn
-      how to install WindowsPerf on Windows on Arm machines and generate sample performance reports
-      for CPU profiling.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers working on laptops and desktops and
-      new to the Arm architecture.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Windows on Arm desktop or development
-      machine.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including WindowsPerf, Windows environments, and Arm platforms
-      such as Cortex-A and Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around WindowsPerf and WindowsPerf cheat sheet.
-# END generated_summary_faq
+
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md
index dac3c04261..08349bd0f6 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md
@@ -21,44 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: e23de84e7e05d771072ab799f5cc802476fd5e3c23da41a202c9c50fe9980e25
-  summary: >-
-    Learn how to use WindowsPerf for performance sampling on Windows on Arm, build CPython from
-    sources, and analyze native workload performance. It is designed for developers keen to understand
-    sampling and who are new to the Arm architecture. By the end, you will be able to use WindowsPerf
-    with native Windows on Arm workload, understand the basics of sampling, and explore the WindowsPerf
-    command line. It focuses on tools and technologies such as WindowsPerf, Python, and perf,
-    Windows environments, and Arm platforms including Cortex-A. The main steps cover CPython Sampling
-    Example Overview, WindowsPerf sample example, and WindowsPerf record example.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will use WindowsPerf with native Windows on Arm workload, understand the basics of sampling,
-      and explore the WindowsPerf command line. Learn how to use WindowsPerf for performance sampling
-      on Windows on Arm, build CPython from sources, and analyze native workload performance.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers keen to understand sampling and who are new
-      to the Arm architecture.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Windows on Arm desktop or development
-      machine with [WindowsPerf installed](/install-guides/wperf); Windows x86_64 desktop machine
-      with [Visual Studio 2022 Community Edition](https://visualstudio.microsoft.com/vs/) installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including WindowsPerf, Python, and perf, Windows environments,
-      and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around CPython Sampling Example Overview, WindowsPerf sample
-      example, and WindowsPerf record example.
-# END generated_summary_faq
+
 
 author: Przemyslaw Wirkus
 
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
index 996b840137..9caefdcd5c 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
@@ -18,43 +18,6 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 1fd4d85855933a5dfc5edf5ae3abf5f4388d94c9ee69aff12b77eb766e88301f
-  summary: >-
-    Learn how to import WindowsPerf data in Windows Performance Analyzer (WPA) and visualize timeline
-    and telemetry data using the WPA plugin. It is designed for software developers interested
-    in using the Windows Performance Analyzer (WPA) plugin for performance analysis. By the end,
-    you will be able to import WindowsPerf data as a .json file in WPA and visualize the timeline
-    and telemetry data in WPA using the WPA plugin. It focuses on tools and technologies such
-    as WindowsPerf, perf, and Windows Performance Analyzer, Windows environments, and Arm platforms
-    including Cortex-A and Neoverse. The main steps cover Visualize data from WindowsPerf using
-    WPA.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will import WindowsPerf data as a .json file in WPA and visualize the timeline and telemetry
-      data in WPA using the WPA plugin. Learn how to import WindowsPerf data in Windows Performance
-      Analyzer (WPA) and visualize timeline and telemetry data using the WPA plugin.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers interested in using the Windows Performance
-      Analyzer (WPA) plugin for performance analysis.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows on Arm laptop with WindowsPerf,
-      Windows Performance Analyzer (WPA), and the WPA plugin installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including WindowsPerf, perf, and Windows Performance Analyzer,
-      Windows environments, and Arm platforms such as Cortex-A and Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Visualize data from WindowsPerf using WPA.
-# END generated_summary_faq
 
 author: Alaaeddine Chakroun
 
diff --git a/content/learning-paths/laptops-and-desktops/wsl2/_index.md b/content/learning-paths/laptops-and-desktops/wsl2/_index.md
index bf36e86248..4ee417fdfc 100644
--- a/content/learning-paths/laptops-and-desktops/wsl2/_index.md
+++ b/content/learning-paths/laptops-and-desktops/wsl2/_index.md
@@ -23,46 +23,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 03d816ff419e6e5b1533f95e9d4ffa087b9c0c9aefeee112f67b70223c8aedc3
-  summary: >-
-    Learn how to configure and run WSL with Linux distributions, graphical applications, remote
-    desktop, and development tools on Windows on Arm computers. It is designed for Software developers
-    with Windows on Arm computers doing Linux or cloud native development. By the end, you will
-    be able to configure and run WSL with various Linux distributions, run graphical Linux applications
-    on Windows, and use ssh to connect to WSL. It focuses on tools and technologies such as WSL
-    and Visual Studio Code, Windows and Linux environments, and Arm platforms including Cortex-A.
-    The main steps cover Configure and run WSL with various Linux distributions, Run graphical
-    Linux applications, Enable systemd in WSL, Use SSH to connect to WSL, and Connect to WSL using
-    RDP and VNC.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will configure and run WSL with various Linux distributions, run graphical Linux applications
-      on Windows, and use ssh to connect to WSL. Learn how to configure and run WSL with Linux
-      distributions, graphical applications, remote desktop, and development tools on Windows
-      on Arm computers.
-  - question: Who is this Learning Path for?
-    answer: >-
-      Software developers with Windows on Arm computers doing Linux or cloud native development.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows on Arm computer such as the
-      Lenovo Thinkpad X13s running Windows 11.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including WSL and Visual Studio Code, Windows and Linux environments,
-      and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Configure and run WSL with various Linux distributions,
-      Run graphical Linux applications, Enable systemd in WSL, Use SSH to connect to WSL, and
-      Connect to WSL using RDP and VNC.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md b/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md
index 6dda13f235..cc705bc3c6 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md
@@ -21,48 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: e4512ad20b372f616409199c51a38d95cb116ec6cf49d9004348d6bb8ee4014d
-  summary: >-
-    Learn how to enable and verify Arm Fixed Rate Compression in Vulkan applications on Android
-    devices to reduce memory footprint and bandwidth. It is designed for Software developers of
-    Android applications and mobile games who are interested in learning how to enable Arm Fixed
-    Rate Compression (AFRC) to improve performance. By the end, you will be able to query for
-    fixed-rate compression support, specify what compression to use, and verify that compression
-    is applied. It focuses on tools and technologies such as Vulkan, Android environments, and
-    Arm platforms including Mali and Immortalis. The main steps cover What is fixed-rate compression?,
-    How to run the code examples, Vulkan Extensions, Query for compression support, and Request
-    fixed-rate compression.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will query for fixed-rate compression support, specify what compression to use, and
-      verify that compression is applied. Learn how to enable and verify Arm Fixed Rate Compression
-      in Vulkan applications on Android devices to reduce memory footprint and bandwidth.
-  - question: Who is this Learning Path for?
-    answer: >-
-      Software developers of Android applications and mobile games who are interested in learning
-      how to enable Arm Fixed Rate Compression (AFRC) to improve performance.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An appropriate Android device (e.g.,
-      Google Pixel 8) supporting the required Vulkan extensions.; Knowledge of the Vulkan API.;
-      A Vulkan application that creates and uses images. This Learning Path shows how to use an
-      API Sample in the [Khronos Vulkan Samples repository](https://github.com/KhronosGroup/Vulkan-Samples/blob/main/scripts/README.adoc#generate-api-sample)
-      as an example.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Vulkan, Android environments, and Arm platforms
-      such as Mali and Immortalis.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around What is fixed-rate compression?, How to run the code
-      examples, Vulkan Extensions, Query for compression support, and Request fixed-rate compression.
-# END generated_summary_faq
+
 
 author: Jose-Emilio Munoz-Lopez
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md b/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md
index d5645b0504..d5f61cc766 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md
@@ -18,47 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: dee181248ecc8cb40e3ad76642fdb216cbf1e7610dde2f2605ba04f111b8926a
-  summary: >-
-    Learn how to build and optimize AI-powered camera pipeline applications on Arm Linux using
-    KleidiAI, KleidiCV, and SME2 to accelerate denoising, background blur, and low-light effects.
-    It is designed for This introductory topic is for mobile and computer-vision developers, camera
-    pipeline engineers, and performance-minded practitioners who want to optimize real-time camera
-    effects on Arm using KleidiAI and KleidiCV. By the end, you will be able to build and run
-    AI-powered camera pipeline applications and use SME2 to improve the performance of real-time
-    camera pipelines. It focuses on tools and technologies such as CPP, Docker, and SME2, Linux
-    and macOS environments, and Arm platforms including Cortex-A and Arm C1. The main steps cover
-    Prerequisites, Overview, Build the pipelines, Run the pipelines, and Performance.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build and run AI-powered camera pipeline applications and use SME2 to improve the
-      performance of real-time camera pipelines. Learn how to build and optimize AI-powered camera
-      pipeline applications on Arm Linux using KleidiAI, KleidiCV, and SME2 to accelerate denoising,
-      background blur, and low-light effects.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This introductory topic is for mobile and computer-vision developers, camera pipeline engineers,
-      and performance-minded practitioners who want to optimize real-time camera effects on Arm
-      using KleidiAI and KleidiCV.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A computer running Arm Linux or macOS
-      with Docker installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including CPP, Docker, and SME2, Linux and macOS environments,
-      and Arm platforms such as Cortex-A and Arm C1.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Prerequisites, Overview, Build the pipelines, Run
-      the pipelines, and Performance.
-# END generated_summary_faq
+
 
 author: Arnaud de Grandmaison
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md b/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
index d11d9ccdf1..449f25b0f2 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
@@ -24,49 +24,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 3d1a743c1b3ee617f52191fc9c9fd33a9d44454ac079f00b6f532e2865103377
-  summary: >-
-    Learn how to use each of the tools supplied with Arm Performance Studio (formerly known as
-    Arm Mobile Studio). It is designed for Android application and games developers new to Arm
-    Performance Studio. By the end, you will be able to learn the basic features of each component
-    of Arm Performance Studio and get started profiling and optimizing your application. It focuses
-    on tools and technologies such as Arm Performance Studio and Arm Mobile Studio, Android environments,
-    and Arm platforms including Cortex-A, Mali, and Immortalis. The main steps cover What is Arm
-    Performance Studio?, Setup tasks, Arm Streamline example capture, Streamline with your application,
-    and Performance Advisor example report.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will learn the basic features of each component of Arm Performance Studio and get started
-      profiling and optimizing your application. Learn how to use each of the tools supplied with
-      Arm Performance Studio (formerly known as Arm Mobile Studio).
-  - question: Who is this Learning Path for?
-    answer: >-
-      Android application and games developers new to Arm Performance Studio.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Android device.; Arm Performance
-      Studio supports applications built with OpenGL ES versions 2.0 to 3.2, or Vulkan versions
-      1.0 to 1.2.; For OpenGL ES applications, your device must be running Android 10 or later.;
-      For Vulkan applications, your device must be running Android 9 or later.; A debuggable build
-      of your application.; Arm Performance Studio installed. Follow the [Arm Performance Studio
-      install guide](/install-guides/ams) for instructions.; Android SDK Platform tools installed.
-      Required for the Android Debug bridge (adb).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Arm Performance Studio and Arm Mobile Studio, Android
-      environments, and Arm platforms such as Cortex-A, Mali, and Immortalis.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around What is Arm Performance Studio?, Setup tasks, Arm
-      Streamline example capture, Streamline with your application, and Performance Advisor example
-      report.
-# END generated_summary_faq
+
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md b/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md
index 756ce990cb..60f9319e12 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md
@@ -22,51 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: d5406f24ab38522b21e348ed3829ede7b1bcdce28e81ec4fddebbec280d2cb89
-  summary: >-
-    Learn how to capture frame data from Android applications and analyze performance inefficiencies
-    using Frame Advisor in Arm Performance Studio. It is designed for Android application developers
-    who want to learn how to use Frame Advisor. By the end, you will be able to capture data from
-    a significant frame in your application and find inefficiencies in the application with Frame
-    Advisor. It focuses on tools and technologies such as Frame Advisor, Android environments,
-    and Arm platforms including Mali GPUs and Immortalis GPUs. The main steps cover What is Frame
-    Advisor?, Capture a trace, Analyze draw calls, Analyze frame construction, and Analyze mesh
-    geometry.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will capture data from a significant frame in your application and find inefficiencies
-      in the application with Frame Advisor. Learn how to capture frame data from Android applications
-      and analyze performance inefficiencies using Frame Advisor in Arm Performance Studio.
-  - question: Who is this Learning Path for?
-    answer: >-
-      Android application developers who want to learn how to use Frame Advisor.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Android device. These [devices](https://developer.arm.com/Tools%20and%20Software/Arm%20Mobile%20Studio#Supported-Devices)
-      have been tested internally within Arm and confirmed to work with Arm Performance Studio.;
-      Arm Performance Studio supports applications built with OpenGL ES versions 2.0 to 3.2 or
-      Vulkan versions 1.0 to 1.2. For OpenGL ES applications, your device must be running Android
-      10 or later. For Vulkan applications, your device must be running Android 9 or later.; A
-      debuggable build of your application.; Download and install Arm Performance Studio from
-      [Product Download Hub](https://developer.arm.com/downloads/view/MOBST-PRO0). It is supported
-      on Windows, Linux, and macOS host platforms.; Download and install [Android SDK Platform
-      tools](https://developer.android.com/studio/releases/platform-tools.html). Required for
-      [Android Debug bridge (adb)](https://developer.android.com/studio/command-line/adb).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Frame Advisor, Android environments, and Arm platforms
-      such as Mali GPUs and Immortalis GPUs.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around What is Frame Advisor?, Capture a trace, Analyze draw
-      calls, Analyze frame construction, and Analyze mesh geometry.
-# END generated_summary_faq
+
 
 author: Julie Gaskin
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md
index f800e90c0a..c1e170c64e 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md
@@ -20,48 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: e63ac917c218aa5e5981f96a9821567d0f8ba9761d5ce6e4c9d7b6dea7ed5c5f
-  summary: >-
-    Learn how to build an Android chatbot app using Arm's AI Chat library to run GGUF models on-device
-    with optimized performance on Arm CPUs. It is designed for developers who want to add a local,
-    on-device LLM chat experience using Arm's AI Chat library, Kotlin, and Android Studio. By
-    the end, you will be able to create a simple Android chatbot app scaffold in Android Studio
-    and load a mobile-friendly GGUF model on-device and run streamed chat inference. It focuses
-    on tools and technologies such as Kotlin, Neon, SVE2, SME2, and LLM, Android environments,
-    and Arm platforms including Arm AI Chat library. The main steps cover Create the Android project,
-    Configure the AI Chat library dependency, Create the UI layouts and message adapter, Implement
-    the main activity logic, and Download a model and run the app.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create a simple Android chatbot app scaffold in Android Studio and load a mobile-friendly
-      GGUF model on-device and run streamed chat inference. Learn how to build an Android chatbot
-      app using Arm's AI Chat library to run GGUF models on-device with optimized performance
-      on Arm CPUs.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers who want to add a local, on-device LLM chat
-      experience using Arm's AI Chat library, Kotlin, and Android Studio.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Android development environment with
-      Android Studio installed; An Android phone for testing, in Developer Mode, with USB cable
-      for connection; Basic familiarity with Kotlin and Android app development.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Kotlin, Neon, SVE2, SME2, and LLM, Android environments,
-      and Arm platforms such as Arm AI Chat library.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Create the Android project, Configure the AI Chat
-      library dependency, Create the UI layouts and message adapter, Implement the main activity
-      logic, and Download a model and run the app.
-# END generated_summary_faq
+
 
 author: Ben Clark
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
index 5e9fee2f0c..45617e14c6 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
@@ -20,50 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: fa04ff97605ae93b17c056c397c37f6fc17cf6f4853bfabe374610ede002e3df
-  summary: >-
-    Learn how to build real-time image processing pipelines using Halide on Android, combining
-    operations for improved performance in Kotlin applications. It is designed for developers
-    interested in learning how to use Halide for image processing. By the end, you will be able
-    to learn the basics of Halide and set up your development environment, build a simple real-time
-    image processing pipeline with Halide, and make your image processing faster by combining
-    operations in Halide. It focuses on tools and technologies such as Android Studio, Halide,
-    CPP, Kotlin, and CMake, Android environments, and Arm platforms including Cortex-A and Cortex-X.
-    The main steps cover Install and configure Halide for Arm development, Build a simple camera
-    image processing workflow, Apply operator fusion in Halide for real-time image processing,
-    Generate optimized Halide pipelines for Android using ahead-of-time cross-compilation, and
-    Integrate Halide into an Android project with Kotlin.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will learn the basics of Halide and set up your development environment, build a simple
-      real-time image processing pipeline with Halide, and make your image processing faster by
-      combining operations in Halide. Learn how to build real-time image processing pipelines
-      using Halide on Android, combining operations for improved performance in Kotlin applications.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers interested in learning how to use Halide for
-      image processing.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Basic C++ knowledge; Android Studio
-      with Android Emulator.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Android Studio, Halide, CPP, Kotlin, and CMake,
-      Android environments, and Arm platforms such as Cortex-A and Cortex-X.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Install and configure Halide for Arm development,
-      Build a simple camera image processing workflow, Apply operator fusion in Halide for real-time
-      image processing, Generate optimized Halide pipelines for Android using ahead-of-time cross-compilation,
-      and Integrate Halide into an Android project with Kotlin.
-# END generated_summary_faq
+
 
 author: Éliás Bálint, Dawid Borycki, Steve Suzuki
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md
index 19a67b4c26..1b333759ac 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md
@@ -19,44 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 2137cec46fe8d57f11e9e4011306a140bba7d8a5b2326a746193c1794a148f75
-  summary: >-
-    Learn how to create and configure an Android project with OpenCV support to process camera
-    images for computer vision applications. It is designed for developers who are interested
-    in creating Computer Vision Applications with OpenCV on Android Devices. By the end, you will
-    be able to describe what OpenCV is, and what it can offer, create and configure a project
-    to add OpenCV support, and process camera images using OpenCV. It focuses on tools and technologies
-    such as Android, Android Studio, Kotlin, and Java, Windows environments, and Arm platforms
-    including Cortex-A. The main steps cover Overview, Create a project and add OpenCV, Get camera
-    images using OpenCV, Process Images, and Summary.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will describe what OpenCV is, and what it can offer, create and configure a project
-      to add OpenCV support, and process camera images using OpenCV. Learn how to create and configure
-      an Android project with OpenCV support to process camera images for computer vision applications.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers who are interested in creating Computer Vision
-      Applications with OpenCV on Android Devices.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A development machine with [Android
-      Studio](https://developer.android.com/studio) installed.; An Android smartphone.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Android, Android Studio, Kotlin, and Java, Windows
-      environments, and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview, Create a project and add OpenCV, Get camera
-      images using OpenCV, Process Images, and Summary.
-# END generated_summary_faq
+
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md
index 66a732a021..6880fa481c 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md
@@ -21,46 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 3a120620bbc56e796aa45fbe01aa1455fbee085a1a4cf0d055416b7e95e72d0d
-  summary: >-
-    Learn how to implement face detection on Android devices using OpenCV, camera frame retrieval,
-    and Haar cascade classifiers. It is designed for developers who are interested in creating
-    Computer Vision applications with OpenCV on Android devices. By the end, you will be able
-    to describe how you can use OpenCV for face detection, use OpenCV to retrieve camera frames,
-    and use Haar cascade classifier for face detection. It focuses on tools and technologies such
-    as Android, Android Studio, and Kotlin, Windows and macOS environments, and Arm platforms
-    including Cortex-A. The main steps cover Background, Create a project, add OpenCV, and read
-    camera frames, and Face detection.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will describe how you can use OpenCV for face detection, use OpenCV to retrieve camera
-      frames, and use Haar cascade classifier for face detection. Learn how to implement face
-      detection on Android devices using OpenCV, camera frame retrieval, and Haar cascade classifiers.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers who are interested in creating Computer Vision
-      applications with OpenCV on Android devices.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A development machine with [Android
-      Studio](https://developer.android.com/studio) installed.; An Android smartphone.; Familiarity
-      with OpenCV, review [Create Computer Vision Applications with OpenCV on Android Devices](/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/)
-      before starting.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Android, Android Studio, and Kotlin, Windows and
-      macOS environments, and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Background, Create a project, add OpenCV, and read
-      camera frames, and Face detection.
-# END generated_summary_faq
+
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
index 7610da1658..763b09f326 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
@@ -21,45 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: 8e05fb124ad18194fba2ed83adae3e52673b2fad41af998ca8d0aa8cb9785f5e
-  summary: >-
-    Learn how to accelerate OpenCV-based Android applications using KleidiCV for enhanced computer
-    vision performance. It is designed for developers who are interested in creating Computer
-    Vision applications with OpenCV and KleidiCV on Android Devices. By the end, you will be able
-    to describe what KleidiCV is, and what it can offer, create and configure a project to add
-    OpenCV support, and process images using OpenCV functionality. It focuses on tools and technologies
-    such as Android, Android Studio, Kotlin, and Java, Android environments, and Arm platforms
-    including Cortex-A. The main steps cover Overview, Create a project and add OpenCV, Define
-    the UI, Processing the Images, and Summary.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will describe what KleidiCV is, and what it can offer, create and configure a project
-      to add OpenCV support, and process images using OpenCV functionality. Learn how to accelerate
-      OpenCV-based Android applications using KleidiCV for enhanced computer vision performance.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers who are interested in creating Computer Vision
-      applications with OpenCV and KleidiCV on Android Devices.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A development machine with [Android
-      Studio](https://developer.android.com/studio) installed.; Familiarity with Android development
-      concepts.; An Android smartphone.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Android, Android Studio, Kotlin, and Java, Android
-      environments, and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview, Create a project and add OpenCV, Define
-      the UI, Processing the Images, and Summary.
-# END generated_summary_faq
+
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md
index ae068545ff..5f72d98890 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md
@@ -20,47 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:55Z'
-  generator: template
-  source_hash: be91053c5abcdd669ff8da7e66b2f717c4adaac27b3fb44ea073b69a68d2dba7
-  summary: >-
-    Get started with Scalable Vector Extension 2 (SVE2) on Android walks you through an end-to-end
-    Arm software workflow. It is designed for software developers interested in learning how to
-    use the Scalable Vector Extension 2 (SVE2) on Arm powered mobile devices running Android.
-    By the end, you will be able to enable Scalable Vector Extension 2 (SVE2) support in Android
-    Studio, implement an Android application that uses the Android Native Development Kit (NDK)
-    to calculate the fused multiply-add (FMA), and measure the performance uplift by using SVE2
-    intrinsics. It focuses on tools and technologies such as Android Studio, Android environments,
-    and Arm platforms including Cortex-A. The main steps cover Enable SVE2 support in Android
-    Studio and Implement vector operations.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will enable Scalable Vector Extension 2 (SVE2) support in Android Studio, implement
-      an Android application that uses the Android Native Development Kit (NDK) to calculate the
-      fused multiply-add (FMA), and measure the performance uplift by using SVE2 intrinsics.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers interested in learning how to use
-      the Scalable Vector Extension 2 (SVE2) on Arm powered mobile devices running Android.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A x86_64 or Apple development machine
-      with Android Studio installed.; A 64-bit Arm powered smartphone running Android.; Knowledge
-      of Single instruction Multi Data (SIMD); Knowledge of [Neon](https://developer.arm.com/documentation/102474/latest);
-      Knowledge of [Scalable Vector Extension (SVE)](https://developer.arm.com/documentation/101726/4-0).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Android Studio, Android environments, and Arm platforms
-      such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Enable SVE2 support in Android Studio and Implement
-      vector operations.
-# END generated_summary_faq
+
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
index 6e07a8ecfc..73dac8bf57 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
@@ -29,48 +29,6 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 8f58429f12e40b53e8a2e0d7eb260f22fbb7ac869ca64554a906ddcd28c9fd75
-  summary: >-
-    Learn how to integrate Dawn WebGPU in an Android application, render 3D objects, and profile
-    the application using Streamline. It is designed for developers who are building GPU-based
-    Android applications and are interested in experimenting with WebGPU. By the end, you will
-    be able to describe the benefits of WebGPU, describe the benefits of using Dawn, and set up
-    a WebGPU development environment. It focuses on tools and technologies such as Java, Kotlin,
-    CPP, and Python, macOS, Linux, Windows, and Android environments, and Arm platforms including
-    Cortex-A. The main steps cover Overview of WebGPU, Set up a development environment, Create
-    an application with Dawn, Using Dawn WebGPU APIs in the application, and Render a simple 3D
-    object.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will describe the benefits of WebGPU, describe the benefits of using Dawn, and set up
-      a WebGPU development environment. Learn how to integrate Dawn WebGPU in an Android application,
-      render 3D objects, and profile the application using Streamline.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers who are building GPU-based Android applications
-      and are interested in experimenting with WebGPU.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Basic knowledge of graphics APIs and
-      experience in developing Android graphics applications.; A development machine with Android
-      Studio, Blender, and Arm Streamline installed.; An Android phone in developer mode.; Android
-      Studio.; Arm Performance Studio.; Python 3.10 or later.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Java, Kotlin, CPP, and Python, macOS, Linux, Windows,
-      and Android environments, and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview of WebGPU, Set up a development environment,
-      Create an application with Dawn, Using Dawn WebGPU APIs in the application, and Render a
-      simple 3D object.
-# END generated_summary_faq
 
 author:
     - Varun Chari
diff --git a/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md b/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
index 482375a63b..ed3a617534 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
@@ -20,49 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: ef70ed2089132df657bd1ebfb9a66a39934d8a118b1e73974148ce11c104fef5
-  summary: >-
-    Learn how to optimize hardware ray tracing with Lumen on Android devices powered by Arm Mali
-    GPUs to maximize performance. It is designed for Unreal Engine developers interested in optimizing
-    hardware ray tracing with Lumen on android devices. By the end, you will be able to learn
-    about ray tracing, understand what an acceleration structure is, and learn about the best
-    practices for getting the maximum performance of hardware ray tracing on Lumen for Arm devices.
-    It focuses on tools and technologies such as Unreal Engine, Android environments, and Arm
-    platforms including Immortalis-G715 and Immortalis-G720. The main steps cover Lumen and Ray
-    Tracing, Acceleration Structure, Only Add Important Objects into Ray Tracing, Take Full Advantage
-    of Instancing, and Optimize Acceleration Structure.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will learn about ray tracing, understand what an acceleration structure is, and learn
-      about the best practices for getting the maximum performance of hardware ray tracing on
-      Lumen for Arm devices. Learn how to optimize hardware ray tracing with Lumen on Android
-      devices powered by Arm Mali GPUs to maximize performance.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for Unreal Engine developers interested in optimizing hardware
-      ray tracing with Lumen on android devices.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A computer capable of running [Unreal
-      Engine 5.3 or later version](https://www.unrealengine.com/en-US/download).; An Android mobile
-      device that has a Mali GPU with hardware ray tracing support.; A USB cable to connect the
-      mobile device to your computer.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Unreal Engine, Android environments, and Arm platforms
-      such as Immortalis-G715 and Immortalis-G720.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Lumen and Ray Tracing, Acceleration Structure, Only
-      Add Important Objects into Ray Tracing, Take Full Advantage of Instancing, and Optimize
-      Acceleration Structure.
-# END generated_summary_faq
+
 
 author: Owen Wu
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md b/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md
index 98d1739e3a..b1f3c47140 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md
@@ -19,47 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: deeb745d72457138cb81252c421205cd8dfb43b5e29ceee3a15c5842cc90cc33
-  summary: >-
-    Learn how to build ONNX Runtime and the generate() API for Android to run a Phi-3 model on
-    Arm-based smartphones. It is designed for software developers interested in learning how to
-    build an Android chat app with ONNX Runtime and ONNX Runtime Generate() API. By the end, you
-    will be able to build ONNX Runtime and ONNX Runtime generate() API for Android and run a Phi-3
-    model using ONNX Runtime on an Arm-based smartphone. It focuses on tools and technologies
-    such as Kotlin, CPP, ONNX Runtime, Android, and Hugging Face, Windows and Android environments,
-    and Arm platforms including Cortex-A. The main steps cover Create a development environment,
-    Build ONNX Runtime, Build ONNX Runtime Generate() API, Run a benchmark on an Android phone,
-    and Build and run an Android chat app.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build ONNX Runtime and ONNX Runtime generate() API for Android and run a Phi-3
-      model using ONNX Runtime on an Arm-based smartphone. Learn how to build ONNX Runtime and
-      the generate() API for Android to run a Phi-3 model on Arm-based smartphones.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers interested in learning how to build an
-      Android chat app with ONNX Runtime and ONNX Runtime Generate() API.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows x86_64 development machine
-      with at least 16GB of RAM.; An Android phone with at least 8GB of RAM. This learning path
-      was tested on Samsung Galaxy S24.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Kotlin, CPP, ONNX Runtime, Android, and Hugging
-      Face, Windows and Android environments, and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Create a development environment, Build ONNX Runtime,
-      Build ONNX Runtime Generate() API, Run a benchmark on an Android phone, and Build and run
-      an Android chat app.
-# END generated_summary_faq
+
 
 author: Koki Mitsunami
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md b/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md
index 24674975ff..b6185b3157 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md
@@ -24,55 +24,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 13ff69755e51c6d7c355616f95703b3f2cefaedcf1f8dbeab31fe2e3db9aec74
-  summary: >-
-    Learn how to build a hands-free selfie Android application using MediaPipe multimodal AI,
-    Kotlin flows, CameraX, and MVVM architecture. It is designed for mobile application developers
-    interested in learning how to build an Android selfie application with Modern MediaPipe Multimodal
-    AI, Kotlin flows, and CameraX, using the Modern Android Development (MAD) architecture design.
-    By the end, you will be able to architect a modern hands-free selfie Android app with MediaPipe,
-    leverage lifecycle-aware components within the Model-View-ViewModel (MVVM) architecture, and
-    combine MediaPipe's face landmark detection and gesture recognition for integration in a multimodel
-    selfie solution. It focuses on tools and technologies such as Android Studio, Kotlin, and
-    MediaPipe, Android environments, and Arm platforms including Cortex-A and Mali GPU. The main
-    steps cover Set up the Development Environment, Manage Camera Permissions, Integrate MediaPipe
-    solutions, Manage UI state with ViewModel, and Use SharedFlow to View Events.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will architect a modern hands-free selfie Android app with MediaPipe, leverage lifecycle-aware
-      components within the Model-View-ViewModel (MVVM) architecture, and combine MediaPipe's
-      face landmark detection and gesture recognition for integration in a multimodel selfie solution.
-      Learn how to build a hands-free selfie Android application using MediaPipe multimodal AI,
-      Kotlin flows, CameraX, and MVVM architecture.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for mobile application developers interested in learning how
-      to build an Android selfie application with Modern MediaPipe Multimodal AI, Kotlin flows,
-      and CameraX, using the Modern Android Development (MAD) architecture design.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A development machine with [Android
-      Studio](https://developer.android.com/studio) installed.; A recent Arm-powered Android phone
-      with a front-facing camera and a USB data cable.; Familiarity with Android development concepts.;
-      Basic knowledge of Modern Android Architecture. See [Modern Android App Architecture](https://developer.android.com/courses/pathways/android-architecture).;
-      Basic knowledge of Kotlin programming language, including [Coroutines](https://kotlinlang.org/docs/coroutines-overview.html)
-      and [Kotlin Flows](https://kotlinlang.org/docs/flow.html).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Android Studio, Kotlin, and MediaPipe, Android environments,
-      and Arm platforms such as Cortex-A and Mali GPU.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Set up the Development Environment, Manage Camera
-      Permissions, Integrate MediaPipe solutions, Manage UI state with ViewModel, and Use SharedFlow
-      to View Events.
-# END generated_summary_faq
+
 
 author: Han Yin
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md b/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md
index 3cdb10cdeb..a5c28184c6 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md
@@ -26,55 +26,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 688b5b6eddc0480fa732308802d225696371c22a468bb6b140c6b74908222bbc
-  summary: >-
-    Learn how to build an Android chat application with Llama models using ExecuTorch, XNNPACK,
-    and KleidiAI for accelerated performance on Arm smartphones. It is designed for software developers
-    interested in learning how to build an Android chat app with Llama, KleidiAI, ExecuTorch,
-    and XNNPACK. By the end, you will be able to set up an ExecuTorch development environment,
-    describe how ExecuTorch uses KleidiAI kernels to accelerate performance on Arm-based platforms,
-    and describe how 4-bit groupwise PTQ quantization reduces model size without significantly
-    sacrificing model accuracy. It focuses on tools and technologies such as Java, CPP, Python,
-    Hugging Face, and ExecuTorch, macOS and Android environments, and Arm platforms including
-    Cortex-A. The main steps cover Create a development environment, ExecuTorch Setup, Understanding
-    Llama models, Prepare Llama models for ExecuTorch, and Run Benchmark on Android phone.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will set up an ExecuTorch development environment, describe how ExecuTorch uses KleidiAI
-      kernels to accelerate performance on Arm-based platforms, and describe how 4-bit groupwise
-      PTQ quantization reduces model size without significantly sacrificing model accuracy. Learn
-      how to build an Android chat application with Llama models using ExecuTorch, XNNPACK, and
-      KleidiAI for accelerated performance on Arm smartphones.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers interested in learning how to build
-      an Android chat app with Llama, KleidiAI, ExecuTorch, and XNNPACK.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Apple M1/M2 development machine with
-      Android Studio installed or a Linux machine with at least 16GB of RAM.; An Arm-powered smartphone
-      with the i8mm feature running Android, with 16GB of RAM.; A USB cable to connect your smartphone
-      to your development machine.; Android Debug Bridge (adb) installed on your device. Follow
-      the steps in [adb](https://developer.android.com/tools/adb) to install Android SDK Platform
-      Tools. The adb tool is included in this package.; Java 17 JDK. Follow the steps in [Java
-      17 JDK](https://www.oracle.com/java/technologies/javase/jdk17-archive-downloads.html) to
-      download and install JDK for host.; Python 3.10.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Java, CPP, Python, Hugging Face, and ExecuTorch,
-      macOS and Android environments, and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Create a development environment, ExecuTorch Setup,
-      Understanding Llama models, Prepare Llama models for ExecuTorch, and Run Benchmark on Android
-      phone.
-# END generated_summary_faq
+
 
 author: 
     - Varun Chari
diff --git a/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md b/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md
index f8148b6921..460ec1afcc 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md
@@ -26,54 +26,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 9ccf2cdd6406694d85c553204479d7ff1f688512056a3c76d4c85266cdc418b1
-  summary: >-
-    Learn how to build a customer support chatbot for Android using Llama 3.2, ExecuTorch, and
-    KleidiAI to run on-device inference on Arm platforms. It is designed for software developers
-    interested in building an on-device customer support chatbot for Android using Meta's Llama
-    models and the ExecuTorch runtime. By the end, you will be able to set up a development environment
-    for building and deploying ExecuTorch-based apps on Android, describe how ExecuTorch uses
-    KleidiAI kernels to accelerate performance on Arm-based platforms, and export a Llama 3.2
-    model to .pte format optimized for on-device inference. It focuses on tools and technologies
-    such as Java, Python, and ExecuTorch, macOS, Linux, and Android environments, and Arm platforms
-    including Cortex-A. The main steps cover Create a development environment, Set up ExecuTorch,
-    Understand Llama models, Prepare Llama models for ExecuTorch, and Run the chatbot on Android.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will set up a development environment for building and deploying ExecuTorch-based apps
-      on Android, describe how ExecuTorch uses KleidiAI kernels to accelerate performance on Arm-based
-      platforms, and export a Llama 3.2 model to .pte format optimized for on-device inference.
-      Learn how to build a customer support chatbot for Android using Llama 3.2, ExecuTorch, and
-      KleidiAI to run on-device inference on Arm platforms.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers interested in building an on-device
-      customer support chatbot for Android using Meta's Llama models and the ExecuTorch runtime.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Apple M1/M2/M3 development machine,
-      or a Linux machine with at least 16GB of RAM; An Arm-powered smartphone with the i8mm feature
-      running Android, with 16GB of RAM; A USB cable to connect your smartphone to your development
-      machine; Android Debug Bridge (adb) installed. Follow the steps in [adb](https://developer.android.com/tools/adb)
-      to install Android SDK Platform Tools; Java 17 JDK. Follow the steps in [Java SE 17 Archive
-      Downloads](https://www.oracle.com/java/technologies/javase/jdk17-archive-downloads.html)
-      to download and install JDK for your host; Python 3.10 or later; A [Hugging Face](https://huggingface.co/)
-      account with access to Meta Llama models.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Java, Python, and ExecuTorch, macOS, Linux, and
-      Android environments, and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Create a development environment, Set up ExecuTorch,
-      Understand Llama models, Prepare Llama models for ExecuTorch, and Run the chatbot on Android.
-# END generated_summary_faq
+
 
 author: Parichay Das
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md b/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md
index c8be0a262c..0a3f311466 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md
@@ -23,50 +23,6 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 95d4fb855552939d1a0e9cccfe46333692ea291ee85dd08b816321db29ceebdc
-  summary: >-
-    Learn how to detect and debug memory safety bugs in Android applications using Arm Memory
-    Tagging Extension (MTE) on a Google Pixel 8 smartphone. It is designed for developers interested
-    in learning how to use the Arm Memory Tagging Extension (MTE) to detect memory safety bugs
-    with Android Studio on a Google Pixel 8 smartphone. By the end, you will be able to recognize
-    common memory safety bugs in Android applications, describe how you can use an Android MTE
-    Test app to implement common memory bugs, and build the MTE Test app in Android Studio. It
-    focuses on tools and technologies such as Android Studio and MTE, Android environments, and
-    Arm platforms including Cortex-A. The main steps cover Background, Implement memory safety
-    bugs with the Android app, Set up the app for debugging with MTE, and Debug in Android Studio
-    with MTE.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will recognize common memory safety bugs in Android applications, describe how you can
-      use an Android MTE Test app to implement common memory bugs, and build the MTE Test app
-      in Android Studio. Learn how to detect and debug memory safety bugs in Android applications
-      using Arm Memory Tagging Extension (MTE) on a Google Pixel 8 smartphone.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for developers interested in learning how to use the Arm Memory
-      Tagging Extension (MTE) to detect memory safety bugs with Android Studio on a Google Pixel
-      8 smartphone.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Google Pixel 8 smartphone.; Android
-      Studio installed on your development computer.; A USB cable to connect your computer to
-      your Google Pixel 8.; Android Debug Bridge (adb) installed on your device. If needed, follow
-      the steps in the [Android Debug Bridge](https://developer.android.com/tools/adb) documentation.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Android Studio and MTE, Android environments, and
-      Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Background, Implement memory safety bugs with the
-      Android app, Set up the app for debugging with MTE, and Debug in Android Studio with MTE.
-# END generated_summary_faq
 
 author: Roberto Lopez Mendez
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md
index a51b1c5d64..ffadc5dafa 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md
@@ -18,47 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: b194edd6ff4ffc5e39e7daa995271d2ed81ec31c8e88ddf1b23a54eb06dd1d06
-  summary: >-
-    Get started with Arm Accuracy Super Resolution (Arm ASR) walks you through an end-to-end Arm
-    software workflow. It is designed for mobile, gaming, and graphics developers who want to
-    install and configure Arm Accuracy Super Resolution (Arm ASR) to enhance performance on complex
-    game content without sacrificing image quality. By the end, you will be able to describe Arm
-    Accuracy Super Resolution, integrate Arm ASR into your game project, and manage how Arm ASR
-    upscales content. It focuses on tools and technologies such as Unreal Engine, Android environments,
-    and Arm platforms including Mali and Immortalis. The main steps cover What is Arm Accuracy
-    Super Resolution?, Using Arm ASR in Unreal Engine, Using Arm ASR in a Custom Engine using
-    the Generic Library, and Acknowledgements and Licensing.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will describe Arm Accuracy Super Resolution, integrate Arm ASR into your game project,
-      and manage how Arm ASR upscales content.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is for mobile, gaming, and graphics developers who want to install and
-      configure Arm Accuracy Super Resolution (Arm ASR) to enhance performance on complex game
-      content without sacrificing image quality.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A game project that uses advanced rendering
-      features (such as hardware ray tracing) that stretch the performance capabilities of everyday
-      smartphones.; A development machine with Git installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Unreal Engine, Android environments, and Arm platforms
-      such as Mali and Immortalis.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around What is Arm Accuracy Super Resolution?, Using Arm
-      ASR in Unreal Engine, Using Arm ASR in a Custom Engine using the Generic Library, and Acknowledgements
-      and Licensing.
-# END generated_summary_faq
+
 
 author: Julie Gaskin
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md
index 92a4fb1ad1..f5bba21888 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md
@@ -19,42 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 23df12c0e165a4120fa25b5573437121bc7af141376758ccd051ddac14e180fb
-  summary: >-
-    Get started with Unity on Android walks you through an end-to-end Arm software workflow. It
-    is designed for Unity developers who want to target Android devices. By the end, you will
-    be able to set up with Unity development, build and deploy to an Android device, and launch
-    the Profiler tool to investigate performance issues. It focuses on tools and technologies
-    such as Unity and C#, Android environments, and Arm platforms including Cortex. The main steps
-    cover Set up, Sample project, Test on Android device, Introduction to profiling, and Profiling
-    on Android.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will set up with Unity development, build and deploy to an Android device, and launch
-      the Profiler tool to investigate performance issues.
-  - question: Who is this Learning Path for?
-    answer: >-
-      Unity developers who want to target Android devices.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Basic knowledge of game engines and
-      programming concepts; Recent Android device, such as a mobile phone or tablet; Desktop computer
-      capable of running Unity.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Unity and C#, Android environments, and Arm platforms
-      such as Cortex.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Set up, Sample project, Test on Android device, Introduction
-      to profiling, and Profiling on Android.
-# END generated_summary_faq
+
 
 author: Joshua Marshall-Law
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md
index 40602be9e3..e1f50235e2 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md
@@ -17,47 +17,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 44e7b5fe3fda52267dc1957319439895293276602b1f533de5665adaf6f7cafe
-  summary: >-
-    Profile Android game performance in Godot with Arm Performance Studio walks you through an
-    end-to-end Arm software workflow. It is designed for Godot developers targeting Android devices
-    who want to optimize game performance on Arm CPUs and Mali GPUs using Arm Performance Studio
-    tools. By the end, you will be able to install the Arm Performance Studio Integration extension
-    in Godot and annotate your Godot game with performance markers for profiling in Streamline
-    and Performance Advisor. It focuses on tools and technologies such as Godot and Arm Performance
-    Studio, Windows, macOS, and Linux environments, and Arm platforms including Cortex-A and Mali.
-    The main steps cover Profile your Godot game with Arm Performance Studio, Install the Arm
-    Performance Studio extension in Godot, Annotate Game Events for Profiling in Godot, Define
-    performance regions in Godot, and Use channels for threaded performance annotations.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install the Arm Performance Studio Integration extension in Godot and annotate
-      your Godot game with performance markers for profiling in Streamline and Performance Advisor.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for Godot developers targeting Android devices who want to
-      optimize game performance on Arm CPUs and Mali GPUs using Arm Performance Studio tools.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Familiarity with Godot; Familiarity
-      with Arm Performance Studio tools.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Godot and Arm Performance Studio, Windows, macOS,
-      and Linux environments, and Arm platforms such as Cortex-A and Mali.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Profile your Godot game with Arm Performance Studio,
-      Install the Arm Performance Studio extension in Godot, Annotate Game Events for Profiling
-      in Godot, Define performance regions in Godot, and Use channels for threaded performance
-      annotations.
-# END generated_summary_faq
+
 
 author: Albin Bernhardsson, Julie Gaskin
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md b/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md
index d46ebcc9ca..57971350cb 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md
@@ -18,44 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 3f35558e0399cb4c851b120eaa2217a63c48336cbba96d23500d1b751e4aec63
-  summary: >-
-    How to Enable Hardware Ray Tracing on Lumen for Android Devices walks you through an end-to-end
-    Arm software workflow. It is designed for Unreal Engine developers interested in using hardware
-    ray tracing with Lumen on Arm devices. By the end, you will be able to learn about Lumen and
-    global illumination and enable hardware ray tracing on Lumen for Arm devices. It focuses on
-    tools and technologies such as Unreal Engine, Android environments, and Arm platforms including
-    Immortalis-G715 and Immortalis-G720. The main steps cover What is Lumen?, What is Global Illumination?,
-    How to Enable Lumen, and How to Enable Hardware Ray Tracing on Lumen.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will learn about Lumen and global illumination and enable hardware ray tracing on Lumen
-      for Arm devices.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for Unreal Engine developers interested in using hardware
-      ray tracing with Lumen on Arm devices.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A computer capable of running [Unreal
-      Engine 5.3 or later version](https://www.unrealengine.com/en-US/download).; An Android mobile
-      device that has a Mali GPU with hardware ray tracing support.; A USB cable to connect the
-      mobile device to your computer.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Unreal Engine, Android environments, and Arm platforms
-      such as Immortalis-G715 and Immortalis-G720.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around What is Lumen?, What is Global Illumination?, How
-      to Enable Lumen, and How to Enable Hardware Ray Tracing on Lumen.
-# END generated_summary_faq
+
 
 author: Owen Wu
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md b/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md
index 55853978b5..9600275b41 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md
@@ -15,35 +15,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: ab171b1ff6cfed7bce1cb8e50d4d9aeb4bee1972e8a409203cb438f94086a320
-  summary: >-
-    Get started with Arm hardware walks you through an end-to-end Arm software workflow. It is
-    designed for Developers new to the Arm architecture and looking for mobile hardware. By the
-    end, you will be able to find mobile hardware to use for software development. It focuses
-    on Android environments and Arm platforms including Cortex-A, Mali, and Immortalis. The main
-    steps cover Find Arm hardware.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will find mobile hardware to use for software development.
-  - question: Who is this Learning Path for?
-    answer: >-
-      Developers new to the Arm architecture and looking for mobile hardware.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: None.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers Android environments and Arm platforms such as Cortex-A, Mali, and Immortalis.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Find Arm hardware.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md b/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md
index 0ce125b7f2..b09c72cfa3 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md
@@ -20,48 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 5a94138f74e7b2266c35dbd555f9966ca0ff29fdbd1842d4724e0da0cd4e46db
-  summary: >-
-    Understand KleidiAI SME2 matmul microkernels walks you through an end-to-end Arm software
-    workflow. It is designed for software developers, performance engineers, and AI practitioners.
-    By the end, you will be able to explain how a KleidiAI microkernel performs matrix multiplication
-    (matmul) with quantized data, identify how SME2 INT8 MOPA (matrix outer product accumulate)
-    instructions map to matmul work, and trace how quantization and packing feed an SME2 matmul
-    microkernel (using GGML Q4_0 and llama.cpp call stacks as a concrete example). It focuses
-    on tools and technologies such as C++, KleidiAI, llama.cpp, and SME2, Android and Linux environments,
-    and Arm platforms including Arm C1. The main steps cover Overview and setup, Matmul tiling
-    and packing, SME2 INT8 MOPA for matmul, Decode the SME2 matmul microkernel, and Repack RHS
-    weights (GGML Q4_0).
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will explain how a KleidiAI microkernel performs matrix multiplication (matmul) with
-      quantized data, identify how SME2 INT8 MOPA (matrix outer product accumulate) instructions
-      map to matmul work, and trace how quantization and packing feed an SME2 matmul microkernel
-      (using GGML Q4_0 and llama.cpp call stacks as a concrete example).
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers, performance engineers, and AI practitioners.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Basic understanding of general matrix
-      multiplication (GEMM) and matmul operations; Basic understanding of quantization concepts
-      for neural networks; (Optional) Access to an Arm CPU with SME2 support (Linux or Android)
-      for hands-on verification steps.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including C++, KleidiAI, llama.cpp, and SME2, Android and
-      Linux environments, and Arm platforms such as Arm C1.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview and setup, Matmul tiling and packing, SME2
-      INT8 MOPA for matmul, Decode the SME2 matmul microkernel, and Repack RHS weights (GGML Q4_0).
-# END generated_summary_faq
+
 
 author: Zenon Zhilong Xiu
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md b/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md
index 71a37cdfe2..40a5d66b1d 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md
@@ -19,48 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 0e51db9ceb25c31c42608f3c6744a4fa8f9d995805ed44a7d7fc83324889ea12
-  summary: >-
-    Learn how to run LLM inference on Android devices using MediaPipe with KleidiAI-enhanced Arm
-    i8mm features to benchmark the Gemma 2B model. It is designed for Android developers who want
-    to efficiently run LLMs on-device. By the end, you will be able to install the prerequisites
-    for cross-compiling new inference engines for Android, run LLM inference on an Android device
-    with the Gemma 2B model using the Google AI Edge's MediaPipe framework, and benchmark LLM
-    inference speed with and without the KleidiAI-enhanced Arm i8mm processor feature. It focuses
-    on tools and technologies such as Java, MediaPipe, Android SDK, Android NDK, and Bazel, Linux
-    environments, and Arm platforms including Cortex-A. The main steps cover Install dependencies,
-    Run the Gemma 2B model using MediaPipe with XNNPACK, and Benchmark the Gemma 2B Model with
-    KleidiAI.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install the prerequisites for cross-compiling new inference engines for Android,
-      run LLM inference on an Android device with the Gemma 2B model using the Google AI Edge's
-      MediaPipe framework, and benchmark LLM inference speed with and without the KleidiAI-enhanced
-      Arm i8mm processor feature. Learn how to run LLM inference on Android devices using MediaPipe
-      with KleidiAI-enhanced Arm i8mm features to benchmark the Gemma 2B model.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for Android developers who want to efficiently run LLMs on-device.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An x86_64 Linux machine running Ubuntu
-      with approximately 500 MB of free space, or a docker daemon that can build and run a provided
-      x86_64 Dockerfile.; An Android phone with support for i8mm (tested on Google Pixel 8 Pro).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Java, MediaPipe, Android SDK, Android NDK, and Bazel,
-      Linux environments, and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Install dependencies, Run the Gemma 2B model using
-      MediaPipe with XNNPACK, and Benchmark the Gemma 2B Model with KleidiAI.
-# END generated_summary_faq
+
 
 author: 
     - Pareena Verma
diff --git a/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md b/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md
index a33bc0d80c..6e7f60d071 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md
@@ -18,45 +18,6 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 68cdc7315795b6ffa794c0a1fd4cf325ab39ebb65e23efd27b7760ece76a2ec9
-  summary: >-
-    Learn how to build the libGPUInfo library using Android NDK and query configuration details
-    of Arm Mali or Immortalis GPUs on Android devices. It is designed for Android developers who
-    want to adjust application complexity to match device performance. By the end, you will be
-    able to build the libGPUInfo library using the Android NDK and run an example application
-    to query the configuration details of an Arm Mali or Arm Immortalis GPU. It focuses on tools
-    and technologies such as Android NDK and adb, Android environments, and Arm platforms including
-    Cortex-A, Mali, and Immortalis. The main steps cover Build and run an example application
-    to obtain Arm GPU configuration information.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build the libGPUInfo library using the Android NDK and run an example application
-      to query the configuration details of an Arm Mali or Arm Immortalis GPU. Learn how to build
-      the libGPUInfo library using Android NDK and query configuration details of Arm Mali or
-      Immortalis GPUs on Android devices.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for Android developers who want to adjust application complexity
-      to match device performance.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A development machine running Ubuntu
-      or Debian Linux with `x86_64` architecture; An Android device with an Arm GPU.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Android NDK and adb, Android environments, and Arm
-      platforms such as Cortex-A, Mali, and Immortalis.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Build and run an example application to obtain Arm
-      GPU configuration information.
-# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md b/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md
index 666350f0c9..0a12f13127 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md
@@ -20,49 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: a744c8118a5a3cb97eee7bee271f768bdd71b4286e723c38a7b4ff6cd9e08d18
-  summary: >-
-    Learn how to accelerate LiteRT model inference on Android using KleidiAI with SME2 instructions
-    and validate performance with the benchmark tool. It is designed for developers looking to
-    leverage Arm's Scalable Matrix Extension 2 (SME2) instructions to accelerate LiteRT model
-    inference on Android. By the end, you will be able to understand how KleidiAI integrates with
-    LiteRT, build the LiteRT benchmark tool and enable XNNPACK and KleidiAI with SME2 support
-    in LiteRT, and create LiteRT models that can be accelerated by SME2 through KleidiAI. It focuses
-    on tools and technologies such as C, Python, and SME2, Android environments, and Arm platforms
-    including Cortex-A, Cortex-X, and Arm C1. The main steps cover Explore LiteRT, XNNPACK, KleidiAI,
-    and SME2, Create LiteRT models, Build the LiteRT benchmark tool, and Benchmark the LiteRT
-    model.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand how KleidiAI integrates with LiteRT, build the LiteRT benchmark tool
-      and enable XNNPACK and KleidiAI with SME2 support in LiteRT, and create LiteRT models that
-      can be accelerated by SME2 through KleidiAI. Learn how to accelerate LiteRT model inference
-      on Android using KleidiAI with SME2 instructions and validate performance with the benchmark
-      tool.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for developers looking to leverage Arm's Scalable Matrix Extension
-      2 (SME2) instructions to accelerate LiteRT model inference on Android.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm64 Linux development machine;
-      An Android device that supports Arm SME2 architecture features - see this [list of devices
-      with SME2 support](/learning-paths/cross-platform/multiplying-matrices-with-sme2/1-get-started/#devices).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including C, Python, and SME2, Android environments, and Arm
-      platforms such as Cortex-A, Cortex-X, and Arm C1.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Explore LiteRT, XNNPACK, KleidiAI, and SME2, Create
-      LiteRT models, Build the LiteRT benchmark tool, and Benchmark the LiteRT model.
-# END generated_summary_faq
+
 
 author: Jiaming Guo
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md b/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md
index fc0da0a7fe..5332acc412 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md
@@ -20,54 +20,6 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: b55d902389806f5e84e674e5ba1f1f2d9e38af370c289ad344eff2dad3475df1
-  summary: >-
-    Learn how to benchmark KleidiAI micro-kernels in ExecuTorch using SME/SME2 instructions on
-    Arm64 platforms with ETDump profiling and analysis. It is designed for developers, performance
-    engineers, and ML framework contributors who want to benchmark and optimize KleidiAI micro-kernels
-    within ExecuTorch to accelerate model inference on Arm64 platforms supporting SME/SME2 instructions.
-    By the end, you will be able to cross-compile ExecuTorch for Arm64 with XNNPACK and KleidiAI
-    enabled, including SME/SME2 instructions, build and export ExecuTorch models that can be accelerated
-    by KleidiAI using SME/SME2 instructions, and use the executor_runner tool to run kernel workloads
-    and collect ETDump profiling data. It focuses on tools and technologies such as Python, ExecuTorch,
-    XNNPACK, and KleidiAI, Linux environments, and Arm platforms including Cortex-A. The main
-    steps cover Set up your environment, Cross-Compile ExecuTorch for the AArch64 platform, Accelerate
-    ExecuTorch operators with KleidiAI micro-kernels, Create and quantize linear layer benchmark
-    model, and Create and quantize convolution layer benchmark model.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will cross-compile ExecuTorch for Arm64 with XNNPACK and KleidiAI enabled, including
-      SME/SME2 instructions, build and export ExecuTorch models that can be accelerated by KleidiAI
-      using SME/SME2 instructions, and use the executor_runner tool to run kernel workloads and
-      collect ETDump profiling data. Learn how to benchmark KleidiAI micro-kernels in ExecuTorch
-      using SME/SME2 instructions on Arm64 platforms with ETDump profiling and analysis.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for developers, performance engineers, and ML framework contributors
-      who want to benchmark and optimize KleidiAI micro-kernels within ExecuTorch to accelerate
-      model inference on Arm64 platforms supporting SME/SME2 instructions.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An x86_64 Linux host machine running
-      Ubuntu, with at least 15 GB of free disk space; An Arm64 target system with support for
-      SME or SME2 - see the Learning Path [Devices with native SME2 support](/learning-paths/cross-platform/multiplying-matrices-with-sme2/1-get-started/#devices).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Python, ExecuTorch, XNNPACK, and KleidiAI, Linux
-      environments, and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Set up your environment, Cross-Compile ExecuTorch
-      for the AArch64 platform, Accelerate ExecuTorch operators with KleidiAI micro-kernels, Create
-      and quantize linear layer benchmark model, and Create and quantize convolution layer benchmark
-      model.
-# END generated_summary_faq
 
 author: Qixiang Xu
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md b/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md
index 9b22f00225..9e7d8312e9 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md
@@ -21,52 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: e145caae5b20f7165251d88e334ba22d90c8b35270902778a2b6fe0ed0b250f6
-  summary: >-
-    Learn how to fine-tune and evaluate Neural Super Sampling (NSS) models using PyTorch and Arm's
-    Model Gym API with hardware-aware optimization. It is designed for developers exploring neural
-    graphics and interested in training and deploying upscaling models like Neural Super Sampling
-    (NSS) using PyTorch and Arm’s hardware-aware backend. By the end, you will be able to understand
-    the principles of neural graphics and how it’s applied to game performance, learn how to fine-tune
-    and evaluate a neural network for Neural Super Sampling (NSS), and use the Model Gym Python
-    API and CLI to configure and train neural graphics models. It focuses on tools and technologies
-    such as PyTorch, Jupyter Notebook, Vulkan, and NX, Linux environments, and Arm platforms including
-    Mali. The main steps cover Install Model Gym and explore neural graphics examples, Set up
-    your environment, Launch the training notebook, Visualize your model with Model Explorer,
-    and Defining your own use cases.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand the principles of neural graphics and how it’s applied to game performance,
-      learn how to fine-tune and evaluate a neural network for Neural Super Sampling (NSS), and
-      use the Model Gym Python API and CLI to configure and train neural graphics models. Learn
-      how to fine-tune and evaluate Neural Super Sampling (NSS) models using PyTorch and Arm's
-      Model Gym API with hardware-aware optimization.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for developers exploring neural graphics and interested in training
-      and deploying upscaling models like Neural Super Sampling (NSS) using PyTorch and Arm’s
-      hardware-aware backend.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Basic understanding of PyTorch and machine
-      learning concepts; A development machine running Ubuntu 22.04, with a CUDA-capable NVIDIA®
-      GPU; CUDA Toolkit version 11.8 or later.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including PyTorch, Jupyter Notebook, Vulkan, and NX, Linux
-      environments, and Arm platforms such as Mali.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Install Model Gym and explore neural graphics examples,
-      Set up your environment, Launch the training notebook, Visualize your model with Model Explorer,
-      and Defining your own use cases.
-# END generated_summary_faq
+
 
 author: Annie Tallund
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md b/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md
index 2ec57d7ef1..b0deb2608c 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md
@@ -16,42 +16,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: a25cb73b70716e397d9477f8ab9729f4a094143d276e4de68cf8c33b273bb1ff
-  summary: >-
-    Learn how to run example C programs on AArch64 Linux to gain an introductory understanding
-    of the Arm Memory Tagging Extension (MTE). It is designed for developers who want to gain
-    some experience with the Arm Memory Tagging Extension (MTE). By the end, you will be able
-    to run an example C program to gain an introductory understanding of MTE. It focuses on tools
-    and technologies such as QEMU, Linux environments, and Arm platforms including Cortex-A. The
-    main steps cover Build and run an example application to learn about MTE.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will run an example C program to gain an introductory understanding of MTE. Learn how
-      to run example C programs on AArch64 Linux to gain an introductory understanding of the
-      Arm Memory Tagging Extension (MTE).
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers who want to gain some experience with the Arm
-      Memory Tagging Extension (MTE).
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An AArch64 Linux development machine.
-      Cloud instances can be used, refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including QEMU, Linux environments, and Arm platforms such
-      as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Build and run an example application to learn about
-      MTE.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md b/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md
index 52f54fb893..4f0ed88d03 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md
@@ -22,49 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: b6a9aa558ec5062c829d61b28fb92e25d5517249c011eb29f03cc36775b6f9af
-  summary: >-
-    Learn how to enable Arm Memory Tagging Extension (MTE) on a Google Pixel 8 smartphone, trigger
-    memory bug crashes, and interpret bug reports. It is designed for developers interested in
-    learning how to enable Arm's Memory Tagging Extension (MTE) on Google's Pixel 8 smartphone
-    and also how to access a memory bug report. By the end, you will be able to enable MTE on
-    your Google Pixel 8 smartphone, understand how MTE works and learn how to make an application
-    crash when it encounters a memory bug, and access the memory bug report. It focuses on tools
-    and technologies such as MTE, adb, and Google Pixel 8, Android environments, and Arm platforms
-    including Cortex-A. The main steps cover Enabling MTE on Pixel 8, Understanding MTE, Testing
-    MTE, Creating the Bug Report, and The Bug Report.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will enable MTE on your Google Pixel 8 smartphone, understand how MTE works and learn
-      how to make an application crash when it encounters a memory bug, and access the memory
-      bug report. Learn how to enable Arm Memory Tagging Extension (MTE) on a Google Pixel 8 smartphone,
-      trigger memory bug crashes, and interpret bug reports.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers interested in learning how to enable Arm's
-      Memory Tagging Extension (MTE) on Google's Pixel 8 smartphone and also how to access a memory
-      bug report.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Google Pixel 8 smartphone; A USB cable
-      to connect your Google Pixel 8 to your desktop machine; Android Debug Bridge (adb) installed
-      on your device. Follow the steps in https://developer.android.com/tools/adb to install Android
-      SDK Platform Tools. The adb tool is included in this package.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including MTE, adb, and Google Pixel 8, Android environments,
-      and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Enabling MTE on Pixel 8, Understanding MTE, Testing
-      MTE, Creating the Bug Report, and The Bug Report.
-# END generated_summary_faq
+
 
 author: Roberto Lopez Mendez
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md b/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md
index 4c895cc514..9ddf1dfd7d 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md
@@ -22,51 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 5ca059af36f0ba375701e76ce82b7803f01ae6beab313336b333bfbdebaa3b1a
-  summary: >-
-    Learn how to capture high-quality frame datasets from Unreal Engine 5.5 gameplay for training
-    and evaluating neural graphics models like Neural Super Sampling. It is designed for Unreal
-    Engine developers who want to generate high-quality frame datasets for training and evaluating
-    neural graphics models. By the end, you will be able to understand why Neural Graphics Data
-    Capture is useful in a neural graphics workflow, install and enable the Neural Graphics Data
-    Capture plugin in Unreal Engine 5.5, and configure a Level Blueprint to start and stop capture
-    with hotkeys. It focuses on tools and technologies such as Unreal Engine, Visual Studio, and
-    NX, Windows environments, and Arm platforms including Mali and Immortalis. The main steps
-    cover Benefits of Neural Graphics Data Capture for game developers, Install and enable the
-    plugin, Configure Level Blueprint capture controls, Run capture and verify outputs, and Capture
-    settings and troubleshooting.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand why Neural Graphics Data Capture is useful in a neural graphics workflow,
-      install and enable the Neural Graphics Data Capture plugin in Unreal Engine 5.5, and configure
-      a Level Blueprint to start and stop capture with hotkeys. Learn how to capture high-quality
-      frame datasets from Unreal Engine 5.5 gameplay for training and evaluating neural graphics
-      models like Neural Super Sampling.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is for Unreal Engine developers who want to generate high-quality frame
-      datasets for training and evaluating neural graphics models.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Windows 11; Unreal Engine 5.5 installed;
-      Visual Studio with C++ game development tools; A C++ Unreal project (such as the Third Person
-      template).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Unreal Engine, Visual Studio, and NX, Windows environments,
-      and Arm platforms such as Mali and Immortalis.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Benefits of Neural Graphics Data Capture for game
-      developers, Install and enable the plugin, Configure Level Blueprint capture controls, Run
-      capture and verify outputs, and Capture settings and troubleshooting.
-# END generated_summary_faq
+
 
 author: 
 - Annie Tallund
diff --git a/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md b/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md
index 5375d9c093..97578a5f82 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md
@@ -24,48 +24,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 7ffbac59b340f3ef5119d2f5ba4a786fd15d521bb294f3a4213b083c9b4ce23d
-  summary: >-
-    Learn how to configure ML Extensions for Vulkan emulation and enable Neural Super Sampling
-    (NSS) in Unreal Engine for real-time upscaling. It is designed for developers experimenting
-    with neural graphics using Unreal Engine® and ML Extensions for Vulkan®. By the end, you will
-    be able to understand how Arm enables neural graphics for game development, configure ML extensions
-    for Vulkan emulation, and enable Neural Super Sampling (NSS) in Unreal Engine. It focuses
-    on tools and technologies such as Unreal Engine, Vulkan SDK, Visual Studio, and NX, Windows
-    environments, and Arm platforms including Mali and Immortalis. The main steps cover Introduction
-    to neural graphics and Neural Super Sampling (NSS), Setting up the emulation layers, Create
-    an example game, Run the example, and Using RenderDoc for Debugging and Analysis.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand how Arm enables neural graphics for game development, configure ML extensions
-      for Vulkan emulation, and enable Neural Super Sampling (NSS) in Unreal Engine. Learn how
-      to configure ML Extensions for Vulkan emulation and enable Neural Super Sampling (NSS) in
-      Unreal Engine for real-time upscaling.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers experimenting with neural graphics using Unreal
-      Engine® and ML Extensions for Vulkan®.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Windows 11; Unreal Engine 4.27 or 5.4
-      or 5.6 (with the Templates and Feature Pack enabled); Visual Studio (with Desktop Development
-      with C++ and .NET desktop build tools).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Unreal Engine, Vulkan SDK, Visual Studio, and NX,
-      Windows environments, and Arm platforms such as Mali and Immortalis.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Introduction to neural graphics and Neural Super Sampling
-      (NSS), Setting up the emulation layers, Create an example game, Run the example, and Using
-      RenderDoc for Debugging and Analysis.
-# END generated_summary_faq
+
 
 author: Annie Tallund
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md b/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md
index 9949a0590a..745d8c71c8 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md
@@ -23,54 +23,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: aba1cea365c0c61e84fb545ada15a64ed52c099f1252dc6312f88ee82385d2d0
-  summary: >-
-    Learn how to build, optimize, and deploy machine learning models using ONNX Runtime on Arm64
-    platforms, including Raspberry Pi, cloud instances, and Android devices. It is designed for
-    developers who want to build, optimize, and deploy machine learning models using ONNX on Arm64-based
-    platforms such as Raspberry Pi, Arm-based laptops, cloud instances, or Android smartphones.
-    By the end, you will be able to explain what ONNX is and how it enables model portability
-    across ML frameworks, build and export a neural network model in Python to ONNX format, and
-    run inference using ONNX Runtime on Arm64 platforms. It focuses on tools and technologies
-    such as Python, PyTorch, TensorFlow, ONNX, and Android, Windows, Linux, macOS, and Android
-    environments, and Arm platforms including Cortex-A and Neoverse. The main steps cover Understand
-    ONNX fundamentals and architecture, Set up your development environment, Generate a synthetic
-    Sudoku digit dataset, Train the digit recognizer, and Run inference and evaluate the model.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will explain what ONNX is and how it enables model portability across ML frameworks,
-      build and export a neural network model in Python to ONNX format, and run inference using
-      ONNX Runtime on Arm64 platforms. Learn how to build, optimize, and deploy machine learning
-      models using ONNX Runtime on Arm64 platforms, including Raspberry Pi, cloud instances, and
-      Android devices.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for developers who want to build, optimize, and deploy machine
-      learning models using ONNX on Arm64-based platforms such as Raspberry Pi, Arm-based laptops,
-      cloud instances, or Android smartphones.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A development machine with Python 3.10
-      or 3.11 installed (Prebuilt ONNX Runtime packages for Arm platforms don't yet support Python
-      3.12); Basic familiarity with PyTorch or TensorFlow; An Arm64 device such as a Raspberry
-      Pi or Android smartphone; Android Studio (required only for the final deployment section).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Python, PyTorch, TensorFlow, ONNX, and Android,
-      Windows, Linux, macOS, and Android environments, and Arm platforms such as Cortex-A and
-      Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Understand ONNX fundamentals and architecture, Set
-      up your development environment, Generate a synthetic Sudoku digit dataset, Train the digit
-      recognizer, and Run inference and evaluate the model.
-# END generated_summary_faq
+
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md b/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md
index 9a4eea0265..4a1741cfdd 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md
@@ -18,42 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 586a505543df4237c943ea47210d735fafe7d5ed2c6ad18b1b99227987ef9b15
-  summary: >-
-    Learn how to optimize vertex representations and analyze Vertex Memory Efficiency using Arm
-    Frame Advisor for improved GPU performance on Android. It is designed for Android graphics
-    application developers aiming to enhance GPU performance through smarter vertex optimization.
-    By the end, you will be able to optimize vertex representations on Arm GPUs and analyze Vertex
-    Memory Efficiency using Arm Frame Advisor. It focuses on tools and technologies such as C
-    and CPP, Android environments, and Arm platforms including Immortalis and Mali. The main steps
-    cover Optimizing graphics vertex efficiency for Arm GPUs.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will optimize vertex representations on Arm GPUs and analyze Vertex Memory Efficiency
-      using Arm Frame Advisor. Learn how to optimize vertex representations and analyze Vertex
-      Memory Efficiency using Arm Frame Advisor for improved GPU performance on Android.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for Android graphics application developers aiming to enhance
-      GPU performance through smarter vertex optimization.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Understanding of vertex attributes.;
-      Familiarity with Arm Frame Advisor (part of Arm Performance Studio).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including C and CPP, Android environments, and Arm platforms
-      such as Immortalis and Mali.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Optimizing graphics vertex efficiency for Arm GPUs.
-# END generated_summary_faq
+
 
 author:
     - Andrew Kilroy
diff --git a/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md
index 87dd4776da..37c9756975 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md
@@ -21,49 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 4962524245ec5e5e07d1207537208a22c2e9569f252f36d758c4f828d2104272
-  summary: >-
-    Learn how to build llama.cpp with KleidiAI and SME2 support to profile and accelerate LLM
-    inference performance on Android devices. It is designed for software developers, performance
-    engineers, and AI practitioners. By the end, you will be able to build llama.cpp with KleidiAI
-    and SME2 support, profile LLM inference performance on Android, and understand how KleidiAI
-    and SME2 accelerate LLM operators. It focuses on tools and technologies such as SME2, C++,
-    and llama.cpp, Android and Linux environments, and Arm platforms including Arm C1. The main
-    steps cover Understand how SME2 and KleidiAI accelerate LLM inference in llama.cpp, Trace
-    how KleidiAI and SME2 accelerate llama.cpp from model load to token decode, Build llama.cpp
-    with KleidiAI and SME2 enabled, and Measure SME2 acceleration in llama.cpp on Android.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build llama.cpp with KleidiAI and SME2 support, profile LLM inference performance
-      on Android, and understand how KleidiAI and SME2 accelerate LLM operators. Learn how to
-      build llama.cpp with KleidiAI and SME2 support to profile and accelerate LLM inference performance
-      on Android devices.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers, performance engineers, and AI practitioners.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Knowledge of KleidiAI and SME2; A Linux
-      host machine (x86_64 or aarch64) for building llama.cpp with the Arm GNU Toolchain; Git,
-      CMake, and Android Debug Bridge (ADB) installed on your host machine; An Android device
-      with Arm SME2 support for running and profiling the executable.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including SME2, C++, and llama.cpp, Android and Linux environments,
-      and Arm platforms such as Arm C1.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Understand how SME2 and KleidiAI accelerate LLM inference
-      in llama.cpp, Trace how KleidiAI and SME2 accelerate llama.cpp from model load to token
-      decode, Build llama.cpp with KleidiAI and SME2 enabled, and Measure SME2 acceleration in
-      llama.cpp on Android.
-# END generated_summary_faq
+
 
 author: Zenon Zhilong Xiu
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md
index 977cb4ad6b..4d177fa652 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md
@@ -21,47 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 1645a1c65527da010edd6fefddad3c865eac0f9cad417ba59709a57bb9dc847a
-  summary: >-
-    Learn how to build ONNX Runtime with KleidiAI and SME2 support for Android and profile ONNX
-    model performance to compare acceleration improvements. It is designed for software developers,
-    performance engineers, and AI practitioners. By the end, you will be able to build ONNX Runtime
-    with KleidiAI and SME2 support for Android, profile ONNX model performance using benchmark
-    tools, and analyze how KleidiAI kernels accelerate ONNX operators with SME2. It focuses on
-    tools and technologies such as C++, ONNX Runtime, and SME2, Android and Linux environments,
-    and Arm platforms including Cortex-A and Arm C1. The main steps cover ONNX Runtime architecture
-    with SME2 acceleration, Integration of KleidiAI to ORT MLAS, Build ONNX Runtime with KleidiAI
-    and SME2 for Android, and Profile ONNX model performance.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build ONNX Runtime with KleidiAI and SME2 support for Android, profile ONNX model
-      performance using benchmark tools, and analyze how KleidiAI kernels accelerate ONNX operators
-      with SME2. Learn how to build ONNX Runtime with KleidiAI and SME2 support for Android and
-      profile ONNX model performance to compare acceleration improvements.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers, performance engineers, and AI practitioners.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Android device with Arm SME2 support;
-      Basic understanding of machine learning model inference; Familiarity with Android NDK and
-      cross-compilation.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including C++, ONNX Runtime, and SME2, Android and Linux environments,
-      and Arm platforms such as Cortex-A and Arm C1.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around ONNX Runtime architecture with SME2 acceleration,
-      Integration of KleidiAI to ORT MLAS, Build ONNX Runtime with KleidiAI and SME2 for Android,
-      and Profile ONNX model performance.
-# END generated_summary_faq
+
 
 author: Zenon Zhilong Xiu
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md b/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md
index 5930c7fc83..7dd0b61b50 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md
@@ -21,51 +21,6 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 01138f6538c655f866fb034a983461b2ac9b793142775465233b2ec8ec18d0c5
-  summary: >-
-    Learn how to profile ML model execution times and application performance on Arm Android devices
-    using Arm Performance Studio and Android Studio Profiler. It is designed for software developers
-    who want to learn how to profile the performance of Machine Learning (ML) models running on
-    Arm devices. By the end, you will be able to profile the execution times of ML models on Arm
-    devices, profile ML application performance on Arm devices, and describe how profiling can
-    help optimize the performance of Machine Learning applications. It focuses on tools and technologies
-    such as Android Studio, LiteRT, and Hugging Face, Android and Linux environments, and Arm
-    platforms including Cortex-A, Mali, and Immortalis. The main steps cover Why should you profile
-    your ML application?, Profile your application with Streamline, Memory Profiling with Android
-    Studio, Profiling the Neural Network, and ML Profiling of a LiteRT model with ExecuteNetwork.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will profile the execution times of ML models on Arm devices, profile ML application
-      performance on Arm devices, and describe how profiling can help optimize the performance
-      of Machine Learning applications. Learn how to profile ML model execution times and application
-      performance on Arm Android devices using Arm Performance Studio and Android Studio Profiler.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to learn how to profile the
-      performance of Machine Learning (ML) models running on Arm devices.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm-powered Android smartphone, and
-      a USB cable to connect to it.; For profiling the ML inference, [Arm NN ExecuteNetwork](https://github.com/ARM-software/armnn/releases)
-      or [ExecuTorch](https://github.com/pytorch/executorch).; For profiling the application,
-      [Arm Performance Studio with Streamline](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio).;
-      Android Studio Profiler.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Android Studio, LiteRT, and Hugging Face, Android
-      and Linux environments, and Arm platforms such as Cortex-A, Mali, and Immortalis.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Why should you profile your ML application?, Profile
-      your application with Streamline, Memory Profiling with Android Studio, Profiling the Neural
-      Network, and ML Profiling of a LiteRT model with ExecuteNetwork.
-# END generated_summary_faq
 
 author: Ben Clark
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md b/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md
index ea27c6fd37..c5e4c7db9a 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md
@@ -21,44 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 9950a58160736e0c60ad80ea602262347e2ba8a37a00e29f25dc96709026ea1f
-  summary: >-
-    Learn how to deploy Unity applications to Android, profile code running on Arm devices, and
-    analyze performance data for optimization. It is designed for Unity developers wanting to
-    analyze the performance of their apps on Android devices. By the end, you will be able to
-    deploy to Android, profile code running on an Android device, and analyze performance data.
-    It focuses on tools and technologies such as Unity and C#, Android environments, and Arm platforms
-    including armv8, aarch32, aarch64, and arm64. The main steps cover Analyzing the sample application,
-    Preparation, Inside the code, Profiling, and Collect performance data.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will deploy to Android, profile code running on an Android device, and analyze performance
-      data. Learn how to deploy Unity applications to Android, profile code running on Arm devices,
-      and analyze performance data for optimization.
-  - question: Who is this Learning Path for?
-    answer: >-
-      Unity developers wanting to analyze the performance of their apps on Android devices.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Recent Android device, such as a mobile
-      phone or tablet; Desktop computer capable of running Unity; Basic knowledge of Unity and
-      programming concepts; The setup described in the Learning Path [Get started with Unity on
-      Android](/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Unity and C#, Android environments, and Arm platforms
-      such as armv8, aarch32, aarch64, and arm64.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Analyzing the sample application, Preparation, Inside
-      the code, Profiling, and Collect performance data.
-# END generated_summary_faq
+
 
 author: Joshua Marshall-Law
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md b/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md
index 5cbfc92883..a28eefeb44 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md
@@ -20,50 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 56d9d0fe9606e42281a2d2be52992c7a4b6846b208376c92e7fe3094647d1c70
-  summary: >-
-    Learn how to apply post-training quantization to PyTorch models using TorchAO and export INT8
-    models to .vgf format with the ExecuTorch Arm backend. It is designed for ML developers who
-    want to reduce latency and memory bandwidth by exporting INT8 models to the `.vgf` file format
-    using the ExecuTorch Arm backend. By the end, you will be able to explain when to use post-training
-    quantization (PTQ) vs quantization-aware training (QAT), prepare and quantize a PyTorch model
-    using TorchAO PT2E quantization APIs, and export the quantized model to TOSA and generate
-    a model artifact with the ExecuTorch Arm backend. It focuses on tools and technologies such
-    as ExecuTorch, TorchAO, Vulkan, TOSA, and NX, Linux, macOS, and Windows environments, and
-    Arm platforms including Mali. The main steps cover Explore PTQ and QAT for ExecuTorch INT8
-    deployment, Set up your environment for ExecuTorch quantization, Apply PTQ and export a quantized
-    VGF model, Apply QAT and export a quantized VGF model, and Inspect the graph with Model Explorer.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will explain when to use post-training quantization (PTQ) vs quantization-aware training
-      (QAT), prepare and quantize a PyTorch model using TorchAO PT2E quantization APIs, and export
-      the quantized model to TOSA and generate a model artifact with the ExecuTorch Arm backend.
-      Learn how to apply post-training quantization to PyTorch models using TorchAO and export
-      INT8 models to .vgf format with the ExecuTorch Arm backend.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for ML developers who want to reduce latency and memory bandwidth
-      by exporting INT8 models to the `.vgf` file format using the ExecuTorch Arm backend.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Basic PyTorch model training and evaluation
-      experience; A development machine with Python 3.10+ and PyTorch installed that runs ExecuTorch.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including ExecuTorch, TorchAO, Vulkan, TOSA, and NX, Linux,
-      macOS, and Windows environments, and Arm platforms such as Mali.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Explore PTQ and QAT for ExecuTorch INT8 deployment,
-      Set up your environment for ExecuTorch quantization, Apply PTQ and export a quantized VGF
-      model, Apply QAT and export a quantized VGF model, and Inspect the graph with Model Explorer.
-# END generated_summary_faq
+
 
 author:
 - Richard Burton
diff --git a/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md b/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md
index af4605f932..73a2998f80 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md
@@ -20,49 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 78f862a7c46fd4591fec45f91d040eb3f1093291c0a7328dddd2203dad72db3f
-  summary: >-
-    Learn how to use the Vulkan ray tracing API to implement realistic shadows, reflections, and
-    refractions in Android applications. It is designed for Vulkan developers who are familiar
-    with rendering and are interested in deploying ray tracing in their applications. By the end,
-    you will be able to describe how the Vulkan ray tracing API works, describe how to use ray
-    tracing to implement realistic shadows, reflections, and refractions, and implement basic
-    ray tracing effects in a Vulkan renderer. It focuses on tools and technologies such as Vulkan,
-    Android environments, and Arm platforms including Mali and Immortalis. The main steps cover
-    What is ray tracing?, Setup: enabling ray tracing, Ray traversal: ray tracing pipeline versus
-    ray query, Acceleration structure, and Bindless materials.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will describe how the Vulkan ray tracing API works, describe how to use ray tracing
-      to implement realistic shadows, reflections, and refractions, and implement basic ray tracing
-      effects in a Vulkan renderer. Learn how to use the Vulkan ray tracing API to implement realistic
-      shadows, reflections, and refractions in Android applications.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is for Vulkan developers who are familiar with rendering and are interested
-      in deploying ray tracing in their applications.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An appropriate Android device that supports
-      the required Vulkan extensions (for example, Vivo X100).; Knowledge of the Vulkan API.;
-      A Vulkan renderer. Most code is generic and should be easy to incorporate into any deferred
-      PBR renderer.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Vulkan, Android environments, and Arm platforms
-      such as Mali and Immortalis.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around What is ray tracing?, Setup: enabling ray tracing,
-      Ray traversal: ray tracing pipeline versus ray query, Acceleration structure, and Bindless
-      materials.
-# END generated_summary_faq
+
 
 author: Iago Calvo Lista
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md b/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md
index 5d27793ae6..29f5910395 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md
@@ -19,48 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: e2f6f3005c119e6f6fe97612d4e2600849106f244bce729d56bfeeedb30637c2
-  summary: >-
-    Learn how to use Frame Advisor's Render Graph view to identify and resolve graphics performance
-    issues in Android applications. It is designed for Mobile application developers who wish
-    to improve graphics performance. By the end, you will be able to understand Frame Advisor's
-    Render Graph view and use the Render Graph view to identify and resolve performance issues
-    in your application. It focuses on tools and technologies such as OpenGL ES and Vulkan, Linux,
-    Windows, macOS, and Android environments, and Arm platforms including Mali and Immortalis.
-    The main steps cover What are render graphs?, Generating a render graph for your application,
-    Understanding your render graph, Problem solving – unused resources, and Problem solving –
-    unwanted execution nodes.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand Frame Advisor's Render Graph view and use the Render Graph view to identify
-      and resolve performance issues in your application. Learn how to use Frame Advisor's Render
-      Graph view to identify and resolve graphics performance issues in Android applications.
-  - question: Who is this Learning Path for?
-    answer: >-
-      Mobile application developers who wish to improve graphics performance.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Frame Advisor, part of Arm Performance
-      Studio, installed. Refer to the [Arm Performance Studio](/install-guides/ams/) install guide.;
-      If you wish to analyze your own applications you will need a supported Android device.;
-      Some basic familiarity with Frame Advisor. Review the [Frame Advisor](/learning-paths/mobile-graphics-and-gaming/ams/fa/)
-      section in [Get started with Arm Performance Studio for mobile](/learning-paths/mobile-graphics-and-gaming/ams/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including OpenGL ES and Vulkan, Linux, Windows, macOS, and
-      Android environments, and Arm platforms such as Mali and Immortalis.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around What are render graphs?, Generating a render graph
-      for your application, Understanding your render graph, Problem solving – unused resources,
-      and Problem solving – unwanted execution nodes.
-# END generated_summary_faq
+
 
 author: Mark Thurman
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md
index df42872803..9b962557ec 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md
@@ -22,51 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 388cab0dcb284c29fe2ad82f2b2934d9243c6356b2885d26db0a97c1a1d4d393
-  summary: >-
-    Learn how to convert and deploy the Stable Audio Open Small text-to-audio model to LiteRT
-    format for audio generation on Android devices and macOS. It is designed for developers looking
-    to deploy the Stable Audio Open Small text-to-audio model using LiteRT on an Android™ device
-    or on a reasonably modern platform with macOS®. By the end, you will be able to download and
-    test the Stable Audio Open Small model, convert the Stable Audio Open Small model to the LiteRT
-    (.tflite) format, and compile the application for an Arm CPU. It focuses on tools and technologies
-    such as CPP, Python, and Hugging Face, Linux and Android environments, and Arm platforms including
-    Cortex-A and Cortex-X. The main steps cover Set up your development environment, Download
-    and test the model, Convert Stable Audio Open Small model to LiteRT, Build LiteRT, and Create
-    a simple program for Android target.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will download and test the Stable Audio Open Small model, convert the Stable Audio Open
-      Small model to the LiteRT (.tflite) format, and compile the application for an Arm CPU.
-      Learn how to convert and deploy the Stable Audio Open Small text-to-audio model to LiteRT
-      format for audio generation on Android devices and macOS.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers looking to deploy the Stable Audio Open Small
-      text-to-audio model using LiteRT on an Android™ device or on a reasonably modern platform
-      with macOS®.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Linux-based x86 or macOS development
-      machine with at least 8 GB of RAM and 50 GB of disk space (tested on Ubuntu 22.04 with x86_64).;
-      A [HuggingFace](https://huggingface.co/) account.; An Android phone in [developer mode](https://developer.android.com/studio/debug/dev-options)
-      and a cable to connect it to your development machine.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including CPP, Python, and Hugging Face, Linux and Android
-      environments, and Arm platforms such as Cortex-A and Cortex-X.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Set up your development environment, Download and
-      test the model, Convert Stable Audio Open Small model to LiteRT, Build LiteRT, and Create
-      a simple program for Android target.
-# END generated_summary_faq
+
 
 author:
     - Nina Drozd
diff --git a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md
index 90f62c62b2..01230a2a84 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md
@@ -20,51 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 7970846a399ddcdb488d90bf19cce3c6b74df6348e88914aed83661b2597fe7b
-  summary: >-
-    Learn how to convert the Stable Audio Open Small model to ExecuTorch format and build an audio
-    generation application for Android or macOS. It is designed for developers who want to deploy
-    the Stable Audio Open Small text-to-audio model using ExecuTorch on an Android device or macOS.
-    By the end, you will be able to download the Stable Audio Open Small model from Hugging Face,
-    convert the Stable Audio Open Small model to ExecuTorch (.pte) format, and build the audio
-    generation application for Arm CPUs. It focuses on tools and technologies such as CPP, Python,
-    Hugging Face, and ExecuTorch, Linux, Android, and macOS environments, and Arm platforms including
-    Cortex-A and Cortex-X. The main steps cover Set up your development environment, Download
-    the Stable Audio Open Small model, Convert the model to ExecuTorch format, Build and run on
-    macOS, and Build and run on Android.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will download the Stable Audio Open Small model from Hugging Face, convert the Stable
-      Audio Open Small model to ExecuTorch (.pte) format, and build the audio generation application
-      for Arm CPUs. Learn how to convert the Stable Audio Open Small model to ExecuTorch format
-      and build an audio generation application for Android or macOS.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers who want to deploy the Stable Audio Open Small
-      text-to-audio model using ExecuTorch on an Android device or macOS.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Linux-based x86 or macOS development
-      machine with at least 8 GB of RAM and 50 GB of disk space (tested on Ubuntu 22.04 with x86_64
-      and macOS with Apple Silicon); A [Hugging Face](https://huggingface.co/) account; An Android
-      phone in [developer mode](https://developer.android.com/studio/debug/dev-options) with at
-      least 8 GB of RAM and a cable to connect it to your development machine.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including CPP, Python, Hugging Face, and ExecuTorch, Linux,
-      Android, and macOS environments, and Arm platforms such as Cortex-A and Cortex-X.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Set up your development environment, Download the
-      Stable Audio Open Small model, Convert the model to ExecuTorch format, Build and run on
-      macOS, and Build and run on Android.
-# END generated_summary_faq
+
 
 author:
     - Adnan AlSinan
diff --git a/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md b/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md
index 66ea500d78..4d63a6ee7d 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md
@@ -21,47 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: dea8c3e15cca703f075c8fa9ba3daf873a90e3eb2ee9975dd05b973dad744b54
-  summary: >-
-    Learn how to build and install a Unity game on an Orange Pi 5 single-board computer running
-    Droid OS. It is designed for software developers who want to build and run a Unity game on
-    an Arm-based single board computer. By the end, you will be able to install Droid OS on an
-    Orange Pi 5, create a build of a Unity game to run on an Orange Pi, and install the Unity
-    game on the Orange Pi. It focuses on tools and technologies such as Unity, 7-Zip, and SDDiskTool,
-    Android environments, and Arm platforms including Cortex-A76 and Cortex-A55. The main steps
-    cover How do I install Droid OS on the Orange Pi 5?, How do I build my game in Unity to run
-    on the Orange Pi 5, and How do I install my Unity game onto the Orange Pi 5?
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install Droid OS on an Orange Pi 5, create a build of a Unity game to run on an
-      Orange Pi, and install the Unity game on the Orange Pi. Learn how to build and install a
-      Unity game on an Orange Pi 5 single-board computer running Droid OS.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to build and run a Unity
-      game on an Arm-based single board computer.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows PC to use Orange Pi's imaging
-      software, which is only available for Windows; An Orange Pi 5; A microSD card (16GB or greater;
-      class 10 or faster); An ethernet connection; A mouse and keyboard connected to the Orange
-      Pi.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Unity, 7-Zip, and SDDiskTool, Android environments,
-      and Arm platforms such as Cortex-A76 and Cortex-A55.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around How do I install Droid OS on the Orange Pi 5?, How
-      do I build my game in Unity to run on the Orange Pi 5, and How do I install my Unity game
-      onto the Orange Pi 5?
-# END generated_summary_faq
+
 
 author: Gabriel Peterson
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md b/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md
index 56c5302e5b..97847ddb0b 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md
@@ -19,45 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 4a990e957658b03bac9306d5f8e6955734cc1d3b063f43c38ac525ed1bf95b79
-  summary: >-
-    Learn how to install Arm integration packages in Unity to view GPU metrics in Unity Profiler
-    and annotate games with markers for Arm Performance Studio. It is designed for Unity developers
-    who are targeting Android devices and want to get more insight into how their game performs
-    on devices with Arm CPUs and GPUs. By the end, you will be able to install the packages in
-    Unity, view Arm GPU metrics in the Unity Profiler, and annotate your Unity game with markers
-    that give context to a profile in Arm Performance Studio tools. It focuses on tools and technologies
-    such as Unity and Arm Performance Studio, Windows, macOS, and Linux environments, and Arm
-    platforms including Cortex-A and Mali. The main steps cover GPU metrics and Arm Performance
-    Studio Unity integrations.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install the packages in Unity, view Arm GPU metrics in the Unity Profiler, and
-      annotate your Unity game with markers that give context to a profile in Arm Performance
-      Studio tools. Learn how to install Arm integration packages in Unity to view GPU metrics
-      in Unity Profiler and annotate games with markers for Arm Performance Studio.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for Unity developers who are targeting Android devices and
-      want to get more insight into how their game performs on devices with Arm CPUs and GPUs.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Familiarity with Unity and the Unity
-      Profiler; Familiarity with Arm Performance Studio tools.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Unity and Arm Performance Studio, Windows, macOS,
-      and Linux environments, and Arm platforms such as Cortex-A and Mali.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around GPU metrics and Arm Performance Studio Unity integrations.
-# END generated_summary_faq
+
 
 author: Julie Gaskin
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md b/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md
index 2e331a4304..c5a2451fd4 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md
@@ -22,45 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: f17ab3c4216495b88cee75a81612c11a4727d874114f914edf97c467f0d1c125
-  summary: >-
-    Learn how to use Arm Neon intrinsics in Unity C# scripts to optimize code on Android and collect
-    performance data using Unity Profiler. It is designed for Developers interested in leveraging
-    the Unity Machine Learning Agents toolkit on Arm devices. By the end, you will be able to
-    use Arm Neon intrinsics in your Unity C# scripts, optimize your code, and collect and compare
-    performance data using the Unity Profiler and Analyzer tools. It focuses on tools and technologies
-    such as Unity and C#, Android environments, and Arm platforms including armv8, aarch64, arm64,
-    and arm architecture. The main steps cover Set up, Arm Neon and the Unity Burst compiler,
-    The sample project, The plain (unoptimized) code, and The optimizations.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will use Arm Neon intrinsics in your Unity C# scripts, optimize your code, and collect
-      and compare performance data using the Unity Profiler and Analyzer tools. Learn how to use
-      Arm Neon intrinsics in Unity C# scripts to optimize code on Android and collect performance
-      data using Unity Profiler.
-  - question: Who is this Learning Path for?
-    answer: >-
-      Developers interested in leveraging the Unity Machine Learning Agents toolkit on Arm devices.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Basic knowledge of Unity and C#; Recent
-      Android device, such as a mobile phone or tablet; Desktop computer capable of running Unity;
-      Unity version compatible with Unity Burst compiler 1.5 or later.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Unity and C#, Android environments, and Arm platforms
-      such as armv8, aarch64, arm64, and arm architecture.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Set up, Arm Neon and the Unity Burst compiler, The
-      sample project, The plain (unoptimized) code, and The optimizations.
-# END generated_summary_faq
+
 
 author: Ben Clark, Joshua Marshall-Law
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md b/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md
index 0719c29ace..e4ca066123 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md
@@ -19,48 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 9cd19738cbe9cde618125b309bd4f2c57ad1799e807251fdaed09707bea2781d
-  summary: >-
-    Learn how to integrate Unity's Machine Learning Agents toolkit into games deployable to Arm-powered
-    Android devices. It is designed for Developers interested in leveraging the Unity Machine
-    Learning Agents toolkit on Arm devices. By the end, you will be able to get the Unity Machine
-    Learning (ML) Agents toolkit running in a game that is deployable to Arm-powered Android devices
-    and note - Instructions on how to deploy Unity games to an Arm-powered Android device and
-    how to profile them are included in separate Learning Paths. It focuses on tools and technologies
-    such as Unity, Android environments, and Arm platforms including Cortex-A. The main steps
-    cover Machine Learning in games, Install Unity and the project, The Dr Arm game, Machine Learning
-    in Unity, and The Unity project.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will get the Unity Machine Learning (ML) Agents toolkit running in a game that is deployable
-      to Arm-powered Android devices and note - Instructions on how to deploy Unity games to an
-      Arm-powered Android device and how to profile them are included in separate Learning Paths.
-      Learn how to integrate Unity's Machine Learning Agents toolkit into games deployable to
-      Arm-powered Android devices.
-  - question: Who is this Learning Path for?
-    answer: >-
-      Developers interested in leveraging the Unity Machine Learning Agents toolkit on Arm devices.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A computer capable of running Unity.
-      (Instructions are for Windows, but could be adapted to other platforms.); An Android mobile
-      device that has a 64-bit processor and supports at least Android 8.; A USB cable to connect
-      the mobile device to your computer.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Unity, Android environments, and Arm platforms such
-      as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Machine Learning in games, Install Unity and the project,
-      The Dr Arm game, Machine Learning in Unity, and The Unity project.
-# END generated_summary_faq
+
 
 author: Arm
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md b/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md
index 98201178fe..1f470f82be 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md
@@ -21,48 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: e7d95c8e7210be2fe4520fe94ccbaa3e437cd0edfa5caca549afaee22fb8b377
-  summary: >-
-    Learn how to download, convert, and deploy Vision Transformers using the Mobile Neural Network
-    framework on Android with KleidiAI micro-kernels for optimized performance. It is designed
-    for developers who want to run Vision Transformers (ViT) efficiently on Android. By the end,
-    you will be able to download a Vision Large Language Model (LLM) from Hugging Face, convert
-    the model to the Mobile Neural Network (MNN) framework, and install an Android demo application
-    using the model to run an inference. It focuses on tools and technologies such as Android
-    Studio and KleidiAI, Android environments, and Arm platforms including Cortex-A. The main
-    steps cover Background, Environment setup and prepare model, Benchmark the Vision Transformer
-    performance with KleidiAI, and Build the MNN Command-line ViT Demo.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will download a Vision Large Language Model (LLM) from Hugging Face, convert the model
-      to the Mobile Neural Network (MNN) framework, and install an Android demo application using
-      the model to run an inference. Learn how to download, convert, and deploy Vision Transformers
-      using the Mobile Neural Network framework on Android with KleidiAI micro-kernels for optimized
-      performance.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is for developers who want to run Vision Transformers (ViT) efficiently
-      on Android.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A development machine with [Android
-      Studio](https://developer.android.com/studio) installed.; A smartphone running Android with
-      support for `i8mm` and `dotprod` instructions.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Android Studio and KleidiAI, Android environments,
-      and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Background, Environment setup and prepare model, Benchmark
-      the Vision Transformer performance with KleidiAI, and Build the MNN Command-line ViT Demo.
-# END generated_summary_faq
+
 
 author:
     - Shuheng Deng
diff --git a/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md b/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md
index b5de5220cc..1b5dffac92 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md
@@ -22,52 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 192f5919261cfef88c107576dc444d2db3a07fbad98100d9c758bb75670a5a00
-  summary: >-
-    Learn how to build and optimize a multimodal Voice Assistant application on Android using
-    KleidiAI and SME2 for accelerated performance. It is designed for developers who want to implement
-    a multimodal pipeline for a Voice Assistant application and accelerate the performance on
-    Android devices using KleidiAI and SME2. By the end, you will be able to learn about the multimodal
-    Voice Assistant pipeline and different components used, learn about the functionality of ML
-    components used and how these can be built and benchmarked on various platforms, and compile
-    and run a multimodal Voice Assistant example based on Android OS. It focuses on tools and
-    technologies such as Java, Kotlin, CPP, and SME2, Android, Linux, and macOS environments,
-    and Arm platforms including Cortex-A and Arm C1. The main steps cover Set up your environment,
-    Overview, Build the Voice Assistant, Run the Voice Assistant, and KleidiAI.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will learn about the multimodal Voice Assistant pipeline and different components used,
-      learn about the functionality of ML components used and how these can be built and benchmarked
-      on various platforms, and compile and run a multimodal Voice Assistant example based on
-      Android OS. Learn how to build and optimize a multimodal Voice Assistant application on
-      Android using KleidiAI and SME2 for accelerated performance.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers who want to implement a multimodal pipeline
-      for a Voice Assistant application and accelerate the performance on Android devices using
-      KleidiAI and SME2.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Android phone that supports the i8mm
-      Arm architecture feature (8-bit integer matrix multiplication).; An Android phone with support
-      for SME (Scalable Matrix Extension) instructions, required for SME performance checking;
-      This Learning Path was tested on a Vivo X300 Pro.; A development machine with [Android Studio](https://developer.android.com/studio)
-      installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Java, Kotlin, CPP, and SME2, Android, Linux, and
-      macOS environments, and Arm platforms such as Cortex-A and Arm C1.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Set up your environment, Overview, Build the Voice
-      Assistant, Run the Voice Assistant, and KleidiAI.
-# END generated_summary_faq
+
 
 author:
     - Arnaud de Grandmaison
diff --git a/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md b/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md
index 4a727e246b..02e8c98de5 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md
@@ -23,52 +23,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 2d8fca8838e759c3098358f131fb4b0884b0d80d5fdb2fd68719bd09fa4c3d32
-  summary: >-
-    Build an end-to-end, on-device voice assistant that understands both speech and emotion using
-    Whisper, HuBERT, ONNX Runtime, and a local LLM with llama.cpp on Arm. It is designed for developers,
-    ML practitioners, and game developers interested in building on-device AI applications, including
-    voice interfaces, real-time interactions with non-player characters (NPCs), and edge AI systems
-    powered by LLMs on Arm platforms. By the end, you will be able to build a voice-to-LLM pipeline
-    using Whisper and llama.cpp, train a voice sentiment classification model using HuBERT on
-    the RAVDESS dataset, and quantize the model and convert into ONNX Runtime for on-device inference.
-    It focuses on tools and technologies such as Python, Transformers, ONNX Runtime, llama.cpp,
-    and Gradio, Linux, Windows, and macOS environments, and Arm platforms including Cortex-A.
-    The main steps cover Understand voice sentiment analysis for on-device AI, Set up your environment,
-    Build the voice-to-LLM pipeline, Train the voice sentiment classification model, and Convert
-    and quantize the model.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build a voice-to-LLM pipeline using Whisper and llama.cpp, train a voice sentiment
-      classification model using HuBERT on the RAVDESS dataset, and quantize the model and convert
-      into ONNX Runtime for on-device inference. Build an end-to-end, on-device voice assistant
-      that understands both speech and emotion using Whisper, HuBERT, ONNX Runtime, and a local
-      LLM with llama.cpp on Arm.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is for developers, ML practitioners, and game developers interested in
-      building on-device AI applications, including voice interfaces, real-time interactions with
-      non-player characters (NPCs), and edge AI systems powered by LLMs on Arm platforms.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Python 3.9 or later for programming.;
-      A working microphone for voice input.; Basic Python and command-line knowledge.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Python, Transformers, ONNX Runtime, llama.cpp, and
-      Gradio, Linux, Windows, and macOS environments, and Arm platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Understand voice sentiment analysis for on-device
-      AI, Set up your environment, Build the voice-to-LLM pipeline, Train the voice sentiment
-      classification model, and Convert and quantize the model.
-# END generated_summary_faq
+
 
 author: Bhanu Arya
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md
index 0b48eeed99..ce3dfb5ed5 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md
@@ -23,49 +23,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: af4f1c8964e0970c187643bcd0330a63a6ce66c38a2e6b4d2fc17857a12a3d7f
-  summary: >-
-    Learn how to set up ML Emulation Layers for Vulkan, run sample applications using ML extensions,
-    and debug the flow with RenderDoc. It is designed for engine developers interested in learning
-    about neural graphics using ML Extensions for Vulkan. By the end, you will be able to explain
-    the purpose of neural graphics and the role of ML Extensions for Vulkan, set up the ML Emulation
-    Layers for Vulkan to enable the extensions, and run a sample Vulkan application that uses
-    the extensions. It focuses on tools and technologies such as Vulkan, RenderDoc, and NX, Windows
-    environments, and Arm platforms including Mali. The main steps cover Run neural graphics workloads
-    with ML Extensions for Vulkan, Setting up the ML Emulation Layers for Vulkan, Simple Tensor
-    and Data Graph, Running a test with the Scenario Runner, and Use RenderDoc to debug and analyze
-    workloads.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will explain the purpose of neural graphics and the role of ML Extensions for Vulkan,
-      set up the ML Emulation Layers for Vulkan to enable the extensions, and run a sample Vulkan
-      application that uses the extensions. Learn how to set up ML Emulation Layers for Vulkan,
-      run sample applications using ML extensions, and debug the flow with RenderDoc.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for engine developers interested in learning about neural graphics
-      using ML Extensions for Vulkan.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Windows 11 development machine; Visual
-      Studio 2022; Visual Studio workload - Desktop development with C++; Visual Studio workload
-      - .NET desktop build tools.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Vulkan, RenderDoc, and NX, Windows environments,
-      and Arm platforms such as Mali.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Run neural graphics workloads with ML Extensions for
-      Vulkan, Setting up the ML Emulation Layers for Vulkan, Simple Tensor and Data Graph, Running
-      a test with the Scenario Runner, and Use RenderDoc to debug and analyze workloads.
-# END generated_summary_faq
+
 
 author: Annie Tallund
 
diff --git a/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md b/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
index e16c252ea3..79f7114113 100644
--- a/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
@@ -22,53 +22,6 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v2
-  generated_at: '2026-05-12T18:20:22Z'
-  generator: template
-  source_hash: 275878b5aa4c27dee394da12ad8b80e4c0d0235b3ddce66b1fe3f0e056ef3da9
-  summary_generated_at: '2026-05-12T18:20:22Z'
-  summary_source_hash: 275878b5aa4c27dee394da12ad8b80e4c0d0235b3ddce66b1fe3f0e056ef3da9
-  faq_generated_at: '2026-05-12T18:20:22Z'
-  faq_source_hash: 275878b5aa4c27dee394da12ad8b80e4c0d0235b3ddce66b1fe3f0e056ef3da9
-  summary: >-
-    Learn how to build and deploy an AI agent application on Arm servers using llama.cpp and llama-cpp-agent
-    with KleidiAI optimization for efficient LLM inference and function calling. It is designed
-    for software developers and ML engineers looking to deploy an optimized AI agent application.
-    By the end, you will be able to set up llama-cpp-python optimized for Arm servers, run optimized
-    Large Language Models (LLMs), and create custom functions for LLMs. It focuses on tools and
-    technologies such as Python, AWS Graviton, and AI, Linux environments, Arm platforms including
-    Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The
-    main steps cover Introduction to AI Agents and Agent Use Cases, Set Up Your Local Environment
-    to Run an AI Application, AI Agent Application, and Explore and Test Your AI Agent.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will set up llama-cpp-python optimized for Arm servers, run optimized Large Language
-      Models (LLMs), and create custom functions for LLMs. Learn how to build and deploy an AI
-      agent application on Arm servers using llama.cpp and llama-cpp-agent with KleidiAI optimization
-      for efficient LLM inference and function calling.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers and ML engineers looking to deploy
-      an optimized AI agent application.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/)
-      from a cloud service provider or an on-premise Arm server.; Basic understanding of Python
-      and prompt engineering.; Understanding of LLM fundamentals.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Python, AWS Graviton, and AI, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
-      Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Introduction to AI Agents and Agent Use Cases, Set
-      Up Your Local Environment to Run an AI Application, AI Agent Application, and Explore and
-      Test Your AI Agent.
-# END generated_summary_faq
 
 author: Andrew Choi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/aks/_index.md b/content/learning-paths/servers-and-cloud-computing/aks/_index.md
index 0fd1303021..07fb0bcbba 100644
--- a/content/learning-paths/servers-and-cloud-computing/aks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/aks/_index.md
@@ -20,49 +20,6 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v2
-  generated_at: '2026-05-12T18:20:22Z'
-  generator: template
-  source_hash: 01c5cc8930650a8d81d67d4c48b62e0f9d7b1ebfc2d09a552e11046fb982fc52
-  summary_generated_at: '2026-05-12T18:20:22Z'
-  summary_source_hash: 01c5cc8930650a8d81d67d4c48b62e0f9d7b1ebfc2d09a552e11046fb982fc52
-  faq_generated_at: '2026-05-12T18:20:22Z'
-  faq_source_hash: 01c5cc8930650a8d81d67d4c48b62e0f9d7b1ebfc2d09a552e11046fb982fc52
-  summary: >-
-    Learn how to automate the deployment of an Arm-based Kubernetes cluster on Azure AKS using
-    Terraform and deploy a sample WordPress application as a workload. It is designed for software
-    developers who want to deploy an Arm-based Kubernetes cluster using Azure Kubernetes Service
-    (AKS). By the end, you will be able to automate the deployment of an Arm-based AKS cluster
-    using Terraform and install Wordpress on AKS as an example workload. It focuses on tools and
-    technologies such as Terraform, Kubernetes, WordPress, and MySQL, Linux environments, Arm
-    platforms including Neoverse, and cloud platforms such as Microsoft Azure. The main steps
-    cover Deploy an Arm-based AKS Cluster using Terraform and Deploy a WordPress Example.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will automate the deployment of an Arm-based AKS cluster using Terraform and install
-      Wordpress on AKS as an example workload. Learn how to automate the deployment of an Arm-based
-      Kubernetes cluster on Azure AKS using Terraform and deploy a sample WordPress application
-      as a workload.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers who want to deploy an Arm-based Kubernetes
-      cluster using Azure Kubernetes Service (AKS).
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Azure account; A machine with [Terraform](/install-guides/terraform/),
-      [Azure CLI](/install-guides/azure-cli), and [Kubectl](/install-guides/kubectl/) installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Terraform, Kubernetes, WordPress, and MySQL, Linux
-      environments, Arm platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Deploy an Arm-based AKS Cluster using Terraform and
-      Deploy a WordPress Example.
-# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md b/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
index 9b654e442e..6987e6a360 100644
--- a/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
@@ -24,59 +24,6 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v2
-  generated_at: '2026-05-12T18:20:22Z'
-  generator: template
-  source_hash: 90b638385267e085ea07a70a1c23f16578aa3caaeabeca1f651bcdcac5bf38fb
-  summary_generated_at: '2026-05-12T18:20:22Z'
-  summary_source_hash: 90b638385267e085ea07a70a1c23f16578aa3caaeabeca1f651bcdcac5bf38fb
-  faq_generated_at: '2026-05-12T18:20:22Z'
-  faq_source_hash: 90b638385267e085ea07a70a1c23f16578aa3caaeabeca1f651bcdcac5bf38fb
-  summary: >-
-    Learn how to deploy Apache Arrow and Arrow Flight on Google Cloud Axion C4A processors for
-    high-throughput columnar data processing and low-latency data transport with MinIO integration.
-    It is designed for data engineers, platform engineers, and developers who aim to build high-performance
-    analytics pipelines on Arm64-based Google Cloud C4A Axion processors using Apache Arrow and
-    Arrow Flight. By the end, you will be able to deploy Apache Arrow–based data processing workloads
-    on Google Cloud C4A Axion processors, set up and run an Arrow Flight server for high-throughput,
-    low-latency data transport, and read and write columnar data formats such as Parquet and ORC
-    using Apache Arrow. It focuses on tools and technologies such as Apache Arrow, Arrow Flight,
-    Python, and MinIO, Linux environments, Arm platforms including Neoverse, and cloud platforms
-    such as Google Cloud. The main steps cover Get started with Apache Arrow and Arrow Flight
-    on Google Axion C4A, Create firewall rules on GCP for Apache Arrow, MinIO, and Arrow Flight,
-    Create a Google Axion C4A arm64 virtual machine on GCP, Set up Apache Arrow and MinIO on arm64,
-    and Analyze columnar data with Apache Arrow on arm64.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will deploy Apache Arrow–based data processing workloads on Google Cloud C4A Axion processors,
-      set up and run an Arrow Flight server for high-throughput, low-latency data transport, and
-      read and write columnar data formats such as Parquet and ORC using Apache Arrow. Learn how
-      to deploy Apache Arrow and Arrow Flight on Google Cloud Axion C4A processors for high-throughput
-      columnar data processing and low-latency data transport with MinIO integration.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for data engineers, platform engineers, and developers who
-      aim to build high-performance analytics pipelines on Arm64-based Google Cloud C4A Axion
-      processors using Apache Arrow and Arrow Flight.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
-      account with billing enabled; Basic familiarity with Python; Basic understanding of data
-      formats such as Parquet or ORC; Familiarity with Linux command-line operations.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Apache Arrow, Arrow Flight, Python, and MinIO, Linux
-      environments, Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Get started with Apache Arrow and Arrow Flight on
-      Google Axion C4A, Create firewall rules on GCP for Apache Arrow, MinIO, and Arrow Flight,
-      Create a Google Axion C4A arm64 virtual machine on GCP, Set up Apache Arrow and MinIO on
-      arm64, and Analyze columnar data with Apache Arrow on arm64.
-# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
index 8a392bff8a..8e5f15f275 100644
--- a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
@@ -22,51 +22,6 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v2
-  generated_at: '2026-05-12T18:20:22Z'
-  generator: template
-  source_hash: 964c1e6a87810dca686a7b3218433ce09d31bd92882db10af355e8c50bb6091c
-  summary_generated_at: '2026-05-12T18:20:22Z'
-  summary_source_hash: 964c1e6a87810dca686a7b3218433ce09d31bd92882db10af355e8c50bb6091c
-  faq_generated_at: '2026-05-12T18:20:22Z'
-  faq_source_hash: 964c1e6a87810dca686a7b3218433ce09d31bd92882db10af355e8c50bb6091c
-  summary: >-
-    Learn how to build llama.cpp, quantize the Arcee AFM-4.5B model, and run optimized inference
-    on AWS Graviton4 instances with perplexity-based quality evaluation. It is designed for developers
-    and ML engineers who want to deploy Arcee's AFM-4.5B small language model on AWS Graviton4
-    instances using Llama.cpp. By the end, you will be able to launch an Arm-based EC2 instance
-    on AWS Graviton4, build and install Llama.cpp from source, and download and quantize the AFM-4.5B
-    model from Hugging Face. It focuses on tools and technologies such as AWS, Hugging Face, Python,
-    and Llama.cpp, Linux environments, Arm platforms including Neoverse, and cloud platforms such
-    as AWS. The main steps cover Overview, Provision your Graviton4 environment, Configure your
-    Graviton4 environment, Build Llama.cpp, and Install Python dependencies.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will launch an Arm-based EC2 instance on AWS Graviton4, build and install Llama.cpp
-      from source, and download and quantize the AFM-4.5B model from Hugging Face. Learn how to
-      build llama.cpp, quantize the Arcee AFM-4.5B model, and run optimized inference on AWS Graviton4
-      instances with perplexity-based quality evaluation.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is for developers and ML engineers who want to deploy Arcee's AFM-4.5B
-      small language model on AWS Graviton4 instances using Llama.cpp.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An [AWS account](https://aws.amazon.com/)
-      with permission to launch Graviton4 (`c8g.4xlarge` or larger) instances; Basic familiarity
-      with Linux and SSH.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including AWS, Hugging Face, Python, and Llama.cpp, Linux
-      environments, Arm platforms such as Neoverse, and cloud platforms such as AWS.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview, Provision your Graviton4 environment, Configure
-      your Graviton4 environment, Build Llama.cpp, and Install Python dependencies.
-# END generated_summary_faq
 
 author: Julien Simon
 
diff --git a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
index 031b7b8c99..c2c34acaae 100644
--- a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
@@ -22,55 +22,6 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v2
-  generated_at: '2026-05-12T18:20:22Z'
-  generator: template
-  source_hash: b2f60d2c31ef380e6e6ef3028671ad9242dd51c98f4a192f2da32bb05b94e185
-  summary_generated_at: '2026-05-12T18:20:22Z'
-  summary_source_hash: b2f60d2c31ef380e6e6ef3028671ad9242dd51c98f4a192f2da32bb05b94e185
-  faq_generated_at: '2026-05-12T18:20:22Z'
-  faq_source_hash: b2f60d2c31ef380e6e6ef3028671ad9242dd51c98f4a192f2da32bb05b94e185
-  summary: >-
-    Learn how to build llama.cpp, quantize the Arcee AFM-4.5B model, and run optimized inference
-    on Google Cloud Axion instances with perplexity-based quality evaluation. It is designed for
-    developers and ML engineers who want to deploy Arcee's AFM-4.5B small language model on Google
-    Cloud Axion instances using Llama.cpp. By the end, you will be able to launch an Arm-based
-    Compute Engine instance on Google Cloud Axion, build and install Llama.cpp from source, and
-    download and quantize the AFM-4.5B model from Hugging Face. It focuses on tools and technologies
-    such as Google Cloud, Hugging Face, Python, and Llama.cpp, Linux environments, Arm platforms
-    including Neoverse, and cloud platforms such as Google Cloud. The main steps cover AFM-4.5B
-    deployment on Google Cloud Axion with Llama.cpp, Provision a Google Cloud Axion Arm64 environment,
-    Configure your Google Cloud Axion Arm64 environment, Build Llama.cpp on Google Cloud Axion
-    Arm64, and Install Python dependencies for Llama.cpp.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will launch an Arm-based Compute Engine instance on Google Cloud Axion, build and install
-      Llama.cpp from source, and download and quantize the AFM-4.5B model from Hugging Face. Learn
-      how to build llama.cpp, quantize the Arcee AFM-4.5B model, and run optimized inference on
-      Google Cloud Axion instances with perplexity-based quality evaluation.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is for developers and ML engineers who want to deploy Arcee's AFM-4.5B
-      small language model on Google Cloud Axion instances using Llama.cpp.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud account](https://console.cloud.google.com/)
-      with permission to launch Axion (`c4a-standard-16` or larger) instances; Basic familiarity
-      with Linux and SSH.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Google Cloud, Hugging Face, Python, and Llama.cpp,
-      Linux environments, Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around AFM-4.5B deployment on Google Cloud Axion with Llama.cpp,
-      Provision a Google Cloud Axion Arm64 environment, Configure your Google Cloud Axion Arm64
-      environment, Build Llama.cpp on Google Cloud Axion Arm64, and Install Python dependencies
-      for Llama.cpp.
-# END generated_summary_faq
 
 author: Julien Simon
 
diff --git a/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
index 83904bafcb..9e34a8506b 100644
--- a/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
@@ -26,60 +26,6 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v2
-  generated_at: '2026-05-12T18:20:22Z'
-  generator: template
-  source_hash: c6106f21859f5a8e04bbd579db47c7177bb5371d4c7f306054e56e81ed9e89a1
-  summary_generated_at: '2026-05-12T18:20:22Z'
-  summary_source_hash: c6106f21859f5a8e04bbd579db47c7177bb5371d4c7f306054e56e81ed9e89a1
-  faq_generated_at: '2026-05-12T18:20:22Z'
-  faq_source_hash: c6106f21859f5a8e04bbd579db47c7177bb5371d4c7f306054e56e81ed9e89a1
-  summary: >-
-    Learn how to deploy and manage applications on Google Cloud GKE Arm64 clusters using Argo
-    CD GitOps workflows with automated sync and self-healing on Axion processors. It is designed
-    for developers and platform engineers who want hands-on experience implementing GitOps using
-    Argo CD on Arm64-based Google Kubernetes Engine (GKE) clusters running on Google Axion (C4A)
-    processors. By the end, you will be able to provision an Arm-based SUSE Linux Enterprise Server
-    (SLES) virtual machine on Google Cloud (C4A with Axion processors), create and connect to
-    a Google Kubernetes Engine (GKE) cluster running on Arm64 (Axion) nodes, and install and validate
-    Argo CD on an Arm-based GKE cluster. It focuses on tools and technologies such as Argo CD,
-    Kubernetes, kubectl, GKE, and Git, Linux environments, Arm platforms including Neoverse, and
-    cloud platforms such as Google Cloud. The main steps cover Get started with Argo CD on Google
-    Axion C4A (Arm-based), Create a Google Axion C4A virtual machine on Google Cloud, Prepare
-    a GKE cluster for Argo CD deployments, Install and access Argo CD on Arm64 GKE, and Deploy
-    applications using GitOps with Argo CD.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will provision an Arm-based SUSE Linux Enterprise Server (SLES) virtual machine on Google
-      Cloud (C4A with Axion processors), create and connect to a Google Kubernetes Engine (GKE)
-      cluster running on Arm64 (Axion) nodes, and install and validate Argo CD on an Arm-based
-      GKE cluster. Learn how to deploy and manage applications on Google Cloud GKE Arm64 clusters
-      using Argo CD GitOps workflows with automated sync and self-healing on Axion processors.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers and platform engineers who want hands-on experience
-      implementing GitOps using Argo CD on Arm64-based Google Kubernetes Engine (GKE) clusters
-      running on Google Axion (C4A) processors.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
-      account with billing enabled; Basic familiarity with [Kubernetes concepts](https://kubernetes.io/docs/concepts/);
-      Basic understanding of Git and GitHub workflows; Familiarity with basic Linux command-line
-      usage.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Argo CD, Kubernetes, kubectl, GKE, and Git, Linux
-      environments, Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Get started with Argo CD on Google Axion C4A (Arm-based),
-      Create a Google Axion C4A virtual machine on Google Cloud, Prepare a GKE cluster for Argo
-      CD deployments, Install and access Argo CD on Arm64 GKE, and Deploy applications using GitOps
-      with Argo CD.
-# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md b/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
index b2fb23a1b5..2d072d1766 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
@@ -20,51 +20,6 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v2
-  generated_at: '2026-05-12T18:20:22Z'
-  generator: template
-  source_hash: 3a4bc8b2ab548be507b6d528844b0593b27f2dab3ab3c9649e807abdf4887ce0
-  summary_generated_at: '2026-05-12T18:20:22Z'
-  summary_source_hash: 3a4bc8b2ab548be507b6d528844b0593b27f2dab3ab3c9649e807abdf4887ce0
-  faq_generated_at: '2026-05-12T18:20:22Z'
-  faq_source_hash: 3a4bc8b2ab548be507b6d528844b0593b27f2dab3ab3c9649e807abdf4887ce0
-  summary: >-
-    Learn how to write correct concurrent C++ code when porting applications from x86 to Arm by
-    understanding memory ordering differences and using best practices to avoid race conditions.
-    It is designed for C++ developers porting applications from x86 to Arm and optimizing performance.
-    By the end, you will be able to describe at a high level what a memory model does, and the
-    types of memory ordering, describe the differences between the Arm and x86 memory model, and
-    employ best practices for writing C++ on Arm to avoid race conditions. It focuses on tools
-    and technologies such as CPP, TSan, and Runbook, Linux environments, and Arm platforms including
-    Neoverse. The main steps cover Introduction to C++ Memory Models, The C++ Memory Model and
-    Atomics, Walk through a Race condition example, and Detecting race conditions.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will describe at a high level what a memory model does, and the types of memory ordering,
-      describe the differences between the Arm and x86 memory model, and employ best practices
-      for writing C++ on Arm to avoid race conditions. Learn how to write correct concurrent C++
-      code when porting applications from x86 to Arm by understanding memory ordering differences
-      and using best practices to avoid race conditions.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for C++ developers porting applications from x86 to Arm and optimizing
-      performance.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Access to an x86 and an Arm cloud instance
-      (virtual machine).; Proficiency in C++ programming.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including CPP, TSan, and Runbook, Linux environments, and
-      Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Introduction to C++ Memory Models, The C++ Memory
-      Model and Atomics, Walk through a Race condition example, and Detecting race conditions.
-# END generated_summary_faq
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md b/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
index 874faf87e3..a5c9d16fb5 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
@@ -23,55 +23,6 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v2
-  generated_at: '2026-05-12T18:20:22Z'
-  generator: template
-  source_hash: b870ac5160d35ddb51955ca8379493e172787ced8125cde8ae79f7700e653a87
-  summary_generated_at: '2026-05-12T18:20:22Z'
-  summary_source_hash: b870ac5160d35ddb51955ca8379493e172787ced8125cde8ae79f7700e653a87
-  faq_generated_at: '2026-05-12T18:20:22Z'
-  faq_source_hash: b870ac5160d35ddb51955ca8379493e172787ced8125cde8ae79f7700e653a87
-  summary: >-
-    Learn how to automate x86-to-Arm application migration using the Arm MCP Server, with AI-assisted
-    compatibility checks, C++ code refactoring, and Docker-based validation on Arm cloud platforms.
-    It is designed for developers who want to use AI-powered tools to migrate x86 applications
-    to Arm-based cloud instances. By the end, you will be able to explain how the Arm MCP Server
-    enables AI-driven x86-to-Arm migration workflows, use AI-assisted checks to inspect Docker
-    images for Arm compatibility, and set up and use the Arm Cloud Migration Agent in GitHub Copilot
-    to automate x86-to-Arm code migration. It focuses on tools and technologies such as MCP, Docker,
-    CPP, and GitHub Copilot, Linux environments, and Arm platforms including Neoverse. The main
-    steps cover Understand the Arm MCP Server for AI-driven x86-to-Arm migration, Verify Docker
-    image compatibility with Arm using AI, Arm Cloud Migration Agent in GitHub Copilot, and Configure
-    other AI agents to automate Arm migration workflows.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will explain how the Arm MCP Server enables AI-driven x86-to-Arm migration workflows,
-      use AI-assisted checks to inspect Docker images for Arm compatibility, and set up and use
-      the Arm Cloud Migration Agent in GitHub Copilot to automate x86-to-Arm code migration. Learn
-      how to automate x86-to-Arm application migration using the Arm MCP Server, with AI-assisted
-      compatibility checks, C++ code refactoring, and Docker-based validation on Arm cloud platforms.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for developers who want to use AI-powered tools to migrate x86
-      applications to Arm-based cloud instances.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An AI-powered IDE such as VS Code, Copilot
-      in VS Code, Kiro (IDE or CLI) or Codex; Basic familiarity with Docker and C/C++ development;
-      Access to an Arm-based cloud instance or local Arm computer running Linux or macOS.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including MCP, Docker, CPP, and GitHub Copilot, Linux environments,
-      and Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Understand the Arm MCP Server for AI-driven x86-to-Arm
-      migration, Verify Docker image compatibility with Arm using AI, Arm Cloud Migration Agent
-      in GitHub Copilot, and Configure other AI agents to automate Arm migration workflows.
-# END generated_summary_faq
 
 author: Joe Stech
 
diff --git a/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md b/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
index 276a92445b..c5d36a031e 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
@@ -24,61 +24,6 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v2
-  generated_at: '2026-05-12T18:20:22Z'
-  generator: template
-  source_hash: 49b3f2b1464efb20f0c8fbcff46e9f30910d856567ca01c01dc348aa1c5740d1
-  summary_generated_at: '2026-05-12T18:20:22Z'
-  summary_source_hash: 49b3f2b1464efb20f0c8fbcff46e9f30910d856567ca01c01dc348aa1c5740d1
-  faq_generated_at: '2026-05-12T18:20:22Z'
-  faq_source_hash: 49b3f2b1464efb20f0c8fbcff46e9f30910d856567ca01c01dc348aa1c5740d1
-  summary: >-
-    Learn how to migrate C applications between Arm platforms using Kiro's AI-assisted tooling
-    to identify hardware dependencies and implement abstraction layers for cross-platform compatibility.
-    It is designed for experienced developers who need to migrate applications between Arm-based
-    platforms using AI-assisted tooling. You will work through a structured, repeatable migration
-    workflow using Kiro Arm SoC Migration Power, moving an application from AWS Graviton3 (Neoverse)
-    to Raspberry Pi 5 (Cortex-A). The techniques apply broadly to cloud-to-edge and cross-architecture
-    migrations across the Arm ecosystem. By the end, you will be able to install and configure
-    Kiro Arm SoC Migration Power, apply a structured migration workflow across Arm platforms,
-    and identify platform-specific and hardware-dependent code using AI-guided analysis. It focuses
-    on tools and technologies such as Kiro, AWS EC2, GCC, C, and CMake, Linux environments, Arm
-    platforms including Neoverse and Cortex-A, and cloud platforms such as AWS, Microsoft Azure,
-    Google Cloud, and Oracle. The main steps cover Install Arm SoC Migration Power, Develop on
-    source platform, Migrate using Arm SoC Migration Power, and Validate migration with testing.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install and configure Kiro Arm SoC Migration Power, apply a structured migration
-      workflow across Arm platforms, and identify platform-specific and hardware-dependent code
-      using AI-guided analysis. Learn how to migrate C applications between Arm platforms using
-      Kiro's AI-assisted tooling to identify hardware dependencies and implement abstraction layers
-      for cross-platform compatibility.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for experienced developers who need to migrate applications between
-      Arm-based platforms using AI-assisted tooling. You will work through a structured, repeatable
-      migration workflow using Kiro Arm SoC Migration Power, moving an application from AWS Graviton3
-      (Neoverse) to Raspberry Pi 5 (Cortex-A). The techniques apply broadly to cloud-to-edge and
-      cross-architecture migrations across the Arm ecosystem.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Access to both source and target Arm
-      platforms (for example, AWS Graviton3 and Raspberry Pi 5); Working knowledge of C programming;
-      Familiarity with Linux development environments and basic embedded or cloud deployment concepts;
-      Experience building applications with GCC and CMake.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Kiro, AWS EC2, GCC, C, and CMake, Linux environments,
-      Arm platforms such as Neoverse and Cortex-A, and cloud platforms such as AWS, Microsoft
-      Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Install Arm SoC Migration Power, Develop on source
-      platform, Migrate using Arm SoC Migration Power, and Validate migration with testing.
-# END generated_summary_faq
 
 author: Daniel Schleicher
 
diff --git a/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md b/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
index bab06b2e72..60770bc549 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
@@ -22,51 +22,6 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v2
-  generated_at: '2026-05-12T18:20:22Z'
-  generator: template
-  source_hash: 67004eb9a75926144eb6f75124104972b3cf20a57a22b698c9359d20db3eca02
-  summary_generated_at: '2026-05-12T18:20:22Z'
-  summary_source_hash: 67004eb9a75926144eb6f75124104972b3cf20a57a22b698c9359d20db3eca02
-  faq_generated_at: '2026-05-12T18:20:22Z'
-  faq_source_hash: 67004eb9a75926144eb6f75124104972b3cf20a57a22b698c9359d20db3eca02
-  summary: >-
-    Learn how to install and configure a Linux kernel with 64K page size support on Arm systems
-    to improve memory efficiency and performance for memory-intensive workloads. It is designed
-    for developers who want to modify the Linux kernel page size on Arm-based systems to improve
-    performance for memory-intensive workloads. By the end, you will be able to explain the differences
-    in page size configuration between Arm64 and x86 architectures, understand how page size affects
-    memory efficiency and system performance, and check the current memory page size on an Arm-based
-    Linux system. It focuses on tools and technologies such as bash, Linux environments, and Arm
-    platforms including Neoverse. The main steps cover Overview, Change page size on Ubuntu, Change
-    page size on Debian, and Change page size on CentOS.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will explain the differences in page size configuration between Arm64 and x86 architectures,
-      understand how page size affects memory efficiency and system performance, and check the
-      current memory page size on an Arm-based Linux system. Learn how to install and configure
-      a Linux kernel with 64K page size support on Arm systems to improve memory efficiency and
-      performance for memory-intensive workloads.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers who want to modify the Linux kernel page size
-      on Arm-based systems to improve performance for memory-intensive workloads.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Access to an Arm-based Linux system
-      running Ubuntu, Debian, or CentOS.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including bash, Linux environments, and Arm platforms such
-      as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview, Change page size on Ubuntu, Change page
-      size on Debian, and Change page size on CentOS.
-# END generated_summary_faq
 
 author: Geremy Cohen
 
diff --git a/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md b/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
index f643feed3a..b17b8c2daa 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
@@ -19,52 +19,6 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v2
-  generated_at: '2026-05-08T18:10:01Z'
-  generator: template
-  source_hash: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
-  summary_generated_at: '2026-05-08T18:10:01Z'
-  summary_source_hash: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
-  faq_generated_at: '2026-05-08T18:10:01Z'
-  faq_source_hash: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
-  summary: >-
-    Learn how to access and use Arm hardware performance counters and the system counter from
-    user space using PAPI, perf_event_open, and assembly code for performance instrumentation.
-    It is designed for software developers who want to instrument hardware event counters or the
-    system counter in software applications. By the end, you will be able to understand different
-    options for accessing counters from user space, use the system counter to measure time in
-    code, and use PAPI to instrument event counters in code. It focuses on tools and technologies
-    such as PAPI, perf, Assembly, GCC, and Runbook, Linux environments, and Arm platforms including
-    Neoverse. The main steps cover Counter access options, Use a system counter, Use PAPI for
-    counting, and Use perf_event_open for counting.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand different options for accessing counters from user space, use the system
-      counter to measure time in code, and use PAPI to instrument event counters in code. Learn
-      how to access and use Arm hardware performance counters and the system counter from user
-      space using PAPI, perf_event_open, and assembly code for performance instrumentation.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers who want to instrument hardware event
-      counters or the system counter in software applications.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm computer running Linux. A bare
-      metal or cloud metal instance is best because they expose more counters. You can use a virtual
-      machine (VM), but fewer counters may be available. These instructions have been tested on
-      the `a1.metal` instance type.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including PAPI, perf, Assembly, GCC, and Runbook, Linux environments,
-      and Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Counter access options, Use a system counter, Use
-      PAPI for counting, and Use perf_event_open for counting.
-# END generated_summary_faq
 
 author: Julio Suarez
 
diff --git a/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md b/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
index 41c1eef932..4a98a1aa32 100644
--- a/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
@@ -19,45 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: ced3882cf8be11a55304d2697f450a2c862a81d87317ab42298148755e9f272c
-  summary: >-
-    Learn how to package multi-architecture container applications and deploy them on AWS Fargate
-    with Graviton processors using the AWS Copilot CLI. It is designed for software developers
-    who want to learn how to use the command line to deploy Arm containers on AWS Fargate. By
-    the end, you will be able to package applications using a multi-architecture containers, deploy
-    containers on AWS Fargate with the AWS Copilot CLI, and configure Copilot to use AWS Graviton
-    processors. It focuses on tools and technologies such as Docker, Linux environments, Arm platforms
-    including Neoverse, and cloud platforms such as AWS. The main steps cover Containerize an
-    example application and Deploy with Copilot.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will package applications using a multi-architecture containers, deploy containers on
-      AWS Fargate with the AWS Copilot CLI, and configure Copilot to use AWS Graviton processors.
-      Learn how to package multi-architecture container applications and deploy them on AWS Fargate
-      with Graviton processors using the AWS Copilot CLI.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to learn how to use the command
-      line to deploy Arm containers on AWS Fargate.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Amazon Web Services (AWS) account;
-      A local computer with Docker, AWS CLI, and AWS Copilot CLI installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Docker, Linux environments, Arm platforms such as
-      Neoverse, and cloud platforms such as AWS.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Containerize an example application and Deploy with
-      Copilot.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md b/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
index 78511f0e12..50baf3c248 100644
--- a/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
@@ -19,47 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 087a4d56914e5b53cec5ad17e8d682e72d04ba6b394237c081efe4a3f939e04e
-  summary: >-
-    Learn how to automate the creation and deployment of AWS Graviton instances using Terraform
-    with jump server access for secure infrastructure management. It is designed for software
-    developers who are new to deploying Arm instances on AWS using Terraform. By the end, you
-    will be able to automate AWS EC2 instance creation using Terraform, deploy Arm instances on
-    AWS and provide access via Jump Server, and provide infrastructure basics, code knowledge
-    and files that could help with future learning paths. It focuses on tools and technologies
-    such as Terraform and Bastion, Linux environments, Arm platforms including Neoverse, and cloud
-    platforms such as AWS. The main steps cover Automate AWS instance creation using Terraform
-    and Deploy Arm instances on AWS and provide access via Jump Server.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will automate AWS EC2 instance creation using Terraform, deploy Arm instances on AWS
-      and provide access via Jump Server, and provide infrastructure basics, code knowledge and
-      files that could help with future learning paths. Learn how to automate the creation and
-      deployment of AWS Graviton instances using Terraform with jump server access for secure
-      infrastructure management.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who are new to deploying Arm instances
-      on AWS using Terraform.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Amazon Web Services (AWS) [account](https://aws.amazon.com/);
-      A computer with [Terraform](/install-guides/terraform) installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Terraform and Bastion, Linux environments, Arm platforms
-      such as Neoverse, and cloud platforms such as AWS.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Automate AWS instance creation using Terraform and
-      Deploy Arm instances on AWS and provide access via Jump Server.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
index 20477665db..d4b9364d2a 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
@@ -21,51 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 1682c0527bb49c417263286e2aba7c82fb9a27fa315ecc6b9ca10978b69cc236
-  summary: >-
-    Learn how to create and deploy Azure Resource Manager templates to provision Arm64-based Cobalt
-    100 virtual machines on Azure using the Azure CLI. It is designed for developers and DevOps
-    engineers who want to automate the deployment of Arm-based Azure Cobalt 100 virtual machines
-    using Azure Resource Manager templates. By the end, you will be able to structure an Azure
-    Resource Manager template with parameters, variables, and resources, specify Arm64 architecture
-    and Cobalt 100 Azure VM sizes, and deploy the template using Azure CLI and verify the deployment.
-    It focuses on tools and technologies such as Azure CLI and JSON, Linux environments, Arm platforms
-    including Neoverse, and cloud platforms such as Microsoft Azure. The main steps cover Getting
-    started with Azure Resource Manager, Create the Azure Resource Manager template, Deploy the
-    Azure Resource Manager template, and Connect to the Cobalt 100 VM and verify Arm64 architecture.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will structure an Azure Resource Manager template with parameters, variables, and resources,
-      specify Arm64 architecture and Cobalt 100 Azure VM sizes, and deploy the template using
-      Azure CLI and verify the deployment. Learn how to create and deploy Azure Resource Manager
-      templates to provision Arm64-based Cobalt 100 virtual machines on Azure using the Azure
-      CLI.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers and DevOps engineers who want to automate the
-      deployment of Arm-based Azure Cobalt 100 virtual machines using Azure Resource Manager templates.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Azure subscription with permissions
-      to create resource groups, virtual machines, and networking resources; Azure CLI installed
-      on your local machine - see the [Azure CLI install guide](/install-guides/azure-cli/); An
-      SSH key pair for authentication.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Azure CLI and JSON, Linux environments, Arm platforms
-      such as Neoverse, and cloud platforms such as Microsoft Azure.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Getting started with Azure Resource Manager, Create
-      the Azure Resource Manager template, Deploy the Azure Resource Manager template, and Connect
-      to the Cobalt 100 VM and verify Arm64 architecture.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
index f20928060c..7942211dfb 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
@@ -20,49 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: b5bd3abde8d9dd3146e0f11f4819ac510417ad7f707b4d0970c02fd63801b358
-  summary: >-
-    Learn how to configure a self-hosted GitHub runner on Azure Cobalt 100, create an AKS cluster
-    with Terraform, and deploy a .NET application using GitHub Actions CI/CD. It is designed for
-    software developers who want to develop cloud-native applications using GitHub Actions and
-    Azure Kubernetes Service (AKS), and run them on Microsoft Azure Cobalt 100 VMs. By the end,
-    you will be able to configure an Azure Cobalt 100 VM as a self-hosted GitHub runner, create
-    an AKS cluster with Arm-based Azure Cobalt 100 nodes using Terraform, and deploy a .NET application
-    to AKS with GitHub Actions using the self-hosted Arm64-based runner. It focuses on tools and
-    technologies such as .NET, Kubernetes, and Docker, Linux environments, Arm platforms including
-    Neoverse, and cloud platforms such as Microsoft Azure. The main steps cover Background and
-    Build and deploy a .NET application.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will configure an Azure Cobalt 100 VM as a self-hosted GitHub runner, create an AKS
-      cluster with Arm-based Azure Cobalt 100 nodes using Terraform, and deploy a .NET application
-      to AKS with GitHub Actions using the self-hosted Arm64-based runner. Learn how to configure
-      a self-hosted GitHub runner on Azure Cobalt 100, create an AKS cluster with Terraform, and
-      deploy a .NET application using GitHub Actions CI/CD.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers who want to develop cloud-native applications
-      using GitHub Actions and Azure Kubernetes Service (AKS), and run them on Microsoft Azure
-      Cobalt 100 VMs.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Microsoft Azure account.; A GitHub
-      account.; A machine with [Terraform](/install-guides/terraform/),[Azure CLI](/install-guides/azure-cli),
-      and [Kubectl](/install-guides/kubectl/) installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including .NET, Kubernetes, and Docker, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Background and Build and deploy a .NET application.
-# END generated_summary_faq
+
 
 author: Pranay Bakre
 
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
index 4028d5cbae..7011a8df88 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
@@ -20,45 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 6713af3d70887933cf54c7fa36bdc88d71a51fa34fb29a4ce90636ff1de624c7
-  summary: >-
-    Learn how to automate the creation of Azure Arm virtual machines using Terraform. It is designed
-    for software developers who are new to deploying Arm instances on Azure using Terraform. By
-    the end, you will be able to automate Arm virtual machine creation using Terraform, deploy
-    Arm VMs on Azure and provide access via Jump Server, and provide infrastructure basics, code
-    knowledge and files that could help with future learning paths. It focuses on tools and technologies
-    such as Terraform and Bastion, Linux environments, Arm platforms including Neoverse, and cloud
-    platforms such as Microsoft Azure. The main steps cover Automate Azure VM creation with Terraform
-    and Deploy Arm VMs on Azure and provide access via Jump Server.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will automate Arm virtual machine creation using Terraform, deploy Arm VMs on Azure
-      and provide access via Jump Server, and provide infrastructure basics, code knowledge and
-      files that could help with future learning paths. Learn how to automate the creation of
-      Azure Arm virtual machines using Terraform.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who are new to deploying Arm instances
-      on Azure using Terraform.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Azure account; A computer with Terraform
-      installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Terraform and Bastion, Linux environments, Arm platforms
-      such as Neoverse, and cloud platforms such as Microsoft Azure.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Automate Azure VM creation with Terraform and Deploy
-      Arm VMs on Azure and provide access via Jump Server.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
index 88e33bb768..8f09adacf2 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
@@ -24,49 +24,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 413467e581e3561425229d0f6118975dbb596d6a9494544a54f0b9ab22c1b89d
-  summary: >-
-    Learn how to create a custom Azure Linux 3.0 VM image using QEMU, upload it to Azure Shared
-    Image Gallery, and deploy it on Arm-based Cobalt 100 processors. It is designed for developers
-    who want to run Azure Linux 3.0 on Arm-based Cobalt 100 processors in a custom virtual machine.
-    By the end, you will be able to use QEMU to create a raw disk image, boot a virtual machine
-    using an AArch64 ISO and install Azure Linux 3.0, and convert the raw disk image to VHD format.
-    It focuses on tools and technologies such as QEMU and Azure CLI, Linux environments, Arm platforms
-    including Neoverse, and cloud platforms such as Microsoft Azure. The main steps cover Build
-    and run Azure Linux 3.0 on an Arm-based Azure virtual machine, Create an Azure Linux image
-    for Arm, Transfer the image to Azure, and Start an Azure virtual machine with the new image.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will use QEMU to create a raw disk image, boot a virtual machine using an AArch64 ISO
-      and install Azure Linux 3.0, and convert the raw disk image to VHD format. Learn how to
-      create a custom Azure Linux 3.0 VM image using QEMU, upload it to Azure Shared Image Gallery,
-      and deploy it on Arm-based Cobalt 100 processors.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for developers who want to run Azure Linux 3.0 on Arm-based Cobalt
-      100 processors in a custom virtual machine.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Microsoft Azure](https://azure.microsoft.com/)
-      account with permission to create resources, including instances using Cobalt 100 processors;
-      A Linux machine with [QEMU](https://www.qemu.org/download/) and the [Azure CLI](/install-guides/azure-cli/)
-      installed and authenticated.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including QEMU and Azure CLI, Linux environments, Arm platforms
-      such as Neoverse, and cloud platforms such as Microsoft Azure.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Build and run Azure Linux 3.0 on an Arm-based Azure
-      virtual machine, Create an Azure Linux image for Arm, Transfer the image to Azure, and Start
-      an Azure virtual machine with the new image.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md b/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
index cc6f7b5204..28eaf31203 100644
--- a/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
@@ -18,52 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: a4cf1d9161b3a32e29694415762eda419752e1c3144662d5e131b6553f0a58e3
-  summary: >-
-    Learn how to deploy and accelerate PyTorch NLP sentiment analysis models from Hugging Face
-    on Arm servers with BFloat16 fast math kernel optimization on Graviton3 processors. It is
-    designed for software developers who want to learn how to run and accelerate the performance
-    of Natural Language Processing (NLP) models on Arm-based servers. By the end, you will be
-    able to deploy PyTorch NLP Sentiment Analysis models from Hugging Face on Arm servers, evaluate
-    the performance of three NLP models using the Sentiment Analysis pipeline, and measure the
-    performance uplift of these models by enabling support for BFloat16 fast math kernels on Arm
-    Neoverse-based AWS Graviton3 Processors. It focuses on tools and technologies such as Python,
-    PyTorch, and Hugging Face, Linux environments, Arm platforms including Neoverse, and cloud
-    platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps cover Measure
-    and accelerate the performance of Natural Language Processing (NLP) models from Hugging Face
-    on Arm servers.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will deploy PyTorch NLP Sentiment Analysis models from Hugging Face on Arm servers,
-      evaluate the performance of three NLP models using the Sentiment Analysis pipeline, and
-      measure the performance uplift of these models by enabling support for BFloat16 fast math
-      kernels on Arm Neoverse-based AWS Graviton3 Processors. Learn how to deploy and accelerate
-      PyTorch NLP sentiment analysis models from Hugging Face on Arm servers with BFloat16 fast
-      math kernel optimization on Graviton3 processors.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to learn how to run and accelerate
-      the performance of Natural Language Processing (NLP) models on Arm-based servers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/)
-      from a cloud service provider or an on-premise Arm server.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Python, PyTorch, and Hugging Face, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
-      Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Measure and accelerate the performance of Natural
-      Language Processing (NLP) models from Hugging Face on Arm servers.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md b/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
index 1b206a68ce..d96fd5d448 100644
--- a/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
@@ -21,51 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 1701b37580fe5d012a5e6fd322307742656a748dfb766fd48914011167386e95
-  summary: >-
-    Learn how to implement and benchmark bitmap scanning operations for database workloads using
-    scalar, Neon, and SVE instructions on Arm-based cloud instances. It is designed for database
-    developers, performance engineers, and anyone interested in optimizing data processing workloads
-    on Arm-based cloud instances. By the end, you will be able to understand bitmap scanning operations
-    in database systems, implement bitmap scanning with scalar, Neon, and SVE instructions, and
-    compare performance between different implementations. It focuses on tools and technologies
-    such as SVE, Neon, and Runbook, Linux environments, Arm platforms including Neoverse, and
-    cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps cover
-    Optimize bitmap scanning in databases with SVE and Neon on Arm servers, Build and manage a
-    bit vector in C, Implement scalar bitmap scanning in C, Vectorized bitmap scanning with Neon
-    and SVE, and Benchmarking bitmap scanning across implementations.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand bitmap scanning operations in database systems, implement bitmap scanning
-      with scalar, Neon, and SVE instructions, and compare performance between different implementations.
-      Learn how to implement and benchmark bitmap scanning operations for database workloads using
-      scalar, Neon, and SVE instructions on Arm-based cloud instances.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for database developers, performance engineers, and anyone
-      interested in optimizing data processing workloads on Arm-based cloud instances.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/)
-      from an appropriate cloud service provider.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including SVE, Neon, and Runbook, Linux environments, Arm
-      platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud,
-      and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Optimize bitmap scanning in databases with SVE and
-      Neon on Arm servers, Build and manage a bit vector in C, Implement scalar bitmap scanning
-      in C, Vectorized bitmap scanning with Neon and SVE, and Benchmarking bitmap scanning across
-      implementations.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md b/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
index bab292ec40..894420b6d4 100644
--- a/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
@@ -27,54 +27,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 8654b656131bf1d529e11d85f874f7a81c01f7207340d2a606b6fd2d80bfad04
-  summary: >-
-    Learn how to identify optimization candidates and apply LLVM BOLT post-link optimization to
-    AArch64 binaries using BRBE, SPE, instrumentation, and PMU profiling techniques. It is designed
-    for developers who have compiled an AArch64 Linux application and want to evaluate whether
-    LLVM BOLT can improve its runtime performance. By the end, you will be able to identify whether
-    a program is a good candidate for code layout optimization, install LLVM BOLT on Linux, and
-    use LLVM BOLT to perform profile-guided post-link optimization of an AArch64 binary with poor
-    spatial locality. It focuses on tools and technologies such as BOLT and perf, Linux environments,
-    and Arm platforms including Neoverse and Cortex-A. The main steps cover Understand BOLT optimization
-    for Arm, Install BOLT on Linux, Prepare your environment, Identify programs for BOLT optimization,
-    and Optimize with BRBE profiling.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will identify whether a program is a good candidate for code layout optimization, install
-      LLVM BOLT on Linux, and use LLVM BOLT to perform profile-guided post-link optimization of
-      an AArch64 binary with poor spatial locality. Learn how to identify optimization candidates
-      and apply LLVM BOLT post-link optimization to AArch64 binaries using BRBE, SPE, instrumentation,
-      and PMU profiling techniques.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers who have compiled an AArch64 Linux application
-      and want to evaluate whether LLVM BOLT can improve its runtime performance.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An AArch64 system running Linux with
-      [perf](/install-guides/perf/) installed; Linux kernel version 6.17 or later to enable Branch
-      Record Buffer Extension ([BRBE profiling](/learning-paths/servers-and-cloud-computing/bolt-demo/brbe/));
-      Linux kernel version 6.14 or later for Arm Statistical Profiling Extension ([SPE profiling](/learning-paths/servers-and-cloud-computing/bolt-demo/spe/));
-      GCC version 13.3 or later to compile the example program ([GCC](/install-guides/gcc/) );
-      A system with with sufficient hardware performance counters to use the [TopDown](/install-guides/topdown-tool)
-      methodology. This typically requires running on bare metal rather than a virtualized environment.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including BOLT and perf, Linux environments, and Arm platforms
-      such as Neoverse and Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Understand BOLT optimization for Arm, Install BOLT
-      on Linux, Prepare your environment, Identify programs for BOLT optimization, and Optimize
-      with BRBE profiling.
-# END generated_summary_faq
+
 
 author: Paschalis Mpeis
 
diff --git a/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md b/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
index 79e89fa11c..85b99e740d 100644
--- a/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
@@ -20,49 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 84a8b96fe7df302e0a2a6e4645bbb6170b45a3e0b55e0ea3682ec47663d34819
-  summary: >-
-    Learn how to optimize Arm application binaries and shared libraries using BOLT profile instrumentation,
-    merge multiple profiles for improved coverage, and integrate optimized libraries. It is designed
-    for performance engineers and software developers targeting Arm platforms who want to optimize
-    application binaries and shared libraries using BOLT. By the end, you will be able to instrument
-    and optimize application binaries for individual workload features using BOLT, collect and
-    merge separate BOLT profiles to improve code coverage, and optimize shared libraries independently
-    of application binaries. It focuses on tools and technologies such as BOLT, perf, and Runbook,
-    Linux environments, and Arm platforms including Neoverse and Cortex-A. The main steps cover
-    Overview, Instrument MySQL with BOLT, Run a new workload using BOLT and merge the results,
-    Instrument shared libraries with BOLT, and Review the performance results.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will instrument and optimize application binaries for individual workload features using
-      BOLT, collect and merge separate BOLT profiles to improve code coverage, and optimize shared
-      libraries independently of application binaries. Learn how to optimize Arm application binaries
-      and shared libraries using BOLT profile instrumentation, merge multiple profiles for improved
-      coverage, and integrate optimized libraries.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for performance engineers and software developers targeting Arm
-      platforms who want to optimize application binaries and shared libraries using BOLT.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm-based Linux system with [BOLT](/install-guides/bolt/)
-      and [Linux Perf](/install-guides/perf/) installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including BOLT, perf, and Runbook, Linux environments, and
-      Arm platforms such as Neoverse and Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview, Instrument MySQL with BOLT, Run a new workload
-      using BOLT and merge the results, Instrument shared libraries with BOLT, and Review the
-      performance results.
-# END generated_summary_faq
+
 
 author: Gayathri Narayana Yegna Narayanan
 
diff --git a/content/learning-paths/servers-and-cloud-computing/bolt/_index.md b/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
index 0a6cbb0c8d..00f5a1c11d 100644
--- a/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
@@ -19,54 +19,6 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v2
-  generated_at: '2026-05-08T18:10:01Z'
-  generator: template
-  source_hash: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
-  summary_generated_at: '2026-05-08T18:10:01Z'
-  summary_source_hash: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
-  faq_generated_at: '2026-05-08T18:10:01Z'
-  faq_source_hash: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
-  summary: >-
-    Learn how to build, profile, and optimize Arm executables using BOLT post-link binary optimization
-    to improve application performance through code layout improvements. It is designed for software
-    developers who want to learn how to use BOLT on an Arm executable. By the end, you will be
-    able to build an application which is ready to be optimized by BOLT, profile an application
-    and collect performance information, and run BOLT to create an optimized executable. It focuses
-    on tools and technologies such as BOLT, perf, and Runbook, Linux environments, and Arm platforms
-    including Neoverse and Cortex-A. The main steps cover Overview of the BOLT optimization process,
-    Prepare your BOLT environment, Use BOLT with Samples, Use BOLT with ETM, and Use BOLT with
-    SPE.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build an application which is ready to be optimized by BOLT, profile an application
-      and collect performance information, and run BOLT to create an optimized executable. Learn
-      how to build, profile, and optimize Arm executables using BOLT post-link binary optimization
-      to improve application performance through code layout improvements.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to learn how to use BOLT
-      on an Arm executable.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm based system running Linux with
-      [BOLT](/install-guides/bolt/) and [Linux Perf](/install-guides/perf/) installed. The Linux
-      kernel should be version 5.15 or later. Earlier kernel versions can be used, but some Linux
-      Perf features may be limited or not available. For [SPE](./bolt-spe) the version should
-      be 6.14 or later.; (Optional) A second, more powerful Linux system to build the software
-      executable and run BOLT.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including BOLT, perf, and Runbook, Linux environments, and
-      Arm platforms such as Neoverse and Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview of the BOLT optimization process, Prepare
-      your BOLT environment, Use BOLT with Samples, Use BOLT with ETM, and Use BOLT with SPE.
-# END generated_summary_faq
 
 author: Jonathan Davies
 
diff --git a/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
index 5353639d2a..d0ed8f59c9 100644
--- a/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
@@ -23,56 +23,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 4bda206717eef380430009f859826d9bcf820442d13492cd3c22a114561e2917
-  summary: >-
-    Learn how to configure Buildkite agents on Google Axion C4A VMs to build and publish multi-architecture
-    Docker images using Docker Buildx for Arm and x86 platforms. It is designed for developers
-    who want to build and run multi-architecture Docker images with Buildkite on Arm-based Google
-    Cloud C4A virtual machines (VM) powered by Google Axion processors. By the end, you will be
-    able to provision an Arm-based VM on Google Cloud running either SUSE Linux Enterprise Server
-    or Ubuntu, install and configure Docker, Docker Buildx, and the Buildkite agent, and write
-    a Dockerfile to containerize a simple Flask-based Python application. It focuses on tools
-    and technologies such as Buildkite, Docker, and Docker Buildx, Linux environments, Arm platforms
-    including Neoverse, and cloud platforms such as Google Cloud. The main steps cover Discover
-    Buildkite on Google Axion C4A instances, Create a Google Axion C4A Arm virtual machine on
-    GCP, Install Buildkite on a Google Axion C4A Arm VM, Set up and connect Buildkite agent on
-    a Google Axion C4A Arm VM, and Create a Flask app and set up the Buildkite pipeline.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will provision an Arm-based VM on Google Cloud running either SUSE Linux Enterprise
-      Server or Ubuntu, install and configure Docker, Docker Buildx, and the Buildkite agent,
-      and write a Dockerfile to containerize a simple Flask-based Python application. Learn how
-      to configure Buildkite agents on Google Axion C4A VMs to build and publish multi-architecture
-      Docker images using Docker Buildx for Arm and x86 platforms.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers who want to build and run multi-architecture
-      Docker images with Buildkite on Arm-based Google Cloud C4A virtual machines (VM) powered
-      by Google Axion processors.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud Platform (GCP) account](https://cloud.google.com/free?utm_source=google&hl=en)
-      with billing enabled; Basic Linux system administration skills, including how to create
-      users, install packages, and manage services; Familiarity with [Docker](https://docs.docker.com/get-started/)
-      and container concepts; A [GitHub account](https://github.com/join) to host your application
-      repository.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Buildkite, Docker, and Docker Buildx, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Discover Buildkite on Google Axion C4A instances,
-      Create a Google Axion C4A Arm virtual machine on GCP, Install Buildkite on a Google Axion
-      C4A Arm VM, Set up and connect Buildkite agent on a Google Axion C4A Arm VM, and Create
-      a Flask app and set up the Buildkite pipeline.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
index 5b45682d82..c52a8950c6 100644
--- a/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
@@ -21,52 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: b8268d4df3b62aeb9943b750b436a936134d47745fa71db6cc08db8c84eb37be
-  summary: >-
-    Learn how to install and configure Apache Cassandra on Google Cloud Axion C4A Arm64 instances
-    and benchmark read/write performance using cassandra-stress. It is designed for software developers
-    migrating Cassandra workloads from x86_64 to Arm-based servers, specifically on Google Cloud
-    C4A virtual machines built on Axion processors. By the end, you will be able to provision
-    an Arm-based SUSE SLES virtual machine on Google Cloud (C4A with Axion processors), install
-    and configure Apache Cassandra on a SUSE Arm64 (C4A) instance, and validate Cassandra functionality
-    using CQLSH and baseline keyspace/table operations. It focuses on tools and technologies such
-    as Apache Cassandra, Java, cqlsh, and cassandra-stress, Linux environments, Arm platforms
-    including Neoverse, and cloud platforms such as Google Cloud. The main steps cover Get started
-    with Cassandra on Google Axion C4A, Create a Google Axion C4A Arm virtual machine, Install
-    Apache Cassandra, Test Cassandra baseline functionality, and Benchmark Cassandra performance.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will provision an Arm-based SUSE SLES virtual machine on Google Cloud (C4A with Axion
-      processors), install and configure Apache Cassandra on a SUSE Arm64 (C4A) instance, and
-      validate Cassandra functionality using CQLSH and baseline keyspace/table operations. Learn
-      how to install and configure Apache Cassandra on Google Cloud Axion C4A Arm64 instances
-      and benchmark read/write performance using cassandra-stress.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers migrating Cassandra workloads from
-      x86_64 to Arm-based servers, specifically on Google Cloud C4A virtual machines built on
-      Axion processors.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
-      account with billing enabled; Familiarity with Cassandra architecture, replication, and
-      [Cassandra partitioning and event-driven I/O](https://cassandra.apache.org/doc/stable/cassandra/architecture/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Apache Cassandra, Java, cqlsh, and cassandra-stress,
-      Linux environments, Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Get started with Cassandra on Google Axion C4A, Create
-      a Google Axion C4A Arm virtual machine, Install Apache Cassandra, Test Cassandra baseline
-      functionality, and Benchmark Cassandra performance.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
index 51289ba3ba..dc87128a0a 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
@@ -20,48 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:56Z'
-  generator: template
-  source_hash: 3947ebbac742399e70001e41ac5face92819bed0deb55aba3e2414f98432023e
-  summary: >-
-    Learn how to run the Arm CCA reference software stack on an FVP with RME support, create a
-    Realm virtual machine, and obtain attestation tokens for confidential computing. It is designed
-    for software developers who want to learn how to run their applications in a Realm using the
-    Arm Confidential Compute Architecture (CCA). By the end, you will be able to run the Arm reference
-    CCA software stack on an Armv-A AEM Base FVP (Fixed Virtual Platform) with support for RME
-    extensions, create a virtual machine in a Realm running guest Linux using a pre-built docker
-    container, and run a simple application in a Realm running guest Linux. It focuses on tools
-    and technologies such as GCC, FVP, RME, CCA, and Docker, Linux environments, and Arm platforms
-    including Neoverse. The main steps cover Overview: Realms, Run the Arm CCA stack using a pre-built
-    docker container, Run an application in a Realm, and Use memory encryption.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will run the Arm reference CCA software stack on an Armv-A AEM Base FVP (Fixed Virtual
-      Platform) with support for RME extensions, create a virtual machine in a Realm running guest
-      Linux using a pre-built docker container, and run a simple application in a Realm running
-      guest Linux. Learn how to run the Arm CCA reference software stack on an FVP with RME support,
-      create a Realm virtual machine, and obtain attestation tokens for confidential computing.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to learn how to run their
-      applications in a Realm using the Arm Confidential Compute Architecture (CCA).
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An AArch64 or x86_64 computer running
-      Linux or macOS. You can use cloud instances, refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including GCC, FVP, RME, CCA, and Docker, Linux environments,
-      and Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview: Realms, Run the Arm CCA stack using a pre-built
-      docker container, Run an application in a Realm, and Use memory encryption.
-# END generated_summary_faq
+
 
 author:
     - Pareena Verma
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
index 37aa1f5f1c..7020ba5440 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
@@ -23,53 +23,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: e21f51feb101ad90245ecceab95fe13e5eb61958faf24dff818eddd11f484b9a
-  summary: >-
-    Learn how Arm CCA Realms interact with I/O devices using VirtIO paravirtualization, SWIOTLB
-    bounce buffers, and PCIe-TDISP secure device attach mechanisms with attestation. It is designed
-    for developers who want to understand how Arm CCA Realms interact with I/O devices using VirtIO,
-    bounce buffers, and secure device attach mechanisms. By the end, you will be able to define
-    device attach and distinguish VirtIO paravirtualized attach from secure physical device attach,
-    summarize what a Realm is and how RME isolates Realm memory, and describe how VirtIO enables
-    paravirtualized I/O without full device emulation. It focuses on tools and technologies such
-    as CCA, RME, and Docker, Linux and macOS environments, and Arm platforms including Neoverse
-    and Cortex-A. The main steps cover About CCA Realms, VirtIO for device attach, Bounce buffers
-    in Realms, and Exercise: observe bounce buffers in a Realm.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will define device attach and distinguish VirtIO paravirtualized attach from secure
-      physical device attach, summarize what a Realm is and how RME isolates Realm memory, and
-      describe how VirtIO enables paravirtualized I/O without full device emulation. Learn how
-      Arm CCA Realms interact with I/O devices using VirtIO paravirtualization, SWIOTLB bounce
-      buffers, and PCIe-TDISP secure device attach mechanisms with attestation.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for developers who want to understand how Arm CCA Realms interact
-      with I/O devices using VirtIO, bounce buffers, and secure device attach mechanisms.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An AArch64 or x86_64 computer running
-      Linux or macOS. You can also use a cloud instance from one of these [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).;
-      Completion of [Get Started with CCA Attestation and Veraison](/learning-paths/servers-and-cloud-computing/cca-veraison)
-      Learning Path; Completion of the [Run an application in a Realm using the Arm Confidential
-      Computing Architecture (CCA)](/learning-paths/servers-and-cloud-computing/cca-container/)
-      Learning Path; Completion of the [Run an end-to-end Attestation Flow](/learning-paths/servers-and-cloud-computing/cca-essentials/)
-      Learning Path.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including CCA, RME, and Docker, Linux and macOS environments,
-      and Arm platforms such as Neoverse and Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around About CCA Realms, VirtIO for device attach, Bounce
-      buffers in Realms, and Exercise: observe bounce buffers in a Realm.
-# END generated_summary_faq
+
 
 author: Arnaud de Grandmaison
 
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
index 6065163e6c..7f240d7c8d 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
@@ -20,54 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: b032debbdfe82cbd017812cf671907520b146fd8684afce0c45c91e7f2287e18
-  summary: >-
-    Learn how to deploy a CCA realm on an FVP with RME support and connect it with attestation
-    services to create an end-to-end confidential computing workflow. It is designed for software
-    developers who want to learn how to run an end-to-end attestation flow with Arm's Confidential
-    Computing Architecture (CCA). By the end, you will be able to describe how you can use attestation
-    with Arm's Confidential Computing Architecture (CCA), deploy a simple workload in a CCA realm
-    on an Armv9-A AEM Base Fixed Virtual Platform (FVP) that has support for RME extensions, and
-    connect the workload with additional software services to create an end-to-end example that
-    uses attestation to unlock the confidential processing of data. It focuses on tools and technologies
-    such as GCC, FVP, RME, CCA, and Docker, Linux environments, and Arm platforms including Neoverse.
-    The main steps cover Overview of the Software Architecture and Run an end-to-end Attestation
-    with Arm CCA.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will describe how you can use attestation with Arm's Confidential Computing Architecture
-      (CCA), deploy a simple workload in a CCA realm on an Armv9-A AEM Base Fixed Virtual Platform
-      (FVP) that has support for RME extensions, and connect the workload with additional software
-      services to create an end-to-end example that uses attestation to unlock the confidential
-      processing of data. Learn how to deploy a CCA realm on an FVP with RME support and connect
-      it with attestation services to create an end-to-end confidential computing workflow.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers who want to learn how to run an end-to-end
-      attestation flow with Arm's Confidential Computing Architecture (CCA).
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An AArch64 or x86_64 computer running
-      Linux. You can use cloud instances, see this list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).;
-      Completion of [Get Started with CCA Attestation and Veraison](/learning-paths/servers-and-cloud-computing/cca-veraison)
-      Learning Path.; Completion of the [Run an application in a Realm using the Arm Confidential
-      Computing Architecture (CCA)](/learning-paths/servers-and-cloud-computing/cca-container/)
-      Learning Path.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including GCC, FVP, RME, CCA, and Docker, Linux environments,
-      and Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview of the Software Architecture and Run an end-to-end
-      Attestation with Arm CCA.
-# END generated_summary_faq
+
 
 author: 
     - Arnaud de Grandmaison
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
index b3c0b4ae11..07197319cf 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
@@ -19,52 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 649957b07b2bde05bc11047bd12bfc4f697c1a55eef1c3a25b5fafc95cda893d
-  summary: >-
-    Learn how to deploy Confidential Containers from encrypted images inside Arm CCA Realms using
-    Trustee services for attestation-based authorization on an FVP with RME support. It is designed
-    for developers who want to understand how Confidential Containers run in Arm CCA Realms. By
-    the end, you will be able to gain an overview of Confidential Containers and their role in
-    confidential computing, understand how Trustee services are used with Arm CCA attestation
-    to authorize and unlock confidential workloads, and deploy a Confidential Container from an
-    encrypted image inside an Arm CCA Realm using an Armv9-A AEM Base Fixed Virtual Platform (FVP)
-    with RME support. It focuses on tools and technologies such as FVP, RME, CCA, Docker, and
-    Veraison, Linux and macOS environments, and Arm platforms including Neoverse and Cortex-A.
-    The main steps cover Overview of Confidential Containers and Arm CCA Attestation with Trustee
-    and Run a confidential container with an encrypted image.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will gain an overview of Confidential Containers and their role in confidential computing,
-      understand how Trustee services are used with Arm CCA attestation to authorize and unlock
-      confidential workloads, and deploy a Confidential Container from an encrypted image inside
-      an Arm CCA Realm using an Armv9-A AEM Base Fixed Virtual Platform (FVP) with RME support.
-      Learn how to deploy Confidential Containers from encrypted images inside Arm CCA Realms
-      using Trustee services for attestation-based authorization on an FVP with RME support.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is for developers who want to understand how Confidential Containers
-      run in Arm CCA Realms.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An AArch64 or x86_64 computer running
-      Linux or macOS. Cloud-based instances can also be used; see the [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/);
-      Completion of the [Run an end-to-end Attestation with Arm CCA and Trustee](/learning-paths/servers-and-cloud-computing/cca-trustee)
-      Learning Path.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including FVP, RME, CCA, Docker, and Veraison, Linux and macOS
-      environments, and Arm platforms such as Neoverse and Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview of Confidential Containers and Arm CCA Attestation
-      with Trustee and Run a confidential container with an encrypted image.
-# END generated_summary_faq
+
 
 author:
   - Anton Antonov
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
index f676911f4f..d31ef8da9f 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
@@ -20,53 +20,6 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 563456f5d151c7ef59bf458f6ac971c321077475a0a78d3b5a3885b97157ba9e
-  summary: >-
-    Learn how to deploy a CCA realm workload on an FVP and connect it with Trustee services to
-    enable attestation-based confidential data processing. It is designed for software developers
-    who want to run an end-to-end attestation flow using Arm Confidential Compute Architecture
-    (CCA) and Trustee services. By the end, you will be able to describe how you can use attestation
-    with Arm's Confidential Computing Architecture (CCA) and Trustee services, deploy a simple
-    workload in a CCA realm on an Armv9-A AEM Base Fixed Virtual Platform (FVP) that has support
-    for RME extensions, and connect the workload with Trustee services to create an end-to-end
-    example that uses attestation to unlock the confidential processing of data. It focuses on
-    tools and technologies such as FVP, RME, CCA, Docker, and Veraison, Linux and macOS environments,
-    and Arm platforms including Neoverse and Cortex-A. The main steps cover Architecture overview
-    for Arm CCA Attestation with Trustee and Run an end-to-end Attestation with Arm CCA and Trustee.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will describe how you can use attestation with Arm's Confidential Computing Architecture
-      (CCA) and Trustee services, deploy a simple workload in a CCA realm on an Armv9-A AEM Base
-      Fixed Virtual Platform (FVP) that has support for RME extensions, and connect the workload
-      with Trustee services to create an end-to-end example that uses attestation to unlock the
-      confidential processing of data. Learn how to deploy a CCA realm workload on an FVP and
-      connect it with Trustee services to enable attestation-based confidential data processing.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is for software developers who want to run an end-to-end attestation
-      flow using Arm Confidential Compute Architecture (CCA) and Trustee services.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An AArch64 or x86_64 computer running
-      Linux or macOS; you can use cloud instances - see the [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/);
-      Completion of the [Get started with CCA attestation and Veraison](/learning-paths/servers-and-cloud-computing/cca-veraison)
-      Learning Path; Completion of the [Run an end-to-end attestation flow with Arm CCA](/learning-paths/servers-and-cloud-computing/cca-essentials/)
-      Learning Path.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including FVP, RME, CCA, Docker, and Veraison, Linux and macOS
-      environments, and Arm platforms such as Neoverse and Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Architecture overview for Arm CCA Attestation with
-      Trustee and Run an end-to-end Attestation with Arm CCA and Trustee.
-# END generated_summary_faq
 
 author:
   - Anton Antonov
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
index bb2f435bb5..e737eed57c 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
@@ -18,49 +18,6 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 55b8032ceaf735d53b1157102a3a1da5aa5cb686e9ec51cf4896ded66d9bf263
-  summary: >-
-    Learn how to deploy a scalable Arm CCA attestation verifier service on AWS using Veraison
-    components with platform endorsement provisioning. It is designed for developers familiar
-    with CCA attestation and the Veraison project. You'll learn how to deploy a scalable CCA attestation
-    verifier service on AWS. By the end, you will be able to build an attestation service on AWS
-    using the Veraison project's components and set up Veraison as a verifier for Arm CCA attestation
-    tokens by provisioning CCA platform endorsements. It focuses on tools and technologies such
-    as CCA, RME, and Runbook, Linux environments, Arm platforms including Neoverse and Cortex-A,
-    and cloud platforms such as AWS. The main steps cover Overview, Prepare AWS Account, Create
-    the Domain and Certificate, Create the Veraison Deployment, and Add CCA Platform Endorsements
-    to Veraison.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build an attestation service on AWS using the Veraison project's components and
-      set up Veraison as a verifier for Arm CCA attestation tokens by provisioning CCA platform
-      endorsements. Learn how to deploy a scalable Arm CCA attestation verifier service on AWS
-      using Veraison components with platform endorsement provisioning.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is for developers familiar with CCA attestation and the Veraison project.
-      You'll learn how to deploy a scalable CCA attestation verifier service on AWS.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An [AWS account](/learning-paths/servers-and-cloud-computing/csp/aws/)
-      with access to AWS services.; An x86 computer running Ubuntu or Arch Linux, authorized for
-      AWS access. If you're using another build environment, you'll need to configure the toolchains
-      for cross-compilation.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including CCA, RME, and Runbook, Linux environments, Arm platforms
-      such as Neoverse and Cortex-A, and cloud platforms such as AWS.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview, Prepare AWS Account, Create the Domain and
-      Certificate, Create the Veraison Deployment, and Add CCA Platform Endorsements to Veraison.
-# END generated_summary_faq
 
 author: Paul Howard
 
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md
index 52bab2273f..42b47f9605 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md
@@ -23,46 +23,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 9e6dee3aef79c1c65cc1a1f4fe3528b9c5542d8046f0b059ef703079ef964d77
-  summary: >-
-    Learn how to inspect and verify Arm CCA attestation tokens using command-line tools and the
-    open-source Veraison attestation verification service. It is designed for developers who would
-    like to learn about attestation in confidential computing, using Arm's Confidential Computing
-    Architecture (CCA). By the end, you will be able to describe the importance of attestation
-    in confidential computing, understand what a CCA attestation token is, and describe its format,
-    and inspect the contents of a CCA attestation token using command-line tools. It focuses on
-    tools and technologies such as CCA, RME, and Runbook, Linux environments, and Arm platforms
-    including Cortex-A. The main steps cover Using this Learning Path, CCA and Attestation, Veraison,
-    Download and inspect the attestation token, and Use the verification service.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will describe the importance of attestation in confidential computing, understand what
-      a CCA attestation token is, and describe its format, and inspect the contents of a CCA attestation
-      token using command-line tools. Learn how to inspect and verify Arm CCA attestation tokens
-      using command-line tools and the open-source Veraison attestation verification service.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is for developers who would like to learn about attestation in confidential
-      computing, using Arm's Confidential Computing Architecture (CCA).
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm-based or x86 computer running
-      Ubuntu. You can use a server instance from a cloud service provider of your choice.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including CCA, RME, and Runbook, Linux environments, and Arm
-      platforms such as Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Using this Learning Path, CCA and Attestation, Veraison,
-      Download and inspect the attestation token, and Use the verification service.
-# END generated_summary_faq
+
 
 author: Paul Howard
 
diff --git a/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
index f393ed4373..611c071ca6 100644
--- a/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
@@ -27,55 +27,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: ec9cdca7aa9a5670f54ea3646f965149460d8e66d9d5d904e1b6dcf09867df39
-  summary: >-
-    Learn how to set up CircleCI self-hosted machine runners on Google Cloud Axion C4A SUSE VMs
-    and execute Arm-native CI/CD workflows using custom resource classes. It is designed for developers
-    and DevOps engineers looking to set up and run CircleCI Arm native workflows on SUSE Linux
-    Arm64 virtual machines (VMs), specifically on Google Cloud C4A with Axion processors, using
-    self-hosted runners. By the end, you will be able to provision a SUSE Arm64 virtual machine
-    on Google Cloud (C4A with Axion processors), install and configure CircleCI self-hosted machine
-    runners on Arm64, and create a cloud-native Node.js demo app to run on the self-hosted Arm
-    runner. It focuses on tools and technologies such as CircleCI, Node.js, npm, and Docker, Linux
-    environments, Arm platforms including Neoverse, and cloud platforms such as Google Cloud.
-    The main steps cover Get started with CircleCI on Google Axion C4A, Create a Google Axion
-    C4A Arm virtual machine on GCP, Install CircleCI, Create a resource class, and Install CircleCI
-    Machine Runner on SUSE Arm.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will provision a SUSE Arm64 virtual machine on Google Cloud (C4A with Axion processors),
-      install and configure CircleCI self-hosted machine runners on Arm64, and create a cloud-native
-      Node.js demo app to run on the self-hosted Arm runner. Learn how to set up CircleCI self-hosted
-      machine runners on Google Cloud Axion C4A SUSE VMs and execute Arm-native CI/CD workflows
-      using custom resource classes.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers and DevOps engineers looking to set up and
-      run CircleCI Arm native workflows on SUSE Linux Arm64 virtual machines (VMs), specifically
-      on Google Cloud C4A with Axion processors, using self-hosted runners.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
-      account with billing enabled; Basic familiarity with Linux command line, Node.js, and npm;
-      Basic understanding of CircleCI concepts such as [workflows](https://circleci.com/docs/guides/orchestrate/workflows/),
-      [jobs](https://circleci.com/docs/guides/orchestrate/jobs-steps/), [resource classes](https://circleci.com/docs/guides/execution-managed/resource-class-overview/),
-      and [runners](https://circleci.com/docs/guides/execution-runner/runner-overview/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including CircleCI, Node.js, npm, and Docker, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Get started with CircleCI on Google Axion C4A, Create
-      a Google Axion C4A Arm virtual machine on GCP, Install CircleCI, Create a resource class,
-      and Install CircleCI Machine Runner on SUSE Arm.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md b/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md
index 7c432a63d2..fe058fe732 100644
--- a/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md
@@ -20,51 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: e04982fd668765fa2ab25f943f020b8d2b4a5e9b5ff3a51b7431cc4d93730180
-  summary: >-
-    Learn how to install and configure CircleCI self-hosted machine runners on AWS Graviton Arm64
-    instances to execute CI/CD workflows natively on Arm. It is designed for developers and DevOps
-    engineers who want to set up and run CircleCI Arm native workflows on Linux Arm64 virtual
-    machines. You'll use AWS EC2 Graviton instances (Neoverse N1) and self-hosted runners. By
-    the end, you will be able to create an AWS EC2 Graviton Arm64 virtual machine, install and
-    configure CircleCI self-hosted machine runners on Arm64, and verify the runner by running
-    a simple workflow and test computation. It focuses on tools and technologies such as CircleCI,
-    Bash, and Git, Linux environments, Arm platforms including Neoverse, and cloud platforms such
-    as AWS. The main steps cover Get Started with CircleCI on AWS Graviton, Create an AWS EC2
-    Arm64 Graviton Instance, Install CircleCI CLI, Create a resource class in CircleCI, and Install
-    CircleCI machine runner on AWS Graviton.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create an AWS EC2 Graviton Arm64 virtual machine, install and configure CircleCI
-      self-hosted machine runners on Arm64, and verify the runner by running a simple workflow
-      and test computation. Learn how to install and configure CircleCI self-hosted machine runners
-      on AWS Graviton Arm64 instances to execute CI/CD workflows natively on Arm.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers and DevOps engineers who want to set up and
-      run CircleCI Arm native workflows on Linux Arm64 virtual machines. You'll use AWS EC2 Graviton
-      instances (Neoverse N1) and self-hosted runners.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An [AWS account](https://aws.amazon.com/free/)
-      with billing enabled; A CircleCI account; Basic understanding of CircleCI workflows, jobs
-      and resource classes.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including CircleCI, Bash, and Git, Linux environments, Arm
-      platforms such as Neoverse, and cloud platforms such as AWS.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Get Started with CircleCI on AWS Graviton, Create
-      an AWS EC2 Arm64 Graviton Instance, Install CircleCI CLI, Create a resource class in CircleCI,
-      and Install CircleCI machine runner on AWS Graviton.
-# END generated_summary_faq
+
 
 author: Annie Tallund
 
diff --git a/content/learning-paths/servers-and-cloud-computing/clair/_index.md b/content/learning-paths/servers-and-cloud-computing/clair/_index.md
index 5930133365..75ea967afa 100644
--- a/content/learning-paths/servers-and-cloud-computing/clair/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/clair/_index.md
@@ -18,47 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: e742eb44fa108bfcc4ee5a241414e6aa05e1fc0e1cceb589fb78a080f59b0d38
-  summary: >-
-    Learn how to install and run Clair on Arm servers using combined and distributed deployment
-    models to scan container images and generate vulnerability reports. It is designed for software
-    developers interested in scanning container images for vulnerabilities on Arm servers. By
-    the end, you will be able to install Clair on an Arm server, run Clair using combined and
-    distributed deployment models, and submit container images using the Clair CLI (command-line
-    interface) and generate vulnerability reports. It focuses on tools and technologies such as
-    Docker, Go, and Clair, Linux environments, Arm platforms including Neoverse, and cloud platforms
-    such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps cover Introduction
-    to Clair deployment models, Create a combined deployment, Create a distributed deployment,
-    and Generate vulnerability reports.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install Clair on an Arm server, run Clair using combined and distributed deployment
-      models, and submit container images using the Clair CLI (command-line interface) and generate
-      vulnerability reports. Learn how to install and run Clair on Arm servers using combined
-      and distributed deployment models to scan container images and generate vulnerability reports.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers interested in scanning container images
-      for vulnerabilities on Arm servers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/)
-      from a cloud service provider or an Arm server with recent versions of Docker and Go installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Docker, Go, and Clair, Linux environments, Arm platforms
-      such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Introduction to Clair deployment models, Create a
-      combined deployment, Create a distributed deployment, and Generate vulnerability reports.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
index b5669c10d4..c17739f02e 100644
--- a/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
@@ -25,56 +25,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: e77cd1373236f39f360afe6844d642fde290960ccdad26544ff1e3342f2a980c
-  summary: >-
-    Learn how to deploy ClickHouse on Google Cloud Axion C4A processors and build a streaming
-    ETL pipeline using Apache Beam, Dataflow, and Pub/Sub for real-time analytics. It is designed
-    for developers deploying and optimizing ClickHouse on Arm-based Linux environments using Google
-    Cloud C4A virtual machines powered by Axion processors, to evaluate ClickHouse performance
-    and behavior on Arm-based infrastructure. By the end, you will be able to provision an Arm-based
-    SUSE SLES virtual machine on Google Cloud using C4A (Axion processors), configure Google Cloud
-    Pub/Sub for real-time log ingestion, and deploy and validate ClickHouse on a SUSE Linux Arm64
-    (Axion) VM. It focuses on tools and technologies such as ClickHouse, Apache Beam, Google Dataflow,
-    Google Cloud Pub/Sub, and Python 3.11, Linux environments, Arm platforms including Neoverse,
-    and cloud platforms such as Google Cloud. The main steps cover Get started with ClickHouse
-    on Google Cloud C4A Arm virtual machines, Create a Firewall Rule on GCP, Create a Google Axion
-    C4A Arm virtual machine on GCP, Set up GCP Pub/Sub and IAM for ClickHouse real-time analytics
-    on Axion, and Install ClickHouse.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will provision an Arm-based SUSE SLES virtual machine on Google Cloud using C4A (Axion
-      processors), configure Google Cloud Pub/Sub for real-time log ingestion, and deploy and
-      validate ClickHouse on a SUSE Linux Arm64 (Axion) VM. Learn how to deploy ClickHouse on
-      Google Cloud Axion C4A processors and build a streaming ETL pipeline using Apache Beam,
-      Dataflow, and Pub/Sub for real-time analytics.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers deploying and optimizing ClickHouse on Arm-based
-      Linux environments using Google Cloud C4A virtual machines powered by Axion processors,
-      to evaluate ClickHouse performance and behavior on Arm-based infrastructure.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
-      account with billing enabled; Basic familiarity with [ClickHouse](https://clickhouse.com/);
-      Basic understanding of databases and SQL.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including ClickHouse, Apache Beam, Google Dataflow, Google
-      Cloud Pub/Sub, and Python 3.11, Linux environments, Arm platforms such as Neoverse, and
-      cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Get started with ClickHouse on Google Cloud C4A Arm
-      virtual machines, Create a Firewall Rule on GCP, Create a Google Axion C4A Arm virtual machine
-      on GCP, Set up GCP Pub/Sub and IAM for ClickHouse real-time analytics on Axion, and Install
-      ClickHouse.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md b/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
index 6a06f37c4d..b87f873b7f 100644
--- a/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
@@ -17,45 +17,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 0d1390198cf2f0e87f509c5269fc2405cffcb2e57311024150d47e60a3273178
-  summary: >-
-    Learn how to install ClickHouse on Arm-based cloud instances and measure database performance
-    using ClickBench to determine appropriate instance configurations. It is designed for software
-    developers who want to use ClickHouse on Arm-based cloud instances. By the end, you will be
-    able to learn how to install and measure ClickHouse performance and determine the appropriate
-    instance configuration needed for your workloads. It focuses on tools and technologies such
-    as ClickHouse and ClickBench, Linux environments, Arm platforms including Neoverse, and cloud
-    platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps cover Run
-    ClickHouse and measure performance.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will learn how to install and measure ClickHouse performance and determine the appropriate
-      instance configuration needed for your workloads. Learn how to install ClickHouse on Arm-based
-      cloud instances and measure database performance using ClickBench to determine appropriate
-      instance configurations.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to use ClickHouse on Arm-based
-      cloud instances.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/)
-      from a cloud service provider or an on-premise Arm server.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including ClickHouse and ClickBench, Linux environments, Arm
-      platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud,
-      and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Run ClickHouse and measure performance.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
index 99bd73c35d..93faac4839 100644
--- a/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
@@ -20,48 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: e4eb84292a669a8d73bff4b63d2fe3f70382dd873d023fc8ff24db5ad6178ab8
-  summary: >-
-    Learn how to deploy an Arm-based Cobalt 100 virtual machine on Azure, connect via SSH, and
-    configure network security group rules for external connectivity. It is designed for developers
-    and DevOps engineers who want to deploy an Arm-based virtual machine on Azure and expose an
-    application port to the internet. By the end, you will be able to deploy an Arm-based Cobalt
-    100 virtual machine (VM) on Microsoft Azure, connect to the Cobalt 100 VM using SSH, and configure
-    an inbound TCP port in the associated Network Security Group (NSG). It focuses on tools and
-    technologies such as Azure Portal and Azure CLI, Linux environments, Arm platforms including
-    Neoverse, and cloud platforms such as Microsoft Azure. The main steps cover Create the Cobalt
-    100 virtual machine, Open inbound ports in the Network Security Group, and Verify connectivity
-    to the Cobalt 100 VM.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will deploy an Arm-based Cobalt 100 virtual machine (VM) on Microsoft Azure, connect
-      to the Cobalt 100 VM using SSH, and configure an inbound TCP port in the associated Network
-      Security Group (NSG). Learn how to deploy an Arm-based Cobalt 100 virtual machine on Azure,
-      connect via SSH, and configure network security group rules for external connectivity.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers and DevOps engineers who want to deploy an
-      Arm-based virtual machine on Azure and expose an application port to the internet.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Microsoft Azure subscription with
-      permissions to create virtual machines and networking resources; Basic familiarity with
-      SSH.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Azure Portal and Azure CLI, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Create the Cobalt 100 virtual machine, Open inbound
-      ports in the Network Security Group, and Verify connectivity to the Cobalt 100 VM.
-# END generated_summary_faq
+
 
 author: Joe Stech
 
diff --git a/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md b/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
index 7d8540703b..b1c91541f1 100644
--- a/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
@@ -18,48 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: e7ea48aaea0e25f624ea1d77c6252b0e537fdaeca3aacca560d7bc9d103bbbb3
-  summary: >-
-    Learn how to automate Docker image creation for Arm using AWS CodeBuild with GitHub integration
-    and run the images on any Arm system with Docker installed. It is designed for software developers
-    interested in using AWS CodeBuild to automate container build tasks. By the end, you will
-    be able to use a GitHub project and AWS CodeBuild to automate Docker image creation and pull
-    and run the created Docker images on any Arm computer with Docker installed. It focuses on
-    tools and technologies such as Docker and AWS CodeBuild, Linux environments, Arm platforms
-    including Neoverse, and cloud platforms such as AWS. The main steps cover Build Docker images
-    using AWS CodeBuild and Run Docker images from Docker Hub and AWS Elastic Container Registry
-    (ECR).
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will use a GitHub project and AWS CodeBuild to automate Docker image creation and pull
-      and run the created Docker images on any Arm computer with Docker installed. Learn how to
-      automate Docker image creation for Arm using AWS CodeBuild with GitHub integration and run
-      the images on any Arm system with Docker installed.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers interested in using AWS CodeBuild to automate
-      container build tasks.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An [AWS account](/learning-paths/servers-and-cloud-computing/csp/aws/)
-      for accessing AWS cloud services.; An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/)
-      from a cloud service provider or any Arm server, laptop, or single-board computer running
-      [Docker](/install-guides/docker/) used to run the created images.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Docker and AWS CodeBuild, Linux environments, Arm
-      platforms such as Neoverse, and cloud platforms such as AWS.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Build Docker images using AWS CodeBuild and Run Docker
-      images from Docker Hub and AWS Elastic Container Registry (ECR).
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/codec/_index.md b/content/learning-paths/servers-and-cloud-computing/codec/_index.md
index 42febb3a63..75e8cf7d7b 100644
--- a/content/learning-paths/servers-and-cloud-computing/codec/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/codec/_index.md
@@ -21,45 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 908a750f2a891a0c4c9d1183c2130ac5ac0fdcfb4a45185cef6ed6da47c9aaa9
-  summary: >-
-    Learn how to build and run the x265 H.265 codec on Arm servers with performance benchmarking
-    across various video resolutions and encoding presets. It is designed for software developers
-    who want to build and run an x265 codec on Arm servers and measure performance. By the end,
-    you will be able to build x265 codec on Arm server and run x265 codec on Arm server with the
-    same video of various resolutions and encoding presets to measure the performance impact.
-    It focuses on tools and technologies such as x265, Linux environments, Arm platforms including
-    Neoverse, and cloud platforms such as AWS and Oracle. The main steps cover Build and Run x265
-    codec on Arm servers.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build x265 codec on Arm server and run x265 codec on Arm server with the same video
-      of various resolutions and encoding presets to measure the performance impact. Learn how
-      to build and run the x265 H.265 codec on Arm servers with performance benchmarking across
-      various video resolutions and encoding presets.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to build and run an x265
-      codec on Arm servers and measure performance.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/)
-      from an appropriate cloud service provider. This Learning Path has been verified on AWS
-      EC2 and Oracle cloud services, running `Ubuntu Linux 20.04.`.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including x265, Linux environments, Arm platforms such as
-      Neoverse, and cloud platforms such as AWS and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Build and Run x265 codec on Arm servers.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/codec1/_index.md b/content/learning-paths/servers-and-cloud-computing/codec1/_index.md
index 84c94b91a4..7b72ed51fe 100644
--- a/content/learning-paths/servers-and-cloud-computing/codec1/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/codec1/_index.md
@@ -6,43 +6,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: c0643a788cdb0b3e33fe645fbb61d99a1899806e3ee197541c1eb8134b2876c1
-  summary: >-
-    Learn how to build and run the AV1 and VP9 video codecs on Arm Linux systems with performance
-    benchmarking across various resolutions and encoding configurations. It is designed for software
-    developers who want to build and run the VP9 and AV1 codecs on Arm servers and measure performance.
-    By the end, you will be able to build the AV1 and VP9 codecs on Arm Linux and run the AV1
-    and VP9 codecs on Arm Linux using example videos with various resolutions and encodings. It
-    focuses on Linux environments and Arm platforms including Neoverse and Cortex-A. The main
-    steps cover Build and Run the AV1 codec and Build and Run the VP9 codec.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build the AV1 and VP9 codecs on Arm Linux and run the AV1 and VP9 codecs on Arm
-      Linux using example videos with various resolutions and encodings. Learn how to build and
-      run the AV1 and VP9 video codecs on Arm Linux systems with performance benchmarking across
-      various resolutions and encoding configurations.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to build and run the VP9
-      and AV1 codecs on Arm servers and measure performance.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm Linux system or an [Arm based
-      instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers Linux environments and Arm platforms such as Neoverse and Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Build and Run the AV1 codec and Build and Run the
-      VP9 codec.
-# END generated_summary_faq
+
 
 author: Odin Shen
 
diff --git a/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
index 660e6bb2df..dc9b8144c5 100644
--- a/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
@@ -21,51 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 0a7d76b8c944a50073c7524813acf83948155c7c61d9c92f9202eae1192c9600
-  summary: >-
-    Learn how to install and configure Couchbase on Google Cloud Axion C4A Arm64 instances and
-    benchmark read/write performance using YCSB workloads. It is designed for developers deploying
-    Couchbase workloads on Arm Linux environments, specifically using Google Cloud C4A virtual
-    machines (VM) powered by Axion processors. By the end, you will be able to provision an Arm-based
-    SUSE Linux Enterprise Server (SLES) virtual machine on Google Cloud (C4A with Axion processors),
-    install Couchbase Server on the SUSE Arm64 (C4A) instance, and verify Couchbase deployment
-    by accessing the web console, creating a test bucket, and confirming cluster health. It focuses
-    on tools and technologies such as Couchbase, Linux environments, Arm platforms including Neoverse,
-    and cloud platforms such as Google Cloud. The main steps cover Get started with Couchbase
-    on Google Axion C4A, Create a firewall rule on GCP, Create a Google Axion C4A Arm virtual
-    machine on GCP, Install Couchbase, and Perform Couchbase baseline testing.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will provision an Arm-based SUSE Linux Enterprise Server (SLES) virtual machine on Google
-      Cloud (C4A with Axion processors), install Couchbase Server on the SUSE Arm64 (C4A) instance,
-      and verify Couchbase deployment by accessing the web console, creating a test bucket, and
-      confirming cluster health. Learn how to install and configure Couchbase on Google Cloud
-      Axion C4A Arm64 instances and benchmark read/write performance using YCSB workloads.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers deploying Couchbase workloads on Arm Linux
-      environments, specifically using Google Cloud C4A virtual machines (VM) powered by Axion
-      processors.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
-      account with billing enabled; Basic familiarity with [Couchbase](https://www.couchbase.com/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Couchbase, Linux environments, Arm platforms such
-      as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Get started with Couchbase on Google Axion C4A, Create
-      a firewall rule on GCP, Create a Google Axion C4A Arm virtual machine on GCP, Install Couchbase,
-      and Perform Couchbase baseline testing.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md b/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
index 13cd53b25d..51a66cb080 100644
--- a/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
@@ -18,44 +18,6 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 22f845b8ea4dbb9ffc63fafb76c17c79aedde14a28417d49e1ab0833bbbc1eba
-  summary: >-
-    Learn how to apply g++ compiler optimization techniques and flags to improve C++ application
-    performance on Arm systems with hands-on examples. It is designed for beginner C++ developers
-    who are looking to optimize applications on Arm-based cloud instances using compiler flags.
-    By the end, you will be able to compile a C++ program for a specific Arm target and use compiler
-    flags to manage optimizations. It focuses on tools and technologies such as CPP and Runbook,
-    Linux environments, Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft
-    Azure, Google Cloud, and Oracle. The main steps cover Compiler basics, Set up Your Environment,
-    Find specific Neoverse features, and Try an example application.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will compile a C++ program for a specific Arm target and use compiler flags to manage
-      optimizations. Learn how to apply g++ compiler optimization techniques and flags to improve
-      C++ application performance on Arm systems with hands-on examples.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is for beginner C++ developers who are looking to optimize applications
-      on Arm-based cloud instances using compiler flags.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Basic understanding of C++.; Basic understanding
-      of compilers.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including CPP and Runbook, Linux environments, Arm platforms
-      such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Compiler basics, Set up Your Environment, Find specific
-      Neoverse features, and Try an example application.
-# END generated_summary_faq
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md b/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md
index 4ce1b42f5e..7435ac0269 100644
--- a/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md
@@ -18,46 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 4e7de348514d0b5a742fa47bb85a2a5814fa9bd587478e7ef08365d16273f6bf
-  summary: >-
-    Learn how to apply profile-guided optimization to C++ applications on Arm systems and measure
-    performance improvements using Google Benchmark. It is designed for Developers looking to
-    optimize C++ performance based on runtime behavior. By the end, you will be able to microbenchmark
-    a function using Google Benchmark and apply profile-guided optimization to build performance-tuned
-    binaries. It focuses on tools and technologies such as Google Benchmark and Runbook, Linux
-    environments, Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft
-    Azure, Google Cloud, and Oracle. The main steps cover Profile-Guided Optimization, Google
-    Benchmark, Example operation, Using Profile Guided Optimization, and Incorporating PGO into
-    a GitHub Actions workflow.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will microbenchmark a function using Google Benchmark and apply profile-guided optimization
-      to build performance-tuned binaries. Learn how to apply profile-guided optimization to C++
-      applications on Arm systems and measure performance improvements using Google Benchmark.
-  - question: Who is this Learning Path for?
-    answer: >-
-      Developers looking to optimize C++ performance based on runtime behavior.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Basic C++ understanding.; Access to
-      an Arm-based Linux machine.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Google Benchmark and Runbook, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
-      Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Profile-Guided Optimization, Google Benchmark, Example
-      operation, Using Profile Guided Optimization, and Incorporating PGO into a GitHub Actions
-      workflow.
-# END generated_summary_faq
+
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md b/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
index 1e44547dae..f8c8b88d49 100644
--- a/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
@@ -18,45 +18,6 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 58dba071f70b4f85b87b3bd27b3a7ba3ff985f80079a42e05b797a998aeaf104
-  summary: >-
-    Learn how to profile and identify CPU hotspots in C++ applications on Arm Neoverse using Arm
-    Performix flame graphs to guide optimization. It is designed for software developers and performance
-    engineers who want to identify code hotspots in applications running on Arm Linux systems.
-    By the end, you will be able to run the Code Hotspots recipe in Arm Performix and identify
-    which functions consume the most CPU cycles and target them for optimization. It focuses on
-    tools and technologies such as Arm Performix, C++, and Runbook, Linux environments, and Arm
-    platforms including Neoverse. The main steps cover Understand flame graphs and profiling tools,
-    Build the example application, Profile baseline performance, and Optimize application performance.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will run the Code Hotspots recipe in Arm Performix and identify which functions consume
-      the most CPU cycles and target them for optimization. Learn how to profile and identify
-      CPU hotspots in C++ applications on Arm Neoverse using Arm Performix flame graphs to guide
-      optimization.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers and performance engineers who want
-      to identify code hotspots in applications running on Arm Linux systems.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Access to Arm Performix; Basic understanding
-      of C++.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Arm Performix, C++, and Runbook, Linux environments,
-      and Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Understand flame graphs and profiling tools, Build
-      the example application, Profile baseline performance, and Optimize application performance.
-# END generated_summary_faq
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/csp/_index.md b/content/learning-paths/servers-and-cloud-computing/csp/_index.md
index 048a5982ab..bcc774460d 100644
--- a/content/learning-paths/servers-and-cloud-computing/csp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/csp/_index.md
@@ -6,44 +6,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 9e94b69eabf35677c48db812bb85ea9cef184efc078a826b96954f540a45e915
-  summary: >-
-    Learn how to start an Arm-based virtual machine instance from major cloud service providers
-    and verify the Arm architecture is being used. It is designed for software developers who
-    are new to Arm-based cloud instances. By the end, you will be able to start an Arm-based instance
-    in the cloud and verify that the instance is using the Arm architecture. It focuses on Linux
-    environments, Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft
-    Azure, Google Cloud, and Oracle. The main steps cover Getting Started with AWS, Getting Started
-    with Microsoft Azure, Getting Started with Google Cloud Platform, Getting Started with Oracle
-    OCI, and Getting Started with Alibaba Cloud Services.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will start an Arm-based instance in the cloud and verify that the instance is using
-      the Arm architecture. Learn how to start an Arm-based virtual machine instance from major
-      cloud service providers and verify the Arm architecture is being used.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who are new to Arm-based cloud instances.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An account with your preferred cloud
-      service provider.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers Linux environments, Arm platforms such as Neoverse, and cloud platforms such as
-      AWS, Microsoft Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Getting Started with AWS, Getting Started with Microsoft
-      Azure, Getting Started with Google Cloud Platform, Getting Started with Oracle OCI, and
-      Getting Started with Alibaba Cloud Services.
-# END generated_summary_faq
+
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md b/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
index d171d85d18..ab43a8d839 100644
--- a/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
@@ -18,47 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: f600fcba0adea12ff1b8b092e75de553577d940dc3bf632e9a247cec22d364a4
-  summary: >-
-    Learn how to deploy and run the DeepSeek-R1 language model on Arm servers using llama.cpp
-    with quantization for efficient CPU inference. It is designed for developers who want to run
-    DeepSeek-R1 on Arm-based servers. By the end, you will be able to clone and build llama.cpp
-    on your Arm-based server, download a pre-quantized DeepSeek-R1 model from Hugging Face, and
-    run the model on your Arm CPU and benchmark its performance. It focuses on tools and technologies
-    such as LLM, Generative AI, and Python, Linux environments, Arm platforms including Neoverse,
-    and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps
-    cover Run a DeepSeek-R1 chatbot on Arm servers and Access the chatbot using the OpenAI-compatible
-    API.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will clone and build llama.cpp on your Arm-based server, download a pre-quantized DeepSeek-R1
-      model from Hugging Face, and run the model on your Arm CPU and benchmark its performance.
-      Learn how to deploy and run the DeepSeek-R1 language model on Arm servers using llama.cpp
-      with quantization for efficient CPU inference.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is for developers who want to run DeepSeek-R1 on Arm-based servers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/)
-      from a cloud provider or an on-premise Arm server. This Learning Path was tested on an AWS
-      Graviton4 r8g.24xlarge instance.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including LLM, Generative AI, and Python, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
-      Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Run a DeepSeek-R1 chatbot on Arm servers and Access
-      the chatbot using the OpenAI-compatible API.
-# END generated_summary_faq
+
 
 author:
     - Tianyu Li
diff --git a/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md b/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md
index e0a35c0451..617c42f589 100644
--- a/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md
@@ -19,48 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: a1ec216948e7cfd4fc52815196bb3b99ab4e76c9c756aa9e9a8e3216ef5e7ce4
-  summary: >-
-    Learn how to use fio to microbenchmark storage performance on Arm systems and monitor storage
-    using iostat, iotop, and pidstat to identify bottlenecks. It is designed for developers looking
-    to optimize storage performance, reduce costs, identify bottlenecks, and evaluate storage
-    options when migrating applications across platforms. By the end, you will be able to describe
-    data flow through storage devices, monitor storage performance using tools like iostat, iotop,
-    and pidstat, and run fio to microbenchmark a block storage device. It focuses on tools and
-    technologies such as bash and Runbook, Linux environments, Arm platforms including Neoverse,
-    and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps
-    cover Fundamentals of storage systems, Analyzing I/O behavior with real workloads, and Benchmarking
-    block storage performance with fio.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will describe data flow through storage devices, monitor storage performance using tools
-      like iostat, iotop, and pidstat, and run fio to microbenchmark a block storage device. Learn
-      how to use fio to microbenchmark storage performance on Arm systems and monitor storage
-      using iostat, iotop, and pidstat to identify bottlenecks.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers looking to optimize storage performance, reduce
-      costs, identify bottlenecks, and evaluate storage options when migrating applications across
-      platforms.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/)
-      from a cloud service provider or an Arm Linux server.; Familiarity with Linux.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including bash and Runbook, Linux environments, Arm platforms
-      such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Fundamentals of storage systems, Analyzing I/O behavior
-      with real workloads, and Benchmarking block storage performance with fio.
-# END generated_summary_faq
+
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md
index b750027384..58fe08379b 100644
--- a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md
@@ -21,50 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 6ac0c7cf1ab4b3efa680acbf1e349b858bee5dc7992baf6689e1f293a83060a4
-  summary: >-
-    Run distributed LLM inference with llama.cpp across multiple AWS Graviton4 instances, covering
-    multi-node setup, coordination, and performance trade-offs. It is designed for This introductory
-    topic is for developers with some experience using llama.cpp who want to learn how to run
-    distributed inference on Arm-based servers. By the end, you will be able to set up a main
-    host and worker nodes with llama.cpp and run a large quantized model (for example, Llama 3.1
-    405B) with distributed CPU inference on Arm machines. It focuses on tools and technologies
-    such as LLM, Generative AI, and AWS, Linux environments, Arm platforms including Neoverse,
-    and cloud platforms such as AWS. The main steps cover Convert model to GGUF and quantize,
-    Configure the worker nodes, and Configure the master node.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will set up a main host and worker nodes with llama.cpp and run a large quantized model
-      (for example, Llama 3.1 405B) with distributed CPU inference on Arm machines. Run distributed
-      LLM inference with llama.cpp across multiple AWS Graviton4 instances, covering multi-node
-      setup, coordination, and performance trade-offs.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This introductory topic is for developers with some experience using llama.cpp who want
-      to learn how to run distributed inference on Arm-based servers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Three AWS c8g.4xlarge instances with
-      at least 500 GB of EBS storage; Python 3 installed on each instance; Access to Meta's gated
-      repository for the Llama 3.1 model family and a Hugging Face token to download models; Familiarity
-      with the Learning Path [Deploy a Large Language Model (LLM) chatbot with llama.cpp using
-      KleidiAI on Arm servers](/learning-paths/servers-and-cloud-computing/llama-cpu); Familiarity
-      with AWS.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including LLM, Generative AI, and AWS, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as AWS.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Convert model to GGUF and quantize, Configure the
-      worker nodes, and Configure the master node.
-# END generated_summary_faq
+
 
 author: 
     - Aryan Bhusari
diff --git a/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md
index c631a54661..ec55673cd9 100644
--- a/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md
@@ -25,56 +25,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 6675df4c91126b157dcbf39c96a773130f1a95e2f5680913979da96f6f6c97cd
-  summary: >-
-    Learn how to deploy a production-grade Django REST API on Google Kubernetes Engine with Arm64
-    Axion node pools integrated with Google Cloud managed data services. It is designed for DevOps
-    engineers and software developers who want to deploy, operate, and benchmark a production-grade
-    Django REST API on Google Kubernetes Engine (GKE) running on Arm64 Axion processors, integrated
-    with managed Google Cloud data services. By the end, you will be able to provision Arm-based
-    Axion compute on Google Cloud using virtual machines and GKE node pools, package a Django
-    REST API into an Arm-native Docker container, and push container images to Google Artifact
-    Registry. It focuses on tools and technologies such as Django, Docker, Kubernetes, Google
-    Artifact Registry, and Cloud SQL (PostgreSQL), Linux environments, Arm platforms including
-    Neoverse, and cloud platforms such as Google Cloud. The main steps cover Get started with
-    Django on Google Axion C4A, Configure firewall rules for Django on Google Cloud, Create a
-    Google Axion C4A Arm virtual machine on GCP, Install Django on your Arm-based VM, and Verify
-    Django installation and run the development server.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will provision Arm-based Axion compute on Google Cloud using virtual machines and GKE
-      node pools, package a Django REST API into an Arm-native Docker container, and push container
-      images to Google Artifact Registry. Learn how to deploy a production-grade Django REST API
-      on Google Kubernetes Engine with Arm64 Axion node pools integrated with Google Cloud managed
-      data services.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for DevOps engineers and software developers who want to deploy,
-      operate, and benchmark a production-grade Django REST API on Google Kubernetes Engine (GKE)
-      running on Arm64 Axion processors, integrated with managed Google Cloud data services.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
-      account with billing enabled; Basic familiarity with [Django](https://www.djangoproject.com/);
-      Basic understanding of containers and Kubernetes concepts.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Django, Docker, Kubernetes, Google Artifact Registry,
-      and Cloud SQL (PostgreSQL), Linux environments, Arm platforms such as Neoverse, and cloud
-      platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Get started with Django on Google Axion C4A, Configure
-      firewall rules for Django on Google Cloud, Create a Google Axion C4A Arm virtual machine
-      on GCP, Install Django on your Arm-based VM, and Verify Django installation and run the
-      development server.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/django/_index.md b/content/learning-paths/servers-and-cloud-computing/django/_index.md
index c1561c4e80..9ba96dcba8 100644
--- a/content/learning-paths/servers-and-cloud-computing/django/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/django/_index.md
@@ -21,52 +21,6 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v2
-  generated_at: '2026-05-08T18:10:02Z'
-  generator: template
-  source_hash: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
-  summary_generated_at: '2026-05-06T17:17:57Z'
-  summary_source_hash: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
-  faq_generated_at: '2026-05-08T18:10:02Z'
-  faq_source_hash: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
-  summary: >-
-    Learn how to create a simple Django web application and deploy it on Arm machines using Nginx
-    and PostgreSQL. It is designed for engineers who want to deploy a Django based application
-    on Arm machines. By the end, you will be able to create a simple Django application, deploy
-    the Django application using Nginx and PostgreSQL, and verify that the Django application
-    is working correctly. It focuses on tools and technologies such as Django, Python, NGINX,
-    and PostgreSQL, Linux environments, Arm platforms including Neoverse, and cloud platforms
-    such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps cover Install the required
-    dependencies, Create the Django application, and Deploy the Django application.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create a simple Django application, deploy the Django application using Nginx and
-      PostgreSQL, and verify that the Django application is working correctly. Learn how to create
-      a simple Django web application and deploy it on Arm machines using Nginx and PostgreSQL.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for engineers who want to deploy a Django based application
-      on Arm machines.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: At least either an [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/)
-      from a cloud service provider, on-premises Arm server, or a Linux virtual machine on your
-      Arm device.; Sudo access to install dependencies and to modify system configuration files.;
-      Be comfortable with SSH/Linux terminal and basic system administration tasks.; To install
-      both [Nginx](/learning-paths/servers-and-cloud-computing/nginx/) and [PostgreSQL](/learning-paths/servers-and-cloud-computing/postgresql/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Django, Python, NGINX, and PostgreSQL, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
-      Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Install the required dependencies, Create the Django
-      application, and Deploy the Django application.
-# END generated_summary_faq
 
 author: Diego Russo
 
diff --git a/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md b/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
index 4860a0d382..cbf4548052 100644
--- a/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
@@ -18,47 +18,6 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 82716c2de19d18a154c85d03b7f2ec01839284262914f7bb3ad04b18a105379d
-  summary: >-
-    Learn how to build and benchmark the Deep Learning Recommendation Model using PyTorch and
-    MLPerf on Arm Neoverse V2 processors. It is designed for software developers who want to set
-    up a pipeline in the cloud for recommendation models. You'll build and run the Deep Learning
-    Recommendation Model (DLRM) and benchmark its performance using MLPerf and PyTorch. By the
-    end, you will be able to build the Deep Learning Recommendation Model (DLRM) and run a modified
-    performant DLRMv2 benchmark and inspect the results. It focuses on tools and technologies
-    such as Docker, MLPerf, and Google Cloud, Linux environments, Arm platforms including Neoverse,
-    and cloud platforms such as AWS and Google Cloud. The main steps cover Overview and setup,
-    Download model weights and data, and Run the benchmark.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build the Deep Learning Recommendation Model (DLRM) and run a modified performant
-      DLRMv2 benchmark and inspect the results. Learn how to build and benchmark the Deep Learning
-      Recommendation Model using PyTorch and MLPerf on Arm Neoverse V2 processors.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to set up a pipeline in the
-      cloud for recommendation models. You'll build and run the Deep Learning Recommendation Model
-      (DLRM) and benchmark its performance using MLPerf and PyTorch.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Any [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/)
-      from a cloud service provider (CSP), or an on-premise Arm server with at least 400GB of
-      RAM and 800 GB of disk space.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Docker, MLPerf, and Google Cloud, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as AWS and Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview and setup, Download model weights and data,
-      and Run the benchmark.
-# END generated_summary_faq
 
 author: 
     - Phalani Paladugu
diff --git a/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md b/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
index f769b225fd..4a15012091 100644
--- a/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
@@ -27,59 +27,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 80785022032bf4e3c65da682e698940a212ee1ee77386698889a9fafbed9f823
-  summary: >-
-    Learn how to use the Docker MCP Toolkit with the Arm MCP Server and GitHub Copilot to automate
-    container and code migration from x86 to Arm64. Through a hands-on example, migrate a legacy
-    C++ application with AVX2 intrinsics to Arm Neon. It is designed for developers and DevOps
-    engineers who want to automate the migration of containerized applications from x86 to Arm64
-    using AI-powered tools in the Docker MCP Toolkit. By the end, you will be able to describe
-    how the Model Context Protocol (MCP) enables AI coding assistants to invoke structured migration
-    tools through the Arm MCP server, explain how the Docker MCP Toolkit connects AI coding assistants
-    to Arm MCP server, and install and configure the Docker MCP Toolkit with the Arm MCP Server,
-    GitHub MCP Server, and Sequential Thinking MCP Server. It focuses on tools and technologies
-    such as Docker, MCP, GitHub Copilot, C++, and VS Code, Linux and macOS environments, and Arm
-    platforms including Neoverse. The main steps cover Simplify Arm migration with the Docker
-    MCP Toolkit and Arm MCP Server, Set up Docker MCP Toolkit with Arm, GitHub, and Sequential
-    Thinking servers, Examine x86 AVX2 intrinsics in the demo application, Automate x86 to Arm
-    migration with GitHub Copilot, and Validate the Arm64 migration and test containers.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will describe how the Model Context Protocol (MCP) enables AI coding assistants to invoke
-      structured migration tools through the Arm MCP server, explain how the Docker MCP Toolkit
-      connects AI coding assistants to Arm MCP server, and install and configure the Docker MCP
-      Toolkit with the Arm MCP Server, GitHub MCP Server, and Sequential Thinking MCP Server.
-      Learn how to use the Docker MCP Toolkit with the Arm MCP Server and GitHub Copilot to automate
-      container and code migration from x86 to Arm64. Through a hands-on example, migrate a legacy
-      C++ application with AVX2 intrinsics to Arm Neon.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for developers and DevOps engineers who want to automate the migration
-      of containerized applications from x86 to Arm64 using AI-powered tools in the Docker MCP
-      Toolkit.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Docker Desktop 4.59 or later with MCP
-      Toolkit enabled; VS Code with the GitHub Copilot extension; A GitHub account with a personal
-      access token; A machine with at least 8 GB RAM (16 GB recommended); Basic familiarity with
-      Docker, C++, and SIMD intrinsics concepts.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Docker, MCP, GitHub Copilot, C++, and VS Code, Linux
-      and macOS environments, and Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Simplify Arm migration with the Docker MCP Toolkit
-      and Arm MCP Server, Set up Docker MCP Toolkit with Arm, GitHub, and Sequential Thinking
-      servers, Examine x86 AVX2 intrinsics in the demo application, Automate x86 to Arm migration
-      with GitHub Copilot, and Validate the Arm64 migration and test containers.
-# END generated_summary_faq
+
 
 author: Ajeet Singh Raina
 
diff --git a/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md b/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md
index cc75f96ded..71597631e7 100644
--- a/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md
@@ -24,50 +24,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 08d8f0c86625ef41476d3a8b24bad9b0a0820797022ef847bf9bb17a976726a7
-  summary: >-
-    Learn how to build and run an OrchardCore CMS .NET application on Azure Cobalt 100 processors,
-    covering AnyCPU configuration and shared C library integration. It is designed for .NET developers
-    who want to take advantage of the performance and cost benefits of Azure Cobalt processors.
-    By the end, you will be able to build and run a basic OrchardCore CMS application, integrate
-    a simple C shared library into a .NET application, and configure architecture-agnostic builds
-    using AnyCPU. It focuses on tools and technologies such as .NET, Orchard Core, and C, Linux
-    environments, Arm platforms including Neoverse, and cloud platforms such as Microsoft Azure.
-    The main steps cover Build and run an OrchardCore CMS app on Azure Cobalt (Arm64), Integrate
-    a C shared library into your .NET OrchardCore app, Configure and run an OrchardCore app, and
-    Evaluate .NET performance across versions on Arm.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build and run a basic OrchardCore CMS application, integrate a simple C shared
-      library into a .NET application, and configure architecture-agnostic builds using AnyCPU.
-      Learn how to build and run an OrchardCore CMS .NET application on Azure Cobalt 100 processors,
-      covering AnyCPU configuration and shared C library integration.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for .NET developers who want to take advantage of the performance
-      and cost benefits of Azure Cobalt processors.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Microsoft Azure account with permissions
-      to deploy virtual machines; .NET SDK 8.0 or later; Basic knowledge of C and C#; GCC installed
-      (Linux) or access to a cross-compiler; OrchardCore application created using the .NET CLI
-      or Visual Studio.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including .NET, Orchard Core, and C, Linux environments, Arm
-      platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Build and run an OrchardCore CMS app on Azure Cobalt
-      (Arm64), Integrate a C shared library into your .NET OrchardCore app, Configure and run
-      an OrchardCore app, and Evaluate .NET performance across versions on Arm.
-# END generated_summary_faq
+
 
 author: Joe Stech
 
diff --git a/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
index d691009614..f8c315543d 100644
--- a/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
@@ -22,55 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 4ef29931bf19dc95bb586440796381725c271ef1b953dcf46d00ad9617eabbb1
-  summary: >-
-    Learn how to deploy Dynatrace OneAgent on Azure Cobalt 100 Arm64 virtual machines and configure
-    ActiveGate for secure infrastructure and application monitoring. It is designed for developers,
-    DevOps engineers, and platform engineers who want to implement infrastructure and application
-    monitoring using Dynatrace on Arm-based cloud environments. By the end, you will be able to
-    deploy Dynatrace OneAgent on Azure Cobalt 100 Arm64 virtual machines, configure Dynatrace
-    ActiveGate for secure monitoring communication, and monitor system resources, processes, and
-    services using Dynatrace. It focuses on tools and technologies such as Dynatrace, NGINX, and
-    ActiveGate, Linux environments, Arm platforms including Neoverse, and cloud platforms such
-    as Microsoft Azure. The main steps cover Overview of Azure Cobalt 100 and Dynatrace, Create
-    an Azure Cobalt 100 Arm64 virtual machine, Create a firewall rule on Azure, Install Dynatrace
-    OneAgent on Azure Ubuntu Arm64 virtual machine, and Install Dynatrace ActiveGate on Azure
-    Ubuntu Arm64 virtual machine.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will deploy Dynatrace OneAgent on Azure Cobalt 100 Arm64 virtual machines, configure
-      Dynatrace ActiveGate for secure monitoring communication, and monitor system resources,
-      processes, and services using Dynatrace. Learn how to deploy Dynatrace OneAgent on Azure
-      Cobalt 100 Arm64 virtual machines and configure ActiveGate for secure infrastructure and
-      application monitoring.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers, DevOps engineers, and platform engineers who
-      want to implement infrastructure and application monitoring using Dynatrace on Arm-based
-      cloud environments.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Microsoft Azure account](https://azure.microsoft.com/)
-      with access to Cobalt 100 based instances (Dpsv6); Basic knowledge of Linux command-line
-      operations; Familiarity with SSH and remote server access; Basic understanding of cloud
-      infrastructure and monitoring concepts.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Dynatrace, NGINX, and ActiveGate, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview of Azure Cobalt 100 and Dynatrace, Create
-      an Azure Cobalt 100 Arm64 virtual machine, Create a firewall rule on Azure, Install Dynatrace
-      OneAgent on Azure Ubuntu Arm64 virtual machine, and Install Dynatrace ActiveGate on Azure
-      Ubuntu Arm64 virtual machine.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/ecs/_index.md b/content/learning-paths/servers-and-cloud-computing/ecs/_index.md
index c964ebdcc2..e0883002b3 100644
--- a/content/learning-paths/servers-and-cloud-computing/ecs/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ecs/_index.md
@@ -19,46 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: ef5f9e7c8844b20b9044b43f4758bc1d74374521093d7738a7f8832d21f1dcac
-  summary: >-
-    Learn how to create an AWS ECS cluster with Fargate and AWS Graviton processors, then create
-    and run containerized tasks on Arm infrastructure. It is designed for developers who want
-    to use AWS Graviton processors with Amazon Elastic Container Service (ECS). By the end, you
-    will be able to create an AWS ECS cluster with Fargate and AWS Graviton processors, create
-    and run an AWS ECS task, and use Terraform to automate deployment of an ECS cluster. It focuses
-    on tools and technologies such as Terraform and AWS Elastic Container Service (ECS), Linux
-    environments, Arm platforms including Neoverse, and cloud platforms such as AWS. The main
-    steps cover Deploy containers using ECS on AWS Graviton processors and Deploy ECS containers
-    on AWS Graviton processor using Terraform.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create an AWS ECS cluster with Fargate and AWS Graviton processors, create and
-      run an AWS ECS task, and use Terraform to automate deployment of an ECS cluster. Learn how
-      to create an AWS ECS cluster with Fargate and AWS Graviton processors, then create and run
-      containerized tasks on Arm infrastructure.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers who want to use AWS Graviton processors with
-      Amazon Elastic Container Service (ECS).
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An AWS account; A computer with Docker,
-      AWS CLI, and Terraform installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Terraform and AWS Elastic Container Service (ECS),
-      Linux environments, Arm platforms such as Neoverse, and cloud platforms such as AWS.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Deploy containers using ECS on AWS Graviton processors
-      and Deploy ECS containers on AWS Graviton processor using Terraform.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md b/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
index 98645ef410..44b9cdae87 100644
--- a/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
@@ -20,50 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: e32bdb090c422d1fb5bb1f9bd3af56c55fdf8989e6a7fe6a101e90dcb6f3eadd
-  summary: >-
-    Learn how to use docker buildx and docker manifest to build and deploy multi-architecture
-    container images with x86/amd64 and arm64 support on Amazon EKS. It is designed for software
-    developers who want to understand how to build and deploy a multi-architecture application
-    with x86/amd64 and arm64-based container images on Amazon EKS. By the end, you will be able
-    to build x86/amd64 and arm64 container images with docker buildx and docker manifest, understand
-    the nuances of building a multi-architecture container image, and deploy a multi-arch container
-    application across multiple architectures in a single Amazon EKS cluster. It focuses on tools
-    and technologies such as Kubernetes and AWS Elastic Kubernetes Service (EKS), Linux environments,
-    Arm platforms including Neoverse, and cloud platforms such as AWS. The main steps cover Build
-    and deploy a multi-arch application on Amazon EKS.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build x86/amd64 and arm64 container images with docker buildx and docker manifest,
-      understand the nuances of building a multi-architecture container image, and deploy a multi-arch
-      container application across multiple architectures in a single Amazon EKS cluster. Learn
-      how to use docker buildx and docker manifest to build and deploy multi-architecture container
-      images with x86/amd64 and arm64 support on Amazon EKS.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers who want to understand how to build and
-      deploy a multi-architecture application with x86/amd64 and arm64-based container images
-      on Amazon EKS.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An [AWS account](https://aws.amazon.com/).
-      Create an account if needed.; A computer with [Amazon eksctl CLI](/install-guides/eksctl)
-      and [kubectl](/install-guides/kubectl/)installed.; Docker installed on local computer [Docker](/install-guides/docker).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Kubernetes and AWS Elastic Kubernetes Service (EKS),
-      Linux environments, Arm platforms such as Neoverse, and cloud platforms such as AWS.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Build and deploy a multi-arch application on Amazon
-      EKS.
-# END generated_summary_faq
+
 
 author: Pranay Bakre
 
diff --git a/content/learning-paths/servers-and-cloud-computing/eks/_index.md b/content/learning-paths/servers-and-cloud-computing/eks/_index.md
index 96a317469d..f8f8ddebb9 100644
--- a/content/learning-paths/servers-and-cloud-computing/eks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/eks/_index.md
@@ -18,43 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 4f1c448eef66300e024bda27c420f9746047c0c4b76e8556d3d8693382206055
-  summary: >-
-    Learn how to provision an Amazon EKS cluster on Arm-based Graviton instances and deploy a
-    WordPress application with MySQL database. It is designed for software developers new to Kubernetes
-    on AWS who want to gain experience with cloud applications. By the end, you will be able to
-    provision an Amazon Elastic Kubernetes Service (EKS) cluster on Arm-based instances and deploy
-    Wordpress with MySQL on EKS. It focuses on tools and technologies such as AWS Elastic Kubernetes
-    Service (EKS), Kubernetes, SQL, MySQL, and WordPress, Linux environments, Arm platforms including
-    Neoverse, and cloud platforms such as AWS. The main steps cover Create an EKS cluster and
-    Deploy WordPress.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will provision an Amazon Elastic Kubernetes Service (EKS) cluster on Arm-based instances
-      and deploy Wordpress with MySQL on EKS. Learn how to provision an Amazon EKS cluster on
-      Arm-based Graviton instances and deploy a WordPress application with MySQL database.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers new to Kubernetes on AWS who want
-      to gain experience with cloud applications.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Amazon Web Services (AWS) [account](https://aws.amazon.com/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including AWS Elastic Kubernetes Service (EKS), Kubernetes,
-      SQL, MySQL, and WordPress, Linux environments, Arm platforms such as Neoverse, and cloud
-      platforms such as AWS.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Create an EKS cluster and Deploy WordPress.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md
index 346eb19737..ea9ab15710 100644
--- a/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md
@@ -21,51 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: f36b1e9a45b6ec29d8041b70997550d917322cf9cdd456db26083169d21175ed
-  summary: >-
-    Learn how to install and configure Envoy proxy on Google Cloud Axion C4A Arm64 instances and
-    benchmark HTTP proxy performance with load testing. It is designed for This introductory topic
-    for software developers migrating Envoy Proxy workloads from x86_64 to Arm-based servers,
-    specifically on Google Cloud C4A virtual machines built on Axion processors. By the end, you
-    will be able to provision an Arm-based C4A VM on Google Cloud Platform (GCP), install and
-    configure Envoy Proxy on a C4A instance, and validate Envoy functionality with baseline tests.
-    It focuses on tools and technologies such as Envoy, Siege, Networking, and Service Mesh, Linux
-    environments, Arm platforms including Neoverse, and cloud platforms such as Google Cloud.
-    The main steps cover Get started with Envoy Proxy on Google Axion C4A (Arm Neoverse V2), Create
-    a Google Axion C4A Arm virtual machine on GCP, Deploy Envoy on Google Axion C4A Arm virtual
-    machines, Run baseline Envoy testing on a Google Axion C4A Arm VM, and Benchmark Envoy on
-    Google Cloud for Arm64 and x86_64 with Siege.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will provision an Arm-based C4A VM on Google Cloud Platform (GCP), install and configure
-      Envoy Proxy on a C4A instance, and validate Envoy functionality with baseline tests. Learn
-      how to install and configure Envoy proxy on Google Cloud Axion C4A Arm64 instances and benchmark
-      HTTP proxy performance with load testing.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This introductory topic for software developers migrating Envoy Proxy workloads from x86_64
-      to Arm-based servers, specifically on Google Cloud C4A virtual machines built on Axion processors.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free?utm_source=google&hl=en)
-      account with billing enabled; Familiarity with networking concepts and the [Envoy architecture](https://www.envoyproxy.io/docs/envoy/latest/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Envoy, Siege, Networking, and Service Mesh, Linux
-      environments, Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Get started with Envoy Proxy on Google Axion C4A (Arm
-      Neoverse V2), Create a Google Axion C4A Arm virtual machine on GCP, Deploy Envoy on Google
-      Axion C4A Arm virtual machines, Run baseline Envoy testing on a Google Axion C4A Arm VM,
-      and Benchmark Envoy on Google Cloud for Arm64 and x86_64 with Siege.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/envoy/_index.md b/content/learning-paths/servers-and-cloud-computing/envoy/_index.md
index 771e3512ed..5bbfc9a96a 100644
--- a/content/learning-paths/servers-and-cloud-computing/envoy/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/envoy/_index.md
@@ -19,43 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: d492b6dce11b7d8f6591ff3b3ce9aa2c382a6a7a749add228c3ac1f6ae57e218
-  summary: >-
-    Learn how to build, install, and run Envoy proxy on Arm servers and configure it as a web
-    server for traffic management. It is designed for engineers who want to use Envoy on Arm.
-    By the end, you will be able to build, install, and run Envoy on Arm servers, setup Envoy
-    as a web server, and verify Envoy is working correctly. It focuses on tools and technologies
-    such as Envoy, Linux environments, Arm platforms including Neoverse, and cloud platforms such
-    as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps cover Build and install
-    Envoy and Run Envoy as a service.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build, install, and run Envoy on Arm servers, setup Envoy as a web server, and
-      verify Envoy is working correctly. Learn how to build, install, and run Envoy proxy on Arm
-      servers and configure it as a web server for traffic management.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for engineers who want to use Envoy on Arm.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: To run Envoy as a web server, you will
-      need at least one [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/)
-      from a cloud service provider or an on-premises Arm server.; Network settings (firewalls
-      and security groups) which allow communication on port 22 (SSH) and port 80 (HTTP).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Envoy, Linux environments, Arm platforms such as
-      Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Build and install Envoy and Run Envoy as a service.
-# END generated_summary_faq
+
 
 author: Zhengjun Xing
 
diff --git a/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md
index 2e8d3189f4..054c9ddf57 100644
--- a/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md
@@ -20,42 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: cbbcc8fa33f864e422f8db7d58fba32c26f6070be2846b246837c63ccf37e1c7
-  summary: >-
-    Learn how to optimize Envoy proxy performance on Arm servers using Transparent Huge Pages
-    and Profile-Guided Optimization techniques. It is designed for software developers who want
-    to use Envoy on Arm. By the end, you will be able to tune Envoy by THP, tune Envoy with PGO,
-    and learn about kernel parameters that can impact Envoy performance. It focuses on tools and
-    technologies such as Envoy and Runbook, Linux environments, Arm platforms including Neoverse,
-    and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps
-    cover Tune Envoy by THP and Tune Envoy by PGO.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will tune Envoy by THP, tune Envoy with PGO, and learn about kernel parameters that
-      can impact Envoy performance. Learn how to optimize Envoy proxy performance on Arm servers
-      using Transparent Huge Pages and Profile-Guided Optimization techniques.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers who want to use Envoy on Arm.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Cloud or bare-metal installation of
-      an Envoy service; Review [Learn how to deploy Envoy](/learning-paths/servers-and-cloud-computing/envoy/)
-      if you do not already have an Envoy setup.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Envoy and Runbook, Linux environments, Arm platforms
-      such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Tune Envoy by THP and Tune Envoy by PGO.
-# END generated_summary_faq
+
 
 author: Zhengjun Xing
 
diff --git a/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md b/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md
index b3176576b0..a0ebef0cd5 100644
--- a/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md
@@ -23,49 +23,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 02eedcebf59a8ab506d4ebbf5bbe20ead367cf3c0700950db5a9059d2f671853
-  summary: >-
-    Learn how stack buffer overflow exploits work on AArch64 by analyzing stack frame layouts,
-    redirecting control flow, and understanding defense mechanisms. It is designed for software
-    developers interested in understanding how memory vulnerability-based exploits work on AArch64
-    and how to defend against them. By the end, you will be able to analyze the stack frame layout
-    to derive which field in user input overwrites the return address stored on the stack and
-    build a basic end-to-end exploit by changing the return address to an attacker-controlled
-    value. It focuses on tools and technologies such as Clang, C, Assembly, and Runbook, Linux
-    environments, and Arm platforms including AArch64. The main steps cover Introduction: "Smashing
-    the stack", Docker Setup, Frame Layout, Stack Buffer Overflow, and Redirect control flow.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will analyze the stack frame layout to derive which field in user input overwrites the
-      return address stored on the stack and build a basic end-to-end exploit by changing the
-      return address to an attacker-controlled value. Learn how stack buffer overflow exploits
-      work on AArch64 by analyzing stack frame layouts, redirecting control flow, and understanding
-      defense mechanisms.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers interested in understanding how memory
-      vulnerability-based exploits work on AArch64 and how to defend against them.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm computer running linux with [Docker](/install-guides/docker/)
-      installed.; Some familiarity with reading and writing basic C code and AArch64 assembly
-      code.; Some familiarity with running linux command line commands.; Some familiarity with
-      using a gdb debugger.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Clang, C, Assembly, and Runbook, Linux environments,
-      and Arm platforms such as AArch64.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Introduction: "Smashing the stack", Docker Setup,
-      Frame Layout, Stack Buffer Overflow, and Redirect control flow.
-# END generated_summary_faq
+
 
 author: Kristof Beyls
 
diff --git a/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md b/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
index 1dc00ae8e6..c2cdf3939e 100644
--- a/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
@@ -20,52 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: dde32f5d14a877313a8b0aafec58acb09cd1e77f4fc9138b9e1bd6d9fedf250a
-  summary: >-
-    Learn how to identify and fix false sharing issues using Perf C2C cache line analysis and
-    Arm Statistical Profiling Extension on Arm-based cloud systems. It is designed for performance-oriented
-    developers working on Arm-based cloud or server systems who want to optimize memory access
-    patterns and investigate cache inefficiencies using Perf C2C and Arm SPE. By the end, you
-    will be able to identify and fix false sharing issues using Perf C2C, a cache line analysis
-    tool, enable and use the Arm Statistical Profiling Extension (SPE) on Linux systems, and investigate
-    cache line performance with Perf C2C. It focuses on tools and technologies such as perf and
-    Runbook, Linux environments, Arm platforms including Neoverse, and cloud platforms such as
-    AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps cover Arm Statistical Profiling
-    Extension and false sharing, Set up your environment for Arm SPE and Perf C2C profiling, False
-    sharing example, and Perform root cause analysis with Perf C2C.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will identify and fix false sharing issues using Perf C2C, a cache line analysis tool,
-      enable and use the Arm Statistical Profiling Extension (SPE) on Linux systems, and investigate
-      cache line performance with Perf C2C. Learn how to identify and fix false sharing issues
-      using Perf C2C cache line analysis and Arm Statistical Profiling Extension on Arm-based
-      cloud systems.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This topic is for performance-oriented developers working on Arm-based cloud or server systems
-      who want to optimize memory access patterns and investigate cache inefficiencies using Perf
-      C2C and Arm SPE.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Access to an Arm-based cloud instance
-      with support for the Arm Statistical Profiling Extension (SPE).; A basic understanding of
-      cache coherency and its impact on performance.; Familiarity with Linux Perf tools.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including perf and Runbook, Linux environments, Arm platforms
-      such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Arm Statistical Profiling Extension and false sharing,
-      Set up your environment for Arm SPE and Perf C2C profiling, False sharing example, and Perform
-      root cause analysis with Perf C2C.
-# END generated_summary_faq
+
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md b/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md
index 68a0d01738..3c3e9dc569 100644
--- a/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md
@@ -20,49 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 978d3da8668e38758c138313c31809f3de5a8cefd1b7c2b47a536c0ee364b692
-  summary: >-
-    Learn how to build custom Linux kernels using tuxmake and Fastpath, then benchmark and compare
-    kernel versions on Arm-based EC2 instances. It is designed for software developers and performance
-    engineers who want to benchmark and compare different Linux kernel versions on Arm servers.
-    By the end, you will be able to build custom Linux kernels for Arm systems using tuxmake and
-    Fastpath, configure and provision Arm-based EC2 instances for kernel testing, and create and
-    execute test plans that compare kernel performance across versions. It focuses on tools and
-    technologies such as Fastpath, tuxmake, and Linux, Linux environments, Arm platforms including
-    Neoverse, and cloud platforms such as AWS. The main steps cover Understand the Fastpath kernel
-    benchmarking workflow, Set up the kernel build host, Set up the Fastpath host, Set up the
-    System Under Test, and Generate and execute the benchmark plan.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build custom Linux kernels for Arm systems using tuxmake and Fastpath, configure
-      and provision Arm-based EC2 instances for kernel testing, and create and execute test plans
-      that compare kernel performance across versions. Learn how to build custom Linux kernels
-      using tuxmake and Fastpath, then benchmark and compare kernel versions on Arm-based EC2
-      instances.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers and performance engineers who want to
-      benchmark and compare different Linux kernel versions on Arm servers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An AWS account with permissions to create
-      EC2 instances; Familiarity with basic Linux administration and SSH.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Fastpath, tuxmake, and Linux, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as AWS.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Understand the Fastpath kernel benchmarking workflow,
-      Set up the kernel build host, Set up the Fastpath host, Set up the System Under Test, and
-      Generate and execute the benchmark plan.
-# END generated_summary_faq
+
 
 author: Geremy Cohen
 
diff --git a/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md b/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md
index d0563cffd8..33067fbe12 100644
--- a/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md
@@ -19,49 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: a67c552b76df6323eccc331c9146d0fcbdb8ad222fe81dbf2e1c2339c7609b61
-  summary: >-
-    Learn how to implement exponential functions using Arm SVE intrinsics with the FEXPA instruction
-    for hardware-accelerated computations on Neoverse processors. It is designed for developers
-    interested in accelerating exponential function computations using Arm's Scalable Vector Extension
-    (SVE). The FEXPA instruction provides hardware acceleration for exponential calculations on
-    Arm Neoverse processors. By the end, you will be able to implement the exponential function
-    using SVE intrinsics and optimize the function with FEXPA. It focuses on tools and technologies
-    such as C and CPP, Linux and macOS environments, Arm platforms including Neoverse, and cloud
-    platforms such as AWS, Microsoft Azure, and Google Cloud. The main steps cover Learn exponential
-    function optimization techniques, Implement exponential with SVE intrinsics, Optimize with
-    FEXPA instruction, and Review benefits and next steps.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will implement the exponential function using SVE intrinsics and optimize the function
-      with FEXPA. Learn how to implement exponential functions using Arm SVE intrinsics with the
-      FEXPA instruction for hardware-accelerated computations on Neoverse processors.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers interested in accelerating exponential function
-      computations using Arm's Scalable Vector Extension (SVE). The FEXPA instruction provides
-      hardware acceleration for exponential calculations on Arm Neoverse processors.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Access to an [AWS Graviton4, Google
-      Axion, or Azure Cobalt 100 virtual machine from a cloud service provider](/learning-paths/servers-and-cloud-computing/csp/);
-      Some familiarity with SIMD programming and SVE intrinsics.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including C and CPP, Linux and macOS environments, Arm platforms
-      such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, and Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Learn exponential function optimization techniques,
-      Implement exponential with SVE intrinsics, Optimize with FEXPA instruction, and Review benefits
-      and next steps.
-# END generated_summary_faq
+
 
 author: 
 - Arnaud Grasset
diff --git a/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md
index 547daeab61..55d68482a6 100644
--- a/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md
@@ -20,52 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: adcf2a1b8a4a77e5834e14a40e46e27b0cbe5e440fbf732a4366ce486d7fafb7
-  summary: >-
-    Learn how to install and configure Apache Flink on Google Cloud Axion C4A Arm64 instances
-    and benchmark stream processing performance with Nexmark. It is designed for developers deploying
-    and optimizing Apache Flink workloads on Linux/Arm64 environments, specifically using Google
-    Cloud C4A virtual machines powered by Axion processors. By the end, you will be able to provision
-    an Arm-based SUSE SLES virtual machine on Google Cloud (C4A with Axion processors), install
-    and configure Apache Flink on an Arm64 instance, and validate Flink functionality by starting
-    the cluster and running a baseline job. It focuses on tools and technologies such as Flink,
-    Java, and Maven, Linux environments, Arm platforms including Neoverse, and cloud platforms
-    such as Google Cloud. The main steps cover Get started with Apache Flink on Google Axion C4A,
-    Create a Google Axion C4A Arm virtual machine, Install Apache Flink, Test Flink baseline functionality,
-    and Benchmark Flink performance.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will provision an Arm-based SUSE SLES virtual machine on Google Cloud (C4A with Axion
-      processors), install and configure Apache Flink on an Arm64 instance, and validate Flink
-      functionality by starting the cluster and running a baseline job. Learn how to install and
-      configure Apache Flink on Google Cloud Axion C4A Arm64 instances and benchmark stream processing
-      performance with Nexmark.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers deploying and optimizing Apache Flink workloads
-      on Linux/Arm64 environments, specifically using Google Cloud C4A virtual machines powered
-      by Axion processors.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
-      account with billing enabled; Basic familiarity with [Apache Flink](https://flink.apache.org/)
-      and its runtime environment.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Flink, Java, and Maven, Linux environments, Arm
-      platforms such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Get started with Apache Flink on Google Axion C4A,
-      Create a Google Axion C4A Arm virtual machine, Install Apache Flink, Test Flink baseline
-      functionality, and Benchmark Flink performance.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/flink/_index.md b/content/learning-paths/servers-and-cloud-computing/flink/_index.md
index c3fe9a4cbe..ac297eed6b 100644
--- a/content/learning-paths/servers-and-cloud-computing/flink/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/flink/_index.md
@@ -18,45 +18,6 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 974d71b1ee968e3aeabe900bfdd52ae4fbfdd0ca7dea420e6c1fc01f5475e8c1
-  summary: >-
-    Learn how to install and run Apache Flink on Arm servers and benchmark stream processing performance
-    using the Nexmark benchmark suite. It is designed for software developers using Flink as their
-    stream processing and batch processing framework on Arm servers. By the end, you will be able
-    to install and run Flink on an Arm server and benchmark the performance of Flink. It focuses
-    on tools and technologies such as Flink, Java, Nexmark, and Runbook, Linux environments, Arm
-    platforms including Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud,
-    and Oracle. The main steps cover Setup and Configure Flink, Setup and Config Nexmark, and
-    Benchmark Flink with nexmark-flink on Arm.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install and run Flink on an Arm server and benchmark the performance of Flink.
-      Learn how to install and run Apache Flink on Arm servers and benchmark stream processing
-      performance using the Nexmark benchmark suite.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers using Flink as their stream processing
-      and batch processing framework on Arm servers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm based instance server from a
-      cloud service provider.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Flink, Java, Nexmark, and Runbook, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
-      Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Setup and Configure Flink, Setup and Config Nexmark,
-      and Benchmark Flink with nexmark-flink on Arm.
-# END generated_summary_faq
 
 author: Ying Yu
 
diff --git a/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md b/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md
index fcfd48e252..ec7912342b 100644
--- a/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md
@@ -23,53 +23,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 85359b9210812675169f149c86bf11a1736ab838c011d18e876b246463d297ee
-  summary: >-
-    Learn how to build scalable machine learning workflow pipelines on Google Cloud C4A Axion
-    processors using Flyte for workflow orchestration and gRPC for distributed service communication.
-    It is designed for developers, data engineers, and ML engineers who want to build scalable
-    machine learning workflow pipelines on Arm64-based Google Cloud C4A Axion processors using
-    Flyte workflow orchestration and gRPC-based microservices. By the end, you will be able to
-    deploy Flyte workflow pipelines on Google Cloud C4A Axion processors, build distributed machine
-    learning pipelines using Flyte tasks, and implement gRPC-based services for feature engineering.
-    It focuses on tools and technologies such as Flyte, Python, and gRPC, Linux environments,
-    Arm platforms including Neoverse, and cloud platforms such as Google Cloud. The main steps
-    cover Understand Flyte and gRPC ML workflows on Google Axion, Create a Google Axion C4A Arm
-    virtual machine, Install Flyte and gRPC tools on Axion, Build a gRPC feature engineering service,
-    and Create ML Training Workflow.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will deploy Flyte workflow pipelines on Google Cloud C4A Axion processors, build distributed
-      machine learning pipelines using Flyte tasks, and implement gRPC-based services for feature
-      engineering. Learn how to build scalable machine learning workflow pipelines on Google Cloud
-      C4A Axion processors using Flyte for workflow orchestration and gRPC for distributed service
-      communication.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers, data engineers, and ML engineers who want
-      to build scalable machine learning workflow pipelines on Arm64-based Google Cloud C4A Axion
-      processors using Flyte workflow orchestration and gRPC-based microservices.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
-      account with billing enabled; Basic familiarity with Python; Basic understanding of machine
-      learning pipelines.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Flyte, Python, and gRPC, Linux environments, Arm
-      platforms such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Understand Flyte and gRPC ML workflows on Google Axion,
-      Create a Google Axion C4A Arm virtual machine, Install Flyte and gRPC tools on Axion, Build
-      a gRPC feature engineering service, and Create ML Training Workflow.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
index ce64de7a86..8a79d94f46 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
@@ -25,53 +25,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 94866800acca2c5f9cd89f76f972af1a343aad5777b6844d49fac09eb764f580
-  summary: >-
-    Learn how to create an Arm64 Azure VM, install .NET SDK, containerize .NET applications, and
-    push Docker images to Azure Container Registry. It is designed for software developers interested
-    in learning how to deploy .NET applications to Microsoft Azure using Arm64-powered Virtual
-    Machines. You will also learn how to containerize .NET applications, and push Docker images
-    to the Azure Container Registry. By the end, you will be able to create a Virtual Machine
-    (VM) in Microsoft Azure, connect to the VM to install app dependencies, including SDK, and
-    create and run the .NET application. It focuses on tools and technologies such as .NET SDK
-    and C#, Linux environments, Arm platforms including Neoverse, and cloud platforms such as
-    Microsoft Azure. The main steps cover Motivation, Creating the Virtual Machine, Connecting
-    to the Virtual Machine, Installing application dependencies and running the application, and
-    Create a Dockerfile using Visual Studio Code.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create a Virtual Machine (VM) in Microsoft Azure, connect to the VM to install
-      app dependencies, including SDK, and create and run the .NET application. Learn how to create
-      an Arm64 Azure VM, install .NET SDK, containerize .NET applications, and push Docker images
-      to Azure Container Registry.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This learning path is for software developers interested in learning how to deploy .NET
-      applications to Microsoft Azure using Arm64-powered Virtual Machines. You will also learn
-      how to containerize .NET applications, and push Docker images to the Azure Container Registry.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A subscription to Azure. Use this link
-      to sign up for a free account: https://azure.microsoft.com/en-us/free/; Visual Studio Code:
-      https://code.visualstudio.com/download; Docker Extension for Visual Studio Code: https://code.visualstudio.com/docs/containers/overview;
-      C# Extension for Visual Studio Code: https://marketplace.visualstudio.com/items?itemName=ms-dotnettools.csharp;
-      [Install Docker on Arm64](/install-guides/docker/docker-desktop/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including .NET SDK and C#, Linux environments, Arm platforms
-      such as Neoverse, and cloud platforms such as Microsoft Azure.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Motivation, Creating the Virtual Machine, Connecting
-      to the Virtual Machine, Installing application dependencies and running the application,
-      and Create a Dockerfile using Visual Studio Code.
-# END generated_summary_faq
+
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md
index 40ce3f9083..36029f15cd 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md
@@ -19,49 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 3823ad3df8ae868acfd59b5b5b541240624572fe739aaf2275185d5cd3578032
-  summary: >-
-    Learn how to create and run Docker containers on Azure Container Instances for Arm64-based
-    containerized application deployment. It is designed for developers interested in learning
-    how to create and run a Docker container in Microsoft Azure using Azure Container Instances.
-    By the end, you will be able to create Azure Container Instances, run a Docker container in
-    Azure Container Instances, and enable Admin in Azure Container Registry, which is required
-    when you are deploying Docker containers from the Azure Container Registry. It focuses on
-    tools and technologies such as ASP.NET Core and Docker, Linux and Windows environments, Arm
-    platforms including Neoverse, and cloud platforms such as Microsoft Azure. The main steps
-    cover Azure Container Instances, Access the application, and Deploying a Docker container
-    from the Azure Container Registry.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create Azure Container Instances, run a Docker container in Azure Container Instances,
-      and enable Admin in Azure Container Registry, which is required when you are deploying Docker
-      containers from the Azure Container Registry. Learn how to create and run Docker containers
-      on Azure Container Instances for Arm64-based containerized application deployment.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers interested in learning how to create and run
-      a Docker container in Microsoft Azure using Azure Container Instances.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Azure subscription. Use this link to
-      sign up for a free account: https://azure.microsoft.com/en-us/free/.; Complete the [first
-      learning path](/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1)
-      of this series.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including ASP.NET Core and Docker, Linux and Windows environments,
-      Arm platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Azure Container Instances, Access the application,
-      and Deploying a Docker container from the Azure Container Registry.
-# END generated_summary_faq
+
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
index b638a9556a..6ae0050e6d 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
@@ -19,47 +19,6 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: a3347a62c3322d88b80fc7848fa5ee1d92ee86333c2a21891b958286f8b70935
-  summary: >-
-    Learn how to create an Azure Kubernetes Service cluster with Arm64 virtual machines and deploy
-    a containerized application to AKS. It is designed for This learning path is dedicated to
-    developers interested in learning how to deploy applications to the Azure Kubernetes Cluster
-    powered by arm64-based virtual machines. By the end, you will be able to create a Kubernetes
-    cluster using the Azure Kubernetes Service and deploy a containerized application to the Azure
-    Kubernetes Service. It focuses on tools and technologies such as ASP.NET Core, Docker, and
-    Kubernetes, Linux environments, Arm platforms including Neoverse, and cloud platforms such
-    as Microsoft Azure. The main steps cover Motivation, Create the Kubernetes cluster with Azure
-    Container Registry, Connecting to the cluster, and Deploying an application.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create a Kubernetes cluster using the Azure Kubernetes Service and deploy a containerized
-      application to the Azure Kubernetes Service. Learn how to create an Azure Kubernetes Service
-      cluster with Arm64 virtual machines and deploy a containerized application to AKS.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This learning path is dedicated to developers interested in learning how to deploy applications
-      to the Azure Kubernetes Cluster powered by arm64-based virtual machines.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Azure subscription. Use this link to
-      sign up for a free account: https://azure.microsoft.com/en-us/free/.; Complete the [first](/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1)
-      and [second](/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2) learning
-      paths of this series.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including ASP.NET Core, Docker, and Kubernetes, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Motivation, Create the Kubernetes cluster with Azure
-      Container Registry, Connecting to the cluster, and Deploying an application.
-# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md
index eb411b3a49..1dbf275fc4 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md
@@ -22,49 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 1510c787979257e191196e13999e98c20ca24d0857f72075649c28520c74c576
-  summary: >-
-    Learn how to automate Azure resource deployment using Infrastructure as Code with Pulumi to
-    provision Azure Container Instances for containerized applications. It is designed for developers
-    interested in learning how to automate their cloud deployments using the Infrastructure as
-    Code (IaC). By the end, you will be able to automate the deployment of all the Azure resources
-    required to deploy a containerized application to the Azure Container Instance, set up Pulumi
-    for Infrastructure as Code (IaC), and automate the provisioning of the Azure resources. It
-    focuses on tools and technologies such as TypeScript and Docker, Windows environments, Arm
-    platforms including Neoverse, and cloud platforms such as Microsoft Azure. The main steps
-    cover Motivation, Pulumi, Pulumi project, and Resources declaration and deployment.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will automate the deployment of all the Azure resources required to deploy a containerized
-      application to the Azure Container Instance, set up Pulumi for Infrastructure as Code (IaC),
-      and automate the provisioning of the Azure resources. Learn how to automate Azure resource
-      deployment using Infrastructure as Code with Pulumi to provision Azure Container Instances
-      for containerized applications.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers interested in learning how to automate their
-      cloud deployments using the Infrastructure as Code (IaC).
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Azure subscription. Use this link to
-      sign up for a free account: https://azure.microsoft.com/en-us/free/; Visual Studio Code;
-      A free Pulumi account and Pulumi CLI (details provided in this learning path); Node.js (details
-      provided in this learning path); Azure CLI (details provided in this learning path).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including TypeScript and Docker, Windows environments, Arm
-      platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Motivation, Pulumi, Pulumi project, and Resources
-      declaration and deployment.
-# END generated_summary_faq
+
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/servers-and-cloud-computing/funasr/_index.md b/content/learning-paths/servers-and-cloud-computing/funasr/_index.md
index 12d454a58c..c93dfb488c 100644
--- a/content/learning-paths/servers-and-cloud-computing/funasr/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/funasr/_index.md
@@ -18,53 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 410ec28d257ce7aa1308f11a741aa87baea80daf1acb113c4149761144269022
-  summary: >-
-    Learn how to deploy the ModelScope FunASR Chinese automatic speech recognition model on Arm-based
-    servers with real-time transcription and sentiment analysis. It is designed for developers
-    interested in learning how to deploy the ModelScope FunASR Chinese Automatic Speech Recognition
-    (ASR) model on Arm-based servers. By the end, you will be able to leverage open-source large
-    language models and tools to build Chinese ASR applications, deploy real-time Chinese speech
-    recognition, punctuation restoration, and sentiment analysis using FunASR, and describe how
-    to accelerate ModelScope models on Arm-based servers for enhanced performance and efficiency.
-    It focuses on tools and technologies such as ModelScope, FunASR, LLM, Generative AI, and Python,
-    Linux environments, Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft
-    Azure, Google Cloud, and Oracle. The main steps cover Introduction to Automatic Speech Recognition,
-    ModelScope - an Open Source Pre-trained AI Models Hub, and Building ASR Applications with
-    ModelScope.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will leverage open-source large language models and tools to build Chinese ASR applications,
-      deploy real-time Chinese speech recognition, punctuation restoration, and sentiment analysis
-      using FunASR, and describe how to accelerate ModelScope models on Arm-based servers for
-      enhanced performance and efficiency. Learn how to deploy the ModelScope FunASR Chinese automatic
-      speech recognition model on Arm-based servers with real-time transcription and sentiment
-      analysis.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers interested in learning how to deploy the ModelScope
-      FunASR Chinese Automatic Speech Recognition (ASR) model on Arm-based servers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/)
-      from a cloud service provider, or a local Arm Linux computer with at least 8 CPUs and 16GB
-      of RAM.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including ModelScope, FunASR, LLM, Generative AI, and Python,
-      Linux environments, Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft
-      Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Introduction to Automatic Speech Recognition, ModelScope
-      - an Open Source Pre-trained AI Models Hub, and Building ASR Applications with ModelScope.
-# END generated_summary_faq
+
 
 author: Odin Shen
 
diff --git a/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
index c2c9e68d4d..699efca619 100644
--- a/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
@@ -22,55 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 85d3d64e0eb0c3cfa0eee29cf1e4199049a6dcbf69634e578dad2b5443ca2b7f
-  summary: >-
-    Learn how to install and configure Gardener Kubernetes management platform on Google Cloud
-    Axion C4A SUSE Arm64 instances and deploy workload clusters. It is designed for software developers
-    deploying and optimizing Gardener workloads on Linux Arm64 environments, specifically using
-    Google Cloud C4A virtual machines powered by Axion processors. By the end, you will be able
-    to provision an Arm-based SUSE Linux Enterprise Server (SLES) virtual machine on Google Cloud
-    (C4A with Axion processors), install and configure Gardener on a SUSE Arm64 (C4A) instance,
-    and deploy Garden, Seed, and Shoot clusters locally using Kubernetes in Docker (KinD). It
-    focuses on tools and technologies such as Gardener, Kubernetes, Docker, KinD, and Helm, Linux
-    environments, Arm platforms including Neoverse, and cloud platforms such as Google Cloud.
-    The main steps cover Get started with Gardener on Google Axion C4A (Arm Neoverse-V2), Create
-    a Google Axion C4A Arm virtual machine for Gardener, Install Gardener on your Arm-based SUSE
-    VM, Verify Gardener cluster health and functionality, and Benchmark Gardener cluster security
-    with kube-bench.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will provision an Arm-based SUSE Linux Enterprise Server (SLES) virtual machine on Google
-      Cloud (C4A with Axion processors), install and configure Gardener on a SUSE Arm64 (C4A)
-      instance, and deploy Garden, Seed, and Shoot clusters locally using Kubernetes in Docker
-      (KinD). Learn how to install and configure Gardener Kubernetes management platform on Google
-      Cloud Axion C4A SUSE Arm64 instances and deploy workload clusters.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers deploying and optimizing Gardener
-      workloads on Linux Arm64 environments, specifically using Google Cloud C4A virtual machines
-      powered by Axion processors.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
-      account with billing enabled; Basic familiarity with [Kubernetes](https://kubernetes.io/);
-      Familiarity with container concepts ([Docker](https://www.docker.com/)).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Gardener, Kubernetes, Docker, KinD, and Helm, Linux
-      environments, Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Get started with Gardener on Google Axion C4A (Arm
-      Neoverse-V2), Create a Google Axion C4A Arm virtual machine for Gardener, Install Gardener
-      on your Arm-based SUSE VM, Verify Gardener cluster health and functionality, and Benchmark
-      Gardener cluster security with kube-bench.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md b/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
index c1a677b168..66fe745de9 100644
--- a/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
@@ -20,44 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 2e53b87d4dc7a7d1984e3bbe035038a60e619aea2f5793cb3082f3b59084bbf9
-  summary: >-
-    Learn how to apply link-time optimization with the GCC toolchain to improve application performance
-    by optimizing across compilation units. It is designed for developers who want to improve
-    application performance using link-time optimization (LTO) with the GCC toolchain. By the
-    end, you will be able to understand how link-time optimization (LTO) works and when to apply
-    it, enable and configure LTO with GCC compiler flags, and evaluate the performance and code
-    size trade-offs of LTO. It focuses on tools and technologies such as GCC, Linux environments,
-    and Arm platforms including Neoverse and Cortex-A. The main steps cover An LTO Primer, Deploying
-    LTO, and Potential Gains.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand how link-time optimization (LTO) works and when to apply it, enable
-      and configure LTO with GCC compiler flags, and evaluate the performance and code size trade-offs
-      of LTO. Learn how to apply link-time optimization with the GCC toolchain to improve application
-      performance by optimizing across compilation units.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers who want to improve application performance
-      using link-time optimization (LTO) with the GCC toolchain.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm Linux system (cloud instance,
-      on-premises hardware, or a virtual machine); A recent version of the [GCC toolchain](/install-guides/gcc/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including GCC, Linux environments, and Arm platforms such
-      as Neoverse and Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around An LTO Primer, Deploying LTO, and Potential Gains.
-# END generated_summary_faq
+
 
 author: Victor Do Nascimento
 
diff --git a/content/learning-paths/servers-and-cloud-computing/gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/gcp/_index.md
index ffe39addef..7ace9bbcf2 100644
--- a/content/learning-paths/servers-and-cloud-computing/gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gcp/_index.md
@@ -20,48 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 24e2ffcf9b7cda919e6e864f18bb9424c0c328242c92f491c1b284e317ed7e1c
-  summary: >-
-    Learn how to automate the creation of Arm virtual machines on Google Cloud Platform using
-    Terraform with jump server access configuration. It is designed for anyone new to using Arm
-    virtual machines in the Google Cloud Platform (GCP). By the end, you will be able to automate
-    Arm virtual machine creation using Terraform, deploy Arm instances on GCP and provide access
-    via Jump Server, and provide infrastructure basics, code knowledge and files that could help
-    with future learning paths. It focuses on tools and technologies such as Terraform and Bastion,
-    Linux environments, Arm platforms including Neoverse, and cloud platforms such as Google Cloud.
-    The main steps cover Automate virtual machine creation with Terraform and Deploy Arm instances
-    on GCP and provide access via Jump Server.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will automate Arm virtual machine creation using Terraform, deploy Arm instances on
-      GCP and provide access via Jump Server, and provide infrastructure basics, code knowledge
-      and files that could help with future learning paths. Learn how to automate the creation
-      of Arm virtual machines on Google Cloud Platform using Terraform with jump server access
-      configuration.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for anyone new to using Arm virtual machines in the Google
-      Cloud Platform (GCP).
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud account](https://console.cloud.google.com/).
-      Create an account if needed.; A computer with [Terraform](/install-guides/terraform) installed.;
-      A computer with [Google Cloud CLI](/install-guides/gcloud) installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Terraform and Bastion, Linux environments, Arm platforms
-      such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Automate virtual machine creation with Terraform and
-      Deploy Arm instances on GCP and provide access via Jump Server.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md b/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
index c28512c376..16e688ca8a 100644
--- a/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
@@ -17,42 +17,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 85a0a44bde6f217bdfc7fd0660ed6a86279b24c7ede302307ef6efb2626d83d9
-  summary: >-
-    Run Geekbench on Arm systems to benchmark CPU performance, interpret the results, and compare
-    different Arm configurations. It is designed for software developers interested in comparing
-    the performance of Arm Linux computers using Geekbench. By the end, you will be able to learn
-    how to install and run Geekbench and use Geekbench to help determine the appropriate hardware
-    configuration for your workload. It focuses on tools and technologies such as Geekbench and
-    Runbook, Linux environments, and Arm platforms including Neoverse. The main steps cover Download
-    and run Geekbench.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will learn how to install and run Geekbench and use Geekbench to help determine the
-      appropriate hardware configuration for your workload. Run Geekbench on Arm systems to benchmark
-      CPU performance, interpret the results, and compare different Arm configurations.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers interested in comparing the performance
-      of Arm Linux computers using Geekbench.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm computer running Linux. You can
-      use a cloud instance, refer to [Get started with Arm-based cloud instances](/learning-paths/servers-and-cloud-computing/csp/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Geekbench and Runbook, Linux environments, and Arm
-      platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Download and run Geekbench.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md b/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
index 9f685b78b1..636d2645f9 100644
--- a/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
@@ -22,54 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 642b67e7ca3717f499ab73efedd924eeab29a0055fad81c2d60fda993dcea195
-  summary: >-
-    Learn how to set up Arm-hosted GitHub runners and train PyTorch ML models using the German
-    Traffic Sign Recognition Benchmark dataset with automated workflows. It is designed for software
-    developers interested in automation for Machine Learning (ML) tasks. By the end, you will
-    be able to set up an Arm-hosted GitHub runner, train and test a PyTorch ML model with the
-    German Traffic Sign Recognition Benchmark (GTSRB) dataset, and compare the performance of
-    two trained PyTorch ML models; one model compiled with OpenBLAS (Open Basic Linear Algebra
-    Subprograms Library) and oneDNN (Deep Neural Network Library), and the other model compiled
-    with Arm Compute Library (ACL). It focuses on tools and technologies such as Python, PyTorch,
-    ACL, and GitHub, Linux environments, and Arm platforms including Neoverse. The main steps
-    cover MLOps background, Understand neural network model training and testing, Automate training
-    and testing with GitHub Actions, Compare the performance of PyTorch backends, and Deploy the
-    application as a container.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will set up an Arm-hosted GitHub runner, train and test a PyTorch ML model with the
-      German Traffic Sign Recognition Benchmark (GTSRB) dataset, and compare the performance of
-      two trained PyTorch ML models; one model compiled with OpenBLAS (Open Basic Linear Algebra
-      Subprograms Library) and oneDNN (Deep Neural Network Library), and the other model compiled
-      with Arm Compute Library (ACL). Learn how to set up Arm-hosted GitHub runners and train
-      PyTorch ML models using the German Traffic Sign Recognition Benchmark dataset with automated
-      workflows.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers interested in automation for Machine
-      Learning (ML) tasks.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A GitHub account with access to Arm-hosted
-      GitHub runners.; A Docker Hub account for storing container images.; Familiarity with the
-      concepts of ML and continuous integration and deployment (CI/CD).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Python, PyTorch, ACL, and GitHub, Linux environments,
-      and Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around MLOps background, Understand neural network model
-      training and testing, Automate training and testing with GitHub Actions, Compare the performance
-      of PyTorch backends, and Deploy the application as a container.
-# END generated_summary_faq
+
 
 author:
     - Pareena Verma
diff --git a/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md b/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
index 3e78307b98..41d01d57a6 100644
--- a/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
@@ -18,44 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: cc72ef1fe1fde9f4f9ea0769c0e04d749731b8073b2efc0e65d7f608665abc2d
-  summary: >-
-    Learn how to install RunsOn self-hosted runner manager in your AWS account to execute GitHub
-    Actions workflows on Arm runners. It is designed for developers who want to use Arm runners
-    offered by AWS to execute GitHub Actions workflows. By the end, you will be able to install
-    RunsOn, a self-hosted runner manager, in your AWS account and execute GitHub Actions workflows
-    on Arm runners. It focuses on tools and technologies such as AWS Cloud Formation, GitHub,
-    and AWS EC2, Linux environments, Arm platforms including Neoverse, and cloud platforms such
-    as AWS. The main steps cover About RunsOn and before you begin, Install RunsOn in your AWS
-    account, and Execute GitHub Actions workflows on Arm runners.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install RunsOn, a self-hosted runner manager, in your AWS account and execute GitHub
-      Actions workflows on Arm runners. Learn how to install RunsOn self-hosted runner manager
-      in your AWS account to execute GitHub Actions workflows on Arm runners.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is for developers who want to use Arm runners offered by AWS to execute
-      GitHub Actions workflows.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An [Amazon Web Services account](/learning-paths/servers-and-cloud-computing/csp/aws/).;
-      A GitHub account (personal or organizational).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including AWS Cloud Formation, GitHub, and AWS EC2, Linux
-      environments, Arm platforms such as Neoverse, and cloud platforms such as AWS.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around About RunsOn and before you begin, Install RunsOn
-      in your AWS account, and Execute GitHub Actions workflows on Arm runners.
-# END generated_summary_faq
+
 
 author: Cyril Rohr
 
diff --git a/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md b/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md
index 0e263f2ee1..e4195f65c2 100644
--- a/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md
@@ -19,48 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: d5df467355a7792f7a04eafc4a6bbd2410353aca000cd2b1aec74a7ed93a9270
-  summary: >-
-    Learn how to provision a Google Axion C4A Arm virtual machine and set up a GitHub Actions
-    self-hosted runner for CI/CD workflows. It is designed for developers who want to deploy a
-    GitHub Actions self-hosted runner on an Arm-based Google Axion C4A instance. By the end, you
-    will be able to provision an Arm virtual machine on the Google Cloud Platform using the C4A
-    Google Axion instance family, set up and validate a GitHub Actions self-hosted runner on the
-    Arm virtual machine, and deploy a basic CI workflow with NGINX and verify execution on Arm
-    infrastructure. It focuses on tools and technologies such as GitHub Actions and GitHub CLI,
-    Linux environments, Arm platforms including Neoverse, and cloud platforms such as Google Cloud.
-    The main steps cover About Google Axion and GitHub Actions, Create the instance, Set up a
-    GitHub Self-Hosted Runner, and Deploy NGINX with the GitHub runner.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will provision an Arm virtual machine on the Google Cloud Platform using the C4A Google
-      Axion instance family, set up and validate a GitHub Actions self-hosted runner on the Arm
-      virtual machine, and deploy a basic CI workflow with NGINX and verify execution on Arm infrastructure.
-      Learn how to provision a Google Axion C4A Arm virtual machine and set up a GitHub Actions
-      self-hosted runner for CI/CD workflows.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers who want to deploy a GitHub Actions self-hosted
-      runner on an Arm-based Google Axion C4A instance.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free?utm_source=google&hl=en)
-      account with billing enabled; A GitHub account; you can [sign up for GitHub](https://github.com/signup).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including GitHub Actions and GitHub CLI, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around About Google Axion and GitHub Actions, Create the
-      instance, Set up a GitHub Self-Hosted Runner, and Deploy NGINX with the GitHub runner.
-# END generated_summary_faq
+
 
 author: Annie Tallund
 
diff --git a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
index e420f14d10..3d35f42573 100644
--- a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
@@ -21,54 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: cf981c67553824c7c57b6dda9c2953ac80926750676ac101e939f6bbff655ab5
-  summary: >-
-    Learn how to create dual-architecture GKE clusters with arm64 and amd64 node pools, build
-    multi-architecture Docker images, and migrate services to Google Axion processors. It is designed
-    for cloud, platform, and site reliability engineers who operate Kubernetes on Google Cloud
-    and need to build multi-architecture images and migrate services from x86 to Arm using Google
-    Axion processors. By the end, you will be able to prepare Dockerfiles for multi-architecture
-    builds by adding arm64 support, create a dual-architecture GKE standard cluster with amd64
-    and arm64 node pools, and build and publish multi-architecture images to Artifact Registry
-    using Docker Buildx. It focuses on tools and technologies such as Kubernetes, GKE, Skaffold,
-    and Cloud Build, Linux environments, Arm platforms including Neoverse, and cloud platforms
-    such as Google Cloud. The main steps cover Explore the benefits of migrating microservices
-    to Arm on GKE, Set up your environment, Create build-ready Dockerfiles for both architectures,
-    Build and deploy multi-architecture images on GKE, and Prepare manifests and deploy on GKE.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will prepare Dockerfiles for multi-architecture builds by adding arm64 support, create
-      a dual-architecture GKE standard cluster with amd64 and arm64 node pools, and build and
-      publish multi-architecture images to Artifact Registry using Docker Buildx. Learn how to
-      create dual-architecture GKE clusters with arm64 and amd64 node pools, build multi-architecture
-      Docker images, and migrate services to Google Axion processors.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for cloud, platform, and site reliability engineers who operate
-      Kubernetes on Google Cloud and need to build multi-architecture images and migrate services
-      from x86 to Arm using Google Axion processors.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud account](https://console.cloud.google.com/)
-      with billing enabled; A local Linux or macOS computer with Docker, Kubernetes CLI (kubectl),
-      Google Cloud CLI (gcloud), and Git installed, or access to Google Cloud Shell; Basic familiarity
-      with Docker, Kubernetes, and gcloud.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Kubernetes, GKE, Skaffold, and Cloud Build, Linux
-      environments, Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Explore the benefits of migrating microservices to
-      Arm on GKE, Set up your environment, Create build-ready Dockerfiles for both architectures,
-      Build and deploy multi-architecture images on GKE, and Prepare manifests and deploy on GKE.
-# END generated_summary_faq
+
 
 author: 
    - Rani Chowdary Mandepudi
diff --git a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
index 19a13fa267..e8a9531f2f 100644
--- a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
@@ -21,48 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 50715640292b60ea4216ee2211140c4212592a27356d628d945e83d9deb7fdcc
-  summary: >-
-    Learn how to add Arm-based Google Axion nodes to an existing x86 GKE cluster and rebuild applications
-    for multi-architecture support. It is designed for software developers who are looking to
-    migrate their existing x86 containerized applications to Arm. By the end, you will be able
-    to add Arm-based nodes (Google Axion) to an existing x86-based GKE cluster, rebuild an x86-based
-    application to make it multi-arch and run on Arm, and learn how to add taints and tolerations
-    to GKE clusters to schedule application pods on architecture specific nodes. It focuses on
-    tools and technologies such as Kubernetes and Runbook, Linux environments, Arm platforms including
-    Neoverse, and cloud platforms such as Google Cloud. The main steps cover Build and deploy
-    a multi-arch application on GKE.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will add Arm-based nodes (Google Axion) to an existing x86-based GKE cluster, rebuild
-      an x86-based application to make it multi-arch and run on Arm, and learn how to add taints
-      and tolerations to GKE clusters to schedule application pods on architecture specific nodes.
-      Learn how to add Arm-based Google Axion nodes to an existing x86 GKE cluster and rebuild
-      applications for multi-architecture support.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers who are looking to migrate their existing
-      x86 containerized applications to Arm.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud account](https://console.cloud.google.com/).
-      Create an account if needed.; A computer with [Google Cloud CLI](/install-guides/gcloud)
-      and [kubectl](/install-guides/kubectl/)installed.; An existing Google Kubernetes Engine
-      (GKE) cluster with x86-based nodes.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Kubernetes and Runbook, Linux environments, Arm
-      platforms such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Build and deploy a multi-arch application on GKE.
-# END generated_summary_faq
+
 
 author: Pranay Bakre
 
diff --git a/content/learning-paths/servers-and-cloud-computing/gke/_index.md b/content/learning-paths/servers-and-cloud-computing/gke/_index.md
index 6967d32458..6f7866c1b7 100644
--- a/content/learning-paths/servers-and-cloud-computing/gke/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gke/_index.md
@@ -17,42 +17,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 7c3d37545c93db584d6e0ce88d8feaf0bf690e0c8786b664a6643be713d0336f
-  summary: >-
-    Learn how to automate the deployment of an Arm-based Google Kubernetes Engine cluster using
-    Terraform for container orchestration. It is designed for software developers who want to
-    deploy an Arm-based Kubernetes cluster using Google Kubernetes Engine (GKE). By the end, you
-    will be able to automate the deployment of an Arm-based GKE cluster using Terraform. It focuses
-    on tools and technologies such as Terraform and Kubernetes, Linux environments, Arm platforms
-    including Neoverse, and cloud platforms such as Google Cloud. The main steps cover Deploy
-    an Arm-based GKE Cluster using Terraform.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will automate the deployment of an Arm-based GKE cluster using Terraform. Learn how
-      to automate the deployment of an Arm-based Google Kubernetes Engine cluster using Terraform
-      for container orchestration.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers who want to deploy an Arm-based Kubernetes
-      cluster using Google Kubernetes Engine (GKE).
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Google Cloud account; A computer with
-      the following tools installed`:` Terraform, Google Cloud CLI (gcloud), Kubernetes CLI (kubectl).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Terraform and Kubernetes, Linux environments, Arm
-      platforms such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Deploy an Arm-based GKE Cluster using Terraform.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md b/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
index 0fad6ac841..cf386f92ff 100644
--- a/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
@@ -19,47 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 74952d68380c7540306543b0a7520f6960f673dd55ad5f164365fcfe1470380e
-  summary: >-
-    Rebuild and benchmark glibc with LSE atomics on Arm servers, then evaluate scalability using
-    MongoDB workloads and guidance on when LSE delivers a measurable uplift. It is designed for
-    software developers interested in learning how to improve the performance of their workloads
-    on Arm servers. By the end, you will be able to build and install glibc with LSE on an Arm
-    server, benchmark workload performance using glibc with LSE optimizations, and benchmark MongoDB
-    using glibc with LSE optimizations. It focuses on tools and technologies such as glibc, LSE,
-    MongoDB, and Runbook, Linux environments, and Arm platforms including Neoverse. The main steps
-    cover Build Glibc with LSE, Start MongoDB utilizing the newly built Glibc with LSE, Benchmark
-    MongoDB with YCSB, and Compare the results with LSE and NoLSE.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build and install glibc with LSE on an Arm server, benchmark workload performance
-      using glibc with LSE optimizations, and benchmark MongoDB using glibc with LSE optimizations.
-      Rebuild and benchmark glibc with LSE atomics on Arm servers, then evaluate scalability using
-      MongoDB workloads and guidance on when LSE delivers a measurable uplift.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers interested in learning how to improve
-      the performance of their workloads on Arm servers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm based instance from a cloud service
-      provider.; Review the learning path on [LSE](/learning-paths/servers-and-cloud-computing/lse/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including glibc, LSE, MongoDB, and Runbook, Linux environments,
-      and Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Build Glibc with LSE, Start MongoDB utilizing the
-      newly built Glibc with LSE, Benchmark MongoDB with YCSB, and Compare the results with LSE
-      and NoLSE.
-# END generated_summary_faq
+
 
 author: Ying Yu
 
diff --git a/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md b/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
index 5c13cacf00..e8b3a2ae64 100644
--- a/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
@@ -19,51 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: aa78434138f08b424352302e92a1cd40d8297459bc65202715dcb41c40de6057
-  summary: >-
-    Learn how to provision Arm64 and x86_64 VM instances on Google Cloud, then install and use
-    Sweet and Benchstat to measure and compare Go application performance. It is designed for
-    This introductory topic is for developers who want to measure and compare the performance
-    of Go applications on Arm-based servers. By the end, you will be able to provision Arm64 and
-    x86_64 VM instances on Google Cloud, install Go, Sweet, and Benchstat on each VM instance,
-    and run benchmarks and use Benchstat to compare Go application performance across architectures.
-    It focuses on tools and technologies such as Go, Linux environments, Arm platforms including
-    Neoverse, and cloud platforms such as Google Cloud. The main steps cover Overview, Launch
-    an Arm-based c4a-standard-4 instance, Launch an Intel Emerald Rapids c4-standard-8 instance,
-    Install Go, Sweet, and Benchstat, and Benchmark types and metrics.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will provision Arm64 and x86_64 VM instances on Google Cloud, install Go, Sweet, and
-      Benchstat on each VM instance, and run benchmarks and use Benchstat to compare Go application
-      performance across architectures. Learn how to provision Arm64 and x86_64 VM instances on
-      Google Cloud, then install and use Sweet and Benchstat to measure and compare Go application
-      performance.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This introductory topic is for developers who want to measure and compare the performance
-      of Go applications on Arm-based servers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud account](https://console.cloud.google.com/).
-      This Learning Path can be run on any cloud provider or on-premises, but it focuses on Google
-      Cloud’s Axion Arm64-based instances.; A local machine with [Google Cloud CLI](/install-guides/gcloud/)
-      installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Go, Linux environments, Arm platforms such as Neoverse,
-      and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview, Launch an Arm-based c4a-standard-4 instance,
-      Launch an Intel Emerald Rapids c4-standard-8 instance, Install Go, Sweet, and Benchstat,
-      and Benchmark types and metrics.
-# END generated_summary_faq
+
 
 author: Geremy Cohen
 
diff --git a/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md
index b4392a7e25..715dae3fca 100644
--- a/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md
@@ -20,56 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 46a0a3df799ac473f56d3820390af405b3301758cf15685a250a04c4854aba9e
-  summary: >-
-    Learn how to provision Azure Cobalt 100 Arm64 virtual machines and deploy Golang applications
-    with performance benchmarking on Arm architecture. It is designed for software developers,
-    DevOps engineers, and cloud architects looking to migrate their Golang (Go) applications from
-    x86_64 to high-performance Arm-based Azure Cobalt 100 virtual machines for improved cost efficiency
-    and performance. By the end, you will be able to provision an Azure Arm64 virtual machine
-    using the Azure portal, with Ubuntu Pro 24.04 LTS as the base image, deploy Golang on an Arm64-based
-    virtual machine running Ubuntu Pro 24.04 LTS, and perform Golang baseline testing and benchmarking
-    on both x86_64 and Arm64 virtual machines. It focuses on tools and technologies such as Golang,
-    Linux environments, Arm platforms including Neoverse, and cloud platforms such as Microsoft
-    Azure. The main steps cover Overview, Create an Azure Cobalt 100 Arm64 virtual machine for
-    Golang deployment, Install and configure Golang on Azure Cobalt 100 Arm64, Perform Golang
-    baseline testing and web server deployment on Azure Cobalt 100, and Run performance tests
-    using go test -bench.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will provision an Azure Arm64 virtual machine using the Azure portal, with Ubuntu Pro
-      24.04 LTS as the base image, deploy Golang on an Arm64-based virtual machine running Ubuntu
-      Pro 24.04 LTS, and perform Golang baseline testing and benchmarking on both x86_64 and Arm64
-      virtual machines. Learn how to provision Azure Cobalt 100 Arm64 virtual machines and deploy
-      Golang applications with performance benchmarking on Arm architecture.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers, DevOps engineers, and cloud architects
-      looking to migrate their Golang (Go) applications from x86_64 to high-performance Arm-based
-      Azure Cobalt 100 virtual machines for improved cost efficiency and performance.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Microsoft Azure](https://azure.microsoft.com/)
-      account with access to Azure Cobalt 100 Arm-based instances (Dpsv6-series); Basic familiarity
-      with the [Go programming language](https://go.dev/) and cloud deployment practices; Understanding
-      of Linux command line and virtual machine management.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Golang, Linux environments, Arm platforms such as
-      Neoverse, and cloud platforms such as Microsoft Azure.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview, Create an Azure Cobalt 100 Arm64 virtual
-      machine for Golang deployment, Install and configure Golang on Azure Cobalt 100 Arm64, Perform
-      Golang baseline testing and web server deployment on Azure Cobalt 100, and Run performance
-      tests using go test -bench.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md
index b4fd31d70f..df222bd7a7 100644
--- a/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md
@@ -27,55 +27,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 87e9d25d5fb1f45d126aa4ca2fa1e13d6a470f2bcdc70968aa4622790106e629
-  summary: >-
-    Learn how to install Helm on Google Cloud Axion C4A SUSE VMs and deploy applications like
-    NGINX, PostgreSQL, and Redis using Helm charts. It is designed for This is an introductory
-    topic intended for developers who want to get hands-on experience using Helm on Linux Arm64
-    systems, specifically Google Cloud C4A virtual machines powered by Axion processors. By the
-    end, you will be able to provision an Arm-based SUSE Linux Enterprise Server (SLES) virtual
-    machine on Google Cloud (C4A with Axion processors), install and configure Helm and kubectl
-    on a SUSE Arm64 (C4A) instance, and create and connect to a Google Kubernetes Engine (GKE)
-    cluster running on Arm-based nodes. It focuses on tools and technologies such as Helm, Kubernetes,
-    kubectl, GKE, and PostgreSQL, Linux environments, Arm platforms including Neoverse, and cloud
-    platforms such as Google Cloud. The main steps cover Get started with Helm on Google Axion
-    C4A (Arm-based), Create a Google Axion C4A virtual machine on Google Cloud, Install Helm,
-    Validate Helm workflows on a Google Axion C4A virtual machine, and Prepare a GKE cluster for
-    Helm deployments.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will provision an Arm-based SUSE Linux Enterprise Server (SLES) virtual machine on Google
-      Cloud (C4A with Axion processors), install and configure Helm and kubectl on a SUSE Arm64
-      (C4A) instance, and create and connect to a Google Kubernetes Engine (GKE) cluster running
-      on Arm-based nodes. Learn how to install Helm on Google Cloud Axion C4A SUSE VMs and deploy
-      applications like NGINX, PostgreSQL, and Redis using Helm charts.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic intended for developers who want to get hands-on experience
-      using Helm on Linux Arm64 systems, specifically Google Cloud C4A virtual machines powered
-      by Axion processors.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
-      account with billing enabled; Basic familiarity with [Kubernetes concepts](https://kubernetes.io/docs/concepts/);
-      Basic understanding of [Helm](https://helm.sh/docs/topics/architecture/) and Kubernetes
-      manifests; Familiarity with basic Linux command-line usage.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Helm, Kubernetes, kubectl, GKE, and PostgreSQL,
-      Linux environments, Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Get started with Helm on Google Axion C4A (Arm-based),
-      Create a Google Axion C4A virtual machine on Google Cloud, Install Helm, Validate Helm workflows
-      on a Google Axion C4A virtual machine, and Prepare a GKE cluster for Helm deployments.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/intro/_index.md b/content/learning-paths/servers-and-cloud-computing/intro/_index.md
index 7a61f69f5d..2b0892c673 100644
--- a/content/learning-paths/servers-and-cloud-computing/intro/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/intro/_index.md
@@ -17,41 +17,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: d2786f42b505823253ae16c647ca2e03c154f371b7c326210fde8523b12a8188
-  summary: >-
-    Learn where Arm architecture is used in servers and cloud computing, and find Arm-based hardware
-    platforms for software development. It is designed for software developers working on server
-    and cloud applications who are new to the Arm architecture. By the end, you will be able to
-    identify where Arm architecture is used in servers and cloud computing and locate server and
-    cloud hardware for software development. It focuses on tools and technologies such as Runbook,
-    Linux environments, and Arm platforms including Neoverse. The main steps cover Arm in Servers
-    and Cloud Computing and Find Arm hardware.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will identify where Arm architecture is used in servers and cloud computing and locate
-      server and cloud hardware for software development. Learn where Arm architecture is used
-      in servers and cloud computing, and find Arm-based hardware platforms for software development.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers working on server and cloud applications
-      who are new to the Arm architecture.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: None.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Runbook, Linux environments, and Arm platforms such
-      as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Arm in Servers and Cloud Computing and Find Arm hardware.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md b/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
index 5efa1e7eb1..12d8a76f19 100644
--- a/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
@@ -21,46 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 6fc608d45c4fdf85a77bddd4f8c26b05a7950d1086e578a8be0dd637feeb5d79
-  summary: >-
-    Analyze and optimize interrupt request (IRQ) patterns on Arm Linux servers to improve network
-    workload performance through IRQ distribution strategies. It is designed for developers and
-    performance engineers who are interested in understanding how network interrupt patterns can
-    impact performance on cloud servers. By the end, you will be able to analyze the current interrupt
-    request (IRQ) layout on an Arm Linux system, experiment with different interrupt options and
-    patterns to improve performance, and configure optimal IRQ distribution strategies for your
-    workload. It focuses on Linux environments and Arm platforms including Neoverse and Cortex-A.
-    The main steps cover Understand and analyze network IRQ configuration, IRQ management patterns
-    for performance optimization, and Conclusion and recommendations.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will analyze the current interrupt request (IRQ) layout on an Arm Linux system, experiment
-      with different interrupt options and patterns to improve performance, and configure optimal
-      IRQ distribution strategies for your workload. Analyze and optimize interrupt request (IRQ)
-      patterns on Arm Linux servers to improve network workload performance through IRQ distribution
-      strategies.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers and performance engineers who are interested
-      in understanding how network interrupt patterns can impact performance on cloud servers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm computer running Linux; Some
-      familiarity with the Linux command line.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers Linux environments and Arm platforms such as Neoverse and Cortex-A.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Understand and analyze network IRQ configuration,
-      IRQ management patterns for performance optimization, and Conclusion and recommendations.
-# END generated_summary_faq
+
 
 author: Kiel Friedt
 
diff --git a/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md b/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
index cc042f5a09..6769bff675 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
@@ -21,49 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: f57dd99bad280f64f53071373519e2d3ba8ae50b94fe1ad0a9cc40d02b458b9b
-  summary: >-
-    Monitor, interpret, and optimize Java Garbage Collector performance on Arm servers by comparing
-    different GCs and tuning parameters for your workload. It is designed for Java developers
-    aiming to optimize application performance on Arm-based servers, especially those migrating
-    applications from x86-based to Arm-based instances. By the end, you will be able to describe
-    the key differences between individual Java Garbage Collectors (GCs), monitor and interpret
-    Garbage Collector performance metrics, and adjust core parameters to optimize performance
-    for your specific workload. It focuses on tools and technologies such as Java and Runbook,
-    Linux environments, Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft
-    Azure, Google Cloud, and Oracle. The main steps cover Overview, Setup, Types of Garbage Collector,
-    Example Application, and Basic GC Tuning Options.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will describe the key differences between individual Java Garbage Collectors (GCs),
-      monitor and interpret Garbage Collector performance metrics, and adjust core parameters
-      to optimize performance for your specific workload. Monitor, interpret, and optimize Java
-      Garbage Collector performance on Arm servers by comparing different GCs and tuning parameters
-      for your workload.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is for Java developers aiming to optimize application performance on
-      Arm-based servers, especially those migrating applications from x86-based to Arm-based instances.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm-based instance from a cloud service
-      provider, or an on-premise Arm server.; Basic understanding of Java.; An [installation of
-      Java](/install-guides/java/) on your machine.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Java and Runbook, Linux environments, Arm platforms
-      such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview, Setup, Types of Garbage Collector, Example
-      Application, and Basic GC Tuning Options.
-# END generated_summary_faq
+
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
index 4938bf789a..0f41ee792c 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
@@ -18,49 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: e6f73fbca45a1be1644f79bbcfbaaec964e31f1aab236e9a872c72a6fd5e4b66
-  summary: >-
-    Deploy and optimize Java applications on Google Cloud Axion processors by testing JDK versions
-    and performance optimization flags. It is designed for software developers who want to learn
-    how to run their Java-based applications on Arm-based Google Axion processors in Google Cloud.
-    Most Java applications will run on Axion with no changes needed, but there are optimizations
-    that can help improve application performance on Axion. By the end, you will be able to create
-    an Arm-based VM instance with Google Axion CPU, deploy a Java application on Axion, and understand
-    Arm performance for different JDK versions. It focuses on tools and technologies such as Java,
-    Google Axion, and Runbook, Linux environments, Arm platforms including Neoverse V2, and cloud
-    platforms such as Google Cloud. The main steps cover Create an Arm-based VM instance with
-    Google Axion CPU, Install the JDK and build an application, and Test performance and optimize.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create an Arm-based VM instance with Google Axion CPU, deploy a Java application
-      on Axion, and understand Arm performance for different JDK versions. Deploy and optimize
-      Java applications on Google Cloud Axion processors by testing JDK versions and performance
-      optimization flags.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to learn how to run their
-      Java-based applications on Arm-based Google Axion processors in Google Cloud. Most Java
-      applications will run on Axion with no changes needed, but there are optimizations that
-      can help improve application performance on Axion.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud](https://cloud.google.com/)
-      account with access to Axion based instances (C4A).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Java, Google Axion, and Runbook, Linux environments,
-      Arm platforms such as Neoverse V2, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Create an Arm-based VM instance with Google Axion
-      CPU, Install the JDK and build an application, and Test performance and optimize.
-# END generated_summary_faq
+
 
 author: Joe Stech
 
diff --git a/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
index 40f68e6181..f4dc576741 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
@@ -19,50 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 58b0bb9f6e4190029d7edc65bdb04a597c7539de55a5e51ed59e88b174e527c3
-  summary: >-
-    Deploy Java on Azure Cobalt 100 Arm virtual machines and benchmark application performance
-    with JMH microbenchmarks. It is designed for This is an introductory topic about Java deployment
-    and benchmarking on Microsoft Azure Cobalt 100 Arm-based virtual machines. It is designed
-    for developers migrating Java applications from x86_64 to Arm architecture. By the end, you
-    will be able to provision an Azure Arm-based Cobalt 100 virtual machine using Azure console,
-    with Ubuntu Pro 24.04 LTS as the base image, deploy Java on the Azure Arm64 virtual machine,
-    and perform Java baseline testing and benchmarking on the Arm64 virtual machines. It focuses
-    on tools and technologies such as Java and JMH, Linux environments, Arm platforms including
-    Neoverse, and cloud platforms such as Microsoft Azure. The main steps cover Overview, Create
-    an Arm-based cloud virtual machine using Microsoft Cobalt 100 CPU, Install Java, Java Baseline
-    Testing, and FIXED, DO NOT MODIFY.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will provision an Azure Arm-based Cobalt 100 virtual machine using Azure console, with
-      Ubuntu Pro 24.04 LTS as the base image, deploy Java on the Azure Arm64 virtual machine,
-      and perform Java baseline testing and benchmarking on the Arm64 virtual machines. Deploy
-      Java on Azure Cobalt 100 Arm virtual machines and benchmark application performance with
-      JMH microbenchmarks.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic about Java deployment and benchmarking on Microsoft Azure
-      Cobalt 100 Arm-based virtual machines. It is designed for developers migrating Java applications
-      from x86_64 to Arm architecture.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Microsoft Azure](https://azure.microsoft.com/)
-      account with access to Cobalt 100 based instances (Dpsv6).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Java and JMH, Linux environments, Arm platforms
-      such as Neoverse, and cloud platforms such as Microsoft Azure.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview, Create an Arm-based cloud virtual machine
-      using Microsoft Cobalt 100 CPU, Install Java, Java Baseline Testing, and FIXED, DO NOT MODIFY.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
index 6c3006b81e..7d88e7ef7e 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
@@ -19,47 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 655180d91bb9b9cf571840b59d8943c1d2c73d8f8aea647db57dc2704ede3af8
-  summary: >-
-    Profile Java applications on Arm Neoverse servers using flame graphs generated with async-profiler
-    and Java agents to identify performance bottlenecks. It is designed for developers who want
-    to analyze the performance of Java applications on Arm Neoverse-based servers using flame
-    graphs. By the end, you will be able to set up a benchmarking environment using Tomcat and
-    wrk2, generate flame graphs using async-profiler, and generate flame graphs using a Java agent.
-    It focuses on tools and technologies such as OpenJDK 21, Apache Tomcat, async-profiler, FlameGraph,
-    and wrk2, Linux environments, and Arm platforms including Neoverse. The main steps cover Set
-    up Tomcat benchmark environment, Generate Java flame graphs using async-profiler, and Generate
-    Java flame graphs using a Java agent.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will set up a benchmarking environment using Tomcat and wrk2, generate flame graphs
-      using async-profiler, and generate flame graphs using a Java agent. Profile Java applications
-      on Arm Neoverse servers using flame graphs generated with async-profiler and Java agents
-      to identify performance bottlenecks.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers who want to analyze the performance of Java
-      applications on Arm Neoverse-based servers using flame graphs.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Access to both Arm-based and x86-based
-      computers running Ubuntu (you can use cloud-based server instances); Basic familiarity with
-      Java applications and performance profiling using flame graphs.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including OpenJDK 21, Apache Tomcat, async-profiler, FlameGraph,
-      and wrk2, Linux environments, and Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Set up Tomcat benchmark environment, Generate Java
-      flame graphs using async-profiler, and Generate Java flame graphs using a Java agent.
-# END generated_summary_faq
+
 
 author: 
   - Ying Yu
diff --git a/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md b/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
index 64d6516760..4ada6d2b97 100644
--- a/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
@@ -25,56 +25,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:57Z'
-  generator: template
-  source_hash: 525d893a796e8e4eb5cc7a9d5996bd7d55a35f8fe413f87b9a275a214d77626f
-  summary: >-
-    Deploy Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate installation,
-    and execute Arm-native CI/CD pipelines including Docker workflows. It is designed for software
-    developers deploying and optimizing Jenkins workloads on Arm Linux environments, specifically
-    on Microsoft Azure Cobalt 100 processors and Google Cloud C4A virtual machines powered by
-    Axion processors. By the end, you will be able to provision an Azure Arm64 virtual machine
-    using the Azure console with Ubuntu Pro 24.04 LTS, provision an Arm-based SUSE Linux virtual
-    machine on Google Cloud (C4A with Axion processors), and install Jenkins LTS with OpenJDK
-    17 on an Arm64 virtual machine. It focuses on tools and technologies such as Jenkins, OpenJDK
-    17, Docker, and Groovy (Jenkins Pipeline), Linux environments, Arm platforms including Neoverse,
-    and cloud platforms such as Microsoft Azure and Google Cloud. The main steps cover Technology
-    stack overview, Create an Arm-based virtual machine using Microsoft Cobalt 100, Create a firewall
-    rule on Azure, Install Jenkins on Azure Ubuntu Arm64 virtual machine, and Create a firewall
-    rule on GCP.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will provision an Azure Arm64 virtual machine using the Azure console with Ubuntu Pro
-      24.04 LTS, provision an Arm-based SUSE Linux virtual machine on Google Cloud (C4A with Axion
-      processors), and install Jenkins LTS with OpenJDK 17 on an Arm64 virtual machine. Deploy
-      Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate installation, and
-      execute Arm-native CI/CD pipelines including Docker workflows.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is for software developers deploying and optimizing Jenkins workloads
-      on Arm Linux environments, specifically on Microsoft Azure Cobalt 100 processors and Google
-      Cloud C4A virtual machines powered by Axion processors.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Microsoft Azure](https://azure.microsoft.com/)
-      account with access to Cobalt 100-based instances (Dpsv6); A [Google Cloud Platform](https://cloud.google.com/)
-      account with access to Arm-based virtual machine instances; Basic understanding of Linux
-      command line; Familiarity with CI/CD concepts and [Jenkins fundamentals](https://www.jenkins.io/doc/book/pipeline/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Jenkins, OpenJDK 17, Docker, and Groovy (Jenkins
-      Pipeline), Linux environments, Arm platforms such as Neoverse, and cloud platforms such
-      as Microsoft Azure and Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Technology stack overview, Create an Arm-based virtual
-      machine using Microsoft Cobalt 100, Create a firewall rule on Azure, Install Jenkins on
-      Azure Ubuntu Arm64 virtual machine, and Create a firewall rule on GCP.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
index d3b085919c..0266bb7f7e 100644
--- a/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
@@ -21,51 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: d510dd43a485e1c2fac404ef9a2e00b5be2449b99b1095c9a1841cd2fcba9b0e
-  summary: >-
-    Deploy Apache Kafka on Azure Cobalt 100 Arm virtual machines and benchmark message throughput
-    performance. It is designed for developers looking to migrate their Apache Kafka workloads
-    from x86_64 to Arm-based platforms, specifically on Microsoft Azure Cobalt 100 (arm64) virtual
-    machines. By the end, you will be able to provision an Azure Arm64 virtual machine using Azure
-    console, with Ubuntu Pro 24.04 LTS as the base image, deploy Kafka on an Ubuntu virtual machine,
-    and perform Kafka baseline testing and benchmarking on Arm64 virtual machines. It focuses
-    on tools and technologies such as Kafka, Linux environments, Arm platforms including Neoverse,
-    and cloud platforms such as Microsoft Azure. The main steps cover Overview, Create an Arm-based
-    cloud virtual machine using Microsoft Cobalt 100 CPU, Install Kafka, Run baseline testing
-    with Kafka on Azure Arm VM, and Benchmark with official Kafka tools.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will provision an Azure Arm64 virtual machine using Azure console, with Ubuntu Pro 24.04
-      LTS as the base image, deploy Kafka on an Ubuntu virtual machine, and perform Kafka baseline
-      testing and benchmarking on Arm64 virtual machines. Deploy Apache Kafka on Azure Cobalt
-      100 Arm virtual machines and benchmark message throughput performance.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for developers looking to migrate their Apache Kafka workloads
-      from x86_64 to Arm-based platforms, specifically on Microsoft Azure Cobalt 100 (arm64) virtual
-      machines.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Microsoft Azure](https://azure.microsoft.com/)
-      account with access to Cobalt 100 based instances (Dpsv6); Basic understanding of Linux
-      command line; Familiarity with the [Apache Kafka architecture](https://kafka.apache.org/)
-      and deployment practices on Arm64 platforms.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Kafka, Linux environments, Arm platforms such as
-      Neoverse, and cloud platforms such as Microsoft Azure.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview, Create an Arm-based cloud virtual machine
-      using Microsoft Cobalt 100 CPU, Install Kafka, Run baseline testing with Kafka on Azure
-      Arm VM, and Benchmark with official Kafka tools.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/kafka/_index.md b/content/learning-paths/servers-and-cloud-computing/kafka/_index.md
index 6e230348f8..9e4ec102cd 100644
--- a/content/learning-paths/servers-and-cloud-computing/kafka/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kafka/_index.md
@@ -21,46 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: b7f36df881c2c436143c1e5579d2c54cb5930f52998904098829c52fa7290f38
-  summary: >-
-    Deploy and configure a Kafka cluster with Zookeeper on Arm servers, test event streaming,
-    and automate deployment on AWS and Google Cloud. It is designed for software developers who
-    want to learn how to use Kafka and Zookeeper. By the end, you will be able to install Zookeeper
-    and Kafka, configure Zookeeper to work with Kafka, and test write/read events into the Kafka
-    cluster. It focuses on tools and technologies such as Kafka and ZooKeeper, Linux environments,
-    Arm platforms including Neoverse, and cloud platforms such as AWS and Google Cloud. The main
-    steps cover Introduction to Kafka and Zookeeper, Setup a 3 node Zookeeper Cluster, Set up
-    a 3 node Kafka Cluster, Verify that the Kafka Cluster is working, and Deploy Cluster Automatically
-    (AWS).
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install Zookeeper and Kafka, configure Zookeeper to work with Kafka, and test write/read
-      events into the Kafka cluster. Deploy and configure a Kafka cluster with Zookeeper on Arm
-      servers, test event streaming, and automate deployment on AWS and Google Cloud.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers who want to learn how to use Kafka and
-      Zookeeper.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Seven physical Arm machines or cloud
-      instances with either Ubuntu or Debian installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Kafka and ZooKeeper, Linux environments, Arm platforms
-      such as Neoverse, and cloud platforms such as AWS and Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Introduction to Kafka and Zookeeper, Setup a 3 node
-      Zookeeper Cluster, Set up a 3 node Kafka Cluster, Verify that the Kafka Cluster is working,
-      and Deploy Cluster Automatically (AWS).
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md b/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
index c00df49175..77aed65690 100644
--- a/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
@@ -21,49 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 6ff33f6dfaf25e926a0ce8756b4e564e5aa37112e5425f9c3dce13f772b54145
-  summary: >-
-    Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing Kedify and
-    KEDA with Helm and testing autoscaling behavior. It is designed for developers running HTTP
-    workloads on Kubernetes who want to enable event-driven autoscaling with KEDA and Kedify.
-    By the end, you will be able to install Kedify (KEDA build, HTTP Scaler, and Kedify Agent)
-    with Helm, verify that Kedify and KEDA components are running in the cluster, and deploy a
-    sample HTTP application and test autoscaling behavior. It focuses on tools and technologies
-    such as Kubernetes, Helm, KEDA, and Kedify, Linux environments, Arm platforms including Neoverse,
-    and cloud platforms such as AWS, Microsoft Azure, and Google Cloud. The main steps cover Install
-    Kedify using Helm, Install an ingress controller, and Autoscale HTTP applications with Kedify
-    and Kubernetes Ingress.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install Kedify (KEDA build, HTTP Scaler, and Kedify Agent) with Helm, verify that
-      Kedify and KEDA components are running in the cluster, and deploy a sample HTTP application
-      and test autoscaling behavior. Enable event-driven autoscaling for HTTP workloads on Kubernetes
-      by installing Kedify and KEDA with Helm and testing autoscaling behavior.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers running HTTP workloads on Kubernetes who want
-      to enable event-driven autoscaling with KEDA and Kedify.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A running Kubernetes cluster (local
-      or cloud); Kubectl and Helm installed; Access to the Kedify Service dashboard to obtain
-      your Organization ID and API key (sign up at [Kedify dashboard](https://dashboard.kedify.io/)).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Kubernetes, Helm, KEDA, and Kedify, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, and Google
-      Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Install Kedify using Helm, Install an ingress controller,
-      and Autoscale HTTP applications with Kedify and Kubernetes Ingress.
-# END generated_summary_faq
+
 
 author: Zbynek Roubalik
 
diff --git a/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md b/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
index 836ef574f1..03c0930e12 100644
--- a/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
@@ -21,48 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 830556dfd51aaa2dd95ee957e64306bab369297f7bc66325e7eb509f541f683c
-  summary: >-
-    Create, train, and evaluate a neural network model on Arm servers using Keras Core with TensorFlow,
-    PyTorch, and JAX backends. It is designed for engineers who want to create a neural network
-    model on Arm machines. By the end, you will be able to create a simple neural network model
-    using Keras Core, train and evaluate your neural network model with different backends, and
-    generate predictions with the trained model. It focuses on tools and technologies such as
-    Python, Keras, TensorFlow, PyTorch, and JAX, Linux environments, Arm platforms including Neoverse,
-    and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps
-    cover Overview of Keras Core, Install the required dependencies, and Run Keras Core.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create a simple neural network model using Keras Core, train and evaluate your
-      neural network model with different backends, and generate predictions with the trained
-      model. Create, train, and evaluate a neural network model on Arm servers using Keras Core
-      with TensorFlow, PyTorch, and JAX backends.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for engineers who want to create a neural network model on
-      Arm machines.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Basic Machine Learning knowledge.; An
-      [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service
-      provider, an on-premises Arm server, or a Linux virtual machine on your Arm device.; Familiarity
-      with SSH, the Linux command line, and basic system administration tasks.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Python, Keras, TensorFlow, PyTorch, and JAX, Linux
-      environments, Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft
-      Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview of Keras Core, Install the required dependencies,
-      and Run Keras Core.
-# END generated_summary_faq
+
 
 author: 
     - Diego Russo
diff --git a/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md b/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
index 4a8e453715..6edb677dda 100644
--- a/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
@@ -22,47 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 004436cf14ff0056136889c58d676295c6dd2088f06f57f397a07ec9004e6b44
-  summary: >-
-    Compile and install custom Linux kernels on Arm cloud instances using TuxMake with configurations
-    for 64 KB page sizes and Fastpath testing. It is designed for software developers building
-    custom Linux kernels on Arm servers and cloud instances. By the end, you will be able to set
-    up a build environment for compiling Linux kernels on Arm cloud instances, build custom Linux
-    kernels with various configurations using TuxMake, and install and verify custom-built kernels.
-    It focuses on tools and technologies such as TuxMake, Linux environments, Arm platforms including
-    Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The
-    main steps cover Set up your Arm instance for kernel building, Build and install custom Linux
-    kernels, and Build kernels for Fastpath validation.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will set up a build environment for compiling Linux kernels on Arm cloud instances,
-      build custom Linux kernels with various configurations using TuxMake, and install and verify
-      custom-built kernels. Compile and install custom Linux kernels on Arm cloud instances using
-      TuxMake with configurations for 64 KB page sizes and Fastpath testing.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers building custom Linux kernels on Arm servers
-      and cloud instances.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm cloud instance with at least
-      24 vCPUs and 200 GB of free storage running Ubuntu 24.04 LTS; Understanding of kernel images
-      and modules; Familiarity with GRUB bootloader and initramfs.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including TuxMake, Linux environments, Arm platforms such
-      as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Set up your Arm instance for kernel building, Build
-      and install custom Linux kernels, and Build kernels for Fastpath validation.
-# END generated_summary_faq
+
 
 author: Geremy Cohen
 
diff --git a/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md b/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
index 921b134036..7bfc638341 100644
--- a/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
@@ -20,47 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 43e4e28b88b9721ca94457418f3b4887d0099017578a54da1db5fcdc11f4a122
-  summary: >-
-    Identify and migrate container images in a Kubernetes cluster to Arm-compatible versions using
-    KubeArchInspect reports. It is designed for software developers who want to ensure containers
-    running in a Kubernetes cluster support the Arm architecture. By the end, you will be able
-    to run KubeArchInspect to generate a report on the containers running in a Kubernetes cluster,
-    discover which images support the Arm architecture, and understand common reasons for an image
-    not supporting Arm. It focuses on tools and technologies such as Kubernetes and Runbook, Linux
-    environments, Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft
-    Azure, Google Cloud, and Oracle. The main steps cover Install KubeArchInspect, Run KubeArchInspect,
-    and Analyze the results.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will run KubeArchInspect to generate a report on the containers running in a Kubernetes
-      cluster, discover which images support the Arm architecture, and understand common reasons
-      for an image not supporting Arm. Identify and migrate container images in a Kubernetes cluster
-      to Arm-compatible versions using KubeArchInspect reports.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to ensure containers running
-      in a Kubernetes cluster support the Arm architecture.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A running Kubernetes cluster accessible
-      with `kubectl`.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Kubernetes and Runbook, Linux environments, Arm
-      platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud,
-      and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Install KubeArchInspect, Run KubeArchInspect, and
-      Analyze the results.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md b/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
index baaa6debad..7f92256edf 100644
--- a/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
@@ -18,42 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 22fa96e29143f2e0dc0c204919422914b3f70d63ddf833cf1067fbf67b525aa0
-  summary: >-
-    Deploy AWS Lambda functions on Graviton processors using Terraform for Python and Node.js
-    runtimes. It is designed for software developers who want to learn how to deploy Lambda functions
-    on AWS Graviton processors. By the end, you will be able to deploy Lambda functions on Graviton
-    processors using Terraform. It focuses on tools and technologies such as Terraform and AWS
-    Lambda, Linux environments, Arm platforms including Neoverse, and cloud platforms such as
-    AWS. The main steps cover Deploy Node.js Lambda functions on Graviton processors with Terraform
-    and Deploy Python Lambda functions on Graviton processors with Terraform.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will deploy Lambda functions on Graviton processors using Terraform. Deploy AWS Lambda
-      functions on Graviton processors using Terraform for Python and Node.js runtimes.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to learn how to deploy Lambda
-      functions on AWS Graviton processors.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A computer with [Terraform](/install-guides/terraform/)
-      and the [AWS CLI](/install-guides/aws-cli/) installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Terraform and AWS Lambda, Linux environments, Arm
-      platforms such as Neoverse, and cloud platforms such as AWS.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Deploy Node.js Lambda functions on Graviton processors
-      with Terraform and Deploy Python Lambda functions on Graviton processors with Terraform.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md b/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
index bf2eac0448..fe6f11160f 100644
--- a/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
@@ -19,44 +19,6 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 66d13edff1371295f0e88a618e924ca67b85bcc8aa72034d1e582a0b267f0d05
-  summary: >-
-    Enable and measure libhugetlbfs performance improvements for MySQL and other workloads on
-    Arm Linux servers. It is designed for engineers looking for ways to increase performance on
-    Arm servers. By the end, you will be able to enable libhugetlbfs on an Arm server running
-    Linux and evaluate performance improvements for workloads such as MySQL. It focuses on tools
-    and technologies such as MySQL, GCC, and Runbook, Linux environments, Arm platforms including
-    Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The
-    main steps cover Enable libhugetlbfs and Enable libhugetlbfs on MySQL.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will enable libhugetlbfs on an Arm server running Linux and evaluate performance improvements
-      for workloads such as MySQL. Enable and measure libhugetlbfs performance improvements for
-      MySQL and other workloads on Arm Linux servers.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for engineers looking for ways to increase performance on Arm
-      servers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm server or virtual machine instance
-      from a cloud service provider with Ubuntu installed; Knowledge of how to build a MySQL server
-      and run the sysbench benchmark test.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including MySQL, GCC, and Runbook, Linux environments, Arm
-      platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud,
-      and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Enable libhugetlbfs and Enable libhugetlbfs on MySQL.
-# END generated_summary_faq
 
 author: Bolt Liu
 
diff --git a/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md b/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
index 5920ca76a0..29cfe2b7c4 100644
--- a/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
@@ -18,46 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: d82aa4faeb599a3a1ac12d477204ebe0a4ddb99564bedced86ca0fc4851e17b9
-  summary: >-
-    Serve the llama.cpp chatbot through an OpenAI-compatible API, enabling existing OpenAI-style
-    clients and applications to run against a persistent Arm-hosted LLM. It is designed for developers
-    interested in running LLMs on Arm-based servers. By the end, you will be able to download
-    and build llama.cpp on your Arm server, download a pre-quantized Llama 3.1 model from Hugging
-    Face, and run the pre-quantized model on your Arm CPU and measure the performance. It focuses
-    on tools and technologies such as LLM, Generative AI, Python, Demo, and Hugging Face, Linux
-    environments, Arm platforms including Neoverse, and cloud platforms such as AWS. The main
-    steps cover Run a Large Language model (LLM) chatbot on Arm servers and Access the chatbot
-    using the OpenAI-compatible API.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will download and build llama.cpp on your Arm server, download a pre-quantized Llama
-      3.1 model from Hugging Face, and run the pre-quantized model on your Arm CPU and measure
-      the performance. Serve the llama.cpp chatbot through an OpenAI-compatible API, enabling
-      existing OpenAI-style clients and applications to run against a persistent Arm-hosted LLM.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers interested in running LLMs on Arm-based servers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An AWS Graviton4 r8g.16xlarge instance
-      to test Arm performance optimizations, or any [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/)
-      from a cloud service provider or an on-premise Arm server.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including LLM, Generative AI, Python, Demo, and Hugging Face,
-      Linux environments, Arm platforms such as Neoverse, and cloud platforms such as AWS.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Run a Large Language model (LLM) chatbot on Arm servers
-      and Access the chatbot using the OpenAI-compatible API.
-# END generated_summary_faq
+
 
 author:
     - Pareena Verma
diff --git a/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md b/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
index b5253c7d47..f31b06c79f 100644
--- a/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
@@ -23,54 +23,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 3e8307a5daf435c21f3cecff635ac51724a63ff9ea4307082d869601563b4204
-  summary: >-
-    Build a production-ready vision chatbot on Google Axion using Streamlit, PyTorch, and Hugging
-    Face Transformers with a quantized Llama 3.2-Vision model. It is designed for software developers
-    and ML engineers who are interested in deploying a production-ready vision chatbot for their
-    application with optimized performance on the Arm Architecture. By the end, you will be able
-    to build a frontend with Streamlit to input images and prompts, build the backend to download
-    a Llama 3.2-Vision model, quantize it, and run it using PyTorch and Hugging Face Transformers,
-    and monitor and analyze inference on Arm CPUs. It focuses on tools and technologies such as
-    Python, PyTorch, Streamlit, and Google Axion, Linux environments, Arm platforms including
-    Neoverse, and cloud platforms such as Google Cloud. The main steps cover Set up an LLM based-Vision
-    Chatbot, Deploy Vision Chatbot LLM backend server, Deploy Vision Chatbot LLM frontend server,
-    and Inference with Vision Chatbot.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build a frontend with Streamlit to input images and prompts, build the backend
-      to download a Llama 3.2-Vision model, quantize it, and run it using PyTorch and Hugging
-      Face Transformers, and monitor and analyze inference on Arm CPUs. Build a production-ready
-      vision chatbot on Google Axion using Streamlit, PyTorch, and Hugging Face Transformers with
-      a quantized Llama 3.2-Vision model.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is for software developers and ML engineers who are interested in deploying
-      a production-ready vision chatbot for their application with optimized performance on the
-      Arm Architecture.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Google Cloud Axion compute instance
-      or [any Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud
-      service provider with at least 32 cores.; Familiarity with REST APIs and web services.;
-      A basic understanding of Python and ML concepts.; A basic understanding of Streamlit.; A
-      basic understanding of LLM fundamentals.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Python, PyTorch, Streamlit, and Google Axion, Linux
-      environments, Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Set up an LLM based-Vision Chatbot, Deploy Vision
-      Chatbot LLM backend server, Deploy Vision Chatbot LLM frontend server, and Inference with
-      Vision Chatbot.
-# END generated_summary_faq
+
 
 author: Nobel Chowdary Mandepudi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
index aa06fb5c7a..df1f9b66b3 100644
--- a/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
@@ -24,52 +24,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 36bf3c45f0b38350714ba41ea88c1551b33f6d299a65b3b0d6668fee2d88d835
-  summary: >-
-    Optimize llama.cpp on Arm CPUs by integrating Streamline Annotations to profile Prefill and
-    Decode stages, analyze operators, and evaluate multi-core execution. It is designed for software
-    developers, performance engineers, and AI practitioners who want to optimize llama.cpp performance
-    on Arm-based CPUs. By the end, you will be able to profile llama.cpp architecture and identify
-    the role of the Prefill and Decode stages, integrate Streamline Annotations into llama.cpp
-    for fine-grained performance insights, and capture and interpret profiling data with Streamline.
-    It focuses on tools and technologies such as Arm Streamline, CPP, llama.cpp, and Profiling,
-    Linux and Android environments, and Arm platforms including Cortex-A and Neoverse. The main
-    steps cover Overview, Explore llama.cpp architecture and the inference workflow, Integrate
-    Streamline Annotations into llama.cpp, Analyze token generation performance with Streamline
-    profiling, and Implement operator-level performance analysis with Annotation Channels.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will profile llama.cpp architecture and identify the role of the Prefill and Decode
-      stages, integrate Streamline Annotations into llama.cpp for fine-grained performance insights,
-      and capture and interpret profiling data with Streamline. Optimize llama.cpp on Arm CPUs
-      by integrating Streamline Annotations to profile Prefill and Decode stages, analyze operators,
-      and evaluate multi-core execution.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers, performance engineers, and AI practitioners
-      who want to optimize llama.cpp performance on Arm-based CPUs.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Basic understanding of llama.cpp; Understanding
-      of transformer models; Knowledge of Arm Streamline usage; An Arm Neoverse or Cortex-A hardware
-      platform running Linux or Android.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Arm Streamline, CPP, llama.cpp, and Profiling, Linux
-      and Android environments, and Arm platforms such as Cortex-A and Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview, Explore llama.cpp architecture and the inference
-      workflow, Integrate Streamline Annotations into llama.cpp, Analyze token generation performance
-      with Streamline profiling, and Implement operator-level performance analysis with Annotation
-      Channels.
-# END generated_summary_faq
+
 
 author: 
     - Zenon Zhilong Xiu
diff --git a/content/learning-paths/servers-and-cloud-computing/lse/_index.md b/content/learning-paths/servers-and-cloud-computing/lse/_index.md
index 8bfce7cead..2d505be42e 100644
--- a/content/learning-paths/servers-and-cloud-computing/lse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/lse/_index.md
@@ -18,45 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 9610291239b88c67e21055f94cd03fe7cf80f7d875d53ebe0d8de7837ce99fa7
-  summary: >-
-    Understand Large System Extensions (LSE) for Arm processors and verify whether applications
-    use LSE for improved atomic operation performance. It is designed for software developers
-    who want to learn about Large System Extensions and use them in an application. By the end,
-    you will be able to learn about Large System Extensions and find out if an application uses
-    Large System Extensions. It focuses on tools and technologies such as GCC and Runbook, Linux
-    environments, Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft
-    Azure, Google Cloud, and Oracle. The main steps cover Introduction to Large System Extensions
-    and Large System Extensions (LSE) Example.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will learn about Large System Extensions and find out if an application uses Large System
-      Extensions. Understand Large System Extensions (LSE) for Arm processors and verify whether
-      applications use LSE for improved atomic operation performance.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to learn about Large System
-      Extensions and use them in an application.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An [AWS account](/learning-paths/servers-and-cloud-computing/csp/aws/)
-      to access instance types with different AWS Graviton processors. If you don't have an AWS
-      account, you can substitute other Arm Linux computers.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including GCC and Runbook, Linux environments, Arm platforms
-      such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Introduction to Large System Extensions and Large
-      System Extensions (LSE) Example.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md b/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
index 73c4f5a370..9064547bf2 100644
--- a/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
@@ -21,51 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: db4ce1c59ad10bd127b4990c82ce876311d7dc7e1ad5d39ec132daf82ceb8b79
-  summary: >-
-    Deploy MariaDB on Arm cloud instances across AWS, Azure, and Google Cloud using Docker, Amazon
-    RDS, and automation with Terraform and Ansible. It is designed for software developers who
-    want to deploy MariaDB on Arm servers. By the end, you will be able to deploy MariaDB on virtual
-    machines from different cloud service providers, deploy MariaDB using Docker, and deploy MariaDB
-    using Amazon RDS (Relational Database Service). It focuses on tools and technologies such
-    as Terraform, Ansible, MariaDB, Docker, and Runbook, Linux environments, Arm platforms including
-    Neoverse, and cloud platforms such as AWS, Microsoft Azure, and Google Cloud. The main steps
-    cover Install MariaDB on an AWS Arm based instance, Deploy MariaDB using RDS(AWS), Install
-    MariaDB on an Azure Arm based instance, Install MariaDB on a GCP Arm based instance, and Deploy
-    MariaDB via Docker.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will deploy MariaDB on virtual machines from different cloud service providers, deploy
-      MariaDB using Docker, and deploy MariaDB using Amazon RDS (Relational Database Service).
-      Deploy MariaDB on Arm cloud instances across AWS, Azure, and Google Cloud using Docker,
-      Amazon RDS, and automation with Terraform and Ansible.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to deploy MariaDB on Arm
-      servers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Cloud service provider accounts for
-      each service you want to use including AWS, Azure, and GCP; A local computer with [Docker](/install-guides/docker/),
-      [Terraform](/install-guides/terraform/), [AWS CLI](/install-guides/aws-cli/), [Azure CLI](/install-guides/azure-cli/),
-      [Google Cloud CLI](/install-guides/gcloud/), and [Ansible](/install-guides/ansible/) installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Terraform, Ansible, MariaDB, Docker, and Runbook,
-      Linux environments, Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft
-      Azure, and Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Install MariaDB on an AWS Arm based instance, Deploy
-      MariaDB using RDS(AWS), Install MariaDB on an Azure Arm based instance, Install MariaDB
-      on a GCP Arm based instance, and Deploy MariaDB via Docker.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/memcached/_index.md b/content/learning-paths/servers-and-cloud-computing/memcached/_index.md
index e346728998..8cc2d7423f 100644
--- a/content/learning-paths/servers-and-cloud-computing/memcached/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memcached/_index.md
@@ -18,42 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: b42ca5cc8f4db6ba6019f3720a5ef46119aa403a2baaef5362e874f898ce0419
-  summary: >-
-    Install memcached on Arm cloud servers and benchmark in-memory key-value store performance
-    using open-source tools. It is designed for developers who want to use memcached as their
-    in-memory key-value store. By the end, you will be able to install and run memcached on your
-    Arm-based cloud server and use an open-source benchmark to test memcached performance. It
-    focuses on tools and technologies such as Runbook and Memcached, Linux environments, Arm platforms
-    including Neoverse, and cloud platforms such as AWS and Oracle. The main steps cover Run and
-    test Memcached on Arm servers.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install and run memcached on your Arm-based cloud server and use an open-source
-      benchmark to test memcached performance. Install memcached on Arm cloud servers and benchmark
-      in-memory key-value store performance using open-source tools.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers who want to use memcached as their in-memory
-      key-value store.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm based instance from an appropriate
-      cloud service provider.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Runbook and Memcached, Linux environments, Arm platforms
-      such as Neoverse, and cloud platforms such as AWS and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Run and test Memcached on Arm servers.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md b/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
index d27d8d5361..e55e548486 100644
--- a/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
@@ -22,55 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 4efe46aaf4580a6b8f263b69e8f072211bfd24fcf7f7a885689c927fc05a4c63
-  summary: >-
-    Deploy Memcached as a cache for MySQL and PostgreSQL on Arm servers. It is designed for developers
-    who want to use memcached as their in-memory key-value store. By the end, you will be able
-    to deploy memcached as a cache for MySQL on AWS, Azure and GCP Arm based Instance and deploy
-    memcached as a cache for PostgreSQL on AWS, Azure and GCP Arm based Instance. It focuses on
-    tools and technologies such as Memcached, SQL, MySQL, and PostgreSQL, Linux environments,
-    Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft Azure, and Google
-    Cloud. The main steps cover Deploy Memcached as a cache for MySQL on an AWS Arm based Instance,
-    Deploy Memcached as a cache for MySQL on an Azure Arm based Instance, Deploy Memcached as
-    a cache for MySQL on a Google Cloud Arm based Instance, Deploy Memcached as a cache for Postgres
-    on an AWS Arm based Instance, and Deploy Memcached as a cache for Postgres on an Azure Arm
-    based Instance.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will deploy memcached as a cache for MySQL on AWS, Azure and GCP Arm based Instance
-      and deploy memcached as a cache for PostgreSQL on AWS, Azure and GCP Arm based Instance.
-      Deploy Memcached as a cache for MySQL and PostgreSQL on Arm servers.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for developers who want to use memcached as their in-memory key-value
-      store.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Amazon Web Services (AWS) [account](https://aws.amazon.com/);
-      An Azure portal [account](https://azure.microsoft.com/en-in/get-started/azure-portal); A
-      Google Cloud [account](https://console.cloud.google.com/); A machine with [Terraform](/install-guides/terraform/),
-      [AWS CLI](/install-guides/aws-cli), [Google Cloud CLI](/install-guides/gcloud), [Azure CLI](/install-guides/azure-cli),
-      [AWS IAM authenticator](https://docs.aws.amazon.com/eks/latest/userguide/install-aws-iam-authenticator.html),
-      and [Ansible](/install-guides/ansible/) installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Memcached, SQL, MySQL, and PostgreSQL, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, and Google
-      Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Deploy Memcached as a cache for MySQL on an AWS Arm
-      based Instance, Deploy Memcached as a cache for MySQL on an Azure Arm based Instance, Deploy
-      Memcached as a cache for MySQL on a Google Cloud Arm based Instance, Deploy Memcached as
-      a cache for Postgres on an AWS Arm based Instance, and Deploy Memcached as a cache for Postgres
-      on an Azure Arm based Instance.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md b/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
index dec4ca386b..1f6c58e824 100644
--- a/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
@@ -23,55 +23,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: d61c9ddcef1c7ffe12b3ee818852fb3f0a62a90cec451692c1186cf2c01de625
-  summary: >-
-    Use ASCT to measure cache latency, streaming bandwidth, and coherency latency on Arm Neoverse
-    systems, and compare results across Graviton generations. It is designed for software developers
-    and performance engineers who want to understand and characterize the CPU-side memory subsystem
-    of Arm Linux systems. By the end, you will be able to identify the core topology, cluster
-    layout, and cache hierarchy of an Arm Linux system using standard tools, measure cache and
-    memory latency using a pointer-chase benchmark, and measure single-core and multi-core streaming
-    bandwidth at each level of the memory hierarchy. It focuses on tools and technologies such
-    as ASCT and Perf, Linux environments, and Arm platforms including Neoverse. The main steps
-    cover Identify Arm CPU topology, cache hierarchy, and NUMA configuration, Analyze Arm cache
-    hierarchy and performance characteristics, Measure Arm cache and memory latency using ASCT
-    pointer chase, Measure Arm single-core memory bandwidth with ASCT, and Measure Arm multi-core
-    memory bandwidth and loaded latency with ASCT.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will identify the core topology, cluster layout, and cache hierarchy of an Arm Linux
-      system using standard tools, measure cache and memory latency using a pointer-chase benchmark,
-      and measure single-core and multi-core streaming bandwidth at each level of the memory hierarchy.
-      Use ASCT to measure cache latency, streaming bandwidth, and coherency latency on Arm Neoverse
-      systems, and compare results across Graviton generations.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers and performance engineers who want to
-      understand and characterize the CPU-side memory subsystem of Arm Linux systems.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Two or more Arm Linux systems with root
-      or sudo access. The examples use AWS Graviton2 and Graviton4 instances, but other systems
-      are possible; Arm System Characterization Tool (ASCT) installed on each system; A good understanding
-      of CPU memory subsystems, including cache hierarchies, cache lines, and DRAM in the memory
-      hierarchy.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including ASCT and Perf, Linux environments, and Arm platforms
-      such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Identify Arm CPU topology, cache hierarchy, and NUMA
-      configuration, Analyze Arm cache hierarchy and performance characteristics, Measure Arm
-      cache and memory latency using ASCT pointer chase, Measure Arm single-core memory bandwidth
-      with ASCT, and Measure Arm multi-core memory bandwidth and loaded latency with ASCT.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md b/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
index 8a1131a83d..9576eac1b1 100644
--- a/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
@@ -23,51 +23,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 6aa61de638be961339d958345225d696ff2b83b8f9cab22bf956f9bf2d15c1aa
-  summary: >-
-    Test and validate thread synchronization approaches in the Arm memory model using Herd7, Litmus7,
-    and Arm hardware with assembly snippets. It is designed for developers seeking practical ways
-    to test thread synchronization approaches in the Arm memory model. By the end, you will be
-    able to test thread synchronization assembly snippets against the formal definition of the
-    Arm memory model, test thread synchronization assembly snippets on Arm hardware, and compare
-    the results of different thread synchronization approaches. It focuses on tools and technologies
-    such as Runbook, Herd7, Litmus7, and Arm ISA, Linux environments, and Arm platforms including
-    Neoverse. The main steps cover Thread Synchronization, Arm Memory Model, and Tools, Herd7
-    and Litmus7 Test Primer, Thread Synchronization Examples, and Additional Resources.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will test thread synchronization assembly snippets against the formal definition of
-      the Arm memory model, test thread synchronization assembly snippets on Arm hardware, and
-      compare the results of different thread synchronization approaches. Test and validate thread
-      synchronization approaches in the Arm memory model using Herd7, Litmus7, and Arm hardware
-      with assembly snippets.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for developers seeking practical ways to test thread synchronization
-      approaches in the Arm memory model.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An understanding of memory consistency
-      models (such as Sequential Consistency, Weak Ordering, Relaxed Consistency, and Processor
-      Consistency).; An understanding of thread synchronization.; Familiarity with Arm assembly
-      language, and the ability to find relevant information on Arm assembly instructions.; Familiarity
-      with general-purpose registers.; Familiarity with memory barriers, including Acquire-Release
-      Semantics.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Runbook, Herd7, Litmus7, and Arm ISA, Linux environments,
-      and Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Thread Synchronization, Arm Memory Model, and Tools,
-      Herd7 and Litmus7 Test Primer, Thread Synchronization Examples, and Additional Resources.
-# END generated_summary_faq
+
 
 author: Julio Suarez
 
diff --git a/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md b/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
index e9fa872160..860f72ed71 100644
--- a/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
@@ -18,40 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: a67d36fb650b77c170c15f193049490286a3f097801d0c79d701d3f5610fe1dc
-  summary: >-
-    This branch-only testing summary is intentionally out of sync with the current Learning Path
-    source content so the workflow report records preserved summary drift for this LP.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      This branch-only testing answer is intentionally stale so the workflow report records preserved
-      FAQ drift for this LP.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for performance engineers, Linux system administrators, and
-      application developers who want to microbenchmark, simulate, or tune the networking performance
-      of distributed systems.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Basic understanding of networking principles
-      such as Transmission Control Protocol/Internet Protocol (TCP/IP) and User Datagram Protocol
-      (UDP).; Access to two [Arm-based cloud instances](/learning-paths/servers-and-cloud-computing/csp/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including iPerf3, Linux environments, Arm platforms such as
-      Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Set up Arm-based Linux systems for network performance
-      testing with iPerf3, Microbenchmark the network connection, Simulate different network conditions,
-      and Tune kernel parameters.
-# END generated_summary_faq
+
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md b/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
index 5d169ce8ad..7652fe328c 100644
--- a/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
@@ -20,48 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 56a38d1d742dd301d48e9ad61ff00e07048559f3f646815e22937113243adcdb
-  summary: >-
-    Scan source code for architecture-specific portability issues using migrate-ease to identify
-    and resolve AArch64 porting challenges before migration. It is designed for developers looking
-    to migrate applications to Arm-based servers using migrate-ease, a code analysis tool that
-    scans source code repositories to identify architecture-specific porting issues before migration.
-    By the end, you will be able to identify architecture-specific dependencies in your application's
-    source code, recognize common migration challenges and how to resolve them, and use migrate-ease
-    to detect and address AArch64 portability issues. It focuses on tools and technologies such
-    as Neon, SVE, Go, and Runbook, Linux environments, and Arm platforms including Neoverse. The
-    main steps cover Assessing your code for migration to Arm, Migrate-ease and supported programming
-    languages, Getting started with migrate-ease, and Try it out.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will identify architecture-specific dependencies in your application's source code,
-      recognize common migration challenges and how to resolve them, and use migrate-ease to detect
-      and address AArch64 portability issues. Scan source code for architecture-specific portability
-      issues using migrate-ease to identify and resolve AArch64 porting challenges before migration.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers looking to migrate applications to Arm-based
-      servers using migrate-ease, a code analysis tool that scans source code repositories to
-      identify architecture-specific porting issues before migration.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Access to an [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/)
-      for testing and validation.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Neon, SVE, Go, and Runbook, Linux environments,
-      and Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Assessing your code for migration to Arm, Migrate-ease
-      and supported programming languages, Getting started with migrate-ease, and Try it out.
-# END generated_summary_faq
+
 
 author: 
     - Odin Shen
diff --git a/content/learning-paths/servers-and-cloud-computing/migration/_index.md b/content/learning-paths/servers-and-cloud-computing/migration/_index.md
index 37b96a6cc1..27fa79e2b7 100644
--- a/content/learning-paths/servers-and-cloud-computing/migration/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/migration/_index.md
@@ -20,48 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 6b2b985c39579c4d0855c8c28b610ba558e25b451a6b755475ff4393e2810670
-  summary: >-
-    Set up an Arm development environment, analyze dependencies, and understand common challenges
-    and scenarios for migrating applications to Arm servers. It is designed for software developers
-    looking to migrate applications to Arm servers. By the end, you will be able to set up an
-    Arm development machine, analyze application dependencies, and learn challenges and tips for
-    application migration. It focuses on tools and technologies such as Neon, SVE, Go, and Runbook,
-    Linux environments, Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft
-    Azure, Google Cloud, and Oracle. The main steps cover Migrating applications to Arm servers,
-    Migrating C/C++ applications, Migrating Java applications, Migrating Go applications, and
-    List of software products supporting Arm.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will set up an Arm development machine, analyze application dependencies, and learn
-      challenges and tips for application migration. Set up an Arm development environment, analyze
-      dependencies, and understand common challenges and scenarios for migrating applications
-      to Arm servers.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers looking to migrate applications to
-      Arm servers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/)
-      from a cloud service provider.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Neon, SVE, Go, and Runbook, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
-      Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Migrating applications to Arm servers, Migrating C/C++
-      applications, Migrating Java applications, Migrating Go applications, and List of software
-      products supporting Arm.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
index 3c094beff7..1648531eeb 100644
--- a/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
@@ -20,47 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 2eb252b19b7535fbb776a3153bfc9f70b6217088e5b4b55deb56d01393abaf3b
-  summary: >-
-    Build a Retrieval-Augmented Generation (RAG) application on Arm servers using Zilliz Cloud
-    for vector search and llama.cpp for LLM inference. It is designed for software developers
-    who want to create a Retrieval-Augmented Generation (RAG) application on Arm servers. By the
-    end, you will be able to create a simple RAG application using Zilliz Cloud and launch an
-    LLM service on Arm servers. It focuses on tools and technologies such as Python, Generative
-    AI, RAG, and Hugging Face, Linux environments, Arm platforms including Neoverse, and cloud
-    platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps cover Overview
-    and Install dependencies, Offline Data Loading, Launch the LLM Server, and Online RAG.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create a simple RAG application using Zilliz Cloud and launch an LLM service on
-      Arm servers. Build a Retrieval-Augmented Generation (RAG) application on Arm servers using
-      Zilliz Cloud for vector search and llama.cpp for LLM inference.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to create a Retrieval-Augmented
-      Generation (RAG) application on Arm servers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A basic understanding of a RAG pipeline.;
-      An AWS Graviton3 C7g.2xlarge instance, or any [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp)
-      from a cloud service provider or an on-premise Arm server.; A [Zilliz account](https://zilliz.com/cloud?utm_source=partner&utm_medium=referral&utm_campaign=2024-10-24_web_arm-dev-hub-data-loading_arm),
-      which you can sign up for with a free trial.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Python, Generative AI, RAG, and Hugging Face, Linux
-      environments, Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft
-      Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview and Install dependencies, Offline Data Loading,
-      Launch the LLM Server, and Online RAG.
-# END generated_summary_faq
+
 
 author: Chen Zhang
 
diff --git a/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
index b9b5ca55f5..3f80c54e9d 100644
--- a/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
@@ -22,52 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 6c438573edec90b3aa4135ace38cd3ae604c58658c1949117ca80dbdd21394bd
-  summary: >-
-    Learn how to deploy and configure MinIO on an Azure Cobalt 100 virtual machine, benchmark
-    object storage throughput, and validate S3 compatibility using the boto3 Python SDK. It is
-    designed for developers, DevOps engineers, and platform engineers who want to deploy MinIO
-    object storage on Microsoft Azure Cobalt 100 virtual machines. By the end, you will be able
-    to provision an Azure Cobalt 100 virtual machine and deploy MinIO, benchmark MinIO storage
-    throughput for large object transfers, and validate S3 API compatibility using the boto3 Python
-    SDK. It focuses on tools and technologies such as MinIO, Python, and boto3, Linux environments,
-    Arm platforms including Neoverse, and cloud platforms such as Microsoft Azure. The main steps
-    cover Overview of Azure Cobalt 100 and MinIO, Create an Azure Cobalt 100 virtual machine,
-    Open MinIO ports in the Azure Network Security Group, Install and configure MinIO on Azure
-    Cobalt 100, and Benchmark MinIO storage performance on Azure Cobalt 100.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will provision an Azure Cobalt 100 virtual machine and deploy MinIO, benchmark MinIO
-      storage throughput for large object transfers, and validate S3 API compatibility using the
-      boto3 Python SDK. Learn how to deploy and configure MinIO on an Azure Cobalt 100 virtual
-      machine, benchmark object storage throughput, and validate S3 compatibility using the boto3
-      Python SDK.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers, DevOps engineers, and platform engineers who
-      want to deploy MinIO object storage on Microsoft Azure Cobalt 100 virtual machines.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Microsoft Azure account](https://azure.microsoft.com/)
-      with access to Cobalt 100-based instances (Dpsv6); Familiarity with SSH and remote server
-      access; Basic understanding of cloud storage concepts.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including MinIO, Python, and boto3, Linux environments, Arm
-      platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview of Azure Cobalt 100 and MinIO, Create an
-      Azure Cobalt 100 virtual machine, Open MinIO ports in the Azure Network Security Group,
-      Install and configure MinIO on Azure Cobalt 100, and Benchmark MinIO storage performance
-      on Azure Cobalt 100.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md b/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
index db4ff79737..fc9acec4e4 100644
--- a/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
@@ -19,44 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 973ba6c1e00fc67e1702606412124d8172e071b9b677a1df53c3be66210bf704
-  summary: >-
-    Benchmark machine learning inference performance on Arm servers using TensorFlow and the MLPerf
-    Inference benchmark suite from MLCommons. It is designed for software developers interested
-    in benchmarking machine learning workloads on Arm servers. By the end, you will be able to
-    install and run TensorFlow on your Arm-based cloud server and use MLPerf Inference benchmark
-    suite, an open-sourced benchmark from MLCommons to test ML performance on your Arm server.
-    It focuses on tools and technologies such as TensorFlow and Runbook, Linux environments, Arm
-    platforms including Neoverse, and cloud platforms such as AWS and Oracle. The main steps cover
-    Measure ML Inference Performance on Arm servers.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install and run TensorFlow on your Arm-based cloud server and use MLPerf Inference
-      benchmark suite, an open-sourced benchmark from MLCommons to test ML performance on your
-      Arm server. Benchmark machine learning inference performance on Arm servers using TensorFlow
-      and the MLPerf Inference benchmark suite from MLCommons.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers interested in benchmarking machine
-      learning workloads on Arm servers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/)
-      from an appropriate cloud service provider or an on-premise Arm server.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including TensorFlow and Runbook, Linux environments, Arm
-      platforms such as Neoverse, and cloud platforms such as AWS and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Measure ML Inference Performance on Arm servers.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
index 5965a078af..47c9460de6 100644
--- a/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
@@ -20,49 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: f270cfc90245c0090685fabf5cbebf62a714edbf34e1ea86c3df0bfbb4699ea4
-  summary: >-
-    Deploy MongoDB on Azure Cobalt 100 Arm virtual machines and benchmark database performance
-    using mongotop and mongostat monitoring tools. It is designed for software developers who
-    want to migrate MongoDB workloads to Arm-based platforms, with a focus on Microsoft Azure
-    Cobalt 100 Arm64 instances. By the end, you will be able to provision an Arm64-based Cobalt
-    100 virtual machine in Azure using Ubuntu Pro 24.04 LTS, deploy MongoDB on the Cobalt 100
-    instance, and run baseline tests and performance benchmarks on MongoDB in the Arm64 environment.
-    It focuses on tools and technologies such as MongoDB, mongotop, and mongostat, Linux environments,
-    Arm platforms including Neoverse, and cloud platforms such as Microsoft Azure. The main steps
-    cover What are Cobalt 100 and MongoDB?, Create an Arm-based cloud virtual machine using Cobalt
-    100, Install MongoDB and Mongosh, MongoDB Baseline Testing, and Monitor MongoDB with mongotop.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will provision an Arm64-based Cobalt 100 virtual machine in Azure using Ubuntu Pro 24.04
-      LTS, deploy MongoDB on the Cobalt 100 instance, and run baseline tests and performance benchmarks
-      on MongoDB in the Arm64 environment. Deploy MongoDB on Azure Cobalt 100 Arm virtual machines
-      and benchmark database performance using mongotop and mongostat monitoring tools.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to migrate MongoDB workloads
-      to Arm-based platforms, with a focus on Microsoft Azure Cobalt 100 Arm64 instances.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Microsoft Azure](https://azure.microsoft.com/)
-      account with access to Cobalt 100 (Dpsv6) instances; Familiarity with the [MongoDB architecture](https://www.mongodb.com/)
-      and deployment practices on Arm64 platforms.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including MongoDB, mongotop, and mongostat, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around What are Cobalt 100 and MongoDB?, Create an Arm-based
-      cloud virtual machine using Cobalt 100, Install MongoDB and Mongosh, MongoDB Baseline Testing,
-      and Monitor MongoDB with mongotop.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
index 539682646f..c8ff6a4c5c 100644
--- a/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
@@ -19,47 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: abbdde22271151fb1485c54bafe463e7797a0b913c7d4c99245d2914a3f9bb82
-  summary: >-
-    Deploy MongoDB on Google Cloud Axion C4A virtual machines and benchmark database performance
-    with Yahoo Cloud Serving Benchmark (YCSB). It is designed for This introductory topic is for
-    software developers who want to migrate MongoDB workloads from x86_64 to Arm-based platforms,
-    specifically on Google Axion-based C4A virtual machines. By the end, you will be able to create
-    an Arm virtual machine on Google Cloud (C4A Axion family), install and run MongoDB on the
-    Arm-based C4A instance, and benchmark MongoDB performance with Yahoo Cloud Serving Benchmark
-    (YCSB). It focuses on tools and technologies such as MongoDB and YCSB, Linux environments,
-    Arm platforms including Neoverse, and cloud platforms such as Google Cloud. The main steps
-    cover About Google Axion C4A series and MongoDB, Create Google Axion instance, Install MongoDB,
-    Baseline Testing, and MongoDB Benchmarking.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create an Arm virtual machine on Google Cloud (C4A Axion family), install and run
-      MongoDB on the Arm-based C4A instance, and benchmark MongoDB performance with Yahoo Cloud
-      Serving Benchmark (YCSB). Deploy MongoDB on Google Cloud Axion C4A virtual machines and
-      benchmark database performance with Yahoo Cloud Serving Benchmark (YCSB).
-  - question: Who is this Learning Path for?
-    answer: >-
-      This introductory topic is for software developers who want to migrate MongoDB workloads
-      from x86_64 to Arm-based platforms, specifically on Google Axion-based C4A virtual machines.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free?utm_source=google&hl=en)
-      account with billing enabled.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including MongoDB and YCSB, Linux environments, Arm platforms
-      such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around About Google Axion C4A series and MongoDB, Create
-      Google Axion instance, Install MongoDB, Baseline Testing, and MongoDB Benchmarking.
-# END generated_summary_faq
+
 
 author: Annie Tallund
 
diff --git a/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md b/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
index 2a487d736e..b9ed8f5283 100644
--- a/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
@@ -5,51 +5,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: b52a1b60a74e19f2a3b60b8a77199ad5369c1ccd3b4d5ce0252a169d9f6123fd
-  summary: >-
-    Install MongoDB on Arm servers and benchmark database performance using Yahoo Cloud Serving
-    Benchmark (YCSB) to compare against other architectures. It is designed for software developers
-    who want to learn how to deploy and measure MongoDB performance on Arm servers. By the end,
-    you will be able to install and run MongoDB on an Arm server, test MongoDB performance using
-    open-source tooling, and measure and compare the performance of MongoDB on Arm versus other
-    architectures with Yahoo Cloud Serving Benchmark (YCSB). It focuses on tools and technologies
-    such as MongoDB, GCC, and Runbook, Linux environments, Arm platforms including Neoverse, and
-    cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps cover
-    Install MongoDB on Arm, Creating MongoDB test scenarios, Benchmark MongoDB on Arm with Yahoo
-    Cloud Serving Benchmark (YCSB), Three node replica set testing with YCSB, and Alternative
-    performance testing of MongoDB.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install and run MongoDB on an Arm server, test MongoDB performance using open-source
-      tooling, and measure and compare the performance of MongoDB on Arm versus other architectures
-      with Yahoo Cloud Serving Benchmark (YCSB). Install MongoDB on Arm servers and benchmark
-      database performance using Yahoo Cloud Serving Benchmark (YCSB) to compare against other
-      architectures.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to learn how to deploy and
-      measure MongoDB performance on Arm servers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/)
-      from a cloud service provider.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including MongoDB, GCC, and Runbook, Linux environments, Arm
-      platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud,
-      and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Install MongoDB on Arm, Creating MongoDB test scenarios,
-      Benchmark MongoDB on Arm with Yahoo Cloud Serving Benchmark (YCSB), Three node replica set
-      testing with YCSB, and Alternative performance testing of MongoDB.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/mpi/_index.md b/content/learning-paths/servers-and-cloud-computing/mpi/_index.md
index 5133e1bb06..0cb38de347 100644
--- a/content/learning-paths/servers-and-cloud-computing/mpi/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mpi/_index.md
@@ -21,45 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 300794f4010658d945212c74c5257635d620cbb6230cac0de92bf23e4e9e29fe
-  summary: >-
-    Debug, profile, and optimize MPI parallel applications on Arm servers using Linaro Forge,
-    gdb, and Arm Performance Libraries. It is designed for HPC software developers writing MPI
-    applications. By the end, you will be able to debug and fix a parallel application, profile
-    and optimize your code, and use optimized routines for common math operations. It focuses
-    on tools and technologies such as Fortran, GCC, Linaro Forge, gdb, and mpi, Linux environments,
-    Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
-    Cloud, and Oracle. The main steps cover Before you start, Debug your application, and Optimize
-    your code.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will debug and fix a parallel application, profile and optimize your code, and use optimized
-      routines for common math operations. Debug, profile, and optimize MPI parallel applications
-      on Arm servers using Linaro Forge, gdb, and Arm Performance Libraries.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for HPC software developers writing MPI applications.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: General knowledge about distributed
-      parallelism (MPI); Some understanding of C, Python, and Linux commands; An Arm computer
-      running Linux. Cloud instances can be used, refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Fortran, GCC, Linaro Forge, gdb, and mpi, Linux
-      environments, Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft
-      Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Before you start, Debug your application, and Optimize
-      your code.
-# END generated_summary_faq
+
 
 author: Florent Lebeau
 
diff --git a/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md b/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md
index e93ca8e3e4..745876fb86 100644
--- a/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md
@@ -7,47 +7,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: a10683fdd14359e93056dc4ba24581856833765c64915979d0466a06887e0755
-  summary: >-
-    Select and apply accuracy modes for vectorized math functions in Libamath to balance performance
-    and precision for your application. It is designed for developers who want to use the different
-    accuracy modes for vectorized math functions in Libamath, a component of Arm Performance Libraries.
-    By the end, you will be able to describe how accuracy is defined and measured in Libamath,
-    select an appropriate accuracy mode for your application, and use Libamath with different
-    vector accuracy modes in practice. It focuses on tools and technologies such as Arm Performance
-    Libraries, GCC, and Libamath, Linux environments, and Arm platforms including Neoverse. The
-    main steps cover Floating-point representation, Units in the last place (ULP), ULP error and
-    accuracy, Accuracy modes in Libamath, and Arm Performance Libraries example.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will describe how accuracy is defined and measured in Libamath, select an appropriate
-      accuracy mode for your application, and use Libamath with different vector accuracy modes
-      in practice. Select and apply accuracy modes for vectorized math functions in Libamath to
-      balance performance and precision for your application.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers who want to use the different accuracy modes
-      for vectorized math functions in Libamath, a component of Arm Performance Libraries.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm computer running Linux with [Arm
-      Performance Libraries](/install-guides/armpl/) version 25.04 or newer installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Arm Performance Libraries, GCC, and Libamath, Linux
-      environments, and Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Floating-point representation, Units in the last place
-      (ULP), ULP error and accuracy, Accuracy modes in Libamath, and Arm Performance Libraries
-      example.
-# END generated_summary_faq
+
 
 author: Joana Cruz
 
diff --git a/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md b/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md
index 9ab33a7f3b..2d1ad5d25a 100644
--- a/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md
@@ -22,51 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: d765afadfe5155f0ecd5bd08373fa12464b2ee5ebb1f8c7831039eb07eba8831
-  summary: >-
-    Build a multi-architecture Kubernetes cluster running nginx on Azure AKS walks you through
-    an end-to-end Arm software workflow. It is designed for developers who want to deploy multi-architecture
-    Kubernetes workloads and compare nginx performance between x86 and Arm-based nodes in Azure
-    Kubernetes Service (AKS) clusters. By the end, you will be able to create a hybrid AKS cluster
-    with both x86 and Arm64 nodes, deploy nginx using multi-architecture container images across
-    different node types, and verify nginx deployment and functionality on each architecture.
-    It focuses on tools and technologies such as nginx, Web Server, Azure, and Kubernetes, Linux
-    environments, Arm platforms including Neoverse, and cloud platforms such as Microsoft Azure.
-    The main steps cover Start your journey with Arm and x86 nginx workloads on a single Kubernetes
-    cluster, Create the AKS cluster, Create the test utility, Deploy nginx on Intel x86, and Deploy
-    nginx on Arm.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create a hybrid AKS cluster with both x86 and Arm64 nodes, deploy nginx using multi-architecture
-      container images across different node types, and verify nginx deployment and functionality
-      on each architecture.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers who want to deploy multi-architecture Kubernetes
-      workloads and compare nginx performance between x86 and Arm-based nodes in Azure Kubernetes
-      Service (AKS) clusters.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An [Azure account](https://azure.microsoft.com/en-us/free/);
-      A local machine with [`jq`](https://jqlang.org/download/), [`curl`](https://curl.se/download.html),
-      [`wrk`](https://github.com/wg/wrk), [Azure CLI](/install-guides/azure-cli/), and [`kubectl`](/install-guides/kubectl/)
-      installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including nginx, Web Server, Azure, and Kubernetes, Linux
-      environments, Arm platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Start your journey with Arm and x86 nginx workloads
-      on a single Kubernetes cluster, Create the AKS cluster, Create the test utility, Deploy
-      nginx on Intel x86, and Deploy nginx on Arm.
-# END generated_summary_faq
+
 
 author:
     - Geremy Cohen
diff --git a/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md b/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md
index 2f7a128e5b..9e71087dcf 100644
--- a/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md
@@ -21,51 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: cd31c217eaeab6faad263728c446ee188cd9d542904d87ae7bfd5120713dfaa4
-  summary: >-
-    Add Arm nodes to your GKE cluster using a multi-architecture Ollama container image walks
-    you through an end-to-end Arm software workflow. It is designed for developers who want to
-    compare the performance of amd64 and arm64 deployments by running inferences on a hybrid GKE
-    cluster using an Ollama multi-architecture container image. By the end, you will be able to
-    create a hybrid GKE cluster with amd64 and arm64 nodes, deploy Ollama services for amd64 and
-    arm64 architectures using a single multi-architecture container image, and validate deployments
-    by pinging, pulling models, and running inferences to compare architecture performance. It
-    focuses on tools and technologies such as LLM, Ollama, and Generative AI, Linux and macOS
-    environments, Arm platforms including Neoverse, and cloud platforms such as Google Cloud.
-    The main steps cover Create the GKE Cluster, Deploy Ollama amd64 to the cluster, Deploy Ollama
-    arm64 to the cluster, and Test functionality and performance.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create a hybrid GKE cluster with amd64 and arm64 nodes, deploy Ollama services
-      for amd64 and arm64 architectures using a single multi-architecture container image, and
-      validate deployments by pinging, pulling models, and running inferences to compare architecture
-      performance.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is for developers who want to compare the performance of amd64 and arm64
-      deployments by running inferences on a hybrid GKE cluster using an Ollama multi-architecture
-      container image.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud account](https://console.cloud.google.com/).;
-      A local machine with [Google Cloud CLI](/install-guides/gcloud/) and [kubectl](/install-guides/kubectl/)
-      installed.; The [GKE Cloud Plugin](https://cloud.google.com/kubernetes-engine/docs/how-to/cluster-access-for-kubectl#gcloud)
-      installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including LLM, Ollama, and Generative AI, Linux and macOS
-      environments, Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Create the GKE Cluster, Deploy Ollama amd64 to the
-      cluster, Deploy Ollama arm64 to the cluster, and Test functionality and performance.
-# END generated_summary_faq
+
 
 author:
     - Geremy Cohen
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md
index 8f4ed98ce9..0041808836 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md
@@ -19,47 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: b9bc1d1f965e4fdeeb9552d853330c201e00597829420c8a0397fa425c3231c4
-  summary: >-
-    Deploy MySQL on Microsoft Azure Cobalt 100 processors walks you through an end-to-end Arm
-    software workflow. It is designed for developers migrating MySQL applications from x86_64
-    to Arm. By the end, you will be able to provision an Azure Arm64 virtual machine using Azure
-    console, with Ubuntu Pro 24.04 LTS as the base image, deploy MySQL on the Ubuntu virtual machine,
-    and perform MySQL baseline testing and benchmarking on Arm64 virtual machines. It focuses
-    on tools and technologies such as MySQL, SQL, and Docker, Linux environments, Arm platforms
-    including Neoverse, and cloud platforms such as Microsoft Azure. The main steps cover Overview,
-    Create an Azure Cobalt 100 Arm64 virtual machine, Deploy MySQL on an Azure Arm64 virtual machine,
-    Validate MySQL functionality on Azure Arm64, and Benchmark MySQL with mysqlslap.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will provision an Azure Arm64 virtual machine using Azure console, with Ubuntu Pro 24.04
-      LTS as the base image, deploy MySQL on the Ubuntu virtual machine, and perform MySQL baseline
-      testing and benchmarking on Arm64 virtual machines.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers migrating MySQL applications from x86_64 to
-      Arm.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Microsoft Azure](https://azure.microsoft.com/)
-      account with access to Cobalt 100 based instances (Dpsv6); Familiarity with relational databases
-      and the basics of [MySQL](https://dev.mysql.com/doc/refman/8.0/en/introduction.html).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including MySQL, SQL, and Docker, Linux environments, Arm
-      platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview, Create an Azure Cobalt 100 Arm64 virtual
-      machine, Deploy MySQL on an Azure Arm64 virtual machine, Validate MySQL functionality on
-      Azure Arm64, and Benchmark MySQL with mysqlslap.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql/_index.md
index ef38e5a7ae..6bbece6118 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql/_index.md
@@ -17,40 +17,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: b9b2ed7f58611c9bc7e1867fe06700f3197eb095ddc62d91c00814021967cf72
-  summary: >-
-    Learn how to deploy MySQL walks you through an end-to-end Arm software workflow. It is designed
-    for software developers who want to deploy MySQL on Arm. By the end, you will be able to learn
-    about the various ways MySQL can be deployed and learn how to interact with a MySQL database
-    using a MySQL client CLI tool. It focuses on tools and technologies such as SQL and MySQL,
-    Linux environments, Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft
-    Azure, Google Cloud, and Oracle. The main steps cover Install, configure and check MySQL.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will learn about the various ways MySQL can be deployed and learn how to interact with
-      a MySQL database using a MySQL client CLI tool.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to deploy MySQL on Arm.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm based instance from a cloud service
-      provider, or an on-premise Arm server.; If you do not have an Arm node, the next section
-      discusses some options.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including SQL and MySQL, Linux environments, Arm platforms
-      such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Install, configure and check MySQL.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md
index 37b4830705..e6b4c0025d 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md
@@ -17,44 +17,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: e473c0724855bbbc59481822130f7dc5e1684593527a6b5688939f84cd32963d
-  summary: >-
-    Benchmarking MySQL with Sysbench walks you through an end-to-end Arm software workflow. It
-    is designed for performance engineers who want to benchmark MySQL using Sysbench and optimize
-    performance on Arm Linux systems. By the end, you will be able to run Sysbench to benchmark
-    a MySQL database server and enable profile-guided optimization (PGO) for MySQL and examine
-    the performance improvements. It focuses on tools and technologies such as MySQL and Sysbench,
-    Linux environments, Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft
-    Azure, Google Cloud, and Oracle. The main steps cover Setup, configure, and run MySQL server,
-    Build and run Sysbench, and Enable profile-guided optimization for MySQL.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will run Sysbench to benchmark a MySQL database server and enable profile-guided optimization
-      (PGO) for MySQL and examine the performance improvements.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for performance engineers who want to benchmark MySQL using
-      Sysbench and optimize performance on Arm Linux systems.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Basic knowledge of [MySQL databases](https://www.mysql.com/);
-      Two Arm servers running Ubuntu 22.04, one for the MySQL server and the other for the Sysbench
-      client.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including MySQL and Sysbench, Linux environments, Arm platforms
-      such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Setup, configure, and run MySQL server, Build and
-      run Sysbench, and Enable profile-guided optimization for MySQL.
-# END generated_summary_faq
+
 
 author: Bolt Liu
 
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md
index 71be8b4837..46ac662963 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md
@@ -15,42 +15,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: faa914d636e03296c6b65ba146745c543326955ec457577f047eec51aa6a3733
-  summary: >-
-    Learn how to Tune MySQL walks you through an end-to-end Arm software workflow. It is designed
-    for software developers and DevOps professionals interested in optimizing MySQL performance
-    on Arm-based VMs in the cloud. By the end, you will be able to tune MySQL to increase performance.
-    It focuses on tools and technologies such as SQL, MySQL, InnoDB, and Runbook, Linux environments,
-    Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
-    Cloud, and Oracle. The main steps cover About MySQL performance tuning, System, Kernel, Compiler,
-    and Libraries, and Tuning MySQL.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will tune MySQL to increase performance.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers and DevOps professionals interested in
-      optimizing MySQL performance on Arm-based VMs in the cloud.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Bare-metal or cloud [installation of
-      MySQL](/learning-paths/servers-and-cloud-computing/mysql/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including SQL, MySQL, InnoDB, and Runbook, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
-      Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around About MySQL performance tuning, System, Kernel, Compiler,
-      and Libraries, and Tuning MySQL.
-# END generated_summary_faq
+
 
 author: Julio Suarez
 
diff --git a/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md b/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md
index 73133e66ec..fe7a44f17a 100644
--- a/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md
@@ -23,54 +23,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 65336a8f8d1d11c4b127ea13982a581afffa94cbfd1af10a9e4df2becbc34b5a
-  summary: >-
-    Develop and Validate Firmware Pre-Silicon on Arm Neoverse CSS V3 walks you through an end-to-end
-    Arm software workflow. It is designed for This advanced topic is for firmware developers,
-    system architects, and silicon validation engineers working on Arm Neoverse CSS platforms
-    who require a pre-silicon workflow for the CSS-V3 reference design using Fixed Virtual Platforms
-    (FVPs). By the end, you will be able to explain the CSS-V3 architecture and the RD-V3 firmware
-    boot sequence (TF-A, RSE, SCP/MCP/LCP, UEFI/GRUB, Linux), set up a containerized build environment
-    and sync sources with a pinned manifest using repo, and build and boot the RD-V3 firmware
-    stack on FVP and map UART consoles to components. It focuses on tools and technologies such
-    as C, Docker, and FVP, Linux environments, Arm platforms including Neoverse, and cloud platforms
-    such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps cover Learn about the
-    Arm RD-V3 Platform, Understand the CSS-V3 boot flow and firmware stack, Build the RD-V3 Reference
-    Platform Software Stack, Simulate RD-V3 Boot Flow on Arm FVP, and Simulate Dual Chip RD-V3-R1
-    Platform.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will explain the CSS-V3 architecture and the RD-V3 firmware boot sequence (TF-A, RSE,
-      SCP/MCP/LCP, UEFI/GRUB, Linux), set up a containerized build environment and sync sources
-      with a pinned manifest using repo, and build and boot the RD-V3 firmware stack on FVP and
-      map UART consoles to components.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This advanced topic is for firmware developers, system architects, and silicon validation
-      engineers working on Arm Neoverse CSS platforms who require a pre-silicon workflow for the
-      CSS-V3 reference design using Fixed Virtual Platforms (FVPs).
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Access to an Arm Neoverse-based Linux
-      machine (cloud or local) with at least 80 GB of free storage; Familiarity with Linux command-line
-      tools and basic scripting; Understanding of firmware boot stages and SoC-level architecture;
-      Docker installed, or a GitHub Codespaces-compatible development environment.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including C, Docker, and FVP, Linux environments, Arm platforms
-      such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Learn about the Arm RD-V3 Platform, Understand the
-      CSS-V3 boot flow and firmware stack, Build the RD-V3 Reference Platform Software Stack,
-      Simulate RD-V3 Boot Flow on Arm FVP, and Simulate Dual Chip RD-V3-R1 Platform.
-# END generated_summary_faq
+
 
 author:
     - Odin Shen
diff --git a/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md b/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md
index c24a6998e9..ce666586a2 100644
--- a/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md
@@ -19,50 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 5a5fd9b66a09de71009ccb098c7ef08a848463a8a7956643020ab1fb1db22fbb
-  summary: >-
-    Run a .NET Aspire application on Arm-based VMs on AWS and GCP walks you through an end-to-end
-    Arm software workflow. It is designed for software developers interested in learning how to
-    deploy .NET Aspire applications on Arm-based virtual machines (VMs) on Amazon Web Services
-    (AWS) and Google Cloud Platform (GCP). By the end, you will be able to demonstrate knowledge
-    and understanding of .NET Aspire developer tools, create a .NET Aspire application, and modify
-    code on a Windows on Arm development machine. It focuses on tools and technologies such as
-    .NET, C#, and Visual Studio Code, Windows and Linux environments, Arm platforms including
-    Neoverse, and cloud platforms such as AWS and Google Cloud. The main steps cover .NET Aspire,
-    Create a project and then an application, Run the application, Modify the Project, and Deploy
-    to AWS EC2.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will demonstrate knowledge and understanding of .NET Aspire developer tools, create
-      a .NET Aspire application, and modify code on a Windows on Arm development machine.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers interested in learning how to deploy
-      .NET Aspire applications on Arm-based virtual machines (VMs) on Amazon Web Services (AWS)
-      and Google Cloud Platform (GCP).
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows on Arm machine, for example
-      the Lenovo Thinkpad X13s running Windows 11 to build the .NET Aspire project.; An [Arm-based
-      instance](/learning-paths/servers-and-cloud-computing/csp/) from AWS or GCP.; Any code editor.
-      [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is
-      an example of a suitable editor.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including .NET, C#, and Visual Studio Code, Windows and Linux
-      environments, Arm platforms such as Neoverse, and cloud platforms such as AWS and Google
-      Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around .NET Aspire, Create a project and then an application,
-      Run the application, Modify the Project, and Deploy to AWS EC2.
-# END generated_summary_faq
+
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md
index 67ac84b30e..ecc09509d0 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md
@@ -19,47 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: e6f6f4d843b4974d1c1d67a35009b4428d08bb356d26c08a441f82726e002fb2
-  summary: >-
-    Deploy NGINX on Azure Cobalt 100 Arm-based virtual machines walks you through an end-to-end
-    Arm software workflow. It is designed for system administrators and developers who want to
-    learn how to deploy and benchmark NGINX on Microsoft Azure Cobalt 100 Arm-based instances.
-    By the end, you will be able to create an Arm64 virtual machine on Azure Cobalt 100 (Dpsv6)
-    using the Azure console with Ubuntu Pro 24.04 LTS as the base image, install and configure
-    the NGINX web server on the Azure Arm64 virtual machine, and configure and test a static website
-    with NGINX on the virtual machine. It focuses on tools and technologies such as NGINX and
-    ApacheBench, Linux environments, Arm platforms including Neoverse, and cloud platforms such
-    as Microsoft Azure. The main steps cover Overview of Azure Cobalt 100 and NGINX, Create an
-    Arm-based Azure VM with Cobalt 100, Install NGINX, NGINX Baseline Testing, and NGINX Benchmarking.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create an Arm64 virtual machine on Azure Cobalt 100 (Dpsv6) using the Azure console
-      with Ubuntu Pro 24.04 LTS as the base image, install and configure the NGINX web server
-      on the Azure Arm64 virtual machine, and configure and test a static website with NGINX on
-      the virtual machine.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for system administrators and developers who want to learn
-      how to deploy and benchmark NGINX on Microsoft Azure Cobalt 100 Arm-based instances.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Microsoft Azure](https://azure.microsoft.com/)
-      account with access to Cobalt 100 based instances (Dpsv6).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including NGINX and ApacheBench, Linux environments, Arm platforms
-      such as Neoverse, and cloud platforms such as Microsoft Azure.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview of Azure Cobalt 100 and NGINX, Create an
-      Arm-based Azure VM with Cobalt 100, Install NGINX, NGINX Baseline Testing, and NGINX Benchmarking.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx/_index.md
index af974d9735..ffeaf26fc8 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx/_index.md
@@ -19,47 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: c5e077458808373c8ce9660235716b5bb55e4e7eb8b6300c162c041ef1c96cb0
-  summary: >-
-    Learn how to deploy Nginx walks you through an end-to-end Arm software workflow. It is designed
-    for engineers who want to use Nginx on Arm. By the end, you will be able to install and run
-    Nginx on Arm servers, set up Nginx as a web server, reverse proxy, or an API Gateway, and
-    verify Nginx is working correctly. It focuses on tools and technologies such as NGINX, Linux
-    environments, Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft
-    Azure, Google Cloud, and Oracle. The main steps cover Install Nginx using a package manager
-    and check the build configuration, Build Nginx from source, Setup a static file server, and
-    Setup a reverse proxy and API gateway.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install and run Nginx on Arm servers, set up Nginx as a web server, reverse proxy,
-      or an API Gateway, and verify Nginx is working correctly.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for engineers who want to use Nginx on Arm.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: To create a file server you will need
-      at least one [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from
-      a cloud service provider or one on-premises Arm server.; To create a reverse proxy or API
-      gateway you will need at least three Arm based instances from a cloud service provider or
-      at least three on-premises Arm servers.; Network settings (firewalls and security groups)
-      which allow communication on port 22 (SSH) and port 443 (HTTPS).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including NGINX, Linux environments, Arm platforms such as
-      Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Install Nginx using a package manager and check the
-      build configuration, Build Nginx from source, Setup a static file server, and Setup a reverse
-      proxy and API gateway.
-# END generated_summary_faq
+
 
 author: Julio Suarez
 
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
index c734ea7ed1..9d74527561 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
@@ -20,47 +20,6 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v2
-  generated_at: '2026-05-08T18:10:03Z'
-  generator: template
-  source_hash: 10f13dee3459577fcadf5966cf80d990f3396321189f638432a3308699e773a8
-  summary_generated_at: '2026-05-08T18:10:03Z'
-  summary_source_hash: 10f13dee3459577fcadf5966cf80d990f3396321189f638432a3308699e773a8
-  faq_generated_at: '2026-05-06T17:17:58Z'
-  faq_source_hash: 10f13dee3459577fcadf5966cf80d990f3396321189f638432a3308699e773a8
-  summary: >-
-    Learn how to tune Nginx walks you through an end-to-end Arm software workflow. It is designed
-    for software developers who want to use Nginx on Arm. By the end, you will be able to describe
-    how kernel parameters can impact Nginx performance, describe how compilers and libraries can
-    impact Nginx performance, and tune a Nginx file server configuration file. It focuses on tools
-    and technologies such as NGINX and Runbook, Linux environments, Arm platforms including Neoverse,
-    and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps
-    cover Before and after tuning Nginx, Kernel, compiler, and libraries, Tune a static file server,
-    Tune a Reverse Proxy or API Gateway, and Test Optimizations.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will describe how kernel parameters can impact Nginx performance, describe how compilers
-      and libraries can impact Nginx performance, and tune a Nginx file server configuration file.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers who want to use Nginx on Arm.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A cloud or bare-metal installation of
-      a Nginx file server or load balancer.; If you do not already have a Nginx setup, a review
-      of [Learn how to deploy Nginx](/learning-paths/servers-and-cloud-computing/nginx/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including NGINX and Runbook, Linux environments, Arm platforms
-      such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Before and after tuning Nginx, Kernel, compiler, and
-      libraries, Tune a static file server, Tune a Reverse Proxy or API Gateway, and Test Optimizations.
-# END generated_summary_faq
 
 author: Julio Suarez
 
diff --git a/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md b/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md
index e84f2fc7ac..5a249266bc 100644
--- a/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md
@@ -16,45 +16,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 81bdee16a18b09da91a7514eb70368771b251ed3f8ec657ec105ca10ecead038
-  summary: >-
-    Run a Natural Language Processing (NLP) model from Hugging Face on Arm servers walks you through
-    an end-to-end Arm software workflow. It is designed for software developers who want to learn
-    how to run a Natural Language Processing (NLP) model from Hugging Face using PyTorch on Arm
-    based servers. By the end, you will be able to deploy a PyTorch NLP model from Hugging Face
-    on an Arm AArch64 CPU and use the PyTorch profiler to analyze the execution time of the model.
-    It focuses on tools and technologies such as Python, PyTorch, and Hugging Face, Linux environments,
-    Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
-    Cloud, and Oracle. The main steps cover Run a Natural Language Processing (NLP) model from
-    Hugging Face on Arm servers.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will deploy a PyTorch NLP model from Hugging Face on an Arm AArch64 CPU and use the
-      PyTorch profiler to analyze the execution time of the model.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to learn how to run a Natural
-      Language Processing (NLP) model from Hugging Face using PyTorch on Arm based servers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/)
-      from a cloud service provider or an on-premise Arm server.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Python, PyTorch, and Hugging Face, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
-      Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Run a Natural Language Processing (NLP) model from
-      Hugging Face on Arm servers.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md
index 8079ff23f1..e76d49e8ee 100644
--- a/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md
@@ -23,51 +23,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 800eed835763098d4b369789d10de642bbdbaa390e8bfe0cb6ea2c4c78f7ecbd
-  summary: >-
-    Deploy Node.js on Google Cloud C4A Arm-based Axion VMs walks you through an end-to-end Arm
-    software workflow. It is designed for software developers migrating Node.js workloads from
-    x86_64 to Arm-based servers, specifically on Google Cloud C4A virtual machines built on Axion
-    processors. By the end, you will be able to provision an Arm-based SUSE Linux Enterprise Server
-    virtual machine on Google Cloud C4A instances with Axion processors, install and configure
-    Node.js on a SUSE Arm64 (C4A) instance, and validate Node.js functionality with baseline HTTP
-    server tests. It focuses on tools and technologies such as Node.js, npm, and Autocannon, Linux
-    environments, Arm platforms including Neoverse, and cloud platforms such as Google Cloud.
-    The main steps cover Getting started with Node.js on Google Axion C4A (Arm Neoverse-V2), Create
-    a Google Axion C4A Arm virtual machine on GCP, Install Node.js using Node Version Manager,
-    Validate Node.js baseline on Google Axion C4A Arm virtual machine, and Benchmark Node.js performance
-    with Autocannon.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will provision an Arm-based SUSE Linux Enterprise Server virtual machine on Google Cloud
-      C4A instances with Axion processors, install and configure Node.js on a SUSE Arm64 (C4A)
-      instance, and validate Node.js functionality with baseline HTTP server tests.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers migrating Node.js workloads from x86_64
-      to Arm-based servers, specifically on Google Cloud C4A virtual machines built on Axion processors.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
-      account with billing enabled; Familiarity with networking concepts and [Node.js event-driven
-      architecture](https://nodejs.org/en/docs/guides/event-loop-timers-and-nexttick).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Node.js, npm, and Autocannon, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Getting started with Node.js on Google Axion C4A (Arm
-      Neoverse-V2), Create a Google Axion C4A Arm virtual machine on GCP, Install Node.js using
-      Node Version Manager, Validate Node.js baseline on Google Axion C4A Arm virtual machine,
-      and Benchmark Node.js performance with Autocannon.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md b/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md
index 5efbc685b4..446854fd6e 100644
--- a/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md
@@ -16,40 +16,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 3ee5dd8d8edeb5dcb1e3be7bb09cdf0b1b2694f0e74e9eb3c6c2f17912399808
-  summary: >-
-    Deploy Arm Instances on Oracle Cloud Infrastructure (OCI) using Terraform walks you through
-    an end-to-end Arm software workflow. It is designed for software developers who are new to
-    deploying Arm instances on Oracle Cloud Infrastructure (OCI) using Terraform. By the end,
-    you will be able to automate Arm virtual machine creation on OCI using Terraform. It focuses
-    on tools and technologies such as Terraform, Linux environments, Arm platforms including Neoverse,
-    and cloud platforms such as Oracle. The main steps cover Automate OCI VM instance creation
-    using Terraform.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will automate Arm virtual machine creation on OCI using Terraform.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who are new to deploying Arm instances
-      on Oracle Cloud Infrastructure (OCI) using Terraform.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An OCI account; A computer with Terraform
-      installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Terraform, Linux environments, Arm platforms such
-      as Neoverse, and cloud platforms such as Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Automate OCI VM instance creation using Terraform.
-# END generated_summary_faq
+
 
 author: Frédéric -lefred- Descamps
 
diff --git a/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md
index 1db4e0c58e..149e4e14b3 100644
--- a/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md
@@ -19,47 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 84ba887426f3ca45b698c79028023d80cbf0a06e6bc2fb71fcbe924943078dd8
-  summary: >-
-    Deploy SqueezeNet 1.0 INT8 model with ONNX Runtime on Azure Cobalt 100 walks you through an
-    end-to-end Arm software workflow. It is designed for developers deploying ONNX-based applications
-    on Arm-based machines. By the end, you will be able to provision an Azure Arm64 virtual machine
-    using Azure console, with Ubuntu Pro 24.04 LTS as the base image and perform ONNX baseline
-    testing and benchmarking on Arm64 virtual machines. It focuses on tools and technologies such
-    as Python and ONNX Runtime, Linux environments, Arm platforms including Neoverse, and cloud
-    platforms such as Microsoft Azure. The main steps cover Overview, Create an Arm-based Azure
-    Cobalt 100 virtual machine, ONNX Installation, Baseline Testing, and Benchmark ONNX runtime
-    performance with onnxruntime_perf_test.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will provision an Azure Arm64 virtual machine using Azure console, with Ubuntu Pro 24.04
-      LTS as the base image and perform ONNX baseline testing and benchmarking on Arm64 virtual
-      machines.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is for developers deploying ONNX-based applications on Arm-based machines.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Microsoft Azure](https://azure.microsoft.com/)
-      account with access to Cobalt 100 based instances (Dpsv6); Basic understanding of Python
-      and machine learning concepts; Familiarity with [ONNX Runtime](https://onnxruntime.ai/docs/)
-      and Azure cloud services.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Python and ONNX Runtime, Linux environments, Arm
-      platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview, Create an Arm-based Azure Cobalt 100 virtual
-      machine, ONNX Installation, Baseline Testing, and Benchmark ONNX runtime performance with
-      onnxruntime_perf_test.
-# END generated_summary_faq
+
 
 author: Pareena Verma    
 
diff --git a/content/learning-paths/servers-and-cloud-computing/onnx/_index.md b/content/learning-paths/servers-and-cloud-computing/onnx/_index.md
index 8da50339b8..8a0bc4a58a 100644
--- a/content/learning-paths/servers-and-cloud-computing/onnx/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/onnx/_index.md
@@ -20,46 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: a9e547c4a9f99e1c7bfbfe582032ede11eb8ff5a74dbb7a93bada275ac435220
-  summary: >-
-    Deploy Phi-4-mini model with ONNX Runtime on Azure Cobalt 100 walks you through an end-to-end
-    Arm software workflow. It is designed for developers, ML engineers, and cloud practitioners
-    looking to deploy Microsoft's Phi Models on Arm-based servers using ONNX Runtime. By the end,
-    you will be able to quantize and run the Phi-4-mini model with ONNX Runtime on Azure and analyze
-    performance on Arm Neoverse N2 based Azure Cobalt 100 VMs. It focuses on tools and technologies
-    such as Python and ONNX Runtime, Linux environments, Arm platforms including Neoverse, and
-    cloud platforms such as Microsoft Azure. The main steps cover Build ONNX Runtime and set up
-    the Phi-4-mini Model, Run the Chatbot Server, and Interact with the Phi-4-mini Chatbot.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will quantize and run the Phi-4-mini model with ONNX Runtime on Azure and analyze performance
-      on Arm Neoverse N2 based Azure Cobalt 100 VMs.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for developers, ML engineers, and cloud practitioners looking
-      to deploy Microsoft's Phi Models on Arm-based servers using ONNX Runtime.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/)
-      from an appropriate cloud service provider. This Learning Path has been tested on an Azure
-      Cobalt 100 virtual machine.; Basic understanding of Python and machine learning concepts.;
-      Familiarity with ONNX Runtime and Azure cloud services.; Knowledge of Large Language Model
-      (LLM) fundamentals.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Python and ONNX Runtime, Linux environments, Arm
-      platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Build ONNX Runtime and set up the Phi-4-mini Model,
-      Run the Chatbot Server, and Interact with the Phi-4-mini Chatbot.
-# END generated_summary_faq
+
 
 author: Nobel Chowdary Mandepudi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md b/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md
index 5923a080b1..34833e5610 100644
--- a/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md
@@ -22,57 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: dec913a7a15e82ebf129e2ac64cba8d9140694840f8f9e817fb091b0c82c4cab
-  summary: >-
-    Simulate OpenBMC and UEFI pre-silicon on Neoverse RD-V3 walks you through an end-to-end Arm
-    software workflow. It is designed for This advanced topic is for firmware developers, platform
-    software engineers, and system integrators working on Arm Neoverse-based platforms. It is
-    especially useful for developers exploring pre-silicon development, testing, and integration
-    of Baseboard Management Controllers (BMC) with UEFI firmware. If you are building or validating
-    server-class reference platforms such as RD-V3, before hardware is available, this Learning
-    Path shows you how to simulate and debug the full boot path using Fixed Virtual Platforms
-    (FVPs). By the end, you will be able to understand the role of OpenBMC and UEFI in the Arm
-    server boot flow, simulate the firmware using the RD-V3 FVP, and build and launch OpenBMC
-    and UEFI images on the RD-V3 FVP. It focuses on tools and technologies such as C, Docker,
-    FVP, OpenBMC, and Yocto/BitBake, Linux environments, and Arm platforms including Neoverse.
-    The main steps cover What are OpenBMC and UEFI?, Set up the development environment for OpenBMC
-    and UEFI, Run OpenBMC and host UEFI simulation on RD-V3 FVP, Monitor and control the host
-    CPU using OpenBMC SOL and web UI, and Customize IPMI commands in OpenBMC.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand the role of OpenBMC and UEFI in the Arm server boot flow, simulate the
-      firmware using the RD-V3 FVP, and build and launch OpenBMC and UEFI images on the RD-V3
-      FVP.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This advanced topic is for firmware developers, platform software engineers, and system
-      integrators working on Arm Neoverse-based platforms. It is especially useful for developers
-      exploring pre-silicon development, testing, and integration of Baseboard Management Controllers
-      (BMC) with UEFI firmware. If you are building or validating server-class reference platforms
-      such as RD-V3, before hardware is available, this Learning Path shows you how to simulate
-      and debug the full boot path using Fixed Virtual Platforms (FVPs).
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm Neoverse-based Linux machine
-      (cloud or local) running Ubuntu 22.04 LTS; At least 80 GB free disk space and 48 GB RAM;
-      Working knowledge of Docker, Git, and common Linux terminal tools; Basic understanding of
-      the server firmware stack (such as UEFI, BMC, and TF-A).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including C, Docker, FVP, OpenBMC, and Yocto/BitBake, Linux
-      environments, and Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around What are OpenBMC and UEFI?, Set up the development
-      environment for OpenBMC and UEFI, Run OpenBMC and host UEFI simulation on RD-V3 FVP, Monitor
-      and control the host CPU using OpenBMC SOL and web UI, and Customize IPMI commands in OpenBMC.
-# END generated_summary_faq
+
 
 author:
   - Odin Shen
diff --git a/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md b/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md
index f4142e51dc..c99a900165 100644
--- a/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md
@@ -21,52 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 778ac2a8b8ad9ffd665f6f0be545541090465f355094c354cc693e784d27559a
-  summary: >-
-    Learn how to profile an example C++ data-processing workload on Arm Linux with Arm Performix,
-    then accelerate random number generation using OpenRNG and Arm Performance Libraries. It is
-    designed for C++ developers who want to profile a data-processing workload on Arm Linux, identify
-    performance bottlenecks with Arm Performix, and accelerate random number generation using
-    OpenRNG and Arm Performance Libraries. By the end, you will be able to build and run a baseline
-    C++ data-processing workload on Arm Linux, use Arm Performix Code Hotspots to identify the
-    highest-impact optimization target, and accelerate random number generation by integrating
-    OpenRNG and Arm Performance Libraries. It focuses on tools and technologies such as CMake,
-    Arm Performix, OpenRNG, and Arm Performance Libraries, Linux environments, and Arm platforms
-    including Neoverse. The main steps cover Set up your environment, Run the baseline data-processing
-    example, Identify code hotspots with Arm Performix, Accelerate distribution generation with
-    OpenRNG, and Measure performance improvements with a microbenchmark.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build and run a baseline C++ data-processing workload on Arm Linux, use Arm Performix
-      Code Hotspots to identify the highest-impact optimization target, and accelerate random
-      number generation by integrating OpenRNG and Arm Performance Libraries. Learn how to profile
-      an example C++ data-processing workload on Arm Linux with Arm Performix, then accelerate
-      random number generation using OpenRNG and Arm Performance Libraries.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for C++ developers who want to profile a data-processing workload
-      on Arm Linux, identify performance bottlenecks with Arm Performix, and accelerate random
-      number generation using OpenRNG and Arm Performance Libraries.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm Linux (aarch64) server, such
-      as an AWS Graviton3 instance; Basic understanding of C++ and CMake.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including CMake, Arm Performix, OpenRNG, and Arm Performance
-      Libraries, Linux environments, and Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Set up your environment, Run the baseline data-processing
-      example, Identify code hotspots with Arm Performix, Accelerate distribution generation with
-      OpenRNG, and Measure performance improvements with a microbenchmark.
-# END generated_summary_faq
+
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/openshift/_index.md b/content/learning-paths/servers-and-cloud-computing/openshift/_index.md
index 11486ff589..a90896e48d 100644
--- a/content/learning-paths/servers-and-cloud-computing/openshift/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openshift/_index.md
@@ -18,41 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 7972fb230273ab1809c6f0917c4bbc49934f495feb2649da665d67bf71a45a3f
-  summary: >-
-    Build multi-architecture applications with Red Hat OpenShift Pipelines on AWS walks you through
-    an end-to-end Arm software workflow. It is designed for OpenShift administrators who want
-    to migrate their applications to Arm. By the end, you will be able to migrate existing OpenShift
-    applications to Arm-based nodes. It focuses on tools and technologies such as Tekton and OpenShift,
-    Linux environments, Arm platforms including Neoverse, and cloud platforms such as AWS. The
-    main steps cover Migrate an x86 workload to Arm on AWS.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will migrate existing OpenShift applications to Arm-based nodes.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This topic is for OpenShift administrators who want to migrate their applications to Arm.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An AWS account with an OpenShift 4.18
-      cluster running x86 compute nodes; Red Hat OpenShift Pipelines (Tekton) Operator installed
-      in your cluster; Familiarity with the `oc` CLI, container fundamentals, and basic Tekton
-      concepts (Task, Pipeline, PipelineRun); Cluster access with cluster-admin or equivalent
-      permissions to configure nodes and pipelines.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Tekton and OpenShift, Linux environments, Arm platforms
-      such as Neoverse, and cloud platforms such as AWS.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Migrate an x86 workload to Arm on AWS.
-# END generated_summary_faq
+
 
 author: Jeff Young
 
diff --git a/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md
index 98bc221b9e..213fe3e63f 100644
--- a/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md
@@ -25,56 +25,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 42628afa923ce6e89693bdfc72469ac0803610c3decb6cd4f36bd1a003874d0d
-  summary: >-
-    Deploy OpenStack on Azure Cobalt 100 Arm64 virtual machines using DevStack for development
-    and Kolla-Ansible for containerized production deployments. It is designed for This learning
-    path is designed for developers, DevOps engineers, and platform engineers who want to deploy
-    and manage OpenStack on Arm-based cloud environments using Kolla-Ansible and DevStack. By
-    the end, you will be able to deploy OpenStack on Azure Cobalt 100 Arm64 virtual machines,
-    configure core OpenStack services (Keystone, Nova, Neutron, Glance, Cinder), and deploy containerized
-    OpenStack using Kolla-Ansible. It focuses on tools and technologies such as OpenStack, Kolla-Ansible,
-    DevStack, Python, and OpenStack CLI, Linux environments, Arm platforms including Neoverse,
-    and cloud platforms such as Microsoft Azure. The main steps cover Understand Azure Cobalt
-    100 and OpenStack, Create an Azure Cobalt 100 Arm64 virtual machine for DevStack, Deploy OpenStack
-    on an Azure Cobalt 100 Arm64 virtual machine using DevStack, Prepare Azure Arm64 virtual machine
-    for Kolla-Ansible, and Deploy OpenStack using Kolla-Ansible on an Azure Ubuntu Arm64 virtual
-    machine.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will deploy OpenStack on Azure Cobalt 100 Arm64 virtual machines, configure core OpenStack
-      services (Keystone, Nova, Neutron, Glance, Cinder), and deploy containerized OpenStack using
-      Kolla-Ansible. Deploy OpenStack on Azure Cobalt 100 Arm64 virtual machines using DevStack
-      for development and Kolla-Ansible for containerized production deployments.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This learning path is designed for developers, DevOps engineers, and platform engineers
-      who want to deploy and manage OpenStack on Arm-based cloud environments using Kolla-Ansible
-      and DevStack.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Microsoft Azure account](https://azure.microsoft.com/)
-      with access to Cobalt 100 based instances (Dpsv6); Basic knowledge of Linux command-line
-      operations; Familiarity with SSH and remote server access; Basic understanding of cloud
-      computing and virtualization concepts.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including OpenStack, Kolla-Ansible, DevStack, Python, and
-      OpenStack CLI, Linux environments, Arm platforms such as Neoverse, and cloud platforms such
-      as Microsoft Azure.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Understand Azure Cobalt 100 and OpenStack, Create
-      an Azure Cobalt 100 Arm64 virtual machine for DevStack, Deploy OpenStack on an Azure Cobalt
-      100 Arm64 virtual machine using DevStack, Prepare Azure Arm64 virtual machine for Kolla-Ansible,
-      and Deploy OpenStack using Kolla-Ansible on an Azure Ubuntu Arm64 virtual machine.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md b/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md
index 80d81ad85c..e62878b713 100644
--- a/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md
@@ -20,54 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: cb4227a3f8375d6ae63801891703d6e615bdc60a01fca099a2f76453775ef460
-  summary: >-
-    Deploy OpenTelemetry on Google Cloud C4A Axion processors walks you through an end-to-end
-    Arm software workflow. It is designed for DevOps engineers, platform engineers, and software
-    developers who want to deploy and observe a cloud-native microservice on Arm64-based Google
-    Cloud C4A Axion processors using OpenTelemetry. By the end, you will be able to deploy an
-    instrumented Python Flask microservice on Google Cloud C4A Axion processors, configure OpenTelemetry
-    Collector to process and route distributed traces and metrics, and integrate Prometheus and
-    Jaeger for comprehensive metrics collection and distributed tracing visualization. It focuses
-    on tools and technologies such as Flask, Docker, Prometheus, and Jaeger, Linux environments,
-    Arm platforms including Neoverse, and cloud platforms such as Google Cloud. The main steps
-    cover Get started with OpenTelemetry on Google Axion C4A, Create firewall rules on GCP for
-    Flask and observability components, Create a Google Axion C4A Arm virtual machine on GCP,
-    Set up OpenTelemetry environment and application on Arm64, and Deploy the OpenTelemetry observability
-    stack on Arm64.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will deploy an instrumented Python Flask microservice on Google Cloud C4A Axion processors,
-      configure OpenTelemetry Collector to process and route distributed traces and metrics, and
-      integrate Prometheus and Jaeger for comprehensive metrics collection and distributed tracing
-      visualization.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This learning path is for DevOps engineers, platform engineers, and software developers
-      who want to deploy and observe a cloud-native microservice on Arm64-based Google Cloud C4A
-      Axion processors using OpenTelemetry.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
-      account with billing enabled; Basic familiarity with Python and Flask; Basic understanding
-      of containers and Kubernetes concepts.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Flask, Docker, Prometheus, and Jaeger, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Get started with OpenTelemetry on Google Axion C4A,
-      Create firewall rules on GCP for Flask and observability components, Create a Google Axion
-      C4A Arm virtual machine on GCP, Set up OpenTelemetry environment and application on Arm64,
-      and Deploy the OpenTelemetry observability stack on Arm64.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/pac/_index.md b/content/learning-paths/servers-and-cloud-computing/pac/_index.md
index 1debef89e1..b8c048f7f9 100644
--- a/content/learning-paths/servers-and-cloud-computing/pac/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pac/_index.md
@@ -19,50 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: fa699cafa5c0d998a63de306371bfbe47680c7453b28fc4b35d4fe2671902b23
-  summary: >-
-    Understand Arm Pointer Authentication walks you through an end-to-end Arm software workflow.
-    It is designed for software developers interested in understanding Arm Pointer Authentication.
-    By the end, you will be able to create a simple application on an Arm server with Pointer
-    Authentication, compile the application with and without Pointer Authentication to inspect
-    the instructions generated, and exploit the applications with and without Pointer Authentication
-    to demonstrate how Pointer Authentication instructions enhance security. It focuses on tools
-    and technologies such as Runbook, Linux environments, Arm platforms including Neoverse, and
-    cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps cover
-    Pointer Authentication on Arm, Example application, and Exploit applications built without
-    Pointer Authentication instructions.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create a simple application on an Arm server with Pointer Authentication, compile
-      the application with and without Pointer Authentication to inspect the instructions generated,
-      and exploit the applications with and without Pointer Authentication to demonstrate how
-      Pointer Authentication instructions enhance security.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers interested in understanding Arm Pointer
-      Authentication.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm based instance from a cloud service
-      provider, or an on-premise Arm server.; If needed, review [Get started with Arm-based cloud
-      instances](/learning-paths/servers-and-cloud-computing/csp/) to learn how to deploy Arm
-      in the cloud. These learning paths also point to more advanced learning paths that show
-      how to automate the deployment of Arm instances at different cloud providers.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Runbook, Linux environments, Arm platforms such
-      as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Pointer Authentication on Arm, Example application,
-      and Exploit applications built without Pointer Authentication instructions.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md b/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md
index d90ee2c215..47fe473bcc 100644
--- a/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md
@@ -23,57 +23,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 0599665da13e9e1a8b017b0dac87c63f323ef769b1a5be62a60a32a36bc82696
-  summary: >-
-    Learn how to use an AI agent and the Performix tool through the Arm MCP Server to run the
-    Code Hotspots recipe on a C++ application, interpret flame graph results, and apply targeted
-    optimizations on Arm Neoverse. It is designed for developers who want to use AI-powered tools
-    to automate performance profiling and optimization of C++ applications on Arm Neoverse servers.
-    By the end, you will be able to describe how the Arm Performix tool in the Arm MCP Server
-    enables AI-driven profiling workflows, configure a GitHub Copilot prompt file to run the Code
-    Hotspots recipe on a remote Arm target, and use an AI agent to interpret flame graph results
-    and identify the hottest functions in a C++ application. It focuses on tools and technologies
-    such as Arm Performix, MCP, C++, and GitHub Copilot, Linux environments, and Arm platforms
-    including Neoverse. The main steps cover Understand AI-driven profiling with Arm Performix
-    MCP, Build the Mandelbrot example on Arm Neoverse, Run Code Hotspots with an AI agent, and
-    Optimize code with AI-driven profiling feedback.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will describe how the Arm Performix tool in the Arm MCP Server enables AI-driven profiling
-      workflows, configure a GitHub Copilot prompt file to run the Code Hotspots recipe on a remote
-      Arm target, and use an AI agent to interpret flame graph results and identify the hottest
-      functions in a C++ application. Learn how to use an AI agent and the Performix tool through
-      the Arm MCP Server to run the Code Hotspots recipe on a C++ application, interpret flame
-      graph results, and apply targeted optimizations on Arm Neoverse.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for developers who want to use AI-powered tools to automate performance
-      profiling and optimization of C++ applications on Arm Neoverse servers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Completion of the [Automate x86-to-Arm
-      application migration using Arm MCP Server](/learning-paths/servers-and-cloud-computing/arm-mcp-server/)
-      Learning Path, or equivalent familiarity with configuring the Arm MCP Server in an AI coding
-      assistant; Access to an Arm-based cloud instance running Linux, such as an AWS Graviton3
-      instance; Access to Arm Performix configured with the remote Arm target. See the [Arm Performix
-      install guide](/install-guides/performix/) for setup instructions; Basic understanding of
-      C++.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Arm Performix, MCP, C++, and GitHub Copilot, Linux
-      environments, and Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Understand AI-driven profiling with Arm Performix
-      MCP, Build the Mandelbrot example on Arm Neoverse, Run Code Hotspots with an AI agent, and
-      Optimize code with AI-driven profiling feedback.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md b/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md
index 4fd38dc4a5..032a8f3f2e 100644
--- a/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md
@@ -21,48 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: ec027f6022cc86a644a71a7d2016bf4601e2c4ac41570e861e9f124468b228ae
-  summary: >-
-    Optimize application performance using Arm Performix CPU microarchitecture analysis walks
-    you through an end-to-end Arm software workflow. It is designed for software developers who
-    want to learn performance analysis methodologies for Linux applications on Arm Neoverse-based
-    servers. By the end, you will be able to identify CPU pipeline bottlenecks using the Arm Performix
-    CPU Microarchitecture recipe, analyze instruction types and SIMD utilization using the Instruction
-    Mix recipe, and optimize application performance using vectorization and compiler flags. It
-    focuses on tools and technologies such as Arm Performix, C, and Runbook, Linux environments,
-    and Arm platforms including Neoverse. The main steps cover Set up the target environment and
-    compile the application, Identify application bottlenecks with the CPU Microarchitecture recipe,
-    and Analyze SIMD utilization with the Instruction Mix recipe.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will identify CPU pipeline bottlenecks using the Arm Performix CPU Microarchitecture
-      recipe, analyze instruction types and SIMD utilization using the Instruction Mix recipe,
-      and optimize application performance using vectorization and compiler flags.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to learn performance analysis
-      methodologies for Linux applications on Arm Neoverse-based servers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm Neoverse-based server running
-      Linux (bare-metal or cloud bare-metal instance preferred for access to hardware performance
-      counters); Familiarity with Linux command line; Basic understanding of CPU performance concepts.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Arm Performix, C, and Runbook, Linux environments,
-      and Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Set up the target environment and compile the application,
-      Identify application bottlenecks with the CPU Microarchitecture recipe, and Analyze SIMD
-      utilization with the Instruction Mix recipe.
-# END generated_summary_faq
+
 
 author:
 - Brendan Long
diff --git a/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md
index 3029dbaba4..8e497525b9 100644
--- a/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md
@@ -21,49 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 719ec8966a776cc5ee97782b7ef7cc2b989a8616d14cb36b9847c9cbd2f16446
-  summary: >-
-    Deploy PHP on Google Cloud C4A Arm-based Axion VMs walks you through an end-to-end Arm software
-    workflow. It is designed for developers migrating Hypertext Preprocessor (PHP) workloads from
-    x86_64 to Arm-based servers, specifically on Google Cloud C4A virtual machines (VM) built
-    on Axion processors. By the end, you will be able to provision a SUSE Linux Enterprise Server
-    (SLES) virtual machine on a Google Cloud C4A Arm-based Axion virtual machine, install PHP
-    on a SUSE Arm64 C4A instance, and validate PHP functionality by running baseline HTTP server
-    tests. It focuses on tools and technologies such as PHP, Apache, and PHPBench, Linux environments,
-    Arm platforms including Neoverse, and cloud platforms such as Google Cloud. The main steps
-    cover Get started with PHP on Google Cloud Axion C4A Arm VMs, Provision a Google Axion C4A
-    Arm virtual machine on GCP, Install PHP, Validate PHP baseline on Google Cloud Axion C4A Arm
-    VM, and PHP Benchmarking.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will provision a SUSE Linux Enterprise Server (SLES) virtual machine on a Google Cloud
-      C4A Arm-based Axion virtual machine, install PHP on a SUSE Arm64 C4A instance, and validate
-      PHP functionality by running baseline HTTP server tests.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers migrating Hypertext Preprocessor (PHP) workloads
-      from x86_64 to Arm-based servers, specifically on Google Cloud C4A virtual machines (VM)
-      built on Axion processors.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
-      account with billing enabled; Basic familiarity with web servers and PHP scripting.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including PHP, Apache, and PHPBench, Linux environments, Arm
-      platforms such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Get started with PHP on Google Cloud Axion C4A Arm
-      VMs, Provision a Google Axion C4A Arm virtual machine on GCP, Install PHP, Validate PHP
-      baseline on Google Cloud Axion C4A Arm VM, and PHP Benchmarking.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md b/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md
index 9b85b168f4..6886618997 100644
--- a/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md
@@ -21,50 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 2fdb45e72a36eb114250486b88d603cc8687b9f6b44bb5bb656e25c37d9795a9
-  summary: >-
-    Optimize application performance with CPU affinity walks you through an end-to-end Arm software
-    workflow. It is designed for developers, performance engineers, and system administrators
-    looking to fine-tune the performance of their workload on many-core Arm-based systems. By
-    the end, you will be able to pin threads to specific CPU cores using taskset and source code
-    modifications, measure cache performance improvements from thread pinning using perf, and
-    evaluate performance trade-offs between throughput and latency consistency. It focuses on
-    tools and technologies such as C++, Python, taskset, perf, and Google Benchmark, Linux environments,
-    Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
-    Cloud, and Oracle. The main steps cover Understand thread pinning and CPU affinity, Create
-    a CPU-intensive program, Pin threads to cores with taskset, and Set CPU affinity in source
-    code.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will pin threads to specific CPU cores using taskset and source code modifications,
-      measure cache performance improvements from thread pinning using perf, and evaluate performance
-      trade-offs between throughput and latency consistency.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for developers, performance engineers, and system administrators
-      looking to fine-tune the performance of their workload on many-core Arm-based systems.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm Linux system with four or more
-      CPU cores; Experience with multi-threaded programming in C++ and Python; Understanding of
-      build systems and computer architecture concepts; Familiarity with Linux command-line tools.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including C++, Python, taskset, perf, and Google Benchmark,
-      Linux environments, Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft
-      Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Understand thread pinning and CPU affinity, Create
-      a CPU-intensive program, Pin threads to cores with taskset, and Set CPU affinity in source
-      code.
-# END generated_summary_faq
+
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md b/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md
index 40c8ccda42..4f08eaf76a 100644
--- a/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md
@@ -19,48 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 3ec6ae40daff557dd05cd092ff7d6b5a63954a1618605030a443f56fe5ffe490
-  summary: >-
-    Implement Code level Performance Analysis using the PMUv3 plugin walks you through an end-to-end
-    Arm software workflow. It is designed for Engineers who want to carry out C/C++ performance
-    analysis by instrumenting code at the block level. By the end, you will be able to generate
-    a fine-grained, precise measurement of functions and other sections of code, instrument your
-    code to analyze a single section or multiple sections using the provided instrumentation scenarios,
-    and run and collect performance metrics and raw event values for any of the 15 event groups
-    (bundles) in a single run. It focuses on tools and technologies such as C, CPP, Python, and
-    Runbook, Linux environments, and Arm platforms including Neoverse. The main steps cover PMUv3
-    plugin features, Download and build the PMUv3 plugin, Instrument one section of code, Plot,
-    visualize, and analyze the results, and Instrument multiple sections of code.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will generate a fine-grained, precise measurement of functions and other sections of
-      code, instrument your code to analyze a single section or multiple sections using the provided
-      instrumentation scenarios, and run and collect performance metrics and raw event values
-      for any of the 15 event groups (bundles) in a single run.
-  - question: Who is this Learning Path for?
-    answer: >-
-      Engineers who want to carry out C/C++ performance analysis by instrumenting code at the
-      block level.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm-based computer running Linux.;
-      Some familiarity with Linux application performance analysis.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including C, CPP, Python, and Runbook, Linux environments,
-      and Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around PMUv3 plugin features, Download and build the PMUv3
-      plugin, Instrument one section of code, Plot, visualize, and analyze the results, and Instrument
-      multiple sections of code.
-# END generated_summary_faq
+
 
 author: Gayathri Narayana Yegna Narayanan
 
diff --git a/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md
index 181c649156..b101781d42 100644
--- a/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md
@@ -22,54 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 57827f45473867b3b5039a9cbca04d2c6d8a93e177ca0ab053999517389014b5
-  summary: >-
-    Deploy PostgreSQL on Azure Cobalt 100 Arm64 virtual machines, load a relational schema with
-    transactional data, and benchmark and optimize query performance using pgbench and pg_stat_statements.
-    It is designed for This learning path is designed for developers, DevOps engineers, and platform
-    engineers who want to deploy, manage, and optimize PostgreSQL databases on Arm-based cloud
-    infrastructure. By the end, you will be able to install and configure PostgreSQL on Azure
-    Cobalt 100 Arm64 virtual machines, deploy a relational database schema for transactional workloads,
-    and execute analytical SQL queries on operational data. It focuses on tools and technologies
-    such as PostgreSQL, SQL, and pgbench, Linux environments, Arm platforms including Neoverse,
-    and cloud platforms such as Microsoft Azure. The main steps cover Understand PostgreSQL on
-    Azure Cobalt 100, Create an Azure Cobalt 100 Arm64 virtual machine, Install and configure
-    PostgreSQL on Cobalt 100, Deploy a relational schema and run queries, and Benchmark and optimize
-    PostgreSQL on Cobalt 100.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install and configure PostgreSQL on Azure Cobalt 100 Arm64 virtual machines, deploy
-      a relational database schema for transactional workloads, and execute analytical SQL queries
-      on operational data. Deploy PostgreSQL on Azure Cobalt 100 Arm64 virtual machines, load
-      a relational schema with transactional data, and benchmark and optimize query performance
-      using pgbench and pg_stat_statements.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This learning path is designed for developers, DevOps engineers, and platform engineers
-      who want to deploy, manage, and optimize PostgreSQL databases on Arm-based cloud infrastructure.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Microsoft Azure account](https://azure.microsoft.com/)
-      with access to Cobalt 100 based instances (Dpsv6); Basic knowledge of Linux command-line
-      operations; Familiarity with SSH and remote server access; Basic understanding of databases
-      and SQL.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including PostgreSQL, SQL, and pgbench, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Understand PostgreSQL on Azure Cobalt 100, Create
-      an Azure Cobalt 100 Arm64 virtual machine, Install and configure PostgreSQL on Cobalt 100,
-      Deploy a relational schema and run queries, and Benchmark and optimize PostgreSQL on Cobalt
-      100.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md b/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md
index 1cd3ac0c14..b479cc2341 100644
--- a/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md
@@ -17,41 +17,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: a60cc2148a83f919467076e9d5a49fc4c9cec85670a919c7ba044cba72bfe219
-  summary: >-
-    Learn how to deploy PostgreSQL walks you through an end-to-end Arm software workflow. It is
-    designed for software developers who want to deploy PostgreSQL on Arm. By the end, you will
-    be able to learn about the various ways PostgreSQL can be deployed and learn how to interact
-    with a PostgreSQL database using the psql client tool. It focuses on tools and technologies
-    such as SQL and PostgreSQL, Linux environments, Arm platforms including Neoverse, and cloud
-    platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps cover How
-    do I install, configure, and check PostgreSQL?
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will learn about the various ways PostgreSQL can be deployed and learn how to interact
-      with a PostgreSQL database using the psql client tool.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to deploy PostgreSQL on Arm.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm based instance from a cloud service
-      provider, or an on-premise Arm server.; If you do not have an Arm node, the next section
-      discusses some options.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including SQL and PostgreSQL, Linux environments, Arm platforms
-      such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around How do I install, configure, and check PostgreSQL?
-# END generated_summary_faq
+
 
 author: Jason Andrews
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md
index 1463d54cae..a06bbfd4a0 100644
--- a/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md
@@ -15,42 +15,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: f30f7fb50000ae7ee28e46d7cbe4e73ba232c3daad2d559cfa41996d630cb936
-  summary: >-
-    Learn how to Tune PostgreSQL walks you through an end-to-end Arm software workflow. It is
-    designed for software developers and DevOps professionals interested in optimizing PostgreSQL
-    performance. By the end, you will be able to tune PostgreSQL to increase performance. It focuses
-    on tools and technologies such as SQL, PostgreSQL, HammerDB, and Runbook, Linux environments,
-    Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
-    Cloud, and Oracle. The main steps cover Before and after tuning PostgreSQL, System, Kernel,
-    compiler, and Libraries, Tuning PostgreSQL, and Testing PostgreSQL Tunings.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will tune PostgreSQL to increase performance.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers and DevOps professionals interested in
-      optimizing PostgreSQL performance.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Bare-metal or cloud [installation of
-      PostgreSQL](/learning-paths/servers-and-cloud-computing/postgresql/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including SQL, PostgreSQL, HammerDB, and Runbook, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
-      Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Before and after tuning PostgreSQL, System, Kernel,
-      compiler, and Libraries, Tuning PostgreSQL, and Testing PostgreSQL Tunings.
-# END generated_summary_faq
+
 
 author: Julio Suarez
 
diff --git a/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md b/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
index 2d388188cc..ee6bbd1279 100644
--- a/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
@@ -17,49 +17,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v2
-  generated_at: '2026-05-08T18:10:03Z'
-  generator: template
-  source_hash: ed85d6171f3c05bfdf1b396065fb0c73ce88724ff63fd1c3c8a93d4c27dd59f8
-  summary_generated_at: '2026-05-06T17:17:58Z'
-  summary_source_hash: ed85d6171f3c05bfdf1b396065fb0c73ce88724ff63fd1c3c8a93d4c27dd59f8
-  faq_generated_at: '2026-05-08T18:10:03Z'
-  faq_source_hash: ed85d6171f3c05bfdf1b396065fb0c73ce88724ff63fd1c3c8a93d4c27dd59f8
-  summary: >-
-    Run Process watch on your Arm machine walks you through an end-to-end Arm software workflow.
-    It is designed for software developers who want to build and run the Process Watch tool on
-    an Arm-based machine. By the end, you will be able to build and run the Process Watch tool
-    on your Arm machine, describe how Process Watch works, and check in real-time whether any
-    workloads are using specific Arm instructions or features. It focuses on tools and technologies
-    such as bpftool, libbpf, Capstone, C, and CPP, Linux environments, and Arm platforms including
-    Cortex-A and Neoverse. The main steps cover Install dependencies, Run Process Watch, Learn
-    how Process Watch works, and Using Process Watch.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build and run the Process Watch tool on your Arm machine, describe how Process
-      Watch works, and check in real-time whether any workloads are using specific Arm instructions
-      or features.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to build and run the Process
-      Watch tool on an Arm-based machine.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm-based system (bare metal server,
-      cloud instance, or developer board) running Linux with kernel version 5.8.0 or later.; Root
-      access, or the ability to run the sudo command.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including bpftool, libbpf, Capstone, C, and CPP, Linux environments,
-      and Arm platforms such as Cortex-A and Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Install dependencies, Run Process Watch, Learn how
-      Process Watch works, and Using Process Watch.
-# END generated_summary_faq
+
 
 author: Graham Woodward
 
diff --git a/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md b/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md
index f7cfa357a1..3a329bdb5b 100644
--- a/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md
@@ -16,44 +16,6 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: ef12378069d6f3538d8daefb5da7fc8e8ce4196277928d372745d12fde6e46a1
-  summary: >-
-    Profiling for Neoverse with Streamline CLI Tools walks you through an end-to-end Arm software
-    workflow. It is designed for This is an introductory guide for developers who want to measure
-    and optimize the performance of applications running on Arm Neoverse™-based servers. By the
-    end, you will be able to describe Arm's top-down profiling methodology and use Streamline
-    CLI tools to capture and analyze performance data from an application. It focuses on tools
-    and technologies such as Streamline CLI and Runbook, Linux environments, and Arm platforms
-    including Neoverse. The main steps cover System compatibility check, Performance analysis
-    concepts, Capture a performance profile, Example report, and Optimization checklist.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will describe Arm's top-down profiling methodology and use Streamline CLI tools to capture
-      and analyze performance data from an application.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory guide for developers who want to measure and optimize the performance
-      of applications running on Arm Neoverse™-based servers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm Neoverse-based (N1, N2 or V1)
-      computer running Linux. For your host OS, you can use Amazon Linux 2023 or newer, Debian
-      10 or newer, RHEL 8 or newer, or Ubuntu 20.04 or newer.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Streamline CLI and Runbook, Linux environments,
-      and Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around System compatibility check, Performance analysis concepts,
-      Capture a performance profile, Example report, and Optimization checklist.
-# END generated_summary_faq
 
 author: Julie Gaskin
 
diff --git a/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md
index 1dabbe8d6d..54301e4e52 100644
--- a/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md
@@ -15,50 +15,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: aeb6a390b5134af2f851e36db17c5d3578d4697b3c372af86c6bd5176765e087
-  summary: >-
-    Deploy Puppet on Google Cloud C4A walks you through an end-to-end Arm software workflow. It
-    is designed for developers deploying and optimizing Puppet workloads on Arm Linux environments,
-    specifically using Google Cloud C4A virtual machines powered by Axion processors. By the end,
-    you will be able to provision an Arm-based SUSE SLES (SUSE Linux Enterprise Server) virtual
-    machine (VM) on Google Cloud C4A with Axion processors, install Puppet on a SUSE Arm64 C4A
-    instance, and verify Puppet by applying a test manifest and confirming successful resource
-    creation on Arm64. It focuses on tools and technologies such as Puppet, Ruby, Facter, and
-    Hiera, Linux environments, Arm platforms including Neoverse, and cloud platforms such as Google
-    Cloud. The main steps cover Get started with Arm-based Google Axion and Puppet, Create a Google
-    Axion C4A Arm virtual machine on GCP, Install Puppet on a GCP VM, Perform Puppet baseline
-    testing, and Benchmark Puppet.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will provision an Arm-based SUSE SLES (SUSE Linux Enterprise Server) virtual machine
-      (VM) on Google Cloud C4A with Axion processors, install Puppet on a SUSE Arm64 C4A instance,
-      and verify Puppet by applying a test manifest and confirming successful resource creation
-      on Arm64.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers deploying and optimizing Puppet workloads on
-      Arm Linux environments, specifically using Google Cloud C4A virtual machines powered by
-      Axion processors.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
-      account with billing enabled; Basic familiarity with [Puppet](https://www.puppet.com/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Puppet, Ruby, Facter, and Hiera, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Get started with Arm-based Google Axion and Puppet,
-      Create a Google Axion C4A Arm virtual machine on GCP, Install Puppet on a GCP VM, Perform
-      Puppet baseline testing, and Benchmark Puppet.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md b/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md
index bc3fa8cdb3..d9f3318e57 100644
--- a/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md
@@ -19,46 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 1c5be7bfc7785ae3b06c60210c06324f00aed7ba75145a0fa65425e6005d4f06
-  summary: >-
-    Run a Large Language Model (LLM) chatbot with PyTorch using KleidiAI on Arm servers walks
-    you through an end-to-end Arm software workflow. It is designed for software developers interested
-    in running LLMs using PyTorch on Arm-based servers. By the end, you will be able to download
-    the Meta Llama 3.1 model from the Meta Hugging Face repository, 4-bit quantize the model using
-    optimized INT4 KleidiAI Kernels for PyTorch, and run an LLM inference using PyTorch on an
-    Arm-based CPU. It focuses on tools and technologies such as LLM, Generative AI, Python, PyTorch,
-    and Hugging Face, Linux environments, Arm platforms including Neoverse, and cloud platforms
-    such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps cover Run a Large Language
-    model (LLM) chatbot on Arm servers and Chatbot with Streamlit Frontend.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will download the Meta Llama 3.1 model from the Meta Hugging Face repository, 4-bit
-      quantize the model using optimized INT4 KleidiAI Kernels for PyTorch, and run an LLM inference
-      using PyTorch on an Arm-based CPU.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers interested in running LLMs using PyTorch
-      on Arm-based servers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/)
-      with at least 16 CPUs from a cloud service provider or an on-premise Arm server.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including LLM, Generative AI, Python, PyTorch, and Hugging
-      Face, Linux environments, Arm platforms such as Neoverse, and cloud platforms such as AWS,
-      Microsoft Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Run a Large Language model (LLM) chatbot on Arm servers
-      and Chatbot with Streamlit Frontend.
-# END generated_summary_faq
+
 
 author:
     - Nikhil Gupta
diff --git a/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md
index b012c7d702..e091864603 100644
--- a/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md
@@ -23,53 +23,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 8b180acd30b3b00484b6e7302b61fd8c3669ef83ff7fca41972d24c5df2682b7
-  summary: >-
-    Learn how to deploy Qdrant on Google Cloud C4A Axion processors, generate vector embeddings
-    with Sentence Transformers, and build a semantic search and chatbot retrieval system on Arm-based
-    infrastructure. It is designed for developers, data engineers, and platform engineers who
-    want to build semantic search systems and chatbot retrieval pipelines on Arm64-based Google
-    Cloud C4A Axion processors using the Qdrant vector database. By the end, you will be able
-    to deploy and run the Qdrant vector database on Google Cloud C4A Axion processors, generate
-    vector embeddings using transformer models, and store and index embeddings efficiently using
-    Qdrant. It focuses on tools and technologies such as Qdrant, Python, Sentence Transformers,
-    and Docker, Linux environments, Arm platforms including Neoverse, and cloud platforms such
-    as Google Cloud. The main steps cover Understand vector search with Qdrant on Google Axion,
-    Create a Google Axion C4A Arm virtual machine, Install and run Qdrant on Axion, Generate and
-    index vector embeddings, and Query vector embeddings with semantic search.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will deploy and run the Qdrant vector database on Google Cloud C4A Axion processors,
-      generate vector embeddings using transformer models, and store and index embeddings efficiently
-      using Qdrant. Learn how to deploy Qdrant on Google Cloud C4A Axion processors, generate
-      vector embeddings with Sentence Transformers, and build a semantic search and chatbot retrieval
-      system on Arm-based infrastructure.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers, data engineers, and platform engineers who
-      want to build semantic search systems and chatbot retrieval pipelines on Arm64-based Google
-      Cloud C4A Axion processors using the Qdrant vector database.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
-      account with billing enabled; Basic familiarity with Python; Basic understanding of machine
-      learning embeddings; Familiarity with Linux command-line operations.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Qdrant, Python, Sentence Transformers, and Docker,
-      Linux environments, Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Understand vector search with Qdrant on Google Axion,
-      Create a Google Axion C4A Arm virtual machine, Install and run Qdrant on Axion, Generate
-      and index vector embeddings, and Query vector embeddings with semantic search.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md
index 9a5fb3aa11..ee3f9467dc 100644
--- a/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md
@@ -23,54 +23,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: b4392f1cf38fa1f3b6c633c7ae1e8d30a51ea8d4e635287586b084cb0d8a556b
-  summary: >-
-    Deploy RabbitMQ on Arm64 Cloud Platforms (Azure and GCP) walks you through an end-to-end Arm
-    software workflow. It is designed for software engineers and platform engineers migrating
-    messaging and event-driven workloads from x86_64 to Arm-based servers, specifically on Microsoft
-    Azure Cobalt 100 Arm processors and Google Cloud C4A virtual machines powered by Axion processors.
-    By the end, you will be able to provision Arm-based Linux virtual machines on Google Cloud
-    (C4A with Axion processors) and Microsoft Azure (Cobalt 100), provision an Arm-based SUSE
-    SLES virtual machine on Google Cloud (C4A with Axion processors), and install and configure
-    RabbitMQ on Arm64 Linux (SUSE SLES on GCP and Ubuntu Pro 24.04 on Azure). It focuses on tools
-    and technologies such as RabbitMQ, Erlang, Python, and pika, Linux environments, Arm platforms
-    including Neoverse, and cloud platforms such as Microsoft Azure and Google Cloud. The main
-    steps cover Learn about Arm-based cloud platforms for RabbitMQ, Create an Azure Cobalt 100
-    virtual machine, Install RabbitMQ on Azure Cobalt 100, Validate RabbitMQ on Azure, and Create
-    a firewall rule for RabbitMQ.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will provision Arm-based Linux virtual machines on Google Cloud (C4A with Axion processors)
-      and Microsoft Azure (Cobalt 100), provision an Arm-based SUSE SLES virtual machine on Google
-      Cloud (C4A with Axion processors), and install and configure RabbitMQ on Arm64 Linux (SUSE
-      SLES on GCP and Ubuntu Pro 24.04 on Azure).
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software engineers and platform engineers migrating messaging
-      and event-driven workloads from x86_64 to Arm-based servers, specifically on Microsoft Azure
-      Cobalt 100 Arm processors and Google Cloud C4A virtual machines powered by Axion processors.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Microsoft Azure](https://azure.microsoft.com/)
-      account with access to Cobalt 100-based instances (Dpsv6).; A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
-      account with billing enabled; Basic understanding of message queues and messaging concepts
-      (publishers, consumers); Familiarity with Linux command-line operations.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including RabbitMQ, Erlang, Python, and pika, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as Microsoft Azure and Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Learn about Arm-based cloud platforms for RabbitMQ,
-      Create an Azure Cobalt 100 virtual machine, Install RabbitMQ on Azure Cobalt 100, Validate
-      RabbitMQ on Azure, and Create a firewall rule for RabbitMQ.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/rag/_index.md b/content/learning-paths/servers-and-cloud-computing/rag/_index.md
index 8eeac1174a..0b3da3fdfb 100644
--- a/content/learning-paths/servers-and-cloud-computing/rag/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rag/_index.md
@@ -23,51 +23,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: d9435cc1dc1fe65432d83934e69993853dd3f294f66f98e9bcfb5f81e6ac6d5f
-  summary: >-
-    Deploy a RAG-based Chatbot with llama-cpp-python using KleidiAI on Google Axion processors
-    walks you through an end-to-end Arm software workflow. It is designed for software developers,
-    ML engineers, and those looking to deploy production-ready LLM chatbots with Retrieval Augmented
-    Generation (RAG) capabilities, knowledge base integration, and performance optimization for
-    Arm Architecture. By the end, you will be able to set up llama-cpp-python optimized for Arm
-    servers, implement RAG architecture using the Facebook AI Similarity Search (FAISS) vector
-    database, and optimize model performance through 4-bit quantization. It focuses on tools and
-    technologies such as Python, Streamlit, Google Axion, Demo, and Hugging Face, Linux environments,
-    Arm platforms including Neoverse, and cloud platforms such as Google Cloud. The main steps
-    cover Set up a RAG based LLM Chatbot, Deploy a RAG-based LLM backend server, Deploy RAG-based
-    LLM frontend server, and The RAG Chatbot and its Performance.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will set up llama-cpp-python optimized for Arm servers, implement RAG architecture using
-      the Facebook AI Similarity Search (FAISS) vector database, and optimize model performance
-      through 4-bit quantization.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is for software developers, ML engineers, and those looking to deploy
-      production-ready LLM chatbots with Retrieval Augmented Generation (RAG) capabilities, knowledge
-      base integration, and performance optimization for Arm Architecture.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Google Cloud Axion (or other Arm)
-      compute instance with at least 16 cores, 8GB of RAM, and 32GB disk space.; Basic understanding
-      of Python and ML concepts.; Familiarity with REST APIs and web services.; Basic knowledge
-      of vector databases.; Understanding of LLM fundamentals.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Python, Streamlit, Google Axion, Demo, and Hugging
-      Face, Linux environments, Arm platforms such as Neoverse, and cloud platforms such as Google
-      Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Set up a RAG based LLM Chatbot, Deploy a RAG-based
-      LLM backend server, Deploy RAG-based LLM frontend server, and The RAG Chatbot and its Performance.
-# END generated_summary_faq
+
 
 author: Nobel Chowdary Mandepudi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/ran/_index.md b/content/learning-paths/servers-and-cloud-computing/ran/_index.md
index d509825c79..7e1160aff0 100644
--- a/content/learning-paths/servers-and-cloud-computing/ran/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ran/_index.md
@@ -19,40 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 2b329c566f7fea90010c52f1ce1cb11bccf9d32cf604aaa720bfca5ef1f85fae
-  summary: >-
-    Get started with the Arm 5G RAN Acceleration Library (ArmRAL) walks you through an end-to-end
-    Arm software workflow. It is designed for software developers new to the Arm RAN Acceleration
-    Library (ArmRAL). By the end, you will be able to build and install the Arm RAN Acceleration
-    Library and test the capabilities of your platform. It focuses on tools and technologies such
-    as ArmRAL, 5G, GCC, and Runbook, Linux environments, and Arm platforms including Neoverse.
-    The main steps cover Build and run ArmRAL.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build and install the Arm RAN Acceleration Library and test the capabilities of
-      your platform.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers new to the Arm RAN Acceleration Library
-      (ArmRAL).
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm computer running Linux. Cloud
-      instances can be used, refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including ArmRAL, 5G, GCC, and Runbook, Linux environments,
-      and Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Build and run ArmRAL.
-# END generated_summary_faq
+
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md
index fe44f7e4f4..21a58bf7a9 100644
--- a/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md
@@ -20,53 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 0877f5f233ec8a50172f4bb9388ad38ae9b890a4d049679ca6ece083d760d872
-  summary: >-
-    Deploy and run distributed AI workloads using Ray on Google Cloud Axion C4A Arm-based VMs,
-    covering parallel tasks, hyperparameter tuning, and model serving with Ray Core, Train, Tune,
-    and Serve. It is designed for DevOps engineers, ML engineers, and software developers who
-    want to deploy and run distributed workloads using Ray on SUSE Linux Enterprise Server (SLES)
-    Arm64, execute parallel tasks, perform hyperparameter tuning, and serve models at scale. By
-    the end, you will be able to install and configure Ray on Google Cloud C4A Axion processors
-    for Arm64, run distributed tasks and parallel workloads using Ray Core, and perform distributed
-    training and hyperparameter tuning using Ray Train and Ray Tune. It focuses on tools and technologies
-    such as Ray, Python, and PyTorch, Linux environments, Arm platforms including Neoverse, and
-    cloud platforms such as Google Cloud. The main steps cover Get started with Ray on Google
-    Axion C4A, Create a firewall rule for Ray Dashboard and Serve, Create a Google Axion C4A Arm
-    virtual machine on GCP, Deploy Ray on GCP SUSE Arm64, and Run Distributed Workloads with Ray.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install and configure Ray on Google Cloud C4A Axion processors for Arm64, run distributed
-      tasks and parallel workloads using Ray Core, and perform distributed training and hyperparameter
-      tuning using Ray Train and Ray Tune. Deploy and run distributed AI workloads using Ray on
-      Google Cloud Axion C4A Arm-based VMs, covering parallel tasks, hyperparameter tuning, and
-      model serving with Ray Core, Train, Tune, and Serve.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for DevOps engineers, ML engineers, and software developers
-      who want to deploy and run distributed workloads using Ray on SUSE Linux Enterprise Server
-      (SLES) Arm64, execute parallel tasks, perform hyperparameter tuning, and serve models at
-      scale.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
-      account with billing enabled; Basic familiarity with Python and distributed systems concepts.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Ray, Python, and PyTorch, Linux environments, Arm
-      platforms such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Get started with Ray on Google Axion C4A, Create a
-      firewall rule for Ray Dashboard and Serve, Create a Google Axion C4A Arm virtual machine
-      on GCP, Deploy Ray on GCP SUSE Arm64, and Run Distributed Workloads with Ray.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md
index 936da54e41..637fcba678 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md
@@ -22,53 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: 9b306bc318491834d0d74dd75221b24081acc20dac7993ccdbd1cb40ba6158ac
-  summary: >-
-    Learn how to install and configure Redis on an Azure Cobalt 100 Arm64 virtual machine, implement
-    real-time messaging with Pub/Sub and Streams, and benchmark throughput and latency on Arm
-    infrastructure. It is designed for developers, DevOps engineers, and platform engineers who
-    want to build real-time messaging systems and event-driven applications using Redis on Arm-based
-    cloud environments. By the end, you will be able to install and configure Redis on Azure Cobalt
-    100 Arm64 virtual machines, implement real-time messaging using Redis Pub/Sub, and build event-driven
-    pipelines using Redis Streams and consumer groups. It focuses on tools and technologies such
-    as Redis and Python, Linux environments, Arm platforms including Neoverse, and cloud platforms
-    such as Microsoft Azure. The main steps cover Understand Redis on Azure Cobalt 100, Create
-    an Azure Cobalt 100 Arm64 virtual machine, Install Redis and build messaging pipelines, and
-    Benchmark Redis performance on Cobalt 100.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install and configure Redis on Azure Cobalt 100 Arm64 virtual machines, implement
-      real-time messaging using Redis Pub/Sub, and build event-driven pipelines using Redis Streams
-      and consumer groups. Learn how to install and configure Redis on an Azure Cobalt 100 Arm64
-      virtual machine, implement real-time messaging with Pub/Sub and Streams, and benchmark throughput
-      and latency on Arm infrastructure.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers, DevOps engineers, and platform engineers who
-      want to build real-time messaging systems and event-driven applications using Redis on Arm-based
-      cloud environments.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Microsoft Azure account](https://azure.microsoft.com/)
-      with access to Cobalt 100 based instances (Dpsv6); Basic knowledge of Linux command-line
-      operations; Familiarity with SSH and remote server access; Basic understanding of databases,
-      caching, and messaging systems.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Redis and Python, Linux environments, Arm platforms
-      such as Neoverse, and cloud platforms such as Microsoft Azure.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Understand Redis on Azure Cobalt 100, Create an Azure
-      Cobalt 100 Arm64 virtual machine, Install Redis and build messaging pipelines, and Benchmark
-      Redis performance on Cobalt 100.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md b/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md
index 63b1b12400..11ab11d4c8 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md
@@ -19,48 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: eb008543ea4248b231be5e6345546164533edf2bc149613693095812bbbba3d9
-  summary: >-
-    Deploy Redis for data searching on Google Cloud C4A walks you through an end-to-end Arm software
-    workflow. It is designed for developers deploying and optimizing Redis-based data searching
-    workloads on Linux/Arm64 environments, specifically using Google Cloud C4A virtual machines
-    powered by Axion processors. By the end, you will be able to provision an Arm-based SUSE SLES
-    virtual machine on Google Cloud (C4A with Axion processors), install Redis on a SUSE Arm64
-    (C4A) instance, and verify Redis functionality by running the server and performing baseline
-    data insertion and retrieval tests on the Arm64 VM. It focuses on tools and technologies such
-    as Redis and redis-benchmark, Linux environments, Arm platforms including Neoverse, and cloud
-    platforms such as Google Cloud. The main steps cover Get started with Redis on Google Axion
-    C4A, Create a Compute Engine instance, Install Redis, Test Redis, and Benchmark Redis.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will provision an Arm-based SUSE SLES virtual machine on Google Cloud (C4A with Axion
-      processors), install Redis on a SUSE Arm64 (C4A) instance, and verify Redis functionality
-      by running the server and performing baseline data insertion and retrieval tests on the
-      Arm64 VM.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers deploying and optimizing Redis-based data searching
-      workloads on Linux/Arm64 environments, specifically using Google Cloud C4A virtual machines
-      powered by Axion processors.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
-      account with billing enabled; Basic familiarity with [Redis](https://redis.io/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Redis and redis-benchmark, Linux environments, Arm
-      platforms such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Get started with Redis on Google Axion C4A, Create
-      a Compute Engine instance, Install Redis, Test Redis, and Benchmark Redis.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/redis/_index.md b/content/learning-paths/servers-and-cloud-computing/redis/_index.md
index e49b427322..3946113a28 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis/_index.md
@@ -18,43 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:58Z'
-  generator: template
-  source_hash: 7b9906a3c16ebd3c6b41618be7db76d7a97cc4b16c4da927257fb39a66753e12
-  summary: >-
-    Deploy Redis on Arm walks you through an end-to-end Arm software workflow. It is designed
-    for developers who want to deploy Redis on Arm based virtual machines. By the end, you will
-    be able to understand Redis deployment configurations and install and run Redis in a single-node
-    Arm based instance. It focuses on tools and technologies such as Redis and Runbook, Linux
-    environments, Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft
-    Azure, Google Cloud, and Oracle. The main steps cover Install, configure and connect to Redis
-    and Configure Redis single-node.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand Redis deployment configurations and install and run Redis in a single-node
-      Arm based instance.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers who want to deploy Redis on Arm based virtual
-      machines.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm based instance from a cloud service
-      provider, or an on-premise Arm server.; If you do not have an Arm node, the next section
-      discusses some options.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Redis and Runbook, Linux environments, Arm platforms
-      such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Install, configure and connect to Redis and Configure
-      Redis single-node.
-# END generated_summary_faq
+
 
 author: Elham Harirpoush
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md b/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md
index 9c1c87227f..985abdac3e 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md
@@ -19,54 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: 9146285feb38ee45171ac234f605eee08467bbf675a77df231a6dc63c38282f2
-  summary: >-
-    Deploy Redis as a cache for MySQL and PostgreSQL on Arm servers walks you through an end-to-end
-    Arm software workflow. It is designed for developers who want to deploy Redis as a cache on
-    Arm based virtual machines. By the end, you will be able to deploy Redis as a cache for MySQL
-    on AWS, Azure and GCP Arm based instance and deploy Redis as a cache for Postgres on AWS,
-    Azure and GCP Arm based instance. It focuses on tools and technologies such as Terraform,
-    Ansible, Redis, SQL, and MySQL, Linux environments, Arm platforms including Neoverse, and
-    cloud platforms such as AWS, Microsoft Azure, and Google Cloud. The main steps cover Deploy
-    Redis as a cache for MySQL on an AWS Arm based Instance, Deploy Redis as a cache for MySQL
-    on an Azure Arm based Instance, Deploy Redis as a cache for MySQL on a GCP Arm based Instance,
-    Deploy Redis as a cache for Postgres on an AWS Arm based Instance, and Deploy Redis as a cache
-    for Postgres on an Azure Arm based Instance.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will deploy Redis as a cache for MySQL on AWS, Azure and GCP Arm based instance and
-      deploy Redis as a cache for Postgres on AWS, Azure and GCP Arm based instance.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for developers who want to deploy Redis as a cache on Arm based
-      virtual machines.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Amazon Web Services (AWS) [account](https://aws.amazon.com/);
-      An Azure portal [account](https://azure.microsoft.com/en-in/get-started/azure-portal); A
-      Google Cloud [account](https://console.cloud.google.com/); A machine with [Terraform](/install-guides/terraform/),
-      [AWS CLI](/install-guides/aws-cli), [Google Cloud CLI](/install-guides/gcloud), [Azure CLI](/install-guides/azure-cli),
-      [AWS IAM authenticator](https://docs.aws.amazon.com/eks/latest/userguide/install-aws-iam-authenticator.html),
-      and [Ansible](/install-guides/ansible/) installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Terraform, Ansible, Redis, SQL, and MySQL, Linux
-      environments, Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft
-      Azure, and Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Deploy Redis as a cache for MySQL on an AWS Arm based
-      Instance, Deploy Redis as a cache for MySQL on an Azure Arm based Instance, Deploy Redis
-      as a cache for MySQL on a GCP Arm based Instance, Deploy Redis as a cache for Postgres on
-      an AWS Arm based Instance, and Deploy Redis as a cache for Postgres on an Azure Arm based
-      Instance.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md
index 7ec56bf112..1fc79cc156 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md
@@ -18,44 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: a6ed103f2d5a00f4cb6c5df1c0812eb68bbad8746d3f351adc959c6880002bbc
-  summary: >-
-    Learn how to tune Redis walks you through an end-to-end Arm software workflow. It is designed
-    for software developers who want to deploy Redis on Arm-based servers and follow best practices
-    to get performance benefits. By the end, you will be able to learn about kernel parameters
-    that can impact Redis performance, learn about compiler and libraries that can impact Redis
-    performance, and tune a Redis configuration file for deployment. It focuses on tools and technologies
-    such as Redis and Runbook, Linux environments, Arm platforms including Neoverse, and cloud
-    platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps cover Kernel,
-    compiler, and OpenSSL settings and Tune Redis.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will learn about kernel parameters that can impact Redis performance, learn about compiler
-      and libraries that can impact Redis performance, and tune a Redis configuration file for
-      deployment.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers who want to deploy Redis on Arm-based
-      servers and follow best practices to get performance benefits.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Cloud or bare-metal installation of
-      an Redis file server; Review [Learn how to deploy Redis](/learning-paths/servers-and-cloud-computing/redis/)
-      if you do not already have Redis setup.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Redis and Runbook, Linux environments, Arm platforms
-      such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Kernel, compiler, and OpenSSL settings and Tune Redis.
-# END generated_summary_faq
+
 
 author: Elham Harirpoush
 
diff --git a/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md b/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md
index 1155363120..f8cdb0483e 100644
--- a/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md
@@ -20,43 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: d060151c5f6ac7a743fb53cd3d2a10aa23f8f09245122a199a84ae17a8ab5ff9
-  summary: >-
-    Debug Neoverse N2 Reference Design with Arm Development Studio walks you through an end-to-end
-    Arm software workflow. It is designed for software developers who are interested in debugging
-    the Arm Neoverse N2 Reference Firmware Stack. By the end, you will be able to create a debug
-    connection, debug a System Control Processor (SCP), and debug Arm TF-A (Trusted Firmware-A).
-    It focuses on tools and technologies such as Arm Development Studio, Linux environments, and
-    Arm platforms including Neoverse. The main steps cover Set up your development environment,
-    Debugging SCP/LCP/RSE, Debugging BL1, Debugging BL31, and Debugging BL33 / UEFI.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create a debug connection, debug a System Control Processor (SCP), and debug Arm
-      TF-A (Trusted Firmware-A).
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software developers who are interested in debugging the Arm
-      Neoverse N2 Reference Firmware Stack.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Arm Development Studio, and a license
-      to use it.; An Arm Neoverse Reference Design (RD) Software Stack.; A Fixed Virtual Platform
-      (FVP).; A basic understanding of Neoverse Reference Design (RD) platform boot.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Arm Development Studio, Linux environments, and
-      Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Set up your development environment, Debugging SCP/LCP/RSE,
-      Debugging BL1, Debugging BL31, and Debugging BL33 / UEFI.
-# END generated_summary_faq
+
 
 author: Daniel Nguyen
 
diff --git a/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md b/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md
index 4a07b1c854..f4ac03c592 100644
--- a/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md
@@ -19,44 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: 8f2515b7c416a821bd397a77297adc263397a8b3cb86e9bb34e646735a21f854
-  summary: >-
-    Get started with the Neoverse Reference Design software stack walks you through an end-to-end
-    Arm software workflow. It is designed for software developers interested in testing the Neoverse
-    Reference Design firmware stack. By the end, you will be able to set up your environment,
-    build the reference firmware stack, and test the reference firmware stack. It focuses on tools
-    and technologies such as Docker, FVP, Arm Development Studio, and Runbook, Linux environments,
-    and Arm platforms including Neoverse. The main steps cover Environment Setup, Build the software
-    stack, and Test With FVP.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will set up your environment, build the reference firmware stack, and test the reference
-      firmware stack.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers interested in testing the Neoverse
-      Reference Design firmware stack.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Some understanding of the [Reference
-      Design software stack architecture](https://neoverse-reference-design.docs.arm.com/en/latest/about/software_stack.html).;
-      Some understanding of the Linux command line.; Optionally a basic understanding of Docker
-      and containers.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Docker, FVP, Arm Development Studio, and Runbook,
-      Linux environments, and Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Environment Setup, Build the software stack, and Test
-      With FVP.
-# END generated_summary_faq
+
 
 author: 
     - Tom Pilar
diff --git a/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md b/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md
index f736df6fbf..e95d65123a 100644
--- a/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md
@@ -7,49 +7,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: d643c9ef25aa5442aec65ac6a8f48264d4789f8e24ffd6270c2efacce48dd8fc
-  summary: >-
-    Enable reproducible math functions across vector extensions with Arm Performance Libraries
-    walks you through an end-to-end Arm software workflow. It is designed for developers who want
-    to produce reproducible code across vector extensions using math functions in Libamath, a
-    component of Arm Performance Libraries. By the end, you will be able to explain what numerical
-    reproducibility means in numerical software, describe generic applications of numerical reproducibility
-    in the industry, and describe how reproducibility is defined and implemented in Libamath.
-    It focuses on tools and technologies such as Arm Performance Libraries, GCC, LLVM, and Libamath,
-    Linux environments, and Arm platforms including Neoverse. The main steps cover Understand
-    numerical reproducibility in floating-point math, Explore where reproducibility is critical,
-    Enable reproducibility in Libamath, and Verify reproducible results across scalar, Neon, and
-    SVE.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will explain what numerical reproducibility means in numerical software, describe generic
-      applications of numerical reproducibility in the industry, and describe how reproducibility
-      is defined and implemented in Libamath.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers who want to produce reproducible code across
-      vector extensions using math functions in Libamath, a component of Arm Performance Libraries.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm computer running Linux with [Arm
-      Performance Libraries](/install-guides/armpl/) version 26.01 or newer installed; A C compiler
-      such as [GCC](/install-guides/gcc/native/) or Clang installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Arm Performance Libraries, GCC, LLVM, and Libamath,
-      Linux environments, and Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Understand numerical reproducibility in floating-point
-      math, Explore where reproducibility is critical, Enable reproducibility in Libamath, and
-      Verify reproducible results across scalar, Neon, and SVE.
-# END generated_summary_faq
+
 
 author: Joana Cruz
 
diff --git a/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md b/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md
index 0dbc068ba7..5dfd612d32 100644
--- a/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md
@@ -18,45 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: 180803cf9c6e34c0dda76cafdfcd0ce67edfaca4563f57ae0c36bfefd2199ae9
-  summary: >-
-    Learn how to create a virtual machine in a Realm using Arm Confidential Compute Architecture
-    (CCA) walks you through an end-to-end Arm software workflow. It is designed for software developers
-    who want to learn about Arm Confidential Compute Architecture (CCA). By the end, you will
-    be able to understand the reference software stack used in Arm CCA, build and run the software
-    stack on an Armv-A AEM Base FVP platform with support for RME extensions, and create a virtual
-    machine in a Realm running guest Linux. It focuses on tools and technologies such as GCC,
-    FVP, RME, CCA, and Runbook, Linux environments, and Arm platforms including Neoverse. The
-    main steps cover Build and run the Arm CCA stack on an Arm FVP.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand the reference software stack used in Arm CCA, build and run the software
-      stack on an Armv-A AEM Base FVP platform with support for RME extensions, and create a virtual
-      machine in a Realm running guest Linux.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to learn about Arm Confidential
-      Compute Architecture (CCA).
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An aarch64 or x86_64 computer running
-      Ubuntu 22.04. Cloud instances can be used, refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).;
-      If you use a client application to access your computer running Ubuntu, make sure that X11
-      forwarding is enabled.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including GCC, FVP, RME, CCA, and Runbook, Linux environments,
-      and Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Build and run the Arm CCA stack on an Arm FVP.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md b/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md
index d6ebf147fa..3b361a9198 100644
--- a/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md
@@ -18,47 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: 27e17ea322cc7dc117f24d9d2007fbc0e6b498840b4097b859b1f376b8d8c9a6
-  summary: >-
-    Run an LLM chatbot with rtp-llm on Arm-based servers walks you through an end-to-end Arm software
-    workflow. It is designed for developers who are interested in running a Large Language Model
-    (LLM) with rtp-llm on Arm-based servers. By the end, you will be able to build rtp-llm on
-    an Arm-based server, download a Qwen model from Hugging Face, and run a Large Language Model
-    with rtp-llm. It focuses on tools and technologies such as LLM, Generative AI, Python, and
-    Hugging Face, Linux environments, Arm platforms including Neoverse, and cloud platforms such
-    as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps cover Background, Run an
-    LLM chatbot with rtp-llm on an Arm server, and Access the chatbot with rtp-llm using the OpenAI-compatible
-    API.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build rtp-llm on an Arm-based server, download a Qwen model from Hugging Face,
-      and run a Large Language Model with rtp-llm.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers who are interested in running a Large Language
-      Model (LLM) with rtp-llm on Arm-based servers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Any Arm Neoverse N2-based or Arm Neoverse
-      V2-based instance running Ubuntu 22.04 LTS from a cloud service provider or an on-premise
-      Arm server.; For the server, at least four cores and 16GB of RAM, with disk storage configured
-      up to at least 32 GB.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including LLM, Generative AI, Python, and Hugging Face, Linux
-      environments, Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft
-      Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Background, Run an LLM chatbot with rtp-llm on an
-      Arm server, and Access the chatbot with rtp-llm using the OpenAI-compatible API.
-# END generated_summary_faq
+
 
 author: Tianyu Li
 
diff --git a/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md b/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md
index 9d76d963a8..1b1b74e17b 100644
--- a/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md
@@ -20,50 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: 092602df259461e2b22dbf0909a682fbacd59464eea38ab901f38cd0b8c6efd3
-  summary: >-
-    Deploy Ruby on Rails on Arm-based Google Cloud C4A virtual machines walks you through an end-to-end
-    Arm software workflow. It is designed for developers deploying and optimizing Ruby on Rails
-    workloads in Linux Arm64 environments, specifically using Google Cloud C4A virtual machines
-    powered by Axion processors. By the end, you will be able to provision an Arm-based SUSE SLES
-    (SUSE Linux Enterprise Server) virtual machine on Google Cloud (C4A with Axion processors),
-    install Ruby on Rails on a SUSE Arm64 (C4A) instance, and validate Ruby on Rails functionality
-    using PostgreSQL as the database. It focuses on tools and technologies such as Ruby, Rails,
-    and PostgreSQL, Linux environments, Arm platforms including Neoverse, and cloud platforms
-    such as Google Cloud. The main steps cover Get started with Ruby on Rails on Google Axion
-    C4A, Create a Google Axion C4A Arm virtual machine on GCP, Install Ruby on Rails on SUSE Linux,
-    Set up Ruby on Rails baseline testing, and Benchmark Ruby on Rails.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will provision an Arm-based SUSE SLES (SUSE Linux Enterprise Server) virtual machine
-      on Google Cloud (C4A with Axion processors), install Ruby on Rails on a SUSE Arm64 (C4A)
-      instance, and validate Ruby on Rails functionality using PostgreSQL as the database.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers deploying and optimizing Ruby on Rails workloads
-      in Linux Arm64 environments, specifically using Google Cloud C4A virtual machines powered
-      by Axion processors.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
-      account with billing enabled; Basic familiarity with Ruby programming, the Rails framework,
-      and the [PostgreSQL Relational Database](https://www.postgresql.org/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Ruby, Rails, and PostgreSQL, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Get started with Ruby on Rails on Google Axion C4A,
-      Create a Google Axion C4A Arm virtual machine on GCP, Install Ruby on Rails on SUSE Linux,
-      Set up Ruby on Rails baseline testing, and Benchmark Ruby on Rails.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md
index 74e5c94c10..1ac08d3389 100644
--- a/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md
@@ -21,51 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: c3dd7a0ff9c645635e41b2d9110f4c52de627b752e33c3c6775e1d2e3ffc381d
-  summary: >-
-    Learn to deploy and benchmark Rust applications on Google Cloud C4A virtual machines powered
-    by Arm-based Axion processors. It is designed for developers deploying and optimizing Rust
-    workloads on Linux/Arm64 environments, specifically using Google Cloud C4A virtual machines
-    powered by Axion processors. By the end, you will be able to provision an Arm-based SUSE SLES
-    virtual machine on Google Cloud (C4A with Axion processors), install Rust and configure the
-    development environment on a SUSE Arm64 (C4A) instance, and verify Rust setup by compiling
-    and running a sample program to ensure toolchain functionality. It focuses on tools and technologies
-    such as Rust, Cargo, and Criterion, Linux environments, Arm platforms including Neoverse,
-    and cloud platforms such as Google Cloud. The main steps cover Get started with Rust on Google
-    Axion C4A (Arm Neoverse-V2), Create a Google Axion C4A Arm virtual machine on GCP, Perform
-    baseline testing, Benchmark Rust performance using Criterion, and FIXED, DO NOT MODIFY.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will provision an Arm-based SUSE SLES virtual machine on Google Cloud (C4A with Axion
-      processors), install Rust and configure the development environment on a SUSE Arm64 (C4A)
-      instance, and verify Rust setup by compiling and running a sample program to ensure toolchain
-      functionality. Learn to deploy and benchmark Rust applications on Google Cloud C4A virtual
-      machines powered by Arm-based Axion processors.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers deploying and optimizing Rust workloads on
-      Linux/Arm64 environments, specifically using Google Cloud C4A virtual machines powered by
-      Axion processors.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
-      account with billing enabled; Basic familiarity with [Rust](https://www.rust-lang.org/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Rust, Cargo, and Criterion, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Get started with Rust on Google Axion C4A (Arm Neoverse-V2),
-      Create a Google Axion C4A Arm virtual machine on GCP, Perform baseline testing, Benchmark
-      Rust performance using Criterion, and FIXED, DO NOT MODIFY.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md b/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md
index 0a6967c31c..2c0dc0146d 100644
--- a/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md
@@ -18,47 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: 3d9b35d09c97557dcde807bb83f994f8c3701c0d109c0a9bb388acc603d81b16
-  summary: >-
-    Perform Sentiment Analysis on X on Arm-based EKS clusters walks you through an end-to-end
-    Arm software workflow. It is designed for software developers who want to build an end-to-end
-    ML sentiment analysis solution on an Arm-based Amazon EKS cluster to analyze live posts on
-    X. By the end, you will be able to deploy a text classification model on Amazon EKS with Apache
-    Spark, use Elasticsearch and a Kibana dashboard to analyze the posts on X, and deploy Prometheus
-    and Grafana dashboards to monitor CPU and RAM usage of Kubernetes nodes. It focuses on tools
-    and technologies such as Kubernetes and AWS Elastic Kubernetes Service (EKS), Linux environments,
-    Arm platforms including Neoverse, and cloud platforms such as AWS. The main steps cover Overview,
-    Monitoring sentiment with Elasticsearch and Kibana, Set up Sentiment Analysis with Amazon
-    EKS, and Monitor the cluster with Prometheus and Grafana.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will deploy a text classification model on Amazon EKS with Apache Spark, use Elasticsearch
-      and a Kibana dashboard to analyze the posts on X, and deploy Prometheus and Grafana dashboards
-      to monitor CPU and RAM usage of Kubernetes nodes.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is for software developers who want to build an end-to-end ML sentiment
-      analysis solution on an Arm-based Amazon EKS cluster to analyze live posts on X .
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An AWS account.; A computer with Docker,
-      Terraform, the Amazon eksctl command-line interface, and kubectl installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Kubernetes and AWS Elastic Kubernetes Service (EKS),
-      Linux environments, Arm platforms such as Neoverse, and cloud platforms such as AWS.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Overview, Monitoring sentiment with Elasticsearch
-      and Kibana, Set up Sentiment Analysis with Amazon EKS, and Monitor the cluster with Prometheus
-      and Grafana.
-# END generated_summary_faq
+
 
 author: 
     - Pranay Bakre
diff --git a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md
index d1886a5d84..897f0387fe 100644
--- a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md
@@ -17,44 +17,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: 0a4dd65c6d0c7eb79479217b351e3d6c5b8917512d510283fd4d8387ff1339f5
-  summary: >-
-    Deploy AWS services using the Serverless Framework walks you through an end-to-end Arm software
-    workflow. It is designed for software developers interested in learning how to deploy AWS
-    cloud resources using the Serverless Framework. By the end, you will be able to learn how
-    to set up Serverless Framework for AWS and create a project and deploy AWS Lambda function.
-    It focuses on tools and technologies such as Node.js and Visual Studio Code, Windows environments,
-    Arm platforms including Neoverse, and cloud platforms such as AWS. The main steps cover Background,
-    Set up Serverless Framework for AWS, and Deploy AWS Lambda.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will learn how to set up Serverless Framework for AWS and create a project and deploy
-      AWS Lambda function.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This learning path is for software developers interested in learning how to deploy AWS cloud
-      resources using the Serverless Framework.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows on Arm computer such as the
-      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
-      Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user)
-      is suitable.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Node.js and Visual Studio Code, Windows environments,
-      Arm platforms such as Neoverse, and cloud platforms such as AWS.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Background, Set up Serverless Framework for AWS, and
-      Deploy AWS Lambda.
-# END generated_summary_faq
+
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md
index 3a50c79857..b91f9c370b 100644
--- a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md
@@ -18,47 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: 2120b6bedf6ea5ae02d8b353a6485f307bcb39d0055c29d9e8a39df37a8b946e
-  summary: >-
-    Deploy and integrate AWS Lambda with DynamoDB using the Serverless Framework walks you through
-    an end-to-end Arm software workflow. It is designed for software developers interested in
-    learning how to deploy serverless applications using the Serverless Framework and Amazon Web
-    Services. It automates several manual deployment steps that developers typically need to perform
-    when deploying microservice-based or IoT applications. By the end, you will be able to create
-    a multi-resource Serverless Framework solution and automate deployment of AWS Lambda function
-    consuming data from DynamoDB. It focuses on tools and technologies such as Node.js and Visual
-    Studio Code, Linux, Windows, and macOS environments, Arm platforms including Neoverse, and
-    cloud platforms such as AWS. The main steps cover Objective, Service declaration, and Deployment.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create a multi-resource Serverless Framework solution and automate deployment of
-      AWS Lambda function consuming data from DynamoDB.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This learning path is for software developers interested in learning how to deploy serverless
-      applications using the Serverless Framework and Amazon Web Services. It automates several
-      manual deployment steps that developers typically need to perform when deploying microservice-based
-      or IoT applications.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows on Arm computer such as the
-      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
-      Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user)
-      is suitable.; Completion of [Deploy AWS services using the Serverless Framework](/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Node.js and Visual Studio Code, Linux, Windows,
-      and macOS environments, Arm platforms such as Neoverse, and cloud platforms such as AWS.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Objective, Service declaration, and Deployment.
-# END generated_summary_faq
+
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md
index d9825df66f..3406f3f1cd 100644
--- a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md
@@ -18,45 +18,6 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: a31c6f9d674bf41cee16066708c884ca587289e181b7754ff75cdbd85bdb30e3
-  summary: >-
-    Deploy a static website to Amazon S3 and integrate with AWS Lambda and DynamoDB using the
-    Serverless Framework walks you through an end-to-end Arm software workflow. It is designed
-    for software developers interested in learning how to deploy serverless applications using
-    the Serverless Framework and Amazon Web Services. By the end, you will be able to create a
-    multi-resource Serverless Framework solution and automate deployment of a static website to
-    Amazon S3. It focuses on tools and technologies such as Node.js and Visual Studio Code, Linux,
-    Windows, and macOS environments, Arm platforms including Neoverse, and cloud platforms such
-    as AWS. The main steps cover Objective, Service declaration, Website, and Deployment.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will create a multi-resource Serverless Framework solution and automate deployment of
-      a static website to Amazon S3.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This learning path is for software developers interested in learning how to deploy serverless
-      applications using the Serverless Framework and Amazon Web Services.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A Windows on Arm computer such as the
-      Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).;
-      Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user)
-      is suitable.; Completion of the Learning Path that shows you how to [Deploy AWS services
-      using the Serverless Framework](/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Node.js and Visual Studio Code, Linux, Windows,
-      and macOS environments, Arm platforms such as Neoverse, and cloud platforms such as AWS.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Objective, Service declaration, Website, and Deployment.
-# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/servers-and-cloud-computing/snappy/_index.md b/content/learning-paths/servers-and-cloud-computing/snappy/_index.md
index 618b203a7d..24bfa1cc8c 100644
--- a/content/learning-paths/servers-and-cloud-computing/snappy/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/snappy/_index.md
@@ -19,41 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: 62edadd9a4163e020b55d33f541a13ceb604c3700ba56787b650563c446a3b0d
-  summary: >-
-    Measure performance of compression libraries on Arm servers walks you through an end-to-end
-    Arm software workflow. It is designed for software developers using compression libraries
-    on Arm servers. By the end, you will be able to install and run lzbench with snappy and zstd
-    and measure compression library performance running on 64-bit Arm AWS EC2 instance. It focuses
-    on tools and technologies such as snappy and Runbook, Linux environments, Arm platforms including
-    Neoverse, and cloud platforms such as AWS and Oracle. The main steps cover Install lzbench
-    and measure algorithm performance.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install and run lzbench with snappy and zstd and measure compression library performance
-      running on 64-bit Arm AWS EC2 instance.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers using compression libraries on Arm
-      servers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/)
-      from an appropriate cloud service provider.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including snappy and Runbook, Linux environments, Arm platforms
-      such as Neoverse, and cloud platforms such as AWS and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Install lzbench and measure algorithm performance.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md b/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md
index 6cff8986bb..c106fca8dc 100644
--- a/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md
@@ -19,45 +19,6 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: 95da7df14cf36fe01e00868356cf117aa46007ac32e61b0df64d2eea28a5466e
-  summary: >-
-    Optimize the performance of Snort 3 using multithreading walks you through an end-to-end Arm
-    software workflow. It is designed for software developers familiar with Snort who want to
-    optimize performance by leveraging the benefits of multithreading. By the end, you will be
-    able to install Snort and dependencies, configure Snort Lua files to enable multithreading,
-    and use multithreading to process capture files and measure performance. It focuses on tools
-    and technologies such as AWS EC2, Snort3, Bash, and GCC, Linux environments, Arm platforms
-    including Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
-    The main steps cover Install Snort 3 and Dependencies and Test Snort 3 multithreading.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install Snort and dependencies, configure Snort Lua files to enable multithreading,
-      and use multithreading to process capture files and measure performance.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is for software developers familiar with Snort who want to optimize performance
-      by leveraging the benefits of multithreading.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm-based instance from a cloud provider,
-      or an Arm server running Ubuntu 20.04 or 22.04.; A basic understanding of Snort's operation
-      and configuration.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including AWS EC2, Snort3, Bash, and GCC, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
-      Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Install Snort 3 and Dependencies and Test Snort 3
-      multithreading.
-# END generated_summary_faq
 
 author: Preema Merlin Dsouza
 
diff --git a/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md
index b2edfbf2a7..13c5547132 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md
@@ -20,54 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: 009f2219f1b949be39b4a1dd43a5e4c1ceb5bb273d9a11c3c155315160d593ac
-  summary: >-
-    Run Spark applications on Microsoft Azure Cobalt 100 processors walks you through an end-to-end
-    Arm software workflow. It is designed for This is an advanced topic that introduces Spark
-    deployment on Microsoft Azure Cobalt 100 (Arm-based) virtual machines. It is designed for
-    developers migrating Spark applications from x86_64 to Arm. By the end, you will be able to
-    provision an Azure Arm64 virtual machine using Azure console, learn how to create an Azure
-    Linux 3.0 Docker container, and deploy a Spark application inside an Azure Linux 3.0 Arm64-based
-    Docker container or an Azure Linux 3.0 custom-image based Azure virtual machine. It focuses
-    on tools and technologies such as Apache Spark, Python, and Docker, Linux environments, Arm
-    platforms including Neoverse, and cloud platforms such as Microsoft Azure. The main steps
-    cover Getting started with Microsoft Azure Cobalt 100, Azure Linux 3.0, and Apache Spark,
-    Create an Azure Cobalt 100 Arm64 virtual machine, Set up an Azure Linux 3.0 environment, Install
-    Apache Spark on Azure Cobalt 100 processors, and Validate Apache Spark on Azure Cobalt 100
-    Arm64 VMs.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will provision an Azure Arm64 virtual machine using Azure console, learn how to create
-      an Azure Linux 3.0 Docker container, and deploy a Spark application inside an Azure Linux
-      3.0 Arm64-based Docker container or an Azure Linux 3.0 custom-image based Azure virtual
-      machine.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic that introduces Spark deployment on Microsoft Azure Cobalt 100
-      (Arm-based) virtual machines. It is designed for developers migrating Spark applications
-      from x86_64 to Arm.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Microsoft Azure](https://azure.microsoft.com/)
-      account with access to Cobalt 100 based instances (Dpsv6); A machine with [Docker](/install-guides/docker/)
-      installed; Familiarity with distributed computing concepts and the [Apache Spark architecture](https://spark.apache.org/docs/latest/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Apache Spark, Python, and Docker, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Getting started with Microsoft Azure Cobalt 100, Azure
-      Linux 3.0, and Apache Spark, Create an Azure Cobalt 100 Arm64 virtual machine, Set up an
-      Azure Linux 3.0 environment, Install Apache Spark on Azure Cobalt 100 processors, and Validate
-      Apache Spark on Azure Cobalt 100 Arm64 VMs.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md
index 299cbbc61f..d27d7e4054 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md
@@ -19,54 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: a4ac73af7e8959a546a66ab776c0bca8b60957e6f1729aa00fd78783e6594c0b
-  summary: >-
-    Deploy Apache Spark on Google Axion processors walks you through an end-to-end Arm software
-    workflow. It is designed for This introductory topic is for software developers interested
-    in migrating their Apache Spark workloads from x86_64 platforms to Arm-based platforms, specifically
-    on Google Axion–based C4A virtual machines. By the end, you will be able to start an Arm virtual
-    machine on Google Cloud Platform (GCP) using the C4A Google Axion instance family with RHEL
-    9 as the base image, install and configure Apache Spark on Arm-based GCP C4A instances, and
-    validate Spark functionality through baseline testing. It focuses on tools and technologies
-    such as Apache Spark and Python, Linux environments, Arm platforms including Neoverse, and
-    cloud platforms such as Google Cloud. The main steps cover Getting started with Apache Spark
-    on Google Axion C4A (Arm Neoverse-V2), How to create a Google Axion C4A Arm virtual machine
-    on GCP, How to deploy Apache Spark on Google Axion C4A Arm virtual machines, Apache Spark
-    baseline testing on Google Axion C4A Arm VM, and Apache Spark performance benchmarks on Arm64
-    and x86_64 in Google Cloud.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will start an Arm virtual machine on Google Cloud Platform (GCP) using the C4A Google
-      Axion instance family with RHEL 9 as the base image, install and configure Apache Spark
-      on Arm-based GCP C4A instances, and validate Spark functionality through baseline testing.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This introductory topic is for software developers interested in migrating their Apache
-      Spark workloads from x86_64 platforms to Arm-based platforms, specifically on Google Axion–based
-      C4A virtual machines.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free?utm_source=google&hl=en)
-      account with billing enabled; Familiarity with distributed computing concepts and the [Apache
-      Spark architecture](https://spark.apache.org/docs/latest/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Apache Spark and Python, Linux environments, Arm
-      platforms such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Getting started with Apache Spark on Google Axion
-      C4A (Arm Neoverse-V2), How to create a Google Axion C4A Arm virtual machine on GCP, How
-      to deploy Apache Spark on Google Axion C4A Arm virtual machines, Apache Spark baseline testing
-      on Google Axion C4A Arm VM, and Apache Spark performance benchmarks on Arm64 and x86_64
-      in Google Cloud.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/spark/_index.md b/content/learning-paths/servers-and-cloud-computing/spark/_index.md
index a818f586c4..8af4d8d9c8 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark/_index.md
@@ -17,41 +17,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: 7a612e45aa64ac93fa9a1fe83da8a7c63ffbc3a4b159184b1a7b04fd46e8ee4b
-  summary: >-
-    Learn how to deploy Spark on AWS Graviton2 walks you through an end-to-end Arm software workflow.
-    It is designed for anyone who wants to deploy Spark on AWS Graviton2. By the end, you will
-    be able to automate Spark EC2 instance creation using Terraform and Ansible and deploy a single
-    instance of Spark on AWS Graviton2. It focuses on tools and technologies such as Terraform,
-    Linux environments, Arm platforms including Neoverse, and cloud platforms such as AWS. The
-    main steps cover Deploy a single node of Spark.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will automate Spark EC2 instance creation using Terraform and Ansible and deploy a single
-      instance of Spark on AWS Graviton2.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for anyone who wants to deploy Spark on AWS Graviton2.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Amazon Web Services (AWS) [account](https://aws.amazon.com/);
-      A machine with [Terraform](/install-guides/terraform/), [AWS CLI](/install-guides/aws-cli/),
-      [AWS IAM authenticator](https://docs.aws.amazon.com/eks/latest/userguide/install-aws-iam-authenticator.html),
-      and [Ansible](/install-guides/ansible/) installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Terraform, Linux environments, Arm platforms such
-      as Neoverse, and cloud platforms such as AWS.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Deploy a single node of Spark.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md b/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md
index 9fe62a9cbb..e8ca1595a0 100644
--- a/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md
@@ -18,46 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: d3fa3b409ed7cf2ecb521fa4d19af8411718909881861ef3885214824031b541
-  summary: >-
-    Access running containers using Supervisor, SSH, and Remote.It walks you through an end-to-end
-    Arm software workflow. It is designed for software developers who want to learn how to run
-    multiple services in a container and access running containers using Supervisor, SSH, and
-    Remote.It during the debug and test phases of a project. By the end, you will be able to use
-    Supervisor to run multiple services in a container and access a container running in AWS Fargate
-    without changing the security group for debug and test. It focuses on tools and technologies
-    such as Docker, Remote.It, and Supervisor, Linux environments, Arm platforms including Neoverse
-    and Cortex-A, and cloud platforms such as AWS. The main steps cover Introduction to remote
-    container access, Install Supervisor, SSH, and Remote.It, and Access the container running
-    in AWS.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will use Supervisor to run multiple services in a container and access a container running
-      in AWS Fargate without changing the security group for debug and test.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to learn how to run multiple
-      services in a container and access running containers using Supervisor, SSH, and Remote.It
-      during the debug and test phases of a project.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm Linux computer running Docker;
-      An AWS account; A Remote.It account.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Docker, Remote.It, and Supervisor, Linux environments,
-      Arm platforms such as Neoverse and Cortex-A, and cloud platforms such as AWS.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Introduction to remote container access, Install Supervisor,
-      SSH, and Remote.It, and Access the container running in AWS.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/sve/_index.md b/content/learning-paths/servers-and-cloud-computing/sve/_index.md
index 393e9c34a7..9e5d8bab10 100644
--- a/content/learning-paths/servers-and-cloud-computing/sve/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sve/_index.md
@@ -18,46 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: 7b2074e39243a5cd0ccb6a01317c700a4797d8c66353f39aa4197237b6672027
-  summary: >-
-    Port Code to Arm Scalable Vector Extension (SVE) walks you through an end-to-end Arm software
-    workflow. It is designed for software developers using SIMD instructions for High-Performance
-    Computing, Machine Learning, Digital Signal Processing, Audio and Video Codec applications.
-    By the end, you will be able to understand the differences between SVE and Neon for vectorization,
-    compile code for SVE-capable Arm processors, and run SVE instructions on any Armv8-A processor.
-    It focuses on tools and technologies such as SVE, Neon, armie, GCC, and armclang, Linux environments,
-    Arm platforms including Neoverse and Cortex-A, and cloud platforms such as AWS, Microsoft
-    Azure, Google Cloud, and Oracle. The main steps cover From Arm Neon to SVE, Compile for SVE,
-    and Run SVE without capable hardware.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand the differences between SVE and Neon for vectorization, compile code
-      for SVE-capable Arm processors, and run SVE instructions on any Armv8-A processor.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers using SIMD instructions for High-Performance
-      Computing, Machine Learning, Digital Signal Processing, Audio and Video Codec applications.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: General knowledge about SIMD processing,
-      vectorization or Arm Neon.; An Arm computer running Linux. Cloud instances can be used,
-      refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including SVE, Neon, armie, GCC, and armclang, Linux environments,
-      Arm platforms such as Neoverse and Cortex-A, and cloud platforms such as AWS, Microsoft
-      Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around From Arm Neon to SVE, Compile for SVE, and Run SVE
-      without capable hardware.
-# END generated_summary_faq
+
 
 author: Florent Lebeau
 
diff --git a/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md b/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md
index fd44bbf71c..15fd2776a3 100644
--- a/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md
@@ -20,47 +20,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: cc3f88ce181b1614f959497a24fafe736b26e55615792faab6b5dce29fac8d77
-  summary: >-
-    Accelerate search performance with SVE2 MATCH on Arm servers walks you through an end-to-end
-    Arm software workflow. It is designed for database developers, performance engineers, and
-    anyone optimizing data processing workloads on Arm-based cloud instances. By the end, you
-    will be able to understand the purpose and function of SVE2 MATCH instructions, implement
-    a search algorithm using both scalar and SVE2-based MATCH approaches, and benchmark and compare
-    performance between scalar and vectorized implementations. It focuses on tools and technologies
-    such as SVE2, Neon, and Runbook, Linux environments, Arm platforms including Neoverse, and
-    cloud platforms such as AWS, Microsoft Azure, and Google Cloud. The main steps cover Compare
-    search performance using scalar and SVE2 MATCH on Arm Servers.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand the purpose and function of SVE2 MATCH instructions, implement a search
-      algorithm using both scalar and SVE2-based MATCH approaches, and benchmark and compare performance
-      between scalar and vectorized implementations.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for database developers, performance engineers, and anyone
-      optimizing data processing workloads on Arm-based cloud instances.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Access to an [AWS Graviton4, Google
-      Axion, or Azure Cobalt 100 virtual machine](/learning-paths/servers-and-cloud-computing/csp/)
-      from a cloud service provider.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including SVE2, Neon, and Runbook, Linux environments, Arm
-      platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, and Google
-      Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Compare search performance using scalar and SVE2 MATCH
-      on Arm Servers.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md b/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md
index 1b424d85f3..facc3d1e46 100644
--- a/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md
@@ -17,46 +17,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: d15c3083cb881838f6500eb56dfafb636d73bb282484a7f1b8f4a855dda37fb4
-  summary: >-
-    Get ready for performance analysis with Sysreport walks you through an end-to-end Arm software
-    workflow. It is designed for software developers who want to use the system capability reporting
-    tool, Sysreport, to understand and configure the performance features of their Arm Linux system.
-    By the end, you will be able to run Sysreport to get a quick report of the system configuration,
-    discover which performance analysis features are available and enabled, and make configuration
-    changes to improve performance information collection. It focuses on tools and technologies
-    such as Python and Runbook, Linux environments, Arm platforms including Cortex-A and Neoverse,
-    and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps
-    cover Before you begin, Run Sysreport, and Analyze the results.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will run Sysreport to get a quick report of the system configuration, discover which
-      performance analysis features are available and enabled, and make configuration changes
-      to improve performance information collection.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to use the system capability
-      reporting tool, Sysreport, to understand and configure the performance features of their
-      Arm Linux system.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm-based system (bare metal server,
-      cloud instance, developer board) running Linux.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Python and Runbook, Linux environments, Arm platforms
-      such as Cortex-A and Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
-      Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Before you begin, Run Sysreport, and Analyze the results.
-# END generated_summary_faq
+
 
 author: James Whitaker
 
diff --git a/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
index 60e5fc767e..a8f7eabfee 100644
--- a/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
@@ -18,54 +18,6 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v2
-  generated_at: '2026-05-08T18:10:04Z'
-  generator: template
-  source_hash: 2c20d30d25a0bb871bdb935d18f7ad8e0740139948133dbd728e10f0add0f94e
-  summary_generated_at: '2026-05-08T18:10:04Z'
-  summary_source_hash: 2c20d30d25a0bb871bdb935d18f7ad8e0740139948133dbd728e10f0add0f94e
-  faq_generated_at: '2026-05-06T17:17:59Z'
-  faq_source_hash: 2c20d30d25a0bb871bdb935d18f7ad8e0740139948133dbd728e10f0add0f94e
-  summary: >-
-    Deploy TensorFlow on Google Cloud C4A (Arm-based Axion VMs) walks you through an end-to-end
-    Arm software workflow. It is designed for software developers deploying and optimizing TensorFlow
-    workloads on Arm64 Linux environments, specifically using Google Cloud C4A virtual machines
-    powered by Axion processors. By the end, you will be able to provision an Arm-based SUSE Linux
-    Enterprise Server (SLES) virtual machine on Google Cloud (C4A with Axion processors), install
-    TensorFlow on a SUSE Arm64 (C4A) instance, and verify TensorFlow by running basic computation
-    and model training tests on Arm64. It focuses on tools and technologies such as TensorFlow,
-    Python, and Keras, Linux environments, Arm platforms including Neoverse, and cloud platforms
-    such as Google Cloud. The main steps cover Get started with TensorFlow on Google Axion C4A,
-    Create a Google Axion C4A Arm virtual machine on GCP, Install TensorFlow, Test TensorFlow
-    baseline performance on Google Axion C4A, and Benchmark TensorFlow model performance using
-    tf.keras.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will provision an Arm-based SUSE Linux Enterprise Server (SLES) virtual machine on Google
-      Cloud (C4A with Axion processors), install TensorFlow on a SUSE Arm64 (C4A) instance, and
-      verify TensorFlow by running basic computation and model training tests on Arm64.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers deploying and optimizing TensorFlow
-      workloads on Arm64 Linux environments, specifically using Google Cloud C4A virtual machines
-      powered by Axion processors.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
-      account with billing enabled; Basic familiarity with [TensorFlow](https://www.tensorflow.org/).
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including TensorFlow, Python, and Keras, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Get started with TensorFlow on Google Axion C4A, Create
-      a Google Axion C4A Arm virtual machine on GCP, Install TensorFlow, Test TensorFlow baseline
-      performance on Google Axion C4A, and Benchmark TensorFlow model performance using tf.keras.
-# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md b/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md
index 760100199a..2aa2a64b0c 100644
--- a/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md
@@ -16,43 +16,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: 0aaee75d902b938e63e37eca421dd28b91f583e784acdf3cccb1e20b8dda71bd
-  summary: >-
-    Run Text Classification with ThirdAI on Arm servers walks you through an end-to-end Arm software
-    workflow. It is designed for software developers who want to learn how to run text classification
-    tasks with ThirdAI on Arm servers. By the end, you will be able to train, evaluate, and deploy
-    a ThirdAI model and set up your Arm server for text classification tasks with ThirdAI. It
-    focuses on tools and technologies such as Python and ThirdAI, Linux environments, Arm platforms
-    including Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
-    The main steps cover Background and Overview of Learning Path, Train a model for text classification,
-    and Evaluate the model.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will train, evaluate, and deploy a ThirdAI model and set up your Arm server for text
-      classification tasks with ThirdAI.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is for software developers who want to learn how to run text classification tasks with
-      ThirdAI on Arm servers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/)
-      from a cloud service provider or an on-premise Arm server.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Python and ThirdAI, Linux environments, Arm platforms
-      such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Background and Overview of Learning Path, Train a
-      model for text classification, and Evaluate the model.
-# END generated_summary_faq
+
 
 author: ThirdAI
 
diff --git a/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md
index d108189428..19c6491cc4 100644
--- a/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md
@@ -19,52 +19,6 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: edf5bebb0440c110723c87ca27e5f4b21e4da588e4208b5391f7091ae93cb73d
-  summary: >-
-    Deploy a live sensor dashboard with TimescaleDB and Grafana on Google Cloud C4A walks you
-    through an end-to-end Arm software workflow. It is designed for DevOps engineers, database
-    engineers, and software developers who want to deploy and operate TimescaleDB on SUSE Linux
-    Enterprise Server (SLES) Arm64, ingest live time-series sensor data, and visualize it in Grafana.
-    By the end, you will be able to install and configure TimescaleDB on Google Cloud C4A Axion
-    processors by building from source for Arm64, create a real-time sensor data ingestion pipeline
-    using Python with hypertables, continuous aggregates, and retention policies, and build a
-    live sensor dashboard with Grafana that automatically refreshes to display time-series data.
-    It focuses on tools and technologies such as TimescaleDB, PostgreSQL, Python, Grafana, and
-    psycopg2, Linux environments, Arm platforms including Neoverse, and cloud platforms such as
-    Google Cloud. The main steps cover Get started with TimescaleDB on Google Axion C4A, Create
-    a firewall rule for Grafana/TimescaleDB, Create a Google Axion C4A Arm virtual machine on
-    GCP, Set up TimescaleDB on Arm64, and Ingest real-time sensor data on Arm64.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install and configure TimescaleDB on Google Cloud C4A Axion processors by building
-      from source for Arm64, create a real-time sensor data ingestion pipeline using Python with
-      hypertables, continuous aggregates, and retention policies, and build a live sensor dashboard
-      with Grafana that automatically refreshes to display time-series data.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for DevOps engineers, database engineers, and software developers
-      who want to deploy and operate TimescaleDB on SUSE Linux Enterprise Server (SLES) Arm64,
-      ingest live time-series sensor data, and visualize it in Grafana.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
-      account with billing enabled; Basic familiarity with SQL, Python, and Grafana.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including TimescaleDB, PostgreSQL, Python, Grafana, and psycopg2,
-      Linux environments, Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Get started with TimescaleDB on Google Axion C4A,
-      Create a firewall rule for Grafana/TimescaleDB, Create a Google Axion C4A Arm virtual machine
-      on GCP, Set up TimescaleDB on Arm64, and Ingest real-time sensor data on Arm64.
-# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md b/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md
index a60f31e57e..a469df4cce 100644
--- a/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md
@@ -18,46 +18,6 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: e6a652fd0b32796433a380012a79def743bc5452a52ffae785007977a2a8e3e0
-  summary: >-
-    Learn the Arm Neoverse N1 performance analysis methodology walks you through an end-to-end
-    Arm software workflow. It is designed for software developers who want to learn about performance
-    analysis methodology for Linux applications running on Arm Neoverse. By the end, you will
-    be able to understand sampling and counting for performance analysis, learn commonly used
-    hardware metrics, and analyze a sample application using the Arm Telemetry Solution and Linux
-    Perf. It focuses on tools and technologies such as perf, Telemetry, and Runbook, Linux environments,
-    and Arm platforms including Neoverse. The main steps cover Introduction to performance analysis,
-    Build an example application, Gather performance metrics, and Optimize the application.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will understand sampling and counting for performance analysis, learn commonly used
-      hardware metrics, and analyze a sample application using the Arm Telemetry Solution and
-      Linux Perf.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to learn about performance
-      analysis methodology for Linux applications running on Arm Neoverse.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm Neoverse N1 computer running
-      Linux. A bare metal or cloud metal instance is best because they expose more counters. You
-      can use a virtual machine (VM), but it may offer fewer counters and some commands might
-      not succeed. These instructions have been tested on the `a1.metal` instance type.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including perf, Telemetry, and Runbook, Linux environments,
-      and Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Introduction to performance analysis, Build an example
-      application, Gather performance metrics, and Optimize the application.
-# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md b/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md
index 80e75a8451..75a05236b6 100644
--- a/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md
@@ -17,47 +17,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: d25121278f9dd40e2fd19d988e35866c6bfa70cfd0b93e2ec4506c8ad8a26d88
-  summary: >-
-    Measure and accelerate PyTorch Inference on Arm servers walks you through an end-to-end Arm
-    software workflow. It is designed for software developers who want to learn how to measure
-    and accelerate the performance of Natural Language Processing (NLP), vision and recommender
-    PyTorch models on Arm-based servers. By the end, you will be able to download and install
-    the PyTorch Benchmarks suite, evaluate PyTorch model inference performance on an Arm-based
-    server using the PyTorch Benchmark suite, and compare the model inference performance using
-    eager mode and `torch.compile` mode in PyTorch. It focuses on tools and technologies such
-    as Python and PyTorch, Linux environments, Arm platforms including Neoverse, and cloud platforms
-    such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps cover Measure and accelerate
-    the inference performance of PyTorch models on Arm servers.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will download and install the PyTorch Benchmarks suite, evaluate PyTorch model inference
-      performance on an Arm-based server using the PyTorch Benchmark suite, and compare the model
-      inference performance using eager mode and `torch.compile` mode in PyTorch.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to learn how to measure and
-      accelerate the performance of Natural Language Processing (NLP), vision and recommender
-      PyTorch models on Arm-based servers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/)
-      from a cloud service provider or an on-premise Arm server.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Python and PyTorch, Linux environments, Arm platforms
-      such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Measure and accelerate the inference performance of
-      PyTorch models on Arm servers.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md
index 95dc898eb3..88486782fd 100644
--- a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md
@@ -17,42 +17,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: 523ff99ccb8d13543f64839fa21040191d2a9ff7ce7c78fa590e8775fac754a6
-  summary: >-
-    Learn about Neoverse Non-cache PMU events using C and Assembly Language walks you through
-    an end-to-end Arm software workflow. It is designed for software and hardware engineers to
-    learn about why common non-cache PMU events occur. By the end, you will be able to describe
-    common non-cache PMU events and understand why specific code triggers specific PMU events
-    on the Neoverse N2 Core. It focuses on tools and technologies such as C, Assembly, and Runbook,
-    Linux environments, and Arm platforms including Neoverse. The main steps cover Introduction
-    to the PMU, Topdown Methodology L1 Events, TLB Events, and Operation Mix Events.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will describe common non-cache PMU events and understand why specific code triggers
-      specific PMU events on the Neoverse N2 Core.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software and hardware engineers to learn about why common
-      non-cache PMU events occur.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Some familiarity with performance analysis.;
-      The ability to read Arm assembly code.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including C, Assembly, and Runbook, Linux environments, and
-      Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Introduction to the PMU, Topdown Methodology L1 Events,
-      TLB Events, and Operation Mix Events.
-# END generated_summary_faq
+
 
 author: Johanna Skinnider
 
diff --git a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md
index 459e47f382..daa5c20d9f 100644
--- a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md
@@ -18,43 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: 1b309980bef983966d948036e309e132b56f767161967187e72c6a9f81f17dce
-  summary: >-
-    Learn about Neoverse Cache PMU Events using C and Assembly Language walks you through an end-to-end
-    Arm software workflow. It is designed for software and hardware engineers who want to learn
-    about the causes of common Neoverse cache Performance Monitoring Unit (PMU) events. By the
-    end, you will be able to describe common cache PMU events, describe why some code triggers
-    PMU events on the Neoverse N2 core, and describe the events triggered during common scenarios.
-    It focuses on tools and technologies such as C, Assembly, and Runbook, Linux environments,
-    and Arm platforms including Neoverse. The main steps cover Introduction to the PMU, L1 Data
-    Cache Events, L1 Instruction Cache Events, L2 Unified Cache Events, and LL Cache Events.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will describe common cache PMU events, describe why some code triggers PMU events on
-      the Neoverse N2 core, and describe the events triggered during common scenarios.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for software and hardware engineers who want to learn about the
-      causes of common Neoverse cache Performance Monitoring Unit (PMU) events.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Knowledge of performance analysis.;
-      The ability to read Arm assembly code.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including C, Assembly, and Runbook, Linux environments, and
-      Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Introduction to the PMU, L1 Data Cache Events, L1
-      Instruction Cache Events, L2 Unified Cache Events, and LL Cache Events.
-# END generated_summary_faq
+
 
 author: Johanna Skinnider
 
diff --git a/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md b/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md
index bfc8483859..307d1ecdb3 100644
--- a/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md
@@ -21,49 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: 13bba1dee1c948f8b751a71479a5106014a2c21dab6ce3968a08bed9e3363de1
-  summary: >-
-    Scan multi-architecture containers with Trivy on Azure Cobalt 100 walks you through an end-to-end
-    Arm software workflow. It is designed for developers and DevOps engineers who want to integrate
-    security scanning into CI/CD pipelines for multi-architecture container images. By the end,
-    you will be able to build and scan multi-architecture container images using Trivy on Azure
-    Cobalt 100, configure self-hosted GitHub Actions Arm runners for CI/CD pipelines, and enforce
-    security gates in CI pipelines based on vulnerability severity. It focuses on tools and technologies
-    such as Trivy, Docker, GitHub Actions, and YAML, Linux environments, Arm platforms including
-    Neoverse, and cloud platforms such as Microsoft Azure. The main steps cover Learn Azure Cobalt
-    100 Arm64 and Use Trivy for Security Scanning, Create an Azure Cobalt 100 Arm64 virtual machine,
-    and Build and scan multi-architecture container images with Trivy.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build and scan multi-architecture container images using Trivy on Azure Cobalt
-      100, configure self-hosted GitHub Actions Arm runners for CI/CD pipelines, and enforce security
-      gates in CI pipelines based on vulnerability severity.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers and DevOps engineers who want to integrate
-      security scanning into CI/CD pipelines for multi-architecture container images.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Microsoft Azure account](https://azure.microsoft.com/)
-      with access to Cobalt 100 based instances (Dpsv6); Docker installed and basic knowledge
-      of containerization; Familiarity with CI/CD concepts; Basic knowledge of Linux command-line
-      operations; Familiarity with GitHub Actions runners.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Trivy, Docker, GitHub Actions, and YAML, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as Microsoft Azure.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Learn Azure Cobalt 100 Arm64 and Use Trivy for Security
-      Scanning, Create an Azure Cobalt 100 Arm64 virtual machine, and Build and scan multi-architecture
-      container images with Trivy.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md b/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md
index db71146b46..26ee944daf 100644
--- a/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md
@@ -21,46 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: 9f2b3f6c5e76ecb754de5c0fd84278ff71f71c5c7906acdc06911f2feff1f5fd
-  summary: >-
-    Tune network workloads on Arm-based bare-metal instances walks you through an end-to-end Arm
-    software workflow. It is designed for engineers who want to tune the performance of network
-    workloads on Arm Neoverse-based bare-metal instances. By the end, you will be able to set
-    up Apache Tomcat and wrk2 to benchmark HTTP on an Arm Neoverse bare‑metal host, establish
-    a reproducible baseline baseline (file‑descriptor limits, logging, thread counts, fixed core
-    set), and tune NIC queue count to match available cores and measure impact. It focuses on
-    tools and technologies such as Apache Tomcat, wrk2, and OpenJDK 21, Linux environments, and
-    Arm platforms including Neoverse. The main steps cover Set up Tomcat, Establish baseline performance,
-    Tune performance with NIC queue counts, NUMA-based tuning, and IOMMU-based tuning.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will set up Apache Tomcat and wrk2 to benchmark HTTP on an Arm Neoverse bare‑metal host,
-      establish a reproducible baseline baseline (file‑descriptor limits, logging, thread counts,
-      fixed core set), and tune NIC queue count to match available cores and measure impact.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an advanced topic for engineers who want to tune the performance of network workloads
-      on Arm Neoverse-based bare-metal instances.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm Neoverse-based bare-metal server
-      running Ubuntu 24.04 to run Apache Tomcat; Access to an x86_64 bare-metal server running
-      Ubuntu 24.04 to run `wrk2`; Basic familiarity with Java applications.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Apache Tomcat, wrk2, and OpenJDK 21, Linux environments,
-      and Arm platforms such as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Set up Tomcat, Establish baseline performance, Tune
-      performance with NIC queue counts, NUMA-based tuning, and IOMMU-based tuning.
-# END generated_summary_faq
+
 
 author: Ying Yu, Ker Liu, Rui Chang
 
diff --git a/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md
index e6462054eb..a97bf8c4d6 100644
--- a/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md
@@ -20,51 +20,6 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: ee805f703acd45612c9a94e60c6322866dc1c8ff25d48de2fb4cd9067948c93e
-  summary: >-
-    Deploy TypeScript on Google Cloud C4A virtual machines walks you through an end-to-end Arm
-    software workflow. It is designed for developers deploying and optimizing TypeScript workloads
-    on Arm64 Linux environments, specifically using Google Cloud C4A virtual machines powered
-    by Axion processors. By the end, you will be able to provision an Arm-based SUSE Linux Enterprise
-    Server (SLES) virtual machine (VM) on Google Cloud, install TypeScript on a SUSE Arm64 C4A
-    instance, and validate TypeScript functionality by creating, compiling, and running a simple
-    TypeScript script on a Arm64 VM. It focuses on tools and technologies such as TypeScript,
-    node.js, and npm, Linux environments, Arm platforms including Neoverse, and cloud platforms
-    such as Google Cloud. The main steps cover Get started with TypeScript on Google Axion C4A
-    instances, Create a Google Axion C4A Arm virtual machine on GCP, Install TypeScript, Establish
-    a TypeScript performance baseline, and Benchmark TypeScript performance.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will provision an Arm-based SUSE Linux Enterprise Server (SLES) virtual machine (VM)
-      on Google Cloud, install TypeScript on a SUSE Arm64 C4A instance, and validate TypeScript
-      functionality by creating, compiling, and running a simple TypeScript script on a Arm64
-      VM.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers deploying and optimizing TypeScript workloads
-      on Arm64 Linux environments, specifically using Google Cloud C4A virtual machines powered
-      by Axion processors.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: A [Google Cloud Platform (GCP)](https://cloud.google.com/free)
-      account with billing enabled; Basic familiarity with [TypeScript](https://www.typescriptlang.org/)
-      and Node.js runtime environment.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including TypeScript, node.js, and npm, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as Google Cloud.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Get started with TypeScript on Google Axion C4A instances,
-      Create a Google Axion C4A Arm virtual machine on GCP, Install TypeScript, Establish a TypeScript
-      performance baseline, and Benchmark TypeScript performance.
-# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md b/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md
index d52aa13a72..925c776f50 100644
--- a/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md
@@ -17,45 +17,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: 035bd363fc8ae772acf52d7e392f2db27087267706aeec86b4098c497e5fe91d
-  summary: >-
-    Migrate applications that leverage performance libraries walks you through an end-to-end Arm
-    software workflow. It is designed for both C and C++ developers who want to migrate applications
-    that rely on optimized performance libraries from x86 to Arm Architecture. By the end, you
-    will be able to describe the differences between standard and performance libraries, incorporate
-    optimized libraries, and port a basic application from x86 to AArch64. It focuses on tools
-    and technologies such as Arm Compiler for Linux, CPP, and Runbook, Linux environments, Arm
-    platforms including Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud,
-    and Oracle. The main steps cover Introduction to Libraries, Set up your environment, Use an
-    optimized math library, and Moving from x86 to AArch64.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will describe the differences between standard and performance libraries, incorporate
-      optimized libraries, and port a basic application from x86 to AArch64.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is for both C and C++ developers who want to migrate applications that
-      rely on optimized performance libraries from x86 to Arm Architecture.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: Access to both an Arm and an x86-based
-      cloud instance.; Intermediate understanding of C++, compilers, and Linux.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Arm Compiler for Linux, CPP, and Runbook, Linux
-      environments, Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft
-      Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Introduction to Libraries, Set up your environment,
-      Use an optimized math library, and Moving from x86 to AArch64.
-# END generated_summary_faq
+
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md b/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md
index 322342e812..c7f2ae3116 100644
--- a/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md
@@ -18,43 +18,6 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: beb244c7766c474b47942b86f1cdd0d124c1cccb74dbe40f0b6c0917d0b3d31a
-  summary: >-
-    Install Vectorscan (Hyperscan on Arm) and use it with Snort 3 walks you through an end-to-end
-    Arm software workflow. It is designed for software developers using Hyperscan who want to
-    migrate to Arm. By the end, you will be able to install and run Vectorscan on an Arm-based
-    instance, install and run Snort 3 on your instance, and run Snort 3 with Vectorscan on capture
-    files and and measure performance. It focuses on tools and technologies such as Vectorscan,
-    Linux environments, Arm platforms including Neoverse, and cloud platforms such as AWS, Microsoft
-    Azure, Google Cloud, and Oracle. The main steps cover Run Vectorscan on Arm and Install Snort3
-    and run it with Vectorscan on Arm.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install and run Vectorscan on an Arm-based instance, install and run Snort 3 on
-      your instance, and run Snort 3 with Vectorscan on capture files and and measure performance.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers using Hyperscan who want to migrate
-      to Arm.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/)
-      from a cloud service provider or an Arm server with Ubuntu 20.04 or Ubuntu 22.04 installed.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Vectorscan, Linux environments, Arm platforms such
-      as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Run Vectorscan on Arm and Install Snort3 and run it
-      with Vectorscan on Arm.
-# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md b/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
index dbccf97839..f657c75f56 100644
--- a/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
@@ -21,53 +21,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: 487e9829c6e08798ad01be7a01f192e10e99cb06b71108575f1919c134eece02
-  summary: >-
-    Run vLLM inference with INT4 quantization on Arm servers walks you through an end-to-end Arm
-    software workflow. It is designed for developers interested in building and optimizing vLLM
-    for Arm-based servers. This Learning Path shows you how to quantize large language models
-    (LLMs) to INT4, serve them using an OpenAI-compatible API, and benchmark model accuracy with
-    the LM Evaluation Harness. By the end, you will be able to build an optimized vLLM for aarch64
-    with oneDNN and the Arm Compute Library (ACL), set up all runtime dependencies including PyTorch,
-    llmcompressor, and Arm-optimized libraries, and quantize an LLM (DeepSeek‑V2‑Lite) to 4-bit
-    integer (INT4) precision. It focuses on tools and technologies such as vLLM, LM Evaluation
-    Harness, LLM, Generative AI, and Python, Linux environments, Arm platforms including Neoverse,
-    and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps
-    cover Build and validate vLLM for inference, Quantize an LLM to INT4, Serve high throughput
-    inference with vLLM, and Evaluate accuracy with LM Evaluation Harness.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build an optimized vLLM for aarch64 with oneDNN and the Arm Compute Library (ACL),
-      set up all runtime dependencies including PyTorch, llmcompressor, and Arm-optimized libraries,
-      and quantize an LLM (DeepSeek‑V2‑Lite) to 4-bit integer (INT4) precision.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers interested in building and optimizing vLLM
-      for Arm-based servers. This Learning Path shows you how to quantize large language models
-      (LLMs) to INT4, serve them using an OpenAI-compatible API, and benchmark model accuracy
-      with the LM Evaluation Harness.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm-based Linux server (Ubuntu 22.04+
-      recommended) with a minimum of 32 vCPUs, 64 GB RAM, and 64 GB free disk space; Python 3.12
-      and basic familiarity with Hugging Face Transformers and quantization.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including vLLM, LM Evaluation Harness, LLM, Generative AI,
-      and Python, Linux environments, Arm platforms such as Neoverse, and cloud platforms such
-      as AWS, Microsoft Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Build and validate vLLM for inference, Quantize an
-      LLM to INT4, Serve high throughput inference with vLLM, and Evaluate accuracy with LM Evaluation
-      Harness.
-# END generated_summary_faq
+
 
 author:
    - Nikhil Gupta
diff --git a/content/learning-paths/servers-and-cloud-computing/vllm/_index.md b/content/learning-paths/servers-and-cloud-computing/vllm/_index.md
index 15fb05a0f8..6621e17c8e 100644
--- a/content/learning-paths/servers-and-cloud-computing/vllm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vllm/_index.md
@@ -18,45 +18,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: db5987ec7987375d9b51de843b0e1faf0e61731b14c5a563d19aaad26f50f6bb
-  summary: >-
-    Build and Run vLLM on Arm Servers walks you through an end-to-end Arm software workflow. It
-    is designed for software developers and AI engineers interested in learning how to use the
-    vLLM library on Arm servers. By the end, you will be able to build vLLM from source on an
-    Arm server, download a Qwen LLM from Hugging Face, and run local batch inference using vLLM.
-    It focuses on tools and technologies such as vLLM, LLM, Generative AI, Python, and Hugging
-    Face, Linux environments, Arm platforms including Neoverse, and cloud platforms such as AWS,
-    Microsoft Azure, Google Cloud, and Oracle. The main steps cover Build a vLLM from Source Code,
-    Run batch inference using vLLM, and Run an OpenAI-compatible server.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build vLLM from source on an Arm server, download a Qwen LLM from Hugging Face,
-      and run local batch inference using vLLM.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers and AI engineers interested in learning
-      how to use the vLLM library on Arm servers.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/)
-      from a cloud service provider, or a local Arm Linux computer with at least 8 CPUs and 16
-      GB RAM.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including vLLM, LLM, Generative AI, Python, and Hugging Face,
-      Linux environments, Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft
-      Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Build a vLLM from Source Code, Run batch inference
-      using vLLM, and Run an OpenAI-compatible server.
-# END generated_summary_faq
+
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md b/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
index 02bdbab923..8a026f2593 100644
--- a/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
@@ -5,44 +5,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: 4ed20056b2d82bd2c9ab1afe3d74657df9ac09808592d843c1b143626a8668ac
-  summary: >-
-    Run the vvenc H.266 encoder on Arm servers walks you through an end-to-end Arm software workflow.
-    It is designed for software developers who want to build and run the VVenC® (Fraunhofer Versatile
-    Video Encoder) H.266 project on Arm servers and measure the performance. By the end, you will
-    be able to build the VVenC® H.266 encoder project on an Arm-based server and run vvenc on
-    an Arm-based server to encode a real 1080p video file and measure the performance. It focuses
-    on tools and technologies such as vvenc, Linux environments, Arm platforms including Neoverse,
-    and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle. The main steps
-    cover Build and run the H.266 VVenC encoder on Arm servers.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build the VVenC® H.266 encoder project on an Arm-based server and run vvenc on
-      an Arm-based server to encode a real 1080p video file and measure the performance.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to build and run the VVenC®
-      (Fraunhofer Versatile Video Encoder) H.266 project on Arm servers and measure the performance.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm Linux system or an [Arm-based
-      instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider.
-      This Learning Path has been tested on an Arm Neoverse N2-based Alibaba cloud ECS instance(g8y),
-      running Ubuntu 22.04.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including vvenc, Linux environments, Arm platforms such as
-      Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Build and run the H.266 VVenC encoder on Arm servers.
-# END generated_summary_faq
+
 
 author: Willen Yang
 
diff --git a/content/learning-paths/servers-and-cloud-computing/whisper/_index.md b/content/learning-paths/servers-and-cloud-computing/whisper/_index.md
index c8d80e3796..9016ec70ea 100644
--- a/content/learning-paths/servers-and-cloud-computing/whisper/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/whisper/_index.md
@@ -22,48 +22,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: e130630b5ae4626641779d0b66d3c1c132b90899cd3d6db07a46df8957c4da38
-  summary: >-
-    Accelerate Whisper on Arm with Hugging Face Transformers walks you through an end-to-end Arm
-    software workflow. It is designed for software developers familiar with basic machine learning
-    concepts and looking to run the OpenAI Whisper Automatic Speech Recognition (ASR) model efficiently,
-    using an Arm-based cloud instance. By the end, you will be able to install the dependencies
-    for the Whisper ASR Model, run the Whisper model using Hugging Face Transformers, and enable
-    performance-enhancing features for running the model on Arm CPUs. It focuses on tools and
-    technologies such as Python, Whisper, Demo, and Hugging Face, Linux environments, Arm platforms
-    including Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google Cloud, and Oracle.
-    The main steps cover Set up the Whisper Model and Run the Whisper Model.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install the dependencies for the Whisper ASR Model, run the Whisper model using
-      Hugging Face Transformers, and enable performance-enhancing features for running the model
-      on Arm CPUs.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This Learning Path is for software developers familiar with basic machine learning concepts
-      and looking to run the OpenAI Whisper Automatic Speech Recognition (ASR) model efficiently,
-      using an Arm-based cloud instance.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An [Arm-based compute instance](/learning-paths/servers-and-cloud-computing/intro/)
-      running Ubuntu with 32 cores, 8GB of RAM, and 32GB of disk space.; Basic knowledge of Python.;
-      Familiarity with machine learning concepts.; Familiarity with the fundamentals of the Whisper
-      ASR Model.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including Python, Whisper, Demo, and Hugging Face, Linux environments,
-      Arm platforms such as Neoverse, and cloud platforms such as AWS, Microsoft Azure, Google
-      Cloud, and Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Set up the Whisper Model and Run the Whisper Model.
-# END generated_summary_faq
+
 
 author: Nobel Chowdary Mandepudi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md b/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
index 93ce4adde8..7a33fa5107 100644
--- a/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
@@ -11,39 +11,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v1
-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: 46f2645fb6ef298508af93569554315cfa2564be9891acabf1c874c94e52d70d
-  summary: >-
-    Deploy MySQL and WordPress on an always free tier Arm shape walks you through an end-to-end
-    Arm software workflow. It is designed for developers who want to install WordPress on Oracle
-    Cloud Infrastructure (OCI) using always free tier. By the end, you will be able to install
-    MySQL and WordPress on an Arm server running in OCI. It focuses on tools and technologies
-    such as MySQL and WordPress, Linux environments, Arm platforms including Neoverse, and cloud
-    platforms such as Oracle. The main steps cover Install WordPress and MySQL on an OCI Arm server.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will install MySQL and WordPress on an Arm server running in OCI.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for developers who want to install WordPress on Oracle Cloud
-      Infrastructure (OCI) using always free tier.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An OCI account; An Arm compute instance
-      deployed on OCI with Oracle Linux.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including MySQL and WordPress, Linux environments, Arm platforms
-      such as Neoverse, and cloud platforms such as Oracle.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Install WordPress and MySQL on an OCI Arm server.
-# END generated_summary_faq
+
 
 author: Frédéric -lefred- Descamps
 
diff --git a/content/learning-paths/servers-and-cloud-computing/zlib/_index.md b/content/learning-paths/servers-and-cloud-computing/zlib/_index.md
index d7eda3ec69..e2edf19e82 100644
--- a/content/learning-paths/servers-and-cloud-computing/zlib/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/zlib/_index.md
@@ -19,50 +19,7 @@ generate_summary_faq: true
 
 rerun_summary: false
 rerun_faqs: false
-# START generated_summary_faq
-generated_summary_faq:
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-  generated_at: '2026-05-06T17:17:59Z'
-  generator: template
-  source_hash: 8916f83f621505dc5406f14e70d04e702ea304950e34b4b76dc1bbc987bcc4e3
-  summary: >-
-    Learn how to build and use zlib-ng on Arm servers, using its Neon SIMD and ARMv8 CRC32 optimizations
-    to improve compression performance compared to the system default zlib. It is designed for
-    software developers who want to improve data compression performance on Arm servers by replacing
-    the default zlib with zlib-ng, an actively maintained fork that includes Neon SIMD and ARMv8
-    CRC32 optimizations. By the end, you will be able to build zlib-ng in zlib-compatible mode
-    on an Arm server, run example applications using zlib-ng as a drop-in replacement, and measure
-    and analyze performance improvements with zlib-ng. It focuses on tools and technologies such
-    as zlib, Linux environments, and Arm platforms including Neoverse. The main steps cover Build
-    and install zlib-ng on Arm servers, Improve Python application performance using zlib-ng,
-    and Use perf to analyze zlib-ng performance.
-  faqs:
-  - question: What will you accomplish in this Learning Path?
-    answer: >-
-      You will build zlib-ng in zlib-compatible mode on an Arm server, run example applications
-      using zlib-ng as a drop-in replacement, and measure and analyze performance improvements
-      with zlib-ng. Learn how to build and use zlib-ng on Arm servers, using its Neon SIMD and
-      ARMv8 CRC32 optimizations to improve compression performance compared to the system default
-      zlib.
-  - question: Who is this Learning Path for?
-    answer: >-
-      This is an introductory topic for software developers who want to improve data compression
-      performance on Arm servers by replacing the default zlib with zlib-ng, an actively maintained
-      fork that includes Neon SIMD and ARMv8 CRC32 optimizations.
-  - question: What do you need before you start?
-    answer: >-
-      Before you start, make sure you have the following: An Arm Linux computer or an [Arm based
-      instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider
-      running Ubuntu 22.04 or Ubuntu 24.04.
-  - question: Which tools, languages, or platforms does it cover?
-    answer: >-
-      It covers tools and languages including zlib, Linux environments, and Arm platforms such
-      as Neoverse.
-  - question: How is the Learning Path structured?
-    answer: >-
-      The Learning Path is organized around Build and install zlib-ng on Arm servers, Improve
-      Python application performance using zlib-ng, and Use perf to analyze zlib-ng performance.
-# END generated_summary_faq
+
 
 author: Pareena Verma
 
diff --git a/reports/generated-summary-faq/test-helper-automotive.yml b/reports/generated-summary-faq/test-helper-automotive.yml
new file mode 100644
index 0000000000..e40e4a7238
--- /dev/null
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+      current_source_hash: 92a6dac2b1674a44cd623a0c8c3189b38438124a6067ed7ca776999ff1d8b5bf
+      generated_at_before: ''
+      generated_at_after: '2026-05-15T21:58:56Z'
+      preview_before: ''
+      preview_after: Learn how to implement functional safety isolation for autonomous driving systems
+        on Arm Neoverse using DDS-based communication, containerized deployment, and ISO 26262 compliance
+        principles. It is de...
+      preview_generated: Learn how to implement functional safety isolation for autonomous driving systems
+        on Arm Neoverse using DDS-based communication, containerized deployment, and ISO 26262 compliance
+        principles. It is de...
+    faqs:
+      action: created
+      missing_before: false
+      rerun_requested: false
+      changed: true
+      drift_detected: false
+      source_hash_before: ''
+      source_hash_after: 92a6dac2b1674a44cd623a0c8c3189b38438124a6067ed7ca776999ff1d8b5bf
+      current_source_hash: 92a6dac2b1674a44cd623a0c8c3189b38438124a6067ed7ca776999ff1d8b5bf
+      generated_at_before: ''
+      generated_at_after: '2026-05-15T21:58:56Z'
+      before_count: 0
+      after_count: 5
+      generated_count: 5
+      change_details:
+        before_count: 0
+        after_count: 5
+        added_questions:
+        - What will you accomplish in this Learning Path?
+        - Who is this Learning Path for?
+        - What do you need before you start?
+        - Which tools, languages, or platforms does it cover?
+        - How is the Learning Path structured?
+        removed_questions: []
+        updated_questions: []
+      generated_diff:
+        before_count: 0
+        after_count: 5
+        added_questions:
+        - What will you accomplish in this Learning Path?
+        - Who is this Learning Path for?
+        - What do you need before you start?
+        - Which tools, languages, or platforms does it cover?
+        - How is the Learning Path structured?
+        removed_questions: []
+        updated_questions: []
+  - path: content/learning-paths/automotive/system76-auto/_index.md
+    status: added
+    changed_on_disk: true
+    managed_block_updated: true
+    rerun_flags_reset: []
+    change_reasons:
+    - initial_generation
+    template_version_before: ''
+    template_version_after: summary-faq-v3
+    summary:
+      action: created
+      missing_before: false
+      rerun_requested: false
+      changed: true
+      drift_detected: false
+      source_hash_before: ''
+      source_hash_after: 2b758d2dcf28a683ab164e28578a736d6d730b81dfbc01a6765619052fcdebd0
+      current_source_hash: 2b758d2dcf28a683ab164e28578a736d6d730b81dfbc01a6765619052fcdebd0
+      generated_at_before: ''
+      generated_at_after: '2026-05-15T21:58:56Z'
+      preview_before: ''
+      preview_after: Learn how to build and run the Arm Automotive Solutions Software Reference Stack
+        locally on the System76 Thelio Astra desktop using Multipass virtualization and Yocto build tools.
+        It is designed for a...
+      preview_generated: Learn how to build and run the Arm Automotive Solutions Software Reference Stack
+        locally on the System76 Thelio Astra desktop using Multipass virtualization and Yocto build tools.
+        It is designed for a...
+    faqs:
+      action: created
+      missing_before: false
+      rerun_requested: false
+      changed: true
+      drift_detected: false
+      source_hash_before: ''
+      source_hash_after: 2b758d2dcf28a683ab164e28578a736d6d730b81dfbc01a6765619052fcdebd0
+      current_source_hash: 2b758d2dcf28a683ab164e28578a736d6d730b81dfbc01a6765619052fcdebd0
+      generated_at_before: ''
+      generated_at_after: '2026-05-15T21:58:56Z'
+      before_count: 0
+      after_count: 5
+      generated_count: 5
+      change_details:
+        before_count: 0
+        after_count: 5
+        added_questions:
+        - What will you accomplish in this Learning Path?
+        - Who is this Learning Path for?
+        - What do you need before you start?
+        - Which tools, languages, or platforms does it cover?
+        - How is the Learning Path structured?
+        removed_questions: []
+        updated_questions: []
+      generated_diff:
+        before_count: 0
+        after_count: 5
+        added_questions:
+        - What will you accomplish in this Learning Path?
+        - Who is this Learning Path for?
+        - What do you need before you start?
+        - Which tools, languages, or platforms does it cover?
+        - How is the Learning Path structured?
+        removed_questions: []
+        updated_questions: []
+  - path: content/learning-paths/automotive/zenacssdebug/_index.md
+    status: added
+    changed_on_disk: true
+    managed_block_updated: true
+    rerun_flags_reset: []
+    change_reasons:
+    - initial_generation
+    template_version_before: ''
+    template_version_after: summary-faq-v3
+    summary:
+      action: created
+      missing_before: false
+      rerun_requested: false
+      changed: true
+      drift_detected: false
+      source_hash_before: ''
+      source_hash_after: 8dccdbdd4d9727f3466987ce918d9df0ed9d4d4f28cd613162bacab01d9c18f3
+      current_source_hash: 8dccdbdd4d9727f3466987ce918d9df0ed9d4d4f28cd613162bacab01d9c18f3
+      generated_at_before: ''
+      generated_at_after: '2026-05-15T21:58:56Z'
+      preview_before: ''
+      preview_after: Learn how to debug the Arm Zena CSS Reference Software Stack using Arm Development
+        Studio on a Fixed Virtual Platform, covering RSE, Safety Island, and Linux kernel debugging workflows.
+        It is designed...
+      preview_generated: Learn how to debug the Arm Zena CSS Reference Software Stack using Arm Development
+        Studio on a Fixed Virtual Platform, covering RSE, Safety Island, and Linux kernel debugging workflows.
+        It is designed...
+    faqs:
+      action: created
+      missing_before: false
+      rerun_requested: false
+      changed: true
+      drift_detected: false
+      source_hash_before: ''
+      source_hash_after: 8dccdbdd4d9727f3466987ce918d9df0ed9d4d4f28cd613162bacab01d9c18f3
+      current_source_hash: 8dccdbdd4d9727f3466987ce918d9df0ed9d4d4f28cd613162bacab01d9c18f3
+      generated_at_before: ''
+      generated_at_after: '2026-05-15T21:58:56Z'
+      before_count: 0
+      after_count: 5
+      generated_count: 5
+      change_details:
+        before_count: 0
+        after_count: 5
+        added_questions:
+        - What will you accomplish in this Learning Path?
+        - Who is this Learning Path for?
+        - What do you need before you start?
+        - Which tools, languages, or platforms does it cover?
+        - How is the Learning Path structured?
+        removed_questions: []
+        updated_questions: []
+      generated_diff:
+        before_count: 0
+        after_count: 5
+        added_questions:
+        - What will you accomplish in this Learning Path?
+        - Who is this Learning Path for?
+        - What do you need before you start?
+        - Which tools, languages, or platforms does it cover?
+        - How is the Learning Path structured?
+        removed_questions: []
+        updated_questions: []
diff --git a/themes/arm-design-system-hugo-theme/layouts/partials/learning-paths/generated-summary-faq.html b/themes/arm-design-system-hugo-theme/layouts/partials/learning-paths/generated-summary-faq.html
index 0af1b2d171..909e4bf8ed 100644
--- a/themes/arm-design-system-hugo-theme/layouts/partials/learning-paths/generated-summary-faq.html
+++ b/themes/arm-design-system-hugo-theme/layouts/partials/learning-paths/generated-summary-faq.html
@@ -1,39 +1,70 @@
 {{/*
 Render a generated summary paragraph and FAQ block for Learning Path introduction pages.
 
-Expected front-matter shape:
-  generated_summary_faq:
-    summary: ...
-    faqs:
-      - question: ...
-        answer: ...
+	Expected front-matter shape:
+	  generated_summary_faq:
+	    ai_assisted: true
+	    summary: ...
+	    faqs:
+	      - question: ...
+	        answer: ...
 */}}
 
 {{ $generated := .Params.generated_summary_faq }}
 {{ if $generated }}
-    {{ $summary := $generated.summary }}
-    {{ $faqs := $generated.faqs }}
+	{{ $summary := $generated.summary }}
+	{{ $faqs := $generated.faqs }}
+	{{ $ai_assisted := $generated.ai_assisted }}
 
-    {{ if or $summary $faqs }}
-        <div id="generated-summary-faq" class="u-margin-top-2">
-            {{ with $summary }}
-                <h3>Summary</h3>
-                <div class="u-margin-left-1">
-                    {{ . | markdownify }}
+	{{ if or $summary $faqs }}
+		<div id="generated-summary-faq" class="u-margin-top-2">
+			{{ if $ai_assisted }}
+				<div class="generated-summary-faq__label u-margin-bottom-1">
+					<span class="generated-summary-faq__badge">AI-assisted</span>
+					<ads-modal id="ai-assisted-summary-faq-modal" modal-title="What AI-assisted means">
+						<div slot="body">
+							<p>
+								This summary and FAQ were drafted with an approved AI-assisted workflow and are reviewed by Arm contributors before publication.
+								Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.
+							</p>
+						</div>
+						<div slot="actions">
+							<ads-cta-button class="ai-assisted-summary-faq-close" level="primary" type="button" sr-label="close">Close</ads-cta-button>
+						</div>
+						<div slot="trigger">
+							<button class="generated-summary-faq__info" type="button" aria-label="Learn what AI-assisted means">?</button>
+						</div>
+					</ads-modal>
+				</div>
+				<script>
+					(() => {
+						const modal = document.getElementById('ai-assisted-summary-faq-modal');
+						if (!modal) return;
+						modal.querySelectorAll('.ai-assisted-summary-faq-close').forEach((button) => {
+							button.addEventListener('click', () => modal.close());
+						});
+					})();
+				</script>
+			{{ end }}
+
+			{{ with $summary }}
+				<h3>Summary</h3>
+				<div class="u-margin-left-1">
+					{{ . | markdownify }}
                 </div>
             {{ end }}
 
-            {{ with $faqs }}
-                <h3 class="u-margin-top-2">Frequently asked questions</h3>
-                {{ range . }}
-                    <details class="u-margin-left-1 u-margin-bottom-1">
-                        <summary>{{ .question }}</summary>
-                        <div class="u-margin-top-1 u-margin-left-1">
-                            {{ .answer | markdownify }}
-                        </div>
-                    </details>
-                {{ end }}
-            {{ end }}
-        </div>
-    {{ end }}
+			{{ with $faqs }}
+				<h3 class="u-margin-top-2">Frequently asked questions</h3>
+				{{ range . }}
+					<ads-expansion-panel class="generated-summary-faq__panel u-margin-left-1 u-margin-bottom-1">
+						<span slot="toggle">{{ .question }}</span>
+						<div slot="content" class="u-margin-top-1">
+							{{ .answer | markdownify }}
+						</div>
+					</ads-expansion-panel>
+				{{ end }}
+			{{ end }}
+		</div>
+	{{ end }}
 {{ end }}
diff --git a/tools/generate_summary_faq.py b/tools/generate_summary_faq.py
index e82bba484d..bef7b1916b 100644
--- a/tools/generate_summary_faq.py
+++ b/tools/generate_summary_faq.py
@@ -3,8 +3,8 @@
 """
 Generate summary and FAQ content for Learning Path _index.md files.
 
-This script is intentionally template-driven so it can run in CI without
-external AI dependencies. It:
+This script uses an OpenAI-compatible endpoint to generate AI-assisted content.
+It keeps a deterministic template fallback for local smoke tests. It:
 
 - selects eligible Learning Paths using a front-matter flag or explicit paths
 - manages a `generated_summary_faq` front-matter block
@@ -22,7 +22,11 @@
     generated_summary_faq:
       template_version: summary-faq-v2
       generated_at: "2026-05-06T19:40:00Z"
-      generator: template
+      generator: ai
+      ai_assisted: true
+      ai_review_required: true
+      model: "..."
+      prompt_template: summary-faq-v3
       source_hash: "..."
       summary_generated_at: "2026-05-06T19:40:00Z"
       summary_source_hash: "..."
@@ -43,8 +47,12 @@
 import copy
 import hashlib
 import json
+import os
 import re
+import ssl
 import sys
+import urllib.error
+import urllib.request
 from dataclasses import dataclass
 from datetime import datetime, timezone
 from pathlib import Path
@@ -56,6 +64,9 @@
 REPO_ROOT = Path(__file__).resolve().parent.parent
 LEARNING_PATH_ROOT = REPO_ROOT / "content" / "learning-paths"
 DEFAULT_REPORT_PATH = REPO_ROOT / "reports" / "generated-summary-faq" / "latest-run.yml"
+DEFAULT_PROMPT_DIR = REPO_ROOT / "tools" / "prompts"
+DEFAULT_OPENAI_BASE_URL = "https://openai-api-proxy.geo.arm.com/api/providers/openai/v1/responses/"
+DEFAULT_OPENAI_MODEL = "gpt-4.1-mini"
 
 ENABLE_FLAG = "generate_summary_faq"
 RERUN_SUMMARY_FLAG = "rerun_summary"
@@ -65,7 +76,8 @@
 MANAGED_START = "# START generated_summary_faq"
 MANAGED_END = "# END generated_summary_faq"
 
-TEMPLATE_VERSION = "summary-faq-v2"
+TEMPLATE_VERSION = "summary-faq-v3"
+PROMPT_TEMPLATE_VERSION = "summary-faq-v3"
 DEFAULT_HISTORY_LIMIT = 20
 
 SUMMARY_SOURCE_HASH_KEY = "summary_source_hash"
@@ -77,6 +89,7 @@
     "created",
     "repaired_missing",
     "rerun_requested",
+    "generator_changed",
     "drift_detected_preserved",
     "unchanged",
 )
@@ -87,11 +100,12 @@
     "missing_faqs",
     "rerun_summary",
     "rerun_faqs",
+    "generator_changed",
     "summary_drift_detected",
     "faq_drift_detected",
     "rerun_flags_reset",
 )
-CHANGE_ACTIONS = {"created", "repaired_missing", "rerun_requested"}
+CHANGE_ACTIONS = {"created", "repaired_missing", "rerun_requested", "generator_changed"}
 
 
 class BlockString(str):
@@ -142,6 +156,11 @@ def parse_args() -> argparse.Namespace:
         default="",
         help="Optional comma/newline-separated list of Learning Path directories or _index.md files.",
     )
+    parser.add_argument(
+        "--category",
+        default="",
+        help="Optional top-level Learning Path category slug, for example servers-and-cloud-computing.",
+    )
     parser.add_argument(
         "--limit",
         type=int,
@@ -153,6 +172,38 @@ def parse_args() -> argparse.Namespace:
         action="store_true",
         help=f"Process explicit or discovered Learning Paths even when `{ENABLE_FLAG}` is not true.",
     )
+    parser.add_argument(
+        "--generation-mode",
+        choices=("ai", "template"),
+        default=os.getenv("SUMMARY_FAQ_GENERATION_MODE", "ai"),
+        help="Use the Arm OpenAI-compatible endpoint, or the deterministic local template fallback.",
+    )
+    parser.add_argument(
+        "--openai-base-url",
+        default=os.getenv("OPENAI_BASE_URL", DEFAULT_OPENAI_BASE_URL),
+        help="OpenAI-compatible Responses endpoint URL. Defaults to Arm's OpenAI proxy.",
+    )
+    parser.add_argument(
+        "--openai-model",
+        default=os.getenv("OPENAI_MODEL", DEFAULT_OPENAI_MODEL),
+        help="Model or deployment name exposed by the configured OpenAI-compatible endpoint.",
+    )
+    parser.add_argument(
+        "--openai-ca-bundle",
+        default=os.getenv("OPENAI_CA_BUNDLE", os.getenv("SSL_CERT_FILE", "")),
+        help="Optional CA bundle file for the OpenAI-compatible endpoint.",
+    )
+    parser.add_argument(
+        "--openai-insecure-skip-verify",
+        action="store_true",
+        default=as_bool(os.getenv("OPENAI_INSECURE_SKIP_VERIFY", "")),
+        help="Skip TLS certificate verification for local endpoint testing only.",
+    )
+    parser.add_argument(
+        "--prompt-dir",
+        default=str(DEFAULT_PROMPT_DIR),
+        help="Directory containing summary/FAQ system and user prompt templates.",
+    )
     parser.add_argument(
         "--write",
         action="store_true",
@@ -168,6 +219,11 @@ def parse_args() -> argparse.Namespace:
         default=str(DEFAULT_REPORT_PATH),
         help="Path to the central run report YAML file.",
     )
+    parser.add_argument(
+        "--log-file",
+        default="",
+        help="Optional text file that captures progress, errors, and summary output for this run.",
+    )
     parser.add_argument(
         "--no-write-report",
         action="store_true",
@@ -183,17 +239,63 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument("--git-ref", default="", help="Optional Git ref or branch name to store in the report.")
     parser.add_argument("--git-sha", default="", help="Optional commit SHA to store in the report.")
     parser.add_argument("--actor", default="", help="Optional workflow actor to store in the report.")
+    parser.add_argument(
+        "--quiet-progress",
+        action="store_true",
+        help="Hide per-Learning Path progress output.",
+    )
 
     args = parser.parse_args()
 
     if args.write and args.dry_run:
         parser.error("Use either --write or --dry-run, not both.")
+    if args.path_filter and args.category:
+        parser.error("Use either --path-filter or --category, not both.")
     if not args.write and not args.dry_run:
         args.dry_run = True
 
     return args
 
 
+def append_log_line(log_file: str, message: str) -> None:
+    if not log_file:
+        return
+
+    path = Path(log_file)
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with path.open("a", encoding="utf-8") as log:
+        log.write(message + "\n")
+
+
+def emit(args: argparse.Namespace, message: str, flush: bool = False) -> None:
+    print(message, flush=flush)
+    append_log_line(args.log_file, message)
+
+
+def initialize_log(args: argparse.Namespace) -> None:
+    if not args.log_file:
+        return
+
+    path = Path(args.log_file)
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text(
+        "\n".join(
+            [
+                "Generate summary/FAQ local run",
+                f"timestamp: {current_timestamp()}",
+                f"mode: {'write' if args.write else 'dry-run'}",
+                f"generation_mode: {args.generation_mode}",
+                f"openai_base_url: {args.openai_base_url if args.generation_mode == 'ai' else ''}",
+                f"openai_model: {args.openai_model if args.generation_mode == 'ai' else ''}",
+                f"category: {args.category or ''}",
+                f"path_filter: {args.path_filter or ''}",
+                "",
+            ]
+        ),
+        encoding="utf-8",
+    )
+
+
 def current_timestamp() -> str:
     return datetime.now(timezone.utc).replace(microsecond=0).isoformat().replace("+00:00", "Z")
 
@@ -263,6 +365,19 @@ def discover_learning_path_indexes() -> List[Path]:
     return [path for path in indexes if path.is_file()]
 
 
+def discover_category_indexes(category: str) -> List[Path]:
+    category_slug = category.strip().strip("/")
+    if not category_slug:
+        return []
+
+    category_path = LEARNING_PATH_ROOT / category_slug
+    if not category_path.is_dir():
+        raise FileNotFoundError(f"Could not resolve Learning Path category from '{category}'.")
+
+    indexes = sorted(category_path.glob("*/_index.md"))
+    return [path for path in indexes if path.is_file()]
+
+
 def as_bool(value: Any) -> bool:
     if isinstance(value, bool):
         return value
@@ -532,6 +647,255 @@ def build_faqs(metadata: Dict[str, Any], steps: Sequence[StepPage]) -> List[Dict
     return faqs
 
 
+def prompt_metadata(metadata: Dict[str, Any]) -> Dict[str, Any]:
+    keys = (
+        "title",
+        "description",
+        "who_is_this_for",
+        "learning_objectives",
+        "prerequisites",
+        "skilllevels",
+        "subjects",
+        "tools_software_languages",
+        "operatingsystems",
+        "armips",
+        "cloud_service_providers",
+        "minutes_to_complete",
+    )
+    return {key: metadata.get(key) for key in keys if metadata.get(key) not in (None, "", [])}
+
+
+def prompt_steps(steps: Sequence[StepPage]) -> List[Dict[str, Any]]:
+    prompt_pages: List[Dict[str, Any]] = []
+    for step in steps:
+        if step.path.name in {"_index.md", "_next-steps.md", "_review.md", "_demo.md"}:
+            continue
+        if as_bool(step.metadata.get("hide_from_navpane", False)):
+            continue
+
+        excerpt = compact_whitespace(strip_markdown_links(step.content))
+        prompt_pages.append(
+            {
+                "file": step.path.name,
+                "title": step.title,
+                "weight": step.weight,
+                "excerpt": excerpt[:1600],
+            }
+        )
+
+    return prompt_pages[:12]
+
+
+def build_learning_path_prompt_context(metadata: Dict[str, Any], steps: Sequence[StepPage]) -> Dict[str, Any]:
+    return {
+        "metadata": prompt_metadata(metadata),
+        "steps": prompt_steps(steps),
+        "output_requirements": {
+            "summary": "One concise paragraph, approximately 70-120 words.",
+            "faqs": "Exactly 5 FAQs. Each answer should be 1-3 sentences.",
+            "voice": "Clear, specific, technically accurate, and aligned with Arm developer education content.",
+            "review": "Output will be reviewed by human contributors before publication.",
+        },
+    }
+
+
+def read_prompt_template(prompt_dir: Path, filename: str) -> str:
+    path = prompt_dir / filename
+    if not path.exists():
+        raise FileNotFoundError(f"Prompt template not found: {path}")
+    return path.read_text(encoding="utf-8").strip()
+
+
+def render_user_prompt(template: str, context: Dict[str, Any]) -> str:
+    context_json = json.dumps(context, ensure_ascii=False, indent=2, sort_keys=True)
+    return template.replace("{{ learning_path_context }}", context_json)
+
+
+def extract_json_object(text: str) -> Dict[str, Any]:
+    cleaned = text.strip()
+    fenced_match = re.search(r"```(?:json)?\s*(.*?)\s*```", cleaned, re.DOTALL)
+    if fenced_match:
+        cleaned = fenced_match.group(1).strip()
+
+    try:
+        payload = json.loads(cleaned)
+    except json.JSONDecodeError:
+        start = cleaned.find("{")
+        end = cleaned.rfind("}")
+        if start == -1 or end == -1 or end <= start:
+            raise
+        payload = json.loads(cleaned[start : end + 1])
+
+    if not isinstance(payload, dict):
+        raise ValueError("AI response must be a JSON object.")
+    return payload
+
+
+def validate_ai_summary_faq(payload: Dict[str, Any]) -> Dict[str, Any]:
+    summary = compact_whitespace(str(payload.get("summary", "")))
+    raw_faqs = payload.get("faqs")
+
+    if not summary:
+        raise ValueError("AI response did not include a non-empty summary.")
+    if not isinstance(raw_faqs, list) or not raw_faqs:
+        raise ValueError("AI response did not include a non-empty faqs list.")
+
+    faqs: List[Dict[str, str]] = []
+    for raw_faq in raw_faqs:
+        if not isinstance(raw_faq, dict):
+            continue
+        question = compact_whitespace(str(raw_faq.get("question", "")))
+        answer = compact_whitespace(str(raw_faq.get("answer", "")))
+        if question and answer:
+            faqs.append({"question": question, "answer": answer})
+
+    if len(faqs) != 5:
+        raise ValueError(f"AI response must include exactly 5 valid FAQs; received {len(faqs)}.")
+
+    return {
+        "summary": summary,
+        "faqs": faqs,
+    }
+
+
+def extract_response_text(response_payload: Dict[str, Any]) -> str:
+    output_text = response_payload.get("output_text")
+    if isinstance(output_text, str) and output_text.strip():
+        return output_text
+
+    output = response_payload.get("output")
+    if isinstance(output, list):
+        chunks: List[str] = []
+        for item in output:
+            if not isinstance(item, dict):
+                continue
+            content = item.get("content")
+            if not isinstance(content, list):
+                continue
+            for content_item in content:
+                if not isinstance(content_item, dict):
+                    continue
+                text = content_item.get("text")
+                if isinstance(text, str):
+                    chunks.append(text)
+        if chunks:
+            return "\n".join(chunks)
+
+    choices = response_payload.get("choices")
+    if isinstance(choices, list) and choices:
+        message = choices[0].get("message") if isinstance(choices[0], dict) else None
+        if isinstance(message, dict) and isinstance(message.get("content"), str):
+            return message["content"]
+
+    raise ValueError("Could not find text output in AI response.")
+
+
+def build_ssl_context(ca_bundle: str = "", insecure_skip_verify: bool = False) -> ssl.SSLContext:
+    if insecure_skip_verify:
+        return ssl._create_unverified_context()
+    if ca_bundle:
+        return ssl.create_default_context(cafile=ca_bundle)
+    return ssl.create_default_context()
+
+
+def post_responses_request(
+    endpoint: str,
+    api_key: str,
+    payload: Dict[str, Any],
+    ca_bundle: str = "",
+    insecure_skip_verify: bool = False,
+) -> Dict[str, Any]:
+    request = urllib.request.Request(
+        endpoint,
+        data=json.dumps(payload).encode("utf-8"),
+        headers={
+            "Authorization": f"Bearer {api_key}",
+            "Content-Type": "application/json",
+        },
+        method="POST",
+    )
+
+    ssl_context = build_ssl_context(ca_bundle=ca_bundle, insecure_skip_verify=insecure_skip_verify)
+
+    try:
+        with urllib.request.urlopen(request, timeout=60, context=ssl_context) as response:
+            body = response.read().decode("utf-8")
+    except urllib.error.HTTPError as exc:
+        error_body = exc.read().decode("utf-8", errors="replace")
+        raise RuntimeError(f"AI endpoint returned HTTP {exc.code}: {error_body}") from exc
+    except urllib.error.URLError as exc:
+        reason = str(exc.reason)
+        if "CERTIFICATE_VERIFY_FAILED" in reason:
+            raise RuntimeError(
+                "Could not verify the AI endpoint TLS certificate. "
+                "Set OPENAI_CA_BUNDLE to your Arm corporate CA bundle, or use "
+                "OPENAI_INSECURE_SKIP_VERIFY=true for local testing only. "
+                f"Original error: {reason}"
+            ) from exc
+        raise RuntimeError(f"Could not reach AI endpoint: {exc.reason}") from exc
+
+    try:
+        parsed = json.loads(body)
+    except json.JSONDecodeError as exc:
+        raise ValueError(f"AI endpoint returned non-JSON response: {body[:500]}") from exc
+
+    if not isinstance(parsed, dict):
+        raise ValueError("AI endpoint response must be a JSON object.")
+
+    return parsed
+
+
+def generate_ai_summary_faq(metadata: Dict[str, Any], steps: Sequence[StepPage], args: argparse.Namespace) -> Dict[str, Any]:
+    api_key = os.getenv("OPENAI_API_KEY", "").strip()
+    if not api_key:
+        raise RuntimeError("OPENAI_API_KEY is required when --generation-mode ai is used.")
+
+    prompt_dir = Path(args.prompt_dir)
+    system_prompt = read_prompt_template(prompt_dir, "summary_faq_system.md")
+    user_template = read_prompt_template(prompt_dir, "summary_faq_user.md")
+    user_prompt = render_user_prompt(
+        user_template,
+        build_learning_path_prompt_context(metadata, steps),
+    )
+
+    prompt_input = (
+        f"{system_prompt}\n\n"
+        "Use the following Learning Path context to produce the required JSON response.\n\n"
+        f"{user_prompt}"
+    )
+
+    response_payload = post_responses_request(
+        endpoint=args.openai_base_url,
+        api_key=api_key,
+        payload={
+            "model": args.openai_model,
+            "input": prompt_input,
+        },
+        ca_bundle=args.openai_ca_bundle,
+        insecure_skip_verify=args.openai_insecure_skip_verify,
+    )
+
+    content = extract_response_text(response_payload)
+    return validate_ai_summary_faq(extract_json_object(content))
+
+
+def build_desired_summary_faq(
+    metadata: Dict[str, Any],
+    steps: Sequence[StepPage],
+    args: argparse.Namespace,
+) -> Dict[str, Any]:
+    if args.generation_mode == "template":
+        return {
+            "summary": build_summary(metadata, steps),
+            "faqs": build_faqs(metadata, steps),
+            "generator": "template",
+        }
+
+    generated = generate_ai_summary_faq(metadata, steps, args)
+    generated["generator"] = "ai"
+    return generated
+
+
 def build_source_hash(metadata: Dict[str, Any], steps: Sequence[StepPage]) -> str:
     relevant = {
         "title": metadata.get("title"),
@@ -775,6 +1139,8 @@ def build_updated_generated_block(
     generated_at: str,
     summary_action: str,
     faq_action: str,
+    generator: str,
+    model: str,
 ) -> Dict[str, Any]:
     summary_matches_current = not summaries_differ(summary_after, desired_summary)
     faqs_match_current = not faq_differences_exist(classify_faq_changes(faqs_after, desired_faqs))
@@ -804,7 +1170,11 @@ def build_updated_generated_block(
     return {
         "template_version": TEMPLATE_VERSION,
         "generated_at": generated_at,
-        "generator": "template",
+        "generator": generator,
+        "ai_assisted": generator == "ai",
+        "ai_review_required": generator == "ai",
+        "model": model if generator == "ai" else "",
+        "prompt_template": PROMPT_TEMPLATE_VERSION if generator == "ai" else "",
         "source_hash": top_level_source_hash,
         SUMMARY_GENERATED_AT_KEY: summary_meta["generated_at"],
         SUMMARY_SOURCE_HASH_KEY: summary_meta["source_hash"],
@@ -902,6 +1272,10 @@ def build_run_report(
         "git_sha": args.git_sha or "",
         "actor": args.actor or "",
         "template_version": TEMPLATE_VERSION,
+        "generation_mode": args.generation_mode,
+        "openai_base_url": args.openai_base_url if args.generation_mode == "ai" else "",
+        "openai_model": args.openai_model if args.generation_mode == "ai" else "",
+        "prompt_template": PROMPT_TEMPLATE_VERSION if args.generation_mode == "ai" else "",
         "totals": totals,
         "section_totals": section_totals,
         "reason_totals": reason_totals,
@@ -939,9 +1313,10 @@ def write_report(report_file: Path, run_report: Dict[str, Any], history_limit: i
     report_file.write_text(report_text, encoding="utf-8")
 
 
-def print_result_summary(run_report: Dict[str, Any]) -> None:
+def print_result_summary(args: argparse.Namespace, run_report: Dict[str, Any]) -> None:
     totals = run_report["totals"]
-    print(
+    emit(
+        args,
         "Processed {processed} Learning Paths: "
         "{added} added, {updated} updated, {drift_detected} drift detected, "
         "{paths_with_drift} paths with drift, "
@@ -950,18 +1325,22 @@ def print_result_summary(run_report: Dict[str, Any]) -> None:
 
     summary_actions = run_report["section_totals"]["summary"]
     faq_actions = run_report["section_totals"]["faqs"]
-    print(
+    emit(
+        args,
         "Summary actions: "
         f"{summary_actions['created']} created, "
         f"{summary_actions['repaired_missing']} repaired_missing, "
         f"{summary_actions['rerun_requested']} rerun_requested, "
+        f"{summary_actions['generator_changed']} generator_changed, "
         f"{summary_actions['drift_detected_preserved']} drift_detected_preserved."
     )
-    print(
+    emit(
+        args,
         "FAQ actions: "
         f"{faq_actions['created']} created, "
         f"{faq_actions['repaired_missing']} repaired_missing, "
         f"{faq_actions['rerun_requested']} rerun_requested, "
+        f"{faq_actions['generator_changed']} generator_changed, "
         f"{faq_actions['drift_detected_preserved']} drift_detected_preserved."
     )
 
@@ -970,12 +1349,20 @@ def print_result_summary(run_report: Dict[str, Any]) -> None:
         summary_action = result.get("summary", {}).get("action", "")
         faq_action = result.get("faqs", {}).get("action", "")
         reasons = ", ".join(result.get("change_reasons", [])) or "none"
-        print(f"- {status.upper():14s} {result['path']} | summary={summary_action} | faqs={faq_action} | reasons={reasons}")
+        line = f"- {status.upper():14s} {result['path']} | summary={summary_action} | faqs={faq_action} | reasons={reasons}"
+        if status == "error":
+            line += f" | error={result.get('error', 'Unknown error')}"
+        emit(args, line)
 
 
 def select_learning_paths(args: argparse.Namespace) -> List[Path]:
     explicit_paths = normalize_path_filter(args.path_filter) if args.path_filter else []
-    selected = explicit_paths or discover_learning_path_indexes()
+    if explicit_paths:
+        selected = explicit_paths
+    elif args.category:
+        selected = discover_category_indexes(args.category)
+    else:
+        selected = discover_learning_path_indexes()
     filtered: List[Path] = []
 
     for index_path in selected:
@@ -1006,11 +1393,15 @@ def process_learning_path(index_path: Path, args: argparse.Namespace) -> Dict[st
 
         generated_at = current_timestamp()
         current_source_hash = build_source_hash(doc.metadata, steps)
-        desired_summary = build_summary(doc.metadata, steps)
-        desired_faqs = build_faqs(doc.metadata, steps)
+        desired_content = build_desired_summary_faq(doc.metadata, steps, args)
+        desired_summary = desired_content["summary"]
+        desired_faqs = desired_content["faqs"]
+        generator = desired_content["generator"]
 
         existing_summary = extract_existing_summary(existing_generated)
         existing_faqs = extract_existing_faqs(existing_generated)
+        existing_generator = compact_whitespace(str((existing_generated or {}).get("generator", "")))
+        generator_changed = bool(existing_generated is not None and existing_generator != generator)
 
         summary_missing_before = existing_generated is not None and not compact_whitespace(existing_summary)
         faqs_missing_before = existing_generated is not None and not existing_faqs
@@ -1028,10 +1419,15 @@ def process_learning_path(index_path: Path, args: argparse.Namespace) -> Dict[st
             change_reasons.append("rerun_summary")
         if rerun_faqs_requested:
             change_reasons.append("rerun_faqs")
+        if generator_changed:
+            change_reasons.append("generator_changed")
 
         if existing_generated is None:
             summary_action = "created"
             summary_after = desired_summary
+        elif generator_changed:
+            summary_action = "generator_changed"
+            summary_after = desired_summary
         elif summary_missing_before:
             summary_action = "repaired_missing"
             summary_after = desired_summary
@@ -1049,6 +1445,9 @@ def process_learning_path(index_path: Path, args: argparse.Namespace) -> Dict[st
         if existing_generated is None:
             faq_action = "created"
             faqs_after = desired_faqs
+        elif generator_changed:
+            faq_action = "generator_changed"
+            faqs_after = desired_faqs
         elif faqs_missing_before:
             faq_action = "repaired_missing"
             faqs_after = desired_faqs
@@ -1098,6 +1497,8 @@ def process_learning_path(index_path: Path, args: argparse.Namespace) -> Dict[st
                 generated_at=generated_at,
                 summary_action=summary_action,
                 faq_action=faq_action,
+                generator=generator,
+                model=args.openai_model,
             )
             updated_front_matter = insert_or_replace_managed_block(updated_front_matter, updated_generated)
             summary_source_hash_after = compact_whitespace(str(updated_generated.get(SUMMARY_SOURCE_HASH_KEY, "")))
@@ -1176,24 +1577,34 @@ def process_learning_path(index_path: Path, args: argparse.Namespace) -> Dict[st
 
 def main() -> int:
     args = parse_args()
+    initialize_log(args)
     selected_paths = select_learning_paths(args)
 
     if not selected_paths:
-        print("No Learning Paths matched the current selection rules.")
+        emit(args, "No Learning Paths matched the current selection rules.")
         run_report = build_run_report(args, [], [])
         if not args.no_write_report:
             write_report(Path(args.report_file), run_report, args.history_limit)
-            print(f"Wrote report to {report_path_for_output(Path(args.report_file))}")
+            emit(args, f"Wrote report to {report_path_for_output(Path(args.report_file))}")
         return 0
 
-    results = [process_learning_path(path, args) for path in selected_paths]
+    results = []
+    total_paths = len(selected_paths)
+    for index, path in enumerate(selected_paths, start=1):
+        if not args.quiet_progress:
+            emit(args, f"[{index}/{total_paths}] Processing {report_path_for_output(path)}", flush=True)
+        result = process_learning_path(path, args)
+        results.append(result)
+        if not args.quiet_progress and result.get("status") == "error":
+            emit(args, f"[{index}/{total_paths}] ERROR {result.get('error', 'Unknown error')}", flush=True)
+
     run_report = build_run_report(args, selected_paths, results)
 
     if not args.no_write_report:
         write_report(Path(args.report_file), run_report, args.history_limit)
-        print(f"Wrote report to {report_path_for_output(Path(args.report_file))}")
+        emit(args, f"Wrote report to {report_path_for_output(Path(args.report_file))}")
 
-    print_result_summary(run_report)
+    print_result_summary(args, run_report)
 
     if run_report["totals"]["errors"] > 0:
         return 1
diff --git a/tools/prompts/summary_faq_system.md b/tools/prompts/summary_faq_system.md
new file mode 100644
index 0000000000..5d9d0a0876
--- /dev/null
+++ b/tools/prompts/summary_faq_system.md
@@ -0,0 +1,23 @@
+You are an expert technical editor for Arm Learning Paths.
+
+Create AI-assisted draft content for developer.arm.com Learning Path pages. The content must be accurate to the supplied Learning Path context, specific to Arm developer education, concise, and ready for human technical review.
+
+Follow these rules:
+- Use only the supplied context. Do not invent products, prerequisites, tools, claims, performance numbers, compatibility details, or outcomes.
+- Keep the tone clear, practical, and engineering-focused.
+- Do not use marketing language, hype, or vague filler.
+- Do not mention that you are an AI model.
+- Do not include citations, markdown headings, YAML, or explanatory notes.
+- Return only a JSON object with this exact shape:
+
+{
+  "summary": "One paragraph summary.",
+  "faqs": [
+    {
+      "question": "Question text?",
+      "answer": "Answer text."
+    }
+  ]
+}
+
+The summary must be one paragraph of approximately 70-120 words. The FAQ list must contain exactly five question/answer pairs. Each FAQ answer must be one to three sentences.
diff --git a/tools/prompts/summary_faq_user.md b/tools/prompts/summary_faq_user.md
new file mode 100644
index 0000000000..f21167f12a
--- /dev/null
+++ b/tools/prompts/summary_faq_user.md
@@ -0,0 +1,13 @@
+Generate an AI-assisted summary paragraph and FAQ section for this Arm Learning Path.
+
+Use the Learning Path title, description, audience, objectives, prerequisites, tags, platform metadata, and step excerpts to produce a useful overview for a developer deciding whether to follow the path.
+
+Prefer concrete phrasing:
+- Say what the learner will build, configure, measure, deploy, or understand.
+- Mention Arm technologies, tools, operating systems, and cloud platforms only when they appear in the context.
+- If prerequisites are absent, say that no explicit prerequisites are listed.
+- Keep FAQ questions useful for readers, not generic.
+
+Learning Path context:
+
+{{ learning_path_context }}
diff --git a/tools/test_summary_faq_ai_local.sh b/tools/test_summary_faq_ai_local.sh
new file mode 100755
index 0000000000..cf8592a232
--- /dev/null
+++ b/tools/test_summary_faq_ai_local.sh
@@ -0,0 +1,162 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+usage() {
+  cat <<'EOF'
+Run the Learning Path summary/FAQ generator locally against the Arm OpenAI proxy.
+
+Usage:
+  tools/test_summary_faq_ai_local.sh [options]
+
+Options:
+  --path PATH        Learning Path directory or _index.md file to test.
+                     Default: content/learning-paths/servers-and-cloud-computing/nginx_tune
+  --category SLUG    Top-level Learning Path category slug to process.
+                     Example: servers-and-cloud-computing
+  --model MODEL      OpenAI model/deployment name exposed by the proxy.
+                     Default: gpt-4.1-mini
+  --base-url URL     OpenAI-compatible Responses endpoint URL.
+                     Default: https://openai-api-proxy.geo.arm.com/api/providers/openai/v1/responses/
+  --ca-bundle FILE   Optional CA bundle file for Python TLS verification.
+  --insecure         Skip TLS certificate verification for local testing only.
+  --log-file FILE    Text file that captures progress, errors, and summary output.
+                     Default: reports/generated-summary-faq/local-run.txt
+  --report-file FILE YAML report file for this run.
+                     Default: reports/generated-summary-faq/local-test.yml
+  --write            Write generated content back to the selected _index.md file.
+                     Default: dry-run
+  --template         Use deterministic template fallback instead of the AI proxy.
+  --help             Show this help text.
+
+Required for AI mode:
+  export OPENAI_API_KEY="..."
+
+Examples:
+  OPENAI_API_KEY="..." tools/test_summary_faq_ai_local.sh
+  tools/test_summary_faq_ai_local.sh --path content/learning-paths/servers-and-cloud-computing/nginx_tune --write
+  tools/test_summary_faq_ai_local.sh --template
+EOF
+}
+
+PATH_FILTER="content/learning-paths/servers-and-cloud-computing/nginx_tune"
+CATEGORY_FILTER=""
+OPENAI_MODEL_VALUE="${OPENAI_MODEL:-gpt-4.1-mini}"
+OPENAI_BASE_URL_VALUE="${OPENAI_BASE_URL:-https://openai-api-proxy.geo.arm.com/api/providers/openai/v1/responses/}"
+OPENAI_CA_BUNDLE_VALUE="${OPENAI_CA_BUNDLE:-${SSL_CERT_FILE:-}}"
+LOG_FILE_VALUE="reports/generated-summary-faq/local-run.txt"
+REPORT_FILE_VALUE="reports/generated-summary-faq/local-test.yml"
+MODE="--dry-run"
+GENERATION_MODE="ai"
+TLS_ARGS=()
+
+while [[ $# -gt 0 ]]; do
+  case "$1" in
+    --path)
+      PATH_FILTER="${2:-}"
+      shift 2
+      ;;
+    --category)
+      CATEGORY_FILTER="${2:-}"
+      PATH_FILTER=""
+      shift 2
+      ;;
+    --model)
+      OPENAI_MODEL_VALUE="${2:-}"
+      shift 2
+      ;;
+    --base-url)
+      OPENAI_BASE_URL_VALUE="${2:-}"
+      shift 2
+      ;;
+    --ca-bundle)
+      OPENAI_CA_BUNDLE_VALUE="${2:-}"
+      shift 2
+      ;;
+    --insecure)
+      TLS_ARGS+=(--openai-insecure-skip-verify)
+      shift
+      ;;
+    --log-file)
+      LOG_FILE_VALUE="${2:-}"
+      shift 2
+      ;;
+    --report-file)
+      REPORT_FILE_VALUE="${2:-}"
+      shift 2
+      ;;
+    --write)
+      MODE="--write"
+      shift
+      ;;
+    --template)
+      GENERATION_MODE="template"
+      shift
+      ;;
+    --help|-h)
+      usage
+      exit 0
+      ;;
+    *)
+      echo "Unknown option: $1" >&2
+      usage >&2
+      exit 2
+      ;;
+  esac
+done
+
+if [[ -z "$PATH_FILTER" && -z "$CATEGORY_FILTER" ]]; then
+  echo "--path cannot be empty." >&2
+  exit 2
+fi
+
+if [[ "$GENERATION_MODE" == "ai" && -z "${OPENAI_API_KEY:-}" ]]; then
+  echo "OPENAI_API_KEY is required for AI mode." >&2
+  echo "Run: export OPENAI_API_KEY=\"...\"" >&2
+  exit 2
+fi
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+REPO_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)"
+
+cd "$REPO_ROOT"
+
+echo "Summary/FAQ local test"
+echo "  generation_mode: $GENERATION_MODE"
+if [[ -n "$CATEGORY_FILTER" ]]; then
+  echo "  category: $CATEGORY_FILTER"
+else
+  echo "  path: $PATH_FILTER"
+fi
+echo "  mode: ${MODE#--}"
+echo "  log_file: $LOG_FILE_VALUE"
+echo "  report_file: $REPORT_FILE_VALUE"
+if [[ "$GENERATION_MODE" == "ai" ]]; then
+  echo "  openai_base_url: $OPENAI_BASE_URL_VALUE"
+  echo "  openai_model: $OPENAI_MODEL_VALUE"
+  if [[ -n "$OPENAI_CA_BUNDLE_VALUE" ]]; then
+    echo "  openai_ca_bundle: $OPENAI_CA_BUNDLE_VALUE"
+  fi
+  if [[ ${#TLS_ARGS[@]} -gt 0 || "${OPENAI_INSECURE_SKIP_VERIFY:-}" == "true" ]]; then
+    echo "  openai_tls_verify: disabled"
+  fi
+fi
+echo
+
+CMD=(
+  python3 tools/generate_summary_faq.py
+  --generation-mode "$GENERATION_MODE" \
+  --openai-base-url "$OPENAI_BASE_URL_VALUE" \
+  --openai-model "$OPENAI_MODEL_VALUE" \
+  --openai-ca-bundle "$OPENAI_CA_BUNDLE_VALUE" \
+  --path-filter "$PATH_FILTER" \
+  --category "$CATEGORY_FILTER" \
+  --report-file "$REPORT_FILE_VALUE" \
+  --log-file "$LOG_FILE_VALUE" \
+  "$MODE"
+)
+
+if [[ ${#TLS_ARGS[@]} -gt 0 ]]; then
+  CMD+=("${TLS_ARGS[@]}")
+fi
+
+"${CMD[@]}"

From 1023e5a7869afe4f875d695173394fafd1bf99db Mon Sep 17 00:00:00 2001
From: Christopher Moroney <christopher.moroney1@gmail.com>
Date: Fri, 15 May 2026 16:03:26 -0700
Subject: [PATCH 17/23] update workflow

---
 .github/workflows/generate-summary-faq.yml       | 16 ++++++++++++++--
 .../cca-trustee/_index.md                        |  1 +
 .../cca-veraison-aws/_index.md                   |  1 +
 .../cplusplus_compilers_flags/_index.md          |  1 +
 .../cpu_hotspot_performix/_index.md              |  1 +
 .../servers-and-cloud-computing/dlrm/_index.md   |  1 +
 .../servers-and-cloud-computing/flink/_index.md  |  1 +
 .../from-iot-to-the-cloud-part3/_index.md        |  1 +
 8 files changed, 21 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/generate-summary-faq.yml b/.github/workflows/generate-summary-faq.yml
index aa67357f54..a284ec4150 100644
--- a/.github/workflows/generate-summary-faq.yml
+++ b/.github/workflows/generate-summary-faq.yml
@@ -7,8 +7,12 @@ on:
         description: "Optional comma or newline separated Learning Path directories or _index.md files. Leave blank to process all eligible paths."
         required: false
         type: string
+      category:
+        description: "Optional top-level Learning Path category slug. Leave blank to process all categories unless paths is set."
+        required: false
+        type: string
       limit:
-        description: "Optional limit when paths is empty. Use 0 to process all eligible Learning Paths."
+        description: "Optional limit when paths and category are empty. Use 0 to process all eligible Learning Paths."
         required: false
         default: "0"
         type: string
@@ -64,6 +68,7 @@ jobs:
       - name: Generate summary and FAQ content
         env:
           INPUT_PATHS: ${{ inputs.paths }}
+          INPUT_CATEGORY: ${{ inputs.category }}
           INPUT_LIMIT: ${{ inputs.limit }}
           INPUT_REQUIRE_FLAG: ${{ inputs.require_flag }}
           INPUT_GENERATION_MODE: ${{ inputs.generation_mode }}
@@ -72,6 +77,7 @@ jobs:
           OPENAI_BASE_URL: ${{ vars.OPENAI_BASE_URL || 'https://openai-api-proxy.geo.arm.com/api/providers/openai/v1/responses/' }}
           INPUT_DRY_RUN: ${{ inputs.dry_run }}
           REPORT_FILE: reports/generated-summary-faq/latest-run.yml
+          LOG_FILE: reports/generated-summary-faq/latest-run.txt
           RUN_URL: ${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}
           GIT_REF_NAME: ${{ github.ref_name }}
           GIT_SHA: ${{ github.sha }}
@@ -80,11 +86,13 @@ jobs:
           CMD=(
             python tools/generate_summary_faq.py
             --path-filter "$INPUT_PATHS"
+            --category "$INPUT_CATEGORY"
             --limit "$INPUT_LIMIT"
             --generation-mode "$INPUT_GENERATION_MODE"
             --openai-base-url "$OPENAI_BASE_URL"
             --openai-model "$INPUT_OPENAI_MODEL"
             --report-file "$REPORT_FILE"
+            --log-file "$LOG_FILE"
             --run-url "$RUN_URL"
             --git-ref "$GIT_REF_NAME"
             --git-sha "$GIT_SHA"
@@ -104,10 +112,13 @@ jobs:
           "${CMD[@]}"
 
       - name: Upload latest run report
+        if: always()
         uses: actions/upload-artifact@v4
         with:
           name: generated-summary-faq-report
-          path: reports/generated-summary-faq/latest-run.yml
+          path: |
+            reports/generated-summary-faq/latest-run.yml
+            reports/generated-summary-faq/latest-run.txt
           retention-days: 14
 
       - name: Configure git
@@ -177,6 +188,7 @@ jobs:
                   "created",
                   "repaired_missing",
                   "rerun_requested",
+                  "generator_changed",
                   "drift_detected_preserved",
                   "unchanged",
               ):
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
index d31ef8da9f..87b94f11b3 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
@@ -21,6 +21,7 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+
 author:
   - Anton Antonov
 
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
index e737eed57c..4e958d5ab0 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
@@ -19,6 +19,7 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+
 author: Paul Howard
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md b/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
index 51a66cb080..cb1eac4fa0 100644
--- a/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
@@ -19,6 +19,7 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+
 author: Kieran Hejmadi
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md b/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
index f8c8b88d49..c4bc0b63c3 100644
--- a/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
@@ -19,6 +19,7 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+
 author: Kieran Hejmadi
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md b/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
index cbf4548052..ddda5c86fb 100644
--- a/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
@@ -19,6 +19,7 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+
 author: 
     - Phalani Paladugu
     - Annie Tallund
diff --git a/content/learning-paths/servers-and-cloud-computing/flink/_index.md b/content/learning-paths/servers-and-cloud-computing/flink/_index.md
index ac297eed6b..8787a5b3c9 100644
--- a/content/learning-paths/servers-and-cloud-computing/flink/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/flink/_index.md
@@ -19,6 +19,7 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+
 author: Ying Yu
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
index 6ae0050e6d..749f9c0b66 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
@@ -20,6 +20,7 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+
 author: Dawid Borycki
 
 ### Tags

From 278c097624963f981d554838419a0c5da4ae17d3 Mon Sep 17 00:00:00 2001
From: Christopher Moroney <christopher.moroney1@gmail.com>
Date: Fri, 15 May 2026 16:21:29 -0700
Subject: [PATCH 18/23] test with self hosted runner

---
 .github/workflows/generate-summary-faq.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/generate-summary-faq.yml b/.github/workflows/generate-summary-faq.yml
index a284ec4150..b473d40b31 100644
--- a/.github/workflows/generate-summary-faq.yml
+++ b/.github/workflows/generate-summary-faq.yml
@@ -50,7 +50,7 @@ permissions:
 
 jobs:
   generate_summary_faq:
-    runs-on: ubuntu-latest
+    runs-on: self-hosted-ubuntu-24.04-x64
     steps:
       - name: Check out current repo
         uses: actions/checkout@v4

From 16a21f6fddf9a43591ee27dee361125114ac6509 Mon Sep 17 00:00:00 2001
From: Christopher Moroney <christopher.moroney1@gmail.com>
Date: Thu, 4 Jun 2026 09:40:36 -0700
Subject: [PATCH 19/23] sample summaries and faqs

---
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 .../intro/_index.md                           |     46 +
 .../irq-tuning-guide/_index.md                |     50 +
 .../java-gc-tuning/_index.md                  |     46 +
 .../java-on-axion/_index.md                   |     47 +
 .../java-on-azure/_index.md                   |     46 +
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 .../kafka-azure/_index.md                     |     47 +
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 .../kedify-http-autoscaling/_index.md         |     49 +
 .../keras-core/_index.md                      |     49 +
 .../kernel-build/_index.md                    |     49 +
 .../kubearchinspect/_index.md                 |     49 +
 .../lambda_functions/_index.md                |     45 +
 .../libhugetlbfs/_index.md                    |     47 +
 .../llama-cpu/_index.md                       |     47 +
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 .../llama_cpp_streamline/_index.md            |     49 +
 .../servers-and-cloud-computing/lse/_index.md |     45 +
 .../mariadb/_index.md                         |     47 +
 .../memcached/_index.md                       |     45 +
 .../memcached_cache/_index.md                 |     49 +
 .../memory-subsystem/_index.md                |     52 +
 .../memory_consistency/_index.md              |     49 +
 .../microbenchmark-network-iperf3/_index.md   |     47 +
 .../migrate-ease/_index.md                    |     47 +
 .../migration/_index.md                       |     47 +
 .../milvus-rag/_index.md                      |     45 +
 .../minio-cobalt/_index.md                    |     49 +
 .../ml-perf/_index.md                         |     45 +
 .../mongodb-on-azure/_index.md                |     48 +
 .../mongodb-on-gcp/_index.md                  |     46 +
 .../mongodb/_index.md                         |     52 +
 .../servers-and-cloud-computing/mpi/_index.md |     50 +
 .../multi-accuracy-libamath/_index.md         |     49 +
 .../multiarch_nginx_on_aks/_index.md          |     51 +
 .../multiarch_ollama_on_gke/_index.md         |     48 +
 .../mysql-azure/_index.md                     |     48 +
 .../mysql-lns-azure/_index.md                 |      2 +-
 .../mysql/_index.md                           |     47 +
 .../mysql_benchmark/_index.md                 |     49 +
 .../mysql_tune/_index.md                      |     47 +
 .../neoverse-rdv3-swstack/_index.md           |     53 +
 .../net-aspire/_index.md                      |     50 +
 .../nginx-on-azure/_index.md                  |     47 +
 .../nginx/_index.md                           |     46 +
 .../nginx_tune/_index.md                      |     51 +
 .../nlp-hugging-face/_index.md                |     44 +
 .../node-js-gcp/_index.md                     |     47 +
 .../oci-terraform/_index.md                   |     45 +
 .../onnx-on-azure/_index.md                   |     48 +
 .../onnx/_index.md                            |     47 +
 .../openbmc-rdv3/_index.md                    |     50 +
 .../openrng-with-performix/_index.md          |     49 +
 .../openshift/_index.md                       |     49 +
 .../openstack-on-azure/_index.md              |     47 +
 .../opentelemetry/_index.md                   |     47 +
 .../servers-and-cloud-computing/pac/_index.md |     47 +
 .../performix-mcp-agent/_index.md             |     52 +
 .../performix-microarchitecture/_index.md     |     48 +
 .../_index.md                                 |      2 +-
 .../php-on-gcp/_index.md                      |     47 +
 .../pinning-threads/_index.md                 |     49 +
 .../pmuv3_plugin_learning_path/_index.md      |     50 +
 .../postgresql-cobalt/_index.md               |     51 +
 .../postgresql/_index.md                      |     46 +
 .../postgresql_tune/_index.md                 |     48 +
 .../processwatch/_index.md                    |     49 +
 .../profiling-for-neoverse/_index.md          |     52 +
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 .../rabbitmq-gcp/_index.md                    |     49 +
 .../servers-and-cloud-computing/rag/_index.md |     49 +
 .../servers-and-cloud-computing/ran/_index.md |     42 +
 .../ray-on-axion/_index.md                    |     49 +
 .../redis-cobalt/_index.md                    |     48 +
 .../redis-data-searching/_index.md            |     48 +
 .../redis/_index.md                           |     45 +
 .../redis_cache/_index.md                     |     48 +
 .../redis_tune/_index.md                      |     48 +
 .../refinfra-debug/_index.md                  |     52 +
 .../refinfra-quick-start/_index.md            |     50 +
 .../reproducible-libamath/_index.md           |     48 +
 .../rme-cca-basics/_index.md                  |     44 +
 .../rtp-llm/_index.md                         |     44 +
 .../ruby-on-rails/_index.md                   |     46 +
 .../rust-on-gcp/_index.md                     |     46 +
 .../sentiment-analysis-eks/_index.md          |     47 +
 .../serverless-framework-aws-intro/_index.md  |     48 +
 .../_index.md                                 |     50 +
 .../serverless-framework-aws-s3/_index.md     |     49 +
 .../snappy/_index.md                          |     45 +
 .../snort3-multithreading/_index.md           |     48 +
 .../spark-on-azure/_index.md                  |     51 +
 .../spark-on-gcp/_index.md                    |     47 +
 .../spark-velox-cobalt/_index.md              |      2 +-
 .../spark/_index.md                           |     44 +
 .../supervisord/_index.md                     |     48 +
 .../servers-and-cloud-computing/sve/_index.md |     48 +
 .../sve2-match/_index.md                      |     48 +
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 .../torchbench/_index.md                      |     46 +
 .../triggering-pmu-events-2/_index.md         |     49 +
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 .../trivy-on-gcpp/_index.md                   |     49 +
 .../_index.md                                 |     49 +
 .../typescript-on-gcp/_index.md               |     48 +
 .../_index.md                                 |     47 +
 .../vLLM-quant/_index.md                      |      2 +-
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 .../vllm/_index.md                            |     48 +
 .../vvenc/_index.md                           |     47 +
 .../whisper/_index.md                         |     47 +
 .../wordpress/_index.md                       |     45 +
 .../zlib/_index.md                            |     46 +
 generate-summary-faq                          |     14 +
 reports/generated-summary-faq/latest-run.yml  | 232184 ++-------------
 .../servers-and-cloud-computing.yml           |  25256 ++
 .../learning-paths/generated-summary-faq.html |     89 +-
 tools/generate-summary-faq                    |    644 +
 tools/generate-summary-faq.md                 |    233 +
 tools/generate_summary_faq.py                 |    667 +-
 tools/prompts/summary_faq_system.md           |     25 +-
 tools/prompts/summary_faq_user.md             |     20 +-
 tools/test_summary_faq_ai_local.sh            |    160 +-
 431 files changed, 68344 insertions(+), 210569 deletions(-)
 create mode 100755 generate-summary-faq
 create mode 100644 reports/generated-summary-faq/servers-and-cloud-computing.yml
 create mode 100755 tools/generate-summary-faq
 create mode 100644 tools/generate-summary-faq.md

diff --git a/.github/workflows/generate-summary-faq.yml b/.github/workflows/generate-summary-faq.yml
index b473d40b31..0f6c0ef2d6 100644
--- a/.github/workflows/generate-summary-faq.yml
+++ b/.github/workflows/generate-summary-faq.yml
@@ -21,14 +21,6 @@ on:
         required: true
         default: true
         type: boolean
-      generation_mode:
-        description: "Use ai for the Arm OpenAI proxy, or template for local deterministic fallback."
-        required: true
-        default: ai
-        type: choice
-        options:
-          - ai
-          - template
       openai_model:
         description: "Model or deployment name exposed by the configured OpenAI-compatible endpoint."
         required: true
@@ -71,13 +63,13 @@ jobs:
           INPUT_CATEGORY: ${{ inputs.category }}
           INPUT_LIMIT: ${{ inputs.limit }}
           INPUT_REQUIRE_FLAG: ${{ inputs.require_flag }}
-          INPUT_GENERATION_MODE: ${{ inputs.generation_mode }}
           INPUT_OPENAI_MODEL: ${{ inputs.openai_model }}
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
           OPENAI_BASE_URL: ${{ vars.OPENAI_BASE_URL || 'https://openai-api-proxy.geo.arm.com/api/providers/openai/v1/responses/' }}
           INPUT_DRY_RUN: ${{ inputs.dry_run }}
           REPORT_FILE: reports/generated-summary-faq/latest-run.yml
           LOG_FILE: reports/generated-summary-faq/latest-run.txt
+          MARKDOWN_REPORT_FILE: reports/generated-summary-faq/latest-run.md
           RUN_URL: ${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}
           GIT_REF_NAME: ${{ github.ref_name }}
           GIT_SHA: ${{ github.sha }}
@@ -88,10 +80,10 @@ jobs:
             --path-filter "$INPUT_PATHS"
             --category "$INPUT_CATEGORY"
             --limit "$INPUT_LIMIT"
-            --generation-mode "$INPUT_GENERATION_MODE"
             --openai-base-url "$OPENAI_BASE_URL"
             --openai-model "$INPUT_OPENAI_MODEL"
             --report-file "$REPORT_FILE"
+            --markdown-report-file "$MARKDOWN_REPORT_FILE"
             --log-file "$LOG_FILE"
             --run-url "$RUN_URL"
             --git-ref "$GIT_REF_NAME"
@@ -119,6 +111,7 @@ jobs:
           path: |
             reports/generated-summary-faq/latest-run.yml
             reports/generated-summary-faq/latest-run.txt
+            reports/generated-summary-faq/latest-run.md
           retention-days: 14
 
       - name: Configure git
diff --git a/.gitignore b/.gitignore
index 312349ca93..ed57c8fe4a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -29,3 +29,5 @@ tags
 .spellcheck-non-draft.yml
 reports/generated-summary-faq/local-test.yml
 reports/generated-summary-faq/*.txt
+reports/generated-summary-faq/*.md
+reports/generated-summary-faq/*/
diff --git a/assets/css/content-pages.css b/assets/css/content-pages.css
index bd70a6f1b1..88b8789908 100644
--- a/assets/css/content-pages.css
+++ b/assets/css/content-pages.css
@@ -265,51 +265,58 @@ html[theme='dark'] .incorrect-explain {color: #e86868} /* 27% lighter than arm's
 
 /* Generated AI-assisted summary/FAQ block
 *******************************************************************/
-.generated-summary-faq__label {
+.generated-summary-faq__heading {
     align-items: center;
     display: flex;
-    gap: 8px;
+    flex-wrap: wrap;
+    column-gap: 12px;
+    margin-bottom: 16px;
+    row-gap: 8px;
 }
 
-.generated-summary-faq__badge {
-    border: 1px solid var(--arm-web-safe-blue);
-    color: var(--arm-web-safe-blue);
-    display: inline-block;
-    font-size: 0.8rem;
-    font-weight: 700;
-    line-height: 1;
-    padding: 5px 8px;
-    text-transform: uppercase;
+.generated-summary-faq__heading h3 {
+    line-height: 1.1;
+    margin: 0;
 }
 
-html[theme='dark'] .generated-summary-faq__badge {
-    border-color: var(--arm-light-blue);
-    color: var(--arm-light-blue);
+.generated-summary-faq__assist {
+    align-items: center;
+    display: inline-flex;
+    gap: 6px;
 }
 
-.generated-summary-faq__info {
-    align-items: center;
-    background: transparent;
-    border: 1px solid var(--arm-web-safe-blue);
-    color: var(--arm-web-safe-blue);
-    cursor: pointer;
+.generated-summary-faq__assist ads-modal {
     display: inline-flex;
-    font-size: 0.8rem;
-    font-weight: 700;
-    height: 24px;
-    justify-content: center;
-    padding: 0;
-    width: 24px;
 }
 
-.generated-summary-faq__info:hover {
-    border-color: var(--arm-green);
-    color: var(--arm-green);
+ads-tag.generated-summary-faq__ai-tag {
+    --ads-tag-background-color: transparent;
+    --ads-tag-background-color-hover: transparent;
+    --ads-tag-border-color: 1px solid var(--arm-web-safe-blue);
+    --ads-tag-border-color-hover: 1px solid var(--arm-green);
+    --ads-tag-color: var(--arm-web-safe-blue);
+    --ads-tag-color-hover: var(--arm-green);
 }
 
-html[theme='dark'] .generated-summary-faq__info {
-    border-color: var(--arm-light-blue);
-    color: var(--arm-light-blue);
+html[theme='dark'] ads-tag.generated-summary-faq__ai-tag {
+    --ads-tag-border-color: 1px solid var(--arm-light-blue);
+    --ads-tag-border-color-hover: 1px solid var(--arm-green);
+    --ads-tag-color: var(--arm-light-blue);
+    --ads-tag-color-hover: var(--arm-green);
+}
+
+ads-button.generated-summary-faq__info {
+    --ads-button-color: var(--arm-web-safe-blue);
+    --ads-button-color-hover: var(--arm-green);
+    --ads-button-border-color: var(--arm-web-safe-blue);
+    --ads-button-border-color-hover: var(--arm-green);
+}
+
+html[theme='dark'] ads-button.generated-summary-faq__info {
+    --ads-button-color: var(--arm-light-blue);
+    --ads-button-color-hover: var(--arm-green);
+    --ads-button-border-color: var(--arm-light-blue);
+    --ads-button-border-color-hover: var(--arm-green);
 }
 
 ads-expansion-panel.generated-summary-faq__panel {
diff --git a/content/learning-paths/automotive/intro/_index.md b/content/learning-paths/automotive/intro/_index.md
index ad69253a86..29191b375f 100644
--- a/content/learning-paths/automotive/intro/_index.md
+++ b/content/learning-paths/automotive/intro/_index.md
@@ -19,7 +19,7 @@ cascade:
 generate_summary_faq: true
 
 rerun_summary: false
-rerun_faqs: false
+rerun_faqs: true
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/automotive/openadkit1_container/_index.md b/content/learning-paths/automotive/openadkit1_container/_index.md
index 1d08a09b58..f03f978f61 100644
--- a/content/learning-paths/automotive/openadkit1_container/_index.md
+++ b/content/learning-paths/automotive/openadkit1_container/_index.md
@@ -20,6 +20,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:26:58Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
+  summary_generated_at: '2026-06-01T20:57:21Z'
+  summary_source_hash: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
+  faq_generated_at: '2026-06-02T21:26:58Z'
+  faq_source_hash: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
+  summary: >-
+    This Learning Path shows how to deploy and run a containerized autonomous driving simulation
+    using Autoware Open AD Kit on Arm Neoverse with Docker, illustrating SOAFEE-aligned Shift-Left
+    development. You will use a Linux Arm Neoverse instance—cloud or on‑prem—and Docker Compose
+    to launch the Open AD Kit demo, which starts a Visualizer and then runs Planning and Simulation
+    services defined in docker/docker-compose.yml. It introduces the SOAFEE architecture plus
+    the roles of ROS 2 and Open AD Kit. Prerequisites are an Arm Neoverse system with at least
+    16 CPUs and 32GB RAM, and familiarity with Docker and Docker Compose. Estimated time is 60
+    minutes; the example was tested on AWS EC2 and an Ampere Altra workstation.
+  faqs:
+  - question: What do I need before running the demo?
+    answer: >-
+      You need an Arm Neoverse cloud instance or a local Arm Neoverse Linux computer with at least
+      16 CPUs and 32GB of RAM. Familiarity with Docker and Docker Compose is also required.
+  - question: Should I use a cloud instance or an on-prem Arm Neoverse system?
+    answer: >-
+      You can use either. The example has been tested on AWS EC2 and an Ampere Altra workstation,
+      so choose the environment you have access to or that best fits your needs.
+  - question: Do I need to install Docker and Docker Compose?
+    answer: >-
+      Yes. Docker is required to run Open AD Kit, and the demo uses Docker Compose; refer to the
+      Docker install guide to set it up on Linux.
+  - question: What should I expect when I start the demo with Docker Compose?
+    answer: >-
+      The Visualizer service starts first in detached mode, followed by continuous execution of
+      the Planning and Simulation components. The ROS 2 commands and service definitions are specified
+      in docker/docker-compose.yml.
+  - question: Where can I inspect or adjust what gets executed?
+    answer: >-
+      Open the docker/docker-compose.yml file to review the service configuration, startup order,
+      and ROS command lines. You can use it as the basis for exploring advanced configurations
+      mentioned in the path.
+# END generated_summary_faq
 
 author: Odin Shen
 
diff --git a/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md b/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
index 84f0614e79..1ce2222140 100644
--- a/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
+++ b/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
@@ -21,6 +21,58 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:27:35Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 92a6dac2b1674a44cd623a0c8c3189b38438124a6067ed7ca776999ff1d8b5bf
+  summary_generated_at: '2026-06-01T20:57:59Z'
+  summary_source_hash: 92a6dac2b1674a44cd623a0c8c3189b38438124a6067ed7ca776999ff1d8b5bf
+  faq_generated_at: '2026-06-02T21:27:35Z'
+  faq_source_hash: 92a6dac2b1674a44cd623a0c8c3189b38438124a6067ed7ca776999ff1d8b5bf
+  summary: >-
+    This advanced Learning Path shows automotive engineers how to prototype safety-critical isolation
+    for autonomous driving workloads on Arm Neoverse running Linux. You apply ISO 26262 concepts
+    (including ASIL and the V-model), use a safety island architectural approach, and separate
+    a simulation platform into independent, safety-isolated components. Communication between
+    components uses DDS in a publish-subscribe pattern, with containerized deployment and tooling
+    that includes Docker, ROS 2, and Python. Prerequisites include two Arm-based Neoverse cloud
+    instances or a local Arm Neoverse Linux system with at least 16 CPUs and 32 GB RAM, completion
+    of the “Deploy Open AD Kit containerized autonomous driving simulation on Arm Neoverse” Learning
+    Path, and basic Docker familiarity. Estimated time to complete is about 60 minutes.
+  faqs:
+  - question: What do I need before running this path?
+    answer: >-
+      You need either two Arm-based Neoverse cloud instances or a local Arm Neoverse Linux system
+      with at least 16 CPUs and 32 GB of RAM. You must also have completed the “Deploy Open AD
+      Kit containerized autonomous driving simulation on Arm Neoverse” Learning Path and be familiar
+      with Docker.
+  - question: Can I use a single local system instead of two cloud instances?
+    answer: >-
+      Yes. A local Arm Neoverse Linux system with at least 16 CPUs and 32 GB of RAM is listed
+      as an alternative to two Arm-based Neoverse cloud instances.
+  - question: Which technologies are used for communication and isolation?
+    answer: >-
+      The path uses DDS with a publish–subscribe architecture and containerized deployment to
+      separate components and communicate between them. Tools referenced include Docker, ROS 2,
+      DDS, and Python on Linux.
+  - question: How are ISO 26262 and ASIL levels applied here?
+    answer: >-
+      The path introduces the ISO 26262 safety lifecycle aligned with the V-model and explains
+      how ASIL levels guide design and testing. You apply prevention and detection principles
+      and plan safe-state behavior as part of the workflow.
+  - question: What result should I expect and how do I know I’m on track?
+    answer: >-
+      Expect to separate the simulation platform into independent, safety-isolated components
+      that communicate via DDS. You should be able to describe a safety island architecture versus
+      a non-safety ECU and relate requirements to verification activities consistent with ISO
+      26262.
+# END generated_summary_faq
 
 author: 
     - Odin Shen
diff --git a/content/learning-paths/automotive/system76-auto/_index.md b/content/learning-paths/automotive/system76-auto/_index.md
index 825f615b05..8ef7f7ec56 100644
--- a/content/learning-paths/automotive/system76-auto/_index.md
+++ b/content/learning-paths/automotive/system76-auto/_index.md
@@ -19,6 +19,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:28:14Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 2b758d2dcf28a683ab164e28578a736d6d730b81dfbc01a6765619052fcdebd0
+  summary_generated_at: '2026-06-01T20:58:28Z'
+  summary_source_hash: 2b758d2dcf28a683ab164e28578a736d6d730b81dfbc01a6765619052fcdebd0
+  faq_generated_at: '2026-06-02T21:28:14Z'
+  faq_source_hash: 2b758d2dcf28a683ab164e28578a736d6d730b81dfbc01a6765619052fcdebd0
+  summary: >-
+    This Learning Path shows how to set up a local automotive software development environment
+    on the Arm-based System76 Thelio Astra and build the Arm Automotive Solutions Software Reference
+    Stack. You will install Multipass on Ubuntu 24.04, create an Ubuntu 20.04 virtual machine,
+    and use Yocto, Docker, and Git to build the stack from the VM. The path introduces the Arm
+    Reference Design-1 AE (RD-1 AE) target, modeled by a Fixed Virtual Platform, and includes
+    running example applications such as a Parsec-enabled TLS demo. By the end, you will have
+    built and run the stack locally in a VM on Thelio Astra; no additional prerequisites beyond
+    the host hardware are listed.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a System76 Thelio Astra desktop computer running Ubuntu 24.04. Before starting,
+      install Multipass using the Multipass install guide for Arm Linux. The path uses Multipass,
+      Yocto, Docker, and Git; no other prerequisites are explicitly listed.
+  - question: Which Ubuntu version should I use inside the Multipass VM?
+    answer: >-
+      The build steps use an Ubuntu 20.04 Multipass virtual machine. Multipass creates a cloud-style
+      VM on your desktop to isolate build and test tasks and split system resources.
+  - question: How do I begin the build of the Arm Automotive Solutions Software Reference Stack?
+    answer: >-
+      From the Ubuntu 20.04 Multipass VM, create a working directory and clone the repository
+      as shown in the steps. A successful clone without errors indicates the environment is ready
+      for the Yocto-based build process.
+  - question: Can I run the demos without RD-1 AE hardware?
+    answer: >-
+      Yes. The example applications demonstrate the software stack running on a Fixed Virtual
+      Platform that models the reference hardware system.
+  - question: What result should I expect from the Parsec demo?
+    answer: >-
+      The Parsec-enabled TLS demo illustrates an HTTPS session where a simple web page is transferred
+      over a TLS connection. This demonstrates use of Parsec’s common API to access security and
+      cryptographic services in the stack.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/automotive/zenacssdebug/_index.md b/content/learning-paths/automotive/zenacssdebug/_index.md
index 2660064c61..f5f9e05553 100644
--- a/content/learning-paths/automotive/zenacssdebug/_index.md
+++ b/content/learning-paths/automotive/zenacssdebug/_index.md
@@ -23,6 +23,59 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:28:53Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 8dccdbdd4d9727f3466987ce918d9df0ed9d4d4f28cd613162bacab01d9c18f3
+  summary_generated_at: '2026-06-01T20:59:08Z'
+  summary_source_hash: 8dccdbdd4d9727f3466987ce918d9df0ed9d4d4f28cd613162bacab01d9c18f3
+  faq_generated_at: '2026-06-02T21:28:53Z'
+  faq_source_hash: 8dccdbdd4d9727f3466987ce918d9df0ed9d4d4f28cd613162bacab01d9c18f3
+  summary: >-
+    This introductory Learning Path shows how to debug the Arm Zena Compute Subsystem (CSS) Reference
+    Software Stack on a Fixed Virtual Platform using Arm Development Studio. You will launch the
+    Zena CSS FVP with the Iris debug server, create and save a custom debug configuration, and
+    set up connections for its heterogeneous subsystems: the Runtime Security Engine (Cortex-M55),
+    the Safety Island (Cortex-R82AE), and the primary compute cores (Cortex-A720AE) running Linux.
+    You will step the RSE from reset with TF-M symbols, attach to SI firmware, and attach to the
+    Linux kernel to debug user space processes. Prerequisites are Ubuntu 22.04, Arm Development
+    Studio 2024.1 or later with a valid license, and basic familiarity with Zena CSS, Armv8-A/Armv9-A,
+    and Linux.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      Use an Ubuntu 22.04 host and Arm Development Studio 2024.1 or later with a valid license.
+      A basic understanding of the Arm Zena CSS software stack, Armv8‑A/Armv9‑A cores, and Linux
+      is assumed.
+  - question: Why can’t Arm Development Studio connect if I launch the FVP from the build environment
+      command?
+    answer: >-
+      Launching with the provided build command does not enable the Iris debug server, so the
+      model cannot be debugged from Arm Development Studio. Re‑launch the model with additional
+      command‑line options that enable Iris; see FVP_RD_Aspen --help and follow the options shown
+      in the Learning Path.
+  - question: Which connection method should I choose in Arm Development Studio for this target?
+    answer: >-
+      Use the Iris interface to create a debug configuration for the Zena CSS FVP. As of Arm Development
+      Studio 2025.0 there is no out‑of‑the‑box configuration, so you will create your own and
+      save the connections as .launch files.
+  - question: How do I hold the RSE at reset and step through early boot?
+    answer: >-
+      Start a new tmux session if needed, then launch the FVP with the Iris server enabled and
+      without running so it stays at reset. Connect from Arm Development Studio, load Trusted
+      Firmware‑M symbols, and step from reset through the early boot sequence.
+  - question: Can I connect to the Safety Island and the Linux kernel simultaneously?
+    answer: >-
+      Yes. Arm Development Studio supports heterogeneous systems like Zena CSS, so you can create
+      separate connections and attach to all processors at the same time, including the Safety
+      Island firmware and the Linux kernel on the primary compute cores.
+# END generated_summary_faq
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/cross-platform/_example-learning-path/_index.md b/content/learning-paths/cross-platform/_example-learning-path/_index.md
index b11e1f3b27..a0825780df 100644
--- a/content/learning-paths/cross-platform/_example-learning-path/_index.md
+++ b/content/learning-paths/cross-platform/_example-learning-path/_index.md
@@ -21,6 +21,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:29:45Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: a58d5eb4c4256f3e4f4374344ef0e73836e8b51ac7223b0a5238b22137ec0819
+  summary_generated_at: '2026-06-01T20:59:38Z'
+  summary_source_hash: a58d5eb4c4256f3e4f4374344ef0e73836e8b51ac7223b0a5238b22137ec0819
+  faq_generated_at: '2026-06-02T21:29:45Z'
+  faq_source_hash: a58d5eb4c4256f3e4f4374344ef0e73836e8b51ac7223b0a5238b22137ec0819
+  summary: >-
+    This introductory path shows content creators and software developers how to create and contribute
+    a new Arm Learning Path in about 60 minutes. You will set up a text editor, Hugo, and Git;
+    fork the GitHub repository; write your tutorial in markdown; choose one of six site categories
+    based on where the software runs; and add required metadata in the _index.md file so pages
+    render consistently. You will use Hugo to review content locally, commit and push changes
+    to your fork, and submit a pull request for review. All Learning Paths are community-created
+    and published under the Creative Commons Attribution-ShareAlike 4.0 International License.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a GitHub account. Three tools are mandatory for authoring: a text editor, the Hugo
+      static site generator, and Git.
+  - question: How do I know whether my topic belongs in a Learning Path?
+    answer: >-
+      A Learning Path is a concise tutorial with detailed steps to complete a specific task. It
+      is not product documentation, marketing material, product or developer news, or a place
+      to embed or link to videos.
+  - question: Which category should I use when adding my Learning Path?
+    answer: >-
+      Choose the category closest to the environment where the software runs: servers-and-cloud-computing,
+      laptops-and-desktops, embedded-and-microcontrollers, iot, mobile-graphics-and-gaming, or
+      automotive. If you are unsure, ask on GitHub.
+  - question: Where do I set the Learning Path metadata, and are there naming rules?
+    answer: >-
+      Add metadata in the _index.md file; it is used by the site to keep Learning Paths consistent.
+      The title should start with a verb, avoid adjectives, and be as concise as possible.
+  - question: How do I contribute my Learning Path for review?
+    answer: >-
+      Commit your changes with Git and push them to your fork on GitHub, then open a pull request.
+      Only you can see changes made to your fork until you submit the pull request.
+# END generated_summary_faq
 
 author: Zach Lasiuk
 
diff --git a/content/learning-paths/cross-platform/adler32/_index.md b/content/learning-paths/cross-platform/adler32/_index.md
index 7541c39763..0a3ecad5e7 100644
--- a/content/learning-paths/cross-platform/adler32/_index.md
+++ b/content/learning-paths/cross-platform/adler32/_index.md
@@ -19,6 +19,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:30:35Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 37b19106fba70423ba8c58f82097d9251f0ae208e882c852f9c4531afcebb46c
+  summary_generated_at: '2026-06-01T21:00:08Z'
+  summary_source_hash: 37b19106fba70423ba8c58f82097d9251f0ae208e882c852f9c4531afcebb46c
+  faq_generated_at: '2026-06-02T21:30:35Z'
+  faq_source_hash: 37b19106fba70423ba8c58f82097d9251f0ae208e882c852f9c4531afcebb46c
+  summary: >-
+    This introductory Learning Path shows C/C++ developers on Arm Linux how to use GitHub Copilot
+    in Visual Studio Code to implement and accelerate the Adler32 checksum with Arm Neon intrinsics.
+    You will start by prompting Copilot to generate a baseline C implementation, create a test
+    program that validates correctness and measures runtime on random inputs from 1 KB to 10 MB,
+    and have Copilot produce a gcc Makefile optimized for Neoverse N1. You then build and run
+    the project to validate results and use Copilot to add Neon intrinsics, aiming for significant
+    speedups over the C baseline. Prerequisites: an Arm computer running Linux with gcc, and VS
+    Code with the GitHub Copilot extension.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an Arm computer running Linux with gcc installed, and Visual Studio Code with the
+      GitHub Copilot extension. No other prerequisites are explicitly listed.
+  - question: Which GitHub Copilot mode or model should I use?
+    answer: >-
+      Open GitHub Copilot, choose the Large Language Model you prefer, and select Agent mode.
+      Results vary by model; the example output shown in the path was produced with Claude 3.7
+      Sonnet.
+  - question: How is the project built, and which CPU is it tuned for?
+    answer: >-
+      Copilot generates a Makefile that builds the project with gcc and selects optimization flags
+      for the Neoverse N1. Use the provided Makefile targets to compile and run the tests.
+  - question: What should I verify when I run the test program?
+    answer: >-
+      Check that the checksum results are correct for all listed data sizes. The test program
+      also measures performance to provide a baseline before introducing Neon intrinsics.
+  - question: When do I implement Neon intrinsics for Adler32?
+    answer: >-
+      After establishing the baseline C implementation and test harness, the path guides you to
+      use GitHub Copilot to write Neon intrinsics for Adler32. This step focuses on leveraging
+      Arm Advanced SIMD to accelerate the algorithm.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md b/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md
index f843ed4313..3b771faedf 100644
--- a/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md
+++ b/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md
@@ -22,6 +22,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:31:11Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 597877722e5f277e5558e29ecd33878ac2262b70a84a9769325b3c3b0ce06e58
+  summary_generated_at: '2026-06-01T21:01:02Z'
+  summary_source_hash: 597877722e5f277e5558e29ecd33878ac2262b70a84a9769325b3c3b0ce06e58
+  faq_generated_at: '2026-06-02T21:31:11Z'
+  faq_source_hash: 597877722e5f277e5558e29ecd33878ac2262b70a84a9769325b3c3b0ce06e58
+  summary: >-
+    This introductory path shows how to automate integration testing of Model Context Protocol
+    (MCP) servers using PyTest and Testcontainers, with local runs and CI on GitHub Actions. You
+    will set up a Python environment with Docker, run a basic Testcontainers example, and build
+    a minimal integration test suite that exercises MCP server behavior over JSON-RPC 2.0 via
+    standard input/output. You will also create a .github/workflows/integration-tests.yml workflow
+    to run tests on pushes and pull requests, with support for Arm64 runners. The path targets
+    Linux, macOS, and Windows, and expects Docker, Python 3.11 or later, Git, and familiarity
+    with Python, PyTest, containers, and the MCP specification.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      Install Docker and Python 3.11 or later with virtual environment support, and have Git available.
+      On Linux, ensure the python3-venv package is installed. You should also be familiar with
+      Python, PyTest, container concepts, and the MCP specification.
+  - question: How do I check if Docker is ready before I start?
+    answer: >-
+      Run the command docker info. If it fails or shows that the daemon is not running, start
+      the Docker daemon and try again.
+  - question: How do MCP servers communicate in these tests?
+    answer: >-
+      MCP uses JSON-RPC 2.0 over standard input and output. Your integration tests interact with
+      the server through this protocol to validate functionality.
+  - question: What result should I expect from the basic Testcontainers example?
+    answer: >-
+      The example starts an alpine:latest container running a long-lived sleep process and then
+      executes a command inside it. You should see the container start successfully and the command
+      complete while the container is alive.
+  - question: Which triggers and runners does the GitHub Actions workflow use, and where is it
+      defined?
+    answer: >-
+      The workflow is defined at .github/workflows/integration-tests.yml and runs on push and
+      pull_request events. It uses GitHub’s native Arm64 runners with Docker pre-installed, and
+      supports parallel execution.
+# END generated_summary_faq
 
 author: Neethu Elizabeth Simon
 
diff --git a/content/learning-paths/cross-platform/avh_cicd/_index.md b/content/learning-paths/cross-platform/avh_cicd/_index.md
index 66d7b7ce4d..5c9e6282d7 100644
--- a/content/learning-paths/cross-platform/avh_cicd/_index.md
+++ b/content/learning-paths/cross-platform/avh_cicd/_index.md
@@ -19,6 +19,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:32:03Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: b6a7439a701f6ae09ce8c61f02150a61fa6ecc1151050e903492e16794999676
+  summary_generated_at: '2026-06-01T21:01:24Z'
+  summary_source_hash: b6a7439a701f6ae09ce8c61f02150a61fa6ecc1151050e903492e16794999676
+  faq_generated_at: '2026-06-02T21:32:03Z'
+  faq_source_hash: b6a7439a701f6ae09ce8c61f02150a61fa6ecc1151050e903492e16794999676
+  summary: >-
+    This introductory path shows embedded developers how to integrate Arm Virtual Hardware (AVH)
+    into a GitHub Actions CI/CD workflow for automated testing and validation of bare‑metal Cortex‑M
+    software. You will prepare a GitHub repository, generate and scope a Personal Access Token
+    to update GitHub Actions workflows, and set up an AVH instance following the Arm Virtual Hardware
+    install guide. The steps cover enabling Actions in your fork and creating a Linux x64 self‑hosted
+    runner that matches your AWS instance. An AWS account and a GitHub account are required, and
+    some familiarity with CI/CD concepts is assumed. By the end, you will have AVH wired into
+    a GitHub Actions flow using a self‑hosted runner.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a GitHub account, an AWS account, and an Arm Virtual Hardware instance set up using
+      the Arm Virtual Hardware install guide. Some familiarity with CI/CD concepts is assumed.
+  - question: How do I create the required GitHub Personal Access Token?
+    answer: >-
+      In GitHub, go to Settings > Developer Settings > Personal access tokens, select Generate
+      new token, and enable the permission to Update GitHub Action workflows. Generate and save
+      the token locally for use during the setup.
+  - question: How do I enable GitHub Actions in my forked repository?
+    answer: >-
+      Open your fork, navigate to Actions, and if workflows are disabled, click the prompt I understand
+      my workflows, go ahead and enable them. This allows the repository’s workflows to run.
+  - question: Which options should I choose when creating the self-hosted runner?
+    answer: >-
+      In the repository, go to Settings > Actions > Runners and create a New self-hosted runner
+      with Runner image set to Linux and Architecture set to x64. These settings should match
+      your AWS instance.
+  - question: Where do I run the commands shown when adding the self-hosted runner?
+    answer: >-
+      Run the displayed registration commands on your AWS instance where Arm Virtual Hardware
+      is set up. These commands connect that instance as the self-hosted runner for your repository.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/cross-platform/avh_cicd2/_index.md b/content/learning-paths/cross-platform/avh_cicd2/_index.md
index 4855850ffa..dd4e09635a 100644
--- a/content/learning-paths/cross-platform/avh_cicd2/_index.md
+++ b/content/learning-paths/cross-platform/avh_cicd2/_index.md
@@ -20,6 +20,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:32:39Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 251b6edf6a016f9fc73eb35216f56b2001465fbca8253bd7b6e6f9f4afcd25e6
+  summary_generated_at: '2026-06-01T21:01:44Z'
+  summary_source_hash: 251b6edf6a016f9fc73eb35216f56b2001465fbca8253bd7b6e6f9f4afcd25e6
+  faq_generated_at: '2026-06-02T21:32:39Z'
+  faq_source_hash: 251b6edf6a016f9fc73eb35216f56b2001465fbca8253bd7b6e6f9f4afcd25e6
+  summary: >-
+    This advanced Learning Path shows how to integrate Arm Virtual Hardware with AWS and GitHub
+    Actions to automate test and validation for bare-metal Cortex-M projects. You will fork the
+    ARM-software/AVH-GetStarted repository, use its included CloudFormation template to prepare
+    your AWS account, and configure repository secrets so GitHub Actions can run an automated
+    build-and-validation example on Arm Virtual Hardware. The steps focus on setting up AWS integration
+    (including region and subnet) and connecting the example CI workflow in your fork. It builds
+    on “Integrate Arm Virtual Hardware into CI/CD workflow 1” and requires valid AWS and GitHub
+    accounts. Estimated time to complete is approximately 30 minutes.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      It builds on “Integrate Arm Virtual Hardware into CI/CD workflow 1” and requires valid AWS
+      and GitHub accounts. No other prerequisites are explicitly listed.
+  - question: Which example repository should I use and where do I find it?
+    answer: >-
+      Fork the Arm example at https://github.com/ARM-software/AVH-GetStarted/fork. It includes
+      a CloudFormation template and documentation for the CI workflow.
+  - question: When is my AWS account ready to connect to GitHub Actions?
+    answer: >-
+      After completing the CloudFormation stack step in “Prepare AWS account for GitHub integration.”
+      Once that is done, proceed to define the GitHub repository secrets.
+  - question: Which GitHub Actions secrets must I create and how do I find their values?
+    answer: >-
+      Create the secrets exactly as named in the Learning Path. Set AWS_DEFAULT_REGION to the
+      same region where the CloudFormation stack was created, and set AWS_SUBNET_ID by selecting
+      any valid Subnet ID from AWS Console > VPC > Subnets.
+  - question: What result should I expect after configuring the workflow?
+    answer: >-
+      The GitHub Actions pipeline will automate build, test, and validation of the example on
+      Arm Virtual Hardware. You should see the example run under CI using your AWS configuration.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/cross-platform/cca_rme/_index.md b/content/learning-paths/cross-platform/cca_rme/_index.md
index b8a7a41474..0aa7ecc84d 100644
--- a/content/learning-paths/cross-platform/cca_rme/_index.md
+++ b/content/learning-paths/cross-platform/cca_rme/_index.md
@@ -20,6 +20,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:33:15Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: a1685fb43efecb9690d03c7f8ee64ab20ae8aece91190e2223705106568b1038
+  summary_generated_at: '2026-06-01T21:02:16Z'
+  summary_source_hash: a1685fb43efecb9690d03c7f8ee64ab20ae8aece91190e2223705106568b1038
+  faq_generated_at: '2026-06-02T21:33:15Z'
+  faq_source_hash: a1685fb43efecb9690d03c7f8ee64ab20ae8aece91190e2223705106568b1038
+  summary: >-
+    This introductory Learning Path shows how to explore Arm Confidential Compute Architecture
+    (CCA) and the Realm Management Extension (RME) using Arm Development Studio. You will import
+    a simple bare-metal example provided with Development Studio (2023.0 or later), run it on
+    the Arm Architecture Envelope Model (AEM) Fixed Virtual Platform included with the tools,
+    and use Arm Debugger features to examine behavior relevant to CCA. The material explains the
+    CCA security states—Normal, Secure, Realm, and Root—and the role of a secure monitor in managing
+    transitions. Prerequisites are a basic understanding of Arm architecture and access to Arm
+    Development Studio. Estimated time to complete is about 30 minutes.
+  faqs:
+  - question: What do I need before running the example?
+    answer: >-
+      Install Arm Development Studio 2023.0 or later and have some understanding of the Arm architecture.
+      The AEM Fixed Virtual Platform and the full bare-metal example are supplied with Development
+      Studio.
+  - question: How do I import the bare-metal RME example into Arm Development Studio?
+    answer: >-
+      Open the IDE and choose File > Import. Select Arm Development Studio > Examples & Programming
+      Libraries, then locate and import the RME bare-metal example provided with the installation.
+  - question: Which target should I run the example on?
+    answer: >-
+      Run the example on the Arm Architecture Envelope Model (AEM) Fixed Virtual Platform, which
+      is supplied with Arm Development Studio.
+  - question: How does this example demonstrate CCA concepts?
+    answer: >-
+      It illustrates RME, the architectural feature needed to implement CCA, highlighting the
+      Realm world in addition to Normal, Secure, and Root worlds. A secure monitor in Root world
+      manages transitions between these states, which you can examine with the Arm Debugger.
+  - question: Do I need Linux or Android to follow this path?
+    answer: >-
+      No. The example is bare-metal and runs on the AEM FVP provided with Arm Development Studio,
+      so no operating system setup is required.
+# END generated_summary_faq
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md b/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md
index 0c71b9ea48..fe1bec2080 100644
--- a/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md
+++ b/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md
@@ -21,6 +21,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:33:42Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: a639e60da034fd04e891c9236e9fe5dab47cca87f6174828275ef5a76c43c32e
+  summary_generated_at: '2026-06-01T21:02:47Z'
+  summary_source_hash: a639e60da034fd04e891c9236e9fe5dab47cca87f6174828275ef5a76c43c32e
+  faq_generated_at: '2026-06-02T21:33:42Z'
+  faq_source_hash: a639e60da034fd04e891c9236e9fe5dab47cca87f6174828275ef5a76c43c32e
+  summary: >-
+    Learn how to improve the runtime of C++ loops on Arm by conveying loop-size boundaries to
+    the compiler. You will start from a baseline program where the loop size is only known at
+    runtime, then modify the code to enforce a multiple-of-4 loop size using integer-division
+    truncation. This developer knowledge enables the compiler to generate better code, potentially
+    including SIMD vectorization, and lets you compare the performance impact on Arm systems.
+    The path runs on Linux and targets Arm CPUs such as Neoverse and Cortex-A. Prerequisite: an
+    Arm computer running Linux, or a Linux VM from a cloud service provider.
+  faqs:
+  - question: What do I need before running the code examples?
+    answer: >-
+      You need an Arm computer running Linux, or you can use a Linux virtual machine from a cloud
+      service provider. No other explicit prerequisites are listed.
+  - question: How is the loop size provided in the baseline program, and why does that matter?
+    answer: >-
+      The baseline program reads max_loop_size from user input at runtime. Because the compiler
+      does not know this bound at compile time, it must generate conservative code.
+  - question: Why does rewriting the loop bound as ((max_loop_size/4)*4) help the compiler?
+    answer: >-
+      Integer division truncates, so (max_loop_size/4)*4 is always divisible by 4. Communicating
+      this constraint can enable SIMD vectorization and better code generation for that specific
+      case.
+  - question: What result should I expect after applying the boundary information?
+    answer: >-
+      The loop will iterate up to the largest multiple of 4 that does not exceed the original
+      input size. You can then compare and analyze runtime behavior and performance impact against
+      the baseline.
+  - question: Do I need any specific tools or compiler options to follow this path?
+    answer: >-
+      The steps focus on C++ source changes using the provided examples. Specific compiler options
+      or additional tools are not explicitly listed.
+# END generated_summary_faq
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/cross-platform/docker-build-cloud/_index.md b/content/learning-paths/cross-platform/docker-build-cloud/_index.md
index b78516eaf9..e15667c5dc 100644
--- a/content/learning-paths/cross-platform/docker-build-cloud/_index.md
+++ b/content/learning-paths/cross-platform/docker-build-cloud/_index.md
@@ -21,6 +21,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:34:59Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 9a94219b689b8e22a175c6067c6bb6d139d6ad22f3aac77c78b6b38970d5a5eb
+  summary_generated_at: '2026-06-01T21:03:46Z'
+  summary_source_hash: 9a94219b689b8e22a175c6067c6bb6d139d6ad22f3aac77c78b6b38970d5a5eb
+  faq_generated_at: '2026-06-02T21:34:59Z'
+  faq_source_hash: 9a94219b689b8e22a175c6067c6bb6d139d6ad22f3aac77c78b6b38970d5a5eb
+  summary: >-
+    This Learning Path shows how to build multi-architecture Docker images for Arm and x86 using
+    Docker Build Cloud, and automate the process with GitHub Actions. You will set up Docker Build
+    Cloud as your builder, create Arm-only and multi-architecture images, and configure a GitHub
+    repository with the required secrets so builds run in the cloud without instruction emulation.
+    It is an introductory, hands-on path aimed at developers who need practical steps to produce
+    images for multiple CPU architectures. Prerequisites are a computer with Docker installed
+    (Windows, macOS, or Linux), a GitHub account, and a Docker Hub account. Estimated time to
+    complete is about 30 minutes.
+  faqs:
+  - question: Do I need an Arm machine to follow this path?
+    answer: >-
+      No. You can use any computer with Docker installed on Windows, macOS, or Linux, and build
+      for Arm and x86 using Docker Build Cloud without local emulation.
+  - question: What do I need before running the builds?
+    answer: >-
+      You need Docker installed on your computer, a GitHub account, and a Docker Hub account.
+      No other prerequisites are explicitly listed.
+  - question: Which method for multi-architecture builds is used here?
+    answer: >-
+      The path explains common methods and focuses on using Docker Build Cloud as the builder
+      to avoid instruction emulation. Emulation is noted as slow for complex builds, so the cloud
+      builder is used instead.
+  - question: How do I set up GitHub Actions for this build?
+    answer: >-
+      Create a new GitHub repository, add a workflow that uses Docker Build Cloud as the builder,
+      and configure the required GitHub secrets referenced by the workflow. The steps guide you
+      through creating the repository and setting up secrets.
+  - question: What should I check if my GitHub Actions workflow fails early?
+    answer: >-
+      Verify that the required secrets referenced by the workflow are defined in the repository
+      settings and that you are using the correct GitHub account. If you are also building locally,
+      ensure Docker is installed and running.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/cross-platform/docker/_index.md b/content/learning-paths/cross-platform/docker/_index.md
index 502ba523e3..546516339d 100644
--- a/content/learning-paths/cross-platform/docker/_index.md
+++ b/content/learning-paths/cross-platform/docker/_index.md
@@ -22,6 +22,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:34:19Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 058f779bc7dd9faef294a57f4524bf525046d757e21a287744825fb9b290935e
+  summary_generated_at: '2026-06-01T21:03:24Z'
+  summary_source_hash: 058f779bc7dd9faef294a57f4524bf525046d757e21a287744825fb9b290935e
+  faq_generated_at: '2026-06-02T21:34:19Z'
+  faq_source_hash: 058f779bc7dd9faef294a57f4524bf525046d757e21a287744825fb9b290935e
+  summary: >-
+    Follow this introductory path to build, run, and share Docker images that support both Arm
+    and x86. You will validate your Docker setup, perform multi-architecture builds with Docker
+    buildx, and use a remote Arm Linux server over SSH to offload Arm image builds when local
+    emulation is slow. The path also covers creating multi-architecture images using Docker manifest
+    (an experimental feature not recommended for production) and checking image architecture support
+    in container registries. You can use a Windows, macOS, or Linux computer with Docker installed,
+    plus access to an Arm Linux server with Docker. By the end, you will have practiced building
+    and publishing images that run on Arm-based systems.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      Use a Windows, macOS, or Linux computer with Docker installed. For remote builds, you also
+      need an Arm Linux server with Docker installed and reachable over SSH without a password.
+  - question: How do I verify my Docker setup before starting builds?
+    answer: >-
+      Run docker run hello-world to confirm Docker is working. Then run docker buildx --help and
+      expect a usage message beginning with 'Usage: docker buildx [OPTIONS] COMMAND'; if you see
+      other output, install the most recent Docker version.
+  - question: When should I use a remote Arm server for builds?
+    answer: >-
+      If building Arm images on a non-Arm machine is slow due to emulation, switch to a remote
+      Arm server. Use docker context to target an Arm machine that has Docker installed and is
+      accessible via passwordless SSH.
+  - question: When should I use docker manifest in this workflow?
+    answer: >-
+      Use docker manifest when you have built separate images for each architecture and want to
+      publish a single multi-architecture image. Note that docker manifest is an experimental
+      feature and is not recommended for production use.
+  - question: How do I check that an image is multi-architecture and supports Arm?
+    answer: >-
+      Inspect the image in your container registry. For example, Docker Hub shows supported OS/ARCH
+      entries, and AWS ECR Public also lists the architectures.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md b/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md
index 931f2b915a..c63cb63d92 100644
--- a/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md
+++ b/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md
@@ -21,6 +21,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:35:52Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: c59308676849aea9b7cfce8c48c7d6e1ca072ef6fb38fb193a34aa5b2597b9b9
+  summary_generated_at: '2026-06-01T21:04:25Z'
+  summary_source_hash: c59308676849aea9b7cfce8c48c7d6e1ca072ef6fb38fb193a34aa5b2597b9b9
+  faq_generated_at: '2026-06-02T21:35:52Z'
+  faq_source_hash: c59308676849aea9b7cfce8c48c7d6e1ca072ef6fb38fb193a34aa5b2597b9b9
+  summary: >-
+    This introductory Learning Path guides you through implementing a simple dynamic memory allocator
+    in C on Linux. You will design and code two functions, simple_malloc and simple_free, to understand
+    how heap allocation works and what malloc/free do under the hood, then build and run provided
+    examples to observe allocation behavior. The project uses a small CMake-based structure (heap.c,
+    heap.h, main.c, CMakeLists.txt) to configure and build the test program. Prerequisites are
+    familiarity with C pointers and access to a Linux machine. The material also highlights some
+    risks of heap allocation and is relevant to developers targeting Arm Cortex-A and Neoverse
+    software. Estimated time to complete is about 120 minutes.
+  faqs:
+  - question: What do I need before running the examples?
+    answer: >-
+      You need a Linux machine and familiarity with C programming, including pointers. No additional
+      prerequisites are explicitly listed.
+  - question: Which allocator functions am I expected to implement?
+    answer: >-
+      You will implement simple_malloc and simple_free. simple_malloc takes a size in bytes and
+      returns a pointer or NULL on failure, and simple_free releases previously allocated memory.
+  - question: How is the project organized in the implementation step?
+    answer: >-
+      The project includes CMakeLists.txt, heap.c (allocator implementation), heap.h (function
+      declarations), and main.c (a test program). Everything required to build and run example
+      allocations is provided.
+  - question: How do I build and run the code on Linux?
+    answer: >-
+      Use the provided CMakeLists.txt to configure and build the project as shown in the Learning
+      Path steps. Building produces a program that exercises simple_malloc and simple_free.
+  - question: How do I know my allocator works as intended?
+    answer: >-
+      Run the included test program and observe that allocations succeed and that simple_malloc
+      returns NULL when memory cannot be allocated. The examples demonstrate basic allocation
+      and freeing behavior.
+# END generated_summary_faq
 
 author: David Spickett
 
diff --git a/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md b/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md
index 7f1bcadeb4..76e146f18c 100644
--- a/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md
+++ b/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md
@@ -19,6 +19,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:36:20Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 39c5e146afbfc74c3076d2cf3f1a67ddc0e4715f39b2cff1ccf76b288415bdc0
+  summary_generated_at: '2026-06-01T21:04:54Z'
+  summary_source_hash: 39c5e146afbfc74c3076d2cf3f1a67ddc0e4715f39b2cff1ccf76b288415bdc0
+  faq_generated_at: '2026-06-02T21:36:20Z'
+  faq_source_hash: 39c5e146afbfc74c3076d2cf3f1a67ddc0e4715f39b2cff1ccf76b288415bdc0
+  summary: >-
+    Learn to use the Eigen C++ linear algebra library on Arm systems that support ASIMD (Neon)
+    and SVE, then build TensorFlow with SVE enabled. You will build and run compact Eigen examples
+    that exercise vectorized operations, including element-wise expressions on a 100×100 matrix
+    and repeated 512×512 matrix multiplications that use FMA. The path then guides you to install
+    TensorFlow build requirements and follow the upstream build-from-source process with slight
+    modifications to enable SVE, producing a build you can run. Target environment is an Arm-based
+    Linux system. Tools include GCC or Clang. Prerequisites: an Arm-based computer running Linux
+    with a recent C++ compiler. Outcome: use Eigen on Arm and produce an SVE-enabled TensorFlow
+    build.
+  faqs:
+  - question: What do I need before running the examples or building TensorFlow?
+    answer: >-
+      You need an Arm-based computer running Linux and a recent version of a C++ compiler (Clang
+      or GCC). No other prerequisites are explicitly listed.
+  - question: Which compiler should I use, and are special flags required for ASIMD or SVE?
+    answer: >-
+      You can use either GCC or Clang. The path demonstrates Eigen on Arm SIMD engines, and specific
+      compiler options for ASIMD or SVE are not explicitly listed.
+  - question: What code do I create and what results indicate the Eigen examples worked?
+    answer: >-
+      You will write small Eigen programs, including a 100×100 matrix example that returns the
+      sum of all elements and a 512×512 matrix multiplication example in a file named eigen-test3.cpp.
+      Successful runs print a numeric result, such as a summed value or a line like "C.norm():
+      <number>".
+  - question: How do I approach building TensorFlow with SVE in this path?
+    answer: >-
+      You follow TensorFlow’s build-from-source instructions with slight modifications. First
+      install the required build dependencies provided in the steps, then build and run the SVE-enabled
+      TensorFlow.
+  - question: What should I do if my Arm system does not support SVE?
+    answer: >-
+      The path covers Eigen on both ASIMD (Neon) and SVE, so you can still work through the Eigen
+      examples using ASIMD. The steps do not list an alternative workflow for building TensorFlow
+      without SVE.
+# END generated_summary_faq
 
 author: Konstantinos Margaritis
 
diff --git a/content/learning-paths/cross-platform/ernie_moe_v9/_index.md b/content/learning-paths/cross-platform/ernie_moe_v9/_index.md
index 28c5e9859b..8e3809cba1 100644
--- a/content/learning-paths/cross-platform/ernie_moe_v9/_index.md
+++ b/content/learning-paths/cross-platform/ernie_moe_v9/_index.md
@@ -20,6 +20,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:37:05Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: e835a550c955b13805c7859ff64e3b8d7fdee68222569225c53a0f15db72f046
+  summary_generated_at: '2026-06-01T21:05:21Z'
+  summary_source_hash: e835a550c955b13805c7859ff64e3b8d7fdee68222569225c53a0f15db72f046
+  faq_generated_at: '2026-06-02T21:37:05Z'
+  faq_source_hash: e835a550c955b13805c7859ff64e3b8d7fdee68222569225c53a0f15db72f046
+  summary: >-
+    This advanced Learning Path shows how to deploy ERNIE-4.5 Mixture of Experts (MoE) models
+    on Armv9 devices using llama.cpp on Linux. You will set up an Armv9 development board (for
+    example, a Radxa Orion O6 with at least 32 GB of available disk space), run and verify inference,
+    and validate multilingual outputs with the ERNIE-4.5 Thinking variant. You then compare the
+    PT and Thinking models, inspect MoE expert routing, and benchmark a baseline CPU build against
+    an Armv9-optimized build that enables SVE, i8mm, and dotprod instructions to measure their
+    impact. The outcome is the ability to deploy, compare, and benchmark ERNIE-4.5 MoE models
+    on Armv9 in about 60 minutes.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      You need an Armv9 device with at least 32 GB of available disk space. The steps assume a
+      Linux environment and use a Radxa Orion O6 as an example platform.
+  - question: Which ERNIE-4.5 variants are used, and what will I compare?
+    answer: >-
+      You will work with the PT and Thinking variants of ERNIE-4.5. The path compares their inference
+      behavior on the same task and shows how to inspect internal MoE expert routing. Both share
+      the same MoE architecture and parameter count (around 21B total, about 3B active at runtime).
+  - question: How do I validate that my setup and model inference are working?
+    answer: >-
+      You verify inference on an Armv9 development board and validate multilingual outputs using
+      the ERNIE Thinking variant. Successful inference confirms the environment and model setup
+      are ready for the comparison and benchmarking steps.
+  - question: What Armv9 optimizations are benchmarked, and how are they tested?
+    answer: >-
+      You measure performance with and without Armv9 vector instruction optimizations. The comparison
+      is between a baseline regular CPU build and an Armv9-specific build with SVE, i8mm, and
+      dotprod enabled.
+  - question: How can I observe which MoE experts are used during generation?
+    answer: >-
+      The path includes steps to inspect internal MoE expert routing behavior while generating
+      outputs. You use this to understand how the PT and Thinking variants route tokens to experts
+      during inference.
+# END generated_summary_faq
 
 author: Odin Shen
 
diff --git a/content/learning-paths/cross-platform/floating-point-behavior/_index.md b/content/learning-paths/cross-platform/floating-point-behavior/_index.md
index cb08e9917e..858d282e32 100644
--- a/content/learning-paths/cross-platform/floating-point-behavior/_index.md
+++ b/content/learning-paths/cross-platform/floating-point-behavior/_index.md
@@ -21,6 +21,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:37:38Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 6427d4466fcd34f7275d16820cbfa2afa3815e1f0306c02e5011b2a4a6afa984
+  summary_generated_at: '2026-06-01T21:05:41Z'
+  summary_source_hash: 6427d4466fcd34f7275d16820cbfa2afa3815e1f0306c02e5011b2a4a6afa984
+  faq_generated_at: '2026-06-02T21:37:38Z'
+  faq_source_hash: 6427d4466fcd34f7275d16820cbfa2afa3815e1f0306c02e5011b2a4a6afa984
+  summary: >-
+    This Learning Path examines IEEE 754 floating-point behavior across x86 and Arm on Linux using
+    C++ examples. You will verify that both architectures produce identical results for all well-defined
+    operations, and learn where differences can appear in edge cases explicitly left undefined
+    by the standard. The path highlights scenarios such as out-of-range floating-point to integer
+    conversions and precision effects related to fused multiply-add (FMAC) in single precision,
+    with an example you can run on both platforms. It is aimed at developers porting applications
+    from x86 to Arm Cortex-A or Neoverse. Prerequisites are access to both an x86 and an Arm Linux
+    machine and familiarity with floating-point numbers. Estimated time is about 30 minutes.
+  faqs:
+  - question: What do I need before running the examples?
+    answer: >-
+      You need access to both an x86 and an Arm Linux machine and familiarity with floating-point
+      numbers. The examples use C++.
+  - question: How do I know if a difference I see is permitted by IEEE 754?
+    answer: >-
+      Check whether your code triggers an undefined case, such as converting an out-of-range floating-point
+      value to an integer. Differences in these cases are allowed by the standard and are not
+      defects in either architecture.
+  - question: Why might two mathematically equivalent C++ functions produce slightly different
+      results across architectures?
+    answer: >-
+      Minor variations can arise from precision and instruction-level choices, including fused
+      multiply-add (FMAC) behavior in single precision. The Learning Path shows an example to
+      help you recognize and reason about these cases.
+  - question: What result should I expect when I run the same C++ code on x86 and Arm?
+    answer: >-
+      For well-defined IEEE 754 operations, results should be identical. Differences should only
+      appear in special undefined cases that the standard permits, which this Learning Path highlights.
+  - question: How should I validate results when comparing x86 and Arm runs?
+    answer: >-
+      Run the provided example on both machines and compare the outputs produced by the program.
+      Use the guidance in the steps to identify whether any differences stem from undefined cases
+      or from the precision topics discussed.
+# END generated_summary_faq
 
 author: 
     - Kieran Hejmadi
diff --git a/content/learning-paths/cross-platform/function-multiversioning/_index.md b/content/learning-paths/cross-platform/function-multiversioning/_index.md
index 45f76f01a4..e0308eefa7 100644
--- a/content/learning-paths/cross-platform/function-multiversioning/_index.md
+++ b/content/learning-paths/cross-platform/function-multiversioning/_index.md
@@ -26,6 +26,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:38:11Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 1c1d745bd1af535315d5edd1b2b9214c96419d46abde3af8fa75bdde299bb65d
+  summary_generated_at: '2026-06-01T21:06:01Z'
+  summary_source_hash: 1c1d745bd1af535315d5edd1b2b9214c96419d46abde3af8fa75bdde299bb65d
+  faq_generated_at: '2026-06-02T21:38:11Z'
+  faq_source_hash: 1c1d745bd1af535315d5edd1b2b9214c96419d46abde3af8fa75bdde299bb65d
+  summary: >-
+    This advanced Learning Path shows how to apply function multiversioning in C/C++ for Arm64
+    targets using GCC or LLVM so your binaries can select the most appropriate implementation
+    at runtime. You will annotate functions with target_version and target_clones, build example
+    programs on Linux, Android, or macOS, and observe how the compiler generates versions specialized
+    for features such as SVE, SVE2, and FEAT_MOPS, including cases using ACLE intrinsics and inline
+    assembly. A dedicated step covers compatibility with Arm streaming mode. By the end, you will
+    be able to create function-level variants that leverage hardware capabilities and reuse the
+    same binary across different Arm64 systems. Prerequisites include basic GNU attributes, ifuncs,
+    loop vectorization, Arm assembly, and LLVM 20 (with runtime support) or GCC 16.
+  faqs:
+  - question: What do I need before running the examples?
+    answer: >-
+      You need either an LLVM 20 compiler with runtime library support or GCC 16. The path assumes
+      basic knowledge of GNU function attributes, familiarity with indirect functions (ifuncs)
+      and loop vectorization, and some familiarity with Arm assembly.
+  - question: Which attribute should I use to define multiple function versions?
+    answer: >-
+      Use __attribute__((target_version("name"))) to define a version keyed to specific features,
+      or __attribute__((target_clones("name", ...))) to create multiple versions at once. The
+      "name" string lists architectural features separated by '+'.
+  - question: Does the order of features in target_clones affect runtime selection?
+    answer: >-
+      No. The examples note that the order in which versions are listed with target_clones does
+      not matter.
+  - question: How do I know which version ran at runtime?
+    answer: >-
+      One example prints a message such as "Running the sve version of dotProduct" when the SVE
+      path executes. In general, the runtime mechanism selects the most appropriate version automatically,
+      and the examples include output cues to validate this.
+  - question: Is multiversioning compatible with Arm streaming mode?
+    answer: >-
+      Yes, as long as all versions of a function use the same calling convention. The examples
+      demonstrate compatibility using attributes like __arm_streaming and a variant specialized
+      for sme2.
+# END generated_summary_faq
 
 author: Alexandros Lamprineas
 
diff --git a/content/learning-paths/cross-platform/github-arm-runners/_index.md b/content/learning-paths/cross-platform/github-arm-runners/_index.md
index 62a123fb24..b58072f02c 100644
--- a/content/learning-paths/cross-platform/github-arm-runners/_index.md
+++ b/content/learning-paths/cross-platform/github-arm-runners/_index.md
@@ -20,6 +20,57 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:38:44Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 3557d534c51f81839cc353ebbd600ec588a60197d2c27c9a58e97c25017d07e4
+  summary_generated_at: '2026-06-01T21:06:27Z'
+  summary_source_hash: 3557d534c51f81839cc353ebbd600ec588a60197d2c27c9a58e97c25017d07e4
+  faq_generated_at: '2026-06-02T21:38:44Z'
+  faq_source_hash: 3557d534c51f81839cc353ebbd600ec588a60197d2c27c9a58e97c25017d07e4
+  summary: >-
+    Learn to build and publish multi-architecture container images for arm64 and amd64 using GitHub
+    Actions with Arm-hosted runners. This introductory path walks you through creating a repository,
+    defining a workflow that runs on Arm-hosted runners, and configuring the secrets needed to
+    automate deployment to Docker Hub. It also explains common build approaches for multi-architecture
+    images, including instruction emulation and using a manifest across multiple machines, noting
+    the performance drawback of emulation for complex builds. By the end, you will be able to
+    build Arm images and multi-architecture images with GitHub Actions. A Linux environment, a
+    GitHub account (Team or Enterprise Cloud for private repositories), and a Docker Hub account
+    are required. Estimated time: 30 minutes.
+  faqs:
+  - question: What do I need before running the workflow?
+    answer: >-
+      You need a GitHub account and a Docker Hub account. For private repositories, a GitHub Team
+      or Enterprise Cloud plan is required. No other explicit prerequisites are listed.
+  - question: Do I need to provision my own machines to run Arm jobs?
+    answer: >-
+      No. Arm-hosted runners are managed by GitHub, so you do not need to provide a server. They
+      are available for public and private repositories, with public repos on free plans subject
+      to standard usage limits.
+  - question: Which approach should I use to build multi-architecture images?
+    answer: >-
+      You can use instruction emulation or a manifest with multiple computers. Emulation is straightforward
+      but can be slow for complex builds, while the manifest approach builds natively on each
+      architecture. This Learning Path uses GitHub Actions with Arm-hosted runners as part of
+      a multi-architecture workflow.
+  - question: Can I use Arm-hosted runners in private repositories, and what runner types exist?
+    answer: >-
+      Yes, you can use them in private repositories with a Team or Enterprise Cloud plan. GitHub-hosted
+      runners include standard and larger runners; larger runners let you adjust RAM, CPU count,
+      and disk space, and offer options like a static IP and runner groups.
+  - question: How do I run the workflow and publish images to Docker Hub?
+    answer: >-
+      Create a new GitHub repository, add a GitHub Actions workflow that targets Arm-hosted runners,
+      and configure the repository secrets required by the workflow. The process builds images
+      for arm64 and amd64 and automates deployment to Docker Hub.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md b/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md
index 00d7e8be99..a463d51a9e 100644
--- a/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md
+++ b/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md
@@ -23,6 +23,50 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:40:28Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: a6ad703d9d894da06ed4aa3725fcc9006d70050cef3f0a3ef9a758bcf4523289
+  summary_generated_at: '2026-06-01T21:07:14Z'
+  summary_source_hash: a6ad703d9d894da06ed4aa3725fcc9006d70050cef3f0a3ef9a758bcf4523289
+  faq_generated_at: '2026-06-02T21:40:28Z'
+  faq_source_hash: a6ad703d9d894da06ed4aa3725fcc9006d70050cef3f0a3ef9a758bcf4523289
+  summary: >-
+    This introductory Learning Path shows how to build a GitLab CI/CD pipeline that runs on GitLab-hosted
+    Arm64 runners. You create or use a GitLab project, write a simple C program, and containerize
+    it for Arm64 with Docker. You configure .gitlab-ci.yml to select Arm64 runner tags, build
+    and push the image to GitLab Container Registry, and run the pipeline on managed Arm infrastructure.
+    You verify execution on Arm64 by reviewing job logs and using lscpu output. The target environment
+    is Linux and GitLab, and no runner provisioning is required. Prerequisite: a GitLab account;
+    the free tier includes access to Arm64 runners.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      You need a GitLab account. The free tier includes access to GitLab-hosted Arm64 runners;
+      no other prerequisites are explicitly listed.
+  - question: How do I configure my pipeline to use Arm64 runners?
+    answer: >-
+      Create a .gitlab-ci.yml in the project root and specify the Arm64 runner tag. GitLab-hosted
+      runners are available to your project without additional setup.
+  - question: Which executor should I use for the jobs in this path?
+    answer: >-
+      Use the Docker executor for containerized builds. The path containerizes a C application
+      and builds it for the Arm64 architecture.
+  - question: What artifact does the pipeline produce and where is it stored?
+    answer: >-
+      The pipeline builds a container image for Arm64 and pushes it to the GitLab Container Registry.
+      After a successful run, you can view the image in your project’s registry.
+  - question: How do I verify the jobs actually ran on Arm64?
+    answer: >-
+      Open the job logs after running the pipeline and check the architecture verification step.
+      The lscpu output should indicate an Arm64 (AArch64) environment.
+# END generated_summary_faq
 
 author: Mohamed Ismail
 
diff --git a/content/learning-paths/cross-platform/gitlab/_index.md b/content/learning-paths/cross-platform/gitlab/_index.md
index e6630b70f9..8252ac8718 100644
--- a/content/learning-paths/cross-platform/gitlab/_index.md
+++ b/content/learning-paths/cross-platform/gitlab/_index.md
@@ -23,6 +23,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:39:28Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: af9eb1c426e1844e2fd20215b7012d95c1efaf737f1d7b5be828931528342316
+  summary_generated_at: '2026-06-01T21:06:50Z'
+  summary_source_hash: af9eb1c426e1844e2fd20215b7012d95c1efaf737f1d7b5be828931528342316
+  faq_generated_at: '2026-06-02T21:39:28Z'
+  faq_source_hash: af9eb1c426e1844e2fd20215b7012d95c1efaf737f1d7b5be828931528342316
+  summary: >-
+    This advanced Learning Path shows how to build a GitLab CI/CD pipeline on Google Cloud using
+    Google Axion-based self-hosted runners. You will create a GitLab runner on Axion (Arm Neoverse)
+    and pair it with a native x86 runner to build a multi-architecture application targeting arm64
+    and amd64. Using GitLab CI/CD with Docker and Kubernetes on Linux, you will configure jobs
+    that produce per-architecture images and combine them into a single multi-arch image with
+    docker manifest, then automate build and deployment. Prerequisites include a Google Cloud
+    account, Google Cloud CLI, kubectl, and a GitLab account. The estimated time to complete is
+    about 30 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Google Cloud account, Google Cloud CLI and kubectl installed on your computer,
+      and a valid GitLab account. The path targets Linux.
+  - question: How do I know my Google Axion self-hosted runner is ready to run jobs?
+    answer: >-
+      After you register the runner, verify it appears in your GitLab project or group runner
+      list with the executor you selected. Continue when it is listed and available for CI/CD
+      jobs.
+  - question: Which approach does the pipeline use to produce a multi-architecture image?
+    answer: >-
+      It uses docker manifest to join separate amd64 and arm64 images into a single multi-architecture
+      image.
+  - question: Do I need both x86 and Arm runners to build the images?
+    answer: >-
+      Yes. The objectives include building multi-architecture Docker images using native GitLab
+      runners on x86 and Arm.
+  - question: Where are the built images stored, and how can I validate the result?
+    answer: >-
+      You create a Docker repository in Google Artifact Registry and push images there as part
+      of the pipeline. Validate by confirming the repository contains amd64 and arm64 images referenced
+      by a manifest after a successful run.
+# END generated_summary_faq
 
 author: Pranay Bakre
 
diff --git a/content/learning-paths/cross-platform/integer-vs-floats/_index.md b/content/learning-paths/cross-platform/integer-vs-floats/_index.md
index 07cc35d957..97dfa6ccc8 100644
--- a/content/learning-paths/cross-platform/integer-vs-floats/_index.md
+++ b/content/learning-paths/cross-platform/integer-vs-floats/_index.md
@@ -18,6 +18,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:41:57Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 1895767d551ba0fa249c5002d3e2e471acacdec06ddb7c2f5314a2fa0df94f2e
+  summary_generated_at: '2026-06-01T21:07:38Z'
+  summary_source_hash: 1895767d551ba0fa249c5002d3e2e471acacdec06ddb7c2f5314a2fa0df94f2e
+  faq_generated_at: '2026-06-02T21:41:57Z'
+  faq_source_hash: 1895767d551ba0fa249c5002d3e2e471acacdec06ddb7c2f5314a2fa0df94f2e
+  summary: >-
+    This Learning Path teaches advanced C/C++ developers on Arm how to identify and fix issues
+    in integer and floating-point conversions. Using an Arm computer running Linux with a recent
+    GCC or Clang, you will review data type ranges, explore explicit and implicit conversions,
+    and examine data type demotions. You will implement concise examples—a Fibonacci-based golden
+    ratio calculator in C and a C++ demotion test—to see where conversions and narrowing can change
+    results. By the end, you will be able to recognize risky conversions and decide when to use
+    explicit casts or different types on AArch64 (Armv8-A/Armv9-A). The estimated time to complete
+    is about 30 minutes.
+  faqs:
+  - question: What do I need before running the examples?
+    answer: >-
+      You need an Arm computer running Linux and a recent version of a C++ compiler, either Clang
+      or GCC. No additional prerequisites are explicitly listed.
+  - question: Which compiler should I use and are any specific flags required?
+    answer: >-
+      You can use either GCC or Clang on Linux. The Learning Path does not specify compiler flags;
+      the focus is on understanding code behavior around conversions.
+  - question: How do I know the golden_ratio.c program worked?
+    answer: >-
+      The program computes the golden ratio from consecutive Fibonacci numbers, so the output
+      should approach 1.618033988749894 as N increases. Compare the printed results to this value
+      to gauge correctness.
+  - question: What should I check if I see unexpected truncation or loss of precision?
+    answer: >-
+      Look for demotions, such as assigning a wider type to a narrower one (for example, double
+      to float or 64-bit to 16-bit) or performing integer division where floating-point was intended.
+      Remember that demotions are not detected in C and only in a few cases in C++, so verify
+      variable types against the ranges reviewed earlier.
+  - question: Which Arm platforms and operating system does this target?
+    answer: >-
+      The path targets AArch64 on Armv8-A and Armv9-A and assumes a Linux environment. The examples
+      are designed to be compiled and run on this setup.
+# END generated_summary_faq
 
 author: Konstantinos Margaritis
 
diff --git a/content/learning-paths/cross-platform/intrinsics/_index.md b/content/learning-paths/cross-platform/intrinsics/_index.md
index 83eaf56c0f..36a23c07ba 100644
--- a/content/learning-paths/cross-platform/intrinsics/_index.md
+++ b/content/learning-paths/cross-platform/intrinsics/_index.md
@@ -23,6 +23,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:42:32Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: ecf51d9a9085f95dedda9c0cfbfa4d6350d0f68d81b61f9461bc51070abd0b69
+  summary_generated_at: '2026-06-01T21:08:20Z'
+  summary_source_hash: ecf51d9a9085f95dedda9c0cfbfa4d6350d0f68d81b61f9461bc51070abd0b69
+  faq_generated_at: '2026-06-02T21:42:32Z'
+  faq_source_hash: ecf51d9a9085f95dedda9c0cfbfa4d6350d0f68d81b61f9461bc51070abd0b69
+  summary: >-
+    This advanced Learning Path shows how to migrate C/C++ code that relies on architecture-specific
+    intrinsics from x64 to Arm. You will learn how to identify intrinsics in your source, understand
+    how compilers expose them, and use header-only libraries to rebuild and run on Arm processors.
+    The path demonstrates two approaches: mapping SSE intrinsics to Neon with sse2neon, and using
+    SIMD Everywhere (SIMDe) for broader coverage, including AVX. It also introduces Porting Advisor
+    for Graviton to locate intrinsics in large codebases. The target environment is Ubuntu Linux
+    on an Arm-based machine or cloud instance; an x86_64 Ubuntu system is optional. By the end,
+    you will have code that compiles and runs on Arm.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      You need some understanding of SIMD concepts and access to an Arm-based machine or cloud
+      instance running Ubuntu Linux. Optionally, have an x86_64 Ubuntu machine available.
+  - question: How do I find architecture-specific intrinsics in a large code base?
+    answer: >-
+      Use the background in this path to spot intrinsics in source and run Porting Advisor for
+      Graviton to assess portability and locate intrinsics. Porting Advisor is a command line
+      tool available on Linux, Windows, and macOS, and the example assumes you run it as an executable
+      in your PATH.
+  - question: 'Which option should I use to port x86 intrinsics: sse2neon or SIMDe?'
+    answer: >-
+      If your code uses MMX or SSE, you can use either sse2neon or SIMDe. If it contains AVX,
+      use SIMDe.
+  - question: What changes are required when porting with sse2neon?
+    answer: >-
+      Adjust SSE-specific header usage for the Arm build, include sse2neon.h to map intrinsics
+      to Neon, and update your g++ compiler flags for the Arm architecture. This approach can
+      get many C/C++ applications compiling and running on an appropriate Arm platform.
+  - question: What are the high-level steps to use SIMD Everywhere (SIMDe)?
+    answer: >-
+      Select the correct SIMDe header using the SIMDEverywhere wiki table, define the required
+      SIMDe configuration macro as shown in the steps, and build for Arm. SIMDe is a header-only
+      library intended to make intrinsic-based code portable across architectures.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/cross-platform/ipexplorer/_index.md b/content/learning-paths/cross-platform/ipexplorer/_index.md
index ea1c8c3e3d..60971c8fb2 100644
--- a/content/learning-paths/cross-platform/ipexplorer/_index.md
+++ b/content/learning-paths/cross-platform/ipexplorer/_index.md
@@ -19,6 +19,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:43:14Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 47cd598f6e33c12d3729c319a1d6a7afcd748de7acd6faea639f2e8600a085ed
+  summary_generated_at: '2026-06-01T21:08:49Z'
+  summary_source_hash: 47cd598f6e33c12d3729c319a1d6a7afcd748de7acd6faea639f2e8600a085ed
+  faq_generated_at: '2026-06-02T21:43:14Z'
+  faq_source_hash: 47cd598f6e33c12d3729c319a1d6a7afcd748de7acd6faea639f2e8600a085ed
+  summary: >-
+    This introductory path shows how to use Arm IP Explorer’s cloud simulation platforms to run
+    and compare custom bare-metal software benchmarks on Arm Cortex-M processors using cycle count
+    analysis. You will run a pre-installed example, then clone the provided software package to
+    create your own benchmark from sample C projects that highlight marked code regions. Optionally
+    build and test locally on Linux using Arm GNU Toolchain or Arm Compiler for Embedded. Next,
+    package your application (custom-software.tgz), upload it via the Simulate Processors workflow,
+    select AC6 in the UI, and run on Cortex-M instances (for example, Cortex-M0 and Cortex-M7).
+    Requires an Arm account with IP Explorer access.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an Arm account that can access IP Explorer. Optionally, have a Linux machine with
+      the desired compilers installed if you plan to build the custom benchmark locally.
+  - question: How do I create and edit the custom benchmark code?
+    answer: >-
+      Clone the software package repository referenced in the steps, which includes sample projects.
+      Use the provided C source file with a marked code region to add or modify the algorithm
+      you want to benchmark.
+  - question: Where do I upload my custom software in IP Explorer, and what file should I select?
+    answer: >-
+      In IP Explorer, go to Simulate Processors, open your Cortex-M instance, then Software Simulation,
+      and click +New. From Select/Upload Software choose +New, upload the custom-software.tgz
+      you created, then select the my_example project, choose AC6 (Arm Compiler for Embedded),
+      and run.
+  - question: How do I compare performance across different Cortex-M processors?
+    answer: >-
+      Run the same benchmark on multiple Cortex-M instances (for example, Cortex-M0 and Cortex-M7).
+      Use the cycle-accurate data produced by the simulation to compare results across cores.
+  - question: What should I check if my Cortex-M instances are not listed?
+    answer: >-
+      Ensure you previously created the instances under Simulate Processors in IP Explorer, as
+      the steps expect them to exist. If they are missing, create the required Cortex-M instances
+      before starting a new Software Simulation.
+# END generated_summary_faq
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/cross-platform/kleidiai-explainer/_index.md b/content/learning-paths/cross-platform/kleidiai-explainer/_index.md
index 6c7a3d9535..8d5671bd39 100644
--- a/content/learning-paths/cross-platform/kleidiai-explainer/_index.md
+++ b/content/learning-paths/cross-platform/kleidiai-explainer/_index.md
@@ -19,6 +19,59 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:44:03Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 907255b3c2c1086b421abe4a1e378b68533de4524a4f4dd81138545a2ff1a5b5
+  summary_generated_at: '2026-06-01T21:09:31Z'
+  summary_source_hash: 907255b3c2c1086b421abe4a1e378b68533de4524a4f4dd81138545a2ff1a5b5
+  faq_generated_at: '2026-06-02T21:44:03Z'
+  faq_source_hash: 907255b3c2c1086b421abe4a1e378b68533de4524a4f4dd81138545a2ff1a5b5
+  summary: >-
+    This introductory path shows how KleidiAI micro-kernels accelerate Generative AI inference
+    on Arm CPUs by optimizing matrix multiplication using architecture features such as Int8 Matrix
+    Multiplication (i8mm). You will explore the KleidiAI GitLab repository, review the organization
+    of quantizing/packing and matmul micro-kernels under /kai/ukernels/matmul, and run a basic
+    C++ matrix multiplication example that highlights the i8mm micro-kernel and its supporting
+    routines. The path also connects core linear algebra operations to how Large Language Models
+    execute. It targets Arm-based Linux systems with i8mm; the example is run on an AWS Graviton
+    3 instance. By the end, you can explain where KleidiAI fits in a software stack and demonstrate
+    its micro-kernel speedup using the provided example. Prerequisites include an Arm-based Linux
+    machine with i8mm and basic linear algebra knowledge.
+  faqs:
+  - question: What do I need before running the example?
+    answer: >-
+      You need an Arm-based Linux machine that implements the Int8 Matrix Multiplication (i8mm)
+      feature; the example is run on an AWS Graviton 3 instance. Instructions on setting up an
+      Arm-based server are found here: /learning-paths/servers-and-cloud-computing/csp/aws/. A
+      basic understanding of dot product and matrix multiplication is also required.
+  - question: How do I know if my ML framework will use KleidiAI automatically?
+    answer: >-
+      If your ML framework integrates KleidiAI and your hardware supports the required Arm instructions
+      for your inference, you will benefit from KleidiAI without any further action. Both conditions
+      must be met.
+  - question: Where do I find the relevant micro-kernels in the KleidiAI repository?
+    answer: >-
+      Navigate to the KleidiAI GitLab repository and go to /kai/ukernels/matmul. Quantizing/packing
+      routines are in the pack directory, and matrix multiplication routines are in the remaining
+      subdirectories there.
+  - question: What should I expect when I run the C++ matrix multiplication example?
+    answer: >-
+      The example highlights the i8mm matrix multiplication micro-kernel along with the enabling
+      quantizing/packing micro-kernels. It is designed to showcase KleidiAI micro-kernel performance
+      rather than require changes to your ML framework.
+  - question: Do I need to modify my ML stack or write assembly to use KleidiAI?
+    answer: >-
+      No. KleidiAI micro-kernels are hand-optimized in Arm assembly, but in practice your ML framework
+      will leverage them automatically if supported. This Learning Path uses a standalone example
+      to illustrate how the micro-kernels work.
+# END generated_summary_faq
 
 author: Zach Lasiuk
 ### Tags
diff --git a/content/learning-paths/cross-platform/llm-fine-tuning-for-web-applications/_index.md b/content/learning-paths/cross-platform/llm-fine-tuning-for-web-applications/_index.md
index cc483dc8bc..57f2ec7a64 100644
--- a/content/learning-paths/cross-platform/llm-fine-tuning-for-web-applications/_index.md
+++ b/content/learning-paths/cross-platform/llm-fine-tuning-for-web-applications/_index.md
@@ -30,7 +30,7 @@ prerequisites:
 generate_summary_faq: true
 
 rerun_summary: false
-rerun_faqs: false
+rerun_faqs: true
 author: Parichay Das
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/loop-reflowing/_index.md b/content/learning-paths/cross-platform/loop-reflowing/_index.md
index 352d0939a6..2c3978cab8 100644
--- a/content/learning-paths/cross-platform/loop-reflowing/_index.md
+++ b/content/learning-paths/cross-platform/loop-reflowing/_index.md
@@ -17,6 +17,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:44:40Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 4218b8b0c1dee3862bb9765a83dfed0cb38555d1da7425e791c5c16b17f98c21
+  summary_generated_at: '2026-06-01T21:10:12Z'
+  summary_source_hash: 4218b8b0c1dee3862bb9765a83dfed0cb38555d1da7425e791c5c16b17f98c21
+  faq_generated_at: '2026-06-02T21:44:40Z'
+  faq_source_hash: 4218b8b0c1dee3862bb9765a83dfed0cb38555d1da7425e791c5c16b17f98c21
+  summary: >-
+    This advanced, 45-minute Learning Path guides C/C++ developers on Arm Linux through practical
+    compiler autovectorization techniques on Arm processors. You will compile small examples (such
+    as addvec, addvec_neon, and dotprod) with GCC or Clang at -O2, generate and inspect assembly
+    with objdump, and learn how to structure loops so compilers can vectorize them. The steps
+    cover using the C99 restrict qualifier, recognizing limits like non-countable loops and branches,
+    and adapting conditionals to enable the vectorizer. Prerequisite: an Arm computer running
+    Linux with a recent GCC or Clang installed. By the end, you will be able to modify loops to
+    help mainstream compilers autovectorize on Arm.
+  faqs:
+  - question: What do I need before running the examples?
+    answer: >-
+      You need an Arm computer running Linux and a recent version of GCC or Clang. The examples
+      use gcc, and the path references the GNU compiler install guide if you need installation
+      help.
+  - question: When should I use the restrict qualifier in the examples?
+    answer: >-
+      The path shows a classic case where adding restrict to pointer parameters removes potential
+      aliasing and enables autovectorization. You will compile both restricted and non-restricted
+      versions and compare their generated assembly.
+  - question: Which commands does the path use to compile and inspect the code?
+    answer: >-
+      It compiles with gcc -O2 addvec.c -o addvec and gcc -O2 addvec_neon.c -o addvec_neon. To
+      view the generated assembly, it uses objdump -D addvec.
+  - question: How do I know if a loop is eligible for autovectorization?
+    answer: >-
+      The path explains that countable loops—where the number of iterations is known before entry—are
+      candidates for vectorization. Examples show that loops with unknown trip counts or early
+      breaks are not vectorized.
+  - question: What should I check if my loop has conditionals and isn’t being vectorized?
+    answer: >-
+      Branches inside loops can inhibit autovectorization. The steps demonstrate when you can
+      adapt or restructure the loop to enable the vectorizer and when an algorithm change or manually
+      optimized code may be required.
+# END generated_summary_faq
 
 author: Konstantinos Margaritis
 
diff --git a/content/learning-paths/cross-platform/matrix/_index.md b/content/learning-paths/cross-platform/matrix/_index.md
index 739b76718e..755b040d8c 100644
--- a/content/learning-paths/cross-platform/matrix/_index.md
+++ b/content/learning-paths/cross-platform/matrix/_index.md
@@ -24,6 +24,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:45:23Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 5611b6d1eebbea167d1860e3ac7f4f7910584561cbb18c3ed696aa27215edce9
+  summary_generated_at: '2026-06-01T21:10:50Z'
+  summary_source_hash: 5611b6d1eebbea167d1860e3ac7f4f7910584561cbb18c3ed696aa27215edce9
+  faq_generated_at: '2026-06-02T21:45:23Z'
+  faq_source_hash: 5611b6d1eebbea167d1860e3ac7f4f7910584561cbb18c3ed696aa27215edce9
+  summary: >-
+    This Learning Path guides you through developing and testing a modern C++ matrix-processing
+    library on an Arm-based machine using CMake and GoogleTest. You will prepare a C++17 toolchain
+    (GCC or Clang), select a build system (GNU Make or Ninja), and set up an IDE and a documentation
+    generator such as Doxygen. Starting from project boilerplate, you implement core matrix types
+    and operations (add, subtract, multiply), separate traversal from data processing, and write
+    unit tests to guard against regressions. The path also discusses practical error-handling
+    trade-offs. By the end, you have a buildable CMake project with a GoogleTest suite running
+    on Linux, macOS, or Windows on Arm. Prerequisites are listed, and the estimated time to complete
+    is about 120 minutes.
+  faqs:
+  - question: What do I need on my Arm-based machine before starting?
+    answer: >-
+      You need an Arm-based computer running Linux, macOS, or Windows; an IDE; CMake; a C++17-capable
+      compiler (GCC or Clang); a build system (GNU Make or Ninja); and Doxygen. The path provides
+      an example installation on Ubuntu using build-essential, clang, ninja-build, cmake, and
+      doxygen.
+  - question: Which compiler, C++ standard, and build system should I use?
+    answer: >-
+      Use GCC or Clang with C++17 support. Either GNU Make or Ninja is suitable as the build system,
+      driven by CMake.
+  - question: How do I know my environment is set up correctly?
+    answer: >-
+      After configuring the project with CMake and adding GoogleTest, build and run the unit tests
+      for the Matrix library. Successful compilation and passing tests indicate the setup is working.
+  - question: What functionality will I implement in the Matrix library?
+    answer: >-
+      You first add the core boilerplate for Matrix objects (construction, assignment, and dump-to-screen),
+      then implement add, subtract, and multiply. The design separates matrix traversal from the
+      data processing to make testing and extension straightforward.
+  - question: How does this path address error handling in the library?
+    answer: >-
+      It explains how to balance safety and security with performance depending on the use case.
+      The path discusses considerations rather than prescribing a single policy.
+# END generated_summary_faq
 
 author: Arnaud de Grandmaison
 
diff --git a/content/learning-paths/cross-platform/mca-godbolt/_index.md b/content/learning-paths/cross-platform/mca-godbolt/_index.md
index 70d430268a..9bad7e1ced 100644
--- a/content/learning-paths/cross-platform/mca-godbolt/_index.md
+++ b/content/learning-paths/cross-platform/mca-godbolt/_index.md
@@ -20,6 +20,51 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:45:53Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: c86322d497541344f92907315793ab990669aa08bef994b0ce9ff1e32a5ba055
+  summary_generated_at: '2026-06-01T21:11:17Z'
+  summary_source_hash: c86322d497541344f92907315793ab990669aa08bef994b0ce9ff1e32a5ba055
+  faq_generated_at: '2026-06-02T21:45:53Z'
+  faq_source_hash: c86322d497541344f92907315793ab990669aa08bef994b0ce9ff1e32a5ba055
+  summary: >-
+    This introductory Learning Path shows how to analyze Arm assembly performance with LLVM Machine
+    Code Analyzer (llvm-mca) and Compiler Explorer. You will run llvm-mca on a small Arm assembly
+    example that sums six values, interpret estimated cycles and hardware resource pressure, and
+    use those metrics to diagnose a possible performance issue and improve the snippet. A brief
+    background section introduces instruction scheduling and pipelines. The path is relevant to
+    Arm cores such as Cortex-A and Neoverse, and notes that LLVM 16 or newer includes support
+    for Neoverse V2. It can be followed on Linux, Windows, or macOS, and also via a browser using
+    Compiler Explorer. Familiarity with Arm assembly is expected; no other explicit prerequisites
+    are listed. Estimated time to complete is about 60 minutes.
+  faqs:
+  - question: Can I use llvm-mca without installing LLVM locally?
+    answer: >-
+      Yes. The path shows how to run llvm-mca in Compiler Explorer at godbolt.org, which provides
+      llvm-mca as an online tool.
+  - question: What do I need to run llvm-mca on my machine?
+    answer: >-
+      You need familiarity with Arm assembly and LLVM version 16 or newer. The path can be followed
+      on Linux, Windows, or macOS.
+  - question: What source code does the path analyze?
+    answer: >-
+      An Arm assembly snippet saved as sum_test1.s that computes the sum of six numbers using
+      add instructions.
+  - question: What output should I expect from llvm-mca, and how is it used?
+    answer: >-
+      Expect estimates of cycles and hardware resource pressure. The path explains the expected
+      output and how to use these metrics to identify a potential performance issue in the example.
+  - question: Which LLVM version includes support for Neoverse V2?
+    answer: >-
+      LLVM 16 or newer includes support for Neoverse V2, as noted in the prerequisites.
+# END generated_summary_faq
 
 author: Asher Dobrescu
 
diff --git a/content/learning-paths/cross-platform/mcp-ai-agent/_index.md b/content/learning-paths/cross-platform/mcp-ai-agent/_index.md
index 5e51b448b3..16ae111e0a 100644
--- a/content/learning-paths/cross-platform/mcp-ai-agent/_index.md
+++ b/content/learning-paths/cross-platform/mcp-ai-agent/_index.md
@@ -23,6 +23,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:46:28Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 39d489081bfa22125a4046c17d5c00a32c0d7b298cba7b6a12373cf3cf6bac04
+  summary_generated_at: '2026-06-01T21:11:51Z'
+  summary_source_hash: 39d489081bfa22125a4046c17d5c00a32c0d7b298cba7b6a12373cf3cf6bac04
+  faq_generated_at: '2026-06-02T21:46:28Z'
+  faq_source_hash: 39d489081bfa22125a4046c17d5c00a32c0d7b298cba7b6a12373cf3cf6bac04
+  summary: >-
+    This Learning Path shows how to deploy a lightweight Model Context Protocol (MCP) server on
+    a Raspberry Pi 5 and connect it to an AI agent built with the OpenAI Agent SDK. You will use
+    uv, a fast Python package manager, to bootstrap a FastMCP server that reads CPU temperature
+    and searches weather data, and expose it to the internet with ngrok. On a Linux Arm development
+    machine, you will create the agent, register custom tools, and point it at the Pi’s MCP endpoint
+    for local inference. Prerequisites include a Raspberry Pi 5 with a Linux-based OS, familiarity
+    with Python and prompt engineering, and a basic understanding of LLMs and AI agents. Estimated
+    time to complete is about 30 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Raspberry Pi 5 with a Linux-based OS installed, familiarity with Python and prompt
+      engineering, a basic understanding of LLMs and local inference, and an understanding of
+      AI agents and the OpenAI Agent SDK (or similar frameworks).
+  - question: Which machine hosts the MCP server and where does the agent run?
+    answer: >-
+      The MCP server runs on the Raspberry Pi 5 (Raspberry Pi OS 64-bit). The AI agent is set
+      up on your development machine, with the commands tested on a Linux Arm system, and it connects
+      to the MCP server on the Pi.
+  - question: How do I install uv and what project files should I see?
+    answer: >-
+      Install uv by running: curl -LsSf https://astral.sh/uv/install.sh | sh. When you initialize
+      a project with uv init, it creates a .venv/ directory and a pyproject.toml file for the
+      project.
+  - question: How do I expose the MCP server running on my Raspberry Pi to the internet?
+    answer: >-
+      Use ngrok to create an HTTPS tunnel to your local MCP server. The steps show how to expose
+      the server so it can be reached remotely.
+  - question: What result should I expect to confirm the setup is working?
+    answer: >-
+      On the Raspberry Pi, the MCP server should be able to read CPU temperature and search weather
+      data. On your development machine, the agent should successfully connect to the Pi’s MCP
+      server, and uv should have created the .venv and pyproject.toml in your agent project.
+# END generated_summary_faq
 
 author: Andrew Choi
 
diff --git a/content/learning-paths/cross-platform/memory-latency/_index.md b/content/learning-paths/cross-platform/memory-latency/_index.md
index 7cc4852065..277993c7e0 100644
--- a/content/learning-paths/cross-platform/memory-latency/_index.md
+++ b/content/learning-paths/cross-platform/memory-latency/_index.md
@@ -18,6 +18,51 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:47:00Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 72e5ffe5091311850d17f30ed3ab4bd7487cbbd862d0d8751cc73bd91fff00e2
+  summary_generated_at: '2026-06-01T21:12:22Z'
+  summary_source_hash: 72e5ffe5091311850d17f30ed3ab4bd7487cbbd862d0d8751cc73bd91fff00e2
+  faq_generated_at: '2026-06-02T21:47:00Z'
+  faq_source_hash: 72e5ffe5091311850d17f30ed3ab4bd7487cbbd862d0d8751cc73bd91fff00e2
+  summary: >-
+    Learn practical ways to reduce the impact of memory latency on Arm processors by experimenting
+    with cache alignment and prefetching in C. You will build and run an example, then create
+    a second version by copying memory-latency1.c to memory-latency2.c, introducing an allocator,
+    adjusting data structure alignment, and adding prefetching to observe effects on execution.
+    The path targets Linux on Arm systems, including Cortex-A and Neoverse, and uses GCC or Clang.
+    Results will vary by processor and system, which is expected and part of the learning. Prerequisite:
+    an Arm computer running Linux with a recent GCC or Clang. Estimated time to complete: about
+    40 minutes.
+  faqs:
+  - question: What do I need before running the examples?
+    answer: >-
+      You need an Arm computer running Linux with recent versions of Clang or GCC installed. No
+      other prerequisites are explicitly listed.
+  - question: What should I expect after copying memory-latency1.c to memory-latency2.c?
+    answer: >-
+      You will have a modified C program that introduces a simple allocator and related bookkeeping.
+      Use it to compare behavior against the original and observe how allocation affects latency.
+  - question: How do I know whether the cache alignment change had an effect?
+    answer: >-
+      Rebuild and run the updated program and compare results with the previous version. Focus
+      on relative differences on your system rather than exact numbers.
+  - question: How far ahead should I prefetch in the loop?
+    answer: >-
+      Prefetch a few iterations ahead; prefetching only the next iteration is not sufficient.
+      The path notes typical RAM latency around 100 ns, so bring data closer earlier.
+  - question: What should I check if my results differ from the sample output?
+    answer: >-
+      This is expected because results depend on the processor and system you use. Focus on the
+      trend between versions, and note that the learning applies to any Arm processor.
+# END generated_summary_faq
 
 author: Konstantinos Margaritis
 
diff --git a/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md b/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md
index f2c4cf40c4..dde2067f67 100644
--- a/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md
+++ b/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md
@@ -23,6 +23,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:47:36Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: bf3183bd62b590d4930f0bcf9c9dc836bbc5206a991be7bec5e18e9c20942352
+  summary_generated_at: '2026-06-01T21:13:18Z'
+  summary_source_hash: bf3183bd62b590d4930f0bcf9c9dc836bbc5206a991be7bec5e18e9c20942352
+  faq_generated_at: '2026-06-02T21:47:36Z'
+  faq_source_hash: bf3183bd62b590d4930f0bcf9c9dc836bbc5206a991be7bec5e18e9c20942352
+  summary: >-
+    This advanced Learning Path shows how to build the MNN (Mobile Neural Network) inference engine
+    natively on an Armv9 Linux device and run a CPU-only Omni multimodal model. You start by verifying
+    a text-only baseline to confirm the core inference path, then run local vision reasoning on
+    retail shelf images to estimate coverage across top, middle, and bottom levels, identify the
+    most sparse priority zone with a short reason, or return NOT_SURE when images are unclear.
+    You also convert a spoken restock note into a single-line, semicolon-separated ticket and
+    combine image and audio inputs into a single-shot restock workflow. Prerequisites include
+    an Armv9 Linux system with 32 GB free space, command-line and CMake/Git experience, and internet
+    access. Estimated time is about 90 minutes.
+  faqs:
+  - question: Do I need a GPU or accelerator to run the demos?
+    answer: >-
+      No. This Learning Path uses a native CPU-only MNN build on an Armv9 Linux system by design.
+  - question: What do I need before building MNN on my Armv9 device?
+    answer: >-
+      You need an Armv9 Linux device with at least 32 GB of available disk space, internet access,
+      and familiarity with the Linux command line, Git, and building C++ projects with CMake.
+  - question: How do I confirm my MNN build and model are ready?
+    answer: >-
+      Verify that the llm_demo binary can load a prebuilt Omni MNN model package on your Armv9
+      system. This confirms the setup needed for the text, vision, and audio demos.
+  - question: What result should I expect from the text-only baseline?
+    answer: >-
+      A reproducible text-only inference run with a simple prompt and predictable output behavior.
+      This validates the MNN runtime, the prompt input path, and token generation before adding
+      vision and audio.
+  - question: What outputs should I expect from the vision and audio steps, and how do they fit
+      together?
+    answer: >-
+      The vision audit estimates shelf coverage for top, middle, and bottom levels, identifies
+      the most sparse priority zone, provides a short reason, and returns NOT_SURE when the image
+      is unclear. The audio step converts a spoken note into a one-line, semicolon-separated ticket.
+      Together, they form a single-shot restock workflow using local image and audio inputs.
+# END generated_summary_faq
 
 author: Odin Shen
 
diff --git a/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md b/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md
index 4e2527ee78..33f9a18f31 100644
--- a/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md
+++ b/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md
@@ -29,6 +29,62 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:48:10Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: eb01b77f36323331c080615edcbddbf8cb56cf005f2249f1ea309ab1dbec8616
+  summary_generated_at: '2026-06-01T21:13:59Z'
+  summary_source_hash: eb01b77f36323331c080615edcbddbf8cb56cf005f2249f1ea309ab1dbec8616
+  faq_generated_at: '2026-06-02T21:48:10Z'
+  faq_source_hash: eb01b77f36323331c080615edcbddbf8cb56cf005f2249f1ea309ab1dbec8616
+  summary: >-
+    This advanced Learning Path shows how to implement, build, and evaluate matrix multiplication
+    using Arm’s Scalable Matrix Extension 2 (SME2) with both assembly and intrinsics. You will
+    set up a development environment on Linux, macOS, or Windows and choose either native SME2
+    hardware (demonstrated on macOS with an M4 chip or some Android phones) or a Linux-based emulation
+    flow. After verifying your toolchain with CMake/Ninja and Clang/LLVM (LLVM 18+), you will
+    create a vanilla C matmul as a correctness reference, then add SME2 intrinsics and assembly,
+    learn how streaming mode and ZA state are handled via ACLE annotations, and benchmark and
+    validate results. Prerequisites include working knowledge of SVE/SME2, intermediate C and
+    Armv9-A assembly, Git, CMake, Ninja, and optionally Docker or Android Development Studio and
+    adb.
+  faqs:
+  - question: What do I need before running the examples?
+    answer: >-
+      You need working knowledge of SVE and SME2, intermediate C and Armv9-A assembly skills,
+      and a computer running Linux, macOS, or Windows. Install Git, CMake, Ninja, and a compiler
+      with SME2 support (for example, LLVM 18+). For emulation, install Docker; for Android targets,
+      install Android Studio and adb, and use a phone with SME2 support.
+  - question: Should I use native SME2 hardware or an emulator?
+    answer: >-
+      Use native SME2 hardware when available for direct execution; this Learning Path demonstrates
+      macOS with an M4 chip and some Android phones with SME2 support. If you lack SME2 hardware,
+      use the Linux-based emulation option. iPhone and iPad are not covered by the instructions,
+      even though they have SME2 support.
+  - question: How do I verify my SME2 toolchain and environment are set up correctly?
+    answer: >-
+      Build the provided code examples with CMake to confirm the compiler, hardware (or emulator),
+      and tools are working. For native builds, you may need to tell CMake which Clang to use
+      if the system default is not suitable. A successful, error-free build indicates your environment
+      is ready.
+  - question: How do I use streaming mode and handle ZA state in SME?
+    answer: >-
+      Annotate the relevant functions to enable streaming mode as defined by the Arm C Language
+      Extensions (ACLE). The compiler manages saving and restoring state, including ZA storage,
+      when streaming-mode functions call each other. No manual state management is required.
+  - question: How do I validate and benchmark the SME2-optimized matrix multiplication?
+    answer: >-
+      First implement the vanilla C matrix multiplication as a correctness reference. Then compile
+      the SME2 intrinsics and assembly implementations and run benchmarks on SME2 hardware or
+      in a Linux-based emulation environment. Compare the performance metrics to the baseline
+      and confirm numerical results match.
+# END generated_summary_faq
 
 author: Arnaud de Grandmaison
 
diff --git a/content/learning-paths/cross-platform/psa-tfm/_index.md b/content/learning-paths/cross-platform/psa-tfm/_index.md
index b0cccb49f4..d199fa4205 100644
--- a/content/learning-paths/cross-platform/psa-tfm/_index.md
+++ b/content/learning-paths/cross-platform/psa-tfm/_index.md
@@ -22,7 +22,7 @@ prerequisites:
 generate_summary_faq: true
 
 rerun_summary: false
-rerun_faqs: false
+rerun_faqs: true
 author: Ronan Synnott
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md b/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md
index c3b0c0ebbd..4983a3ddd4 100644
--- a/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md
+++ b/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md
@@ -27,6 +27,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:48:30Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 1251465f13b67ee80292b66ef22069a1b6cc2ca67bb602cdd5e393a278576ec8
+  summary_generated_at: '2026-06-01T21:14:41Z'
+  summary_source_hash: 1251465f13b67ee80292b66ef22069a1b6cc2ca67bb602cdd5e393a278576ec8
+  faq_generated_at: '2026-06-02T21:48:30Z'
+  faq_source_hash: 1251465f13b67ee80292b66ef22069a1b6cc2ca67bb602cdd5e393a278576ec8
+  summary: >-
+    This advanced Learning Path guides you through preparing a PyTorch development environment,
+    downloading and organizing the MNIST dataset, and creating, training, and saving a feedforward
+    neural network for digit classification. You then create an Android application that loads
+    the pre-trained model, prepares input data consistently with training, and measures inference
+    time. The path also shows how to apply quantization and fusing to optimize the network and
+    deploy the optimized model in the app. You need a machine capable of running Python3, Visual
+    Studio Code, and Android Studio on Windows, Linux, or macOS. Estimated time to complete is
+    about 160 minutes.
+  faqs:
+  - question: What do I need installed before running the training and Android steps?
+    answer: >-
+      You need a machine that can run Python3, Visual Studio Code, and Android Studio. You can
+      use Windows, Linux, or macOS.
+  - question: How do I download MNIST and create DataLoaders in this path?
+    answer: >-
+      Use torchvision.datasets.MNIST with download=True and transforms.ToTensor, then create DataLoader
+      objects for the training and test sets. The example shows a batch size of 32 and uses a
+      data/ folder as the root.
+  - question: How do I know the training step worked and the model is saved?
+    answer: >-
+      The training step saves the trained model to a file that you load later for inference. After
+      saving, proceed to the inference step to validate loading and predictions.
+  - question: During inference, how should I preprocess inputs so they match training?
+    answer: >-
+      Apply the same preprocessing used during training, such as tensor conversion and normalization.
+      Ensure inputs are formatted like MNIST (28x28) before feeding them to the model.
+  - question: When do I apply quantization and fusing, and what gets deployed to Android?
+    answer: >-
+      Apply quantization and fusing after training to produce an optimized model. The Learning
+      Path then deploys this optimized model in an Android application and measures inference
+      time.
+# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/cross-platform/remoteit/_index.md b/content/learning-paths/cross-platform/remoteit/_index.md
index 7545103ab7..5a57870ca4 100644
--- a/content/learning-paths/cross-platform/remoteit/_index.md
+++ b/content/learning-paths/cross-platform/remoteit/_index.md
@@ -22,6 +22,62 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:49:07Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 927cfebb8ebf9595922dad115c9a8d10900e43c4f80e73e6102a71e3e4ca2da1
+  summary_generated_at: '2026-06-01T21:15:32Z'
+  summary_source_hash: 927cfebb8ebf9595922dad115c9a8d10900e43c4f80e73e6102a71e3e4ca2da1
+  faq_generated_at: '2026-06-02T21:49:07Z'
+  faq_source_hash: 927cfebb8ebf9595922dad115c9a8d10900e43c4f80e73e6102a71e3e4ca2da1
+  summary: >-
+    This introductory Learning Path shows how to install and configure Remote.It to access remote
+    devices using SSH and other services, and how to choose between proxy and peer-to-peer connection
+    options. You will install the Remote.It device package on a target device, connect from an
+    initiator computer, and use the Web Dashboard or CLI to create connections. The path applies
+    to Windows, macOS, and Linux environments and supports devices ranging from laptops and Raspberry
+    Pi to cloud-hosted targets such as Arm Virtual Hardware or within AWS. Prerequisites include
+    a Windows, macOS, or Linux computer for setup, control access to the target before deploying
+    Remote.It, and a decision on whether the target must also connect to other Remote.It devices.
+  faqs:
+  - question: What do I need before running the setup?
+    answer: >-
+      Have a Windows, macOS, or Linux computer to configure and connect, plus a target device
+      (Windows, Mac, or Linux) you can control locally or through another remote solution before
+      deploying Remote.It. Targets can include development kits like Raspberry Pi or cloud-hosted
+      systems such as Arm Virtual Hardware or AWS. Also determine whether your target will need
+      to make connections to other Remote.It devices.
+  - question: How do I install the Remote.It device package when I already have access to the
+      target?
+    answer: >-
+      Use a local console or SSH to access the target and follow the steps to install the Remote.It
+      device package. If you need SSH on the target, refer to the SSH guidance referenced in the
+      path.
+  - question: Do I need to install anything on the initiator computer to connect?
+    answer: >-
+      If you use the Remote.It Web Dashboard, proxy connections require only standard tools like
+      SSH on the initiator and no additional Remote.It software. For headless use or automation,
+      install the Remote.It CLI; if you already installed the Desktop software, the CLI binary
+      is included. On Linux, ensure the CLI binary has execute permission.
+  - question: Which connection type should I use, proxy or peer-to-peer?
+    answer: >-
+      The Web Dashboard creates proxy connections and is the easiest to set up because only the
+      target needs Remote.It installed; all traffic is routed through a Remote.It server. Peer-to-peer
+      connections are direct between initiator and target. Choose proxy for the simplest setup,
+      or peer-to-peer when you want a direct connection.
+  - question: What result should I expect after completing the steps, and how do I know it worked?
+    answer: >-
+      You should be able to initiate an SSH session to your Remote.It-enabled target from another
+      location using the connection type you configured. For proxy connections, traffic will route
+      through a Remote.It server; for peer-to-peer, the link is direct. A successful SSH login
+      indicates the setup is working.
+# END generated_summary_faq
 
 author: Brenda Strech
 
diff --git a/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md b/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
index 1d572f2e5b..54a3e31ce4 100644
--- a/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
+++ b/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
@@ -18,6 +18,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:49:44Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 9825df99004e981fadce5884b40b2152f4e43064cbba2cd2129fd90006090678
+  summary_generated_at: '2026-06-01T21:16:11Z'
+  summary_source_hash: 9825df99004e981fadce5884b40b2152f4e43064cbba2cd2129fd90006090678
+  faq_generated_at: '2026-06-02T21:49:44Z'
+  faq_source_hash: 9825df99004e981fadce5884b40b2152f4e43064cbba2cd2129fd90006090678
+  summary: >-
+    This Learning Path shows C developers on Arm Linux how to use the C99 restrict keyword to
+    indicate non-overlapping memory regions so compilers can apply stronger optimizations, including
+    vectorization on AArch64. You will examine a case where overlapping pointers limit optimization,
+    learn the rule-of-thumb for when restrict is valid, and study an SVE2 example with generated
+    code. The steps reference GCC 13 with -O3 -march=armv9-a and compare results with Clang. After
+    completing the path, you will know when and how to apply restrict safely in your own functions.
+    Prerequisites: an Arm computer running Linux with a recent GCC or Clang installed. Estimated
+    time: 30 minutes.
+  faqs:
+  - question: What do I need before running the examples?
+    answer: >-
+      Use an Arm computer running Linux with a recent version of GCC or Clang installed. No additional
+      prerequisites are explicitly listed.
+  - question: Which compiler and options are used in the SVE2 example?
+    answer: >-
+      The example shows output from gcc-13 built with -O3 -march=armv9-a. In this case, GCC 13
+      produced a better result than Clang for the demonstrated function.
+  - question: How do I decide if I can add restrict to a function’s pointer parameters?
+    answer: >-
+      Add restrict when you are certain the pointer arguments refer to non-overlapping memory
+      and those objects are not accessed by any other means inside the function. The path provides
+      a rule of thumb and a counterexample to guide this decision.
+  - question: How do I know that restrict enabled vectorization on Arm?
+    answer: >-
+      Inspect the compiler’s generated output and compare versions with and without restrict.
+      In the SVE2 example, vectorization appears as SVE2 instructions operating on z registers
+      (for example, ld1b and add on z registers).
+  - question: What should I avoid when considering restrict?
+    answer: >-
+      Do not use restrict if the memory regions referenced by pointer arguments may overlap or
+      if the objects can be accessed through other pointers within the function. The path includes
+      a counterexample illustrating when restrict is not appropriate.
+# END generated_summary_faq
 
 author: Konstantinos Margaritis
 
diff --git a/content/learning-paths/cross-platform/rust_armds/_index.md b/content/learning-paths/cross-platform/rust_armds/_index.md
index 386b528c6f..4fd89aae61 100644
--- a/content/learning-paths/cross-platform/rust_armds/_index.md
+++ b/content/learning-paths/cross-platform/rust_armds/_index.md
@@ -20,6 +20,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:50:10Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: f237cd239e5886b21bd5bee88dcd9f95d88eda9a5c2eea363cc9db252e0c6e9f
+  summary_generated_at: '2026-06-01T21:16:47Z'
+  summary_source_hash: f237cd239e5886b21bd5bee88dcd9f95d88eda9a5c2eea363cc9db252e0c6e9f
+  faq_generated_at: '2026-06-02T21:50:10Z'
+  faq_source_hash: f237cd239e5886b21bd5bee88dcd9f95d88eda9a5c2eea363cc9db252e0c6e9f
+  summary: >-
+    This introductory path guides you through building a bare-metal embedded Rust application
+    for Armv7-M, running it on a Fixed Virtual Platform, and debugging with Arm Development Studio.
+    You will install the Rust compiler with cross-compilation support, build the example, and
+    run it on the FVP_MPS2_Cortex-M3 model included with Arm Development Studio. The steps show
+    how to launch the FVP with the built binary and verify the runtime output; an option is provided
+    to disable visualization to reduce startup time. Prerequisites are an installation of Arm
+    Development Studio (license-managed) and a basic understanding of Rust. The path is designed
+    to be completed in about 60 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an installation of Arm Development Studio and a basic understanding of Rust programming.
+      Arm Development Studio is license-managed.
+  - question: Which Arm architecture and FVP model does the example use?
+    answer: >-
+      The example targets Armv7-M and runs on the FVP_MPS2_Cortex-M3 model that comes with Arm
+      Development Studio. Cross-compilation support for the chosen Arm architecture is added following
+      the Rust for Embedded Applications Install Guide.
+  - question: How do I run the built application on the FVP?
+    answer: >-
+      Use the FVP provided by Arm Development Studio with the command: FVP_MPS2_Cortex-M3.exe
+      -a target/thumbv7m-none-eabi/debug/examples/armds. This launches the model and executes
+      the example to completion.
+  - question: How can I reduce the FVP start time?
+    answer: >-
+      Add the option -C fvp_mps2.mps2_visualisation.disable-visualisation=1 to disable visualization.
+      This reduces startup time and has no other effect on FVP behavior.
+  - question: What result should I expect when the program runs on the FVP?
+    answer: >-
+      The application should run to completion and print messages similar to "Total sum to 1 is
+      1" and "Calculated sum is 1." Seeing this output confirms the run succeeded.
+# END generated_summary_faq
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/cross-platform/simd-info-demo/_index.md b/content/learning-paths/cross-platform/simd-info-demo/_index.md
index 76ababaf4c..b0ce904dd0 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/_index.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/_index.md
@@ -19,6 +19,57 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:50:47Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 7a40efa6d83b4629888f9622260e6e9aa9192db836b203fa4bf388cbe636b7e6
+  summary_generated_at: '2026-06-01T21:17:19Z'
+  summary_source_hash: 7a40efa6d83b4629888f9622260e6e9aa9192db836b203fa4bf388cbe636b7e6
+  faq_generated_at: '2026-06-02T21:50:47Z'
+  faq_source_hash: 7a40efa6d83b4629888f9622260e6e9aa9192db836b203fa4bf388cbe636b7e6
+  summary: >-
+    Learn how to use SIMD.info to port SIMD intrinsics between architectures with a practical,
+    code-centric walkthrough. You will examine a short C example that uses Intel SSE4.2 intrinsics
+    on Linux, then use SIMD.info’s navigation, search, and comparison features to identify Arm
+    Neon/ASIMD equivalents for operations such as compare, add, multiply, and square root. The
+    path highlights SIMD.info’s intrinsic metadata (Purpose, Result, Example) and emphasizes correctness
+    of results over performance. It targets AArch64 on Armv8-A/Armv9-A and assumes a basic understanding
+    of SIMD plus access to an Arm platform with a SIMD engine and a recent C compiler (GCC or
+    Clang). Estimated time to complete is 30 minutes.
+  faqs:
+  - question: What do I need before running the example and porting steps?
+    answer: >-
+      You need basic SIMD knowledge and access to an Arm platform with a SIMD-supported engine
+      and a recent C compiler such as Clang or GCC. The example starts on an x86_64 Linux development
+      machine before being ported to Arm Neon/ASIMD.
+  - question: How do I use SIMD.info to find Neon equivalents for the SSE4.2 intrinsics in the
+      example?
+    answer: >-
+      Use SIMD.info’s navigation, search, and comparison features to look up each SSE4.2 intrinsic.
+      Review the Purpose, Result, and Example sections to identify the corresponding Arm Neon/ASIMD
+      intrinsic and understand its behavior.
+  - question: Which intrinsics from the example should I look up on SIMD.info?
+    answer: >-
+      The example uses _mm_cmpgt_ps, _mm_add_ps, _mm_mul_ps, and _mm_sqrt_ps. Look up each of
+      these to find the Arm Neon/ASIMD equivalents that perform the same comparison, addition,
+      multiplication, and square root operations.
+  - question: How should vector initialization and storing change when moving from SSE4.2 to Neon?
+    answer: >-
+      Replace the SSE4.2 _mm_set_ps macro with Neon’s brace {} initialization for vectors. Also
+      update the store operations to follow Neon’s way of moving data from vectors to arrays,
+      as outlined in the step-by-step guidance.
+  - question: How do I verify my Neon port is correct, and should I focus on performance now?
+    answer: >-
+      Compare the results of your Arm Neon build with the outputs from the original SSE example
+      to validate correctness. In this path, the integrity and accuracy of calculations are the
+      primary focus; performance is a secondary concern.
+# END generated_summary_faq
 
 author: 
     - Georgios Mermigkis
diff --git a/content/learning-paths/cross-platform/simd-loops/_index.md b/content/learning-paths/cross-platform/simd-loops/_index.md
index a784ea7d36..c2b273893e 100644
--- a/content/learning-paths/cross-platform/simd-loops/_index.md
+++ b/content/learning-paths/cross-platform/simd-loops/_index.md
@@ -23,6 +23,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:51:17Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: b1c43e1bf971db4582ca358c98dab2c7e6e047d6c79bfcc0db148bc575f33679
+  summary_generated_at: '2026-06-01T21:18:06Z'
+  summary_source_hash: b1c43e1bf971db4582ca358c98dab2c7e6e047d6c79bfcc0db148bc575f33679
+  faq_generated_at: '2026-06-02T21:51:17Z'
+  faq_source_hash: b1c43e1bf971db4582ca358c98dab2c7e6e047d6c79bfcc0db148bc575f33679
+  summary: >-
+    This advanced Learning Path shows how to use Arm’s Scalable Vector Extension (SVE), SVE2,
+    and Scalable Matrix Extension (SME/SME2) with the SIMD Loops project. You will clone the repository,
+    explore how kernels are organized across scalar, Neon, SVE/SVE2, and SME2 variants, and study
+    loop 202, a single-precision matrix multiplication example that ties inner_loop_202 to matmul_fp32.
+    You then build and run selected kernels with the provided runner, validate results against
+    the C reference, and choose build targets to compare Neon, SVE/SVE2, and SME2 implementations.
+    The path targets AArch64 systems on Linux or macOS and expects recent GCC or Clang toolchains
+    with SVE/SME support.
+  faqs:
+  - question: What do I need before running the examples?
+    answer: >-
+      Use an AArch64 computer running Linux or macOS, with a recent toolchain that supports SVE/SME
+      (GCC 13+ or Clang 16+ recommended). Some familiarity with SIMD programming and Neon intrinsics
+      is expected. You can use Arm-based cloud instances if local hardware is not available.
+  - question: How do I know my machine is Arm-based?
+    answer: >-
+      Run uname -m. On Linux, the expected output is aarch64; on macOS, the expected output is
+      arm64.
+  - question: Where are the loop kernels listed, and how are they organized?
+    answer: >-
+      The source for loops is under the loops directory. The complete list of loops, with brief
+      descriptions, is documented in the loops.inc file.
+  - question: Which example does this path use to explain the project structure, and what does
+      it compute?
+    answer: >-
+      It uses loop 202, which implements single-precision floating-point matrix multiplication
+      C[M×N] = A[M×K] × B[K×N]. You will examine inner_loop_202() in loops/loop_202.c and the
+      matmul_fp32 routine in loops/matmul_fp32.c.
+  - question: How do I build, run, and validate a kernel implementation?
+    answer: >-
+      Build and run a selected kernel using the project's runner and validate correctness against
+      the C reference implementation. Choose the appropriate build target to compare Neon, SVE/SVE2,
+      and SME2 variants as demonstrated in the Learning Path.
+# END generated_summary_faq
 
 author:
     - Alejandro Martinez Vicente
diff --git a/content/learning-paths/cross-platform/simd-on-rust/_index.md b/content/learning-paths/cross-platform/simd-on-rust/_index.md
index ad254c97d0..e764863290 100644
--- a/content/learning-paths/cross-platform/simd-on-rust/_index.md
+++ b/content/learning-paths/cross-platform/simd-on-rust/_index.md
@@ -21,6 +21,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:51:46Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: a051c519c1a4969f30d5a81e46823e77f0c15f163011b3a38f4c05104d853249
+  summary_generated_at: '2026-06-01T21:18:44Z'
+  summary_source_hash: a051c519c1a4969f30d5a81e46823e77f0c15f163011b3a38f4c05104d853249
+  faq_generated_at: '2026-06-02T21:51:46Z'
+  faq_source_hash: a051c519c1a4969f30d5a81e46823e77f0c15f163011b3a38f4c05104d853249
+  summary: >-
+    This advanced path teaches you to write SIMD code on Arm using Rust on Linux. You will use
+    Rust’s std::arch Neon intrinsics and portable std::simd, apply feature detection and target
+    attributes for architecture-specific optimizations, and compare C and Rust implementations
+    and their disassembly. Hands-on steps include building and running examples for pairwise averaging,
+    a dot-product-based SAD using vdotq_u32, a 4x4 matrix transpose, and a DCT butterfly operation.
+    The target environment is an Arm-based Linux system with a recent C compiler (Clang or GCC)
+    and a Rust compiler installed. By the end, you can implement and assess SIMD routines for
+    Arm Cortex-A and Neoverse CPUs.
+  faqs:
+  - question: What do I need before running the examples?
+    answer: >-
+      Use an Arm-based computer running Linux with recent versions of a C compiler (GCC or Clang)
+      and a Rust compiler installed. No additional prerequisites are explicitly listed.
+  - question: Which compiler should I use to build the C examples?
+    answer: >-
+      You can use either GCC or Clang with a recent version on your Arm-based Linux system. A
+      Rust compiler is also required for the Rust portions.
+  - question: Which source files will I create, and what do they demonstrate?
+    answer: >-
+      You will create average_neon.c (pairwise averages), dotprod1.c (SAD using vdotq_u32), and
+      transpose1.c (4x4 uint16_t matrix transpose). You will also implement a DCT butterfly (fdct_round_shift),
+      with Rust equivalents introduced where appropriate.
+  - question: When should I use std::simd versus Neon intrinsics in Rust?
+    answer: >-
+      Use std::simd for portable SIMD across platforms and Neon intrinsics via std::arch for Arm-specific
+      code paths. The path shows how to combine this with feature detection and target attributes
+      for architecture-specific optimizations.
+  - question: How do I know the SIMD code is working and producing the right instructions?
+    answer: >-
+      The examples compute concrete results (averages, SAD, matrix transpose, and the butterfly
+      operation) that you can compare between C and Rust versions. You will also compare disassembly
+      output to examine the generated instructions.
+# END generated_summary_faq
 
 author: Konstantinos Margaritis
 
diff --git a/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md b/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md
index 92f6efddc4..7567c227e6 100644
--- a/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md
+++ b/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md
@@ -25,6 +25,57 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:52:19Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 36f7dfe13c8c940abbaad2b61620b3b1c6d97f580de15e573b45ef7760753797
+  summary_generated_at: '2026-06-01T21:19:22Z'
+  summary_source_hash: 36f7dfe13c8c940abbaad2b61620b3b1c6d97f580de15e573b45ef7760753797
+  faq_generated_at: '2026-06-02T21:52:19Z'
+  faq_source_hash: 36f7dfe13c8c940abbaad2b61620b3b1c6d97f580de15e573b45ef7760753797
+  summary: >-
+    This advanced Learning Path shows how to profile ExecuTorch models on Arm with SME2 acceleration
+    in approximately 90 minutes. You will set up a reusable Apple Silicon macOS workspace (Python
+    3.9+ and CMake 3.29+), build ExecuTorch runner binaries with SME2 enabled and disabled, export
+    PyTorch models to .pte, and run a two-pass analysis (timing-only and then trace-enabled).
+    The model-agnostic workflow produces operator-level and operator-category breakdowns (for
+    example, convolution, GEMM, data movement) so you can see how latency shifts when compute
+    speeds up under SME2. Optionally, you can run on an Android Armv9 device with SME2 after configuring
+    power management for consistent measurements. By the end, you can compare execution profiles
+    and make evidence-based optimization decisions.
+  faqs:
+  - question: What do I need on my host machine before starting the setup?
+    answer: >-
+      Use an Apple Silicon macOS system with Python 3.9 or later and CMake 3.29 or later. Basic
+      familiarity with ExecuTorch or PyTorch is expected.
+  - question: Do I need an Android device, and how should it be configured if I use one?
+    answer: >-
+      An Android device is optional and should have Armv9 with SME2 support for on-device testing.
+      If you use one, configure its power management settings to keep performance measurements
+      consistent.
+  - question: Which model format should I export, and is the profiling pipeline model-specific?
+    answer: >-
+      Export your model to ExecuTorch .pte format. After that, the same runners, scripts, and
+      analysis steps apply regardless of model architecture; see the EfficientSAM example in executorch/examples/models
+      for a concrete onboarding reference.
+  - question: How do I collect profiling data for comparison?
+    answer: >-
+      Build ExecuTorch runner binaries with SME2 enabled and disabled, then run the two-run analysis
+      consisting of a timing-only pass and a trace-enabled pass. The Learning Path also provides
+      structured agent skills that you can use to automate these actions in an AI assistant or
+      CI system.
+  - question: What result should I expect when enabling SME2, and how do I interpret the profiles?
+    answer: >-
+      Inference latency often improves significantly with SME2 enabled, which can shift execution
+      time to other parts of the model. Use the operator-level and operator-category breakdowns
+      to identify which operators benefit most and which become the new bottlenecks.
+# END generated_summary_faq
 
 author: Jason Zhu, Tyler Mullenbach, Damien Dooley 
 
diff --git a/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md b/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md
index a3d0643afa..7b18b7e44b 100644
--- a/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md
+++ b/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md
@@ -24,6 +24,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:52:57Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 09142517ecc64fba821062e1af4db3ac9d72f9adcd3f10c12d6fd5a4cf3daaf4
+  summary_generated_at: '2026-06-01T21:20:11Z'
+  summary_source_hash: 09142517ecc64fba821062e1af4db3ac9d72f9adcd3f10c12d6fd5a4cf3daaf4
+  faq_generated_at: '2026-06-02T21:52:57Z'
+  faq_source_hash: 09142517ecc64fba821062e1af4db3ac9d72f9adcd3f10c12d6fd5a4cf3daaf4
+  summary: >-
+    This Learning Path shows how to deploy Tinkerblox UltraEdge HPC-I on Arm for AI and mixed
+    workloads. You start by understanding the UltraEdge layered architecture (core, boost, prime),
+    then provision a Google Axion C4A VM on Google Cloud to build a Yocto image targeting the
+    NXP S32G‑VNP‑GLDBOX3. You install UltraEdge on Debian or Ubuntu by registering a device in
+    the Uncloud dashboard, and use the Tinkerblox CLI to deploy MicroPacs, inspect system state,
+    and observe runtime behavior. By the end, you can build with the UltraEdge MicroStack, deploy
+    MicroPacs on Linux-based compute, and prepare edge–cloud data flows. This advanced path assumes
+    Linux, container runtime/CNI, protocol, and edge‑cloud orchestration knowledge.
+  faqs:
+  - question: What do I need before running the Yocto image build steps?
+    answer: >-
+      Provision a Google Axion C4A VM on Google Cloud using the c4a-standard-32 type (16 vCPUs,
+      128 GB memory). An Ubuntu 22.04 environment with about 100 GB of disk space works well for
+      this Learning Path, and supported host architectures include AArch64 (arm64) and ARMv7.
+  - question: Which Ubuntu releases are supported as Yocto build hosts right now?
+    answer: >-
+      Tested build hosts include Ubuntu 20.04 LTS (AArch64) and Ubuntu 22.04 LTS (AArch64). As
+      of publication, Ubuntu 24.04 LTS is not a supported Yocto build host OS.
+  - question: How do I register a Debian or Ubuntu device for UltraEdge?
+    answer: >-
+      Log in to the Uncloud Dashboard and navigate to Device Management, then choose New Device.
+      This initializes and registers your edge device in the Uncloud ecosystem for subsequent
+      UltraEdge installation and management.
+  - question: How do I deploy and validate a sample microservice on UltraEdge?
+    answer: >-
+      Download a sample MPAC file from the Tinkerblox support repository and install it on your
+      device using the Tinkerblox CLI. Use the CLI to inspect system state and observe the microservice’s
+      runtime behavior.
+  - question: Do I need Docker or Kubernetes to run workloads in this Learning Path?
+    answer: >-
+      No. UltraEdge uses a lean, deterministic execution stack, and the procedures use the Tinkerblox
+      CLI rather than Docker or Kubernetes.
+# END generated_summary_faq
 
 author: Tinkerblox
 
diff --git a/content/learning-paths/cross-platform/topdown-compare/_index.md b/content/learning-paths/cross-platform/topdown-compare/_index.md
index b13dfbebb2..05a050e38e 100644
--- a/content/learning-paths/cross-platform/topdown-compare/_index.md
+++ b/content/learning-paths/cross-platform/topdown-compare/_index.md
@@ -22,6 +22,57 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:53:36Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 5f4b05e3d962476c67c4a4400c8fa71f04412f3f0354d5c3123f38af57791304
+  summary_generated_at: '2026-06-01T21:20:45Z'
+  summary_source_hash: 5f4b05e3d962476c67c4a4400c8fa71f04412f3f0354d5c3123f38af57791304
+  faq_generated_at: '2026-06-02T21:53:36Z'
+  faq_source_hash: 5f4b05e3d962476c67c4a4400c8fa71f04412f3f0354d5c3123f38af57791304
+  summary: >-
+    This advanced Learning Path shows how to compare Arm Neoverse and Intel x86 top-down performance
+    analysis on Linux using PMU counters. You will review Intel’s multilevel hierarchical model
+    and Arm’s two-stage approach for Neoverse V2, then build and run a backend-bound C benchmark
+    with GCC or Clang. Using Linux Perf on x86 and topdown-tool on Arm, you will collect and contrast
+    Retiring, Bad Speculation, Frontend Bound, and Backend Bound metrics, and evaluate differences
+    in slot-based accounting across the two architectures. Prerequisites include familiarity with
+    Perf and PMU counters, access to both an Intel x86 and an Arm Neoverse V2 Linux system, and
+    a basic understanding of CPU pipelines. Estimated time to complete is about 30 minutes.
+  faqs:
+  - question: What do I need before running the cross-platform example?
+    answer: >-
+      You need access to both an Arm Neoverse V2 Linux system and an Intel x86 Linux system. Familiarity
+      with Perf and PMU counters, and a basic understanding of CPU pipelines and bottlenecks,
+      are expected.
+  - question: Which tools should I install on each platform?
+    answer: >-
+      Install GCC or Clang and Perf on both systems. On Arm systems, also install topdown-tool;
+      use your Linux distribution’s package manager for installation information.
+  - question: How do I build and run the provided benchmark?
+    answer: >-
+      Copy the example source to a file named core-bound-div-chain.c and compile it with GCC or
+      Clang. Run the resulting executable with an iterations argument as indicated by the code
+      comment: ./core-bound-div-chain <iterations>.
+  - question: What result should I expect when I run the benchmark?
+    answer: >-
+      The benchmark is intended to be backend/core-bound via an FP64 divide chain. Collect measurements
+      with Perf on x86 and topdown-tool on Arm, and examine the Backend Bound, Frontend Bound,
+      Bad Speculation, and Retiring categories.
+  - question: How should I compare results across Arm and Intel given different counters and slot
+      models?
+    answer: >-
+      Counter names and formulas differ, and Intel uses issue-slot accounting while Neoverse V2
+      uses eight rename slots per cycle. Focus on comparing the shared top-level categories and
+      methodology rather than one-to-one event mappings; details will differ for other Neoverse
+      processors.
+# END generated_summary_faq
 
 author:
     - Jason Andrews
diff --git a/content/learning-paths/cross-platform/vectorization-comparison/_index.md b/content/learning-paths/cross-platform/vectorization-comparison/_index.md
index 8a503d001b..d2c3925f5f 100644
--- a/content/learning-paths/cross-platform/vectorization-comparison/_index.md
+++ b/content/learning-paths/cross-platform/vectorization-comparison/_index.md
@@ -21,6 +21,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:54:01Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 26450ae17f7ed4242c52456c2780ffce5fad36b56dfc4a8482e8f236f855e134
+  summary_generated_at: '2026-06-01T21:21:21Z'
+  summary_source_hash: 26450ae17f7ed4242c52456c2780ffce5fad36b56dfc4a8482e8f236f855e134
+  faq_generated_at: '2026-06-02T21:54:01Z'
+  faq_source_hash: 26450ae17f7ed4242c52456c2780ffce5fad36b56dfc4a8482e8f236f855e134
+  summary: >-
+    This advanced Learning Path shows how to migrate x86-64 SIMD code to Arm64 by mapping Intel
+    SSE/AVX/AMX to Arm Neon, SVE, and SME. You review migration strategies using autovectorization,
+    intrinsics, or library substitution, then work through a SAXPY kernel implemented in plain
+    C and with vector extensions on both Arm (Neon, SVE) and x86 (AVX2, AVX-512). On a Linux system
+    with Neon and SVE support, you build and run each version using GCC or Clang and observe how
+    vector width influences throughput. The expected outcome is an understanding of how Arm vector
+    extensions relate to x86 equivalents and a practical plan for porting existing SIMD code.
+    No additional prerequisites are listed beyond those stated; estimated duration is about 30
+    minutes.
+  faqs:
+  - question: What do I need before running the examples?
+    answer: >-
+      You should be comfortable with SIMD programming and compiler intrinsics, and have access
+      to Linux systems with Neon and SVE support. GCC or Clang are used to build the examples.
+  - question: Which compiler should I use to build the code?
+    answer: >-
+      Use GCC or Clang as listed in the Learning Path tools. The steps show how to build and run
+      the Arm and x86 variants of the SAXPY example.
+  - question: How do I map x86 SIMD intrinsics to Arm equivalents?
+    answer: >-
+      The overview explains how SSE, AVX, and AMX map to Arm Neon, SVE, and SME. Use this mapping
+      to guide intrinsics substitution or decide when autovectorization or libraries are more
+      appropriate.
+  - question: What result should I expect when I run the SAXPY variants?
+    answer: >-
+      You will build and run C, Neon, SVE, AVX2, and AVX-512 versions of a SAXPY kernel that computes
+      y[i] = a * x[i] + y[i]. The run results let you compare SIMD behavior and see how vector
+      width affects throughput.
+  - question: When should I use a library instead of writing intrinsics?
+    answer: >-
+      If a tuned library provides the operation (for example, BLAS for SAXPY), prefer the library.
+      The intrinsics-based examples are provided for learning and comparison.
+# END generated_summary_faq
 
 author:
     - Jason Andrews
diff --git a/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md b/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md
index aad9435bb0..2f8d3fa972 100644
--- a/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md
+++ b/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md
@@ -18,6 +18,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:54:37Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 14a22194d3fc73ebebb62db9c7cfbeabe0bd9acdaf340b2df52072be65d98655
+  summary_generated_at: '2026-06-01T21:21:48Z'
+  summary_source_hash: 14a22194d3fc73ebebb62db9c7cfbeabe0bd9acdaf340b2df52072be65d98655
+  faq_generated_at: '2026-06-02T21:54:37Z'
+  faq_source_hash: 14a22194d3fc73ebebb62db9c7cfbeabe0bd9acdaf340b2df52072be65d98655
+  summary: >-
+    This advanced Learning Path guides C/C++ developers on Arm Linux through restructuring data
+    from Array-of-Structures to Structure-of-Arrays to make SIMD vectorization more effective.
+    You will study data layout and alignment issues (such as 3D vec3 triplets versus 4-wide float
+    operations), incrementally modify a particle simulation, and progress to hand-written SIMD
+    using Arm Neon intrinsics. The path also references practical examples with Neon and SVE intrinsics.
+    Working on an Arm computer with GCC or Clang, you will create successive source files (simulation1.c
+    to simulation4.c) that illustrate alignment fixes, boundary checks, manual intrinsics, and
+    SoA transformations. The expected outcome is a clear understanding of why data layout matters
+    for SIMD on Arm and how to restructure code accordingly.
+  faqs:
+  - question: What do I need before running the examples?
+    answer: >-
+      You need an Arm computer running Linux with a recent version of Clang or GCC installed.
+      No other prerequisites are explicitly listed.
+  - question: How do I know if my current data layout is blocking vectorization?
+    answer: >-
+      If operations are organized as x, y, z triplets, the compiler may not emit SIMD instructions
+      because 32-bit float SIMD requires 4 elements. The path shows a memory layout diagram of
+      the object struct and highlights a 12-byte alignment issue that interferes with vectorization.
+  - question: Which files do I edit and in what order?
+    answer: >-
+      You start from simulation1.c, copy it to simulation2.c to add boundary checks and new types
+      (such as ctr4 and a box constant), then copy to simulation3.c for hand-written SIMD. Finally,
+      you create simulation4.c from provided code to study a Structure-of-Arrays version.
+  - question: When should I switch to hand-written intrinsics, and which ones are used?
+    answer: >-
+      If the compiler is not vectorizing as much as it could, the path has you convert the program
+      to hand-written SIMD in simulation3.c. This uses Arm Neon intrinsics and includes the <arm_neon.h>
+      header.
+  - question: Does this Learning Path cover both Neon and SVE intrinsics?
+    answer: >-
+      Yes. The description states practical examples using Neon and SVE intrinsics, though the
+      step-by-step code shown uses Neon explicitly.
+# END generated_summary_faq
 
 author: Konstantinos Margaritis
 
diff --git a/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md b/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
index 586431d3a1..1bd5773c7d 100644
--- a/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
+++ b/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
@@ -25,6 +25,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:55:45Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: bf887ef2481b51cf6c32e49e3eb1d5577346b7b9b1e4d6a604c559597b5306f0
+  summary_generated_at: '2026-06-01T21:22:34Z'
+  summary_source_hash: bf887ef2481b51cf6c32e49e3eb1d5577346b7b9b1e4d6a604c559597b5306f0
+  faq_generated_at: '2026-06-02T21:55:45Z'
+  faq_source_hash: bf887ef2481b51cf6c32e49e3eb1d5577346b7b9b1e4d6a604c559597b5306f0
+  summary: >-
+    This introductory path shows how to sample and profile CPU instructions on Windows on Arm
+    using WindowsPerf with the Arm Statistical Profiling Extension (SPE), demonstrated on a CPython
+    workload. You will install and use the SPE-enabled WindowsPerf build, verify that your Windows
+    on Arm machine supports SPE, and build CPython from source for AArch64 with Visual Studio
+    and Git. The steps pin the CPython debug binary to a specific core, run a large integer computation,
+    and use WindowsPerf sampling and record commands to collect and explore SPE events (such as
+    load events) from the workload. By the end, you will understand the basics of SPE sampling
+    and have hands-on experience collecting instruction-level samples for a native Windows on
+    Arm application.
+  faqs:
+  - question: What do I need before running the examples?
+    answer: >-
+      You need a Windows on Arm machine with CPU support for Arm SPE, WindowsPerf (driver and
+      wperf CLI) installed, Visual Studio, and Git. These are explicitly listed in the Setup step.
+  - question: How do I check if my Arm CPU supports SPE?
+    answer: >-
+      The Setup step includes guidance on verifying CPU support for Arm SPE. Follow that section
+      before proceeding with sampling or recording.
+  - question: Which WindowsPerf build should I use for SPE?
+    answer: >-
+      WindowsPerf release 3.8.0 includes a separate build with Arm SPE support located in the
+      SPE/ subdirectory of the release assets. Use that build when following the SPE steps.
+  - question: What workload is used to exercise CPython during sampling?
+    answer: >-
+      The path uses a debug-built CPython (python_d.exe) to compute 10**10**100, and pins the
+      process to CPU core 1. The Windows start command is used to launch and pin the process as
+      shown in the steps.
+  - question: In the wperf record example, what does the “--” mean and what data is captured?
+    answer: >-
+      The double dash separates wperf options from the arguments passed to the profiled program
+      (python_d.exe). The example records Arm SPE load events using arm_spe_0/ld=1/ on core 1
+      for 5 seconds, producing a recording you can inspect.
+# END generated_summary_faq
 
 author: Przemyslaw Wirkus
 
diff --git a/content/learning-paths/cross-platform/woa_azure/_index.md b/content/learning-paths/cross-platform/woa_azure/_index.md
index e63fc22cea..d92ac8f1a7 100644
--- a/content/learning-paths/cross-platform/woa_azure/_index.md
+++ b/content/learning-paths/cross-platform/woa_azure/_index.md
@@ -20,6 +20,51 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:56:12Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 367566dfec0a76685b1504c2c1a996e50c55e67b2f4c5515057c0f0f1384ef98
+  summary_generated_at: '2026-06-01T21:23:14Z'
+  summary_source_hash: 367566dfec0a76685b1504c2c1a996e50c55e67b2f4c5515057c0f0f1384ef98
+  faq_generated_at: '2026-06-02T21:56:12Z'
+  faq_source_hash: 367566dfec0a76685b1504c2c1a996e50c55e67b2f4c5515057c0f0f1384ef98
+  summary: >-
+    Learn how to deploy a Windows on Arm virtual machine in Microsoft Azure and connect to it
+    using Remote Desktop. This introductory path guides you through signing in to Azure, using
+    the Azure Marketplace to locate Arm-based images, creating a Windows on Arm VM, and establishing
+    an RDP session. You will also see how to discover other Arm-based offerings, with a note that
+    the same flow applies if you choose a Linux image instead of Windows. Prerequisites are an
+    Azure Cloud account and an RDP client. By the end, you will have a running Windows on Arm
+    instance in Azure.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an Azure Cloud account and an RDP client. You can sign in using either your personal
+      subscription or your organization’s subscription.
+  - question: Where do I start creating the Windows on Arm VM in Azure?
+    answer: >-
+      Sign in to your Azure account and use the Azure Marketplace to select a Windows on Arm image,
+      then create a virtual machine from that listing. The steps walk you through initiating the
+      VM from the Marketplace.
+  - question: How do I discover Arm-based image offerings in Azure?
+    answer: >-
+      Open the Azure Image Marketplace and review the available Arm-based listings. The path highlights
+      how to locate Windows on Arm and other Arm-based images.
+  - question: How do I connect to the VM after it is created?
+    answer: >-
+      Use your RDP client to connect to the Windows on Arm instance. The path uses RDP for access
+      so you can sign in to the Windows session.
+  - question: Can I use the same instructions to deploy a Linux image on Arm?
+    answer: >-
+      Yes. Select a Linux distribution instead of Windows during image selection, as noted in
+      the path.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md b/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
index c85fc13f92..76adc6f37b 100644
--- a/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
+++ b/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
@@ -22,6 +22,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:56:40Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: a56dce27c6a846bcf35b6e9505142f1445b22ff71530f1189700049d7b14e994
+  summary_generated_at: '2026-06-01T21:23:51Z'
+  summary_source_hash: a56dce27c6a846bcf35b6e9505142f1445b22ff71530f1189700049d7b14e994
+  faq_generated_at: '2026-06-02T21:56:40Z'
+  faq_source_hash: a56dce27c6a846bcf35b6e9505142f1445b22ff71530f1189700049d7b14e994
+  summary: >-
+    Learn to build and deploy a distributed Eclipse Zenoh system on Arm Linux devices, including
+    Raspberry Pi 4/5 and Arm servers or cloud instances. You will install the Rust-based Zenoh
+    stack, build core examples, and run a two-node publish/subscribe test, then add in-memory
+    storage and querying using a Zenoh daemon with z_put and z_get. The path also shows how to
+    containerize Zenoh with Docker to streamline multi-node distribution and repeatable testing.
+    Prerequisites include at least two Cortex-A devices running Linux and experience with ROS
+    2 applications. By the end, you can stand up and validate a basic multi-node Zenoh deployment
+    on Arm.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need at least two local Cortex-A devices running Linux, such as Raspberry Pi 4 or 5.
+      You can also use Arm servers or cloud instances. Experience with ROS 2 applications is expected.
+  - question: Do I have to use Docker to deploy across multiple devices?
+    answer: >-
+      No. You can copy the compiled binaries from ~/zenoh/target/release/ to each device. The
+      path also shows how to containerize with Docker for streamlined and consistent multi-node
+      testing.
+  - question: How do I know the Zenoh build on Raspberry Pi completed correctly?
+    answer: >-
+      After building Zenoh and its core examples, you should see release binaries under ~/zenoh/target/release/.
+      You will use these binaries in the deployment and example steps to confirm they run on your
+      devices.
+  - question: What network setup and topics are used in the pub/sub example?
+    answer: >-
+      Run the example across two devices on the same local network. The subscriber listens on
+      the key expression demo/example/**, and you should see it receive messages published under
+      that prefix.
+  - question: How do I validate the storage and query example is working?
+    answer: >-
+      Start the Zenoh daemon with in-memory storage, publish values with z_put, and retrieve them
+      with z_get. Being able to query previously published data—even after the publisher is offline—confirms
+      the storage engine is functioning.
+# END generated_summary_faq
 
 author: 
     - Odin Shen
diff --git a/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md b/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
index 8a0261f9c7..09e034bcda 100644
--- a/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
@@ -22,6 +22,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:57:22Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: d8c48c293b2d316d5e68e18ab9e81157ad114a64cf2efa6951374a55d25238d6
+  summary_generated_at: '2026-06-01T21:24:18Z'
+  summary_source_hash: d8c48c293b2d316d5e68e18ab9e81157ad114a64cf2efa6951374a55d25238d6
+  faq_generated_at: '2026-06-02T21:57:22Z'
+  faq_source_hash: d8c48c293b2d316d5e68e18ab9e81157ad114a64cf2efa6951374a55d25238d6
+  summary: >-
+    This Learning Path guides you through designing and integrating a custom AXI-Lite peripheral
+    with the Cortex-A9 Processing System on a Zybo Z7-10 board using Xilinx Vivado, then generating
+    a bitstream and writing a bare-metal C application in Vitis to read board switches and drive
+    LEDs. You will set up a Windows-based workspace, create and package a new AXI-Lite peripheral,
+    connect GPIO-style ports to the Zynq PS, and build a simple end-to-end system that demonstrates
+    LEDs reflecting switch states. This introductory path assumes some Verilog and basic SoC design
+    knowledge and requires a Zybo Z7-10. Estimated time to complete is about 60 minutes.
+  faqs:
+  - question: What do I need before starting this Learning Path?
+    answer: >-
+      You need a Zybo Z7-10 development board, some familiarity with Verilog, and a basic understanding
+      of System on Chip design. The flow targets a bare-metal application on a Cortex-A9.
+  - question: What project setup should I use in Vivado?
+    answer: >-
+      Create a new RTL Project in Vivado. On Windows, place your workspace in a path without spaces
+      (for example, C:/Workspace).
+  - question: Which option should I use to create the custom AXI-Lite peripheral?
+    answer: >-
+      In Vivado, select Tools -> Create and Package New IP, then choose the option to create a
+      new AXI4 peripheral. Provide a name for the IP and accept the default IP location if appropriate.
+  - question: How do I expose LEDs and switches from the custom peripheral?
+    answer: >-
+      Create ports in the block design: an led output (4 bits) and an sw input, then connect them
+      appropriately in the Vivado diagram. Ensure directions and widths match the intended board
+      connections.
+  - question: What steps complete the design and what should I expect when running the application?
+    answer: >-
+      Create the HDL Wrapper and generate the bitstream in Vivado, then use the Xilinx Vitis IDE
+      to write and run a bare-metal C program on the Cortex-A9. The program reads the switch state
+      and lights the LEDs based on the status of the switches.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md b/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
index 88f943f28d..63867bf1de 100644
--- a/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
@@ -25,6 +25,60 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:57:47Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 8585bb59efa67b3a92314e4382339fc5c0a14bb458931c0d98f2748379003857
+  summary_generated_at: '2026-06-01T21:24:49Z'
+  summary_source_hash: 8585bb59efa67b3a92314e4382339fc5c0a14bb458931c0d98f2748379003857
+  faq_generated_at: '2026-06-02T21:57:47Z'
+  faq_source_hash: 8585bb59efa67b3a92314e4382339fc5c0a14bb458931c0d98f2748379003857
+  summary: >-
+    This advanced Learning Path guides you through deploying a MobileNetV2 image classification
+    model to the Alif Ensemble E8 DevKit and running inference on the Ethos‑U85 NPU from the Cortex‑M55
+    High‑Performance core. You will compile the model with ExecuTorch’s ahead‑of‑time compiler
+    on an Arm‑based cloud instance, build ExecuTorch static libraries for a bare‑metal target,
+    create a CMSIS project in VS Code by cloning a Blinky template, integrate SEGGER RTT, and
+    adjust memory and linker settings. By the end, you will flash the firmware, run real‑time
+    inference on a test image, and verify results over RTT. Prerequisites include C/C++ experience,
+    the E8 DevKit with J‑Link, macOS on Apple Silicon with VS Code, and Arm‑based cloud access;
+    estimated time is 120 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need C/C++ and embedded development experience, an Alif Ensemble E8 DevKit with a USB-C
+      cable, and a SEGGER J-Link (included with the DevKit). You also need a macOS machine on
+      Apple Silicon with Visual Studio Code, plus an AWS account or access to an Arm-based cloud
+      instance.
+  - question: Why should I build on an Arm-based cloud instance instead of my local host?
+    answer: >-
+      ExecuTorch’s Arm backend build scripts are designed for native Arm compilation, and components
+      like the Vela compiler and CMSIS-NN target Arm. Using a Graviton-based EC2 instance avoids
+      the complexity of cross-compilation and lets you compile the model and build the ExecuTorch
+      static libraries natively.
+  - question: When creating the firmware project, which components must be included?
+    answer: >-
+      Duplicate the Blinky example to a new CMSIS project (mv2_runner) and include the ExecuTorch
+      libraries, the compiled MobileNetV2 model, and SEGGER RTT for debug output. Update the project
+      references so they point to mv2_runner rather than the original Blinky.
+  - question: How should I configure memory and linker settings for this workload?
+    answer: >-
+      Reconfigure MRAM allocation, stack/heap sizes, and the linker script to match the ML workload.
+      The embedded model is about 3.7 MB (MRAM/flash), the runtime and application code add roughly
+      800 KB, and inference needs approximately 7.6 MB of SRAM for memory pools and intermediate
+      tensors.
+  - question: What result should I expect after flashing, and how do I verify it?
+    answer: >-
+      The application initializes the Ethos-U85, loads the MobileNetV2 model via ExecuTorch, runs
+      inference on an embedded test image, and prints the classification result. Use SEGGER RTT
+      to view and verify the output in real time.
+# END generated_summary_faq
 
 author: Gabriel Peterson
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md b/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
index 22546a9728..722f7d2ae3 100644
--- a/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
@@ -25,6 +25,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T21:58:43Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 3ddb97eb97a4c7ede7951410086198ee793a9d452a79b607f211873971bd375d
+  summary_generated_at: '2026-06-01T21:25:21Z'
+  summary_source_hash: 3ddb97eb97a4c7ede7951410086198ee793a9d452a79b607f211873971bd375d
+  faq_generated_at: '2026-06-02T21:58:43Z'
+  faq_source_hash: 3ddb97eb97a4c7ede7951410086198ee793a9d452a79b607f211873971bd375d
+  summary: >-
+    Build a motion-detection device on a Raspberry Pi Pico (RP2040 Cortex‑M0+) using the Arduino
+    IDE on baremetal. This introductory Learning Path explains the differences between application
+    and embedded stacks, then walks you through writing a simple embedded application, adding
+    hardware interrupt handlers for a PIR motion sensor, and running it on the Pico with a piezo‑electric
+    buzzer to signal motion. You will practice interrupt-driven programming on Arm Cortex‑M and
+    deploy to real hardware in about 60 minutes. Prerequisites: Arduino IDE with the RP2040 board
+    support package installed, a Raspberry Pi Pico, a PIR sensor, and a piezo‑electric buzzer.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      Install the Arduino IDE with the RP2040 board support package and have a Raspberry Pi Pico,
+      a PIR sensor, and a piezo-electric buzzer. No other prerequisites are explicitly listed.
+  - question: How do I know the Arduino IDE is ready for RP2040 development?
+    answer: >-
+      Verify that the RP2040 board support package is installed so you can build and upload for
+      the Raspberry Pi Pico. The Learning Path assumes you are using Arduino IDE configured for
+      RP2040.
+  - question: Is an RTOS used, or is this bare-metal Arduino on RP2040?
+    answer: >-
+      This project runs on baremetal and uses hardware interrupts. The Learning Path also explains
+      how embedded stacks and RTOS-based designs differ from traditional application stacks.
+  - question: What result should I expect when I run the program on the Pico?
+    answer: >-
+      A simple motion-detection device: when the PIR sensor detects movement, the application
+      responds via an interrupt and signals using the piezo-electric buzzer. You will run this
+      on the Raspberry Pi Pico.
+  - question: What should I check if the buzzer doesn’t sound when motion is detected?
+    answer: >-
+      Confirm you can program the Raspberry Pi Pico from the Arduino IDE and that the PIR sensor
+      and buzzer are correctly connected for the GPIOs used in your program. Also check that your
+      interrupt handler is attached to the PIR input as described in the steps.
+# END generated_summary_faq
 
 author: Michael Hall
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/armds/_index.md b/content/learning-paths/embedded-and-microcontrollers/armds/_index.md
index ab1dc6d200..9a81b8cc4f 100644
--- a/content/learning-paths/embedded-and-microcontrollers/armds/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/armds/_index.md
@@ -19,6 +19,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:00:01Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 0103f51d42c230dbe75ff5b78ac15a33dfd2f2c0f4906fb665a8dd681512d2e1
+  summary_generated_at: '2026-06-01T21:25:49Z'
+  summary_source_hash: 0103f51d42c230dbe75ff5b78ac15a33dfd2f2c0f4906fb665a8dd681512d2e1
+  faq_generated_at: '2026-06-02T22:00:01Z'
+  faq_source_hash: 0103f51d42c230dbe75ff5b78ac15a33dfd2f2c0f4906fb665a8dd681512d2e1
+  summary: >-
+    Learn how to get productive with Arm Development Studio by importing and building an example
+    bare-metal project, then debugging it on a Fixed Virtual Platform (FVP) or on hardware using
+    a DSTREAM debug probe. Starting from a working, licensed installation, you launch the IDE,
+    open the workspace, and use the supplied Cortex-M3 FVP (a digital twin of the MPS2+ AN385
+    platform) with a ready-to-use .launch configuration to step through the code without target
+    hardware. The path also shows how to select a different Arm Compiler for Embedded version
+    at the project level. Some familiarity with embedded programming is assumed.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      Have Arm Development Studio installed with a valid license configured. The path assumes
+      some familiarity with embedded programming.
+  - question: How do I launch the IDE and set up the workspace?
+    answer: >-
+      Start the IDE from your OS applications menu or run the armds_ide command. On first launch,
+      accept the default workspace configuration by clicking Finish; the workspace is a base directory
+      on your host.
+  - question: Can I run the example without hardware, and which FVP does it target?
+    answer: >-
+      Yes. Arm Development Studio provides FVPs, and the supplied Cortex-M FVPs are digital twins
+      of the MPS2+ platform programmed for Cortex-M3 (AN385). If you have hardware and a DSTREAM
+      probe, you can choose to run on the board instead.
+  - question: Where is the FVP debug configuration and how do I use it?
+    answer: >-
+      The project includes startup_Cortex-M3_AC6_FVP.launch in the project folder. Double-click
+      it to inspect settings; it is a ready-to-use configuration to start an FVP debug session
+      from the IDE.
+  - question: How do I select a different Arm Compiler for Embedded version for my project?
+    answer: >-
+      Install the required compiler version using the Arm Compiler for Embedded install guide.
+      Then open Project Properties and adjust the C/C++ Build settings to select the desired compiler
+      version; Development Studio ships with the latest available at its release.
+# END generated_summary_faq
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/asm/_index.md b/content/learning-paths/embedded-and-microcontrollers/asm/_index.md
index 9654cf0bbb..d3f613c61b 100644
--- a/content/learning-paths/embedded-and-microcontrollers/asm/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/asm/_index.md
@@ -9,6 +9,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:01:17Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 24245102b7b6cefd0d4f67fbfaff1fb27c913de33665929ec3cb15293a1b3b51
+  summary_generated_at: '2026-06-01T21:26:11Z'
+  summary_source_hash: 24245102b7b6cefd0d4f67fbfaff1fb27c913de33665929ec3cb15293a1b3b51
+  faq_generated_at: '2026-06-02T22:01:17Z'
+  faq_source_hash: 24245102b7b6cefd0d4f67fbfaff1fb27c913de33665929ec3cb15293a1b3b51
+  summary: >-
+    Learn to write mixed C and assembly for Cortex-M microcontrollers using Keil MDK, following
+    the Arm Procedure Call Standard. You will set up a bare-metal Cortex-M4 project either in
+    Keil Studio (VS Code, CMSIS Solution) or in μVision, target the ARMCM4 device, and, in μVision,
+    add CMSIS Core and Device Startup components and use the Models Cortex-M Debugger with the
+    Cortex-M4 Fixed Virtual Platform. You will implement assembly subroutines (my_strcpy and my_capitalize),
+    call them from C, and step through execution to understand their operation. This introductory
+    path expects some familiarity with C/Assembly and an installed Keil MDK, and takes about 60
+    minutes to complete.
+  faqs:
+  - question: Which Keil environment should I use, and what setup steps differ?
+    answer: >-
+      You can use Keil Studio (VS Code) or µVision. In Keil Studio, create a CMSIS Solution (csolution),
+      select the ARMCM4 target, choose Blank Solution, and ensure Arm Compiler 6 is selected.
+      In µVision, create a new project, select ARMCM4, and add CMSIS > Core and Device > Startup.
+  - question: How do I run the example on a Cortex-M4 Fixed Virtual Platform instead of hardware?
+    answer: >-
+      This path uses the Cortex‑M4 FVP provided with MDK. In µVision, set the Debug option to
+      Models Cortex-M Debugger and open Settings to configure it for the FVP.
+  - question: How do I add the main C file in each environment?
+    answer: >-
+      In µVision, right‑click Source Group 1, choose Add New Item, select C file (.c), and name
+      it main.c. In Keil Studio, open main.c within the Source Files group of your CMSIS Solution.
+  - question: What assembly functions do I implement and how are they called?
+    answer: >-
+      Implement my_strcpy(const char *src, char *dst) and my_capitalize(char *str). The main C
+      function creates character arrays and calls these subroutines to copy and capitalize a string.
+  - question: What calling convention should the assembly subroutines follow?
+    answer: >-
+      Write the subroutines to conform to the Arm Procedure Call Standard, using Arm register
+      calling conventions. This enables the C code to call the assembly routines as shown in the
+      example.
+# END generated_summary_faq
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
index e6f5bc7caf..3bd693307b 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
@@ -23,6 +23,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:02:28Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 983afd3fcc565266c8f12588086e36d736540e23d0e748c94b5d2a45ab53d6ab
+  summary_generated_at: '2026-06-01T21:26:34Z'
+  summary_source_hash: 983afd3fcc565266c8f12588086e36d736540e23d0e748c94b5d2a45ab53d6ab
+  faq_generated_at: '2026-06-02T22:02:28Z'
+  faq_source_hash: 983afd3fcc565266c8f12588086e36d736540e23d0e748c94b5d2a45ab53d6ab
+  summary: >-
+    This introductory Learning Path shows how to prepare a custom Balena OS image, run it on Arm
+    Virtual Hardware as a virtual Raspberry Pi 4, and deploy a pre-built IoT application from
+    Balena Hub. Working from a Linux machine with root access, you create a Balena Cloud fleet,
+    start a Raspberry Pi 4 instance in Arm Virtual Hardware, and upload the balenaos_rpi4b.zip
+    firmware. You then deploy a Grafana dashboard from Balena Hub to view the state of your device.
+    Prerequisites include a Balena Cloud account, an Arm Virtual Hardware account, and some familiarity
+    with embedded Linux. By the end, you will have a managed virtual device and a running application
+    in about 30 minutes.
+  faqs:
+  - question: What do I need before I start?
+    answer: >-
+      You need a Balena Cloud account, an Arm Virtual Hardware account, and a Linux machine with
+      root access. A free Balena Cloud account supports up to 10 devices, and this path uses one
+      device. If you create a new AVH account, you are automatically enrolled in a free 30-day
+      trial.
+  - question: When and why do I create a fleet in Balena Cloud?
+    answer: >-
+      After signing up for Balena Cloud, create a fleet. A fleet groups devices and acts as the
+      single deployment target for your applications.
+  - question: In Arm Virtual Hardware, which device should I select and how do I provide the OS
+      image?
+    answer: >-
+      From the AVH dashboard, click Create Device and select Raspberry Pi 4. When prompted for
+      firmware, choose Upload your own firmware and provide the balenaos_rpi4b.zip image you prepared.
+  - question: How do I open Balena Hub and which example application should I deploy?
+    answer: >-
+      Use the Balena Hub button in the top right of the Balena Cloud dashboard to open Balena
+      Hub. Go to Apps and search for balena-app, which deploys a Grafana dashboard showing the
+      state of your Balena OS device.
+  - question: Can I follow this path without using the hosted Balena Cloud service?
+    answer: >-
+      OpenBalena can deploy Balena applications without the hosted service, but this Learning
+      Path uses Balena Cloud. Follow the steps as written to use the hosted workflow.
+# END generated_summary_faq
 
 author: Michael Hall
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
index 5ee92c8e18..945354e03f 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
@@ -21,6 +21,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:03:50Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 85845fd4a47960bca053aed8d87c1d1442a7f5de0be5caff349fc92c6980b07e
+  summary_generated_at: '2026-06-01T21:27:06Z'
+  summary_source_hash: 85845fd4a47960bca053aed8d87c1d1442a7f5de0be5caff349fc92c6980b07e
+  faq_generated_at: '2026-06-02T22:03:50Z'
+  faq_source_hash: 85845fd4a47960bca053aed8d87c1d1442a7f5de0be5caff349fc92c6980b07e
+  summary: >-
+    This introductory Learning Path guides embedded Linux developers through running a virtual
+    Raspberry Pi 4 on Arm Virtual Hardware and deploying AWS IoT Greengrass components to it.
+    You will create or use existing accounts for Arm Virtual Hardware and AWS, start a Raspberry
+    Pi Arm Virtual Hardware instance, set up AWS IoT Greengrass Core on the device, and use the
+    AWS IoT console to define and launch a Greengrass deployment of pre-built components. The
+    steps focus on essential configuration and deployment actions in a Linux environment. Prerequisites
+    include an Amazon AWS account, an Arm Virtual Hardware account, and some familiarity with
+    embedded Linux.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an Amazon AWS account, an Arm Virtual Hardware (AVH) account, and some familiarity
+      with embedded Linux. These are the only explicit prerequisites.
+  - question: Will I be charged by AWS or Arm Virtual Hardware during this tutorial?
+    answer: >-
+      AWS requires a credit card, but this Learning Path uses free tier only and can be completed
+      without incurring charges. New AVH accounts are automatically enrolled in a free 30-day
+      trial.
+  - question: Which virtual device does this Learning Path use?
+    answer: >-
+      It uses a Raspberry Pi 4 virtual device provided by Arm Virtual Hardware. You will start
+      this virtual device as part of the steps.
+  - question: Where do I create the AWS IoT Greengrass deployment?
+    answer: >-
+      In the AWS console, open the IoT Core service and navigate to Manage -> Greengrass devices
+      -> Deployments. Click Create to start a new Greengrass deployment.
+  - question: How do I change what runs on the device after deployment?
+    answer: >-
+      Modify the Greengrass deployment to change component configurations, add components, or
+      remove components. Deployments are designed to be updated in place.
+# END generated_summary_faq
 
 author: Michael Hall
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
index 7e83aa907d..2680af7ac4 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
@@ -21,6 +21,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:04:34Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: bd39d50c93219201ead5de5da627447a91a82ea9c65e232350facd0c3516bedc
+  summary_generated_at: '2026-06-01T21:27:33Z'
+  summary_source_hash: bd39d50c93219201ead5de5da627447a91a82ea9c65e232350facd0c3516bedc
+  faq_generated_at: '2026-06-02T22:04:34Z'
+  faq_source_hash: bd39d50c93219201ead5de5da627447a91a82ea9c65e232350facd0c3516bedc
+  summary: >-
+    This introductory Learning Path guides embedded developers through building and running Matter
+    reference examples on Arm Virtual Hardware, demonstrating communication between two Raspberry
+    Pi 4 virtual targets, and automating development with GitHub Actions on Linux. You will instantiate
+    AVH instances, fork and clone the connectedhomeip repository, run an example application,
+    and configure a self-hosted runner with a simplified workflow. You will also integrate the
+    AVH API—using JavaScript in this path—and add a GitHub secret to drive chip-tool commands
+    automatically. Prerequisites include an Arm Virtual Hardware 3rd Party Hardware account, a
+    GitHub account, and a Personal Access Token enabled to update GitHub Action workflows. Some
+    familiarity with embedded programming is assumed.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an Arm Virtual Hardware 3rd Party Hardware user account and a GitHub account. Generate
+      a GitHub Personal Access Token with permission to Update GitHub Action workflows and save
+      it locally. Some familiarity with embedded programming is assumed.
+  - question: Which Arm Virtual Hardware targets should I create, and how many?
+    answer: >-
+      Prepare Raspberry Pi 4 instances of Arm Virtual Hardware. You will use multiple instances
+      to demonstrate communication between two virtual hardware targets.
+  - question: How do I get the Matter sources into my AVH instances?
+    answer: >-
+      Fork the public connectedhomeip repository to your personal GitHub account. From the console
+      of each AVH instance, clone your fork so you can build and run the examples there.
+  - question: What should I do before configuring GitHub Actions in the repository?
+    answer: >-
+      If the lighting-app is still running, stop it with Ctrl+C. Then in .github/workflows, remove
+      the existing workflow files so you can add the new workflow used by this path with a self-hosted
+      runner.
+  - question: How do I enable API-based control of AVH in the workflow, and what result should
+      I expect?
+    answer: >-
+      Generate an AVH API Token from Profile > API and add it as a GitHub secret. The workflow
+      is extended (using JavaScript) to transmit chip-tool commands to your virtual devices via
+      the AVH API.
+# END generated_summary_faq
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
index 07c76a1e34..4af65d9c64 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
@@ -22,6 +22,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:05:16Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 398077be1406d0787b0a5d8dc254472da0d125b51b0907769cd4a3516ce9111b
+  summary_generated_at: '2026-06-01T21:28:14Z'
+  summary_source_hash: 398077be1406d0787b0a5d8dc254472da0d125b51b0907769cd4a3516ce9111b
+  faq_generated_at: '2026-06-02T22:05:16Z'
+  faq_source_hash: 398077be1406d0787b0a5d8dc254472da0d125b51b0907769cd4a3516ce9111b
+  summary: >-
+    This introductory Learning Path shows how to export a PaddlePaddle inference model for text
+    recognition, compile it with TVMC, and deploy it on the Arm Corstone-300 Fixed Virtual Platform
+    (FVP) with Cortex-M55 using Arm Virtual Hardware. You will work with a PaddleOCR text recognition
+    model in a bare-metal target environment, using tools listed in the path such as TVMC, GCC,
+    Paddle, and Arm Virtual Hardware. The steps include an OCR overview and an end-to-end workflow
+    from model preparation through final execution on the FVP. Prerequisites include basic familiarity
+    with embedded and AI/ML development and an AWS account to subscribe to the Arm Virtual Hardware
+    AMI. Expected duration is about 30 minutes.
+  faqs:
+  - question: What do I need before running the workflow?
+    answer: >-
+      You need an AWS account to subscribe to the Arm Virtual Hardware AMI, basic familiarity
+      with embedded programming, and some experience with AI/ML development. No other prerequisites
+      are explicitly listed.
+  - question: Do I need to train a model, or does this use a pre-trained PaddlePaddle model?
+    answer: >-
+      The steps deploy pre-trained PaddlePaddle models. You export a Paddle inference model and
+      compile it with TVMC before deployment.
+  - question: Which Arm platform and runtime does this target?
+    answer: >-
+      The deployment targets the Corstone-300 FVP with an Arm Cortex-M55 processor, included with
+      Arm Virtual Hardware. The operating system context is bare-metal.
+  - question: How do I start the environment on AWS?
+    answer: >-
+      Begin by launching the Arm Virtual Hardware AMI on AWS. The Learning Path then guides you
+      through the end-to-end flow from model export to execution on the Corstone-300 FVP.
+  - question: What result should I expect after completing the steps?
+    answer: >-
+      You should complete model export and TVMC compilation and see the PaddleOCR text recognition
+      model execute on the Corstone-300 FVP with Cortex-M55. Successful final execution on the
+      FVP indicates the deployment worked.
+# END generated_summary_faq
 
 author: Liliya Wu
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
index a7aa92aeec..a287d819ec 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
@@ -19,6 +19,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:06:31Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: aaff31b8917320c53825f3e7410b703bc7c7e273b1963fd80727b6b874f50566
+  summary_generated_at: '2026-06-01T21:28:41Z'
+  summary_source_hash: aaff31b8917320c53825f3e7410b703bc7c7e273b1963fd80727b6b874f50566
+  faq_generated_at: '2026-06-02T22:06:31Z'
+  faq_source_hash: aaff31b8917320c53825f3e7410b703bc7c7e273b1963fd80727b6b874f50566
+  summary: >-
+    This introductory Learning Path guides you to create and integrate a virtual LED peripheral
+    using the Virtual IO (VIO) interface in Arm Virtual Hardware (AVH) to simulate real-world
+    peripherals. You will work in an AWS environment by launching the AVH Amazon Machine Image
+    (AMI), install the Tkinter Python package, and use an example project that demonstrates connecting
+    a virtual LED to a bare-metal application. The steps focus on using AVH virtual interfaces
+    and navigating the provided leds_example project. Intended for developers new to AVH and relevant
+    to Cortex-M and Corstone use cases, prerequisites include a valid AWS account and some familiarity
+    with Python; no other requirements are explicitly listed. Estimated completion time is about
+    20 minutes.
+  faqs:
+  - question: What do I need before running the example?
+    answer: >-
+      You need a valid AWS account and some familiarity with Python. No other prerequisites are
+      explicitly listed.
+  - question: How do I launch the environment used in this Learning Path?
+    answer: >-
+      Launch the Arm Virtual Hardware AMI in your AWS account. For full instructions, refer to
+      the Arm Virtual Hardware install guide.
+  - question: How do I install the Tkinter dependency in the AVH instance?
+    answer: >-
+      In the AVH terminal, install it with: sudo apt install -y python3-tk. This provides the
+      Tkinter Python interface to Tcl/Tk used by the example.
+  - question: How do I obtain the example project files?
+    answer: >-
+      In your AVH terminal, clone the example project repository and navigate into the leds_example
+      directory. The steps guide you through cloning and changing to the correct directory.
+  - question: Do I need physical hardware to test the LED peripheral?
+    answer: >-
+      No. The example uses AVH Virtual Interfaces, specifically the Virtual IO (VIO) interface,
+      to simulate a real-world LED peripheral.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md b/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
index 2a41c67206..f5c101d603 100644
--- a/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
@@ -25,6 +25,57 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:07:49Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 0e653bdbf5e5b08a6a00ba68e5ed215a0082e22c634d7d632d088851d48bb01c
+  summary_generated_at: '2026-06-01T21:29:13Z'
+  summary_source_hash: 0e653bdbf5e5b08a6a00ba68e5ed215a0082e22c634d7d632d088851d48bb01c
+  faq_generated_at: '2026-06-02T22:07:49Z'
+  faq_source_hash: 0e653bdbf5e5b08a6a00ba68e5ed215a0082e22c634d7d632d088851d48bb01c
+  summary: >-
+    This advanced Learning Path guides you through building an end-to-end IoT solution in Azure
+    for Arm devices using Python and Visual Studio Code. You will set up Azure IoT Hub, register
+    a device, and stream telemetry using the Azure IoT SDK. The path shows how to process data
+    with Azure Stream Analytics, persist it in Azure Cosmos DB, trigger alerts and aggregate readings
+    with Azure Functions, and publish aggregated results to a public-facing web app hosted on
+    Azure Blob Storage. The steps target Windows, Linux, and macOS. Prerequisites include Python
+    3, Visual Studio Code, and an Azure account with permissions to create IoT Hub, Functions,
+    and Cosmos DB resources.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need Python 3, Visual Studio Code, and an active Azure account with permissions to create
+      resources such as IoT Hub, Azure Functions, and Cosmos DB. Development can be done on Windows,
+      Linux, or macOS.
+  - question: Do I need physical Arm hardware, or can I simulate device telemetry?
+    answer: >-
+      The path includes a Python-based telemetry simulator that generates sensor readings. Physical
+      hardware requirements are not explicitly listed, though the content references streaming
+      from an Arm64-powered IoT device.
+  - question: Which Azure services will I create and how are they used in the workflow?
+    answer: >-
+      You will use Azure IoT Hub for device communication, Azure Stream Analytics to process and
+      route telemetry, and Azure Cosmos DB to store data. Azure Functions monitor thresholds and
+      aggregate readings, and results are published to a web app hosted on Azure Blob Storage.
+  - question: How do I know my simulator is successfully sending data to Azure IoT Hub?
+    answer: >-
+      After configuring your Python app to connect securely to IoT Hub, you should observe continuous,
+      real-time telemetry streaming as described in the steps. Subsequent Stream Analytics inputs
+      and queries confirm that messages from IoT Hub are being received and processed.
+  - question: What outcome should I expect after configuring Stream Analytics and Cosmos DB?
+    answer: >-
+      Stream Analytics will process and route incoming telemetry, and the data will be persisted
+      into Cosmos DB. This stored data enables the next steps: Azure Functions for threshold-based
+      alerts and aggregation, followed by publishing aggregated results to a public-facing Blob
+      Storage web app.
+# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md b/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md
index 87c3e071a1..acf959d621 100644
--- a/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md
@@ -22,6 +22,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:08:50Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 6521f17d1a10ab8871d13917923f7f64320f69301b4c0c5f5b528163d3cb43c6
+  summary_generated_at: '2026-06-01T21:29:37Z'
+  summary_source_hash: 6521f17d1a10ab8871d13917923f7f64320f69301b4c0c5f5b528163d3cb43c6
+  faq_generated_at: '2026-06-02T22:08:50Z'
+  faq_source_hash: 6521f17d1a10ab8871d13917923f7f64320f69301b4c0c5f5b528163d3cb43c6
+  summary: >-
+    Build and run a bare-metal Armv8-A “Hello World” on a Fixed Virtual Platform, then extend
+    it with minimal boot code, UART output, and basic exception handling. You will use Arm Development
+    Studio or the standalone Arm Compiler for Embedded with Arm Fixed Virtual Platforms (FVP),
+    targeting the FVP_Base_AEMvA model that implements four processors. The steps include creating
+    a project, adding a reset handler to park secondary cores and run on one, replacing semihosting-based
+    printf with PL011 UART output, and enabling asynchronous exceptions with GICv3 and a timer
+    interrupt routed to EL3. Some familiarity with embedded programming is assumed. Estimated
+    time to complete is about 60 minutes.
+  faqs:
+  - question: What tools do I need before starting?
+    answer: >-
+      Install Arm Development Studio and configure your license, or install Arm Compiler for Embedded
+      and Arm Fixed Virtual Platforms individually. The Learning Path also references an example
+      Docker image that includes these tools.
+  - question: Which Fixed Virtual Platform should I use to run the example?
+    answer: >-
+      Use the FVP_Base_AEMvA Architecture Envelope Model. It is a generic Arm Architecture platform
+      implementing 4 processors.
+  - question: How do I ensure the application runs on a single core after reset?
+    answer: >-
+      Create a minimal reset handler at EL3 that reads MPIDR_EL1 to identify the core and parks
+      all but one processor. The application then executes on the selected processor.
+  - question: How do I know if printf is using semihosting and how do I redirect output?
+    answer: >-
+      Import the symbol __use_no_semihosting to detect or disable semihosting. Modify the code
+      to send output to the PL011 UART provided by the FVP; note that semihosting uses HLT and
+      would halt on real hardware without a debugger.
+  - question: How are interrupts configured in the event-driven example?
+    answer: >-
+      Asynchronous exceptions are enabled and routed to EL3. You initialize GICv3 in gic.s and
+      configure the timer as an interrupt source.
+# END generated_summary_faq
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md b/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
index d8de531b83..c061574554 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
@@ -22,6 +22,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:09:54Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 3394bf994039e441d57dced2abb64d725077d4672e0e68dc71f1de50e58f0408
+  summary_generated_at: '2026-06-01T21:29:58Z'
+  summary_source_hash: 3394bf994039e441d57dced2abb64d725077d4672e0e68dc71f1de50e58f0408
+  faq_generated_at: '2026-06-02T22:09:54Z'
+  faq_source_hash: 3394bf994039e441d57dced2abb64d725077d4672e0e68dc71f1de50e58f0408
+  summary: >-
+    This introductory path shows how to deploy containerized embedded applications and firmware
+    to a Cortex-M core from a Linux-based Cortex-A application core using the OCI-compatible hybrid-runtime
+    with containerd and K3s on Arm Virtual Hardware. You provision an i.MX 8M Plus model in AVH,
+    review the hybrid-runtime components, run a Hello World firmware container via containerd’s
+    io.containerd.hybrid runtime, and verify creation with ctr commands. You also set up a single-node
+    K3s cluster configured to use containerd with selected components disabled for embedded use.
+    Objectives include building the hybrid-runtime components and a firmware container image.
+    Prerequisites are an AVH account and, if you will build locally, an Arm Linux host machine.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      You need a valid Arm Virtual Hardware account. If you plan to build your own runtime and
+      container image, you also need access to an Arm Linux host machine.
+  - question: Which Arm Virtual Hardware device should I create?
+    answer: >-
+      Create a device in the Default Project and select the i.MX 8M Plus platform. This model
+      runs four Cortex-A53 processors and is used for the hybrid edge setup.
+  - question: Which runtime should I specify when starting a container with containerd?
+    answer: >-
+      Use the hybrid runtime by passing --runtime io.containerd.hybrid to ctr run. The example
+      image is ghcr.io/smarter-project/hybrid-runtime/hello_world_imx8mp:latest with a container
+      name such as test.
+  - question: How do I verify that the container started correctly with containerd?
+    answer: >-
+      Run ctr c ls to list containers. You should see your container (for example, test) with
+      the hello_world_imx8mp:latest image and the io.containerd.hybrid runtime.
+  - question: How should I install and configure K3s for this demo?
+    answer: >-
+      Set INSTALL_K3S_EXEC for a single-node server and include the provided flags to disable
+      traefik, metrics-server, coredns, and local-storage, set flannel-backend=none, cluster-dns
+      to 169.254.0.2, and point to containerd via --container-runtime-endpoint. Then run: curl
+      -sfL https://get.k3s.io | INSTALL_K3S_EXEC=$INSTALL_K3S_EXEC sh -s -
+# END generated_summary_faq
 
 author: Basma El Gaabouri
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md
index a9e7047df0..1052d9d3db 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md
@@ -19,6 +19,58 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:10:56Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: d8c73d658fa7a8051131276b8567cbd7376aac3b8f6e87c8ecdf224443c3ef99
+  summary_generated_at: '2026-06-01T21:30:25Z'
+  summary_source_hash: d8c73d658fa7a8051131276b8567cbd7376aac3b8f6e87c8ecdf224443c3ef99
+  faq_generated_at: '2026-06-02T22:10:56Z'
+  faq_source_hash: d8c73d658fa7a8051131276b8567cbd7376aac3b8f6e87c8ecdf224443c3ef99
+  summary: >-
+    Learn how to create, build, and debug a basic RTX5 RTOS application using Keil μVision in
+    Keil MDK. You will install or update CMSIS packs, initialize RTX5 via the CMSIS-RTOS2 API
+    (including SysTick setup using SystemCoreClockUpdate), implement main() and an app_main thread
+    that launches three RTOS threads, then build and run the project on an FVP from within the
+    IDE. You will observe RTOS activity with the RTX RTOS watch window and route printf output,
+    using Event Recorder when semihosting is unavailable. The path is introductory, targets Cortex‑M
+    development, and includes notes for Arm Development Studio users. Prerequisites are an installation
+    of Keil MDK or Arm Development Studio (MDK recommended) and some familiarity with CMSIS.
+  faqs:
+  - question: What do I need installed before running the steps, and which IDE should I use?
+    answer: >-
+      Install Arm Keil MDK or Arm Development Studio; Keil MDK is recommended. Some familiarity
+      with CMSIS is assumed. If you use Arm Keil Studio for Visual Studio Code, follow the separate
+      path for Keil Studio (VS Code).
+  - question: How do I install the required CMSIS components for the project?
+    answer: >-
+      Open the Pack Installer and install the latest CMSIS packs. The Learning Path assumes you
+      use the most up-to-date CMSIS content.
+  - question: Which source files do I create, and where do I add them in the project?
+    answer: >-
+      Create main.c and app_main.c by right-clicking the Source folder under the FVP target, choosing
+      Add new item, and selecting C file (.c). main.c contains the system and RTX5 initialization;
+      app_main starts and manages additional threads.
+  - question: How do I build, start the FVP, and observe the RTOS during debug in Keil MDK?
+    answer: >-
+      Save all files, build with F7, then click Debug (Ctrl+F5) to launch the FVP and enter debug
+      mode. Use View > Watch Windows > RTX RTOS to inspect RTOS features and View > Serial Windows
+      > Debug (printf) for printf output. Click Run (F5) to start and Stop when finished.
+  - question: How do I enable Event Recorder for printf output in Keil MDK, and when should I
+      use it?
+    answer: >-
+      Because Keil MDK does not support semihosting here, use CMSIS-View Event Recorder for printf
+      functionality. In Manage Run-Time Environment, enable CMSIS-View > Event Recorder (DAP variant),
+      set CMSIS-Compiler > STDOUT (API) to Event recorder, and enable CMSIS-Compiler > Core. In
+      Arm Development Studio, Event Recorder and Component Viewer are not supported, so skip this
+      section.
+# END generated_summary_faq
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md
index ad350298d4..5f76d8982c 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md
@@ -21,6 +21,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:11:43Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: f4d1ab3448590c5654e2479937c9eec3e378d05229b29a45b8675139f40ac177
+  summary_generated_at: '2026-06-01T21:30:42Z'
+  summary_source_hash: f4d1ab3448590c5654e2479937c9eec3e378d05229b29a45b8675139f40ac177
+  faq_generated_at: '2026-06-02T22:11:43Z'
+  faq_source_hash: f4d1ab3448590c5654e2479937c9eec3e378d05229b29a45b8675139f40ac177
+  summary: >-
+    Learn to create, configure, and debug a basic RTX5 RTOS application for Arm Cortex-M using
+    Keil Studio for VS Code and the CMSIS-RTOS2 API. You will set up a new csolution project,
+    configure the Run-Time Environment (including C Startup), initialize the kernel by setting
+    up SysTick with SystemCoreClockUpdate(), and implement an app_main thread that creates multiple
+    RTOS threads. The steps target the supplied Cortex-M4 Fixed Virtual Platform (FVP), with build
+    and debug driven from the VS Code environment. By the end, you can build, run, and observe
+    thread output in the Debug Console. Prerequisites are installation of Arm Keil Studio for
+    VS Code and some familiarity with CMSIS.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      Install Arm Keil Studio for VS Code. Some familiarity with CMSIS is assumed.
+  - question: Which target does this use, and can I run it on other hardware?
+    answer: >-
+      The steps are written for the supplied Cortex-M4 Fixed Virtual Platform (FVP). You can also
+      run the project on other devices supported by CMSIS-Pack.
+  - question: What project should I create and what initial setup is required?
+    answer: >-
+      Create a csolution project in Keil Studio for VS Code. When configuring the project's Run-Time
+      Environment, add the system initialization code (C Startup).
+  - question: How do I set up the OS and create threads?
+    answer: >-
+      Configure the SysTick timer using SystemCoreClockUpdate(), then initialize and start RTX5.
+      Implement app_main to create threads with the CMSIS-RTOS2 API (the example uses three threads,
+      but the number and names are flexible).
+  - question: How do I build, debug, and verify that it works?
+    answer: >-
+      In the CMSIS Extension view, save your files and click the hammer icon to build. Start debugging
+      with the Debug icon or the Run and Debug view, select your configured debug connection to
+      launch the FVP, and expect to see thread messages printed in the Debug Console once the
+      OS is initialized.
+# END generated_summary_faq
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md b/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
index d30fce6604..82af008880 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
@@ -24,6 +24,57 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:13:13Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 69526ee8b840c8f5d77d49349d2f0cc7ff4594fec5e4311984ef72a0672d3462
+  summary_generated_at: '2026-06-01T21:31:12Z'
+  summary_source_hash: 69526ee8b840c8f5d77d49349d2f0cc7ff4594fec5e4311984ef72a0672d3462
+  faq_generated_at: '2026-06-02T22:13:13Z'
+  faq_source_hash: 69526ee8b840c8f5d77d49349d2f0cc7ff4594fec5e4311984ef72a0672d3462
+  summary: >-
+    This advanced Learning Path shows how to prototype DSP algorithms in Python using the CMSIS-DSP
+    Python package and understand how the Python API maps to the CMSIS-DSP C implementation for
+    Arm Cortex-M and Cortex-A platforms. On Linux, Windows, or macOS, you will set up a Python
+    virtual environment, install cmsisdsp, Jupyter, and NumPy, and use a Jupyter notebook to load
+    a sample “yes/no” audio file from an Arm repository. You will implement a simple energy-based
+    voice activity detector, apply overlapping Hanning windows, and build a NumPy reference for
+    a noise-suppression workflow. Prerequisites include familiarity with Python and DSP concepts,
+    working knowledge of C, prior exposure to CMSIS-DSP, and Python installed.
+  faqs:
+  - question: What do I need before running the notebook?
+    answer: >-
+      You need Python installed, familiarity with Python and DSP concepts, working knowledge of
+      C, and prior exposure to CMSIS-DSP. The path targets Linux, Windows, and macOS. No additional
+      prerequisites are explicitly listed.
+  - question: Should I create a Python virtual environment and which packages do I install?
+    answer: >-
+      Yes, the steps use a Python virtual environment. Install cmsisdsp (which also installs NumPy)
+      and then install the jupyter package.
+  - question: Where does the sample audio come from and how is it used?
+    answer: >-
+      The notebook loads a yesno.wav file from an Arm demo repository on GitHub using urlopen.
+      You play it in the notebook with an Audio widget to inspect the noisy speech used in the
+      subsequent steps.
+  - question: How do I know my VAD and noise suppression steps are working?
+    answer: >-
+      You implement a simple energy-based VAD with a manually tuned threshold and then build a
+      NumPy reference for noise suppression using overlapping windows and a Hanning window via
+      dsp.arm_hanning_f32. The path relies on iterative tuning and listening/inspection in the
+      notebook; specific validation criteria beyond this are not explicitly listed.
+  - question: How does the Python code relate to the CMSIS-DSP C implementation on Arm cores?
+    answer: >-
+      The CMSIS-DSP Python package provides APIs that map to CMSIS-DSP C functions, helping you
+      prototype in Python before building and porting to C. The underlying C library is optimized
+      for Arm Cortex-M and Cortex-A, including DSP extensions on M4/M7, Helium on M55/M85, and
+      Neon on Cortex-A55 and other Cortex-A cores.
+# END generated_summary_faq
 
 author: Christophe Favergeon
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md b/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
index db6fe71c57..94a407cfe7 100644
--- a/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
@@ -21,6 +21,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:14:04Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 0b7a5895a87de83903c223215845b6c840602663838c0682f46e50d139d69c59
+  summary_generated_at: '2026-06-01T21:31:37Z'
+  summary_source_hash: 0b7a5895a87de83903c223215845b6c840602663838c0682f46e50d139d69c59
+  faq_generated_at: '2026-06-02T22:14:04Z'
+  faq_source_hash: 0b7a5895a87de83903c223215845b6c840602663838c0682f46e50d139d69c59
+  summary: >-
+    This introductory path shows how to implement context switching on Arm Cortex-M processors
+    in a bare-metal environment using the Memory Protection Unit (MPU) and the SysTick exception.
+    You will build and run an open-source example from the Armv8-M Memory Model and MPU User Guide
+    repository that demonstrates simple real-time kernel context switching between two threads
+    using MPU regions. The workflow uses Arm Development Studio 2022.1 with Arm Compiler for Embedded
+    6.18, Fast Models Fixed Virtual Platforms 11.18, and CMSIS 5.8.0. By completing the steps,
+    you will understand context switching basics, program the MPU, apply SysTick with context
+    switching operations, and successfully build and run the example. Basic familiarity with Cortex-M
+    is expected.
+  faqs:
+  - question: Where do I get the example project used in this Learning Path?
+    answer: >-
+      The source code is available in the GitHub repository that accompanies the Armv8-M Memory
+      Model and MPU User Guide. The example demonstrates simple real-time kernel context switching
+      between two threads.
+  - question: Which tool versions should I use to build and run the example?
+    answer: >-
+      Use Arm Development Studio 2022.1, Arm Compiler for Embedded 6.18, Fast Models Fixed Virtual
+      Platforms (FVP) 11.18, and CMSIS 5.8.0 as listed in the Learning Path.
+  - question: Where is the example intended to run?
+    answer: >-
+      The example targets a bare-metal environment on Arm Cortex-M processors. The listed tools
+      include Fast Models Fixed Virtual Platforms (FVP) 11.18 for running the example.
+  - question: How do MPU and SysTick feature in the example?
+    answer: >-
+      You will program MPU regions and use the SysTick exception as part of the context switching
+      operations. The example shows switching between two threads using these features.
+  - question: What should I check if the project does not build or run as expected?
+    answer: >-
+      Confirm you are using the specified tool versions and CMSIS 5.8.0. Also ensure you are building
+      the example from the GitHub repository associated with the Armv8-M Memory Model and MPU
+      User Guide.
+# END generated_summary_faq
 
 author: Uma Ramalingam
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md b/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
index d2b5ac0825..00c482eeb1 100644
--- a/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
@@ -19,6 +19,50 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:15:46Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: f989929b11c48df42c2701dc71516cec0b8637b4c639c3dbbf6ac5e7440c8a56
+  summary_generated_at: '2026-06-01T21:31:57Z'
+  summary_source_hash: f989929b11c48df42c2701dc71516cec0b8637b4c639c3dbbf6ac5e7440c8a56
+  faq_generated_at: '2026-06-02T22:15:46Z'
+  faq_source_hash: f989929b11c48df42c2701dc71516cec0b8637b4c639c3dbbf6ac5e7440c8a56
+  summary: >-
+    Learn to configure and run code coverage in Keil MDK using Fixed Virtual Platforms (FVPs)
+    for Cortex-M targets. You will import and build the CMSIS-RTOS2 Blinky (uVision Simulator)
+    example for ARMCM3 from the Pack Installer, set up debugging on the supplied Cortex-M FVP,
+    execute the application, and view the Code Coverage report to see which code paths your tests
+    exercise, such as all cases of a C switch statement. This introductory path assumes basic
+    familiarity with Keil MDK and takes about 15 minutes. By the end, you can run a project on
+    an FVP and understand the basics of the coverage report.
+  faqs:
+  - question: Do I need real target hardware to follow this path?
+    answer: >-
+      No. While MDK can perform code coverage with FVPs or real hardware, this Learning Path uses
+      the supplied Cortex-M FVP, so you can complete it without hardware.
+  - question: What do I need before I start?
+    answer: >-
+      You must have Keil MDK installed, and basic familiarity with MDK is assumed. No other explicit
+      prerequisites are listed.
+  - question: Which device and example should I select in the Pack Installer?
+    answer: >-
+      In the Devices tree, select ARM > ARM Cortex M3 > ARMCM3. Then open the Examples tab and
+      copy the CMSIS-RTOS2 Blinky (uVision Simulator) example, open it in MDK, and build.
+  - question: Can I use a different project instead of the CMSIS-RTOS2 Blinky example?
+    answer: >-
+      Yes. You can perform code coverage on any project that runs on a suitable target, but this
+      path uses a standard RTX example that runs on the supplied Cortex-M FVP.
+  - question: What should I look for in the Code Coverage report?
+    answer: >-
+      The report highlights which areas of your code were executed by your tests. A common check
+      is verifying that all cases in a C switch statement have been exercised.
+# END generated_summary_faq
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md b/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
index b02cf4e982..302a094d16 100644
--- a/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
@@ -20,6 +20,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:16:41Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 9d9e4c170fa318a9875c888c2c812ceb27c59f56c4b0dd7e0ae87dd31bb5783c
+  summary_generated_at: '2026-06-01T21:38:11Z'
+  summary_source_hash: 9d9e4c170fa318a9875c888c2c812ceb27c59f56c4b0dd7e0ae87dd31bb5783c
+  faq_generated_at: '2026-06-02T22:16:41Z'
+  faq_source_hash: 9d9e4c170fa318a9875c888c2c812ceb27c59f56c4b0dd7e0ae87dd31bb5783c
+  summary: >-
+    Learn how to establish peer-to-peer device-to-device communication at the edge using the Device
+    Connect Edge SDK in a Python environment, with no hardware required. You will build two simulated
+    devices on the same mesh: a sensor that publishes temperature and humidity readings, and a
+    threshold monitor that subscribes and raises an alert when a configurable limit is crossed.
+    Along the way, you will work with the SDK’s developer model (DeviceDriver, decorators, and
+    DeviceRuntime) and see how discovery, pub/sub, and RPC fit together. The walkthrough uses
+    uv to manage the project and dependencies, and includes using Device Connect agent tools to
+    discover devices and invoke their RPCs. Target platforms include Linux, macOS, and Windows.
+    Prerequisite: basic familiarity with Python and the command line. Estimated time: about 25
+    minutes.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      You should be comfortable with Python and the command line. The steps support Linux, macOS,
+      and Windows, and no hardware is required.
+  - question: Do I need a broker or cloud service to complete the device-to-device setup?
+    answer: >-
+      No. The walkthrough stands up peer-to-peer communication between two devices with no broker
+      or cloud service in between.
+  - question: Which tool is used to manage the Python project and dependencies?
+    answer: >-
+      The walkthrough uses uv to manage the project and its Python dependencies. uv will resolve
+      a compatible Python for the environment.
+  - question: How are devices defined and brought online with Device Connect?
+    answer: >-
+      You subclass DeviceDriver from device_connect_edge.drivers and annotate methods and properties
+      with primitives. DeviceRuntime brings the driver online and wires it into discovery, pub/sub,
+      and RPC.
+  - question: How do I know the two simulated devices are discoverable and callable?
+    answer: >-
+      You will use the Device Connect agent tools to discover both devices on the mesh and invoke
+      their RPCs. Successful discovery and RPC calls indicate the setup is working as intended.
+# END generated_summary_faq
 
 author:
     - Kavya Sri Chennoju
diff --git a/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md b/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md
index 328f9398b8..e007708cda 100644
--- a/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md
@@ -22,6 +22,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:17:42Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: c31d398d3ce965a4193fca45cd025182d788a2fc603fbe8c828dfbd5a1c599ba
+  summary_generated_at: '2026-06-01T21:38:36Z'
+  summary_source_hash: c31d398d3ce965a4193fca45cd025182d788a2fc603fbe8c828dfbd5a1c599ba
+  faq_generated_at: '2026-06-02T22:17:42Z'
+  faq_source_hash: c31d398d3ce965a4193fca45cd025182d788a2fc603fbe8c828dfbd5a1c599ba
+  summary: >-
+    Learn to connect AI agents to Arm-based edge devices using Device Connect for structured device
+    access and Strands for agent orchestration. You will set up a Python environment from source
+    by cloning the strands-labs/robots repository and running its setup script to install dependencies
+    and create a Python 3.12 virtual environment. Then you will start a simulated robot that registers
+    on the local network and use Device Connect agent tools with the robot_mesh Strands tool to
+    discover and invoke it. An optional section runs Zenoh, etcd, and a registry via Docker and
+    connects a Raspberry Pi for a full device-to-device setup. Target platforms are Linux and
+    macOS; prerequisites are Git and basic command-line skills. Core steps take about 30 minutes.
+  faqs:
+  - question: What do I need before cloning the repository?
+    answer: >-
+      Use a Linux or macOS machine with git installed and basic command-line familiarity. Docker
+      is only required for the optional infrastructure section. A Raspberry Pi is optional if
+      you want to test a full device-to-device setup.
+  - question: How do I set up the Python environment?
+    answer: >-
+      Clone the robots repository and run the provided setup.sh script. The script installs uv,
+      creates a Python 3.12 virtual environment, and installs all required packages; then source
+      the environment as directed in the steps.
+  - question: Which option should I choose for device discovery and control?
+    answer: >-
+      Choose the single-machine option to follow a conceptual implementation using two terminal
+      windows on your machine. Choose the real hardware option if you have an external device;
+      your machine acts as the agent machine and the external device serves as the remote device.
+  - question: How do I know the agent discovered the robot?
+    answer: >-
+      After starting the simulated robot, it registers on the local network and is discovered
+      automatically by the agent. Use the Device Connect agent tools and the robot_mesh Strands
+      tool to list and invoke the robot.
+  - question: What changes when I run with the full Device Connect infrastructure?
+    answer: >-
+      You will run a Zenoh router, an etcd state store, and a registry service on your machine
+      using Docker, then connect a Raspberry Pi on the same network as the remote device. This
+      goes beyond the local-only discovery used in the earlier section.
+# END generated_summary_faq
 
 author: 
     - Annie Tallund
diff --git a/content/learning-paths/embedded-and-microcontrollers/docker/_index.md b/content/learning-paths/embedded-and-microcontrollers/docker/_index.md
index 327977b314..19a6be86e1 100644
--- a/content/learning-paths/embedded-and-microcontrollers/docker/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/docker/_index.md
@@ -19,6 +19,51 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:19:02Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: a4b522c46c68d3c6f5c4af7c76b00c7bde48bb638147c201376bc19a4de79cca
+  summary_generated_at: '2026-06-01T21:39:09Z'
+  summary_source_hash: a4b522c46c68d3c6f5c4af7c76b00c7bde48bb638147c201376bc19a4de79cca
+  faq_generated_at: '2026-06-02T22:19:02Z'
+  faq_source_hash: a4b522c46c68d3c6f5c4af7c76b00c7bde48bb638147c201376bc19a4de79cca
+  summary: >-
+    Build a containerized Arm embedded development environment by creating a Dockerfile, constructing
+    an Ubuntu-based Docker image that includes Arm Compiler for Embedded and a library of Fixed
+    Virtual Platforms (FVPs), and testing the image. This introductory path is aimed at embedded
+    software developers new to Docker and focuses on a basic build-and-run setup for bare-metal
+    development. The host machine can be Windows or Linux, and Linux users may need sudo for Docker
+    commands. Before starting, install Docker for your host and download the installation packages
+    you will copy into the image. By the end, you will have a tested Docker image suitable for
+    running compiler and FVP tasks.
+  faqs:
+  - question: What do I need before running docker build?
+    answer: >-
+      Install Docker for your host platform and download the installation packages for Arm Compiler
+      for Embedded and the Fixed Virtual Platforms you plan to include. These files are copied
+      into the image during the build.
+  - question: Which host operating systems can I use to follow this path?
+    answer: >-
+      You can use Windows or Linux as the host. On Linux, you may need to prefix Docker commands
+      with sudo because the Docker daemon runs as root.
+  - question: What base operating system does the container use?
+    answer: >-
+      The Docker image uses Ubuntu as the operating system inside the container.
+  - question: What will the resulting Docker image contain?
+    answer: >-
+      It will contain Arm Compiler for Embedded and a library of Fixed Virtual Platforms (FVPs).
+      This provides a basic build and run environment for Arm embedded development.
+  - question: How do I know the image works after the build?
+    answer: >-
+      The steps include testing the containerized environment. You should be able to run the container
+      and exercise the compiler and FVPs without errors.
+# END generated_summary_faq
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/edge/_index.md b/content/learning-paths/embedded-and-microcontrollers/edge/_index.md
index b3b3399731..1b143b0939 100644
--- a/content/learning-paths/embedded-and-microcontrollers/edge/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/edge/_index.md
@@ -24,6 +24,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:20:08Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 8a7ec29d10a89649662ebb0fa4f14712984786902b6e7343a195abb1f3cbc00b
+  summary_generated_at: '2026-06-01T21:39:32Z'
+  summary_source_hash: 8a7ec29d10a89649662ebb0fa4f14712984786902b6e7343a195abb1f3cbc00b
+  faq_generated_at: '2026-06-02T22:20:08Z'
+  faq_source_hash: 8a7ec29d10a89649662ebb0fa4f14712984786902b6e7343a195abb1f3cbc00b
+  summary: >-
+    This introductory Learning Path guides you through building a TinyML audio command demo on
+    the Arduino Nano RP2040 Connect. You will use Edge Impulse to collect and preprocess audio
+    data, train a simple voice-command classifier, and export a library for deployment. After
+    setting up the Arduino IDE with RP2040 board support, you will integrate the generated library
+    into a new sketch, build, and upload to the board to run real-time inference on a bare-metal
+    Cortex-M class microcontroller. Prerequisites include an Edge Impulse Studio account, the
+    Arduino IDE with RP2040 support, and an Arduino Nano RP2040 Connect. By the end, you will
+    have an LED that turns on and off when it recognizes the words “on” and “off,” in about 90
+    minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an Arduino Nano RP2040 Connect, the Arduino IDE with the RP2040 board support package
+      installed, and an Edge Impulse Studio account. If you are an absolute beginner, complete
+      the Arduino on Raspberry Pi Pico path first.
+  - question: Which platform and tools does this project use?
+    answer: >-
+      It targets the Arduino Nano RP2040 Connect (RP2040 on Arm Cortex-M) running bare-metal.
+      You will use Edge Impulse for data collection and model training, and the Arduino IDE to
+      build and upload the sketch.
+  - question: How do I get the Edge Impulse model into my Arduino sketch?
+    answer: >-
+      Train a voice-command classification model in Edge Impulse, then use the library generated
+      by Edge Impulse. Add that library to your Arduino sketch before building and uploading to
+      the board.
+  - question: What result should I expect after deployment?
+    answer: >-
+      Your board will run real-time audio inference and control an LED based on predictions. Saying
+      "on" or "off" should toggle the LED accordingly.
+  - question: What should I check if the LED does not react to voice commands?
+    answer: >-
+      Verify the RP2040 board support is installed and the correct board is selected in the Arduino
+      IDE. Ensure the Edge Impulse–generated library is included in your sketch, rebuild the model
+      in Edge Impulse if needed, and re-upload the program.
+# END generated_summary_faq
 
 author: Bright Edudzi Gershon Kordorwu
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/_index.md b/content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/_index.md
index 932265347c..830c08b2c3 100644
--- a/content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/_index.md
@@ -27,7 +27,7 @@ prerequisites:
 generate_summary_faq: true
 
 rerun_summary: false
-rerun_faqs: false
+rerun_faqs: true
 author: Doug Anson
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md b/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md
index ffb43b34e6..02858cc79d 100644
--- a/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md
@@ -21,6 +21,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:21:11Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 66024a2e3da90dcda298bf66b5de07952ed1e355d22172bc2812c46ad4fcda7f
+  summary_generated_at: '2026-06-01T21:40:03Z'
+  summary_source_hash: 66024a2e3da90dcda298bf66b5de07952ed1e355d22172bc2812c46ad4fcda7f
+  faq_generated_at: '2026-06-02T22:21:11Z'
+  faq_source_hash: 66024a2e3da90dcda298bf66b5de07952ed1e355d22172bc2812c46ad4fcda7f
+  summary: >-
+    This Learning Path walks you through building a convolutional neural network for image classification
+    using the CIFAR-10 dataset in a Jupyter Notebook environment set up with Anaconda, then deploying
+    and running it on an Arm Cortex-M–based STM32 B-L475E-IOT01A2 (STM32L4 Discovery) board. You
+    will import the trained model into an STM32CubeMX project using STM32Cube.AI, target a bare-metal
+    configuration, and exercise the model on hardware with a provided ST Python tool that sends
+    images to the board. It is aimed at advanced embedded developers. Prerequisites include familiarity
+    with ML concepts, C programming on microcontrollers, and access to the specified STM32 board.
+    The steps note using X-CUBE-AI 7.0.0 for the testing tool.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      You need an STM32 B-L475E-IOT01A2 board, familiarity with ML concepts, and familiarity with
+      C programming on microcontrollers. No other explicit prerequisites are listed.
+  - question: How do I open and run the training notebook?
+    answer: >-
+      From an Anaconda Prompt, run "jupyter notebook" and open lab.ipynb from the extracted project
+      files. Click Run to execute each cell; In[] means not started, In[*] means running, and
+      In[N] indicates the cell completed.
+  - question: Which dataset and model are used for training?
+    answer: >-
+      The model is a CNN trained on the CIFAR-10 dataset, which contains 60,000 images across
+      10 categories. The model takes an RGB image as input and predicts its category.
+  - question: Which STM32Cube tools and versions should I use during deployment?
+    answer: >-
+      Install STM32CubeMX using the Windows installer and add the STM32Cube.AI extension. Select
+      X-CUBE-AI 7.0.0, as the provided testing tool was written for this version and later versions
+      may not connect successfully.
+  - question: How do I run the testing tool and what if the board is not detected?
+    answer: >-
+      Activate your Anaconda environment (conda activate ml_lab), install opencv-python, protobuf==3.20,
+      and tqdm==4.50.2, then run "python ui_python_ai_runner.py" from the Misc folder. If the
+      board is not detected, press the black reset button on the board and try again.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/intro/_index.md b/content/learning-paths/embedded-and-microcontrollers/intro/_index.md
index 1603c7b30f..af7a6e4b14 100644
--- a/content/learning-paths/embedded-and-microcontrollers/intro/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/intro/_index.md
@@ -19,6 +19,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:22:16Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: ac69e979101f6befe55abee1429299c163c70b9a886d87a8f64779babd9fe5af
+  summary_generated_at: '2026-06-01T21:40:30Z'
+  summary_source_hash: ac69e979101f6befe55abee1429299c163c70b9a886d87a8f64779babd9fe5af
+  faq_generated_at: '2026-06-02T22:22:16Z'
+  faq_source_hash: ac69e979101f6befe55abee1429299c163c70b9a886d87a8f64779babd9fe5af
+  summary: >-
+    This introductory Learning Path explains where Arm architecture appears in microcontrollers
+    and helps you identify hardware options for software development on Arm Cortex-M processors.
+    You will review common use contexts, then compare evaluation boards (starter kits) for early
+    development with other small-form-factor boards and modules that can be designed into final
+    products. The content is oriented to bare-metal and RTOS environments and focuses on selection
+    and understanding rather than hands-on tooling. No explicit prerequisites are listed. After
+    completing the path, you should be able to describe where Cortex-M microcontrollers are used
+    and choose suitable board types for prototyping or product integration, with pointers to additional
+    reading and training.
+  faqs:
+  - question: Do I need any prerequisites or hardware to start this Learning Path?
+    answer: >-
+      No prerequisites are listed. The content helps you understand Cortex‑M use cases and discover
+      suitable hardware; you do not need a board to follow the overview.
+  - question: How do evaluation boards differ from edge computing boards or SBCs?
+    answer: >-
+      Evaluation boards (starter kits) are used for early software development, prototyping, and
+      demonstrations, and are typically used stand‑alone. Edge boards, modules, or SBCs can be
+      designed directly into a final product and often use small form factors, with features such
+      as debug interfaces.
+  - question: Which operating environments are in scope for the examples and guidance?
+    answer: >-
+      The path targets microcontroller development on bare‑metal and RTOS operating environments.
+  - question: Will this help if I’m migrating an application from another architecture?
+    answer: >-
+      Yes, it provides context on where Arm microcontrollers are used and how to find Cortex‑M
+      hardware options. It is not a detailed migration guide.
+  - question: Where can I find additional learning resources after finishing?
+    answer: >-
+      The path lists example books on Cortex‑M processors, a free Arm Helium Technology (M‑Profile
+      Vector Extension) e‑book for Cortex‑M, and Arm on‑demand training that includes M‑Profile
+      architecture.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md b/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
index ab49177811..3159183138 100644
--- a/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
@@ -23,6 +23,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:23:20Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: aa49e4a1651367965e79d36c5f6af16e9b341c392d48cf051f24cbb21070e972
+  summary_generated_at: '2026-06-01T21:40:55Z'
+  summary_source_hash: aa49e4a1651367965e79d36c5f6af16e9b341c392d48cf051f24cbb21070e972
+  faq_generated_at: '2026-06-02T22:23:20Z'
+  faq_source_hash: aa49e4a1651367965e79d36c5f6af16e9b341c392d48cf051f24cbb21070e972
+  summary: >-
+    This introductory path explains what differentiates TinyML from other AI domains and why Arm-based
+    edge devices are a good fit. You set up a Linux-hosted TinyML environment using PyTorch, ExecuTorch,
+    and the Corstone-320 Fixed Virtual Platform (FVP), a pre-silicon virtual platform that models
+    Cortex-M processors and Arm Ethos-U NPUs. The steps include installing and configuring ExecuTorch,
+    running scripts to provision the Corstone-320 FVP, and defining a small PyTorch feedforward
+    network that you export with ExecuTorch tooling to validate the setup. By the end, you can
+    describe TinyML trade-offs, identify suitable Arm devices, and work with a basic TinyML sandbox
+    on Linux. Prerequisites: basic ML knowledge and a Linux computer.
+  faqs:
+  - question: What do I need before running the setup?
+    answer: >-
+      You need basic knowledge of Machine Learning concepts and a Linux computer. No other explicit
+      prerequisites are listed.
+  - question: Do I need physical Arm hardware to complete this path?
+    answer: >-
+      No. The Corstone-320 Fixed Virtual Platform provides a virtual representation of Arm-based
+      microcontrollers so you can develop and test before boards are available.
+  - question: What does the Corstone-320 FVP provide for this workflow?
+    answer: >-
+      It is a pre-silicon software development environment designed for AI and ML workloads, with
+      support for Arm Ethos-U NPUs and Cortex-M processors. It enables early software validation
+      and development for embedded AI applications.
+  - question: How do I validate that ExecuTorch and the environment are installed correctly?
+    answer: >-
+      Follow the steps to run the setup scripts for the Corstone-320 reference package and then
+      execute the provided example. Being able to run the example without errors indicates the
+      environment is ready.
+  - question: What code artifact will I create in the modeling step?
+    answer: >-
+      You will create a Python file (simple_nn.py) that defines a small feedforward neural network
+      for a classification task. The example uses PyTorch export utilities and ExecuTorch conversion
+      APIs to target edge execution.
+# END generated_summary_faq
 
 author: Dominica Abena O. Amanfo
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md b/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
index 9abf0f8c90..2cb40a7fc1 100644
--- a/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
@@ -20,6 +20,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:25:19Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 11fc64eb1a0595b1d8e625e465be9fa87a4de04f59492004204ccbe61a92a5b7
+  summary_generated_at: '2026-06-01T21:41:45Z'
+  summary_source_hash: 11fc64eb1a0595b1d8e625e465be9fa87a4de04f59492004204ccbe61a92a5b7
+  faq_generated_at: '2026-06-02T22:25:19Z'
+  faq_source_hash: 11fc64eb1a0595b1d8e625e465be9fa87a4de04f59492004204ccbe61a92a5b7
+  summary: >-
+    This introductory path shows how to build Open-IoT-SDK examples and run them on Corstone-300
+    virtual hardware using Arm Virtual Hardware. You set up an AVH instance, install the required
+    Python environment, then build and run a keyword example to observe ML inference logs on a
+    bare-metal or RTOS stack. The flow highlights how Arm Trusted Firmware-M and the Arm ML Evaluation
+    Kit integrate within Arm Total Solutions for IoT, and how the keyword and speech examples
+    can connect to AWS IoT. Tools listed include Arm Virtual Hardware, FVP, and Arm Compiler for
+    Embedded. Prerequisites are some embedded programming familiarity and an AWS account (required
+    for AVH). Estimated time is about 30 minutes.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      Some familiarity with embedded programming is expected, and you need an AWS account to use
+      Arm Virtual Hardware. No other prerequisites are explicitly listed.
+  - question: How do I set up Arm Virtual Hardware and install the required software?
+    answer: >-
+      Create and set up your AVH instance by following the Arm Virtual Hardware install guide.
+      In the AVH instance, run: sudo apt update; sudo apt install python3.8-venv -y; sudo cp /usr/local/bin/pip3.8
+      /usr/bin.
+  - question: How do I build and run the keyword example?
+    answer: >-
+      From the project, run: ./ats.sh build-n-run keyword. The build takes a few minutes and runs
+      on Corstone-300 virtual hardware within AVH.
+  - question: What result should I expect in the terminal when the example runs successfully?
+    answer: >-
+      Look for logs such as "ML interface initialised" and inference output showing a label and
+      score, for example: label: on, score: 0.996127; threshold: 0.700000. You may also see markers
+      like ML_HEARD_O.
+  - question: How is AWS connectivity used in the examples, and what should I configure?
+    answer: >-
+      The keyword and speech examples implement AWS cloud connectivity. You can create an AWS
+      thing to send data from the simulated Corstone-300 device to AWS IoT cloud services.
+# END generated_summary_faq
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md b/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
index 38086bd277..74256e6dc2 100644
--- a/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
@@ -21,6 +21,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:25:46Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: fdf582f1b54f372768a2c896e1da152c50d22c3bd238848253cdbe18cc68222d
+  summary_generated_at: '2026-06-01T21:42:08Z'
+  summary_source_hash: fdf582f1b54f372768a2c896e1da152c50d22c3bd238848253cdbe18cc68222d
+  faq_generated_at: '2026-06-02T22:25:46Z'
+  faq_source_hash: fdf582f1b54f372768a2c896e1da152c50d22c3bd238848253cdbe18cc68222d
+  summary: >-
+    This introductory path shows how to bring up a Jetson Orin Nano on Linux with a MIPI CSI-2
+    camera and run real-time object detection using DetectNet and TensorRT. You will download
+    the latest Jetson Orin Nano developer kit image from the NVIDIA site and write it to a microSD
+    card with balenaEtcher, then clone and launch the jetson-inference Docker container. From
+    there, you will run DetectNet on a live CSI camera stream and on image files, including adjusting
+    detection thresholds. Required hardware is a Jetson Orin Nano, a 64GB (UHS-1 recommended)
+    microSD card, and a MIPI CSI-2 camera with a 22‑pin connector. The estimated completion time
+    is about 60 minutes.
+  faqs:
+  - question: What do I need before starting the setup?
+    answer: >-
+      You need a Jetson Orin Nano, a microSD card (64GB UHS-1 or larger is recommended), and a
+      MIPI CSI-2 camera with a 22-pin connector. No other prerequisites are explicitly listed.
+  - question: How do I write the Jetson image to the microSD card?
+    answer: >-
+      Download the Jetson Orin Nano Developer Kit image from the NVIDIA developer website (expand
+      the Jetson Xavier NX & Orin Nano section, then select the Jetson Orin Nano Developer Kit).
+      Use balenaEtcher, choose Flash from file, and select the downloaded zip file without unzipping
+      it.
+  - question: How do I download and start the jetson-inference Docker container?
+    answer: >-
+      Clone the repository with git clone --recursive --depth=1 https://github.com/dusty-nv/jetson-inference,
+      change into the jetson-inference directory, and run docker/run.sh to download and launch
+      the container.
+  - question: How do I check that the Docker container is running and find its ID?
+    answer: >-
+      Run sudo docker ps -q to print the container ID. A hex string (for example, 174055df45cd)
+      indicates the container is active.
+  - question: How do I run DetectNet on the live camera and adjust sensitivity?
+    answer: >-
+      Inside the container, change to build/aarch64/bin and run ./detectnet csi://0 to process
+      the live camera stream. Adjust sensitivity with --threshold (default 0.5). The first run
+      can take longer, and you should see object labels rendered in real time.
+# END generated_summary_faq
 
 author: Gabriel Peterson
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md b/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
index 278f8b4545..bcfca046ea 100644
--- a/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
@@ -20,6 +20,50 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:27:21Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: f3a01adf18ad93027b6ab61ceb5b0c470e6b5c298f9d4f944989a96ff64eec81
+  summary_generated_at: '2026-06-01T21:42:40Z'
+  summary_source_hash: f3a01adf18ad93027b6ab61ceb5b0c470e6b5c298f9d4f944989a96ff64eec81
+  faq_generated_at: '2026-06-02T22:27:21Z'
+  faq_source_hash: f3a01adf18ad93027b6ab61ceb5b0c470e6b5c298f9d4f944989a96ff64eec81
+  summary: >-
+    This introductory Learning Path shows how to import and build an example Cortex-M project
+    in Keil Studio Cloud and run it on Arm Virtual Hardware. Using the browser-based IDE with
+    Arm Compiler for Embedded and CMSIS, you learn the core workflow to build, optionally debug,
+    and execute the example without requiring a physical board. Some familiarity with embedded
+    programming is assumed, and an Arm Account (or an existing Mbed account) is required to sign
+    in. If you later connect a board over USB, use a desktop browser with WebUSB support such
+    as Google Chrome or Microsoft Edge (Chromium). The path is designed to take about 30 minutes.
+  faqs:
+  - question: What do I need before I can access Keil Studio Cloud?
+    answer: >-
+      You need an Arm Account to sign in. If you already have an Mbed account, you can use it
+      to access Keil Studio. Some familiarity with embedded programming is assumed.
+  - question: Which browser should I use, especially if I plan to connect a board over USB?
+    answer: >-
+      For USB-connected boards, use a desktop browser that supports WebUSB: Google Chrome or Microsoft
+      Edge (Chromium). All other features are supported in the latest versions of Chrome, Edge,
+      Opera, Safari, and Firefox.
+  - question: Can I complete this Learning Path without physical hardware?
+    answer: >-
+      Yes. One of the objectives is to run the example on Arm Virtual Hardware, so you can run
+      the project in a virtual environment.
+  - question: How do I check if my development board is supported by Keil Studio Cloud?
+    answer: >-
+      Go to keil.arm.com and click the Hardware menu to see the list of supported hardware.
+  - question: What targets and tools are used in the example project?
+    answer: >-
+      The path targets Cortex-M devices and uses Arm Compiler for Embedded, Arm Virtual Hardware,
+      and CMSIS. Examples may be bare-metal or RTOS-based.
+# END generated_summary_faq
 
 author: Christopher Seidl 
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md b/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
index 3a15934426..35bb703984 100644
--- a/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
@@ -25,6 +25,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:28:35Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 567387e8e513458a360f74c14d3f4d02c4af392bc573620e605c93976e4d8b4f
+  summary_generated_at: '2026-06-01T21:43:12Z'
+  summary_source_hash: 567387e8e513458a360f74c14d3f4d02c4af392bc573620e605c93976e4d8b4f
+  faq_generated_at: '2026-06-02T22:28:35Z'
+  faq_source_hash: 567387e8e513458a360f74c14d3f4d02c4af392bc573620e605c93976e4d8b4f
+  summary: >-
+    This Learning Path shows how to bring up Linux on the NXP FRDM i.MX 93 board and prepare it
+    for on-device development. You will boot and log in over the DBG serial console, create a
+    non-root user with sudo access, connect to WiFi using ConnMan, and transfer files to the board
+    with OpenSSH scp or a USB drive. An optional step configures the WiFi driver to load at boot
+    so ConnMan can reconnect automatically after a reboot. It targets embedded developers and
+    ML engineers working with Arm Cortex-A55–based hardware. Prerequisites include the FRDM i.MX
+    93 board, a Linux or macOS host, and USB-C cables for power and serial. Estimated time: 120
+    minutes.
+  faqs:
+  - question: What do I need before powering the board?
+    answer: >-
+      You need an NXP FRDM i.MX 93 board, a Linux or macOS host computer, a USB-C cable for the
+      DBG serial connection, and a USB-C power supply for the POWER port. These are the explicit
+      prerequisites.
+  - question: How do I access the Linux console on the board?
+    answer: >-
+      Connect your host to the board’s DBG serial port over USB-C and use a serial console tool
+      such as picocom. You will boot the board and log in over the serial console as described
+      in the steps.
+  - question: Which tool should I use to connect to WiFi, and how do I verify it worked?
+    answer: >-
+      Use ConnMan (via connmanctl) to join your WiFi network. To verify connectivity, run ifconfig
+      and look for the WiFi interface (often mlan0) and its inet address.
+  - question: How do I transfer files to the board during development?
+    answer: >-
+      Use scp over WiFi by targeting the board’s IP address and destination path. If WiFi is unavailable,
+      you can move files with a USB drive.
+  - question: What should I check if WiFi does not reconnect after a reboot?
+    answer: >-
+      Load the WiFi driver module after boot using the provided modprobe command so ConnMan can
+      reconnect to the saved network. Give it up to a minute to establish a link, then confirm
+      with ifconfig.
+# END generated_summary_faq
 
 author: Waheed Brown
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md b/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md
index 28ba51f1c0..82d3930d9e 100644
--- a/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md
@@ -20,6 +20,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:29:22Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 5943d817827bef274f175f7c14249cad769b2160094b3c65d7476aca6cbf0a1d
+  summary_generated_at: '2026-06-01T21:43:52Z'
+  summary_source_hash: 5943d817827bef274f175f7c14249cad769b2160094b3c65d7476aca6cbf0a1d
+  faq_generated_at: '2026-06-02T22:29:22Z'
+  faq_source_hash: 5943d817827bef274f175f7c14249cad769b2160094b3c65d7476aca6cbf0a1d
+  summary: >-
+    This introductory Learning Path shows how to boot a Linux software stack on Arm Fixed Virtual
+    Platforms (FVPs) and then debug Trusted Firmware-A (TF-A) and the Linux kernel using Arm Development
+    Studio. You will configure TF-A build flags to include cpu_ops for CPU-specific initialization,
+    adjust the device tree for CPU FVPs by removing unsupported PCI and SMMU nodes and setting
+    correct CPU affinity, launch the stack on an FVP, and verify expected build outputs and UART
+    logs. The target environment is a Linux-based x86-64 host with Arm Development Studio installed.
+    FVPs are fast, functional simulation models of Arm hardware, so you can develop and debug
+    without physical silicon. Estimated time to complete is approximately 60 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Linux-based x86-64 host computer with Arm Development Studio installed, and a
+      basic understanding of Assembly and C programming. No other prerequisites are explicitly
+      listed.
+  - question: How should I modify the device tree for CPU FVPs?
+    answer: >-
+      Remove PCI and SMMU nodes and ensure CPU affinity values are set correctly. Leaving PCI
+      or SMMU nodes in place can cause a kernel panic during boot on CPU FVPs.
+  - question: How do I confirm that cpu_ops is enabled in my TF-A build?
+    answer: >-
+      Follow the steps to configure TF-A build flags to include cpu_ops support for your CPU.
+      If the proper cpu_ops are missing, Linux may fail to boot; the steps describe enabling the
+      correct CPU-specific implementations.
+  - question: What result should I expect from the build output?
+    answer: >-
+      In the output directory (for example, output/aemfvp-a/aemfvp-a/), expect files such as Image
+      and Image.defconfig, often as symlinks to the component outputs. If these are missing, revisit
+      the build and configuration steps.
+  - question: How do I run and debug the software stack on an FVP?
+    answer: >-
+      Use the provided command templates, substituting <SRC_PATH> and <PATH_TO_LOG>, and capture
+      the UART output to verify the boot. For debugging, Arm Development Studio v2022.2 or later
+      is recommended for DWARF 5 support; launch the IDE and follow the steps to create a debug
+      configuration for TF-A and the Linux kernel.
+# END generated_summary_faq
 
 author: Qixiang Xu
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md b/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md
index 2ec9f1da56..89f72d28df 100644
--- a/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md
@@ -21,6 +21,51 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:30:54Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: aa6252610c0603acfdfc8d4555bb7da93cbdd5b9d92ba140c5dc5f63d23e2b07
+  summary_generated_at: '2026-06-01T21:44:40Z'
+  summary_source_hash: aa6252610c0603acfdfc8d4555bb7da93cbdd5b9d92ba140c5dc5f63d23e2b07
+  faq_generated_at: '2026-06-02T22:30:54Z'
+  faq_source_hash: aa6252610c0603acfdfc8d4555bb7da93cbdd5b9d92ba140c5dc5f63d23e2b07
+  summary: >-
+    This introductory Learning Path guides you through running a local LLM chatbot on a Raspberry
+    Pi 5. You install the Python version of llama.cpp on Raspberry Pi OS (64-bit), download a
+    model from Hugging Face, assess model memory size and performance, and run the model using
+    Python bindings. The 8GB RAM Raspberry Pi 5 is preferred for exploring an LLM, and with minor
+    modifications the approach can be adapted to other Arm Linux computers. By the end, you will
+    have a working local chatbot and a basic understanding of its resource and performance characteristics
+    on this device. Estimated time to complete is about 30 minutes. No additional prerequisites
+    are explicitly listed beyond a Raspberry Pi 5 running Raspberry Pi OS.
+  faqs:
+  - question: How should I prepare the SD card and which Raspberry Pi OS build should I choose?
+    answer: >-
+      Use Raspberry Pi Imager as recommended in the Raspberry Pi documentation to prepare the
+      SD card. Install the 64-bit version of Raspberry Pi OS for this Learning Path.
+  - question: Do I need the 8GB RAM Raspberry Pi 5 model?
+    answer: >-
+      The 8GB RAM Raspberry Pi 5 model is preferred for exploring an LLM. The Learning Path requires
+      a Raspberry Pi 5 running Raspberry Pi OS.
+  - question: Can I follow these steps on another Arm Linux computer?
+    answer: >-
+      Yes, the instructions can be used on any Arm Linux computer with minor modifications. The
+      Learning Path focuses on Raspberry Pi 5 as the primary target.
+  - question: Where do I obtain the model and how is it executed?
+    answer: >-
+      You will download an LLM from Hugging Face. It is run using the Python version of llama.cpp
+      through its Python bindings.
+  - question: What result should I expect after completing the steps, and how long will it take?
+    answer: >-
+      In about 30 minutes, you will have a local LLM chatbot running on your Raspberry Pi 5 using
+      Python. You will also assess the model’s memory size and performance on your device.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/migration/_index.md b/content/learning-paths/embedded-and-microcontrollers/migration/_index.md
index 623c215903..482e4e999c 100644
--- a/content/learning-paths/embedded-and-microcontrollers/migration/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/migration/_index.md
@@ -22,6 +22,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:32:21Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 81a15ff0d5579be9529a8c9c444be57521ebc48bbf5234f042f5a47a8a709b5c
+  summary_generated_at: '2026-06-01T21:45:31Z'
+  summary_source_hash: 81a15ff0d5579be9529a8c9c444be57521ebc48bbf5234f042f5a47a8a709b5c
+  faq_generated_at: '2026-06-02T22:32:21Z'
+  faq_source_hash: 81a15ff0d5579be9529a8c9c444be57521ebc48bbf5234f042f5a47a8a709b5c
+  summary: >-
+    This advanced Learning Path guides you through migrating an x86_64 Linux application to aarch64
+    using a practical porting methodology. You will set up an aarch64 GCC development environment
+    in a Docker container on a Linux host, analyze a Sobel filter workload implemented as non-SIMD
+    C++, x86_64 intrinsics, and OpenCV, and iteratively port code to Arm, including translating
+    intrinsics to Neon using SIMDe. You will build and run the application and evaluate console
+    timing results and image outputs. Emulation, remote hardware, or physical Arm hardware can
+    be used; physical hardware is not required. Prerequisites include introductory container knowledge,
+    familiarity with build workflows, and access to an aarch64 or x86_64 Linux machine. The path
+    also introduces using Arm compilers and libraries.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an introductory understanding of software containers, knowledge about building
+      workflows, and access to a Linux machine on either aarch64 or x86_64. This is an advanced
+      topic aimed at developers migrating Linux workloads.
+  - question: Can I complete this Learning Path without physical Arm hardware?
+    answer: >-
+      Yes. Physical Arm hardware is not required; you can use emulation or remote hardware to
+      run the aarch64 application.
+  - question: Which compiler and environment should I use for the port?
+    answer: >-
+      The example uses GCC and recommends matching the original GCC version when possible. Set
+      up an aarch64 GCC development container with Docker and run all build and test commands
+      inside that container.
+  - question: How should I handle x86 SIMD intrinsics during the port?
+    answer: >-
+      Use SIMD Everywhere (SIMDe) to port AVX intrinsics. This enables keeping a single source
+      base while targeting aarch64.
+  - question: What result should I expect when I run the ported application?
+    answer: >-
+      The program prints execution time measurements in microseconds for the non-SIMD, SIMD, and
+      OpenCV implementations, and opens four image windows including the original and processed
+      outputs. The example runs on CPU only (no hardware acceleration).
+# END generated_summary_faq
 
 author: Kasper Mecklenburg
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md b/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md
index 0f4d85f759..65eab51fff 100644
--- a/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md
@@ -20,6 +20,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:33:38Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: acf43df3c6f344b55bb413b7483d60e26d0e137489ac148bca64500e443140ab
+  summary_generated_at: '2026-06-01T21:46:02Z'
+  summary_source_hash: acf43df3c6f344b55bb413b7483d60e26d0e137489ac148bca64500e443140ab
+  faq_generated_at: '2026-06-02T22:33:38Z'
+  faq_source_hash: acf43df3c6f344b55bb413b7483d60e26d0e137489ac148bca64500e443140ab
+  summary: >-
+    This Learning Path shows how to build sample applications from the Arm Machine Learning Evaluation
+    Kit (MLEK) and run them on an Arm Ecosystem Fixed Virtual Platform (FVP) for bare-metal ML
+    development on microcontrollers. You will set up the Corstone-320 FVP, build the MLEK examples
+    using a supported toolchain (such as GCC or Arm Compiler for Embedded), and locate the generated
+    .axf images in the cmake-*/bin directory. You then launch an application on the FVP, selecting
+    the binary with -a and configuring the Ethos-U NPU MACs using -C mps4_board.subsystem.ethosu.num_macs
+    so they match the build. Targets include Cortex-M55 with Ethos-U85. Prerequisites are familiarity
+    with embedded programming and an Ubuntu Linux host (20.04 or 22.04 on x86_64 recommended).
+    Estimated time: about 30 minutes.
+  faqs:
+  - question: What do I need on my host machine before running the steps?
+    answer: >-
+      Use a Linux host running Ubuntu; 20.04 or 22.04 is recommended. The instructions have been
+      tested on x86_64 and assume some familiarity with embedded programming.
+  - question: Which FVP should I install to run the examples?
+    answer: >-
+      Install the Corstone-320 Ecosystem FVP on your local machine. You can download Arm Ecosystem
+      FVPs from the Arm Developer website and follow the Fast Model and Fixed Virtual Platform
+      install guide.
+  - question: Where will the built binaries be located after compiling MLEK?
+    answer: >-
+      The built examples are .axf files found under a cmake-*/bin directory, which depends on
+      your build configuration. An example path is similar to cmake-build-mps4-sse-320-ethos-u85-256-gnu/bin/.
+  - question: How do I choose and run a specific example on the FVP?
+    answer: >-
+      Use the -a option to specify the application image (.axf) to load when launching the FVP.
+      Configure the Ethos-U component using -C mps4_board.subsystem.ethosu.num_macs to match your
+      build.
+  - question: What Arm IP and reference system do these examples target?
+    answer: >-
+      The examples let you investigate the software stack and evaluate performance on Cortex-M55
+      and Ethos-U85. They are run on Arm Corstone reference systems, such as the Corstone-320
+      FVP; similar steps apply to other platforms.
+# END generated_summary_faq
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md b/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md
index 1bc04d2527..f8e155508c 100644
--- a/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md
@@ -13,6 +13,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:35:12Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 0cfaa21be515b980097232ed38092b80d046df308120887e5503513a3384a1d7
+  summary_generated_at: '2026-06-01T21:46:50Z'
+  summary_source_hash: 0cfaa21be515b980097232ed38092b80d046df308120887e5503513a3384a1d7
+  faq_generated_at: '2026-06-02T22:35:12Z'
+  faq_source_hash: 0cfaa21be515b980097232ed38092b80d046df308120887e5503513a3384a1d7
+  summary: >-
+    This introductory path helps embedded developers plan Machine Learning workflows on Arm Cortex-M
+    with Ethos-U by choosing suitable physical and virtual targets, identifying core tools, and
+    locating example applications. You will compare development options that include Corstone-based
+    designs such as the MPS3 FPGA Prototyping Board and virtual platforms like Arm Virtual Hardware
+    and Fixed Virtual Platforms (FVPs). The path outlines host setup on an x86_64 Windows or Linux
+    machine, noting that some tools work only on Linux and that the Arm ML Evaluation Kit (MLEK)
+    is not fully supported on Windows. By the end, you will be prepared to select a target, set
+    up a bare-metal toolchain (GCC or Arm Compiler for Embedded), and find relevant examples to
+    study.
+  faqs:
+  - question: I don’t have an Ethos-U board—what platform should I start with?
+    answer: >-
+      Use a virtual platform. The path highlights virtual options such as FVP and Arm Virtual
+      Hardware, which let you begin ML development without physical hardware.
+  - question: Can I follow this path on Windows, or do I need Linux?
+    answer: >-
+      An x86_64 machine running Windows or Linux is suitable, but the Arm ML Evaluation Kit is
+      not fully supported on Windows and some required tools are Linux-only. If you plan to use
+      MLEK extensively, Linux is recommended.
+  - question: Which compilers can I use to build ML applications for Cortex-M and Ethos-U?
+    answer: >-
+      You can build C/C++ applications with GCC or Arm Compiler for Embedded. These toolchains
+      are appropriate for the targets described in the path.
+  - question: What physical hardware options exist today for Ethos-U development?
+    answer: >-
+      Readily available development boards with Ethos-U are currently limited. The Arm MPS3 FPGA
+      Prototyping Board can be programmed with Corstone reference system images to support ML
+      development.
+  - question: Does this path assume bare-metal or an RTOS, and what prior experience is needed?
+    answer: >-
+      The path targets bare-metal development. It assumes some familiarity with microcontroller
+      software development.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md b/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md
index b396ffa87d..740393539b 100644
--- a/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md
@@ -19,6 +19,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:35:33Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: d2c403c7fd1001dbd985697c9eb018951a955358c2be39c727183a87a3dfdf51
+  summary_generated_at: '2026-06-01T21:47:16Z'
+  summary_source_hash: d2c403c7fd1001dbd985697c9eb018951a955358c2be39c727183a87a3dfdf51
+  faq_generated_at: '2026-06-02T22:35:33Z'
+  faq_source_hash: d2c403c7fd1001dbd985697c9eb018951a955358c2be39c727183a87a3dfdf51
+  summary: >-
+    This introductory Learning Path shows how to add new debug targets in Arm Development Studio
+    for both virtual platforms and physical development boards. You will create debugger connections
+    to Arm Fast Models for bare-metal software bring-up and to boards via the Arm DSTREAM family
+    of probes. The steps outline when to use each DSTREAM variant and how to connect over USB
+    or Ethernet, so you can attach the debugger to Cortex-A, Cortex-R, Cortex-M, or Neoverse based
+    systems. It assumes Arm Development Studio and Arm Fast Models are installed and that you
+    have some familiarity with embedded debug. After completing the path, you will have working
+    debug configurations for your chosen target.
+  faqs:
+  - question: What do I need installed before creating a Fast Models debug connection in Arm Development
+      Studio?
+    answer: >-
+      It is assumed that Arm Development Studio and Arm Fast Models are installed, and that you
+      have some familiarity with embedded debug. Installation steps are not covered in this path.
+  - question: Do I need a physical development board to follow this path?
+    answer: >-
+      Not for the virtual platform step; Fast Models let you connect the debugger to a model as
+      if it were real hardware. For the hardware step, you will use an Arm DSTREAM probe with
+      a development board.
+  - question: Which DSTREAM probe should I choose for my board?
+    answer: >-
+      DSTREAM-ST provides full debug over JTAG and SWD, plus on-chip and low-bandwidth (4-bit)
+      external trace. If you require higher-bandwidth trace and your SoC and platform support
+      it, select DSTREAM-PT, DSTREAM-HT, or DSTREAM-XT.
+  - question: Should I connect DSTREAM to my host over USB or Ethernet?
+    answer: >-
+      The DSTREAM family supports both high-speed USB and Ethernet connections to the host. Use
+      whichever is available and appropriate for your setup.
+  - question: What result should I expect after creating each debug configuration?
+    answer: >-
+      For Fast Models, the Arm Development Studio debugger should attach to the virtual platform
+      and let you interact with it like real hardware. For a development board, the debugger should
+      connect through DSTREAM and provide debug (and trace, where supported) according to the
+      probe and target capabilities.
+# END generated_summary_faq
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md b/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
index 3fae9b8d9a..5fb589e1d3 100644
--- a/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
@@ -23,6 +23,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:36:25Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: be2b3571d4d2ed75a16bc97589abc22c970d83be868b7e94bb60962a4ba853da
+  summary_generated_at: '2026-06-01T21:47:49Z'
+  summary_source_hash: be2b3571d4d2ed75a16bc97589abc22c970d83be868b7e94bb60962a4ba853da
+  faq_generated_at: '2026-06-02T22:36:25Z'
+  faq_source_hash: be2b3571d4d2ed75a16bc97589abc22c970d83be868b7e94bb60962a4ba853da
+  summary: >-
+    This Learning Path guides you through deploying ExecuTorch on the NXP FRDM i.MX 93 to accelerate
+    inference with the Arm Ethos-U65. You will bring up a custom executor_runner firmware on the
+    Cortex-M33 using Linux RemoteProc from the Linux-based application processor, compile ExecuTorch
+    .pte models for Ethos-U65, and run them on the device. The steps cover board boot and serial
+    console access, setting up a consistent build environment (including an Ubuntu container on
+    macOS), and building and installing ExecuTorch. You will produce an executor_runner ELF and
+    a .pte model and see how these components work across Cortex-A, Cortex-M, and the NPU. Prerequisites
+    include the FRDM i.MX 93 board, a USB cable, basic ML knowledge, prior Linux setup on the
+    board, and a host computer.
+  faqs:
+  - question: What do I need before running the steps on the FRDM i.MX 93?
+    answer: >-
+      You need an NXP FRDM i.MX 93 board, a suitable USB cable, a host computer to compile ExecuTorch
+      libraries, and basic ML knowledge. Complete the Learning Path “Use Linux on an NXP FRDM
+      i.MX 93 board” to set up Linux, serial console access, and file transfer.
+  - question: How should I set up the ExecuTorch build environment on macOS?
+    answer: >-
+      Use an Ubuntu Docker container on macOS to build ExecuTorch. This container is a build-only
+      environment that produces prebuilt ExecuTorch libraries and .pte model files you later move
+      onto the FRDM i.MX 93.
+  - question: How do I connect to the board’s serial console, especially on macOS?
+    answer: >-
+      Connect your host to the board’s DEBUG USB-C port and open a serial terminal. On macOS,
+      install the Silicon Labs USB-to-UART driver and picocom via Homebrew before connecting.
+  - question: How can I verify that ExecuTorch installed correctly in my environment?
+    answer: >-
+      After running the installation, check that the package is present with: pip list | grep
+      executorch. If it appears in the list, the install succeeded.
+  - question: Which artifacts do I deploy, and how do they run on this heterogeneous system?
+    answer: >-
+      Deploy a .pte model compiled for Ethos-U65 and an executor_runner ELF firmware for the Cortex-M33.
+      Linux on the Cortex-A side uses RemoteProc to bring up the firmware, which loads the model
+      and invokes the NPU for accelerated inference.
+# END generated_summary_faq
 
 author: 
 - Waheed Brown
diff --git a/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md b/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
index cc086bf5d9..89c26ab54f 100644
--- a/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
@@ -20,6 +20,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:37:02Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 8039812773c6c1ac45cceb4039cee801e627d67871b625283c1bb5b3fa2960b3
+  summary_generated_at: '2026-06-01T21:48:37Z'
+  summary_source_hash: 8039812773c6c1ac45cceb4039cee801e627d67871b625283c1bb5b3fa2960b3
+  faq_generated_at: '2026-06-02T22:37:02Z'
+  faq_source_hash: 8039812773c6c1ac45cceb4039cee801e627d67871b625283c1bb5b3fa2960b3
+  summary: >-
+    This path shows maintainers how to migrate a CMSIS v5-based CMSIS-Pack with device support
+    to CMSIS v6 and update example projects for compatibility. You will update device support
+    by switching from assembly-based startup to C-based startup files and creating scatter files,
+    then migrate example projects from Arm Compiler 5 to Arm Compiler 6 and convert them to the
+    CMSIS-Toolbox project standard (csolution/cproject). CMSIS v6 supports Arm Compiler for Embedded
+    v6+, Arm GNU Toolchain v12+, LLVM v16+, and IAR EWARM v9.30+, and this path uses Arm Compiler
+    for Embedded v6. Prerequisites are a solid understanding of CMSIS-Packs and a CMSIS v5 device-support
+    pack. Target environments include Baremetal and RTOS.
+  faqs:
+  - question: Which toolchains can I use for CMSIS v6, and which one is used in this path?
+    answer: >-
+      CMSIS v6 supports Arm Compiler for Embedded (v6 and above), Arm GNU Toolchain (v12 and above),
+      LLVM (v16 and above), and IAR Embedded Workbench for Arm (v9.30 and above). This Learning
+      Path uses Arm Compiler for Embedded v6.
+  - question: What do I need before running the migration steps?
+    answer: >-
+      You need a good understanding of CMSIS-Packs and a CMSIS-Pack with device support that was
+      created for CMSIS v5. This path targets maintainers responsible for such packs.
+  - question: What changes are required in device support when moving to CMSIS v6?
+    answer: >-
+      Switch from assembly-based startup code to C-based startup files and create scatter files.
+      These updates prepare the device support for CMSIS v6 compatibility.
+  - question: My example projects use Arm Compiler 5. What should I do first?
+    answer: >-
+      Migrate the projects to Arm Compiler for Embedded v6 before attempting conversion to the
+      new CMSIS-Toolbox project format. In µVision, install the newly created device family pack,
+      set the compiler to use default version 6 under Options for Target > Target, and configure
+      defines in the C/C++ [AC6] tab.
+  - question: When can I convert projects to the CMSIS-Toolbox csolution/cproject format?
+    answer: >-
+      After migrating your examples to Arm Compiler for Embedded v6, they can be automatically
+      converted to the CMSIS-Toolbox project standard (csolution/cproject). The path assumes this
+      conversion follows the compiler migration.
+# END generated_summary_faq
 
 author: Christopher Seidl
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md b/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md
index 9fbb8673c5..2db2e2d7c6 100644
--- a/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md
@@ -21,6 +21,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:37:32Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 7a192506fc8a15423d1257fe92e7655053e38be85aad8310dae29602e483fbba
+  summary_generated_at: '2026-06-01T21:48:57Z'
+  summary_source_hash: 7a192506fc8a15423d1257fe92e7655053e38be85aad8310dae29602e483fbba
+  faq_generated_at: '2026-06-02T22:37:32Z'
+  faq_source_hash: 7a192506fc8a15423d1257fe92e7655053e38be85aad8310dae29602e483fbba
+  summary: >-
+    Learn how to migrate CMSIS v5 projects to CMSIS v6 for Cortex-M targets on bare-metal or RTOS.
+    Verify your toolchain (Arm Compiler for Embedded v6+, Arm GNU Toolchain v12+, LLVM v16+, or
+    IAR Embedded Workbench for Arm v9.30+; Arm Compiler v5 is not supported), install the required
+    CMSIS-Packs (ARM.CMSIS.6.0.0, ARM.Cortex_DFP.1.0.0, ARM.CMSIS-RTX.5.8.0), and update device
+    selection using the Cortex_DFP mappings. If your project used Keil.ARM_Compiler, install ARM.CMSIS-View.1.1.0
+    and ARM.CMSIS_Compiler.2.0. A troubleshooting section addresses common issues including missing
+    devices, RTE component errors, linker messages, and RTX5 runtime problems. Prerequisites:
+    a CMSIS v5 project and basic CMSIS-Pack knowledge.
+  faqs:
+  - question: Which toolchain versions are supported for CMSIS v6?
+    answer: >-
+      Use one of the following: Arm Compiler for Embedded v6 and above, Arm GNU Toolchain v12
+      and above, LLVM Toolchain v16 and above, or IAR Embedded Workbench for Arm v9.30 and above.
+      Arm Compiler v5 is not supported, and earlier versions of the listed toolchains are not
+      guaranteed to work.
+  - question: Which CMSIS-Packs do I need when migrating from CMSIS v5 to v6?
+    answer: >-
+      Install ARM.CMSIS.6.0.0, ARM.Cortex_DFP.1.0.0, and ARM.CMSIS-RTX.5.8.0. These replace the
+      monolithic ARM.CMSIS.5.x.x pack for CMSIS v6.
+  - question: I used the Keil.ARM_Compiler pack. Which packs replace it for CMSIS v6?
+    answer: >-
+      Install ARM.CMSIS-View.1.1.0 and ARM.CMSIS_Compiler.2.0. The Keil.ARM_Compiler pack is deprecated
+      and its content has moved into these packs.
+  - question: How do I map my CMSIS v5 device to the Cortex_DFP pack?
+    answer: >-
+      Switch your device selection to a supported device in the Cortex_DFP pack using the provided
+      mapping table. For example, ARMCM4/ARMCM4_FP maps to ARMCM4 (SP_FPU, MPU) and ARMCM7/ARMCM7_SP/ARMCM7_DP
+      maps to ARMCM7 (DP_FPU, MPU).
+  - question: What should I do if I’m using a Keil MDK v5 uvprojx project?
+    answer: >-
+      Use the project format conversion guidance to move from uvprojx to the Open-CMSIS-Pack csolution
+      format. Follow the referenced learning path to import, convert, and build in Keil Studio
+      for VS Code or on the command line.
+# END generated_summary_faq
 
 author: Christopher Seidl
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md b/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md
index b6fd139c65..2938f6393f 100644
--- a/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md
@@ -24,6 +24,58 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:37:58Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: a0145c77b3d4a8bb25a32c62adaa3ad378e65fccbe6db88d9a46a569897d238a
+  summary_generated_at: '2026-06-01T21:50:06Z'
+  summary_source_hash: a0145c77b3d4a8bb25a32c62adaa3ad378e65fccbe6db88d9a46a569897d238a
+  faq_generated_at: '2026-06-02T22:37:58Z'
+  faq_source_hash: a0145c77b3d4a8bb25a32c62adaa3ad378e65fccbe6db88d9a46a569897d238a
+  summary: >-
+    This introductory Learning Path guides you through building a fully local, privacy-first smart
+    home assistant on Raspberry Pi 5 with an Arm Cortex-A76 CPU. You install Python and required
+    libraries, set up Ollama to run a local large language model, and validate GPIO by wiring
+    an LED with a resistor to GPIO 17 and controlling it from a Python script. You then clone
+    a GitHub project that initializes devices, exposes a local FastAPI web server, and uses the
+    model’s JSON responses to execute actions from natural-language commands. Prerequisites include
+    a Raspberry Pi 5 running Raspberry Pi OS, basic electronics components, and familiarity with
+    Python and Raspberry Pi GPIO.
+  faqs:
+  - question: What do I need before running the setup?
+    answer: >-
+      You need an Arm-based single board computer such as a Raspberry Pi 5 running Raspberry Pi
+      OS with network connectivity. Have a breadboard, LEDs, 220Ω resistors, and jumper wires
+      for GPIO testing, plus familiarity with Python, the Raspberry Pi GPIO pinout, and basic
+      electronics.
+  - question: How should I connect to my Raspberry Pi 5 to install dependencies?
+    answer: >-
+      Connect the Raspberry Pi 5 to an external display through a micro‑HDMI port for local access.
+      The Learning Path assumes Raspberry Pi OS and network connectivity are already configured.
+  - question: How do I wire and verify the GPIO LED test?
+    answer: >-
+      Connect the LED anode in series with a 220Ω resistor to GPIO 17 (physical pin 11), and connect
+      the cathode to a GND pin. Create and run the testgpio.py script as shown; the LED should
+      respond to the script, confirming the wiring and GPIO control.
+  - question: Where do I get the assistant code and what does the main script do?
+    answer: >-
+      The assistant is available on GitHub; clone the repository and navigate to the project directory
+      as directed in the steps. Running smart_home_assistant.py initializes devices on specific
+      GPIO pins, starts a local web server, and uses a local model via Ollama to parse JSON commands
+      and control devices.
+  - question: How do I interact with the assistant and what behavior should I expect from the
+      LLM?
+    answer: >-
+      You can issue commands from the terminal or use the local web interface started by the script.
+      The Learning Path notes the system can achieve 15+ tokens per second while operating without
+      cloud services for a privacy-first setup.
+# END generated_summary_faq
 
 author: Fidel Makatia Omusilibwa
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md b/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md
index 228f705634..a0a3c9531c 100644
--- a/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md
@@ -25,6 +25,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:38:37Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: ed2034f5c95c4148fca355f6d545219c320f2e73267a0725934c792ebc4c0c59
+  summary_generated_at: '2026-06-01T21:50:55Z'
+  summary_source_hash: ed2034f5c95c4148fca355f6d545219c320f2e73267a0725934c792ebc4c0c59
+  faq_generated_at: '2026-06-02T22:38:37Z'
+  faq_source_hash: ed2034f5c95c4148fca355f6d545219c320f2e73267a0725934c792ebc4c0c59
+  summary: >-
+    This introductory Learning Path guides you through building and running a voice-controlled
+    ChatGPT bot on a Raspberry Pi 4 or 5 using Raspberry Pi OS (64-bit, Linux). You will install
+    the OS with Raspberry Pi Imager, configure and test audio I/O, and create a Python application
+    that listens for a wake word with Porcupine, converts speech to text using Google Speech Recognition,
+    sends text to ChatGPT’s gpt-4-turbo-preview model via API, generates speech via ChatGPT’s
+    text-to-speech API, and plays the audio reply. You will work in a Python virtual environment
+    and use packages including pyaudio, SpeechRecognition, pydub, openai, python-dotenv, and pvporcupine.
+    Prerequisites include a Raspberry Pi, a 16GB microSD card, and a USB microphone and speakers.
+  faqs:
+  - question: What Raspberry Pi hardware and OS do I need before starting?
+    answer: >-
+      Use a Raspberry Pi 4 or 5 (earlier models may also work), a microSD card with at least 16GB,
+      and Raspberry Pi OS (64-bit) installed via Raspberry Pi Imager. You also need a Linux compatible
+      USB microphone and USB speakers or a combined USB audio device.
+  - question: How do I verify my microphone and speakers are set up correctly?
+    answer: >-
+      Plug in the devices, then right-click the speaker icon on the desktop to select your speakers.
+      In a terminal, run arecord -d 5 test.wav to create a short recording; if the file is not
+      created or contains no audio, adjust audio settings manually and retry.
+  - question: Which Python version and packages does the application use?
+    answer: >-
+      Raspberry Pi OS includes Python 3.11.2. Create a virtual environment and install pyaudio,
+      SpeechRecognition, pydub, openai, python-dotenv, and pvporcupine; you can optionally run
+      pip freeze to capture versions for troubleshooting.
+  - question: How do I run and stop the bot?
+    answer: >-
+      Activate your virtual environment, then run python main.py from the project directory. The
+      application runs indefinitely until you press Ctrl+C to stop it.
+  - question: What behavior should I expect when I say the wake word?
+    answer: >-
+      Say “computer,” pause briefly, then ask a question. After detection, the app converts your
+      speech to text, sends it to ChatGPT’s gpt-4-turbo-preview model, converts the reply to speech
+      using ChatGPT’s text-to-speech model, and plays the audio response.
+# END generated_summary_faq
 
 author: Gabriel Peterson
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
index dc8dffae58..fb8922b357 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
@@ -25,6 +25,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:39:40Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 9cd2c3082c4874dc596e1b7b59c3dc2143aaf1ce3e66948ab8b6af190ffa3776
+  summary_generated_at: '2026-06-01T21:51:49Z'
+  summary_source_hash: 9cd2c3082c4874dc596e1b7b59c3dc2143aaf1ce3e66948ab8b6af190ffa3776
+  faq_generated_at: '2026-06-02T22:39:40Z'
+  faq_source_hash: 9cd2c3082c4874dc596e1b7b59c3dc2143aaf1ce3e66948ab8b6af190ffa3776
+  summary: >-
+    This introductory Learning Path shows how to compile the Llama 3 large language model with
+    ExecuTorch using a Docker container that runs Raspberry Pi OS on an Arm Linux machine or Arm
+    cloud instance, then deploy it to a Raspberry Pi 5. You will create an isolated Python environment
+    for ExecuTorch, build the binaries required for the device, and review quantization techniques
+    relevant to running LLMs in embedded environments. Finally, you will install the 64-bit Raspberry
+    Pi OS on the Raspberry Pi 5 and run the model, experimenting with prompts and settings to
+    observe behavior on-device. Prerequisites are an Arm Linux machine or Arm cloud instance and
+    a Raspberry Pi 5. Estimated time: 60 minutes.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      You need an Arm Linux machine or an Arm cloud instance, and a Raspberry Pi 5. No other explicit
+      prerequisites are listed.
+  - question: Where do I build the binaries for deployment?
+    answer: >-
+      You build in a Docker container running Raspberry Pi OS on your Arm Linux machine. Inside
+      that container, set up ExecuTorch with an isolated Python environment before compiling the
+      model.
+  - question: Which Raspberry Pi OS should I install on the device?
+    answer: >-
+      Install the 64-bit version of Raspberry Pi OS using the Raspberry Pi documentation. The
+      steps rely on the 64-bit image on the Raspberry Pi 5.
+  - question: Do I need to quantize the Llama 3 model for the Raspberry Pi 5?
+    answer: >-
+      The Learning Path explains quantization and why it is often used to reduce the memory footprint
+      of large models in memory-constrained environments. Follow the guidance in the steps to
+      decide when to apply it.
+  - question: How do I validate that the model is running correctly on the Raspberry Pi 5?
+    answer: >-
+      After deployment, experiment with different prompts and settings on the device as shown
+      in the final section. You should observe the model generating text responses to your prompts.
+# END generated_summary_faq
 
 author: Annie Tallund
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
index 04298b7825..c288ec4a89 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
@@ -23,6 +23,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:40:08Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 17721158fe10e97314af364f138c32b7bcf53fdc88fe11fffeb5765f11e26b79
+  summary_generated_at: '2026-06-01T21:52:22Z'
+  summary_source_hash: 17721158fe10e97314af364f138c32b7bcf53fdc88fe11fffeb5765f11e26b79
+  faq_generated_at: '2026-06-02T22:40:08Z'
+  faq_source_hash: 17721158fe10e97314af364f138c32b7bcf53fdc88fe11fffeb5765f11e26b79
+  summary: >-
+    This advanced Learning Path shows how to cut compile time for embedded Linux work by building
+    the MXNet machine learning framework on an Arm Linux server using a Raspberry Pi OS file system,
+    then deploying the result to a Raspberry Pi. You will set up an Arm server (Ubuntu 22.04 was
+    tested), enter the Raspberry Pi OS environment, install build dependencies, and compile MXNet
+    as the pi user. The steps cover exporting the resulting Raspberry Pi image from the server
+    via scp, writing it to an SD card, and testing on a Raspberry Pi 3 or 4. Prerequisites include
+    an Arm computer running Linux (on-premises or cloud) and, optionally, a Raspberry Pi board
+    for testing.
+  faqs:
+  - question: What do I need on the Arm server before starting?
+    answer: >-
+      An Arm Linux server or an Arm cloud instance running Ubuntu is required; the instructions
+      were tested on Ubuntu 22.04. Verify you can use SSH to connect. A Raspberry Pi 3 or 4 is
+      only needed to test the compiled application, and that step is optional if a board is not
+      available.
+  - question: How do I know I am inside the Raspberry Pi OS file system before installing dependencies?
+    answer: >-
+      Proceed when you have a root shell inside the Raspberry Pi OS file system; the prompt appears
+      as #. The steps then run apt to update and install packages in that environment.
+  - question: Which user should compile MXNet, and where should I run the build?
+    answer: >-
+      After installing packages as root, switch to user pi (su pi). Use the pi home directory
+      ($HOME) to build the application.
+  - question: Which packages are required to build MXNet in this path?
+    answer: >-
+      Run apt update/upgrade and install: git, cmake, ninja-build, gfortran, liblapack*, libblas*,
+      libopencv*, libopenblas*, python3-dev, python3-pip, python-dev, and virtualenv. Then install
+      Cython with pip3.
+  - question: How do I transfer the built image and deploy it on a Raspberry Pi?
+    answer: >-
+      From your local machine, use scp with your SSH key and server IP to download the image (for
+      example: scp -i <your-key.pem> ubuntu@<your-ip-addr>:~/2023-02-21-raspios-bullseye-arm64-lite.img
+      .). Write the image to an SD card and insert it into a Raspberry Pi 3 or 4 to test.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md
index 5ee8ae6702..348a4ab047 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md
@@ -20,6 +20,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:39:04Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 5e5fad1563b67ed38d1cf3399d8e0d62162e76cae8d6848c3be7638f67cd037c
+  summary_generated_at: '2026-06-01T21:51:21Z'
+  summary_source_hash: 5e5fad1563b67ed38d1cf3399d8e0d62162e76cae8d6848c3be7638f67cd037c
+  faq_generated_at: '2026-06-02T22:39:04Z'
+  faq_source_hash: 5e5fad1563b67ed38d1cf3399d8e0d62162e76cae8d6848c3be7638f67cd037c
+  summary: >-
+    This introductory Learning Path walks you through setting up a Raspberry Pi 4 with 64-bit
+    Raspberry Pi OS and an Arm-based cloud instance, then running comparable software examples
+    on both to understand relative performance. You will identify hardware characteristics with
+    uname, build the Linux kernel, and install and run a TensorFlow quickstart using tensorflow-aarch64
+    and tensorflow_io. The path also includes Docker applications, as indicated in the overview.
+    By the end, you will have built and executed multiple examples on the Raspberry Pi 4 and contrasted
+    the results with an Arm cloud server. Prerequisites are a Raspberry Pi 4 and an Arm-based
+    instance from a cloud service provider. Estimated time to complete is about 90 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Raspberry Pi 4 and access to an Arm-based instance from a cloud service provider.
+      Both systems should run 64-bit Linux for the comparisons in this path.
+  - question: Which Raspberry Pi OS should I install and how?
+    answer: >-
+      Install the 64-bit version of Raspberry Pi OS. Use Raspberry Pi Imager on Windows, Linux,
+      or macOS as recommended by the Raspberry Pi documentation.
+  - question: How do I verify that both systems are 64-bit Arm and running Linux?
+    answer: >-
+      Run uname -a on the Raspberry Pi 4 and on the Arm cloud instance. You should see aarch64
+      GNU/Linux in the output; exact kernel details may differ between systems.
+  - question: How do I install and test TensorFlow in this path?
+    answer: >-
+      Install Python and pip with sudo apt install python-is-python3 python3-pip, then run pip
+      install tensorflow-aarch64 tensorflow_io. Validate by running the TensorFlow quickstart
+      code provided in the path.
+  - question: What result should I expect from the Linux kernel compile comparison?
+    answer: >-
+      You will compile the Linux kernel on both platforms to observe relative performance. Recent
+      cloud servers are faster than a Raspberry Pi 4; the goal is to understand the differences,
+      not to achieve a specific metric.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
index ed54b56838..cea22423d9 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
@@ -22,6 +22,50 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:41:08Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: d9fe3cfc8f7a7092f40786763fc28e371697e4b57dba99b2c9b191dae4273911
+  summary_generated_at: '2026-06-01T21:52:41Z'
+  summary_source_hash: d9fe3cfc8f7a7092f40786763fc28e371697e4b57dba99b2c9b191dae4273911
+  faq_generated_at: '2026-06-02T22:41:08Z'
+  faq_source_hash: d9fe3cfc8f7a7092f40786763fc28e371697e4b57dba99b2c9b191dae4273911
+  summary: >-
+    This introductory path shows how to set up the Raspberry Pi Pico C/C++ SDK on a Raspberry
+    Pi development computer and write bare-metal applications for the Arm Cortex-M0+ on the Pico.
+    You will install the SDK using the pico_setup.sh script from GitHub, build and run a Hello
+    World that prints over USB and blinks the on-board LED with GCC and CMake, measure execution
+    cycles using the SysTick timer by comparing two Fibonacci implementations, and perform interactive
+    debugging over SWD from the command line with gdb. Prerequisites are a Raspberry Pi Pico and
+    a Raspberry Pi 3, 4, 400, or 5 as the host. The estimated time to complete is about 30 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Raspberry Pi Pico board and a Raspberry Pi 3, 4, 400, or 5 to use as the development
+      computer. No other prerequisites are explicitly listed.
+  - question: Which tools does the Pico SDK use to build applications?
+    answer: >-
+      The Pico SDK uses the GCC compiler and CMake to build applications. The installation script
+      is provided as pico_setup.sh in GitHub.
+  - question: How do I know the Hello World example worked?
+    answer: >-
+      The program prints “Hello” over USB and blinks the on-board LED. Seeing the repeated USB
+      printout and the LED toggling confirms a successful build and run.
+  - question: How can I measure the number of cycles a code section takes on the Pico?
+    answer: >-
+      Use the 24-bit SysTick system timer on Cortex-M0+. The example measures cycles while computing
+      the Fibonacci series in two different ways and reports the counts.
+  - question: How can I load and debug without pressing the BOOTSEL button each time?
+    answer: >-
+      Connect the three SWD debug pins on the Raspberry Pi Pico and load programs from the command
+      line. You can then use gdb for interactive debugging over SWD.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md b/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
index e547bcff7c..9f02ca8be2 100644
--- a/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
@@ -25,6 +25,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:41:38Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 0b8de63a15d3d77b9c9972103b1317d6c48907875ac625c3d1c1b8db5360879a
+  summary_generated_at: '2026-06-01T21:53:04Z'
+  summary_source_hash: 0b8de63a15d3d77b9c9972103b1317d6c48907875ac625c3d1c1b8db5360879a
+  faq_generated_at: '2026-06-02T22:41:38Z'
+  faq_source_hash: 0b8de63a15d3d77b9c9972103b1317d6c48907875ac625c3d1c1b8db5360879a
+  summary: >-
+    This advanced Learning Path shows how to profile Linux kernel modules on Arm-based systems
+    using Arm Streamline, part of Arm Performance Studio. You will prepare a Buildroot-based environment,
+    implement a simple cache-unfriendly character device as an out-of-tree module, and then integrate
+    the same driver in-tree to profile with the kernel’s vmlinux for symbolized analysis. The
+    path explains Streamline’s sampling workflow and introduces using Statistical Profiling Extension
+    (SPE) for deeper kernel insights. Prerequisites include basic Linux kernel and module development
+    knowledge, an Arm-based Linux target with SSH access, and a host machine that meets Buildroot
+    requirements. Expect to analyze bottlenecks in both out-of-tree and in-tree scenarios in about
+    60 minutes.
+  faqs:
+  - question: What do I need before running the steps on hardware?
+    answer: >-
+      You need an Arm-based Linux target device with SSH access and a host machine that meets
+      the Buildroot system requirements. A basic understanding of Linux kernel development and
+      module programming is also expected.
+  - question: Which system should I use to install Buildroot prerequisites and run the build steps?
+    answer: >-
+      Use an AArch64-based Linux system as your host and run the package installation commands
+      there. The setup step shows updating package lists and installing required dependencies
+      before building.
+  - question: How does the example kernel module create measurable behavior for profiling?
+    answer: >-
+      It traverses a two-dimensional array in column-major order to induce cache misses. This
+      cache-unfriendly pattern helps expose hotspots and memory access inefficiencies in Streamline.
+  - question: What should I add in Streamline to profile an in-tree driver with kernel symbols?
+    answer: >-
+      Add the kernel’s vmlinux file in the capture settings. This enables analysis of function
+      calls, call paths, and specific kernel code sections for the in-tree build.
+  - question: How is the Statistical Profiling Extension (SPE) used in this path?
+    answer: >-
+      SPE is introduced for deeper kernel profiling insights alongside Streamline’s sampling.
+      Use it when available on your Arm-based system to expand the analysis beyond basic metrics.
+# END generated_summary_faq
 
 author: Yahya Abouelseoud
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md b/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
index 98080ed5f5..0b7f062e28 100644
--- a/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
@@ -21,6 +21,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:42:04Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: b5714494f00fff407c39e6f2f7bf37de94cde61d7569ba147412fec1b2f537c2
+  summary_generated_at: '2026-06-01T21:53:37Z'
+  summary_source_hash: b5714494f00fff407c39e6f2f7bf37de94cde61d7569ba147412fec1b2f537c2
+  faq_generated_at: '2026-06-02T22:42:04Z'
+  faq_source_hash: b5714494f00fff407c39e6f2f7bf37de94cde61d7569ba147412fec1b2f537c2
+  summary: >-
+    This Learning Path guides you through building a letter recognition neural network in TensorFlow
+    using accelerometer data from an STM32 B-L475E-IOT01A2 board, then deploying it to the device
+    with STM32Cube.AI. You will set up a Python environment with Anaconda, work in a Jupyter notebook
+    to collect data and train a multi-layer perceptron, and create a feature-based model using
+    mean and standard deviation per axis. Finally, you will configure an STM32CubeMX project and
+    run the model on the Arm Cortex-M4–based board in a bare-metal configuration. This advanced
+    path assumes familiarity with ML concepts and C programming on microcontrollers, and access
+    to the specified STM32 board.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      Have the STM32 B-L475E-IOT01A2 board and be comfortable with ML concepts and C programming
+      on microcontrollers. Install Anaconda for the Python environment and download STM32CubeMX;
+      STM32Cube.AI will be used within STM32CubeMX to import the trained model.
+  - question: How should I run the Jupyter notebook steps, and how do I know each cell finished?
+    answer: >-
+      Run the cells in order using the Run button. Jupyter shows In[ ] before execution, In[*]
+      while running, and In[N] when a cell has completed.
+  - question: What data do I train on, and how is it prepared?
+    answer: >-
+      You will use accelerometer data from the STM32 board to recognize letters. The dataset is
+      stored as CSV files in a samples directory and is first used as raw sequences; later you
+      extract features (mean and standard deviation per axis) and retrain.
+  - question: Which model architecture should I define in TensorFlow?
+    answer: >-
+      Define a multi-layer perceptron with three dense layers and dropout using TensorFlow/Keras,
+      as shown in the notebook. Labels are converted to categorical form before training.
+  - question: Which option should I use in STM32CubeMX to target the board and import the model?
+    answer: >-
+      Open STM32CubeMX and use Access to Board Selector to find the B-L475E-IOT01A board and start
+      a new project, then set the project name and location. Under Pinout & Configuration, proceed
+      with setup and use the STM32Cube.AI extension to import the trained ML model.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md b/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md
index 219e5a3c2f..a15a60f35a 100644
--- a/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md
@@ -21,6 +21,50 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:43:17Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 8d8f9df559ff1b1570dc98baf640fc2d4c4d225d69f22529e1881b62d4017752
+  summary_generated_at: '2026-06-01T21:54:11Z'
+  summary_source_hash: 8d8f9df559ff1b1570dc98baf640fc2d4c4d225d69f22529e1881b62d4017752
+  faq_generated_at: '2026-06-02T22:43:17Z'
+  faq_source_hash: 8d8f9df559ff1b1570dc98baf640fc2d4c4d225d69f22529e1881b62d4017752
+  summary: >-
+    This introductory Learning Path shows how to build and run the reference Trusted Firmware-M
+    (TF-M) tests and example application on the Corstone-300 Fixed Virtual Platform (FVP). Working
+    in a bare-metal environment for Armv8-M/Armv8.1-M, you use Arm Virtual Hardware FVP to exercise
+    the Secure Processing Environment (SPE) reference implementation aligned with PSA Certified
+    guidelines. The steps assume an Ubuntu 22.04 LTS (Jammy) host and basic familiarity with embedded
+    C. By the end, you will have compiled the supplied TF-M tests and reference example and executed
+    them on the Corstone-300 FVP, providing a practical starting point for secure microcontroller
+    development with TF-M.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      You need a machine running Ubuntu Linux and some familiarity with embedded C programming.
+      No other prerequisites are explicitly listed.
+  - question: Which platform should I use to run the TF-M tests and example?
+    answer: >-
+      Use the Corstone-300 Fixed Virtual Platform (FVP). It is available from the Arm Ecosystem
+      FVP page.
+  - question: Is an RTOS required or is this a bare-metal setup?
+    answer: >-
+      This path targets a bare-metal setup. No operating system is used on the target.
+  - question: Which Ubuntu version is assumed, and what initial setup step should I run?
+    answer: >-
+      The instructions assume Ubuntu 22.04-LTS (jammy). Begin by updating your system package
+      lists with: sudo apt update.
+  - question: What result should I expect after completing the steps, and how long will it take?
+    answer: >-
+      You will build the supplied TF-M tests and reference example and run them on the Corstone-300
+      FVP. The estimated time to complete is about 15 minutes.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md b/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md
index d0c6c64bf1..34d3adca6e 100644
--- a/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md
@@ -24,6 +24,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:43:51Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 20c500fd5174c9901058676d4619981ee1c8a8cf8e331affea8c0292112651c9
+  summary_generated_at: '2026-06-01T21:54:57Z'
+  summary_source_hash: 20c500fd5174c9901058676d4619981ee1c8a8cf8e331affea8c0292112651c9
+  faq_generated_at: '2026-06-02T22:43:51Z'
+  faq_source_hash: 20c500fd5174c9901058676d4619981ee1c8a8cf8e331affea8c0292112651c9
+  summary: >-
+    This Learning Path walks you through training a small CNN in PyTorch to classify images of
+    the letters R, P, and S into rock, paper, or scissors, exporting the model to an ExecuTorch
+    program (.pte), and running it as a simple interactive mini-game. You then compile and execute
+    the model on the Corstone-320 Fixed Virtual Platform (FVP), completing an end-to-end TinyML
+    workflow for Arm-based edge devices. The path uses synthetic data generation when real data
+    is limited and employs the Ahead-of-Time Arm compiler with delegation to the Ethos-U NPU.
+    Prerequisites include basic ML knowledge, Python/PyTorch familiarity, the prior TinyML Learning
+    Path, and an x86 Ubuntu 22.04+ Linux host.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need basic ML knowledge, familiarity with Python and PyTorch, completion of the “Introduction
+      to TinyML on Arm using PyTorch and ExecuTorch” Learning Path, and an x86 Linux host or VM
+      running Ubuntu 22.04 or later. The path targets the Corstone-320 FVP, so additional Arm
+      hardware is not explicitly required.
+  - question: Where should I create the script and start training?
+    answer: >-
+      Navigate to $HOME/executorch/examples/arm and create rps_tiny.py there. Train and export/play
+      using the provided commands, for example: python rps_tiny.py --epochs 8 --export --play.
+  - question: Do I need a real image dataset to train the model?
+    answer: >-
+      No. The path uses synthetic data generation for training when real data is limited.
+  - question: What artifact should I expect after exporting the model?
+    answer: >-
+      Exporting produces an ExecuTorch program (.pte). You will then compile and build it for
+      the Corstone-320 FVP using the Ahead-of-Time Arm compiler with delegation to the Ethos-U
+      NPU.
+  - question: What should I expect when I run the mini-game or the FVP build?
+    answer: >-
+      The --play option runs an interactive CLI mini-game that classifies the letters R, P, and
+      S as rock, paper, or scissors. The FVP build runs the trained model on a simulated Arm-based
+      edge device to demonstrate on-device inference.
+# END generated_summary_faq
 
 author: Dominica Abena O. Amanfo
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md b/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md
index 3c6f1f95e6..ec5a95246e 100644
--- a/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md
@@ -23,6 +23,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:45:23Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 6c81f3c9595df1128ca6f4f89446f093826d2242fa789ffa2d37465854be1f8e
+  summary_generated_at: '2026-06-01T21:55:22Z'
+  summary_source_hash: 6c81f3c9595df1128ca6f4f89446f093826d2242fa789ffa2d37465854be1f8e
+  faq_generated_at: '2026-06-02T22:45:23Z'
+  faq_source_hash: 6c81f3c9595df1128ca6f4f89446f093826d2242fa789ffa2d37465854be1f8e
+  summary: >-
+    This introductory path shows how to set up Keil MDK with Arm Compiler for Embedded on Windows
+    and run a bare-metal TrustZone hello world on the NXP LPCXpresso55S69. You will obtain the
+    example using the Keil µVision Pack Installer, which provides two sub-projects (hello_ns and
+    hello_s) representing the non-secure and secure worlds. You will build, run, and start a debug
+    session to examine TrustZone behavior, including security state switching and how non-secure
+    code calls secure functions. Prerequisites include familiarity with C programming on microcontrollers,
+    comfort with Windows, and access to an LPCXpresso55S69 board. The path is designed to be completed
+    in about 20 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      Install Keil MDK and Arm Compiler for Embedded on a Windows machine and connect an NXP LPCXpresso55S69
+      board. You should also be comfortable with C programming on microcontrollers and using Windows.
+  - question: How do I obtain the TrustZone hello world example in Keil μVision?
+    answer: >-
+      Open the Pack Installer, select the LPC55S69 device, and copy the hello_ns and hello_s examples
+      into your workspace. These provide the non-secure and secure sub-projects used in the tutorial.
+  - question: Which project should I open to build and run the example?
+    answer: >-
+      From μVision, choose Project -> Open Project and select the hello_world_s example. This
+      example uses the hello_s (secure) and hello_ns (non-secure) sub-projects.
+  - question: What result should I expect when starting a debug session?
+    answer: >-
+      At reset, the secure startup code runs, and the program counter will be at the start of
+      main() in hello_world_s.c. You can then run or step through the code as directed.
+  - question: How do I explore security state switching and secure function calls?
+    answer: >-
+      Start another debug session and step through the example as described in the path to observe
+      transitions between secure and non-secure states and how secure functions are invoked from
+      the non-secure world.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md b/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
index 1138856ddf..1d0351154d 100644
--- a/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
@@ -30,6 +30,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:46:11Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 57e05139cdea1de2f4a4ce239d701f0cef634b6ac11fd437cba47cc071e14098
+  summary_generated_at: '2026-06-01T21:55:43Z'
+  summary_source_hash: 57e05139cdea1de2f4a4ce239d701f0cef634b6ac11fd437cba47cc071e14098
+  faq_generated_at: '2026-06-02T22:46:11Z'
+  faq_source_hash: 57e05139cdea1de2f4a4ce239d701f0cef634b6ac11fd437cba47cc071e14098
+  summary: >-
+    This Learning Path shows you how to 3D print parts and assemble a universal rack mount system
+    for single board computers in a 4U chassis. You will print bay bodies and covers using PETG,
+    cut and prepare 8-32 stainless steel threaded rods, and build chassis bays with nuts, wing
+    nuts, and washers. You then mount SBCs to card plates with screws, standoffs, and hex nuts,
+    and slide the finished assemblies into bay slots. The path lists Fusion 360 as a tool and
+    targets an introductory audience working with Linux-based SBC projects. Expected outcome:
+    a 4U chassis populated with modular bays ready to house multiple SBCs. Prerequisites, including
+    a 3D printer and specific hardware, are explicitly provided.
+  faqs:
+  - question: What do I need before I start printing and assembling the rack?
+    answer: >-
+      You need a 3D printer, PETG filament, a hack saw or chop saw, and a 4U server chassis with
+      the insides removed. Hardware includes 8‑32 stainless threaded rods (cut to 405 mm), #8
+      washers, 8‑32 hex nuts, 8‑32 wing nuts (counts per bay row are listed), and 18‑8 stainless
+      socket head screws and hex nuts for each card.
+  - question: Which filament should I use for the printed parts and why?
+    answer: >-
+      Use PETG. It flexes for parts with squeeze tabs, is non‑toxic so it doesn’t require extra
+      ventilation like ABS, and withstands higher temperatures than PLA.
+  - question: How many printed parts do I need per bay?
+    answer: >-
+      Print bay bodies and bay covers, with the number of each per bay depending on the spacer
+      size you use. Spacers are also required. The exact counts depend on your chosen spacing
+      and are not explicitly listed.
+  - question: How should I prepare and assemble the chassis bays?
+    answer: >-
+      Wash grease off the threaded rods with soap and hot water, then cut each rod to 405 mm.
+      Follow the bay assembly steps to install the nuts, wing nuts, and washers for each bay row.
+  - question: How do I mount an SBC to a card plate and check orientation?
+    answer: >-
+      Insert bolts through the SBC, add standoffs on the back, align the SBC with the appropriate
+      card plate holes, and secure with hex nuts. The design places the SBC’s back edge flush
+      with the grip side of the card plate before you slide the card and plate into a bay slot.
+# END generated_summary_faq
 
 author: Gabriel Peterson
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md b/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
index 50850b4111..7448650cf7 100644
--- a/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
@@ -12,6 +12,57 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:47:15Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 27ced3e9f69dbe51e75dcf13999ae1745a994137f31c86d6f17d4de341c7fa2a
+  summary_generated_at: '2026-06-01T21:56:36Z'
+  summary_source_hash: 27ced3e9f69dbe51e75dcf13999ae1745a994137f31c86d6f17d4de341c7fa2a
+  faq_generated_at: '2026-06-02T22:47:15Z'
+  faq_source_hash: 27ced3e9f69dbe51e75dcf13999ae1745a994137f31c86d6f17d4de341c7fa2a
+  summary: >-
+    This advanced Learning Path guides you through debugging Cortex-M software in Arm Keil µVision
+    using a Blinky example on the Corstone-300 Ecosystem FVP. You will build the project, start
+    a debug session, and use run/stop with hardware breakpoints. It then introduces Event Recorder,
+    including printf to the Debug (printf) Viewer, and Serial Wire Viewer for real-time data (note:
+    SWV is not supported in simulation). You will also explore ETM instruction trace on Armv7-M/Armv8-M
+    devices for execution profiling and code coverage analysis, and measure power with ULINKplus
+    using Event Statistics. Prerequisites include familiarity with embedded programming, a Windows
+    machine, an Arm Account, Keil MDK with an active MDK-Community license, and the Corstone-300
+    Ecosystem FVP.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an Arm Account, a Windows machine, Arm Keil MDK with an active MDK-Community license,
+      and the Corstone-300 Ecosystem FVP installed. Some familiarity with embedded programming
+      is assumed. Clone or download the Blinky example project and open Blinky.Debug+AVH.uvprojx
+      in µVision.
+  - question: How can I print debug text without a UART?
+    answer: >-
+      Use the Event Recorder’s printf utility and view output in the Debug (printf) Viewer window.
+      Event Recorder uses CoreSight DAP for data output and requires some system RAM.
+  - question: What should I check if Serial Wire Viewer (SWV) shows no data?
+    answer: >-
+      SWV is not supported in simulation mode. Connect a debug adapter to real target hardware
+      before using SWV.
+  - question: When should I enable ETM Trace, and what results should I expect?
+    answer: >-
+      Enable ETM Trace on Armv7-M/Armv8-M devices that include ETM to capture instruction trace.
+      In µVision, you can review historical execution sequences, perform execution profiling and
+      performance analysis, and generate code coverage. It also helps diagnose issues like pointer
+      problems and illegal instructions or data aborts.
+  - question: How do I measure power with ULINKplus and configure it?
+    answer: >-
+      Use an Arm Keil ULINKplus to add serial-wire debug, CPU core clock measurement, and power
+      measurement to your session. You can create power profiles with Event Statistics and configure
+      ULINKplus using an initialization file (debug script) that runs when debug mode starts.
+# END generated_summary_faq
 
 author: Christopher Seidl
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md b/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
index e33a99a965..9bd4f41202 100644
--- a/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
@@ -23,6 +23,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:48:13Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 179b0318bbef9cef31ca6bb82de853597a609f61d6868aaaa85cfb40a27a051a
+  summary_generated_at: '2026-06-01T21:57:17Z'
+  summary_source_hash: 179b0318bbef9cef31ca6bb82de853597a609f61d6868aaaa85cfb40a27a051a
+  faq_generated_at: '2026-06-02T22:48:13Z'
+  faq_source_hash: 179b0318bbef9cef31ca6bb82de853597a609f61d6868aaaa85cfb40a27a051a
+  summary: >-
+    This Learning Path shows how to migrate existing µVision uvprojx-based Cortex-M projects to
+    the csolution format required by CMSIS-Toolbox. You will convert projects using three workflows:
+    Keil Studio in VS Code, µVision’s built-in export, and the uv2csolution command-line tool
+    on Windows, Linux, or macOS. The steps highlight what gets generated (for example, .csolution.yaml,
+    .cproject.yaml, and a vcpkg configuration) and how to confirm a successful conversion in the
+    output views. Prerequisites include installed Keil Studio, µVision, and uv2csolution for the
+    CLI flow; the project must use Arm Compiler 6. After conversion, you can use the project with
+    CMSIS-Toolbox or Keil Studio. Estimated time to complete is about 10 minutes.
+  faqs:
+  - question: What do I need installed before running the conversion?
+    answer: >-
+      Install Keil Studio and µVision, and install uv2csolution if you plan to use the command-line
+      flow. The µVision project must use Arm Compiler 6 as the default toolchain; Arm Compiler
+      5 is not supported.
+  - question: How do I start and verify the conversion in Keil Studio?
+    answer: >-
+      In VS Code, open the folder containing the uvprojx, right-click the uvprojx file, and select
+      “Convert µVision project to csolution.” The Output window shows a successful conversion,
+      and the vcpkg configuration file is automatically activated so you will see “Arm Tools”
+      available.
+  - question: What files should I expect after a successful conversion?
+    answer: >-
+      You should see files such as <project>.csolution.yaml, <project>.cproject.yaml, and vcpkg-configuration.json,
+      along with any related support files.
+  - question: How do I export from µVision and confirm it worked?
+    answer: >-
+      Use Project → Export → Save Project to csolution format in µVision. The Build Output window
+      will show a successful conversion, and you can then use the project with CMSIS-Toolbox or
+      Keil Studio.
+  - question: What should I check if my project currently uses Arm Compiler 5?
+    answer: >-
+      Arm Compiler 5 is not supported; set Arm Compiler 6 as the default toolchain in your µVision
+      project before converting.
+# END generated_summary_faq
 
 author: Christopher Seidl
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md b/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
index 3bc2a5c68d..b181bf540b 100644
--- a/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
@@ -24,6 +24,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:49:26Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 0c3422e1cd571e6abff676c28ec32c2ec69a626a406167f9166e57daa6829252
+  summary_generated_at: '2026-06-01T21:57:53Z'
+  summary_source_hash: 0c3422e1cd571e6abff676c28ec32c2ec69a626a406167f9166e57daa6829252
+  faq_generated_at: '2026-06-02T22:49:26Z'
+  faq_source_hash: 0c3422e1cd571e6abff676c28ec32c2ec69a626a406167f9166e57daa6829252
+  summary: >-
+    Use vcpkg on Linux, Windows, or macOS to create reproducible command-line installations of
+    tools used in Arm Cortex-M development. You will install and initialize vcpkg in each new
+    terminal session, create a vcpkg-configuration.json to ensure consistent, cross-platform tool
+    setup, activate the tools defined by your configuration, and handle license activation for
+    Arm tools using armlm. The path also covers removing vcpkg when you are finished. Prerequisites
+    are a basic understanding of development tools for Arm Cortex-M and command-line access. After
+    completing the steps, you will be able to stand up a consistent tool installation via vcpkg
+    and verify that licensing is active.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a basic understanding of the development tools for Arm Cortex-M and command-line
+      access to your machine. No other explicit prerequisites are listed.
+  - question: Which initialization command should I use on my OS, and when should I run it?
+    answer: >-
+      Run the vcpkg init command in every new Terminal window: Windows (cmd): %USERPROFILE%\.vcpkgcpkg-init.cmd,
+      Windows (PowerShell): . ~/.vcpkg/vcpkg-init.ps1, Linux/macOS: . ~/.vcpkg/vcpkg-init. This
+      ensures your shell session is set up to use vcpkg.
+  - question: What is the purpose of vcpkg-configuration.json?
+    answer: >-
+      It ensures a consistent installation of tools across all platforms by selecting the correct
+      binaries for your host OS and architecture. Creating this configuration file is the first
+      step before using the tools.
+  - question: How do I activate the tools and confirm activation worked?
+    answer: >-
+      Use vcpkg-shell activate to activate the tools specified in your vcpkg-configuration.json.
+      You should see a list of artifacts with their Status (for example, "installed") such as
+      Arm distributed Open-CMSIS-Pack CLI tools or Arm Compiler for Embedded. A warning that vcpkg-artifacts
+      is experimental may appear and is shown in the example output.
+  - question: When do I need to activate a license, and how can I verify it?
+    answer: >-
+      Before compiling with Arm Compiler for Embedded, you must install a license. You can activate
+      an MDK-Community license with: armlm activate -product KEMDK-COM0 -server https://mdk-preview.keil.arm.com.
+      Verify the license with armlm inspect, which shows active products in your local cache.
+# END generated_summary_faq
 
 author: Christopher Seidl
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md b/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md
index a4c78d454e..1cbfc24ed8 100644
--- a/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md
@@ -23,6 +23,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:50:53Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: dcdbc4cecc376ed4c0df9377d13cb4fe792ad7c68acfe0610d75cf41898804ff
+  summary_generated_at: '2026-06-01T21:59:12Z'
+  summary_source_hash: dcdbc4cecc376ed4c0df9377d13cb4fe792ad7c68acfe0610d75cf41898804ff
+  faq_generated_at: '2026-06-02T22:50:53Z'
+  faq_source_hash: dcdbc4cecc376ed4c0df9377d13cb4fe792ad7c68acfe0610d75cf41898804ff
+  summary: >-
+    This introductory Learning Path shows how to evaluate TinyML workloads on Arm virtual hardware
+    before physical boards are available. You will set up an ExecuTorch development environment
+    on Linux or macOS, install and configure the Corstone-320 Fixed Virtual Platform (FVP), and
+    deploy a MobileNet V2 model to exercise the Ethos-U NPU in a virtual system. The steps explain
+    how ExecuTorch uses ahead-of-time compilation and hybrid CPU/NPU execution, then guide you
+    to run the example and visualize execution with the FVP graphical interface. Prerequisites
+    are basic machine learning familiarity and a Linux or macOS host with Python 3. The path is
+    designed to be completed in about 120 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need familiarity with basic machine learning concepts and a Linux or macOS computer
+      with Python 3 installed. The path is introductory and assumes no prior TinyML experience.
+  - question: I’m using macOS—are there extra steps to run the FVP?
+    answer: >-
+      Yes. The path notes additional setup is required on macOS for FVP execution and points to
+      the FVPs-on-Mac GitHub repository for the necessary steps.
+  - question: Where is the example model and how do I run it?
+    answer: >-
+      The MobileNet V2 Python code is located in executorch/examples/models/mobilenet_v2/model.py
+      within your local ExecuTorch repository. You deploy it using the run.sh script with extra
+      parameters as shown in the steps.
+  - question: How do I know the FVP and ExecuTorch setup worked?
+    answer: >-
+      After setup, you should be able to start the Corstone-320 FVP and run an ExecuTorch-compiled
+      model. You can then visualize model execution in the FVP graphical interface.
+  - question: Do I need physical hardware to test Ethos-U NPU performance?
+    answer: >-
+      No. The Corstone-320 FVP simulates an Arm-based embedded system so you can deploy and test
+      TinyML models, including visualization, without any hardware.
+# END generated_summary_faq
 
 author: Waheed Brown
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md b/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md
index 7762a1275e..8d2f23b6c3 100644
--- a/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md
@@ -20,6 +20,50 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:52:18Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 3bcfc51edbab56e3c8416f27045a61b069333e6f0cd130e8689b9a4d9fde1dd6
+  summary_generated_at: '2026-06-01T21:59:52Z'
+  summary_source_hash: 3bcfc51edbab56e3c8416f27045a61b069333e6f0cd130e8689b9a4d9fde1dd6
+  faq_generated_at: '2026-06-02T22:52:18Z'
+  faq_source_hash: 3bcfc51edbab56e3c8416f27045a61b069333e6f0cd130e8689b9a4d9fde1dd6
+  summary: >-
+    Learn how to build a minimal Yocto Linux image for a generic 64-bit Arm (Cortex-A class) target
+    and run it under QEMU. Working on a Linux host (Ubuntu 22.04) with at least 60 GB of disk
+    space, you use the Yocto Project—starting from the Poky reference distribution—to configure
+    and produce a bootable image, then launch it on a 64-bit Arm QEMU machine. This introductory
+    path is aimed at developers who want the basics of Yocto for embedded Arm. By the end, you
+    will have built a minimal image and verified it by running it in QEMU. Some familiarity with
+    embedded Linux is expected.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      Use a Linux host running Ubuntu 22.04 with at least 60 GB of disk space. Some familiarity
+      with embedded Linux is also expected.
+  - question: Which Yocto distribution should I use to start the build?
+    answer: >-
+      Poky, the Yocto Project reference distribution, is used as the starting point to build a
+      minimal image. You will work with Yocto recipes as part of this process.
+  - question: Do I need physical Arm hardware to complete this Learning Path?
+    answer: >-
+      No. The image is run on a 64-bit QEMU Arm target, so you can complete the steps without
+      physical Arm hardware.
+  - question: Which target architecture is used when running under QEMU?
+    answer: >-
+      The steps target a generic 64-bit Arm platform (Cortex-A class) and boot the built image
+      under QEMU.
+  - question: What result should I expect after the build, and how do I run it?
+    answer: >-
+      Expect a minimal Yocto Linux image produced by the Yocto build system. You will launch QEMU
+      and boot this image as shown in the steps to validate the build.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md b/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md
index b0ab9b1ad6..c7e62bc4ab 100644
--- a/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md
@@ -25,6 +25,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:54:16Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: b0cb917bdcfb601c054e6cac93b2d04aca3ffe84b5a1963288fd7b89dd9bad3b
+  summary_generated_at: '2026-06-01T22:00:17Z'
+  summary_source_hash: b0cb917bdcfb601c054e6cac93b2d04aca3ffe84b5a1963288fd7b89dd9bad3b
+  faq_generated_at: '2026-06-02T22:54:16Z'
+  faq_source_hash: b0cb917bdcfb601c054e6cac93b2d04aca3ffe84b5a1963288fd7b89dd9bad3b
+  summary: >-
+    Build and deploy a YOLO object detection application on the Himax WiseEye2 platform (Arm Cortex-M55
+    with Ethos-U55) using the Seeed Grove Vision AI Module V2. You will prepare a Linux or macOS
+    host, install Python, clone the Himax examples repository, build the Himax SDK to generate
+    a firmware image, and flash the microcontroller using Xmodem. After connecting the OV5647
+    camera via the FPC cable and USB-C, you will run the firmware to view a live camera feed and
+    explore additional models by editing a makefile and selecting a model in the web toolkit.
+    Prerequisites include the Grove Vision AI Module V2, OV5647 camera, FPC and USB-C cables,
+    and an x86 Linux machine or a Mac. Estimated time: 90 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Seeed Grove Vision AI Module V2, an OV5647-62 camera module, a Flexible Printed
+      Circuit (FPC) cable, a USB-C cable, and an x86 Linux machine or a Mac running macOS.
+  - question: Which operating systems are supported, and can I use Windows?
+    answer: >-
+      The path has been validated on Ubuntu 22.04 LTS and macOS. If you use Windows, you can run
+      Ubuntu through Windows Subsystem for Linux 2 (WSL2).
+  - question: How do I clone the Himax project with all required submodules?
+    answer: >-
+      Clone the repository recursively so that subrepositories are included: git clone --recursive
+      https://github.com/HimaxWiseEyePlus/Seeed_Grove_Vision_AI_Module_V2.git. Then change into
+      the cloned directory to proceed with the build steps.
+  - question: How do I install Xmodem for flashing the firmware?
+    answer: >-
+      From the repository root, run: cd $HOME/Seeed_Grove_Vision_AI_Module_V2 and pip install
+      -r xmodem/requirements.txt. Xmodem is used to transfer the compiled firmware image to the
+      microcontroller.
+  - question: How do I select and run different models, such as YOLO object detection?
+    answer: >-
+      Edit the makefile in $HOME/Seeed_Grove_Vision_AI_Module_V2/EPII_CM55M_APP_S/ and set APP_TYPE
+      (for example, tflm_yolov8_od for object detection). Use the corresponding model argument
+      with the --model option in the Xmodem command; after flashing, you will view a live camera
+      feed with the application running.
+# END generated_summary_faq
 
 author:
     - Chaodong Gong
diff --git a/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md b/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md
index 514959da2d..4afb8a851f 100644
--- a/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md
@@ -21,6 +21,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:56:13Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 89f599ab33721a2d578d4fc39e505b5aed8d930aabac71f22d1ba28bfc4a5cf8
+  summary_generated_at: '2026-06-01T22:00:54Z'
+  summary_source_hash: 89f599ab33721a2d578d4fc39e505b5aed8d930aabac71f22d1ba28bfc4a5cf8
+  faq_generated_at: '2026-06-02T22:56:13Z'
+  faq_source_hash: 89f599ab33721a2d578d4fc39e505b5aed8d930aabac71f22d1ba28bfc4a5cf8
+  summary: >-
+    This Learning Path shows how to build and run Zephyr RTOS applications on the Arm Corstone-300
+    Fixed Virtual Platform (FVP) using Arm Virtual Hardware. You will obtain the Zephyr source,
+    install the Zephyr SDK, build Zephyr sample applications, and execute them on a virtual Corstone-300
+    system targeting Cortex-M. This introductory path is designed for developers getting started
+    with Zephyr on Arm and can be completed in about 30 minutes. Prerequisites are some familiarity
+    with embedded C and either a Linux machine running Ubuntu or an AWS account to use Arm Virtual
+    Hardware. By the end, you will have verified Zephyr builds running on the Corstone-300 FVP.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need some familiarity with embedded C programming and either a Linux machine running
+      Ubuntu or an AWS account to use Arm Virtual Hardware. No other prerequisites are explicitly
+      listed.
+  - question: Do I need physical hardware for this Learning Path?
+    answer: >-
+      No. The applications are run on the Corstone-300 Fixed Virtual Platform using Arm Virtual
+      Hardware, so no physical hardware is required.
+  - question: 'Which environment should I use: local Ubuntu or Arm Virtual Hardware on AWS?'
+    answer: >-
+      Use a local Ubuntu machine if you prefer to run the tools on your own system, or choose
+      an AWS account to access Arm Virtual Hardware in the cloud. The path supports either option
+      as indicated in the prerequisites.
+  - question: What will I build and run in this path?
+    answer: >-
+      You will get the Zephyr source, install the Zephyr SDK, build sample Zephyr applications,
+      and run them on the Corstone-300 FVP.
+  - question: How do I know the application ran correctly on the Corstone-300 FVP?
+    answer: >-
+      You should be able to launch the Corstone-300 FVP and see the sample application run without
+      errors. If it fails, recheck that the Zephyr SDK is installed and the Zephyr source was
+      obtained as shown in the steps.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md b/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md
index d7c59de272..a3d8a25ce4 100644
--- a/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md
@@ -25,6 +25,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:57:52Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 808f1c99409f9ca3bb72419eaf950bc097f1c7af6c2dcade6385be97fdbbf713
+  summary_generated_at: '2026-06-01T22:01:28Z'
+  summary_source_hash: 808f1c99409f9ca3bb72419eaf950bc097f1c7af6c2dcade6385be97fdbbf713
+  faq_generated_at: '2026-06-02T22:57:52Z'
+  faq_source_hash: 808f1c99409f9ca3bb72419eaf950bc097f1c7af6c2dcade6385be97fdbbf713
+  summary: >-
+    This introductory path shows how to install and configure the Workbench for Zephyr extension
+    in Visual Studio Code, set up the Zephyr SDK and toolchain, and create, build, and debug Zephyr
+    RTOS applications on Arm Cortex-M boards. You will follow a workflow demonstrated with an
+    NXP FRDM-MCXN947 board, but the same steps apply to any Zephyr-supported Cortex-M target,
+    with board-specific debug runners selected as needed. The path also covers generating memory
+    usage reports to understand ROM and RAM consumption and applying basic optimization techniques.
+    Prerequisites include basic embedded C skills, VS Code, a Cortex-M development board, and
+    a host running Windows 10+ (64-bit), macOS with Homebrew, or Linux (preferably Ubuntu 20.04+).
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need basic familiarity with embedded C, Visual Studio Code, and access to a Cortex-M
+      development board. Use Windows 10+ (64-bit), macOS with Homebrew, or Linux (preferably Ubuntu
+      20.04+) as your host system.
+  - question: How do I know if my Arm Cortex-M board will work for this path?
+    answer: >-
+      The process works for any Zephyr-supported Arm Cortex-M board. The path demonstrates with
+      an NXP FRDM-MCXN947, and you can confirm your board on the Zephyr Supported Boards list.
+  - question: Which debug runner should I use for my board?
+    answer: >-
+      The required runner depends on your board. Follow your board’s Zephyr documentation and
+      the path’s guidance, as you might need to install and select a different debug tool (runner)
+      in Workbench for Zephyr.
+  - question: What result should I expect after I build the sample application in Workbench?
+    answer: >-
+      You should get a successful Zephyr build along with a memory usage report showing ROM and
+      RAM consumption. You can then proceed to live debugging and memory analysis within Workbench.
+  - question: What should I check if the build or debug setup fails?
+    answer: >-
+      Verify that the Workbench for Zephyr extension is installed and that it completed SDK and
+      toolchain setup. Ensure your board is Zephyr-supported and that the appropriate debug runner
+      is configured for your target.
+# END generated_summary_faq
 
 author: 
     - Ayoub Bourjilat
diff --git a/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md b/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md
index 790c3afcb5..254caf4369 100644
--- a/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md
+++ b/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md
@@ -23,6 +23,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T22:58:53Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 137e974aee0ccba78e3375a1c8179af392e54a0304cfecdcd53cf4ac5c38917b
+  summary_generated_at: '2026-06-01T22:01:54Z'
+  summary_source_hash: 137e974aee0ccba78e3375a1c8179af392e54a0304cfecdcd53cf4ac5c38917b
+  faq_generated_at: '2026-06-02T22:58:53Z'
+  faq_source_hash: 137e974aee0ccba78e3375a1c8179af392e54a0304cfecdcd53cf4ac5c38917b
+  summary: >-
+    This introductory Learning Path shows how to create and run an Ubuntu 24.04 LXC container
+    on ChromeOS (Crostini) from the Termina shell on an Arm-based Chromebook. You will set up
+    ChromeOS integration for selective folder sharing from the Files app and enable Linux GUI
+    applications through Sommelier, using a minimal desktop environment and a test app to validate.
+    You will also manage the container lifecycle with common LXC commands, including start, stop,
+    exec, list, info, and delete. Prerequisites are a ChromeOS device with the Linux development
+    environment enabled (the Lenovo Chromebook Plus 14 is recommended) and basic Linux command-line
+    familiarity. Estimated time to complete is about 60 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a ChromeOS device with the Linux development environment enabled and basic Linux
+      command-line knowledge. The Lenovo Chromebook Plus 14 is recommended.
+  - question: Where do I run the LXC and setup commands on ChromeOS?
+    answer: >-
+      Run all container management and setup commands in the Termina shell provided by the ChromeOS
+      Linux development environment.
+  - question: How do I start, stop, and access my Ubuntu container, and check its status?
+    answer: >-
+      From Termina, use lxc start u1 to start, lxc stop u1 to stop, and lxc exec u1 -- bash to
+      enter the container shell. Use lxc list to view all containers and lxc info u1 for detailed
+      information such as status and architecture.
+  - question: How do I share folders between ChromeOS and the Ubuntu container?
+    answer: >-
+      In the ChromeOS Files app, right-click a folder and select Share with Linux to make it available
+      to the container. Only folders (not individual files) can be shared, and access is two-way
+      for Linux apps and the command line.
+  - question: How do I enable and test Linux GUI applications from the container?
+    answer: >-
+      Install a minimal desktop environment and configure the display environment variables so
+      applications use Sommelier. You can install a test GUI application (for example, a terminal
+      emulator like terminator) to confirm that windows open in the ChromeOS desktop.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md
index c599af362d..1074ada65c 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md
@@ -24,6 +24,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:00:20Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: eda533c727ea7094202e8784bf1ed2240cfea2573cefc73aca1a86797840778c
+  summary_generated_at: '2026-06-01T22:02:33Z'
+  summary_source_hash: eda533c727ea7094202e8784bf1ed2240cfea2573cefc73aca1a86797840778c
+  faq_generated_at: '2026-06-02T23:00:20Z'
+  faq_source_hash: eda533c727ea7094202e8784bf1ed2240cfea2573cefc73aca1a86797840778c
+  summary: >-
+    This advanced Learning Path shows how to build, configure, and run NVIDIA Isaac Sim and Isaac
+    Lab on an Arm-based NVIDIA DGX Spark system powered by the Grace–Blackwell (GB10) architecture.
+    You will verify the DGX Spark configuration, install required build dependencies, build Isaac
+    Sim, and set up Isaac Lab on top. You will launch and control a sample Cartpole simulation
+    using Python to understand Isaac Sim’s simulation loop. You will then train and evaluate a
+    reinforcement learning policy for the Unitree H1 humanoid robot using Isaac Lab with RSL-RL
+    (PPO). Prerequisites include a DGX Spark with about 50 GB free disk space, Linux command-line
+    skills, experience with Python virtual environments, and a basic understanding of RL concepts.
+    Estimated time to complete is 90 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need access to an NVIDIA DGX Spark system with at least 50 GB of free disk space. Familiarity
+      with Linux command-line tools, Python scripting and virtual environments, and basic RL concepts
+      is expected.
+  - question: How long does installation usually take, and how much storage is required?
+    answer: >-
+      The setup typically takes 15–20 minutes on a DGX Spark system. Plan for approximately 50
+      GB of available disk space.
+  - question: How are Isaac Sim and Isaac Lab arranged in the environment?
+    answer: >-
+      You first build and configure Isaac Sim, then set up Isaac Lab on top of the Isaac Sim environment.
+      The path begins by verifying the DGX Spark configuration and installing required build dependencies.
+  - question: Which simulation do I run first, and how do I confirm it worked?
+    answer: >-
+      You start with the Cartpole environment by launching a pre-built scene in Isaac Sim. Successful
+      setup is indicated by the scene loading and your ability to interact with it programmatically
+      using Python while exploring the simulation loop.
+  - question: Which RL framework and algorithm are used for training the humanoid policy?
+    answer: >-
+      Training uses Isaac Lab’s integration with the RSL-RL library implementing PPO. You configure
+      the task and environment for the Unitree H1 humanoid to walk over rough terrain, then train
+      and evaluate the policy.
+# END generated_summary_faq
 
 author:
     - Johnny Nunez
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md
index 5e89b06086..d517ce3e3b 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md
@@ -26,6 +26,57 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:01:47Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: ada333cc887badfd57815708ef93e172543da74f2c995b46a916817917e92394
+  summary_generated_at: '2026-06-01T22:03:05Z'
+  summary_source_hash: ada333cc887badfd57815708ef93e172543da74f2c995b46a916817917e92394
+  faq_generated_at: '2026-06-02T23:01:47Z'
+  faq_source_hash: ada333cc887badfd57815708ef93e172543da74f2c995b46a916817917e92394
+  summary: >-
+    This Learning Path shows how to build and validate both CUDA-enabled and CPU-only versions
+    of llama.cpp on an Arm-based NVIDIA DGX Spark system with the Grace–Blackwell (GB10) architecture
+    running Linux. You will review GB10 fundamentals, including the Grace CPU with Armv9 Cortex‑X925
+    and Cortex‑A725 cores, verify system readiness (CPU, OS, Blackwell GPU, and CUDA toolkit),
+    compile llama.cpp for GPU and CPU targets, and confirm both builds function on DGX Spark.
+    You then analyze Armv9 SIMD behavior on the Grace CPU using Process Watch, observing instruction
+    usage during quantized LLM inference. Prerequisites include DGX Spark access (15 GB free),
+    Linux CLI skills, CUDA basics, quantized LLM knowledge, and experience building with CMake
+    and make.
+  faqs:
+  - question: What do I need before running the steps on DGX Spark?
+    answer: >-
+      You need access to an NVIDIA DGX Spark system with at least 15 GB of available disk space.
+      Familiarity with Linux and the command line, CUDA programming basics, knowledge of quantized
+      LLMs, and experience building from source with CMake and make are expected.
+  - question: How do I confirm my DGX Spark is ready for building llama.cpp?
+    answer: >-
+      Verify your Grace CPU configuration and operating system, ensure the Blackwell GPU and CUDA
+      drivers are active, and confirm that the CUDA toolkit is installed. The readiness section
+      guides you through these checks before you start building.
+  - question: 'Which llama.cpp build should I use on GB10: GPU or CPU-only?'
+    answer: >-
+      Use the CUDA-enabled GPU build when the Blackwell GPU and a CUDA 13 environment are available
+      for quantized LLM workloads. Use the CPU-only build to run entirely on the Grace CPU, which
+      leverages Armv9 vector capabilities such as SVE2, BFloat16, and I8MM.
+  - question: What result should I expect after completing the builds?
+    answer: >-
+      You will have both a CUDA-enabled and a CPU-only llama.cpp build ready to run on DGX Spark.
+      The Learning Path includes steps to validate that each build functions correctly on the
+      platform.
+  - question: How do I analyze the Armv9 instruction mix during CPU inference?
+    answer: >-
+      Use Process Watch to observe Neon SIMD instruction execution on the Grace CPU. The path
+      also explains why SVE and SVE2 remain inactive under the current kernel configuration during
+      this analysis.
+# END generated_summary_faq
 
 author: Odin Shen
 
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md
index c243177a2b..cfa9bdd13c 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md
@@ -21,6 +21,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:02:18Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: facf9c504a3caceb62ef898a04a6760e8aafeb7e802e5727cb80d3a7e7e344d0
+  summary_generated_at: '2026-06-01T22:03:59Z'
+  summary_source_hash: facf9c504a3caceb62ef898a04a6760e8aafeb7e802e5727cb80d3a7e7e344d0
+  faq_generated_at: '2026-06-02T23:02:18Z'
+  faq_source_hash: facf9c504a3caceb62ef898a04a6760e8aafeb7e802e5727cb80d3a7e7e344d0
+  summary: >-
+    This advanced Learning Path guides you through building a hybrid Retrieval-Augmented Generation
+    (RAG) pipeline on Arm-based NVIDIA DGX Spark (Grace–Blackwell/GB10). You will set up a Python
+    environment on Linux, prepare the e5-base-v2 embedding model and the Llama 3.1 8B Instruct
+    LLM, load a sample document corpus, and index it with FAISS for vector search on Arm Grace
+    CPUs. You will run GPU-accelerated inference via the llama.cpp REST server on Blackwell GPUs
+    while CPU-managed retrieval orchestrates requests. Finally, you will monitor unified memory
+    behavior and GPU utilization to validate zero-copy data sharing. Prerequisite: an NVIDIA DGX
+    Spark with at least 15 GB free disk space; related llama.cpp background is recommended.
+  faqs:
+  - question: Do I need to complete another Learning Path before starting this one?
+    answer: >-
+      It is recommended to first complete “Unlock quantized LLM performance on Arm-based NVIDIA
+      DGX Spark” to learn about CPU and GPU builds of llama.cpp. That background helps when deploying
+      the RAG solution in this path.
+  - question: What platform and resources are required to follow the steps?
+    answer: >-
+      You need an NVIDIA DGX Spark (Grace–Blackwell/GB10) system running Linux with at least 15
+      GB of available disk space. No other explicit prerequisites are listed.
+  - question: Which models and libraries does the RAG pipeline use?
+    answer: >-
+      The pipeline uses e5-base-v2 for embeddings and Llama 3.1 8B Instruct for generation. It
+      relies on Python, Hugging Face tooling, FAISS for vector search, and the llama.cpp REST
+      server for GPU-accelerated inference.
+  - question: How should I set up the Python environment for this project?
+    answer: >-
+      Create a Python virtual environment and upgrade pip. Then install torch from the PyTorch
+      CPU wheel index along with transformers==4.46.2 and sentence-transformers==2.7 as shown
+      in the steps.
+  - question: How do I verify the pipeline is working and monitor performance?
+    answer: >-
+      After integration, run the RAG model server and issue a query against your document corpus
+      to exercise retrieval and GPU-backed generation. Use the monitoring steps to observe unified
+      memory and GPU utilization from separate terminals and confirm zero-copy data sharing during
+      inference.
+# END generated_summary_faq
 
 author: Odin Shen
 
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md
index c453374dbb..9c146039bc 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md
@@ -23,6 +23,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:03:30Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 4ccde526ec4dd9fc18672e162e067108c7251161c1da0375a0bb0374a2f3a4ea
+  summary_generated_at: '2026-06-01T22:04:24Z'
+  summary_source_hash: 4ccde526ec4dd9fc18672e162e067108c7251161c1da0375a0bb0374a2f3a4ea
+  faq_generated_at: '2026-06-02T23:03:30Z'
+  faq_source_hash: 4ccde526ec4dd9fc18672e162e067108c7251161c1da0375a0bb0374a2f3a4ea
+  summary: >-
+    This advanced Learning Path guides you through building a private, offline voice chatbot on
+    Arm-based DGX Spark running Linux. You will capture real-time audio from a USB microphone
+    using PyAudio with Voice Activity Detection, transcribe speech locally using faster-whisper
+    on CPU, and generate responses with vLLM for on-device inference. The steps cover installing
+    and validating faster-whisper, constructing a real-time STT pipeline, fine-tuning segmentation
+    parameters, and integrating vLLM to complete the end-to-end system. By the end, you will deploy
+    and run the full pipeline on DGX Spark, with a focus on adjusting segmentation and prompt
+    strategies to balance latency and response quality. Prerequisites include a DGX Spark system
+    with at least 15 GB free disk space and a USB microphone, using Docker and Python.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an NVIDIA DGX Spark system with at least 15 GB of available disk space, a USB microphone,
+      and a Linux environment. The path uses Python and Docker.
+  - question: Which components run on CPU versus GPU in this workflow?
+    answer: >-
+      The path builds a real-time speech-to-text pipeline on the CPU using faster-whisper. It
+      then adds vLLM for local language generation, which runs on GPU.
+  - question: How do I verify that faster-whisper is installed correctly?
+    answer: >-
+      The setup step focuses on confirming that faster-whisper can reliably transcribe audio.
+      Run a short recording or sample through the tool and check that you get accurate text output
+      before proceeding.
+  - question: How is audio captured and segmented for transcription?
+    answer: >-
+      You capture real-time audio with PyAudio and apply VAD-based segmentation and smart turn
+      detection. The path evolves from a simple 10-second recorder to a multithreaded, VAD-enhanced
+      STT engine.
+  - question: What result should I expect when the full pipeline is running?
+    answer: >-
+      Speaking into the microphone yields transcribed text from faster-whisper, and vLLM generates
+      a local response. You can fine-tune segmentation and prompt strategies to improve latency
+      and response quality.
+# END generated_summary_faq
 
 author: Odin Shen
 
diff --git a/content/learning-paths/laptops-and-desktops/docker-models/_index.md b/content/learning-paths/laptops-and-desktops/docker-models/_index.md
index a40d34d255..a6a37243dc 100644
--- a/content/learning-paths/laptops-and-desktops/docker-models/_index.md
+++ b/content/learning-paths/laptops-and-desktops/docker-models/_index.md
@@ -21,6 +21,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:04:02Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: eae0a23635e7a025e1a73baaf5ccbd01f2c031ec76725c68893ca02190e36deb
+  summary_generated_at: '2026-06-01T22:04:54Z'
+  summary_source_hash: eae0a23635e7a025e1a73baaf5ccbd01f2c031ec76725c68893ca02190e36deb
+  faq_generated_at: '2026-06-02T23:04:02Z'
+  faq_source_hash: eae0a23635e7a025e1a73baaf5ccbd01f2c031ec76725c68893ca02190e36deb
+  summary: >-
+    This introductory path shows how to run pre-trained large language models locally on Windows
+    or macOS using Docker Model Runner, an official Docker extension that leverages llama.cpp
+    without requiring you to install AI frameworks. You will start local LLM inference and then
+    use Docker Compose to deploy a simple containerized AI chat application with a Flask frontend
+    and a Model Runner backend. The provided example can interact with local models such as Llama
+    3.2 or Gemma 3. Prerequisites are Docker Desktop 4.40+ (16GB RAM recommended), basic Docker
+    CLI knowledge, and familiarity with LLM concepts. The workflow is designed to run across environments,
+    including Arm-based systems, and takes about 45 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need Docker Desktop version 4.40 or later on Windows or macOS, a system with at least
+      16GB of RAM recommended, basic understanding of Docker CLI and concepts, and familiarity
+      with LLM concepts.
+  - question: Do I need to install any LLM frameworks or toolchains locally?
+    answer: >-
+      No. Docker Model Runner uses llama.cpp under the hood, so you do not need to download, build,
+      or install any LLM frameworks.
+  - question: Will this work on Arm-based systems?
+    answer: >-
+      Yes. Docker Model Runner is designed to run models across different environments, including
+      Arm-based systems; the steps target Windows or macOS with Docker Desktop.
+  - question: Which models can I try with the example chat app?
+    answer: >-
+      The example supports interacting with local AI models such as Llama 3.2 or Gemma 3.
+  - question: What result should I expect after deploying with Docker Compose?
+    answer: >-
+      You will run a simple web-based AI chat application where a Flask frontend communicates
+      with a Docker Model Runner backend. You should be able to enter prompts in the web interface
+      and receive model-generated responses.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/laptops-and-desktops/electron/_index.md b/content/learning-paths/laptops-and-desktops/electron/_index.md
index b3b22e3b07..e103089cd8 100644
--- a/content/learning-paths/laptops-and-desktops/electron/_index.md
+++ b/content/learning-paths/laptops-and-desktops/electron/_index.md
@@ -21,6 +21,50 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:04:46Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 8491a5e83e9e6436721f6078085e9b367121d19fdf228634dba859b3e1a0802a
+  summary_generated_at: '2026-06-01T22:05:20Z'
+  summary_source_hash: 8491a5e83e9e6436721f6078085e9b367121d19fdf228634dba859b3e1a0802a
+  faq_generated_at: '2026-06-02T23:04:46Z'
+  faq_source_hash: 8491a5e83e9e6436721f6078085e9b367121d19fdf228634dba859b3e1a0802a
+  summary: >-
+    This Learning Path shows how to develop a simple Electron desktop application on Windows on
+    Arm (Arm64) and build it for multiple architectures. You will set up a Windows on Arm device
+    or virtual machine with Node.js for Arm64 and a code editor, create an Electron app using
+    web technologies, and configure cross-platform builds. The steps introduce Electron Builder
+    and the required changes to package.json so you can produce builds targeting Arm64 and x64.
+    Designed for an introductory audience, the path takes about 30 minutes and provides a practical
+    workflow for getting an Electron app running on Windows on Arm and preparing multi-architecture
+    outputs.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      Have a Windows on Arm computer (for example, a Lenovo ThinkPad X13s running Windows 11)
+      or a Windows on Arm virtual machine, Node.js for Arm64 installed, and a code editor. Visual
+      Studio Code for Arm64 is recommended.
+  - question: How long should I plan to spend on this Learning Path?
+    answer: >-
+      The estimated time to complete is 30 minutes.
+  - question: How do I add Electron Builder to my project?
+    answer: >-
+      From your project folder, run: npm install electron-builder --save-dev. The console output
+      will be similar to the example shown in the steps and may include npm audit messages.
+  - question: Where do I configure the project for cross-platform builds?
+    answer: >-
+      Modify the package.json file in your project folder as shown in the Learning Path to enable
+      building for multiple architectures.
+  - question: Which architectures will the final build target?
+    answer: >-
+      The build is configured to run on both Arm64 and x64.
+# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md b/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md
index d0758d366c..4b6e9d31dc 100644
--- a/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md
+++ b/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md
@@ -21,6 +21,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:06:25Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 7e108d774eb0fd0b2b72fa8b5d65e1efec0ff4ecf3d80d4e511e82cdf3862284
+  summary_generated_at: '2026-06-01T22:05:42Z'
+  summary_source_hash: 7e108d774eb0fd0b2b72fa8b5d65e1efec0ff4ecf3d80d4e511e82cdf3862284
+  faq_generated_at: '2026-06-02T23:06:25Z'
+  faq_source_hash: 7e108d774eb0fd0b2b72fa8b5d65e1efec0ff4ecf3d80d4e511e82cdf3862284
+  summary: >-
+    This introductory Learning Path shows how to automate Windows application builds on Arm architecture
+    using GitHub Arm-hosted runners and GitHub Actions. You will learn what Arm-hosted Windows
+    runners are, how to target them in your workflows, and how to automate builds for a sample
+    rotating 3D cube application that is also used in the Optimize Windows applications using
+    Arm Performance Libraries Learning Path. The steps emphasize running CI on Arm hardware without
+    operating your own infrastructure and introduce options for configuring a larger runner if
+    required. Prerequisites are a GitHub account and familiarity with GitHub Actions. Estimated
+    time to complete is about 20 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a GitHub account and familiarity with GitHub Actions. No other explicit prerequisites
+      are listed.
+  - question: How do I target a GitHub Arm-hosted Windows runner in my workflow?
+    answer: >-
+      Configure your GitHub Actions workflow to run on the Arm-hosted Windows runner as described
+      in the path steps. The workflow will then execute on Arm architecture without additional
+      infrastructure.
+  - question: Do I need to provide my own server or a self-hosted runner?
+    answer: >-
+      No. An Arm-hosted runner is managed by GitHub, so you do not need to provide or manage a
+      server to run your Actions workflows.
+  - question: Which application is used as the example, and where are the detailed build instructions?
+    answer: >-
+      The example is a rotating 3D cube application used in the “Optimize Windows applications
+      using Arm Performance Libraries” Learning Path. This path provides a basic overview; see
+      the referenced Learning Path for detailed build instructions.
+  - question: Can I configure a larger runner if my build needs more resources?
+    answer: >-
+      Yes. The introduction covers how to configure your own larger runner.
+# END generated_summary_faq
 
 author: 
     - Pareena Verma
diff --git a/content/learning-paths/laptops-and-desktops/hyper-v/_index.md b/content/learning-paths/laptops-and-desktops/hyper-v/_index.md
index 39046c1fb8..60b1be2476 100644
--- a/content/learning-paths/laptops-and-desktops/hyper-v/_index.md
+++ b/content/learning-paths/laptops-and-desktops/hyper-v/_index.md
@@ -18,6 +18,50 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:07:08Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 829130636ec6969f791826ef731b38f7bb87c025d910218822a113ecdef62306
+  summary_generated_at: '2026-06-01T22:06:03Z'
+  summary_source_hash: 829130636ec6969f791826ef731b38f7bb87c025d910218822a113ecdef62306
+  faq_generated_at: '2026-06-02T23:07:08Z'
+  faq_source_hash: 829130636ec6969f791826ef731b38f7bb87c025d910218822a113ecdef62306
+  summary: >-
+    This introductory Learning Path shows how to create and manage Arm-based Linux virtual machines
+    using Hyper-V on Windows on Arm devices. Working on Windows 11 version 22H2 or newer with
+    Hyper-V installed, you will use an Ubuntu 24.04 ISO image for Arm as the example Linux distribution,
+    with guidance that can be applied to other distributions. The steps call out a key requirement
+    specific to Windows on Arm: do not use Hyper-V Quick Create. By the end, you will have created
+    a Linux virtual machine in Hyper-V on your Windows on Arm computer (for example, a Lenovo
+    Thinkpad X13s). Estimated time to complete is about 60 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Windows on Arm computer with Hyper-V installed and Windows 11 version 22H2 or
+      newer. A device such as the Lenovo Thinkpad X13s meets the requirement.
+  - question: Which Ubuntu image should I download for this setup?
+    answer: >-
+      Download the Ubuntu 24.04 ISO file for Arm. Make sure you select the Arm build, not an x86
+      image.
+  - question: Can I use Hyper-V Quick Create on Windows on Arm?
+    answer: >-
+      No. Do not use Quick Create with Windows on Arm devices; follow the manual creation steps
+      described in the path.
+  - question: How do I proceed if I want a different Linux distribution?
+    answer: >-
+      Use the same process shown for Ubuntu and obtain the Arm ISO for your chosen distribution.
+      The instructions indicate you can follow the Ubuntu steps for other distributions.
+  - question: How long will this take and what result should I expect?
+    answer: >-
+      Plan for about 60 minutes. You will create an Arm-based Linux virtual machine running under
+      Hyper-V on your Windows on Arm device.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/laptops-and-desktops/intro/_index.md b/content/learning-paths/laptops-and-desktops/intro/_index.md
index fe861fa54f..6aa5f77642 100644
--- a/content/learning-paths/laptops-and-desktops/intro/_index.md
+++ b/content/learning-paths/laptops-and-desktops/intro/_index.md
@@ -19,6 +19,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:07:34Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 5a5e4437a7e71182ede45ee091ae49117446fd1c34b02be92df75a41f4fd1f0d
+  summary_generated_at: '2026-06-01T22:06:27Z'
+  summary_source_hash: 5a5e4437a7e71182ede45ee091ae49117446fd1c34b02be92df75a41f4fd1f0d
+  faq_generated_at: '2026-06-02T23:07:34Z'
+  faq_source_hash: 5a5e4437a7e71182ede45ee091ae49117446fd1c34b02be92df75a41f4fd1f0d
+  summary: >-
+    This introductory Learning Path explains where Arm architecture is used in modern laptops
+    and desktops and helps you identify hardware suitable for software development. You will review
+    platform choices across Windows, Linux, and ChromeOS, see which companies supply Arm processors
+    for client systems (Qualcomm, MediaTek, and Rockchip), and explore example Arm-based Chromebooks
+    such as the Lenovo Chromebook Plus 14 with the MediaTek Kompanio Ultra and the Lenovo Duet
+    Gen 9. The path also highlights why using the same Arm architecture locally and on servers
+    or cloud instances can simplify build and test workflows. No explicit prerequisites are listed,
+    and the material is designed for developers new to Arm on laptops and desktops, taking about
+    10 minutes to complete.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      No explicit prerequisites are listed. You can start immediately with the provided guidance.
+  - question: Which operating systems are covered for Arm laptops and desktops?
+    answer: >-
+      Windows, Linux, and ChromeOS are covered. The path points to Arm-based options available
+      on each of these platforms.
+  - question: Which processor vendors are mentioned for Arm-based laptops and desktops?
+    answer: >-
+      Qualcomm, MediaTek, and Rockchip are mentioned as creating processors for laptops and desktops.
+  - question: What Chromebook models are highlighted as Arm-based options?
+    answer: >-
+      The Lenovo Chromebook Plus 14 with the MediaTek Kompanio Ultra is highlighted, along with
+      the Lenovo Duet Gen 9. These are presented as examples of Chromebooks suitable for software
+      development.
+  - question: How does this path help me align my local machine with my server or cloud architecture?
+    answer: >-
+      The background explains that adopting the same architecture locally as in servers and cloud
+      can simplify building and testing. The path then points you to Arm-based laptop and desktop
+      options across common operating systems.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md b/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md
index a5d535a521..510e16f53c 100644
--- a/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md
+++ b/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md
@@ -22,6 +22,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:07:54Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 3e93048b46c24514a89ccc2f277b53111a419c049b53662e1461be38a22e83f7
+  summary_generated_at: '2026-06-01T22:06:52Z'
+  summary_source_hash: 3e93048b46c24514a89ccc2f277b53111a419c049b53662e1461be38a22e83f7
+  faq_generated_at: '2026-06-02T23:07:54Z'
+  faq_source_hash: 3e93048b46c24514a89ccc2f277b53111a419c049b53662e1461be38a22e83f7
+  summary: >-
+    This introductory path shows how to download, build, and test Arm KleidiCV on macOS using
+    an Apple Silicon Mac (M4 generation or newer). You will compile the library, run its API tests,
+    and verify Scalable Matrix Extensions (SME) backend support, including checking for increased
+    SME performance where available. KleidiCV provides optimized implementations for Arm Neon,
+    SVE2, and SME2 and automatically selects the fastest path for your hardware, so you do not
+    need to change existing CV code. Prerequisites are Xcode command line tools and basic Terminal
+    familiarity. In about 30 minutes, you will confirm a working KleidiCV build and execute example
+    tests on macOS.
+  faqs:
+  - question: What do I need before running the build steps?
+    answer: >-
+      You need a Mac with Apple Silicon (M4 generation or newer), Xcode command line tools installed,
+      and basic familiarity with using the Terminal and command-line tools. No other prerequisites
+      are explicitly listed.
+  - question: How do I run the KleidiCV API test and what result should I expect?
+    answer: >-
+      Run the API test binary at ./build-kleidicv-benchmark-SME/test/api/kleidicv-api-test. The
+      output shows the number of tests run and their results, with lines similar to “Vector length
+      is set to 16 bytes,” a seed value, and test progress markers.
+  - question: How do I verify that the SME backend is enabled and see its impact?
+    answer: >-
+      Follow the steps to enable SME and then run the tests to confirm SME backend support. The
+      Learning Path guides you to verify increased SME performance after enabling SME.
+  - question: Do I need to change my code to use Neon, SVE2, or SME2 with KleidiCV?
+    answer: >-
+      No. KleidiCV automatically detects your hardware and selects the fastest available implementation,
+      so you do not need to adjust your application code.
+  - question: Do I need a specific computer vision framework to complete this path?
+    answer: >-
+      No. You can use KleidiCV with any CV framework, and this path focuses on building and testing
+      KleidiCV itself. The steps also include running KleidiCV and OpenCV tests.
+# END generated_summary_faq
 
 author: Jett Zhou
 
diff --git a/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md b/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md
index 43334786e2..176e969d28 100644
--- a/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md
+++ b/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md
@@ -19,6 +19,50 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:09:11Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 631134875aac73e168b60b86d1ca7b4e898196f98cb2825a4238f62129d2d862
+  summary_generated_at: '2026-06-01T22:07:27Z'
+  summary_source_hash: 631134875aac73e168b60b86d1ca7b4e898196f98cb2825a4238f62129d2d862
+  faq_generated_at: '2026-06-02T23:09:11Z'
+  faq_source_hash: 631134875aac73e168b60b86d1ca7b4e898196f98cb2825a4238f62129d2d862
+  summary: >-
+    This introductory Learning Path shows how to configure the native LLVM toolchain in Visual
+    Studio to compile a Windows on Arm application, using the open-source PuTTY project as the
+    example. You will set up Visual Studio 2022 or later with LLVM support, install the required
+    32-bit x86 Strawberry Perl package, and then build PuTTY with Clang for Windows on Arm. The
+    path targets developers working on a Windows on Arm computer or a Windows on Arm virtual machine
+    and is designed to be completed in about 60 minutes. By the end, you will have compiled PuTTY
+    natively for Windows on Arm using the LLVM toolchain integrated with Visual Studio.
+  faqs:
+  - question: Do I need Arm hardware, or can I use a virtual machine?
+    answer: >-
+      You can use either. The prerequisites list a Windows on Arm computer such as the Lenovo
+      ThinkPad X13s running Windows 11 or a Windows on Arm virtual machine.
+  - question: Which version of Visual Studio and components are required?
+    answer: >-
+      Use Visual Studio 2022 or higher and install LLVM support in Visual Studio. The steps assume
+      LLVM is available through the Visual Studio installer.
+  - question: Which Strawberry Perl package should I install on Windows on Arm?
+    answer: >-
+      Install the 32-bit x86 version of Strawberry Perl. There is currently no Arm version available.
+  - question: Which compiler and build system are used to compile PuTTY?
+    answer: >-
+      The path uses Clang from the LLVM toolchain within Visual Studio to build a CMake application.
+      The example application is PuTTY.
+  - question: What result should I expect after the build completes?
+    answer: >-
+      You should have a successful build of the PuTTY application for Windows on Arm using the
+      native LLVM toolchain. A completed build produces PuTTY artifacts in your configured build
+      output.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md b/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md
index 5bae458289..7885442862 100644
--- a/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md
+++ b/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md
@@ -21,6 +21,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:09:52Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 87d4afe4ce7f0cef113cd61fd712fde073cca0eaafbe86a2066b76a117328d11
+  summary_generated_at: '2026-06-01T22:08:16Z'
+  summary_source_hash: 87d4afe4ce7f0cef113cd61fd712fde073cca0eaafbe86a2066b76a117328d11
+  faq_generated_at: '2026-06-02T23:09:52Z'
+  faq_source_hash: 87d4afe4ce7f0cef113cd61fd712fde073cca0eaafbe86a2066b76a117328d11
+  summary: >-
+    This advanced Learning Path shows how to add Arm Memory Tagging Extension (MTE) to a C dynamic
+    memory allocator on Linux. Using the provided project (CMakeLists.txt, heap.c/.h, mte_utils.c/.h,
+    and main.c), you will enable tagged addressing for the process, request memory with tag storage,
+    and implement tagging in allocator operations. The steps focus on MTE-specific changes and
+    include runnable examples that illustrate how tag checks catch common allocation and use errors.
+    It targets developers who already understand MTE and dynamic memory allocators. Estimated
+    time to complete is 120 minutes. The outcome is a working demo allocator that applies MTE
+    for learning and experimentation.
+  faqs:
+  - question: What do I need before running the code in this Learning Path?
+    answer: >-
+      You need a Linux computer, basic knowledge of how MTE works, and familiarity with how a
+      dynamic memory allocator can be implemented. The referenced Learning Paths on MTE and writing
+      a dynamic memory allocator provide the necessary background.
+  - question: Which source files contain the allocator and MTE-specific logic?
+    answer: >-
+      The project includes CMakeLists.txt, heap.c and heap.h for the allocator, mte_utils.c and
+      mte_utils.h for tag handling helpers, and main.c for the demo application. Review these
+      files to see how tagging is integrated into allocation and use sites.
+  - question: How is MTE enabled and memory with tag storage requested in the allocator?
+    answer: >-
+      Memory with tag storage is not allocated by the kernel by default, so the application must
+      request it. The heap does this in simple_heap_init using prctl(PR_SET_TAGGED_ADDR_CTRL,
+      PR_TAGGED_ADDR_ENABLE | PR_MTE_TCF_SYNC | (0xfffe << PR_MTE_TAG_SHIFT), ...). The provided
+      code shows the exact initialization used.
+  - question: How do I exercise the examples and what result should I expect?
+    answer: >-
+      The demo starts in main.c, where each example exploit is a function you can call from main.
+      When a tagged pointer accesses memory with a mismatched allocation tag, MTE raises an exception,
+      demonstrating how common mistakes are caught.
+  - question: Is the allocator implementation intended for production use?
+    answer: >-
+      No. It is a demo that illustrates concepts and does not make optimal use of MTE from a security
+      or performance perspective. Any production code should be rigorously tested.
+# END generated_summary_faq
 
 author: David Spickett
 
diff --git a/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md b/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
index 3e96049087..1838a7baed 100644
--- a/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
+++ b/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
@@ -21,6 +21,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:10:51Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: e1befed4cafab0eaee29a31c2f259a6a90a14d9f8230c570b6c10a9840b761d5
+  summary_generated_at: '2026-06-01T22:08:50Z'
+  summary_source_hash: e1befed4cafab0eaee29a31c2f259a6a90a14d9f8230c570b6c10a9840b761d5
+  faq_generated_at: '2026-06-02T23:10:51Z'
+  faq_source_hash: e1befed4cafab0eaee29a31c2f259a6a90a14d9f8230c570b6c10a9840b761d5
+  summary: >-
+    This advanced Learning Path shows how to install and configure Arch Linux for Arm on a Pinebook
+    Pro, then set up the i3 window manager and optionally configure Neovim for development. You
+    will prepare a bootable microSD card from a second computer (instructions target Linux), install
+    Arch Linux on the Pinebook Pro, and perform user-level i3 configuration, including practical
+    tweaks like setting display brightness. An optional section demonstrates a Neovim-based editing
+    workflow. The focus is turning the Pinebook Pro into an Arm Linux development machine. Prerequisites
+    are a Pinebook Pro and a class‑10 or faster microSD card (8GB or larger); no other explicit
+    prerequisites are listed.
+  faqs:
+  - question: Do I need a second computer to prepare the microSD card, and which OS is covered?
+    answer: >-
+      Yes. You will write the Arch Linux image to the microSD card from a second computer, and
+      the instructions are written for Linux. You can use macOS, but the partitioning steps differ
+      and are not included here.
+  - question: What hardware do I need before starting?
+    answer: >-
+      You need a Pinebook Pro laptop and a microSD card that is at least 8GB and class 10 or faster.
+      These are required to install Arch Linux for Arm.
+  - question: Which account should I use when installing and running the i3 window manager?
+    answer: >-
+      Use your created user account, not root. The instructions use sudo for package installation,
+      and you will run i3 from your user account.
+  - question: How do I set the Pinebook Pro display to maximum brightness under i3?
+    answer: >-
+      Run the command: echo 4095 > /sys/class/backlight/edp-backlight/brightness. This sets the
+      laptop display to maximum brightness.
+  - question: Is the Neovim setup required, and what should I expect the first time I open it?
+    answer: >-
+      The Neovim section is optional. On first launch it looks much like vim, but it is more customizable
+      with Lua extensibility and still supports Vimscript; the majority of vim plugins work as
+      expected.
+# END generated_summary_faq
 
 author: Gabriel Peterson
 
diff --git a/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md b/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
index 02e132e286..fa94dc3614 100644
--- a/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
+++ b/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
@@ -22,6 +22,51 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:11:51Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: aa2a78baf3e52172e37506c3f75254968d775b4eb516f9696a0a6998aba50e97
+  summary_generated_at: '2026-06-01T22:09:20Z'
+  summary_source_hash: aa2a78baf3e52172e37506c3f75254968d775b4eb516f9696a0a6998aba50e97
+  faq_generated_at: '2026-06-02T23:11:51Z'
+  faq_source_hash: aa2a78baf3e52172e37506c3f75254968d775b4eb516f9696a0a6998aba50e97
+  summary: >-
+    Learn how to fine-tune the Llama 3.2 3B language model on domain data using PyTorch and Hugging
+    Face on an NVIDIA DGX Spark with an Arm-based Grace CPU and a Blackwell GPU. You will configure
+    Docker on Linux, pull a pre-built PyTorch container, prepare a JSONL dataset from Raspberry
+    Pi datasheet content for supervised fine-tuning, and run a provided PyTorch script to train
+    the model. Finally, you will serve the base and fine-tuned models using a vLLM container to
+    compare responses and confirm factual accuracy improvements. Prerequisites are a Hugging Face
+    account with access token and access to a DGX Spark workstation.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Hugging Face account with an access token and an NVIDIA DGX Spark workstation.
+      The Learning Path targets a Linux environment.
+  - question: Do I need to install Docker on DGX Spark?
+    answer: >-
+      No. Docker is pre-installed on the DGX Spark, and you only need to configure permissions
+      as described in the setup step.
+  - question: Which model and dataset format are used for fine-tuning?
+    answer: >-
+      You will fine-tune Llama 3.2 3B. The workflow expects a custom JSONL dataset prepared for
+      supervised fine-tuning.
+  - question: Which containers are used for training and serving?
+    answer: >-
+      You pull a pre-built PyTorch container to run fine-tuning. For inference and comparison,
+      you use an NVIDIA-provided vLLM container.
+  - question: How do I know the fine-tuned model improved factual accuracy?
+    answer: >-
+      Serve both the base and fine-tuned models with vLLM and compare answers to domain questions.
+      For example, after fine-tuning on Raspberry Pi datasheets, the model should answer that
+      the RP2350 supports up to 150 MHz instead of the incorrect 1.8 GHz.
+# END generated_summary_faq
 
 author: Michael Hall
 
diff --git a/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md b/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
index d7e33d8833..935967026b 100644
--- a/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
+++ b/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
@@ -22,6 +22,51 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:12:53Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: a851b2f81b6aac54aef84acf7537a1cc6b99f66ce31ffd26631d6a966401e4ed
+  summary_generated_at: '2026-06-01T22:09:45Z'
+  summary_source_hash: a851b2f81b6aac54aef84acf7537a1cc6b99f66ce31ffd26631d6a966401e4ed
+  faq_generated_at: '2026-06-02T23:12:53Z'
+  faq_source_hash: a851b2f81b6aac54aef84acf7537a1cc6b99f66ce31ffd26631d6a966401e4ed
+  summary: >-
+    This introductory Learning Path shows how to build a GitHub Actions CI/CD pipeline that uses
+    a self-hosted Arm64 runner to compile a .NET application and publish an Arm64 Docker image
+    to DockerHub. You will create a private DockerHub repository, import a starter GitHub repository,
+    configure repository secrets for Docker credentials, and prepare an Arm64 Ubuntu 22.04 runner
+    by installing the .NET SDK and Docker. The workflow builds the container image and pushes
+    it to your DockerHub repository. Prerequisites include an Arm64-powered machine, a GitHub
+    account, and a DockerHub account. The path targets Linux environments and uses .NET and Visual
+    Studio Code.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an Arm64-powered machine (the demonstration uses an Ubuntu 22.04 VM), a DockerHub
+      account, and a GitHub account. No other prerequisites are explicitly listed.
+  - question: Which DockerHub repository settings should I use, and what push command will I see?
+    answer: >-
+      Create a repository named sampleapp and set its visibility to Private. You should see a
+      push command in the form: docker push <YOUR_ACCOUNT_NAME>/sampleapp:tagname.
+  - question: How do I bring the sample application into my GitHub account?
+    answer: >-
+      Use GitHub’s Import repository and provide https://github.com/dawidborycki/arm-lp-ci-cd-net.git
+      as the source URL. Set a repository name (for example, lp-ci-cd-net) and start the import.
+  - question: Which secrets should I add to the GitHub repository?
+    answer: >-
+      Create two secrets that store your DockerHub username and a DockerHub token. These are used
+      by the workflow to authenticate when pushing images.
+  - question: What software must be installed on the self-hosted Arm64 runner?
+    answer: >-
+      Install the .NET SDK and Docker on your Arm64 machine, and keep the OS patched. For Ubuntu
+      22.04, the Learning Path provides Docker installation steps.
+# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/_index.md b/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
index f925f06639..41078980d7 100644
--- a/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
@@ -19,6 +19,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:13:24Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: d24bf30aef690451942789bb66df63b7a3483ecc3cd2c4b3e60ce15fad90cb91
+  summary_generated_at: '2026-06-01T22:10:09Z'
+  summary_source_hash: d24bf30aef690451942789bb66df63b7a3483ecc3cd2c4b3e60ce15fad90cb91
+  faq_generated_at: '2026-06-02T23:13:24Z'
+  faq_source_hash: d24bf30aef690451942789bb66df63b7a3483ecc3cd2c4b3e60ce15fad90cb91
+  summary: >-
+    This Learning Path shows how to build the OpenCV library from source on Windows on Arm and
+    create a small test application using either MSVC or Clang. You will work on a Windows on
+    Arm machine or an Azure virtual machine, install CMake and Git, and, for the MSVC route, use
+    Visual Studio 2022 or higher. The steps clone the OpenCV repository and build version 4.10.0
+    with CMake from the command line. By the end, you will have a working OpenCV build on Windows
+    on Arm and a C++ application linked against it. No additional prerequisites are explicitly
+    listed. The estimated time to complete is about 90 minutes.
+  faqs:
+  - question: What do I need before building OpenCV on Windows on Arm?
+    answer: >-
+      You need a Windows on Arm machine such as the Lenovo Thinkpad X13s, or an Azure virtual
+      machine. Install CMake (tested with 3.28.1) and Git for Windows on Arm. For the MSVC flow,
+      install Visual Studio 2022 or higher; Clang is used in the alternative flow.
+  - question: Which compiler should I use, MSVC or Clang?
+    answer: >-
+      This Learning Path includes separate sections for MSVC and Clang. Choose one compiler and
+      follow the corresponding steps to build OpenCV and the test application.
+  - question: Where do I run the commands to fetch and configure OpenCV?
+    answer: >-
+      Open a Windows PowerShell, clone the OpenCV repository, and check out the 4.10.0 tag. Then
+      use CMake from the command line to run the pre-build configuration.
+  - question: Can I use a newer OpenCV version than 4.10.0?
+    answer: >-
+      The instructions have been tested with OpenCV 4.10.0. You might be able to use a later version,
+      but 4.10.0 is the version verified by this path.
+  - question: What result should I expect after completing the steps?
+    answer: >-
+      You will have a built OpenCV library for Windows on Arm and a test application that uses
+      the library. This provides a working base to start developing OpenCV applications on your
+      device or Azure VM.
+# END generated_summary_faq
 
 author: Koki Mitsunami
 
diff --git a/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md b/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md
index 4c246b932f..eaa2f92cf8 100644
--- a/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md
@@ -21,6 +21,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:14:27Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: fc0d79605640cc8e7a36070a044323d6e278335484949921d0aa4e9cd163d4bd
+  summary_generated_at: '2026-06-01T22:10:37Z'
+  summary_source_hash: fc0d79605640cc8e7a36070a044323d6e278335484949921d0aa4e9cd163d4bd
+  faq_generated_at: '2026-06-02T23:14:27Z'
+  faq_source_hash: fc0d79605640cc8e7a36070a044323d6e278335484949921d0aa4e9cd163d4bd
+  summary: >-
+    Learn how to measure application resource and power usage on Windows on Arm using FFmpeg and
+    PowerShell. You will set up FFmpeg, encode a test video, and run a decoding workload while
+    PowerShell scripts record CPU and memory usage to CSV for later analysis. You will also sample
+    battery status to measure power consumption without external equipment. The workflow includes
+    running the same tests with an x86_64 FFmpeg binary under Windows instruction emulation and
+    an Arm64 native build to compare behavior. Prerequisites are a Windows on Arm device such
+    as a Lenovo Thinkpad X13s running Windows 11 and a code editor such as Visual Studio Code
+    for Windows on Arm. Estimated time: 60 minutes.
+  faqs:
+  - question: What do I need before running the scripts?
+    answer: >-
+      You need a Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 and
+      a code editor such as Visual Studio Code for Windows on Arm. The Learning Path uses FFmpeg
+      and PowerShell.
+  - question: Which FFmpeg binaries should I use for the tests?
+    answer: >-
+      Run the same tests with both the x86_64 binary (using Windows instruction emulation) and
+      the Arm64 native binary. This lets you compare results on the same device.
+  - question: How do I capture CPU and memory usage during decoding, and what output should I
+      expect?
+    answer: >-
+      Use the provided PowerShell script saved as sample_decoding.ps1. It launches the decoding
+      process, periodically records CPU and memory statistics, and writes them to a CSV file.
+  - question: How is power usage measured without extra hardware?
+    answer: >-
+      Use the sample_power.ps1 PowerShell script to sample battery status while the decoding task
+      runs. The script logs readings to a CSV file for analysis.
+  - question: How should I compare results between Arm64 and x86_64 runs?
+    answer: >-
+      Execute identical workloads with each binary and compare the generated CSV files for CPU,
+      memory, and battery metrics. Use these data to benchmark the encoding task and analyze decoding
+      resource usage.
+# END generated_summary_faq
 
 author: Ruifeng Wang
 
diff --git a/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md b/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
index d140b69879..e476bde3be 100644
--- a/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
@@ -21,6 +21,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:15:47Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 915f6eb5e95bd42ed09b727b4599855d965125e67a8761fbd48a124e9e74b1bc
+  summary_generated_at: '2026-06-01T22:11:02Z'
+  summary_source_hash: 915f6eb5e95bd42ed09b727b4599855d965125e67a8761fbd48a124e9e74b1bc
+  faq_generated_at: '2026-06-02T23:15:47Z'
+  faq_source_hash: 915f6eb5e95bd42ed09b727b4599855d965125e67a8761fbd48a124e9e74b1bc
+  summary: >-
+    This introductory path shows how to install and run Windows 11 on Arm virtual machines on
+    an Arm Linux system using QEMU, KVM, and two Bash automation scripts. You will clone a GitHub
+    project, understand the script structure to customize options, and create a complete VM with
+    a single command that stores its data in a directory you choose. You will then launch the
+    VM with a run script that checks status, starts headless when needed, and connects over RDP
+    using Remmina. The path is intended for developers and system administrators building or testing
+    on Windows on Arm. Prerequisite: an Arm Linux host with KVM support, at least 8 GB RAM and
+    50 GB free disk space.
+  faqs:
+  - question: What do I need before running the VM automation scripts?
+    answer: >-
+      An Arm Linux system with KVM support and at least 8GB RAM and 50GB free disk space. This
+      path assumes you will run QEMU/KVM on that host.
+  - question: How do I get the automation scripts onto my Arm Linux system?
+    answer: >-
+      Clone the GitHub repository and change into the project directory: git clone https://github.com/jasonrandrews/win11arm.git;
+      cd win11arm.
+  - question: Which command should I use to create a new Windows on Arm VM quickly?
+    answer: >-
+      Run: ./create-win11-vm.sh all $HOME/win11-vm. This uses default values for all configurable
+      parameters and stores the VM data in $HOME/win11-vm while Windows installs automatically.
+  - question: How do I start and connect to the VM after it is created?
+    answer: >-
+      Run: ./run-win11-vm.sh $HOME/win11-vm. The script checks if the VM is already running, starts
+      it in headless mode if needed, and connects via RDP using Remmina.
+  - question: What should I check if VM creation or startup fails?
+    answer: >-
+      Confirm your system meets the prerequisites and that KVM is available on your Arm Linux
+      host. Verify the VM directory path you pass to the scripts is correct, then re-run the command;
+      the Learning Path includes guidance for troubleshooting common setup and runtime issues.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md b/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
index 46b586e318..8ad5b86777 100644
--- a/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
@@ -19,6 +19,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:17:05Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 4069c5bce1ce4b689a7a67d740fc077dc55c9b0bfbab392f5984ba0bdd9e59c3
+  summary_generated_at: '2026-06-01T22:11:42Z'
+  summary_source_hash: 4069c5bce1ce4b689a7a67d740fc077dc55c9b0bfbab392f5984ba0bdd9e59c3
+  faq_generated_at: '2026-06-02T23:17:05Z'
+  faq_source_hash: 4069c5bce1ce4b689a7a67d740fc077dc55c9b0bfbab392f5984ba0bdd9e59c3
+  summary: >-
+    This Learning Path shows how to use Arm64EC on Windows 11 on Arm to build native Arm applications
+    and begin migrating existing x86 or x64 code. Working on a Windows on Arm computer (for example,
+    a Lenovo ThinkPad X13s) with Visual Studio 2022 or later, you will configure projects to target
+    the Arm64EC application binary interface, build and run on-device, and compare the performance
+    of a simple application across different build configurations. The topic is introductory and
+    focused on practical steps developers can follow on Windows, with no additional prerequisites
+    explicitly listed beyond the required hardware and Visual Studio installation.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Windows on Arm computer such as a Lenovo ThinkPad X13s running Windows 11 and
+      Visual Studio 2022 or higher installed. No other prerequisites are explicitly listed.
+  - question: Which option should I use to migrate an existing x86 or x64 application?
+    answer: >-
+      Use Arm64EC to migrate existing x86 or x64 applications to devices using the Arm architecture.
+      The path also covers building new native Arm applications so you can compare configurations.
+  - question: What should I check if I do not see Arm64EC options in Visual Studio?
+    answer: >-
+      Verify that you are using Visual Studio 2022 or higher on a Windows 11 on Arm computer.
+      The Learning Path does not list additional components beyond installing Visual Studio.
+  - question: How do I compare performance across build configurations?
+    answer: >-
+      Build the same simple application using different configurations and then run them to observe
+      differences. The steps guide you through creating those builds and comparing the results;
+      no specific performance targets are stated.
+  - question: How do I verify that my build was successful?
+    answer: >-
+      After building in Visual Studio, you should get a runnable application on your Windows 11
+      on Arm device. Launch it and proceed to the comparison step to confirm it behaves as expected
+      in the selected configuration.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md b/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
index 0e72738f25..3d3629bd7a 100644
--- a/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
@@ -23,6 +23,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:18:19Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 3b3ecd451ed8b634c7fbe194248cdf1d33432633efbcbef5de713495041ff425
+  summary_generated_at: '2026-06-01T22:12:11Z'
+  summary_source_hash: 3b3ecd451ed8b634c7fbe194248cdf1d33432633efbcbef5de713495041ff425
+  faq_generated_at: '2026-06-02T23:18:19Z'
+  faq_source_hash: 3b3ecd451ed8b634c7fbe194248cdf1d33432633efbcbef5de713495041ff425
+  summary: >-
+    This Learning Path shows how to port a Qt-based Python desktop application with C/C++ dependencies
+    to Arm64 on Windows using Arm64EC. You will build the app, create C/C++ DLLs, and port each
+    DLL to Arm64 by configuring Arm64EC targets with both CMake (editing CMakePresets.json) and
+    MSBuild in Visual Studio 2022. Arm64EC allows Arm64 binaries and existing x64 dependencies
+    to run in the same process, enabling staged migration. The target environment is Windows on
+    Arm hardware running Windows 11 or a Windows on Arm virtual machine. Prerequisites are Visual
+    Studio 2022 with Arm build tools and a code editor such as Visual Studio Code for Arm64. Estimated
+    time to complete is about 90 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Windows on Arm computer running Windows 11 or a Windows on Arm virtual machine,
+      any code editor (Visual Studio Code for Arm64 is suitable), and Visual Studio 2022 with
+      Arm build tools installed.
+  - question: 'Which option should I use to port DLLs: CMake or MSBuild?'
+    answer: >-
+      Use the option that matches your project. This path demonstrates both: CMake (used earlier
+      in the path) and MSBuild with Visual Studio 2022.
+  - question: How do I enable Arm64EC for a CMake project in this path?
+    answer: >-
+      Modify the CMakePresets.json file by adding the final statement block shown in the steps
+      to configure the build target for Arm64EC. This config lets you build the DLLs for Arm64EC.
+  - question: How do I set up an MSBuild project for Arm64EC in Visual Studio 2022?
+    answer: >-
+      Create a new Console Application project in Visual Studio 2022 and set the build target
+      to Arm64EC. The steps provide example solution and project names to guide the configuration.
+  - question: What result should I expect after building with Arm64EC?
+    answer: >-
+      Your application can load existing x64 dependencies in the same process as your Arm64 binaries,
+      easing the transition of x64 apps to Arm64. As described in the introduction, this approach
+      can improve app performance without changing code.
+# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md b/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md
index 4ef101fa33..cd94ccad28 100644
--- a/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md
@@ -20,6 +20,50 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:20:12Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 63389742eced4df89f85bdf56a01e489e52a9702d446b557f8f55312f9d31f20
+  summary_generated_at: '2026-06-01T22:12:37Z'
+  summary_source_hash: 63389742eced4df89f85bdf56a01e489e52a9702d446b557f8f55312f9d31f20
+  faq_generated_at: '2026-06-02T23:20:12Z'
+  faq_source_hash: 63389742eced4df89f85bdf56a01e489e52a9702d446b557f8f55312f9d31f20
+  summary: >-
+    This Learning Path shows how to build and run a Qt-based desktop application on Windows on
+    Arm (WoA) and investigate native Arm64 performance characteristics. You work on a WoA device
+    such as a Lenovo Thinkpad X13s running Windows 11, or a WoA virtual machine, using the Qt
+    framework (including the Qt for Open Source Development option). The content is introductory
+    and is designed to be completed in about 20 minutes. By the end, you will have a Qt application
+    running natively on Arm64 and a foundation for exploring the performance improvements available
+    with native WoA development.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Windows on Arm computer such as a Lenovo Thinkpad X13s running Windows 11, or
+      a Windows on Arm virtual machine. You also need the Qt framework or Qt for Open Source Development.
+  - question: Which Qt package or version should I install for Windows on Arm?
+    answer: >-
+      You can use the Qt framework or Qt for Open Source Development. Qt v6.2 supports native
+      development for Windows on Arm.
+  - question: Can I use a virtual machine instead of physical hardware?
+    answer: >-
+      Yes. A Windows on Arm virtual machine is listed as an alternative to a physical device.
+  - question: Do I need to use Qt Creator for this Learning Path?
+    answer: >-
+      The path references the Qt framework and notes that Qt provides tools including Qt Creator.
+      It does not explicitly require a specific IDE.
+  - question: What result should I expect and how long will it take?
+    answer: >-
+      You will build and run a Qt-based desktop application on Windows on Arm and investigate
+      performance improvements from running natively on Arm64. The estimated time to complete
+      is about 20 minutes.
+# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md b/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
index 1a7f742f08..8bc391b36e 100644
--- a/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
@@ -22,6 +22,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:20:57Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: e60ce02e9d1872da06bc5bfeb18c7ec65f2bc17defb2b75292f930fb3ad41711
+  summary_generated_at: '2026-06-01T22:13:07Z'
+  summary_source_hash: e60ce02e9d1872da06bc5bfeb18c7ec65f2bc17defb2b75292f930fb3ad41711
+  faq_generated_at: '2026-06-02T23:20:57Z'
+  faq_source_hash: e60ce02e9d1872da06bc5bfeb18c7ec65f2bc17defb2b75292f930fb3ad41711
+  summary: >-
+    Follow this advanced, approximately 30-minute Learning Path to build and run an ASP.NET Core
+    8 Web API on Windows on Arm (Arm64). You will create a project that uses dependency injection
+    for services, build it with the .NET 8 SDK for arm64, run it locally, and confirm from console
+    output that the server is listening on localhost. The target environment is a Windows 11 on
+    Arm device such as a Lenovo ThinkPad X13s or a Windows on Arm virtual machine, using any code
+    editor (Visual Studio Code for Arm64 recommended). By the end, you will have a working ASP.NET
+    Core 8 web server suitable as a starting point for headless IoT scenarios on Arm.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      You need a Windows on Arm computer running Windows 11 or a Windows on Arm virtual machine,
+      the .NET 8 SDK for arm64, and a code editor. Visual Studio Code for Arm64 is recommended,
+      but any editor will work.
+  - question: How do I create and run the ASP.NET Core Web API project on Windows on Arm?
+    answer: >-
+      Follow the steps to create the Web API project, then open a command prompt, change to the
+      project folder, and run the application. The path shows using dotnet run to build and start
+      the server.
+  - question: What result should I expect when the server starts successfully?
+    answer: >-
+      The console output will indicate the server is listening on a localhost URL and that the
+      application has started in the Development environment. The example output shows a line
+      like “Now listening on: http://localhost:5203”.
+  - question: What should I check if dotnet run doesn’t show a listening address?
+    answer: >-
+      Confirm the .NET 8 SDK for arm64 is installed and that you are in the project’s directory
+      before running the command. Rebuild from the project folder and review the console output
+      for build errors.
+  - question: How are dependency injection services used in this path?
+    answer: >-
+      You will create services and consume them via ASP.NET Core’s built-in dependency injection.
+      The steps guide you to register and use these services from your Web API.
+# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
index 9c649c7d48..7d85d00e67 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
@@ -21,6 +21,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:21:33Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: cddfb7b83e82f0daa513558b1e7ee09b55c63e2ff95675d67be7d4408d391aa4
+  summary_generated_at: '2026-06-01T22:13:29Z'
+  summary_source_hash: cddfb7b83e82f0daa513558b1e7ee09b55c63e2ff95675d67be7d4408d391aa4
+  faq_generated_at: '2026-06-02T23:21:33Z'
+  faq_source_hash: cddfb7b83e82f0daa513558b1e7ee09b55c63e2ff95675d67be7d4408d391aa4
+  summary: >-
+    This Learning Path shows how to build a Node.js IoT application on Windows on Arm that streams
+    synthesized sensor data to AWS IoT Core over MQTT. You will register a device using the AWS
+    IoT Core “Connect one device” wizard, verify connectivity with the provided ping command,
+    connect an emulator, and send data to the cloud. You will then validate the stream using the
+    MQTT Test Client by subscribing to a specific topic. The path targets developers working on
+    Windows on Arm devices or a Windows-on-Arm virtual machine, uses a code editor (Visual Studio
+    Code is suitable), and takes about 120 minutes. It assumes access to the AWS Console but lists
+    no additional prerequisites.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      Use a Windows-on-Arm computer such as a Lenovo ThinkPad X13s running Windows 11, or a Windows-on-Arm
+      virtual machine, and any code editor (Visual Studio Code is suitable). The path uses Node.js;
+      no other explicit prerequisites are listed.
+  - question: Where do I register and connect the device in AWS IoT Core?
+    answer: >-
+      In the AWS Console, open IoT Core and select Connect one device. The wizard guides you through
+      Register and secure your device and subsequent steps.
+  - question: How do I check network connectivity to AWS IoT Core before sending data?
+    answer: >-
+      Use the ping command shown in the Connect one device wizard to confirm your device can reach
+      the AWS IoT Core endpoint. Verify the ping succeeds before proceeding.
+  - question: Which MQTT topic should I subscribe to in the test client to view messages?
+    answer: >-
+      Subscribe to Emulators/Weather/SensorReadings in the AWS IoT Core MQTT test client. This
+      is where the emulator’s synthesized sensor data is published.
+  - question: How do I know the data stream from the emulator is working?
+    answer: >-
+      After subscribing in the MQTT test client, you should see data from the emulator appear
+      in the message pane. If no messages appear, re-check the connection steps in the wizard.
+# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md
index 4dda9edfaa..b4bdfeab8a 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md
@@ -22,6 +22,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:22:02Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: f25597c6c9e69e09e9a86fa7c02d0ace9c347f293a21a11c00a6d3498300052c
+  summary_generated_at: '2026-06-01T22:13:59Z'
+  summary_source_hash: f25597c6c9e69e09e9a86fa7c02d0ace9c347f293a21a11c00a6d3498300052c
+  faq_generated_at: '2026-06-02T23:22:02Z'
+  faq_source_hash: f25597c6c9e69e09e9a86fa7c02d0ace9c347f293a21a11c00a6d3498300052c
+  summary: >-
+    This Learning Path guides you through configuring AWS IoT Core to parse MQTT messages and
+    store IoT data in Amazon DynamoDB from a Windows on Arm environment. Building on the previously
+    completed weather-station emulator and AWS IoT setup, you will run the IoT application that
+    streams data to AWS IoT Core and create an IoT Core rule (send_message_to_dynamodb) that writes
+    parsed messages to DynamoDB. The path targets Windows on Arm devices and uses a code editor
+    such as Visual Studio Code; .NET is listed in the metadata. Prerequisites include a Windows
+    on Arm PC or VM, any code editor, and completion of the prior “Create IoT applications with
+    Windows on Arm and AWS IoT Core” Learning Path.
+  faqs:
+  - question: What do I need before running these steps?
+    answer: >-
+      You need a Windows on Arm computer such as the Lenovo ThinkPad X13s running Windows 11,
+      or a Windows on Arm virtual machine, and any code editor; Visual Studio Code for Arm64 is
+      suitable. Complete the "Create IoT applications with Windows on Arm and AWS IoT Core" Learning
+      Path to prepare the weather station emulator and connect it to AWS IoT Core.
+  - question: Where do I create the AWS IoT Core rule?
+    answer: >-
+      In AWS IoT Core, go to Message routing and select Rules. Click Create rule to open the Create
+      rule view.
+  - question: What should I name the rule?
+    answer: >-
+      Use send_message_to_dynamodb as the rule name when prompted. Then proceed through the configuration
+      views as described in the steps.
+  - question: Do I need to modify or rebuild the IoT application for this path?
+    answer: >-
+      The path expects you to run the existing IoT application from the prerequisite to stream
+      data to AWS IoT Core. The focus here is on configuring the AWS IoT Core rule that writes
+      to DynamoDB.
+  - question: What result should I expect after completing the configuration?
+    answer: >-
+      The rule parses incoming MQTT messages from AWS IoT Core and writes the data to Amazon DynamoDB.
+      This connects your Arm64-based Windows workload to persistent storage in DynamoDB.
+# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md
index d8d9bf15d1..6db5268208 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md
@@ -23,6 +23,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:23:16Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: f685f45be9e5fc05b278e28590f9d421a920d43cc36ccb572545de4eaf4a799a
+  summary_generated_at: '2026-06-01T22:14:31Z'
+  summary_source_hash: f685f45be9e5fc05b278e28590f9d421a920d43cc36ccb572545de4eaf4a799a
+  faq_generated_at: '2026-06-02T23:23:16Z'
+  faq_source_hash: f685f45be9e5fc05b278e28590f9d421a920d43cc36ccb572545de4eaf4a799a
+  summary: >-
+    This Learning Path shows how to process IoT data on Arm64 by connecting AWS IoT Core to an
+    AWS Lambda function from a Windows on Arm device. You will reuse the weather-station IoT emulator
+    from the prerequisite path, create an AWS IoT Core rule to route temperature messages, and
+    implement a Lambda function that checks a threshold and uses Amazon SNS to send email notifications.
+    The target environment is Windows on Arm, using tools such as .NET and Visual Studio Code.
+    By the end, you will have an event-driven flow from IoT Core to Lambda and SNS. Prerequisites
+    include a Windows on Arm machine or VM and completion of the prior AWS IoT Core Learning Path.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Windows on Arm computer or a Windows on Arm virtual machine, a code editor such
+      as Visual Studio Code for Arm64, and completion of the “Create IoT applications with Windows
+      on Arm and AWS IoT Core” Learning Path to set up the weather station emulator and connect
+      it to AWS IoT Core.
+  - question: Where do I create the AWS IoT Core rule that triggers the Lambda function?
+    answer: >-
+      In AWS IoT Core, open Message routing and select Rules. Click Create rule and configure
+      it in the Create rule view.
+  - question: Which AWS services are used and how do they interact in this path?
+    answer: >-
+      AWS IoT Core receives temperature messages from the emulator, an IoT Core rule triggers
+      an AWS Lambda function, and the Lambda function uses Amazon Simple Notification Service
+      (SNS) to send an email when the temperature exceeds a predefined threshold.
+  - question: How do I know the Lambda trigger and notifications are working?
+    answer: >-
+      Publish a temperature value above the defined threshold from the emulator. You should receive
+      an email notification when the Lambda function is invoked by the IoT Core rule.
+  - question: What should I check if I do not receive an email after sending a high temperature
+      reading?
+    answer: >-
+      Confirm the emulator is connected to AWS IoT Core and sending data to the expected topic,
+      verify the IoT Core rule targets your Lambda function, and review the threshold logic in
+      the Lambda implementation and its use of SNS for email delivery.
+# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md
index 617b86c4c7..e856878b7e 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md
@@ -21,6 +21,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:23:55Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: f5a93346a0fd55659b7c0a6df501db97742ec71755b6443051b654f3ce871cdf
+  summary_generated_at: '2026-06-01T22:14:53Z'
+  summary_source_hash: f5a93346a0fd55659b7c0a6df501db97742ec71755b6443051b654f3ce871cdf
+  faq_generated_at: '2026-06-02T23:23:55Z'
+  faq_source_hash: f5a93346a0fd55659b7c0a6df501db97742ec71755b6443051b654f3ce871cdf
+  summary: >-
+    This Learning Path shows how to implement and test an AWS Lambda function on Windows on Arm
+    that scans and aggregates IoT data stored in Amazon DynamoDB. You will create a Lambda function
+    in the AWS console using the Node.js 20.x runtime, implement the handler as an ES module (index.mjs)
+    to scan a table (SensorReadings) and compute an average temperature value, then deploy and
+    invoke a test event to view results. The path assumes your DynamoDB table already contains
+    records written by an IoT emulator from a prior exercise. Prerequisites include a Windows
+    on Arm computer or VM, a code editor such as Visual Studio Code for Arm64, and completion
+    of the earlier Windows on Arm and AWS IoT Core Learning Path. Estimated time: 45 minutes.
+  faqs:
+  - question: What do I need before running these steps?
+    answer: >-
+      You need a Windows on Arm computer (for example, a Lenovo ThinkPad X13s running Windows
+      11 or a Windows on Arm virtual machine), any code editor such as Visual Studio Code for
+      Arm64, and completion of the “Create IoT applications with Windows on Arm and AWS IoT Core”
+      Learning Path.
+  - question: Which options should I choose when creating the Lambda function?
+    answer: >-
+      In the AWS Lambda console, select Create function, choose Author from scratch, set the Function
+      name to GetAverageTemperature, and select Node.js 20.x as the runtime.
+  - question: Where do I add the code and what file name should I use?
+    answer: >-
+      Paste the code in the Code source section under index.mjs. The .mjs extension indicates
+      the Lambda entry file is an ECMAScript (ES) module.
+  - question: How do I populate data and test the function?
+    answer: >-
+      Launch the IoT emulator to write data to your DynamoDB table, then click Deploy in the function
+      dashboard. Click Test, create a test event named Test, and run it to see execution status
+      and the average temperature in the console.
+  - question: What should I check if the function returns no average or errors?
+    answer: >-
+      Verify the DynamoDB table SensorReadings exists in the eu-central-1 region and contains
+      items with a temperature attribute. Also confirm you completed the prior steps that write
+      records to the table.
+# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md
index db0d583c73..f7dc9afaa3 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md
@@ -21,6 +21,57 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:24:33Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 32186a4879e98aa113f461d2a2c705dee099404ed2020ef6fdb981a28bb0c0c3
+  summary_generated_at: '2026-06-01T22:15:20Z'
+  summary_source_hash: 32186a4879e98aa113f461d2a2c705dee099404ed2020ef6fdb981a28bb0c0c3
+  faq_generated_at: '2026-06-02T23:24:33Z'
+  faq_source_hash: 32186a4879e98aa113f461d2a2c705dee099404ed2020ef6fdb981a28bb0c0c3
+  summary: >-
+    This Learning Path guides you through hosting a static IoT website on Amazon S3 from a Windows
+    on Arm environment. You will create a simple site (index.html, styles.css, index.js), connect
+    it to an existing GetAverageTemperature AWS Lambda function by retrieving its Function URL,
+    and deploy the site to S3 using AWS CLI v2. The path is intended for advanced developers and
+    takes about 30 minutes. Prerequisites include a Windows on Arm computer or virtual machine,
+    a code editor (Visual Studio Code for Arm64 is suitable), and prior completion of the Use
+    AWS Lambda for IoT applications Learning Path. Tools referenced include Node.js and Visual
+    Studio Code.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Windows on Arm computer or a Windows on Arm virtual machine, a code editor (Visual
+      Studio Code for Arm64 is suitable), and completion of the "Use AWS Lambda for IoT applications"
+      Learning Path. Node.js is listed among the tools.
+  - question: How should I structure the static website files, and what does each file do?
+    answer: >-
+      Create a folder (for example, IoTPage) with three files: index.html, styles.css, and index.js.
+      The HTML defines the page structure, the CSS handles styling, and index.js contains logic
+      to fetch data from AWS Lambda and display it on the page.
+  - question: Where do I find the AWS Lambda Function URL to use in my website?
+    answer: >-
+      In the AWS Lambda console, open the GetAverageTemperature function, go to the Configuration
+      tab, and select Function URL, then create the Function URL. Ensure the GetAverageTemperature
+      function is prepared as described in the related Learning Path that integrates AWS Lambda
+      with DynamoDB.
+  - question: How do I set up AWS CLI to deploy to Amazon S3?
+    answer: >-
+      Install AWS CLI version 2, create an AWS CLI user, and generate access keys following the
+      AWS CLI authentication tutorial. Run aws configure and provide your access key details,
+      then use the CLI to deploy the website to S3.
+  - question: How do I know the website is working after deployment?
+    answer: >-
+      When the site loads, it should call your configured AWS Lambda Function URL and display
+      the retrieved IoT data on the page. If no data appears, verify that the Function URL in
+      index.js matches the URL shown in the Lambda console.
+# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_cef/_index.md b/content/learning-paths/laptops-and-desktops/win_cef/_index.md
index cbb2154b6c..9af97f64e8 100644
--- a/content/learning-paths/laptops-and-desktops/win_cef/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_cef/_index.md
@@ -20,6 +20,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:25:17Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 5df8cc6d859c505ad79f8297056b06233956c92203a6d2352d9be8e498a42547
+  summary_generated_at: '2026-06-01T22:15:54Z'
+  summary_source_hash: 5df8cc6d859c505ad79f8297056b06233956c92203a6d2352d9be8e498a42547
+  faq_generated_at: '2026-06-02T23:25:17Z'
+  faq_source_hash: 5df8cc6d859c505ad79f8297056b06233956c92203a6d2352d9be8e498a42547
+  summary: >-
+    This introductory Learning Path guides you through creating and building a Chromium Embedded
+    Framework (CEF) desktop application on Windows on Arm using CMake. Working in Visual Studio
+    2022 on a Windows on Arm device or a Windows on Arm virtual machine, you will set up a C++
+    CEF project and then modify and style the application using HTML, JavaScript, and CSS. The
+    focus is on building the project for Arm-based Windows systems (Arm Cortex-A) and applying
+    basic UI changes with familiar web technologies. Prerequisites are a Windows on Arm computer
+    such as a Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm VM, and Visual Studio
+    2022. Estimated time: about 30 minutes.
+  faqs:
+  - question: What do I need before starting this Learning Path?
+    answer: >-
+      You need a Windows on Arm computer such as the Lenovo ThinkPad X13s running Windows 11,
+      or a Windows on Arm virtual machine, and Visual Studio 2022. These are the only explicit
+      prerequisites.
+  - question: Which tools and languages will I use to build the application?
+    answer: >-
+      You will use CMake with Visual Studio 2022 to configure and build a CEF project written
+      in C++. You will also work with HTML, JavaScript, and CSS to modify and style the application.
+  - question: What environment does the resulting application target?
+    answer: >-
+      The application targets Windows on Arm (WoA) devices. The metadata indicates an Arm Cortex-A
+      CPU class for the platform.
+  - question: What result should I expect when I finish the steps?
+    answer: >-
+      You will have created and built a CEF project on Windows on Arm and applied basic styling
+      or modifications using web technologies. Expect a CEF-based desktop application that incorporates
+      web content.
+  - question: Is this suitable if I am new to CEF or Windows on Arm, and how long will it take?
+    answer: >-
+      Yes. The Learning Path is introductory and is designed to be completed in about 30 minutes.
+# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_forms/_index.md b/content/learning-paths/laptops-and-desktops/win_forms/_index.md
index 2cff72e0e9..de2c075cba 100644
--- a/content/learning-paths/laptops-and-desktops/win_forms/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_forms/_index.md
@@ -20,6 +20,50 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:25:52Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: d43413097704af29b9233dfe33fb675ffd5c6d5a172154d6a7542e77d6625c00
+  summary_generated_at: '2026-06-01T22:16:20Z'
+  summary_source_hash: d43413097704af29b9233dfe33fb675ffd5c6d5a172154d6a7542e77d6625c00
+  faq_generated_at: '2026-06-02T23:25:52Z'
+  faq_source_hash: d43413097704af29b9233dfe33fb675ffd5c6d5a172154d6a7542e77d6625c00
+  summary: >-
+    This introductory path shows how to create and build a Windows Forms desktop application in
+    C#/.NET on Windows on Arm using Visual Studio 2022. You will configure build settings, including
+    creating an ARM64 solution platform in Configuration Manager, then run the app under different
+    settings and compare matrix multiplication computation times to observe execution behavior
+    on Arm64. The target environment is a Windows on Arm device or a Windows on Arm virtual machine.
+    Prerequisites are Visual Studio 2022 with the .NET Desktop Development workload and access
+    to Windows on Arm. By the end, you will have a working WinForms app and a basic method to
+    measure code performance on Arm64.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Windows on Arm computer running Windows 11 or a Windows on Arm virtual machine,
+      plus Visual Studio 2022 with the .NET Desktop Development workload installed.
+  - question: Which language and framework does the sample use?
+    answer: >-
+      The application is built with Windows Forms using C# on .NET.
+  - question: How do I switch the project to build for ARM64 in Visual Studio?
+    answer: >-
+      Open the target platform dropdown (default is Any CPU), choose Configuration Manager, select
+      New in Active solution platform, and pick ARM64 in the New Solution Platform dialog.
+  - question: How do I confirm I’m building and running the ARM64 configuration?
+    answer: >-
+      Check that the Active solution platform in Visual Studio shows ARM64 before building and
+      launching the application.
+  - question: What result should I expect when comparing performance settings?
+    answer: >-
+      You will run the application under different build settings and compare the matrix multiplication
+      computation times reported by the app to evaluate execution performance on Arm64.
+# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_net/_index.md b/content/learning-paths/laptops-and-desktops/win_net/_index.md
index 8af0b81fed..14008b025e 100644
--- a/content/learning-paths/laptops-and-desktops/win_net/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_net/_index.md
@@ -18,6 +18,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:26:32Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 9cc8f77f98bc8f4afb7b77344e42615e420be421f85583f9fc4f5ec76516e6eb
+  summary_generated_at: '2026-06-01T22:16:47Z'
+  summary_source_hash: 9cc8f77f98bc8f4afb7b77344e42615e420be421f85583f9fc4f5ec76516e6eb
+  faq_generated_at: '2026-06-02T23:26:32Z'
+  faq_source_hash: 9cc8f77f98bc8f4afb7b77344e42615e420be421f85583f9fc4f5ec76516e6eb
+  summary: >-
+    This introductory Learning Path shows how to build and run a native .NET 6 Windows Presentation
+    Foundation (WPF) application on a Windows on Arm system. You will prepare your environment
+    by installing Visual Studio 2022 or later with the .NET desktop development workload, then
+    build and execute the application on a Windows on Arm computer or a Windows on Arm virtual
+    machine. The steps focus on installing the required tools and validating a working WPF app
+    running natively on Arm. Prerequisites are a Windows on Arm computer (for example, a Lenovo
+    ThinkPad X13s running Windows 11) or a Windows on Arm VM; no other explicit prerequisites
+    are listed. Estimated time to complete is about 20 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need access to a Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows
+      11, or a Windows on Arm virtual machine. Install Visual Studio 2022 or higher.
+  - question: Which Visual Studio components should I install?
+    answer: >-
+      Install the .NET desktop development workload component in Visual Studio. This is the required
+      workload for this Learning Path.
+  - question: How do I add the .NET desktop development workload to an existing Visual Studio
+      installation?
+    answer: >-
+      Open the Windows Start Menu, launch Visual Studio Installer, and select Modify. On the Workloads
+      tab, select the .NET desktop development workload.
+  - question: Can I use a Windows on Arm virtual machine instead of physical hardware?
+    answer: >-
+      Yes. A Windows on Arm virtual machine is listed as an acceptable environment for this Learning
+      Path.
+  - question: What result should I expect after completing the steps, and how long will it take?
+    answer: >-
+      You will build and run a .NET 6 Windows Presentation Foundation (WPF) application on a Windows
+      on Arm machine. The estimated time to complete is about 20 minutes.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/laptops-and-desktops/win_net8/_index.md b/content/learning-paths/laptops-and-desktops/win_net8/_index.md
index 1af7fca2ab..fea5a55b9d 100644
--- a/content/learning-paths/laptops-and-desktops/win_net8/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_net8/_index.md
@@ -22,6 +22,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:27:37Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 218fd5b33e104360d5de9f632f9338e2f24041ea70f7a01fad0af6be2a11619c
+  summary_generated_at: '2026-06-01T22:17:04Z'
+  summary_source_hash: 218fd5b33e104360d5de9f632f9338e2f24041ea70f7a01fad0af6be2a11619c
+  faq_generated_at: '2026-06-02T23:27:37Z'
+  faq_source_hash: 218fd5b33e104360d5de9f632f9338e2f24041ea70f7a01fad0af6be2a11619c
+  summary: >-
+    This introductory path shows how to build, run, and benchmark .NET 8 Console applications
+    on Windows on Arm, with a focus on measuring execution performance on Arm64. You will set
+    up your development environment, verify your .NET installation, clone a sample repository,
+    and implement custom benchmarks using System.Diagnostics.Stopwatch. Prerequisites include
+    a Windows on Arm device or VM, the .NET 8 SDK for both x64 and arm64, and any code editor
+    (Visual Studio Code for Arm64 is recommended). In about 20 minutes, you will be able to run
+    the sample app and create simple, repeatable measurements to understand how your .NET code
+    performs on Windows on Arm.
+  faqs:
+  - question: What do I need before running the benchmarks?
+    answer: >-
+      You need a Windows on Arm computer or a Windows on Arm virtual machine, the .NET 8 SDK for
+      both x64 and arm64, and a code editor (Visual Studio Code for Arm64 is recommended). These
+      are listed in the prerequisites.
+  - question: How do I verify that .NET 8 is installed correctly on Windows on Arm?
+    answer: >-
+      Follow the “Before you begin” step to check your .NET installation. If anything is missing,
+      install the .NET 8 SDK for both x64 and arm64 as listed in the prerequisites.
+  - question: How do I get the sample application used in this Learning Path?
+    answer: >-
+      Clone the repository by running: git clone https://github.com/dawidborycki/Arm64.Performance.DotNet.git.
+      The sample is a .NET console application created with dotnet new console.
+  - question: How are the custom benchmarks implemented in this path?
+    answer: >-
+      They use the System.Diagnostics.Stopwatch class. The sample includes a PerformanceHelper
+      static class with reusable timing methods and a PerformanceTests static class to organize
+      test code.
+  - question: How should I compare performance between x64 and Arm64 on Windows on Arm?
+    answer: >-
+      Install both the x64 and arm64 .NET 8 SDKs as listed, then run the same Stopwatch-based
+      benchmarks as directed in the steps. Compare the reported execution times to observe differences.
+# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md b/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md
index 8582af6b81..1145392ae4 100644
--- a/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md
@@ -20,6 +20,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:28:34Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 037509ebca3a7c5a58bef247532919cd8196c7993e946ce519585cdc82073daa
+  summary_generated_at: '2026-06-01T22:17:25Z'
+  summary_source_hash: 037509ebca3a7c5a58bef247532919cd8196c7993e946ce519585cdc82073daa
+  faq_generated_at: '2026-06-02T23:28:34Z'
+  faq_source_hash: 037509ebca3a7c5a58bef247532919cd8196c7993e946ce519585cdc82073daa
+  summary: >-
+    This path shows how to create and build a cross-platform .NET MAUI application on Windows
+    on Arm and measure code execution performance uplift on Arm64. Using Visual Studio 2022, you
+    will start a new MAUI project, add C# helper classes to generate pseudo-random double-precision
+    vectors and compute a*b+c, measure execution time with a PerformanceHelper, and present results
+    in a list view. Prerequisites are a Windows on Arm computer such as a Lenovo Thinkpad X13s
+    running Windows 11, or a Windows on Arm virtual machine, plus Visual Studio 2022 with .NET
+    Multi-platform App UI development and Universal Windows Platform development installed. The
+    path is introductory and takes about 30 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      Use a Windows on Arm computer such as a Lenovo ThinkPad X13s running Windows 11, or a Windows
+      on Arm virtual machine. Install Visual Studio 2022 with the .NET Multi-platform App UI development
+      and Universal Windows Platform development workloads.
+  - question: Which Visual Studio components should I install?
+    answer: >-
+      Install Visual Studio 2022 with the .NET Multi-platform App UI development workload and
+      the Universal Windows Platform development workload. These are explicitly listed prerequisites.
+  - question: Which project type should I create in Visual Studio?
+    answer: >-
+      Create a .NET MAUI project. The Learning Path focuses on building and running it on Windows
+      on Arm.
+  - question: What code will I add to measure performance and what does it compute?
+    answer: >-
+      You will add a PerformanceHelper class to measure code execution time and a VectorHelper
+      class that implements AdditionOfProduct, computing a*b+c over pseudo-random double-precision
+      vectors. A list view displays the processing results.
+  - question: How do I know the performance measurement part worked?
+    answer: >-
+      Build and run the app on Windows on Arm and check that the UI displays processing results
+      and execution times. The Learning Path does not specify expected numbers; you use the reported
+      timings to observe Arm64 performance.
+# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md b/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md
index f22c2b73ec..ff01df5636 100644
--- a/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md
@@ -18,6 +18,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:29:03Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 606702e7afc9a29976d027eceab021570cf3f91ec408ef2dd2051df0b05d9bda
+  summary_generated_at: '2026-06-01T22:17:48Z'
+  summary_source_hash: 606702e7afc9a29976d027eceab021570cf3f91ec408ef2dd2051df0b05d9bda
+  faq_generated_at: '2026-06-02T23:29:03Z'
+  faq_source_hash: 606702e7afc9a29976d027eceab021570cf3f91ec408ef2dd2051df0b05d9bda
+  summary: >-
+    This Learning Path shows how to build ONNX Runtime with the Generate() API on Windows on Arm
+    and run inference on the Phi-3 Mini (3.3B) model with KleidiAI acceleration. You will clone
+    and build ONNX Runtime and the Generate() API from source using Visual Studio and CMake, then
+    download the short-context (4K) Phi-3 Mini ONNX model (quantized to 4-bits) and execute a
+    simple model runner that reports performance metrics. The target environment is a Windows
+    on Arm device or a Windows on Arm virtual machine. Tools used include Visual Studio, C++,
+    Python, Git, CMake, and ONNX Runtime. Outcome: a working build and a validated Phi-3 inference
+    run on WoA.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need access to a Windows on Arm computer such as a Lenovo ThinkPad X13 running Windows
+      11, or a Windows on Arm virtual machine. No other explicit prerequisites are listed.
+  - question: Which Phi-3 model variant should I use in this path?
+    answer: >-
+      Use the short-context (4K) Phi-3 Mini (3.3B) model in ONNX format, quantized to 4-bits.
+      This version consumes less memory than the long-context (128K) model and is the variant
+      used in the steps.
+  - question: How is the ONNX Runtime Generate() API used here?
+    answer: >-
+      You build the Generate() API from source and use it for text generation with Phi-3. It handles
+      pre- and post-processing, inference with ONNX Runtime (including logits processing), search
+      and sampling, and KV cache management.
+  - question: How do I know the build and run were successful?
+    answer: >-
+      You should be able to run the simple model runner without build errors, see generated model
+      outputs, and observe reported performance metrics. These results indicate a successful build
+      and inference run.
+  - question: Do I need extra configuration to use KleidiAI acceleration?
+    answer: >-
+      The path runs inference with KleidiAI acceleration, but specific configuration steps beyond
+      building ONNX Runtime and the Generate() API are not explicitly listed. Follow the provided
+      build and run instructions.
+# END generated_summary_faq
 
 author: Barbara Corriero
 
diff --git a/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md b/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md
index b2054b0f99..175fac5558 100644
--- a/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md
@@ -21,6 +21,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:29:33Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: b975cc83674410ec50ca49a31744eee4ac5a63c994dd28e46ed84f7afda0568d
+  summary_generated_at: '2026-06-01T22:18:09Z'
+  summary_source_hash: b975cc83674410ec50ca49a31744eee4ac5a63c994dd28e46ed84f7afda0568d
+  faq_generated_at: '2026-06-02T23:29:33Z'
+  faq_source_hash: b975cc83674410ec50ca49a31744eee4ac5a63c994dd28e46ed84f7afda0568d
+  summary: >-
+    This Learning Path guides you through applying Profile-Guided Optimization (PGO) to C++ code
+    and measuring the impact with Google Benchmark on Windows on Arm. You start by understanding
+    PGO fundamentals, then create a baseline microbenchmark of an integer division function. Using
+    MSVC on a Windows on Arm system, you build an instrumented binary, run it to collect profile
+    data, and rebuild using that profile to produce a PGO-optimized binary. You then compare benchmark
+    results between the baseline and optimized builds. Prerequisites are C++ command-line experience
+    and a Windows on Arm machine with Visual Studio and the C++ desktop development tools installed.
+    Estimated time: about 30 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Windows on Arm machine with Visual Studio and the C++ desktop development tools
+      installed. Familiarity with C++ and compiling from the command line is expected.
+  - question: Which build environment should I use on Windows on Arm?
+    answer: >-
+      Open an ARM64 Native Tools Command Prompt and use PowerShell if instructed. Navigate to
+      your project directory and set any environment variables (such as VCPKG) as shown in the
+      steps.
+  - question: What does the baseline benchmark measure, and why was it chosen?
+    answer: >-
+      The baseline measures an integer division operation. Division is used because it typically
+      has higher latency and lower throughput than addition, subtraction, or multiplication, making
+      changes measurable.
+  - question: How do I apply PGO here, and how do I know it worked?
+    answer: >-
+      You build an instrumented binary, run it to collect profile data, and rebuild using that
+      profile with MSVC. You then run Google Benchmark to compare the optimized build against
+      the baseline and observe the measured differences.
+  - question: Do I need to install Google Benchmark before starting?
+    answer: >-
+      No. The path first introduces Google Benchmark, then guides you through setting up your
+      environment and running your first benchmark in the following section.
+# END generated_summary_faq
 
 author: Tom Dunkle
 
diff --git a/content/learning-paths/laptops-and-desktops/win_python/_index.md b/content/learning-paths/laptops-and-desktops/win_python/_index.md
index a38a94b4f8..40166cab32 100644
--- a/content/learning-paths/laptops-and-desktops/win_python/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_python/_index.md
@@ -21,6 +21,49 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:30:23Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: d953214fe33ad89a683f79e7c29ce9348e5169e6529b808804329cb35060b431
+  summary_generated_at: '2026-06-01T22:18:32Z'
+  summary_source_hash: d953214fe33ad89a683f79e7c29ce9348e5169e6529b808804329cb35060b431
+  faq_generated_at: '2026-06-02T23:30:23Z'
+  faq_source_hash: d953214fe33ad89a683f79e7c29ce9348e5169e6529b808804329cb35060b431
+  summary: >-
+    This introductory path shows how to build native Python applications on Windows on Arm and
+    work with platform-dependent packages using Arm64. Using a Windows on Arm PC or virtual machine,
+    a code editor (Visual Studio Code for Arm64 recommended), and Visual Studio 2022 with Arm
+    build tools, you will create a small NumPy-based application that synthesizes noisy sine waves,
+    runs FFTs for varying input sizes, and measures execution time. You will examine the platform-specificity
+    of Python packages and use native Arm64 builds where applicable. By the end, you will have
+    a working sample.py and timing results to analyze on an Arm64 Windows environment.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Windows on Arm computer (for example, a Lenovo ThinkPad X13s running Windows
+      11) or a Windows on Arm virtual machine, a code editor such as Visual Studio Code for Arm64,
+      and Visual Studio 2022 with Arm build tools.
+  - question: Can I use a Windows on Arm virtual machine instead of physical hardware?
+    answer: >-
+      Yes. The prerequisites explicitly list a Windows on Arm virtual machine as an option.
+  - question: Do I need Visual Studio 2022 if I plan to edit code in VS Code?
+    answer: >-
+      Yes. Visual Studio 2022 with Arm build tools is listed as a prerequisite, while Visual Studio
+      Code for Arm64 is the recommended editor.
+  - question: What should I create and what does the sample application do?
+    answer: >-
+      Create a file named sample.py. It uses NumPy to generate noisy sine waves, runs FFTs over
+      multiple input sizes, and measures execution time.
+  - question: Where can I find the complete sample code?
+    answer: >-
+      The Learning Path references that the complete code is available on GitHub.
+# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_python_onnx/_index.md b/content/learning-paths/laptops-and-desktops/win_python_onnx/_index.md
index 7b101afe13..d87dab085a 100644
--- a/content/learning-paths/laptops-and-desktops/win_python_onnx/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_python_onnx/_index.md
@@ -22,7 +22,7 @@ prerequisites:
 generate_summary_faq: true
 
 rerun_summary: false
-rerun_faqs: false
+rerun_faqs: true
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md b/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md
index 957fa2ac1c..b0b691c59f 100644
--- a/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md
@@ -21,6 +21,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:31:07Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 055e3a3d8c0dc717e2246e3e8e7ebb00c7d2cea3ff9faabf7125ca1ebfff7a31
+  summary_generated_at: '2026-06-01T22:19:03Z'
+  summary_source_hash: 055e3a3d8c0dc717e2246e3e8e7ebb00c7d2cea3ff9faabf7125ca1ebfff7a31
+  faq_generated_at: '2026-06-02T23:31:07Z'
+  faq_source_hash: 055e3a3d8c0dc717e2246e3e8e7ebb00c7d2cea3ff9faabf7125ca1ebfff7a31
+  summary: >-
+    This Learning Path shows how to use Windows Sandbox on a Windows on Arm PC as a self-hosted
+    Arm64 GitHub Actions runner, then run a CI/CD workflow that builds and runs a .NET 8 Windows
+    Presentation Foundation (WPF) sample solving the Traveling Salesman Problem. You will configure
+    the sandboxed runner and use the workflow definition in .github/workflows/dotnet_sandbox.yml
+    to trigger builds manually or on pushes to the main branch. The focus is on getting a working
+    pipeline that executes inside Windows Sandbox. Prerequisites are a Windows on Arm computer
+    (for example, a Lenovo ThinkPad X13s) running Windows 11 Version 22H2 with Windows Sandbox
+    enabled, and a GitHub account.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 Version
+      22H2 with Windows Sandbox enabled, and a valid GitHub account. Follow the Windows Sandbox
+      enablement guide linked in the prerequisites.
+  - question: Which GitHub Actions runner is configured in this Learning Path?
+    answer: >-
+      You will configure a self-hosted Arm64 runner inside Windows Sandbox on your Windows on
+      Arm machine. This runner executes the jobs defined in your workflow.
+  - question: Where is the workflow file located and how is it triggered?
+    answer: >-
+      The workflow is defined in .github/workflows/dotnet_sandbox.yml. It runs on push events
+      to the main branch and can also be triggered manually from the Actions tab.
+  - question: What result should I expect when I run the pipeline?
+    answer: >-
+      The pipeline builds a .NET 8 WPF sample application and verifies it runs on your Windows
+      Sandbox self-hosted runner. The workflow should complete successfully with jobs executed
+      on the self-hosted Arm64 runner, and it may publish the app as configured.
+  - question: What should I check if my jobs are queued and do not run in Windows Sandbox?
+    answer: >-
+      Confirm Windows Sandbox is enabled and that you have configured and started the self-hosted
+      runner in the Sandbox as described in the steps. Also ensure the repository contains .github/workflows/dotnet_sandbox.yml
+      and you triggered the workflow as specified.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md b/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md
index a0a4ec2eb1..5de376efb6 100644
--- a/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md
@@ -22,6 +22,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:31:50Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: cacf4c6fc3cd3c2a9ab194f7a04981fd4820fca5482317a06c6b9fa02a1c9da2
+  summary_generated_at: '2026-06-01T22:19:36Z'
+  summary_source_hash: cacf4c6fc3cd3c2a9ab194f7a04981fd4820fca5482317a06c6b9fa02a1c9da2
+  faq_generated_at: '2026-06-02T23:31:50Z'
+  faq_source_hash: cacf4c6fc3cd3c2a9ab194f7a04981fd4820fca5482317a06c6b9fa02a1c9da2
+  summary: >-
+    This introductory Learning Path shows how to create a C/C++ Win32 DLL, use it from a Windows
+    console application, and port the library to Arm64 for Windows on Arm. You work on a Windows
+    on Arm device such as a Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm virtual
+    machine, using Visual Studio 2022 with Arm build tools. The steps include a brief overview
+    of Armv8-A Arm64 concepts and focus on building and running the console app that consumes
+    your DLL on Arm64. By the end, you will have exercised a practical migration workflow for
+    a Win32 library to Arm64.
+  faqs:
+  - question: What do I need installed before starting?
+    answer: >-
+      Use a Windows on Arm computer or a Windows on Arm virtual machine and refer to the Visual
+      Studio 2022 with Arm build tools installation guide. No other prerequisites are explicitly
+      listed.
+  - question: Can I complete this on a virtual machine instead of physical hardware?
+    answer: >-
+      Yes. The prerequisites explicitly allow a Windows on Arm virtual machine as an alternative
+      to a Windows on Arm device.
+  - question: What will I build and target by the end?
+    answer: >-
+      You will create a C/C++ Win32 DLL and a Windows console application that uses it, with both
+      projects targeting Arm64 on Windows on Arm.
+  - question: How do I choose the correct build target for Arm64?
+    answer: >-
+      The steps show how to configure your projects in Visual Studio 2022 with Arm build tools
+      to build for Arm64. Follow the project configuration guidance provided in the path.
+  - question: What should I check if my Arm64 build fails or the app cannot load the DLL?
+    answer: >-
+      Verify that Visual Studio 2022 with Arm build tools is installed, the project target is
+      set to Arm64, and you are building and running on a Windows on Arm environment. Revisit
+      the configuration steps to confirm the platform settings.
+# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_winui3/_index.md b/content/learning-paths/laptops-and-desktops/win_winui3/_index.md
index 844750a91e..a561363f19 100644
--- a/content/learning-paths/laptops-and-desktops/win_winui3/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_winui3/_index.md
@@ -20,6 +20,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:32:13Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 383dcf17bce86b24a2d9c8d0982fa6e9ddf954955c16b420463ada951753dbfa
+  summary_generated_at: '2026-06-01T22:20:00Z'
+  summary_source_hash: 383dcf17bce86b24a2d9c8d0982fa6e9ddf954955c16b420463ada951753dbfa
+  faq_generated_at: '2026-06-02T23:32:13Z'
+  faq_source_hash: 383dcf17bce86b24a2d9c8d0982fa6e9ddf954955c16b420463ada951753dbfa
+  summary: >-
+    This Learning Path shows how to create and build a Windows UI Library (WinUI 3) application
+    in C#/.NET using Visual Studio 2022 on Windows on Arm, then compare code execution performance
+    on Arm64 versus x64. You will configure Visual Studio for Release builds, select the target
+    architecture, launch the app, and use matrix multiplication to measure and compare computation
+    times across the two architectures. Prerequisites are a Windows on Arm computer (or a Windows
+    on Arm virtual machine) and Visual Studio 2022 with the .NET desktop development and Universal
+    Windows Platform development workloads installed. Designed for an introductory audience focused
+    on migration to Arm, the path takes about 30 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Windows on Arm device such as a Lenovo ThinkPad X13s running Windows 11 or a
+      Windows on Arm virtual machine. Install Visual Studio 2022 with the .NET desktop development
+      and Universal Windows Platform development workloads.
+  - question: Which Visual Studio settings should I use to build and run for each architecture?
+    answer: >-
+      Set the Configuration to Release. Then choose the target architecture as x64 or ARM64 and
+      select Arm64.WinUIApp (Package) when targeting ARM64.
+  - question: How do I run the performance comparison between x64 and ARM64?
+    answer: >-
+      Launch the application for x64 first, perform the matrix multiplication calculations as
+      described, then switch the architecture to ARM64 and repeat. Record the computation times
+      to compare the results.
+  - question: How do I confirm I built the app for ARM64?
+    answer: >-
+      In Visual Studio, verify the Configuration is set to Release and the Architecture dropdown
+      shows ARM64. Ensure the startup item is Arm64.WinUIApp (Package) before running.
+  - question: Can I complete this Learning Path without a physical Arm device?
+    answer: >-
+      Yes. A Windows on Arm virtual machine is listed as an acceptable environment in the prerequisites.
+# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_wpf/_index.md b/content/learning-paths/laptops-and-desktops/win_wpf/_index.md
index cba4d7c225..9567ea8182 100644
--- a/content/learning-paths/laptops-and-desktops/win_wpf/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_wpf/_index.md
@@ -20,6 +20,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:33:26Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: cc1a494e7b03672122ff84073b677a93659eca7df601b3f77c7e7fd536cc1af9
+  summary_generated_at: '2026-06-01T22:20:26Z'
+  summary_source_hash: cc1a494e7b03672122ff84073b677a93659eca7df601b3f77c7e7fd536cc1af9
+  faq_generated_at: '2026-06-02T23:33:26Z'
+  faq_source_hash: cc1a494e7b03672122ff84073b677a93659eca7df601b3f77c7e7fd536cc1af9
+  summary: >-
+    This Learning Path shows how to create and build a Windows Presentation Foundation (WPF) desktop
+    application on Windows on Arm and compare execution times between ARM64 and x86_64 builds
+    using Visual Studio 2022. You will work with WPF and XAML to define the UI, then use Visual
+    Studio’s Configuration Manager to add an ARM64 Solution Platform and run the app under different
+    settings to measure code execution performance uplift on Arm64. The target environment is
+    a Windows on Arm computer (or a Windows on Arm virtual machine) with the .NET desktop development
+    workload installed. By the end, you will have built and executed a WPF app and gathered timing
+    comparisons across build configurations.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Windows on Arm computer (such as a Lenovo ThinkPad X13s) or a Windows on Arm
+      virtual machine, and Visual Studio 2022 with .NET desktop development installed. No additional
+      prerequisites are explicitly listed.
+  - question: Which Visual Studio option do I use to target ARM64?
+    answer: >-
+      Use the Any CPU drop-down, choose Configuration Manager, then select New from the Active
+      Solution Platform menu. In the New Solution Platform window, choose ARM64 and click OK.
+  - question: Do I also need an x86_64 configuration for comparison?
+    answer: >-
+      Yes. The procedure prepares both ARM64 and x86_64 builds so you can compare computation
+      times. Repeat the New Solution Platform steps to create the additional architecture.
+  - question: How do I run the app to compare execution times across configurations?
+    answer: >-
+      Select the desired platform in Active Solution Platform, build, and launch the app from
+      Visual Studio. Run it under each configuration and compare the computation times as instructed
+      in the steps.
+  - question: How do I know the app is running as ARM64 rather than x86_64?
+    answer: >-
+      Ensure ARM64 is selected as the Active Solution Platform before building and launching.
+      The app will run using the architecture of the active platform you selected.
+# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md b/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md
index 864c45d668..0ee73389a6 100644
--- a/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md
@@ -21,6 +21,51 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:34:00Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 16b7f8c67050ea336402791c0ecca2c688d5da9373c571986e12dcf646c8093c
+  summary_generated_at: '2026-06-01T22:20:57Z'
+  summary_source_hash: 16b7f8c67050ea336402791c0ecca2c688d5da9373c571986e12dcf646c8093c
+  faq_generated_at: '2026-06-02T23:34:00Z'
+  faq_source_hash: 16b7f8c67050ea336402791c0ecca2c688d5da9373c571986e12dcf646c8093c
+  summary: >-
+    This introductory Learning Path shows how to create and build a Xamarin Forms application
+    on Windows on Arm using Visual Studio 2022. You will apply the Model-View-ViewModel (MVVM)
+    pattern by adding a Models folder and a DataPoint2d class to structure app logic, and measure
+    code execution performance uplift on Arm64. The path targets developers exploring cross-platform
+    development with C# and .NET while running on Arm-based Windows systems. Prerequisites are
+    a Windows on Arm computer such as a Lenovo ThinkPad X13s or a Windows on Arm virtual machine,
+    and Visual Studio 2022 with the .NET desktop development and Universal Windows Platform workloads
+    installed. By the end, you will have built the app and measured code execution on Arm64.
+  faqs:
+  - question: What do I need installed before starting on Windows on Arm?
+    answer: >-
+      Use a Windows on Arm computer or a Windows on Arm virtual machine, and install Visual Studio
+      2022 with the .NET desktop development and Universal Windows Platform development workloads.
+      No other prerequisites are explicitly listed.
+  - question: Can I complete this Learning Path using a virtual machine instead of physical hardware?
+    answer: >-
+      Yes. A Windows on Arm virtual machine is listed as an acceptable environment.
+  - question: Which Visual Studio workloads should I select for this Xamarin Forms project?
+    answer: >-
+      Install the .NET desktop development and Universal Windows Platform development workloads
+      in Visual Studio 2022 as specified in the prerequisites.
+  - question: Where should I place the DataPoint2d model when implementing MVVM?
+    answer: >-
+      Create a folder named Models in your Arm64.MobileApp.XamarinForms project and add a new
+      class file named DataPoint2d.cs inside it. This establishes the Model part of the MVVM structure.
+  - question: How will I measure code execution performance uplift on Arm64 in this path?
+    answer: >-
+      The Learning Path includes steps to measure code execution performance uplift on Arm64.
+      Follow the provided instructions in the steps to perform and interpret the measurement.
+# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md b/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md
index f83a0f0d03..d2d34fa612 100644
--- a/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md
@@ -19,6 +19,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:34:43Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: f058f628cefb7766ef0728e594c9ef5b57bde97c1850395786d54cc591271503
+  summary_generated_at: '2026-06-01T22:21:28Z'
+  summary_source_hash: f058f628cefb7766ef0728e594c9ef5b57bde97c1850395786d54cc591271503
+  faq_generated_at: '2026-06-02T23:34:43Z'
+  faq_source_hash: f058f628cefb7766ef0728e594c9ef5b57bde97c1850395786d54cc591271503
+  summary: >-
+    This introductory path guides you through setting up Visual Studio 2022 on a Windows on Arm
+    device, creating and running a simple console application, and then building and profiling
+    a sample that renders a rotating 3D cube. You will install Git for Windows on Arm to clone
+    the SpinTheCubeInGDI repository, open its Visual Studio solution, and review core components
+    such as shape generation, rotation, and drawing. The final steps install and deploy Arm Performance
+    Libraries and explore performance differences after using these math libraries compared to
+    multithreaded code. Prerequisite: a Windows on Arm computer such as a Lenovo ThinkPad X13s
+    running Windows 11. Estimated time to complete is about 60 minutes.
+  faqs:
+  - question: Which Visual Studio edition should I install on Windows on Arm?
+    answer: >-
+      Any of the three Visual Studio 2022 editions can be used. The Community Edition is a free,
+      fully featured option suitable for individual developers.
+  - question: How do I create the initial Windows on Arm project in Visual Studio?
+    answer: >-
+      From the Start window, select Create a new project, choose Console App, provide a project
+      name, and click Create. Then build and run the project to verify your setup.
+  - question: How do I get the SpinTheCubeInGDI example used in this path?
+    answer: >-
+      Install Git for Windows on Arm if needed, navigate to an empty directory, and clone the
+      repository: git clone https://github.com/arm/SpinTheCubeInGDI.git. This repository contains
+      the Visual Studio solution for the example.
+  - question: How do I open and run the spinning cube example in Visual Studio?
+    answer: >-
+      In Windows File Explorer, double-click SpinTheCubeInGDI.sln to open the solution in Visual
+      Studio. Build and run it to see the rotating 3D cube; the project includes shape generation,
+      rotation, and drawing logic implemented in SpinTheCubeInGDI.cpp.
+  - question: How do I use Arm Performance Libraries with this example?
+    answer: >-
+      Follow the Arm Performance Libraries install guide to set up the libraries on Windows. After
+      installation, use the project to explore differences when numerical routines are backed
+      by Arm Performance Libraries, which provide BLAS, LAPACK, FFT, and sparse implementations
+      built with OpenMP.
+# END generated_summary_faq
 
 author: Odin Shen
 
diff --git a/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md b/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md
index 97128786e3..cebcee3c65 100644
--- a/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md
@@ -21,6 +21,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:35:23Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 828e4022f387698d2736d94010de5d93efa9f38ffd3a2e4f15252410e9ccedb2
+  summary_generated_at: '2026-06-01T22:22:06Z'
+  summary_source_hash: 828e4022f387698d2736d94010de5d93efa9f38ffd3a2e4f15252410e9ccedb2
+  faq_generated_at: '2026-06-02T23:35:23Z'
+  faq_source_hash: 828e4022f387698d2736d94010de5d93efa9f38ffd3a2e4f15252410e9ccedb2
+  summary: >-
+    Set up a GitHub self-hosted runner on a Windows on Arm machine or cloud instance and run a
+    minimal GitHub Actions workflow to validate a basic CI/CD flow on this platform. You will
+    create a new GitHub repository, configure the runner on Windows on Arm, and use the Actions
+    Simple workflow to generate a minimal blank.yml under .github/workflows (optionally renamed,
+    for example to hello.yml) that executes a hello world command on the Windows Arm VM. Prerequisites
+    are a valid GitHub account, a Microsoft Azure account if you use a virtual machine, and some
+    familiarity with CI/CD concepts. This introductory path is intended for developers interested
+    in running CI on Windows on Arm and can be completed in about 30 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a valid GitHub account and some familiarity with CI/CD concepts. If you plan to
+      use a virtual machine, you also need a Microsoft Azure account.
+  - question: Can I use a virtual machine instead of physical Windows on Arm hardware?
+    answer: >-
+      Yes. You can use a cloud instance as the Windows on Arm host; if you choose a virtual machine,
+      an Azure account is required.
+  - question: How do I create the repository used for testing the workflow?
+    answer: >-
+      Log in to GitHub in your browser, select New to create a repository, give it a name, and
+      click Create Repository. This repo will host the workflow you run on the Windows on Arm
+      runner.
+  - question: How do I set up the Windows on Arm self-hosted runner, and what does it do?
+    answer: >-
+      Follow the path’s runner preparation steps to configure a GitHub self-hosted runner on your
+      Windows on Arm machine or cloud instance. The runner is the machine that executes your GitHub
+      Actions jobs.
+  - question: How do I create and run the sample GitHub Actions workflow, and what file should
+      I expect?
+    answer: >-
+      In your repository, select Actions, choose the Simple workflow option, and click Configure.
+      GitHub creates .github/workflows/blank.yml, which you can optionally rename (for example,
+      hello.yml); this minimal workflow runs a hello world command to validate the setup.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md
index 9145318eab..2d7f7d3c6f 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md
@@ -22,6 +22,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:36:56Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 1dd709b14537e06c80b9cdee58729cfa7066f44f0de4ceabe5450b33288731aa
+  summary_generated_at: '2026-06-02T02:37:03Z'
+  summary_source_hash: 1dd709b14537e06c80b9cdee58729cfa7066f44f0de4ceabe5450b33288731aa
+  faq_generated_at: '2026-06-02T23:36:56Z'
+  faq_source_hash: 1dd709b14537e06c80b9cdee58729cfa7066f44f0de4ceabe5450b33288731aa
+  summary: >-
+    This introductory Learning Path shows how to install and use the WindowsPerf Visual Studio
+    extension on Windows on Arm to generate counting and sampling reports and analyze performance
+    data in Windows Performance Analyzer (WPA). You configure Visual Studio 2022 Community Edition
+    with WindowsPerf, the WindowsPerf extension, and the WPA tooling, then run counting and sampling
+    sessions from within Visual Studio. You review results in Visual Studio and in WPA, and, if
+    your hardware supports it, explore the SPE subset of the sampling feature. By the end, you
+    can produce and examine WindowsPerf reports as part of a Windows on Arm development workflow.
+    No additional prerequisites are listed beyond the required tools.
+  faqs:
+  - question: What do I need installed before I start?
+    answer: >-
+      You need a Windows on Arm desktop or laptop with Visual Studio 2022 Community Edition, WindowsPerf,
+      the WindowsPerf Visual Studio extension, and Windows Performance Analyzer (WPA). The Learning
+      Path provides install guides for each tool.
+  - question: How do I open and configure the counting settings in Visual Studio?
+    answer: >-
+      In Visual Studio 2022, open the View menu and select Counting Settings to open the dialog.
+      From there, configure the counting parameters as shown in the Learning Path.
+  - question: How do I generate a counting report and review it in WPA?
+    answer: >-
+      After configuring counting, generate a report in Visual Studio and explore the data in the
+      IDE. You can then review the report in Windows Performance Analyzer (WPA) using the WindowsPerf
+      WPA plugin described in the Learning Path.
+  - question: Where do I find the sampling tools and set sampling preferences?
+    answer: >-
+      Open the View menu in Visual Studio 2022 and select Sampling Explorer. In the Sampling Explorer
+      window, use the Configure the sampling command icon to set your preferences.
+  - question: What should I check if the SPE feature does not work on my system?
+    answer: >-
+      The SPE section requires hardware that supports the Arm Statistical Profiling Extension.
+      If your CPU does not support SPE, this feature will not function and you should proceed
+      with the general Sampling feature instead.
+# END generated_summary_faq
 
 author: 
   - Nader Zouaoui
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf/_index.md
index 35d977371a..8daa49fb2c 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf/_index.md
@@ -19,6 +19,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:36:06Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 61a6390d42ce6bfc37a3bb981710dc23b8566070733f7979f9ec37bc63ab7d78
+  summary_generated_at: '2026-06-02T02:36:31Z'
+  summary_source_hash: 61a6390d42ce6bfc37a3bb981710dc23b8566070733f7979f9ec37bc63ab7d78
+  faq_generated_at: '2026-06-02T23:36:06Z'
+  faq_source_hash: 61a6390d42ce6bfc37a3bb981710dc23b8566070733f7979f9ec37bc63ab7d78
+  summary: >-
+    This introductory Learning Path shows how to install WindowsPerf on a Windows on Arm desktop
+    or development machine and generate sample CPU profiling reports. You will use the wperf command-line
+    interface to count ARM64 PMU events with wperf stat and to collect samples with wperf sample
+    and wperf record, producing example outputs at function, basic block, or instruction granularity.
+    The steps focus on practical, minimal commands and a cheat sheet to help you run counting
+    and sampling quickly. By the end, you will have WindowsPerf installed and be able to execute
+    basic profiling runs and view sample results. No additional prerequisites are explicitly listed
+    beyond a Windows on Arm machine.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Windows on Arm desktop or development machine. No additional prerequisites are
+      explicitly listed.
+  - question: Which wperf command should I use for counting versus sampling?
+    answer: >-
+      Use wperf stat for counting occurrences of PMU events. Use wperf sample or wperf record
+      for sampling to analyze where events occur in your code.
+  - question: How do I limit a count to a specific core and time window?
+    answer: >-
+      The cheat sheet includes an example: wperf stat -e inst_spec,vfp_spec,ase_spec,ld_spec -c
+      0 --timeout 3. This counts the listed events on core 0 for 3 seconds.
+  - question: What result should I expect from counting and sampling runs?
+    answer: >-
+      Counting provides aggregate totals of selected PMU events. Sampling reports event frequencies
+      attributed to program locations at the function, basic block, and/or instruction levels.
+  - question: Where can I find example PMU events and metrics to try?
+    answer: >-
+      Refer to the WindowsPerf cheat sheet, which shows practical examples including events like
+      inst_spec, vfp_spec, ase_spec, ld_spec and a metric example such as imix with an additional
+      event.
+# END generated_summary_faq
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md
index 08349bd0f6..4ebb9cade2 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md
@@ -22,6 +22,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:37:32Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: e23de84e7e05d771072ab799f5cc802476fd5e3c23da41a202c9c50fe9980e25
+  summary_generated_at: '2026-06-02T02:37:39Z'
+  summary_source_hash: e23de84e7e05d771072ab799f5cc802476fd5e3c23da41a202c9c50fe9980e25
+  faq_generated_at: '2026-06-02T23:37:32Z'
+  faq_source_hash: e23de84e7e05d771072ab799f5cc802476fd5e3c23da41a202c9c50fe9980e25
+  summary: >-
+    This Learning Path shows how to use WindowsPerf to sample a native Windows on Arm workload
+    by building CPython from sources for the ARM64 target and analyzing its runtime. You will
+    create a debug build, run CPython interactively, pin python_d.exe to a selected core, and
+    collect both counting and sampling data to locate hot code paths using PMU event frequencies.
+    The path also shows how to streamline the workflow with the WindowsPerf record command to
+    spawn and pin the process and forward arguments. Prerequisites include a Windows on Arm machine
+    with WindowsPerf installed and a Windows x86_64 desktop with Visual Studio 2022 Community
+    Edition. After completing, you will understand basic sampling and the WindowsPerf command
+    line for this scenario.
+  faqs:
+  - question: What do I need before running the examples?
+    answer: >-
+      You need a Windows on Arm desktop or development machine with WindowsPerf installed, and
+      a Windows x86_64 desktop with Visual Studio 2022 Community Edition installed. The sampling
+      examples are run on a native ARM64 Windows on Arm machine.
+  - question: Which CPython build should I use during the sampling exercises?
+    answer: >-
+      Use the debug build of CPython targeting ARM64 that you built from sources in the previous
+      step. The examples reference these pre-built ARM64 debug binaries.
+  - question: Which WindowsPerf command should I use to spawn and pin CPython to a core?
+    answer: >-
+      Use the record command with the -c option to pin to a specific core. You can specify the
+      process with --pe_file or place the process to spawn at the end of the wperf command.
+  - question: How do I pass command-line arguments to my program when using WindowsPerf?
+    answer: >-
+      Place all application arguments after the WindowsPerf options. They are passed verbatim
+      to the spawned program.
+  - question: What result should I expect when I run counting and sampling on the Googolplex workload?
+    answer: >-
+      Counting provides aggregate event counts, while sampling reports frequencies of PMU events.
+      Together they help you see hot locations in the CPython runtime image under the chosen workload.
+# END generated_summary_faq
 
 author: Przemyslaw Wirkus
 
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
index 9caefdcd5c..8fbb724789 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
@@ -19,6 +19,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:38:01Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 1fd4d85855933a5dfc5edf5ae3abf5f4388d94c9ee69aff12b77eb766e88301f
+  summary_generated_at: '2026-06-02T02:38:21Z'
+  summary_source_hash: 1fd4d85855933a5dfc5edf5ae3abf5f4388d94c9ee69aff12b77eb766e88301f
+  faq_generated_at: '2026-06-02T23:38:01Z'
+  faq_source_hash: 1fd4d85855933a5dfc5edf5ae3abf5f4388d94c9ee69aff12b77eb766e88301f
+  summary: >-
+    This Learning Path shows how to take performance data collected with WindowsPerf on a Windows
+    on Arm laptop and analyze it in Windows Performance Analyzer (WPA) using the WPA plugin. You
+    will generate a .json report from a WindowsPerf wperf stat run, import that file into WPA,
+    and use the plugin to visualize timeline and telemetry data. The focus is practical: connect
+    WindowsPerf output to WPA and inspect the resulting views. Prerequisites are explicitly listed:
+    a Windows on Arm laptop with WindowsPerf, Windows Performance Analyzer, and the WPA plugin
+    installed. The path is introductory and designed to be completed in about 15 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Windows on Arm laptop with WindowsPerf, Windows Performance Analyzer (WPA), and
+      the WPA plugin installed. The WPA plugin install guide includes installation of WPA.
+  - question: How do I create the .json file that WPA will import?
+    answer: >-
+      Run the WindowsPerf wperf stat command on your Windows on Arm machine and save the output
+      as a .json file using the --output option. This .json file is the input for WPA.
+  - question: Where should I run the wperf stat command?
+    answer: >-
+      Run wperf stat on a Windows on Arm machine. The .json output from that run is what you will
+      import into WPA.
+  - question: How do I know the import into WPA worked?
+    answer: >-
+      After importing the .json file, use the WPA plugin to view timeline and telemetry data.
+      If you can open the file and see these views, the import succeeded.
+  - question: What should I check if I do not see the plugin views in WPA?
+    answer: >-
+      Verify that the WPA plugin is installed and that you imported a .json file generated by
+      WindowsPerf using --output. Recreate the .json with wperf stat if needed and try the import
+      again.
+# END generated_summary_faq
+
 author: Alaaeddine Chakroun
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/wsl2/_index.md b/content/learning-paths/laptops-and-desktops/wsl2/_index.md
index 4ee417fdfc..20d5e9b576 100644
--- a/content/learning-paths/laptops-and-desktops/wsl2/_index.md
+++ b/content/learning-paths/laptops-and-desktops/wsl2/_index.md
@@ -24,6 +24,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:38:39Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 03d816ff419e6e5b1533f95e9d4ffa087b9c0c9aefeee112f67b70223c8aedc3
+  summary_generated_at: '2026-06-02T02:38:49Z'
+  summary_source_hash: 03d816ff419e6e5b1533f95e9d4ffa087b9c0c9aefeee112f67b70223c8aedc3
+  faq_generated_at: '2026-06-02T23:38:39Z'
+  faq_source_hash: 03d816ff419e6e5b1533f95e9d4ffa087b9c0c9aefeee112f67b70223c8aedc3
+  summary: >-
+    This Learning Path shows how to configure and run Windows Subsystem for Linux (WSL) on Windows
+    on Arm computers to support Linux and cloud-native development. You will set up WSL with various
+    Linux distributions, run graphical Linux applications on Windows 11, enable systemd so services
+    start automatically, and use SSH when remote access is required. You will also configure remote
+    desktop access with RDP and VNC, learn multiple options for running Visual Studio Code, and
+    import or export WSL file systems for backup. The intended audience is developers using Windows
+    on Arm systems; no additional prerequisites are explicitly listed beyond a Windows 11 device
+    such as a Lenovo ThinkPad X13s. Estimated time to complete is about 90 minutes.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      You need a Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11.
+      No other explicit prerequisites are listed.
+  - question: How do I know systemd is enabled and running in my WSL distribution?
+    answer: >-
+      Add systemd=true to /etc/wsl.conf, terminate the distribution, and restart it. Then run
+      systemctl list-unit-files --type=service to confirm systemd-managed services are available;
+      services such as SSH and docker will start automatically when systemd is enabled.
+  - question: How can I run and verify a graphical Linux application on Windows 11?
+    answer: >-
+      Install the application from the Linux command line in WSL (for example, terminator on Ubuntu
+      22.04) and launch it. A new window should appear on your Windows desktop, and the app will
+      show on the Windows taskbar with a penguin icon.
+  - question: Do I need SSH to move files between Windows and WSL on the same machine?
+    answer: >-
+      No. WSL mounts the Windows C: drive at /mnt/c, so you can copy files directly (for example,
+      cp /mnt/c/Users/<username>/Downloads/<filename> .). Use SSH only if you need to access WSL
+      from a different machine.
+  - question: What should I check if RDP does not display the Linux desktop?
+    answer: >-
+      Verify xfce4 and xrdp are installed, set XFCE4 as the default session (echo xfce4-session
+      > ~/.xsession), and restart the xrdp service. Check systemctl status xrdp and start it if
+      it is not running.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md b/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md
index cc705bc3c6..4ce1551c85 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md
@@ -22,6 +22,57 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:39:11Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: e4512ad20b372f616409199c51a38d95cb116ec6cf49d9004348d6bb8ee4014d
+  summary_generated_at: '2026-06-02T02:39:32Z'
+  summary_source_hash: e4512ad20b372f616409199c51a38d95cb116ec6cf49d9004348d6bb8ee4014d
+  faq_generated_at: '2026-06-02T23:39:11Z'
+  faq_source_hash: e4512ad20b372f616409199c51a38d95cb116ec6cf49d9004348d6bb8ee4014d
+  summary: >-
+    This Learning Path shows how to enable and verify Arm Fixed Rate Compression (AFRC) in Vulkan
+    applications on Android. You will check for VK_EXT_image_compression_control support (and
+    VK_EXT_image_compression_control_swapchain for swapchain images), query whether specific VkImage
+    configurations support fixed-rate compression, and request AFRC by chaining VkImageCompressionControlEXT
+    at image creation. The steps reference a Vulkan API Sample from the Khronos Vulkan Samples
+    repository as a test application. Prerequisites include an Android device (for example, Google
+    Pixel 8) that supports the required Vulkan extensions, familiarity with the Vulkan API, and
+    a Vulkan app that creates and uses images. By the end, you can confirm that compression is
+    applied to reduce memory footprint and bandwidth.
+  faqs:
+  - question: How do I know if my Android device supports the required Vulkan extensions for AFRC?
+    answer: >-
+      Use vkEnumerateDeviceExtensionProperties to look for VK_EXT_image_compression_control, and
+      include VK_EXT_image_compression_control_swapchain if you need swapchain images. If these
+      are not listed, the device does not meet the prerequisites for this path.
+  - question: Where do I enable the Vulkan extensions in my application?
+    answer: >-
+      Add the required extension names to VkDeviceCreateInfo::ppEnabledExtensionNames before calling
+      vkCreateDevice. This enables VK_EXT_image_compression_control (and the swapchain variant
+      when needed) for use in your Vulkan device.
+  - question: How do I query whether a specific image setup supports fixed-rate compression?
+    answer: >-
+      Define your intended VkImageCreateInfo properties (such as format, type, tiling, and usage)
+      and use them to query support on your platform before creating the image. The path shows
+      using those properties to drive the support check.
+  - question: How do I request fixed-rate compression at image creation time?
+    answer: >-
+      Provide a VkImageCompressionControlEXT structure in the pNext chain of VkImageCreateInfo
+      and set flags = VK_IMAGE_COMPRESSION_FIXED_RATE_DEFAULT_EXT. Then create the VkImage with
+      these settings applied.
+  - question: What result should I expect when verifying that compression was applied?
+    answer: >-
+      VK_EXT_image_compression_control can be used to verify whether default compression was applied
+      and to confirm your fixed-rate request. The verification indicates the compression chosen
+      for the image based on device support and your request.
+# END generated_summary_faq
 
 author: Jose-Emilio Munoz-Lopez
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md b/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md
index d5f61cc766..484944a44c 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md
@@ -19,6 +19,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:39:57Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: dee181248ecc8cb40e3ad76642fdb216cbf1e7610dde2f2605ba04f111b8926a
+  summary_generated_at: '2026-06-02T02:40:04Z'
+  summary_source_hash: dee181248ecc8cb40e3ad76642fdb216cbf1e7610dde2f2605ba04f111b8926a
+  faq_generated_at: '2026-06-02T23:39:57Z'
+  faq_source_hash: dee181248ecc8cb40e3ad76642fdb216cbf1e7610dde2f2605ba04f111b8926a
+  summary: >-
+    Build and run AI-powered camera pipeline applications on Arm using SME2 with KleidiAI and
+    KleidiCV. You will clone the ai-camera-pipelines repository with Git LFS, build a Docker container,
+    compile the C++ pipelines, apply background blur, denoising, and low-light effects, and run
+    provided benchmark binaries to exercise the hot loop and observe improvements from KleidiCV
+    and KleidiAI. The steps target an Arm64 system with SME2 support, with instructions tested
+    on Ubuntu 24.04. Prerequisites list a computer running Arm Linux or macOS with Docker installed,
+    plus Git and Git LFS. After completing the path, you can build, run, and benchmark these real-time
+    camera effects.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      Use an Arm64 machine with SME2 support; the instructions were tested on Ubuntu 24.04. The
+      prerequisites list a computer running Arm Linux or macOS with Docker installed, plus Git
+      and Git LFS.
+  - question: Which repository do I clone and why is Git LFS required?
+    answer: >-
+      Clone git.gitlab.arm.com/kleidi/kleidi-examples/ai-camera-pipelines.git. Git LFS is needed
+      to fetch the large files referenced by the project.
+  - question: How do I build the container used to compile the pipelines?
+    answer: >-
+      Build the Docker image from docker/Dockerfile with tag ai-camera-pipelines, passing the
+      build args DOCKERHUB_MIRROR=docker.io and CI_UID=$(id -u), targeting the docker/ directory.
+      Then start a shell in the container to compile the pipelines as shown in the steps.
+  - question: How do I run a background blur or other effect and verify success?
+    answer: >-
+      Create a Python virtual environment, install numpy, opencv-python, pillow, and torch, then
+      run the provided binaries from the bin directory. For background blur, run cinematic_mode
+      with resources/test_input.png and expect an output image like test_output_cinematic_mode.png.
+  - question: How do I run benchmarks and what result should I expect?
+    answer: >-
+      Use the benchmark executables: cinematic_mode_benchmark, low_light_image_enhancement_benchmark,
+      and neural_denoiser_temporal_benchmark_4K. They run the core processing loop multiple times
+      and demonstrate improvements enabled by KleidiCV (OpenCV kernels on Arm) and KleidiAI (LiteRT+XNNPack
+      inference micro-kernels).
+# END generated_summary_faq
 
 author: Arnaud de Grandmaison
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md b/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
index 449f25b0f2..665d87c92f 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
@@ -25,6 +25,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:41:00Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 3d1a743c1b3ee617f52191fc9c9fd33a9d44454ac079f00b6f532e2865103377
+  summary_generated_at: '2026-06-02T02:40:50Z'
+  summary_source_hash: 3d1a743c1b3ee617f52191fc9c9fd33a9d44454ac079f00b6f532e2865103377
+  faq_generated_at: '2026-06-02T23:41:00Z'
+  faq_source_hash: 3d1a743c1b3ee617f52191fc9c9fd33a9d44454ac079f00b6f532e2865103377
+  summary: >-
+    This introductory path shows Android developers how to start profiling apps on devices with
+    Mali-based GPUs using Arm Performance Studio. You will install the tools, connect an Android
+    device over adb, explore a provided Streamline example capture, then profile your own debuggable
+    build and generate a Performance Advisor HTML report using streamline-cli. The walkthrough
+    focuses on Streamline and Performance Advisor basics. Prerequisites include an Android device,
+    a debuggable app using OpenGL ES 2.0–3.2 or Vulkan 1.0–1.2 (Android 10+ for OpenGL ES, Android
+    9+ for Vulkan), Arm Performance Studio installed, Android SDK Platform Tools (adb), and Python
+    3.8 or later for the Performance Advisor script.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an Android device, a debuggable build of your application, Arm Performance Studio
+      installed, and the Android SDK Platform Tools (adb). For Performance Advisor’s connection
+      script, install Python 3.8 or later.
+  - question: Which graphics APIs and Android versions are supported?
+    answer: >-
+      Arm Performance Studio supports OpenGL ES versions 2.0 to 3.2 and Vulkan versions 1.0 to
+      1.2. For OpenGL ES applications your device must run Android 10 or later; for Vulkan applications
+      your device must run Android 9 or later.
+  - question: How do I connect my Android device in Streamline?
+    answer: >-
+      Launch the Performance Studio Hub and open Streamline, then in the Start view select Android
+      (adb) and choose your device. Streamline installs the gatord daemon and connects to the
+      device; if your device is not listed, check that adb from the Android SDK Platform Tools
+      is installed.
+  - question: How do I open the example Streamline capture?
+    answer: >-
+      In Streamline, select File > Import, then choose Import Streamline Sample Captures and select
+      the Android example. After import, double-click the report in Streamline Data to view it.
+  - question: How do I generate a Performance Advisor report from a Streamline capture?
+    answer: >-
+      From a terminal, navigate to the capture and run the streamline-cli command with the -pa
+      option on the .apc file. The capture is processed and an HTML report is generated, with
+      warnings shown where applicable.
+# END generated_summary_faq
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md b/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md
index 60f9319e12..0147dfb24f 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md
@@ -23,6 +23,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:42:06Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: d5406f24ab38522b21e348ed3829ede7b1bcdce28e81ec4fddebbec280d2cb89
+  summary_generated_at: '2026-06-02T02:41:30Z'
+  summary_source_hash: d5406f24ab38522b21e348ed3829ede7b1bcdce28e81ec4fddebbec280d2cb89
+  faq_generated_at: '2026-06-02T23:42:06Z'
+  faq_source_hash: d5406f24ab38522b21e348ed3829ede7b1bcdce28e81ec4fddebbec280d2cb89
+  summary: >-
+    This introductory Learning Path shows how to use Frame Advisor in Arm Performance Studio to
+    capture a significant frame from an Android application and analyze where time is spent. You
+    will connect a supported device, start a trace from Frame Advisor, and examine the captured
+    frame’s render passes and draw calls, including primitive counts. You will use the Render
+    Graph to understand how data flows between passes and to spot attachments that do not contribute
+    to the final output, and the Content Metrics view to identify complex meshes by sorting and
+    navigating to high-primitive draw calls. Prerequisites include a debuggable build, Arm Performance
+    Studio on Windows, Linux, or macOS, Android SDK Platform-tools (adb), and an Android device
+    running OpenGL ES 2.0–3.2 (Android 10+) or Vulkan 1.0–1.2 (Android 9+).
+  faqs:
+  - question: What do I need before running Frame Advisor?
+    answer: >-
+      You need a supported Android device, a debuggable build of your app, Arm Performance Studio
+      installed on Windows, Linux, or macOS, and Android SDK Platform Tools (adb). Frame Advisor
+      supports OpenGL ES 2.0–3.2 on Android 10+ and Vulkan 1.0–1.2 on Android 9+.
+  - question: How do I start a capture trace from my device?
+    answer: >-
+      Open Frame Advisor and choose New Trace. Select your connected device and the target application,
+      switch the API to Vulkan if needed, then click Next to start the capture session; the app
+      launches automatically on the device.
+  - question: How do I know the capture and analysis worked?
+    answer: >-
+      When analysis completes, the Analysis screen appears with the Frame Hierarchy listing captured
+      frames, render passes, and draw calls. You can step through draw calls to see how the scene
+      is built.
+  - question: Which view helps me find unused render passes or attachments?
+    answer: >-
+      Use the render graph. It visualizes how data flows between render passes and resources so
+      you can spot passes or attachments that are not used in the final output to the swapchain.
+  - question: How can I locate the most complex meshes in my scene?
+    answer: >-
+      Open the Content Metrics view, select Draws, and sort by the highest number of primitives
+      (Prims). Right-click a top entry and choose Navigate to call to select it in the Frame Hierarchy
+      and view it in the Framebuffers view.
+# END generated_summary_faq
 
 author: Julie Gaskin
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md
index c1e170c64e..edc6403385 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md
@@ -21,6 +21,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:42:41Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: e63ac917c218aa5e5981f96a9821567d0f8ba9761d5ce6e4c9d7b6dea7ed5c5f
+  summary_generated_at: '2026-06-02T02:41:57Z'
+  summary_source_hash: e63ac917c218aa5e5981f96a9821567d0f8ba9761d5ce6e4c9d7b6dea7ed5c5f
+  faq_generated_at: '2026-06-02T23:42:41Z'
+  faq_source_hash: e63ac917c218aa5e5981f96a9821567d0f8ba9761d5ce6e4c9d7b6dea7ed5c5f
+  summary: >-
+    Build a simple Android chatbot app that runs a local LLM on-device using Arm’s AI Chat library.
+    You will create a new Android Studio project, verify google() and mavenCentral() repositories,
+    add the library dependency, design a basic chat UI, and implement MainActivity in Kotlin to
+    load a GGUF model and stream chat responses. The library wraps llama.cpp with Arm CPU optimizations
+    for GGUF models. You will download a mobile-friendly GGUF, such as google_gemma-3-4b-it-Q4_0.gguf,
+    sized for your device and run the app on a physical Android phone in Developer Mode. Prerequisites
+    include Android Studio, a USB-connected Android phone, and basic familiarity with Kotlin and
+    Android app development. Reference implementations include the Arm AI Chat app on Google Play.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      Install Android Studio, have an Android phone in Developer Mode with a USB cable, and be
+      comfortable with basic Kotlin and Android app development. No other prerequisites are explicitly
+      listed.
+  - question: Which repositories should be in settings.gradle.kts to resolve the AI Chat library?
+    answer: >-
+      Ensure the top-level repositories include google() and mavenCentral(). This allows Gradle
+      to find the AI Chat library from Maven Central.
+  - question: Where do I add the AI Chat dependency and what is the coordinate?
+    answer: >-
+      Add the dependency in the app module’s build file (app/build.gradle.kts), not the project-level
+      file. Use implementation "com.arm:ai-chat:0.1.0".
+  - question: How do I choose a mobile-compatible GGUF model, and is there an example?
+    answer: >-
+      Pick a model that is significantly smaller than your device’s RAM to leave room for the
+      OS and other apps. A good example provided is google_gemma-3-4b-it-Q4_0.gguf.
+  - question: What result should I expect when I run the app, and how do I know it’s working?
+    answer: >-
+      The app should load your selected GGUF model on-device and produce streamed chat responses
+      in the UI. If the build cannot resolve the library, re-check that google() and mavenCentral()
+      are configured and that the dependency was added to app/build.gradle.kts.
+# END generated_summary_faq
 
 author: Ben Clark
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
index 45617e14c6..1651156bc8 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
@@ -21,6 +21,57 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:43:24Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: fa04ff97605ae93b17c056c397c37f6fc17cf6f4853bfabe374610ede002e3df
+  summary_generated_at: '2026-06-02T02:42:22Z'
+  summary_source_hash: fa04ff97605ae93b17c056c397c37f6fc17cf6f4853bfabe374610ede002e3df
+  faq_generated_at: '2026-06-02T23:43:24Z'
+  faq_source_hash: fa04ff97605ae93b17c056c397c37f6fc17cf6f4853bfabe374610ede002e3df
+  summary: >-
+    This introductory Learning Path shows how to build and integrate real-time image processing
+    pipelines with Halide on Android. You start by installing and configuring Halide, then build
+    a camera pipeline that captures frames using OpenCV, applies Gaussian (binomial) blur and
+    thresholding, and measures performance while exploring Halide scheduling (parallelization
+    and tiling). You then apply operator fusion and learn when to materialize intermediates with
+    compute_root() or compute_at(), using print_loop_nest() to inspect the schedule. Next, you
+    perform ahead-of-time cross-compilation on the host to generate a library for Android (for
+    example, arm64-v8a), and integrate it into an Android app written in Kotlin using Android
+    Studio. Prerequisites are basic C++ knowledge and Android Studio with Android Emulator.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      You need basic C++ knowledge and Android Studio with Android Emulator. No other prerequisites
+      are explicitly listed.
+  - question: What result should I expect from the initial pipeline, and how do I confirm it worked?
+    answer: >-
+      The pipeline applies Gaussian blur followed by thresholding to produce a binary output that
+      highlights prominent features. If you see smoothed frames and a clear binary image derived
+      from the captured frames, the pipeline is running as intended. You will also measure performance
+      as part of the steps.
+  - question: Which Halide scheduling options will I use, and how can I inspect the schedule?
+    answer: >-
+      You will explore parallelization and tiling to improve throughput. Use print_loop_nest()
+      to see how Halide arranges the computation loops under your chosen schedule.
+  - question: When should I use operator fusion versus materializing intermediates?
+    answer: >-
+      Use fusion to compute stages inside their consumers to reduce memory traffic and improve
+      cache efficiency. Materialize intermediates with compute_root() or compute_at() for large
+      filters or when results are reused by multiple stages.
+  - question: Where does Android compilation happen, and what target should I build for?
+    answer: >-
+      Compilation occurs on the host using Halide’s ahead-of-time cross-compilation to produce
+      an Android pipeline library. The example targets an ABI such as arm64-v8a and avoids building
+      Halide or performing JIT on the device, preparing the library for integration into a Kotlin
+      Android app.
+# END generated_summary_faq
 
 author: Éliás Bálint, Dawid Borycki, Steve Suzuki
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_neon/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_neon/_index.md
index 96247658a5..6e6dce1bb7 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_neon/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_neon/_index.md
@@ -23,7 +23,7 @@ prerequisites:
 generate_summary_faq: true
 
 rerun_summary: false
-rerun_faqs: false
+rerun_faqs: true
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md
index 1b333759ac..3bc3cff8a6 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md
@@ -20,6 +20,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:44:45Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 2137cec46fe8d57f11e9e4011306a140bba7d8a5b2326a746193c1794a148f75
+  summary_generated_at: '2026-06-02T02:42:50Z'
+  summary_source_hash: 2137cec46fe8d57f11e9e4011306a140bba7d8a5b2326a746193c1794a148f75
+  faq_generated_at: '2026-06-02T23:44:45Z'
+  faq_source_hash: 2137cec46fe8d57f11e9e4011306a140bba7d8a5b2326a746193c1794a148f75
+  summary: >-
+    Build an introductory Android camera app that uses OpenCV to process images on an Arm-based
+    smartphone. Working in Android Studio on Windows, you create a Kotlin project, integrate the
+    OpenCV library, enable camera permissions, and capture real-time frames using JavaCameraView.
+    You then manage Mat objects and implement adaptive thresholding with OpenCV’s Imgproc.adaptiveThreshold,
+    controlled by a simple UI toggle for real-time processing. The result is a runnable app on
+    an Android smartphone that demonstrates live camera capture and basic computer vision processing.
+    No additional prerequisites are explicitly listed beyond Android Studio on your development
+    machine and an Android smartphone. Estimated time to complete is about 30 minutes.
+  faqs:
+  - question: Which Android Studio version should I use for this path?
+    answer: >-
+      The example uses Android Studio Jellyfish | 2023.3.1 Patch 1. The Learning Path does not
+      list other versions, so follow the steps as shown with that release.
+  - question: Do I need to develop on Windows to follow the steps?
+    answer: >-
+      Yes, the target operating system for the development machine in this Learning Path is Windows.
+      The instructions and tooling are presented with that environment in mind.
+  - question: Should I use Kotlin or Java for the project?
+    answer: >-
+      The steps configure the project to use Kotlin and show edits in MainActivity.kt. Java is
+      listed among the tools, but the provided code examples use Kotlin.
+  - question: How do I know OpenCV is integrated correctly?
+    answer: >-
+      Follow the steps to add the OpenCV library and imports like org.opencv.imgproc.Imgproc.
+      A successful build and the ability to run the app with JavaCameraView and adaptive thresholding
+      indicate the integration is working.
+  - question: What result should I expect when I run the app on my phone?
+    answer: >-
+      You should see a camera preview from JavaCameraView. When you check the provided checkbox,
+      adaptive thresholding is applied to the live frames; unchecking it shows the unprocessed
+      preview.
+# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md
index 6880fa481c..3afcee7b85 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md
@@ -22,6 +22,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:45:29Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 3a120620bbc56e796aa45fbe01aa1455fbee085a1a4cf0d055416b7e95e72d0d
+  summary_generated_at: '2026-06-02T02:43:15Z'
+  summary_source_hash: 3a120620bbc56e796aa45fbe01aa1455fbee085a1a4cf0d055416b7e95e72d0d
+  faq_generated_at: '2026-06-02T23:45:29Z'
+  faq_source_hash: 3a120620bbc56e796aa45fbe01aa1455fbee085a1a4cf0d055416b7e95e72d0d
+  summary: >-
+    Build an introductory Android app that detects faces in real time using OpenCV. Working in
+    Android Studio on Windows or macOS, you will create a Kotlin project, add OpenCV, retrieve
+    camera frames, and apply a Haar cascade classifier using a pre-trained XML file. The steps
+    focus on the essentials for camera access and classical face detection with OpenCV on Android
+    devices, relevant to Arm Cortex-A based smartphones. Prerequisites include Android Studio
+    on your development machine, an Android smartphone, and familiarity with OpenCV (with a recommended
+    prior Learning Path for review). The estimated time to complete is about 30 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need Android Studio installed on a Windows or macOS machine, an Android smartphone,
+      and familiarity with OpenCV. The path recommends reviewing the “Create Computer Vision Applications
+      with OpenCV on Android Devices” Learning Path first.
+  - question: Do I need a specific version of Android Studio?
+    answer: >-
+      The example uses Android Studio Jellyfish | 2023.3.1 Patch 1. If you use a different version,
+      expect minor UI differences when creating the project.
+  - question: Which Haar cascade file should I use and how is it included?
+    answer: >-
+      This path uses OpenCV’s pre-trained Haar cascades, which are XML files. The steps indicate
+      which cascade to use and how to include it in your project.
+  - question: How do I know OpenCV is correctly added and camera frames are being read?
+    answer: >-
+      After following the setup steps, you should be able to build the project and retrieve camera
+      frames via OpenCV without errors. If frame retrieval works, proceed to the face detection
+      step with the Haar cascade.
+  - question: What should I check if faces are not being detected?
+    answer: >-
+      Confirm that the correct Haar cascade XML file is included and loaded, and that valid camera
+      frames are being passed to the classifier. Revisit the steps to ensure OpenCV integration
+      and frame retrieval are configured as shown.
+# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
index 763b09f326..ec75159169 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
@@ -22,6 +22,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:46:04Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 8e05fb124ad18194fba2ed83adae3e52673b2fad41af998ca8d0aa8cb9785f5e
+  summary_generated_at: '2026-06-02T02:43:45Z'
+  summary_source_hash: 8e05fb124ad18194fba2ed83adae3e52673b2fad41af998ca8d0aa8cb9785f5e
+  faq_generated_at: '2026-06-02T23:46:04Z'
+  faq_source_hash: 8e05fb124ad18194fba2ed83adae3e52673b2fad41af998ca8d0aa8cb9785f5e
+  summary: >-
+    This introductory Android Learning Path shows how to build an OpenCV-based app accelerated
+    with KleidiCV. You will create a new Android Studio project, add OpenCV with KleidiCV support,
+    define a simple UI, and implement image processing on an input image. The app uses an ImageOperation
+    enum (for tasks such as Gaussian blur, resizing, and rotation), an ImageProcessor that works
+    with OpenCV Mat objects, and a PerformanceMetrics class that reports statistics like average
+    and standard deviation. The path targets Android development in Android Studio using Kotlin
+    or Java. Prerequisites include Android Studio, familiarity with Android development concepts,
+    and an Android smartphone. Estimated completion time is about 45 minutes.
+  faqs:
+  - question: What do I need before running through the steps?
+    answer: >-
+      You need a development machine with Android Studio installed, familiarity with Android development
+      concepts, and an Android smartphone. No other prerequisites are explicitly listed.
+  - question: Which Android Studio version is referenced in the example?
+    answer: >-
+      The example uses Android Studio Ladybug 2024.2.1, Patch 3. If you are using a different
+      version, menu names or screens may vary slightly from the instructions.
+  - question: Where should I place the test image, and does it have to be PNG?
+    answer: >-
+      Create an assets folder under src/main and add an img.png file there. The app will convert
+      the image as needed, and any image file can be used; the Learning Path uses a cameraman
+      image.
+  - question: Which files do I edit to define the UI and application logic?
+    answer: >-
+      Replace the contents of app/src/main/res/layout/activity_main.xml to define the UI. For
+      logic, create the ImageOperation enum, ImageProcessor class, and PerformanceMetrics class
+      as outlined in the steps.
+  - question: What result should I expect when I run the app on my device?
+    answer: >-
+      The app processes the bundled image using operations such as Gaussian blur, resizing, and
+      rotation. It also displays performance metrics, including average and standard deviation,
+      for the executed operations.
+# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md
index 5f72d98890..24765ff8c0 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md
@@ -21,6 +21,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:46:40Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: be91053c5abcdd669ff8da7e66b2f717c4adaac27b3fb44ea073b69a68d2dba7
+  summary_generated_at: '2026-06-02T02:44:16Z'
+  summary_source_hash: be91053c5abcdd669ff8da7e66b2f717c4adaac27b3fb44ea073b69a68d2dba7
+  faq_generated_at: '2026-06-02T23:46:40Z'
+  faq_source_hash: be91053c5abcdd669ff8da7e66b2f717c4adaac27b3fb44ea073b69a68d2dba7
+  summary: >-
+    This Learning Path guides you through enabling Scalable Vector Extension 2 (SVE2) in Android
+    Studio and implementing a native Android NDK example that computes vector fused multiply-add
+    (a * b + c) using SVE2 intrinsics. You will write C++ code to generate pseudo-random input
+    data, add helper functions, and create a reusable measureExecutionTime template to time N
+    invocations of FMA implementations with and without SVE2 on a 64-bit Arm smartphone running
+    Android. The introduction places SVE2 in the context of the ARMv9-A architecture. Prerequisites
+    include Android Studio on an x86_64 or Apple development machine, a 64-bit Arm Android device,
+    and familiarity with SIMD, Neon, and SVE. The expected outcome is a working project that builds,
+    runs, and compares execution times.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need Android Studio installed on an x86_64 or Apple development machine and access to
+      a 64-bit Arm smartphone running Android. Prior knowledge of SIMD, Neon, and SVE is expected.
+  - question: How do I enable SVE2 support in Android Studio for this project?
+    answer: >-
+      Follow the step titled “Enable SVE2 support in Android Studio” to configure the project
+      so SVE2 intrinsics compile for your NDK code. The path guides you through the necessary
+      project changes to build and run on a 64-bit Arm device.
+  - question: Which source file do I modify to add the FMA and timing code?
+    answer: >-
+      You will modify native-lib.cpp located under app/cpp/. This file is updated to implement
+      the FMA routines and the measureExecutionTime template function.
+  - question: How is performance measured, and what result should I expect to see?
+    answer: >-
+      A measureExecutionTime template function runs N invocations of the FMA implementations with
+      and without SVE2 intrinsics and returns their execution times. You should see timing results
+      that let you compare the two paths; specific numbers are not provided.
+  - question: Can I complete this path without a physical Arm-based Android device?
+    answer: >-
+      A 64-bit Arm-powered smartphone running Android is listed as a prerequisite. The path does
+      not list an emulator or non-Arm alternative.
+# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
index 73dac8bf57..c37df66b1e 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
@@ -30,6 +30,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:47:07Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 8f58429f12e40b53e8a2e0d7eb260f22fbb7ac869ca64554a906ddcd28c9fd75
+  summary_generated_at: '2026-06-02T02:44:35Z'
+  summary_source_hash: 8f58429f12e40b53e8a2e0d7eb260f22fbb7ac869ca64554a906ddcd28c9fd75
+  faq_generated_at: '2026-06-02T23:47:07Z'
+  faq_source_hash: 8f58429f12e40b53e8a2e0d7eb260f22fbb7ac869ca64554a906ddcd28c9fd75
+  summary: >-
+    This Learning Path shows how to integrate Dawn WebGPU into a C++-based Android Game Activity,
+    render a simple 3D object using WebGPU APIs, and profile the application with Arm Streamline.
+    You will set up a development environment on macOS, Linux, or Windows with Android Studio
+    (including the NDK), Arm Performance Studio, Blender, and Python 3.10, and use an Android
+    phone in developer mode. The steps introduce WebGPU fundamentals, create and configure the
+    Android Studio project, add Dawn and renderer sources, build and run the app, and capture
+    and analyze profiling data. By the end, you have a working WebGPU Android application and
+    a Streamline capture to review.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need Android Studio, Arm Performance Studio, Python 3.10 or later, and Blender installed
+      on your development machine. You also need an Android phone in developer mode. Basic knowledge
+      of graphics APIs and experience developing Android graphics applications are expected.
+  - question: Which Android Studio project template should I start with?
+    answer: >-
+      Create a new project using the Game Activity (C++) template. Name the project dawnwebgpu
+      and accept the default selections until the project is created in ~/AndroidStudioProjects.
+  - question: How should I set up the Android SDK and NDK for this project?
+    answer: >-
+      Install the latest Android Studio and the Android NDK. In Settings > Languages & Frameworks
+      > Android SDK, enable the Android 14.0 (UpsideDownCake) platform, then use the SDK Tools
+      tab to install the required tools including the NDK.
+  - question: After integrating Dawn, which project files do I keep or add?
+    answer: >-
+      Delete all files from the top-level cpp directory except CMakeLists.txt. Add webgpuRenderer.cpp
+      and webgpuRenderer.h, and use the provided commands to copy in a new main.cpp and the WebGPU
+      renderer files.
+  - question: When should I profile the app with Streamline and what is the expected outcome?
+    answer: >-
+      After the application builds and runs on your Android device, use Arm Performance Studio’s
+      Streamline to profile it. You will capture and analyze profiling data to understand the
+      app’s behavior as described in the steps.
+# END generated_summary_faq
+
 author:
     - Varun Chari
     - Albin Bernhardsson
diff --git a/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md b/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
index ed3a617534..f3bc862b76 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
@@ -21,6 +21,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:47:45Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: ef70ed2089132df657bd1ebfb9a66a39934d8a118b1e73974148ce11c104fef5
+  summary_generated_at: '2026-06-02T02:45:10Z'
+  summary_source_hash: ef70ed2089132df657bd1ebfb9a66a39934d8a118b1e73974148ce11c104fef5
+  faq_generated_at: '2026-06-02T23:47:45Z'
+  faq_source_hash: ef70ed2089132df657bd1ebfb9a66a39934d8a118b1e73974148ce11c104fef5
+  summary: >-
+    This introductory Learning Path shows Unreal Engine developers how to improve hardware ray
+    tracing with Lumen on Android devices powered by Arm Mali GPUs, including Immortalis series.
+    In approximately 30 minutes, you learn the basics of ray tracing and acceleration structures,
+    then apply best practices in Unreal Engine 5.3 or later to get the most from Lumen on Arm
+    devices. You will trim the acceleration structure by excluding non‑contributing and very small
+    actors, use instancing so BLAS data is shared, and minimize mesh overlap. The path uses Unreal
+    Editor Ray Tracing Debug tools such as the Instance Overlap view and r.RayTracing.Debug.PickerDomain
+    to inspect changes. Prerequisites are a machine that can run Unreal Engine 5.3+, an Android
+    device with hardware ray tracing on a Mali GPU, and a USB cable.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a computer capable of running Unreal Engine 5.3 or later, an Android device with
+      a Mali GPU that supports hardware ray tracing, and a USB cable to connect the device to
+      your computer. No other prerequisites are explicitly listed.
+  - question: Should I enable Lumen hardware ray tracing before following these optimizations?
+    answer: >-
+      If you are not familiar with Lumen and global illumination, review the guidance on enabling
+      hardware ray tracing for Lumen on Android devices before proceeding. This path focuses on
+      optimization once hardware ray tracing is available.
+  - question: How do I exclude actors that don’t help lighting from ray tracing?
+    answer: >-
+      In Unreal Editor, use the actor details panel to turn off the appropriate ray tracing visibility
+      for objects that do not contribute to lighting and for very small actors. This reduces geometry
+      in the acceleration structure and can cut noise in indirect lighting.
+  - question: How can I check and use instancing to improve efficiency?
+    answer: >-
+      Instanced actors share geometry in the BLAS, reducing memory and improving cache behavior.
+      To inspect instancing, run the command r.RayTracing.Debug.PickerDomain 1 and use the Ray
+      Tracing Debug Picker in the Unreal Editor.
+  - question: How do I identify and reduce mesh overlap in the acceleration structure?
+    answer: >-
+      Open the Instance Overlap view under Ray Tracing Debug to visualize overlap in your level.
+      Aim for tight actor bounding boxes with minimal empty space to lower traversal cost.
+# END generated_summary_faq
 
 author: Owen Wu
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md b/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md
index b1f3c47140..489e39eda8 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md
@@ -20,6 +20,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:48:15Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: deeb745d72457138cb81252c421205cd8dfb43b5e29ceee3a15c5842cc90cc33
+  summary_generated_at: '2026-06-02T02:45:40Z'
+  summary_source_hash: deeb745d72457138cb81252c421205cd8dfb43b5e29ceee3a15c5842cc90cc33
+  faq_generated_at: '2026-06-02T23:48:15Z'
+  faq_source_hash: deeb745d72457138cb81252c421205cd8dfb43b5e29ceee3a15c5842cc90cc33
+  summary: >-
+    This advanced Learning Path guides you through cross-compiling ONNX Runtime and its generate()
+    API for Android on a Windows x86_64 host, then running a Phi-3 model on an Arm-based (Cortex-A)
+    smartphone. You will set up Android Studio, the Android NDK (tested with 27.3.13750724), Python
+    3.13, CMake 4.1.0, and Ninja 1.12.1; build ONNX Runtime and the onnxruntime-genai Generate()
+    API; prepare and run a Phi-3-mini model; and view performance metrics using a command-line
+    model runner. You will also build and run a Kotlin-based Android chat demo from the onnxruntime-inference-examples
+    repository. Prerequisites include a Windows machine with at least 16GB RAM and an Android
+    phone with at least 8GB RAM (tested on Samsung Galaxy S24).
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Windows x86_64 development machine with at least 16GB of RAM and an Android phone
+      with at least 8GB of RAM. This path was tested on a Samsung Galaxy S24. The operating systems
+      used are Windows and Android.
+  - question: Which software versions should I install for the build environment?
+    answer: >-
+      Install Android Studio (latest recommended), Android NDK tested with version 27.3.13750724,
+      Python 3.13, CMake tested with version 4.1.0, and Ninja tested with version 1.12.1. These
+      versions are referenced in the steps.
+  - question: What is the build target for ONNX Runtime and the generate() API?
+    answer: >-
+      You cross-compile ONNX Runtime and the generate() API for Android CPU. The steps use the
+      Android NDK toolchain during the build.
+  - question: Where should the CMake toolchain file point when building the model runner?
+    answer: >-
+      Set -DCMAKE_TOOLCHAIN_FILE to the android.toolchain.cmake file inside your installed Android
+      NDK. The example path in the steps references NDK 27.3.13750724 under the Android SDK; update
+      it to match your local installation.
+  - question: What result should I expect when running the benchmark on the phone?
+    answer: >-
+      The benchmark prepares and runs a Phi-3-mini model on your Android device. You should be
+      able to view performance metrics produced by the model runner.
+# END generated_summary_faq
 
 author: Koki Mitsunami
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md b/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md
index b6185b3157..79a2107b99 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md
@@ -25,6 +25,57 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:49:17Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 13ff69755e51c6d7c355616f95703b3f2cefaedcf1f8dbeab31fe2e3db9aec74
+  summary_generated_at: '2026-06-02T02:46:24Z'
+  summary_source_hash: 13ff69755e51c6d7c355616f95703b3f2cefaedcf1f8dbeab31fe2e3db9aec74
+  faq_generated_at: '2026-06-02T23:49:17Z'
+  faq_source_hash: 13ff69755e51c6d7c355616f95703b3f2cefaedcf1f8dbeab31fe2e3db9aec74
+  summary: >-
+    Build a hands-free selfie Android app that runs on a recent Arm-powered Android phone using
+    MediaPipe multimodal AI, Kotlin Flows, CameraX, and an MVVM architecture. You will set up
+    Android Studio, connect a device with USB debugging, manage camera permissions, add MediaPipe
+    dependencies, and incorporate Jetpack Lifecycle components. The path shows how to combine
+    MediaPipe face landmark detection and gesture recognition, access camera features with CameraX,
+    and handle multiple asynchronous data streams with SharedFlow and StateFlow. Prerequisites
+    include Android Studio, a front-facing camera device, familiarity with Android development
+    and Modern Android Architecture, and basic Kotlin knowledge (Coroutines and Flows). Estimated
+    time to complete is about 120 minutes.
+  faqs:
+  - question: What do I need before running the app on a device?
+    answer: >-
+      Install Android Studio on your development machine and have a recent Arm-powered Android
+      phone with a front-facing camera and a USB data cable. You should be familiar with Android
+      development, Modern Android Architecture, Kotlin Coroutines, and Kotlin Flows.
+  - question: How do I know my Android Studio setup is complete before coding?
+    answer: >-
+      Open Android Studio, accept license agreements, download all required assets, and choose
+      the default or recommended settings. These steps prepare the environment used throughout
+      the Learning Path.
+  - question: How do I set up and verify device debugging over USB?
+    answer: >-
+      Enable USB debugging on your device, then connect it by USB and tap OK on the Allow USB
+      debugging dialog. Check Always allow from this computer so Android Studio can deploy and
+      debug the app without repeated prompts.
+  - question: Which option should I use to access the camera in this app?
+    answer: >-
+      Use JetPack CameraX to access camera features. Camera permissions are handled in a dedicated
+      step before running the app on your device.
+  - question: How do I add MediaPipe and handle UI state and events?
+    answer: >-
+      Add MediaPipe dependencies by updating libs.versions.toml and project settings as shown
+      in the steps that introduce MediaPipe Solutions and Tasks. Manage UI state with ViewModel
+      and Jetpack Lifecycle, and use SharedFlow and StateFlow to emit and observe UI events and
+      state across multiple subscribers.
+# END generated_summary_faq
 
 author: Han Yin
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md b/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md
index a5c28184c6..01318920d9 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md
@@ -27,6 +27,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:49:55Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 688b5b6eddc0480fa732308802d225696371c22a468bb6b140c6b74908222bbc
+  summary_generated_at: '2026-06-02T02:46:55Z'
+  summary_source_hash: 688b5b6eddc0480fa732308802d225696371c22a468bb6b140c6b74908222bbc
+  faq_generated_at: '2026-06-02T23:49:55Z'
+  faq_source_hash: 688b5b6eddc0480fa732308802d225696371c22a468bb6b140c6b74908222bbc
+  summary: >-
+    Learn how to build and deploy a simple LLM-based Android chat app using ExecuTorch with XNNPACK
+    and KleidiAI on Arm smartphones. You will set up an ExecuTorch development environment, prepare
+    the Llama 3.2 1B Instruct model, and understand how KleidiAI kernels and the i8mm feature
+    accelerate quantized LLMs, along with the role of 4-bit groupwise PTQ. The path covers building
+    the ExecuTorch runtime and JNI libraries, cross-compiling a Llama runner with the Android
+    NDK, and running benchmarks on device. Prerequisites include an Apple M1/M2 or Linux host,
+    an Arm-powered Android phone with i8mm (both with 16GB RAM), USB, adb, Java 17 JDK, and Python
+    3.10. Estimated time: about 60 minutes.
+  faqs:
+  - question: Do I need macOS or Linux for the host, and what resources are required?
+    answer: >-
+      Use an Apple M1/M2 development machine with Android Studio installed, or a Linux machine
+      with at least 16GB of RAM. Python 3.10 and Java 17 JDK are also required.
+  - question: What Android device requirements should I confirm before starting?
+    answer: >-
+      Use an Arm-powered smartphone running Android that includes the i8mm feature and has 16GB
+      of RAM. You also need a USB cable and adb (from Android SDK Platform Tools) installed on
+      your host.
+  - question: When setting up ExecuTorch, should I use a Python virtual environment or Conda?
+    answer: >-
+      Create an isolated Python environment for ExecuTorch; you can use either a Python virtual
+      environment or a Conda environment. You only need one of these options.
+  - question: How do I obtain and prepare the Llama model used in this path?
+    answer: >-
+      Request access to Llama from Meta’s Llama Downloads page, accept the Responsible Use Guide,
+      and use the provided 24-hour download link. Install the llama-stack package from pip, then
+      download the Llama 3.2 1B Instruct model following the provided steps.
+  - question: What should I set before cross-compiling the Llama runner for Android, and what
+      outputs should I expect?
+    answer: >-
+      Set ANDROID_NDK to your NDK path and ensure the CMake android.toolchain.cmake file is available.
+      The build produces the ExecuTorch runtime with KleidiAI, associated libraries (including
+      JNI libraries), and a Llama runner binary for Android.
+# END generated_summary_faq
 
 author: 
     - Varun Chari
diff --git a/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md b/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md
index 460ec1afcc..54787f0f5e 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md
@@ -27,6 +27,59 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:50:22Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 9ccf2cdd6406694d85c553204479d7ff1f688512056a3c76d4c85266cdc418b1
+  summary_generated_at: '2026-06-02T02:47:30Z'
+  summary_source_hash: 9ccf2cdd6406694d85c553204479d7ff1f688512056a3c76d4c85266cdc418b1
+  faq_generated_at: '2026-06-02T23:50:22Z'
+  faq_source_hash: 9ccf2cdd6406694d85c553204479d7ff1f688512056a3c76d4c85266cdc418b1
+  summary: >-
+    Learn to build and deploy an on-device customer support chatbot for Android using Meta’s Llama
+    3.2 and the ExecuTorch runtime with KleidiAI integrated through XNNPACK on Arm. You set up
+    a development environment on macOS or Linux, install ExecuTorch in a Python virtual environment,
+    obtain the Llama 3.2 1B Instruct model, and export it to .pte for on-device inference. You
+    then cross-compile ExecuTorch and a Llama runner with the Android NDK and CMake, enabling
+    KleidiAI kernels for Arm chips with the i8mm feature, and run the model on an Arm-powered
+    Android phone to verify inference performance. Prerequisites include a compatible host, an
+    Android device with i8mm and 16GB RAM, adb, Java 17 JDK, Python 3.10+, and a Hugging Face
+    account with Llama access.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a macOS (Apple M1/M2/M3) or Linux machine with at least 16GB RAM, and an Arm-powered
+      Android smartphone with the i8mm feature and 16GB RAM. Also prepare a USB cable, adb (Android
+      SDK Platform Tools), Java 17 JDK, Python 3.10 or later, and a Hugging Face account with
+      access to Meta Llama models.
+  - question: Should I use a Python virtual environment for ExecuTorch, and which Python version
+      is required?
+    answer: >-
+      Yes, the best practice is to create an isolated Python virtual environment before installing
+      ExecuTorch dependencies. Use Python 3.10 or later.
+  - question: How do I obtain and prepare the Llama model for ExecuTorch?
+    answer: >-
+      Request access on Meta’s Llama Downloads page, accept the Responsible Use Guide, and use
+      the time-limited download link you receive. Install the llama-stack package from pip, download
+      the model, and export it to .pte format optimized for on-device inference as described in
+      the path.
+  - question: Which Llama model variant does this path use, and can I try others?
+    answer: >-
+      This path uses the Llama 3.2 1B Instruct model. The same instructions apply to other variants
+      with minimal modification.
+  - question: How do I build and run the chatbot on Android, and how do I confirm it works?
+    answer: >-
+      Set the ANDROID_NDK path, ensure the CMake Android toolchain file is available, then use
+      CMake to cross-compile ExecuTorch and libraries with KleidiAI and build the Llama runner
+      for Android. Deploy to the phone, run the model, and follow the path’s steps to verify on-device
+      inference performance without cloud dependency.
+# END generated_summary_faq
 
 author: Parichay Das
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md b/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md
index 0a3f311466..ebece99ae9 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md
@@ -24,6 +24,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:51:15Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 95d4fb855552939d1a0e9cccfe46333692ea291ee85dd08b816321db29ceebdc
+  summary_generated_at: '2026-06-02T02:48:04Z'
+  summary_source_hash: 95d4fb855552939d1a0e9cccfe46333692ea291ee85dd08b816321db29ceebdc
+  faq_generated_at: '2026-06-02T23:51:15Z'
+  faq_source_hash: 95d4fb855552939d1a0e9cccfe46333692ea291ee85dd08b816321db29ceebdc
+  summary: >-
+    This Learning Path shows how to detect and debug memory safety bugs in Android applications
+    using Arm Memory Tagging Extension (MTE) on a Google Pixel 8. You will clone an Android MTE
+    Test app from GitHub, open it in Android Studio, explore common native memory bug patterns,
+    enable or disable MTE via the AndroidManifest, then build and debug the app on a connected
+    Pixel 8. The path targets advanced developers and takes about 20 minutes. Prerequisites include
+    a Google Pixel 8 smartphone, Android Studio on your development computer, a USB cable, and
+    Android Debug Bridge (adb) installed.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Google Pixel 8 smartphone, Android Studio on your development computer, a USB
+      cable, and adb. If adb is not installed, follow the Android Debug Bridge documentation linked
+      in the prerequisites.
+  - question: How do I get the MTE Test app project into Android Studio?
+    answer: >-
+      Clone the repository from GitHub using the provided git clone command, then launch Android
+      Studio and open the cloned project. The path guides you to view the project files as needed.
+  - question: Which file do I edit to enable or disable MTE?
+    answer: >-
+      Use the AndroidManifest.xml in the app module. Switch the project view to Project Files,
+      navigate to app -> src -> main -> res, and open AndroidManifest.xml to apply the settings
+      shown in the steps.
+  - question: How do I run and debug the app on my Pixel 8?
+    answer: >-
+      Connect the Pixel 8 via USB, ensure it appears in the device selector in Android Studio,
+      and press the Debug button to build and start debugging. On the device, you will see a startup
+      message, then the app interface appears for you to continue debugging.
+  - question: What should I check if my Pixel 8 does not appear in Android Studio?
+    answer: >-
+      Verify the USB connection and confirm that adb is installed as listed in the prerequisites.
+      Reconnect the device and reopen the project if it still does not show up.
+# END generated_summary_faq
+
 author: Roberto Lopez Mendez
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/gemma4-kleidiai-sme2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/gemma4-kleidiai-sme2/_index.md
index 0556545fb3..54c0d6378c 100755
--- a/content/learning-paths/mobile-graphics-and-gaming/gemma4-kleidiai-sme2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/gemma4-kleidiai-sme2/_index.md
@@ -22,7 +22,7 @@ prerequisites:
 generate_summary_faq: true
 
 rerun_summary: false
-rerun_faqs: false
+rerun_faqs: true
 author: Annie Tallund
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md
index ffadc5dafa..887908adcc 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md
@@ -19,6 +19,51 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:51:46Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: b194edd6ff4ffc5e39e7daa995271d2ed81ec31c8e88ddf1b23a54eb06dd1d06
+  summary_generated_at: '2026-06-02T02:48:29Z'
+  summary_source_hash: b194edd6ff4ffc5e39e7daa995271d2ed81ec31c8e88ddf1b23a54eb06dd1d06
+  faq_generated_at: '2026-06-02T23:51:46Z'
+  faq_source_hash: b194edd6ff4ffc5e39e7daa995271d2ed81ec31c8e88ddf1b23a54eb06dd1d06
+  summary: >-
+    Learn how to install and integrate Arm Accuracy Super Resolution (Arm ASR)—a mobile-optimized
+    temporal upscaling technique derived from AMD Fidelity Super Resolution 2 v2.2.2—into Android
+    game projects. You will add the ASR plugin to an Unreal Engine project (UE 5.3–5.5 recommended)
+    and complete common setup tasks, or integrate the generic ASR library into a custom engine
+    using either a quick standalone backend or a tight renderer backend. You will manage how ASR
+    upscales content by configuring quality presets, shader variants, and input resources. Prerequisites
+    are a game project that pushes smartphone performance (for example, with hardware ray tracing)
+    and a development machine with Git installed. Estimated completion time is about 40 minutes.
+  faqs:
+  - question: Which Unreal Engine versions should I use for this Learning Path?
+    answer: >-
+      Unreal Engine 5.3–5.5 is recommended. The Arm ASR plugin is available for UE 5.3, 5.4, and
+      5.5.
+  - question: What do I need before running the steps?
+    answer: >-
+      Have a game project that uses advanced rendering features that push everyday smartphones,
+      and a development machine with Git installed. The path targets Android.
+  - question: I’m not using Unreal Engine—how can I integrate Arm ASR?
+    answer: >-
+      Use the generic library. You can choose Quick Integration with the standalone backend or
+      Tight Integration using your engine’s backend/renderer.
+  - question: What configuration areas will I manage when integrating ASR?
+    answer: >-
+      You will work with quality presets, shader variants and extensions, and input resources.
+      These areas control how ASR upscales your content.
+  - question: How is Arm ASR related to AMD FSR2?
+    answer: >-
+      Arm ASR is a mobile-optimized temporal upscaling technique derived from AMD Fidelity Super
+      Resolution 2 v2.2.2, with optimizations for resource-constrained mobile gaming.
+# END generated_summary_faq
 
 author: Julie Gaskin
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md
index f5bba21888..0b7002cabd 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md
@@ -20,6 +20,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:52:25Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 23df12c0e165a4120fa25b5573437121bc7af141376758ccd051ddac14e180fb
+  summary_generated_at: '2026-06-02T02:48:56Z'
+  summary_source_hash: 23df12c0e165a4120fa25b5573437121bc7af141376758ccd051ddac14e180fb
+  faq_generated_at: '2026-06-02T23:52:25Z'
+  faq_source_hash: 23df12c0e165a4120fa25b5573437121bc7af141376758ccd051ddac14e180fb
+  summary: >-
+    This introductory path shows how to set up Unity for Android, build and deploy a simple sample
+    to a real device, and begin investigating performance with the Unity Profiler. You will install
+    the latest Unity with Android Build Support, open a provided scene that includes a small C#
+    script, switch the project to the Android platform, and run it on a recent Android phone or
+    tablet. You then launch the Profiler in the editor and on a connected device to review CPU,
+    graphics, and memory timelines and start diagnosing why a basic scene runs slowly. Prerequisites
+    are basic game‑engine knowledge and a desktop capable of running Unity; the estimated time
+    to complete is about 30 minutes.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      You need basic knowledge of game engines and programming concepts, a recent Android device
+      (phone or tablet), and a desktop computer capable of running Unity. No other explicit prerequisites
+      are listed.
+  - question: Which Unity components should I install to target Android?
+    answer: >-
+      Install the latest version of Unity and add Android Build Support. The setup step in the
+      path calls out both items before you open the sample project.
+  - question: How do I open and inspect the sample project and scene?
+    answer: >-
+      Extract the sample project from the path, open it in Unity, and double-click SampleScene
+      in the Project tab. Select the Cube object to view the attached Spin.cs script.
+  - question: How do I switch the project to Android and build for my device?
+    answer: >-
+      Open File -> Build Profile to access the window where you switch the active platform to
+      Android. Then use the Build Settings workflow described in the steps to produce and deploy
+      the Android build.
+  - question: Should I profile in the editor or on my Android device?
+    answer: >-
+      Use the Unity Profiler in the editor for quick, high-level checks, then profile on your
+      Android device to reflect end-user characteristics. Expect a per-frame timeline with CPU,
+      graphics, and memory data when profiling is active.
+# END generated_summary_faq
 
 author: Joshua Marshall-Law
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md
index e1f50235e2..80777cc993 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md
@@ -18,6 +18,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:52:55Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 44e7b5fe3fda52267dc1957319439895293276602b1f533de5665adaf6f7cafe
+  summary_generated_at: '2026-06-02T02:49:25Z'
+  summary_source_hash: 44e7b5fe3fda52267dc1957319439895293276602b1f533de5665adaf6f7cafe
+  faq_generated_at: '2026-06-02T23:52:55Z'
+  faq_source_hash: 44e7b5fe3fda52267dc1957319439895293276602b1f533de5665adaf6f7cafe
+  summary: >-
+    Learn how to profile Android games built with Godot using Arm Performance Studio. You install
+    the Arm Performance Studio Integration extension from the Godot Asset Library, then add annotations
+    in GDScript with the PerformanceStudio class, including single markers, regions, and threaded
+    channels. You visualize these annotations in Streamline and Performance Advisor to correlate
+    game events with CPU and GPU activity on Arm-based Android devices, including Mali GPUs. The
+    path is introductory, takes about 15 minutes, and targets Godot 4.3 or later on Windows, macOS,
+    or Linux. Prerequisites include familiarity with Godot and Arm Performance Studio tools. By
+    the end, you will have a project instrumented for profiling and ready to analyze with Arm’s
+    tools.
+  faqs:
+  - question: Which Godot versions support the Arm Performance Studio extension?
+    answer: >-
+      The extension is compatible with Godot 4.3 and later.
+  - question: How do I install the Arm Performance Studio Integration in my Godot project?
+    answer: >-
+      Open your project, select AssetLib, search for "Arm Performance Studio Integration," then
+      double-click the result and choose Download. When prompted, you can change the install folder
+      before completing the installation.
+  - question: How do I add a basic marker and where will I see it?
+    answer: >-
+      Create an instance of the PerformanceStudio class in your script and call marker("Label").
+      These markers appear on the Streamline timeline to help correlate game behavior with performance
+      data.
+  - question: How do I define a performance region and how is it reported?
+    answer: >-
+      Emit a pair of markers with labels prefixed by "Region Start <Name>" and "Region End <Name>".
+      Regions appear on the frame rate analysis chart in the Performance Advisor report, with
+      dedicated charts for each region at the end of the report.
+  - question: When should I use channels, and what do they capture?
+    answer: >-
+      Use channels for threaded, duration-based annotations tied to a specific software thread.
+      Define a PerformanceStudio_Channel, then add annotations with labels (and optional color)
+      to trace tasks like asset loading or enemy spawning.
+# END generated_summary_faq
 
 author: Albin Bernhardsson, Julie Gaskin
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md b/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md
index 57971350cb..24726aeb83 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md
@@ -19,6 +19,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:53:37Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 3f35558e0399cb4c851b120eaa2217a63c48336cbba96d23500d1b751e4aec63
+  summary_generated_at: '2026-06-02T02:49:43Z'
+  summary_source_hash: 3f35558e0399cb4c851b120eaa2217a63c48336cbba96d23500d1b751e4aec63
+  faq_generated_at: '2026-06-02T23:53:37Z'
+  faq_source_hash: 3f35558e0399cb4c851b120eaa2217a63c48336cbba96d23500d1b751e4aec63
+  summary: >-
+    This introductory Learning Path guides Unreal Engine developers through enabling hardware
+    ray tracing for Lumen on Android devices with Arm Mali GPUs, including those based on Immortalis-G715
+    or G720. You will first review Lumen and global illumination, then configure an Unreal Engine
+    5.3+ project to use Lumen for Global Illumination and Reflections. The steps cover Android-specific
+    requirements for Lumen’s hardware ray tracing, including enabling the SM5 shader format (via
+    Support Vulkan Desktop [Experimental]) and selecting deferred shading mode, with an option
+    to enable Lumen via a Post Process Volume. Prerequisites include a computer capable of running
+    Unreal Engine, an Android device with a Mali GPU that supports hardware ray tracing, and a
+    USB cable.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a computer capable of running Unreal Engine 5.3 or later, an Android mobile device
+      with a Mali GPU that supports hardware ray tracing, and a USB cable to connect the device
+      to your computer.
+  - question: Where do I enable Lumen for Global Illumination and Reflections?
+    answer: >-
+      Open Project Settings, go to Engine - Rendering, and select Lumen in the Global Illumination
+      section and also select Lumen in the Reflections section.
+  - question: Can I enable Lumen per scene instead of project-wide?
+    answer: >-
+      Yes. Add a Post Process Volume actor to your scene and select Lumen in the Global Illumination
+      section of the volume’s details panel.
+  - question: How do I enable the SM5 shader format for Android?
+    answer: >-
+      In Project Settings under Platforms - Android, enable Support Vulkan Desktop (Experimental)
+      to activate SM5 shader format support.
+  - question: Which shading path should I choose when using Lumen?
+    answer: >-
+      Use deferred shading. Lumen exclusively supports deferred shading mode, which you configure
+      under Engine - Rendering in Project Settings.
+# END generated_summary_faq
 
 author: Owen Wu
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md b/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md
index 9600275b41..23ca58a0d4 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md
@@ -16,6 +16,50 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:54:18Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: ab171b1ff6cfed7bce1cb8e50d4d9aeb4bee1972e8a409203cb438f94086a320
+  summary_generated_at: '2026-06-02T02:50:02Z'
+  summary_source_hash: ab171b1ff6cfed7bce1cb8e50d4d9aeb4bee1972e8a409203cb438f94086a320
+  faq_generated_at: '2026-06-02T23:54:18Z'
+  faq_source_hash: ab171b1ff6cfed7bce1cb8e50d4d9aeb4bee1972e8a409203cb438f94086a320
+  summary: >-
+    This introductory Learning Path helps developers new to Arm identify Android smartphones suitable
+    for software development and performance analysis. You will learn how to read device specifications
+    to confirm an Arm-based CPU (such as Cortex-A) and to check for Arm Mali or Arm Immortalis
+    GPUs. The path highlights that different devices offer varying levels of performance analysis
+    depending on the CPU and GPU, and introduces tools such as Arm Performance Studio for Mobile
+    for analyzing mobile application performance. No explicit prerequisites are listed. By the
+    end, you will be better equipped to select mobile hardware aligned with your development and
+    analysis needs.
+  faqs:
+  - question: How do I know if a smartphone I’m considering uses Arm hardware?
+    answer: >-
+      Almost all modern smartphones have Arm-based CPUs. Check the device specifications for an
+      Arm Mali or Arm Immortalis GPU to confirm the graphics processor.
+  - question: Which devices should I consider if I plan to analyze performance?
+    answer: >-
+      Different mobile devices offer differing levels of performance analysis depending on the
+      CPU and GPU used. Availability and depth of performance counters can vary by device.
+  - question: Do I need to install any tools or set up accounts before starting this path?
+    answer: >-
+      No explicit prerequisites are listed. This path focuses on identifying suitable mobile hardware
+      rather than installing tools.
+  - question: How does Arm Performance Studio for Mobile fit into this path?
+    answer: >-
+      It is mentioned as a tool that can help optimize mobile application performance once you
+      have hardware. This path does not cover installing or using the tool.
+  - question: What platform does this target, and how long will it take?
+    answer: >-
+      The path targets Android devices and takes about 5 minutes to complete.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md b/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md
index b09c72cfa3..ef4624d68b 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md
@@ -21,6 +21,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:54:55Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 5a94138f74e7b2266c35dbd555f9966ca0ff29fdbd1842d4724e0da0cd4e46db
+  summary_generated_at: '2026-06-02T02:50:20Z'
+  summary_source_hash: 5a94138f74e7b2266c35dbd555f9966ca0ff29fdbd1842d4724e0da0cd4e46db
+  faq_generated_at: '2026-06-02T23:54:55Z'
+  faq_source_hash: 5a94138f74e7b2266c35dbd555f9966ca0ff29fdbd1842d4724e0da0cd4e46db
+  summary: >-
+    This advanced Learning Path explains how KleidiAI implements matrix multiplication microkernels
+    for quantized inference on Arm CPUs using SME2 INT8 MOPA instructions. You will decode a specific
+    SME2 matmul microkernel, understand its tiling and packing parameters (mr, nr, bl, kr), and
+    trace how quantized GGML Q4_0 weights from llama.cpp are repacked and consumed. Using a simplified
+    example with FP32 activations [16,64] and Q4_0 weights [64,64], you will connect normal matmul
+    semantics to the SME2 inner loop and see where MOPA instructions appear. Optional hands-on
+    steps include source inspection and disassembly on Linux or Android systems with SME2 support.
+    Prerequisites are basic GEMM/matmul and quantization knowledge; no other explicit prerequisites
+    are listed.
+  faqs:
+  - question: Do I need a device with SME2 support to follow this Learning Path?
+    answer: >-
+      No. SME2-capable hardware is optional and only required for the hands-on verification steps
+      such as disassembly. The core explanations and examples can be followed without hardware.
+  - question: How do I verify that SME2 INT8 MOPA instructions are used in the microkernel?
+    answer: >-
+      The path shows where these instructions appear in the inner loop and suggests basic checks
+      via source inspection. If you have SME2 hardware, you can optionally confirm via disassembly.
+  - question: Which llama.cpp operations route through the SME2 matmul microkernel in this context?
+    answer: >-
+      The heavy matmul work in attention (K/Q/V projections) and feed-forward network (FFN) layers
+      can run through the SME2 matmul microkernel. In these cases, the LHS activations are FP32
+      and the RHS weights use GGML Q4_0.
+  - question: Which tiling and packing parameters should I pay attention to?
+    answer: >-
+      Focus on mr, nr, bl, and kr, which define the output tile shape and inner-loop step sizes.
+      The microkernel computes C in tiles and expects inputs to be quantized and packed accordingly.
+  - question: What SVL and matrix sizes does the example assume, and how do I interpret 1vlx4vl?
+    answer: >-
+      The example assumes an SME2 streaming vector length (SVL) of 512 bits and a simplified matmul
+      of LHS [16, 64] by RHS [64, 64]. The 1vlx4vl suffix means each inner-loop iteration computes
+      a 1VL × 4VL submatrix of the output, with exact element counts depending on the hardware
+      SVL.
+# END generated_summary_faq
 
 author: Zenon Zhilong Xiu
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md b/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md
index 40a5d66b1d..32d45e56ec 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md
@@ -20,6 +20,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:55:26Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 0e51db9ceb25c31c42608f3c6744a4fa8f9d995805ed44a7d7fc83324889ea12
+  summary_generated_at: '2026-06-02T02:50:53Z'
+  summary_source_hash: 0e51db9ceb25c31c42608f3c6744a4fa8f9d995805ed44a7d7fc83324889ea12
+  faq_generated_at: '2026-06-02T23:55:26Z'
+  faq_source_hash: 0e51db9ceb25c31c42608f3c6744a4fa8f9d995805ed44a7d7fc83324889ea12
+  summary: >-
+    Learn to cross-compile and run LLM inference on Android using Google AI Edge’s MediaPipe with
+    XNNPACK and KleidiAI-enhanced Arm i8mm. Starting from an x86_64 Ubuntu host (or a provided
+    Docker setup), you install Android SDK/NDK and Bazel prerequisites, build a CPU inference
+    engine, and run the Gemma 2B model on an Android device that supports i8mm (tested on Google
+    Pixel 8 Pro). You then benchmark inference with the i8mm build flag enabled and disabled to
+    compare performance. The path targets advanced Android developers and uses tools including
+    MediaPipe, XNNPACK, Bazel, Android SDK/NDK, and Hugging Face. Expected outcomes are a working
+    inference binary and benchmark results on your device.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an x86_64 Linux machine running Ubuntu with about 500 MB free space or a Docker
+      daemon to build and run the provided x86_64 Dockerfile, plus an Android phone that supports
+      i8mm (tested on Google Pixel 8 Pro).
+  - question: Should I install dependencies with Docker or directly on Ubuntu?
+    answer: >-
+      The path provides two options: build a Docker container with the dependencies or install
+      them directly on an x86_64 Ubuntu machine. Choose Docker if you prefer a contained setup;
+      use native installation if you already work on Ubuntu.
+  - question: Which Bazel options target Android Arm64 and enable i8mm?
+    answer: >-
+      Use --config=android_arm64 to target Android Arm64 and --define=xnn_enable_arm_i8mm=true
+      to enable i8mm. These flags are applied when building the inference tool.
+  - question: How do I confirm the inference engine built correctly?
+    answer: >-
+      After the build completes, check for the binary in bazel-bin/mediapipe/tasks/cc/genai/inference/c/.
+      The executable is named llm_inference_engine_cpu_main.
+  - question: What result should I expect when running inference and benchmarking?
+    answer: >-
+      The inference tool runs an LLM on the Android device and produces output from an initial
+      prompt. For benchmarking, you will cross-compile with and without the i8mm build flag to
+      demonstrate performance differences using KleidiAI through XNNPACK.
+# END generated_summary_faq
 
 author: 
     - Pareena Verma
diff --git a/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md b/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md
index 6e7f60d071..f3f52aaf3d 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md
@@ -19,6 +19,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:55:55Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 68cdc7315795b6ffa794c0a1fd4cf325ab39ebb65e23efd27b7760ece76a2ec9
+  summary_generated_at: '2026-06-02T02:51:15Z'
+  summary_source_hash: 68cdc7315795b6ffa794c0a1fd4cf325ab39ebb65e23efd27b7760ece76a2ec9
+  faq_generated_at: '2026-06-02T23:55:55Z'
+  faq_source_hash: 68cdc7315795b6ffa794c0a1fd4cf325ab39ebb65e23efd27b7760ece76a2ec9
+  summary: >-
+    Learn to build the libGPUInfo C++ library with the Android NDK and run an example application
+    on an Android device to query configuration details of Arm Mali or Arm Immortalis GPUs. Working
+    from a Debian or Ubuntu x86_64 host, you will install the Android NDK, use adb to deploy and
+    run the sample, and read GPU feature and performance-level information reported by the device.
+    The outcome is the ability to retrieve device-specific GPU configuration at runtime to inform
+    application settings. Prerequisites are a development machine running Ubuntu or Debian Linux
+    and an Android device with an Arm GPU; no additional prerequisites are explicitly listed.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a development machine running Ubuntu or Debian Linux with x86_64 architecture and
+      an Android device with an Arm GPU. The steps use the Android NDK and adb.
+  - question: Which Android GPUs and devices does this target?
+    answer: >-
+      The example targets Arm Mali and Arm Immortalis GPUs on Android. Use an Android device that
+      includes an Arm GPU.
+  - question: Does this Learning Path include installing the Android NDK and using adb?
+    answer: >-
+      Yes. You download and install the Android NDK and use adb as part of building libGPUInfo
+      and running the example on a connected device.
+  - question: What result should I expect from the example application?
+    answer: >-
+      The example queries the device to read Arm GPU hardware configuration information. The results
+      identify available features and performance levels.
+  - question: How would I use libGPUInfo in my own application?
+    answer: >-
+      libGPUInfo is a C++ library that can be integrated into applications to gather Arm GPU configuration
+      details at runtime. You can use this information to guide runtime settings and application
+      complexity choices.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ##### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md b/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md
index 0a12f13127..eac77a18d8 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md
@@ -21,6 +21,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:56:24Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: a744c8118a5a3cb97eee7bee271f768bdd71b4286e723c38a7b4ff6cd9e08d18
+  summary_generated_at: '2026-06-02T02:51:34Z'
+  summary_source_hash: a744c8118a5a3cb97eee7bee271f768bdd71b4286e723c38a7b4ff6cd9e08d18
+  faq_generated_at: '2026-06-02T23:56:24Z'
+  faq_source_hash: a744c8118a5a3cb97eee7bee271f768bdd71b4286e723c38a7b4ff6cd9e08d18
+  summary: >-
+    This advanced Learning Path shows how to accelerate LiteRT (Lite Runtime) model inference
+    on Android by enabling KleidiAI with Scalable Matrix Extension 2 (SME2) via XNNPACK, then
+    validating the results with the benchmark_model tool. You will examine how LiteRT, XNNPACK,
+    and KleidiAI fit together; create LiteRT models that match the subset of operators currently
+    accelerated by SME2; build two benchmark_model binaries (one with KleidiAI+SME2 and one baseline
+    using Neon micro-kernels); and run benchmarks on an SME2-capable Android device. Prerequisites
+    are an Arm64 Linux development machine and an Android device with SME2 support (a device list
+    is linked in the path). By the end, you can compare benchmark outputs to evaluate SME2 acceleration
+    for your models.
+  faqs:
+  - question: What do I need before building and benchmarking?
+    answer: >-
+      You need an Arm64 Linux development machine and an Android device that supports Arm SME2.
+      You also need a LiteRT model (for example, fc_fp32.tflite) and two benchmark_model binaries
+      built with and without KleidiAI and SME2.
+  - question: How do I check if my Android device supports SME2?
+    answer: >-
+      On the device, use an ADB shell and run cat /proc/cpuinfo. Look for a feature entry indicating
+      SME2 support, and you can also refer to the linked list of SME2-capable devices in the prerequisites.
+  - question: Which parts of my LiteRT model are accelerated through KleidiAI SME2?
+    answer: >-
+      Only a subset of KleidiAI SME2 micro-kernels has been integrated into XNNPACK. Supported
+      operator data types and quantization configurations are listed in the path; other operators
+      use XNNPACK’s default implementation.
+  - question: Why build two versions of the benchmark_model tool?
+    answer: >-
+      You build one version with KleidiAI and SME2 enabled and another without SME2 to establish
+      a Neon-based baseline. Running both lets you evaluate and validate the acceleration provided
+      by SME2-enabled micro-kernels.
+  - question: What should I check if my benchmark does not reflect SME2 acceleration?
+    answer: >-
+      Confirm the device reports SME2 support and that your model uses operators and data types
+      covered by the integrated SME2 micro-kernels. If not supported, LiteRT will use XNNPACK’s
+      default implementation during inference.
+# END generated_summary_faq
 
 author: Jiaming Guo
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md b/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md
index 5332acc412..4fa8c7f11b 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md
@@ -21,6 +21,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:56:51Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: b55d902389806f5e84e674e5ba1f1f2d9e38af370c289ad344eff2dad3475df1
+  summary_generated_at: '2026-06-02T02:51:58Z'
+  summary_source_hash: b55d902389806f5e84e674e5ba1f1f2d9e38af370c289ad344eff2dad3475df1
+  faq_generated_at: '2026-06-02T23:56:51Z'
+  faq_source_hash: b55d902389806f5e84e674e5ba1f1f2d9e38af370c289ad344eff2dad3475df1
+  summary: >-
+    This Learning Path shows how to benchmark KleidiAI micro-kernels in ExecuTorch on Arm64 platforms
+    that support SME or SME2. You will set up an isolated Python environment on an x86_64 Ubuntu
+    host, cross-compile ExecuTorch for AArch64 with XNNPACK and KleidiAI (including SME/SME2),
+    and build and export quantized benchmark models for Fully Connected and Conv2d operators.
+    On an Arm64 target with SME/SME2, you will run workloads with executor_runner, measure throughput
+    and latency, and collect ETDump traces. You will then use the ExecuTorch Inspector API to
+    inspect ETRecord and ETDump files to understand kernel-level behavior across GEMM variants.
+    Prerequisites are explicitly listed in the path.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an x86_64 Linux host running Ubuntu with at least 15 GB of free disk space and
+      an Arm64 target device that supports SME or SME2. The path targets Linux environments.
+  - question: Should I use a Python virtual environment, and how long should it stay active?
+    answer: >-
+      Yes. Create and activate a Python virtual environment before building ExecuTorch, and keep
+      it activated while completing the steps so dependencies install and run in the correct location.
+  - question: Which toolchain should I install to cross-compile ExecuTorch for AArch64?
+    answer: >-
+      Install the GNU Arm cross-compilation toolchain on your x86_64 host along with Ninja as
+      the CMake build backend. The path cross-compiles ExecuTorch with XNNPACK and KleidiAI enabled
+      for the Arm64 target.
+  - question: How do I know if KleidiAI micro-kernels are being used for my operators?
+    answer: >-
+      ExecuTorch automatically dispatches to KleidiAI within XNNPACK when an operator’s data types
+      and quantization match supported configurations. You can confirm by collecting ETDump data
+      and inspecting it with the ExecuTorch Inspector API.
+  - question: What results should I expect after running executor_runner?
+    answer: >-
+      executor_runner runs kernel workloads and collects ETDump profiling data. Use the ExecuTorch
+      Inspector API to examine ETRecord and ETDump files and review kernel-level behavior, along
+      with throughput and latency measurements produced during the runs.
+# END generated_summary_faq
+
 author: Qixiang Xu
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md b/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md
index 9e7d8312e9..78ec20af83 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md
@@ -22,6 +22,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:57:36Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: e145caae5b20f7165251d88e334ba22d90c8b35270902778a2b6fe0ed0b250f6
+  summary_generated_at: '2026-06-02T02:52:24Z'
+  summary_source_hash: e145caae5b20f7165251d88e334ba22d90c8b35270902778a2b6fe0ed0b250f6
+  faq_generated_at: '2026-06-02T23:57:36Z'
+  faq_source_hash: e145caae5b20f7165251d88e334ba22d90c8b35270902778a2b6fe0ed0b250f6
+  summary: >-
+    This advanced Learning Path shows how to fine-tune and evaluate a Neural Super Sampling (NSS)
+    upscaler using PyTorch with Arm’s Model Gym API and CLI on Ubuntu 22.04. You will set up a
+    Python 3.10+ environment, install required system packages, clone open-source example notebooks,
+    and launch a Jupyter Notebook to configure training and evaluation with hardware-aware optimization.
+    The workflow exports models in .vgf format, which you then inspect using Model Explorer with
+    the VGF adapter to review architecture, tensor shapes, and graph connectivity. The final section
+    demonstrates how to register and train your own model via the Python API by subclassing BaseNGModel.
+    Prerequisites include basic PyTorch knowledge, a CUDA-capable NVIDIA GPU, and CUDA Toolkit
+    11.8 or later.
+  faqs:
+  - question: What do I need before running the notebooks?
+    answer: >-
+      You need basic familiarity with PyTorch and machine learning, a development machine running
+      Ubuntu 22.04 with a CUDA-capable NVIDIA GPU, and CUDA Toolkit 11.8 or later.
+  - question: How do I set up Python and system dependencies on Ubuntu?
+    answer: >-
+      Verify Python 3.10+ with: python3 --version. Then install dependencies with: sudo apt update
+      followed by sudo apt install python3-venv python-is-python3 gcc make python3-dev -y.
+  - question: How do I get the example notebooks used in this Learning Path?
+    answer: >-
+      Clone the open-source examples repository from GitHub using git clone. The exact repository
+      URL is provided in the setup step of the Learning Path.
+  - question: What result should I expect after training the NSS model?
+    answer: >-
+      You will produce a fine-tuned NSS model and export it as a .vgf file using the Model Gym
+      toolchain. The .vgf can be opened in Model Explorer for inspection with the VGF adapter.
+  - question: Can I integrate my own model into Model Gym?
+    answer: >-
+      Yes. Create a Python class that inherits from BaseNGModel, register it with the toolkit,
+      and use the same Python API to run training, evaluation, and export as demonstrated for
+      NSS.
+# END generated_summary_faq
 
 author: Annie Tallund
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md b/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md
index b0deb2608c..59b5085353 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md
@@ -17,6 +17,49 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:58:26Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: a25cb73b70716e397d9477f8ab9729f4a094143d276e4de68cf8c33b273bb1ff
+  summary_generated_at: '2026-06-02T02:52:58Z'
+  summary_source_hash: a25cb73b70716e397d9477f8ab9729f4a094143d276e4de68cf8c33b273bb1ff
+  faq_generated_at: '2026-06-02T23:58:26Z'
+  faq_source_hash: a25cb73b70716e397d9477f8ab9729f4a094143d276e4de68cf8c33b273bb1ff
+  summary: >-
+    Learn how to build and run a small C program on AArch64 Linux to explore the Arm Memory Tagging
+    Extension (MTE). MTE, available in Armv8.5-A and Armv9-A processors, helps detect memory safety
+    issues such as buffer overflows and use-after-free. In about 20 minutes, you will follow practical
+    steps to compile and execute an example that illustrates how MTE works on a recent AArch64
+    system. The only explicit prerequisite is an AArch64 Linux development machine; cloud instances
+    from Arm cloud service providers are suitable. QEMU is listed among the tools. After completing
+    the path, you will have an introductory understanding of MTE based on a working example.
+  faqs:
+  - question: What do I need before running the example C program?
+    answer: >-
+      You need an AArch64 Linux development machine. No other explicit prerequisites are listed.
+  - question: Can I use a cloud-based AArch64 instance for this path?
+    answer: >-
+      Yes. Cloud instances can be used, and the path references a list of Arm cloud service providers.
+  - question: Is QEMU required for this Learning Path?
+    answer: >-
+      QEMU is listed among the tools. The core activity is to build and run a small C program
+      on AArch64 Linux; follow the steps to see if QEMU is used in your setup.
+  - question: How do I know if my environment supports MTE?
+    answer: >-
+      MTE is a feature of Armv8.5-A and Armv9-A processors, and the path suggests using a recent
+      AArch64 Linux machine. The steps do not provide a specific detection command.
+  - question: What result should I expect when I build and run the example?
+    answer: >-
+      The example demonstrates how MTE detects memory safety issues like buffer overflows and
+      use-after-free. Expect behavior that shows MTE in action; the exact output is determined
+      by the provided example.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md b/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md
index 4f0ed88d03..083c132cd7 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md
@@ -23,6 +23,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:59:06Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: b6a9aa558ec5062c829d61b28fb92e25d5517249c011eb29f03cc36775b6f9af
+  summary_generated_at: '2026-06-02T02:53:24Z'
+  summary_source_hash: b6a9aa558ec5062c829d61b28fb92e25d5517249c011eb29f03cc36775b6f9af
+  faq_generated_at: '2026-06-02T23:59:06Z'
+  faq_source_hash: b6a9aa558ec5062c829d61b28fb92e25d5517249c011eb29f03cc36775b6f9af
+  summary: >-
+    This Learning Path shows how to enable Arm’s Memory Tagging Extension (MTE) on a Google Pixel
+    8, trigger memory-bug crashes using a test app, and examine the resulting Android bug report.
+    You will enable Developer options, turn on MTE on the device, install MTE_test.apk, and use
+    the app to execute code with common memory bugs so that MTE forces a crash. You then capture
+    a Bug report from the Developer options menu, unzip the generated archive on your desktop,
+    and review diagnostics—especially tombstone files—to understand where and why the crash occurred.
+    Prerequisites are a Pixel 8, a USB cable, and adb from Android SDK Platform Tools. Estimated
+    time: about 10 minutes.
+  faqs:
+  - question: What do I need before enabling MTE on my Pixel 8?
+    answer: >-
+      You need a Google Pixel 8 smartphone, a USB cable, and Android Debug Bridge (adb) installed
+      from the Android SDK Platform Tools. These are the only prerequisites explicitly listed.
+  - question: How do I turn on Developer options to access MTE settings?
+    answer: >-
+      Go to Settings -> About phone -> Build number and tap the Build number seven times until
+      you see “You are now a developer!”. Then return to System and open Developer options.
+  - question: How do I confirm that MTE is active after I enable it?
+    answer: >-
+      Install and run MTE_test.apk and press any button in the app to execute code with a memory
+      bug. If MTE is enabled, the app will crash and the bug report will include MTE-specific
+      information about the violation.
+  - question: How do I capture a bug report after the test app crashes?
+    answer: >-
+      Open Developer options and select the Bug report option, then tap Report to start generation.
+      You can watch the progress on the device; the result is a zip file that you can move to
+      your desktop and unzip.
+  - question: Which files should I inspect in the bug report, and why might the filename include
+      'husky'?
+    answer: >-
+      After unzipping, review the main bugreport text file and the tombstone file under FS/data/tombstones
+      for detailed crash information. “Husky” is the code name for Google Pixel 8 Pro and may
+      appear in the generated bug report filename.
+# END generated_summary_faq
 
 author: Roberto Lopez Mendez
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md b/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md
index 9ddf1dfd7d..434fb8111f 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md
@@ -23,6 +23,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-02T23:59:40Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 5ca059af36f0ba375701e76ce82b7803f01ae6beab313336b333bfbdebaa3b1a
+  summary_generated_at: '2026-06-02T02:53:51Z'
+  summary_source_hash: 5ca059af36f0ba375701e76ce82b7803f01ae6beab313336b333bfbdebaa3b1a
+  faq_generated_at: '2026-06-02T23:59:40Z'
+  faq_source_hash: 5ca059af36f0ba375701e76ce82b7803f01ae6beab313336b333bfbdebaa3b1a
+  summary: >-
+    Learn how to capture high-quality frame datasets from Unreal Engine 5.5 gameplay using the
+    Neural Graphics Data Capture plugin on Windows. You will install and enable the plugin in
+    a C++ Unreal project, wire up a Level Blueprint to start (C) and stop (V) capture, run in
+    Standalone Game mode to record frames at the correct dimensions, and verify the exported dataset.
+    The path also introduces key capture settings, including UpscalingRatio, SupersamplingRatio,
+    FixedFrameRate, camera cut thresholds, and output path controls (DatasetDir, CaptureName).
+    Prerequisites include Windows 11, Unreal Engine 5.5, Visual Studio with C++ game development
+    tools, and a C++ project. Suitable for developers preparing data for Neural Super Sampling
+    and related temporal upscalers.
+  faqs:
+  - question: What do I need before running the capture workflow?
+    answer: >-
+      You need Windows 11, Unreal Engine 5.5 installed, Visual Studio with C++ game development
+      tools, and a C++ Unreal project. A template project like Third Person is suitable.
+  - question: How do I install and enable the Neural Graphics Data Capture plugin in my project?
+    answer: >-
+      Clone the plugin’s GitHub repository, then copy the NeuralGraphicsDataCapture folder into
+      your project’s Plugins directory. Open the project so the module compiles via Visual Studio,
+      then enable the plugin in Unreal.
+  - question: How do I set up hotkeys to start and stop capture?
+    answer: >-
+      Open your Level Blueprint and paste the prepared snippet provided by this Learning Path
+      (downloaded on Windows via PowerShell wget). The snippet binds C to start capture and V
+      to stop capture.
+  - question: Where can I configure capture parameters and output locations?
+    answer: >-
+      Adjust settings in NGDCRenderingSettings and NGDCExportSettings. You can set UpscalingRatio,
+      SupersamplingRatio, FixedFrameRate, camera cut thresholds, and control DatasetDir and CaptureName
+      for output organization.
+  - question: What should I check if my captured frame dimensions look wrong?
+    answer: >-
+      Use Standalone Game from the Play mode menu instead of New Editor Window (PIE). PIE can
+      produce frame dimensions that differ from expected output sizes.
+# END generated_summary_faq
 
 author: 
 - Annie Tallund
diff --git a/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md b/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md
index 97578a5f82..2bfbc38afb 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md
@@ -25,6 +25,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:00:50Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 7ffbac59b340f3ef5119d2f5ba4a786fd15d521bb294f3a4213b083c9b4ce23d
+  summary_generated_at: '2026-06-02T02:54:18Z'
+  summary_source_hash: 7ffbac59b340f3ef5119d2f5ba4a786fd15d521bb294f3a4213b083c9b4ce23d
+  faq_generated_at: '2026-06-03T00:00:50Z'
+  faq_source_hash: 7ffbac59b340f3ef5119d2f5ba4a786fd15d521bb294f3a4213b083c9b4ce23d
+  summary: >-
+    This Learning Path shows how to configure ML Extensions for Vulkan emulation and enable Arm
+    Neural Super Sampling (NSS) in Unreal Engine on Windows 11. You will install the Vulkan SDK
+    and activate the ML Emulation Layer with Vulkan Configurator, download the NSS Unreal plugin
+    (including its VGF model), create a C++ Third Person template project, and run the level to
+    visualize real-time upscaling. The steps explain how Arm enables neural graphics in Unreal
+    and how to verify NSS using console commands and plugin settings, with optional frame capture
+    in RenderDoc for analysis. Prerequisites include Windows 11, Unreal Engine 4.27 or 5.4 or
+    5.6, and Visual Studio with C++ and .NET desktop build tools. Estimated time: about 30 minutes.
+  faqs:
+  - question: Which Unreal Engine versions should I use for this path?
+    answer: >-
+      The prerequisites list Unreal Engine 4.27 or 5.4 or 5.6. The Unreal Engine 5.5 plugin is
+      deprecated; refer to the plugin repository documentation for details.
+  - question: Do I need the Vulkan SDK, and how are the ML emulation layers enabled?
+    answer: >-
+      Yes. The Vulkan SDK is required to use the Vulkan Configurator, which sets up the emulation
+      layers used to run ML extensions for Vulkan workloads. The Vulkan layer configuration activates
+      the ML Emulation Layer so it runs with the Unreal Engine plugin.
+  - question: Where do I get the NSS plugin and what does it include?
+    answer: >-
+      Download the latest release .zip from the Neural Super Sampling Unreal Engine Plugin GitHub
+      repository. The release package contains the plugin and the VGF model file; extract it on
+      your Windows machine.
+  - question: How do I verify that NSS is active and view its output in Unreal?
+    answer: >-
+      Press Play, then run ShowFlag.VisualizeTemporalUpscaler 1 to see NSS listed in the rendering
+      summary (use 0 to hide it). To visualize the model’s output in real time, run r.NSS.Debug
+      1. You can also view and configure the active neural network model under Project Settings
+      > Plugins > Neural Super Sampling.
+  - question: When should I use RenderDoc during this workflow?
+    answer: >-
+      Use RenderDoc to capture and inspect frames when you see unexpected visual output or need
+      to analyze the rendering sequence. It lets you step through Vulkan API calls, examine shader
+      inputs/outputs, and review resource state, with additional features available for Arm GPUs.
+# END generated_summary_faq
 
 author: Annie Tallund
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md b/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md
index 745d8c71c8..9a5c8bd089 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md
@@ -24,6 +24,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:01:17Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: aba1cea365c0c61e84fb545ada15a64ed52c099f1252dc6312f88ee82385d2d0
+  summary_generated_at: '2026-06-02T02:54:49Z'
+  summary_source_hash: aba1cea365c0c61e84fb545ada15a64ed52c099f1252dc6312f88ee82385d2d0
+  faq_generated_at: '2026-06-03T00:01:17Z'
+  faq_source_hash: aba1cea365c0c61e84fb545ada15a64ed52c099f1252dc6312f88ee82385d2d0
+  summary: >-
+    This advanced Learning Path shows how to build, optimize, and deploy ONNX models for Arm64
+    platforms using ONNX Runtime. You will create a small digit-recognition CNN in Python, export
+    it to ONNX, validate numerical parity with ONNX Runtime, and apply model optimization techniques
+    such as layer fusion. The steps target Arm64 devices including Raspberry Pi, Arm-based servers,
+    Windows on Arm, and Android, with Apple Silicon suitable for development. You will culminate
+    in deploying an optimized model inside an Android application, using the CPU execution provider
+    on edge devices and NNAPI on Android. Prerequisites include Python 3.10 or 3.11, basic PyTorch
+    or TensorFlow familiarity, an Arm64 device, and Android Studio for the final deployment stage.
+  faqs:
+  - question: Which Python version should I install for this Learning Path?
+    answer: >-
+      Use Python 3.10 or 3.11. Prebuilt ONNX Runtime packages for Arm platforms don't yet support
+      Python 3.12.
+  - question: Which Arm64 hardware can I use, and can I develop on macOS or Windows on Arm?
+    answer: >-
+      You can use Raspberry Pi 4/5 (64-bit OS), Jetson (Arm64 CPU; GPU via CUDA if using NVIDIA
+      stack), or Arm-based servers. Apple Silicon (macOS/Arm64) and Windows on Arm are suitable
+      for development, with deployment to Arm64 Linux later.
+  - question: How do I know ONNX Runtime is using the expected execution provider on my device?
+    answer: >-
+      In the setup step you verify that ONNX Runtime detects and uses the available execution
+      providers. On edge Arm64 devices the CPU execution provider is used; on Android the NNAPI
+      execution provider is targeted.
+  - question: Do I need to prepare a dataset before training the digit recognizer?
+    answer: >-
+      No manual collection is required. The path guides you to generate a custom Sudoku digit
+      dataset, starting from a Hugging Face parquet dataset.
+  - question: What artifacts should I expect after training and evaluation, and when is the model
+      ready for Android deployment?
+    answer: >-
+      You will have a PyTorch checkpoint and an exported ONNX model, with numerical parity verified
+      against ONNX Runtime. After reviewing evaluation outputs such as the confusion matrix and
+      visual diagnostics, proceed to integrate the optimized model into the Android application.
+# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md b/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md
index 4a1741cfdd..eebd9e3af8 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md
@@ -19,6 +19,51 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:02:15Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 586a505543df4237c943ea47210d735fafe7d5ed2c6ad18b1b99227987ef9b15
+  summary_generated_at: '2026-06-02T02:55:06Z'
+  summary_source_hash: 586a505543df4237c943ea47210d735fafe7d5ed2c6ad18b1b99227987ef9b15
+  faq_generated_at: '2026-06-03T00:02:15Z'
+  faq_source_hash: 586a505543df4237c943ea47210d735fafe7d5ed2c6ad18b1b99227987ef9b15
+  summary: >-
+    This advanced Learning Path guides Android graphics developers through diagnosing and improving
+    vertex data efficiency on Arm GPUs. Using Arm Frame Advisor (part of Arm Performance Studio),
+    you will profile frames to analyze the Vertex Memory Efficiency metric, identify low-efficiency
+    draw calls (for example, shadow map passes), and apply vertex representation optimizations
+    in your C/C++ rendering code. The focus is on understanding what drives poor VME on Arm Immortalis
+    and Mali devices and making targeted changes to reduce vertex bandwidth pressure. Prerequisites
+    include knowledge of vertex attributes and familiarity with Arm Frame Advisor. Estimated time
+    to complete is about 10 minutes.
+  faqs:
+  - question: How do I know if Vertex Memory Efficiency is low in my frame?
+    answer: >-
+      Profile your frame with Arm Frame Advisor and inspect the VME metric for each draw call.
+      Low VME values indicate inefficient vertex data usage that warrants investigation.
+  - question: What should I check if the shadow map draw calls report low VME?
+    answer: >-
+      Review the vertex representations and attributes used by those draw calls. The Learning
+      Path guides you through approaches to address inefficiency identified by Frame Advisor.
+  - question: What do I need before running the steps in this path?
+    answer: >-
+      You should understand vertex attributes and be familiar with Arm Frame Advisor, which is
+      part of Arm Performance Studio. The content assumes an advanced level of graphics knowledge.
+  - question: Which platforms and GPUs does this apply to?
+    answer: >-
+      The Learning Path targets Android applications running on Arm GPUs, including Arm Immortalis
+      and Mali. The analysis is performed with Arm Frame Advisor.
+  - question: How do I validate that my changes improved vertex efficiency?
+    answer: >-
+      Re-profile the frame in Arm Frame Advisor and compare VME for the affected draw calls before
+      and after your changes. An increase in VME indicates improved vertex efficiency.
+# END generated_summary_faq
 
 author:
     - Andrew Kilroy
diff --git a/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md
index 37c9756975..2dd6e70b29 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md
@@ -22,6 +22,57 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:02:40Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 4962524245ec5e5e07d1207537208a22c2e9569f252f36d758c4f828d2104272
+  summary_generated_at: '2026-06-02T02:55:26Z'
+  summary_source_hash: 4962524245ec5e5e07d1207537208a22c2e9569f252f36d758c4f828d2104272
+  faq_generated_at: '2026-06-03T00:02:40Z'
+  faq_source_hash: 4962524245ec5e5e07d1207537208a22c2e9569f252f36d758c4f828d2104272
+  summary: >-
+    This advanced Learning Path shows you how to build a statically linked llama.cpp (llama-cli)
+    with Arm KleidiAI and Scalable Matrix Extension 2 (SME2) to measure LLM inference performance
+    on Android. You cross-compile on a Linux host (x86_64 or aarch64) using the Linux-hosted Arm
+    GNU Toolchain and GCC 14.2 or later, then deploy to an SME2-capable Android device via ADB.
+    Along the way you trace how acceleration flows from llama.cpp through the ggml-cpu backend
+    into KleidiAI microkernels that can use SME2, I8MM, or DotProd, and learn how to verify that
+    SME2 kernels are active. You then compare performance with SME2 enabled and disabled using
+    a 3B-parameter GGUF model. Prerequisites include knowledge of KleidiAI and SME2, plus Git,
+    CMake, and ADB.
+  faqs:
+  - question: What do I need before building and running this path?
+    answer: >-
+      You need a Linux host machine (x86_64 or aarch64), the Linux‑hosted Arm GNU Toolchain, Git,
+      CMake, and ADB. You also need an Android device with Arm SME2 support and prior knowledge
+      of KleidiAI and SME2.
+  - question: Which compiler and target should I use to enable SME2 in llama.cpp?
+    answer: >-
+      Use the aarch64 GCC cross‑compile toolchain with the aarch64‑none‑linux‑gnu‑ prefix from
+      the Linux‑hosted Arm GNU Toolchain. GCC version 14.2 or later is required for SME2, and
+      the build produces a statically linked llama-cli.
+  - question: How do I put the model and binary onto the Android device?
+    answer: >-
+      ADB is the recommended way to transfer files and open a shell on the device. Download the
+      Llama-3.2-3B-Instruct-Q4_0.gguf model from Hugging Face using curl on your host, then use
+      ADB to move both the model and the built binary to the device.
+  - question: How do I verify that SME2 microkernels are being used during inference?
+    answer: >-
+      The steps show how to confirm SME2 microkernels are active and trace the selection path
+      from llama.cpp through ggml‑cpu to KleidiAI. Follow the verification guidance when running
+      on the SME2‑capable Android device.
+  - question: What should I check if SME2 is not selected at runtime?
+    answer: >-
+      Verify the target Android device supports Arm SME2 and that you built with GCC 14.2+ and
+      the SME2‑enabled configuration. If SME2 is unavailable, the KleidiAI integration may select
+      I8MM or DotProd microkernels depending on hardware support.
+# END generated_summary_faq
 
 author: Zenon Zhilong Xiu
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md
index 4d177fa652..4345015604 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md
@@ -22,6 +22,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:03:23Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 1645a1c65527da010edd6fefddad3c865eac0f9cad417ba59709a57bb9dc847a
+  summary_generated_at: '2026-06-02T02:55:45Z'
+  summary_source_hash: 1645a1c65527da010edd6fefddad3c865eac0f9cad417ba59709a57bb9dc847a
+  faq_generated_at: '2026-06-03T00:03:23Z'
+  faq_source_hash: 1645a1c65527da010edd6fefddad3c865eac0f9cad417ba59709a57bb9dc847a
+  summary: >-
+    This Learning Path shows how to build ONNX Runtime for Android with KleidiAI micro-kernels
+    and Arm Scalable Matrix Extension 2 (SME2) support, then profile model performance to assess
+    acceleration. You will cross-compile ONNX Runtime (v1.23.2) using the Android NDK (r26b or
+    newer, with r27 recommended), CMake, and Ninja on a Linux environment, and run benchmarks
+    on an Android device that supports SME2. Using onnxruntime_perf_test and a ResNet-50 v2 model,
+    you will measure execution, see how MLAS dispatches to KleidiAI kernels when SME2 is detected,
+    and compare standard versus SME2-optimized runs. Prerequisites include an SME2-capable Android
+    device, basic ML inference knowledge, and familiarity with the Android NDK and cross-compilation.
+  faqs:
+  - question: What do I need before building ONNX Runtime for Android in this path?
+    answer: >-
+      Install the Android NDK r26b or newer (r27 recommended), and ensure CMake and Ninja are
+      in your PATH. You also need an Android device with Arm SME2 support, plus familiarity with
+      NDK cross-compilation and basic model inference.
+  - question: Which ONNX Runtime version is used and how do I check it out?
+    answer: >-
+      This path uses ONNX Runtime v1.23.2. Clone the repository and checkout v1.23.2 as shown
+      in the build step.
+  - question: How does ONNX Runtime select KleidiAI SME2 kernels at runtime?
+    answer: >-
+      ORT’s MLAS checks CPU capabilities for SME2 and, when available, dispatches to KleidiAI
+      micro-kernels. Examples include Conv and GEMM via ArmKleidiAI::MlasConv, ArmKleidiAI::MlasGemmBatch,
+      and ArmKleidiAI::MlasDynamicQGemmBatch.
+  - question: How do I prepare the example model on the device for profiling?
+    answer: >-
+      Download the ResNet-50 v2 package, push it to /data/local/tmp with adb, and extract it on
+      the device in that directory. You then run profiling with onnxruntime_perf_test against
+      the extracted model files.
+  - question: What should I check if I don’t observe SME2-optimized execution?
+    answer: >-
+      Verify the Android device supports Arm SME2, because MLAS only dispatches to KleidiAI when
+      SME2 is detected. Without SME2, ONNX Runtime uses its default kernels (for example, Neon),
+      and SME2-focused comparisons will not apply.
+# END generated_summary_faq
 
 author: Zenon Zhilong Xiu
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md b/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md
index 7dd0b61b50..ca768c04e7 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md
@@ -22,6 +22,58 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:04:25Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 01138f6538c655f866fb034a983461b2ac9b793142775465233b2ec8ec18d0c5
+  summary_generated_at: '2026-06-02T02:56:03Z'
+  summary_source_hash: 01138f6538c655f866fb034a983461b2ac9b793142775465233b2ec8ec18d0c5
+  faq_generated_at: '2026-06-03T00:04:25Z'
+  faq_source_hash: 01138f6538c655f866fb034a983461b2ac9b793142775465233b2ec8ec18d0c5
+  summary: >-
+    Learn how to profile ML model execution times and end-to-end application behavior on Arm-powered
+    Android devices using Arm Performance Studio (Streamline), Android Studio Profiler, and framework-level
+    tools. This introductory path shows how to identify bottlenecks across CPU, memory, cache,
+    and GPU counters with a sampling profiler, monitor Android app memory usage and leaks, and
+    extract per-layer timings from ML models. You will connect an Arm-based Android smartphone
+    via USB, run profiling sessions, and interpret timeline and layer-level outputs. Prerequisites
+    include an Arm-powered Android smartphone with a USB cable, Android Studio Profiler, Arm Performance
+    Studio with Streamline, and either Arm NN ExecuteNetwork or ExecuTorch. Estimated time to
+    complete is about 60 minutes.
+  faqs:
+  - question: What do I need before running the profiling steps?
+    answer: >-
+      You need an Arm-powered Android smartphone and a USB cable. For inference profiling, have
+      Arm NN ExecuteNetwork or ExecuTorch. For application profiling, install Arm Performance
+      Studio with Streamline and use Android Studio Profiler.
+  - question: How do I set up Android Studio Profiler to examine memory?
+    answer: >-
+      Open your project in Android Studio, go to View > Tool Windows > Profiler to open the Profiler
+      window. Connect your device in Developer Mode via USB and select your app’s process. You
+      can then monitor memory usage and look for leaks.
+  - question: Which profiler should I use for system behavior versus memory analysis?
+    answer: >-
+      Use Streamline (part of Arm Performance Studio) for system-wide sampling of performance
+      metrics with low overhead. Use Android Studio Profiler to focus on app memory usage and
+      leak detection.
+  - question: What output should I expect from Arm NN ExecuteNetwork when profiling a LiteRT model?
+    answer: >-
+      ExecuteNetwork runs the model outside the rest of the app and reports per-layer timings
+      and other useful information. This helps pinpoint bottlenecks inside the network. If you
+      are using LiteRT without Arm NN, treat the output as indicative rather than definitive.
+  - question: Which performance metrics does Streamline provide during sampling?
+    answer: >-
+      Streamline samples system counters such as memory, CPU activity and cycles, cache misses,
+      and many parts of the GPU. It also provides a timeline view to visualize how these metrics
+      evolve during execution.
+# END generated_summary_faq
+
 author: Ben Clark
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md b/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md
index c5e4c7db9a..f8ac62d8d7 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md
@@ -22,6 +22,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:04:52Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 9950a58160736e0c60ad80ea602262347e2ba8a37a00e29f25dc96709026ea1f
+  summary_generated_at: '2026-06-02T02:56:23Z'
+  summary_source_hash: 9950a58160736e0c60ad80ea602262347e2ba8a37a00e29f25dc96709026ea1f
+  faq_generated_at: '2026-06-03T00:04:52Z'
+  faq_source_hash: 9950a58160736e0c60ad80ea602262347e2ba8a37a00e29f25dc96709026ea1f
+  summary: >-
+    This introductory Learning Path guides Unity developers through deploying a sample app to
+    an Android device, collecting frame-level performance data with the Unity Profiler, and comparing
+    captures in the Profile Analyzer. You will create a blank 3D (URP) Core project, import a
+    sample from the Unity Asset Store, and run three code paths—Plain, Burst, and Neon with Arm
+    Neon intrinsics—to observe differences. The steps emphasize recording datasets for the unoptimized
+    and Neon modes, then loading them into the Analyzer to visualize and compare results. Prerequisites
+    are a recent Android phone or tablet, a desktop capable of running Unity, basic Unity/programming
+    knowledge, and the setup from Get started with Unity on Android.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      Have a recent Android device, a desktop capable of running Unity, and basic Unity/programming
+      knowledge. Complete the setup described in Get started with Unity on Android before proceeding.
+  - question: Which Unity project template should I use when creating the project?
+    answer: >-
+      Create a blank project in Unity Hub using the 3D (URP) Core template. Even though the sample
+      is a project, you will import it into this blank project.
+  - question: How are the Profiler and Profile Analyzer used differently in this path?
+    answer: >-
+      You will use the Profiler to record data over a series of frames and drill into specific
+      frames and timings. Then you will load the captured data into the Profile Analyzer to visualize
+      and compare datasets.
+  - question: Which sample modes should I run, and what do they represent?
+    answer: >-
+      The sample has three modes: Plain (unoptimized), Burst (code tagged for the Burst compiler
+      to enable auto-vectorization), and Neon (uses Arm Neon intrinsics). You will collect data
+      from the unoptimized (Plain) and optimized (Neon) versions for comparison.
+  - question: How should I run the sample on the device during data collection?
+    answer: >-
+      Run the app in landscape orientation, as it works best on Android in this mode. The sample
+      displays information in the bottom-right of the screen.
+# END generated_summary_faq
 
 author: Joshua Marshall-Law
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md b/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md
index a28eefeb44..9b662c38b8 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md
@@ -21,6 +21,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:05:22Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 56d9d0fe9606e42281a2d2be52992c7a4b6846b208376c92e7fe3094647d1c70
+  summary_generated_at: '2026-06-02T02:56:51Z'
+  summary_source_hash: 56d9d0fe9606e42281a2d2be52992c7a4b6846b208376c92e7fe3094647d1c70
+  faq_generated_at: '2026-06-03T00:05:22Z'
+  faq_source_hash: 56d9d0fe9606e42281a2d2be52992c7a4b6846b208376c92e7fe3094647d1c70
+  summary: >-
+    This advanced Learning Path guides ML developers through applying post-training quantization
+    (PTQ) and quantization-aware training (QAT) to PyTorch models using TorchAO PT2E APIs, then
+    exporting INT8 models to the .vgf format via the ExecuTorch Arm backend. You start with a
+    complete, runnable CIFAR-10-based example to generate a VGF artifact intended for Arm hardware
+    with dedicated neural accelerators (NX), export to TOSA, and validate the graph using Google’s
+    Model Explorer. The steps cover environment setup, PTQ and QAT workflows, and graph inspection
+    to spot issues such as unexpected layout conversions. Prerequisites include basic PyTorch
+    training/evaluation experience and a machine with Python 3.10+ and PyTorch that runs ExecuTorch
+    on Linux, macOS, or Windows.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need basic PyTorch model training and evaluation experience and a development machine
+      with Python 3.10+ and PyTorch installed that runs ExecuTorch. The path supports Linux, macOS,
+      and Windows.
+  - question: Should I start with PTQ or QAT in this workflow?
+    answer: >-
+      Start with PTQ using the end-to-end CIFAR-10 example to quickly generate a .vgf artifact
+      and validate the export path. Then extend the same example with QAT to compare PTQ and QAT
+      outputs using the same model and data.
+  - question: Where will the .vgf files be generated, and what result should I expect?
+    answer: >-
+      Running the provided example produces a .vgf artifact as part of the ExecuTorch Arm backend
+      export. The path uses default output directories such as ./output/ for PTQ and ./output_qat/
+      for QAT.
+  - question: How do I inspect the exported graph and what should I look for?
+    answer: >-
+      Install and launch Model Explorer with the VGF adapter, then open the .vgf files from the
+      output directories. Check for unexpected layout conversions (for example, extra transpose
+      operations) and operators you did not intend to run on your GPU.
+  - question: Can I apply this quantization and export flow to my own model?
+    answer: >-
+      Yes. After running the CIFAR-10 example end to end, reuse the same PTQ (and optionally QAT)
+      logic with your model and calibration data to export your own .vgf artifact.
+# END generated_summary_faq
 
 author:
 - Richard Burton
diff --git a/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md b/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md
index 73a2998f80..e8195329f1 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md
@@ -21,6 +21,61 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:05:48Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 78f862a7c46fd4591fec45f91d040eb3f1093291c0a7328dddd2203dad72db3f
+  summary_generated_at: '2026-06-02T02:57:13Z'
+  summary_source_hash: 78f862a7c46fd4591fec45f91d040eb3f1093291c0a7328dddd2203dad72db3f
+  faq_generated_at: '2026-06-03T00:05:48Z'
+  faq_source_hash: 78f862a7c46fd4591fec45f91d040eb3f1093291c0a7328dddd2203dad72db3f
+  summary: >-
+    Learn how to add ray tracing to Android renderers using the Vulkan ray tracing API. This Learning
+    Path explains core concepts, compares the ray tracing pipeline and ray query approaches, shows
+    how to create acceleration structures, and uses bindless materials to access hit data efficiently.
+    You will implement basic effects for realistic shadows, reflections, and refractions in an
+    existing Vulkan renderer. The target is Android devices that support the required Vulkan extensions;
+    Immortalis GPUs (such as Immortalis-G715, Immortalis-G720, and Immortalis-G925) support ray
+    tracing, while support on some Mali G7-series devices varies by phone model. Prerequisites
+    include a compatible Android device (for example, Vivo X100), knowledge of the Vulkan API,
+    and access to a renderer, ideally a deferred PBR design.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an appropriate Android device that supports the required Vulkan extensions (for
+      example, a Vivo X100), prior knowledge of the Vulkan API, and a Vulkan renderer. The material
+      is written so most code can be integrated into a deferred PBR renderer.
+  - question: How do I know if my Android device or GPU supports Vulkan ray tracing?
+    answer: >-
+      Immortalis GPUs such as Arm Immortalis-G715, Immortalis-G720, and Immortalis-G925 support
+      ray tracing. Some Arm Mali G7‑series GPUs after Mali‑G715 may or may not support it, depending
+      on the phone model. Vulkan uses the same ray tracing API on PC and mobile, so you can prototype
+      on PC and deploy to Android.
+  - question: Which Vulkan approach should I use to launch rays?
+    answer: >-
+      The path introduces two options: the ray tracing pipeline (VK_KHR_ray_tracing_pipeline)
+      and ray queries. The ray tracing pipeline is a more driver‑managed approach with dedicated
+      shader stages such as Ray Generation and Intersection. Choose the approach that best fits
+      your renderer; the path covers them conceptually.
+  - question: What acceleration structures will I build for ray tracing?
+    answer: >-
+      You will represent the scene using VK_KHR_acceleration_structure. These implementation‑defined,
+      typically tree‑like structures accelerate intersection tests, and the API provides options
+      to control topology and balancing. Constructing them is the first step before launching
+      rays.
+  - question: Are bindless materials required for the examples?
+    answer: >-
+      No. VK_EXT_descriptor_indexing (a core feature since Vulkan 1.2) is independent of ray tracing,
+      but it simplifies accessing data for intersected objects by letting shaders index arrays
+      of buffers and textures with dynamic, non‑uniform indices. It helps organize resources in
+      lookup tables.
+# END generated_summary_faq
 
 author: Iago Calvo Lista
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md b/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md
index 29f5910395..eb9668f62b 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md
@@ -20,6 +20,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:06:38Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: e2f6f3005c119e6f6fe97612d4e2600849106f244bce729d56bfeeedb30637c2
+  summary_generated_at: '2026-06-02T02:57:43Z'
+  summary_source_hash: e2f6f3005c119e6f6fe97612d4e2600849106f244bce729d56bfeeedb30637c2
+  faq_generated_at: '2026-06-03T00:06:38Z'
+  faq_source_hash: e2f6f3005c119e6f6fe97612d4e2600849106f244bce729d56bfeeedb30637c2
+  summary: >-
+    Learn to analyze Android graphics workloads using Frame Advisor’s Render Graph view in Arm
+    Performance Studio. You will capture GPU data with Streamline Performance Analyzer, then inspect
+    the directed acyclic graph of workloads and resources to find GPU‑heavy sections, spot unused
+    resources, and detect unwanted execution nodes. The path explains render graph concepts, shows
+    how to generate a capture, and demonstrates actionable fixes such as removing unnecessary
+    API calls. It applies to applications using OpenGL ES or Vulkan. Prerequisites include having
+    Frame Advisor installed; a supported Android device is needed if you plan to analyze your
+    own applications. Basic familiarity with Frame Advisor is recommended. Estimated time to complete
+    is about 30 minutes on Linux, Windows, or macOS hosts.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      Install Frame Advisor (part of Arm Performance Studio). If you plan to analyze your own
+      applications, use a supported Android device. Basic familiarity with Frame Advisor is recommended;
+      review the “Get started with Arm Performance Studio for mobile” section.
+  - question: Which Streamline capture settings should I use to record GPU data for the render
+      graph?
+    answer: >-
+      In Streamline’s Start view, open Configure Capture and enable GPU data collection. For an
+      Arm GPU, deselect “Use advanced mode” and select the “Capture Arm GPU” checkbox.
+  - question: What result should I expect from the Render Graph view?
+    answer: >-
+      You will see a directed acyclic graph of nodes and edges that summarizes GPU workloads (execution
+      nodes) and resources for a single frame. It shows how data flows between passes and where
+      outputs are consumed.
+  - question: What should I check if the graph shows resources that are never consumed?
+    answer: >-
+      Identify outputs from a render or transfer node that have no downstream consumers in the
+      graph. These indicate data written but not used in the frame and are candidates for review
+      or removal in your application.
+  - question: How do I decide whether an execution node can be removed?
+    answer: >-
+      If all outputs from a node are unnecessary, the computation is unnecessary and you can remove
+      the corresponding API calls. Make changes carefully and verify the application after adjustments.
+# END generated_summary_faq
 
 author: Mark Thurman
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md
index 9b962557ec..27d27063d9 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md
@@ -23,6 +23,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:07:29Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 388cab0dcb284c29fe2ad82f2b2934d9243c6356b2885d26db0a97c1a1d4d393
+  summary_generated_at: '2026-06-02T02:58:12Z'
+  summary_source_hash: 388cab0dcb284c29fe2ad82f2b2934d9243c6356b2885d26db0a97c1a1d4d393
+  faq_generated_at: '2026-06-03T00:07:29Z'
+  faq_source_hash: 388cab0dcb284c29fe2ad82f2b2934d9243c6356b2885d26db0a97c1a1d4d393
+  summary: >-
+    This Learning Path shows how to take the Stable Audio Open Small text-to-audio model from
+    Hugging Face, convert its submodules to LiteRT (.tflite), build LiteRT from the TensorFlow
+    repository using Bazel, and compile a simple C++ application for Arm-based Android (arm64-v8a)
+    with the Android NDK and CMake. You will run the app on an Android smartphone to generate
+    an audio snippet from a text prompt. The path assumes a Linux-based x86 or macOS development
+    machine, a Hugging Face account, and an Android device in developer mode with a USB cable.
+    macOS is mentioned as a platform, but the provided steps focus on Android deployment.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Linux-based x86 or macOS development machine with at least 8 GB RAM and 50 GB
+      of disk, a Hugging Face account, and an Android phone in developer mode with a USB cable.
+      These are the only explicit prerequisites listed.
+  - question: Which model files do I download from Hugging Face, and how do I verify them?
+    answer: >-
+      Download model_config.json and model.ckpt from the Stable Audio Open Small page and copy
+      them into your workspace. Verify that both files exist in the workspace before proceeding.
+  - question: Which tool versions are required for the environment setup?
+    answer: >-
+      Install Android NDK r27b or newer, Python 3.10 or newer (tested with 3.10), and CMake 3.16.0
+      or newer (tested with 3.28.1). LiteRT is built using Bazel, but a specific Bazel version
+      is not listed.
+  - question: How are the model components converted to LiteRT format?
+    answer: >-
+      You will clone a repository that provides scripts to convert the model’s three submodules
+      into LiteRT (.tflite) and generate the inference application. Follow the steps to run these
+      scripts after downloading the model assets.
+  - question: What result should I expect when running the Android app, and how do I configure
+      the build?
+    answer: >-
+      Configure CMake with the Android NDK toolchain, set ANDROID_ABI=arm64-v8a, and pass the
+      TensorFlow include and library paths. The app takes a text prompt and outputs an audio file;
+      successful generation confirms the pipeline is working.
+# END generated_summary_faq
 
 author:
     - Nina Drozd
diff --git a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md
index 01230a2a84..c7240abae0 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md
@@ -21,6 +21,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:08:09Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 7970846a399ddcdb488d90bf19cce3c6b74df6348e88914aed83661b2597fe7b
+  summary_generated_at: '2026-06-02T02:58:48Z'
+  summary_source_hash: 7970846a399ddcdb488d90bf19cce3c6b74df6348e88914aed83661b2597fe7b
+  faq_generated_at: '2026-06-03T00:08:09Z'
+  faq_source_hash: 7970846a399ddcdb488d90bf19cce3c6b74df6348e88914aed83661b2597fe7b
+  summary: >-
+    This Learning Path shows how to download the Stable Audio Open Small model from Hugging Face,
+    convert it to ExecuTorch (.pte), and build an audio generation application targeting Arm CPUs.
+    You will set up a Python 3.10+ environment, install ExecuTorch 1.0.0, and build with CMake;
+    Android builds use the Android NDK, and macOS (Apple Silicon) uses ExecuTorch with XNNPack
+    and Arm KleidiAI. You will then run the application on an Android smartphone or macOS to generate
+    short audio snippets. Prerequisites include a Linux-based x86 or macOS development machine
+    with 8 GB RAM and 50 GB disk space, a Hugging Face account, and an Android phone in developer
+    mode with at least 8 GB RAM and a cable; Android devices should have an Arm CPU with FEAT_DotProd
+    (dotprod).
+  faqs:
+  - question: What do I need before running the conversion and build steps?
+    answer: >-
+      Use a Linux-based x86 or macOS development machine with at least 8 GB RAM and 50 GB of disk
+      space, and sign in to a Hugging Face account. For Android, enable developer mode on a phone
+      with at least 8 GB RAM and an Arm CPU that supports FEAT_DotProd; Python 3.10+ and CMake
+      3.16+ are required, and the Android NDK is referenced (version not fully specified in the
+      excerpt).
+  - question: Which ExecuTorch installation option should I use?
+    answer: >-
+      You can install executorch==1.0.0 from PyPI, which is the simplest path. Alternatively,
+      clone the ExecuTorch repository, check out v1.0.0, and run the provided installation script.
+  - question: How should I set up the Python environment for conversion?
+    answer: >-
+      Create and activate a Python 3.10 virtual environment in the audiogen-et directory to isolate
+      dependencies. Then install ExecuTorch before running the conversion step.
+  - question: How do I know the model conversion to ExecuTorch succeeded?
+    answer: >-
+      The conversion produces a .pte file for Stable Audio Open Small. Proceed to the build steps
+      once this file is created.
+  - question: What should I check if the Android build or run fails?
+    answer: >-
+      Confirm you are targeting an Arm64 Android device with FEAT_DotProd and sufficient memory
+      (8 GB recommended) and that developer mode is enabled. Ensure required tools like CMake
+      and the Android NDK are installed, and follow the cross-compilation steps for Android.
+# END generated_summary_faq
 
 author:
     - Adnan AlSinan
diff --git a/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md b/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md
index 4d63a6ee7d..e2b6d26483 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md
@@ -22,6 +22,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:08:53Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: dea8c3e15cca703f075c8fa9ba3daf873a90e3eb2ee9975dd05b973dad744b54
+  summary_generated_at: '2026-06-02T02:59:30Z'
+  summary_source_hash: dea8c3e15cca703f075c8fa9ba3daf873a90e3eb2ee9975dd05b973dad744b54
+  faq_generated_at: '2026-06-03T00:08:53Z'
+  faq_source_hash: dea8c3e15cca703f075c8fa9ba3daf873a90e3eb2ee9975dd05b973dad744b54
+  summary: >-
+    This introductory Learning Path shows how to install Droid OS on an Arm-based Orange Pi 5,
+    build a Unity game for Android, and deploy the resulting APK to the board. You will use a
+    Windows PC to obtain the Orange Pi OS (Droid) TF Card image from the Orange Pi 5 support page
+    and write it to a microSD card with SDDiskTool, using 7‑Zip as needed for archives. Then you
+    will configure Unity Build Settings for Android, add Android Build Support in Unity Hub if
+    required, and produce an APK. Finally, you will transfer the APK to the Orange Pi 5 (for example
+    via USB, microSD, or a cloud drive over Ethernet) and install it. Prerequisites are explicitly
+    listed.
+  faqs:
+  - question: Do I need a Windows PC to flash Droid OS to the microSD card?
+    answer: >-
+      Yes. The Orange Pi imaging software used in this path is only available for Windows, so
+      the flashing step must be done on a Windows PC.
+  - question: Where do I download the correct Droid OS image for Orange Pi 5?
+    answer: >-
+      Go to the Orange Pi 5 support page, select Orange Pi OS (Droid) > TF Card Image, and download
+      the latest release. An example filename provided is OrangePI-OS_Droid_orangepi5_en_v0.0.6_beta.tar.gz.
+  - question: Which Unity components are required to build for the Orange Pi 5?
+    answer: >-
+      In Unity Hub, add the Android Build Support module for the Unity version used by your project.
+      In Build Settings, select Android (Unity may prompt a restart), and ensure all needed Android
+      subcomponents are included.
+  - question: What microSD card should I use for Droid OS on the Orange Pi 5?
+    answer: >-
+      Use a microSD card that is 16GB or larger and Class 10 or faster. This capacity and speed
+      are listed as prerequisites for the path.
+  - question: How can I move my Unity APK onto the Orange Pi 5?
+    answer: >-
+      You can copy the APK via a USB thumb drive if the file systems are compatible, place it
+      directly on the microSD card if formats allow, or upload it to a cloud drive and download
+      it from Droid OS on the board.
+# END generated_summary_faq
 
 author: Gabriel Peterson
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md b/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md
index 97847ddb0b..6eb005285d 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md
@@ -20,6 +20,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:09:23Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 4a990e957658b03bac9306d5f8e6955734cc1d3b063f43c38ac525ed1bf95b79
+  summary_generated_at: '2026-06-02T02:59:56Z'
+  summary_source_hash: 4a990e957658b03bac9306d5f8e6955734cc1d3b063f43c38ac525ed1bf95b79
+  faq_generated_at: '2026-06-03T00:09:23Z'
+  faq_source_hash: 4a990e957658b03bac9306d5f8e6955734cc1d3b063f43c38ac525ed1bf95b79
+  summary: >-
+    Learn how to install Arm integration packages in Unity to profile games targeting Android
+    devices with Arm CPUs and GPUs. In about 20 minutes, you add the System Metrics Mali package
+    to enable Arm GPU hardware counters in the Unity Profiler (supported in Unity 2021.2 and later)
+    and integrate annotations that appear in Arm Performance Studio tools, Streamline and Performance
+    Advisor. You work on Windows, macOS, or Linux. By the end, you can configure Unity to display
+    Arm GPU metrics and annotate your project with markers and custom counters to add context
+    in Arm Performance Studio. Prerequisites include familiarity with Unity, the Unity Profiler,
+    and Arm Performance Studio tools.
+  faqs:
+  - question: Do I need a specific Unity version to view Arm GPU metrics?
+    answer: >-
+      Yes. The System Metrics Mali package is supported in Unity versions 2021.2 and later.
+  - question: How do I install the System Metrics Mali package in Unity?
+    answer: >-
+      Open Window > Package Manager, click the + button, and choose Add package by name. Enter
+      com.unity.profiling.systemmetrics.mali to add the package.
+  - question: What result should I expect in the Unity Profiler after installing the Mali metrics
+      package?
+    answer: >-
+      You will be able to read and display GPU hardware counters from Arm GPUs in the Unity Profiler.
+  - question: How do I enable annotations for Arm Performance Studio from my Unity project?
+    answer: >-
+      Use the Arm Performance Studio Unity integration package to add annotations. It lets you
+      mark the timeline with events and custom counters that provide context alongside performance
+      data in Streamline and are visible to Performance Advisor.
+  - question: What should I check if the Mali metrics package is not available or GPU metrics
+      do not appear?
+    answer: >-
+      Verify that your project uses Unity 2021.2 or later and that you added the package by name
+      as com.unity.profiling.systemmetrics.mali. After installation, open the Unity Profiler to
+      view the Arm GPU hardware counters.
+# END generated_summary_faq
 
 author: Julie Gaskin
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md b/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md
index c5a2451fd4..55264d7dc5 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md
@@ -23,6 +23,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:10:08Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: f17ab3c4216495b88cee75a81612c11a4727d874114f914edf97c467f0d1c125
+  summary_generated_at: '2026-06-02T03:00:21Z'
+  summary_source_hash: f17ab3c4216495b88cee75a81612c11a4727d874114f914edf97c467f0d1c125
+  faq_generated_at: '2026-06-03T00:10:08Z'
+  faq_source_hash: f17ab3c4216495b88cee75a81612c11a4727d874114f914edf97c467f0d1c125
+  summary: >-
+    This advanced Learning Path guides you through using Arm Neon intrinsics in Unity C# scripts
+    for Android, compiled with the Unity Burst compiler, and measuring results with the Unity
+    Profiler and Analyzer tools. You will install Unity with Android build support, open a provided
+    sample, configure an unoptimized baseline, then enable Burst and Neon intrinsics to compare
+    performance across versions. The path was written using Unity v6.3 and Burst 1.8.28, though
+    any Unity version compatible with Burst 1.5 or later is suitable. Prerequisites include basic
+    Unity and C# knowledge, a desktop capable of running Unity, and a recent Android device for
+    testing. Expect to complete the steps in around 90 minutes.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      You need basic knowledge of Unity and C#, a recent Android device, a desktop capable of
+      running Unity, and a Unity version compatible with the Burst compiler 1.5 or later. Android
+      build support for Unity is required.
+  - question: Which Unity and Burst versions are assumed?
+    answer: >-
+      Use a Unity version compatible with Burst 1.5 or later. The Learning Path was written using
+      Unity v6.3 and Burst 1.8.28.
+  - question: How do I enable the Burst package in my Unity project?
+    answer: >-
+      Open Window > Package Manager, set the Packages filter to Unity Registry, search for "Burst,"
+      select it, and install or enable it. Follow the project setup described to allow Burst to
+      compile the targeted code paths.
+  - question: How do I switch the sample project between unoptimized, Burst, and Neon modes?
+    answer: >-
+      Edit Assets/BurstNeonCollisions/Scripts/CollisionCalculationScript.cs and set the codeMode
+      constant (for example, Mode.Plain for unoptimized as shown). The Neon version will not function
+      correctly on computers without Neon support, so run and profile that mode on an Android
+      device.
+  - question: How do I validate that the performance comparison worked?
+    answer: >-
+      Use the Unity Profiler and Analyzer to capture data for each mode—unoptimized, Burst, and
+      Neon—on your Android device. You should see separate measurements that let you compare the
+      collision-detection workload across modes.
+# END generated_summary_faq
 
 author: Ben Clark, Joshua Marshall-Law
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md b/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md
index e4ca066123..4105236f9c 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md
@@ -20,6 +20,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:10:44Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 9cd19738cbe9cde618125b309bd4f2c57ad1799e807251fdaed09707bea2781d
+  summary_generated_at: '2026-06-02T03:01:07Z'
+  summary_source_hash: 9cd19738cbe9cde618125b309bd4f2c57ad1799e807251fdaed09707bea2781d
+  faq_generated_at: '2026-06-03T00:10:44Z'
+  faq_source_hash: 9cd19738cbe9cde618125b309bd4f2c57ad1799e807251fdaed09707bea2781d
+  summary: >-
+    This Learning Path shows how to use Unity’s Machine Learning Agents toolkit inside a Unity
+    project that can be deployed to Arm-powered Android devices. You will install Unity (via Unity
+    Hub), open the provided Dr Arm sample project, review how observations and actions map to
+    a neural network “brain,” and prepare the scene and scripts for the training stage. The toolkit
+    includes a C# API and Python scripts; you will need Python and a few extra libraries before
+    running training, but you can begin by setting up Unity. Prerequisites include a computer
+    capable of running Unity (instructions target Windows), an Android device with a 64-bit processor
+    running Android 8 or later, and a USB cable. Deployment and profiling are covered in separate
+    Learning Paths.
+  faqs:
+  - question: Do I need to install Python before I start, or can I begin with Unity only?
+    answer: >-
+      You will need Python and some additional libraries before the training stage, but to get
+      started quickly you can install Unity first. The C# API is used inside Unity, while Python
+      scripts run outside Unity during training.
+  - question: Which Unity components should I install through Unity Hub?
+    answer: >-
+      Install Unity via the Unity Hub and include Visual Studio Community Edition with the Unity
+      support module. The Hub helps manage multiple Unity installations and add required support
+      modules.
+  - question: Which scene should I open in the Dr Arm project to follow the steps?
+    answer: >-
+      Open Assets -> #DevSummit2022 -> Scenes and load the Level DevSummit2022 scene. Ignore the
+      “Ready to Play” version and use this incomplete scene to apply the ML setup changes.
+  - question: What Android device requirements should I check before proceeding?
+    answer: >-
+      Use an Android device with a 64-bit processor running Android 8 or later and have a USB
+      cable to connect it to your computer. These are the explicitly listed prerequisites.
+  - question: Does this Learning Path include Android deployment and profiling steps?
+    answer: >-
+      No. Instructions for deploying Unity games to Arm-powered Android devices and profiling
+      them are provided in separate Learning Paths.
+# END generated_summary_faq
 
 author: Arm
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md b/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md
index 1f470f82be..a06cfa3ee5 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md
@@ -22,6 +22,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:11:12Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: e7d95c8e7210be2fe4520fe94ccbaa3e437cd0edfa5caca549afaee22fb8b377
+  summary_generated_at: '2026-06-02T03:01:36Z'
+  summary_source_hash: e7d95c8e7210be2fe4520fe94ccbaa3e437cd0edfa5caca549afaee22fb8b377
+  faq_generated_at: '2026-06-03T00:11:12Z'
+  faq_source_hash: e7d95c8e7210be2fe4520fe94ccbaa3e437cd0edfa5caca549afaee22fb8b377
+  summary: >-
+    This Learning Path guides you through running Vision Transformer (ViT) inference on Android
+    using the Mobile Neural Network (MNN) framework and KleidiAI micro-kernels. You will download
+    a Vision LLM from Hugging Face, prepare the Qwen vision model, convert it to MNN, and build
+    a demo Android app from the Vision Language Models repository in Android Studio to create
+    an APK. You also compile command-line binaries, push an example image to the device with adb,
+    and run inference. Finally, you benchmark runs with and without KleidiAI kernels to compare
+    performance. Prerequisites include Android Studio and an Android smartphone with i8mm and
+    dotprod support. The path is introductory and takes about 30 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need Android Studio installed on your development machine and a smartphone running Android
+      that supports i8mm and dotprod instructions. No other prerequisites are explicitly listed.
+  - question: Which NDK and CMake versions are used, and how do I install them?
+    answer: >-
+      This path was tested with NDK 28.0.12916984 and CMake 4.0.0-rc1. Install the NDK via Android
+      Studio (Tools > SDK Manager > SDK Tools > NDK (Side by side)), and on Ubuntu/Debian install
+      CMake and git‑lfs with: sudo apt update and sudo apt install cmake git-lfs -y.
+  - question: Where do I get the source code for the Android demo app?
+    answer: >-
+      Clone the examples repository with: git clone https://gitlab.arm.com/kleidi/kleidi-examples/vision-language-models.
+      Open the project in Android Studio and build to generate an APK.
+  - question: How is the model prepared for use with MNN?
+    answer: >-
+      You will download a Vision LLM from Hugging Face and convert it to the MNN format. The setup
+      steps prepare the Qwen vision model as part of this process.
+  - question: How do I run the benchmark and what input image should I use?
+    answer: >-
+      Build the command-line ViT demo and prepare an example image named example.png. Push it
+      to the device with adb push example.png /data/local/tmp, then follow the steps to compare
+      inference runs with and without KleidiAI micro-kernels.
+# END generated_summary_faq
 
 author:
     - Shuheng Deng
diff --git a/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md b/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md
index 1b5dffac92..95b06526b6 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md
@@ -23,6 +23,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:11:41Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 192f5919261cfef88c107576dc444d2db3a07fbad98100d9c758bb75670a5a00
+  summary_generated_at: '2026-06-02T03:02:05Z'
+  summary_source_hash: 192f5919261cfef88c107576dc444d2db3a07fbad98100d9c758bb75670a5a00
+  faq_generated_at: '2026-06-03T00:11:41Z'
+  faq_source_hash: 192f5919261cfef88c107576dc444d2db3a07fbad98100d9c758bb75670a5a00
+  summary: >-
+    Build and run a multimodal Voice Assistant on Android and explore how KleidiAI and SME2 can
+    accelerate its performance. You will set up Android Studio and supporting command-line tools
+    (cmake, python3, git, adb), clone the Real-Time-Voice-Assistant repository, compile the project
+    in Android Studio, and deploy it to a USB-connected phone in developer mode. The application
+    implements a Speech-to-Text → LLM (via Llama.cpp) → Text-to-Speech pipeline, with KleidiAI
+    micro-kernels and SME2 highlighted for Arm-specific acceleration on supported hardware. Prerequisites
+    include an Android phone with i8mm and SME support and a development machine with Android
+    Studio. This introductory path takes about 30 minutes and results in a working app and a clear
+    understanding of the acceleration points.
+  faqs:
+  - question: What do I need before starting?
+    answer: >-
+      An Android phone that supports the i8mm Arm architecture feature and SME (Scalable Matrix
+      Extension) instructions, and a development machine with Android Studio installed. This path
+      was tested on a Vivo X300 Pro and uses a USB connection to deploy via adb.
+  - question: Which command-line tools should I install and why?
+    answer: >-
+      Install cmake, python3, git, and adb. Python is used by the project to fetch dependencies
+      and models, and adb is required to communicate with and control the Android device.
+  - question: How do I build the app in Android Studio?
+    answer: >-
+      Open the downloaded project in Android Studio and click the Make Module VoiceAssistant.app
+      button (hammer icon). Android Studio will build the application with the default settings.
+  - question: How do I install and run the app on my phone?
+    answer: >-
+      Enable developer mode on the Android device, connect it via USB, and select it as the target
+      in Android Studio. Click Run to transfer and start the application on the phone.
+  - question: How are KleidiAI, SME2, and Llama.cpp used in this application?
+    answer: >-
+      The application combines local LLM inference and speech recognition optimized for Arm CPUs
+      using Llama.cpp and the KleidiAI library of tuned micro-kernels. SME support is required
+      for SME performance checking, and the path focuses on using KleidiAI and SME2 to accelerate
+      the workload on supported devices.
+# END generated_summary_faq
 
 author:
     - Arnaud de Grandmaison
diff --git a/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md b/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md
index 02e8c98de5..f5fbc31e96 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md
@@ -24,6 +24,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:12:36Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 2d8fca8838e759c3098358f131fb4b0884b0d80d5fdb2fd68719bd09fa4c3d32
+  summary_generated_at: '2026-06-02T03:02:45Z'
+  summary_source_hash: 2d8fca8838e759c3098358f131fb4b0884b0d80d5fdb2fd68719bd09fa4c3d32
+  faq_generated_at: '2026-06-03T00:12:36Z'
+  faq_source_hash: 2d8fca8838e759c3098358f131fb4b0884b0d80d5fdb2fd68719bd09fa4c3d32
+  summary: >-
+    Build an end-to-end, on-device voice assistant on Arm that understands both speech and emotion.
+    You will set up an isolated Python environment (Linux, Windows, or macOS), install dependencies
+    including ffmpeg for Whisper, and create a baseline pipeline that records from a microphone,
+    transcribes with Whisper, and queries a locally hosted LLM via llama.cpp. You then train a
+    HuBERT-based sentiment classifier on the RAVDESS dataset (neutral, happy, angry), export the
+    model to ONNX, and apply post-training quantization for on-device inference with ONNX Runtime.
+    Finally, you integrate sentiment inference into the voice-to-LLM flow to generate context-aware
+    responses. Prerequisites include Python 3.9+, a working microphone, and basic Python/CLI skills.
+    Estimated time: 90 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need Python 3.9 or later, a working microphone, and basic Python and command-line knowledge.
+      No other prerequisites are explicitly listed.
+  - question: Which operating systems are supported, and how should I set up the environment?
+    answer: >-
+      The instructions support Ubuntu, macOS, and Windows. You will create a project workspace
+      and use an isolated UV environment, and install required system tools first; ffmpeg is required
+      by Whisper for audio decoding.
+  - question: What result should I expect when the baseline voice-to-LLM pipeline is working?
+    answer: >-
+      After recording audio from your microphone, Whisper transcribes it to text and sends the
+      text to a locally hosted LLM. You should see the model’s response displayed.
+  - question: Which dataset and sentiment labels are used for training the classifier?
+    answer: >-
+      Training uses the RAVDESS dataset with three sentiment classes: neutral, happy, and angry.
+      The same approach can be extended to more classes or other datasets.
+  - question: How do I verify the ONNX conversion and quantization steps?
+    answer: >-
+      You should obtain an exported ONNX model and a quantized version with reduced file size
+      for on-device inference with ONNX Runtime. If export fails, ensure the trained HuBERT checkpoint
+      from the previous section exists and can be loaded; ONNX export may take a few seconds.
+# END generated_summary_faq
 
 author: Bhanu Arya
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md
index ce3dfb5ed5..c3f40fc995 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md
@@ -24,6 +24,57 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:13:13Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: af4f1c8964e0970c187643bcd0330a63a6ce66c38a2e6b4d2fc17857a12a3d7f
+  summary_generated_at: '2026-06-02T03:03:08Z'
+  summary_source_hash: af4f1c8964e0970c187643bcd0330a63a6ce66c38a2e6b4d2fc17857a12a3d7f
+  faq_generated_at: '2026-06-03T00:13:13Z'
+  faq_source_hash: af4f1c8964e0970c187643bcd0330a63a6ce66c38a2e6b4d2fc17857a12a3d7f
+  summary: >-
+    This Learning Path shows how to enable neural graphics workflows on Windows by using ML Extensions
+    for Vulkan. You install the ML Emulation Layers to simulate VK_ARM_data_graph and VK_ARM_tensors,
+    set up build tools (CMake, Python 3, Git), and use Visual Studio 2022 on a Windows 11 development
+    machine. You then build and run the Vulkan Samples fork, starting with the Simple Tensor and
+    Data Graph example that executes a 2D average pooling operation via a data graph pipeline.
+    You also run an end-to-end inference test with the Scenario Runner from Arm’s ML SDK for Vulkan,
+    and debug or inspect frames with RenderDoc. By the end, you can run sample workloads using
+    the ML extensions and analyze their execution.
+  faqs:
+  - question: What do I need installed before building and running the samples?
+    answer: >-
+      Use a Windows 11 development machine with Visual Studio 2022 and the Desktop development
+      with C++ and .NET desktop build tools workloads. Install CMake (3.12+), Python 3, and Git,
+      then download the ML Emulation Layers for Vulkan. You can verify tools with commands like
+      cmake --version and python3 --version.
+  - question: Which Vulkan ML extensions does this path use, and how are they enabled?
+    answer: >-
+      The path uses VK_ARM_data_graph and VK_ARM_tensors. These are enabled on your machine by
+      installing the ML Emulation Layers for Vulkan, which simulate the extensions so the samples
+      can run.
+  - question: How do I get and build the first sample?
+    answer: >-
+      Clone Arm’s fork of Vulkan Samples on the tensor_and_data_graph branch with submodules as
+      shown in the steps. Build it with the tools you installed; the Simple Tensor and Data Graph
+      sample demonstrates a 2D average pooling operation via a data graph pipeline.
+  - question: How do I run a complete inference test beyond the simple sample?
+    answer: >-
+      Use the Scenario Runner from Arm’s ML SDK for Vulkan. The Learning Path points to Arm’s
+      Hugging Face page where you can download binaries and assets that demonstrate the ML extensions
+      in action.
+  - question: When should I use RenderDoc with these samples, and what can I inspect?
+    answer: >-
+      Use RenderDoc to capture frames when you need to visualize and debug ML-integrated rendering.
+      You can step through frames, inspect Vulkan API calls, view shader inputs and outputs, examine
+      tensors, and review GPU resource states and memory usage.
+# END generated_summary_faq
 
 author: Annie Tallund
 
diff --git a/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md b/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
index 79f7114113..9d96e5cd66 100644
--- a/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
@@ -22,6 +22,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:14:25Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 275878b5aa4c27dee394da12ad8b80e4c0d0235b3ddce66b1fe3f0e056ef3da9
+  summary_generated_at: '2026-06-02T03:03:34Z'
+  summary_source_hash: 275878b5aa4c27dee394da12ad8b80e4c0d0235b3ddce66b1fe3f0e056ef3da9
+  faq_generated_at: '2026-06-03T00:14:25Z'
+  faq_source_hash: 275878b5aa4c27dee394da12ad8b80e4c0d0235b3ddce66b1fe3f0e056ef3da9
+  summary: >-
+    This Learning Path shows how to build and deploy an AI agent application on Arm servers using
+    llama.cpp, llama-cpp-python, and llama-cpp-agent with KleidiAI optimization. You will configure
+    an Arm-optimized environment on Ubuntu 22.04 LTS, build llama.cpp, download a quantized Llama
+    3.1 8B model, and implement a Python script (agent.py) that adds custom functions and exercises
+    function calling. The path targets Arm-based cloud or on-prem servers and was tested on an
+    AWS EC2 Graviton3 m7g.xlarge instance. Plan for at least 4 CPU cores, 16 GB RAM, and 32 GB
+    disk. Prerequisites include basic Python and prompt engineering skills and understanding of
+    LLM fundamentals. By the end, you will have a working, Arm-optimized AI agent suitable for
+    application integration.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      Use an Arm-based server running Ubuntu 22.04 LTS with at least 4 CPU cores, 16 GB RAM, and
+      32 GB disk space. You should also have a basic understanding of Python, prompt engineering,
+      and LLM fundamentals.
+  - question: Which environment or instance type is assumed?
+    answer: >-
+      Any Arm-based instance from a supported cloud service provider or an on-premise Arm server
+      that meets the resource requirements. The instructions were tested on an AWS EC2 Graviton3
+      m7g.xlarge instance.
+  - question: Which model is used in the example and how is it referenced?
+    answer: >-
+      The example uses a quantized Llama 3.1 8B model. Ensure you download this model and that
+      the model_path in agent.py points to the location where you stored it.
+  - question: Do I need special configuration to use KleidiAI optimizations?
+    answer: >-
+      This path sets up llama.cpp and llama-cpp-python optimized for Arm with KleidiAI as part
+      of the procedure. Follow the steps in order on Ubuntu 22.04 LTS; no additional configuration
+      beyond what is shown is listed.
+  - question: How do I know the AI agent is working after I create agent.py?
+    answer: >-
+      When you run the script, the agent should load the quantized Llama 3.1 8B model and select
+      predefined functions based on your input. You should see outputs that reflect function calls
+      and responses generated by the model.
+# END generated_summary_faq
 
 author: Andrew Choi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/aks/_index.md b/content/learning-paths/servers-and-cloud-computing/aks/_index.md
index 07fb0bcbba..db2052e293 100644
--- a/content/learning-paths/servers-and-cloud-computing/aks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/aks/_index.md
@@ -20,6 +20,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:15:11Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 01c5cc8930650a8d81d67d4c48b62e0f9d7b1ebfc2d09a552e11046fb982fc52
+  summary_generated_at: '2026-06-02T03:03:59Z'
+  summary_source_hash: 01c5cc8930650a8d81d67d4c48b62e0f9d7b1ebfc2d09a552e11046fb982fc52
+  faq_generated_at: '2026-06-03T00:15:11Z'
+  faq_source_hash: 01c5cc8930650a8d81d67d4c48b62e0f9d7b1ebfc2d09a552e11046fb982fc52
+  summary: >-
+    This Learning Path shows how to automate the creation of an Arm-based Azure Kubernetes Service
+    (AKS) cluster using Terraform and then deploy a WordPress example workload backed by MySQL.
+    You will target Azure Dpsv5 virtual machines featuring Ampere Altra Arm-based processors and
+    use the Azure CLI and kubectl alongside Terraform. The steps include provisioning the AKS
+    cluster and applying three Kubernetes manifests (kustomization.yaml, mysql-deployment.yaml,
+    and wordpress-deployment.yaml) adapted from the Kubernetes WordPress tutorial. Prerequisites
+    are an Azure account and a machine with Terraform, Azure CLI, and kubectl installed; the instructions
+    target a Linux environment. By the end, you will have a running AKS cluster and a deployed
+    WordPress example.
+  faqs:
+  - question: What do I need before running the Terraform deployment?
+    answer: >-
+      You need an Azure account and a machine with Terraform, Azure CLI, and kubectl installed.
+      The Learning Path assumes these tools are ready before you start.
+  - question: Which Azure VM series is used for Arm-based AKS nodes in this path?
+    answer: >-
+      The path uses the Azure Dpsv5 Virtual Machine series featuring Ampere Altra Arm-based processors.
+      AKS can run on this series to provide Arm-based compute.
+  - question: Can I run the setup steps from my local computer or a virtual machine?
+    answer: >-
+      Yes. Any computer with the required tools installed can be used, including your desktop,
+      laptop, or a virtual machine.
+  - question: What files do I create to deploy the WordPress example?
+    answer: >-
+      You will create three Kubernetes YAML files: kustomization.yaml, mysql-deployment.yaml,
+      and wordpress-deployment.yaml. These are modified from the Kubernetes WordPress Tutorial.
+  - question: How do I know I’m ready to deploy WordPress to the cluster?
+    answer: >-
+      You should have an AKS cluster already running from the previous topic. Once the cluster
+      is provisioned, proceed to create the YAML files and deploy the example workload.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md b/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
index 6987e6a360..2f19161973 100644
--- a/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
@@ -24,6 +24,59 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:15:38Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 90b638385267e085ea07a70a1c23f16578aa3caaeabeca1f651bcdcac5bf38fb
+  summary_generated_at: '2026-06-02T03:04:20Z'
+  summary_source_hash: 90b638385267e085ea07a70a1c23f16578aa3caaeabeca1f651bcdcac5bf38fb
+  faq_generated_at: '2026-06-03T00:15:38Z'
+  faq_source_hash: 90b638385267e085ea07a70a1c23f16578aa3caaeabeca1f651bcdcac5bf38fb
+  summary: >-
+    This Learning Path shows how to deploy Apache Arrow and Arrow Flight on Arm-based Google Cloud
+    C4A Axion instances for high-throughput columnar analytics and low-latency data transport.
+    You will provision a c4a-standard-4 arm64 VM running Linux (SUSE Linux Enterprise Server),
+    configure Google Cloud firewall rules for MinIO and Arrow Flight, install Arrow and MinIO,
+    and assemble a single-node analytics stack. Using Python, you will read and write Parquet
+    and ORC datasets stored in MinIO and explore predicate pushdown and column pruning. The path
+    also integrates Arrow Flight and includes guidance to validate performance benefits on Arm-based
+    infrastructure. Prerequisites include a GCP account with billing enabled and basic familiarity
+    with Python, Parquet/ORC, and the Linux command line.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Google Cloud Platform (GCP) account with billing enabled, basic familiarity with
+      Python, a basic understanding of Parquet or ORC, and comfort with Linux command-line operations.
+      No other prerequisites are explicitly listed.
+  - question: Which Google Cloud machine type and operating system are used?
+    answer: >-
+      You will create an Axion C4A arm64 virtual machine using the c4a-standard-4 type, which
+      provides 4 vCPUs and 16 GB of memory. The environment is based on SUSE Linux Enterprise
+      Server (SLES) for arm64.
+  - question: Which firewall ports should I open for MinIO and Arrow Flight?
+    answer: >-
+      Open port 9000 for the MinIO S3 API as listed in the path. Additional ports for the MinIO
+      Web UI and Arrow Flight are required; follow the port list provided in the firewall setup
+      step.
+  - question: How is MinIO used, and how does Apache Arrow access data?
+    answer: >-
+      MinIO provides S3-compatible object storage for analytical datasets. Apache Arrow uses its
+      Dataset API to read and write Parquet and ORC files stored in MinIO.
+  - question: What result should I expect after the analysis and Arrow Flight steps, and how can
+      I validate success?
+    answer: >-
+      You should be able to store datasets in MinIO and use Apache Arrow to read and write Parquet
+      and ORC while exploring predicate pushdown and column pruning. You also set up and run an
+      Arrow Flight server for low-latency data transport, with access allowed by your firewall
+      rules. The path includes validation of performance benefits on Arm-based C4A, but it does
+      not provide specific metrics.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
index 8e5f15f275..aeaedde83c 100644
--- a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
@@ -22,6 +22,51 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:16:17Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 964c1e6a87810dca686a7b3218433ce09d31bd92882db10af355e8c50bb6091c
+  summary_generated_at: '2026-06-02T03:04:42Z'
+  summary_source_hash: 964c1e6a87810dca686a7b3218433ce09d31bd92882db10af355e8c50bb6091c
+  faq_generated_at: '2026-06-03T00:16:17Z'
+  faq_source_hash: 964c1e6a87810dca686a7b3218433ce09d31bd92882db10af355e8c50bb6091c
+  summary: >-
+    Follow a concise workflow to deploy Arcee’s AFM-4.5B small language model on Arm-based AWS
+    Graviton4 using Llama.cpp. You will launch a Graviton4 EC2 instance (c8g.4xlarge or larger),
+    configure a Linux environment with system packages and a Python virtual environment, build
+    Llama.cpp from source, download the AFM-4.5B model from Hugging Face, quantize it, and run
+    inference. The path includes evaluating model quality using perplexity. Prerequisites are
+    an AWS account with permission to launch Graviton4 instances, at least 128 GB of available
+    storage, and basic Linux and SSH familiarity. The estimated time to complete is about 30 minutes.
+  faqs:
+  - question: Do I need specific AWS access or resources before starting?
+    answer: >-
+      Yes. You need an AWS account with permission to launch Graviton4 EC2 instances and at least
+      128 GB of available storage. Basic familiarity with Linux and SSH is also expected.
+  - question: Which EC2 instance type should I launch for this workflow?
+    answer: >-
+      Use an Arm-based AWS Graviton4 instance of type c8g.4xlarge or larger. The steps assume
+      a Linux environment on this instance.
+  - question: How do I connect to the EC2 instance?
+    answer: >-
+      Create an SSH key pair in the EC2 console as part of the provisioning steps. You will use
+      this key pair to establish an SSH connection to your Graviton4 instance.
+  - question: Which Llama.cpp repository should I use for AFM-4.5B?
+    answer: >-
+      Use the standard upstream repository at https://github.com/ggerganov/llama.cpp. Arcee AI
+      has contributed the necessary modeling code upstream, so no custom fork is required.
+  - question: What are the main steps after provisioning the instance?
+    answer: >-
+      Install system packages and a Python environment, then build Llama.cpp from source. Next,
+      download the AFM-4.5B model from Hugging Face, quantize it, run inference with Llama.cpp,
+      and evaluate quality by measuring perplexity.
+# END generated_summary_faq
 
 author: Julien Simon
 
diff --git a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
index c2c34acaae..03aac5c90e 100644
--- a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
@@ -22,6 +22,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:16:57Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: b2f60d2c31ef380e6e6ef3028671ad9242dd51c98f4a192f2da32bb05b94e185
+  summary_generated_at: '2026-06-02T03:05:06Z'
+  summary_source_hash: b2f60d2c31ef380e6e6ef3028671ad9242dd51c98f4a192f2da32bb05b94e185
+  faq_generated_at: '2026-06-03T00:16:57Z'
+  faq_source_hash: b2f60d2c31ef380e6e6ef3028671ad9242dd51c98f4a192f2da32bb05b94e185
+  summary: >-
+    This Learning Path guides you through deploying Arcee’s AFM-4.5B small language model on Arm-based
+    Google Cloud Axion instances using Llama.cpp. You will provision a Linux Compute Engine VM
+    (c4a-standard-16 or larger), install system and Python dependencies, build Llama.cpp from
+    source, download the model from Hugging Face, quantize it, and run inference. You will also
+    evaluate model quality by measuring perplexity. It targets developers and ML engineers and
+    is scoped to about 30 minutes. Prerequisites include a Google Cloud account with permission
+    and quota to launch Axion instances, at least 128 GB of available storage, and basic familiarity
+    with Linux and SSH.
+  faqs:
+  - question: What do I need in my Google Cloud project before launching the VM?
+    answer: >-
+      You need permission and sufficient quota to launch a Google Cloud Axion instance of type
+      c4a-standard-16 (or larger). Ensure at least 128 GB of available storage for the model and
+      dependencies.
+  - question: Which Llama.cpp repository should I clone for AFM-4.5B support?
+    answer: >-
+      Use the standard Llama.cpp repository: git clone https://github.com/ggerganov/llama.cpp.
+      AFM-4.5B support is available because Arcee AI contributed the necessary modeling code upstream.
+  - question: Do I need a Hugging Face account or token to download AFM-4.5B?
+    answer: >-
+      The Learning Path states that you will download the AFM-4.5B model from Hugging Face, but
+      it does not explicitly list whether a Hugging Face account or token is required. Follow
+      the steps as provided in the path.
+  - question: Why create a Python virtual environment for Llama.cpp, and how is it set up here?
+    answer: >-
+      A virtual environment isolates dependencies and prevents conflicts. In this path, you create
+      one with virtualenv env-llama-cpp before installing the required Python packages.
+  - question: What result should I expect after completing the steps?
+    answer: >-
+      You will have built Llama.cpp on a Google Cloud Axion Arm64 VM, downloaded and quantized
+      AFM-4.5B, and run inference. You will also evaluate model quality by measuring perplexity.
+# END generated_summary_faq
 
 author: Julien Simon
 
diff --git a/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
index 9e34a8506b..58cf2954de 100644
--- a/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
@@ -26,6 +26,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:17:35Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: c6106f21859f5a8e04bbd579db47c7177bb5371d4c7f306054e56e81ed9e89a1
+  summary_generated_at: '2026-06-02T03:05:29Z'
+  summary_source_hash: c6106f21859f5a8e04bbd579db47c7177bb5371d4c7f306054e56e81ed9e89a1
+  faq_generated_at: '2026-06-03T00:17:35Z'
+  faq_source_hash: c6106f21859f5a8e04bbd579db47c7177bb5371d4c7f306054e56e81ed9e89a1
+  summary: >-
+    Learn how to deploy and manage applications on Arm-based Google Kubernetes Engine (GKE) using
+    Argo CD and GitOps. You will provision an Arm-based SUSE Linux Enterprise Server VM on a Google
+    Axion C4A instance, create and connect to a GKE cluster running on Arm64 nodes, and install
+    Argo CD using official manifests. The path covers configuring browser and CLI access, deploying
+    a production-ready NGINX application from a Git repository, and enabling automated sync, pruning,
+    and self-healing. By the end, you will verify application health and access to services on
+    GKE. Prerequisites include a GCP account with billing enabled, basic Kubernetes and Git/GitHub
+    knowledge, and Linux CLI familiarity.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Google Cloud Platform (GCP) account with billing enabled and basic familiarity
+      with Kubernetes, Git/GitHub workflows, and Linux command-line usage. The path uses a SUSE
+      Linux Arm64 VM that you provision on Google Cloud as part of the steps.
+  - question: Which Google Cloud VM and OS are used for the setup host?
+    answer: >-
+      The example provisions a Google Axion C4A VM using the c4a-standard-4 machine type (4 vCPUs,
+      16 GB memory) running SUSE Linux Enterprise Server (SLES) for Arm64. This VM is used to
+      prepare and interact with the GKE environment.
+  - question: What type of GKE cluster should I create for this path?
+    answer: >-
+      Create a production-ready Arm64 GKE cluster running on Axion (C4A) nodes to support GitOps
+      deployments with Argo CD. The steps guide you to prepare the cluster from the SLES Arm64
+      VM.
+  - question: How do I know Argo CD is installed and accessible?
+    answer: >-
+      Argo CD is installed using the official upstream manifests into a dedicated namespace. You
+      should be able to access the Argo CD UI in a browser, retrieve the admin credentials, and
+      authenticate with the Argo CD CLI.
+  - question: What repository do I need for the GitOps deployment?
+    answer: >-
+      You need a GitHub repository to store the GitOps manifests; an empty repository is sufficient
+      to start. Argo CD continuously reconciles the cluster to match the desired state defined
+      in this repo.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md b/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
index 2d072d1766..49dc756b1f 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
@@ -20,6 +20,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:18:10Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 3a4bc8b2ab548be507b6d528844b0593b27f2dab3ab3c9649e807abdf4887ce0
+  summary_generated_at: '2026-06-02T03:06:00Z'
+  summary_source_hash: 3a4bc8b2ab548be507b6d528844b0593b27f2dab3ab3c9649e807abdf4887ce0
+  faq_generated_at: '2026-06-03T00:18:10Z'
+  faq_source_hash: 3a4bc8b2ab548be507b6d528844b0593b27f2dab3ab3c9649e807abdf4887ce0
+  summary: >-
+    This Learning Path helps experienced C++ developers port concurrent code from x86 to Arm by
+    explaining the C++ memory model, highlighting key memory ordering differences, and demonstrating
+    how subtle races can appear on Arm. You will run a simple race condition example on both x86
+    and Arm cloud instances (Linux), with an example using an Arm-based AWS t4g.xlarge instance
+    running Ubuntu 22.04 LTS, though other instance types can be used. You will use ThreadSanitizer
+    (TSan) to detect infrequent data races and learn best practices for writing correct C++ on
+    Arm. Prerequisites include access to both an x86 and an Arm VM and proficiency in C++. Estimated
+    time to complete is about 45 minutes.
+  faqs:
+  - question: What do I need before running the example?
+    answer: >-
+      You need access to both an x86 and an Arm cloud instance (virtual machine) and proficiency
+      in C++ programming. The Learning Path assumes a Linux environment.
+  - question: Which Arm instance and OS are used in the walkthrough?
+    answer: >-
+      The example uses an AWS t4g.xlarge instance running Ubuntu 22.04 LTS. You can use other
+      Arm instance types if preferred.
+  - question: Which compiler/toolchain should I use for ThreadSanitizer (TSan)?
+    answer: >-
+      Use a recent version of the clang toolchain that includes TSan support. TSan instruments
+      the code at compile time to detect data races.
+  - question: How do I know if the race condition has been reproduced?
+    answer: >-
+      Expect differences in program behavior between x86 and Arm due to memory ordering, as illustrated
+      by the example. When you run with TSan, it will report data races if they are present, including
+      details to help you debug.
+  - question: What operating system is assumed for this Learning Path?
+    answer: >-
+      Linux is the target operating system. The example specifically references Ubuntu 22.04 LTS
+      on an Arm instance.
+# END generated_summary_faq
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md b/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
index a5c9d16fb5..a27d363d57 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
@@ -23,6 +23,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:18:38Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: b870ac5160d35ddb51955ca8379493e172787ced8125cde8ae79f7700e653a87
+  summary_generated_at: '2026-06-02T03:06:30Z'
+  summary_source_hash: b870ac5160d35ddb51955ca8379493e172787ced8125cde8ae79f7700e653a87
+  faq_generated_at: '2026-06-03T00:18:38Z'
+  faq_source_hash: b870ac5160d35ddb51955ca8379493e172787ced8125cde8ae79f7700e653a87
+  summary: >-
+    This Learning Path guides you through automating x86-to-Arm application migration using the
+    Arm MCP Server. You will connect an AI-powered IDE or agent to the MCP Server to run AI-assisted
+    checks on Docker images for arm64 support, refactor C++ (including SIMD intrinsics cases)
+    with the Arm Cloud Migration Agent in GitHub Copilot, and validate the migrated application
+    in Docker on Arm-based systems. You also configure the same migration workflow in other agentic
+    tools. Prerequisites include an AI-enabled IDE (for example VS Code with Copilot, Kiro, or
+    Codex), basic Docker and C/C++ knowledge, and access to an Arm-based Linux or macOS system.
+    Estimated time to complete is about 20 minutes.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      Have an AI-powered IDE (for example, VS Code with GitHub Copilot, Kiro, or Codex), basic
+      familiarity with Docker and C/C++ development, and access to an Arm-based cloud instance
+      or a local Arm machine running Linux or macOS.
+  - question: How do I check if a Docker base image supports arm64 during migration?
+    answer: >-
+      Use natural language prompts with the Arm MCP Server to ask about arm64 compatibility. This
+      avoids manual manifest inspection and returns an AI-assisted compatibility assessment you
+      can act on.
+  - question: I’m not using GitHub Copilot—how do I follow the migration workflow?
+    answer: >-
+      Skip to the section on configuring other agentic systems and set up persistent instructions
+      (such as steering documents or prompt files) for your tool. The goal is to let your AI assistant
+      use the Arm MCP Server to execute the same multi-step migration workflow.
+  - question: What should I do if my C++ code uses x86 SIMD intrinsics?
+    answer: >-
+      Use the Arm Cloud Migration Agent in GitHub Copilot to guide refactoring from SSE/AVX/AVX2
+      intrinsics to Arm Neon or SVE equivalents. Follow the agent’s structured steps to address
+      architecture-specific vector code.
+  - question: How do I validate the migrated C++ application on Arm?
+    answer: >-
+      Run and validate the application in Docker on an Arm-based system as outlined in the path.
+      You should be able to execute the container on Arm Linux or macOS and confirm the application
+      runs as expected.
+# END generated_summary_faq
 
 author: Joe Stech
 
diff --git a/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md b/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
index c5d36a031e..a8f4612678 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
@@ -24,6 +24,57 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:19:04Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 49b3f2b1464efb20f0c8fbcff46e9f30910d856567ca01c01dc348aa1c5740d1
+  summary_generated_at: '2026-06-02T03:06:57Z'
+  summary_source_hash: 49b3f2b1464efb20f0c8fbcff46e9f30910d856567ca01c01dc348aa1c5740d1
+  faq_generated_at: '2026-06-03T00:19:04Z'
+  faq_source_hash: 49b3f2b1464efb20f0c8fbcff46e9f30910d856567ca01c01dc348aa1c5740d1
+  summary: >-
+    This advanced Learning Path shows how to migrate a C application between Arm platforms using
+    Kiro Arm SoC Migration Power. You install Kiro IDE on your local machine, enable the Migration
+    Power, and use an Arm MCP server deployed as a Docker-based backend for Arm-specific guidance.
+    You build and validate a sensor-monitor application on an AWS Graviton3 source platform, then
+    follow an AI-guided workflow—discovery, architecture analysis, abstraction design, and platform-specific
+    implementation—to target a Raspberry Pi 5. Finally, you validate on both platforms using testing
+    recommendations for functional correctness, platform compatibility, hardware interaction,
+    and performance comparison. Prerequisites include access to both platforms, C experience,
+    Linux familiarity, and the ability to build with GCC and CMake. Estimated time is 60 minutes.
+  faqs:
+  - question: What do I need before running the migration workflow?
+    answer: >-
+      You need access to both source and target Arm platforms (for example, AWS Graviton3 and
+      Raspberry Pi 5), working knowledge of C, familiarity with Linux development, and experience
+      with GCC and CMake. Basic embedded or cloud deployment concepts are also assumed.
+  - question: How do I set up Kiro and the required backend services?
+    answer: >-
+      Install Kiro IDE on your local machine, enable Kiro Arm SoC Migration Power, and run the
+      Arm MCP server as a containerized backend using Docker. You also provision an AWS Graviton3
+      instance to serve as the source platform for the example.
+  - question: Which application and platforms are used in the example?
+    answer: >-
+      The example uses a sensor-monitor application. The migration demonstrates moving from AWS
+      Graviton3 (Neoverse) to Raspberry Pi 5 (Cortex-A76), though the workflow applies to other
+      Arm-to-Arm scenarios.
+  - question: How do I know the analysis phase is working during migration?
+    answer: >-
+      The Migration Power highlights platform-specific and hardware-dependent code and guides
+      abstraction boundaries. Use these findings to design and implement a hardware abstraction
+      layer before adding platform-specific implementations.
+  - question: What should I check to confirm the migration is successful?
+    answer: >-
+      Use the Power’s testing recommendations on both source and target platforms to verify functional
+      correctness, confirm platform compatibility, validate hardware interaction, and compare
+      performance characteristics. Migration is complete when these checks pass on both environments.
+# END generated_summary_faq
 
 author: Daniel Schleicher
 
diff --git a/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md b/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
index 60770bc549..ce78d097c7 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
@@ -22,6 +22,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:19:30Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 67004eb9a75926144eb6f75124104972b3cf20a57a22b698c9359d20db3eca02
+  summary_generated_at: '2026-06-02T03:07:24Z'
+  summary_source_hash: 67004eb9a75926144eb6f75124104972b3cf20a57a22b698c9359d20db3eca02
+  faq_generated_at: '2026-06-03T00:19:30Z'
+  faq_source_hash: 67004eb9a75926144eb6f75124104972b3cf20a57a22b698c9359d20db3eca02
+  summary: >-
+    This Learning Path shows how to install and boot a Linux kernel configured for 64K page size
+    on Arm-based systems to improve memory efficiency and performance for memory‑intensive workloads.
+    You will learn the role of page size, how Arm64 differs from x86, and how page size impacts
+    efficiency and performance. The steps cover checking the current page size, switching to a
+    64K kernel on Ubuntu 22.04 LTS or later, Debian 11 “Bullseye” or later (compiled from source),
+    and CentOS 9 or later, then confirming the change and optionally reverting to a 4K kernel.
+    Prerequisite: access to an Arm-based Linux system running Ubuntu, Debian, or CentOS. The path
+    uses bash and is introductory.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need access to an Arm-based Linux system running Ubuntu, Debian, or CentOS. No other
+      explicit prerequisites are listed.
+  - question: Which Linux distributions and versions are covered?
+    answer: >-
+      Ubuntu 22.04 LTS or later, Debian 11 “Bullseye” or later, and CentOS 9 or later. The steps
+      are distribution-specific.
+  - question: How do I check my current memory page size and kernel?
+    answer: >-
+      Run getconf PAGESIZE and uname -r. A first line of 4096 indicates a 4K base-page-size kernel;
+      if it is different, you are already using a non-4K page size.
+  - question: On Debian, do I need to compile a 64K kernel and which source should I use?
+    answer: >-
+      Yes. Debian does not provide a 64K kernel package, so you must compile from source; you
+      can use kernel.org or the Debian source package, and this path uses the Debian source package.
+  - question: How do I verify the 64K page size is active, and can I revert to 4K?
+    answer: >-
+      Re-run getconf PAGESIZE after booting the new kernel; it should no longer report 4096 and
+      should reflect the 64K configuration. The path includes an optional step to revert to the
+      default 4K page size kernel.
+# END generated_summary_faq
 
 author: Geremy Cohen
 
diff --git a/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md b/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
index b17b8c2daa..c5e27624fc 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
@@ -19,6 +19,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:20:03Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
+  summary_generated_at: '2026-06-02T03:07:54Z'
+  summary_source_hash: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
+  faq_generated_at: '2026-06-03T00:20:03Z'
+  faq_source_hash: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
+  summary: >-
+    Learn how to access Arm hardware performance counters (PMU) and the system counter from user
+    space on Linux. You will measure time using the system counter with small assembly snippets
+    (MRS/MSR), instrument event counters with PAPI, and use the Linux perf_event_open system call
+    to read both single counters and groups (without multiplexing). The path covers installing
+    PAPI, setting environment variables (PAPI_DIR and, if needed, LD_LIBRARY_PATH), enabling user-space
+    access to counters, and building example programs with GCC. Target environment is an Arm computer
+    running Linux; bare-metal or cloud metal instances expose more counters, and the steps were
+    tested on the a1.metal instance type. You will finish with working code that reads hardware
+    and system counter values.
+  faqs:
+  - question: What do I need before running the examples?
+    answer: >-
+      You need an Arm computer running Linux. A bare metal or cloud metal instance is recommended
+      because it exposes more counters, while a VM may provide fewer counters. The instructions
+      were tested on an a1.metal instance.
+  - question: How do I decide between using the system counter, PAPI, or perf_event_open?
+    answer: >-
+      Use the system counter via MRS/MSR if you only need to measure time or cycles from user
+      space. Use PAPI to instrument event counters in application code, or use the perf_event_open
+      system call to read hardware event counters directly.
+  - question: Which environment variables and permissions are required for the PAPI steps?
+    answer: >-
+      Set PAPI_DIR to the PAPI installation path, and you might need to add $PAPI_DIR/lib to LD_LIBRARY_PATH.
+      The steps also include enabling user space access to counters using a sudo command to change
+      a kernel setting.
+  - question: What does the perf_event_open section demonstrate, and does it support multiplexing?
+    answer: >-
+      It provides two examples: reading a single hardware counter and reading a group of counters
+      without multiplexing. perf_event_open does not support multiplexing.
+  - question: What should I check if I cannot access certain hardware counters?
+    answer: >-
+      Confirm that user space access to counters has been enabled as shown in the steps. Also
+      note that VMs may expose fewer counters; using a bare metal or cloud metal instance typically
+      provides more.
+# END generated_summary_faq
 
 author: Julio Suarez
 
diff --git a/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md b/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
index 4a98a1aa32..6f65c1e941 100644
--- a/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
@@ -20,6 +20,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:20:41Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: ced3882cf8be11a55304d2697f450a2c862a81d87317ab42298148755e9f272c
+  summary_generated_at: '2026-06-02T03:08:36Z'
+  summary_source_hash: ced3882cf8be11a55304d2697f450a2c862a81d87317ab42298148755e9f272c
+  faq_generated_at: '2026-06-03T00:20:41Z'
+  faq_source_hash: ced3882cf8be11a55304d2697f450a2c862a81d87317ab42298148755e9f272c
+  summary: >-
+    This Learning Path shows how to package a multi-architecture container and deploy it to AWS
+    Fargate using the AWS Copilot CLI, configured to run on AWS Graviton processors. You will
+    containerize an example service, use copilot init to build locally, push the image to Amazon
+    ECR, and provision a load balanced web service on Fargate. It explains Copilot’s default amd64
+    behavior and where to set the architecture to Arm for Graviton. Prerequisites are an AWS account
+    and a local machine with Docker, AWS CLI, and AWS Copilot CLI installed. The guide is applicable
+    to Linux and macOS users.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an AWS account and a local environment with Docker, AWS CLI, and the AWS Copilot
+      CLI installed. The path targets Linux, and the steps note macOS is also applicable.
+  - question: What architecture does Copilot use by default, and how does this affect deploying
+      on Graviton?
+    answer: >-
+      Copilot defaults to amd64. To run on AWS Graviton processors with Fargate, you must explicitly
+      set the architecture to Arm as described in the steps.
+  - question: How do I deploy the sample service with Copilot?
+    answer: >-
+      Use the copilot init command shown in the path to build from your Dockerfile, create a Load
+      Balanced Web Service, and deploy to an environment. Copilot builds locally, pushes the image
+      to Amazon ECR, and provisions the Fargate resources.
+  - question: Can I use an existing container image instead of building from a Dockerfile?
+    answer: >-
+      Yes. Use the --image option instead of --dockerfile, and ensure the image is multi-architecture.
+  - question: What result should I expect after a successful deployment?
+    answer: >-
+      A running service on AWS Fargate with the image stored in Amazon ECR, configured as a Load
+      Balanced Web Service. Copilot will have created the required infrastructure in the specified
+      environment.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md b/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
index 50baf3c248..f7585c1164 100644
--- a/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
@@ -20,6 +20,51 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:21:25Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 087a4d56914e5b53cec5ad17e8d682e72d04ba6b394237c081efe4a3f939e04e
+  summary_generated_at: '2026-06-02T03:09:04Z'
+  summary_source_hash: 087a4d56914e5b53cec5ad17e8d682e72d04ba6b394237c081efe4a3f939e04e
+  faq_generated_at: '2026-06-03T00:21:25Z'
+  faq_source_hash: 087a4d56914e5b53cec5ad17e8d682e72d04ba6b394237c081efe4a3f939e04e
+  summary: >-
+    This Learning Path shows how to automate the provisioning of Arm-based AWS Graviton instances
+    using Terraform, with access provided through a Jump Server (bastion) for secure infrastructure
+    management. You will use Terraform Cloud to define and deploy EC2 resources on AWS and work
+    with reusable infrastructure-as-code files that you can adapt for future Learning Paths. Prerequisites
+    are an AWS account and a computer with Terraform installed; any desktop, laptop, or VM with
+    the required tools is suitable. By the end, you will have Arm instances deployed on AWS with
+    jump server access and a foundation for modifying the provided Terraform for related exercises.
+    Estimated time to complete is about 60 minutes.
+  faqs:
+  - question: What do I need before running the Terraform steps?
+    answer: >-
+      You need an AWS account and a computer with Terraform installed. Any computer with the required
+      tools can be used.
+  - question: Does this path use Terraform Cloud or local Terraform?
+    answer: >-
+      The steps use Terraform Cloud to automate the creation of Arm instances. Follow the instructions
+      in the path to run the workflow in Terraform Cloud.
+  - question: What infrastructure gets created by the configuration?
+    answer: >-
+      It provisions AWS EC2 Arm instances (Graviton) and sets up access through a Jump Server
+      (bastion). The Jump Server provides a supervised, secure channel between networks.
+  - question: How do I access the deployed instances?
+    answer: >-
+      Access is provided via the Jump Server (bastion). Traffic is funneled through this intermediary
+      host to add a security barrier between networks.
+  - question: Can I reuse or modify the Terraform files for other Learning Paths?
+    answer: >-
+      Yes. The Terraform files are intended as a platform you can adapt to support other Learning
+      Paths that require one or more server nodes.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
index d4b9364d2a..2c5fba26a9 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
@@ -22,6 +22,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:21:51Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 1682c0527bb49c417263286e2aba7c82fb9a27fa315ecc6b9ca10978b69cc236
+  summary_generated_at: '2026-06-02T03:09:38Z'
+  summary_source_hash: 1682c0527bb49c417263286e2aba7c82fb9a27fa315ecc6b9ca10978b69cc236
+  faq_generated_at: '2026-06-03T00:21:51Z'
+  faq_source_hash: 1682c0527bb49c417263286e2aba7c82fb9a27fa315ecc6b9ca10978b69cc236
+  summary: >-
+    Learn how to automate the provisioning of Arm64-based Azure Cobalt 100 virtual machines using
+    Azure Resource Manager (ARM) templates and the Azure CLI. You will author a JSON template
+    with parameters, variables, resources, and outputs to define a Linux VM, networking, security
+    settings, and SSH access. The path shows how to specify Arm64 and choose a Cobalt 100 VM size,
+    deploy the template to a resource group, and verify the VM by connecting over SSH and checking
+    uname -m for aarch64. Prerequisites include an Azure subscription with sufficient permissions,
+    the Azure CLI installed, and an SSH key pair. By the end, you can reproducibly deploy a Cobalt
+    100 VM and validate the Arm64 environment.
+  faqs:
+  - question: What do I need before running the template?
+    answer: >-
+      You need an Azure subscription with permissions to create resource groups, virtual machines,
+      and networking resources, the Azure CLI installed, and an SSH key pair. These are the only
+      explicit prerequisites listed.
+  - question: Which Azure region and VM size should I use for Cobalt 100?
+    answer: >-
+      Create a resource group in your preferred region, then query available VM SKUs in that region
+      and filter for the Dpsv6 series to find Cobalt 100 sizes. The Learning Path shows an az
+      vm list-skus command and grep filter you can run to confirm availability.
+  - question: How is the ARM template structured and how do I customize it?
+    answer: >-
+      The template is organized into $schema, contentVersion, parameters, variables, resources,
+      and outputs. You customize deployments by defining inputs in parameters, computing values
+      in variables, and specifying resources that include the VM, networking, security settings,
+      and SSH authentication.
+  - question: How do I get the VM’s public IP to connect over SSH?
+    answer: >-
+      Use the public IP address recorded during the deployment step or retrieve it from your template
+      outputs. The path then uses that IP with your SSH key to connect.
+  - question: What result should I expect after deployment, and how do I verify Arm64?
+    answer: >-
+      You should have a Linux VM powered by Cobalt 100 with its networking and SSH access configured
+      in your resource group. After connecting via SSH, run uname -m and expect aarch64; lscpu
+      will show additional CPU details.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
index 7942211dfb..cc4485c1ab 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
@@ -21,6 +21,51 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:22:28Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: b5bd3abde8d9dd3146e0f11f4819ac510417ad7f707b4d0970c02fd63801b358
+  summary_generated_at: '2026-06-02T03:10:03Z'
+  summary_source_hash: b5bd3abde8d9dd3146e0f11f4819ac510417ad7f707b4d0970c02fd63801b358
+  faq_generated_at: '2026-06-03T00:22:28Z'
+  faq_source_hash: b5bd3abde8d9dd3146e0f11f4819ac510417ad7f707b4d0970c02fd63801b358
+  summary: >-
+    This Learning Path shows how to configure a self-hosted GitHub Actions Arm64 runner on an
+    Azure Cobalt 100 VM, create an Arm-based Azure Kubernetes Service (AKS) cluster with Terraform,
+    and deploy a .NET 8 web application using GitHub Actions CI/CD. You will work on Linux and
+    use Azure CLI, kubectl, and Terraform to automate infrastructure and deployment on Microsoft’s
+    Armv9 Neoverse-N2–based Cobalt 100 instances (Dpsv6/Dplsv6/Epsv6). By the end, you will have
+    a running application on AKS triggered from a self-hosted runner. Prerequisites are a Microsoft
+    Azure account, a GitHub account, and a machine with Terraform, Azure CLI, and kubectl installed.
+    Estimated time to complete is about 60 minutes.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      You need a Microsoft Azure account, a GitHub account, and a machine with Terraform, Azure
+      CLI, and kubectl installed. The procedures use Linux.
+  - question: Which Azure Cobalt 100 VM series should I use for the runner or AKS nodes?
+    answer: >-
+      The path does not prescribe a specific series. Azure Cobalt 100 offers general-purpose Dpsv6
+      and Dplsv6 VMs and a memory-optimized Epsv6 series; select from these in the steps as appropriate.
+  - question: Can I use GitHub-hosted Arm64 runners instead of a self-hosted runner?
+    answer: >-
+      GitHub-hosted Arm64 runners are generally available for Team and Enterprise Cloud accounts.
+      This path demonstrates using a self-hosted Arm64 runner on an Azure Cobalt 100 VM.
+  - question: What does the Terraform configuration create?
+    answer: >-
+      An Azure Kubernetes Service (AKS) cluster with Arm-based Azure Cobalt 100 nodes. The cluster
+      is the target environment for deploying the .NET application.
+  - question: What should I expect after the GitHub Actions workflow runs?
+    answer: >-
+      The .NET 8-based web application is built and deployed to the AKS cluster using the self-hosted
+      Arm64 runner. The steps guide you through the CI/CD pipeline to reach this state.
+# END generated_summary_faq
 
 author: Pranay Bakre
 
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
index 7011a8df88..916aa26064 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
@@ -21,6 +21,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:23:24Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 6713af3d70887933cf54c7fa36bdc88d71a51fa34fb29a4ce90636ff1de624c7
+  summary_generated_at: '2026-06-02T03:10:40Z'
+  summary_source_hash: 6713af3d70887933cf54c7fa36bdc88d71a51fa34fb29a4ce90636ff1de624c7
+  faq_generated_at: '2026-06-03T00:23:24Z'
+  faq_source_hash: 6713af3d70887933cf54c7fa36bdc88d71a51fa34fb29a4ce90636ff1de624c7
+  summary: >-
+    This Learning Path shows how to automate the creation of Arm-based virtual machines on Microsoft
+    Azure using Terraform and Terraform Cloud. You will deploy Azure Arm VMs (Neoverse) and configure
+    access through a Jump Server (bastion host), using provided Terraform code you can adapt for
+    future Learning Paths. The steps require an Azure account and a computer with Terraform installed;
+    any desktop, laptop, or VM with the required tools will work. Positioned as an introductory
+    topic for developers new to deploying Arm instances on Azure with Terraform, the same instructions
+    can be used to deploy Linux as well. By the end, you will have automated infrastructure and
+    a controlled access path to your instances.
+  faqs:
+  - question: What do I need before running the Terraform steps?
+    answer: >-
+      You need an Azure account and a computer with Terraform installed. Any desktop, laptop,
+      or virtual machine with the required tools can be used. You will also need access to the
+      Azure portal.
+  - question: Which Terraform workflow does this Learning Path use?
+    answer: >-
+      It uses Terraform Cloud to automate the instantiation of Arm instances on Azure. The provided
+      Terraform files form the basis of the deployment.
+  - question: Can I deploy Linux or Windows on Arm with these instructions?
+    answer: >-
+      The same instructions can be used to deploy Linux. A related guide for deploying a Windows
+      on Arm virtual machine on Microsoft Azure is referenced.
+  - question: How is access to the deployed VMs provided?
+    answer: >-
+      Access is provided via a Jump Server (also known as a bastion host). The Jump Server funnels
+      traffic through firewalls using a supervised secure channel to create a barrier between
+      networks.
+  - question: What should I expect to have at the end of this Learning Path?
+    answer: >-
+      You will have Arm virtual machines deployed on Azure and access set up through a Jump Server.
+      You will also have Terraform files that you can modify and reuse as a platform for other
+      Learning Paths.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
index 8f09adacf2..ba8dd46620 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
@@ -25,6 +25,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:24:06Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 413467e581e3561425229d0f6118975dbb596d6a9494544a54f0b9ab22c1b89d
+  summary_generated_at: '2026-06-02T03:11:18Z'
+  summary_source_hash: 413467e581e3561425229d0f6118975dbb596d6a9494544a54f0b9ab22c1b89d
+  faq_generated_at: '2026-06-03T00:24:06Z'
+  faq_source_hash: 413467e581e3561425229d0f6118975dbb596d6a9494544a54f0b9ab22c1b89d
+  summary: >-
+    This advanced Learning Path guides you through building a custom Azure Linux 3.0 image for
+    Arm and deploying it on Microsoft Azure Cobalt 100 processors. You will use QEMU on a Linux
+    host to create a raw disk image, install Azure Linux 3.0 from an AArch64 ISO, convert the
+    disk to a fixed-size VHD, upload it to Azure, and register it in Azure Shared Image Gallery.
+    Finally, you will create an Azure VM using the Azure CLI and your custom image. Prerequisites
+    include an Azure account with permissions for Cobalt 100 resources and a Linux machine with
+    QEMU and the Azure CLI installed and authenticated. Estimated time: about 120 minutes.
+  faqs:
+  - question: What do I need before running these steps?
+    answer: >-
+      You need a Microsoft Azure account with permission to create resources, including instances
+      using Cobalt 100 processors. You also need a Linux machine with QEMU and the Azure CLI installed
+      and authenticated.
+  - question: Which Azure Linux ISO and architecture should I use with QEMU?
+    answer: >-
+      Use the Azure Linux 3.0 AArch64 (Arm64) ISO. The Learning Path points to the Azure Linux
+      3.0 project README, which includes links to ISO downloads.
+  - question: What artifacts should I have before uploading to Azure?
+    answer: >-
+      After installing Azure Linux 3.0 in QEMU, you should have a raw disk image that you convert
+      to a fixed-size VHD. The VHD file is the artifact you upload to Azure.
+  - question: How is the VHD registered so I can reuse it to create VMs?
+    answer: >-
+      You upload the VHD to Azure Blob Storage using the Azure CLI, then register it in Azure
+      Shared Image Gallery. This process produces a custom image you can reference by its image
+      ID.
+  - question: How do I launch a VM on Cobalt 100 using my custom image?
+    answer: >-
+      Use az vm create with the image ID from the Shared Image Gallery and specify the VM size
+      targeting Cobalt 100 Arm-based processors. Provide the resource group, VM name, image ID,
+      size, admin username, and optionally generate an SSH key as shown in the example.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md b/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
index 28eaf31203..080a7f6e10 100644
--- a/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
@@ -19,6 +19,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:24:45Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: a4cf1d9161b3a32e29694415762eda419752e1c3144662d5e131b6553f0a58e3
+  summary_generated_at: '2026-06-02T03:11:52Z'
+  summary_source_hash: a4cf1d9161b3a32e29694415762eda419752e1c3144662d5e131b6553f0a58e3
+  faq_generated_at: '2026-06-03T00:24:45Z'
+  faq_source_hash: a4cf1d9161b3a32e29694415762eda419752e1c3144662d5e131b6553f0a58e3
+  summary: >-
+    Learn to deploy and evaluate Hugging Face Sentiment Analysis models with PyTorch on Arm servers
+    running Linux. You will run three NLP models using the Sentiment Analysis pipeline, then enable
+    BFloat16 fast math kernels on Arm Neoverse-based AWS Graviton3 processors to measure performance
+    uplift. The instructions target Ubuntu 22.04 LTS on an Arm server with at least four cores
+    and 8GB of RAM, and have been tested on AWS Graviton3 (c7g). You will use Python, PyTorch,
+    and Hugging Face to complete the workflow. Prerequisite: access to an Arm-based instance from
+    a cloud provider or an on-premise Arm server.
+  faqs:
+  - question: What do I need before running the examples?
+    answer: >-
+      You need an Arm-based instance from a cloud service provider or an on-prem Arm server. The
+      instructions assume Ubuntu 22.04 LTS with at least four cores and 8GB RAM and have been
+      tested on AWS Graviton3 (c7g) instances.
+  - question: Which platforms can I use for this path?
+    answer: >-
+      You can use an Arm-based instance from AWS, Microsoft Azure, Google Cloud, or Oracle, or
+      an on-prem Arm server. The procedure is written for any Arm server running Ubuntu 22.04
+      LTS.
+  - question: What should I install first to follow the steps?
+    answer: >-
+      Install PyTorch on your Arm machine. PyTorch is the framework used to deploy and run the
+      Hugging Face NLP models in this path.
+  - question: How do I know the sentiment analysis models ran successfully?
+    answer: >-
+      You should be able to execute the Sentiment Analysis pipeline for three Hugging Face models
+      in PyTorch and capture performance measurements. The path then has you compare results before
+      and after enabling BFloat16 fast math kernels.
+  - question: How do I enable and validate BFloat16 fast math kernels on Graviton3?
+    answer: >-
+      Follow the steps to enable support for BFloat16 fast math kernels on Arm Neoverse-based
+      AWS Graviton3 processors. Validate by re-running the same workloads and comparing the measured
+      performance uplift reported in the path.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md b/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
index d96fd5d448..6bc8ce56bf 100644
--- a/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
@@ -22,6 +22,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:25:22Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 1701b37580fe5d012a5e6fd322307742656a748dfb766fd48914011167386e95
+  summary_generated_at: '2026-06-02T03:12:13Z'
+  summary_source_hash: 1701b37580fe5d012a5e6fd322307742656a748dfb766fd48914011167386e95
+  faq_generated_at: '2026-06-03T00:25:22Z'
+  faq_source_hash: 1701b37580fe5d012a5e6fd322307742656a748dfb766fd48914011167386e95
+  summary: >-
+    Learn how to implement and benchmark bitmap scanning for database workloads on Arm-based cloud
+    instances running Linux. You will build a simple bitmap data structure and multiple scanning
+    routines in C—covering scalar, Neon, and SVE—and add a benchmarking harness to compare their
+    behavior. The steps focus on Arm Neoverse servers, with examples targeting Neoverse V2–based
+    instances such as AWS Graviton4, so you can measure performance differences across implementations.
+    This introductory path is aimed at database developers and performance engineers; the only
+    explicit prerequisite is access to an Arm-based instance from a cloud provider such as AWS,
+    Microsoft Azure, Google Cloud, or Oracle.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      Provision an Arm-based instance from an appropriate cloud service provider running Linux.
+      For the SVE sections, use a Neoverse V2–based server such as AWS Graviton4.
+  - question: Where do I put the code for this Learning Path?
+    answer: >-
+      Create a file named bitvector_scan_benchmark.c with a text editor and copy in the provided
+      code sections. This single file will contain the bit vector data structure, scalar implementations,
+      Neon and SVE versions, and the benchmarking code.
+  - question: Which bitmap scanning implementations will I build and compare?
+    answer: >-
+      You will implement a per-bit scalar baseline, an optimized scalar approach for sparse data,
+      and vectorized versions using Neon and SVE. These are all placed in the same C source file
+      for side-by-side benchmarking.
+  - question: What results should I expect from the benchmarking step?
+    answer: >-
+      The benchmarking framework times multiple iterations using CLOCK_MONOTONIC and reports how
+      many set-bit positions are found. You will be able to compare the relative performance of
+      the scalar, Neon, and SVE implementations; specific numeric results are not provided.
+  - question: How do I validate that all implementations are correct?
+    answer: >-
+      Use the same generated bitmaps and compare the counts (and positions if captured) returned
+      by each implementation. The Learning Path includes helper functions to generate and analyze
+      bitmaps so you can verify consistency across scalar, Neon, and SVE scans.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md b/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
index 894420b6d4..040e004910 100644
--- a/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
@@ -28,6 +28,58 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:26:35Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 8654b656131bf1d529e11d85f874f7a81c01f7207340d2a606b6fd2d80bfad04
+  summary_generated_at: '2026-06-02T03:13:32Z'
+  summary_source_hash: 8654b656131bf1d529e11d85f874f7a81c01f7207340d2a606b6fd2d80bfad04
+  faq_generated_at: '2026-06-03T00:26:35Z'
+  faq_source_hash: 8654b656131bf1d529e11d85f874f7a81c01f7207340d2a606b6fd2d80bfad04
+  summary: >-
+    This introductory Learning Path shows how to assess AArch64 programs for code layout optimization
+    and apply LLVM BOLT to a deliberately inefficient, BubbleSort-based example on Linux. You
+    install LLVM BOLT (22.1.0 or later), prepare a small workspace, compile the example with GCC
+    13.3 or later, and collect profiles using BRBE, SPE, instrumentation, and PMU event sampling.
+    Using a subset of Arm TopDown indicators, you check for front-end bound behavior and poor
+    instruction locality, then run BOLT to reorganize code layout. You finish by evaluating the
+    effect using performance metrics and the collected profiling data. Prerequisites include an
+    AArch64 Linux system with perf, recent kernels for BRBE/SPE, and sufficient hardware counters
+    (typically on bare metal).
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an AArch64 Linux system with perf installed, GCC 13.3 or later, and sufficient
+      hardware performance counters for TopDown analysis. BRBE profiling requires Linux 6.17 or
+      later, and SPE profiling requires Linux 6.14 or later.
+  - question: Which BOLT version should I install, and what if my package manager provides an
+      older one?
+    answer: >-
+      Use LLVM BOLT 22.1.0 or later to access the required options and SPE profiling support.
+      If your package manager is older, install BOLT from a prebuilt LLVM release and verify the
+      installed version before continuing.
+  - question: How should I set up the example and organize outputs?
+    answer: >-
+      Download bsort.cpp into a working directory and create subdirectories named out, prof, and
+      heatmap. The out directory stores output binaries, while prof and heatmap hold profile data
+      and generated visualizations.
+  - question: How do I know if my application is a good candidate for BOLT?
+    answer: >-
+      Use hardware performance metrics and the Arm TopDown methodology to look for front-end bound
+      behavior and poor code locality. If instruction delivery is inefficient, the program is
+      a strong candidate for BOLT code layout optimization.
+  - question: What does BRBE profiling capture and why is it useful here?
+    answer: >-
+      BRBE records the most recent taken branches in a circular buffer (typically 32 or 64 entries,
+      depending on hardware). This edge-based, low-overhead data is well-suited for BOLT to derive
+      code layout profiles.
+# END generated_summary_faq
 
 author: Paschalis Mpeis
 
diff --git a/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md b/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
index 85b99e740d..9ae75d13be 100644
--- a/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
@@ -21,6 +21,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:27:14Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 84a8b96fe7df302e0a2a6e4645bbb6170b45a3e0b55e0ea3682ec47663d34819
+  summary_generated_at: '2026-06-02T03:14:28Z'
+  summary_source_hash: 84a8b96fe7df302e0a2a6e4645bbb6170b45a3e0b55e0ea3682ec47663d34819
+  faq_generated_at: '2026-06-03T00:27:14Z'
+  faq_source_hash: 84a8b96fe7df302e0a2a6e4645bbb6170b45a3e0b55e0ea3682ec47663d34819
+  summary: >-
+    This advanced path shows how to instrument and optimize Arm application binaries and shared
+    libraries on Linux using BOLT and Linux perf. You will build the MySQL server (mysqld) from
+    source, create an instrumented binary, run read- and write-heavy workloads to collect profiles,
+    and merge the profiles to broaden coverage before applying BOLT optimizations. You will also
+    rebuild OpenSSL to produce instrumentable libssl.so and libcrypto.so, optimize these libraries
+    with BOLT, and integrate them into the application. Finally, you will use Sysbench with --time=0
+    --events=10000 to compare baseline, isolated, and merged optimization scenarios. Prerequisite:
+    an Arm-based Linux system with BOLT and Linux perf installed.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an Arm-based Linux system with BOLT and Linux Perf installed. The path builds and
+      instruments MySQL and OpenSSL from source during the steps.
+  - question: How do I generate profiles for BOLT to use with mysqld?
+    answer: >-
+      Instrument the MySQL server binary with BOLT, then run targeted workloads to collect profile
+      data. The result is workload-specific .fdata files that BOLT uses to optimize code layout.
+  - question: When should I merge profiles, and what does that produce?
+    answer: >-
+      After creating separate profiles for read-heavy and write-only workloads, merge them to
+      broaden code coverage. The merged profile is then used to optimize the final mysqld binary.
+  - question: What should I do if libssl.so or libcrypto.so are stripped and lack relocations?
+    answer: >-
+      Rebuild OpenSSL from source and include relocations so BOLT can instrument and optimize
+      the libraries. The path shows configuring OpenSSL with the linker option -Wl,--emit-relocs.
+  - question: How do I compare baseline and BOLT-optimized results?
+    answer: >-
+      Use Sysbench with --time=0 --events=10000 and run consistent read-only, write-only, and
+      read+write tests. Compare the baseline binaries to BOLT-optimized binaries and to runs that
+      include optimized shared libraries.
+# END generated_summary_faq
 
 author: Gayathri Narayana Yegna Narayanan
 
diff --git a/content/learning-paths/servers-and-cloud-computing/bolt/_index.md b/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
index 00f5a1c11d..e9e35460b7 100644
--- a/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
@@ -19,6 +19,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:25:57Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
+  summary_generated_at: '2026-06-02T03:12:45Z'
+  summary_source_hash: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
+  faq_generated_at: '2026-06-03T00:25:57Z'
+  faq_source_hash: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
+  summary: >-
+    This Learning Path shows how to build, profile, and post-link optimize an Arm Linux executable
+    with BOLT. You will collect runtime profiles on an Arm-based target using Linux Perf (via
+    samples, ETM, or SPE), convert the profile into the format BOLT expects, and run BOLT to produce
+    a new optimized binary with improved code layout. You can work on a single Arm Linux system
+    or split tasks across two systems, using a second, more powerful Linux host for building and
+    running BOLT if preferred. Prerequisites include an Arm system running Linux with BOLT and
+    Linux Perf installed, a kernel version 5.15 or later (earlier versions may limit Perf features),
+    and for SPE, version 6.14 or later. Estimated time: about 30 minutes.
+  faqs:
+  - question: Do I need one or two Linux systems for this workflow?
+    answer: >-
+      You can complete all steps on a single Arm Linux system. Alternatively, profile on an Arm
+      Linux target system and use a second, more powerful Linux system to build the executable
+      and run BOLT.
+  - question: 'Which profiling option should I choose: samples, ETM, or SPE?'
+    answer: >-
+      Use the samples method for a straightforward profile, ETM if ETM tracing is available, or
+      SPE when you need SPE branch information. The SPE workflow requires Linux Perf version 6.14
+      or later; follow the dedicated steps for each option.
+  - question: What versions of Linux kernel and Perf are required before I start?
+    answer: >-
+      Use a Linux kernel version 5.15 or later; earlier kernels can work but some Perf features
+      may be limited or unavailable. For SPE, use Linux Perf version 6.14 or later, and the prerequisites
+      note that 6.14 or later is required for SPE.
+  - question: How do I collect the performance profile and verify that it worked?
+    answer: >-
+      For samples, run: perf record -e cycles:u -o perf.data -- ./executable. For ETM, run: perf
+      record -e cs_etm//u -o perf.data -- ./executable. Perf reports the total number of samples
+      and/or the perf.data size; confirm that perf.data is created.
+  - question: What does BOLT produce after profiling, and how is it used?
+    answer: >-
+      After collecting perf.data, convert the profile to BOLT’s format and run BOLT to create
+      a new optimized executable. The optimized binary is saved separately, and the expected outcome
+      is improved performance compared to the original executable.
+# END generated_summary_faq
 
 author: Jonathan Davies
 
diff --git a/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
index d0ed8f59c9..a5e1c04c85 100644
--- a/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
@@ -24,6 +24,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:27:49Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 4bda206717eef380430009f859826d9bcf820442d13492cd3c22a114561e2917
+  summary_generated_at: '2026-06-02T03:15:44Z'
+  summary_source_hash: 4bda206717eef380430009f859826d9bcf820442d13492cd3c22a114561e2917
+  faq_generated_at: '2026-06-03T00:27:49Z'
+  faq_source_hash: 4bda206717eef380430009f859826d9bcf820442d13492cd3c22a114561e2917
+  summary: >-
+    This Learning Path shows how to use Buildkite on Arm-based Google Axion C4A virtual machines
+    to build and publish multi-architecture Docker images. You will provision a c4a-standard-4
+    VM on Google Cloud running Ubuntu or SUSE Linux Enterprise Server, install Docker, Docker
+    Buildx, and the Buildkite agent, and create a simple Flask-based Python application with a
+    Dockerfile. You then configure a Buildkite pipeline to produce a multi-architecture image
+    for Arm and x86 and push it to Docker Hub, before starting the application and verifying it
+    runs. Prerequisites include a GCP account with billing enabled, a GitHub account, basic Linux
+    administration skills, and familiarity with Docker. Estimated time to complete is about 40
+    minutes.
+  faqs:
+  - question: What do I need before provisioning the Google Axion C4A VM?
+    answer: >-
+      You need a Google Cloud Platform account with billing enabled, basic Linux administration
+      skills, familiarity with Docker, and a GitHub account to host your repository. These are
+      the only explicit prerequisites listed.
+  - question: Which instance type and operating systems does this path use?
+    answer: >-
+      The steps use a c4a-standard-4 instance with 4 vCPUs and 16 GB of memory in the Google Cloud
+      Console. The VM can run either Ubuntu or SUSE Linux Enterprise Server.
+  - question: How do I install the Buildkite agent on the C4A VM?
+    answer: >-
+      Update packages and install the listed prerequisites using your distribution’s package manager
+      (apt on Ubuntu, zypper on SUSE), then run the provided one-line Buildkite installer. The
+      path shows the exact commands for each supported distribution.
+  - question: How do I know my Buildkite agent is ready to run jobs?
+    answer: >-
+      Create an agent token in your Buildkite organization, configure the agent with that token,
+      and assign it to a queue. In the Buildkite UI, verify the agent shows as online and is listed
+      in the configured queue.
+  - question: What does the pipeline build and where is it published?
+    answer: >-
+      The pipeline uses Docker Buildx to build a multi-architecture Docker image for Arm and x86
+      from your Flask app’s Dockerfile and then pushes it to Docker Hub. The repository contains
+      the Dockerfile and app.py used by the build.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
index c52a8950c6..3f9d69074c 100644
--- a/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
@@ -22,6 +22,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:28:19Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: b8268d4df3b62aeb9943b750b436a936134d47745fa71db6cc08db8c84eb37be
+  summary_generated_at: '2026-06-02T03:16:24Z'
+  summary_source_hash: b8268d4df3b62aeb9943b750b436a936134d47745fa71db6cc08db8c84eb37be
+  faq_generated_at: '2026-06-03T00:28:19Z'
+  faq_source_hash: b8268d4df3b62aeb9943b750b436a936134d47745fa71db6cc08db8c84eb37be
+  summary: >-
+    Follow this introductory path to provision a Google Cloud Axion C4A Arm64 virtual machine,
+    install Apache Cassandra with Java 17 on SUSE or Ubuntu, validate basic database operations,
+    and benchmark read/write performance using cassandra-stress. You will create a C4A instance
+    (the example uses c4a-standard-4), start Cassandra, confirm health via logs and nodetool,
+    and use cqlsh for baseline keyspace/table operations before running cassandra-stress. This
+    path targets developers moving Cassandra workloads to Arm on Google Cloud and takes about
+    30 minutes. Prerequisites are a Google Cloud account with billing enabled and familiarity
+    with Cassandra architecture, replication, and partitioning/event-driven I/O.
+  faqs:
+  - question: What do I need before provisioning the VM on Google Cloud?
+    answer: >-
+      You need a Google Cloud Platform account with billing enabled. Familiarity with Cassandra
+      architecture, replication, and partitioning/event-driven I/O is expected.
+  - question: Which Google Cloud machine type is used in this guide?
+    answer: >-
+      The steps use an Axion C4A instance with the c4a-standard-4 machine type (4 vCPUs, 16 GB
+      memory) created from the Google Cloud Console.
+  - question: Which Linux distributions does the installation cover?
+    answer: >-
+      The installation shows how to set up Cassandra on Ubuntu or SUSE Linux. The learning objectives
+      emphasize SUSE on Arm64 (C4A).
+  - question: How do I verify that Cassandra started correctly?
+    answer: >-
+      Start Cassandra in the background and check the system.log for the message “Startup complete.”
+      Then run nodetool status to confirm the node is up before proceeding.
+  - question: How do I confirm cassandra-stress is available and what does it test?
+    answer: >-
+      cassandra-stress is included in the Cassandra distribution under tools/bin; list that directory
+      and check the tool’s help to confirm it’s present. It measures performance for write, read,
+      and mixed workloads as used in this path.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
index dc87128a0a..b8626fd8f5 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
@@ -21,6 +21,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:28:52Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 3947ebbac742399e70001e41ac5face92819bed0deb55aba3e2414f98432023e
+  summary_generated_at: '2026-06-02T03:16:50Z'
+  summary_source_hash: 3947ebbac742399e70001e41ac5face92819bed0deb55aba3e2414f98432023e
+  faq_generated_at: '2026-06-03T00:28:52Z'
+  faq_source_hash: 3947ebbac742399e70001e41ac5face92819bed0deb55aba3e2414f98432023e
+  summary: >-
+    This Learning Path shows how to bring up the Arm Confidential Compute Architecture (CCA) reference
+    software stack on an Armv-A AEM Fixed Virtual Platform (FVP) with Realm Management Extension
+    (RME) support using a pre-built Docker image (armswdev/cca-learning-path:cca-simulation-v3).
+    You will create a Realm that runs a guest Linux virtual machine, inject and run a simple application
+    inside that Realm, and obtain a CCA attestation token from the guest. You also run the CCA
+    stack with Memory Encryption Contexts (MEC). The path targets developers on AArch64 or x86_64
+    hosts running Linux or macOS and is introductory, with an estimated completion time of about
+    120 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      Use an AArch64 or x86_64 computer running Linux or macOS and install Docker Engine. You
+      can use cloud instances; a list of Arm cloud service providers is referenced.
+  - question: Which Docker image should I pull, and how do I verify it downloaded?
+    answer: >-
+      Pull armswdev/cca-learning-path:cca-simulation-v3. Verify with docker image list and check
+      that the image appears with its ID and sizes.
+  - question: What runs inside the Realm, and what result should I expect regarding attestation?
+    answer: >-
+      A guest Linux virtual machine runs inside the Realm. As part of the steps, you will obtain
+      a CCA attestation token from the virtual guest.
+  - question: How do I run my own application inside the Realm in this example?
+    answer: >-
+      Inject the application into the guest filesystem of the Realm. The path demonstrates this
+      with a simple hello application that runs under the Realm’s protections.
+  - question: When do I use Memory Encryption Contexts (MEC), and what does it change?
+    answer: >-
+      The MEC section shows how to run the CCA software stack using MEC after downloading the
+      same container. MEC extends RME to support multiple encryption contexts in the Realm Physical
+      Address Space, with each access tagged by a MECID.
+# END generated_summary_faq
 
 author:
     - Pareena Verma
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
index 7020ba5440..eacf51b470 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
@@ -24,6 +24,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:29:35Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: e21f51feb101ad90245ecceab95fe13e5eb61958faf24dff818eddd11f484b9a
+  summary_generated_at: '2026-06-02T03:17:31Z'
+  summary_source_hash: e21f51feb101ad90245ecceab95fe13e5eb61958faf24dff818eddd11f484b9a
+  faq_generated_at: '2026-06-03T00:29:35Z'
+  faq_source_hash: e21f51feb101ad90245ecceab95fe13e5eb61958faf24dff818eddd11f484b9a
+  summary: >-
+    This advanced Learning Path explains how Arm CCA Realms interact with I/O devices, contrasting
+    VirtIO paravirtualized attach with secure physical device attach. You will review what a Realm
+    is, how the Realm Management Extension (RME) isolates Realm memory, and when SWIOTLB bounce
+    buffers are used. A hands-on exercise uses Docker to run the CCA Key Broker demo inside a
+    Realm and employs kernel tracing to confirm bounce buffer activity for VirtIO network I/O.
+    The path also describes how PCIe‑TDISP and PCIe‑IDE support secure device attach and attestation.
+    It targets developers on AArch64 or x86_64 systems running Linux or macOS, including Arm cloud
+    instances, and assumes completion of three prerequisite CCA Learning Paths.
+  faqs:
+  - question: What do I need before running the exercise?
+    answer: >-
+      Use an AArch64 or x86_64 computer running Linux or macOS, or a cloud instance from the Arm
+      cloud service providers page. Complete the CCA Attestation and Veraison, Run an application
+      in a Realm using CCA, and Run an end-to-end Attestation Flow Learning Paths.
+  - question: How is attestation covered when discussing secure physical device attach?
+    answer: >-
+      The Learning Path describes how PCIe‑TDISP and PCIe‑IDE support secure physical device attach
+      with attestation. It builds on prior attestation knowledge from the prerequisite Learning
+      Paths.
+  - question: How do I start the Key Broker server (KBS) used in the exercise?
+    answer: >-
+      Pull and run the Docker image armswdev/cca-learning-path:cca-key-broker-v2. The steps provide
+      the exact docker pull and docker run commands.
+  - question: How do I confirm that SWIOTLB bounce buffers are being used inside the Realm?
+    answer: >-
+      Follow the exercise to enable kernel tracing in the Realm while generating VirtIO network
+      I/O with the Key Broker demo. You should observe trace evidence indicating SWIOTLB activity
+      for the transfers.
+  - question: How can I check network interfaces during the exercise?
+    answer: >-
+      Use the ip -c a command as shown in the steps to list network interfaces and verify the
+      environment during the demo.
+# END generated_summary_faq
 
 author: Arnaud de Grandmaison
 
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
index 7f240d7c8d..29037baa7c 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
@@ -21,6 +21,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:30:18Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: b032debbdfe82cbd017812cf671907520b146fd8684afce0c45c91e7f2287e18
+  summary_generated_at: '2026-06-02T03:18:08Z'
+  summary_source_hash: b032debbdfe82cbd017812cf671907520b146fd8684afce0c45c91e7f2287e18
+  faq_generated_at: '2026-06-03T00:30:18Z'
+  faq_source_hash: b032debbdfe82cbd017812cf671907520b146fd8684afce0c45c91e7f2287e18
+  summary: >-
+    This advanced Learning Path guides you through running an end-to-end attestation flow with
+    Arm’s Confidential Computing Architecture (CCA). You will deploy a simple workload inside
+    a confidential Linux realm on an Armv9-A AEM Base Fixed Virtual Platform (FVP) with Realm
+    Management Extension (RME) support, then connect it to attestation services so confidential
+    data is released only after the realm’s isolation is assessed. Using Docker and Veraison,
+    you will run a minimal, educational Key Broker Server (KBS) and integrate it with the realm.
+    A Linux host (AArch64 or x86_64) is required, and prior completion of the CCA attestation/Veraison
+    and CCA realm application Learning Paths is expected.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      You need a Linux computer on AArch64 or x86_64; cloud instances are acceptable. You must
+      also complete the “Get Started with CCA Attestation and Veraison” and “Run an application
+      in a Realm using the Arm Confidential Computing Architecture (CCA)” Learning Paths.
+  - question: Which FVP and Arm features does the example require?
+    answer: >-
+      Use the Armv9-A AEM Base Fixed Virtual Platform (FVP) with support for RME extensions. The
+      target compute environment is a Linux realm.
+  - question: How do I run the Key Broker Server (KBS) used in this path?
+    answer: >-
+      A pre-built Docker container image for the KBS is provided, and you will pull the image
+      and run the container. The KBS comes from the Veraison project and is intentionally minimal
+      for educational use, not for production.
+  - question: What result should I expect when attestation succeeds?
+    answer: >-
+      Attestation assesses whether the realm offers a provable level of confidential isolation.
+      When it succeeds, confidential data can be released to the Linux realm for processing as
+      part of the end-to-end flow.
+  - question: How long does this take and which tools will I use?
+    answer: >-
+      The estimated time to complete is about 120 minutes. You will use GCC, FVP, RME, CCA, Docker,
+      Veraison, and a runbook on a Linux host.
+# END generated_summary_faq
 
 author: 
     - Arnaud de Grandmaison
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
index 07197319cf..d5a55f84f9 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
@@ -20,6 +20,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:31:07Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 649957b07b2bde05bc11047bd12bfc4f697c1a55eef1c3a25b5fafc95cda893d
+  summary_generated_at: '2026-06-02T03:19:07Z'
+  summary_source_hash: 649957b07b2bde05bc11047bd12bfc4f697c1a55eef1c3a25b5fafc95cda893d
+  faq_generated_at: '2026-06-03T00:31:07Z'
+  faq_source_hash: 649957b07b2bde05bc11047bd12bfc4f697c1a55eef1c3a25b5fafc95cda893d
+  summary: >-
+    Learn to deploy a Confidential Container from an encrypted image inside an Arm CCA Realm using
+    Trustee for attestation-based authorization. Working on the Armv9-A AEM Base Fixed Virtual
+    Platform (FVP) with RME support, you will start the Trustee services (AS, KBS, RVPS) and a
+    local Docker registry, publish an encrypted image, then launch and verify the container running
+    in a Realm. The path includes an overview of Confidential Containers and how Arm CCA attestation
+    integrates with Trustee. Prerequisites are an AArch64 or x86_64 Linux or macOS host (cloud
+    instances are acceptable) and completion of the prior CCA + Trustee attestation path. Tools
+    include FVP, RME, CCA, Docker, Veraison, Trustee, and Kata Containers.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      Use an AArch64 or x86_64 computer running Linux or macOS; a cloud-based instance is also
+      acceptable. Complete the “Run an end-to-end Attestation with Arm CCA and Trustee” Learning
+      Path first.
+  - question: Which platform does the container run on in this workflow?
+    answer: >-
+      The container runs on an Armv9-A AEM Base FVP with RME support. The procedure is FVP-based
+      and does not specify running on physical hardware.
+  - question: Which services must be started before launching the confidential container?
+    answer: >-
+      Start the Trustee services (AS, KBS, RVPS) and a local Docker registry. The steps also guide
+      you to install Docker if it is not already present.
+  - question: How do I create and publish the encrypted container image?
+    answer: >-
+      Follow the steps to encrypt the image and push it to the local Docker registry. The Learning
+      Path provides the exact sequence to publish the encrypted image.
+  - question: How do I know the container is running inside an Arm CCA Realm?
+    answer: >-
+      After launching the workload, the Learning Path includes a verification step to confirm
+      it is running inside an Arm CCA Realm. Follow those checks to validate success.
+# END generated_summary_faq
 
 author:
   - Anton Antonov
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
index 87b94f11b3..fcc0f4a497 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
@@ -21,6 +21,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:31:41Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 563456f5d151c7ef59bf458f6ac971c321077475a0a78d3b5a3885b97157ba9e
+  summary_generated_at: '2026-06-02T03:20:03Z'
+  summary_source_hash: 563456f5d151c7ef59bf458f6ac971c321077475a0a78d3b5a3885b97157ba9e
+  faq_generated_at: '2026-06-03T00:31:41Z'
+  faq_source_hash: 563456f5d151c7ef59bf458f6ac971c321077475a0a78d3b5a3885b97157ba9e
+  summary: >-
+    This Learning Path shows how to run an end-to-end attestation flow using Arm Confidential
+    Computing Architecture (CCA) and Trustee services. On a Linux or macOS host (AArch64 or x86_64),
+    you will use an Armv9-A AEM Base Fixed Virtual Platform (FVP) with RME extensions to launch
+    a Linux realm, deploy a simple workload, and connect it to Trustee services (AS, KBS, RVPS)
+    with Docker. You will generate attestation evidence, see an initial secret request fail under
+    policy, endorse the realm initial measurement (RIM), re-attest, and retrieve the secret. Prerequisites
+    include completing the CCA attestation and Veraison and CCA end-to-end Learning Paths. Estimated
+    time: 60 minutes.
+  faqs:
+  - question: What do I need before running the exercises?
+    answer: >-
+      You need an AArch64 or x86_64 computer running Linux or macOS. Complete the “Get started
+      with CCA attestation and Veraison” and “Run an end-to-end attestation flow with Arm CCA”
+      Learning Paths first.
+  - question: Can I use a cloud instance as the host machine?
+    answer: >-
+      Yes. You can use cloud instances; see the Arm cloud service providers link referenced in
+      the prerequisites.
+  - question: Which FVP and realm environment does this path use?
+    answer: >-
+      You will deploy a simple workload in a CCA realm on an Armv9-A AEM Base FVP that has support
+      for RME extensions. The target compute environment is a Linux realm.
+  - question: Which Trustee components are started during the flow?
+    answer: >-
+      You will run the Trustee services: AS, KBS, and RVPS. These are used in the attestation
+      flow and policy-controlled secret release.
+  - question: What result should I expect when I request a secret?
+    answer: >-
+      The first request intentionally fails due to attestation policy. After endorsing the realm
+      initial measurement (RIM) and re-attesting, the request succeeds and the secret is retrieved.
+# END generated_summary_faq
 
 author:
   - Anton Antonov
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
index 4e958d5ab0..e59a3dd135 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
@@ -19,6 +19,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:32:36Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 55b8032ceaf735d53b1157102a3a1da5aa5cb686e9ec51cf4896ded66d9bf263
+  summary_generated_at: '2026-06-02T03:21:13Z'
+  summary_source_hash: 55b8032ceaf735d53b1157102a3a1da5aa5cb686e9ec51cf4896ded66d9bf263
+  faq_generated_at: '2026-06-03T00:32:36Z'
+  faq_source_hash: 55b8032ceaf735d53b1157102a3a1da5aa5cb686e9ec51cf4896ded66d9bf263
+  summary: >-
+    This advanced Learning Path shows how to build and deploy a scalable Arm CCA attestation verifier
+    on AWS using Veraison. You will prepare your AWS account, install and authenticate the AWS
+    CLI, create a public domain in Route 53 and an HTTPS certificate, then use Veraison’s bootstrap
+    process to clone sources and launch an automated deployment that typically completes in 30–60
+    minutes. After the services are online, you will provision Arm CCA platform endorsements using
+    the Linaro endorsement tool so Veraison can verify CCA attestation tokens. The path targets
+    Linux and assumes an active AWS account and an x86 machine running Ubuntu or Arch Linux; other
+    build environments require cross-compilation setup.
+  faqs:
+  - question: What do I need before starting the deployment?
+    answer: >-
+      You need an AWS account with access to AWS services and an x86 computer running Ubuntu or
+      Arch Linux that is authorized for AWS access. The path assumes administrator-level privileges
+      for your AWS account.
+  - question: How should I authenticate the AWS CLI before deploying Veraison?
+    answer: >-
+      Set up your local environment to authenticate with AWS before you begin the deployment.
+      Follow the AWS documentation to install the latest AWS CLI and configure authentication.
+  - question: Do I need a public domain, and how is it used?
+    answer: >-
+      Yes. You create a domain in Route53 because the Veraison services are published on the internet
+      over HTTPS using RESTful APIs, and they need a domain to be accessible. You also create
+      a certificate for the chosen domain.
+  - question: What should I expect when running the Veraison deployment?
+    answer: >-
+      The process is highly automated and typically takes 30 to 60 minutes as several AWS resources
+      are created. You start with a bootstrap step that clones the Veraison source from GitHub
+      and sets up your build environment, including dependencies.
+  - question: How do I add CCA platform endorsements so the verifier can process tokens?
+    answer: >-
+      Clone the Linaro endorsement tool from the provided Git server, configure it for AWS, and
+      use it to provision the CCA platform endorsements. This enables the deployed Veraison services
+      to act as a verifier for Arm CCA attestation tokens.
+# END generated_summary_faq
 
 author: Paul Howard
 
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md
index 42b47f9605..40cf980532 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md
@@ -24,6 +24,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:32:10Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 9e6dee3aef79c1c65cc1a1f4fe3528b9c5542d8046f0b059ef703079ef964d77
+  summary_generated_at: '2026-06-02T03:20:26Z'
+  summary_source_hash: 9e6dee3aef79c1c65cc1a1f4fe3528b9c5542d8046f0b059ef703079ef964d77
+  faq_generated_at: '2026-06-03T00:32:10Z'
+  faq_source_hash: 9e6dee3aef79c1c65cc1a1f4fe3528b9c5542d8046f0b059ef703079ef964d77
+  summary: >-
+    Learn how to work with Arm Confidential Computing Architecture (CCA) attestation by obtaining
+    an example CCA attestation token, inspecting its contents with command-line tools on Ubuntu,
+    and evaluating it using a publicly hosted Veraison-based verifier from Linaro. The path covers
+    key concepts including Trusted Execution Environments and how Armv9 Realm Management Extension
+    (RME) provides the secure boundary, then moves into hands-on token formats and workflows.
+    You will install the Go language to run the required tools. No explicit prerequisites beyond
+    an Arm-based or x86 Ubuntu system are listed, and a cloud instance can be used. In about 30
+    minutes, you will be able to parse a CCA token and submit it to an attestation verification
+    service, and understand the purpose of the open-source Veraison project.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an Arm-based or x86 computer running Ubuntu. A server instance from a cloud service
+      provider is acceptable. No other explicit prerequisites are listed.
+  - question: How do I install Go for this Learning Path?
+    answer: >-
+      The steps start by removing any existing Go installation, then download and extract Go 1.23.3
+      using the provided commands. You then export the installation path and add it to your PATH
+      as shown in the instructions.
+  - question: What is Veraison used for here?
+    answer: >-
+      Veraison provides the verification components and tools used to evaluate CCA attestation
+      tokens. It originated within Arm and is now an open-source project within the Confidential
+      Computing Consortium.
+  - question: How do I obtain and inspect the example CCA attestation token?
+    answer: >-
+      You will obtain an example token in the steps and use command-line tools to inspect its
+      contents. This gives hands-on experience with the token format and common attestation data.
+  - question: Which service should I use to verify the token, and what tokens does it support?
+    answer: >-
+      Use the publicly hosted Linaro attestation verifier service for pre-silicon CCA platforms
+      such as FVP. It verifies CCA attestation tokens from emulated Arm platforms, including the
+      example token used in this exercise.
+# END generated_summary_faq
 
 author: Paul Howard
 
diff --git a/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
index 611c071ca6..771523dc82 100644
--- a/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
@@ -28,6 +28,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:33:06Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: ec9cdca7aa9a5670f54ea3646f965149460d8e66d9d5d904e1b6dcf09867df39
+  summary_generated_at: '2026-06-02T03:22:01Z'
+  summary_source_hash: ec9cdca7aa9a5670f54ea3646f965149460d8e66d9d5d904e1b6dcf09867df39
+  faq_generated_at: '2026-06-03T00:33:06Z'
+  faq_source_hash: ec9cdca7aa9a5670f54ea3646f965149460d8e66d9d5d904e1b6dcf09867df39
+  summary: >-
+    Set up a SUSE Linux Arm64 virtual machine on Google Cloud C4A with Axion processors and run
+    CircleCI Arm-native CI/CD workflows using self-hosted machine runners. You will provision
+    a c4a instance via the Google Cloud Console, install the CircleCI CLI and Machine Runner on
+    SUSE, create a custom resource class in the CircleCI dashboard, and target it from a workflow.
+    The path also includes creating a simple Node.js demo app and testing workflows locally to
+    understand job execution on Arm64 runners. Prerequisites include a GCP account with billing
+    enabled, basic Linux command line, Node.js/npm familiarity, and a basic understanding of CircleCI
+    workflows, jobs, resource classes, and runners. Estimated time to complete is about 45 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Google Cloud Platform account with billing enabled, plus basic familiarity with
+      the Linux command line, Node.js and npm. You should also understand CircleCI concepts such
+      as workflows, jobs, resource classes, and runners.
+  - question: Which Google Cloud VM type and OS should I use for the self-hosted runner?
+    answer: >-
+      Provision a SUSE Linux (Arm64) VM using the C4A series, specifically c4a-standard-4 in the
+      Google Cloud Console. This VM runs on Google’s Axion processors based on Arm Neoverse-V2
+      cores.
+  - question: How is the CircleCI CLI used in this path?
+    answer: >-
+      The CLI lets you validate CircleCI configuration, run jobs locally, and manage runners from
+      the terminal. You will install it on SUSE Arm64 to test workflows and interact with your
+      setup.
+  - question: How do resource classes direct jobs to my Arm VM?
+    answer: >-
+      You create a custom resource class in the CircleCI dashboard that links your self-hosted
+      runner to your organization. Reference this resource class in your workflow so jobs target
+      the SUSE Arm64 VM.
+  - question: How do I know the self-hosted runner is working with my Node.js demo workflow?
+    answer: >-
+      Run the provided CircleCI workflow that specifies your custom Arm resource class; the job
+      should execute on the SUSE Arm64 VM. You can also use the CircleCI CLI to test and validate
+      the configuration locally.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md b/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md
index fe058fe732..f46bdc9232 100644
--- a/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md
@@ -21,6 +21,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:33:29Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: e04982fd668765fa2ab25f943f020b8d2b4a5e9b5ff3a51b7431cc4d93730180
+  summary_generated_at: '2026-06-02T03:22:34Z'
+  summary_source_hash: e04982fd668765fa2ab25f943f020b8d2b4a5e9b5ff3a51b7431cc4d93730180
+  faq_generated_at: '2026-06-03T00:33:29Z'
+  faq_source_hash: e04982fd668765fa2ab25f943f020b8d2b4a5e9b5ff3a51b7431cc4d93730180
+  summary: >-
+    Learn how to set up CircleCI self-hosted machine runners on AWS EC2 Graviton (Arm64) to execute
+    CI/CD jobs natively on Arm. You will create a Linux Arm64 VM on an m6g.large instance, install
+    the CircleCI CLI, register a resource class in the CircleCI dashboard, and install and configure
+    the machine runner. Finally, you verify the setup by running a simple workflow and test computation
+    on the runner. This introductory path targets developers and DevOps engineers using CircleCI,
+    Bash, and Git. Prerequisites include an AWS account with billing enabled, a CircleCI account,
+    and a basic understanding of CircleCI workflows, jobs, and resource classes. Estimated time:
+    about 30 minutes.
+  faqs:
+  - question: Which EC2 instance type and OS should I use for this setup?
+    answer: >-
+      The steps use an AWS Graviton Arm64 instance with the m6g.large type. Choose an appropriate
+      Linux AMI, such as an Ubuntu AMI, during instance creation.
+  - question: What do I need before launching the instance and configuring CircleCI?
+    answer: >-
+      You need an AWS account with billing enabled, a CircleCI account, and a basic understanding
+      of CircleCI workflows, jobs, and resource classes. No other prerequisites are explicitly
+      listed.
+  - question: How do I install the CircleCI CLI on the Graviton instance?
+    answer: >-
+      Update your package index and install tools like curl, tar, gzip, coreutils, gpg, and git.
+      Then download and extract the CircleCI CLI binary as described in the steps.
+  - question: How do I register and link a self-hosted runner to my CircleCI organization?
+    answer: >-
+      Create a resource class in the CircleCI Web Dashboard, which uniquely identifies your runner
+      and links it to your namespace. If you do not have an organization, create one first to
+      access the dashboard features.
+  - question: How is the CircleCI machine runner installed on the EC2 instance?
+    answer: >-
+      Add the official CircleCI package repository for Debian/Ubuntu on Arm64, then install the
+      CircleCI Runner via apt and configure it to use your resource class. Follow the path steps
+      to complete the configuration.
+# END generated_summary_faq
 
 author: Annie Tallund
 
diff --git a/content/learning-paths/servers-and-cloud-computing/clair/_index.md b/content/learning-paths/servers-and-cloud-computing/clair/_index.md
index 75ea967afa..190d0f610f 100644
--- a/content/learning-paths/servers-and-cloud-computing/clair/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/clair/_index.md
@@ -19,6 +19,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:33:54Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: e742eb44fa108bfcc4ee5a241414e6aa05e1fc0e1cceb589fb78a080f59b0d38
+  summary_generated_at: '2026-06-02T03:23:30Z'
+  summary_source_hash: e742eb44fa108bfcc4ee5a241414e6aa05e1fc0e1cceb589fb78a080f59b0d38
+  faq_generated_at: '2026-06-03T00:33:54Z'
+  faq_source_hash: e742eb44fa108bfcc4ee5a241414e6aa05e1fc0e1cceb589fb78a080f59b0d38
+  summary: >-
+    This Learning Path shows how to install and run Clair on Arm-based Linux servers to scan container
+    images and generate vulnerability reports. You will deploy Clair using both combined (single-process)
+    and distributed (separate indexer, matcher, notifier) models, then use the clairctl CLI to
+    submit image manifests for static analysis. The path targets advanced developers working with
+    containers on Arm infrastructure, including Arm instances from major cloud providers. Prerequisites
+    are an Arm server or cloud instance running Linux with recent versions of Docker and Go installed;
+    the instructions are tested on Ubuntu. By the end, you will have a running Clair deployment
+    and can produce vulnerability reports from your images.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      Use an Arm-based instance from a cloud provider or an Arm server running Linux, with recent
+      versions of Docker and Go installed. The instructions are tested on Ubuntu; other Linux
+      distributions may require adjustments.
+  - question: Which Clair deployment model should I use?
+    answer: >-
+      Use the combined deployment if you want the simplest setup, as all Clair components run
+      in a single process. Choose the distributed deployment if you want to run the indexer, matcher,
+      and notifier as separate services.
+  - question: How do I know when Clair is ready to scan images?
+    answer: >-
+      Wait 5–10 minutes after starting Clair before submitting manifests so vulnerabilities can
+      populate in the PostgreSQL database. Submitting too early can produce a clean (empty) report.
+  - question: How do I submit a container image for scanning?
+    answer: >-
+      With Clair running (combined or distributed), use clairctl to submit a manifest to your
+      deployment. The Learning Path steps guide you to generate a vulnerability report from this
+      submission.
+  - question: What result should I expect after submitting a manifest?
+    answer: >-
+      Clair performs static analysis of the image layers and returns a vulnerability report. It
+      does not run the container image as part of the analysis.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
index c17739f02e..e2ebe06676 100644
--- a/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
@@ -26,6 +26,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:34:45Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: e77cd1373236f39f360afe6844d642fde290960ccdad26544ff1e3342f2a980c
+  summary_generated_at: '2026-06-02T03:24:37Z'
+  summary_source_hash: e77cd1373236f39f360afe6844d642fde290960ccdad26544ff1e3342f2a980c
+  faq_generated_at: '2026-06-03T00:34:45Z'
+  faq_source_hash: e77cd1373236f39f360afe6844d642fde290960ccdad26544ff1e3342f2a980c
+  summary: >-
+    Follow this introductory Learning Path to deploy ClickHouse on Arm-based Google Cloud Axion
+    C4A virtual machines and build a real-time analytics pipeline. You will provision a SUSE Linux
+    (Arm64) VM using the c4a-standard-4 type, configure a firewall rule for TCP 8123, and install
+    ClickHouse and the Google Cloud CLI. You will create Pub/Sub resources (including a logs-topic)
+    and IAM roles, then implement a streaming ETL with Apache Beam and run it on Google Dataflow
+    to ingest events into ClickHouse. Finally, you will validate end-to-end ingestion and run
+    baseline and analytical queries to measure and report latency, including p95, on Axion processors.
+    Prerequisites are a GCP account with billing enabled and basic familiarity with ClickHouse
+    and SQL.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Google Cloud Platform account with billing enabled. Basic familiarity with ClickHouse
+      and a basic understanding of databases and SQL are also listed.
+  - question: Which VM type and OS should I use on Google Cloud?
+    answer: >-
+      Use a Google Axion C4A instance with the c4a-standard-4 machine type (4 vCPUs, 16 GB memory).
+      The VM runs SUSE SLES on Arm64.
+  - question: Which network port must be opened for this setup?
+    answer: >-
+      Create a VPC firewall rule to allow inbound TCP traffic on port 8123. The steps guide you
+      through doing this in the Google Cloud Console.
+  - question: How should I configure Pub/Sub for ingestion?
+    answer: >-
+      Create a Pub/Sub topic named logs-topic using default settings. The path also covers setting
+      up the required IAM so Dataflow and the VM can communicate with Pub/Sub.
+  - question: What outcome should I expect after deployment and configuration?
+    answer: >-
+      You will ingest real-time data from Pub/Sub into ClickHouse using Dataflow and validate
+      end-to-end data flow. You will also run baseline and analytical query benchmarks and measure
+      query latency, including p95, on Axion processors.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md b/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
index b87f873b7f..89305c6ea6 100644
--- a/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
@@ -18,6 +18,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:34:16Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 0d1390198cf2f0e87f509c5269fc2405cffcb2e57311024150d47e60a3273178
+  summary_generated_at: '2026-06-02T03:24:05Z'
+  summary_source_hash: 0d1390198cf2f0e87f509c5269fc2405cffcb2e57311024150d47e60a3273178
+  faq_generated_at: '2026-06-03T00:34:16Z'
+  faq_source_hash: 0d1390198cf2f0e87f509c5269fc2405cffcb2e57311024150d47e60a3273178
+  summary: >-
+    This Learning Path shows how to install ClickHouse on an Arm-based cloud instance or Arm server
+    running Ubuntu for Arm, then measure query latency with ClickBench using a web‑analytics dataset.
+    It is an introductory, hands-on path for developers evaluating ClickHouse on Arm to choose
+    appropriate instance configurations across cloud providers or on‑premises. You will set up
+    ClickHouse, run ClickBench to capture processing times, and use the results to inform instance
+    selection for your workloads. Prerequisites include access to an Arm-based instance and sufficient
+    storage for the dataset; no additional tools beyond ClickHouse and ClickBench are listed.
+    Expected duration is about 45 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an Arm-based instance from a cloud service provider or an on-premise Arm server.
+      Ensure it runs a recent version of Ubuntu for Arm and has enough storage for the web-analytics
+      dataset used in the benchmark.
+  - question: Which cloud platforms can I use for the Arm instance?
+    answer: >-
+      You can use Arm-based instances from AWS, Microsoft Azure, Google Cloud, or Oracle. An on-premise
+      Arm server is also suitable.
+  - question: Which operating system should I run on the instance?
+    answer: >-
+      Use a recent version of Ubuntu for Arm. The path assumes a Linux environment.
+  - question: What result should I expect after running ClickBench?
+    answer: >-
+      ClickBench reports processing time (query latency) for ClickHouse on the web-analytics workload.
+      You can use these measurements to evaluate performance and inform your instance configuration
+      choices.
+  - question: What should I check if the benchmark fails or seems unusually slow?
+    answer: >-
+      Confirm you are using an Arm-based instance with a recent Ubuntu for Arm and that sufficient
+      storage is available for the dataset. Also ensure the dataset required by the steps is present
+      before running ClickBench.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
index 93faac4839..45933d462c 100644
--- a/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
@@ -21,6 +21,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:35:17Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: e4eb84292a669a8d73bff4b63d2fe3f70382dd873d023fc8ff24db5ad6178ab8
+  summary_generated_at: '2026-06-02T03:25:26Z'
+  summary_source_hash: e4eb84292a669a8d73bff4b63d2fe3f70382dd873d023fc8ff24db5ad6178ab8
+  faq_generated_at: '2026-06-03T00:35:17Z'
+  faq_source_hash: e4eb84292a669a8d73bff4b63d2fe3f70382dd873d023fc8ff24db5ad6178ab8
+  summary: >-
+    This Learning Path walks you through deploying a Linux-based Cobalt 100 virtual machine on
+    Microsoft Azure, connecting via SSH, and configuring Network Security Group (NSG) rules to
+    expose an application port for testing. Using the Azure Portal, you create an Arm-based VM
+    powered by Microsoft’s Cobalt 100 (Armv9 Neoverse-N2), open inbound TCP ports 22 and 8080,
+    and verify external connectivity to the newly opened port. You will copy the VM’s public IP,
+    establish an SSH session, and optionally start a temporary HTTP server to confirm reachability.
+    Prerequisites include an Azure subscription with permissions to create VMs and networking
+    resources, and basic familiarity with SSH. Azure Portal and Azure CLI are listed tools; the
+    steps focus on the Portal.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Microsoft Azure subscription with permissions to create virtual machines and
+      networking resources, and basic familiarity with SSH. No other prerequisites are explicitly
+      listed.
+  - question: Which Cobalt 100 VM series should I choose during creation?
+    answer: >-
+      Azure offers Cobalt 100–powered VMs in Dpsv6 and Dplsv6 (general-purpose) and Epsv6 (memory-optimized)
+      series. Select the series that aligns with your general-purpose or memory-optimized needs.
+  - question: How do I find the public IP to SSH into the VM?
+    answer: >-
+      Open the VM’s Overview page in the Azure Portal and copy the Public IP address. Use that
+      address in your SSH command.
+  - question: What SSH command and username should I use to connect?
+    answer: >-
+      From a terminal, run: ssh -i [path to your pem file] azureuser@[public IP], replacing the
+      placeholders with your key path and the VM’s public IP. Use azureuser unless you specified
+      a different admin username during VM creation.
+  - question: How do I open and test an application port like 8080?
+    answer: >-
+      Add an inbound rule in the VM’s Network Security Group to allow TCP 8080, typically scoped
+      to your IP for testing. Start your application (or a temporary HTTP server) on the VM listening
+      on 8080, then access the VM on that port from your client to verify connectivity.
+# END generated_summary_faq
 
 author: Joe Stech
 
diff --git a/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md b/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
index b1c91541f1..6244859b3c 100644
--- a/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
@@ -19,6 +19,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:35:39Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: e7ea48aaea0e25f624ea1d77c6252b0e537fdaeca3aacca560d7bc9d103bbbb3
+  summary_generated_at: '2026-06-02T03:25:50Z'
+  summary_source_hash: e7ea48aaea0e25f624ea1d77c6252b0e537fdaeca3aacca560d7bc9d103bbbb3
+  faq_generated_at: '2026-06-03T00:35:39Z'
+  faq_source_hash: e7ea48aaea0e25f624ea1d77c6252b0e537fdaeca3aacca560d7bc9d103bbbb3
+  summary: >-
+    Automate building Arm AArch64 Docker images with AWS CodeBuild using a GitHub project, then
+    publish them to Docker Hub and the Amazon ECR Public Gallery and run them on any Arm system
+    with Docker installed. The path targets Linux and uses AWS Graviton-backed CodeBuild to create
+    images for Arm. You will also validate your runtime environment by checking uname -m returns
+    aarch64 and then pull and run the completed images. Prerequisites include an AWS account and
+    access to an Arm-based instance or any Arm server, laptop, or single-board computer with Docker
+    installed. This advanced, 30‑minute path is aimed at developers comfortable with Docker and
+    CI/CD concepts.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an AWS account and an Arm-based system with Docker installed to run the created
+      images. The path assumes Linux and mentions prior Docker experience is helpful, but no other
+      prerequisites are explicitly listed.
+  - question: How do I verify that my machine is Arm AArch64 before running the images?
+    answer: >-
+      On Linux, run uname -m. The expected output is aarch64; if you see a different result, you
+      are not on a 64-bit Arm Linux machine.
+  - question: Where will the built Docker images be published?
+    answer: >-
+      The images are published to the Amazon ECR Public Gallery and Docker Hub. Both images are
+      identical.
+  - question: When should I pull and run the images on my Arm machine?
+    answer: >-
+      Wait until the AWS CodeBuild process completes. Once complete, you can pull and run the
+      images from either Docker Hub or ECR on any Arm system with Docker installed.
+  - question: Do I need a GitHub repository to follow this path?
+    answer: >-
+      Yes. The path uses a GitHub project integrated with AWS CodeBuild to automate Docker image
+      creation.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/codec/_index.md b/content/learning-paths/servers-and-cloud-computing/codec/_index.md
index 75e8cf7d7b..821a51ecec 100644
--- a/content/learning-paths/servers-and-cloud-computing/codec/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/codec/_index.md
@@ -22,6 +22,51 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:36:00Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 908a750f2a891a0c4c9d1183c2130ac5ac0fdcfb4a45185cef6ed6da47c9aaa9
+  summary_generated_at: '2026-06-02T03:26:25Z'
+  summary_source_hash: 908a750f2a891a0c4c9d1183c2130ac5ac0fdcfb4a45185cef6ed6da47c9aaa9
+  faq_generated_at: '2026-06-03T00:36:00Z'
+  faq_source_hash: 908a750f2a891a0c4c9d1183c2130ac5ac0fdcfb4a45185cef6ed6da47c9aaa9
+  summary: >-
+    Build and run the x265 H.265 encoder on Arm servers and benchmark its performance across different
+    video resolutions and encoding presets. You will use an Arm-based cloud instance—verified
+    on AWS EC2 and Oracle Cloud Services—running Ubuntu Linux 20.04, install GCC, CMake, and required
+    packages, then compile x265 and execute the same video under varied configurations to observe
+    performance impact. The open-source libx265 includes optimizations for Arm Neoverse with Neon,
+    and optimized code is available on Bitbucket. This introductory path focuses on practical
+    build-and-run steps so you finish with a working x265 on Arm and comparative measurements.
+    Estimated time to complete is about 10 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      An Arm-based instance from a cloud service provider. This Learning Path has been verified
+      on AWS EC2 and Oracle Cloud, running Ubuntu Linux 20.04.
+  - question: Which packages should I install to build x265 on Ubuntu?
+    answer: >-
+      Update apt and install wget, git, cmake, cmake-curses-gui, and build-essential. You also
+      need GCC for your Arm Linux distribution.
+  - question: Where do the Arm optimizations for x265 come from?
+    answer: >-
+      The path uses the open-source libx265, which includes optimizations for Arm Neoverse platforms
+      with Neon support. The optimized code is available on Bitbucket.
+  - question: How will I measure the performance impact of different settings?
+    answer: >-
+      You will run x265 on the same video using various resolutions and encoding presets. Compare
+      the results to assess the performance impact of those choices.
+  - question: Which operating systems and platforms are validated for these steps?
+    answer: >-
+      The steps target Linux and have been verified on Ubuntu 20.04 running on Arm-based servers
+      from AWS EC2 and Oracle Cloud. Other operating systems are not explicitly listed.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/codec1/_index.md b/content/learning-paths/servers-and-cloud-computing/codec1/_index.md
index 7b72ed51fe..736aa10d62 100644
--- a/content/learning-paths/servers-and-cloud-computing/codec1/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/codec1/_index.md
@@ -7,6 +7,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:36:28Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: c0643a788cdb0b3e33fe645fbb61d99a1899806e3ee197541c1eb8134b2876c1
+  summary_generated_at: '2026-06-02T03:27:16Z'
+  summary_source_hash: c0643a788cdb0b3e33fe645fbb61d99a1899806e3ee197541c1eb8134b2876c1
+  faq_generated_at: '2026-06-03T00:36:28Z'
+  faq_source_hash: c0643a788cdb0b3e33fe645fbb61d99a1899806e3ee197541c1eb8134b2876c1
+  summary: >-
+    Learn how to build and run the AV1 (libaom) and VP9 (libvpx) video codecs on Arm Linux, then
+    benchmark them on example videos using multiple resolutions and encoding configurations. You
+    will install build dependencies such as CMake and the GNU compiler, obtain the codec sources,
+    compile on an Arm server or Arm-based cloud instance, and execute encoding and decoding workloads.
+    The reference implementations include Arm-focused optimizations, including use of Neon and
+    SVE2 on Arm Neoverse platforms. By the end, you will be able to run these codecs on your Arm
+    system and record performance results. No further prerequisites are listed beyond access to
+    an Arm Linux environment.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need access to an Arm Linux system or an Arm-based instance from a cloud service provider.
+      No other explicit prerequisites are listed.
+  - question: Which codecs and libraries are used in this path?
+    answer: >-
+      AV1 is built and run using the libxaom reference implementation, and VP9 is built and run
+      using libvpx. Both libraries support encoding and decoding.
+  - question: Which development tools do I need to install to build the codecs?
+    answer: >-
+      You need various development tools including CMake and the GNU compiler. The steps provide
+      installation instructions for the required packages.
+  - question: Where do I obtain the source code for the codecs?
+    answer: >-
+      For VP9, the path clones libvpx from https://chromium.googlesource.com/webm/libvpx. For
+      AV1, the reference implementation and Arm-optimized code for libxaom are available on Google
+      Git.
+  - question: What results should I expect after completing the path?
+    answer: >-
+      You will have built the AV1 and VP9 codecs on Arm Linux and run them on example videos at
+      various resolutions and encodings. You will also collect performance measurements to compare
+      configurations, with notes on Arm Neoverse optimizations using Neon and SVE2.
+# END generated_summary_faq
 
 author: Odin Shen
 
diff --git a/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
index dc9b8144c5..235a03bf2d 100644
--- a/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
@@ -22,6 +22,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:36:56Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 0a7d76b8c944a50073c7524813acf83948155c7c61d9c92f9202eae1192c9600
+  summary_generated_at: '2026-06-02T03:28:12Z'
+  summary_source_hash: 0a7d76b8c944a50073c7524813acf83948155c7c61d9c92f9202eae1192c9600
+  faq_generated_at: '2026-06-03T00:36:56Z'
+  faq_source_hash: 0a7d76b8c944a50073c7524813acf83948155c7c61d9c92f9202eae1192c9600
+  summary: >-
+    Follow this introductory path to deploy Couchbase Server on Arm-based Google Cloud Axion C4A
+    virtual machines and run basic performance checks. You will provision a SUSE Linux Enterprise
+    Server (SLES) VM on the c4a-standard-4 machine type, create a Google Cloud firewall rule to
+    open TCP port 8091, install Couchbase on Arm64, and initialize the cluster from the web console
+    by creating a test bucket and confirming node health. You then benchmark Couchbase using YCSB
+    workloads to record operations per second, memory utilization, and disk behavior on the Arm
+    platform. Prerequisites are a GCP account with billing enabled and basic familiarity with
+    Couchbase.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Google Cloud Platform account with billing enabled and basic familiarity with
+      Couchbase. No additional prerequisites are explicitly listed.
+  - question: Which Google Cloud VM type and OS should I use?
+    answer: >-
+      Provision an Arm-based Google Axion C4A VM using the c4a-standard-4 machine type (4 vCPUs,
+      16 GB memory). The Learning Path targets SUSE Linux Enterprise Server (Arm64).
+  - question: How do I allow and test access to the Couchbase Web Console?
+    answer: >-
+      Create a VPC firewall rule in Google Cloud Console to allow inbound TCP port 8091. Then
+      open http://VM_PUBLIC_IP:8091 in your browser to reach the console.
+  - question: How do I know Couchbase installed correctly on the VM?
+    answer: >-
+      You should be able to access the Couchbase Web Console, complete the initial cluster setup,
+      see your node reported as healthy, and create a test bucket. These checks confirm the deployment
+      is ready for benchmarking.
+  - question: What should I capture when running the YCSB benchmarks?
+    answer: >-
+      Measure and record operations per second (ops/sec), memory utilization, and disk performance
+      for the Couchbase workload on the Arm-based instance. The Learning Path guides you to prepare
+      the bucket and run YCSB workloads to collect these results.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md b/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
index cb1eac4fa0..f33d4589a0 100644
--- a/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
@@ -19,6 +19,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:37:16Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 22f845b8ea4dbb9ffc63fafb76c17c79aedde14a28417d49e1ab0833bbbc1eba
+  summary_generated_at: '2026-06-02T03:28:45Z'
+  summary_source_hash: 22f845b8ea4dbb9ffc63fafb76c17c79aedde14a28417d49e1ab0833bbbc1eba
+  faq_generated_at: '2026-06-03T00:37:16Z'
+  faq_source_hash: 22f845b8ea4dbb9ffc63fafb76c17c79aedde14a28417d49e1ab0833bbbc1eba
+  summary: >-
+    Learn how to apply g++ compiler optimization flags when building C++ applications for Arm-based
+    servers. You will provision and connect to an AWS Graviton4 (r8g.xlarge) instance running
+    Ubuntu 24.04 LTS, then build and run a sample C++ program on Linux while selecting an appropriate
+    target architecture and optimization strategy. The path also reviews Neoverse-based instance
+    generations (for example, Graviton3/V1 and Graviton4/V2) to inform choices like the -march
+    flag for portability, size, or focusing on a specific CPU. This introductory, 60‑minute path
+    assumes a basic understanding of C++ and compilers and focuses on compiling for a specific
+    Arm target and managing optimizations with g++.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a basic understanding of C++ and compilers, and access to an AWS account to create
+      a Graviton4 (r8g.xlarge) instance running Ubuntu 24.04 LTS. You also need a way to connect
+      to the instance.
+  - question: Which -march value should I use for my build?
+    answer: >-
+      Choose the lowest Arm architecture among the systems you plan to run on if you need portability.
+      If you want the highest performance on a specific processor, target that processor (for
+      example, AWS Graviton4) instead.
+  - question: How do I know my environment and compiler are ready?
+    answer: >-
+      After connecting to the instance, the path has you run commands to confirm the OS and compiler
+      setup on Ubuntu 24.04 LTS. Proceed once you have verified that your build environment is
+      available.
+  - question: What result should I expect after I build and run the example?
+    answer: >-
+      You will produce a compiled C++ application built with the selected g++ optimization flags
+      for an Arm target and run it on the AWS Graviton4 instance. The path helps you compare choices
+      such as portability versus targeting a specific CPU or optimizing for size.
+  - question: Can I follow this on other Arm-based cloud instances?
+    answer: >-
+      Most cloud providers offer Arm-based instances on Neoverse, but the hands-on steps use AWS
+      Graviton4. If you plan to run across different Arm servers, select an -march value that
+      matches the lowest target in your set.
+# END generated_summary_faq
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md b/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md
index 7435ac0269..6df9cb1509 100644
--- a/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md
@@ -19,6 +19,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:37:40Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 4e7de348514d0b5a742fa47bb85a2a5814fa9bd587478e7ef08365d16273f6bf
+  summary_generated_at: '2026-06-02T03:29:36Z'
+  summary_source_hash: 4e7de348514d0b5a742fa47bb85a2a5814fa9bd587478e7ef08365d16273f6bf
+  faq_generated_at: '2026-06-03T00:37:40Z'
+  faq_source_hash: 4e7de348514d0b5a742fa47bb85a2a5814fa9bd587478e7ef08365d16273f6bf
+  summary: >-
+    Learn to measure and tune C++ code on Arm-based Linux systems using Profile-Guided Optimization
+    (PGO) and Google Benchmark. You will compile an instrumented binary with GCC/G++ using -fprofile-generate,
+    run it to emit profile data (.gcda), and rebuild with -fprofile-use to create a profile-tuned
+    executable. An integer division example demonstrates microbenchmarking and comparing baseline
+    versus PGO builds with Google Benchmark. The path also shows how to integrate PGO into a Makefile
+    and a GitHub Actions workflow, with cautions on when PGO is appropriate. Prerequisites are
+    basic C++ knowledge and access to an Arm-based Linux machine. Estimated time is 15 minutes;
+    the approach applies to Arm environments, including cloud instances on AWS, Microsoft Azure,
+    Google Cloud, or Oracle.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need basic C++ understanding and access to an Arm-based Linux machine. The path uses
+      GCC/G++ and Google Benchmark to build and run the examples.
+  - question: Which compiler options should I use for PGO with GCC/G++ and in what order?
+    answer: >-
+      First compile with -fprofile-generate to create an instrumented binary, then run that binary
+      to collect profile data. Recompile the program with -fprofile-use to apply the collected
+      data during optimization.
+  - question: How do I know the profiling run succeeded and where are the files?
+    answer: >-
+      After running the instrumented binary, expect profile data files (typically .gcda) to appear
+      in the same directory. Their presence indicates that execution generated the data needed
+      for the -fprofile-use rebuild.
+  - question: What will I benchmark in this path and why that example?
+    answer: >-
+      You will benchmark a simple integer division operation. Division is chosen because it is
+      typically more expensive than addition, subtraction, or multiplication, making performance
+      differences easier to observe.
+  - question: When should I apply PGO in my project or CI workflow?
+    answer: >-
+      Use PGO for performance-critical code that is heavily influenced by runtime behavior, and
+      consider integrating it via a Makefile or GitHub Actions. Be aware that PGO adds build steps
+      and time, and it may not be ideal for early-stage development or highly variable workloads.
+# END generated_summary_faq
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md b/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
index c4bc0b63c3..d25df95620 100644
--- a/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
@@ -19,6 +19,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:38:16Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 58dba071f70b4f85b87b3bd27b3a7ba3ff985f80079a42e05b797a998aeaf104
+  summary_generated_at: '2026-06-02T03:30:14Z'
+  summary_source_hash: 58dba071f70b4f85b87b3bd27b3a7ba3ff985f80079a42e05b797a998aeaf104
+  faq_generated_at: '2026-06-03T00:38:16Z'
+  faq_source_hash: 58dba071f70b4f85b87b3bd27b3a7ba3ff985f80079a42e05b797a998aeaf104
+  summary: >-
+    This Learning Path shows how to find code hotspots in C++ applications running on Arm Linux
+    systems using Arm Performix on Arm Neoverse. You will build and run a C++11 Mandelbrot example
+    that generates a 1920×1080 bitmap, profile baseline performance with the Code Hotspots recipe,
+    and read the resulting flame graph to identify functions that dominate CPU time. The steps
+    then use those insights to focus potential improvements, such as investigating calls like
+    __hypot within Mandelbrot::getIterations. This is an introductory path aimed at developers
+    and performance engineers. Prerequisites are access to Arm Performix and a basic understanding
+    of C++. By the end, you will be able to run the recipe and pinpoint CPU-intensive functions
+    for deeper analysis.
+  faqs:
+  - question: Which Arm Performix feature should I run to find hotspots?
+    answer: >-
+      Use the Code Hotspots recipe. It samples execution and produces a flame graph that highlights
+      the functions consuming the most CPU time.
+  - question: What do I need before running the steps?
+    answer: >-
+      You need access to Arm Performix and a basic understanding of C++. The example runs on an
+      Arm Linux system, as described by the Learning Path.
+  - question: What do I build and what output should I expect from the example?
+    answer: >-
+      You will build a C++11 program that computes the Mandelbrot set and writes a 1920×1080 bitmap
+      image. The source is provided so you can rebuild, profile, and relate flame graph results
+      back to specific functions and loops.
+  - question: How do I know profiling worked?
+    answer: >-
+      After running the Code Hotspots recipe, you should see a flame graph that clearly shows
+      the hottest functions. In the example, __hypot appears as a hotspot invoked by Mandelbrot::getIterations.
+  - question: What should I check if the image file is missing when profiling under Arm Performix?
+    answer: >-
+      The Learning Path notes that myplot.draw() uses a relative path (./images/green.bmp) and
+      that Arm Performix launches the binary in a different location. Follow the step guidance
+      to ensure the output is written to the intended directory.
+# END generated_summary_faq
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/csp/_index.md b/content/learning-paths/servers-and-cloud-computing/csp/_index.md
index bcc774460d..dc35c9a54c 100644
--- a/content/learning-paths/servers-and-cloud-computing/csp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/csp/_index.md
@@ -7,6 +7,51 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:38:48Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 9e94b69eabf35677c48db812bb85ea9cef184efc078a826b96954f540a45e915
+  summary_generated_at: '2026-06-02T03:30:48Z'
+  summary_source_hash: 9e94b69eabf35677c48db812bb85ea9cef184efc078a826b96954f540a45e915
+  faq_generated_at: '2026-06-03T00:38:48Z'
+  faq_source_hash: 9e94b69eabf35677c48db812bb85ea9cef184efc078a826b96954f540a45e915
+  summary: >-
+    This introductory Learning Path shows how to launch a Linux virtual machine on Arm-based instances
+    from major cloud providers and confirm that it is running on Arm architecture. You will use
+    each provider’s standard VM service: AWS EC2 with Graviton, Microsoft Azure Virtual Machines
+    (Azure Cobalt 100 or previous Ampere generations), Google Cloud Compute Engine with Axion
+    C4A (example c4a-standard-4), Oracle Cloud Infrastructure compute with Ampere, and Alibaba
+    Cloud ECS. The steps focus on selecting an Arm-based machine type and performing a brief post-launch
+    verification. An active account with your chosen provider is required; no other prerequisites
+    are explicitly listed. The path takes about 15 minutes to complete.
+  faqs:
+  - question: What do I need before starting?
+    answer: >-
+      You need an account with your preferred cloud service provider. The path uses provider consoles
+      and documentation to guide VM creation.
+  - question: Which instance types should I choose to get an Arm VM on each cloud?
+    answer: >-
+      Use AWS EC2 with Graviton, Azure Arm-based VMs (Cobalt 100 or Ampere generations), Google
+      Cloud Axion C4A (for example c4a-standard-4), Oracle Cloud Infrastructure with Ampere, and
+      Alibaba Cloud ECS with Arm-based processors.
+  - question: Which operating system is used in the examples?
+    answer: >-
+      Linux is used for the examples in this Learning Path.
+  - question: How do I verify that the VM is Arm-based once it’s running?
+    answer: >-
+      Follow the verification step in the path to confirm that the instance reports an Arm CPU
+      architecture. The process is performed from the running Linux VM.
+  - question: What result should I expect after completing the steps?
+    answer: >-
+      You will have a running Linux VM on your chosen cloud that is using an Arm-based processor,
+      and you will have verified the architecture on the instance.
+# END generated_summary_faq
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md b/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
index ab43a8d839..2155d00854 100644
--- a/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
@@ -19,6 +19,51 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:39:18Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: f600fcba0adea12ff1b8b092e75de553577d940dc3bf632e9a247cec22d364a4
+  summary_generated_at: '2026-06-02T03:31:44Z'
+  summary_source_hash: f600fcba0adea12ff1b8b092e75de553577d940dc3bf632e9a247cec22d364a4
+  faq_generated_at: '2026-06-03T00:39:18Z'
+  faq_source_hash: f600fcba0adea12ff1b8b092e75de553577d940dc3bf632e9a247cec22d364a4
+  summary: >-
+    This Learning Path shows how to deploy and run the DeepSeek-R1 671B language model on Arm-based
+    servers using llama.cpp with quantization for CPU inference. You will clone and build llama.cpp,
+    download a pre-quantized DeepSeek-R1 model from Hugging Face, start the llama.cpp server,
+    and access it via an OpenAI-compatible API. The instructions target Ubuntu 24.04 LTS on an
+    Arm server with at least 64 cores, 512 GB RAM, and 400 GB of disk space; they were tested
+    on an AWS Graviton4 r8g.24xlarge instance. By the end, you will have a running chatbot on
+    your Arm CPU and benchmark its performance. Prerequisite: an Arm-based instance from a cloud
+    provider or an on-prem Arm server.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      Use an Arm-based server running Ubuntu 24.04 LTS with at least 64 CPU cores, 512 GB RAM,
+      and 400 GB of disk space. An Arm-based instance from a cloud provider or an on-prem Arm
+      server is suitable; the instructions were tested on an AWS Graviton4 r8g.24xlarge instance.
+  - question: Where do I get the DeepSeek-R1 model and what format is expected?
+    answer: >-
+      Download a pre-quantized DeepSeek-R1 model from Hugging Face as directed in the Learning
+      Path. The steps assume a pre-quantized artifact appropriate for llama.cpp.
+  - question: How do I start and access the model server during this Learning Path?
+    answer: >-
+      After building llama.cpp, start its server mode as shown in the steps. The server provides
+      an OpenAI-compatible API and can be accessed locally or over the network from another machine.
+  - question: Do I need any extra tools to query or work with the API responses?
+    answer: >-
+      Yes. The steps require jq for this section; install it with: sudo apt install jq -y.
+  - question: What should I check if the llama.cpp server binary is missing?
+    answer: >-
+      The server executable is built when you run make in the previous section. Ensure you completed
+      the llama.cpp build step before attempting to start the server.
+# END generated_summary_faq
 
 author:
     - Tianyu Li
diff --git a/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md b/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md
index 617c42f589..ffb8c01431 100644
--- a/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md
@@ -20,6 +20,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:39:53Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: a1ec216948e7cfd4fc52815196bb3b99ab4e76c9c756aa9e9a8e3216ef5e7ce4
+  summary_generated_at: '2026-06-02T03:32:18Z'
+  summary_source_hash: a1ec216948e7cfd4fc52815196bb3b99ab4e76c9c756aa9e9a8e3216ef5e7ce4
+  faq_generated_at: '2026-06-03T00:39:53Z'
+  faq_source_hash: a1ec216948e7cfd4fc52815196bb3b99ab4e76c9c756aa9e9a8e3216ef5e7ce4
+  summary: >-
+    This introductory Learning Path shows how to monitor and microbenchmark storage on Arm-based
+    Linux systems. You will review storage fundamentals and key workload attributes (IOPS, I/O
+    size, throughput, read/write ratio, and access patterns), analyze a real workload using FFMPEG
+    on an AWS t4g.medium (Graviton2) instance, and then install and run fio to benchmark SSD-based
+    block devices. The steps use iostat, iotop, and pidstat to observe I/O behavior and identify
+    bottlenecks. An example demonstrates attaching and identifying two AWS EBS volumes (io2 and
+    gp2) before testing. Prerequisites are an Arm-based cloud instance or Arm Linux server and
+    familiarity with Linux. Expected outcomes include describing data flow, monitoring storage
+    activity, and running fio microbenchmarks.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an Arm-based instance from a cloud service provider or an Arm Linux server, and
+      familiarity with Linux. No other explicit prerequisites are listed.
+  - question: Can I use a cloud provider other than AWS?
+    answer: >-
+      Yes. The prerequisite allows any Arm-based instance from a cloud service provider, but the
+      example steps use AWS. Setup details for other providers are not explicitly listed.
+  - question: Which instance type and example workload are used in the path?
+    answer: >-
+      The example uses an AWS t4g.medium (Graviton2) instance with two vCPUs and 4 GiB of memory.
+      FFMPEG is used as a real workload to analyze I/O behavior.
+  - question: Which block storage devices are benchmarked and how are they created?
+    answer: >-
+      Two SSD-based EBS volumes are used: an io2 volume (8 GiB, 400 provisioned IOPS, same Availability
+      Zone as the instance) and a gp2 volume. They are created in the AWS Console and added to
+      the EC2 instance before identifying them on the system.
+  - question: How should I monitor and validate storage behavior while running fio?
+    answer: >-
+      Use iostat, iotop, and pidstat to observe activity and relate results to workload attributes
+      such as IOPS, I/O size, throughput, read/write ratio, and random vs. sequential access.
+      If results look incorrect or devices are missing, verify the volumes are in the same Availability
+      Zone as the instance and properly attached.
+# END generated_summary_faq
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md
index 58fe08379b..2314a7d91b 100644
--- a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md
@@ -22,6 +22,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:40:36Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 6ac0c7cf1ab4b3efa680acbf1e349b858bee5dc7992baf6689e1f293a83060a4
+  summary_generated_at: '2026-06-02T03:33:12Z'
+  summary_source_hash: 6ac0c7cf1ab4b3efa680acbf1e349b858bee5dc7992baf6689e1f293a83060a4
+  faq_generated_at: '2026-06-03T00:40:36Z'
+  faq_source_hash: 6ac0c7cf1ab4b3efa680acbf1e349b858bee5dc7992baf6689e1f293a83060a4
+  summary: >-
+    Learn to run distributed LLM inference with llama.cpp across multiple Arm-based AWS Graviton4
+    instances on Linux. You will set up a master (main) host and worker nodes, download a Meta
+    Llama 3.1 model, convert safetensors to a single GGUF file, quantize 16-bit weights to 4-bit,
+    configure node coordination using llama.cpp’s distributed RPC feature, verify connectivity,
+    and run the model across machines. This introductory path targets developers with some llama.cpp
+    experience and familiarity with AWS. Prerequisites include three AWS c8g.4xlarge instances,
+    Python 3 on each, and access to Meta’s gated repository with a Hugging Face token. The expected
+    outcome is a working multi-node CPU inference run of a large quantized model.
+  faqs:
+  - question: What AWS resources do I need before starting?
+    answer: >-
+      You need three AWS c8g.4xlarge instances with at least 500 GB of EBS storage, running Linux.
+      This path targets Arm-based AWS Graviton4.
+  - question: Which model is used and how is it prepared?
+    answer: >-
+      The steps download Meta’s Llama 3.1 70B model, convert the safetensors files into a single
+      GGUF file, and quantize the 16-bit GGUF weights to 4-bit. The resulting 4-bit GGUF file
+      is what llama.cpp loads for inference.
+  - question: How do I register worker nodes on the master node?
+    answer: >-
+      After setting up the workers, export the worker_ips environment variable on the master using
+      entries like ip:50052. You can find each instance’s IP address in the AWS console.
+  - question: How do I verify that the master can reach a worker node?
+    answer: >-
+      From the master node, run a telnet command to the worker’s IP on port 50052. If the backend
+      server is set up correctly on the worker, you should see the backend server output.
+  - question: What access and prior knowledge do I need to download and run the model?
+    answer: >-
+      You need Python 3 installed on each instance, access to Meta’s gated repository for the
+      Llama 3.1 family, and a Hugging Face token. Familiarity with AWS and the Learning Path on
+      deploying a llama.cpp chatbot using KleidiAI is also expected.
+# END generated_summary_faq
 
 author: 
     - Aryan Bhusari
diff --git a/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md
index ec55673cd9..1b1af3a5c7 100644
--- a/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md
@@ -26,6 +26,57 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:41:54Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 6675df4c91126b157dcbf39c96a773130f1a95e2f5680913979da96f6f6c97cd
+  summary_generated_at: '2026-06-02T03:34:24Z'
+  summary_source_hash: 6675df4c91126b157dcbf39c96a773130f1a95e2f5680913979da96f6f6c97cd
+  faq_generated_at: '2026-06-03T00:41:54Z'
+  faq_source_hash: 6675df4c91126b157dcbf39c96a773130f1a95e2f5680913979da96f6f6c97cd
+  summary: >-
+    This Learning Path shows how to deploy a production-grade Django REST API on Google Cloud
+    using Arm-based Axion compute. You will provision Arm64 Axion C4A virtual machines and GKE
+    node pools, package the application into an Arm-native Docker image, push it to Google Artifact
+    Registry, and deploy on GKE using Kubernetes manifests (Deployment, Service, ConfigMap, Secrets).
+    The path integrates Django with Cloud SQL (PostgreSQL) over private IP and Memorystore (Redis),
+    exposes the service via a LoadBalancer, and validates connectivity to both services. You also
+    set up a SUSE Linux Enterprise Server VM, open port 8000, run the Django development server,
+    and benchmark Gunicorn on Arm with ApacheBench to measure throughput and p95 latency. Prerequisites
+    are a GCP account with billing enabled and basic familiarity with Django, containers, and
+    Kubernetes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Google Cloud Platform account with billing enabled. The path assumes basic familiarity
+      with Django and a basic understanding of containers and Kubernetes.
+  - question: How do I run and reach the Django development server on the Axion VM?
+    answer: >-
+      You will install Python 3.11 on a SUSE Linux Enterprise Server VM, create a Django project,
+      and start the development server. Then, create a firewall rule to allow inbound traffic
+      on port 8000 so you can access the server from your browser.
+  - question: Which container and registry steps are included before deploying to GKE?
+    answer: >-
+      You will package the Django REST API into an Arm-native Docker container and push the image
+      to Google Artifact Registry. These steps prepare the application for deployment on Arm64
+      GKE node pools.
+  - question: Which Kubernetes resources and exposure method are used on GKE?
+    answer: >-
+      The deployment uses Kubernetes manifests including a Deployment, Service, ConfigMap, and
+      Secrets. The application is exposed externally using a LoadBalancer Service on Arm64 Axion
+      node pools.
+  - question: How does the app connect to managed data services and how is performance evaluated?
+    answer: >-
+      Django is integrated with Cloud SQL (PostgreSQL) over private IP and Memorystore (Redis)
+      for caching and sessions, with steps to validate application connectivity. Performance is
+      measured using ApacheBench to report throughput and p95 latency against Gunicorn on Arm.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/django/_index.md b/content/learning-paths/servers-and-cloud-computing/django/_index.md
index 9ba96dcba8..20454e51a7 100644
--- a/content/learning-paths/servers-and-cloud-computing/django/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/django/_index.md
@@ -21,6 +21,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:41:18Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
+  summary_generated_at: '2026-06-02T03:33:56Z'
+  summary_source_hash: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
+  faq_generated_at: '2026-06-03T00:41:18Z'
+  faq_source_hash: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
+  summary: >-
+    Build and deploy a simple Django web application on Arm-based Linux machines using Nginx and
+    PostgreSQL. This introductory path uses Ubuntu 22.04 LTS and walks you through creating a
+    Django project, configuring its PostgreSQL database settings, creating the database and user,
+    deploying behind Nginx, and verifying the application is working. You can run the steps on
+    an Arm instance from AWS, Microsoft Azure, Google Cloud, or Oracle, on an on-premises Arm
+    server, or on a Linux VM on your Arm device. Prerequisites include sudo access, comfort with
+    SSH and basic Linux administration, and the ability to install Nginx and PostgreSQL.
+  faqs:
+  - question: What environment do I need to run this?
+    answer: >-
+      Use an Arm-based instance from a cloud provider, an on-premises Arm server, or a Linux VM
+      on your Arm device. The instructions use Ubuntu 22.04 LTS and are the same regardless of
+      the Arm machine type.
+  - question: Do I need a specific Python version or a virtual environment?
+    answer: >-
+      Ubuntu 22.04 provides Python 3.10, which you can use, or you may optionally install a newer
+      Python via the Deadsnakes PPA. The steps assume you are working in a terminal with a Python
+      virtual environment activated.
+  - question: Do I need to install Nginx and PostgreSQL before deploying?
+    answer: >-
+      Yes. Installing both Nginx and PostgreSQL is listed as a prerequisite for this path. Follow
+      the referenced Learning Paths for Nginx and PostgreSQL if you need installation guidance.
+  - question: How do I know the Django project was created correctly?
+    answer: >-
+      After running django-admin startproject myproject, you should see a myproject directory
+      with manage.py and a myproject package containing asgi.py, __init__.py, settings.py, urls.py,
+      and wsgi.py. You can start the development server from the project directory to quickly
+      validate it runs.
+  - question: Which PostgreSQL settings should I use and how do I create the database?
+    answer: >-
+      In settings.py set ENGINE to django.db.backends.postgresql with NAME myprojectdb, USER usr,
+      PASSWORD mypassword, HOST as localhost or your machine’s IP, and PORT 5432. Open the PostgreSQL
+      prompt with sudo -u postgres psql and create the database and user to match those values.
+# END generated_summary_faq
 
 author: Diego Russo
 
diff --git a/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md b/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
index ddda5c86fb..83eb1e09f3 100644
--- a/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
@@ -19,6 +19,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:42:37Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 82716c2de19d18a154c85d03b7f2ec01839284262914f7bb3ad04b18a105379d
+  summary_generated_at: '2026-06-02T03:35:11Z'
+  summary_source_hash: 82716c2de19d18a154c85d03b7f2ec01839284262914f7bb3ad04b18a105379d
+  faq_generated_at: '2026-06-03T00:42:37Z'
+  faq_source_hash: 82716c2de19d18a154c85d03b7f2ec01839284262914f7bb3ad04b18a105379d
+  summary: >-
+    This Learning Path shows how to build and benchmark the Deep Learning Recommendation Model
+    (DLRM) on Arm Neoverse V2 processors using PyTorch and MLPerf. You will prepare a Linux Arm-based
+    cloud instance or on‑prem server, obtain data and model weights with rclone, and use provided
+    scripts to run a modified DLRMv2 benchmark. The path uses PyTorch 2.9.0+cpu with Arm-focused
+    optimizations and Docker-based tooling where applicable. By the end, you will have built DLRM,
+    executed the MLPerf benchmark tailored for Arm systems, and inspected the resulting performance
+    outputs. Prerequisites include an Arm-based instance (AWS or Google Cloud) or on‑prem Arm
+    server with at least 400GB RAM and 800GB of disk space.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      Use any Arm-based instance from a cloud service provider such as AWS or Google Cloud, or
+      an on-premise Arm server. The prerequisites are at least 400GB of RAM and 800GB of disk
+      space on a Linux system.
+  - question: Which operating system and processors does this target?
+    answer: >-
+      The steps assume Linux and target Arm Neoverse V2 CPUs. The procedures and benchmarks are
+      written for Arm-based systems.
+  - question: How do I download the DLRM data and model weights?
+    answer: >-
+      Create data and model directories in your home folder, then install rclone using the provided
+      installation script. Run rclone config and use it to download the required datasets and
+      weights as shown in the steps.
+  - question: Which frameworks and versions are used to run the benchmark?
+    answer: >-
+      You will run a modified MLPerf benchmark for DLRM using PyTorch 2.9.0+cpu. The steps use
+      a repository of scripts tailored for Arm-based systems.
+  - question: How do I run the benchmark and confirm it completed successfully?
+    answer: >-
+      Clone the provided repository and run the included scripts to execute the DLRM benchmark.
+      After the run, inspect the generated results as directed in the Learning Path to validate
+      completion.
+# END generated_summary_faq
 
 author: 
     - Phalani Paladugu
diff --git a/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md b/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
index 4a15012091..380557e5a9 100644
--- a/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
@@ -28,6 +28,60 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:43:28Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 80785022032bf4e3c65da682e698940a212ee1ee77386698889a9fafbed9f823
+  summary_generated_at: '2026-06-02T03:37:07Z'
+  summary_source_hash: 80785022032bf4e3c65da682e698940a212ee1ee77386698889a9fafbed9f823
+  faq_generated_at: '2026-06-03T00:43:28Z'
+  faq_source_hash: 80785022032bf4e3c65da682e698940a212ee1ee77386698889a9fafbed9f823
+  summary: >-
+    This advanced path shows how to use the Docker MCP Toolkit with the Arm MCP Server and GitHub
+    Copilot in VS Code to automate migration of a containerized C++ app from x86 AVX2 intrinsics
+    to Arm64 Neon. You will enable the MCP Toolkit in Docker Desktop, connect the MCP Gateway
+    to VS Code, and configure the Arm, GitHub, and Sequential Thinking MCP servers. Using a provided
+    demo repository, you will scan for x86-specific code, generate Neon equivalents, create a
+    pull request, and review changes. Finally, you will build for linux/arm64 with docker buildx
+    and run the benchmark to validate output on Arm64. Prerequisites include Docker Desktop 4.59+,
+    VS Code with GitHub Copilot, a GitHub PAT, and basic Docker/C++ and SIMD knowledge. Estimated
+    time: 45 minutes on Linux or macOS.
+  faqs:
+  - question: What do I need before starting the migration steps?
+    answer: >-
+      You need Docker Desktop 4.59 or later with MCP Toolkit enabled, VS Code with the GitHub
+      Copilot extension, a GitHub account with a Personal Access Token that allows repository
+      access, a machine with at least 8 GB RAM (16 GB recommended), and basic familiarity with
+      Docker, C++, and SIMD intrinsics concepts.
+  - question: Which MCP servers should I configure, and how do I make them available to Copilot
+      in VS Code?
+    answer: >-
+      Configure the Arm MCP Server, GitHub MCP Server, and Sequential Thinking MCP Server in the
+      Docker MCP Toolkit. In VS Code, ensure the MCP_DOCKER server is running (Extensions > MCP_DOCKER
+      > Start Server) so GitHub Copilot can invoke these servers through the MCP Gateway.
+  - question: Where do I get the demo application and open it in VS Code?
+    answer: >-
+      Clone the repository with: git clone https://github.com/JoeStech/docker-blog-arm-migration
+      and cd into docker-blog-arm-migration. Open it in VS Code with: code .
+  - question: How do I direct GitHub Copilot to perform the x86-to-Arm64 migration?
+    answer: >-
+      Open GitHub Copilot Chat in VS Code and paste the provided prompt that instructs it to use
+      the Arm MCP Server tools for migration. Copilot will scan for x86-specific dependencies
+      and intrinsics, automate AVX2-to-Neon conversions using the Arm MCP Server knowledge base,
+      and propose changes via a pull request using the GitHub MCP Server.
+  - question: What result should I expect after building and running the Arm64 container?
+    answer: >-
+      After building with docker buildx for --platform linux/arm64 and running the container,
+      the benchmark output should indicate it’s running on Arm64 with NEON optimizations and display
+      matrix multiplication timings and a result sum. This confirms the migrated code executes
+      on Arm64 as intended.
+# END generated_summary_faq
 
 author: Ajeet Singh Raina
 
diff --git a/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md b/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md
index 71597631e7..6b0bb6ee4f 100644
--- a/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md
@@ -25,6 +25,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:44:01Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 08d8f0c86625ef41476d3a8b24bad9b0a0820797022ef847bf9bb17a976726a7
+  summary_generated_at: '2026-06-02T03:38:20Z'
+  summary_source_hash: 08d8f0c86625ef41476d3a8b24bad9b0a0820797022ef847bf9bb17a976726a7
+  faq_generated_at: '2026-06-03T00:44:01Z'
+  faq_source_hash: 08d8f0c86625ef41476d3a8b24bad9b0a0820797022ef847bf9bb17a976726a7
+  summary: >-
+    Learn how to migrate and run an OrchardCore CMS .NET application on Azure Cobalt 100 Arm-based
+    virtual machines. You will build and run the app on Ubuntu 24.04 with port 8080 open, integrate
+    a simple C shared library that is invoked from C# via DllImport, and configure .NET AnyCPU
+    so the same build runs on both Arm and x86. The path also reviews .NET version choices and
+    support status to help you evaluate behavior on Arm. Prerequisites include an Azure account
+    with VM permissions, .NET SDK 8.0 or later, GCC or a cross-compiler, basic C and C# knowledge,
+    and an OrchardCore app created with the .NET CLI or Visual Studio.
+  faqs:
+  - question: What do I need in Azure before I start?
+    answer: >-
+      You need a Microsoft Azure account with permissions to deploy virtual machines. The path
+      assumes you can create and configure an Azure Cobalt 100 instance.
+  - question: Which VM image and network settings should I use for the OrchardCore app?
+    answer: >-
+      Launch an Azure Cobalt 100 (Arm-based) VM running Ubuntu 24.04 and open port 8080 to the
+      internet. If you need help creating the VM, see the Create an Azure Cobalt 100 VM Learning
+      Path.
+  - question: What tools and project setup are required on the VM?
+    answer: >-
+      Install .NET SDK 8.0 or later and ensure GCC is available on Linux (or use a cross-compiler).
+      You should also have an OrchardCore application created using the .NET CLI or Visual Studio,
+      and basic knowledge of C and C#.
+  - question: How do I build the C shared library and verify it is called from .NET?
+    answer: >-
+      Compile the C source with: gcc -shared -o libmylib.so -fPIC mylib.c, which produces libmylib.so.
+      Call the function from C# via DllImport; when invoked, it prints: Hello from the C library!.
+  - question: How do I run the same build on both Arm and x86 machines?
+    answer: >-
+      Use .NET’s AnyCPU configuration to produce an architecture-agnostic build. The path shows
+      how to configure and run the OrchardCore app so it can execute on Arm-based cloud VMs as
+      well as x86 systems.
+# END generated_summary_faq
 
 author: Joe Stech
 
diff --git a/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
index f8c315543d..2c89371b19 100644
--- a/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
@@ -23,6 +23,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:44:58Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 4ef29931bf19dc95bb586440796381725c271ef1b953dcf46d00ad9617eabbb1
+  summary_generated_at: '2026-06-02T03:39:49Z'
+  summary_source_hash: 4ef29931bf19dc95bb586440796381725c271ef1b953dcf46d00ad9617eabbb1
+  faq_generated_at: '2026-06-03T00:44:58Z'
+  faq_source_hash: 4ef29931bf19dc95bb586440796381725c271ef1b953dcf46d00ad9617eabbb1
+  summary: >-
+    This Learning Path shows how to monitor Azure Cobalt 100 Arm64 virtual machines with Dynatrace.
+    You will create an Azure VM in the Dpsv6 series, install Dynatrace OneAgent on Ubuntu 24.04
+    LTS Arm64, and configure Dynatrace ActiveGate as a secure gateway to the Dynatrace SaaS platform.
+    You will open TCP port 9999 in the Azure Network Security Group to allow ActiveGate traffic,
+    then verify host and application visibility by monitoring system resources, processes, and
+    services, and validating with a sample NGINX workload. Prerequisites include an Azure account
+    with access to Cobalt 100 instances, basic Linux command-line skills, SSH familiarity, and
+    a basic understanding of cloud and monitoring concepts.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Microsoft Azure account with access to Cobalt 100 based instances (Dpsv6), basic
+      Linux command-line skills, familiarity with SSH, and a basic understanding of cloud infrastructure
+      and monitoring concepts. The path connects to a Dynatrace SaaS environment, but specific
+      account details are not explicitly listed.
+  - question: Which Azure VM type and operating system should I use?
+    answer: >-
+      Use a general-purpose VM in the Dpsv6 series running on Azure Cobalt 100 processors. The
+      installation steps target Ubuntu 24.04 LTS Arm64.
+  - question: How do I allow Dynatrace ActiveGate traffic to the VM?
+    answer: >-
+      Create a Network Security Group rule in the Azure Portal to allow inbound TCP traffic on
+      port 9999. Apply the rule to the NSG attached to the VM’s network interface or subnet.
+  - question: How do I know if OneAgent and ActiveGate are installed correctly?
+    answer: >-
+      After installation, OneAgent runs as a host monitoring agent, connects to your Dynatrace
+      SaaS environment, and begins monitoring system processes and services automatically. ActiveGate
+      runs as a system service, listens on port 9999, and communicates with Dynatrace.
+  - question: What result should I expect when validating with the sample NGINX workload?
+    answer: >-
+      You should see NGINX detected in Dynatrace with process and service monitoring data from
+      the Arm64 VM. This confirms that application monitoring is functioning through OneAgent
+      and, if configured, via ActiveGate.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/ecs/_index.md b/content/learning-paths/servers-and-cloud-computing/ecs/_index.md
index e0883002b3..e09f4b655e 100644
--- a/content/learning-paths/servers-and-cloud-computing/ecs/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ecs/_index.md
@@ -20,6 +20,51 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:45:33Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: ef5f9e7c8844b20b9044b43f4758bc1d74374521093d7738a7f8832d21f1dcac
+  summary_generated_at: '2026-06-02T03:40:50Z'
+  summary_source_hash: ef5f9e7c8844b20b9044b43f4758bc1d74374521093d7738a7f8832d21f1dcac
+  faq_generated_at: '2026-06-03T00:45:33Z'
+  faq_source_hash: ef5f9e7c8844b20b9044b43f4758bc1d74374521093d7738a7f8832d21f1dcac
+  summary: >-
+    Learn to deploy containerized applications on Amazon Elastic Container Service (ECS) using
+    Fargate with AWS Graviton processors. You will create an ECS cluster, configure required identity
+    settings, and run a container task on Arm-based infrastructure. The path also shows how to
+    automate the same workflow with Terraform by incrementally building a main.tf file, including
+    creating an Amazon ECR repository and deploying an example Nginx service. This introductory,
+    Linux-focused path targets developers new to ECS on Graviton. Prerequisites are an AWS account
+    and a computer with Docker, AWS CLI, and Terraform installed. By the end, you will have a
+    running ECS task on Fargate and a Terraform configuration that reproduces the deployment.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an AWS account and a computer with Docker, AWS CLI, and Terraform installed. The
+      path targets Linux.
+  - question: Do I need to manage EC2 instances for this deployment?
+    answer: >-
+      No. The path uses the Fargate launch type, which is serverless, so you do not provision
+      or maintain EC2 instances.
+  - question: Which architecture should my container image target to run on AWS Graviton?
+    answer: >-
+      Build your container image for the Arm architecture. Fargate supports AWS Graviton processors
+      so your containers can run on Arm.
+  - question: Where will I store and pull my container images in this workflow?
+    answer: >-
+      The path creates a repository in Amazon Elastic Container Registry (ECR). The Terraform
+      section builds a main.tf that sets up ECR and uses it for the ECS deployment.
+  - question: What result should I expect after completing the Terraform section?
+    answer: >-
+      You will have a main.tf that automates the same steps for deploying Nginx on ECS. This includes
+      provisioning the required ECS resources and using an ECR repository for the container image.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md b/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
index 44b9cdae87..dfd6eb3de0 100644
--- a/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
@@ -21,6 +21,51 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:46:43Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: e32bdb090c422d1fb5bb1f9bd3af56c55fdf8989e6a7fe6a101e90dcb6f3eadd
+  summary_generated_at: '2026-06-02T03:43:01Z'
+  summary_source_hash: e32bdb090c422d1fb5bb1f9bd3af56c55fdf8989e6a7fe6a101e90dcb6f3eadd
+  faq_generated_at: '2026-06-03T00:46:43Z'
+  faq_source_hash: e32bdb090c422d1fb5bb1f9bd3af56c55fdf8989e6a7fe6a101e90dcb6f3eadd
+  summary: >-
+    This Learning Path shows how to build and deploy a multi-architecture container application
+    for x86/amd64 and arm64 on Amazon EKS using docker buildx and docker manifest. You will create
+    a hybrid EKS cluster with both x86 and Arm-based (Graviton) nodes, then build images for each
+    architecture and understand the key nuances of multi-arch container builds. The environment
+    assumes Linux, and you need an AWS account plus eksctl, kubectl, and Docker installed locally.
+    By the end, you will have deployed a multi-arch application to a single EKS cluster that can
+    run across both architectures. The topic is advanced and is designed for developers targeting
+    multi-arch Kubernetes on AWS.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an AWS account and a Linux machine with eksctl, kubectl, and Docker installed.
+      No other prerequisites are explicitly listed.
+  - question: Which tools are used to build multi-architecture images, and where do I run them?
+    answer: >-
+      You will use docker buildx and docker manifest. These are run with your local Docker installation.
+  - question: How is the Amazon EKS cluster set up for multiple architectures?
+    answer: >-
+      You create a hybrid EKS cluster that includes both x86/amd64 and Arm-based (Graviton) nodes.
+      This lets you run workloads across both architectures in a single cluster.
+  - question: What result should I expect after deployment?
+    answer: >-
+      A multi-architecture container application runs on a single Amazon EKS cluster that supports
+      both arm64 and amd64. The image you build is suitable for both architectures using a multi-arch
+      manifest.
+  - question: What should I check if the application only runs on one node type?
+    answer: >-
+      Confirm that you built images for both amd64 and arm64 and that your docker manifest includes
+      both. Also verify your EKS cluster has both x86 and Arm-based (Graviton) nodes available.
+# END generated_summary_faq
 
 author: Pranay Bakre
 
diff --git a/content/learning-paths/servers-and-cloud-computing/eks/_index.md b/content/learning-paths/servers-and-cloud-computing/eks/_index.md
index f8f8ddebb9..02790ae4c9 100644
--- a/content/learning-paths/servers-and-cloud-computing/eks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/eks/_index.md
@@ -19,6 +19,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:45:59Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 4f1c448eef66300e024bda27c420f9746047c0c4b76e8556d3d8693382206055
+  summary_generated_at: '2026-06-02T03:42:15Z'
+  summary_source_hash: 4f1c448eef66300e024bda27c420f9746047c0c4b76e8556d3d8693382206055
+  faq_generated_at: '2026-06-03T00:45:59Z'
+  faq_source_hash: 4f1c448eef66300e024bda27c420f9746047c0c4b76e8556d3d8693382206055
+  summary: >-
+    Provision an Amazon EKS cluster on Arm-based Graviton instances and deploy a WordPress application
+    with a MySQL database. Working from a machine with the AWS CLI, EKS CLI, and Kubernetes CLI
+    installed, you will configure AWS credentials, create the cluster, and use three Kubernetes
+    YAML files (kustomization.yaml, mysql-deployment.yaml, and wordpress-deployment.yaml) to deploy
+    the application with kubectl. The path is introductory and aimed at developers new to Kubernetes
+    on AWS. It focuses on practical setup and deployment steps, including setting a MySQL password
+    via Kustomize. An AWS account is required; no other explicit prerequisites are listed. Estimated
+    time to complete is about 60 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an AWS account and must configure your AWS access key ID and secret access key.
+      Install the EKS CLI, AWS CLI, and Kubernetes CLI, and confirm you can run the aws, ekscli,
+      and kubectl commands.
+  - question: Which machine can I use to run the setup?
+    answer: >-
+      Any computer with the required tools installed can be used. The operating system listed
+      for this path is Linux.
+  - question: How do I create an EKS cluster on Arm-based instances?
+    answer: >-
+      Follow the Create an EKS cluster step to provision an Amazon EKS cluster on Arm-based Graviton
+      instances. You will use the EKS CLI together with the AWS CLI during this step.
+  - question: Which files are required to deploy WordPress and where do I set the MySQL password?
+    answer: >-
+      You need kustomization.yaml, mysql-deployment.yaml, and wordpress-deployment.yaml. In kustomization.yaml,
+      the secretGenerator named mysql-pass sets the database password using a literal such as
+      password=YourPassword.
+  - question: How do I apply the deployment and know it targets my EKS cluster?
+    answer: >-
+      Use kubectl with the kustomization.yaml that references the MySQL and WordPress resources.
+      Ensure kubectl is configured to communicate with your newly created EKS cluster before applying
+      the files.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md
index ea9ab15710..f35811a4b3 100644
--- a/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md
@@ -22,6 +22,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:48:02Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: f36b1e9a45b6ec29d8041b70997550d917322cf9cdd456db26083169d21175ed
+  summary_generated_at: '2026-06-02T03:44:25Z'
+  summary_source_hash: f36b1e9a45b6ec29d8041b70997550d917322cf9cdd456db26083169d21175ed
+  faq_generated_at: '2026-06-03T00:48:02Z'
+  faq_source_hash: f36b1e9a45b6ec29d8041b70997550d917322cf9cdd456db26083169d21175ed
+  summary: >-
+    This Learning Path shows how to deploy Envoy Proxy on Google Cloud Axion C4A Arm64 virtual
+    machines built on Arm Neoverse V2 cores, then validate and benchmark it. You will provision
+    a c4a-standard-4 instance (4 vCPUs, 16 GB) in the Google Cloud Console, install Envoy v1.30.0
+    on RHEL 9 using the official static Arm64 binary, and run a minimal configuration that forwards
+    traffic to httpbin.org to verify a 200 OK response on port 10000. You will also build and
+    use Siege to generate HTTP load and record availability, throughput, response time, and failure
+    rates, comparing results on Arm64 (AArch64) and x86_64. Prerequisites include a GCP account
+    with billing enabled and familiarity with networking and Envoy architecture.
+  faqs:
+  - question: What do I need before provisioning the C4A VM on GCP?
+    answer: >-
+      You need a Google Cloud Platform account with billing enabled, plus familiarity with networking
+      concepts and the Envoy architecture. For general GCP setup assistance, see the Learning
+      Path Getting started with Google Cloud Platform.
+  - question: Which C4A machine type is used, and where do I create it?
+    answer: >-
+      The path uses c4a-standard-4 (4 vCPUs, 16 GB memory). Create it in the Google Cloud Console
+      under Compute Engine > VM instances by selecting Create instance and choosing the C4A machine
+      type.
+  - question: What Envoy build is installed on the C4A instance?
+    answer: >-
+      Envoy Proxy v1.30.0 is installed on RHEL 9 using the official static Arm64 (AArch64) binary.
+      You install required dependencies and then download the binary with curl to /usr/local/bin/envoy.
+  - question: How do I validate Envoy after installation, and what result should I expect?
+    answer: >-
+      Create a minimal Envoy configuration, start Envoy with it, and issue a request using curl.
+      Envoy should listen on port 10000, forward requests to httpbin.org, and return a 200 OK
+      response.
+  - question: How do I run the benchmarks and what metrics does Siege report?
+    answer: >-
+      Build Siege from source after installing Development Tools, then run load tests against
+      Envoy. Siege reports availability, throughput, response time, and failure rates; repeat
+      the same procedure on Arm64 and x86_64 to compare results.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/envoy/_index.md b/content/learning-paths/servers-and-cloud-computing/envoy/_index.md
index 5bbfc9a96a..1bacb9518a 100644
--- a/content/learning-paths/servers-and-cloud-computing/envoy/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/envoy/_index.md
@@ -20,6 +20,51 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:47:28Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: d492b6dce11b7d8f6591ff3b3ce9aa2c382a6a7a749add228c3ac1f6ae57e218
+  summary_generated_at: '2026-06-02T03:43:48Z'
+  summary_source_hash: d492b6dce11b7d8f6591ff3b3ce9aa2c382a6a7a749add228c3ac1f6ae57e218
+  faq_generated_at: '2026-06-03T00:47:28Z'
+  faq_source_hash: d492b6dce11b7d8f6591ff3b3ce9aa2c382a6a7a749add228c3ac1f6ae57e218
+  summary: >-
+    This Learning Path shows how to build, install, and run Envoy on Arm-based Linux servers and
+    configure it as a basic web server for traffic management. You will provision an Arm instance
+    in the cloud (AWS, Microsoft Azure, Google Cloud, or Oracle) or use an on-premises Arm server,
+    ensure network access on SSH (22) and HTTP (80), and then set up Envoy with a sample configuration
+    to run it as a service. The steps focus on practical setup and conclude with checks to verify
+    Envoy is working correctly. Aimed at an introductory audience, the path is designed to be
+    completed in about 60 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need at least one Arm-based instance from a cloud service provider or an on-premises
+      Arm server. Ensure your network settings (firewalls and security groups) allow communication
+      on port 22 (SSH) and port 80 (HTTP).
+  - question: Which platforms can I use for the Arm-based instance?
+    answer: >-
+      You can use Arm-based instances from AWS, Microsoft Azure, Google Cloud, or Oracle. An on-premises
+      Arm server also works for this Learning Path.
+  - question: Which operating system do the steps target?
+    answer: >-
+      The steps target Linux. Ensure your Arm instance is running a Linux distribution.
+  - question: How do I run Envoy as a service in this path?
+    answer: >-
+      You will create a sample configuration file at configs/config-http.yaml and use it to start
+      Envoy. The sample config defines a listener on port 80 with an HTTP connection manager.
+  - question: What should I check if I cannot reach the Envoy web server?
+    answer: >-
+      Verify that your security groups and firewalls allow inbound traffic on port 80 and that
+      SSH access on port 22 is permitted for management. Also confirm that Envoy is running with
+      the provided configuration file.
+# END generated_summary_faq
 
 author: Zhengjun Xing
 
diff --git a/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md
index 054c9ddf57..c45cb63ab4 100644
--- a/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md
@@ -21,6 +21,51 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:48:49Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: cbbcc8fa33f864e422f8db7d58fba32c26f6070be2846b246837c63ccf37e1c7
+  summary_generated_at: '2026-06-02T03:45:17Z'
+  summary_source_hash: cbbcc8fa33f864e422f8db7d58fba32c26f6070be2846b246837c63ccf37e1c7
+  faq_generated_at: '2026-06-03T00:48:49Z'
+  faq_source_hash: cbbcc8fa33f864e422f8db7d58fba32c26f6070be2846b246837c63ccf37e1c7
+  summary: >-
+    Learn how to tune Envoy on Arm servers running Linux—on bare metal or Arm instances from AWS,
+    Microsoft Azure, Google Cloud, or Oracle—using Transparent Huge Pages (THP) and Profile-Guided
+    Optimization (PGO). You will review kernel parameters that affect Envoy, check THP configuration
+    (with an Ubuntu example), and rebuild Envoy with Bazel and LLVM/Clang to apply PGO, using
+    the latest compiler and a recent Bazel as recommended. This advanced path expects an existing
+    Envoy service; if you do not have one, follow the Deploy Envoy Learning Path first. By the
+    end, you will have applied THP settings and produced a PGO-built Envoy binary.
+  faqs:
+  - question: What do I need before running these tuning steps?
+    answer: >-
+      You need a cloud or bare-metal installation of an Envoy service. If you do not already have
+      Envoy set up, review Learn how to deploy Envoy.
+  - question: Which environments does this Learning Path target?
+    answer: >-
+      Linux on Arm servers, including Arm Neoverse in the cloud (AWS, Microsoft Azure, Google
+      Cloud, Oracle) or on bare metal. The guidance is for developers running Envoy on Arm.
+  - question: How do I check my Linux kernel configuration for THP on Ubuntu?
+    answer: >-
+      Run: cat /boot/config-$(uname -r) to inspect your kernel configuration. Use this to verify
+      settings relevant to Transparent Huge Pages.
+  - question: Which toolchain should I use to build Envoy with PGO?
+    answer: >-
+      Build Envoy using Bazel and LLVM/Clang, and use the latest compiler version. It is advisable
+      to build Bazel from the most recent source; refer to the LLVM and Clang documentation for
+      details.
+  - question: What performance improvement should I expect from THP or PGO?
+    answer: >-
+      The Learning Path notes that applying THP can result in an 18% enhancement in performance,
+      and PGO can result in a 10% enhancement. These figures are presented as general guidance.
+# END generated_summary_faq
 
 author: Zhengjun Xing
 
diff --git a/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md b/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md
index a0ebef0cd5..3011d6225d 100644
--- a/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md
@@ -24,6 +24,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:49:39Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 02eedcebf59a8ab506d4ebbf5bbe20ead367cf3c0700950db5a9059d2f671853
+  summary_generated_at: '2026-06-02T03:46:23Z'
+  summary_source_hash: 02eedcebf59a8ab506d4ebbf5bbe20ead367cf3c0700950db5a9059d2f671853
+  faq_generated_at: '2026-06-03T00:49:39Z'
+  faq_source_hash: 02eedcebf59a8ab506d4ebbf5bbe20ead367cf3c0700950db5a9059d2f671853
+  summary: >-
+    This advanced Learning Path shows how stack buffer overflow exploits work on AArch64 Linux
+    by building and analyzing small, controlled examples. You will create a Docker-based lab on
+    an Arm machine using an Ubuntu 22.04 image that installs Clang and GDB and disables ASLR for
+    repeatable experiments. Working through C programs, you will inspect compiler-generated stack
+    frame layouts, see how an out-of-bounds write can overwrite a saved return address, and construct
+    a minimal end-to-end exploit that redirects control flow to an attacker-chosen value without
+    crashing. Prerequisites include an Arm computer running Linux with Docker installed, plus
+    familiarity with C, AArch64 assembly, the Linux command line, and GDB.
+  faqs:
+  - question: What do I need before running the exercises?
+    answer: >-
+      You need an Arm computer running Linux with Docker installed. You should be comfortable
+      with basic C and AArch64 assembly, Linux command line commands, and using gdb.
+  - question: Why does the Dockerfile disable ASLR, and what happens if I skip that step?
+    answer: >-
+      ASLR is an on-by-default mitigation that would block some of the experiments in this path.
+      The Dockerfile sets kernel.randomize_va_space to 0 so you can reproduce the stack layouts
+      and control-flow redirection reliably.
+  - question: Where should I save the example source files when using Docker?
+    answer: >-
+      Save the files in the directory where you run docker run. You can create or edit these files
+      outside the container.
+  - question: Which tools will I use inside the container to build and inspect the examples?
+    answer: >-
+      The container installs Clang and gdb. You will compile the provided C programs with Clang
+      and may use gdb as part of inspecting behavior during the steps.
+  - question: How do I know if the control-flow redirection worked?
+    answer: >-
+      The goal is to modify program behavior without causing a crash by overwriting the saved
+      return address with a chosen value. You should see the program’s behavior change as described
+      in the steps rather than terminating with a fault.
+# END generated_summary_faq
 
 author: Kristof Beyls
 
diff --git a/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md b/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
index c2cdf3939e..265a972ada 100644
--- a/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
@@ -21,6 +21,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:50:12Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: dde32f5d14a877313a8b0aafec58acb09cd1e77f4fc9138b9e1bd6d9fedf250a
+  summary_generated_at: '2026-06-02T03:47:24Z'
+  summary_source_hash: dde32f5d14a877313a8b0aafec58acb09cd1e77f4fc9138b9e1bd6d9fedf250a
+  faq_generated_at: '2026-06-03T00:50:12Z'
+  faq_source_hash: dde32f5d14a877313a8b0aafec58acb09cd1e77f4fc9138b9e1bd6d9fedf250a
+  summary: >-
+    Learn how to detect and address false sharing on Arm-based cloud systems using Linux perf
+    C2C and the Arm Statistical Profiling Extension (SPE). You will set up a Linux environment
+    on an Arm Neoverse-based instance with SPE support, verify kernel and tool access to the required
+    performance events, and compile a multithreaded C example that contrasts cache-aligned and
+    unaligned data. Using perf stat and perf c2c, you will compare the two builds, investigate
+    cache line behavior, and trace memory contention to source lines. Prerequisites include access
+    to an Arm-based cloud instance with SPE, a basic understanding of cache coherency, and familiarity
+    with Linux perf tools. No additional prerequisites are explicitly listed.
+  faqs:
+  - question: How do I know if my cloud instance supports Arm SPE?
+    answer: >-
+      Follow the setup steps to check both hardware and kernel support for SPE and to validate
+      that Linux perf can access the required events. Choose an Arm-based instance that exposes
+      SPE to the OS.
+  - question: Which cloud platforms can I use for this path?
+    answer: >-
+      You can use an Arm-based instance on AWS, Microsoft Azure, Google Cloud, or Oracle, as long
+      as the instance supports Arm SPE.
+  - question: Which perf commands will I use during the analysis?
+    answer: >-
+      You will use perf stat to compare the runtime and metrics of aligned and unaligned binaries,
+      and perf c2c to record and analyze cache line behavior and memory contention.
+  - question: What result should I expect from the false sharing example?
+    answer: >-
+      After compiling and running both versions, expect a runtime difference and c2c analysis
+      that highlights cache line contention in the unaligned case. The steps show how to relate
+      those findings back to the source code.
+  - question: What should I check if perf c2c does not show the expected events?
+    answer: >-
+      Verify that SPE is enabled and supported by your hardware and kernel, that the perf tools
+      are installed, and that perf can access the necessary performance monitoring events as described
+      in the setup section.
+# END generated_summary_faq
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md b/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md
index 3c3e9dc569..5cab4a9eaf 100644
--- a/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md
@@ -21,6 +21,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:50:51Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 978d3da8668e38758c138313c31809f3de5a8cefd1b7c2b47a536c0ee364b692
+  summary_generated_at: '2026-06-02T03:48:10Z'
+  summary_source_hash: 978d3da8668e38758c138313c31809f3de5a8cefd1b7c2b47a536c0ee364b692
+  faq_generated_at: '2026-06-03T00:50:51Z'
+  faq_source_hash: 978d3da8668e38758c138313c31809f3de5a8cefd1b7c2b47a536c0ee364b692
+  summary: >-
+    This advanced Learning Path guides you through building custom Linux kernels with tuxmake,
+    provisioning Arm-based AWS EC2 instances, and benchmarking multiple kernel versions using
+    Fastpath. You will set up three machines: a CPU-optimized kernel build host, a Fastpath host
+    on Ubuntu 24.04 LTS to orchestrate testing, and a System Under Test (SUT) on Ubuntu 24.04
+    LTS to run workloads. You will generate a YAML benchmark plan (plan.yaml), execute benchmarks,
+    and analyze collected results to compare kernel performance across versions. Prerequisites
+    include an AWS account with permissions to create EC2 instances and familiarity with basic
+    Linux administration and SSH. The estimated time to complete is about 90 minutes.
+  faqs:
+  - question: What do I need before provisioning the EC2 instances?
+    answer: >-
+      You need an AWS account with permissions to create EC2 instances. The path assumes familiarity
+      with basic Linux administration and SSH.
+  - question: Which EC2 instance types and images are used for each role?
+    answer: >-
+      The example build host and SUT use AWS Graviton m6g.12xlarge instances. The Fastpath host
+      is a separate EC2 instance using the Ubuntu 24.04 LTS (Arm) AMI, and the SUT also runs Ubuntu
+      24.04 LTS; other instance details beyond these examples are not explicitly listed.
+  - question: Can I use the AWS Management Console or the AWS CLI to create the instances?
+    answer: >-
+      You can use either the AWS Management Console or the AWS CLI to perform the EC2 instance
+      creation steps. The Learning Path supports both approaches.
+  - question: Where are kernels built and which tools are used?
+    answer: >-
+      Kernels are built on the kernel build host using tuxmake. The Learning Path then prepares
+      those kernels for testing with Fastpath.
+  - question: How do I generate and run the Fastpath benchmark plan, and what should I expect?
+    answer: >-
+      You use a provided helper script to generate a YAML plan (plan.yaml) that defines the SUT,
+      kernels to deploy, and workloads to run. Executing the plan on the Fastpath host installs
+      each kernel on the SUT, runs benchmarks, collects results, and enables you to compare performance
+      across kernel versions; connectivity between the Fastpath host and SUT is validated during
+      setup.
+# END generated_summary_faq
 
 author: Geremy Cohen
 
diff --git a/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md b/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md
index 33067fbe12..18db4105be 100644
--- a/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md
@@ -20,6 +20,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:51:30Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: a67c552b76df6323eccc331c9146d0fcbdb8ad222fe81dbf2e1c2339c7609b61
+  summary_generated_at: '2026-06-02T03:49:04Z'
+  summary_source_hash: a67c552b76df6323eccc331c9146d0fcbdb8ad222fe81dbf2e1c2339c7609b61
+  faq_generated_at: '2026-06-03T00:51:30Z'
+  faq_source_hash: a67c552b76df6323eccc331c9146d0fcbdb8ad222fe81dbf2e1c2339c7609b61
+  summary: >-
+    Learn how to implement the exponential function on Arm Neoverse processors using SVE intrinsics
+    and then refine it with the FEXPA instruction. You will review range reduction and polynomial
+    approximation trade-offs, write a C implementation with SVE intrinsics, and build it with
+    gcc on a cloud VM. The path lists Linux and macOS, shows installing gcc on Linux, and was
+    tested on an AWS Graviton4 r8g.medium instance. You will apply FEXPA to reduce the polynomial
+    degree needed for a target precision, with SME support noted for integrating the approximation
+    into matrix computation paths. Prerequisites include access to an AWS Graviton4, Google Axion,
+    or Azure Cobalt 100 VM and some familiarity with SIMD programming and SVE intrinsics.
+  faqs:
+  - question: What do I need before running the example?
+    answer: >-
+      You need access to an AWS Graviton4, Google Axion, or Azure Cobalt 100 virtual machine,
+      plus some familiarity with SIMD programming and SVE intrinsics. The path uses gcc on Linux
+      or macOS.
+  - question: Which instance type should I pick, and what was used to validate the steps?
+    answer: >-
+      You can use an Arm-based VM from AWS Graviton4, Google Axion, or Azure Cobalt 100. The steps
+      were tested on an AWS Graviton4 r8g.medium instance.
+  - question: How do I set up the build environment and source file?
+    answer: >-
+      Install gcc; the steps show using apt on Linux to install it. Then create the exp_sve.c
+      file with the provided SVE-based implementation.
+  - question: What changes when I enable FEXPA compared to the initial SVE implementation?
+    answer: >-
+      You begin with a polynomial approximation using SVE intrinsics, then apply FEXPA for hardware-accelerated
+      exponential computation. With FEXPA, the approximation can reach a specified target precision
+      using a lower-degree polynomial than alternative implementations.
+  - question: I’m on macOS—what should I do if the Linux package commands don’t work?
+    answer: >-
+      macOS is listed as supported, but the explicit setup commands use Linux’s apt. Use a C compiler
+      available on macOS; the path does not provide macOS-specific install commands.
+# END generated_summary_faq
 
 author: 
 - Arnaud Grasset
diff --git a/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md
index 55d68482a6..a7c81bd46e 100644
--- a/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md
@@ -21,6 +21,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:53:07Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: adcf2a1b8a4a77e5834e14a40e46e27b0cbe5e440fbf732a4366ce486d7fafb7
+  summary_generated_at: '2026-06-02T03:51:50Z'
+  summary_source_hash: adcf2a1b8a4a77e5834e14a40e46e27b0cbe5e440fbf732a4366ce486d7fafb7
+  faq_generated_at: '2026-06-03T00:53:07Z'
+  faq_source_hash: adcf2a1b8a4a77e5834e14a40e46e27b0cbe5e440fbf732a4366ce486d7fafb7
+  summary: >-
+    Learn how to deploy Apache Flink on Google Cloud C4A virtual machines powered by Axion processors
+    (Arm Neoverse-V2) using a SUSE Linux Arm64 environment. You will provision a c4a-standard-4
+    VM through the Google Cloud Console, install Java 17 and Flink, and validate your setup by
+    starting the Flink cluster and running a baseline job. The path then guides you to install
+    Maven and benchmark Flink using the official JMH-based flink-benchmarks suite, including the
+    Remote Channel Throughput Benchmark. By the end, you will have a working Flink environment
+    on Arm-based Google Cloud infrastructure and baseline microbenchmark results. Prerequisites
+    are a GCP account with billing enabled and basic familiarity with Flink.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      You need a Google Cloud Platform account with billing enabled and basic familiarity with
+      Apache Flink and its runtime. Sudo access on the VM is implied because the steps install
+      packages and place files under system directories.
+  - question: Which Google Cloud VM and OS should I create for the exercises?
+    answer: >-
+      Create an Axion C4A Arm instance, using the c4a-standard-4 machine type in the Google Cloud
+      Console under Compute Engine > VM Instances. The steps assume a SUSE SLES Arm64 virtual
+      machine.
+  - question: Which Java version is required on the VM?
+    answer: >-
+      Install Java 17 (OpenJDK) along with the development package on the SUSE system. The steps
+      use zypper to install java-17-openjdk and java-17-openjdk-devel.
+  - question: Where should I install Flink and how do I confirm it works?
+    answer: >-
+      The path downloads and installs the official Flink distribution under /opt on the VM. You
+      will validate the installation by starting the Flink cluster and running a baseline job
+      to confirm the JobManager and TaskManager execute successfully.
+  - question: Which benchmarks will I run and how are they executed?
+    answer: >-
+      You will clone the official apache/flink-benchmarks repository, build it with Maven, and
+      run JMH-based microbenchmarks. The steps demonstrate running the Remote Channel Throughput
+      Benchmark to assess Flink performance on the C4A instance.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/flink/_index.md b/content/learning-paths/servers-and-cloud-computing/flink/_index.md
index 8787a5b3c9..0023452ef7 100644
--- a/content/learning-paths/servers-and-cloud-computing/flink/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/flink/_index.md
@@ -19,6 +19,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:52:15Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 974d71b1ee968e3aeabe900bfdd52ae4fbfdd0ca7dea420e6c1fc01f5475e8c1
+  summary_generated_at: '2026-06-02T03:50:28Z'
+  summary_source_hash: 974d71b1ee968e3aeabe900bfdd52ae4fbfdd0ca7dea420e6c1fc01f5475e8c1
+  faq_generated_at: '2026-06-03T00:52:15Z'
+  faq_source_hash: 974d71b1ee968e3aeabe900bfdd52ae4fbfdd0ca7dea420e6c1fc01f5475e8c1
+  summary: >-
+    This Learning Path shows how to install and run Apache Flink on an Arm-based Linux server
+    and benchmark its stream processing performance using the Nexmark suite. You will set up Java,
+    configure a Flink Standalone Cluster, prepare the Nexmark environment (including Maven and
+    SSH), and execute benchmark queries. The steps use common Linux tooling and Flink/Nexmark
+    scripts to start the cluster, set up the benchmark, and run queries, with optional additional
+    query runs. The target audience is developers using Flink on Arm servers. A prerequisite is
+    an Arm-based instance from a cloud service provider. The estimated time to complete is about
+    30 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      Provision an Arm-based Linux instance from a cloud provider such as AWS, Microsoft Azure,
+      Google Cloud, or Oracle. You will also need Java installed because Flink runs on the JVM.
+  - question: Which Java version should I install for this setup?
+    answer: >-
+      Install a JDK 11, using either Oracle JDK or OpenJDK. Nexmark requires JDK 1.8+ tools, so
+      JDK 11 satisfies both Flink and Nexmark needs.
+  - question: What are the Nexmark setup requirements I must have in place?
+    answer: >-
+      You need a Flink standalone cluster, JDK 1.8.x or higher, and ssh with sshd running for
+      the scripts that manage remote components. Maven must be installed, and any required environment
+      variables for the scripts should be configured.
+  - question: Where do I run the commands to start Flink and the benchmark?
+    answer: >-
+      Run them on the master node. Use these scripts in order: ~/flink-benchmark/flink-1.17.2/bin/start-cluster.sh,
+      then ~/flink-benchmark/nexmark-flink/bin/setup_cluster.sh, and finally ~/flink-benchmark/nexmark-flink/bin/run_query.sh.
+  - question: What should I check if the Nexmark scripts fail to start components?
+    answer: >-
+      Verify that sshd is running, Java is installed and available, and Maven is installed. Ensure
+      the Flink standalone cluster is set up and any required environment variables for the scripts
+      are defined.
+# END generated_summary_faq
 
 author: Ying Yu
 
diff --git a/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md b/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md
index ec7912342b..320db60cc6 100644
--- a/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md
@@ -24,6 +24,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:53:45Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 85359b9210812675169f149c86bf11a1736ab838c011d18e876b246463d297ee
+  summary_generated_at: '2026-06-02T03:53:08Z'
+  summary_source_hash: 85359b9210812675169f149c86bf11a1736ab838c011d18e876b246463d297ee
+  faq_generated_at: '2026-06-03T00:53:45Z'
+  faq_source_hash: 85359b9210812675169f149c86bf11a1736ab838c011d18e876b246463d297ee
+  summary: >-
+    This Learning Path shows how to build and run an introductory machine learning workflow on
+    Arm-based Google Cloud C4A Axion processors using Flyte for orchestration and gRPC for distributed
+    service communication. You will provision a c4a-standard-4 Arm64 VM in Google Cloud, prepare
+    a SUSE Linux Enterprise Server (SLES) development environment, install Flyte and gRPC tools,
+    implement a gRPC feature engineering service, and create a Flyte workflow that loads data,
+    preprocesses it, generates features via the service, trains a model, and evaluates results.
+    It targets Linux on Arm infrastructure and takes about 30 minutes. Prerequisites include a
+    GCP account with billing enabled, plus basic Python and ML pipeline familiarity.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      You need a Google Cloud Platform account with billing enabled, basic familiarity with Python,
+      and a basic understanding of machine learning pipelines. No other prerequisites are explicitly
+      listed.
+  - question: Which Google Cloud VM type should I create for the exercises?
+    answer: >-
+      Use the c4a-standard-4 machine type on Google Axion C4A, which provides 4 vCPUs and 16 GB
+      of memory. This VM hosts the Flyte ML workflow and gRPC applications.
+  - question: Which operating system and architecture are used on the VM?
+    answer: >-
+      The development environment uses a SUSE Linux Enterprise Server (SLES) arm64 virtual machine.
+      The tools run natively on the Arm-based Axion C4A processors.
+  - question: How does the Flyte workflow interact with the gRPC feature engineering service?
+    answer: >-
+      The Flyte workflow calls the external gRPC-based feature engineering service during execution
+      to generate features used by downstream tasks. This integrates distributed services directly
+      into the pipeline.
+  - question: What result should I expect after running the workflow?
+    answer: >-
+      The pipeline loads a dataset, preprocesses it, generates features via the gRPC service,
+      trains a machine learning model, and evaluates the model’s performance. You will have a
+      working example of a Flyte-orchestrated ML workflow running on Axion C4A.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
index 8a79d94f46..736aad4157 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
@@ -26,6 +26,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:54:23Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 94866800acca2c5f9cd89f76f972af1a343aad5777b6844d49fac09eb764f580
+  summary_generated_at: '2026-06-02T03:53:58Z'
+  summary_source_hash: 94866800acca2c5f9cd89f76f972af1a343aad5777b6844d49fac09eb764f580
+  faq_generated_at: '2026-06-03T00:54:23Z'
+  faq_source_hash: 94866800acca2c5f9cd89f76f972af1a343aad5777b6844d49fac09eb764f580
+  summary: >-
+    This introductory path shows how to deploy a .NET application on Arm64 in Microsoft Azure.
+    You will create a Linux Arm64 virtual machine, connect over SSH using Azure Cloud Shell, install
+    the .NET 7 SDK and git, then build and run the app. You will configure the VM’s network security
+    group to expose the application over the Internet. Next, you will containerize the People.WebApp
+    with a Dockerfile in Visual Studio Code and push the resulting image to Azure Container Registry.
+    Prerequisites include an Azure subscription, Visual Studio Code with the Docker and C# extensions,
+    and Docker on Arm64. By the end, you have a running app and an image stored in ACR.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an Azure subscription, Visual Studio Code with the Docker and C# extensions, and
+      Docker installed on an Arm64 system. These prerequisites are listed so you can build the
+      app locally and containerize it before pushing to Azure Container Registry.
+  - question: How do I connect to the VM and which IP address should I use?
+    answer: >-
+      Connect over SSH using Azure Cloud Shell from the portal. Always use the public IP address
+      of your own VM shown in the Azure portal and not the sample IP provided in the tutorial
+      text.
+  - question: Which SDK and tools are installed on the VM to build the app?
+    answer: >-
+      You install the .NET 7 SDK using the dotnet-install.sh script and also install git to clone
+      the application sources. These are used to build and run the .NET application on the VM.
+  - question: How will the application be accessible from the internet?
+    answer: >-
+      You will configure the VM’s network security group to expose the application. This step
+      opens access so the running app can be reached externally.
+  - question: Where should I build the Docker image and how is it published to Azure?
+    answer: >-
+      You can containerize the People.WebApp using Visual Studio Code on a Windows on Arm device
+      (via WSL) or on the previously created VM, then push the local Docker image to Azure Container
+      Registry. The application sources are cloned from https://github.com/dawidborycki/People.WebApp.git.
+# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md
index 36029f15cd..47b6aedf64 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md
@@ -20,6 +20,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:55:15Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 3823ad3df8ae868acfd59b5b5b541240624572fe739aaf2275185d5cd3578032
+  summary_generated_at: '2026-06-02T03:54:18Z'
+  summary_source_hash: 3823ad3df8ae868acfd59b5b5b541240624572fe739aaf2275185d5cd3578032
+  faq_generated_at: '2026-06-03T00:55:15Z'
+  faq_source_hash: 3823ad3df8ae868acfd59b5b5b541240624572fe739aaf2275185d5cd3578032
+  summary: >-
+    This introductory Learning Path shows how to create an Azure Container Instance (ACI) and
+    run a Docker container on Microsoft Azure. You will provision ACI through the Azure Portal
+    and Cloud Shell, enable the Admin account in Azure Container Registry (ACR) when deploying
+    from ACR, and verify the containerized ASP.NET sample application by browsing to the instance’s
+    public IP on port 8080. At the time of writing, ACI was not yet compatible with Arm64 Docker
+    containers, so the steps use a sample image from the Microsoft Container Registry. Prerequisites
+    are an active Azure subscription and completion of the first part of this series. The path
+    can be followed from Linux or Windows.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      You need an active Azure subscription and you must complete the first learning path in this
+      series. No other explicit prerequisites are listed.
+  - question: Which container image should I use for Azure Container Instances in this path?
+    answer: >-
+      Use the sample ASP.NET application image from the Microsoft Container Registry: mcr.microsoft.com/dotnet/samples:aspnetapp.
+      At the time of writing, Azure Container Instances was not yet compatible with arm64 Docker
+      containers.
+  - question: Where do I run the Azure CLI commands shown in the steps?
+    answer: >-
+      Open the Azure Portal and launch Cloud Shell using the icon in the top-right corner. Run
+      the provided commands directly in Cloud Shell.
+  - question: How do I enable and verify the Azure Container Registry Admin account?
+    answer: >-
+      Enable the Admin account in your Azure Container Registry because it is required by Azure
+      Container Instances when deploying from ACR. In Cloud Shell, run az acr list -o table and
+      check the ADMIN ENABLED column to confirm.
+  - question: How do I access the running application and what port should I use?
+    answer: >-
+      In the Container Instance Overview tab, copy the public IP address and open it in a browser
+      using port 8080 (for example, http://IP_ADDRESS:8080). If the deployment succeeded, the
+      application should load in the browser.
+# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
index 749f9c0b66..db0d175fa0 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
@@ -20,6 +20,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:55:54Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: a3347a62c3322d88b80fc7848fa5ee1d92ee86333c2a21891b958286f8b70935
+  summary_generated_at: '2026-06-02T03:55:07Z'
+  summary_source_hash: a3347a62c3322d88b80fc7848fa5ee1d92ee86333c2a21891b958286f8b70935
+  faq_generated_at: '2026-06-03T00:55:54Z'
+  faq_source_hash: a3347a62c3322d88b80fc7848fa5ee1d92ee86333c2a21891b958286f8b70935
+  summary: >-
+    This introductory Learning Path shows how to create an Azure Kubernetes Service (AKS) cluster
+    backed by arm64-based virtual machines, connect to it, and deploy a containerized application.
+    You will provision the cluster in the Azure Portal with integration to Azure Container Registry,
+    then use Azure Cloud Shell and kubectl to access and manage it. Deployment is driven by Kubernetes
+    YAML that defines a Deployment and a Service. Tools listed include Docker, Kubernetes, and
+    ASP.NET Core, targeting Linux. Prerequisites are an active Azure subscription and completion
+    of the first and second parts of this series. By the end, you will have an AKS cluster on
+    Arm64 with an application running on it.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an Azure subscription and you must complete the first and second learning paths
+      in this series. No other explicit prerequisites are listed.
+  - question: How do I connect to the AKS cluster once it’s created?
+    answer: >-
+      Open Azure Cloud Shell and run: az aks get-credentials -g rg-arm64 -n aks-people. After
+      the command completes, manage the cluster with kubectl.
+  - question: Where do the container images for deployment come from?
+    answer: >-
+      The cluster is created with integration to Azure Container Registry. Images stored in that
+      registry are available to the cluster for deployment as shown in this path.
+  - question: What result should I expect after applying the Kubernetes YAML?
+    answer: >-
+      The Deployment creates one or more Pods, and the Service exposes the application. You should
+      see the Pods running and the Service present in the cluster.
+  - question: What should I check if kubectl commands fail after connecting?
+    answer: >-
+      Confirm you ran az aks get-credentials in Azure Cloud Shell and used the correct resource
+      group and cluster name (for example, rg-arm64 and aks-people). If issues persist, re-run
+      the command and try again from Cloud Shell as shown in the path.
+# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md
index 1dbf275fc4..19366a8775 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md
@@ -23,6 +23,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:56:53Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 1510c787979257e191196e13999e98c20ca24d0857f72075649c28520c74c576
+  summary_generated_at: '2026-06-02T03:56:05Z'
+  summary_source_hash: 1510c787979257e191196e13999e98c20ca24d0857f72075649c28520c74c576
+  faq_generated_at: '2026-06-03T00:56:53Z'
+  faq_source_hash: 1510c787979257e191196e13999e98c20ca24d0857f72075649c28520c74c576
+  summary: >-
+    Learn how to use Infrastructure as Code with Pulumi to automate Azure resource deployment
+    on Windows. You will install and configure Node.js, the Pulumi CLI, and the Azure CLI, then
+    create a Pulumi TypeScript project in Visual Studio Code. The path shows the Pulumi project
+    structure and how to declare an Azure Resource Group and an Azure Container Instance that
+    runs a sample container image. By the end, you will provision the required Azure resources
+    with Pulumi. Prerequisites include an Azure subscription, Visual Studio Code, a free Pulumi
+    account with the Pulumi CLI, Node.js, and the Azure CLI. No additional prerequisites are explicitly
+    listed.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Windows environment, an Azure subscription (a free account link is provided),
+      Visual Studio Code, a free Pulumi account with the Pulumi CLI, Node.js, and the Azure CLI.
+      The setup step in the path provides installer links.
+  - question: Which installers should I use on Windows?
+    answer: >-
+      Install Node.js for Arm64 using the MSI linked in the setup step, then install the Pulumi
+      CLI and the Azure CLI using the Windows installers provided. Follow the path’s setup instructions
+      in order.
+  - question: Which Pulumi runtime and language does this path use?
+    answer: >-
+      The project uses TypeScript on Node.js with Pulumi’s Azure Native provider. You will edit
+      index.ts to declare Azure resources.
+  - question: After creating the Pulumi app, what should I see in the project?
+    answer: >-
+      Open the azure-aci folder in Visual Studio Code and expect a typical Node.js layout plus
+      Pulumi.yaml. Pulumi.yaml contains the global project configuration such as name, runtime,
+      and description.
+  - question: What result should I expect after updating index.ts and deploying?
+    answer: >-
+      The Pulumi deployment provisions an Azure Resource Group and an Azure Container Instance
+      using a sample ASP.NET Docker image. You should see these resources created in your Azure
+      subscription.
+# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/servers-and-cloud-computing/funasr/_index.md b/content/learning-paths/servers-and-cloud-computing/funasr/_index.md
index c93dfb488c..0569db89f1 100644
--- a/content/learning-paths/servers-and-cloud-computing/funasr/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/funasr/_index.md
@@ -19,6 +19,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:57:43Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 410ec28d257ce7aa1308f11a741aa87baea80daf1acb113c4149761144269022
+  summary_generated_at: '2026-06-02T03:56:56Z'
+  summary_source_hash: 410ec28d257ce7aa1308f11a741aa87baea80daf1acb113c4149761144269022
+  faq_generated_at: '2026-06-03T00:57:43Z'
+  faq_source_hash: 410ec28d257ce7aa1308f11a741aa87baea80daf1acb113c4149761144269022
+  summary: >-
+    Deploy the ModelScope FunASR Chinese ASR model on Arm-based Linux servers to enable real-time
+    transcription, punctuation restoration, and sentiment analysis. This introductory path walks
+    you through the essentials of ModelScope and FunASR, including installing FunASR via pip and
+    using it from Python to run speech recognition tasks. You will learn how to leverage open-source
+    large language models and tools for Chinese ASR, and describe approaches to accelerate ModelScope
+    models on Arm servers. The target environment is Ubuntu 22.04 LTS (or later) on an Arm-based
+    instance or local Arm Linux machine with at least 8 CPUs, 16GB RAM, and 30GB disk. Estimated
+    time to complete is about 60 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      Use an Arm-based server or a local Arm Linux computer running Ubuntu 22.04 LTS or later
+      with at least 8 CPU cores, 16GB of RAM, and 30GB of disk space. This environment is required
+      for the examples in the Learning Path.
+  - question: Which FunASR version should I install and how?
+    answer: >-
+      Install FunASR version 1.2.3 using the command: pip3 install funasr==1.2.3. The examples
+      in this Learning Path use 1.2.3, and results might vary with other versions.
+  - question: Can I run this on a cloud provider and which ones are suitable?
+    answer: >-
+      Yes. Use an Arm-based instance from a cloud service provider; AWS, Microsoft Azure, Google
+      Cloud, and Oracle are listed options, or use a local Arm Linux machine.
+  - question: How do I know FunASR is working correctly after installation?
+    answer: >-
+      Run the speech recognition example provided in the Learning Path and confirm that an audio
+      input produces transcribed text output. FunASR provides a simple interface for transcription
+      that you can use to validate your setup.
+  - question: What output should I expect from the deployment?
+    answer: >-
+      You should be able to perform real-time Chinese speech-to-text transcription with punctuation
+      restoration and sentiment analysis using FunASR. The steps guide you through enabling these
+      capabilities on an Arm-based server.
+# END generated_summary_faq
 
 author: Odin Shen
 
diff --git a/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
index 699efca619..de973da3e8 100644
--- a/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
@@ -23,6 +23,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:58:45Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 85d3d64e0eb0c3cfa0eee29cf1e4199049a6dcbf69634e578dad2b5443ca2b7f
+  summary_generated_at: '2026-06-02T03:57:32Z'
+  summary_source_hash: 85d3d64e0eb0c3cfa0eee29cf1e4199049a6dcbf69634e578dad2b5443ca2b7f
+  faq_generated_at: '2026-06-03T00:58:45Z'
+  faq_source_hash: 85d3d64e0eb0c3cfa0eee29cf1e4199049a6dcbf69634e578dad2b5443ca2b7f
+  summary: >-
+    Learn how to provision a Google Cloud C4A virtual machine powered by Axion (Arm Neoverse-V2)
+    and install Gardener on SUSE Linux Enterprise Server (Arm64). You will set up Gardener Local,
+    deploy Garden, Seed, and Shoot clusters using Kubernetes in Docker (KinD), and validate functionality
+    by deploying workloads into a Shoot cluster. The path uses tools including Kubernetes, Docker,
+    KinD, Helm, and kube-bench, and includes baseline security benchmarking against CIS Kubernetes
+    guidelines. Prerequisites are a Google Cloud account with billing enabled plus basic familiarity
+    with Kubernetes and Docker. The steps focus on a c4a-standard-4 VM configuration suitable
+    for running Gardener Local on Arm.
+  faqs:
+  - question: What do I need before creating the Axion C4A VM on Google Cloud?
+    answer: >-
+      You need a Google Cloud Platform account with billing enabled. Basic familiarity with Kubernetes
+      and Docker is also assumed.
+  - question: Which VM type and operating system does this path use for Gardener?
+    answer: >-
+      You will use a c4a-standard-4 instance (4 vCPUs, 16 GB memory) on Google Cloud C4A with
+      SUSE Linux Enterprise Server. This configuration is sufficient for running Gardener Local
+      with Garden, Seed, and Shoot clusters.
+  - question: Do the Garden, Seed, and Shoot clusters run in the cloud or locally?
+    answer: >-
+      They run locally on the C4A VM using Kubernetes in Docker (KinD). The path deploys Garden,
+      Seed, and Shoot clusters without requiring a separate managed Kubernetes service.
+  - question: How do I point kubectl at the Gardener Local cluster to validate the setup?
+    answer: >-
+      Set the KUBECONFIG environment variable to $PWD/example/gardener-local/kind/local/kubeconfig.
+      Then follow the verification steps to check cluster health and confirm Garden and Shoot
+      resources report Ready states.
+  - question: What should be ready before running kube-bench, and what output should I expect?
+    answer: >-
+      Ensure Gardener Local is running with Garden and Shoot clusters in Ready state and Docker
+      is available. kube-bench will check the cluster against CIS Kubernetes benchmarks and produce
+      a baseline security report.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md b/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
index 66fe745de9..79cc9db07b 100644
--- a/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
@@ -21,6 +21,51 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T00:59:35Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 2e53b87d4dc7a7d1984e3bbe035038a60e619aea2f5793cb3082f3b59084bbf9
+  summary_generated_at: '2026-06-02T03:57:53Z'
+  summary_source_hash: 2e53b87d4dc7a7d1984e3bbe035038a60e619aea2f5793cb3082f3b59084bbf9
+  faq_generated_at: '2026-06-03T00:59:35Z'
+  faq_source_hash: 2e53b87d4dc7a7d1984e3bbe035038a60e619aea2f5793cb3082f3b59084bbf9
+  summary: >-
+    This introductory Learning Path shows how to enable and use GCC link-time optimization (LTO)
+    on an Arm Linux system to improve application performance by optimizing across compilation
+    units. You will learn how LTO works, when to apply it, and how to build with -flto during
+    both compilation and linking. The steps cover deploying LTO with GCC on Linux and evaluating
+    performance and code size trade-offs, with context on standardized benchmarks that illustrate
+    potential gains. Prerequisites are an Arm Linux environment (cloud, on-premises, or VM) and
+    a recent GCC toolchain. After completing the path, you will be able to configure LTO in your
+    builds and compare results against non-LTO builds.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an Arm Linux system (cloud instance, on‑premises hardware, or a virtual machine)
+      and a recent version of the GCC toolchain. No other prerequisites are explicitly listed.
+  - question: Which GCC flags do I use to enable LTO?
+    answer: >-
+      Pass -flto during both compilation and linking. The examples also use -O2 alongside -flto.
+  - question: Do I need to compile every translation unit with -flto?
+    answer: >-
+      Yes. In a stepwise build, compile each translation unit with -flto so the object files embed
+      LTO information, and then link with -flto to trigger whole‑program optimization.
+  - question: Can I build a small program with a single gcc command?
+    answer: >-
+      Yes. For small programs, the path notes you can simplify the build into a single gcc invocation
+      that both compiles and links with -flto.
+  - question: How should I evaluate the impact of LTO on my workload?
+    answer: >-
+      The path discusses evaluating performance and code size trade‑offs and references SPEC CPU2017
+      integer rate as a standardized way to illustrate potential gains. Actual results will depend
+      on your application.
+# END generated_summary_faq
 
 author: Victor Do Nascimento
 
diff --git a/content/learning-paths/servers-and-cloud-computing/gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/gcp/_index.md
index 7ace9bbcf2..01b3db3d41 100644
--- a/content/learning-paths/servers-and-cloud-computing/gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gcp/_index.md
@@ -21,6 +21,51 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:00:09Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 24e2ffcf9b7cda919e6e864f18bb9424c0c328242c92f491c1b284e317ed7e1c
+  summary_generated_at: '2026-06-02T03:58:46Z'
+  summary_source_hash: 24e2ffcf9b7cda919e6e864f18bb9424c0c328242c92f491c1b284e317ed7e1c
+  faq_generated_at: '2026-06-03T01:00:09Z'
+  faq_source_hash: 24e2ffcf9b7cda919e6e864f18bb9424c0c328242c92f491c1b284e317ed7e1c
+  summary: >-
+    Learn to automate the deployment of Arm-based virtual machines on Google Cloud Platform using
+    Terraform, with secure access configured through a Jump Server (bastion host). You will generate
+    an SSH key pair, obtain GCP user credentials so Terraform can authenticate, and apply Terraform
+    files that serve as a base for future Learning Paths that need one or more server nodes. This
+    introductory, Linux-based path targets developers new to Arm VMs on GCP and takes about 20
+    minutes. By the end, you will have Terraform-managed infrastructure that deploys Arm instances
+    on GCP and provides access via a Jump Server, along with reusable Terraform code you can modify
+    for related tasks.
+  faqs:
+  - question: What do I need before running the Terraform steps?
+    answer: >-
+      You need a Google Cloud account and a computer with Terraform and the Google Cloud CLI installed.
+      These are the only explicit prerequisites listed.
+  - question: Do I need to generate a new SSH key pair, and where should it be located?
+    answer: >-
+      Generate an SSH key pair with ssh-keygen if you do not already have one. If you have keys
+      in the ~/.ssh directory, you can skip key generation and use the existing pair.
+  - question: How do I authenticate Terraform with my Google Cloud project?
+    answer: >-
+      Obtain GCP user credentials by following the provided guide so Terraform can communicate
+      with GCP. This authentication step is required before running Terraform.
+  - question: What gets created when I apply the Terraform configuration?
+    answer: >-
+      The configuration deploys Arm-based virtual machine instances on GCP and provides access
+      via a Jump Server (bastion). Any additional resources are not explicitly listed.
+  - question: How do I access the deployed Arm instances after provisioning?
+    answer: >-
+      Use SSH via the Jump Server (bastion) with the SSH key pair you generated or reused. The
+      Learning Path explains how to configure this bastion-based access.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md b/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
index 16e688ca8a..d24f7f27b4 100644
--- a/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
@@ -18,6 +18,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:01:02Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 85a0a44bde6f217bdfc7fd0660ed6a86279b24c7ede302307ef6efb2626d83d9
+  summary_generated_at: '2026-06-02T03:59:40Z'
+  summary_source_hash: 85a0a44bde6f217bdfc7fd0660ed6a86279b24c7ede302307ef6efb2626d83d9
+  faq_generated_at: '2026-06-03T01:01:02Z'
+  faq_source_hash: 85a0a44bde6f217bdfc7fd0660ed6a86279b24c7ede302307ef6efb2626d83d9
+  summary: >-
+    This introductory Learning Path shows how to download and run Geekbench on Arm Linux systems
+    to benchmark CPU performance. You will install and execute Geekbench, obtain single-core and
+    multi-core scores, and use the results to compare different Arm configurations when selecting
+    hardware for your workload. The path targets Arm computers running Linux, including cloud
+    instances, and takes about 15 minutes to complete. Tools include Geekbench (with Preview Versions
+    available for Linux on Arm) and a Runbook. By the end, you will be able to run Geekbench on
+    an Arm Linux system, interpret the reported core scores, and apply them to basic hardware
+    selection decisions.
+  faqs:
+  - question: What do I need before running this benchmark?
+    answer: >-
+      You need an Arm computer running Linux. A cloud instance is acceptable; refer to Get started
+      with Arm-based cloud instances.
+  - question: Which Geekbench package should I download for Arm Linux?
+    answer: >-
+      Use a Geekbench Preview Version for Linux on Arm. Check the Geekbench downloads area for
+      the appropriate Arm Linux build.
+  - question: What result should I expect after a successful run?
+    answer: >-
+      Geekbench reports a single-core score, a multi-core score, and individual performance scores.
+      You will use these values to assess and compare systems.
+  - question: How should I compare different Arm systems using Geekbench?
+    answer: >-
+      Run Geekbench on each system you want to evaluate and compare the reported single-core,
+      multi-core, and individual performance scores. Use these comparisons to help determine a
+      suitable hardware configuration for your workload.
+  - question: Can I use an operating system other than Linux for this path?
+    answer: >-
+      This path targets Linux on Arm. Geekbench provides downloads for additional operating systems,
+      but those are not covered here.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md b/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
index 636d2645f9..0100c279c6 100644
--- a/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
@@ -23,6 +23,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:01:51Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 642b67e7ca3717f499ab73efedd924eeab29a0055fad81c2d60fda993dcea195
+  summary_generated_at: '2026-06-02T04:00:17Z'
+  summary_source_hash: 642b67e7ca3717f499ab73efedd924eeab29a0055fad81c2d60fda993dcea195
+  faq_generated_at: '2026-06-03T01:01:51Z'
+  faq_source_hash: 642b67e7ca3717f499ab73efedd924eeab29a0055fad81c2d60fda993dcea195
+  summary: >-
+    This Learning Path shows how to automate an end-to-end MLOps workflow on Linux using Arm-hosted
+    GitHub runners and GitHub Actions. You will fork an example repository, set up workflows to
+    train and test a PyTorch model on the German Traffic Sign Recognition Benchmark (GTSRB) dataset,
+    and save the trained model as a workflow artifact. You will compare inference performance
+    by switching from a PyTorch 2.3.0 Docker image compiled with OpenBLAS to a oneDNN backend
+    with the Arm Compute Library (ACL). Finally, you will containerize the model using the provided
+    Dockerfile, push the image to Docker Hub, and deploy the container for API-based access. Prerequisites
+    include GitHub access to Arm-hosted runners, a Docker Hub account, and familiarity with ML
+    and CI/CD.
+  faqs:
+  - question: What do I need before running the workflows?
+    answer: >-
+      You need a GitHub account with access to Arm-hosted GitHub runners and a Docker Hub account.
+      Familiarity with ML and CI/CD is expected, and the path targets Linux.
+  - question: Where should I fork the example repository, and what if the name conflicts?
+    answer: >-
+      Fork https://github.com/Arm-Labs/gh_armrunner_mlops_gtsrb into a GitHub Organization or
+      Team where you have access to Arm-hosted GitHub runners. If a repository with the same name
+      already exists there, rename it during the fork.
+  - question: Which workflow trains the model and what should I expect as output?
+    answer: >-
+      The training is automated by .github/workflows/train-model.yml, which runs scripts/train_model.py
+      inside a PyTorch 2.3.0 Docker image compiled with OpenBLAS. When it completes, the trained
+      model is saved as a workflow artifact.
+  - question: How do I compare inference performance across PyTorch backends?
+    answer: >-
+      Use the comparison step to change the backend used for testing to oneDNN with Arm Compute
+      Library (ACL) and run the workflow to measure inference time. Compare those results with
+      the OpenBLAS-based run.
+  - question: How do I containerize and publish the trained model, and how is deployment validated?
+    answer: >-
+      Build an image using the Dockerfile in the repository and push it to Docker Hub; the Dockerfile
+      uses armswdev/pytorch-arm-neoverse:r24.07-torch-2.3.0-onednn-acl as the base. After deployment,
+      access the model using API calls as described in the steps.
+# END generated_summary_faq
 
 author:
     - Pareena Verma
diff --git a/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md b/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
index 41d01d57a6..000dd569b2 100644
--- a/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
@@ -19,6 +19,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:02:59Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: cc72ef1fe1fde9f4f9ea0769c0e04d749731b8073b2efc0e65d7f608665abc2d
+  summary_generated_at: '2026-06-02T04:01:04Z'
+  summary_source_hash: cc72ef1fe1fde9f4f9ea0769c0e04d749731b8073b2efc0e65d7f608665abc2d
+  faq_generated_at: '2026-06-03T01:02:59Z'
+  faq_source_hash: cc72ef1fe1fde9f4f9ea0769c0e04d749731b8073b2efc0e65d7f608665abc2d
+  summary: >-
+    This Learning Path shows how to install RunsOn, a self-hosted runner manager, in your AWS
+    account to run GitHub Actions on Arm-based AWS EC2 instances. You will set up RunsOn using
+    AWS CloudFormation and a GitHub App, then modify your workflow files to target Arm runners,
+    including AWS Graviton instances based on Arm Neoverse processors. The steps highlight account
+    setup, installation flow, and the minimal workflow changes needed to launch Arm runners, which
+    typically come online in under 30 seconds. Prerequisites are an AWS account and a GitHub account.
+    The path is introductory and designed to be completed in about 15 minutes on Linux.
+  faqs:
+  - question: What do I need before running the installation?
+    answer: >-
+      You need an AWS account and a GitHub account. It is best to install RunsOn in its own AWS
+      sub-account for isolation and security.
+  - question: How do I install RunsOn in my AWS account?
+    answer: >-
+      Log in to the AWS console for the target account, then follow the official RunsOn installation
+      guide to create the CloudFormation stack and the GitHub app. Use the link at the top of
+      the guide to obtain your license key before proceeding.
+  - question: Which EC2 instance types and Arm processors can I use for runners?
+    answer: >-
+      You can select any instance types offered by AWS, including Arm instances with AWS Graviton
+      processors. With Graviton, you can run on Neoverse N1, Neoverse V1, and Neoverse V2 processors.
+  - question: How do I change my GitHub Actions workflow to target an Arm runner?
+    answer: >-
+      Edit the runs-on setting in your workflow file. For example, replace runs-on: ubuntu-22.04
+      with runs-on entries that include runner=1cpu-linux-arm64 and run-id=${{ github.run_id }}
+      to invoke a new Arm runner in your AWS account.
+  - question: What outcome and timing should I expect after triggering a workflow?
+    answer: >-
+      After installation, new runners launch in less than 30 seconds and your job should start
+      shortly. The runner will be an AWS EC2 Arm instance with 1 vCPU running Ubuntu 22.04.
+# END generated_summary_faq
 
 author: Cyril Rohr
 
diff --git a/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md b/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md
index e4195f65c2..eb53e37f5c 100644
--- a/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md
@@ -20,6 +20,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:03:50Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: d5df467355a7792f7a04eafc4a6bbd2410353aca000cd2b1aec74a7ed93a9270
+  summary_generated_at: '2026-06-02T04:01:33Z'
+  summary_source_hash: d5df467355a7792f7a04eafc4a6bbd2410353aca000cd2b1aec74a7ed93a9270
+  faq_generated_at: '2026-06-03T01:03:50Z'
+  faq_source_hash: d5df467355a7792f7a04eafc4a6bbd2410353aca000cd2b1aec74a7ed93a9270
+  summary: >-
+    This Learning Path shows how to provision a Google Axion C4A Arm virtual machine on Google
+    Cloud and use it as a self-hosted runner for GitHub Actions. You will create a c4a-standard-4
+    instance from the Google Cloud Console, install Git and the GitHub CLI on Linux, authenticate
+    with GitHub, and register the runner so workflows execute on Arm infrastructure. To validate
+    the setup, you will deploy a basic CI workflow that installs and starts NGINX when changes
+    are pushed to the main branch. Prerequisites are a Google Cloud account with billing enabled
+    and a GitHub account; no other prerequisites are explicitly listed.
+  faqs:
+  - question: What do I need before creating the VM and runner?
+    answer: >-
+      You need a Google Cloud Platform account with billing enabled and a GitHub account. No other
+      prerequisites are explicitly listed.
+  - question: Which Google Cloud machine type is used in the steps?
+    answer: >-
+      The path uses the c4a-standard-4 machine type (4 vCPUs, 16 GB memory) from the Google Axion
+      C4A family. Other sizes are not covered in the instructions.
+  - question: Which operating system is assumed on the VM?
+    answer: >-
+      The steps assume a Linux VM on an Arm64 Google Axion C4A instance. A specific Linux distribution
+      is not explicitly listed.
+  - question: How do I set up the self-hosted runner on the VM?
+    answer: >-
+      Install Git and GitHub CLI with apt, configure your Git identity, authenticate with GitHub,
+      and register the runner as shown in the steps. This enables your CI/CD workflows to target
+      the self-hosted Arm runner.
+  - question: How do I verify that the workflow executed on the Arm runner?
+    answer: >-
+      Push to the main branch to trigger the provided workflow, which installs and starts NGINX
+      on the self-hosted runner. A successful job run and a started NGINX service on the VM indicate
+      it executed on your Arm infrastructure.
+# END generated_summary_faq
 
 author: Annie Tallund
 
diff --git a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
index 3d35f42573..9cdbb9e305 100644
--- a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
@@ -22,6 +22,58 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:05:58Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: cf981c67553824c7c57b6dda9c2953ac80926750676ac101e939f6bbff655ab5
+  summary_generated_at: '2026-06-02T04:03:32Z'
+  summary_source_hash: cf981c67553824c7c57b6dda9c2953ac80926750676ac101e939f6bbff655ab5
+  faq_generated_at: '2026-06-03T01:05:58Z'
+  faq_source_hash: cf981c67553824c7c57b6dda9c2953ac80926750676ac101e939f6bbff655ab5
+  summary: >-
+    This advanced Learning Path walks you through migrating a microservices application from x86
+    to Arm on Google Kubernetes Engine using multi-architecture container images and Google Axion
+    processors. You will prepare Dockerfiles for arm64, create a dual-architecture GKE standard
+    cluster with separate amd64 and arm64 node pools, and build and publish images to Artifact
+    Registry with Docker Buildx. You will deploy Online Boutique on amd64 and migrate to arm64
+    using Kustomize overlays, with optional automation using Cloud Build and Skaffold. Prerequisites
+    include a billing-enabled Google Cloud account and a Linux or macOS environment with Docker,
+    kubectl, gcloud, and Git installed, or access to Cloud Shell. Estimated time to complete is
+    about 90 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Google Cloud account with billing enabled and either a local Linux or macOS system
+      with Docker, kubectl, gcloud, and Git installed, or access to Google Cloud Shell. Basic
+      familiarity with Docker, Kubernetes, and gcloud is expected.
+  - question: Which GKE cluster configuration and networking are used?
+    answer: >-
+      You will create a GKE standard cluster with two node pools: one amd64 and one arm64. GKE
+      uses VPC-native (IP aliasing) with two secondary ranges for Pods and Services; for the default
+      VPC these ranges are created automatically.
+  - question: Which Online Boutique services require Dockerfile changes for multi-architecture
+      builds?
+    answer: >-
+      Four services need updates: emailservice, recommendationservice, loadgenerator, and cartservice.
+      The changes ensure the correct compiler headers and runtime libraries are present for each
+      architecture.
+  - question: How are the multi-architecture images built and published?
+    answer: >-
+      Images are built with Docker Buildx on the cluster using separate BuildKit pods per architecture,
+      so no QEMU emulation is required. The resulting multi-architecture images are pushed to
+      Google Artifact Registry.
+  - question: How do I deploy on amd64 first and then migrate to Arm?
+    answer: >-
+      Update the base Kubernetes manifests to reference your Artifact Registry images, then create
+      Kustomize overlays that select nodes by CPU architecture. Deploy to the amd64 node pool
+      first, then apply the arm64 overlay to migrate to the Arm node pool.
+# END generated_summary_faq
 
 author: 
    - Rani Chowdary Mandepudi
diff --git a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
index e8a9531f2f..1db623b923 100644
--- a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
@@ -22,6 +22,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:05:07Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 50715640292b60ea4216ee2211140c4212592a27356d628d945e83d9deb7fdcc
+  summary_generated_at: '2026-06-02T04:02:44Z'
+  summary_source_hash: 50715640292b60ea4216ee2211140c4212592a27356d628d945e83d9deb7fdcc
+  faq_generated_at: '2026-06-03T01:05:07Z'
+  faq_source_hash: 50715640292b60ea4216ee2211140c4212592a27356d628d945e83d9deb7fdcc
+  summary: >-
+    This Learning Path shows how to extend an existing x86-based Google Kubernetes Engine (GKE)
+    cluster with Arm-based Google Axion nodes and rebuild an x86 application for multi-architecture
+    support. You will add C4A virtual machine nodes (based on Google Axion with Armv9 Neoverse
+    V2 CPUs), rebuild your container to run on Arm, and use Kubernetes taints and tolerations
+    to schedule pods on architecture-specific nodes. The path targets Linux and uses Google Cloud
+    with Kubernetes tooling, including the Google Cloud CLI and kubectl. By the end, you will
+    run a multi-arch application across both Arm and x86 within a single GKE cluster and validate
+    placement through scheduling controls.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Google Cloud account, Google Cloud CLI, kubectl installed on your local machine,
+      and an existing Google Kubernetes Engine (GKE) cluster with x86-based nodes.
+  - question: Which VM type should I use for the Arm-based node pool?
+    answer: >-
+      Use the C4A family of virtual machines. These nodes are based on Google Axion, built using
+      the Armv9 Neoverse V2 CPU.
+  - question: How do I rebuild my existing x86 application for multi-architecture?
+    answer: >-
+      The path guides you to rebuild your container image so it supports both Arm and x86. You
+      then deploy the new multi-arch image to the hybrid GKE cluster.
+  - question: How will I control which pods run on Arm versus x86 nodes?
+    answer: >-
+      You will add taints to the Arm-based nodes and apply matching tolerations to your workloads
+      so only compatible pods schedule there. The steps show how to configure these settings for
+      architecture-specific placement.
+  - question: How do I know the application is running on the intended architecture?
+    answer: >-
+      Use kubectl to inspect pod placement and confirm pods are scheduled onto Arm-based nodes
+      and x86 nodes as configured. The path explains the checks to perform after deployment.
+# END generated_summary_faq
 
 author: Pranay Bakre
 
diff --git a/content/learning-paths/servers-and-cloud-computing/gke/_index.md b/content/learning-paths/servers-and-cloud-computing/gke/_index.md
index 6f7866c1b7..8a4b3c8db4 100644
--- a/content/learning-paths/servers-and-cloud-computing/gke/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gke/_index.md
@@ -18,6 +18,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:04:26Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 7c3d37545c93db584d6e0ce88d8feaf0bf690e0c8786b664a6643be713d0336f
+  summary_generated_at: '2026-06-02T04:01:56Z'
+  summary_source_hash: 7c3d37545c93db584d6e0ce88d8feaf0bf690e0c8786b664a6643be713d0336f
+  faq_generated_at: '2026-06-03T01:04:26Z'
+  faq_source_hash: 7c3d37545c93db584d6e0ce88d8feaf0bf690e0c8786b664a6643be713d0336f
+  summary: >-
+    Automate the creation of an Arm-based Kubernetes cluster on Google Cloud using Terraform.
+    This advanced Learning Path focuses on deploying Google Kubernetes Engine (GKE) on Tau T2A
+    virtual machines powered by Ampere Altra Arm-based processors. You will prepare a Linux environment
+    with Terraform, the Google Cloud CLI (gcloud), and kubectl, create a new Google Cloud project,
+    and use infrastructure-as-code to provision the cluster for container orchestration. The expected
+    outcome is a deployed Arm-based GKE cluster managed via Terraform. Prerequisites are a Google
+    Cloud account and a computer with Terraform, gcloud, and kubectl installed; no other prerequisites
+    are explicitly listed.
+  faqs:
+  - question: What do I need before running the Terraform configuration?
+    answer: >-
+      You need a Google Cloud account and a computer with Terraform, Google Cloud CLI (gcloud),
+      and kubectl installed. The Learning Path lists Linux as the operating system and notes that
+      any computer with the required tools can be used.
+  - question: How do I ensure the GKE nodes are Arm-based?
+    answer: >-
+      Configure the cluster to use the Tau T2A VM family in GKE. Tau T2A is powered by Ampere
+      Altra Arm-based processors.
+  - question: Will I create a new Google Cloud project or use an existing one?
+    answer: >-
+      The steps include creating a new Google Cloud project before provisioning the cluster with
+      Terraform. Using an existing project is not explicitly listed.
+  - question: What result should I expect when the Terraform apply completes?
+    answer: >-
+      A GKE cluster will be deployed on Google Cloud with Arm-based nodes (Tau T2A). You can then
+      interact with the cluster using kubectl.
+  - question: Does this Learning Path cover deploying workloads or only cluster creation?
+    answer: >-
+      The objective is to automate the deployment of an Arm-based GKE cluster using Terraform.
+      Additional tasks such as deploying applications or tearing down the cluster are not explicitly
+      listed.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md b/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
index cf386f92ff..17a24b09c8 100644
--- a/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
@@ -20,6 +20,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:06:30Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 74952d68380c7540306543b0a7520f6960f673dd55ad5f164365fcfe1470380e
+  summary_generated_at: '2026-06-02T04:04:16Z'
+  summary_source_hash: 74952d68380c7540306543b0a7520f6960f673dd55ad5f164365fcfe1470380e
+  faq_generated_at: '2026-06-03T01:06:30Z'
+  faq_source_hash: 74952d68380c7540306543b0a7520f6960f673dd55ad5f164365fcfe1470380e
+  summary: >-
+    This advanced path shows how to rebuild and install glibc with Armv8-A Large System Extensions
+    (LSE) on an Arm server running Linux, then benchmark the impact on MongoDB. You will build
+    MongoDB 5.3.2 from source to run with the LSE-enabled glibc, drive workloads using YCSB, and
+    compare results against a No-LSE baseline. The steps focus on measuring throughput and runtime
+    characteristics and provide guidance on when LSE can deliver a measurable uplift for multi-threaded
+    workloads. Prerequisites are an Arm-based instance from a cloud service provider and a prior
+    review of the LSE learning path. Expected duration is about 60 minutes.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      You need an Arm-based instance from a cloud service provider running Linux. You should also
+      review the separate Learning Path on LSE before starting. This is an advanced topic intended
+      for experienced developers.
+  - question: Do I need to rebuild glibc on the instance, and why?
+    answer: >-
+      Yes. The steps have you build and install glibc with LSE so library routines can use LSE
+      atomic operations available on ARMv8-A, which you will then evaluate with workloads.
+  - question: Which MongoDB version is used and how is it installed?
+    answer: >-
+      MongoDB 5.3.2 is built from source using the provided commands. You clone the repository,
+      check out r5.3.2, install the listed dependencies, and build with SCons.
+  - question: How do I run and validate the benchmarks with and without LSE?
+    answer: >-
+      You run YCSB against MongoDB configured to use the newly built glibc with LSE, then repeat
+      with a No-LSE configuration. Compare the YCSB output, focusing on lines such as [OVERALL]
+      Throughput(ops/sec), as shown in the examples.
+  - question: What result should I expect from the No-LSE baseline?
+    answer: >-
+      YCSB prints summary lines including overall runtime, throughput in ops/sec, and GC metrics.
+      The path provides a sample No-LSE output format that you can use to compare against the
+      LSE run.
+# END generated_summary_faq
 
 author: Ying Yu
 
diff --git a/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md b/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
index e8b3a2ae64..d1f9beacce 100644
--- a/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
@@ -20,6 +20,50 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:07:15Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: aa78434138f08b424352302e92a1cd40d8297459bc65202715dcb41c40de6057
+  summary_generated_at: '2026-06-02T04:05:11Z'
+  summary_source_hash: aa78434138f08b424352302e92a1cd40d8297459bc65202715dcb41c40de6057
+  faq_generated_at: '2026-06-03T01:07:15Z'
+  faq_source_hash: aa78434138f08b424352302e92a1cd40d8297459bc65202715dcb41c40de6057
+  summary: >-
+    Provision Arm64 and x86_64 Linux VM instances on Google Cloud and use Go benchmarking tools
+    to compare performance across architectures. You will create an Arm-based c4a-standard-4 and
+    an Intel Emerald Rapids c4-standard-8 instance, install Go, Sweet, and Benchstat on both,
+    then run Go Benchmarks with Sweet and analyze results with Benchstat (text or CSV). Prerequisites
+    are a Google Cloud account and the Google Cloud CLI on your local machine. The path focuses
+    on Google Cloud’s Axion Arm64-based instances but can also be run on other clouds or on-premises.
+    Estimated time to complete is about 60 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Google Cloud account and the Google Cloud CLI installed on your local machine.
+      No other explicit prerequisites are listed.
+  - question: Which VM types should I create for the comparison?
+    answer: >-
+      Create an Arm-based c4a-standard-4 VM named "c4a" and an Intel-based Emerald Rapids c4-standard-8
+      VM named "c4". The steps show how to launch each in the Google Cloud console.
+  - question: Do I install Go, Sweet, and Benchstat on both VMs, and where should I run the install?
+    answer: >-
+      Yes, install on both VMs. The steps assume you run the installation from your home directory
+      ($HOME), which results in a $HOME/benchmarks/sweet directory.
+  - question: How do I execute and compare the benchmarks?
+    answer: >-
+      Run sweet on each VM to generate raw performance data. Then use benchstat to compare results
+      from the different VMs.
+  - question: What output should I expect from Benchstat?
+    answer: >-
+      Benchstat compares results to highlight performance differences and outputs text by default.
+      It can also produce CSV output.
+# END generated_summary_faq
 
 author: Geremy Cohen
 
diff --git a/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md
index 715dae3fca..c8eaddbe32 100644
--- a/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md
@@ -21,6 +21,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:07:53Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 46a0a3df799ac473f56d3820390af405b3301758cf15685a250a04c4854aba9e
+  summary_generated_at: '2026-06-02T04:05:37Z'
+  summary_source_hash: 46a0a3df799ac473f56d3820390af405b3301758cf15685a250a04c4854aba9e
+  faq_generated_at: '2026-06-03T01:07:53Z'
+  faq_source_hash: 46a0a3df799ac473f56d3820390af405b3301758cf15685a250a04c4854aba9e
+  summary: >-
+    This introductory Learning Path guides you through provisioning an Arm64 Azure Cobalt 100
+    (Dpsv6-series) virtual machine using the Azure portal with Ubuntu Pro 24.04 LTS, installing
+    the Go toolchain, deploying a simple Go web server for baseline validation, and running performance
+    tests with go test -bench (and -benchmem). You will use the official Arm64 Go distribution,
+    confirm compilation, networking, and runtime on the VM, and perform basic benchmarking, with
+    objectives that include comparing results on both x86_64 and Arm64 virtual machines. Prerequisites
+    include an Azure account with access to Cobalt 100 instances, familiarity with Go and cloud
+    deployment practices, and understanding of the Linux command line and VM management. After
+    completing the path, you can provision, deploy, and benchmark Go workloads on Azure Cobalt
+    100.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      You need a Microsoft Azure account with access to Azure Cobalt 100 Arm-based instances (Dpsv6-series),
+      basic familiarity with Go and cloud deployment practices, and an understanding of the Linux
+      command line and virtual machine management.
+  - question: Which VM series and operating system image should I choose?
+    answer: >-
+      Use a general-purpose Dpsv6-series virtual machine and select Ubuntu Pro 24.04 LTS (Arm64)
+      as the base image in the Azure portal.
+  - question: Which Go distribution should I install on the Arm64 VM?
+    answer: >-
+      Download the official Arm64-optimized Go distribution from the Go website and install it
+      on Ubuntu Pro 24.04 LTS. The steps guide you to fetch the tarball directly from go.dev.
+  - question: What result should I expect from the baseline Go web server test?
+    answer: >-
+      You will build and run a simple Go web application that serves an HTML page, confirming
+      that compilation, networking, and runtime execution work correctly on the Azure Cobalt 100
+      Arm64 VM.
+  - question: How do I run and interpret the performance benchmarks, and compare with x86_64?
+    answer: >-
+      Use go test -bench to run benchmarks and add -benchmem to capture memory usage, which reports
+      ns/op, B/op, and allocs/op. To compare architectures, run the same benchmark suite on an
+      x86_64 VM and evaluate the reported metrics side by side.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md
index df222bd7a7..e45a8a5c29 100644
--- a/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md
@@ -28,6 +28,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:08:45Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 87e9d25d5fb1f45d126aa4ca2fa1e13d6a470f2bcdc70968aa4622790106e629
+  summary_generated_at: '2026-06-02T04:06:17Z'
+  summary_source_hash: 87e9d25d5fb1f45d126aa4ca2fa1e13d6a470f2bcdc70968aa4622790106e629
+  faq_generated_at: '2026-06-03T01:08:45Z'
+  faq_source_hash: 87e9d25d5fb1f45d126aa4ca2fa1e13d6a470f2bcdc70968aa4622790106e629
+  summary: >-
+    Follow this introductory, hands-on path to install and validate Helm on Arm-based Google Cloud
+    Axion C4A virtual machines running SUSE Linux Enterprise Server. You will provision a C4A
+    instance, install Docker, kubectl, Helm, and KinD, and verify Helm workflows on a local Arm64
+    Kubernetes cluster. Next, you create and connect to a Google Kubernetes Engine (GKE) cluster
+    on Arm-based nodes and deploy PostgreSQL, Redis, and NGINX using official Helm charts. You
+    will perform install, upgrade, rollback, and uninstall operations, check application readiness
+    and service access, and observe Helm behavior under concurrent CLI operations. Prerequisites
+    include a GCP account with billing enabled and basic familiarity with Kubernetes, Helm, and
+    the Linux command line.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Google Cloud Platform account with billing enabled, basic familiarity with Kubernetes
+      concepts, a basic understanding of Helm and Kubernetes manifests, and comfort with the Linux
+      command line.
+  - question: Which Google Cloud machine type is used for the C4A VM in this path?
+    answer: >-
+      The steps use the c4a-standard-4 machine type, which provides 4 vCPUs and 16 GB of memory.
+  - question: Which tools are installed on the SUSE Arm64 VM to prepare for Helm testing?
+    answer: >-
+      You install Docker, kubectl, Helm, and KinD, and enable the SUSE Containers Module. This
+      setup lets you create and verify a local Kubernetes cluster for validating Helm workflows.
+  - question: How do I confirm that Helm and the chart repository are set up correctly?
+    answer: >-
+      Add the Bitnami chart repository and run a repository update. You should see output indicating
+      that "bitnami" was added and the repositories were successfully updated.
+  - question: What is deployed to GKE, and how does that differ from the local KinD cluster?
+    answer: >-
+      On GKE you deploy PostgreSQL, Redis, and NGINX using official Helm charts, and verify readiness
+      and service access. The earlier KinD-based cluster is used only for local validation before
+      targeting GKE; verify kubectl availability with the command kubectl version -.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/intro/_index.md b/content/learning-paths/servers-and-cloud-computing/intro/_index.md
index 2b0892c673..f51225ac02 100644
--- a/content/learning-paths/servers-and-cloud-computing/intro/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/intro/_index.md
@@ -18,6 +18,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:09:37Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: d2786f42b505823253ae16c647ca2e03c154f371b7c326210fde8523b12a8188
+  summary_generated_at: '2026-06-02T04:07:02Z'
+  summary_source_hash: d2786f42b505823253ae16c647ca2e03c154f371b7c326210fde8523b12a8188
+  faq_generated_at: '2026-06-03T01:09:37Z'
+  faq_source_hash: d2786f42b505823253ae16c647ca2e03c154f371b7c326210fde8523b12a8188
+  summary: >-
+    This short, introductory Learning Path helps software developers new to Arm understand where
+    Arm architecture appears in servers and cloud computing and how to find Arm-based hardware
+    for development. In about 10 minutes, it outlines how server vendors and cloud service providers
+    use Arm Neoverse processors for data center and on‑premises workloads, with an emphasis on
+    Linux environments. You will learn that cloud providers offer Arm instances based on Neoverse
+    and how creating a CSP account can be a practical first step, often with pay‑as‑you‑go access.
+    By the end, you can identify where Arm fits in server and cloud stacks and locate hardware
+    options to start exploring. No explicit prerequisites are listed.
+  faqs:
+  - question: Do I need my own Arm server to follow this path?
+    answer: >-
+      No. There are no prerequisites, and the path explains that creating an account with a cloud
+      service provider is the easiest way to try Arm instances.
+  - question: Which operating system does this path assume?
+    answer: >-
+      Linux is the target environment mentioned in the metadata.
+  - question: How do I choose an Arm-based instance in the cloud?
+    answer: >-
+      The path states that cloud providers offer Arm instances based on Neoverse processors. When
+      browsing your provider’s catalog, select an Arm-based option to evaluate.
+  - question: Does this path include step-by-step migration or tuning guidance?
+    answer: >-
+      No. It introduces where Arm is used and how to find Arm-based hardware, but it does not
+      provide detailed migration procedures or performance tuning steps.
+  - question: What outcome should I expect, and how long will it take?
+    answer: >-
+      In about 10 minutes, you will be able to identify where Arm is used in servers and cloud
+      computing and locate Arm-based hardware from CSPs and server vendors. You will also understand
+      common starting points to access Arm hardware through pay‑as‑you‑go models and introductory
+      credits.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md b/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
index 12d8a76f19..0367f2eaf9 100644
--- a/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
@@ -22,6 +22,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:10:27Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 6fc608d45c4fdf85a77bddd4f8c26b05a7950d1086e578a8be0dd637feeb5d79
+  summary_generated_at: '2026-06-02T04:07:50Z'
+  summary_source_hash: 6fc608d45c4fdf85a77bddd4f8c26b05a7950d1086e578a8be0dd637feeb5d79
+  faq_generated_at: '2026-06-03T01:10:27Z'
+  faq_source_hash: 6fc608d45c4fdf85a77bddd4f8c26b05a7950d1086e578a8be0dd637feeb5d79
+  summary: >-
+    Learn how to analyze and adjust network interrupt (IRQ) distribution on Arm Linux servers
+    to improve network workload performance. You will inspect the current IRQ layout, experiment
+    with different IRQ management patterns using scripts provided in the Learning Path, and configure
+    a distribution strategy appropriate for your workload, including making changes persistent.
+    The guidance reflects observations across multiple cloud platforms and VM sizes, with specific
+    notes for smaller systems (16 vCPUs or fewer). This introductory path is aimed at developers
+    and performance engineers with an Arm computer running Linux and basic command-line familiarity.
+    In about 20 minutes, you will be able to evaluate IRQ placement and apply a practical distribution
+    approach for your system.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      You need an Arm computer running Linux and some familiarity with the Linux command line.
+      No additional prerequisites are explicitly listed.
+  - question: How do I know how my NIC IRQs are currently distributed?
+    answer: >-
+      The steps show you how to understand and analyze the current IRQ configuration on your Arm
+      Linux system. You will review how network interrupts are assigned across CPU cores before
+      making changes.
+  - question: Which IRQ distribution strategies can I try, and how are they applied?
+    answer: >-
+      The Learning Path presents multiple IRQ distribution strategies and provides scripts to
+      implement them on your systems. You will experiment with assigning network IRQs to specific
+      cores to improve cache locality and reduce contention, depending on your workload.
+  - question: How should I choose a strategy for my system size or workload?
+    answer: >-
+      Effectiveness depends on system size and workload characteristics, and there is no single
+      best approach. For systems with 16 vCPUs or less, recommendations include concentrating
+      network IRQs on one or two CPU cores.
+  - question: How do I make my IRQ configuration persistent and confirm it worked?
+    answer: >-
+      The Learning Path covers implementing persistent IRQ management solutions so your configuration
+      survives reboots. You can validate changes by repeating the analysis steps and observing
+      IRQ distribution under your workload.
+# END generated_summary_faq
 
 author: Kiel Friedt
 
diff --git a/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md b/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
index 6769bff675..761409b4a7 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
@@ -22,6 +22,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:11:10Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: f57dd99bad280f64f53071373519e2d3ba8ae50b94fe1ad0a9cc40d02b458b9b
+  summary_generated_at: '2026-06-02T04:08:34Z'
+  summary_source_hash: f57dd99bad280f64f53071373519e2d3ba8ae50b94fe1ad0a9cc40d02b458b9b
+  faq_generated_at: '2026-06-03T01:11:10Z'
+  faq_source_hash: f57dd99bad280f64f53071373519e2d3ba8ae50b94fe1ad0a9cc40d02b458b9b
+  summary: >-
+    Learn to monitor, interpret, and tune Java Garbage Collection on Arm-based Linux servers.
+    Using an Arm instance on AWS, Microsoft Azure, Google Cloud, Oracle, or an on‑premise Arm
+    server, you will verify your JDK with java --version, review the differences among commonly
+    used production collectors, and run a small Java program that rapidly fills the heap to expose
+    GC behavior. The path shows how to select a collector for your application and adjust core
+    parameters, with guidance on updating to a recent LTS JDK. Prerequisites include basic Java
+    knowledge and a working Java installation. Estimated time: 45 minutes.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      You need an Arm-based instance from a cloud provider (or an on-premise Arm server), a basic
+      understanding of Java, and a working Java installation. Linux is the target operating system.
+  - question: How do I check which JDK version I am using?
+    answer: >-
+      Run the command: java --version. The output shows your JDK release and build details so
+      you can proceed with the appropriate GC options.
+  - question: How do I find which Garbage Collectors are available with my JDK?
+    answer: >-
+      Different JDK versions ship with different collectors, so first confirm your version with
+      java --version. Then follow the identification step in the path to see which collectors
+      your JDK includes.
+  - question: How do I use the example application to observe GC behavior?
+    answer: >-
+      Create the provided HeapUsageExample.java file and run it to allocate a large number of
+      objects and fill the heap. This makes GC activity easy to observe while you vary GC choices
+      and tuning parameters.
+  - question: What should I do if I’m on an older JDK release?
+    answer: >-
+      Update to a recent long-term-support JDK, because newer releases include GC improvements.
+      Use java --version to verify the upgrade before repeating your measurements.
+# END generated_summary_faq
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
index 0f41ee792c..dd1372a6ad 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
@@ -19,6 +19,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:12:09Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: e6f73fbca45a1be1644f79bbcfbaaec964e31f1aab236e9a872c72a6fd5e4b66
+  summary_generated_at: '2026-06-02T04:09:25Z'
+  summary_source_hash: e6f73fbca45a1be1644f79bbcfbaaec964e31f1aab236e9a872c72a6fd5e4b66
+  faq_generated_at: '2026-06-03T01:12:09Z'
+  faq_source_hash: e6f73fbca45a1be1644f79bbcfbaaec964e31f1aab236e9a872c72a6fd5e4b66
+  summary: >-
+    Learn how to deploy and evaluate a Java workload on Google Cloud Axion instances built on
+    Armv9 Neoverse V2. You will create an Arm-based VM using the gcloud CLI, install Java on Ubuntu
+    24.04, and build and deploy the Spring Petclinic application. The path then uses jmeter (with
+    a provided JMX file) to exercise the application, compare JDK versions, and test common JVM
+    performance optimization flags. You can also compare Axion results with previous-generation
+    Google Cloud Arm instances. This introductory path targets developers running Java on Arm
+    in Google Cloud. Prerequisite: a Google Cloud account with access to Axion-based (C4A) instances.
+  faqs:
+  - question: What do I need before creating the VM?
+    answer: >-
+      You need a Google Cloud account with access to Axion-based instances (C4A). No other explicit
+      prerequisites are listed.
+  - question: Which method should I use to create the Axion VM?
+    answer: >-
+      There are multiple options: Google Cloud console, the gcloud CLI, or Infrastructure as Code.
+      This guide uses the gcloud CLI.
+  - question: How do I connect to the instance, and which OS is used?
+    answer: >-
+      Use the Google Cloud console’s SSH button to open a shell to the VM. The guide uses an Ubuntu
+      24.04 image on the Axion instance.
+  - question: Which Java package should I install and how do I verify it?
+    answer: >-
+      Install the default JRE using apt and verify with java -version. The example output shows
+      an OpenJDK 21.x release.
+  - question: What application and tool are used for performance testing, and how should I run
+      the tests?
+    answer: >-
+      You deploy the Spring Petclinic application and test it with jmeter using the JMX file in
+      the spring-petclinic repo. Open a new SSH terminal so the running application is not interrupted,
+      then compare results across JDK versions and common JVM flags; comparing Axion to previous-generation
+      Arm instances is optional.
+# END generated_summary_faq
 
 author: Joe Stech
 
diff --git a/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
index f4dc576741..477394f2b3 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
@@ -20,6 +20,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:13:27Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 58b0bb9f6e4190029d7edc65bdb04a597c7539de55a5e51ed59e88b174e527c3
+  summary_generated_at: '2026-06-02T04:09:50Z'
+  summary_source_hash: 58b0bb9f6e4190029d7edc65bdb04a597c7539de55a5e51ed59e88b174e527c3
+  faq_generated_at: '2026-06-03T01:13:27Z'
+  faq_source_hash: 58b0bb9f6e4190029d7edc65bdb04a597c7539de55a5e51ed59e88b174e527c3
+  summary: >-
+    Provision an Arm-based Azure Cobalt 100 virtual machine using the Azure portal, install Java
+    on Ubuntu Pro 24.04 LTS (Arm64), and measure application performance with JVM-aware microbenchmarks.
+    This introductory path is aimed at developers migrating Java workloads to Arm and walks through
+    creating a Cobalt 100 (Dpsv6) VM, installing the JRE and JDK via the Ubuntu package manager,
+    validating the installation, and running a simple Tomcat-like Java baseline before benchmarking
+    with JMH. You will learn how to set up the environment, run baseline tests, and execute JMH
+    to assess Java performance on Arm. A Microsoft Azure account with access to Cobalt 100 instances
+    is required. Estimated time to complete: 30 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Microsoft Azure account with access to Cobalt 100 based instances (Dpsv6). No
+      other explicit prerequisites are listed.
+  - question: How should I create the VM and which OS image should I choose?
+    answer: >-
+      Use the Azure portal to create an Arm64 VM with the Cobalt 100 processor. Select Ubuntu
+      Pro 24.04 LTS as the base image, following the VM creation steps in the portal.
+  - question: Which Java package should I install on Ubuntu Pro 24.04 LTS (Arm64)?
+    answer: >-
+      Install OpenJDK using the default-jdk package, which provides both the JRE and JDK. Run:
+      sudo apt update and then sudo apt install -y default-jdk.
+  - question: Why start with a Tomcat-like baseline instead of deploying a full Tomcat server?
+    answer: >-
+      A Tomcat-like baseline lets you measure how efficiently raw Java executes simple operations
+      before adding server components. Full servers introduce complexity such as request parsing,
+      thread management, and I/O handling.
+  - question: How will I benchmark the Java code and what results should I look for?
+    answer: >-
+      You will use JMH (Java Microbenchmark Harness) to run JVM-aware microbenchmarks. JMH accounts
+      for JIT and warmup and enables you to measure throughput for small code snippets.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
index 7d88e7ef7e..c9c471b2e8 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
@@ -20,6 +20,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:14:15Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 655180d91bb9b9cf571840b59d8943c1d2c73d8f8aea647db57dc2704ede3af8
+  summary_generated_at: '2026-06-02T04:10:23Z'
+  summary_source_hash: 655180d91bb9b9cf571840b59d8943c1d2c73d8f8aea647db57dc2704ede3af8
+  faq_generated_at: '2026-06-03T01:14:15Z'
+  faq_source_hash: 655180d91bb9b9cf571840b59d8943c1d2c73d8f8aea647db57dc2704ede3af8
+  summary: >-
+    Learn how to analyze Java application performance on Arm Neoverse-based Linux servers by benchmarking
+    a Tomcat deployment and generating flame graphs. You will set up Apache Tomcat, drive HTTP
+    load with wrk2, and profile on the same Arm machine using two approaches: async-profiler and
+    a perf-based Java agent (libperf-jvmti) with the FlameGraph toolkit. The path uses OpenJDK
+    21 and focuses on producing actionable flame graphs to help identify bottlenecks under load.
+    Prerequisites include access to Arm- and x86-based Ubuntu systems (cloud instances are acceptable)
+    and basic familiarity with Java applications and flame graph profiling. Estimated time to
+    complete is about 30 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need access to both Arm-based and x86-based computers running Ubuntu (cloud instances
+      are acceptable) and basic familiarity with Java applications and flame-graph-based profiling.
+      The path uses OpenJDK 21, Apache Tomcat, async-profiler, FlameGraph, and wrk2.
+  - question: Can I perform the steps on an x86 server?
+    answer: >-
+      The procedures target an Arm Neoverse-based Linux server for profiling. Access to an x86
+      Ubuntu system is listed as a prerequisite, but the profiling steps in this path are executed
+      on the Arm machine.
+  - question: Where should I run async-profiler relative to Tomcat?
+    answer: >-
+      Install and run async-profiler on the same Arm-based Linux machine where Tomcat is running
+      to ensure accurate profiling.
+  - question: How are flame graphs generated with the Java agent approach?
+    answer: >-
+      Configure Tomcat to load the libperf-jvmti.so JVMTI agent so perf can record stacks with
+      Java method names, then use the FlameGraph toolkit to build the flame graph. This complements
+      the async-profiler method provided earlier in the path.
+  - question: Do I need to generate load during profiling, and how should I do that?
+    answer: >-
+      Yes. The path sets up a Tomcat benchmark and uses wrk2 to simulate HTTP load while you collect
+      profiles, so the flame graphs reflect realistic request handling.
+# END generated_summary_faq
 
 author: 
   - Ying Yu
diff --git a/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md b/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
index 4ada6d2b97..a63b2303da 100644
--- a/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
@@ -26,6 +26,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:14:54Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 525d893a796e8e4eb5cc7a9d5996bd7d55a35f8fe413f87b9a275a214d77626f
+  summary_generated_at: '2026-06-02T04:11:14Z'
+  summary_source_hash: 525d893a796e8e4eb5cc7a9d5996bd7d55a35f8fe413f87b9a275a214d77626f
+  faq_generated_at: '2026-06-03T01:14:54Z'
+  faq_source_hash: 525d893a796e8e4eb5cc7a9d5996bd7d55a35f8fe413f87b9a275a214d77626f
+  summary: >-
+    This Learning Path guides you through deploying Jenkins LTS on Arm-based cloud servers and
+    validating Arm-native CI/CD pipelines. You provision an Azure Cobalt 100 (Dpsv6) virtual machine
+    using the Azure console with Ubuntu Pro 24.04 LTS, and a Google Cloud C4A instance powered
+    by Axion processors on SUSE Linux. You configure cloud firewall rules to expose Jenkins on
+    TCP port 8080, install Jenkins with OpenJDK 17 on Arm64, and verify the setup via service
+    checks, UI access, and pipeline execution. You then run Arm-native Jenkins pipelines, including
+    Docker-based workflows. No additional prerequisites are listed beyond cloud account access,
+    basic Linux skills, and familiarity with CI/CD and Jenkins fundamentals.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an Azure account with access to Cobalt 100-based Dpsv6 instances and a Google Cloud
+      account with access to Arm-based VMs. You should be comfortable with the Linux command line
+      and basic CI/CD and Jenkins concepts.
+  - question: Which VM types and operating systems are used in this path?
+    answer: >-
+      On Azure, you provision an Arm64 VM from the Dpsv6 (Cobalt 100) series using Ubuntu Pro
+      24.04 LTS. On Google Cloud, you provision an Arm-based SUSE Linux VM on the C4A family powered
+      by Axion processors.
+  - question: How do I expose the Jenkins web UI to my browser?
+    answer: >-
+      Open TCP port 8080. On Azure, create an NSG rule for the VM’s network interface or subnet;
+      on Google Cloud, create a VPC firewall rule allowing inbound TCP 8080 to the instance.
+  - question: How do I validate that Jenkins installed correctly on the Azure VM?
+    answer: >-
+      Confirm the Jenkins service is running, then access the web UI on port 8080. The setup verifies
+      Jenkins on Arm64 (aarch64) with Java 17 as part of the installation outcome.
+  - question: What should I check if I plan to run Docker-based pipelines?
+    answer: >-
+      This path includes CI use cases that use Docker-based pipelines on Arm64. Follow the steps
+      when they introduce Docker workflows and ensure your environment meets those requirements.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
index 0266bb7f7e..076b321260 100644
--- a/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
@@ -22,6 +22,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:16:31Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: d510dd43a485e1c2fac404ef9a2e00b5be2449b99b1095c9a1841cd2fcba9b0e
+  summary_generated_at: '2026-06-02T04:12:27Z'
+  summary_source_hash: d510dd43a485e1c2fac404ef9a2e00b5be2449b99b1095c9a1841cd2fcba9b0e
+  faq_generated_at: '2026-06-03T01:16:31Z'
+  faq_source_hash: d510dd43a485e1c2fac404ef9a2e00b5be2449b99b1095c9a1841cd2fcba9b0e
+  summary: >-
+    Provision an Arm-based Microsoft Azure Cobalt 100 (Dpsv6) virtual machine using the Azure
+    portal, install Apache Kafka on Ubuntu Pro 24.04 LTS (arm64), and validate end-to-end messaging
+    before running official Kafka benchmarks. You will set up Java, deploy Kafka, start the broker
+    in KRaft mode, and perform a baseline producer/consumer test to confirm the environment is
+    working. Finally, use Kafka’s bundled performance tools to measure throughput and latency
+    on the Arm64 VM. This advanced path targets developers migrating Kafka workloads to Arm on
+    Azure. Prerequisites include an Azure account with access to Cobalt 100 instances, basic Linux
+    command-line skills, and familiarity with Kafka architecture and Arm64 deployment practices.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      You need a Microsoft Azure account with access to Cobalt 100 based instances (Dpsv6), basic
+      Linux command-line skills, and familiarity with the Apache Kafka architecture and deployment
+      on Arm64. No other prerequisites are explicitly listed.
+  - question: Which Azure VM size and OS image should I select?
+    answer: >-
+      Use a Dpsv6 series virtual machine based on the Arm-based Cobalt 100 CPU and select Ubuntu
+      Pro 24.04 LTS (Arm64) as the base image. The steps use the Azure portal to create the VM.
+  - question: Do I need ZooKeeper for this Kafka setup?
+    answer: >-
+      No. Kafka 4.1.0 in KRaft mode integrates the control and data planes and removes the need
+      for ZooKeeper, simplifying deployment.
+  - question: How do I know the baseline test worked?
+    answer: >-
+      Start the Kafka broker in KRaft mode and run the producer and consumer in separate terminals.
+      Successful end-to-end message production and consumption indicates the setup is working.
+  - question: Which tools are used for benchmarking and what should be running first?
+    answer: >-
+      Use kafka-producer-perf-test.sh and kafka-consumer-perf-test.sh bundled with Kafka to measure
+      throughput, latency, and end-to-end efficiency. Ensure your Kafka broker is already active
+      in a separate terminal before running these tools.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/kafka/_index.md b/content/learning-paths/servers-and-cloud-computing/kafka/_index.md
index 9e4ec102cd..d930f84991 100644
--- a/content/learning-paths/servers-and-cloud-computing/kafka/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kafka/_index.md
@@ -22,6 +22,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:15:30Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: b7f36df881c2c436143c1e5579d2c54cb5930f52998904098829c52fa7290f38
+  summary_generated_at: '2026-06-02T04:11:38Z'
+  summary_source_hash: b7f36df881c2c436143c1e5579d2c54cb5930f52998904098829c52fa7290f38
+  faq_generated_at: '2026-06-03T01:15:30Z'
+  faq_source_hash: b7f36df881c2c436143c1e5579d2c54cb5930f52998904098829c52fa7290f38
+  summary: >-
+    This advanced Learning Path guides you through deploying a production-style Kafka event streaming
+    cluster on Arm-based Linux servers. You will install and configure a three-node ZooKeeper
+    ensemble and a three-node Kafka cluster on Ubuntu or Debian, then validate the setup by creating
+    a topic and writing/reading events from a client node. The path also covers automating deployment
+    on AWS Graviton processors using Terraform and Ansible, with objectives that include automation
+    on Google Cloud. You need seven Arm machines or cloud instances and appropriate network ports
+    opened. After about 90 minutes, you will have a working Kafka cluster on Arm and a repeatable
+    deployment approach for cloud environments.
+  faqs:
+  - question: What do I need before running the setup?
+    answer: >-
+      You need seven physical Arm machines or cloud instances running Ubuntu or Debian. Ensure
+      ports 8080, 2888, 3888, 2181, and 9092 are open in the security groups for these machines.
+  - question: How should I assign roles to the seven machines?
+    answer: >-
+      Use three machines for the ZooKeeper cluster, three machines for the Kafka cluster, and
+      one machine as the client.
+  - question: Which configuration values do I change on Kafka nodes to connect to ZooKeeper?
+    answer: >-
+      Edit config/server.properties on each Kafka node and replace zk_1_ip, zk_2_ip, and zk_3_ip
+      with the IP addresses of your three ZooKeeper nodes.
+  - question: Where do I run the validation and what result should I expect?
+    answer: >-
+      Install Kafka on the client machine, create a topic, write events to it, and read them back.
+      Successfully reading the events you produced confirms the Kafka cluster is working.
+  - question: Which options are available for automated deployment on cloud platforms?
+    answer: >-
+      The path covers automated deployment on AWS and Google Cloud. On AWS, Terraform and Ansible
+      are used to deploy a three-node ZooKeeper cluster, a three-node Kafka cluster, and one client
+      on AWS Graviton, and you should have the required tools installed on a computer you can
+      run them from.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md b/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
index 77aed65690..3e61458755 100644
--- a/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
@@ -22,6 +22,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:17:17Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 6ff33f6dfaf25e926a0ce8756b4e564e5aa37112e5425f9c3dce13f772b54145
+  summary_generated_at: '2026-06-02T04:12:58Z'
+  summary_source_hash: 6ff33f6dfaf25e926a0ce8756b4e564e5aa37112e5425f9c3dce13f772b54145
+  faq_generated_at: '2026-06-03T01:17:17Z'
+  faq_source_hash: 6ff33f6dfaf25e926a0ce8756b4e564e5aa37112e5425f9c3dce13f772b54145
+  summary: >-
+    This Learning Path shows how to enable event-driven autoscaling for HTTP workloads on Kubernetes
+    using KEDA and Kedify. You will use Helm to add the Kedify chart repository and install three
+    charts—the KEDA (Kedify build), the HTTP Scaler, and the Kedify Agent—then verify they are
+    running. If needed, you will install an ingress controller (NGINX) and target arm64 nodes.
+    Next, you will deploy a sample web service, expose it via Kubernetes Ingress, rely on Kedify’s
+    autowiring to route traffic, and generate load to observe scale-out, scale-in, and scale-to-zero
+    behavior. It targets Linux and works on local or cloud clusters (EKS, GKE, AKS). Prerequisites
+    include kubectl, Helm, a running cluster, and Kedify dashboard credentials.
+  faqs:
+  - question: What do I need before I start the installation?
+    answer: >-
+      You need a running Kubernetes cluster (local or cloud), kubectl and Helm installed, and
+      access to the Kedify Service dashboard to obtain your Organization ID and API key. The path
+      targets Linux.
+  - question: Do I need an ingress controller, and which one is used here?
+    answer: >-
+      Yes, an ingress controller is required to handle HTTP traffic. This path installs the NGINX
+      Ingress Controller with Helm and targets arm64 nodes; if your cluster already has a working
+      ingress controller, you can skip this step.
+  - question: Which Helm charts are installed to enable HTTP autoscaling?
+    answer: >-
+      You install three charts from the Kedify repository: KEDA (Kedify build) for event-driven
+      autoscaling, the HTTP Scaler for HTTP-based scaling, and the Kedify Agent to connect your
+      cluster to Kedify’s cloud service.
+  - question: How do I know Kedify and KEDA are running correctly?
+    answer: >-
+      The Learning Path includes a verification step to check that the Kedify and KEDA components
+      are running in your cluster. Follow those checks before proceeding to application deployment.
+  - question: What behavior should I expect when testing the sample HTTP app?
+    answer: >-
+      After deploying the app and enabling autoscaling with a scaled object, generating HTTP load
+      should trigger scale out. When the app becomes idle, you should observe scale in, including
+      scale-to-zero.
+# END generated_summary_faq
 
 author: Zbynek Roubalik
 
diff --git a/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md b/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
index 03c0930e12..b2bb2cf9c4 100644
--- a/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
@@ -22,6 +22,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:17:46Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 830556dfd51aaa2dd95ee957e64306bab369297f7bc66325e7eb509f541f683c
+  summary_generated_at: '2026-06-02T04:13:36Z'
+  summary_source_hash: 830556dfd51aaa2dd95ee957e64306bab369297f7bc66325e7eb509f541f683c
+  faq_generated_at: '2026-06-03T01:17:46Z'
+  faq_source_hash: 830556dfd51aaa2dd95ee957e64306bab369297f7bc66325e7eb509f541f683c
+  summary: >-
+    This introductory Learning Path shows how to create, train, and evaluate a simple neural network
+    on Arm servers using Keras Core with TensorFlow, PyTorch, and JAX backends. You work on Ubuntu
+    22.04 LTS on an Arm-based instance or server, including cloud instances from AWS, Microsoft
+    Azure, Google Cloud, or Oracle. After installing the required Python environment, you write
+    and run a compact ml.py script that defines and compiles a model in Keras Core, then train,
+    evaluate, and generate predictions using different backends. The path targets Linux and takes
+    about 30 minutes. Prerequisites include basic machine learning knowledge and familiarity with
+    SSH, the Linux command line, and basic system administration tasks.
+  faqs:
+  - question: What environment should I prepare before starting?
+    answer: >-
+      Use an Arm-based machine running Ubuntu 22.04 LTS: a cloud instance on AWS, Microsoft Azure,
+      Google Cloud, or Oracle; an on-premises Arm server; or a Linux VM on your Arm device. Access
+      it via SSH (for remote servers) or open a terminal locally.
+  - question: Which Python version should I use on Ubuntu 22.04, and do I need pip and venv?
+    answer: >-
+      Ubuntu 22.04 includes Python 3.10 by default, which you can use, or you can install a newer
+      Python version. In either case, install the python3-pip and python3-venv packages to manage
+      dependencies and an isolated environment.
+  - question: How do I switch between TensorFlow, PyTorch, and JAX backends in Keras Core?
+    answer: >-
+      Keras Core supports multiple backends and the Learning Path runs the same model with TensorFlow,
+      PyTorch, and JAX. Follow the step that specifies how to choose the backend before executing
+      the script; the exact selection method is provided there.
+  - question: What script do I run, and what should I expect as output?
+    answer: >-
+      You will create an ml.py script that defines a simple model with Keras Core, then compile,
+      train, evaluate, and generate predictions. When it runs successfully, you should see training
+      progress and evaluation results, followed by prediction output.
+  - question: What input shape and data type does the example model expect?
+    answer: >-
+      The example model uses an input shape of 784 elements with dtype float16. If your data has
+      a different shape or dtype, adjust the Input layer in the script accordingly.
+# END generated_summary_faq
 
 author: 
     - Diego Russo
diff --git a/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md b/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
index 6edb677dda..9146bff7f0 100644
--- a/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
@@ -23,6 +23,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:18:47Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 004436cf14ff0056136889c58d676295c6dd2088f06f57f397a07ec9004e6b44
+  summary_generated_at: '2026-06-02T04:14:17Z'
+  summary_source_hash: 004436cf14ff0056136889c58d676295c6dd2088f06f57f397a07ec9004e6b44
+  faq_generated_at: '2026-06-03T01:18:47Z'
+  faq_source_hash: 004436cf14ff0056136889c58d676295c6dd2088f06f57f397a07ec9004e6b44
+  summary: >-
+    Learn how to build and install custom Linux kernels on Arm cloud instances using TuxMake.
+    You will provision an Ubuntu 24.04 LTS Arm server (minimum 24 vCPUs and 200 GB free storage),
+    configure a build environment, compile specific kernel versions, and install or package the
+    resulting kernels. The path covers standard workflows for general-purpose kernels as well
+    as configurations for 64 KB page sizes. It also explains how to produce Fastpath-enabled builds
+    for testing workflows, which are build-only. Examples use AWS, but the steps apply to any
+    provider offering 64-bit Arm Ubuntu instances. By the end, you will be able to build, install,
+    and prepare kernels for Fastpath validation on Arm cloud machines.
+  faqs:
+  - question: What do I need on my Arm cloud instance before starting?
+    answer: >-
+      Use an Ubuntu 24.04 LTS Arm instance with at least 24 vCPUs and 200 GB of free storage,
+      and ensure you have SSH access. The example uses AWS, but any provider offering 64-bit Arm
+      Ubuntu instances is suitable.
+  - question: How do I choose which Linux kernel version to build with TuxMake?
+    answer: >-
+      Specify your desired version with the --tags flag. The versions shown in the examples (such
+      as v6.18.1) are valid but arbitrary, so you can substitute the version you need.
+  - question: What result should I expect from a standard TuxMake build workflow?
+    answer: >-
+      Standard workflows produce general-purpose kernels suitable for production deployment, development
+      testing, or packaging. You can build for direct installation on the instance or create artifacts
+      for downstream packaging, and configure options such as 64 KB page sizes.
+  - question: What is the correct workflow for Fastpath builds?
+    answer: >-
+      Fastpath builds are build-only; do not combine --fastpath true (or the demo shortcut) with
+      --kernel-install or any --install-from commands. Build with Fastpath enabled, then copy
+      the flat artifacts (for example, the kernel image and modules) to your Fastpath test environment.
+  - question: What should I check if compilation is very slow or runs out of memory?
+    answer: >-
+      Use a sufficiently large Arm instance because smaller instances take longer or can run out
+      of memory during compilation. Meeting the minimum of 24 vCPUs and ample free storage helps
+      avoid resource-related build failures.
+# END generated_summary_faq
 
 author: Geremy Cohen
 
diff --git a/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md b/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
index 7bfc638341..84c3ff6855 100644
--- a/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
@@ -21,6 +21,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:19:42Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 43e4e28b88b9721ca94457418f3b4887d0099017578a54da1db5fcdc11f4a122
+  summary_generated_at: '2026-06-02T04:14:53Z'
+  summary_source_hash: 43e4e28b88b9721ca94457418f3b4887d0099017578a54da1db5fcdc11f4a122
+  faq_generated_at: '2026-06-03T01:19:42Z'
+  faq_source_hash: 43e4e28b88b9721ca94457418f3b4887d0099017578a54da1db5fcdc11f4a122
+  summary: >-
+    Learn how to assess and migrate Kubernetes container images to Arm-compatible versions using
+    KubeArchInspect. You will install KubeArchInspect on Linux, ensure kubectl is configured to
+    your cluster, run kubearchinspect images to inventory running images, and generate a report
+    by querying source registries for available architectures. You will analyze the results using
+    clear indicators (arm64 supported, not available, available in newer version, or error) and
+    make configuration changes to upgrade images that include Arm support. This introductory path
+    targets developers who want to confirm Arm readiness for their workloads and can be applied
+    to Kubernetes clusters, including those on major cloud providers. Prerequisite: a running
+    Kubernetes cluster accessible with kubectl. Estimated time: 15 minutes.
+  faqs:
+  - question: What do I need before running KubeArchInspect?
+    answer: >-
+      You need a running Kubernetes cluster and kubectl configured to access it. Install kubearchinspect
+      on a Linux environment. No other explicit prerequisites are listed.
+  - question: Which command should I use to generate the image report?
+    answer: >-
+      Run: kubearchinspect images. This connects to your cluster and produces a report of images
+      in use and their available architectures.
+  - question: How does KubeArchInspect determine whether an image supports Arm?
+    answer: >-
+      It queries the image’s source registry and checks which architectures are available. The
+      report highlights whether arm64 is present for each image.
+  - question: How do I interpret the output symbols in the report?
+    answer: >-
+      A green tick (✅) means the image already supports arm64. A red cross (❌) means arm64 is
+      not available; a blue up (🆙) indicates a newer version includes arm64; a red cross mark
+      (🚫) signals an error occurred when checking the image, which may indicate a registry connectivity
+      issue.
+  - question: What should I do after running the report?
+    answer: >-
+      Use the results to plan configuration changes that upgrade images to versions with arm64
+      support. Prioritize images marked with 🆙 for straightforward upgrades, and review ❌ entries
+      to determine your next steps.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md b/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
index 7f92256edf..c7a415a25a 100644
--- a/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
@@ -19,6 +19,51 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:20:35Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 22fa96e29143f2e0dc0c204919422914b3f70d63ddf833cf1067fbf67b525aa0
+  summary_generated_at: '2026-06-02T04:15:30Z'
+  summary_source_hash: 22fa96e29143f2e0dc0c204919422914b3f70d63ddf833cf1067fbf67b525aa0
+  faq_generated_at: '2026-06-03T01:20:35Z'
+  faq_source_hash: 22fa96e29143f2e0dc0c204919422914b3f70d63ddf833cf1067fbf67b525aa0
+  summary: >-
+    This introductory Learning Path shows how to deploy AWS Lambda functions on AWS Graviton processors
+    using Terraform. From a Linux host with Terraform and the AWS CLI installed, you will provision
+    Lambda functions configured with the arm64 architecture and implement examples in both Node.js
+    and Python. The steps demonstrate selecting the runtime, specifying the target architecture
+    in Terraform, and reusing the workflow across languages, including a simple Python function
+    that assembles a greeting message from event fields. By the end, you will be able to deploy
+    Lambda functions on Graviton with Terraform and adapt the same approach for either runtime.
+    No other prerequisites are explicitly listed. Estimated time: 30 minutes.
+  faqs:
+  - question: Which architecture should I select in Terraform to run the function on Graviton?
+    answer: >-
+      Choose arm64 for the function architecture in your Terraform configuration. This setting
+      deploys the Lambda function on Graviton processors.
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a computer with Terraform and the AWS CLI installed. No other explicit prerequisites
+      are listed, and the path targets Linux.
+  - question: Can I reuse the same deployment approach for Python and Node.js?
+    answer: >-
+      Yes. Follow the Node.js deployment workflow and, for Python, replace the Node.js code with
+      the provided Python Lambda function.
+  - question: How do I know the sample Python function is behaving as expected?
+    answer: >-
+      The Python handler constructs a message using the event’s first_name and last_name fields.
+      When invoked with those fields, expect a response containing the formatted greeting.
+  - question: What should I check if Terraform deployment does not work as expected?
+    answer: >-
+      Verify that Terraform and the AWS CLI are installed and accessible. Also confirm that the
+      Lambda function architecture is set to arm64 as shown in the path.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md b/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
index fe6f11160f..7c848b8085 100644
--- a/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
@@ -20,6 +20,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:21:05Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 66d13edff1371295f0e88a618e924ca67b85bcc8aa72034d1e582a0b267f0d05
+  summary_generated_at: '2026-06-02T04:16:07Z'
+  summary_source_hash: 66d13edff1371295f0e88a618e924ca67b85bcc8aa72034d1e582a0b267f0d05
+  faq_generated_at: '2026-06-03T01:21:05Z'
+  faq_source_hash: 66d13edff1371295f0e88a618e924ca67b85bcc8aa72034d1e582a0b267f0d05
+  summary: >-
+    This Learning Path shows how to enable libhugetlbfs on an Arm server running Ubuntu Linux
+    and measure its impact on memory-intensive workloads. You will configure hugepages so application
+    text, data, malloc, and shared memory can use larger pages, then apply the approach to MySQL
+    by modifying its build flags and benchmarking with sysbench to compare results. The target
+    environment is an Arm server or a cloud VM (for example, from AWS, Microsoft Azure, Google
+    Cloud, or Oracle) with Ubuntu installed. This advanced path expects familiarity with building
+    MySQL and running sysbench. Tools referenced include GCC and MySQL. Estimated time to complete
+    is about 60 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an Arm server or virtual machine with Ubuntu installed, plus knowledge of how to
+      build a MySQL server and run the sysbench benchmark test. No additional prerequisites are
+      explicitly listed.
+  - question: Can I use a cloud VM for this Learning Path?
+    answer: >-
+      Yes. An Arm-based instance from AWS, Microsoft Azure, Google Cloud, or Oracle with Ubuntu
+      installed meets the environment requirement.
+  - question: Where do I add libhugetlbfs build options when compiling MySQL?
+    answer: >-
+      Add the options to both -DCMAKE_C_FLAGS and -DCMAKE_CXX_FLAGS during the MySQL build configuration
+      as described in the steps. The path shows the required flags to enable libhugetlbfs.
+  - question: Do I need to change both build and run settings for MySQL?
+    answer: >-
+      Yes. The steps explain how to modify both the build and the run of the MySQL server to enable
+      libhugetlbfs.
+  - question: How should I evaluate the effect of enabling libhugetlbfs?
+    answer: >-
+      Run your baseline workload, then enable libhugetlbfs and repeat the same test to compare
+      results. For MySQL, you are expected to use sysbench to measure before-and-after performance.
+# END generated_summary_faq
+
 author: Bolt Liu
 
 skilllevels: Advanced
diff --git a/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md b/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
index 29cfe2b7c4..ad2a650f0d 100644
--- a/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
@@ -19,6 +19,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:21:51Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: d82aa4faeb599a3a1ac12d477204ebe0a4ddb99564bedced86ca0fc4851e17b9
+  summary_generated_at: '2026-06-02T04:16:47Z'
+  summary_source_hash: d82aa4faeb599a3a1ac12d477204ebe0a4ddb99564bedced86ca0fc4851e17b9
+  faq_generated_at: '2026-06-03T01:21:51Z'
+  faq_source_hash: d82aa4faeb599a3a1ac12d477204ebe0a4ddb99564bedced86ca0fc4851e17b9
+  summary: >-
+    Deploy a pre-quantized Llama‑3.1‑8B chatbot on an Arm server using llama.cpp with KleidiAI,
+    and expose it through an OpenAI‑compatible API. You will download and build llama.cpp, fetch
+    the pre‑quantized model from Hugging Face, run it on your Arm CPU, and measure performance.
+    The path targets Ubuntu 24.04 LTS on Arm with a minimum of 4 CPU cores, 8 GB RAM, and 32 GB
+    disk; it was tested on an AWS Graviton4 r8g.16xlarge instance but can run on other Arm‑based
+    instances or on‑prem Arm servers. You will also install jq and start the llama.cpp server,
+    which listens on port 8080 and can be accessed by OpenAI‑style clients over the network.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      Use an Arm server running Ubuntu 24.04 LTS with at least four cores, 8 GB RAM, and 32 GB
+      of disk. The instructions were tested on an AWS Graviton4 r8g.16xlarge instance, but any
+      Arm-based instance or on-prem Arm server meeting these resources is acceptable.
+  - question: Which LLM model should I download for this setup?
+    answer: >-
+      Download a pre-quantized Llama 3.1 model from Hugging Face. The Learning Path guides you
+      to use that model with llama.cpp on your Arm CPU.
+  - question: How do I start and access the OpenAI-compatible server?
+    answer: >-
+      After building llama.cpp (via make in a prior step), start the server binary; it listens
+      on port 8080. You can submit requests using an OpenAI-compatible API from the same machine
+      or over the network.
+  - question: Is any extra package required to interact with the API responses?
+    answer: >-
+      Yes. Install jq to work with JSON responses (for example, using sudo apt install jq -y on
+      Ubuntu). This helps format and inspect the server’s OpenAI-compatible output.
+  - question: Can I measure performance during inference, and how is it covered?
+    answer: >-
+      Yes. The Learning Path includes running the pre-quantized model on your Arm CPU and measuring
+      performance as part of the procedure; it does not list additional tools beyond those in
+      the steps.
+# END generated_summary_faq
 
 author:
     - Pareena Verma
diff --git a/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md b/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
index f31b06c79f..0a5f4a0a3a 100644
--- a/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
@@ -24,6 +24,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:22:45Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 3e8307a5daf435c21f3cecff635ac51724a63ff9ea4307082d869601563b4204
+  summary_generated_at: '2026-06-02T04:17:37Z'
+  summary_source_hash: 3e8307a5daf435c21f3cecff635ac51724a63ff9ea4307082d869601563b4204
+  faq_generated_at: '2026-06-03T01:22:45Z'
+  faq_source_hash: 3e8307a5daf435c21f3cecff635ac51724a63ff9ea4307082d869601563b4204
+  summary: >-
+    This Learning Path shows how to deploy a production-ready, vision-enabled chatbot on Arm-based
+    servers using Google Cloud Axion. You will build a Flask backend that downloads a Llama 3.2‑Vision
+    model from Hugging Face, applies 4‑bit quantization, and serves inference with PyTorch and
+    Transformers, and a Streamlit frontend that accepts images and text prompts. The instructions
+    target Ubuntu 24.04 LTS and were tested on a Google Cloud c4a-standard-64 instance; an Arm
+    server with at least 32 CPU cores is required. You will launch the web UI on port 8501 and
+    monitor and analyze inference on Arm CPUs. Prerequisites include basic Python/ML, Streamlit,
+    LLM fundamentals, and familiarity with REST and web services.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      You need a Google Cloud Axion compute instance or any Arm-based instance with at least 32
+      CPU cores. Familiarity with REST APIs and web services, basic Python and ML concepts, Streamlit,
+      and LLM fundamentals is expected.
+  - question: Which environment is targeted and what instance was used for testing?
+    answer: >-
+      The steps are tailored for Arm servers running Ubuntu 24.04 LTS. They were tested on a Google
+      Cloud c4a-standard-64 instance.
+  - question: Which model is used and how is it prepared for inference?
+    answer: >-
+      The backend downloads the Llama 3.2‑Vision model from Hugging Face and performs 4‑bit quantization.
+      It then serves the model with PyTorch on Arm CPUs.
+  - question: How do I access the web application once the services are running?
+    answer: >-
+      Open your browser to http://[your instance ip]:8501. On Google Cloud, you may need to allow
+      inbound TCP traffic on port 8501 in your instance’s firewall rules.
+  - question: What result should I expect to validate that inference is working?
+    answer: >-
+      From the Streamlit UI, upload an image and enter a text prompt; the app should return a
+      generated text response that uses the image as context. The backend Flask service streams
+      the model’s output to the frontend.
+# END generated_summary_faq
 
 author: Nobel Chowdary Mandepudi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
index df1f9b66b3..515eaa12f2 100644
--- a/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
@@ -25,6 +25,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:23:50Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 36bf3c45f0b38350714ba41ea88c1551b33f6d299a65b3b0d6668fee2d88d835
+  summary_generated_at: '2026-06-02T04:18:16Z'
+  summary_source_hash: 36bf3c45f0b38350714ba41ea88c1551b33f6d299a65b3b0d6668fee2d88d835
+  faq_generated_at: '2026-06-03T01:23:50Z'
+  faq_source_hash: 36bf3c45f0b38350714ba41ea88c1551b33f6d299a65b3b0d6668fee2d88d835
+  summary: >-
+    Learn how to profile llama.cpp inference on Arm CPUs using Arm Streamline. This advanced path
+    guides you to integrate Streamline Annotation Markers and Annotation Channels into the llama.cpp
+    codebase to visualize and analyze the Prefill and Decode stages, and to perform operator-level
+    timing during token generation. You will build llama-cli, configure the gator daemon, and
+    prepare your Arm Neoverse or Cortex-A target running Linux or Android with the required executables
+    and model files. By the end, you will capture and interpret Streamline data and evaluate multi-core
+    and multi-thread execution characteristics. Prerequisites include familiarity with llama.cpp,
+    transformer models, and Arm Streamline.
+  faqs:
+  - question: What do I need before running the profiling steps?
+    answer: >-
+      You need an Arm Neoverse or Cortex-A hardware platform running Linux or Android, plus a
+      basic understanding of llama.cpp, transformer models, and Arm Streamline usage. These are
+      listed prerequisites for this Learning Path.
+  - question: Which option should I use to visualize the Prefill and Decode stages?
+    answer: >-
+      Use Streamline’s Annotation Marker feature and insert markers in llama.cpp to tag the Prefill
+      and Decode stages. These markers appear in the Streamline timeline to correlate performance
+      data with token generation phases.
+  - question: How can I analyze operator-level performance during token generation?
+    answer: >-
+      Use Streamline Annotation Channels to group related operations and track their timing as
+      separate visual channels. This lets you examine execution time per node in the compute graph
+      and see concurrent operations over time.
+  - question: How do I evaluate multi-core or multi-thread execution in this path?
+    answer: >-
+      Capture a Streamline profile while running llama.cpp and use your annotations to correlate
+      activity during Prefill and Decode across threads and cores. The Learning Path guides you
+      through assessing multi-core and multi-thread execution on Arm CPUs.
+  - question: What should I check if Streamline is not collecting data from my target?
+    answer: >-
+      Verify that the gator daemon is configured and running on the target system. Also ensure
+      the required executables and model files are present on the device before capturing data.
+# END generated_summary_faq
 
 author: 
     - Zenon Zhilong Xiu
diff --git a/content/learning-paths/servers-and-cloud-computing/lse/_index.md b/content/learning-paths/servers-and-cloud-computing/lse/_index.md
index 2d505be42e..76a00521c7 100644
--- a/content/learning-paths/servers-and-cloud-computing/lse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/lse/_index.md
@@ -19,6 +19,51 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:24:38Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 9610291239b88c67e21055f94cd03fe7cf80f7d875d53ebe0d8de7837ce99fa7
+  summary_generated_at: '2026-06-02T04:18:56Z'
+  summary_source_hash: 9610291239b88c67e21055f94cd03fe7cf80f7d875d53ebe0d8de7837ce99fa7
+  faq_generated_at: '2026-06-03T01:24:38Z'
+  faq_source_hash: 9610291239b88c67e21055f94cd03fe7cf80f7d875d53ebe0d8de7837ce99fa7
+  summary: >-
+    This Learning Path introduces Large System Extensions (LSE) on Arm processors and shows how
+    to check whether your application and toolchain use LSE for atomic operations. You will build
+    and run a short C example on a Linux system to observe multi-threaded atomic increments and
+    verify if the compiler emits LSE instructions. The path is introductory and relevant to developers
+    targeting Arm servers based on Neoverse, using AWS Graviton instances or another Arm Linux
+    machine. You will use GCC and follow a runbook to create the sample, compile it, and assess
+    LSE usage. No additional prerequisites are explicitly listed beyond access to an Arm Linux
+    environment; an AWS account is suggested for convenient access to Arm instances.
+  faqs:
+  - question: What do I need before running the example?
+    answer: >-
+      You need access to an Arm Linux computer. An AWS account is recommended to use instance
+      types with different AWS Graviton processors, but you can substitute other Arm Linux systems
+      if you prefer.
+  - question: Which compiler should I use to build the example program?
+    answer: >-
+      Use GCC on your Arm Linux computer. The Learning Path uses GCC to build the C example that
+      exercises atomic operations.
+  - question: How do I know if my build is using Large System Extensions?
+    answer: >-
+      The steps guide you to build and run an example and then verify whether the compiler generated
+      LSE instructions. Follow the verification instructions in the path to confirm LSE usage.
+  - question: Can I complete this Learning Path without an AWS account?
+    answer: >-
+      Yes. If you do not have an AWS account, you can use any other Arm Linux computer as a substitute.
+  - question: What result should I expect after running the example program?
+    answer: >-
+      You will compile and run a multithreaded C program that uses atomic operations. The expected
+      outcome is that you can determine whether LSE instructions were generated for the example.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md b/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
index 9064547bf2..14d3976fa6 100644
--- a/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
@@ -22,6 +22,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:25:42Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: db4ce1c59ad10bd127b4990c82ce876311d7dc7e1ad5d39ec132daf82ceb8b79
+  summary_generated_at: '2026-06-02T04:19:21Z'
+  summary_source_hash: db4ce1c59ad10bd127b4990c82ce876311d7dc7e1ad5d39ec132daf82ceb8b79
+  faq_generated_at: '2026-06-03T01:25:42Z'
+  faq_source_hash: db4ce1c59ad10bd127b4990c82ce876311d7dc7e1ad5d39ec132daf82ceb8b79
+  summary: >-
+    Learn how to deploy MariaDB on Arm-based cloud infrastructure across AWS, Microsoft Azure,
+    and Google Cloud using Terraform, Ansible, Docker, and Amazon RDS. You will provision single
+    virtual machines on each provider with automation, deploy MariaDB in a Docker container using
+    Ansible, and create a managed MariaDB database with Amazon RDS via Terraform. The steps target
+    Linux hosts and assume cloud accounts for the services you plan to use. Work from a local
+    computer with Docker, Terraform, the AWS/Azure/Google Cloud CLIs, and Ansible installed. This
+    introductory path takes about 90 minutes and results in MariaDB running on Arm servers and
+    as a managed RDS service.
+  faqs:
+  - question: What do I need installed locally before starting?
+    answer: >-
+      Install Docker, Terraform, AWS CLI, Azure CLI, Google Cloud CLI, and Ansible on your local
+      computer. You also need cloud accounts for the services you plan to use.
+  - question: Can I follow only the sections for the cloud provider I use?
+    answer: >-
+      Yes. The Learning Path includes AWS, Azure, and GCP; complete the sections that match the
+      accounts and services you have available.
+  - question: Which tools does each deployment method use?
+    answer: >-
+      EC2, Azure, and GCP VM deployments use Terraform and Ansible to provision a single Arm-based
+      instance and install MariaDB. The Amazon RDS section uses Terraform. The Docker section
+      uses Ansible to deploy a MariaDB container.
+  - question: What additional setup is required for the Docker-based deployment?
+    answer: >-
+      You need a cloud instance, VM, or physical machine with Ubuntu installed, running, and ready
+      to deploy MariaDB. Ansible must be installed locally, and you can reuse the same SSH key
+      pair.
+  - question: What credentials are required for the Amazon RDS section?
+    answer: >-
+      An AWS account is required along with an AWS access key ID and secret access key. You also
+      need Terraform and the AWS CLI installed on the computer you use to run the steps.
+# END generated_summary_faq
 
 author: Jason Andrews
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/memcached/_index.md b/content/learning-paths/servers-and-cloud-computing/memcached/_index.md
index 8cc2d7423f..5c7032072e 100644
--- a/content/learning-paths/servers-and-cloud-computing/memcached/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memcached/_index.md
@@ -19,6 +19,51 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:26:50Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: b42ca5cc8f4db6ba6019f3720a5ef46119aa403a2baaef5362e874f898ce0419
+  summary_generated_at: '2026-06-02T04:20:04Z'
+  summary_source_hash: b42ca5cc8f4db6ba6019f3720a5ef46119aa403a2baaef5362e874f898ce0419
+  faq_generated_at: '2026-06-03T01:26:50Z'
+  faq_source_hash: b42ca5cc8f4db6ba6019f3720a5ef46119aa403a2baaef5362e874f898ce0419
+  summary: >-
+    This introductory Learning Path shows how to install and run Memcached on an Arm-based Ubuntu
+    Linux cloud instance and measure its performance with the open-source memtier_benchmark tool.
+    You will provision an Arm server (tested on AWS and Oracle Cloud), install gcc and required
+    development libraries such as libevent, and set up the packages needed to build and run the
+    benchmark. By the end, you will have Memcached running and will execute a benchmark workload
+    to generate performance results for your environment. Prerequisite: access to an Arm-based
+    instance from a cloud service provider; no other explicit prerequisites are listed.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need access to an Arm-based instance from a cloud service provider running Ubuntu Linux.
+      Install gcc on the instance by following the GNU compiler install guide.
+  - question: Which cloud platforms are referenced in this Learning Path?
+    answer: >-
+      The steps have been tested on AWS and Oracle Cloud. Any appropriate Arm-based cloud instance
+      running Ubuntu Linux is suitable.
+  - question: Which packages should I install to prepare for memcached and the benchmark?
+    answer: >-
+      Install libevent-dev for memcached. For memtier_benchmark, install build-essential, autoconf,
+      automake, libpcre3-dev, libevent-dev, pkg-config, zlib1g-dev, libssl-dev, wget, and git.
+  - question: Which benchmark tool is used to measure memcached performance?
+    answer: >-
+      The path uses the open-source memtier_benchmark. You install its required build and runtime
+      dependencies and then run it against your memcached service.
+  - question: How do I know the setup worked?
+    answer: >-
+      After starting memcached, run memtier_benchmark; a successful connection and benchmark output
+      indicate the service is running and being exercised. The benchmark will report performance
+      metrics you can review.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md b/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
index e55e548486..460314a406 100644
--- a/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
@@ -23,6 +23,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:27:30Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 4efe46aaf4580a6b8f263b69e8f072211bfd24fcf7f7a885689c927fc05a4c63
+  summary_generated_at: '2026-06-02T04:20:49Z'
+  summary_source_hash: 4efe46aaf4580a6b8f263b69e8f072211bfd24fcf7f7a885689c927fc05a4c63
+  faq_generated_at: '2026-06-03T01:27:30Z'
+  faq_source_hash: 4efe46aaf4580a6b8f263b69e8f072211bfd24fcf7f7a885689c927fc05a4c63
+  summary: >-
+    Learn how to deploy Memcached as a cache for MySQL and PostgreSQL on Arm-based cloud instances
+    using Terraform and Ansible. You will provision Linux instances on AWS, Microsoft Azure, and
+    Google Cloud, then install and configure Memcached to serve as a cache layer for your database
+    workload. The path includes sections for MySQL on AWS, Azure, and GCP, and for PostgreSQL
+    on AWS and Azure; PostgreSQL on GCP is not explicitly listed in the provided steps. No explicit
+    prior Terraform knowledge is required, but related automation guides are referenced. Prerequisites
+    include cloud accounts and a machine with Terraform, AWS CLI, Google Cloud CLI, Azure CLI,
+    AWS IAM authenticator, and Ansible installed. Estimated time to complete is 60 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need AWS, Azure, and Google Cloud accounts, and a machine with Terraform, AWS CLI, Google
+      Cloud CLI, Azure CLI, AWS IAM authenticator, and Ansible installed. These tools are required
+      to provision infrastructure and configure Memcached.
+  - question: Which database and cloud combinations are covered in the sections?
+    answer: >-
+      MySQL on AWS, Azure, and Google Cloud Arm-based instances, and PostgreSQL on AWS and Azure.
+      PostgreSQL on Google Cloud is listed in the objectives but is not explicitly shown in the
+      provided section excerpts.
+  - question: Where do I run Terraform and Ansible from?
+    answer: >-
+      From any computer that has the required tools installed; a desktop or laptop is suitable.
+      The deployed target instances run Linux.
+  - question: I'm new to Terraform—what should I read first?
+    answer: >-
+      Each cloud-specific section recommends reviewing the corresponding “Automate [cloud] instance
+      creation using Terraform” guide before you start. Use the AWS, Azure, or GCP guide referenced
+      by the section you plan to follow.
+  - question: What result should I expect after completing a section?
+    answer: >-
+      A running Arm-based cloud instance with Memcached configured to act as a cache for the chosen
+      database (MySQL or PostgreSQL). The deployment is performed with Terraform and configured
+      with Ansible as described in the section.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md b/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
index 1f6c58e824..7aa8ce979b 100644
--- a/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
@@ -24,6 +24,58 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:28:09Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: d61c9ddcef1c7ffe12b3ee818852fb3f0a62a90cec451692c1186cf2c01de625
+  summary_generated_at: '2026-06-02T04:21:33Z'
+  summary_source_hash: d61c9ddcef1c7ffe12b3ee818852fb3f0a62a90cec451692c1186cf2c01de625
+  faq_generated_at: '2026-06-03T01:28:09Z'
+  faq_source_hash: d61c9ddcef1c7ffe12b3ee818852fb3f0a62a90cec451692c1186cf2c01de625
+  summary: >-
+    This advanced Learning Path shows how to characterize the CPU-side memory subsystem on Arm
+    Neoverse-based Linux systems using the Arm System Characterization Tool (ASCT). You will identify
+    CPU topology, cache hierarchy, and NUMA layout, then measure cache and memory latency with
+    a pointer-chase benchmark. You will also measure single-core and multi-core streaming bandwidth
+    across L1, L2, last-level cache, and DRAM, evaluate latency under bandwidth pressure, and
+    examine coherency latency. Examples use AWS Graviton2 and Graviton4, but any two or more Arm
+    Linux systems with ASCT installed and root or sudo access can be used. By the end, you can
+    compare results across Arm systems and draw practical conclusions.
+  faqs:
+  - question: What do I need before running these tests?
+    answer: >-
+      You need two or more Arm Linux systems with root or sudo access, and ASCT installed on each
+      system. The examples use AWS Graviton2 and Graviton4, but other Arm systems are possible.
+      A good understanding of cache hierarchies and DRAM is assumed.
+  - question: How do I identify core, cache, and NUMA topology on my system?
+    answer: >-
+      Use standard Linux tools to determine the CPU topology, cache hierarchy, and NUMA configuration
+      before testing. This context helps you interpret where cache-level transitions and bandwidth
+      limits should appear in the results.
+  - question: Which ASCT benchmarks should I run to measure latency and bandwidth?
+    answer: >-
+      Run the pointer-chase benchmark to measure dependent-load latency at each level of the memory
+      hierarchy. Use the single-core bandwidth sweep to measure per-core streaming bandwidth,
+      then run the multi-core peak-bandwidth and loaded-latency benchmarks to characterize scaling
+      and contention.
+  - question: How do I know the latency and bandwidth measurements are reasonable?
+    answer: >-
+      Expect step-like increases in latency as working sets exceed L1, then L2, then LLC and fall
+      into DRAM, and look for bandwidth plateaus consistent with each level. Pointer chasing defeats
+      hardware prefetching and out-of-order execution, so the latency curves should reflect true
+      dependent-load behavior.
+  - question: How should I compare results across systems like Graviton2 and Graviton4?
+    answer: >-
+      Run the same ASCT benchmarks under similar conditions on each system, then compare latency
+      curves, bandwidth sweeps, and the points where scaling saturates. Use these comparisons
+      to draw conclusions about cache hierarchy behavior and shared-resource limits across generations.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md b/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
index 9576eac1b1..fb24945d52 100644
--- a/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
@@ -24,6 +24,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:28:34Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 6aa61de638be961339d958345225d696ff2b83b8f9cab22bf956f9bf2d15c1aa
+  summary_generated_at: '2026-06-02T04:22:41Z'
+  summary_source_hash: 6aa61de638be961339d958345225d696ff2b83b8f9cab22bf956f9bf2d15c1aa
+  faq_generated_at: '2026-06-03T01:28:34Z'
+  faq_source_hash: 6aa61de638be961339d958345225d696ff2b83b8f9cab22bf956f9bf2d15c1aa
+  summary: >-
+    This advanced Learning Path guides you through testing and validating thread synchronization
+    in the Arm memory model on Linux using Herd7, Litmus7, and Arm hardware. You will create and
+    run litmus tests, including an abbreviated MP.litmus example, to compare formal model predictions
+    against observed hardware behavior. The exercises focus on Arm ISA acquire-release semantics
+    with LDAR and STLR, and show how to compare results from different synchronization approaches.
+    The path is intended for developers with knowledge of memory consistency models, thread synchronization,
+    Arm assembly, general-purpose registers, and memory barriers (including acquire-release semantics).
+    No additional prerequisites are explicitly listed beyond these skills.
+  faqs:
+  - question: Do I need access to Arm hardware, and what operating system is used?
+    answer: >-
+      Yes, testing on Arm hardware is part of the Learning Path. The target operating system is
+      Linux, and no specific hardware platform or distribution is explicitly listed.
+  - question: Which tools should I use for modeling versus running on hardware?
+    answer: >-
+      Use Herd7 to test snippets against the formal definition of the Arm memory model, and Litmus7
+      to run litmus tests on Arm hardware. A Runbook structures the steps; diy7 is referenced
+      only in additional resources.
+  - question: How do I start with a litmus test in this path?
+    answer: >-
+      Follow the Herd7 and Litmus7 primer to create the provided abbreviated MP.litmus example
+      as test.litmus. Run it with Herd7 to confirm the syntax is correct and to produce results
+      you can later compare with hardware runs.
+  - question: Which Arm synchronization instructions are covered in the examples?
+    answer: >-
+      The path focuses on acquire-release ordering using LDAR (load-acquire) and STLR (store-release).
+      Other atomic instructions like CAS, SWP, LDADD, and STADD are mentioned but are outside
+      the scope of this Learning Path.
+  - question: What results should I expect to compare when I finish?
+    answer: >-
+      You will compare the observed outcomes of different thread synchronization approaches between
+      the formal model (Herd7) and runs on Arm hardware (Litmus7). Specific expected result values
+      are not listed in the context.
+# END generated_summary_faq
 
 author: Julio Suarez
 
diff --git a/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md b/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
index 860f72ed71..6fa4fd1775 100644
--- a/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
@@ -19,6 +19,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:29:10Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: a67d36fb650b77c170c15f193049490286a3f097801d0c79d701d3f5610fe1dc
+  summary_generated_at: '2026-06-02T04:23:38Z'
+  summary_source_hash: a67d36fb650b77c170c15f193049490286a3f097801d0c79d701d3f5610fe1dc
+  faq_generated_at: '2026-06-03T01:29:10Z'
+  faq_source_hash: a67d36fb650b77c170c15f193049490286a3f097801d0c79d701d3f5610fe1dc
+  summary: >-
+    Learn to microbenchmark and tune network performance on Arm-based Linux systems using iPerf3
+    and Linux traffic control (tc). You will provision two Arm-based instances—such as AWS EC2
+    with Graviton within a VPC or equivalent Arm-based VMs from other cloud providers—and run
+    TCP/UDP tests in cloud-to-cloud and local-to-cloud scenarios. The steps show how to start
+    iPerf3, simulate latency and packet loss with tc, and adjust basic Linux kernel parameters,
+    then compare results across environments. This introductory path assumes a basic understanding
+    of TCP/IP and UDP and access to two Arm-based cloud instances. By the end, you can run accurate
+    iPerf3 tests, model adverse network conditions, and apply simple tunables to evaluate behavior.
+  faqs:
+  - question: What do I need before running the tests?
+    answer: >-
+      You need two Arm-based Linux cloud instances and a basic understanding of TCP/IP and UDP.
+      Ensure the systems can reach each other over the network, and if you use AWS, the setup
+      follows EC2 instances within a VPC.
+  - question: How do I start the iPerf3 server and confirm it’s ready?
+    answer: >-
+      Run iperf3 -s on the server node. You should see “Server listening on 5201” by default;
+      if that port is in use, start the server with -p to select another port.
+  - question: Can I use a cloud provider other than AWS for this Learning Path?
+    answer: >-
+      Yes. While the setup examples use AWS EC2 with Graviton, you can use Linux virtual machines
+      from other cloud service providers.
+  - question: How do I simulate latency or packet loss with tc and which interface should I modify?
+    answer: >-
+      First, identify the network interface on the client system using ip addr show. Apply tc
+      rules (such as delay or loss) to that interface to simulate different network conditions.
+  - question: What should I check if a local-to-cloud test cannot connect?
+    answer: >-
+      Update the cloud server’s security group to allow incoming TCP connections from your local
+      machine. Also ensure iPerf3 is installed on the local system as described in the iPerf3
+      installation guide.
+# END generated_summary_faq
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md b/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
index 7652fe328c..cec0d389c1 100644
--- a/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
@@ -21,6 +21,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:29:38Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 56a38d1d742dd301d48e9ad61ff00e07048559f3f646815e22937113243adcdb
+  summary_generated_at: '2026-06-02T04:24:02Z'
+  summary_source_hash: 56a38d1d742dd301d48e9ad61ff00e07048559f3f646815e22937113243adcdb
+  faq_generated_at: '2026-06-03T01:29:38Z'
+  faq_source_hash: 56a38d1d742dd301d48e9ad61ff00e07048559f3f646815e22937113243adcdb
+  summary: >-
+    Use migrate-ease to scan your source code for architecture-specific issues before migrating
+    applications to Arm-based servers. This introductory, Linux-focused path shows how to set
+    up dependencies, clone the migrate-ease repository, and run a code scan that targets AArch64,
+    including an example that analyzes Protobuf v2.5.0 and writes a JSON report. Migrate-ease
+    is a read-only tool designed to examine x86_64-oriented code and suggest changes for AArch64
+    on Linux, and it runs on either x86_64 or Arm AArch64 machines. The path emphasizes identifying
+    architecture-dependent code and common migration challenges. An Arm-based instance is required
+    for testing and validation. Estimated completion time is about 45 minutes.
+  faqs:
+  - question: What do I need before running migrate-ease?
+    answer: >-
+      You need access to an Arm-based instance for testing and validation. You also need a Linux
+      machine (x86_64 or Arm AArch64) to run the tool, with Git and Python 3 available as shown
+      in the setup steps.
+  - question: Can I run migrate-ease on x86_64, or do I need an Arm machine?
+    answer: >-
+      You can run migrate-ease on either x86_64 or Arm AArch64 Linux systems. The tool targets
+      migration to AArch64 but does not require Arm hardware to perform the analysis.
+  - question: Which packages should I install on my distro before cloning the tool?
+    answer: >-
+      On Ubuntu 22.04 or Debian 13: python3, python3-pip, python3-venv, unzip, libmagic1, and
+      git. On Fedora 42: python3, python3-pip, unzip, and git.
+  - question: Which command does the path use to scan the Protobuf v2.5.0 source and write a report?
+    answer: >-
+      Run: python3 -m cpp --git-repo https://github.com/protocolbuffers/protobuf.git --branch
+      v2.5.0 --output result.json --march armv8-a protobuf. This analyzes the specified branch
+      and writes findings to a JSON file.
+  - question: What result should I expect, and how do I verify it?
+    answer: >-
+      Expect a JSON report named result.json containing AArch64-related findings. Verify the file
+      was created and populated; migrate-ease is read-only and will not modify your source code.
+# END generated_summary_faq
 
 author: 
     - Odin Shen
diff --git a/content/learning-paths/servers-and-cloud-computing/migration/_index.md b/content/learning-paths/servers-and-cloud-computing/migration/_index.md
index 27fa79e2b7..ca8ab8960a 100644
--- a/content/learning-paths/servers-and-cloud-computing/migration/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/migration/_index.md
@@ -21,6 +21,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:30:21Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 6b2b985c39579c4d0855c8c28b610ba558e25b451a6b755475ff4393e2810670
+  summary_generated_at: '2026-06-02T04:24:49Z'
+  summary_source_hash: 6b2b985c39579c4d0855c8c28b610ba558e25b451a6b755475ff4393e2810670
+  faq_generated_at: '2026-06-03T01:30:21Z'
+  faq_source_hash: 6b2b985c39579c4d0855c8c28b610ba558e25b451a6b755475ff4393e2810670
+  summary: >-
+    Learn the essentials of migrating applications to Arm servers on Linux. This introductory
+    path guides you to set up an Arm-based development machine (typically a cloud instance), analyze
+    application dependencies, and review common migration challenges and scenarios. It provides
+    practical, language-specific guidance for C/C++ on Arm Neoverse with current compilers, Java
+    on Arm (including areas to investigate for JVM performance), and Go (with emphasis on using
+    recent releases). You also learn where to check third‑party software support using the Software
+    Ecosystem Dashboard for Arm and the AWS Graviton Technical Guide. The only explicit prerequisite
+    is access to an Arm-based instance from a cloud service provider.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an Arm-based instance from a cloud service provider running Linux. A Linux Arm
+      development machine can also be set up using a virtual machine such as Multipass.
+  - question: Which C/C++ compiler versions should I use on Arm Neoverse?
+    answer: >-
+      Use the latest GCC or Clang/LLVM available for your Linux distribution. If a newer version
+      is available beyond the distribution default, install that newer version.
+  - question: How should I install Java on Arm Linux, and are there JVM options to consider?
+    answer: >-
+      There are several ways to install Java on Arm Linux; refer to the Java install guide linked
+      from the path. Java runs well on Arm, and you should review which JVM flags impact performance.
+  - question: Which Go version should I install for Arm servers?
+    answer: >-
+      Install the latest Go compiler and toolchain. Go 1.18 introduced a significant performance
+      improvement, so staying current is recommended; refer to Go releases and the Go install
+      guide.
+  - question: Where can I check if my application’s dependencies or ISV software support Arm?
+    answer: >-
+      Use the Software Ecosystem Dashboard for Arm to review supported software. The AWS Graviton
+      Technical Guide also lists ISV products with Arm support, and both resources accept GitHub
+      issues for feedback.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
index 1648531eeb..98b29abdf0 100644
--- a/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
@@ -21,6 +21,51 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:30:54Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 2eb252b19b7535fbb776a3153bfc9f70b6217088e5b4b55deb56d01393abaf3b
+  summary_generated_at: '2026-06-02T04:25:25Z'
+  summary_source_hash: 2eb252b19b7535fbb776a3153bfc9f70b6217088e5b4b55deb56d01393abaf3b
+  faq_generated_at: '2026-06-03T01:30:54Z'
+  faq_source_hash: 2eb252b19b7535fbb776a3153bfc9f70b6217088e5b4b55deb56d01393abaf3b
+  summary: >-
+    Build a Retrieval-Augmented Generation application on Arm-based servers using Zilliz Cloud
+    for vector search and llama.cpp for LLM inference. You will create a Dedicated Zilliz Cloud
+    cluster on AWS using Arm-based machines, then build and run a local llama.cpp server that
+    exposes an OpenAI-compatible API with the Llama‑3.1‑8B model. In Python, prepare embeddings
+    and call the local LLM to perform an online RAG query, validating by printing the embedding
+    dimension and sample values. Prerequisites: basic RAG knowledge, access to an Arm-based instance
+    (for example, an AWS Graviton3 C7g.2xlarge or other Arm server), a Zilliz account, and Linux.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a basic understanding of a RAG pipeline, access to an Arm-based server (for example,
+      an AWS Graviton3 C7g.2xlarge or any Arm-based instance from a cloud provider or on-prem),
+      and a Zilliz Cloud account. The environment is Linux.
+  - question: Which Zilliz Cloud cluster should I create for this path?
+    answer: >-
+      Create a Dedicated cluster deployed in AWS using Arm-based machines. You can alternatively
+      use self-hosted Milvus, but this is more complicated to set up.
+  - question: Do I need to request access to the Llama 3.1 model before launching llama.cpp?
+    answer: >-
+      Yes. Before using the Llama 3.1-8B model, visit the Llama website and fill in the form to
+      request access.
+  - question: Do I need an OpenAI API key when using the OpenAI SDK with the local llama.cpp server?
+    answer: >-
+      No. Because the LLM service is running locally via llama.cpp, you do not need to provide
+      an API key.
+  - question: What output should I see when I test the embedding model in the Python script?
+    answer: >-
+      The script prints the embedding dimension and the first few elements. The example output
+      shows 384 followed by several floating-point values.
+# END generated_summary_faq
 
 author: Chen Zhang
 
diff --git a/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
index 3f80c54e9d..26eb6776c2 100644
--- a/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
@@ -23,6 +23,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:31:21Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 6c438573edec90b3aa4135ace38cd3ae604c58658c1949117ca80dbdd21394bd
+  summary_generated_at: '2026-06-02T04:26:22Z'
+  summary_source_hash: 6c438573edec90b3aa4135ace38cd3ae604c58658c1949117ca80dbdd21394bd
+  faq_generated_at: '2026-06-03T01:31:21Z'
+  faq_source_hash: 6c438573edec90b3aa4135ace38cd3ae604c58658c1949117ca80dbdd21394bd
+  summary: >-
+    This Learning Path shows how to deploy a single-node, S3-compatible MinIO server on an Arm-based
+    Azure Cobalt 100 virtual machine and verify it end to end. You will provision a Dpsv6 instance
+    (Ubuntu 24.04), open MinIO ports 9000 and 9001 in the Azure Network Security Group, connect
+    over SSH, and install and configure MinIO. You will generate a 1 GB test dataset, benchmark
+    large-object upload throughput using MinIO tooling, and validate S3 API compatibility with
+    the boto3 Python SDK. The path is introductory, takes about 30 minutes, and is intended for
+    developers and platform/DevOps engineers with an Azure account, SSH familiarity, and basic
+    cloud storage knowledge.
+  faqs:
+  - question: Which provisioning method, VM size, and OS are used in this path?
+    answer: >-
+      The steps use the Azure Portal to create an Arm-based Azure Cobalt 100 virtual machine from
+      the Dpsv6 series. The architecture shows Ubuntu 24.04 as the OS for the VM. Other provisioning
+      methods exist, but this path focuses on the Azure Portal workflow.
+  - question: Which network ports must I open for MinIO, and where do I configure them?
+    answer: >-
+      Open TCP ports 9000 and 9001 in the Azure Network Security Group (NSG) attached to the VM’s
+      network interface or subnet. You configure these rules in the Azure Portal under your VM’s
+      Networking settings.
+  - question: How do I connect to the Azure Cobalt 100 VM?
+    answer: >-
+      Use SSH with the private key you downloaded and the VM’s public IP address. For example:
+      ssh -i <your-key>.pem azureuser@<VM-IP>.
+  - question: How do I run the throughput benchmark and what result should I expect to see?
+    answer: >-
+      Create a 1 GB dataset with dd, then measure upload time using time mc cp --recursive dataset
+      local/ as shown. dd prints record counts and the total bytes written; time reports the command’s
+      duration. The path does not specify expected performance values.
+  - question: How is S3 API compatibility validated in this path?
+    answer: >-
+      You use the Python boto3 SDK to interact with MinIO and confirm S3 compatibility. Ensure
+      Python and boto3 are available, then run simple bucket and object operations as directed
+      in the steps.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md b/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
index fc9acec4e4..dc778d7039 100644
--- a/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
@@ -20,6 +20,51 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:31:54Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 973ba6c1e00fc67e1702606412124d8172e071b9b677a1df53c3be66210bf704
+  summary_generated_at: '2026-06-02T04:26:55Z'
+  summary_source_hash: 973ba6c1e00fc67e1702606412124d8172e071b9b677a1df53c3be66210bf704
+  faq_generated_at: '2026-06-03T01:31:54Z'
+  faq_source_hash: 973ba6c1e00fc67e1702606412124d8172e071b9b677a1df53c3be66210bf704
+  summary: >-
+    Set up an Arm-based Linux server and benchmark machine learning inference using TensorFlow
+    and the MLPerf Inference benchmark suite from MLCommons. You will launch an Arm instance running
+    Ubuntu 20.04 or 22.04, install required system and Python packages, then configure and run
+    TensorFlow with the MLPerf Inference suite to measure performance. This introductory path
+    has been tested on AWS and Oracle cloud platforms and also applies to on-premise Arm servers.
+    The only explicit prerequisite is access to an Arm-based instance. By the end, you will have
+    executed MLPerf Inference on your Arm server and obtained benchmark results in about 20 minutes.
+  faqs:
+  - question: What do I need before running the benchmarks?
+    answer: >-
+      You need an Arm-based instance from a cloud service provider or an on-premise Arm server
+      running Ubuntu 20.04 or Ubuntu 22.04. This path has been tested on AWS and Oracle. No other
+      explicit prerequisites are listed.
+  - question: Which Ubuntu version should I choose for this path?
+    answer: >-
+      Use Ubuntu 20.04 or Ubuntu 22.04, as shown in the steps. Both were tested on AWS and Oracle.
+  - question: Which packages do I install to prepare the environment?
+    answer: >-
+      Update apt and install build-essential, python3-pip, and git, then use pip to install the
+      Python packages listed in the steps (for example, opencv-python-headless and Cython). Follow
+      the exact apt-get and pip commands provided.
+  - question: How are TensorFlow and MLPerf Inference used here?
+    answer: >-
+      You will install and run TensorFlow on your Arm server, then use the MLPerf Inference benchmark
+      suite from MLCommons to test ML inference performance.
+  - question: How long will this take and what result should I expect?
+    answer: >-
+      The path is estimated to take about 20 minutes. On completion, you will have run TensorFlow
+      and executed the MLPerf Inference suite to produce benchmark output on your Arm server.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
index 47c9460de6..e148cbdae3 100644
--- a/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
@@ -21,6 +21,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:33:04Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: f270cfc90245c0090685fabf5cbebf62a714edbf34e1ea86c3df0bfbb4699ea4
+  summary_generated_at: '2026-06-02T04:28:12Z'
+  summary_source_hash: f270cfc90245c0090685fabf5cbebf62a714edbf34e1ea86c3df0bfbb4699ea4
+  faq_generated_at: '2026-06-03T01:33:04Z'
+  faq_source_hash: f270cfc90245c0090685fabf5cbebf62a714edbf34e1ea86c3df0bfbb4699ea4
+  summary: >-
+    This Learning Path shows how to run MongoDB on Arm-based Microsoft Azure Cobalt 100 virtual
+    machines. You will provision a Dpsv6 instance using the Azure console with Ubuntu Pro 24.04
+    LTS (Arm64), install MongoDB and mongosh, and validate the deployment. The steps include baseline
+    checks such as service health, a quick storage test with fio, and CRUD verification, followed
+    by monitoring database activity with mongotop and using mongostat for additional runtime metrics.
+    By the end, you will have a working MongoDB setup on Cobalt 100 and initial observations from
+    light benchmarking on Arm64. Prerequisites are an Azure account with access to Cobalt 100
+    and familiarity with MongoDB architecture and Arm64 deployments.
+  faqs:
+  - question: What do I need before creating the Azure VM?
+    answer: >-
+      You need a Microsoft Azure account with access to Cobalt 100 (Dpsv6) instances. Familiarity
+      with MongoDB architecture and deployment practices on Arm64 platforms is also expected.
+  - question: Which Azure VM series and OS image should I select?
+    answer: >-
+      Use the Dpsv6 general-purpose series for the Arm-based Cobalt 100 processor. The path targets
+      Ubuntu Pro 24.04 LTS (Arm64).
+  - question: How do I verify that MongoDB was installed and is working?
+    answer: >-
+      Start mongod locally, check service health, and connect with mongosh to validate CRUD operations.
+      Run a quick storage baseline with fio and perform light query, index, and concurrency checks.
+  - question: How is access control handled during the exercises and how can I enable remote access
+      later?
+    answer: >-
+      For this exercise, access control is disabled by default and mongod should remain bound
+      to 127.0.0.1. To accept remote connections later, set --bind_ip (or bindIp in the config)
+      and enable authorization.
+  - question: How do I monitor MongoDB activity and what should be running first?
+    answer: >-
+      Use mongotop (and mongostat) to observe real-time activity, ensuring mongod is running locally
+      and that the long_system_load.js script is generating traffic. The path includes benchmark
+      results from Azure Arm64 VMs as a latency reference.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
index c8ff6a4c5c..8ede273c4e 100644
--- a/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
@@ -20,6 +20,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:33:34Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: abbdde22271151fb1485c54bafe463e7797a0b913c7d4c99245d2914a3f9bb82
+  summary_generated_at: '2026-06-02T04:28:40Z'
+  summary_source_hash: abbdde22271151fb1485c54bafe463e7797a0b913c7d4c99245d2914a3f9bb82
+  faq_generated_at: '2026-06-03T01:33:34Z'
+  faq_source_hash: abbdde22271151fb1485c54bafe463e7797a0b913c7d4c99245d2914a3f9bb82
+  summary: >-
+    Learn how to deploy MongoDB on Arm-based Google Axion C4A virtual machines and benchmark it
+    with the Yahoo Cloud Serving Benchmark (YCSB). You will create a c4a-standard-4 VM in Google
+    Cloud using the Console, install MongoDB and mongosh on Red Hat Enterprise Linux with Arm64
+    (aarch64) binaries for RHEL 9, and verify the server locally. Then you will build YCSB’s MongoDB
+    binding from source (Maven/Java 11), load a starter dataset, and run workloads to capture
+    a quick baseline and benchmark results. The C4A family uses Google’s Axion CPU based on Arm
+    Neoverse‑V2 cores. Prerequisite: a Google Cloud Platform account with billing enabled.
+  faqs:
+  - question: What do I need before creating the VM on Google Cloud?
+    answer: >-
+      You need a Google Cloud Platform (GCP) account with billing enabled. All setup and deployment
+      takes place in your GCP project.
+  - question: Which VM configuration does this path use for Axion C4A?
+    answer: >-
+      The steps create an Arm-based C4A VM using the c4a-standard-4 machine type (4 vCPUs, 16
+      GB memory). You create it in the Google Cloud Console under Compute Engine by selecting
+      the C4A series.
+  - question: Which operating system and MongoDB package are assumed?
+    answer: >-
+      The installation targets Red Hat Enterprise Linux on Arm. The steps fetch the Arm64 (aarch64)
+      MongoDB binaries for RHEL 9.3.
+  - question: How do I verify that MongoDB is running correctly?
+    answer: >-
+      Connect locally with mongosh using mongodb://127.0.0.1:27017. Create a test database and
+      collection, perform basic CRUD operations, and record a quick insert-time baseline.
+  - question: How do I install and run YCSB for MongoDB, and what data size is loaded initially?
+    answer: >-
+      Install git, Maven, and Java 11, clone the YCSB repository, and build the MongoDB binding
+      with Maven. Use YCSB to load the starter dataset, which defaults to 1,000 records, and then
+      run the workloads to benchmark MongoDB.
+# END generated_summary_faq
 
 author: Annie Tallund
 
diff --git a/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md b/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
index b9ed8f5283..76be244668 100644
--- a/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
@@ -6,6 +6,58 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:32:28Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: b52a1b60a74e19f2a3b60b8a77199ad5369c1ccd3b4d5ce0252a169d9f6123fd
+  summary_generated_at: '2026-06-02T04:27:23Z'
+  summary_source_hash: b52a1b60a74e19f2a3b60b8a77199ad5369c1ccd3b4d5ce0252a169d9f6123fd
+  faq_generated_at: '2026-06-03T01:32:28Z'
+  faq_source_hash: b52a1b60a74e19f2a3b60b8a77199ad5369c1ccd3b4d5ce0252a169d9f6123fd
+  summary: >-
+    Learn how to install MongoDB Community Edition 8.0 on Arm-based Linux servers and evaluate
+    database performance using Yahoo Cloud Serving Benchmark (YCSB). You will provision an Arm
+    instance from a cloud provider (such as AWS, Microsoft Azure, Google Cloud, or Oracle) and
+    deploy MongoDB on supported distributions including Ubuntu 20.04/22.04/24.04, RHEL/CentOS
+    8/9, and Amazon Linux 2023. The path covers configuring a three-node MongoDB replica set,
+    installing supporting packages (Maven, Make, GCC), and running common YCSB workloads (95/5,
+    100/0, 90/10) with warm-up and load-tuning guidance. An alternative Java-based MongoDB performance
+    test tool using OpenJDK is also included. By the end, you can measure and compare MongoDB
+    performance on Arm in about 30 minutes.
+  faqs:
+  - question: Which Linux distributions are supported for installing MongoDB Community Edition
+      8.0 in this path?
+    answer: >-
+      Ubuntu 20.04, 22.04, and 24.04; RHEL/CentOS 8 and 9; and Amazon Linux 2023 are listed as
+      supported. Refer to the Platform Support Matrix for additional details.
+  - question: How should I structure the MongoDB environment for testing with YCSB?
+    answer: >-
+      Use two parts: one instance running YCSB and one or more instances running MongoDB. The
+      recommended setup is a three-node replica set of equal-sized nodes, with one primary (the
+      target for test traffic) and two secondary nodes.
+  - question: What additional packages are required to run YCSB, and how do I install them on
+      Ubuntu?
+    answer: >-
+      Additional software packages are required for YCSB. On Ubuntu, install maven, make, and
+      gcc using: sudo apt install -y maven make gcc.
+  - question: Which YCSB workloads should I run, for how long, and how do I know the system is
+      exercised enough?
+    answer: >-
+      Common workloads are 95/5, 100/0, and 90/10, with 95/5 recommended for real-world testing.
+      After loading the dataset, run the test for about five minutes to warm up, then target high
+      CPU utilization (90%+) by adjusting threads, operationscount, and recordscount.
+  - question: Is there an alternative to YCSB for testing MongoDB performance in this path?
+    answer: >-
+      Yes. The MongoDB performance test tool is an open source Java application that measures
+      latency and throughput across operations like Inserts, Updates, Deletes, Counts, and Finds.
+      To use it, install the appropriate OpenJDK run-time environment.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/mpi/_index.md b/content/learning-paths/servers-and-cloud-computing/mpi/_index.md
index 0cb38de347..55b5c6f186 100644
--- a/content/learning-paths/servers-and-cloud-computing/mpi/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mpi/_index.md
@@ -22,6 +22,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:34:00Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 300794f4010658d945212c74c5257635d620cbb6230cac0de92bf23e4e9e29fe
+  summary_generated_at: '2026-06-02T04:29:07Z'
+  summary_source_hash: 300794f4010658d945212c74c5257635d620cbb6230cac0de92bf23e4e9e29fe
+  faq_generated_at: '2026-06-03T01:34:00Z'
+  faq_source_hash: 300794f4010658d945212c74c5257635d620cbb6230cac0de92bf23e4e9e29fe
+  summary: >-
+    This advanced Learning Path is for HPC developers building MPI applications on Arm-based Linux
+    servers or cloud instances. You will install and validate Linaro Forge, then build, debug,
+    and profile a parallel matrix multiplication example implemented in C, Fortran, and Python.
+    The steps show how to compile with -O0 -g -fsanitize=address to expose bugs and memory issues,
+    use gdb and Forge for debugging, and compare profiling results across compiler options and
+    alternative libraries, including Arm Performance Libraries for common math routines. The path
+    was tested on Ubuntu 20.04 and assumes general MPI knowledge plus some familiarity with C,
+    Python, and Linux commands. Cloud instances from AWS, Microsoft Azure, Google Cloud, or Oracle
+    may be used.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an Arm computer running Linux; cloud instances from AWS, Microsoft Azure, Google
+      Cloud, or Oracle can be used. General MPI knowledge and some familiarity with C, Python,
+      and Linux commands are expected. The instructions are tested on Ubuntu 20.04; other distributions
+      may require adjustments.
+  - question: How do I verify that Linaro Forge installed correctly?
+    answer: >-
+      Run ddt --version. If the command is not found or does not report a version, revisit the
+      Linaro Forge install guide and confirm your PATH and environment are set.
+  - question: Where is the example application and which languages are available?
+    answer: >-
+      The parallel matrix multiplication application is in the src directory. Implementations
+      are provided in C, Fortran, and Python, and each contains a bug that must be fixed.
+  - question: Which build flags should I use for debugging and where do I set them?
+    answer: >-
+      Edit make.def in the src directory and set CFLAGS = -O0 -g -fsanitize=address. This disables
+      compiler optimizations, adds debug symbols, and enables AddressSanitizer to help find memory
+      issues.
+  - question: How should I approach profiling and comparing alternatives?
+    answer: >-
+      Profile a baseline build with -O0, then enable compiler optimizations and compare results.
+      You can also try alternative coding approaches and libraries implementing equivalent functions,
+      including Arm Performance Libraries for common math routines.
+# END generated_summary_faq
 
 author: Florent Lebeau
 
diff --git a/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md b/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md
index 745876fb86..7afbde272b 100644
--- a/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md
@@ -8,6 +8,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:34:28Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: a10683fdd14359e93056dc4ba24581856833765c64915979d0466a06887e0755
+  summary_generated_at: '2026-06-02T04:29:42Z'
+  summary_source_hash: a10683fdd14359e93056dc4ba24581856833765c64915979d0466a06887e0755
+  faq_generated_at: '2026-06-03T01:34:28Z'
+  faq_source_hash: a10683fdd14359e93056dc4ba24581856833765c64915979d0466a06887e0755
+  summary: >-
+    Learn how to control floating-point accuracy for vectorized math functions in Libamath, a
+    component of Arm Performance Libraries, on Linux. This path introduces IEEE-754 representation,
+    Units in the Last Place (ULP), and the ULP error metric used to assess function accuracy.
+    You will see how Libamath offers multiple accuracy modes, how to recognize them by function-name
+    suffixes (for example, _u10 for results within 1 ULP), and how to select a mode that balances
+    precision and speed for your workload. A concise C example invokes the Neon single-precision
+    exp function across modes and computes ULP error using a provided helper header. Prerequisite:
+    an Arm computer running Linux with Arm Performance Libraries 25.04 or newer installed.
+  faqs:
+  - question: What do I need before running the example code?
+    answer: >-
+      You need an Arm computer running Linux with Arm Performance Libraries version 25.04 or newer
+      installed. The path uses C code and Libamath, and assumes a typical GCC-based environment.
+  - question: How do I select a specific Libamath accuracy mode in my code?
+    answer: >-
+      Accuracy modes are encoded in the function symbol suffix. For example, a suffix of _u10
+      indicates a high-accuracy variant (≤ 1 ULP); other modes are exposed via their documented
+      suffixes when available.
+  - question: How is ULP error computed when checking results?
+    answer: >-
+      ULP error is defined as |want − got| divided by ULP(want). Because it scales with floating-point
+      spacing, it provides a more meaningful accuracy measure than absolute error across different
+      magnitudes.
+  - question: What files should I have to build the example?
+    answer: >-
+      Create example.c using the provided code and ensure ulp_error.h from the previous section
+      is available. The example includes amath.h and calls Libamath Neon single-precision exp
+      variants to compare accuracy.
+  - question: What should I check if the build fails with missing headers or vector types?
+    answer: >-
+      Verify Arm Performance Libraries 25.04+ is installed and accessible, and that you included
+      both amath.h and ulp_error.h. Build on an Arm Linux system; the example uses AArch64 vector
+      calling conventions and Neon vector types.
+# END generated_summary_faq
 
 author: Joana Cruz
 
diff --git a/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md b/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md
index 2d1ad5d25a..d9bdafc6fa 100644
--- a/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md
@@ -23,6 +23,57 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:35:04Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: d765afadfe5155f0ecd5bd08373fa12464b2ee5ebb1f8c7831039eb07eba8831
+  summary_generated_at: '2026-06-02T04:31:12Z'
+  summary_source_hash: d765afadfe5155f0ecd5bd08373fa12464b2ee5ebb1f8c7831039eb07eba8831
+  faq_generated_at: '2026-06-03T01:35:04Z'
+  faq_source_hash: d765afadfe5155f0ecd5bd08373fa12464b2ee5ebb1f8c7831039eb07eba8831
+  summary: >-
+    This Learning Path walks you through building a hybrid Azure Kubernetes Service (AKS) cluster
+    with both Arm-based and x86 node pools on Linux, then deploying nginx using a multi-architecture
+    image to each architecture. You authenticate with Azure CLI, create the cluster with two node
+    pools, and verify access. You also create a small utility script to streamline kubectl operations
+    and testing. The path adds a namespace and a shared ConfigMap with performance-optimized nginx
+    settings, then creates architecture-specific deployments and LoadBalancer services. You validate
+    that pods land on the intended nodes and use wrk to exercise traffic and compare behavior
+    across architectures. Plan about 60 minutes to complete. Prerequisites are an Azure account
+    and a local machine with jq, curl, wrk, Azure CLI, and kubectl installed.
+  faqs:
+  - question: What do I need before running the setup?
+    answer: >-
+      You need an Azure account and a local machine with jq, curl, wrk, Azure CLI, and kubectl
+      installed. The path assumes a Linux environment.
+  - question: How do I know my AKS cluster includes both x86 and Arm nodes?
+    answer: >-
+      During cluster creation, you provision two distinct node pools—one x86 and one Arm—and then
+      verify connectivity. You will confirm that both node types are available from your AKS environment
+      using the tools introduced in the steps.
+  - question: Which files set up nginx on Intel, and what should I expect after applying them?
+    answer: >-
+      You use namespace.yaml and nginx-configmap.yaml along with the Intel-specific deployment
+      manifest described in the steps. The result is an nginx deployment in a dedicated namespace
+      and a load balancer service that exposes nginx to the Internet.
+  - question: How is the Arm nginx deployment created and exposed?
+    answer: >-
+      Applying arm_nginx.yaml creates nginx-arm-deployment and nginx-arm-svc. It pulls a multi-architecture
+      nginx image from DockerHub, schedules a pod on the Arm node, mounts the shared ConfigMap
+      at /etc/nginx/nginx.conf, and exposes it via a load balancer service targeting pods with
+      app: nginx-multiarch and arch: arm labels.
+  - question: How do I compare performance between the x86 and Arm nginx instances?
+    answer: >-
+      Use the provided utility script and tools like wrk to send requests to each load-balanced
+      service and observe results. Validate that both endpoints respond as expected, then compare
+      behavior and performance across architectures.
+# END generated_summary_faq
 
 author:
     - Geremy Cohen
diff --git a/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md b/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md
index 9e71087dcf..3a3f354a69 100644
--- a/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md
@@ -22,6 +22,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:35:29Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: cd31c217eaeab6faad263728c446ee188cd9d542904d87ae7bfd5120713dfaa4
+  summary_generated_at: '2026-06-02T04:32:13Z'
+  summary_source_hash: cd31c217eaeab6faad263728c446ee188cd9d542904d87ae7bfd5120713dfaa4
+  faq_generated_at: '2026-06-03T01:35:29Z'
+  faq_source_hash: cd31c217eaeab6faad263728c446ee188cd9d542904d87ae7bfd5120713dfaa4
+  summary: >-
+    This Learning Path shows how to extend a Google Kubernetes Engine (GKE) cluster with Arm-based
+    nodes and deploy Ollama using a single multi-architecture container image. You begin with
+    an amd64 node running an Ollama Deployment and Service, then add an arm64 node pool and mirror
+    the deployment to form a hybrid cluster. You create the Kubernetes namespace, apply architecture-specific
+    services, and exercise a multi-architecture service that can route to either backend. You
+    validate by pinging the service, pulling models, and running LLM inferences while observing
+    which pod and node serve requests. Prerequisites are a Google Cloud account, gcloud, kubectl,
+    and the GKE Cloud Plugin on Linux or macOS.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Google Cloud account and a local machine with the Google Cloud CLI, kubectl,
+      and the GKE Cloud Plugin installed. The steps target Linux and macOS.
+  - question: How is the initial amd64 deployment organized in Kubernetes?
+    answer: >-
+      You create an ollama namespace, then deploy an Ollama Deployment and Service for amd64.
+      This simulates an existing cluster before adding Arm nodes.
+  - question: What settings should I use when adding the Arm node pool?
+    answer: >-
+      In the GKE console, select the ollama-on-multiarch cluster, choose Add node pool, name it
+      arm64-pool, set Size to 1, and specify the us-central1-a location. Follow the step guidance
+      to complete node settings.
+  - question: How do I verify that requests can reach either architecture in the hybrid cluster?
+    answer: >-
+      Use the provided script: ./model_util.sh multiarch hello. The response includes the pod
+      and deployment that handled the request, and repeating the command may route to different
+      pods.
+  - question: How do I compare amd64 and arm64 behavior and performance in this setup?
+    answer: >-
+      Validate by pinging services, pulling models, and running inferences on both the amd64 and
+      arm64 deployments. Use the multiarch service to observe request routing or target each architecture’s
+      service to compare results.
+# END generated_summary_faq
 
 author:
     - Geremy Cohen
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md
index 0041808836..b247d4baba 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md
@@ -20,6 +20,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:36:24Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: b9bc1d1f965e4fdeeb9552d853330c201e00597829420c8a0397fa425c3231c4
+  summary_generated_at: '2026-06-02T04:33:49Z'
+  summary_source_hash: b9bc1d1f965e4fdeeb9552d853330c201e00597829420c8a0397fa425c3231c4
+  faq_generated_at: '2026-06-03T01:36:24Z'
+  faq_source_hash: b9bc1d1f965e4fdeeb9552d853330c201e00597829420c8a0397fa425c3231c4
+  summary: >-
+    Learn how to provision an Arm64 virtual machine on Microsoft Azure Cobalt 100 (Neoverse-N2)
+    using the Azure Portal with Ubuntu Pro 24.04 LTS, deploy and secure MySQL, validate the service,
+    and run baseline benchmarks with mysqlslap. Aimed at developers migrating MySQL applications
+    from x86_64 to Arm, this introductory path focuses on Dpsv6 series VMs and walks through installation,
+    configuration, and functional checks to confirm the database is ready for use. You will also
+    perform baseline testing with mysqlslap to understand MySQL behavior on Azure Arm64. Prerequisites
+    include an Azure account with access to Cobalt 100 instances (Dpsv6) and familiarity with
+    relational databases and MySQL basics.
+  faqs:
+  - question: Which Azure VM size and base image should I use?
+    answer: >-
+      Use a general-purpose VM in the Dpsv6 series (Cobalt 100, Arm64) and select Ubuntu Pro 24.04
+      LTS as the base image. The path provisions the VM via the Azure Portal.
+  - question: Can I create the VM with Azure CLI or IaC instead of the Azure Portal?
+    answer: >-
+      Yes, Azure CLI and IaC are common alternatives, but this path demonstrates the Azure Portal
+      workflow. If you prefer CLI or IaC, you can adapt the same choices (Dpsv6, Ubuntu Pro 24.04
+      LTS), though those steps are not covered here.
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Microsoft Azure account with access to Cobalt 100 based instances (Dpsv6). Familiarity
+      with relational databases and the basics of MySQL is also expected.
+  - question: How do I know MySQL started and is ready for use?
+    answer: >-
+      Start and enable MySQL using systemctl as shown in the validation step. Then perform the
+      functional checks to confirm queries run, users can authenticate, and the environment is
+      correctly configured for cloud workloads.
+  - question: How do I benchmark MySQL in this setup, and what does mysqlslap measure?
+    answer: >-
+      Use the built-in mysqlslap tool to run baseline tests on the Azure Cobalt 100 (Arm64) VM.
+      It simulates multiple clients and reports read/write throughput, query response times, and
+      overall MySQL server performance under different workloads.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql-lns-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql-lns-azure/_index.md
index de5b2ede22..fe2ef8e769 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql-lns-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql-lns-azure/_index.md
@@ -24,7 +24,7 @@ prerequisites:
 generate_summary_faq: true
 
 rerun_summary: false
-rerun_faqs: false
+rerun_faqs: true
 author: Doug Anson
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql/_index.md
index 6bbece6118..6eba9e6668 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql/_index.md
@@ -18,6 +18,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:35:55Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: b9b2ed7f58611c9bc7e1867fe06700f3197eb095ddc62d91c00814021967cf72
+  summary_generated_at: '2026-06-02T04:33:29Z'
+  summary_source_hash: b9b2ed7f58611c9bc7e1867fe06700f3197eb095ddc62d91c00814021967cf72
+  faq_generated_at: '2026-06-03T01:35:55Z'
+  faq_source_hash: b9b2ed7f58611c9bc7e1867fe06700f3197eb095ddc62d91c00814021967cf72
+  summary: >-
+    This introductory Learning Path shows how to deploy MySQL on Arm-based Linux systems and interact
+    with it using the MySQL client CLI. You will review common deployment options on Arm, including
+    bare metal, cloud VMs, and managed SQL services from providers such as AWS, Microsoft Azure,
+    Google Cloud, and Oracle. The practical steps focus on installing, configuring, and checking
+    a MySQL instance, then running basic interactions from a CLI. Prerequisites include access
+    to an Arm-based instance from a cloud service provider or an on-premise Arm server; if you
+    do not have one, the path discusses options. Estimated time to complete is about 30 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need access to a Linux system on an Arm-based instance from a cloud provider or an on‑premise
+      Arm server. No other explicit prerequisites are listed.
+  - question: I don’t have an Arm node—what should I do?
+    answer: >-
+      The path discusses options to obtain Arm capacity, including Arm Cloud VMs and a separate
+      learning path for getting started with Arm-based cloud instances. Cloud providers referenced
+      include AWS, Microsoft Azure, Google Cloud, and Oracle.
+  - question: Which deployment approach should I choose for MySQL on Arm?
+    answer: >-
+      This path introduces multiple options: bare metal, cloud VMs, and cloud providers’ SQL services.
+      Choose based on your available infrastructure and whether you prefer managing MySQL yourself
+      or using a managed service.
+  - question: How do I know the installation worked?
+    answer: >-
+      The steps include checking the installation and interacting with the database using the
+      MySQL client CLI tool. You should be able to connect and run simple SQL commands to validate
+      the deployment.
+  - question: Does this path cover performance tuning?
+    answer: >-
+      No. If you already know how to deploy MySQL and want to focus on performance, follow the
+      separate “Learn how to Tune MySQL” learning path.
+# END generated_summary_faq
 
 author: Jason Andrews
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md
index e6b4c0025d..51100a1ca4 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md
@@ -18,6 +18,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:37:09Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: e473c0724855bbbc59481822130f7dc5e1684593527a6b5688939f84cd32963d
+  summary_generated_at: '2026-06-02T04:34:09Z'
+  summary_source_hash: e473c0724855bbbc59481822130f7dc5e1684593527a6b5688939f84cd32963d
+  faq_generated_at: '2026-06-03T01:37:09Z'
+  faq_source_hash: e473c0724855bbbc59481822130f7dc5e1684593527a6b5688939f84cd32963d
+  summary: >-
+    This Learning Path shows how to benchmark MySQL on Arm Linux using Sysbench and apply profile-guided
+    optimization (PGO) with GCC. You will build, configure, and run a MySQL server on one Arm
+    server running Ubuntu 22.04, then build and run Sysbench on a second Arm Linux system. On
+    the client, you also build and install MySQL to provide the libraries required by Sysbench.
+    You will run Sysbench against the server, rebuild MySQL to generate and then use profile data,
+    and examine the resulting performance changes. Prerequisites include basic MySQL knowledge
+    and access to two Arm servers (200 GB disk on the server, 30 GB on the client).
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need two Arm servers running Ubuntu 22.04: one for the MySQL server and one for the
+      Sysbench client. Ensure at least 200 GB of free disk space on the server and 30 GB on the
+      client. Basic knowledge of MySQL is also required.
+  - question: Which packages should I install to build MySQL on Ubuntu 22.04?
+    answer: >-
+      Install: git, make, automake, libtool, bison, pkg-config, cmake, g++, openssl, libssl-dev,
+      libncurses5-dev, libtirpc-dev, rpcsvc-proto, libaio-dev, libssl-dev. These packages are
+      used to build, install, and run the MySQL server from source.
+  - question: Why do I need to build MySQL on the Sysbench client as well?
+    answer: >-
+      Sysbench requires MySQL libraries to build and run the MySQL tests. On the client system
+      you only build and install MySQL to provide these libraries; you do not configure or run
+      the MySQL server there.
+  - question: Can I use a different Linux distribution or Ubuntu version?
+    answer: >-
+      The steps assume Ubuntu 22.04 on Arm, but other Linux distributions and Ubuntu versions
+      may also work. The path does not list specific adjustments for other distributions.
+  - question: How is PGO applied to MySQL in this path, and which compiler is used?
+    answer: >-
+      The path uses GCC to apply PGO: first rebuild MySQL with profile generation to collect data,
+      then rebuild with profile use to apply the collected profiles. The initial installation
+      referenced is at /home/mysql/mysql_install_8.0.33, and the PGO workflow creates two additional
+      installations (one for profile collection and one for profile use).
+# END generated_summary_faq
 
 author: Bolt Liu
 
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md
index 46ac662963..2f6857c85a 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md
@@ -16,6 +16,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:37:44Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: faa914d636e03296c6b65ba146745c543326955ec457577f047eec51aa6a3733
+  summary_generated_at: '2026-06-02T04:34:44Z'
+  summary_source_hash: faa914d636e03296c6b65ba146745c543326955ec457577f047eec51aa6a3733
+  faq_generated_at: '2026-06-03T01:37:44Z'
+  faq_source_hash: faa914d636e03296c6b65ba146745c543326955ec457577f047eec51aa6a3733
+  summary: >-
+    This advanced Learning Path guides you through tuning MySQL for better performance on Arm-based
+    (Neoverse) cloud VMs running Linux. You will review system-level considerations such as storage
+    technology and filesystem choices, then apply MySQL server settings using configuration files
+    under the mysqld group or the mysqld command line, with a preference for version-controlled
+    config files. The guidance is workload-focused: start from defaults, change only when needed,
+    and evaluate results. Tools and concepts include MySQL, SQL, InnoDB, and a runbook approach
+    to track changes. Prerequisite: a bare-metal or cloud installation of MySQL from the referenced
+    setup path. Estimated time to complete is about 30 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an existing MySQL installation on either bare-metal or in the cloud, as referenced
+      by the prerequisite Learning Path. No other explicit prerequisites are listed.
+  - question: Which platforms and Arm targets does this path focus on?
+    answer: >-
+      It targets Arm-based VMs in AWS, Microsoft Azure, Google Cloud, and Oracle. The Arm focus
+      is Neoverse.
+  - question: How should I choose storage and filesystem for MySQL?
+    answer: >-
+      Locally attached SSD storage generally performs best, though network storage can also perform
+      well. Start with the xfs filesystem, with ext4 as an alternative, and evaluate disk scheduling
+      and other options for your workload.
+  - question: Where should I place MySQL tuning parameters, and can I use command-line options?
+    answer: >-
+      Place configuration under the mysqld group in a MySQL configuration file, following the
+      Specifying Program Options section of the MySQL documentation. You can set options on the
+      mysqld command line, but configuration files are preferred for version control.
+  - question: Should I change many MySQL settings at once?
+    answer: >-
+      No. It is usually best to leave most settings at their defaults and change them only when
+      you suspect or know they affect your workload, since there is no one-size-fits-all configuration.
+# END generated_summary_faq
 
 author: Julio Suarez
 
diff --git a/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md b/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md
index fe7a44f17a..2b5962ac09 100644
--- a/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md
@@ -24,6 +24,59 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:38:16Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 65336a8f8d1d11c4b127ea13982a581afffa94cbfd1af10a9e4df2becbc34b5a
+  summary_generated_at: '2026-06-02T04:35:16Z'
+  summary_source_hash: 65336a8f8d1d11c4b127ea13982a581afffa94cbfd1af10a9e4df2becbc34b5a
+  faq_generated_at: '2026-06-03T01:38:16Z'
+  faq_source_hash: 65336a8f8d1d11c4b127ea13982a581afffa94cbfd1af10a9e4df2becbc34b5a
+  summary: >-
+    This advanced Learning Path shows how to develop and validate firmware pre-silicon for Arm
+    Neoverse CSS‑V3 using the RD‑V3 reference design and Arm Fixed Virtual Platforms (FVPs). You
+    will examine the CSS‑V3 architecture and coordinated boot sequence (TF‑A, RSE, SCP/MCP/LCP,
+    UEFI/GRUB, Linux), set up a containerized build environment, sync sources with a pinned repo
+    manifest, then build and boot the RD‑V3 firmware stack on an FVP. The steps include mapping
+    UART consoles, interpreting boot logs, and bringing the stack to a Linux shell with Buildroot.
+    You will also modify platform control firmware and run a dual‑chip RD‑V3‑R1 simulation. This
+    path takes about 90 minutes and assumes an Arm Neoverse‑based Linux machine, Docker or Codespaces,
+    and prior firmware knowledge.
+  faqs:
+  - question: What do I need before running the build and simulation steps?
+    answer: >-
+      You need access to an Arm Neoverse‑based Linux machine with at least 80 GB of free storage,
+      Docker installed or a GitHub Codespaces‑compatible environment, and familiarity with Linux
+      command‑line tools and basic scripting. An understanding of firmware boot stages and SoC‑level
+      architecture is also required.
+  - question: Which FVP model version should I use with my RD‑V3 release tag?
+    answer: >-
+      Each RD‑V3 release tag maps to a specific FVP version. For example, the RD‑INFRA‑2025.07.03
+      tag is designed to work with FVP version 11.29.35; consult the RD‑V3 Release Tags to select
+      and install the matching model.
+  - question: What result should I expect when the FVP simulation completes successfully?
+    answer: >-
+      The simulation brings up the full firmware stack from BL1 to a Linux shell using Buildroot.
+      You should see boot logs across the mapped UART consoles for components including TF‑A,
+      RSE, SCP/MCP/LCP, and UEFI/GRUB, ending at a Linux shell prompt.
+  - question: How do I diagnose issues if the boot sequence stalls?
+    answer: >-
+      Use the mapped UART consoles and boot logs to identify the active or failing stage and verify
+      the expected handoffs across TF‑A, RSE, SCP/MCP/LCP, and UEFI. The steps show how to interpret
+      logs to verify bring‑up and locate boot‑stage issues.
+  - question: What is different about running the dual‑chip RD‑V3‑R1 simulation, and what should
+      I verify?
+    answer: >-
+      RD‑V3‑R1 models a dual‑chip platform with two application processors and a Management Control
+      Processor (Cortex‑M7) for cross‑die management. You will launch the dual‑chip simulation
+      and verify AP/MCP coordination and the chiplet‑style boot flow.
+# END generated_summary_faq
 
 author:
     - Odin Shen
diff --git a/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md b/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md
index ce666586a2..7aa1c11463 100644
--- a/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md
@@ -20,6 +20,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:38:53Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 5a5fd9b66a09de71009ccb098c7ef08a848463a8a7956643020ab1fb1db22fbb
+  summary_generated_at: '2026-06-02T04:36:04Z'
+  summary_source_hash: 5a5fd9b66a09de71009ccb098c7ef08a848463a8a7956643020ab1fb1db22fbb
+  faq_generated_at: '2026-06-03T01:38:53Z'
+  faq_source_hash: 5a5fd9b66a09de71009ccb098c7ef08a848463a8a7956643020ab1fb1db22fbb
+  summary: >-
+    This introductory Learning Path guides you through creating, running, modifying, and deploying
+    a .NET Aspire application using a Windows on Arm development machine and Arm-based virtual
+    machines on AWS and Google Cloud. You will verify .NET 8.0 or later, install the Aspire workload,
+    generate and run the project (including trusting the HTTPS development certificate and using
+    the Aspire dashboard), and add a computation service to simulate intensive work. The path
+    then shows how to deploy to an Arm-powered EC2 instance, such as AWS Graviton; Google Cloud
+    Arm-based VMs are also targeted. Prerequisites include a Windows on Arm device, an Arm-based
+    instance from AWS or GCP, and a code editor (for example, Visual Studio Code for Arm64).
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Windows on Arm machine (for example, a Lenovo ThinkPad X13s running Windows 11),
+      access to an Arm-based instance on AWS or GCP, and a code editor. Visual Studio Code for
+      Arm64 is an example of a suitable editor.
+  - question: How do I check my .NET version and install the Aspire workload?
+    answer: >-
+      Open a PowerShell terminal and run dotnet --version to confirm .NET 8.0 or later is installed.
+      Then install the Aspire workload with dotnet workload install aspire and wait for the download
+      and installation to complete without errors.
+  - question: How do I run the application locally and confirm it started correctly?
+    answer: >-
+      First trust the HTTPS development certificate by running dotnet dev-certs https --trust.
+      Then change into the project directory and run dotnet run --project NetAspire.Arm.AppHost;
+      you should see build output, an Aspire version line, and messages such as “Distributed application
+      starting.”
+  - question: Where do I add the computational code, and what does it do?
+    answer: >-
+      Add a new file named ComputationService.cs in the NetAspire.Arm.ApiService project. The
+      provided code performs matrix multiplication to mimic computationally intensive work.
+  - question: Which cloud targets are supported, and how do I begin with AWS?
+    answer: >-
+      The path targets Arm-based VMs on AWS and Google Cloud. For AWS, sign in to the AWS Management
+      Console, navigate to the EC2 service, and choose an Arm-powered instance type such as those
+      based on AWS Graviton.
+# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md
index ecc09509d0..8b23345645 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md
@@ -20,6 +20,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:39:34Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: e6f6f4d843b4974d1c1d67a35009b4428d08bb356d26c08a441f82726e002fb2
+  summary_generated_at: '2026-06-02T04:37:18Z'
+  summary_source_hash: e6f6f4d843b4974d1c1d67a35009b4428d08bb356d26c08a441f82726e002fb2
+  faq_generated_at: '2026-06-03T01:39:34Z'
+  faq_source_hash: e6f6f4d843b4974d1c1d67a35009b4428d08bb356d26c08a441f82726e002fb2
+  summary: >-
+    This Learning Path shows how to deploy and validate NGINX on an Arm-based Microsoft Azure
+    Cobalt 100 virtual machine. Using the Azure portal, you create a general-purpose Dpsv6 Arm64
+    VM with Ubuntu Pro 24.04 LTS, install and enable NGINX, and replace the default page with
+    a simple static site to confirm the server is working. You then install ApacheBench (ab) to
+    run baseline NGINX performance tests and review the output, with a sample result from a D4ps_v6
+    configuration. The path is introductory and Linux-focused, takes about 30 minutes, and is
+    intended for system administrators and developers. Prerequisite: an Azure account with access
+    to Cobalt 100 (Dpsv6) instances.
+  faqs:
+  - question: What do I need before I start creating the VM on Azure?
+    answer: >-
+      You need a Microsoft Azure account with access to Cobalt 100 based instances (Dpsv6). This
+      access is required to select the Arm64 Cobalt 100 VM used in the steps.
+  - question: Which Azure VM series and OS image should I select?
+    answer: >-
+      Use a general-purpose D-series VM in the Dpsv6 size series and choose Ubuntu Pro 24.04 LTS
+      as the base image for Arm64.
+  - question: Can I use Azure CLI or IaC instead of the portal to create the VM?
+    answer: >-
+      There are multiple ways to create a Cobalt 100 VM, but this Learning Path uses the Azure
+      portal. CLI and IaC workflows are not covered here.
+  - question: How do I know NGINX is installed and serving content?
+    answer: >-
+      After installation and enabling, NGINX should serve its default welcome page. Then create
+      /var/www/my-static-site with a simple HTML file to replace the default page and confirm
+      it is delivered by the server.
+  - question: How do I install and verify ApacheBench (ab) on Ubuntu Pro 24.04 LTS?
+    answer: >-
+      Install the apache2-utils package and verify the tool with ab -V. You can then run a basic
+      benchmark and review the key metrics, with a sample result provided for an Azure D4ps_v6
+      instance.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx/_index.md
index ffeaf26fc8..4fe85d0343 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx/_index.md
@@ -20,6 +20,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:39:16Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: c5e077458808373c8ce9660235716b5bb55e4e7eb8b6300c162c041ef1c96cb0
+  summary_generated_at: '2026-06-02T04:36:50Z'
+  summary_source_hash: c5e077458808373c8ce9660235716b5bb55e4e7eb8b6300c162c041ef1c96cb0
+  faq_generated_at: '2026-06-03T01:39:16Z'
+  faq_source_hash: c5e077458808373c8ce9660235716b5bb55e4e7eb8b6300c162c041ef1c96cb0
+  summary: >-
+    Deploy the open source Nginx on Arm-based Linux servers and configure it as a minimal HTTPS
+    static file server and as a reverse proxy and API gateway. You will first install Nginx using
+    a package manager and review its build configuration, then optionally build Nginx from source
+    with the features you need. Next, you will create a key and certificate, add a basic Nginx
+    configuration, and start the server. Finally, you will set up a third node to act as a reverse
+    proxy and API gateway that load balances across two upstream file servers. Prerequisites include
+    Arm-based instances (AWS, Microsoft Azure, Google Cloud, or Oracle) or on-prem Arm servers,
+    and network access on ports 22 and 443. No other explicit prerequisites are listed.
+  faqs:
+  - question: Which Nginx edition does this path use?
+    answer: >-
+      The path uses the open source version of Nginx. Nginx Plus is mentioned for context but
+      is not used here.
+  - question: How many Arm-based instances do I need to complete the exercises?
+    answer: >-
+      You need at least one instance to create a static file server. For the reverse proxy and
+      API gateway, you need at least three instances: two file servers and one reverse proxy/API
+      gateway node.
+  - question: Should I install Nginx from a package manager or build from source?
+    answer: >-
+      The path covers both approaches. It recommends inspecting the build configuration of a prebuilt
+      package first to inform which features you enable when compiling from source.
+  - question: What network settings should I configure before starting?
+    answer: >-
+      Ensure your firewalls and security groups allow communication on port 22 (SSH) and port
+      443 (HTTPS). These are required for access and for serving HTTPS.
+  - question: What should be ready before configuring the reverse proxy and API gateway?
+    answer: >-
+      Set up two static file servers using the earlier section. The third node will run the reverse
+      proxy/API gateway and load balance across the two upstream file servers.
+# END generated_summary_faq
 
 author: Julio Suarez
 
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
index 9d74527561..dcc7ec0cd2 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
@@ -20,6 +20,57 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:40:13Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 10f13dee3459577fcadf5966cf80d990f3396321189f638432a3308699e773a8
+  summary_generated_at: '2026-06-02T04:38:02Z'
+  summary_source_hash: 10f13dee3459577fcadf5966cf80d990f3396321189f638432a3308699e773a8
+  faq_generated_at: '2026-06-03T01:40:13Z'
+  faq_source_hash: 10f13dee3459577fcadf5966cf80d990f3396321189f638432a3308699e773a8
+  summary: >-
+    This advanced Learning Path shows how to tune Nginx on Arm-based Linux servers in about 60
+    minutes. You will review how Linux kernel parameters, compiler and library choices, and Nginx
+    configuration affect performance. The steps walk through tuned examples for a static file
+    server (/etc/nginx/nginx.conf) and a Reverse Proxy/API Gateway (/etc/nginx/conf.d/loadbalancer.conf),
+    and present a practical method to test changes using wrk2. A cloud or bare-metal Nginx file
+    server or load balancer is required to follow along; if you do not already have one, first
+    review Learn how to deploy Nginx. By the end, you will be able to apply workload-aware tuning
+    and validate the impact with targeted load testing.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a cloud or bare-metal installation of an Nginx file server or load balancer on
+      Linux. If you do not already have a setup, review the “Learn how to deploy Nginx” Learning
+      Path first.
+  - question: How do I list and change the Linux kernel networking parameters mentioned in the
+      tuning guidance?
+    answer: >-
+      Run sudo sysctl -a to list kernel parameters. You can change values in /etc/sysctl.conf
+      or apply them using the sysctl command; see the Linux source admin-guide and networking
+      documentation for parameter details.
+  - question: Which Nginx configuration files will I tune?
+    answer: >-
+      You will work with the top-level /etc/nginx/nginx.conf and, for Reverse Proxy and API Gateway
+      use cases, /etc/nginx/conf.d/loadbalancer.conf. The Learning Path focuses on performance-relevant
+      directives in these files.
+  - question: Do I have to use wrk2 for performance testing?
+    answer: >-
+      No. If you already have a performance test methodology for your deployment, you can skip
+      the wrk2 section; otherwise, the path shows how wrk2 is typically used for testing Nginx
+      at Arm.
+  - question: What result should I expect after tuning, and how do I validate it?
+    answer: >-
+      There is no single recommended setting set; outcomes depend on your workload and use case.
+      Validate by measuring before and after with your test methodology (or wrk2) and compare
+      results for your specific scenario.
+# END generated_summary_faq
 
 author: Julio Suarez
 
diff --git a/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md b/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md
index 5a249266bc..d9bfe9247a 100644
--- a/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md
@@ -17,6 +17,50 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:40:53Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 81bdee16a18b09da91a7514eb70368771b251ed3f8ec657ec105ca10ecead038
+  summary_generated_at: '2026-06-02T04:38:40Z'
+  summary_source_hash: 81bdee16a18b09da91a7514eb70368771b251ed3f8ec657ec105ca10ecead038
+  faq_generated_at: '2026-06-03T01:40:53Z'
+  faq_source_hash: 81bdee16a18b09da91a7514eb70368771b251ed3f8ec657ec105ca10ecead038
+  summary: >-
+    Learn how to run a Hugging Face Natural Language Processing (NLP) model with PyTorch on Arm
+    servers. Using an Arm-based cloud instance or on-prem Arm server running Ubuntu 22.04 LTS,
+    you will install PyTorch, load an NLP model from Hugging Face, execute the model on an Arm
+    AArch64 CPU, and use the PyTorch profiler to analyze its execution time. The path focuses
+    on practical setup and measurement using Python, PyTorch, and Hugging Face. No explicit prerequisites
+    are listed beyond access to an Arm-based server. This introductory Learning Path is designed
+    to be completed in about 20 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need access to an Arm based instance from a cloud service provider or an on-premise
+      Arm server. No other explicit prerequisites are listed.
+  - question: Which operating system should my server use?
+    answer: >-
+      The instructions are written for an Arm server running Ubuntu 22.04 LTS on Linux. Other
+      operating systems are not covered in this path.
+  - question: Can I use AWS, Microsoft Azure, Google Cloud, or Oracle Cloud for this?
+    answer: >-
+      Yes. You can use an Arm based instance from any of these cloud service providers, or an
+      on-premise Arm server.
+  - question: Do I need a GPU to run the model?
+    answer: >-
+      No. This path focuses on deploying and running the model on an Arm AArch64 CPU. GPU use
+      is not covered.
+  - question: How do I know the deployment and profiling worked?
+    answer: >-
+      You should be able to run inference on the model and collect execution-time data using the
+      PyTorch profiler. Seeing profiler output for the model run indicates success.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md
index e76d49e8ee..0294f070d1 100644
--- a/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md
@@ -24,6 +24,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:41:19Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 800eed835763098d4b369789d10de642bbdbaa390e8bfe0cb6ea2c4c78f7ecbd
+  summary_generated_at: '2026-06-02T04:39:16Z'
+  summary_source_hash: 800eed835763098d4b369789d10de642bbdbaa390e8bfe0cb6ea2c4c78f7ecbd
+  faq_generated_at: '2026-06-03T01:41:19Z'
+  faq_source_hash: 800eed835763098d4b369789d10de642bbdbaa390e8bfe0cb6ea2c4c78f7ecbd
+  summary: >-
+    Learn how to deploy and evaluate Node.js on Google Cloud C4A virtual machines powered by Axion
+    processors built on Arm Neoverse-V2 cores. You will provision a SUSE Linux Enterprise Server
+    VM (for example, c4a-standard-4) in the Google Cloud Console, install and manage Node.js with
+    Node Version Manager (NVM), validate the runtime with baseline REPL and HTTP server tests,
+    and benchmark using Autocannon on Arm64 (AArch64). This introductory path is aimed at developers
+    migrating Node.js workloads from x86_64 to Arm on GCP. Prerequisites include a GCP account
+    with billing enabled and familiarity with networking concepts and the Node.js event-driven
+    architecture.
+  faqs:
+  - question: What do I need before provisioning the VM?
+    answer: >-
+      You need a Google Cloud Platform account with billing enabled. Familiarity with networking
+      concepts and Node.js’s event-driven architecture is also expected.
+  - question: Which Google Cloud instance type and OS image are used in the steps?
+    answer: >-
+      The path uses Google Cloud C4A Arm-based instances, with c4a-standard-4 (4 vCPUs, 16 GB
+      memory) shown as an example. The VM runs SUSE Linux Enterprise Server on Arm64 (AArch64).
+  - question: How do I install Node.js on the Arm VM?
+    answer: >-
+      Use Node Version Manager (NVM). Run the provided NVM install script, load NVM in your shell,
+      then install and select a Node.js version using the official Node.js packages.
+  - question: How do I confirm the Node.js setup before benchmarking?
+    answer: >-
+      Start the Node.js REPL and print a message such as "Hello from Node.js" to verify the runtime.
+      Then run the simple HTTP server baseline test to confirm the server starts and responds.
+  - question: What should I expect from the Autocannon benchmark, and what should I check if it
+      fails?
+    answer: >-
+      Autocannon reports metrics such as throughput and latency for your HTTP server on Arm64.
+      If it fails to reach the server, first confirm the baseline HTTP server is running and reachable,
+      then rerun the benchmark.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md b/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md
index 446854fd6e..6594009561 100644
--- a/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md
@@ -17,6 +17,51 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:41:50Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 3ee5dd8d8edeb5dcb1e3be7bb09cdf0b1b2694f0e74e9eb3c6c2f17912399808
+  summary_generated_at: '2026-06-02T04:39:57Z'
+  summary_source_hash: 3ee5dd8d8edeb5dcb1e3be7bb09cdf0b1b2694f0e74e9eb3c6c2f17912399808
+  faq_generated_at: '2026-06-03T01:41:50Z'
+  faq_source_hash: 3ee5dd8d8edeb5dcb1e3be7bb09cdf0b1b2694f0e74e9eb3c6c2f17912399808
+  summary: >-
+    Learn how to automate the creation of Arm (Neoverse) virtual machine instances on Oracle Cloud
+    Infrastructure (OCI) using Terraform. This Learning Path is aimed at developers new to deploying
+    Arm instances on OCI and uses Terraform to define and provision the required resources. The
+    command examples assume you work from a Linux environment, though any computer with the required
+    tools can be used. You need an OCI account and a computer with Terraform installed. In about
+    60 minutes, you will run Terraform to stand up an Arm VM on OCI and understand the basic workflow
+    for repeatable infrastructure deployment.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      You need an Oracle Cloud Infrastructure (OCI) account and a computer with Terraform installed.
+      The commands are written for a Linux environment.
+  - question: Do I have to use Linux to follow the commands?
+    answer: >-
+      The command format assumes you are working on a Linux machine. Any computer can be used
+      if it has the required tools installed.
+  - question: Is there anything I should review before starting with OCI?
+    answer: >-
+      You may want to review the Learning Path “Getting Started with Oracle OCI” before you begin.
+      It helps establish the basics you will build on here.
+  - question: How long does this take and what experience level is expected?
+    answer: >-
+      The estimated time to complete is 60 minutes. The skill level is listed as Advanced, but
+      the topic is positioned as introductory for developers new to deploying Arm instances on
+      OCI using Terraform.
+  - question: What result should I expect after completing the steps?
+    answer: >-
+      You will have automated the creation of Arm virtual machine instances on OCI using Terraform.
+      The outcome is a repeatable infrastructure-as-code workflow for provisioning Arm-based VMs.
+# END generated_summary_faq
 
 author: Frédéric -lefred- Descamps
 
diff --git a/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md
index 149e4e14b3..a2e706276c 100644
--- a/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md
@@ -20,6 +20,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:43:11Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 84ba887426f3ca45b698c79028023d80cbf0a06e6bc2fb71fcbe924943078dd8
+  summary_generated_at: '2026-06-02T04:41:17Z'
+  summary_source_hash: 84ba887426f3ca45b698c79028023d80cbf0a06e6bc2fb71fcbe924943078dd8
+  faq_generated_at: '2026-06-03T01:43:11Z'
+  faq_source_hash: 84ba887426f3ca45b698c79028023d80cbf0a06e6bc2fb71fcbe924943078dd8
+  summary: >-
+    Provision an Arm-based Azure Cobalt 100 (Dpsv6) virtual machine using the Azure portal and
+    Ubuntu Pro 24.04 LTS, then set up a clean Python environment to run ONNX Runtime with a SqueezeNet
+    1.0 INT8 model. You will validate your setup by performing a simple baseline latency test
+    in Python and then run onnxruntime_perf_test for more systematic benchmarking on Arm64. This
+    introductory path targets developers deploying ONNX-based applications on Arm-based machines
+    and takes about 60 minutes. Prerequisites include an Azure account with access to Cobalt 100
+    instances, basic Python and machine learning knowledge, and familiarity with ONNX Runtime
+    and Azure services.
+  faqs:
+  - question: What do I need before provisioning the VM?
+    answer: >-
+      You need a Microsoft Azure account with access to Cobalt 100 based instances (Dpsv6). Basic
+      understanding of Python and machine learning, and familiarity with ONNX Runtime and Azure
+      cloud services, are also assumed.
+  - question: When creating the VM, which size series and OS image should I choose?
+    answer: >-
+      Use a general-purpose D-series Dpsv6 Arm64 VM and select Ubuntu Pro 24.04 LTS as the base
+      image. The Learning Path guides you through creation using the Azure portal.
+  - question: Can I use the Azure CLI or IaC to create the VM instead of the portal?
+    answer: >-
+      There are multiple ways to create a Cobalt 100 VM, but this Learning Path uses the Azure
+      portal. Other methods are not covered in the steps.
+  - question: How should I prepare the Python environment for ONNX Runtime on the VM?
+    answer: >-
+      Install Python 3, pip, and venv on Ubuntu Pro 24.04 LTS, then create and activate a virtual
+      environment as shown in the steps. This prepares the environment to run ONNX models with
+      ONNX Runtime.
+  - question: How do I run and validate the SqueezeNet INT8 baseline and benchmark?
+    answer: >-
+      Use the provided baseline.py to load squeezenet-int8.onnx and time a single inference to
+      confirm ONNX Runtime is working. Then run onnxruntime_perf_test for detailed statistics;
+      successful runs finish without errors and report latency or benchmark metrics.
+# END generated_summary_faq
 
 author: Pareena Verma    
 
diff --git a/content/learning-paths/servers-and-cloud-computing/onnx/_index.md b/content/learning-paths/servers-and-cloud-computing/onnx/_index.md
index 8a0bc4a58a..0fce1cd121 100644
--- a/content/learning-paths/servers-and-cloud-computing/onnx/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/onnx/_index.md
@@ -21,6 +21,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:42:17Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: a9e547c4a9f99e1c7bfbfe582032ede11eb8ff5a74dbb7a93bada275ac435220
+  summary_generated_at: '2026-06-02T04:40:35Z'
+  summary_source_hash: a9e547c4a9f99e1c7bfbfe582032ede11eb8ff5a74dbb7a93bada275ac435220
+  faq_generated_at: '2026-06-03T01:42:17Z'
+  faq_source_hash: a9e547c4a9f99e1c7bfbfe582032ede11eb8ff5a74dbb7a93bada275ac435220
+  summary: >-
+    This advanced Learning Path guides you through quantizing and deploying Microsoft’s Phi-4-mini
+    model with ONNX Runtime on Arm-based Azure Cobalt 100 virtual machines running Ubuntu 24.04
+    LTS. You will build and configure ONNX Runtime, convert and quantize the model, and create
+    a minimal Python chatbot server (phi4.py) using onnxruntime_genai. You will run prompts and
+    analyze performance on Neoverse N2–based Cobalt 100 instances, observing metrics such as tokens
+    per second and time to first token. Prerequisites include an Arm-based cloud instance (tested
+    on an Azure Cobalt 100 VM), familiarity with Python, ONNX Runtime, Azure services, and LLM
+    fundamentals. By the end, you can interactively serve Phi-4-mini inference on Azure Arm CPUs.
+  faqs:
+  - question: What kind of Azure instance should I use to follow this path?
+    answer: >-
+      Use an Arm-based instance. The steps were tested on an Azure Cobalt 100 Dpls_v6 VM with
+      32 cores, 64GB of RAM, and 32GB of disk space.
+  - question: Which operating system and environment are the instructions written for?
+    answer: >-
+      The procedures target Linux and were validated on Ubuntu 24.04 LTS running on Azure Cobalt
+      100 servers.
+  - question: Do I need to quantize the Phi-4-mini model before running inference?
+    answer: >-
+      Yes. The setup includes quantizing and converting Phi-4-mini before deploying it with ONNX
+      Runtime.
+  - question: How do I run the chatbot server and which arguments matter?
+    answer: >-
+      Create the provided phi4.py script and run it with a model path (args.model_path) to your
+      converted Phi-4-mini model. You can also set an execution provider (args.execution_provider)
+      and enable verbose or timing output if needed.
+  - question: How do I know the deployment worked and what results should I expect?
+    answer: >-
+      After starting the server, send a text prompt and check the terminal for generated tokens
+      and performance metrics. You should see tokens/second and time to first token; the example
+      output shows about 57 tokens/s and ~0.2 s to first token.
+# END generated_summary_faq
 
 author: Nobel Chowdary Mandepudi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md b/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md
index 34833e5610..90666bdb39 100644
--- a/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md
@@ -23,6 +23,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:43:38Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: dec913a7a15e82ebf129e2ac64cba8d9140694840f8f9e817fb091b0c82c4cab
+  summary_generated_at: '2026-06-02T04:41:47Z'
+  summary_source_hash: dec913a7a15e82ebf129e2ac64cba8d9140694840f8f9e817fb091b0c82c4cab
+  faq_generated_at: '2026-06-03T01:43:38Z'
+  faq_source_hash: dec913a7a15e82ebf129e2ac64cba8d9140694840f8f9e817fb091b0c82c4cab
+  summary: >-
+    This advanced Learning Path shows how to build and simulate OpenBMC and UEFI firmware pre-silicon
+    on the Arm Neoverse RD-V3 r1 Fixed Virtual Platform (FVP). You will set up a Docker-based
+    build environment, compile OpenBMC and host UEFI images, launch the RD-V3 FVP, and observe
+    the boot across multiple UART consoles. You will validate host–BMC communication using UART
+    and Serial over LAN (SoL), access the host console through the OpenBMC web UI, and implement
+    a custom IPMI command in C++ for validation. Prerequisites include an Arm Neoverse-based Ubuntu
+    22.04 LTS system with 80 GB free disk space, 48 GB RAM, and familiarity with Docker, Git,
+    and Linux tools. Tools include OpenBMC, Yocto/BitBake, FVP, C/C++, and ipmitool.
+  faqs:
+  - question: What do I need before running the builds?
+    answer: >-
+      Use an Arm Neoverse-based Linux machine running Ubuntu 22.04 LTS with at least 80 GB free
+      disk space and 48 GB RAM. You should be comfortable with Docker, Git, common Linux terminal
+      tools, and have a basic understanding of UEFI, BMC, and TF-A.
+  - question: How do I know the RD-V3 FVP booted OpenBMC and UEFI correctly?
+    answer: >-
+      After launching the FVP, you should see multiple UART consoles for subsystems such as Neoverse
+      V3, Cortex-M55, Cortex-M7, and the Cortex-A BMC. Successful boot is indicated by visible
+      boot logs on these consoles for both the BMC and the host UEFI firmware.
+  - question: What should I check if the UART console windows do not appear?
+    answer: >-
+      The simulation opens multiple graphical UART terminals and requires a desktop session. If
+      you connect over SSH only, these consoles will not render; switch to a desktop environment
+      or an appropriate session that supports GUI windows.
+  - question: How do I access the host console through OpenBMC?
+    answer: >-
+      Use OpenBMC Serial over LAN (SoL). Create a virtual UART bridge with socat between the host-side
+      and BMC-side ports (for example, tcp:localhost:5005 to tcp:localhost:5067), verify the mappings,
+      then open the host console from the BMC web UI.
+  - question: How do I add and validate a custom IPMI command in OpenBMC?
+    answer: >-
+      Implement a custom IPMI command handler in C++, package it with Yocto/BitBake, and rebuild
+      the OpenBMC image. Run it on the FVP and confirm it returns the expected simple string response,
+      using the steps provided (you can invoke it with ipmitool).
+# END generated_summary_faq
 
 author:
   - Odin Shen
diff --git a/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md b/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md
index c99a900165..120706ca79 100644
--- a/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md
@@ -22,6 +22,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:44:04Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 778ac2a8b8ad9ffd665f6f0be545541090465f355094c354cc693e784d27559a
+  summary_generated_at: '2026-06-02T04:42:23Z'
+  summary_source_hash: 778ac2a8b8ad9ffd665f6f0be545541090465f355094c354cc693e784d27559a
+  faq_generated_at: '2026-06-03T01:44:04Z'
+  faq_source_hash: 778ac2a8b8ad9ffd665f6f0be545541090465f355094c354cc693e784d27559a
+  summary: >-
+    Learn to profile and accelerate a C++ data-processing workload on Arm Linux (aarch64) using
+    Arm Performix and OpenRNG from Arm Performance Libraries. You will build and run a baseline
+    application, use Performix Code Hotspots to identify the most impactful functions to optimize,
+    then integrate OpenRNG’s vector API to speed up random number generation. Finally, you will
+    run a microbenchmark sweep to measure runtime across input sizes from 2^8 to 2^15 elements
+    and compare baseline versus accelerated builds. This introductory path targets C++ developers
+    and assumes access to an Arm Linux server, such as an AWS Graviton3 instance, plus basic knowledge
+    of C++ and CMake.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an Arm Linux (aarch64) server, such as an AWS Graviton3 instance, and a basic understanding
+      of C++ and CMake. No other explicit prerequisites are listed.
+  - question: Which packages should I install, and what if I’m not using Amazon Linux?
+    answer: >-
+      Install git, cmake, g++, environment-modules, and python3 with your package manager. The
+      steps use dnf on Amazon Linux 2023, but you can substitute apt on Ubuntu or Debian.
+  - question: How do I decide which function to optimize after running the baseline?
+    answer: >-
+      Use Arm Performix Code Hotspots to profile the entire program with hardware performance
+      counters and identify the routines with the highest impact. This avoids relying on manual
+      timers that might miss hotspots like both generateDistribution and min_length.
+  - question: When integrating OpenRNG, which API should I use and what changes am I making?
+    answer: >-
+      Use OpenRNG’s Vector Statistical Library (VSL) API to generate Gaussian values in bulk via
+      a stream object. Replace the baseline’s one-sample-at-a-time generation with this vectorized,
+      bulk generation.
+  - question: What result should I expect from the microbenchmark sweep, and how do I compare
+      builds?
+    answer: >-
+      The microbenchmark isolates generateDistribution and times it in microseconds across sizes
+      from 2^8 to 2^15. Run both the baseline and accelerated builds and compare the recorded
+      times to see how the speedup from OpenRNG scales with input size.
+# END generated_summary_faq
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/openshift/_index.md b/content/learning-paths/servers-and-cloud-computing/openshift/_index.md
index a90896e48d..c515155947 100644
--- a/content/learning-paths/servers-and-cloud-computing/openshift/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openshift/_index.md
@@ -19,6 +19,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:44:23Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 7972fb230273ab1809c6f0917c4bbc49934f495feb2649da665d67bf71a45a3f
+  summary_generated_at: '2026-06-02T04:43:07Z'
+  summary_source_hash: 7972fb230273ab1809c6f0917c4bbc49934f495feb2649da665d67bf71a45a3f
+  faq_generated_at: '2026-06-03T01:44:23Z'
+  faq_source_hash: 7972fb230273ab1809c6f0917c4bbc49934f495feb2649da665d67bf71a45a3f
+  summary: >-
+    Learn how to use Red Hat OpenShift Pipelines (Tekton) on AWS to migrate existing OpenShift
+    applications from x86 compute nodes to Arm 64-bit (arm64) nodes and build multi-architecture
+    container images. You will assess workload compatibility, enable multi-architecture support
+    in OpenShift, configure Arm64 nodes, rebuild and verify images, and transition deployments
+    safely. The example uses the OpenShift Pipelines Tutorial as a baseline running on x86 infrastructure.
+    Prerequisites include an AWS account with an OpenShift 4.18 cluster on x86 compute nodes,
+    the OpenShift Pipelines (Tekton) Operator installed, cluster-admin access, and familiarity
+    with the oc CLI, container fundamentals, and core Tekton concepts. Estimated time to complete
+    is about 30 minutes.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      You need an AWS account with an OpenShift 4.18 cluster running x86 compute nodes, the Red
+      Hat OpenShift Pipelines (Tekton) Operator installed, and cluster-admin or equivalent permissions.
+      Familiarity with the oc CLI, container fundamentals, and basic Tekton concepts (Task, Pipeline,
+      PipelineRun) is also required.
+  - question: Which environment does the example start from?
+    answer: >-
+      It uses the OpenShift Pipelines Tutorial as the baseline, running on an OpenShift 4.18 cluster
+      on AWS with x86 compute nodes. The procedures assume this x86 starting point.
+  - question: Do I need Arm64 worker nodes already available?
+    answer: >-
+      Not explicitly. The Learning Path shows how to configure Arm64 nodes and enable multi-architecture
+      support as part of the migration steps.
+  - question: How do I know my application can run on Arm (arm64)?
+    answer: >-
+      Begin with the assessment step to confirm workload compatibility with the 64-bit Arm architecture.
+      Proceed only after verifying that your applications can run on arm64.
+  - question: What result should I expect after completing the steps?
+    answer: >-
+      You will rebuild and verify container images with multi-architecture support and transition
+      deployments to Arm-based nodes on AWS. The steps focus on a safe migration using OpenShift
+      Pipelines.
+# END generated_summary_faq
 
 author: Jeff Young
 
diff --git a/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md
index 213fe3e63f..6ca4afe920 100644
--- a/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md
@@ -26,6 +26,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:44:49Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 42628afa923ce6e89693bdfc72469ac0803610c3decb6cd4f36bd1a003874d0d
+  summary_generated_at: '2026-06-02T04:43:52Z'
+  summary_source_hash: 42628afa923ce6e89693bdfc72469ac0803610c3decb6cd4f36bd1a003874d0d
+  faq_generated_at: '2026-06-03T01:44:49Z'
+  faq_source_hash: 42628afa923ce6e89693bdfc72469ac0803610c3decb6cd4f36bd1a003874d0d
+  summary: >-
+    Learn how to deploy OpenStack on Arm-based Microsoft Azure Cobalt 100 (Arm64) virtual machines.
+    You will provision an Azure Dpsv6 series VM and use DevStack to bring up a single-node development
+    environment with core services. Then you will prepare a second Ubuntu 24.04 Arm64 VM with
+    two NICs and a data disk, and use Kolla-Ansible to deploy containerized OpenStack services.
+    Along the way, you will configure networking and storage, launch and manage instances, and
+    access OpenStack via the CLI and Horizon dashboard. Prerequisites include an Azure account
+    with access to Cobalt 100 instances, basic Linux command-line skills, familiarity with SSH,
+    and a basic understanding of cloud and virtualization concepts.
+  faqs:
+  - question: What do I need before I start?
+    answer: >-
+      You need a Microsoft Azure account with access to Cobalt 100 based instances (Dpsv6), basic
+      Linux command-line skills, familiarity with SSH and remote access, and a basic understanding
+      of cloud computing and virtualization concepts.
+  - question: Which Azure VM size and disk setup should I use for the DevStack deployment?
+    answer: >-
+      Use a single-NIC D4ps_v6 instance with at least 80 GB of disk. The steps in this Learning
+      Path use the Azure Portal to create the VM.
+  - question: Can I run DevStack and Kolla-Ansible on the same VM?
+    answer: >-
+      No. You can't run DevStack and Kolla-Ansible on the same VM; the Kolla-Ansible deployment
+      must run on a separate Azure VM.
+  - question: What specifications and OS are required for the Kolla-Ansible host?
+    answer: >-
+      Create a separate Azure VM with 4 vCPUs (8 recommended), 16 GB RAM (recommended), a 100
+      GB OS disk, a 32 GB data disk (for Cinder/Docker), and two NICs. Use Ubuntu 24.04 on Arm64.
+  - question: After deployment, how do I access OpenStack and what should I expect to be running?
+    answer: >-
+      Access OpenStack using the CLI and the Horizon dashboard. The environment runs core services
+      such as Nova, Neutron, Keystone, and Glance on Arm64 and allows launching virtual machine
+      instances.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md b/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md
index e62878b713..b7c83349cf 100644
--- a/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md
@@ -21,6 +21,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:45:22Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: cb4227a3f8375d6ae63801891703d6e615bdc60a01fca099a2f76453775ef460
+  summary_generated_at: '2026-06-02T04:44:29Z'
+  summary_source_hash: cb4227a3f8375d6ae63801891703d6e615bdc60a01fca099a2f76453775ef460
+  faq_generated_at: '2026-06-03T01:45:22Z'
+  faq_source_hash: cb4227a3f8375d6ae63801891703d6e615bdc60a01fca099a2f76453775ef460
+  summary: >-
+    This Learning Path guides you through deploying and observing a Python Flask microservice
+    on Arm64-based Google Cloud C4A Axion processors. You will provision a c4a-standard-4 VM running
+    SUSE Linux, configure GCP firewall rules for the service and observability endpoints, and
+    prepare container tooling to run an instrumented Flask app that emits OpenTelemetry traces
+    and metrics. You then deploy the OpenTelemetry Collector and integrate Prometheus and Jaeger
+    so metrics and traces flow from the service through the collector to their UIs. By the end,
+    you can generate and analyze telemetry for the service on Arm infrastructure. Prerequisites
+    include a GCP account with billing enabled and basic Python/Flask and container/Kubernetes
+    familiarity.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Google Cloud Platform account with billing enabled, basic familiarity with Python
+      and Flask, and a basic understanding of containers and Kubernetes concepts.
+  - question: Which Google Cloud VM and operating system does this path use?
+    answer: >-
+      You will create a Google Axion C4A Arm-based VM using the c4a-standard-4 machine type with
+      4 vCPUs and 16 GB of memory. The setup uses an arm64-based SUSE Linux virtual machine.
+  - question: Which firewall ports should I open and why?
+    answer: >-
+      Open TCP ports: 8080 for the Flask application, 16686 for the Jaeger UI, 9090 for the Prometheus
+      UI, 4317 for OTLP gRPC ingestion, and 4318 for OTLP HTTP ingestion.
+  - question: How are the telemetry components connected in this setup?
+    answer: >-
+      The Flask microservice uses the OpenTelemetry SDK to emit telemetry to the OpenTelemetry
+      Collector. The Collector routes metrics to Prometheus and traces to Jaeger.
+  - question: How do I validate that telemetry is flowing end-to-end?
+    answer: >-
+      Access the Prometheus UI on port 9090 and the Jaeger UI on port 16686 to verify data from
+      the Flask service. Generate requests to the Flask app on port 8080 to produce new traces
+      and metrics.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/pac/_index.md b/content/learning-paths/servers-and-cloud-computing/pac/_index.md
index b8c048f7f9..178f596184 100644
--- a/content/learning-paths/servers-and-cloud-computing/pac/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pac/_index.md
@@ -20,6 +20,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:45:44Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: fa699cafa5c0d998a63de306371bfbe47680c7453b28fc4b35d4fe2671902b23
+  summary_generated_at: '2026-06-02T04:45:16Z'
+  summary_source_hash: fa699cafa5c0d998a63de306371bfbe47680c7453b28fc4b35d4fe2671902b23
+  faq_generated_at: '2026-06-03T01:45:44Z'
+  faq_source_hash: fa699cafa5c0d998a63de306371bfbe47680c7453b28fc4b35d4fe2671902b23
+  summary: >-
+    Use a Linux Arm server to explore Arm Pointer Authentication (PAC) by building and analyzing
+    a small, vulnerable C program. You will compile the application with and without PAC, inspect
+    the generated instructions, and use pwntools to exploit the non-PAC binary (main_nopac) to
+    redirect control flow to an unintended function that launches a shell, then compare behavior
+    with PAC enabled to see how the protection changes the outcome. This advanced path targets
+    Arm-based instances in the cloud or on-premise and takes about 30 minutes. Prerequisite: access
+    to an Arm-based instance; if needed, consult the referenced Get started with Arm-based cloud
+    instances learning paths.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need access to a Linux Arm-based instance from a cloud provider or an on‑prem Arm server.
+      No other prerequisites are explicitly listed.
+  - question: Can I use any cloud provider for the Arm instance?
+    answer: >-
+      Yes. You can use an Arm-based instance from AWS, Microsoft Azure, Google Cloud, or Oracle,
+      or use an on‑prem Arm server.
+  - question: Which tools do I install to run the exploit code?
+    answer: >-
+      Install pwntools and its dependencies as shown in the steps. The path uses Python 3 and
+      pip to set up pwntools.
+  - question: Which binary should I target when running the exploit?
+    answer: >-
+      Target the application built without Pointer Authentication, referred to as main_nopac in
+      the steps.
+  - question: What result should I expect when the exploit works, and how do I compare with Pointer
+      Authentication enabled?
+    answer: >-
+      A successful exploit will execute func2(), print "Hello from func2!", and spawn a shell.
+      Then build the Pointer Authentication version and follow the steps to inspect the generated
+      instructions and compare behavior.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md b/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md
index 47fe473bcc..97caf0d6af 100644
--- a/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md
@@ -24,6 +24,58 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:46:30Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 0599665da13e9e1a8b017b0dac87c63f323ef769b1a5be62a60a32a36bc82696
+  summary_generated_at: '2026-06-02T04:46:21Z'
+  summary_source_hash: 0599665da13e9e1a8b017b0dac87c63f323ef769b1a5be62a60a32a36bc82696
+  faq_generated_at: '2026-06-03T01:46:30Z'
+  faq_source_hash: 0599665da13e9e1a8b017b0dac87c63f323ef769b1a5be62a60a32a36bc82696
+  summary: >-
+    Use an AI coding assistant with the Arm MCP Server to run Arm Performix Code Hotspots on a
+    C++ application and act on the results on Arm Neoverse. You configure a GitHub Copilot prompt
+    file to launch profiling on a remote Linux-based Arm instance, interpret flame graph output,
+    and apply agent-suggested changes to the Mandelbrot example, such as a squared-magnitude check,
+    raw double arithmetic instead of std::complex, and compiling with -O3. Prerequisites include
+    familiarity with configuring the Arm MCP Server in an AI assistant (or completion of the related
+    Learning Path), access to an Arm-based cloud instance (for example, AWS Graviton3) with Arm
+    Performix set up for the target, and basic C++ knowledge.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      You need familiarity with configuring the Arm MCP Server in an AI coding assistant (or completion
+      of the referenced Learning Path), access to an Arm-based Linux cloud instance such as an
+      AWS Graviton3 instance, access to Arm Performix configured with the remote Arm target, and
+      a basic understanding of C++.
+  - question: Do I have to use Visual Studio Code and GitHub Copilot?
+    answer: >-
+      The steps use Visual Studio Code with GitHub Copilot as the example AI assistant. Equivalent
+      configurations for other AI agents (Kiro and OpenAI Codex) are referenced at the end of
+      the section.
+  - question: Which prompt file should I use to run the Code Hotspots recipe?
+    answer: >-
+      Use the Arm MCP arm-hotspots-optimization prompt file with GitHub Copilot. It drives the
+      Code Hotspots recipe through the MCP Server, confirms your target details, runs the collection,
+      and returns structured profiling results.
+  - question: How do I know Arm Performix can reach my remote Arm target?
+    answer: >-
+      You will build the Mandelbrot C++ application on the remote server and follow a step that
+      confirms Performix can access the target. Complete this confirmation before launching the
+      Code Hotspots run.
+  - question: What result should I expect, and what optimizations are applied?
+    answer: >-
+      Expect structured profiling output and a flame graph that highlights the hottest functions
+      in the Mandelbrot application. The path applies AI-suggested changes: replacing std::abs
+      with a squared-magnitude check, replacing std::complex<double> with raw double arithmetic,
+      and rebuilding with -O3; the agent can edit the remote source via SSH through the MCP Server.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md b/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md
index 032a8f3f2e..d06308cfd1 100644
--- a/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md
@@ -22,6 +22,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:47:08Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: ec027f6022cc86a644a71a7d2016bf4601e2c4ac41570e861e9f124468b228ae
+  summary_generated_at: '2026-06-02T04:47:05Z'
+  summary_source_hash: ec027f6022cc86a644a71a7d2016bf4601e2c4ac41570e861e9f124468b228ae
+  faq_generated_at: '2026-06-03T01:47:08Z'
+  faq_source_hash: ec027f6022cc86a644a71a7d2016bf4601e2c4ac41570e861e9f124468b228ae
+  summary: >-
+    Analyze and improve a Linux application’s performance on Arm Neoverse-based servers using
+    Arm Performix Runbook. You will configure a Performix connection, build a C Mandelbrot set
+    generator, then run the CPU Microarchitecture recipe to identify pipeline bottlenecks and
+    the Instruction Mix recipe to examine instruction types and SIMD utilization. Using these
+    insights, apply vectorization and compiler flags and compare performance profiles to measure
+    execution changes. Target environment: an Arm Neoverse server running Linux, with bare-metal
+    or cloud bare-metal preferred for access to hardware performance counters. This introductory
+    path assumes familiarity with the Linux command line and basic CPU performance concepts and
+    is designed to be completed in about 60 minutes.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      You need a Linux system on an Arm Neoverse-based server, with bare-metal or cloud bare-metal
+      access preferred for hardware performance counters. You should be comfortable with the Linux
+      command line and have a basic understanding of CPU performance concepts.
+  - question: How do I know the sample Mandelbrot application built and runs correctly?
+    answer: >-
+      The program generates a 1920×1080 bitmap image of the fractal when it runs successfully.
+      Use this output as a quick validation before launching Arm Performix analyses.
+  - question: Which option should I select for the Instruction Mix recipe?
+    answer: >-
+      Choose Dynamic for the Analysis Mode. This path uses the Dynamic mode to report instruction
+      types and SIMD utilization.
+  - question: What should I look for in the CPU Microarchitecture recipe results?
+    answer: >-
+      Identify which instruction pipeline stages dominate program latency. Use those findings
+      to focus subsequent changes on the most impactful bottlenecks.
+  - question: How do I confirm whether my workload is using SIMD, and what if it isn’t?
+    answer: >-
+      Run the Instruction Mix recipe and review the SIMD utilization alongside the integer and
+      floating-point instruction counts. If SIMD usage is absent, proceed with vectorization and
+      appropriate compiler flags, then compare performance profiles to measure the effect.
+# END generated_summary_faq
 
 author:
 - Brendan Long
diff --git a/content/learning-paths/servers-and-cloud-computing/performix-system-characterization/_index.md b/content/learning-paths/servers-and-cloud-computing/performix-system-characterization/_index.md
index 05cb2a6884..a3fb62fe10 100644
--- a/content/learning-paths/servers-and-cloud-computing/performix-system-characterization/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/performix-system-characterization/_index.md
@@ -22,7 +22,7 @@ prerequisites:
 generate_summary_faq: true
 
 rerun_summary: false
-rerun_faqs: false
+rerun_faqs: true
 author:
 - Brendan Long
 - David Wong
diff --git a/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md
index 8e497525b9..3c600d1fe3 100644
--- a/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md
@@ -22,6 +22,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:48:24Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 719ec8966a776cc5ee97782b7ef7cc2b989a8616d14cb36b9847c9cbd2f16446
+  summary_generated_at: '2026-06-02T04:47:26Z'
+  summary_source_hash: 719ec8966a776cc5ee97782b7ef7cc2b989a8616d14cb36b9847c9cbd2f16446
+  faq_generated_at: '2026-06-03T01:48:24Z'
+  faq_source_hash: 719ec8966a776cc5ee97782b7ef7cc2b989a8616d14cb36b9847c9cbd2f16446
+  summary: >-
+    Follow this introductory path to deploy and validate a PHP stack on Arm-based Google Cloud
+    C4A virtual machines built on Axion processors. You will provision a SUSE Linux Enterprise
+    Server instance (c4a-standard-4), install PHP, Apache, and common PHP extensions, and configure
+    PHP-FPM. The path guides you through running baseline HTTP server tests to confirm the setup
+    and benchmarking PHP performance with PHPBench on Arm64. It is intended for developers migrating
+    PHP workloads from x86_64 to Arm on Google Cloud. Prerequisites include a Google Cloud Platform
+    account with billing enabled and basic familiarity with web servers and PHP scripting.
+  faqs:
+  - question: What do I need before provisioning the instance on Google Cloud?
+    answer: >-
+      You need a Google Cloud Platform account with billing enabled and basic familiarity with
+      web servers and PHP scripting. The path references a separate Learning Path for general
+      GCP setup support.
+  - question: Which Google Cloud VM configuration does this path use?
+    answer: >-
+      You will create a Google Cloud C4A Arm-based Axion VM using the c4a-standard-4 machine type
+      (four vCPUs, 16 GB memory). Provisioning is performed in the Google Cloud Console.
+  - question: Which operating system and architecture are targeted?
+    answer: >-
+      The path uses SUSE Linux Enterprise Server on an Arm64 Google Cloud C4A instance. Steps
+      refer to installing and configuring software on a SUSE Arm-based virtual machine.
+  - question: How do I install the PHP stack on the SUSE instance?
+    answer: >-
+      Update the system with zypper and then install PHP, PHP-FPM, Apache, and commonly used PHP
+      extensions. The steps use zypper refresh, zypper update, and zypper install to add the required
+      packages.
+  - question: How do I validate the setup and what should I look for in benchmarks?
+    answer: >-
+      Configure a PHP-FPM pool, connect it to Apache, and run baseline HTTP server tests to verify
+      FastCGI and dynamic PHP are working. For benchmarking, use PHPBench and review metrics like
+      mode time, variance, and throughput on sample operations such as string and array handling.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md b/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md
index 6886618997..9b52accce6 100644
--- a/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md
@@ -22,6 +22,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:49:12Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 2fdb45e72a36eb114250486b88d603cc8687b9f6b44bb5bb656e25c37d9795a9
+  summary_generated_at: '2026-06-02T04:47:41Z'
+  summary_source_hash: 2fdb45e72a36eb114250486b88d603cc8687b9f6b44bb5bb656e25c37d9795a9
+  faq_generated_at: '2026-06-03T01:49:12Z'
+  faq_source_hash: 2fdb45e72a36eb114250486b88d603cc8687b9f6b44bb5bb656e25c37d9795a9
+  summary: >-
+    This advanced Learning Path teaches you how to control where your workloads run on many-core
+    Arm-based Linux systems by setting CPU affinity for processes and threads. You will pin threads
+    to specific cores using taskset and source-level changes in C++ and Python, create a single-threaded
+    Python benchmark and C++ examples, and use perf (and Google Benchmark where applicable) to
+    measure cache behavior and compare default scheduling with pinned execution. You will also
+    evaluate throughput versus latency consistency and apply CPU affinity strategies for co-located
+    workloads. The path runs on any Arm Linux system with four or more CPU cores; an example uses
+    an AWS Graviton 3 m7g.4xlarge with Ubuntu 24.04 LTS, and you will check NUMA topology with
+    lscpu. Prerequisites are explicitly listed.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an Arm Linux system with four or more CPU cores. Experience with multi-threaded
+      C++ and Python, build systems, computer architecture concepts, and familiarity with Linux
+      command-line tools is expected.
+  - question: Do I have to use the AWS Graviton3 instance mentioned in the setup?
+    answer: >-
+      No. The steps work on any Arm Linux system with four or more cores; the AWS Graviton3 m7g.4xlarge
+      on Ubuntu 24.04 LTS (Neoverse V1) is provided as an example.
+  - question: How do I check whether my system has a single NUMA node before choosing cores?
+    answer: >-
+      Run lscpu | grep -i numa. On the example m7g.4xlarge instance, all 16 cores are reported
+      in the same NUMA node.
+  - question: How do I validate that thread pinning changed behavior?
+    answer: >-
+      Compare runs before and after pinning using the provided benchmarks and use perf to measure
+      cache performance differences. Use the results to assess throughput and latency consistency
+      trade-offs.
+  - question: When is thread pinning most useful in this Learning Path?
+    answer: >-
+      Pinning is presented as a fine-tuning technique for workloads that aim to consume as many
+      CPU cycles as possible while co-located with other workloads. Use it when you want more
+      consistent execution by constraining where threads run.
+# END generated_summary_faq
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md b/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md
index 4f08eaf76a..bb326211d5 100644
--- a/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md
@@ -20,6 +20,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:50:24Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 3ec6ae40daff557dd05cd092ff7d6b5a63954a1618605030a443f56fe5ffe490
+  summary_generated_at: '2026-06-02T04:48:11Z'
+  summary_source_hash: 3ec6ae40daff557dd05cd092ff7d6b5a63954a1618605030a443f56fe5ffe490
+  faq_generated_at: '2026-06-03T01:50:24Z'
+  faq_source_hash: 3ec6ae40daff557dd05cd092ff7d6b5a63954a1618605030a443f56fe5ffe490
+  summary: >-
+    This Learning Path shows how to instrument C/C++ applications on Arm-based Linux systems for
+    precise, code-level performance analysis using the PMUv3 plugin. You will prepare the plugin,
+    enable user-space access to Arm PMUv3 performance counters, and instrument one or multiple
+    code sections to collect fine-grained metrics. The path demonstrates running a single collection
+    over any of 15 event groups (bundles) and using a Python tool to plot raw PMU event values
+    alongside KPIs such as MPKI, stalls, and IPC for visualization. Prerequisites include an Arm-based
+    computer running Linux and some familiarity with Linux application performance analysis; no
+    additional prerequisites are explicitly listed.
+  faqs:
+  - question: How do I enable and verify userspace access to the PMU counters?
+    answer: >-
+      Run: sudo sysctl kernel/perf_user_access=1. Verify with: cat /proc/sys/kernel/perf_user_access
+      and expect a value of 1. This setting enables access until the next reboot.
+  - question: How should I organize my directories before instrumenting code?
+    answer: >-
+      Keep three parallel directories: the Linux kernel source tree, the PMUv3 plugin source code,
+      and a test directory for integrating the plugin into an application. If you use a different
+      layout, adjust build commands to locate headers and libraries accordingly.
+  - question: Which events and metrics can I collect in a single run?
+    answer: >-
+      You can collect raw event values and performance metrics for any of the 15 event groups
+      (bundles) in a single run. The results can later be plotted with KPIs such as MPKI, stalls,
+      and IPC.
+  - question: How do I instrument multiple sections of code in C?
+    answer: >-
+      Include the two required headers, initialize the plugin with pmuv3_bundle_init() using the
+      desired bundle number, then use start and stop functions with markers to identify each profiled
+      segment. Cleanup steps are the same as for the single-section scenario.
+  - question: How do I set up the Python environment to plot and analyze results?
+    answer: >-
+      On Ubuntu, install python-is-python3, python3-pip, and python3-venv, create and activate
+      a virtual environment, then pip install pandas, pyyaml, matplotlib, and PyPDF2. Download
+      the provided Python application to generate plots of raw PMU events and KPIs from your collected
+      data.
+# END generated_summary_faq
 
 author: Gayathri Narayana Yegna Narayanan
 
diff --git a/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md
index b101781d42..8c0445f7df 100644
--- a/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md
@@ -23,6 +23,57 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:52:07Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 57827f45473867b3b5039a9cbca04d2c6d8a93e177ca0ab053999517389014b5
+  summary_generated_at: '2026-06-02T04:48:52Z'
+  summary_source_hash: 57827f45473867b3b5039a9cbca04d2c6d8a93e177ca0ab053999517389014b5
+  faq_generated_at: '2026-06-03T01:52:07Z'
+  faq_source_hash: 57827f45473867b3b5039a9cbca04d2c6d8a93e177ca0ab053999517389014b5
+  summary: >-
+    Deploy PostgreSQL on Arm-based Microsoft Azure Cobalt 100 virtual machines and validate it
+    for transactional and analytical workloads in about 30 minutes. You will provision a Dpsv6
+    VM, install PostgreSQL on Ubuntu 24.04 Pro Arm64, configure the service for remote access,
+    and load a relational schema with transactional data. The path then runs analytical SQL queries,
+    benchmarks using pgbench, and monitors query execution with built-in PostgreSQL tools such
+    as pg_stat_statements. You will also add indexes and apply basic tuning for better query execution
+    on Arm. Prerequisites include an Azure account with access to Cobalt 100 instances, basic
+    Linux CLI skills, SSH familiarity, and a basic understanding of databases and SQL.
+  faqs:
+  - question: What do I need in Azure before creating the VM?
+    answer: >-
+      You need a Microsoft Azure account with access to Cobalt 100-based instances (Dpsv6). The
+      path also assumes basic Linux, SSH, and SQL knowledge.
+  - question: Which option should I use to provision the Cobalt 100 VM?
+    answer: >-
+      The path walks through creating the VM in the Azure Portal and targets general-purpose Dpsv6
+      instances. You can also use the Azure CLI or an IaC tool, but the instructions focus on
+      the Portal workflow.
+  - question: How do I confirm PostgreSQL is installed and ready for connections?
+    answer: >-
+      At the end of the installation section, PostgreSQL is installed, running as a service, configured
+      for remote access, and ready for application workloads. You can verify by connecting as
+      the postgres user to the appdb database.
+  - question: What schema and data are created before running queries?
+    answer: >-
+      The path creates a relational schema with two tables to simulate a transactional application
+      and loads sample transactional data. You work in the appdb database and then run analytical
+      SQL queries using the application user.
+  - question: What should I expect after running the pgbench initialization, and how do I monitor
+      queries?
+    answer: >-
+      Running pgbench -i -s 50 appdb creates standard benchmarking tables and loads data for testing,
+      with output indicating the initialization steps. For monitoring and tuning, you use PostgreSQL
+      built-in extensions such as pg_stat_statements and apply indexing techniques described in
+      the path.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md b/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md
index b479cc2341..b307f0e9c2 100644
--- a/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md
@@ -18,6 +18,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:51:17Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: a60cc2148a83f919467076e9d5a49fc4c9cec85670a919c7ba044cba72bfe219
+  summary_generated_at: '2026-06-02T04:48:33Z'
+  summary_source_hash: a60cc2148a83f919467076e9d5a49fc4c9cec85670a919c7ba044cba72bfe219
+  faq_generated_at: '2026-06-03T01:51:17Z'
+  faq_source_hash: a60cc2148a83f919467076e9d5a49fc4c9cec85670a919c7ba044cba72bfe219
+  summary: >-
+    This introductory Learning Path shows how to deploy PostgreSQL on Arm-based infrastructure
+    running Linux. In about 30 minutes, you will review deployment choices on Arm, including bare
+    metal, cloud VMs, and SQL services from AWS, Microsoft Azure, Google Cloud, and Oracle. You
+    will consider installation and configuration options, learn how to check your database, and
+    use the psql client tool to interact with PostgreSQL. A prerequisite is access to an Arm-based
+    instance from a cloud service provider or an on-premise Arm server; if you do not yet have
+    an Arm node, the path outlines options. The content targets Arm server platforms, including
+    Neoverse-based systems.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      You need access to an Arm-based instance from a cloud provider or an on-premise Arm server.
+      The path targets Linux. If you do not have an Arm node, the next section discusses options.
+  - question: Which Arm deployment options does this path cover?
+    answer: >-
+      It discusses deploying PostgreSQL on bare metal, on cloud virtual machines, and via managed
+      SQL services. Cloud providers listed include AWS, Microsoft Azure, Google Cloud, and Oracle.
+  - question: Will I use the psql client, and for what?
+    answer: >-
+      Yes. You will use the psql client tool to interact with the PostgreSQL database, run SQL,
+      and validate connectivity.
+  - question: How do I know my PostgreSQL installation is working?
+    answer: >-
+      The steps include configuring and checking your PostgreSQL database, then connecting with
+      psql. Successful connection and basic SQL interaction indicate that the database is running.
+  - question: Can I skip any sections if I already have experience or hardware?
+    answer: >-
+      If you already know how to deploy PostgreSQL, you can skip this path and explore the Learn
+      how to Tune PostgreSQL path. If you already have an Arm system, you can skip the subsection
+      about Arm deployment options and continue reading.
+# END generated_summary_faq
 
 author: Jason Andrews
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md
index a06bbfd4a0..1d53d53ced 100644
--- a/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md
@@ -16,6 +16,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:52:41Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: f30f7fb50000ae7ee28e46d7cbe4e73ba232c3daad2d559cfa41996d630cb936
+  summary_generated_at: '2026-06-02T04:49:21Z'
+  summary_source_hash: f30f7fb50000ae7ee28e46d7cbe4e73ba232c3daad2d559cfa41996d630cb936
+  faq_generated_at: '2026-06-03T01:52:41Z'
+  faq_source_hash: f30f7fb50000ae7ee28e46d7cbe4e73ba232c3daad2d559cfa41996d630cb936
+  summary: >-
+    This advanced Learning Path guides developers and DevOps engineers through tuning PostgreSQL
+    on Linux, with relevance to Arm Neoverse-based servers and common cloud providers. You will
+    review system considerations such as storage technology and file system selection (with xfs
+    as a good starting point), apply PostgreSQL configuration changes via configuration files
+    (including connection and prepared transaction settings), and measure their impact using HammerDB
+    TPROC-C. The content emphasizes that tuning is workload-specific and should be validated with
+    testing. A bare-metal or cloud installation of PostgreSQL is required, and you need a machine
+    or cloud node with PostgreSQL installed and configured for the test steps. Estimated time
+    to complete is about 30 minutes.
+  faqs:
+  - question: What do I need before running the tuning and tests?
+    answer: >-
+      You need a physical machine or a cloud node with PostgreSQL installed and configured. The
+      prerequisite is a bare-metal or cloud installation of PostgreSQL.
+  - question: How should I apply the provided PostgreSQL configuration parameters?
+    answer: >-
+      The parameters shown can be pasted directly into a PostgreSQL configuration file. The path
+      references the Setting Parameters documentation for ways to set these values.
+  - question: Which storage and file system options should I start with?
+    answer: >-
+      Storage technology and file system format can significantly impact performance. In general,
+      locally attached SSDs perform best, but network-based storage can also perform well; xfs
+      is a good start. You should study and experiment with options for your workload.
+  - question: Do I need to use HammerDB if I already have a performance test?
+    answer: >-
+      No. Skip the HammerDB section if you already have a performance test methodology; otherwise,
+      the path demonstrates testing with HammerDB TPROC-C.
+  - question: Should I increase max_connections or max_prepared_transactions?
+    answer: >-
+      Increase these only if your use case requires high client connection counts or prepared
+      transactions. The path notes that max_connections does not directly impact query performance
+      but helps avoid rejecting client requests; test any changes.
+# END generated_summary_faq
 
 author: Julio Suarez
 
diff --git a/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md b/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
index ee6bbd1279..57b8a4474e 100644
--- a/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
@@ -18,6 +18,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:53:34Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: ed85d6171f3c05bfdf1b396065fb0c73ce88724ff63fd1c3c8a93d4c27dd59f8
+  summary_generated_at: '2026-06-02T04:49:56Z'
+  summary_source_hash: ed85d6171f3c05bfdf1b396065fb0c73ce88724ff63fd1c3c8a93d4c27dd59f8
+  faq_generated_at: '2026-06-03T01:53:34Z'
+  faq_source_hash: ed85d6171f3c05bfdf1b396065fb0c73ce88724ff63fd1c3c8a93d4c27dd59f8
+  summary: >-
+    This Learning Path shows you how to build and run the Process Watch tool on an Arm-based Linux
+    machine to monitor, in real time, whether workloads use specific Arm instructions and features.
+    You will install required build dependencies (such as CMake, Clang/LLVM, and libelf), clone
+    the Process Watch repository with submodules, and run the tool—preferably as root or by configuring
+    capabilities and sysctl settings for non-root use. It explains how Process Watch samples retired
+    instructions via Linux perf_events and a BPF program, and how to interpret output fields like
+    PID, NAME, NEON, SVE, and SVE2. You will compile and run a simple C workload to observe instruction
+    usage, including a no-optimization case. Prerequisites are an Arm-based Linux system (kernel
+    5.8+), with root or sudo access.
+  faqs:
+  - question: What do I need before running the steps in this Learning Path?
+    answer: >-
+      Use an Arm-based system running Linux with kernel version 5.8.0 or later, and have root
+      access or the ability to use sudo. No other prerequisites are explicitly listed.
+  - question: Which packages should I install on Ubuntu 20.04 or later?
+    answer: >-
+      Run: sudo apt-get update, then sudo apt-get install libelf-dev cmake clang llvm llvm-dev
+      -y. These provide CMake, Clang/LLVM, and libelf required to build Process Watch.
+  - question: How should I clone the Process Watch repository to include all submodules?
+    answer: >-
+      Clone with submodules using: git clone --recursive https://github.com/intel/processwatch.git.
+      The --recursive option ensures all submodules are fetched.
+  - question: Should I run Process Watch as root, or can I enable it for non-root users?
+    answer: >-
+      Running as root is recommended. To allow a non-root user, run these as root: sudo setcap
+      CAP_PERFMON,CAP_BPF=+ep ./processwatch, sudo sysctl -w kernel.perf_event_paranoid=-1, and
+      sudo sysctl kernel.unprivileged_bpf_disabled=0.
+  - question: How do I run Process Watch and interpret its output for NEON or SVE usage?
+    answer: >-
+      View options with: sudo ./processwatch -h, then run the tool and observe columns like FPARMv8,
+      NEON, SVE, SVE2, %TOTAL, and TOTAL. Create and run the provided C workload with different
+      optimization settings; the NEON and SVE columns indicate whether those instruction sets
+      are being exercised.
+# END generated_summary_faq
 
 author: Graham Woodward
 
diff --git a/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md b/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md
index 3a329bdb5b..858c9537a7 100644
--- a/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md
@@ -17,6 +17,58 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:54:25Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: ef12378069d6f3538d8daefb5da7fc8e8ce4196277928d372745d12fde6e46a1
+  summary_generated_at: '2026-06-02T04:50:40Z'
+  summary_source_hash: ef12378069d6f3538d8daefb5da7fc8e8ce4196277928d372745d12fde6e46a1
+  faq_generated_at: '2026-06-03T01:54:25Z'
+  faq_source_hash: ef12378069d6f3538d8daefb5da7fc8e8ce4196277928d372745d12fde6e46a1
+  summary: >-
+    This introductory Learning Path shows how to profile applications on Arm Neoverse-based Linux
+    servers using Streamline CLI tools and Arm’s top-down performance methodology. You begin by
+    checking hardware-assisted profiling support with Arm Sysreport, examining perf counters and
+    SPE availability (best results are on systems with at least 6 CPU counters). You then capture
+    raw samples with sl-record, preprocess with sl-analyze, and format function-attributed metrics
+    with sl-format.py. The path explains Frontend, Backend, and Retire concepts, demonstrates
+    interpreting Retiring%, FE bound%, Bad spec%, and BE bound% in a sample report, and provides
+    a short optimization checklist. Prerequisite: an Arm Neoverse (N1, N2, or V1) Linux system;
+    supported host OS options include Amazon Linux 2023+, Debian 10+, RHEL 8+, or Ubuntu 20.04+.
+  faqs:
+  - question: What do I need before running the profiling steps?
+    answer: >-
+      You need an Arm Neoverse-based (N1, N2, or V1) computer running Linux. Supported host OS
+      options include Amazon Linux 2023 or newer, Debian 10 or newer, RHEL 8 or newer, or Ubuntu
+      20.04 or newer.
+  - question: How do I know if my system supports hardware-assisted profiling?
+    answer: >-
+      Run the Arm Sysreport utility as described in the referenced guide. In the report, perf
+      counters shows how many CPU counters are available and perf sampling indicates if SPE is
+      available; systems with at least 6 available CPU counters provide better profiles.
+  - question: Do I need to rebuild my application before profiling?
+    answer: >-
+      Yes. Build your application with debug information so the profiler can map instructions
+      to source code and attribute metrics to functions.
+  - question: Which Streamline CLI tools should I run and in what order?
+    answer: >-
+      Use sl-record to capture raw sampled data, sl-analyze to generate function-attributed counters
+      and metrics, and sl-format.py to produce a human-readable report. Follow this sequence for
+      each profiling run.
+  - question: What result should I expect, and how do I interpret low Retiring%?
+    answer: >-
+      After sl-format.py, expect a functions report with top-down metrics: Retiring%, FE bound%,
+      Bad spec%, and BE bound%. A low Retiring% indicates inefficient use of processing resources;
+      if a function is frontend bound with high instruction cache miss rate, the checklist suggests
+      applying profile-guided optimization to reduce less important code.
+# END generated_summary_faq
+
 author: Julie Gaskin
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md
index 54301e4e52..888ea80b2c 100644
--- a/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md
@@ -16,6 +16,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:54:50Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: aeb6a390b5134af2f851e36db17c5d3578d4697b3c372af86c6bd5176765e087
+  summary_generated_at: '2026-06-02T04:51:08Z'
+  summary_source_hash: aeb6a390b5134af2f851e36db17c5d3578d4697b3c372af86c6bd5176765e087
+  faq_generated_at: '2026-06-03T01:54:50Z'
+  faq_source_hash: aeb6a390b5134af2f851e36db17c5d3578d4697b3c372af86c6bd5176765e087
+  summary: >-
+    Learn how to deploy and validate Puppet on Arm-based Google Cloud C4A virtual machines powered
+    by Axion processors. You will provision a SUSE Linux Arm64 VM (c4a-standard-4), install Puppet
+    by setting up dependencies and building Ruby 3.1.4 from source, then verify the installation
+    by checking Puppet and Facter versions, applying a simple manifest, and confirming system
+    facts collection. The path concludes with a standalone benchmark of Puppet on Arm64 to measure
+    catalog compile time, apply speed, and resource usage—without a Puppet Master. Prerequisites
+    include a GCP account with billing enabled and basic familiarity with Puppet. Expected duration
+    is about 30 minutes.
+  faqs:
+  - question: What do I need before provisioning the VM?
+    answer: >-
+      You need a Google Cloud Platform account with billing enabled and basic familiarity with
+      Puppet. No other prerequisites are explicitly listed.
+  - question: Which Google Cloud machine type and OS should I select?
+    answer: >-
+      Use the c4a-standard-4 machine type (4 vCPUs, 16 GB memory) and a SUSE Linux Arm64 (SUSE
+      Linux Enterprise Server) image. The VM is created from the Google Cloud Console under Compute
+      Engine.
+  - question: Do I need to build Ruby, and which version is used?
+    answer: >-
+      Yes. You will install required development tools and libraries, then build Ruby 3.1.4 from
+      source to prepare the environment for Puppet and avoid compatibility issues.
+  - question: How do I verify that Puppet installed correctly?
+    answer: >-
+      Run a version check such as puppet --version (the example output shown is 8.10.0), then
+      run basic Puppet commands. Apply a simple manifest and confirm that resources are created
+      and Facter reports system facts.
+  - question: Does the benchmark require a Puppet Master, and what does it measure?
+    answer: >-
+      No. The benchmark runs standalone on the SUSE Arm64 VM and measures local execution, including
+      catalog compile time, apply speed, and resource usage on Arm64.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md b/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md
index d9f3318e57..6d0c5ac397 100644
--- a/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md
@@ -20,6 +20,51 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:55:09Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 1c5be7bfc7785ae3b06c60210c06324f00aed7ba75145a0fa65425e6005d4f06
+  summary_generated_at: '2026-06-02T04:51:34Z'
+  summary_source_hash: 1c5be7bfc7785ae3b06c60210c06324f00aed7ba75145a0fa65425e6005d4f06
+  faq_generated_at: '2026-06-03T01:55:09Z'
+  faq_source_hash: 1c5be7bfc7785ae3b06c60210c06324f00aed7ba75145a0fa65425e6005d4f06
+  summary: >-
+    Learn how to run a Meta Llama 3.1 LLM chatbot on Arm-based servers using PyTorch and KleidiAI
+    INT4 kernels. You will use an Ubuntu 24.04 LTS Arm instance with at least 16 cores, 64 GB
+    RAM, and 50 GB disk; the steps were tested on an AWS Graviton4 r8g.4xlarge. The path covers
+    downloading the model from the Meta Hugging Face repository, applying 4-bit quantization,
+    running CPU inference with PyTorch, and exposing the service through a Streamlit frontend
+    backed by the Torchchat framework. You will also measure performance metrics for the inference
+    run. Estimated time to complete is about 30 minutes. No additional explicit prerequisites
+    are listed beyond access to an Arm-based server.
+  faqs:
+  - question: What infrastructure and OS should I use to follow this path?
+    answer: >-
+      Use an Arm server running Ubuntu 24.04 LTS with at least 16 cores, 64 GB of RAM, and around
+      50 GB of disk space. The instructions were tested on an AWS Graviton4 r8g.4xlarge instance.
+  - question: Do I need a GPU to run the example?
+    answer: >-
+      No. The Learning Path runs LLM inference on an Arm-based CPU using PyTorch.
+  - question: Where do I obtain the model used in the example?
+    answer: >-
+      Download the Meta Llama 3.1 model from the Meta Hugging Face repository as shown in the
+      steps.
+  - question: How is quantization performed, and what role does KleidiAI play?
+    answer: >-
+      The model is 4-bit quantized using optimized INT4 KleidiAI Kernels for PyTorch. This setup
+      is used to run the LLM on Arm-based CPUs.
+  - question: Which packages are required for the frontend, and how do I avoid HTTP client issues?
+    answer: >-
+      Activate the torch_env virtual environment, install openai version 1.45.0, and roll back
+      httpx to a version before 0.28. Then start the backend server and launch the Streamlit app
+      to access the chatbot in your browser.
+# END generated_summary_faq
 
 author:
     - Nikhil Gupta
diff --git a/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md
index e091864603..7393d3b4b6 100644
--- a/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md
@@ -24,6 +24,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:55:47Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 8b180acd30b3b00484b6e7302b61fd8c3669ef83ff7fca41972d24c5df2682b7
+  summary_generated_at: '2026-06-02T04:52:49Z'
+  summary_source_hash: 8b180acd30b3b00484b6e7302b61fd8c3669ef83ff7fca41972d24c5df2682b7
+  faq_generated_at: '2026-06-03T01:55:47Z'
+  faq_source_hash: 8b180acd30b3b00484b6e7302b61fd8c3669ef83ff7fca41972d24c5df2682b7
+  summary: >-
+    This Learning Path shows how to deploy the Qdrant vector database on Arm-based Google Cloud
+    C4A Axion processors, generate text embeddings with Sentence Transformers in Python, and run
+    semantic similarity search to power a simple chatbot retrieval system. You will provision
+    a c4a-standard-4 Arm64 VM in Google Compute Engine, prepare a SLES Linux environment, install
+    and run Qdrant, create and index embeddings, and issue vector queries. Tools used include
+    Qdrant, Python, Sentence Transformers, and Docker. Prerequisites are a GCP account with billing
+    enabled, basic Python skills, a basic understanding of embeddings, and familiarity with the
+    Linux command line. The path is introductory and designed to complete in about 30 minutes.
+  faqs:
+  - question: Do I need anything set up in Google Cloud before I start?
+    answer: >-
+      Yes. You need a Google Cloud Platform account with billing enabled to provision the Axion
+      C4A VM used in this Learning Path.
+  - question: Which Google Cloud instance and operating system should I create?
+    answer: >-
+      Create a c4a-standard-4 Arm-based VM (4 vCPUs, 16 GB memory) and use a SUSE Linux Enterprise
+      Server (SLES) arm64 image to host Qdrant.
+  - question: How do I confirm that Qdrant is installed and running on the VM?
+    answer: >-
+      Start Qdrant and check that the service or container initializes without errors and is reachable
+      from your client code on the VM. The steps guide you through deploying Qdrant on the Arm64
+      instance.
+  - question: Which Sentence Transformers model should I use to generate embeddings?
+    answer: >-
+      A specific model is not explicitly listed. Use a Sentence Transformers model suitable for
+      text embeddings to follow the embedding and indexing steps.
+  - question: What result should I expect when I run a semantic similarity query?
+    answer: >-
+      You should see a ranked list of the most relevant documents by meaning and context rather
+      than exact keyword matches. Successful results indicate your embeddings were stored and
+      indexed correctly in Qdrant.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md
index ee3f9467dc..cfb1e007c0 100644
--- a/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md
@@ -24,6 +24,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:56:11Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: b4392f1cf38fa1f3b6c633c7ae1e8d30a51ea8d4e635287586b084cb0d8a556b
+  summary_generated_at: '2026-06-02T04:53:31Z'
+  summary_source_hash: b4392f1cf38fa1f3b6c633c7ae1e8d30a51ea8d4e635287586b084cb0d8a556b
+  faq_generated_at: '2026-06-03T01:56:11Z'
+  faq_source_hash: b4392f1cf38fa1f3b6c633c7ae1e8d30a51ea8d4e635287586b084cb0d8a556b
+  summary: >-
+    Learn how to deploy RabbitMQ on Arm64 infrastructure across Microsoft Azure and Google Cloud.
+    You will provision Arm-based Linux virtual machines on Azure Cobalt 100 (Dpsv6) and Google
+    Cloud C4A with Axion processors, install RabbitMQ 4.2.0 with the required Erlang (OTP 26)
+    on Ubuntu Pro 24.04 for Azure, and configure a SUSE SLES VM on GCP. The path covers baseline
+    validation steps for RabbitMQ, including service and version checks, and setting up a GCP
+    firewall rule to expose the management interface (TCP 15672). It targets engineers migrating
+    messaging workloads and uses RabbitMQ, Erlang, Python, and pika. Prerequisites include Azure
+    and GCP accounts, basic messaging concepts, and Linux command-line familiarity.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Microsoft Azure account with access to Cobalt 100-based Dpsv6 instances, a Google
+      Cloud account with billing enabled, a basic understanding of message queuing concepts, and
+      familiarity with Linux command-line operations.
+  - question: Which Azure VM series and creation method does this path use?
+    answer: >-
+      The path uses Azure Cobalt 100 Dpsv6 instances and creates the VM through the Azure console.
+      Other methods like the Azure CLI or IaC are mentioned but are not used in the walkthrough.
+  - question: How do I verify RabbitMQ and Erlang after installation on Azure?
+    answer: >-
+      Check the service status with sudo systemctl status rabbitmq and confirm the Erlang runtime
+      with the provided erl -eval command (OTP 26). The path also includes a step to confirm the
+      installed RabbitMQ version.
+  - question: How do I expose the RabbitMQ management interface on GCP?
+    answer: >-
+      Create a VPC firewall rule that allows TCP port 15672. In Google Cloud Console, go to VPC
+      Network > Firewall > Create firewall rule, name it (for example, allow-tcp-15672), select
+      your network, and allow ingress on TCP 15672.
+  - question: What should I check if baseline validation fails?
+    answer: >-
+      Verify that the RabbitMQ service is running, confirm Erlang is OTP 26 using the provided
+      command, and follow the RabbitMQ version check step. If accessing the management UI on GCP,
+      ensure the firewall rule for TCP 15672 is correctly configured.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/rag/_index.md b/content/learning-paths/servers-and-cloud-computing/rag/_index.md
index 0b3da3fdfb..035925c70d 100644
--- a/content/learning-paths/servers-and-cloud-computing/rag/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rag/_index.md
@@ -24,6 +24,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:56:43Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: d9435cc1dc1fe65432d83934e69993853dd3f294f66f98e9bcfb5f81e6ac6d5f
+  summary_generated_at: '2026-06-02T04:54:26Z'
+  summary_source_hash: d9435cc1dc1fe65432d83934e69993853dd3f294f66f98e9bcfb5f81e6ac6d5f
+  faq_generated_at: '2026-06-03T01:56:43Z'
+  faq_source_hash: d9435cc1dc1fe65432d83934e69993853dd3f294f66f98e9bcfb5f81e6ac6d5f
+  summary: >-
+    Build and deploy a Retrieval Augmented Generation (RAG) chatbot on Arm-based Google Cloud
+    Axion processors using llama-cpp-python with KleidiAI. You will provision an Arm server running
+    Ubuntu 22.04 LTS, set up a Python backend that integrates an LLM with the FAISS vector database
+    and Hugging Face embeddings, apply 4-bit quantization, and expose REST endpoints. You will
+    also create a Streamlit web interface for document upload and chat, then access the application
+    via your instance’s external URL and review inference performance metrics. This advanced path
+    targets an Arm instance with at least 16 cores, 8GB RAM, and 32GB disk, and assumes familiarity
+    with Python, ML and LLM fundamentals, REST APIs, and vector databases.
+  faqs:
+  - question: What do I need before running this on Google Cloud Axion?
+    answer: >-
+      Use an Arm server instance with at least 16 cores, 8GB of RAM, and 32GB of disk space. The
+      instructions target Ubuntu 22.04 LTS. You should also be comfortable with Python, ML concepts,
+      REST APIs, vector databases, and LLM fundamentals.
+  - question: Which ports and URLs are used by the backend and frontend?
+    answer: >-
+      The frontend is accessed at http://[your instance ip]:8501. The frontend is configured to
+      call the backend at http://localhost:5000. If you access the frontend externally, you may
+      need to allow inbound TCP traffic on port 8501.
+  - question: How do I know the RAG pipeline is working after I start the servers?
+    answer: >-
+      Upload documents or PDFs in the Streamlit UI and submit a query that should reference those
+      documents. The backend integrates LlamaCpp with FAISS and Hugging Face embeddings, so responses
+      should include context drawn from your uploaded content.
+  - question: How is model performance addressed in this Learning Path?
+    answer: >-
+      You will apply 4-bit quantization with llama-cpp-python and monitor/analyze inference performance
+      metrics as part of the deployment. The provided scripts include logging and callbacks to
+      surface runtime behavior.
+  - question: Do I need a specific LLM or a GPU to complete the steps?
+    answer: >-
+      The path uses open-source LLMs via llama-cpp-python and does not specify a single required
+      model. The prerequisites do not list any GPU requirement.
+# END generated_summary_faq
 
 author: Nobel Chowdary Mandepudi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/ran/_index.md b/content/learning-paths/servers-and-cloud-computing/ran/_index.md
index 7e1160aff0..3d0dd9a9ad 100644
--- a/content/learning-paths/servers-and-cloud-computing/ran/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ran/_index.md
@@ -20,6 +20,48 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:57:23Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 2b329c566f7fea90010c52f1ce1cb11bccf9d32cf604aaa720bfca5ef1f85fae
+  summary_generated_at: '2026-06-02T04:55:40Z'
+  summary_source_hash: 2b329c566f7fea90010c52f1ce1cb11bccf9d32cf604aaa720bfca5ef1f85fae
+  faq_generated_at: '2026-06-03T01:57:23Z'
+  faq_source_hash: 2b329c566f7fea90010c52f1ce1cb11bccf9d32cf604aaa720bfca5ef1f85fae
+  summary: >-
+    This introductory Learning Path shows how to build and install the Arm RAN Acceleration Library
+    (ArmRAL) on an Arm-based Linux system and then exercise it to test your platform’s capabilities.
+    You will use a development machine—either a local Arm server, laptop, or desktop, or an Arm-based
+    cloud instance—compile the open-source BSD-licensed library with GCC, and run it to validate
+    the environment. The path is designed for developers new to ArmRAL and 5G RAN acceleration
+    and focuses on practical build-and-run steps that take about 15 minutes. No additional prerequisites
+    are explicitly listed beyond access to an Arm computer running Linux.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an Arm computer running Linux and a development environment on that machine. You
+      can use a local Arm server, laptop, or desktop, or an Arm-based cloud instance.
+  - question: Can I use an Arm-based cloud instance instead of local hardware?
+    answer: >-
+      Yes. You can use an Arm-based instance from a cloud service provider; see the list of Arm
+      cloud service providers referenced in the prerequisites.
+  - question: Which operating system do the instructions target?
+    answer: >-
+      Linux on Arm. A specific distribution is not explicitly listed in the provided context.
+  - question: Which compiler is used to build ArmRAL in this path?
+    answer: >-
+      GCC is used to build the library according to the tools listed for this Learning Path.
+  - question: What result should I expect after completing the steps?
+    answer: >-
+      You will have ArmRAL built and installed, and you will run basic tests to check your platform’s
+      capabilities. A successful outcome is a clean build and tests completing without errors.
+# END generated_summary_faq
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md
index 21a58bf7a9..59b861babb 100644
--- a/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md
@@ -21,6 +21,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:57:59Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 0877f5f233ec8a50172f4bb9388ad38ae9b890a4d049679ca6ece083d760d872
+  summary_generated_at: '2026-06-02T04:56:29Z'
+  summary_source_hash: 0877f5f233ec8a50172f4bb9388ad38ae9b890a4d049679ca6ece083d760d872
+  faq_generated_at: '2026-06-03T01:57:59Z'
+  faq_source_hash: 0877f5f233ec8a50172f4bb9388ad38ae9b890a4d049679ca6ece083d760d872
+  summary: >-
+    This Learning Path shows how to deploy and run distributed AI workloads with Ray on Google
+    Cloud Axion C4A Arm-based VMs. You will provision a c4a-standard-4 instance (4 vCPUs, 16 GB)
+    running SUSE Linux Enterprise Server (SLES) Arm64, configure a firewall rule for the Ray Dashboard
+    and Ray Serve API, install Ray and required dependencies, and initialize a single-node Ray
+    cluster. You will run parallel tasks with Ray Core, perform distributed training and hyperparameter
+    tuning using Ray Train and Ray Tune, and deploy an API with Ray Serve to validate end-to-end
+    execution. Prerequisites are a GCP account with billing enabled and basic familiarity with
+    Python and distributed systems. Estimated time: 30 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Google Cloud Platform (GCP) account with billing enabled and basic familiarity
+      with Python and distributed systems concepts. The path provisions the required Arm-based
+      VM during the steps.
+  - question: Which VM type should I create for this path?
+    answer: >-
+      Create a Google Axion C4A Arm VM using the c4a-standard-4 machine type, which provides 4
+      vCPUs and 16 GB of memory. This instance will host your Ray application.
+  - question: Which Ray components will I use, and for what?
+    answer: >-
+      Use Ray Core to run distributed tasks and parallel workloads. Use Ray Train and Ray Tune
+      for distributed training and hyperparameter tuning, and Ray Serve to deploy a scalable API
+      and validate end-to-end execution.
+  - question: How do I expose the Ray Dashboard and Ray Serve endpoints?
+    answer: >-
+      In the Google Cloud Console, go to VPC Network > Firewall and create a firewall rule that
+      allows the required ports for the Ray Dashboard and Ray Serve API. If you need help with
+      GCP setup, see the Learning Path Getting started with Google Cloud Platform.
+  - question: How do I verify that Ray is set up correctly?
+    answer: >-
+      Run the provided Python script that calls a @ray.remote function and aggregates results
+      with ray.get. You should see output like “Results: [...]” containing the squared numbers
+      from the sample.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md
index 637fcba678..e4abc2e5d5 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md
@@ -23,6 +23,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:59:01Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 9b306bc318491834d0d74dd75221b24081acc20dac7993ccdbd1cb40ba6158ac
+  summary_generated_at: '2026-06-02T04:58:08Z'
+  summary_source_hash: 9b306bc318491834d0d74dd75221b24081acc20dac7993ccdbd1cb40ba6158ac
+  faq_generated_at: '2026-06-03T01:59:01Z'
+  faq_source_hash: 9b306bc318491834d0d74dd75221b24081acc20dac7993ccdbd1cb40ba6158ac
+  summary: >-
+    Learn how to deploy Redis on Azure Cobalt 100 Arm64 virtual machines running Linux, then build
+    and validate real-time messaging and event-driven processing on Arm. You will provision a
+    Cobalt 100 VM in the Dpsv6 series (via the Azure Portal, with options to use the CLI or IaC),
+    install and configure Redis, implement Pub/Sub for low-latency messaging, and use Redis Streams
+    with consumer groups to create scalable pipelines. You will simulate workloads with Python
+    and benchmark throughput and latency to validate performance on Arm-based infrastructure.
+    Prerequisites include an Azure account with Cobalt 100 access, basic Linux and SSH skills,
+    and familiarity with databases and messaging.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Microsoft Azure account with access to Cobalt 100 based instances (Dpsv6), basic
+      Linux command-line skills, familiarity with SSH, and a basic understanding of databases,
+      caching, and messaging systems.
+  - question: Which Azure VM type and creation method should I use?
+    answer: >-
+      The Learning Path focuses on general-purpose Cobalt 100 Arm-based virtual machines in the
+      Dpsv6 series. You can create the VM via the Azure Portal (used in this path), or use the
+      Azure CLI or an infrastructure as code tool if that better fits your workflow.
+  - question: How do I confirm I’m using an Arm-based Cobalt 100 VM?
+    answer: >-
+      During provisioning, ensure you select a Cobalt 100 Arm64 instance in the Dpsv6 series.
+      The path targets an Arm-based Linux VM on Azure Cobalt 100.
+  - question: Do I need Python, and where is it used?
+    answer: >-
+      Yes. Python is used to simulate workloads when validating and benchmarking Redis in the
+      final section.
+  - question: What result should I expect after completing the examples and benchmarks?
+    answer: >-
+      You will have Redis installed and running on a Cobalt 100 VM, with working Pub/Sub messaging
+      and Streams using consumer groups. You will also collect throughput and latency measurements
+      to validate Redis performance on Arm infrastructure.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md b/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md
index 11ab11d4c8..f9f7864368 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md
@@ -20,6 +20,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:59:24Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: eb008543ea4248b231be5e6345546164533edf2bc149613693095812bbbba3d9
+  summary_generated_at: '2026-06-02T04:58:42Z'
+  summary_source_hash: eb008543ea4248b231be5e6345546164533edf2bc149613693095812bbbba3d9
+  faq_generated_at: '2026-06-03T01:59:24Z'
+  faq_source_hash: eb008543ea4248b231be5e6345546164533edf2bc149613693095812bbbba3d9
+  summary: >-
+    This Learning Path guides you through deploying Redis for data searching on Google Cloud C4A
+    virtual machines powered by Axion processors (Arm Neoverse-V2 cores). You will provision a
+    SUSE Linux (SLES) Arm64 instance in Compute Engine, build and install Redis from source with
+    TLS support, verify the server using redis-cli, and run baseline data insertion and retrieval
+    tests. You will then measure Redis SET and GET throughput and latency using the official redis-benchmark
+    tool on Arm64. It is introductory in scope and intended for developers working with Redis-based
+    data searching on Linux/Arm64. Prerequisites are a Google Cloud account with billing enabled
+    and basic familiarity with Redis.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      You need a Google Cloud Platform account with billing enabled and basic familiarity with
+      Redis. No other explicit prerequisites are listed.
+  - question: Which Google Cloud instance and OS should I use?
+    answer: >-
+      Use the C4A family with the c4a-standard-4 machine type (4 vCPUs, 16 GB memory). Provision
+      a SUSE SLES Arm64 virtual machine from the Google Cloud Console.
+  - question: How is Redis installed on the SUSE Arm64 VM?
+    answer: >-
+      You install build prerequisites using zypper, then download Redis 8.2.2 from the official
+      GitHub repository and build from source. Building from source ensures compatibility on Arm
+      and enables TLS support.
+  - question: How do I start Redis and confirm it is running?
+    answer: >-
+      Start the server in the background with redis-server & and verify responsiveness with redis-cli
+      ping, which should return PONG. The steps then insert and retrieve sample data to validate
+      baseline functionality.
+  - question: How do I benchmark Redis and what results should I look for?
+    answer: >-
+      Use the official redis-benchmark tool; the path demonstrates SET testing with redis-benchmark
+      -t set -n 100000 -c 50 and also measures GET. Review requests per second and latency metrics
+      reported by the tool.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/redis/_index.md b/content/learning-paths/servers-and-cloud-computing/redis/_index.md
index 3946113a28..0fddb8dd9c 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis/_index.md
@@ -19,6 +19,51 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:58:39Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 7b9906a3c16ebd3c6b41618be7db76d7a97cc4b16c4da927257fb39a66753e12
+  summary_generated_at: '2026-06-02T04:57:15Z'
+  summary_source_hash: 7b9906a3c16ebd3c6b41618be7db76d7a97cc4b16c4da927257fb39a66753e12
+  faq_generated_at: '2026-06-03T01:58:39Z'
+  faq_source_hash: 7b9906a3c16ebd3c6b41618be7db76d7a97cc4b16c4da927257fb39a66753e12
+  summary: >-
+    Deploy Redis on Arm is an introductory, 30-minute path that guides you through installing,
+    configuring, and connecting to Redis on an Arm-based Linux instance. You will learn about
+    Redis deployment configurations and set up a single-node server, including adjusting the default
+    binding so the service is reachable beyond localhost on the default port 6379. The path applies
+    to Arm virtual machines from major cloud providers (AWS, Microsoft Azure, Google Cloud, Oracle)
+    or an on-premise Arm server. The outcome is a running Redis instance on Arm and a clear understanding
+    of the core setup choices; tuning and advanced configuration are covered in a separate path.
+    Prerequisite: access to an Arm node.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need access to an Arm based instance on a cloud service provider or an on-premise Arm
+      server running Linux. If you do not have an Arm node, the next section discusses options.
+  - question: Which cloud providers can I use for the Arm instance?
+    answer: >-
+      You can use AWS, Microsoft Azure, Google Cloud, or Oracle. The path targets Arm-based virtual
+      machines on these platforms.
+  - question: How do I enable remote access to my single-node Redis server?
+    answer: >-
+      By default Redis binds to 127.0.0.1 on port 6379. To accept remote connections, set the
+      bind option in redis.conf to 0.0.0.0.
+  - question: What port does Redis use in this setup?
+    answer: >-
+      Redis runs on port 6379 by default. The path focuses on adjusting the bind address for a
+      single-node deployment; changing the port is not explicitly listed.
+  - question: What should I do after I have Redis running with the default configuration?
+    answer: >-
+      Once Redis is working, follow the Learn how to Tune Redis learning path. It is recommended
+      for improving the configuration beyond the default setup.
+# END generated_summary_faq
 
 author: Elham Harirpoush
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md b/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md
index 985abdac3e..54f2cb1c41 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md
@@ -20,6 +20,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T01:59:56Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 9146285feb38ee45171ac234f605eee08467bbf675a77df231a6dc63c38282f2
+  summary_generated_at: '2026-06-02T04:59:18Z'
+  summary_source_hash: 9146285feb38ee45171ac234f605eee08467bbf675a77df231a6dc63c38282f2
+  faq_generated_at: '2026-06-03T01:59:56Z'
+  faq_source_hash: 9146285feb38ee45171ac234f605eee08467bbf675a77df231a6dc63c38282f2
+  summary: >-
+    Deploy Redis as a cache for MySQL and PostgreSQL on Arm Neoverse-based Linux virtual machines
+    across AWS, Microsoft Azure, and Google Cloud. Using Terraform and Ansible, you will provision
+    cloud instances and configure Redis as a caching layer for your databases. The path provides
+    provider-specific sections; MySQL deployments are covered on AWS, Azure, and GCP, and PostgreSQL
+    deployments on AWS and Azure. Prerequisites include active accounts on the three clouds and
+    a machine with Terraform, AWS CLI, Google Cloud CLI, Azure CLI, AWS IAM authenticator, and
+    Ansible installed. Expect to complete the hands-on steps in about 90 minutes and finish with
+    repeatable automation for your target platform.
+  faqs:
+  - question: What do I need before running the deployment steps?
+    answer: >-
+      You need AWS, Azure, and Google Cloud accounts, plus Terraform, AWS CLI, Azure CLI, Google
+      Cloud CLI, AWS IAM authenticator, and Ansible installed. You can run the steps from any
+      computer that has these tools installed.
+  - question: Which section should I follow for my database and cloud provider?
+    answer: >-
+      Use the MySQL sections for AWS, Azure, or Google Cloud. Use the PostgreSQL sections for
+      AWS or Azure.
+  - question: I am new to Terraform—what should I read before starting?
+    answer: >-
+      Each section references an introductory guide: Automate AWS EC2 instance creation using
+      Terraform, Automate Azure instance creation using Terraform, or Automate GCP instance creation
+      using Terraform. Review the guide that matches your target cloud before proceeding.
+  - question: What result should I expect, and how long will it take?
+    answer: >-
+      Expect a provisioned Arm-based Linux instance on your chosen cloud with Redis configured
+      as a cache for the selected database. The Learning Path is designed to take approximately
+      90 minutes.
+  - question: Is there a section for deploying Redis as a cache for PostgreSQL on Google Cloud?
+    answer: >-
+      A PostgreSQL section for Google Cloud is not explicitly listed in the provided steps. Follow
+      the available PostgreSQL sections for AWS or Azure.
+# END generated_summary_faq
 
 author: Jason Andrews
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md
index 1fc79cc156..f1de4fd8c0 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md
@@ -19,6 +19,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:00:24Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: a6ed103f2d5a00f4cb6c5df1c0812eb68bbad8746d3f351adc959c6880002bbc
+  summary_generated_at: '2026-06-02T05:00:17Z'
+  summary_source_hash: a6ed103f2d5a00f4cb6c5df1c0812eb68bbad8746d3f351adc959c6880002bbc
+  faq_generated_at: '2026-06-03T02:00:24Z'
+  faq_source_hash: a6ed103f2d5a00f4cb6c5df1c0812eb68bbad8746d3f351adc959c6880002bbc
+  summary: >-
+    This advanced Learning Path shows how to tune Redis on Arm-based servers built on Neoverse,
+    running Linux in the cloud (AWS, Microsoft Azure, Google Cloud, Oracle) or on bare metal.
+    You will review Linux kernel parameters along with compiler and OpenSSL settings that can
+    impact Redis performance, then apply guidance to tune a Redis configuration file for deployment.
+    The material emphasizes workload-specific choices rather than a single preset and introduces
+    practical mechanisms such as /proc and sysctl for memory-related adjustments. Prerequisite:
+    a cloud or bare-metal installation of a Redis file server; if Redis is not set up, review
+    Learn how to deploy Redis first. Estimated time to complete: 30 minutes.
+  faqs:
+  - question: What do I need before running the tuning steps?
+    answer: >-
+      You need a cloud or bare-metal installation of a Redis file server. If you do not already
+      have Redis set up, review Learn how to deploy Redis before starting.
+  - question: Where do I change Linux memory-related kernel parameters during this path?
+    answer: >-
+      You can change them temporarily through the /proc filesystem or permanently using the sysctl
+      command. The path discusses these options as part of general guidance.
+  - question: How should I decide which kernel, compiler, and OpenSSL settings to use?
+    answer: >-
+      There is no one-size-fits-all configuration; the right choices depend on your client request
+      profile and use case. Use the provided guidance to evaluate and select settings that match
+      your workload characteristics.
+  - question: Which Redis configuration does this path focus on?
+    answer: >-
+      It focuses on Redis file configuration and references the Configure Redis single-node section
+      of the Learn how to deploy Redis path. Cluster-specific configuration is not explicitly
+      listed.
+  - question: Can I follow these steps on my preferred cloud provider?
+    answer: >-
+      Yes. The path targets Linux on Arm-based servers and can be used in cloud or bare-metal
+      environments, including AWS, Microsoft Azure, Google Cloud, and Oracle; provider-specific
+      instructions are not detailed.
+# END generated_summary_faq
 
 author: Elham Harirpoush
 
diff --git a/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md b/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md
index f8cdb0483e..08c0fb1f00 100644
--- a/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md
@@ -21,6 +21,58 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:00:58Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: d060151c5f6ac7a743fb53cd3d2a10aa23f8f09245122a199a84ae17a8ab5ff9
+  summary_generated_at: '2026-06-02T05:00:55Z'
+  summary_source_hash: d060151c5f6ac7a743fb53cd3d2a10aa23f8f09245122a199a84ae17a8ab5ff9
+  faq_generated_at: '2026-06-03T02:00:58Z'
+  faq_source_hash: d060151c5f6ac7a743fb53cd3d2a10aa23f8f09245122a199a84ae17a8ab5ff9
+  summary: >-
+    Learn how to debug the Neoverse N2 Reference Design firmware stack using Arm Development Studio
+    on Linux. This path shows how to create a debug connection to an associated Fixed Virtual
+    Platform (FVP), step through early firmware stages, and work with SCP/LCP/RSE and Arm TF-A
+    (BL1 and BL31). You adjust SCP build settings for easier debugging, apply a BL1 workaround
+    to allow early attachment, and use the Functions view to set precise breakpoints. The path
+    is advanced, takes about 30 minutes, and assumes Arm Development Studio with a valid license,
+    the Neoverse RD-N2 Software Stack, an FVP, and a basic understanding of the Neoverse RD platform
+    boot sequence.
+  faqs:
+  - question: What do I need before running the debug steps?
+    answer: >-
+      You need Arm Development Studio with a valid license, the Neoverse RD-N2 Reference Design
+      Software Stack, an associated FVP, and a basic understanding of RD platform boot. The environment
+      targets Linux.
+  - question: Which optimization flag should I use for SCP firmware debug, and how do I change
+      it?
+    answer: >-
+      SCP firmware debug uses -Og by default, which can optimize variables in ways that hinder
+      debugging. To switch to -O0, edit rd-infra/scp/cmake/Toolchain/<compiler>-Baremetal.cmake
+      and change string(APPEND CMAKE_${language}_FLAGS_DEBUG_INIT "-Og") to use "-O0".
+  - question: Why can’t I start the debugger at BL1, and what’s the workaround?
+    answer: >-
+      RSE CPU wait hold means AP cores are powered off, so you cannot start the debugger until
+      RSE powers them. As a workaround, modify BL1 to spin on entry by adding a "b ." in the bl1_entrypoint
+      function at <workspace>/rd-infra/tf-a/bl1/aarch64/bl1_entrypoint.S.
+  - question: How do I set a breakpoint for BL31?
+    answer: >-
+      Open the Functions view in Arm Development Studio, search for bl31_entrypoint, and set a
+      breakpoint there. Continue execution and observe the console as TF-A advances from BL1 to
+      BL2 to BL31, where the breakpoint will be hit.
+  - question: How do I add symbols to debug BL33/UEFI?
+    answer: >-
+      First boot the FVP once without debugging to capture symbol locations and relocation addresses
+      from the Non-secure AP console. Repeat the same actions in the same order during debugging,
+      and use the recorded addresses; the log files are stored under your workspace in the rd-infr
+      directory as indicated in the steps.
+# END generated_summary_faq
 
 author: Daniel Nguyen
 
diff --git a/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md b/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md
index f4ac03c592..671032a54b 100644
--- a/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md
@@ -20,6 +20,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:01:19Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 8f2515b7c416a821bd397a77297adc263397a8b3cb86e9bb34e646735a21f854
+  summary_generated_at: '2026-06-02T05:02:22Z'
+  summary_source_hash: 8f2515b7c416a821bd397a77297adc263397a8b3cb86e9bb34e646735a21f854
+  faq_generated_at: '2026-06-03T02:01:19Z'
+  faq_source_hash: 8f2515b7c416a821bd397a77297adc263397a8b3cb86e9bb34e646735a21f854
+  summary: >-
+    Learn how to set up a Linux host, build, and test the Neoverse Reference Design (RD-N2) firmware
+    stack using containers and an Arm Ecosystem FVP. You will prepare an Ubuntu 22.04 AArch64
+    or x86_64 machine, then build a busybox root filesystem and a firmware stack that includes
+    TF-A, UEFI, SCP, and a lightweight OS loader to exercise the UEFI ExitBootServices transition.
+    Finally, you will validate the build by booting it on the RD-N2 FVP. Tools referenced include
+    Docker, Arm Ecosystem FVPs, Arm Development Studio, and Runbook. Prerequisites include familiarity
+    with the Reference Design software stack architecture and the Linux command line, plus optional
+    Docker basics. Expect to allocate 64GB disk and 32GB RAM (48GB recommended) and about 30 minutes
+    to complete.
+  faqs:
+  - question: Which host platforms and OS versions can I use?
+    answer: >-
+      Use either an AArch64 or x86_64 host machine running Ubuntu Linux 22.04. Other host operating
+      systems are not listed.
+  - question: How much disk space and memory do I need to sync and build the software stack?
+    answer: >-
+      Allocate at least 64 GB of free disk space and 32 GB of RAM. 48 GB of RAM is recommended
+      for the build.
+  - question: How do I launch the build environment and start the build?
+    answer: >-
+      Launch the container using the provided script (for example: bash ./container-scripts/container.sh
+      -v /home/ubuntu/rd-infra/ run). Then run the build script from the build-scripts/ directory
+      inside the container as instructed in the steps.
+  - question: Which FVP should I download for testing, and how do I install it?
+    answer: >-
+      Download the Neoverse N2 Reference Design FVP from Arm Ecosystem FVPs, for example with:
+      wget https://developer.arm.com/-/cdn-downloads/permalink/FVPs-Neoverse-Infrastructure/RD-N2/FVP_RD_N2_11.25_23_Linux64.tgz.
+      Unpack it (tar -xf …) and run the installer with: ./FVP_RD_N2.sh --i-agree-to-the-contained-eula
+      --no-interactive, then export the path to the model binary in the MODEL environment variable.
+  - question: What result should I expect when I test the firmware on the FVP?
+    answer: >-
+      The firmware implementation should build and boot on the RD‑N2 FVP into a lightweight BusyBox
+      shell. This path exercises the UEFI ExitBootServices transition to validate the firmware
+      stack.
+# END generated_summary_faq
 
 author: 
     - Tom Pilar
diff --git a/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md b/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md
index e95d65123a..50ef8547c6 100644
--- a/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md
@@ -8,6 +8,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:01:52Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: d643c9ef25aa5442aec65ac6a8f48264d4789f8e24ffd6270c2efacce48dd8fc
+  summary_generated_at: '2026-06-02T05:03:04Z'
+  summary_source_hash: d643c9ef25aa5442aec65ac6a8f48264d4789f8e24ffd6270c2efacce48dd8fc
+  faq_generated_at: '2026-06-03T02:01:52Z'
+  faq_source_hash: d643c9ef25aa5442aec65ac6a8f48264d4789f8e24ffd6270c2efacce48dd8fc
+  summary: >-
+    This Learning Path shows you how to enable and use reproducible math functions in Libamath,
+    a component of Arm Performance Libraries, on Linux-based Arm systems. You will learn what
+    numerical reproducibility means and where it matters, then configure Libamath so supported
+    functions produce bitwise-identical floating‑point results across scalar, Neon (AdvSIMD),
+    and SVE implementations while operating in the default accuracy mode (within 3.5 ULP). The
+    hands-on example verifies reproducibility using the single‑precision expf function across
+    vector paths. Prerequisites include an Arm computer running Linux with Arm Performance Libraries
+    26.01 or newer, and a C compiler such as GCC or Clang.
+  faqs:
+  - question: What do I need before running the example?
+    answer: >-
+      You need an Arm computer running Linux with Arm Performance Libraries version 26.01 or newer
+      installed, and a C compiler such as GCC or Clang. No other prerequisites are explicitly
+      listed.
+  - question: Which vector extensions are covered by reproducibility in this path?
+    answer: >-
+      On Linux, Libamath supports bitwise-reproducible results across scalar, Neon (AdvSIMD),
+      and SVE implementations for a subset of math functions.
+  - question: Which math functions are reproducible in Libamath?
+    answer: >-
+      Reproducibility is provided for a subset of Libamath functions on Linux. This path demonstrates
+      expf; the complete list of supported functions is not explicitly listed here.
+  - question: How do I compile and link the example against Arm Performance Libraries?
+    answer: >-
+      Set CC to your compiler (for example, gcc), and export C_INCLUDE_PATH, LIBRARY_PATH, and
+      LD_LIBRARY_PATH to point to $ARMPL_DIR/include and $ARMPL_DIR/lib. Then build your C code
+      so it includes Libamath headers and links against the Arm Performance Libraries.
+  - question: What result should I expect when verifying reproducibility?
+    answer: >-
+      For the same inputs, the floating-point results should be bitwise identical across scalar,
+      Neon, and SVE code paths for supported Libamath functions. These routines operate in the
+      default accuracy mode, within 3.5 ULP of the correctly rounded value.
+# END generated_summary_faq
 
 author: Joana Cruz
 
diff --git a/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md b/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md
index 5dfd612d32..f1026074e7 100644
--- a/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md
@@ -19,6 +19,50 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:02:26Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 180803cf9c6e34c0dda76cafdfcd0ce67edfaca4563f57ae0c36bfefd2199ae9
+  summary_generated_at: '2026-06-02T05:04:08Z'
+  summary_source_hash: 180803cf9c6e34c0dda76cafdfcd0ce67edfaca4563f57ae0c36bfefd2199ae9
+  faq_generated_at: '2026-06-03T02:02:26Z'
+  faq_source_hash: 180803cf9c6e34c0dda76cafdfcd0ce67edfaca4563f57ae0c36bfefd2199ae9
+  summary: >-
+    Build and run the Arm Confidential Compute Architecture (CCA) reference software stack on
+    an Armv-A AEM Base FVP with RME support, then create a guest Linux virtual machine inside
+    a Realm. This introductory path targets developers exploring CCA and uses GCC, FVP, RME, CCA,
+    and a Runbook. You will work on Ubuntu 22.04 on aarch64 or x86_64, including cloud instances,
+    with X11 forwarding enabled if you access the machine via a client application. Allocate at
+    least 30 GB of free disk space and install git, gcc, telnet, xterm, net-tools, and build-essential.
+    Estimated time to complete is about two hours.
+  faqs:
+  - question: What do I need on my Ubuntu host before building the Arm CCA stack?
+    answer: >-
+      Use Ubuntu 22.04 on aarch64 or x86_64 with at least 30 GB of free disk space. Install git,
+      gcc, telnet, xterm, net-tools, and build-essential before starting.
+  - question: Which FVP should I use to run the CCA stack?
+    answer: >-
+      Use the Armv-A AEM Base FVP with support for RME extensions. The steps in the path specify
+      the required FVP configuration.
+  - question: Can I complete this Learning Path on a cloud instance?
+    answer: >-
+      Yes. Cloud instances can be used; the path links to a list of Arm cloud service providers.
+  - question: Do I need to enable X11 forwarding?
+    answer: >-
+      Enable X11 forwarding if you use a client application to access your Ubuntu machine. This
+      supports any steps that open X11 applications such as xterm.
+  - question: What outcome should I expect when everything runs correctly?
+    answer: >-
+      You will build and run the Arm CCA reference software stack on the FVP and create a Realm
+      that hosts a guest Linux virtual machine. This demonstrates the CCA flow from build to launching
+      a Realm-based VM.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md b/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md
index 3b361a9198..59a0b1f0aa 100644
--- a/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md
@@ -19,6 +19,50 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:02:59Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 27e17ea322cc7dc117f24d9d2007fbc0e6b498840b4097b859b1f376b8d8c9a6
+  summary_generated_at: '2026-06-02T05:05:13Z'
+  summary_source_hash: 27e17ea322cc7dc117f24d9d2007fbc0e6b498840b4097b859b1f376b8d8c9a6
+  faq_generated_at: '2026-06-03T02:02:59Z'
+  faq_source_hash: 27e17ea322cc7dc117f24d9d2007fbc0e6b498840b4097b859b1f376b8d8c9a6
+  summary: >-
+    This introductory Learning Path guides you through running a Large Language Model (LLM) chatbot
+    on an Arm-based CPU using rtp-llm. You will build rtp-llm, set up Python 3.10 with micromamba,
+    install Bazelisk and build tools, then download the Qwen2-0.5B-Instruct model from Hugging
+    Face. You will start the rtp-llm server and send OpenAI-compatible API requests so applications
+    can interact with the model locally or over the network. The target environment is an Arm
+    Neoverse N2- or V2-based Ubuntu 22.04 LTS server with at least 4 CPU cores, 16 GB RAM, and
+    32 GB storage. By the end, you will have a working LLM chatbot service running on an Arm server.
+  faqs:
+  - question: What hardware and OS do I need before running the steps?
+    answer: >-
+      Use an Arm Neoverse N2- or V2-based server running Ubuntu 22.04 LTS with at least four cores,
+      16GB of RAM, and 32GB of disk. This can be a cloud instance or an on-premise Arm server.
+  - question: Which Python version and location does the rtp-llm build expect?
+    answer: >-
+      The build expects Python 3.10 installed at /opt/conda310. The steps use micromamba to create
+      this environment.
+  - question: Which tools do I need to build rtp-llm?
+    answer: >-
+      Install bazelisk to build rtp-llm, and install git, gcc, g++, and build-essential. These
+      packages are used to fetch sources and compile the project.
+  - question: Which model will I run and how is it obtained?
+    answer: >-
+      You will run the Qwen2-0.5B-Instruct model. It is downloaded from Hugging Face in the steps.
+  - question: How do I interact with the model after starting the server?
+    answer: >-
+      Use the rtp-llm server program and submit requests through its OpenAI-compatible API. Install
+      jq to follow the API examples in the steps. The server can be accessed over the network
+      from another machine.
+# END generated_summary_faq
 
 author: Tianyu Li
 
diff --git a/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md b/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md
index 1b1b74e17b..f36fac898d 100644
--- a/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md
@@ -21,6 +21,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:03:26Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 092602df259461e2b22dbf0909a682fbacd59464eea38ab901f38cd0b8c6efd3
+  summary_generated_at: '2026-06-02T05:06:21Z'
+  summary_source_hash: 092602df259461e2b22dbf0909a682fbacd59464eea38ab901f38cd0b8c6efd3
+  faq_generated_at: '2026-06-03T02:03:26Z'
+  faq_source_hash: 092602df259461e2b22dbf0909a682fbacd59464eea38ab901f38cd0b8c6efd3
+  summary: >-
+    This Learning Path guides you through deploying Ruby on Rails on Arm-based Google Cloud C4A
+    virtual machines powered by Axion processors. You will provision a SUSE Linux Enterprise Server
+    instance—illustrated with the c4a-standard-4 type via Google Cloud Console—install Ruby, Rails,
+    and supporting packages, and set up PostgreSQL including development headers required by the
+    pg gem. You will validate a Rails app’s connectivity to PostgreSQL and run Ruby’s built-in
+    Benchmark library to measure execution time for inserts, queries, and CPU tasks on Arm64.
+    Prerequisites are a Google Cloud Platform account with billing enabled and basic familiarity
+    with Ruby, Rails, and PostgreSQL. Estimated time to complete is about 40 minutes.
+  faqs:
+  - question: What do I need before running this Learning Path?
+    answer: >-
+      You need a Google Cloud Platform (GCP) account with billing enabled. Basic familiarity with
+      Ruby, Rails, and PostgreSQL is also expected.
+  - question: Which Google Cloud machine type and OS does this path use?
+    answer: >-
+      You will create a Google Axion C4A Arm VM using the c4a-standard-4 machine type (4 vCPUs,
+      16 GB memory) in the Google Cloud Console. The instance runs SUSE Linux Enterprise Server
+      (SLES) on Arm64.
+  - question: Where in Google Cloud Console do I create the C4A instance?
+    answer: >-
+      Navigate to Compute Engine > VM Instances and select Create Instance. Choose the C4A Arm-based
+      machine type during configuration.
+  - question: How should I prepare SUSE SLES for installing Ruby on Rails?
+    answer: >-
+      Update system packages first using zypper (for example, sudo zypper update). Then install
+      Ruby, Rails, and the essential development tools as directed in the steps.
+  - question: Which PostgreSQL packages are needed for Rails on SUSE SLES?
+    answer: >-
+      Install postgresql-server and postgresql-devel. The development headers are required so
+      the pg gem can compile and allow Rails to communicate with PostgreSQL.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md
index 1ac08d3389..c1cd93d7ba 100644
--- a/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md
@@ -22,6 +22,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:04:00Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: c3dd7a0ff9c645635e41b2d9110f4c52de627b752e33c3c6775e1d2e3ffc381d
+  summary_generated_at: '2026-06-02T05:07:00Z'
+  summary_source_hash: c3dd7a0ff9c645635e41b2d9110f4c52de627b752e33c3c6775e1d2e3ffc381d
+  faq_generated_at: '2026-06-03T02:04:00Z'
+  faq_source_hash: c3dd7a0ff9c645635e41b2d9110f4c52de627b752e33c3c6775e1d2e3ffc381d
+  summary: >-
+    This introductory Learning Path shows how to deploy and benchmark Rust on Google Cloud C4A
+    virtual machines powered by Arm-based Axion processors (Arm Neoverse-V2 cores). You will provision
+    a SUSE SLES Arm64 instance in the Google Cloud Console (for example, c4a-standard-4 with 4
+    vCPUs and 16 GB memory), install Rust with rustup and essential build tools, verify the toolchain
+    by building and running a simple program, and run cargo bench with Criterion to measure execution
+    speed and stability. It targets developers working on Linux/Arm64 in Google Cloud and takes
+    about 30 minutes. Prerequisites are a GCP account with billing enabled and basic Rust familiarity.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Google Cloud Platform (GCP) account with billing enabled and basic familiarity
+      with Rust. No other explicit prerequisites are listed.
+  - question: Which VM type and OS should I create on Google Cloud?
+    answer: >-
+      Use a Google Axion C4A Arm instance, specifically the c4a-standard-4 machine type in the
+      Google Cloud Console. The Learning Path provisions a SUSE SLES Arm64 environment on this
+      instance.
+  - question: How do I install Rust and build tools on the SUSE Arm64 VM?
+    answer: >-
+      Update the system with zypper and install curl, gcc, and make. Then install Rust using rustup
+      to prepare the environment for building and benchmarking Rust applications.
+  - question: How do I verify that the Rust toolchain is working?
+    answer: >-
+      Create a new project with cargo new hello, then run it with cargo run. A successful build
+      prints the standard Hello, world! message and shows normal Cargo compile and run output.
+  - question: How do I set up and run benchmarks with Criterion?
+    answer: >-
+      Create a new project, add criterion = "0.5" to Cargo.toml, and define a bench target (for
+      example, my_benchmark with harness = false). Place your benchmark code under benches/ and
+      run cargo bench to execute Criterion and collect performance measurements on Arm64.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md b/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md
index 2c0dc0146d..3d58d75bec 100644
--- a/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md
@@ -19,6 +19,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:04:23Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 3d9b35d09c97557dcde807bb83f994f8c3701c0d109c0a9bb388acc603d81b16
+  summary_generated_at: '2026-06-02T05:07:57Z'
+  summary_source_hash: 3d9b35d09c97557dcde807bb83f994f8c3701c0d109c0a9bb388acc603d81b16
+  faq_generated_at: '2026-06-03T02:04:23Z'
+  faq_source_hash: 3d9b35d09c97557dcde807bb83f994f8c3701c0d109c0a9bb388acc603d81b16
+  summary: >-
+    Build an end-to-end sentiment analysis workflow on an Arm-based Amazon EKS cluster. You will
+    deploy a text classification model with Apache Spark, index and analyze posts from X using
+    Elasticsearch, and explore results through a Kibana dashboard. The path also adds cluster
+    observability by deploying Prometheus and Grafana dashboards to track CPU and RAM usage of
+    Kubernetes nodes. Prerequisites include an AWS account and a Linux workstation with Docker,
+    Terraform, eksctl, and kubectl installed; the steps also use the AWS CLI (configured with
+    access keys) and Java. You will clone a provided GitHub repository to bootstrap the environment
+    and validate results in Kibana and Grafana.
+  faqs:
+  - question: What do I need before running the setup commands?
+    answer: >-
+      You need an AWS account and a Linux computer with Docker, Terraform, eksctl, kubectl, the
+      AWS CLI, and Java installed. The path assumes these tools are available on your machine.
+  - question: How do I provide AWS credentials for the deployment tools?
+    answer: >-
+      Generate AWS access keys and configure the AWS CLI following the AWS Credentials Install
+      Guide. The CLI must be authenticated before creating or managing EKS resources.
+  - question: Where do I get the code and configurations used in this path?
+    answer: >-
+      Clone the repository: git clone https://github.com/koleini/spark-sentiment-analysis.git.
+      Work from the cloned directory as the Learning Path steps reference files from that repo.
+  - question: Which dashboards will I use and what data should I expect to see?
+    answer: >-
+      Use Kibana to explore posts on X stored in Elasticsearch through customizable visualizations.
+      Use Grafana, backed by Prometheus, to view Kubernetes metrics such as CPU and memory usage
+      of nodes.
+  - question: How do I know the deployment succeeded?
+    answer: >-
+      You should have a text classification model running on EKS with Apache Spark, a Kibana dashboard
+      to analyze X posts, and Grafana dashboards showing CPU and RAM usage. If any part is missing,
+      repeat the corresponding deployment step in the path.
+# END generated_summary_faq
 
 author: 
     - Pranay Bakre
diff --git a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md
index 897f0387fe..a4cc0c84f8 100644
--- a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md
@@ -18,6 +18,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:04:56Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 0a4dd65c6d0c7eb79479217b351e3d6c5b8917512d510283fd4d8387ff1339f5
+  summary_generated_at: '2026-06-02T05:08:42Z'
+  summary_source_hash: 0a4dd65c6d0c7eb79479217b351e3d6c5b8917512d510283fd4d8387ff1339f5
+  faq_generated_at: '2026-06-03T02:04:56Z'
+  faq_source_hash: 0a4dd65c6d0c7eb79479217b351e3d6c5b8917512d510283fd4d8387ff1339f5
+  summary: >-
+    Learn to set up the Serverless Framework on a Windows on Arm system and deploy an AWS Lambda
+    function using an introductory, step-by-step workflow. You will install Node.js (version 18.20.3
+    or later) and npm, install the Serverless Framework CLI, configure AWS credentials, and use
+    the interactive serverless command to create a new service with the AWS / Node.js / Simple
+    Function template. The target environment is Windows 11 on Arm hardware or a Windows on Arm
+    virtual machine, using any code editor such as Visual Studio Code for Arm64. By the end, you
+    will have created a basic Serverless project and deployed a Lambda function to AWS. Prerequisites
+    include a Windows on Arm computer, a code editor, and AWS credentials.
+  faqs:
+  - question: What do I need before running the setup steps?
+    answer: >-
+      Use a Windows on Arm computer (or Windows on Arm VM) with Windows 11, and install Node.js
+      18.20.3 or later with npm. Any code editor works; Visual Studio Code for Arm64 is suitable.
+      You also need AWS credentials to deploy to AWS.
+  - question: How do I install the Serverless Framework on Windows on Arm?
+    answer: >-
+      After installing Node.js 18.20.3 or greater, open a terminal or command prompt and run:
+      npm install -g serverless. This adds the Serverless Framework globally so you can use the
+      serverless command.
+  - question: How do I start creating the project and choose the correct template?
+    answer: >-
+      Run the serverless command in a terminal to start the wizard. Use the arrow keys to select
+      the AWS / Node.js / Simple Function template and press Enter.
+  - question: What does the wizard generate for me?
+    answer: >-
+      It scaffolds a new Serverless service configured for a simple Node.js Lambda function targeting
+      AWS. You will then proceed to deploy the function as shown in the steps.
+  - question: How do I know my AWS credentials are ready for deployment?
+    answer: >-
+      This path includes configuring AWS credentials, which are required to deploy to AWS. Ensure
+      you have valid AWS credentials created and available locally before running the deployment
+      steps.
+# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md
index b91f9c370b..834704c106 100644
--- a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md
@@ -19,6 +19,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:05:18Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 2120b6bedf6ea5ae02d8b353a6485f307bcb39d0055c29d9e8a39df37a8b946e
+  summary_generated_at: '2026-06-02T05:09:22Z'
+  summary_source_hash: 2120b6bedf6ea5ae02d8b353a6485f307bcb39d0055c29d9e8a39df37a8b946e
+  faq_generated_at: '2026-06-03T02:05:18Z'
+  faq_source_hash: 2120b6bedf6ea5ae02d8b353a6485f307bcb39d0055c29d9e8a39df37a8b946e
+  summary: >-
+    Learn to define and deploy a small AWS serverless application that integrates AWS Lambda with
+    DynamoDB using the Serverless Framework. You will declare a service that provisions a DynamoDB
+    table for sample sensor data, two Lambda functions (one to write temperatures and one to return
+    an average), and an IAM role with the necessary read/write permissions, then deploy everything
+    with a single serverless deploy command. This introductory path takes about 30 minutes. Prerequisites
+    include a Windows on Arm computer or Windows on Arm virtual machine, a code editor such as
+    Visual Studio Code for Arm64, and completion of the prior Serverless Framework on AWS Learning
+    Path. The path uses Node.js and Visual Studio Code.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      Use a Windows on Arm computer or a Windows on Arm virtual machine, and have a code editor;
+      Visual Studio Code for Arm64 is suitable. Complete the “Deploy AWS services using the Serverless
+      Framework” Learning Path first. This path uses Node.js and Visual Studio Code.
+  - question: Which AWS resources does this service create?
+    answer: >-
+      It creates a DynamoDB table to store timestamps and randomly generated temperatures, two
+      AWS Lambda functions, and an IAM role. One Lambda writes temperature data to the table,
+      and the other retrieves the average temperature.
+  - question: Which command do I use to deploy and where should I run it?
+    answer: >-
+      Run serverless deploy from a terminal in the AwsServerlessDynamoDbLambda directory. The
+      Serverless Framework will validate your serverless.yml and deploy the declared resources
+      to AWS.
+  - question: What result should I expect after running the deploy command?
+    answer: >-
+      The deploy command should complete without errors and provision the DynamoDB table, Lambda
+      functions, and IAM role as defined in serverless.yml. The framework handles the orchestration
+      of these resources for you.
+  - question: What should I check if deployment fails?
+    answer: >-
+      Confirm you are in the AwsServerlessDynamoDbLambda folder and that your service is declared
+      as described. Ensure your serverless.yml is valid and that you have completed the prerequisite
+      Learning Path.
+# END generated_summary_faq
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md
index 3406f3f1cd..f52a6fac91 100644
--- a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md
@@ -19,6 +19,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:05:45Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: a31c6f9d674bf41cee16066708c884ca587289e181b7754ff75cdbd85bdb30e3
+  summary_generated_at: '2026-06-02T05:10:00Z'
+  summary_source_hash: a31c6f9d674bf41cee16066708c884ca587289e181b7754ff75cdbd85bdb30e3
+  faq_generated_at: '2026-06-03T02:05:45Z'
+  faq_source_hash: a31c6f9d674bf41cee16066708c884ca587289e181b7754ff75cdbd85bdb30e3
+  summary: >-
+    Build and deploy a multi-resource serverless application on AWS using the Serverless Framework.
+    You will declare a service that provisions an Amazon S3 bucket to host a static website, a
+    DynamoDB table for sample sensor data, two AWS Lambda functions (one to write temperatures
+    and one to return an average), and the required IAM role. You will add website files (including
+    index.html) under your Serverless project and deploy the stack using Serverless Framework
+    commands. This introductory path targets developers using Windows on Arm and assumes completion
+    of the introductory Serverless Framework on AWS Learning Path. Estimated time to complete
+    is about 60 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      Have a Windows on Arm computer or a Windows on Arm virtual machine, a code editor such as
+      Visual Studio Code for Arm64, and completion of the Learning Path on deploying AWS services
+      with the Serverless Framework. This path uses Node.js and npm.
+  - question: Where should I create the website files?
+    answer: >-
+      Create a subfolder under the folder where you created the serverless project (for example,
+      AwsServerlessDynamoDbLambdaS3). Inside that website folder, create index.html as shown in
+      the steps.
+  - question: Which AWS resources does the service declare and deploy?
+    answer: >-
+      A DynamoDB table for hypothetical sensor data, two AWS Lambda functions (one writes temperatures,
+      the other retrieves the average), an IAM role granting the functions access to the table,
+      and an S3 bucket to host the static website.
+  - question: From which directory and with which commands do I deploy?
+    answer: >-
+      Open a terminal and navigate to the AwsServerlessDynamoDbLambda folder. Run npm install
+      --save-dev serverless, then run serverless deploy.
+  - question: What result should I expect after deployment?
+    answer: >-
+      You should see packaging and deployment logs, including a line like "Deploying 'AwsServerlessDynamoDbLambdaS3'
+      to stage 'dev' (us-east-1)". This indicates the service and its AWS resources were deployed.
+# END generated_summary_faq
+
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/snappy/_index.md b/content/learning-paths/servers-and-cloud-computing/snappy/_index.md
index 24bfa1cc8c..dd4fcc2ae2 100644
--- a/content/learning-paths/servers-and-cloud-computing/snappy/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/snappy/_index.md
@@ -20,6 +20,51 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:06:13Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 62edadd9a4163e020b55d33f541a13ceb604c3700ba56787b650563c446a3b0d
+  summary_generated_at: '2026-06-02T05:10:41Z'
+  summary_source_hash: 62edadd9a4163e020b55d33f541a13ceb604c3700ba56787b650563c446a3b0d
+  faq_generated_at: '2026-06-03T02:06:13Z'
+  faq_source_hash: 62edadd9a4163e020b55d33f541a13ceb604c3700ba56787b650563c446a3b0d
+  summary: >-
+    This Learning Path guides you through installing and running lzbench with Snappy and Zstandard
+    to measure compression library performance on Arm servers. It targets Linux and has been tested
+    on AWS EC2 and Oracle OCI Arm-based instances running Ubuntu 20.04, with Snappy and Zstandard
+    also supported on Amazon Linux 2, RHEL/CentOS 8, and Ubuntu 22.04/20.04/18.04. You will install
+    required packages (gcc, g++, unzip, make), install lzbench, and run benchmarks to collect
+    performance measurements on a 64-bit Arm instance. It is introductory and intended for developers
+    using compression libraries on Arm Neoverse-based servers. The only explicit prerequisite
+    is access to an Arm-based cloud instance, and it takes about 10 minutes to complete.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need access to an Arm-based instance from an appropriate cloud service provider. The
+      steps have been tested on AWS EC2 and Oracle OCI Arm-based servers running Ubuntu 20.04.
+  - question: Which Linux distributions are supported for Snappy and Zstandard in this path?
+    answer: >-
+      Amazon Linux 2, RHEL/CentOS 8, and Ubuntu 18.04, 20.04, and 22.04 are supported. The detailed
+      steps were validated on Ubuntu 20.04.
+  - question: Which packages should I install on the instance before building or running lzbench?
+    answer: >-
+      Install GNU gcc and g++ for your Arm Linux distribution, along with unzip and make. On Ubuntu,
+      the packages are gcc, g++, unzip, and make.
+  - question: Which compression libraries are benchmarked and how are they executed?
+    answer: >-
+      The path benchmarks Snappy and Zstandard using lzbench. You install lzbench and run it to
+      measure these libraries on your Arm-based server.
+  - question: What result should I expect after running the benchmarks?
+    answer: >-
+      You should obtain lzbench performance measurements for Snappy and Zstandard on your instance.
+      Use these results to assess compression performance on a 64-bit Arm AWS EC2 environment.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md b/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md
index c106fca8dc..ce49107ebe 100644
--- a/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md
@@ -20,6 +20,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:06:38Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 95da7df14cf36fe01e00868356cf117aa46007ac32e61b0df64d2eea28a5466e
+  summary_generated_at: '2026-06-02T05:11:39Z'
+  summary_source_hash: 95da7df14cf36fe01e00868356cf117aa46007ac32e61b0df64d2eea28a5466e
+  faq_generated_at: '2026-06-03T02:06:38Z'
+  faq_source_hash: 95da7df14cf36fe01e00868356cf117aa46007ac32e61b0df64d2eea28a5466e
+  summary: >-
+    Learn how to install Snort 3 on an Arm-based Linux server and configure it to use multithreading
+    for processing capture files. You will adjust Snort’s Lua configuration to set the number
+    of packet-processing threads, prepare the system by enabling Transparent HugePages and setting
+    CPU isolation and affinity via GRUB, set up a rule set, and download PCAPs to test and measure
+    performance. The path targets developers with a basic understanding of Snort and applies to
+    Arm platforms, including Neoverse. It runs on Ubuntu 20.04 or 22.04 on an Arm server or an
+    Arm-based cloud instance such as AWS EC2. Estimated time to complete is about 45 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an Arm-based instance from a cloud provider, or an Arm server running Ubuntu 20.04
+      or 22.04, and a basic understanding of Snort’s operation and configuration. No other explicit
+      prerequisites are listed.
+  - question: Which platforms and services can I use for the Arm instance?
+    answer: >-
+      You can use Arm-based instances from AWS, Microsoft Azure, Google Cloud, or Oracle. The
+      tools list includes AWS EC2, but the procedure does not require a specific provider.
+  - question: How do I enable multithreading in Snort 3?
+    answer: >-
+      Edit the Snort 3 Lua configuration to specify the number of threads that process network
+      traffic. The steps show where to set the thread count for a single Snort instance.
+  - question: How do I configure CPU affinity and memory settings before testing?
+    answer: >-
+      Append the provided kernel parameter line to /etc/default/grub to enable Transparent HugePages
+      (THP) and set CPU isolation and affinity. The path includes an example for systems with
+      CPUs 0–95, pinning CPUs 0–9 to Snort; adjust the CPU numbers for your hardware.
+  - question: What should I expect when processing PCAP files with multithreading enabled?
+    answer: >-
+      Snort 3 will concurrently process packets from the capture files using multiple threads
+      within one instance. You will measure performance as described in the steps, and alerts
+      will be produced according to your configured rule set.
+# END generated_summary_faq
+
 author: Preema Merlin Dsouza
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md
index 13c5547132..4c025b5c10 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md
@@ -21,6 +21,57 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:07:30Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 009f2219f1b949be39b4a1dd43a5e4c1ceb5bb273d9a11c3c155315160d593ac
+  summary_generated_at: '2026-06-02T05:12:43Z'
+  summary_source_hash: 009f2219f1b949be39b4a1dd43a5e4c1ceb5bb273d9a11c3c155315160d593ac
+  faq_generated_at: '2026-06-03T02:07:30Z'
+  faq_source_hash: 009f2219f1b949be39b4a1dd43a5e4c1ceb5bb273d9a11c3c155315160d593ac
+  summary: >-
+    Learn how to deploy and validate Apache Spark on Microsoft Azure Cobalt 100 (Arm-based) virtual
+    machines using Azure Linux 3.0. You will provision an Arm64 VM via the Azure portal, choose
+    between running Spark in an Azure Linux 3.0 Docker container or on a custom-image VM, and
+    install the required components (Java, Python, and Spark). The path includes a simple PySpark
+    functional test and guidance to run a suite of Spark benchmarks to understand performance
+    on the Cobalt 100 platform. This advanced path targets developers migrating Spark workloads
+    from x86_64 to Arm. Prerequisites include an Azure account with access to Cobalt 100 (Dpsv6),
+    a machine with Docker installed, and familiarity with distributed computing and the Apache
+    Spark architecture.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need a Microsoft Azure account with access to Cobalt 100 instances (Dpsv6), a machine
+      with Docker installed, and familiarity with distributed computing concepts and the Apache
+      Spark architecture.
+  - question: How do I make sure I’m creating the correct Arm64 VM in Azure?
+    answer: >-
+      Use the Azure portal to create a virtual machine and select a Cobalt 100-based Arm64 size
+      such as Dpsv6. Then choose the image appropriate for your deployment as shown in the steps.
+  - question: Should I deploy Spark in an Azure Linux 3.0 Docker container or on a custom-image
+      VM?
+    answer: >-
+      This Learning Path supports both options on Arm64. Pick one approach and follow the corresponding
+      instructions for an Azure Linux 3.0 container or a VM created from a custom Azure Linux
+      3.0 image.
+  - question: Which packages do I install before setting up Spark, and how do I verify Java?
+    answer: >-
+      Install Java 17 (runtime and devel), Python 3, pip, and common tools such as git and maven
+      using tdnf as shown. Verify the installation by running java -version; it should report
+      OpenJDK 17.
+  - question: How do I validate that Spark is working after installation?
+    answer: >-
+      Create the provided test_spark.py, run it as directed in the steps, and confirm that df.show()
+      prints the sample rows. This verifies that Spark can initialize, process a small job, and
+      exit on the Arm64 environment.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md
index d27d7e4054..a0d3a4d602 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md
@@ -20,6 +20,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:07:54Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: a4ac73af7e8959a546a66ab776c0bca8b60957e6f1729aa00fd78783e6594c0b
+  summary_generated_at: '2026-06-02T05:13:16Z'
+  summary_source_hash: a4ac73af7e8959a546a66ab776c0bca8b60957e6f1729aa00fd78783e6594c0b
+  faq_generated_at: '2026-06-03T02:07:54Z'
+  faq_source_hash: a4ac73af7e8959a546a66ab776c0bca8b60957e6f1729aa00fd78783e6594c0b
+  summary: >-
+    Learn how to deploy Apache Spark on Arm-based Google Axion C4A virtual machines in Google
+    Cloud. You will provision a c4a-standard-4 instance with RHEL 9, install Java, Scala, Maven,
+    and Spark, then validate the setup by running a simple Scala Spark job. The path concludes
+    with running Spark’s built-in SQL micro-benchmarks using the SBT-based framework to produce
+    results you can use to compare Arm64 C4A performance with x86_64 platforms. This path targets
+    developers evaluating migration of Spark workloads to Arm Neoverse-V2–based systems. Prerequisites
+    include a GCP account with billing enabled and familiarity with distributed computing and
+    the Apache Spark architecture.
+  faqs:
+  - question: What do I need before creating the VM?
+    answer: >-
+      You need a Google Cloud Platform account with billing enabled. Familiarity with distributed
+      computing concepts and the Apache Spark architecture is expected.
+  - question: Which VM configuration and OS image should I use on GCP?
+    answer: >-
+      Use a Google Axion C4A Arm VM with the c4a-standard-4 machine type (4 vCPUs, 16 GB memory).
+      The Learning Path uses Red Hat Enterprise Linux 9 as the base image.
+  - question: How do I access the instance to install Spark and its dependencies?
+    answer: >-
+      SSH into the C4A VM you created in the Google Cloud Console. From there, install Java, Scala,
+      Maven, and Apache Spark on the RHEL 9 system.
+  - question: How do I confirm that my Spark installation works on the C4A VM?
+    answer: >-
+      Create and run a simple Scala Spark job that parallelizes a small dataset and performs a
+      basic transformation and action. Successful execution with the expected output indicates
+      the installation is correct.
+  - question: How are the performance benchmarks run and what do they measure?
+    answer: >-
+      Clone the Apache Spark source and use the SBT-based framework to run the built-in SQL micro-benchmarks.
+      These cover areas such as SQL execution, aggregations, joins, and data source reads and
+      can be used to compare Arm64 results with x86_64 runs.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/spark-velox-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/spark-velox-cobalt/_index.md
index 358eb5e9eb..0a9f182832 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark-velox-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark-velox-cobalt/_index.md
@@ -25,7 +25,7 @@ prerequisites:
 generate_summary_faq: true
 
 rerun_summary: false
-rerun_faqs: false
+rerun_faqs: true
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/spark/_index.md b/content/learning-paths/servers-and-cloud-computing/spark/_index.md
index 8af4d8d9c8..3d0d3dd8e5 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark/_index.md
@@ -18,6 +18,50 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:07:05Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 7a612e45aa64ac93fa9a1fe83da8a7c63ffbc3a4b159184b1a7b04fd46e8ee4b
+  summary_generated_at: '2026-06-02T05:12:06Z'
+  summary_source_hash: 7a612e45aa64ac93fa9a1fe83da8a7c63ffbc3a4b159184b1a7b04fd46e8ee4b
+  faq_generated_at: '2026-06-03T02:07:05Z'
+  faq_source_hash: 7a612e45aa64ac93fa9a1fe83da8a7c63ffbc3a4b159184b1a7b04fd46e8ee4b
+  summary: >-
+    Deploy a single-node Apache Spark environment on an AWS Graviton2 EC2 instance using Terraform
+    and Ansible on Linux. This Learning Path focuses on automating instance creation with Terraform
+    and configuring Spark with Ansible, targeting Arm Neoverse-based Graviton2 hardware on AWS.
+    It is presented as an advanced topic and estimated to take about 60 minutes. Prerequisites
+    include an AWS account and a local setup with Terraform, AWS CLI, AWS IAM authenticator, and
+    Ansible. If you are new to Terraform, review the Automate AWS EC2 instance creation using
+    Terraform Learning Path before starting. By the end, you will have deployed a single Spark
+    instance on AWS Graviton2.
+  faqs:
+  - question: What do I need before running the deployment?
+    answer: >-
+      You need an AWS account and a machine with Terraform, AWS CLI, AWS IAM authenticator, and
+      Ansible installed. These are the explicit prerequisites for the Learning Path.
+  - question: Do I need prior Terraform experience to follow this path?
+    answer: >-
+      If you are new to Terraform, you should review the Automate AWS EC2 instance creation using
+      Terraform Learning Path first. This will help you follow the provisioning steps more easily.
+  - question: What result should I expect after completing the steps?
+    answer: >-
+      You will deploy a single Apache Spark instance on an AWS EC2 instance using AWS Graviton2.
+      The deployment is automated with Terraform and Ansible.
+  - question: Which operating system and platform does this deployment target?
+    answer: >-
+      The deployment targets Linux on AWS. The EC2 instance is based on AWS Graviton2 (Arm Neoverse).
+  - question: Do I need to choose a specific AWS instance type or region?
+    answer: >-
+      A specific instance type or region is not explicitly listed. Use the Terraform configuration
+      provided in the Learning Path and ensure the EC2 instance uses AWS Graviton2.
+# END generated_summary_faq
 
 author: Jason Andrews
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md b/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md
index e8ca1595a0..a01f141db8 100644
--- a/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md
@@ -19,6 +19,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:08:16Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: d3fa3b409ed7cf2ecb521fa4d19af8411718909881861ef3885214824031b541
+  summary_generated_at: '2026-06-02T05:13:51Z'
+  summary_source_hash: d3fa3b409ed7cf2ecb521fa4d19af8411718909881861ef3885214824031b541
+  faq_generated_at: '2026-06-03T02:08:16Z'
+  faq_source_hash: d3fa3b409ed7cf2ecb521fa4d19af8411718909881861ef3885214824031b541
+  summary: >-
+    Learn how to run multiple services in a single container with Supervisor and access that container
+    for debugging and testing without opening SSH ports or changing AWS security groups. You will
+    update a Dockerfile to add Supervisor, enable SSH, and install and configure Remote.It, then
+    build and run the container on an Arm Linux system using Docker. The path then demonstrates
+    launching the container on AWS ECS with a Fargate launch type using the AWS Copilot CLI and
+    connecting to it through Remote.It. Prerequisites are an Arm Linux computer running Docker,
+    an AWS account, and a Remote.It account. After completing the steps, you can reach running
+    containers for debug and test using Supervisor and Remote.It.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an Arm Linux computer running Docker, an AWS account, and a Remote.It account.
+      No additional prerequisites are explicitly listed.
+  - question: Which changes should I make in the Dockerfile to run multiple services and enable
+      access?
+    answer: >-
+      Install and configure Supervisor, OpenSSH (with password login), and Remote.It, and add
+      a Supervisor configuration file. The example uses Ubuntu 24.04 and also installs common
+      debug/test utilities.
+  - question: How do I access a container running in AWS Fargate without changing security groups?
+    answer: >-
+      Use the AWS Copilot CLI to launch the container on AWS ECS with Fargate, then connect to
+      it using Remote.It. This avoids opening any port for SSH access.
+  - question: How do I know the container is ready to accept SSH via Remote.It?
+    answer: >-
+      After building and running the image with the provided Supervisor configuration, both the
+      SSH daemon and Remote.It should start in the container. You should be able to initiate a
+      Remote.It session and open an SSH shell.
+  - question: Can I adapt this approach to other container runtimes besides AWS Fargate?
+    answer: >-
+      Yes. The example demonstrates AWS ECS with Fargate, but you can adapt the technique to any
+      container runtime environment.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/sve/_index.md b/content/learning-paths/servers-and-cloud-computing/sve/_index.md
index 9e5d8bab10..bca38e3f30 100644
--- a/content/learning-paths/servers-and-cloud-computing/sve/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sve/_index.md
@@ -19,6 +19,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:08:37Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 7b2074e39243a5cd0ccb6a01317c700a4797d8c66353f39aa4197237b6672027
+  summary_generated_at: '2026-06-02T05:14:53Z'
+  summary_source_hash: 7b2074e39243a5cd0ccb6a01317c700a4797d8c66353f39aa4197237b6672027
+  faq_generated_at: '2026-06-03T02:08:37Z'
+  faq_source_hash: 7b2074e39243a5cd0ccb6a01317c700a4797d8c66353f39aa4197237b6672027
+  summary: >-
+    This introductory Learning Path shows how to port SIMD code to Arm Scalable Vector Extension
+    (SVE) on Linux. You will compare Neon and SVE to understand how SVE reduces fixed-length vector
+    constraints, compile C and Fortran code for SVE-capable Arm processors using the GNU toolchain,
+    and run SVE instructions on any Armv8-A system using QEMU or the Arm Instruction Emulator
+    (ArmIE) when dedicated SVE hardware is unavailable. You will build and run a small example
+    and inspect compiler vectorization via disassembly. Prerequisites are general SIMD or Arm
+    Neon knowledge and access to an Arm Linux machine; cloud instances from AWS, Microsoft Azure,
+    Google Cloud, or Oracle can be used. Estimated time to complete is about 30 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need general knowledge of SIMD processing and Arm Neon, and access to an Arm computer
+      running Linux. Arm-based cloud instances can be used; see the listed cloud service providers.
+  - question: Which GCC options enable SVE for my build?
+    answer: >-
+      Use -march=armv8-a+sve when compiling (for example, gcc -march=armv8-a+sve myapp.c -o myapp_c.out
+      or gfortran -march=armv8-a+sve myapp.f90 -o myapp_f90.out). Autovectorization with GCC is
+      enabled with -O3 and can be disabled with -fno-tree-vectorize.
+  - question: How can I run SVE instructions if my system lacks SVE hardware?
+    answer: >-
+      Use QEMU or the Arm Instruction Emulator (ArmIE). The path demonstrates both approaches
+      on an Armv8-A system running Ubuntu 22.04 without SVE support.
+  - question: How do I know if the compiler vectorized my code?
+    answer: >-
+      The steps have you compare the disassembly of a simple program with and without autovectorization
+      enabled. You should observe differences in the generated code when building with -O3 versus
+      disabling vectorization.
+  - question: What should I consider when moving from Neon to SVE?
+    answer: >-
+      Neon uses 32 fixed 128-bit vector registers (V0–V31) for integer and floating-point types,
+      while SVE reduces restrictions related to fixed-length vector sizes. The path introduces
+      these differences to guide porting decisions.
+# END generated_summary_faq
 
 author: Florent Lebeau
 
diff --git a/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md b/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md
index 15fd2776a3..c6609ed182 100644
--- a/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md
@@ -21,6 +21,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:09:11Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: cc3f88ce181b1614f959497a24fafe736b26e55615792faab6b5dce29fac8d77
+  summary_generated_at: '2026-06-02T05:15:37Z'
+  summary_source_hash: cc3f88ce181b1614f959497a24fafe736b26e55615792faab6b5dce29fac8d77
+  faq_generated_at: '2026-06-03T02:09:11Z'
+  faq_source_hash: cc3f88ce181b1614f959497a24fafe736b26e55615792faab6b5dce29fac8d77
+  summary: >-
+    Implement and benchmark scalar and SVE2 MATCH-based search functions on Arm Neoverse servers
+    to evaluate vectorized search performance on Linux. Working on a cloud VM with SVE2 support—AWS
+    Graviton4, Google Axion, or Azure Cobalt 100—you will compare scalar and vectorized approaches,
+    measure performance, and analyze speedups and efficiency. The path introduces the purpose
+    and function of SVE2 MATCH instructions and contrasts them with a scalar implementation. Tools
+    and technologies referenced include SVE2, Neon, and Runbook. No explicit prerequisites are
+    listed beyond access to one of the specified cloud instances. By the end, you can assess when
+    to apply SVE2 MATCH for search tasks and interpret your benchmarking results.
+  faqs:
+  - question: What do I need before running the exercises?
+    answer: >-
+      You need access to an AWS Graviton4, Google Axion, or Azure Cobalt 100 virtual machine.
+      The steps target a Linux environment. No other explicit prerequisites are listed.
+  - question: Which cloud instance should I choose to use SVE2 MATCH?
+    answer: >-
+      Use one of the Arm Neoverse-based instances listed in the prerequisites: AWS Graviton4,
+      Google Axion, or Azure Cobalt 100. The Learning Path focuses on running SVE2-based code
+      on these servers.
+  - question: What will I implement and benchmark during the path?
+    answer: >-
+      You will implement a scalar search function and a vectorized version using SVE2 MATCH instructions.
+      You will then benchmark both to compare performance and analyze speedups on the target instance.
+  - question: How do I know my results are correct or meaningful?
+    answer: >-
+      You should obtain timing results for both scalar and SVE2-based implementations and observe
+      a performance comparison. The path expects analysis of speedups and efficiency, but no specific
+      numbers are provided.
+  - question: Is Neon or Runbook required, or is the focus only on SVE2 MATCH?
+    answer: >-
+      SVE2, Neon, and Runbook are listed as tools, but the core tasks center on SVE2 MATCH versus
+      scalar implementations. Neon- or Runbook-specific steps are not explicitly detailed in the
+      provided context.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md b/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md
index facc3d1e46..ae397f6521 100644
--- a/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md
@@ -18,6 +18,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:09:35Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: d15c3083cb881838f6500eb56dfafb636d73bb282484a7f1b8f4a855dda37fb4
+  summary_generated_at: '2026-06-02T05:16:32Z'
+  summary_source_hash: d15c3083cb881838f6500eb56dfafb636d73bb282484a7f1b8f4a855dda37fb4
+  faq_generated_at: '2026-06-03T02:09:35Z'
+  faq_source_hash: d15c3083cb881838f6500eb56dfafb636d73bb282484a7f1b8f4a855dda37fb4
+  summary: >-
+    Use Sysreport to quickly assess the performance-related capabilities of an Arm Linux system
+    and decide what to configure before profiling. This introductory path walks you through running
+    the command-line tool on Arm Cortex-A and Neoverse-based platforms, including cloud instances
+    (AWS, Microsoft Azure, Google Cloud, Oracle), bare metal servers, developer boards, and Raspberry
+    Pi devices. You will verify access to the system shell, confirm Python (invoked as python3)
+    and Git are available, run Sysreport, and analyze its on-screen summary of hardware and operating
+    system configuration. By the end, you can identify which performance analysis features are
+    present or enabled and determine any configuration changes needed to improve performance information
+    collection. Estimated time to complete: about 10 minutes.
+  faqs:
+  - question: What do I need before running Sysreport on my Arm system?
+    answer: >-
+      You need an Arm-based system running Linux and the ability to log in via SSH or use a local
+      console, with comfort on the Linux command line. The path asks you to confirm that Python
+      and Git are installed.
+  - question: Which Python command should I use for the steps?
+    answer: >-
+      The path assumes Python is invoked with the python3 command. If your environment uses a
+      different command, adjust accordingly.
+  - question: How do I confirm Python is installed?
+    answer: >-
+      Run python3 --version and look for a version string, for example “Python 3.9.5.” If no version
+      is shown, Python may not be installed or python3 may not be the correct command on your
+      system.
+  - question: What result should I expect after running Sysreport?
+    answer: >-
+      Sysreport prints an on-screen summary of system configuration oriented toward performance
+      analysis. It includes hardware and operating system details and indicates which performance
+      features are available and enabled.
+  - question: What should I check if a feature I expected is missing in the report?
+    answer: >-
+      Use the report to decide whether to switch to a different system or make configuration changes
+      to reach the desired state for performance analysis. The path guides you to examine the
+      output and consider changes where needed.
+# END generated_summary_faq
 
 author: James Whitaker
 
diff --git a/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
index a8f7eabfee..cb53ebab9b 100644
--- a/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
@@ -18,6 +18,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:10:01Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 2c20d30d25a0bb871bdb935d18f7ad8e0740139948133dbd728e10f0add0f94e
+  summary_generated_at: '2026-06-02T05:17:09Z'
+  summary_source_hash: 2c20d30d25a0bb871bdb935d18f7ad8e0740139948133dbd728e10f0add0f94e
+  faq_generated_at: '2026-06-03T02:10:01Z'
+  faq_source_hash: 2c20d30d25a0bb871bdb935d18f7ad8e0740139948133dbd728e10f0add0f94e
+  summary: >-
+    Provision an Arm-based SUSE Linux Enterprise Server (SLES) VM on Google Cloud C4A (Axion,
+    Neoverse-V2) and set up a working TensorFlow environment on Arm64. You will create a c4a-standard-4
+    instance, install Python 3.11 with pip and virtual environment support, and install TensorFlow
+    on SLES. The path guides you to verify the installation by listing available devices and running
+    basic TensorFlow operations and a simple training test on the CPU. You then benchmark ResNet50,
+    MobileNetV2, and InceptionV3 with tf.keras using dummy data to measure average inference time
+    and throughput. Prerequisites are a GCP account with billing enabled and basic familiarity
+    with TensorFlow. Estimated time to complete is about 30 minutes.
+  faqs:
+  - question: What do I need before running this Learning Path, and how long will it take?
+    answer: >-
+      You need a Google Cloud Platform (GCP) account with billing enabled and basic familiarity
+      with TensorFlow. The path is introductory and is designed to take about 30 minutes.
+  - question: Which VM configuration and OS should I select on Google Cloud to match the steps?
+    answer: >-
+      Choose the C4A series and the c4a-standard-4 machine type (4 vCPUs, 16 GB memory). Use a
+      SUSE Linux Enterprise Server (SLES) Arm64 image, then set your region and zone.
+  - question: Which Python version is used and how do I install the prerequisites for TensorFlow?
+    answer: >-
+      The steps install Python 3.11, pip, and virtual environment support using zypper on SLES.
+      After that, you install TensorFlow and continue with testing and benchmarking on Arm64.
+  - question: How do I verify that TensorFlow is correctly installed and recognizes the hardware?
+    answer: >-
+      Run: python -c "import tensorflow as tf; print(tf.config.list_physical_devices())". On most
+      VMs you should see a CPU device listed; the baseline section also runs simple ops and a
+      small training test.
+  - question: What models are benchmarked and what metrics are collected in this path?
+    answer: >-
+      You benchmark ResNet50, MobileNetV2, and InceptionV3 using tf.keras with dummy input data.
+      The procedure measures average inference time and throughput on the CPU of the Arm-based
+      VM.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md b/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md
index 2aa2a64b0c..cfcab64e41 100644
--- a/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md
@@ -17,6 +17,50 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:10:25Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 0aaee75d902b938e63e37eca421dd28b91f583e784acdf3cccb1e20b8dda71bd
+  summary_generated_at: '2026-06-02T05:17:40Z'
+  summary_source_hash: 0aaee75d902b938e63e37eca421dd28b91f583e784acdf3cccb1e20b8dda71bd
+  faq_generated_at: '2026-06-03T02:10:25Z'
+  faq_source_hash: 0aaee75d902b938e63e37eca421dd28b91f583e784acdf3cccb1e20b8dda71bd
+  summary: >-
+    Learn how to run a text classification workflow with ThirdAI on Arm servers running Linux.
+    You will provision an Arm-based instance in the cloud (AWS, Microsoft Azure, Google Cloud,
+    or Oracle) or use an on-prem Arm server, install Python and ThirdAI, create a virtual environment,
+    and follow an introductory example to train, evaluate, and deploy a model. The steps use Ubuntu
+    commands, though other Linux distributions can be used. By the end, you will have a trained
+    and evaluated text classifier and know how to invoke ThirdAI’s high-level APIs to make predictions.
+    No explicit prerequisites are listed beyond access to an Arm-based server.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need access to an Arm-based instance from a cloud service provider or an on-premise
+      Arm server. The steps assume a Linux environment.
+  - question: Can I follow the instructions on Linux distributions other than Ubuntu?
+    answer: >-
+      Yes. The instructions show Ubuntu commands, but you can use other Linux distributions.
+  - question: Which setup commands prepare Python and an isolated environment?
+    answer: >-
+      Install python3-pip and python3-venv, then create and activate a virtual environment. The
+      terminal prompt showing a (thirdai) prefix indicates the environment is active.
+  - question: How do I install and activate ThirdAI for this example?
+    answer: >-
+      Install the package inside the virtual environment with pip3 install thirdai. The evaluation
+      script includes a thirdai.licensing.activate(...) call; use that line as shown in the example.
+  - question: How do I evaluate the trained model and what result should I expect?
+    answer: >-
+      Use the provided evaluate.py script to load the saved model (sentiment_analysis.model) and
+      run evaluation on the test file (amazon_polarity_test.csv). The script reports categorical_accuracy
+      and includes a sample prediction; specific metric values are not listed.
+# END generated_summary_faq
 
 author: ThirdAI
 
diff --git a/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md
index 19c6491cc4..c3691845c5 100644
--- a/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md
@@ -20,6 +20,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:10:49Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: edf5bebb0440c110723c87ca27e5f4b21e4da588e4208b5391f7091ae93cb73d
+  summary_generated_at: '2026-06-02T05:18:38Z'
+  summary_source_hash: edf5bebb0440c110723c87ca27e5f4b21e4da588e4208b5391f7091ae93cb73d
+  faq_generated_at: '2026-06-03T02:10:49Z'
+  faq_source_hash: edf5bebb0440c110723c87ca27e5f4b21e4da588e4208b5391f7091ae93cb73d
+  summary: >-
+    Deploy a live sensor dashboard on Google Cloud Axion C4A Arm instances by provisioning a c4a-standard-4
+    VM running SUSE Linux Enterprise Server (SLES) Arm64, building PostgreSQL 15 with the TimescaleDB
+    2.25.0 extension from source, and configuring access for Grafana on TCP port 3000. You will
+    simulate real-time sensor data with a Python script using psycopg2, create a TimescaleDB hypertable
+    along with continuous aggregates and retention policies, and visualize the stream in a Grafana
+    dashboard that auto-refreshes. The path concludes with validating end-to-end data flow from
+    ingestion through TimescaleDB to Grafana. Prerequisites: a GCP account with billing enabled
+    and basic familiarity with SQL, Python, and Grafana.
+  faqs:
+  - question: What do I need before provisioning the VM on Google Cloud?
+    answer: >-
+      You need a Google Cloud Platform account with billing enabled. Basic familiarity with SQL,
+      Python, and Grafana is expected.
+  - question: Which Google Cloud VM and operating system are used in this path?
+    answer: >-
+      You will create a c4a-standard-4 instance (4 vCPUs, 16 GB memory) on Google Axion C4A. The
+      VM runs SUSE Linux Enterprise Server (SLES) on Arm64.
+  - question: Why does the path build TimescaleDB from source on Arm64, and which versions are
+      used?
+    answer: >-
+      Building from source ensures the TimescaleDB extension is fully optimized for Arm64. The
+      environment uses PostgreSQL 15 with the TimescaleDB 2.25.0 extension.
+  - question: Which firewall port should I open, and what is it for?
+    answer: >-
+      Open TCP port 3000 in a Google Cloud VPC firewall rule. This exposes the Grafana interface
+      used to view the time-series dashboards.
+  - question: How do I know the ingestion and visualization are working?
+    answer: >-
+      The Python sensor script (using psycopg2) continuously writes readings into a TimescaleDB
+      hypertable, and Grafana automatically refreshes to display the data. Successful completion
+      shows end-to-end flow from ingestion through TimescaleDB to Grafana visualization.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md b/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md
index a469df4cce..1e6d815c48 100644
--- a/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md
@@ -19,6 +19,60 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:11:21Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: e6a652fd0b32796433a380012a79def743bc5452a52ffae785007977a2a8e3e0
+  summary_generated_at: '2026-06-02T05:19:33Z'
+  summary_source_hash: e6a652fd0b32796433a380012a79def743bc5452a52ffae785007977a2a8e3e0
+  faq_generated_at: '2026-06-03T02:11:21Z'
+  faq_source_hash: e6a652fd0b32796433a380012a79def743bc5452a52ffae785007977a2a8e3e0
+  summary: >-
+    Learn how to analyze Linux application performance on Arm Neoverse N1 using the Arm Telemetry
+    Solution and Linux perf. You will build a slightly modified DynamoRIO stride benchmark, collect
+    sampling and counting data, and interpret commonly used hardware metrics. Following the Telemetry
+    Solution install guide, you will set up the required tools (including Python and perf) and
+    use g++ to compile the example. You will then enable software prefetching with compile-time
+    defines, rerun measurements, and compare results to assess the impact of the change. The steps
+    target an Arm Neoverse N1 system; bare metal or cloud metal instances are recommended, and
+    results vary by hardware. Estimated time: about 60 minutes.
+  faqs:
+  - question: Do I need a bare-metal Neoverse N1 system, or can I use a VM?
+    answer: >-
+      Use an Arm Neoverse N1 computer running Linux. A bare metal or cloud metal instance is best
+      because they expose more counters; a VM may offer fewer counters and some commands might
+      not succeed. These instructions have been tested on the a1.metal instance type.
+  - question: Which tools must be installed before I build and profile the example?
+    answer: >-
+      Follow the Arm Telemetry Solution install guide to install the required tools on your Arm
+      Neoverse server; this includes Python and Linux perf. You also need the GNU C++ compiler
+      (g++), as described in the GNU Compiler install guide.
+  - question: What application is used as the example, and what does it measure?
+    answer: >-
+      The example is the DynamoRIO stride benchmark, a pointer-chasing micro-benchmark that accesses
+      values in a 16 MB array with positions determined by the chased pointers. The provided code
+      is slightly modified to increase the number of iterations. The original source is at https://github.com/DynamoRIO/dynamorio/blob/master/clients/drcachesim/tests/stride_benchmark.cpp.
+  - question: Can I run this Learning Path on hardware other than the N1SDP, and how will results
+      differ?
+    answer: >-
+      Yes. The white paper uses the Neoverse N1 Software Development Platform (N1SDP), which differs
+      from Neoverse N1 servers and cloud instances, so your results will be different. You can
+      also run on single board computers with Cortex-A76 processors such as Raspberry Pi 5, Khadas
+      Edge2, or Orange Pi 5; the example output provided is from a Khadas Edge2.
+  - question: How do I enable and tune software prefetching in the sample application?
+    answer: >-
+      Recompile the application with two compile-time defines to enable data prefetching, as shown
+      in the steps. You can experiment with values of DIST to observe performance impact; the
+      white paper shows performance saturating at DIST=40 on N1SDP, but your results will vary
+      by hardware.
+# END generated_summary_faq
+
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md b/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md
index 75a05236b6..703f4ddd7b 100644
--- a/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md
@@ -18,6 +18,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:11:47Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: d25121278f9dd40e2fd19d988e35866c6bfa70cfd0b93e2ec4506c8ad8a26d88
+  summary_generated_at: '2026-06-02T05:20:50Z'
+  summary_source_hash: d25121278f9dd40e2fd19d988e35866c6bfa70cfd0b93e2ec4506c8ad8a26d88
+  faq_generated_at: '2026-06-03T02:11:47Z'
+  faq_source_hash: d25121278f9dd40e2fd19d988e35866c6bfa70cfd0b93e2ec4506c8ad8a26d88
+  summary: >-
+    Learn to measure PyTorch inference on Arm-based servers using the PyTorch Benchmarks suite.
+    You will install the benchmarks on Ubuntu 22.04 LTS, run model inference tests with Python
+    and PyTorch, and compare performance between eager mode and torch.compile across NLP, vision,
+    and recommender workloads. The instructions were tested on AWS Graviton3 (c7g.4xlarge) and
+    apply to any Arm server meeting a baseline of 4 CPU cores and 8 GB RAM, whether provisioned
+    on AWS, Microsoft Azure, Google Cloud, Oracle, or on-premises. Prerequisite: access to an
+    Arm-based instance or Arm server. Estimated time to complete: about 20 minutes.
+  faqs:
+  - question: What do I need before running the benchmarks?
+    answer: >-
+      You need access to an Arm-based instance from a cloud service provider or an on-premise
+      Arm server. The instructions target Ubuntu 22.04 LTS and the example assumes at least four
+      cores and 8GB of RAM.
+  - question: Can I run this on AWS, Azure, Google Cloud, or Oracle Cloud?
+    answer: >-
+      Yes. Any Arm-based instance from these cloud providers works, and the steps apply to any
+      Arm server running Ubuntu 22.04 LTS.
+  - question: How do I know the PyTorch Benchmarks suite installed correctly?
+    answer: >-
+      After installation, you will be able to run the benchmark suite and see inference timing
+      output for selected PyTorch models. The Learning Path guides you through producing and reviewing
+      those results.
+  - question: Which PyTorch execution modes should I compare?
+    answer: >-
+      You will compare inference performance between PyTorch eager mode and torch.compile mode.
+      Follow the steps to run both and examine the reported latency.
+  - question: What results should I expect to collect and for which model types?
+    answer: >-
+      You will measure inference latency for PyTorch models in NLP, vision, and recommender categories.
+      The outcome is a side-by-side comparison of latency between eager and torch.compile runs.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md
index 88486782fd..ff6beb1819 100644
--- a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md
@@ -18,6 +18,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:12:45Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 523ff99ccb8d13543f64839fa21040191d2a9ff7ce7c78fa590e8775fac754a6
+  summary_generated_at: '2026-06-02T05:21:57Z'
+  summary_source_hash: 523ff99ccb8d13543f64839fa21040191d2a9ff7ce7c78fa590e8775fac754a6
+  faq_generated_at: '2026-06-03T02:12:45Z'
+  faq_source_hash: 523ff99ccb8d13543f64839fa21040191d2a9ff7ce7c78fa590e8775fac754a6
+  summary: >-
+    This advanced Learning Path shows how to describe common non-cache PMU events and understand
+    why specific C and Arm assembly sequences trigger them on the Arm Neoverse N2 core. You will
+    run compact examples that exercise Topdown Methodology L1 metrics, TLB effectiveness and walks,
+    and operation mix groups (SIMD, scalar floating point, integer, branch, load, store), then
+    examine the resulting PMU counts. The examples require a way to print to a console and can
+    run in simulation or on hardware; on Linux you may see slight variations due to OS overhead.
+    Prerequisites are familiarity with performance analysis and the ability to read Arm assembly.
+    Estimated time to complete is about 30 minutes.
+  faqs:
+  - question: What do I need before running the examples?
+    answer: >-
+      You should be comfortable with performance analysis and able to read Arm assembly. You also
+      need an environment that can print to a console (printf support) to view event counts.
+  - question: Which execution environment should I use for the code?
+    answer: >-
+      You can use any simulation environment or hardware with printf support. The provided examples
+      were run bare-metal in EL3; running under Linux is also possible but may introduce slight
+      variations in PMU counts.
+  - question: How do I know the ITLB-related events were exercised correctly?
+    answer: >-
+      The ITLB example uses self-modifying code to execute an instruction from a previously unaccessed
+      address, causing an ITLB miss and walk. After running it, check that counts for events such
+      as PMU_EVENT_L1I_TLB, PMU_EVENT_L1I_TLB_REFILL, PMU_EVENT_L2D_TLB, PMU_EVENT_L2D_TLB_REFILL,
+      PMU_EVENT_ITLB_WALK, and PMU_EVENT_INST_RETIRED increase as expected.
+  - question: What result should I expect from the SIMD operation mix example?
+    answer: >-
+      The example demonstrates SIMD activity and, in the provided run, produced counts like INST_SPEC
+      = 12, ASE_SPEC = 1, and ASE_INST_SPEC = 3. Your values may differ slightly, especially if
+      running under an operating system.
+  - question: Where can I find the definitions and behavior of the PMU events used here?
+    answer: >-
+      Refer to the Arm Neoverse N2 PMU guide for event behavior details. Additional Neoverse core
+      PMU guides are available on developer.arm.com.
+# END generated_summary_faq
 
 author: Johanna Skinnider
 
diff --git a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md
index daa5c20d9f..088f29af3a 100644
--- a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md
@@ -19,6 +19,56 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:12:13Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 1b309980bef983966d948036e309e132b56f767161967187e72c6a9f81f17dce
+  summary_generated_at: '2026-06-02T05:21:26Z'
+  summary_source_hash: 1b309980bef983966d948036e309e132b56f767161967187e72c6a9f81f17dce
+  faq_generated_at: '2026-06-03T02:12:13Z'
+  faq_source_hash: 1b309980bef983966d948036e309e132b56f767161967187e72c6a9f81f17dce
+  summary: >-
+    This advanced Learning Path shows how simple C and assembly code patterns trigger common cache
+    Performance Monitoring Unit (PMU) events on Arm Neoverse, with a focus on the Neoverse N2
+    core, in a Linux environment. You will review example snippets that issue stores to Normal
+    Cacheable memory and see how they map to PMU metric groups for L1 data and instruction caches,
+    the unified L2 cache, and the last-level (LL) cache. The steps explain why events such as
+    L1D_CACHE_REFILL, L1D_CACHE, and INST_RETIRED are observed in common scenarios, and include
+    example event counts to compare against. Prerequisites are knowledge of performance analysis
+    and the ability to read Arm assembly. Estimated time to complete is 30 minutes.
+  faqs:
+  - question: What do I need before running the examples?
+    answer: >-
+      You should be comfortable with performance analysis and able to read Arm assembly. The content
+      targets Linux and focuses on the Neoverse N2 core. No other explicit prerequisites are listed.
+  - question: Which PMU events are used to evaluate each cache level?
+    answer: >-
+      L1 Data Cache: PMU_EVENT_L1D_CACHE_REFILL, PMU_EVENT_L1D_CACHE, PMU_EVENT_INST_RETIRED.
+      L1 Instruction Cache: PMU_EVENT_L1I_CACHE_REFILL, PMU_EVENT_L1I_CACHE, PMU_EVENT_INST_RETIRED,
+      PMU_EVENT_INST_SPEC. L2 Unified Cache: PMU_EVENT_L2D_CACHE_REFILL, PMU_EVENT_L2D_CACHE,
+      PMU_EVENT_L2D_CACHE_WR, PMU_EVENT_L2D_CACHE_RD, PMU_EVENT_L1D_CACHE_WR, PMU_EVENT_INST_RETIRED.
+      LL Cache: PMU_EVENT_LL_CACHE_RD, PMU_EVENT_LL_CACHE_MISS_RD, PMU_EVENT_INST_RETIRED.
+  - question: How do the code samples trigger the intended cache PMU events?
+    answer: >-
+      They execute stores to Normal Cacheable memory to allocate and access cache lines. To highlight
+      L2 activity, the examples first fill the L1 D-cache with many stores; LL activity can appear
+      when stores cause writebacks or involve shared cache lines.
+  - question: How do I know if my run matched the expected behavior?
+    answer: >-
+      Compare your observed PMU event counts and relationships to the examples shown in the path.
+      For instance, the L1 D-cache section provides example counts you can use as a reference.
+  - question: What should I check if LL cache events remain low or zero?
+    answer: >-
+      LL cache activity is highlighted when excessive stores lead to writebacks into the LL cache
+      or when there are shared cache lines. Ensure your workload issues enough stores, as illustrated,
+      to overflow earlier cache levels and reach the LL cache.
+# END generated_summary_faq
 
 author: Johanna Skinnider
 
diff --git a/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md b/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md
index 307d1ecdb3..a7cc8cff4a 100644
--- a/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md
@@ -22,6 +22,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:13:05Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 13bba1dee1c948f8b751a71479a5106014a2c21dab6ce3968a08bed9e3363de1
+  summary_generated_at: '2026-06-02T05:22:29Z'
+  summary_source_hash: 13bba1dee1c948f8b751a71479a5106014a2c21dab6ce3968a08bed9e3363de1
+  faq_generated_at: '2026-06-03T02:13:05Z'
+  faq_source_hash: 13bba1dee1c948f8b751a71479a5106014a2c21dab6ce3968a08bed9e3363de1
+  summary: >-
+    This Learning Path guides you through building and scanning multi-architecture container images
+    with Trivy on Microsoft Azure Cobalt 100 Arm64 virtual machines. You will provision a Dpsv6
+    series VM via the Azure Portal, configure Docker Buildx, create a demo container, push a multi-architecture
+    image to Docker Hub, install and verify Trivy on Ubuntu, run local vulnerability scans, and
+    generate reports. It also covers configuring self-hosted GitHub Actions Arm runners and adding
+    severity-based security gates to CI pipelines. Prerequisites include an Azure account with
+    access to Cobalt 100 instances, Docker knowledge, familiarity with CI/CD and GitHub Actions
+    runners, basic Linux command-line skills, and a Docker Hub account.
+  faqs:
+  - question: What do I need before starting this Learning Path?
+    answer: >-
+      You need a Microsoft Azure account with access to Cobalt 100 instances (Dpsv6), Docker installed
+      with basic containerization knowledge, familiarity with CI/CD concepts and GitHub Actions
+      runners, and basic Linux command-line skills. For the build and scan steps, ensure you have
+      an Arm64 Ubuntu VM running on Cobalt 100 and a Docker Hub account.
+  - question: Which Azure VM size and operating system should I use?
+    answer: >-
+      Use a general-purpose Dpsv6 series VM with the Arm-based Azure Cobalt 100 processor. The
+      steps use an Arm64 Ubuntu VM.
+  - question: Can I create the VM with Azure CLI or infrastructure as code instead of the Portal?
+    answer: >-
+      Yes, Azure CLI and IaC are common options. This Learning Path focuses on using the Azure
+      Portal to create the Cobalt 100 VM.
+  - question: How do I build a multi-architecture container image on the VM?
+    answer: >-
+      You will configure Docker Buildx for multi-architecture builds, create a demo container
+      application, and push the resulting image to Docker Hub. The steps guide you through enabling
+      Buildx and validating the build on the Arm64 VM.
+  - question: What should I expect from Trivy scanning and how is it used in CI?
+    answer: >-
+      You will install and verify Trivy on the Arm64 VM, run local vulnerability scans, and generate
+      reports with findings categorized by severity. In CI pipelines, you will configure self-hosted
+      GitHub Actions Arm runners and enforce security gates based on vulnerability severity.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md b/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md
index 26ee944daf..8eeb67d44a 100644
--- a/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md
@@ -22,6 +22,55 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:13:37Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 9f2b3f6c5e76ecb754de5c0fd84278ff71f71c5c7906acdc06911f2feff1f5fd
+  summary_generated_at: '2026-06-02T05:23:06Z'
+  summary_source_hash: 9f2b3f6c5e76ecb754de5c0fd84278ff71f71c5c7906acdc06911f2feff1f5fd
+  faq_generated_at: '2026-06-03T02:13:37Z'
+  faq_source_hash: 9f2b3f6c5e76ecb754de5c0fd84278ff71f71c5c7906acdc06911f2feff1f5fd
+  summary: >-
+    This advanced Learning Path shows how to benchmark and tune an HTTP network workload on Arm
+    Neoverse-based bare‑metal servers using Apache Tomcat, wrk2, and OpenJDK 21 on Ubuntu 24.04.
+    You will set up Tomcat on an Arm Neoverse host and wrk2 on an x86_64 host, establish a reproducible
+    baseline (file‑descriptor limits, logging, thread counts, and a fixed core set), then apply
+    targeted tuning: adjust NIC queue counts to match available CPUs, improve NUMA locality by
+    running Tomcat on the NIC’s NUMA node, and compare IOMMU strict versus passthrough modes.
+    Validated on an AWS c8g.metal‑48xl instance, the expected outcome is a clear, repeatable process
+    to measure and refine throughput and latency for your workload.
+  faqs:
+  - question: What do I need before running the benchmark?
+    answer: >-
+      You need an Arm Neoverse-based bare-metal server with Ubuntu 24.04 to run Apache Tomcat,
+      and an x86_64 bare-metal server with Ubuntu 24.04 to run wrk2. Basic familiarity with Java
+      applications is assumed. Tomcat runs on OpenJDK 21.
+  - question: Do I need to raise file descriptor limits on both the client and server?
+    answer: >-
+      Yes. Increase the file-descriptor limit on both the Tomcat server and the wrk2 client to
+      avoid running out under load (for example, set it to 65535).
+  - question: How should I choose the NIC queue count during tuning?
+    answer: >-
+      Match the number of NIC transmit/receive queues to the number of CPUs you keep online. Reducing
+      queues when using a small CPU set helps distribute interrupts more evenly and can stabilize
+      throughput and latency on Arm Neoverse systems.
+  - question: How do I decide where to place Tomcat for NUMA locality?
+    answer: >-
+      Use numactl -H to inspect NUMA topology and relative latencies; cross‑NUMA latency is higher
+      than intra‑NUMA. Place Tomcat on the NIC’s NUMA node and align worker threads with the cores
+      on that node.
+  - question: How do I compare IOMMU strict mode with passthrough?
+    answer: >-
+      Update the kernel command line via GRUB to set iommu.strict=0 and iommu.passthrough=1, then
+      reboot and benchmark again. Compare results with strict mode enabled and select the configuration
+      that performs best for your workload.
+# END generated_summary_faq
 
 author: Ying Yu, Ker Liu, Rui Chang
 
diff --git a/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md
index a97bf8c4d6..5138b15aa8 100644
--- a/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md
@@ -21,6 +21,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:14:07Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: ee805f703acd45612c9a94e60c6322866dc1c8ff25d48de2fb4cd9067948c93e
+  summary_generated_at: '2026-06-02T05:23:36Z'
+  summary_source_hash: ee805f703acd45612c9a94e60c6322866dc1c8ff25d48de2fb4cd9067948c93e
+  faq_generated_at: '2026-06-03T02:14:07Z'
+  faq_source_hash: ee805f703acd45612c9a94e60c6322866dc1c8ff25d48de2fb4cd9067948c93e
+  summary: >-
+    Provision a SUSE Linux Enterprise Server (SLES) VM on Google Cloud’s Arm-based C4A instances
+    powered by Axion processors, install a TypeScript toolchain, validate it, and benchmark it.
+    You will create a c4a-standard-4 VM via the Google Cloud Console, then install Node.js, npm,
+    TypeScript, and ts-node on an Arm64 environment. After initializing a minimal project, you
+    will compile and run a simple TypeScript file to confirm the setup. Finally, you will implement
+    a JMH-style benchmark using Node.js perf_hooks to collect average runtime across multiple
+    iterations. Prerequisites are a GCP account with billing enabled and basic familiarity with
+    TypeScript and Node.js. Estimated time to complete is about 30 minutes.
+  faqs:
+  - question: What do I need before creating the VM on Google Cloud?
+    answer: >-
+      You need a Google Cloud Platform (GCP) account with billing enabled. Basic familiarity with
+      TypeScript and the Node.js runtime is assumed.
+  - question: Which machine type and OS should I use for the instance?
+    answer: >-
+      Use the c4a-standard-4 machine type, which provides four virtual CPUs and 16 GB of memory.
+      Provision a SUSE Linux Enterprise Server (SLES) Arm64 VM from the Google Cloud Console under
+      Compute Engine > VM Instances.
+  - question: Which packages are installed to run TypeScript on the SUSE Arm64 VM?
+    answer: >-
+      You install Node.js, npm, TypeScript, and ts-node. These components enable you to develop,
+      compile, and run TypeScript code on the Arm64 instance.
+  - question: How do I verify the TypeScript environment is working?
+    answer: >-
+      Create a minimal project, then create, compile, and run a simple TypeScript file. Successful
+      compilation and execution confirm the environment is ready for benchmarking.
+  - question: What result should I expect from the benchmarking step?
+    answer: >-
+      The JMH-style benchmark implemented with Node.js perf_hooks runs multiple iterations and
+      reports an average runtime. This produces stable, repeatable performance data for your workload
+      on the C4A Arm64 VM.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ##### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md b/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md
index 925c776f50..2deb32107f 100644
--- a/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md
@@ -18,6 +18,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:14:44Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 035bd363fc8ae772acf52d7e392f2db27087267706aeec86b4098c497e5fe91d
+  summary_generated_at: '2026-06-02T05:24:08Z'
+  summary_source_hash: 035bd363fc8ae772acf52d7e392f2db27087267706aeec86b4098c497e5fe91d
+  faq_generated_at: '2026-06-03T02:14:44Z'
+  faq_source_hash: 035bd363fc8ae772acf52d7e392f2db27087267706aeec86b4098c497e5fe91d
+  summary: >-
+    This Learning Path guides C and C++ developers through migrating applications that depend
+    on optimized performance libraries from x86 to Arm Architecture on Linux. You will compare
+    the C++ standard library with performance libraries, set up an Arm-based AWS instance running
+    Ubuntu 22.04 LTS, install build tools and Arm Performance Libraries, and use libamath to access
+    optimized math routines. You will then port a basic application that uses Intel’s Vector Statistics
+    Library (VSL) to AArch64 using OpenRNG as a drop-in replacement. Prerequisites include access
+    to both an Arm and an x86 cloud instance and intermediate knowledge of C++, compilers, and
+    Linux. Estimated time to complete is about 60 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need access to both an Arm-based and an x86-based cloud instance, plus an intermediate
+      understanding of C++, compilers, and Linux. No additional prerequisites are explicitly listed.
+  - question: Which Arm instance and OS are used in the setup example?
+    answer: >-
+      The example uses an Arm-based AWS instance, such as t4g.2xlarge, running Ubuntu 22.04 LTS.
+      You connect to the instance via SSH before installing packages.
+  - question: Which compiler should I use to build the examples?
+    answer: >-
+      The setup installs GCC and G++ from the Ubuntu repositories for building the examples. Although
+      Arm Compiler for Linux is listed as a tool, the walkthrough uses GCC.
+  - question: How do I install Arm Performance Libraries on the instance?
+    answer: >-
+      After connecting via SSH, run apt update and install gcc, g++, and make, then download and
+      install Arm Performance Libraries using the commands provided in the Learning Path. For
+      details, follow the Arm Performance Libraries install guide referenced in the steps.
+  - question: How do I replace Intel Vector Statistics Library when migrating to AArch64?
+    answer: >-
+      Use OpenRNG, included with Arm Performance Libraries 24.04, as a drop-in replacement for
+      Intel’s Vector Statistics Library. It supports a range of RNG types and utilities to help
+      transition existing code to Arm.
+# END generated_summary_faq
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/vLLM-quant/_index.md b/content/learning-paths/servers-and-cloud-computing/vLLM-quant/_index.md
index 260f44aaf1..a9e434c645 100644
--- a/content/learning-paths/servers-and-cloud-computing/vLLM-quant/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vLLM-quant/_index.md
@@ -25,7 +25,7 @@ prerequisites:
 generate_summary_faq: true
 
 rerun_summary: false
-rerun_faqs: false
+rerun_faqs: true
 author: 
    - Rani Chowdary Mandepudi
    - Phalani Paladugu
diff --git a/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md b/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md
index c7f2ae3116..b7fed74a39 100644
--- a/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md
@@ -19,6 +19,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:15:15Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: beb244c7766c474b47942b86f1cdd0d124c1cccb74dbe40f0b6c0917d0b3d31a
+  summary_generated_at: '2026-06-02T05:24:57Z'
+  summary_source_hash: beb244c7766c474b47942b86f1cdd0d124c1cccb74dbe40f0b6c0917d0b3d31a
+  faq_generated_at: '2026-06-03T02:15:15Z'
+  faq_source_hash: beb244c7766c474b47942b86f1cdd0d124c1cccb74dbe40f0b6c0917d0b3d31a
+  summary: >-
+    Learn how to migrate regex-based workloads from Hyperscan to Arm by installing and running
+    Vectorscan on an Arm-based Ubuntu instance, then integrating it with Snort 3. You will set
+    up on Ubuntu 20.04 or 22.04, install Snort 3 and its dependencies, and run Snort 3 with Vectorscan
+    on capture files to measure performance. This introductory path targets developers familiar
+    with Hyperscan who want to adopt Arm, including Arm-based cloud instances from AWS, Microsoft
+    Azure, Google Cloud, or Oracle, or an Arm server. The steps are concise and practical, designed
+    to complete in about 15 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an Arm-based instance from a cloud service provider or an Arm server with Ubuntu
+      20.04 or Ubuntu 22.04 installed. No other explicit prerequisites are listed.
+  - question: Should I install Hyperscan or Vectorscan on Arm?
+    answer: >-
+      Install Vectorscan. Hyperscan runs only on x86_64, and Vectorscan is the architecture-inclusive
+      fork that supports Arm.
+  - question: Can I use a cloud instance from AWS, Microsoft Azure, Google Cloud, or Oracle?
+    answer: >-
+      Yes. Any Arm-based instance from these cloud providers is in scope, as long as it runs Ubuntu
+      20.04 or Ubuntu 22.04.
+  - question: Which Ubuntu versions are these steps intended for?
+    answer: >-
+      Ubuntu 20.04 and Ubuntu 22.04 on Arm are explicitly listed and are the tested environments
+      for this Learning Path.
+  - question: What result should I expect after completing the steps?
+    answer: >-
+      You will have Vectorscan installed and running on your Arm instance, and Snort 3 installed
+      and run with Vectorscan on capture files. You will also measure performance as directed
+      in the steps.
+# END generated_summary_faq
+
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md b/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
index f657c75f56..7851256336 100644
--- a/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
@@ -22,6 +22,58 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:16:14Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 487e9829c6e08798ad01be7a01f192e10e99cb06b71108575f1919c134eece02
+  summary_generated_at: '2026-06-02T05:25:49Z'
+  summary_source_hash: 487e9829c6e08798ad01be7a01f192e10e99cb06b71108575f1919c134eece02
+  faq_generated_at: '2026-06-03T02:16:14Z'
+  faq_source_hash: 487e9829c6e08798ad01be7a01f192e10e99cb06b71108575f1919c134eece02
+  summary: >-
+    This Learning Path shows how to build an aarch64-optimized vLLM with oneDNN and the Arm Compute
+    Library on an Arm-based Linux server, set up runtime dependencies (including PyTorch and llmcompressor),
+    quantize the DeepSeek‑V2‑Lite model to INT4, and serve both INT4 and BF16 variants through
+    OpenAI‑compatible endpoints. You will configure key vLLM batching parameters (max_model_len
+    and max_num_batched_tokens) and evaluate accuracy using the LM Evaluation Harness to compare
+    BF16 and INT4 deployments. Prerequisites include an Arm-based Ubuntu 22.04+ server with at
+    least 32 vCPUs, 64 GB RAM, 64 GB free disk space, and Python 3.12 with basic Hugging Face
+    and quantization familiarity. Estimated time to complete is about 60 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      Use an Arm-based Linux server (Ubuntu 22.04+ recommended) with at least 32 vCPUs, 64 GB
+      RAM, and 64 GB free disk space. Install Python 3.12 and be comfortable with Hugging Face
+      Transformers and basic quantization concepts.
+  - question: How do I build and verify vLLM is optimized for aarch64 with oneDNN and ACL?
+    answer: >-
+      Follow the build step to target aarch64 and include oneDNN and the Arm Compute Library.
+      You validate the build by running inference as described in the path to confirm the binary
+      loads and serves a model.
+  - question: Which packages do I install to quantize the model, and why are they needed?
+    answer: >-
+      Install compressed-tensors and llmcompressor as shown in the quantization step. compressed-tensors
+      provides tensor storage and compression utilities for quantized formats, and llmcompressor
+      supplies quantization utilities compatible with Hugging Face Transformers and vLLM to quantize
+      deepseek-ai/DeepSeek‑V2‑Lite to INT4.
+  - question: How should I set vLLM batch sizing parameters when serving the model?
+    answer: >-
+      Use max_model_len to cap tokens per request and max_num_batched_tokens to bound total tokens
+      across concurrent requests. These parameters determine memory usage and how effectively
+      CPU threads are saturated; choose values based on expected prompt/generation lengths and
+      concurrency on your server.
+  - question: How do I evaluate accuracy for BF16 vs INT4 with LM Evaluation Harness?
+    answer: >-
+      Install the LM Evaluation Harness with vLLM support, then run benchmarks against your BF16
+      and INT4 models served by vLLM. Compare the reported metrics across precisions; results
+      vary based on your CPU, datasets, and runtime settings.
+# END generated_summary_faq
 
 author:
    - Nikhil Gupta
diff --git a/content/learning-paths/servers-and-cloud-computing/vllm/_index.md b/content/learning-paths/servers-and-cloud-computing/vllm/_index.md
index 6621e17c8e..4d1338a5bd 100644
--- a/content/learning-paths/servers-and-cloud-computing/vllm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vllm/_index.md
@@ -19,6 +19,54 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:15:37Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: db5987ec7987375d9b51de843b0e1faf0e61731b14c5a563d19aaad26f50f6bb
+  summary_generated_at: '2026-06-02T05:25:21Z'
+  summary_source_hash: db5987ec7987375d9b51de843b0e1faf0e61731b14c5a563d19aaad26f50f6bb
+  faq_generated_at: '2026-06-03T02:15:37Z'
+  faq_source_hash: db5987ec7987375d9b51de843b0e1faf0e61731b14c5a563d19aaad26f50f6bb
+  summary: >-
+    Learn to build vLLM from source on an Arm-based Ubuntu 24.04 LTS server, verify BFloat16 support,
+    and run both local batch inference and an OpenAI-compatible server. The path uses a Qwen model
+    from Hugging Face and shows how vLLM automatically downloads models on first run, with optional
+    Hugging Face token authentication for gated models. You can follow the steps on an Arm instance
+    from AWS, Microsoft Azure, Google Cloud, or Oracle, or on a local Arm Linux machine with at
+    least 8 CPUs, 16 GB RAM, and 50 GB of storage. By the end, you will run batch prompts locally
+    and serve requests through a local OpenAI-compatible API. No additional prerequisites are
+    explicitly listed.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      Use an Arm server running Ubuntu 24.04 LTS with at least 8 cores, 16 GB RAM, and 50 GB disk
+      space. You also need a processor that supports BFloat16. You can use an Arm-based instance
+      from a supported cloud provider or a local Arm Linux computer.
+  - question: How do I know if my Arm CPU supports BFloat16?
+    answer: >-
+      Run: lscpu | grep bf16. If the Flags are printed, your processor includes BFloat16 support
+      as required by the steps.
+  - question: Do I need to download the model from Hugging Face ahead of time?
+    answer: >-
+      No. vLLM downloads the required model automatically on first run. For models that require
+      access approval or terms, authenticate with Hugging Face using huggingface-cli login and
+      a token generated from your Hugging Face account.
+  - question: Which model is used in this Learning Path?
+    answer: >-
+      A Qwen LLM from the Hugging Face Hub is used. The path guides you to obtain it via vLLM’s
+      first-run download.
+  - question: When should I use batch inference versus the OpenAI-compatible server?
+    answer: >-
+      Use batch inference for a quick local run from Python. Start the OpenAI-compatible server
+      when you need an API endpoint on your Arm server; this avoids external APIs and supports
+      the privacy, cost, and offline advantages described in the steps.
+# END generated_summary_faq
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md b/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
index 8a026f2593..528ef80f3c 100644
--- a/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
@@ -6,6 +6,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:16:42Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 4ed20056b2d82bd2c9ab1afe3d74657df9ac09808592d843c1b143626a8668ac
+  summary_generated_at: '2026-06-02T05:26:21Z'
+  summary_source_hash: 4ed20056b2d82bd2c9ab1afe3d74657df9ac09808592d843c1b143626a8668ac
+  faq_generated_at: '2026-06-03T02:16:42Z'
+  faq_source_hash: 4ed20056b2d82bd2c9ab1afe3d74657df9ac09808592d843c1b143626a8668ac
+  summary: >-
+    Learn how to build and run the open-source VVenC (vvenc) H.266/VVC encoder on Arm-based Linux
+    servers to encode a real 1080p video and measure performance. This introductory path targets
+    Arm Neoverse platforms and highlights available optimizations in vvenc for Neon and SVE/SVE2,
+    with optimized code in the project’s GitHub repository. You will build the vvenc project and
+    run an encode on an Arm server to gather performance measurements. Prerequisites are an Arm
+    Linux system or an Arm-based cloud instance; the path was tested on an Arm Neoverse N2-based
+    Alibaba Cloud ECS instance (g8y) running Ubuntu 22.04. Estimated completion time is about
+    20 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an Arm Linux system or an Arm-based instance from a cloud service provider. This
+      Learning Path has been tested on a Neoverse N2-based Alibaba Cloud ECS (g8y) running Ubuntu
+      22.04. No other prerequisites are explicitly listed.
+  - question: Which cloud platforms can I use for the Arm instance?
+    answer: >-
+      The path lists AWS, Microsoft Azure, Google Cloud, and Oracle as cloud service providers.
+      It was tested on an Alibaba Cloud ECS instance with a Neoverse N2 CPU and Ubuntu 22.04.
+  - question: Where do I get the encoder source and which tool will I run?
+    answer: >-
+      The optimized code for Arm Neoverse platforms is available in the vvenc GitHub repository.
+      You will build the project and run the vvenc encoder to process a real 1080p video.
+  - question: Do I need Neon or SVE/SVE2 to follow this path?
+    answer: >-
+      The encoder includes optimizations for Arm Neoverse that use Neon and SVE/SVE2 instructions.
+      The path does not list specific instruction-set requirements as prerequisites.
+  - question: What result should I expect after running vvenc on a 1080p video?
+    answer: >-
+      You should complete an encode of a real 1080p video and gather performance measurements
+      as directed. The steps focus on building vvenc, running the encoder, and measuring performance
+      on the Arm server.
+# END generated_summary_faq
 
 author: Willen Yang
 
diff --git a/content/learning-paths/servers-and-cloud-computing/whisper/_index.md b/content/learning-paths/servers-and-cloud-computing/whisper/_index.md
index 9016ec70ea..28575b7e44 100644
--- a/content/learning-paths/servers-and-cloud-computing/whisper/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/whisper/_index.md
@@ -23,6 +23,53 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:17:11Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: e130630b5ae4626641779d0b66d3c1c132b90899cd3d6db07a46df8957c4da38
+  summary_generated_at: '2026-06-02T05:26:50Z'
+  summary_source_hash: e130630b5ae4626641779d0b66d3c1c132b90899cd3d6db07a46df8957c4da38
+  faq_generated_at: '2026-06-03T02:17:11Z'
+  faq_source_hash: e130630b5ae4626641779d0b66d3c1c132b90899cd3d6db07a46df8957c4da38
+  summary: >-
+    This Learning Path shows how to run the OpenAI Whisper ASR model on Arm-based cloud servers
+    using Hugging Face Transformers. You will install the required Python dependencies, configure
+    environment variables to enable Arm-friendly execution, and run the whisper-large-v3-turbo
+    model as an application that accepts audio input and generates a text transcript. The steps
+    target Ubuntu 24.04 LTS on an Arm instance with 32 cores, at least 8GB RAM, and 32GB of disk;
+    they were tested on an AWS Graviton4 c8g.8xlarge. You will also evaluate transcript generation
+    times. This introductory path assumes basic Python skills, familiarity with ML concepts, and
+    Whisper fundamentals, and takes about 15 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an Arm-based server running Ubuntu 24.04 LTS with 32 cores, at least 8GB of RAM,
+      and 32GB of disk space. You should also have basic Python knowledge, familiarity with machine
+      learning concepts, and fundamentals of the Whisper ASR model.
+  - question: Which Whisper model and libraries does this path use?
+    answer: >-
+      The path runs the whisper-large-v3-turbo model. It uses Hugging Face Transformers in Python
+      to load and execute the model.
+  - question: Which settings will I change to improve performance on Arm CPUs?
+    answer: >-
+      You will configure environment variables that enable low-level, Arm-targeted kernels to
+      accelerate key parts of inference. The specific variables to set are provided in the steps
+      before you run the model.
+  - question: What result should I expect after running the demo?
+    answer: >-
+      You will provide an audio input and receive a text transcript generated by Whisper. The
+      steps also guide you to evaluate transcript generation times.
+  - question: Where were these steps tested?
+    answer: >-
+      The procedure was tested on an AWS Graviton4 c8g.8xlarge instance. The instructions are
+      designed for Arm servers running Ubuntu 24.04 LTS.
+# END generated_summary_faq
 
 author: Nobel Chowdary Mandepudi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md b/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
index 7a33fa5107..449d80f668 100644
--- a/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
@@ -12,6 +12,51 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:17:46Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 46f2645fb6ef298508af93569554315cfa2564be9891acabf1c874c94e52d70d
+  summary_generated_at: '2026-06-02T05:27:17Z'
+  summary_source_hash: 46f2645fb6ef298508af93569554315cfa2564be9891acabf1c874c94e52d70d
+  faq_generated_at: '2026-06-03T02:17:46Z'
+  faq_source_hash: 46f2645fb6ef298508af93569554315cfa2564be9891acabf1c874c94e52d70d
+  summary: >-
+    This introductory Learning Path shows how to install MySQL Community Server and WordPress
+    on an Arm virtual machine running Oracle Linux in Oracle Cloud Infrastructure (OCI), targeting
+    an always free tier Arm shape. You will follow practical steps to set up the database and
+    application stack on an Arm (Ampere) compute instance. The path notes that an Arm instance
+    can be deployed through the OCI Console or Terraform. Prerequisites are an OCI account and
+    an Arm compute instance on OCI with Oracle Linux. On completion, you will have MySQL and WordPress
+    installed on your OCI Arm server. The estimated time to complete is about 30 minutes.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      You need an Oracle Cloud Infrastructure (OCI) account and an Arm compute instance deployed
+      on OCI with Oracle Linux. The path suggests reviewing “Getting Started with Oracle OCI”
+      if you want a quick orientation before you begin.
+  - question: Which OCI shape and operating system should I use for the instance?
+    answer: >-
+      Use an always free tier Arm shape in OCI targeting an Arm (Ampere) compute instance. The
+      prerequisite specifies Oracle Linux as the operating system.
+  - question: How can I provision the Arm compute instance?
+    answer: >-
+      You can deploy the instance through the OCI Console or by using Terraform. The Learning
+      Path supports either approach.
+  - question: Which software will I install during this Learning Path?
+    answer: >-
+      You will install MySQL Community Server and WordPress on the Arm virtual machine. These
+      are the only tools explicitly listed.
+  - question: What result should I expect, and how long will it take?
+    answer: >-
+      Plan for about 30 minutes to complete. By the end, MySQL Community Server and WordPress
+      will be installed on your Arm server running in OCI.
+# END generated_summary_faq
 
 author: Frédéric -lefred- Descamps
 
diff --git a/content/learning-paths/servers-and-cloud-computing/zlib/_index.md b/content/learning-paths/servers-and-cloud-computing/zlib/_index.md
index e2edf19e82..db5518ad9a 100644
--- a/content/learning-paths/servers-and-cloud-computing/zlib/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/zlib/_index.md
@@ -20,6 +20,52 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-06-03T02:18:20Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 8916f83f621505dc5406f14e70d04e702ea304950e34b4b76dc1bbc987bcc4e3
+  summary_generated_at: '2026-06-02T05:27:47Z'
+  summary_source_hash: 8916f83f621505dc5406f14e70d04e702ea304950e34b4b76dc1bbc987bcc4e3
+  faq_generated_at: '2026-06-03T02:18:20Z'
+  faq_source_hash: 8916f83f621505dc5406f14e70d04e702ea304950e34b4b76dc1bbc987bcc4e3
+  summary: >-
+    Build and use zlib-ng on an Arm Linux server to take advantage of Neon SIMD and ARMv8 CRC32
+    enhancements for compression-heavy workloads. You will compile zlib-ng in zlib-compatible
+    mode, run example applications as a drop-in replacement for the system zlib, and compare a
+    Python file-compression workload before and after switching to zlib-ng. The path also shows
+    how to install and use Linux perf to analyze where time is spent, including enabling access
+    to PMU registers and kernel symbols. Prerequisite: an Arm Linux computer or Arm-based cloud
+    instance running Ubuntu 22.04 or 24.04; no other explicit prerequisites are listed.
+  faqs:
+  - question: What do I need before running the steps?
+    answer: >-
+      Use an Arm Linux computer or an Arm-based cloud instance running Ubuntu 22.04 or Ubuntu
+      24.04. The steps use sudo to install packages and adjust perf settings.
+  - question: Which zlib-ng build mode should I use for a drop-in replacement?
+    answer: >-
+      Build zlib-ng in zlib-compatible mode. This enables the zlib API so existing applications
+      can use zlib-ng without source changes.
+  - question: What result should I expect after running the Python compression example?
+    answer: >-
+      You will compress a large file with Python and measure the time difference when using zlib-ng
+      versus the system zlib. The outcome is a measured performance comparison rather than a specific
+      numeric target.
+  - question: Which packages are installed during this Learning Path?
+    answer: >-
+      The steps install python-is-python3 for running Python and the perf tooling via linux-tools-common,
+      linux-tools-generic, and linux-tools-uname -r. These enable running the example and analyzing
+      performance.
+  - question: What should I check if perf reports permission or access errors?
+    answer: >-
+      Follow the steps that allow user access to PMU registers and kernel symbol addresses using
+      the provided sudo commands. After applying those settings, rerun perf to collect data.
+# END generated_summary_faq
 
 author: Pareena Verma
 
diff --git a/generate-summary-faq b/generate-summary-faq
new file mode 100755
index 0000000000..9138e9b801
--- /dev/null
+++ b/generate-summary-faq
@@ -0,0 +1,14 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+# Team-friendly shortcut for the AI-assisted Learning Path summary/FAQ generator.
+# With no arguments, run the normal full generation workflow for all opted-in paths.
+# With arguments, pass them through to the full tool for category/path/dry-run control.
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+
+if [[ $# -eq 0 ]]; then
+  exec "$SCRIPT_DIR/tools/generate-summary-faq" --all --write
+fi
+
+exec "$SCRIPT_DIR/tools/generate-summary-faq" "$@"
diff --git a/reports/generated-summary-faq/latest-run.yml b/reports/generated-summary-faq/latest-run.yml
index 6ade0b66ab..0fd3972081 100644
--- a/reports/generated-summary-faq/latest-run.yml
+++ b/reports/generated-summary-faq/latest-run.yml
@@ -1,198649 +1,175 @@
 latest_run:
-  timestamp: '2026-05-12T18:20:25Z'
+  timestamp: '2026-06-03T02:18:43Z'
+  run_name: all-learning-paths-20260602-212658
   mode: write
-  require_enable_flag: true
-  path_filter: ''
-  limit: 0
-  run_url: https://github.com/chrismoroney/arm-learning-paths/actions/runs/25753610365
-  git_ref: STESOL-345
-  git_sha: f79ee7e5f40a1d01f4a801847647eec92d7f41cc
-  actor: chrismoroney
-  template_version: summary-faq-v2
-  totals:
-    processed: 407
-    added: 10
-    updated: 0
-    unchanged: 396
-    drift_detected: 1
-    paths_with_drift: 1
-    skipped: 0
-    errors: 0
-    removed: 0
-    summary_changed: 10
-    faq_changed: 10
-    rerun_flags_reset: 0
-  section_totals:
-    summary:
-      created: 10
-      repaired_missing: 0
-      rerun_requested: 0
-      drift_detected_preserved: 1
-      unchanged: 396
-    faqs:
-      created: 10
-      repaired_missing: 0
-      rerun_requested: 0
-      drift_detected_preserved: 1
-      unchanged: 396
-  reason_totals:
-    initial_generation: 10
-    missing_summary: 0
-    missing_faqs: 0
-    rerun_summary: 0
-    rerun_faqs: 0
-    summary_drift_detected: 1
-    faq_drift_detected: 1
-    rerun_flags_reset: 0
-  paths:
-  - path: content/learning-paths/automotive/openadkit1_container/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
-    summary:
-      action: unchanged
-      missing_before: false
-      rerun_requested: false
-      changed: false
-      drift_detected: false
-      source_hash_before: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
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-      current_source_hash: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
-      generated_at_before: '2026-05-06T17:17:53Z'
-      generated_at_after: '2026-05-06T17:17:53Z'
-      preview_before: Learn how to deploy and run containerized autonomous driving simulations using Autoware
-        Open AD Kit on Arm Neoverse with Docker, demonstrating SOAFEE-based Shift-Left development workflows.
-        It is desi...
-      preview_after: Learn how to deploy and run containerized autonomous driving simulations using Autoware
-        Open AD Kit on Arm Neoverse with Docker, demonstrating SOAFEE-based Shift-Left development workflows.
-        It is desi...
-      preview_generated: Learn how to deploy and run containerized autonomous driving simulations using
-        Autoware Open AD Kit on Arm Neoverse with Docker, demonstrating SOAFEE-based Shift-Left development
-        workflows. It is desi...
-    faqs:
-      action: unchanged
-      missing_before: false
-      rerun_requested: false
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-      before_count: 5
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-        updated_questions: []
-      generated_diff:
-        before_count: 5
-        after_count: 5
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-        removed_questions: []
-        updated_questions: []
-  - path: content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
-    summary:
-      action: unchanged
-      missing_before: false
-      rerun_requested: false
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-      preview_before: Learn how to implement functional safety isolation for autonomous driving systems
-        on Arm Neoverse using DDS-based communication, containerized deployment, and ISO 26262 compliance
-        principles. It is de...
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-        on Arm Neoverse using DDS-based communication, containerized deployment, and ISO 26262 compliance
-        principles. It is de...
-      preview_generated: Learn how to implement functional safety isolation for autonomous driving systems
-        on Arm Neoverse using DDS-based communication, containerized deployment, and ISO 26262 compliance
-        principles. It is de...
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-      generated_diff:
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-  - path: content/learning-paths/automotive/system76-auto/_index.md
-    status: unchanged
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-    managed_block_updated: false
-    rerun_flags_reset: []
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-    template_version_before: summary-faq-v1
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-      preview_before: Learn how to build and run the Arm Automotive Solutions Software Reference Stack
-        locally on the System76 Thelio Astra desktop using Multipass virtualization and Yocto build tools.
-        It is designed for a...
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-        locally on the System76 Thelio Astra desktop using Multipass virtualization and Yocto build tools.
-        It is designed for a...
-      preview_generated: Learn how to build and run the Arm Automotive Solutions Software Reference Stack
-        locally on the System76 Thelio Astra desktop using Multipass virtualization and Yocto build tools.
-        It is designed for a...
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-  - path: content/learning-paths/automotive/zenacssdebug/_index.md
-    status: unchanged
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-      preview_before: Learn how to debug the Arm Zena CSS Reference Software Stack using Arm Development
-        Studio on a Fixed Virtual Platform, covering RSE, Safety Island, and Linux kernel debugging workflows.
-        It is designed...
-      preview_after: Learn how to debug the Arm Zena CSS Reference Software Stack using Arm Development
-        Studio on a Fixed Virtual Platform, covering RSE, Safety Island, and Linux kernel debugging workflows.
-        It is designed...
-      preview_generated: Learn how to debug the Arm Zena CSS Reference Software Stack using Arm Development
-        Studio on a Fixed Virtual Platform, covering RSE, Safety Island, and Linux kernel debugging workflows.
-        It is designed...
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-  - path: content/learning-paths/cross-platform/_example-learning-path/_index.md
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-      preview_before: Learn what type of content belongs in a Learning Path and how to format it. It is
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-        as a step-by-step guid...
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-        as a step-by-step guid...
-      preview_generated: Learn what type of content belongs in a Learning Path and how to format it. It
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-  - path: content/learning-paths/cross-platform/adler32/_index.md
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-      preview_before: Learn how to use GitHub Copilot to write Neon intrinsics that accelerate the Adler32
-        checksum algorithm on Arm platforms, achieving significant performance improvements over standard
-        C implementations...
-      preview_after: Learn how to use GitHub Copilot to write Neon intrinsics that accelerate the Adler32
-        checksum algorithm on Arm platforms, achieving significant performance improvements over standard
-        C implementations...
-      preview_generated: Learn how to use GitHub Copilot to write Neon intrinsics that accelerate the
-        Adler32 checksum algorithm on Arm platforms, achieving significant performance improvements over
-        standard C implementations...
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+    rerun_flags_reset: 407
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       unchanged: 407
     faqs:
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       repaired_missing: 0
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   reason_totals:
     initial_generation: 0
     missing_summary: 0
     missing_faqs: 0
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-    rerun_faqs: 0
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-    rerun_flags_reset: 0
+    rerun_flags_reset: 407
+    draft: 11
   paths:
+  - path: content/learning-paths/automotive/intro/_index.md
+    status: skipped
+    skip_reason: draft
+    change_reasons:
+    - draft
+    ai_requested: false
+    summary:
+      action: skipped
+    faqs:
+      action: skipped
+    category: automotive
   - path: content/learning-paths/automotive/openadkit1_container/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -198653,51 +179,78 @@ history:
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-      generated_at_before: '2026-05-06T17:17:53Z'
-      generated_at_after: '2026-05-06T17:17:53Z'
-      preview_before: Learn how to deploy and run containerized autonomous driving simulations using Autoware
-        Open AD Kit on Arm Neoverse with Docker, demonstrating SOAFEE-based Shift-Left development workflows.
-        It is desi...
-      preview_after: Learn how to deploy and run containerized autonomous driving simulations using Autoware
-        Open AD Kit on Arm Neoverse with Docker, demonstrating SOAFEE-based Shift-Left development workflows.
-        It is desi...
-      preview_generated: Learn how to deploy and run containerized autonomous driving simulations using
-        Autoware Open AD Kit on Arm Neoverse with Docker, demonstrating SOAFEE-based Shift-Left development
-        workflows. It is desi...
+      generated_at_before: '2026-06-01T20:57:21Z'
+      generated_at_after: '2026-06-01T20:57:21Z'
+      preview_before: This Learning Path shows how to deploy and run a containerized
+        autonomous driving simulation using Autoware Open AD Kit on Arm Neoverse with
+        Docker, illustrating SOAFEE-aligned Shift-Left development....
+      preview_after: This Learning Path shows how to deploy and run a containerized
+        autonomous driving simulation using Autoware Open AD Kit on Arm Neoverse with
+        Docker, illustrating SOAFEE-aligned Shift-Left development....
+      preview_generated: Learn to deploy and run Autoware Open AD Kit autonomous driving
+        simulations as Docker containers on Arm Neoverse, demonstrating a SOAFEE-based
+        Shift-Left workflow. You will review SOAFEE concepts for ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
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       drift_detected: false
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-      generated_at_after: '2026-05-06T17:17:53Z'
+      generated_at_before: '2026-06-01T20:57:21Z'
+      generated_at_after: '2026-06-02T21:26:58Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the demo?
+        - Should I use a cloud instance or an on-prem Arm Neoverse system?
+        - Do I need to install Docker and Docker Compose?
+        - What should I expect when I start the demo with Docker Compose?
+        - Where can I inspect or adjust what gets executed?
+        removed_questions:
+        - What hardware and operating system do I need to complete this Learning Path?
+        - What tools must be installed before I start?
+        - Can I run the simulation in the cloud or on-premises, and what setups have
+          been tested?
+        - How do I launch the Open AD Kit demo and what indicates it is running correctly?
+        - What background knowledge and time commitment are expected?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the demo?
+        - Should I use a cloud instance or an on-prem Arm Neoverse system?
+        - Do I need to install Docker and Docker Compose?
+        - What should I expect when I start the demo with Docker Compose?
+        - Where can I inspect or adjust what gets executed?
+        removed_questions:
+        - What hardware and operating system do I need to complete this Learning Path?
+        - What tools must be installed before I start?
+        - Can I run the simulation in the cloud or on-premises, and what setups have
+          been tested?
+        - How do I launch the Open AD Kit demo and what indicates it is running correctly?
+        - What background knowledge and time commitment are expected?
+        updated_questions: []
+    category: automotive
   - path: content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -198707,51 +260,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:53Z'
-      generated_at_after: '2026-05-06T17:17:53Z'
-      preview_before: Learn how to implement functional safety isolation for autonomous driving systems
-        on Arm Neoverse using DDS-based communication, containerized deployment, and ISO 26262 compliance
-        principles. It is de...
-      preview_after: Learn how to implement functional safety isolation for autonomous driving systems
-        on Arm Neoverse using DDS-based communication, containerized deployment, and ISO 26262 compliance
-        principles. It is de...
-      preview_generated: Learn how to implement functional safety isolation for autonomous driving systems
-        on Arm Neoverse using DDS-based communication, containerized deployment, and ISO 26262 compliance
-        principles. It is de...
+      generated_at_before: '2026-06-01T20:57:59Z'
+      generated_at_after: '2026-06-01T20:57:59Z'
+      preview_before: This advanced Learning Path shows automotive engineers how to
+        prototype safety-critical isolation for autonomous driving workloads on Arm
+        Neoverse running Linux. You apply ISO 26262 concepts (includin...
+      preview_after: This advanced Learning Path shows automotive engineers how to
+        prototype safety-critical isolation for autonomous driving workloads on Arm
+        Neoverse running Linux. You apply ISO 26262 concepts (includin...
+      preview_generated: "This advanced path shows how to prototype safety-critical\
+        \ isolation for autonomous driving systems on Arm Neoverse using DDS-based\
+        \ publish\u2013subscribe communication and containerized deployment on Linux..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
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       drift_detected: false
       source_hash_before: 92a6dac2b1674a44cd623a0c8c3189b38438124a6067ed7ca776999ff1d8b5bf
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-      generated_at_before: '2026-05-06T17:17:53Z'
-      generated_at_after: '2026-05-06T17:17:53Z'
+      generated_at_before: '2026-06-01T20:57:59Z'
+      generated_at_after: '2026-06-02T21:27:35Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this path?
+        - Can I use a single local system instead of two cloud instances?
+        - Which technologies are used for communication and isolation?
+        - How are ISO 26262 and ASIL levels applied here?
+        - "What result should I expect and how do I know I\u2019m on track?"
+        removed_questions:
+        - What environment do I need to follow this Learning Path?
+        - Can I complete this on a single machine instead of two instances?
+        - What prior knowledge or preparation is required?
+        - What will I implement during the path?
+        - Does this cover ISO 26262 certification steps?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this path?
+        - Can I use a single local system instead of two cloud instances?
+        - Which technologies are used for communication and isolation?
+        - How are ISO 26262 and ASIL levels applied here?
+        - "What result should I expect and how do I know I\u2019m on track?"
+        removed_questions:
+        - What environment do I need to follow this Learning Path?
+        - Can I complete this on a single machine instead of two instances?
+        - What prior knowledge or preparation is required?
+        - What will I implement during the path?
+        - Does this cover ISO 26262 certification steps?
+        updated_questions: []
+    category: automotive
   - path: content/learning-paths/automotive/system76-auto/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -198761,51 +339,78 @@ history:
       source_hash_before: 2b758d2dcf28a683ab164e28578a736d6d730b81dfbc01a6765619052fcdebd0
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-      generated_at_before: '2026-05-06T17:17:53Z'
-      generated_at_after: '2026-05-06T17:17:53Z'
-      preview_before: Learn how to build and run the Arm Automotive Solutions Software Reference Stack
-        locally on the System76 Thelio Astra desktop using Multipass virtualization and Yocto build tools.
-        It is designed for a...
-      preview_after: Learn how to build and run the Arm Automotive Solutions Software Reference Stack
-        locally on the System76 Thelio Astra desktop using Multipass virtualization and Yocto build tools.
-        It is designed for a...
-      preview_generated: Learn how to build and run the Arm Automotive Solutions Software Reference Stack
-        locally on the System76 Thelio Astra desktop using Multipass virtualization and Yocto build tools.
-        It is designed for a...
+      generated_at_before: '2026-06-01T20:58:28Z'
+      generated_at_after: '2026-06-01T20:58:28Z'
+      preview_before: This Learning Path shows how to set up a local automotive software
+        development environment on the Arm-based System76 Thelio Astra and build the
+        Arm Automotive Solutions Software Reference Stack. You w...
+      preview_after: This Learning Path shows how to set up a local automotive software
+        development environment on the Arm-based System76 Thelio Astra and build the
+        Arm Automotive Solutions Software Reference Stack. You w...
+      preview_generated: This Learning Path shows how to set up a local automotive
+        software development workflow on a System76 Thelio Astra Arm desktop (Ampere
+        Altra, Arm Neoverse N1) running Ubuntu 24.04. You will install Mu...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 2b758d2dcf28a683ab164e28578a736d6d730b81dfbc01a6765619052fcdebd0
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-      generated_at_before: '2026-05-06T17:17:53Z'
-      generated_at_after: '2026-05-06T17:17:53Z'
+      generated_at_before: '2026-06-01T20:58:28Z'
+      generated_at_after: '2026-06-02T21:28:14Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Ubuntu version should I use inside the Multipass VM?
+        - How do I begin the build of the Arm Automotive Solutions Software Reference
+          Stack?
+        - Can I run the demos without RD-1 AE hardware?
+        - What result should I expect from the Parsec demo?
+        removed_questions:
+        - What hardware and OS do I need to follow this Learning Path?
+        - How is the development environment created and where do builds run?
+        - What will I build and how can I validate it works?
+        - Which target platform does the software stack address in this path?
+        - Which tools and technologies are used?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Ubuntu version should I use inside the Multipass VM?
+        - How do I begin the build of the Arm Automotive Solutions Software Reference
+          Stack?
+        - Can I run the demos without RD-1 AE hardware?
+        - What result should I expect from the Parsec demo?
+        removed_questions:
+        - What hardware and OS do I need to follow this Learning Path?
+        - How is the development environment created and where do builds run?
+        - What will I build and how can I validate it works?
+        - Which target platform does the software stack address in this path?
+        - Which tools and technologies are used?
+        updated_questions: []
+    category: automotive
   - path: content/learning-paths/automotive/zenacssdebug/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -198815,51 +420,84 @@ history:
       source_hash_before: 8dccdbdd4d9727f3466987ce918d9df0ed9d4d4f28cd613162bacab01d9c18f3
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-      generated_at_before: '2026-05-06T17:17:53Z'
-      generated_at_after: '2026-05-06T17:17:53Z'
-      preview_before: Learn how to debug the Arm Zena CSS Reference Software Stack using Arm Development
-        Studio on a Fixed Virtual Platform, covering RSE, Safety Island, and Linux kernel debugging workflows.
-        It is designed...
-      preview_after: Learn how to debug the Arm Zena CSS Reference Software Stack using Arm Development
-        Studio on a Fixed Virtual Platform, covering RSE, Safety Island, and Linux kernel debugging workflows.
-        It is designed...
-      preview_generated: Learn how to debug the Arm Zena CSS Reference Software Stack using Arm Development
-        Studio on a Fixed Virtual Platform, covering RSE, Safety Island, and Linux kernel debugging workflows.
-        It is designed...
+      generated_at_before: '2026-06-01T20:59:08Z'
+      generated_at_after: '2026-06-01T20:59:08Z'
+      preview_before: This introductory Learning Path shows how to debug the Arm Zena
+        Compute Subsystem (CSS) Reference Software Stack on a Fixed Virtual Platform
+        using Arm Development Studio. You will launch the Zena CSS ...
+      preview_after: This introductory Learning Path shows how to debug the Arm Zena
+        Compute Subsystem (CSS) Reference Software Stack on a Fixed Virtual Platform
+        using Arm Development Studio. You will launch the Zena CSS ...
+      preview_generated: Follow this Learning Path to debug the Arm Zena Compute Subsystem
+        (CSS) Reference Software Stack on a Fixed Virtual Platform (FVP) using Arm
+        Development Studio. You will launch the FVP with the Iris d...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 8dccdbdd4d9727f3466987ce918d9df0ed9d4d4f28cd613162bacab01d9c18f3
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-      generated_at_before: '2026-05-06T17:17:53Z'
-      generated_at_after: '2026-05-06T17:17:53Z'
+      generated_at_before: '2026-06-01T20:59:08Z'
+      generated_at_after: '2026-06-02T21:28:53Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - "Why can\u2019t Arm Development Studio connect if I launch the FVP from\
+          \ the build environment command?"
+        - Which connection method should I choose in Arm Development Studio for this
+          target?
+        - How do I hold the RSE at reset and step through early boot?
+        - Can I connect to the Safety Island and the Linux kernel simultaneously?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - How should I launch the Zena CSS FVP so it can be debugged from Arm Development
+          Studio?
+        - Which targets within Zena CSS will I connect to and debug?
+        - Does Arm Development Studio provide a built-in debug configuration for the
+          Zena CSS FVP?
+        - How do I know my debug setup is working?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - "Why can\u2019t Arm Development Studio connect if I launch the FVP from\
+          \ the build environment command?"
+        - Which connection method should I choose in Arm Development Studio for this
+          target?
+        - How do I hold the RSE at reset and step through early boot?
+        - Can I connect to the Safety Island and the Linux kernel simultaneously?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - How should I launch the Zena CSS FVP so it can be debugged from Arm Development
+          Studio?
+        - Which targets within Zena CSS will I connect to and debug?
+        - Does Arm Development Studio provide a built-in debug configuration for the
+          Zena CSS FVP?
+        - How do I know my debug setup is working?
+        updated_questions: []
+    category: automotive
   - path: content/learning-paths/cross-platform/_example-learning-path/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -198869,51 +507,76 @@ history:
       source_hash_before: a58d5eb4c4256f3e4f4374344ef0e73836e8b51ac7223b0a5238b22137ec0819
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-      generated_at_before: '2026-05-06T17:17:53Z'
-      generated_at_after: '2026-05-06T17:17:53Z'
-      preview_before: Learn what type of content belongs in a Learning Path and how to format it. It is
-        designed for content creators and software developers who want to share Arm related information
-        as a step-by-step guid...
-      preview_after: Learn what type of content belongs in a Learning Path and how to format it. It is
-        designed for content creators and software developers who want to share Arm related information
-        as a step-by-step guid...
-      preview_generated: Learn what type of content belongs in a Learning Path and how to format it. It
-        is designed for content creators and software developers who want to share Arm related information
-        as a step-by-step guid...
+      generated_at_before: '2026-06-01T20:59:38Z'
+      generated_at_after: '2026-06-01T20:59:38Z'
+      preview_before: This introductory path shows content creators and software developers
+        how to create and contribute a new Arm Learning Path in about 60 minutes.
+        You will set up a text editor, Hugo, and Git; fork the G...
+      preview_after: This introductory path shows content creators and software developers
+        how to create and contribute a new Arm Learning Path in about 60 minutes.
+        You will set up a text editor, Hugo, and Git; fork the G...
+      preview_generated: This introductory path teaches you how to create and contribute
+        a new Arm Learning Path from start to finish. You will set up a local authoring
+        environment with a text editor, Hugo, and Git; fork the ...
     faqs:
-      action: unchanged
+      action: rerun_requested
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       change_details:
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         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I know whether my topic belongs in a Learning Path?
+        - Which category should I use when adding my Learning Path?
+        - Where do I set the Learning Path metadata, and are there naming rules?
+        - How do I contribute my Learning Path for review?
+        removed_questions:
+        - What do I need before starting?
+        - Which tools are mandatory, and what are they used for?
+        - How do I start and submit my contribution?
+        - How do I choose the right category for my Learning Path?
+        - What content belongs in a Learning Path, and can I include videos?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I know whether my topic belongs in a Learning Path?
+        - Which category should I use when adding my Learning Path?
+        - Where do I set the Learning Path metadata, and are there naming rules?
+        - How do I contribute my Learning Path for review?
+        removed_questions:
+        - What do I need before starting?
+        - Which tools are mandatory, and what are they used for?
+        - How do I start and submit my contribution?
+        - How do I choose the right category for my Learning Path?
+        - What content belongs in a Learning Path, and can I include videos?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/adler32/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
       missing_before: false
@@ -198923,51 +586,74 @@ history:
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-      preview_before: Learn how to use GitHub Copilot to write Neon intrinsics that accelerate the Adler32
-        checksum algorithm on Arm platforms, achieving significant performance improvements over standard
-        C implementations...
-      preview_after: Learn how to use GitHub Copilot to write Neon intrinsics that accelerate the Adler32
-        checksum algorithm on Arm platforms, achieving significant performance improvements over standard
-        C implementations...
-      preview_generated: Learn how to use GitHub Copilot to write Neon intrinsics that accelerate the
-        Adler32 checksum algorithm on Arm platforms, achieving significant performance improvements over
-        standard C implementations...
+      generated_at_before: '2026-06-01T21:00:08Z'
+      generated_at_after: '2026-06-01T21:00:08Z'
+      preview_before: This introductory Learning Path shows C/C++ developers on Arm
+        Linux how to use GitHub Copilot in Visual Studio Code to implement and accelerate
+        the Adler32 checksum with Arm Neon intrinsics. You will ...
+      preview_after: This introductory Learning Path shows C/C++ developers on Arm
+        Linux how to use GitHub Copilot in Visual Studio Code to implement and accelerate
+        the Adler32 checksum with Arm Neon intrinsics. You will ...
+      preview_generated: This Learning Path shows how to use GitHub Copilot to implement
+        and then accelerate the Adler32 checksum on Arm using Neon (Advanced SIMD).
+        You start by prompting Copilot to generate a baseline C vers...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
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-      generated_at_after: '2026-05-06T17:17:53Z'
+      generated_at_before: '2026-06-01T21:00:08Z'
+      generated_at_after: '2026-06-02T21:30:35Z'
       before_count: 5
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       change_details:
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         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How is the project built, and which CPU is it tuned for?
+        - What should I verify when I run the test program?
+        - When do I implement Neon intrinsics for Adler32?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - What code and artifacts will I create?
+        - How do I validate that the implementation works and assess performance?
+        - Will I write Neon intrinsics, or is this only a C baseline?
+        updated_questions:
+        - Which GitHub Copilot mode or model should I use?
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How is the project built, and which CPU is it tuned for?
+        - What should I verify when I run the test program?
+        - When do I implement Neon intrinsics for Adler32?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - What code and artifacts will I create?
+        - How do I validate that the implementation works and assess performance?
+        - Will I write Neon intrinsics, or is this only a C baseline?
+        updated_questions:
+        - Which GitHub Copilot mode or model should I use?
+    category: cross-platform
   - path: content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
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+    rerun_flags_reset:
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+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -198977,51 +663,78 @@ history:
       source_hash_before: 597877722e5f277e5558e29ecd33878ac2262b70a84a9769325b3c3b0ce06e58
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-      generated_at_before: '2026-05-06T17:17:53Z'
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-      preview_before: Learn how to automate integration testing of MCP servers using Testcontainers and
-        PyTest, with hands-on examples and GitHub Actions CI/CD configuration. It is designed for software
-        developers and QA e...
-      preview_after: Learn how to automate integration testing of MCP servers using Testcontainers and
-        PyTest, with hands-on examples and GitHub Actions CI/CD configuration. It is designed for software
-        developers and QA e...
-      preview_generated: Learn how to automate integration testing of MCP servers using Testcontainers
-        and PyTest, with hands-on examples and GitHub Actions CI/CD configuration. It is designed for
-        software developers and QA e...
+      generated_at_before: '2026-06-01T21:01:02Z'
+      generated_at_after: '2026-06-01T21:01:02Z'
+      preview_before: This introductory path shows how to automate integration testing
+        of Model Context Protocol (MCP) servers using PyTest and Testcontainers, with
+        local runs and CI on GitHub Actions. You will set up a Py...
+      preview_after: This introductory path shows how to automate integration testing
+        of Model Context Protocol (MCP) servers using PyTest and Testcontainers, with
+        local runs and CI on GitHub Actions. You will set up a Py...
+      preview_generated: This Learning Path shows how to automate integration testing
+        of Model Context Protocol (MCP) servers using PyTest and Testcontainers. You
+        will set up a Python 3.11+ and Docker environment on Linux, ma...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
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-      generated_at_before: '2026-05-06T17:17:53Z'
-      generated_at_after: '2026-05-06T17:17:53Z'
+      generated_at_before: '2026-06-01T21:01:02Z'
+      generated_at_after: '2026-06-02T21:31:11Z'
       before_count: 5
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       generated_count: 5
       change_details:
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         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I check if Docker is ready before I start?
+        - How do MCP servers communicate in these tests?
+        - What result should I expect from the basic Testcontainers example?
+        - Which triggers and runners does the GitHub Actions workflow use, and where
+          is it defined?
+        removed_questions:
+        - What do I need installed before starting?
+        - Which operating systems are supported, and how do I verify Docker is ready?
+        - What will I build or configure in this path?
+        - How do the integration tests interact with the MCP server?
+        - Can I run the tests in CI on Arm runners, and when are they triggered?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I check if Docker is ready before I start?
+        - How do MCP servers communicate in these tests?
+        - What result should I expect from the basic Testcontainers example?
+        - Which triggers and runners does the GitHub Actions workflow use, and where
+          is it defined?
+        removed_questions:
+        - What do I need installed before starting?
+        - Which operating systems are supported, and how do I verify Docker is ready?
+        - What will I build or configure in this path?
+        - How do the integration tests interact with the MCP server?
+        - Can I run the tests in CI on Arm runners, and when are they triggered?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/avh_cicd/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -199031,51 +744,74 @@ history:
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-      preview_before: Learn how to integrate Arm Virtual Hardware into a GitHub Actions CI/CD workflow
-        for automated embedded software testing and validation. It is designed for embedded software developers
-        new to Arm Virt...
-      preview_after: Learn how to integrate Arm Virtual Hardware into a GitHub Actions CI/CD workflow
-        for automated embedded software testing and validation. It is designed for embedded software developers
-        new to Arm Virt...
-      preview_generated: Learn how to integrate Arm Virtual Hardware into a GitHub Actions CI/CD workflow
-        for automated embedded software testing and validation. It is designed for embedded software developers
-        new to Arm Virt...
+      generated_at_before: '2026-06-01T21:01:24Z'
+      generated_at_after: '2026-06-01T21:01:24Z'
+      preview_before: "This introductory path shows embedded developers how to integrate\
+        \ Arm Virtual Hardware (AVH) into a GitHub Actions CI/CD workflow for automated\
+        \ testing and validation of bare\u2011metal Cortex\u2011M software. ..."
+      preview_after: "This introductory path shows embedded developers how to integrate\
+        \ Arm Virtual Hardware (AVH) into a GitHub Actions CI/CD workflow for automated\
+        \ testing and validation of bare\u2011metal Cortex\u2011M software. ..."
+      preview_generated: This Learning Path shows how to integrate Arm Virtual Hardware
+        (AVH) into a GitHub Actions CI/CD workflow to automate embedded software testing
+        and validation for bare-metal projects. You will prepare...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
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-      generated_at_after: '2026-05-06T17:17:53Z'
+      generated_at_before: '2026-06-01T21:01:24Z'
+      generated_at_after: '2026-06-02T21:32:03Z'
       before_count: 5
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       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I create the required GitHub Personal Access Token?
+        - Which options should I choose when creating the self-hosted runner?
+        - Where do I run the commands shown when adding the self-hosted runner?
+        removed_questions:
+        - What accounts and setup do I need before starting?
+        - How do I create the self-hosted runner and what settings should I choose?
+        - What target and software type does this Learning Path focus on?
+        - How can I confirm the integration is working?
+        updated_questions:
+        - How do I enable GitHub Actions in my forked repository?
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I create the required GitHub Personal Access Token?
+        - Which options should I choose when creating the self-hosted runner?
+        - Where do I run the commands shown when adding the self-hosted runner?
+        removed_questions:
+        - What accounts and setup do I need before starting?
+        - How do I create the self-hosted runner and what settings should I choose?
+        - What target and software type does this Learning Path focus on?
+        - How can I confirm the integration is working?
+        updated_questions:
+        - How do I enable GitHub Actions in my forked repository?
+    category: cross-platform
   - path: content/learning-paths/cross-platform/avh_cicd2/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -199085,51 +821,78 @@ history:
       source_hash_before: 251b6edf6a016f9fc73eb35216f56b2001465fbca8253bd7b6e6f9f4afcd25e6
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-      preview_before: Learn how to integrate Arm Virtual Hardware with AWS and GitHub Actions for automated
-        CI/CD workflows, including CloudFormation setup and test automation. It is designed for DevOps
-        integrating AVH int...
-      preview_after: Learn how to integrate Arm Virtual Hardware with AWS and GitHub Actions for automated
-        CI/CD workflows, including CloudFormation setup and test automation. It is designed for DevOps
-        integrating AVH int...
-      preview_generated: Learn how to integrate Arm Virtual Hardware with AWS and GitHub Actions for automated
-        CI/CD workflows, including CloudFormation setup and test automation. It is designed for DevOps
-        integrating AVH int...
+      generated_at_before: '2026-06-01T21:01:44Z'
+      generated_at_after: '2026-06-01T21:01:44Z'
+      preview_before: This advanced Learning Path shows how to integrate Arm Virtual
+        Hardware with AWS and GitHub Actions to automate test and validation for bare-metal
+        Cortex-M projects. You will fork the ARM-software/AVH...
+      preview_after: This advanced Learning Path shows how to integrate Arm Virtual
+        Hardware with AWS and GitHub Actions to automate test and validation for bare-metal
+        Cortex-M projects. You will fork the ARM-software/AVH...
+      preview_generated: "This advanced Learning Path shows how to integrate Arm Virtual\
+        \ Hardware with AWS and GitHub Actions to automate test and validation for\
+        \ Cortex-M bare\u2011metal software. You will fork the ARM\u2011software/AVH..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_after: '2026-05-06T17:17:53Z'
+      generated_at_before: '2026-06-01T21:01:44Z'
+      generated_at_after: '2026-06-02T21:32:39Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which example repository should I use and where do I find it?
+        - When is my AWS account ready to connect to GitHub Actions?
+        - Which GitHub Actions secrets must I create and how do I find their values?
+        - What result should I expect after configuring the workflow?
+        removed_questions:
+        - Do I need to complete the first CI/CD Learning Path before starting this
+          one?
+        - What accounts and tools are required?
+        - Which example repository should I fork, and what does it provide?
+        - How do I configure GitHub Actions to access AWS?
+        - How can I verify that the integration is working?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which example repository should I use and where do I find it?
+        - When is my AWS account ready to connect to GitHub Actions?
+        - Which GitHub Actions secrets must I create and how do I find their values?
+        - What result should I expect after configuring the workflow?
+        removed_questions:
+        - Do I need to complete the first CI/CD Learning Path before starting this
+          one?
+        - What accounts and tools are required?
+        - Which example repository should I fork, and what does it provide?
+        - How do I configure GitHub Actions to access AWS?
+        - How can I verify that the integration is working?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/cca_rme/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -199139,51 +902,76 @@ history:
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-      preview_before: Learn how to use Arm Development Studio to explore Realm Management Extension (RME)
-        and Arm Confidential Compute Architecture (CCA) through a bare-metal example running on the Arm
-        Architecture Envelop...
-      preview_after: Learn how to use Arm Development Studio to explore Realm Management Extension (RME)
-        and Arm Confidential Compute Architecture (CCA) through a bare-metal example running on the Arm
-        Architecture Envelop...
-      preview_generated: Learn how to use Arm Development Studio to explore Realm Management Extension
-        (RME) and Arm Confidential Compute Architecture (CCA) through a bare-metal example running on
-        the Arm Architecture Envelop...
+      generated_at_before: '2026-06-01T21:02:16Z'
+      generated_at_after: '2026-06-01T21:02:16Z'
+      preview_before: This introductory Learning Path shows how to explore Arm Confidential
+        Compute Architecture (CCA) and the Realm Management Extension (RME) using
+        Arm Development Studio. You will import a simple bare-me...
+      preview_after: This introductory Learning Path shows how to explore Arm Confidential
+        Compute Architecture (CCA) and the Realm Management Extension (RME) using
+        Arm Development Studio. You will import a simple bare-me...
+      preview_generated: Use Arm Development Studio to explore the Realm Management
+        Extension (RME) and the Arm Confidential Compute Architecture (CCA) with a
+        provided bare-metal example that runs on the Arm Architecture Enve...
     faqs:
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+      action: rerun_requested
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       generated_count: 5
       change_details:
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         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the example?
+        - How do I import the bare-metal RME example into Arm Development Studio?
+        - Which target should I run the example on?
+        - How does this example demonstrate CCA concepts?
+        - Do I need Linux or Android to follow this path?
+        removed_questions:
+        - What are the prerequisites to follow this Learning Path?
+        - Do I need physical hardware to run the example?
+        - How do I import the bare-metal example into Arm Development Studio?
+        - Is an operating system required for this example?
+        - What CCA and RME behavior will I be able to examine?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the example?
+        - How do I import the bare-metal RME example into Arm Development Studio?
+        - Which target should I run the example on?
+        - How does this example demonstrate CCA concepts?
+        - Do I need Linux or Android to follow this path?
+        removed_questions:
+        - What are the prerequisites to follow this Learning Path?
+        - Do I need physical hardware to run the example?
+        - How do I import the bare-metal example into Arm Development Studio?
+        - Is an operating system required for this example?
+        - What CCA and RME behavior will I be able to examine?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/cpp-loop-size-context/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -199193,51 +981,78 @@ history:
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-      preview_before: Learn how to optimize C++ loop performance on Arm by providing boundary information
-        to the compiler, enabling SIMD vectorization and reducing runtime through compile-time context.
-        It is designed for C...
-      preview_after: Learn how to optimize C++ loop performance on Arm by providing boundary information
-        to the compiler, enabling SIMD vectorization and reducing runtime through compile-time context.
-        It is designed for C...
-      preview_generated: Learn how to optimize C++ loop performance on Arm by providing boundary information
-        to the compiler, enabling SIMD vectorization and reducing runtime through compile-time context.
-        It is designed for C...
+      generated_at_before: '2026-06-01T21:02:47Z'
+      generated_at_after: '2026-06-01T21:02:47Z'
+      preview_before: Learn how to improve the runtime of C++ loops on Arm by conveying
+        loop-size boundaries to the compiler. You will start from a baseline program
+        where the loop size is only known at runtime, then modify...
+      preview_after: Learn how to improve the runtime of C++ loops on Arm by conveying
+        loop-size boundaries to the compiler. You will start from a baseline program
+        where the loop size is only known at runtime, then modify...
+      preview_generated: Learn how to express loop boundary information in C++ so
+        the compiler can make stronger assumptions, potentially enabling SIMD vectorization
+        and reducing runtime. You start from a baseline program tha...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
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-      generated_at_after: '2026-05-06T17:17:53Z'
+      generated_at_before: '2026-06-01T21:02:47Z'
+      generated_at_after: '2026-06-02T21:33:42Z'
       before_count: 5
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       generated_count: 5
       change_details:
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         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the code examples?
+        - How is the loop size provided in the baseline program, and why does that
+          matter?
+        - Why does rewriting the loop bound as ((max_loop_size/4)*4) help the compiler?
+        - What result should I expect after applying the boundary information?
+        - Do I need any specific tools or compiler options to follow this path?
+        removed_questions:
+        - What setup do I need to follow this Learning Path?
+        - What does the baseline C++ example do?
+        - How do I communicate loop boundary information to the compiler?
+        - Do I need a specific compiler or extra tools?
+        - How will I know if the optimization helped, and how long will this take?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the code examples?
+        - How is the loop size provided in the baseline program, and why does that
+          matter?
+        - Why does rewriting the loop bound as ((max_loop_size/4)*4) help the compiler?
+        - What result should I expect after applying the boundary information?
+        - Do I need any specific tools or compiler options to follow this path?
+        removed_questions:
+        - What setup do I need to follow this Learning Path?
+        - What does the baseline C++ example do?
+        - How do I communicate loop boundary information to the compiler?
+        - Do I need a specific compiler or extra tools?
+        - How will I know if the optimization helped, and how long will this take?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/docker/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -199247,51 +1062,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:53Z'
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-      preview_before: Learn how to build, run, and share multi-architecture Docker images for Arm and
-        x86 platforms using buildx, manifest, and remote builders. It is designed for software developers
-        who want to learn abou...
-      preview_after: Learn how to build, run, and share multi-architecture Docker images for Arm and x86
-        platforms using buildx, manifest, and remote builders. It is designed for software developers
-        who want to learn abou...
-      preview_generated: Learn how to build, run, and share multi-architecture Docker images for Arm and
-        x86 platforms using buildx, manifest, and remote builders. It is designed for software developers
-        who want to learn abou...
+      generated_at_before: '2026-06-01T21:03:24Z'
+      generated_at_after: '2026-06-01T21:03:24Z'
+      preview_before: Follow this introductory path to build, run, and share Docker
+        images that support both Arm and x86. You will validate your Docker setup,
+        perform multi-architecture builds with Docker buildx, and use a...
+      preview_after: Follow this introductory path to build, run, and share Docker
+        images that support both Arm and x86. You will validate your Docker setup,
+        perform multi-architecture builds with Docker buildx, and use a...
+      preview_generated: This introductory Learning Path shows how to build, run,
+        and share Docker container images for Arm and x86, with a focus on creating
+        multi-architecture artifacts. You start by validating Docker, then ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
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-      generated_at_after: '2026-05-06T17:17:53Z'
+      generated_at_before: '2026-06-01T21:03:24Z'
+      generated_at_after: '2026-06-02T21:34:19Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
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         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I verify my Docker setup before starting builds?
+        - When should I use a remote Arm server for builds?
+        - When should I use docker manifest in this workflow?
+        - How do I check that an image is multi-architecture and supports Arm?
+        removed_questions:
+        - What do I need before starting?
+        - How do I verify that Docker and buildx are ready to use?
+        - Do I need an Arm machine to build Arm images?
+        - How are multi-architecture images produced in this path?
+        - How can I check if an image supports Arm or multiple architectures?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I verify my Docker setup before starting builds?
+        - When should I use a remote Arm server for builds?
+        - When should I use docker manifest in this workflow?
+        - How do I check that an image is multi-architecture and supports Arm?
+        removed_questions:
+        - What do I need before starting?
+        - How do I verify that Docker and buildx are ready to use?
+        - Do I need an Arm machine to build Arm images?
+        - How are multi-architecture images produced in this path?
+        - How can I check if an image supports Arm or multiple architectures?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/docker-build-cloud/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
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+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -199301,51 +1141,76 @@ history:
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-      preview_before: Learn how to build multi-architecture Docker images for Arm and x86 using Docker
-        Build Cloud, with GitHub Actions automation for faster builds without emulation. It is designed
-        for software developers...
-      preview_after: Learn how to build multi-architecture Docker images for Arm and x86 using Docker
-        Build Cloud, with GitHub Actions automation for faster builds without emulation. It is designed
-        for software developers...
-      preview_generated: Learn how to build multi-architecture Docker images for Arm and x86 using Docker
-        Build Cloud, with GitHub Actions automation for faster builds without emulation. It is designed
-        for software developers...
+      generated_at_before: '2026-06-01T21:03:46Z'
+      generated_at_after: '2026-06-01T21:03:46Z'
+      preview_before: This Learning Path shows how to build multi-architecture Docker
+        images for Arm and x86 using Docker Build Cloud, and automate the process
+        with GitHub Actions. You will set up Docker Build Cloud as you...
+      preview_after: This Learning Path shows how to build multi-architecture Docker
+        images for Arm and x86 using Docker Build Cloud, and automate the process
+        with GitHub Actions. You will set up Docker Build Cloud as you...
+      preview_generated: This Learning Path shows how to build multi-architecture
+        Docker images for Arm and x86 using Docker Build Cloud, and automate the process
+        with GitHub Actions. You will configure Docker Build Cloud as ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
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       drift_detected: false
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-      generated_at_after: '2026-05-06T17:17:53Z'
+      generated_at_before: '2026-06-01T21:03:46Z'
+      generated_at_after: '2026-06-02T21:34:59Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - Do I need an Arm machine to follow this path?
+        - What do I need before running the builds?
+        - Which method for multi-architecture builds is used here?
+        - How do I set up GitHub Actions for this build?
+        - What should I check if my GitHub Actions workflow fails early?
+        removed_questions:
+        - Do I need an Arm machine locally to build Arm images?
+        - What accounts and tools are required before starting?
+        - What architectures will the images target?
+        - What will I set up in GitHub Actions for these builds?
+        - How do I know the path worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - Do I need an Arm machine to follow this path?
+        - What do I need before running the builds?
+        - Which method for multi-architecture builds is used here?
+        - How do I set up GitHub Actions for this build?
+        - What should I check if my GitHub Actions workflow fails early?
+        removed_questions:
+        - Do I need an Arm machine locally to build Arm images?
+        - What accounts and tools are required before starting?
+        - What architectures will the images target?
+        - What will I set up in GitHub Actions for these builds?
+        - How do I know the path worked?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -199355,51 +1220,76 @@ history:
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-      generated_at_after: '2026-05-06T17:17:53Z'
-      preview_before: Learn how to implement a dynamic memory allocator in C, understanding heap management
-        and how malloc and free work under the hood with practical examples. It is designed for software
-        developers learni...
-      preview_after: Learn how to implement a dynamic memory allocator in C, understanding heap management
-        and how malloc and free work under the hood with practical examples. It is designed for software
-        developers learni...
-      preview_generated: Learn how to implement a dynamic memory allocator in C, understanding heap management
-        and how malloc and free work under the hood with practical examples. It is designed for software
-        developers learni...
+      generated_at_before: '2026-06-01T21:04:25Z'
+      generated_at_after: '2026-06-01T21:04:25Z'
+      preview_before: This introductory Learning Path guides you through implementing
+        a simple dynamic memory allocator in C on Linux. You will design and code
+        two functions, simple_malloc and simple_free, to understand ho...
+      preview_after: This introductory Learning Path guides you through implementing
+        a simple dynamic memory allocator in C on Linux. You will design and code
+        two functions, simple_malloc and simple_free, to understand ho...
+      preview_generated: Learn how dynamic memory allocation works in C by designing
+        and implementing a simple heap allocator on Linux. You will define and build
+        two functions, simple_malloc and simple_free, to mirror the bas...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: c59308676849aea9b7cfce8c48c7d6e1ca072ef6fb38fb193a34aa5b2597b9b9
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-      generated_at_before: '2026-05-06T17:17:53Z'
-      generated_at_after: '2026-05-06T17:17:53Z'
+      generated_at_before: '2026-06-01T21:04:25Z'
+      generated_at_after: '2026-06-02T21:35:52Z'
       before_count: 5
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       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - Which allocator functions am I expected to implement?
+        - How is the project organized in the implementation step?
+        - How do I build and run the code on Linux?
+        - How do I know my allocator works as intended?
+        removed_questions:
+        - What prerequisites do I need before starting?
+        - What exactly will I implement in this path?
+        - How is the project organized and built?
+        - How can I validate that the allocator works?
+        - Do I need specific Arm hardware to follow this path?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - Which allocator functions am I expected to implement?
+        - How is the project organized in the implementation step?
+        - How do I build and run the code on Linux?
+        - How do I know my allocator works as intended?
+        removed_questions:
+        - What prerequisites do I need before starting?
+        - What exactly will I implement in this path?
+        - How is the project organized and built?
+        - How can I validate that the allocator works?
+        - Do I need specific Arm hardware to follow this path?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
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+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -199409,51 +1299,78 @@ history:
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-      generated_at_before: '2026-05-06T17:17:53Z'
-      generated_at_after: '2026-05-06T17:17:53Z'
-      preview_before: Learn how to use the Eigen linear algebra library on Arm systems with ASIMD and
-        SVE vectorization, including building TensorFlow with SVE support for optimized performance. It
-        is designed for C/C++ de...
-      preview_after: Learn how to use the Eigen linear algebra library on Arm systems with ASIMD and SVE
-        vectorization, including building TensorFlow with SVE support for optimized performance. It is
-        designed for C/C++ de...
-      preview_generated: Learn how to use the Eigen linear algebra library on Arm systems with ASIMD and
-        SVE vectorization, including building TensorFlow with SVE support for optimized performance. It
-        is designed for C/C++ de...
+      generated_at_before: '2026-06-01T21:04:54Z'
+      generated_at_after: '2026-06-01T21:04:54Z'
+      preview_before: Learn to use the Eigen C++ linear algebra library on Arm systems
+        that support ASIMD (Neon) and SVE, then build TensorFlow with SVE enabled.
+        You will build and run compact Eigen examples that exercise ...
+      preview_after: Learn to use the Eigen C++ linear algebra library on Arm systems
+        that support ASIMD (Neon) and SVE, then build TensorFlow with SVE enabled.
+        You will build and run compact Eigen examples that exercise ...
+      preview_generated: This advanced path shows how to use the Eigen C++ linear
+        algebra library on Arm systems to take advantage of ASIMD (Neon) and SVE vectorization,
+        then guides you to build TensorFlow with SVE enabled. Y...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_after: '2026-05-06T17:17:53Z'
+      generated_at_before: '2026-06-01T21:04:54Z'
+      generated_at_after: '2026-06-02T21:36:20Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the examples or building TensorFlow?
+        - Which compiler should I use, and are special flags required for ASIMD or
+          SVE?
+        - What code do I create and what results indicate the Eigen examples worked?
+        - How do I approach building TensorFlow with SVE in this path?
+        - What should I do if my Arm system does not support SVE?
+        removed_questions:
+        - What hardware and software do I need before starting?
+        - Which compilers are supported for the examples and builds?
+        - Does this path include installing Eigen, or is it assumed to be available?
+        - What will I run to see Eigen using Arm SIMD features?
+        - What is the outcome of the TensorFlow step?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the examples or building TensorFlow?
+        - Which compiler should I use, and are special flags required for ASIMD or
+          SVE?
+        - What code do I create and what results indicate the Eigen examples worked?
+        - How do I approach building TensorFlow with SVE in this path?
+        - What should I do if my Arm system does not support SVE?
+        removed_questions:
+        - What hardware and software do I need before starting?
+        - Which compilers are supported for the examples and builds?
+        - Does this path include installing Eigen, or is it assumed to be available?
+        - What will I run to see Eigen using Arm SIMD features?
+        - What is the outcome of the TensorFlow step?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/ernie_moe_v9/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -199463,51 +1380,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:53Z'
-      generated_at_after: '2026-05-06T17:17:53Z'
-      preview_before: Learn how to deploy ERNIE-4.5 Mixture of Experts models on Armv9 devices using llama.cpp,
-        compare PT and Thinking variants, and measure Armv9-specific hardware optimization impact. It
-        is designed for ...
-      preview_after: Learn how to deploy ERNIE-4.5 Mixture of Experts models on Armv9 devices using llama.cpp,
-        compare PT and Thinking variants, and measure Armv9-specific hardware optimization impact. It
-        is designed for ...
-      preview_generated: Learn how to deploy ERNIE-4.5 Mixture of Experts models on Armv9 devices using
-        llama.cpp, compare PT and Thinking variants, and measure Armv9-specific hardware optimization
-        impact. It is designed for ...
+      generated_at_before: '2026-06-01T21:05:21Z'
+      generated_at_after: '2026-06-01T21:05:21Z'
+      preview_before: This advanced Learning Path shows how to deploy ERNIE-4.5 Mixture
+        of Experts (MoE) models on Armv9 devices using llama.cpp on Linux. You will
+        set up an Armv9 development board (for example, a Radxa Or...
+      preview_after: This advanced Learning Path shows how to deploy ERNIE-4.5 Mixture
+        of Experts (MoE) models on Armv9 devices using llama.cpp on Linux. You will
+        set up an Armv9 development board (for example, a Radxa Or...
+      preview_generated: This advanced Learning Path guides you through deploying
+        ERNIE-4.5 Mixture of Experts (MoE) models on Armv9 devices using llama.cpp.
+        You will prepare a Linux-based Armv9 development board (for example...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_after: '2026-05-06T17:17:53Z'
+      generated_at_before: '2026-06-01T21:05:21Z'
+      generated_at_after: '2026-06-02T21:37:05Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which ERNIE-4.5 variants are used, and what will I compare?
+        - How do I validate that my setup and model inference are working?
+        - What Armv9 optimizations are benchmarked, and how are they tested?
+        - How can I observe which MoE experts are used during generation?
+        removed_questions:
+        - What hardware and storage do I need to follow this path?
+        - What operating system and tools are used?
+        - Which ERNIE-4.5 variants are covered, and what will I compare?
+        - How do I validate that the deployment is working before benchmarking?
+        - How is performance evaluated with Armv9-specific features?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which ERNIE-4.5 variants are used, and what will I compare?
+        - How do I validate that my setup and model inference are working?
+        - What Armv9 optimizations are benchmarked, and how are they tested?
+        - How can I observe which MoE experts are used during generation?
+        removed_questions:
+        - What hardware and storage do I need to follow this path?
+        - What operating system and tools are used?
+        - Which ERNIE-4.5 variants are covered, and what will I compare?
+        - How do I validate that the deployment is working before benchmarking?
+        - How is performance evaluated with Armv9-specific features?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/floating-point-behavior/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -199517,51 +1459,78 @@ history:
       source_hash_before: 6427d4466fcd34f7275d16820cbfa2afa3815e1f0306c02e5011b2a4a6afa984
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-      generated_at_before: '2026-05-06T17:17:53Z'
-      generated_at_after: '2026-05-06T17:17:53Z'
-      preview_before: Learn how Arm and x86 floating-point implementations follow IEEE 754 standards,
-        identify rare undefined behavior differences, and write portable code across architectures. It
-        is designed for This is a...
-      preview_after: Learn how Arm and x86 floating-point implementations follow IEEE 754 standards, identify
-        rare undefined behavior differences, and write portable code across architectures. It is designed
-        for This is a...
-      preview_generated: Learn how Arm and x86 floating-point implementations follow IEEE 754 standards,
-        identify rare undefined behavior differences, and write portable code across architectures. It
-        is designed for This is a...
+      generated_at_before: '2026-06-01T21:05:41Z'
+      generated_at_after: '2026-06-01T21:05:41Z'
+      preview_before: This Learning Path examines IEEE 754 floating-point behavior
+        across x86 and Arm on Linux using C++ examples. You will verify that both
+        architectures produce identical results for all well-defined oper...
+      preview_after: This Learning Path examines IEEE 754 floating-point behavior
+        across x86 and Arm on Linux using C++ examples. You will verify that both
+        architectures produce identical results for all well-defined oper...
+      preview_generated: This Learning Path explains how Arm and x86 implement IEEE
+        754 floating-point and what to expect when porting code between them. You
+        will compare behavior across an x86 and an Arm Linux machine using ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 6427d4466fcd34f7275d16820cbfa2afa3815e1f0306c02e5011b2a4a6afa984
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-      generated_at_before: '2026-05-06T17:17:53Z'
-      generated_at_after: '2026-05-06T17:17:53Z'
+      generated_at_before: '2026-06-01T21:05:41Z'
+      generated_at_after: '2026-06-02T21:37:38Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - How do I know if a difference I see is permitted by IEEE 754?
+        - Why might two mathematically equivalent C++ functions produce slightly different
+          results across architectures?
+        - What result should I expect when I run the same C++ code on x86 and Arm?
+        - How should I validate results when comparing x86 and Arm runs?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - Do Arm and x86 produce the same floating-point results?
+        - Which undefined cases are covered in this path?
+        - How does fused multiply-add (FMAC) relate to observed differences?
+        - What is the expected outcome and scope of this path?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - How do I know if a difference I see is permitted by IEEE 754?
+        - Why might two mathematically equivalent C++ functions produce slightly different
+          results across architectures?
+        - What result should I expect when I run the same C++ code on x86 and Arm?
+        - How should I validate results when comparing x86 and Arm runs?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - Do Arm and x86 produce the same floating-point results?
+        - Which undefined cases are covered in this path?
+        - How does fused multiply-add (FMAC) relate to observed differences?
+        - What is the expected outcome and scope of this path?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/function-multiversioning/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -199571,51 +1540,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:53Z'
-      generated_at_after: '2026-05-06T17:17:53Z'
-      preview_before: Learn how to optimize C/C++ applications using function multiversioning on Arm64
-        targets with GCC or LLVM, enabling automatic runtime selection of hardware-optimized function
-        versions. It is designed ...
-      preview_after: Learn how to optimize C/C++ applications using function multiversioning on Arm64
-        targets with GCC or LLVM, enabling automatic runtime selection of hardware-optimized function
-        versions. It is designed ...
-      preview_generated: Learn how to optimize C/C++ applications using function multiversioning on Arm64
-        targets with GCC or LLVM, enabling automatic runtime selection of hardware-optimized function
-        versions. It is designed ...
+      generated_at_before: '2026-06-01T21:06:01Z'
+      generated_at_after: '2026-06-01T21:06:01Z'
+      preview_before: This advanced Learning Path shows how to apply function multiversioning
+        in C/C++ for Arm64 targets using GCC or LLVM so your binaries can select the
+        most appropriate implementation at runtime. You wil...
+      preview_after: This advanced Learning Path shows how to apply function multiversioning
+        in C/C++ for Arm64 targets using GCC or LLVM so your binaries can select the
+        most appropriate implementation at runtime. You wil...
+      preview_generated: This advanced path shows how to use function multiversioning
+        to optimize C/C++ applications on Arm64 targets with GCC or LLVM. You will
+        annotate functions with target_version and target_clones to prod...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_before: '2026-05-06T17:17:53Z'
-      generated_at_after: '2026-05-06T17:17:53Z'
+      generated_at_before: '2026-06-01T21:06:01Z'
+      generated_at_after: '2026-06-02T21:38:11Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - Which attribute should I use to define multiple function versions?
+        - Does the order of features in target_clones affect runtime selection?
+        - How do I know which version ran at runtime?
+        - Is multiversioning compatible with Arm streaming mode?
+        removed_questions:
+        - What compilers and versions are required?
+        - Which operating systems and targets does this cover?
+        - How do I declare multiple function versions?
+        - What hardware features and coding styles are demonstrated?
+        - How can I tell that runtime selection is working?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - Which attribute should I use to define multiple function versions?
+        - Does the order of features in target_clones affect runtime selection?
+        - How do I know which version ran at runtime?
+        - Is multiversioning compatible with Arm streaming mode?
+        removed_questions:
+        - What compilers and versions are required?
+        - Which operating systems and targets does this cover?
+        - How do I declare multiple function versions?
+        - What hardware features and coding styles are demonstrated?
+        - How can I tell that runtime selection is working?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/github-arm-runners/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -199625,51 +1619,78 @@ history:
       source_hash_before: 3557d534c51f81839cc353ebbd600ec588a60197d2c27c9a58e97c25017d07e4
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-      generated_at_before: '2026-05-06T17:17:53Z'
-      generated_at_after: '2026-05-06T17:17:53Z'
-      preview_before: Learn how to use GitHub Actions with Arm-hosted runners to build multi-architecture
-        container images for arm64 and amd64 platforms and automate deployment to Docker Hub. It is designed
-        for software de...
-      preview_after: Learn how to use GitHub Actions with Arm-hosted runners to build multi-architecture
-        container images for arm64 and amd64 platforms and automate deployment to Docker Hub. It is designed
-        for software de...
-      preview_generated: Learn how to use GitHub Actions with Arm-hosted runners to build multi-architecture
-        container images for arm64 and amd64 platforms and automate deployment to Docker Hub. It is designed
-        for software de...
+      generated_at_before: '2026-06-01T21:06:27Z'
+      generated_at_after: '2026-06-01T21:06:27Z'
+      preview_before: Learn to build and publish multi-architecture container images
+        for arm64 and amd64 using GitHub Actions with Arm-hosted runners. This introductory
+        path walks you through creating a repository, definin...
+      preview_after: Learn to build and publish multi-architecture container images
+        for arm64 and amd64 using GitHub Actions with Arm-hosted runners. This introductory
+        path walks you through creating a repository, definin...
+      preview_generated: "Learn how to build and publish multi-architecture container\
+        \ images targeting arm64 and amd64 using GitHub Actions with Arm-hosted runners.\
+        \ The path explains two common build approaches\u2014instruction emu..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 3557d534c51f81839cc353ebbd600ec588a60197d2c27c9a58e97c25017d07e4
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-      generated_at_before: '2026-05-06T17:17:53Z'
-      generated_at_after: '2026-05-06T17:17:53Z'
+      generated_at_before: '2026-06-01T21:06:27Z'
+      generated_at_after: '2026-06-02T21:38:44Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the workflow?
+        - Do I need to provision my own machines to run Arm jobs?
+        - Which approach should I use to build multi-architecture images?
+        - Can I use Arm-hosted runners in private repositories, and what runner types
+          exist?
+        - How do I run the workflow and publish images to Docker Hub?
+        removed_questions:
+        - Do I need a paid GitHub plan to use Arm-hosted runners?
+        - What architectures will the container images target?
+        - What accounts or prerequisites are required before I start?
+        - Do I need to provide my own Arm server to run the builds?
+        - How will I know the workflow worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the workflow?
+        - Do I need to provision my own machines to run Arm jobs?
+        - Which approach should I use to build multi-architecture images?
+        - Can I use Arm-hosted runners in private repositories, and what runner types
+          exist?
+        - How do I run the workflow and publish images to Docker Hub?
+        removed_questions:
+        - Do I need a paid GitHub plan to use Arm-hosted runners?
+        - What architectures will the container images target?
+        - What accounts or prerequisites are required before I start?
+        - Do I need to provide my own Arm server to run the builds?
+        - How will I know the workflow worked?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/gitlab/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -199679,51 +1700,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:53Z'
-      generated_at_after: '2026-05-06T17:17:53Z'
-      preview_before: Learn how to build a GitLab CI/CD pipeline using Google Axion-based self-hosted
-        runners. It is designed for DevOps professionals who are looking to build a CI/CD pipeline with
-        GitLab on Google Axion b...
-      preview_after: Learn how to build a GitLab CI/CD pipeline using Google Axion-based self-hosted runners.
-        It is designed for DevOps professionals who are looking to build a CI/CD pipeline with GitLab
-        on Google Axion b...
-      preview_generated: Learn how to build a GitLab CI/CD pipeline using Google Axion-based self-hosted
-        runners. It is designed for DevOps professionals who are looking to build a CI/CD pipeline with
-        GitLab on Google Axion b...
+      generated_at_before: '2026-06-01T21:06:50Z'
+      generated_at_after: '2026-06-01T21:06:50Z'
+      preview_before: This advanced Learning Path shows how to build a GitLab CI/CD
+        pipeline on Google Cloud using Google Axion-based self-hosted runners. You
+        will create a GitLab runner on Axion (Arm Neoverse) and pair it...
+      preview_after: This advanced Learning Path shows how to build a GitLab CI/CD
+        pipeline on Google Cloud using Google Axion-based self-hosted runners. You
+        will create a GitLab runner on Axion (Arm Neoverse) and pair it...
+      preview_generated: "This Learning Path shows how to build a GitLab CI/CD pipeline\
+        \ on Google Cloud using Google Axion Arm-based self-hosted runners. You will\
+        \ provision a Google Axion\u2013based GitLab runner, register it with ..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_after: '2026-05-06T17:17:53Z'
+      generated_at_before: '2026-06-01T21:06:50Z'
+      generated_at_after: '2026-06-02T21:39:28Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I know my Google Axion self-hosted runner is ready to run jobs?
+        - Which approach does the pipeline use to produce a multi-architecture image?
+        - Do I need both x86 and Arm runners to build the images?
+        - Where are the built images stored, and how can I validate the result?
+        removed_questions:
+        - What do I need before I start?
+        - What will I set up on Google Cloud?
+        - Which architectures and build method does the pipeline use?
+        - Do I need both Arm and x86 runners?
+        - How do I know the pipeline worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I know my Google Axion self-hosted runner is ready to run jobs?
+        - Which approach does the pipeline use to produce a multi-architecture image?
+        - Do I need both x86 and Arm runners to build the images?
+        - Where are the built images stored, and how can I validate the result?
+        removed_questions:
+        - What do I need before I start?
+        - What will I set up on Google Cloud?
+        - Which architectures and build method does the pipeline use?
+        - Do I need both Arm and x86 runners?
+        - How do I know the pipeline worked?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/gitlab-managed-runners/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -199733,51 +1779,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:53Z'
-      generated_at_after: '2026-05-06T17:17:53Z'
-      preview_before: Learn how to build GitLab CI/CD pipelines using GitLab-hosted Arm64 runners to containerize
-        C applications, store images in GitLab Container Registry, and deploy on Arm infrastructure. It
-        is designed ...
-      preview_after: Learn how to build GitLab CI/CD pipelines using GitLab-hosted Arm64 runners to containerize
-        C applications, store images in GitLab Container Registry, and deploy on Arm infrastructure. It
-        is designed ...
-      preview_generated: Learn how to build GitLab CI/CD pipelines using GitLab-hosted Arm64 runners to
-        containerize C applications, store images in GitLab Container Registry, and deploy on Arm infrastructure.
-        It is designed ...
+      generated_at_before: '2026-06-01T21:07:14Z'
+      generated_at_after: '2026-06-01T21:07:14Z'
+      preview_before: This introductory Learning Path shows how to build a GitLab
+        CI/CD pipeline that runs on GitLab-hosted Arm64 runners. You create or use
+        a GitLab project, write a simple C program, and containerize it f...
+      preview_after: This introductory Learning Path shows how to build a GitLab CI/CD
+        pipeline that runs on GitLab-hosted Arm64 runners. You create or use a GitLab
+        project, write a simple C program, and containerize it f...
+      preview_generated: This Learning Path shows how to build a GitLab CI/CD pipeline
+        that runs on GitLab-hosted Arm64 runners. You will create or use a GitLab
+        project, add a .gitlab-ci.yml that targets Arm64, and containeri...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: a6ad703d9d894da06ed4aa3725fcc9006d70050cef3f0a3ef9a758bcf4523289
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-      generated_at_before: '2026-05-06T17:17:53Z'
-      generated_at_after: '2026-05-06T17:17:53Z'
+      generated_at_before: '2026-06-01T21:07:14Z'
+      generated_at_after: '2026-06-02T21:40:28Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - How do I configure my pipeline to use Arm64 runners?
+        - Which executor should I use for the jobs in this path?
+        - What artifact does the pipeline produce and where is it stored?
+        - How do I verify the jobs actually ran on Arm64?
+        removed_questions:
+        - Do I need to set up my own runner infrastructure?
+        - Can I use an existing GitLab project or do I need a new one?
+        - How do I ensure my jobs run on Arm64 runners?
+        - What does the pipeline produce and where is it stored?
+        - Does this Learning Path include deployment to Arm infrastructure?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - How do I configure my pipeline to use Arm64 runners?
+        - Which executor should I use for the jobs in this path?
+        - What artifact does the pipeline produce and where is it stored?
+        - How do I verify the jobs actually ran on Arm64?
+        removed_questions:
+        - Do I need to set up my own runner infrastructure?
+        - Can I use an existing GitLab project or do I need a new one?
+        - How do I ensure my jobs run on Arm64 runners?
+        - What does the pipeline produce and where is it stored?
+        - Does this Learning Path include deployment to Arm infrastructure?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/integer-vs-floats/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -199787,51 +1858,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:53Z'
-      generated_at_after: '2026-05-06T17:17:53Z'
-      preview_before: Learn how to identify and fix potential problems with integer and floating-point
-        conversions in C/C++ code on Arm, including explicit conversions, implicit conversions, and type
-        demotion issues. It is...
-      preview_after: Learn how to identify and fix potential problems with integer and floating-point
-        conversions in C/C++ code on Arm, including explicit conversions, implicit conversions, and type
-        demotion issues. It is...
-      preview_generated: Learn how to identify and fix potential problems with integer and floating-point
-        conversions in C/C++ code on Arm, including explicit conversions, implicit conversions, and type
-        demotion issues. It is...
+      generated_at_before: '2026-06-01T21:07:38Z'
+      generated_at_after: '2026-06-01T21:07:38Z'
+      preview_before: This Learning Path teaches advanced C/C++ developers on Arm
+        how to identify and fix issues in integer and floating-point conversions.
+        Using an Arm computer running Linux with a recent GCC or Clang, yo...
+      preview_after: This Learning Path teaches advanced C/C++ developers on Arm how
+        to identify and fix issues in integer and floating-point conversions. Using
+        an Arm computer running Linux with a recent GCC or Clang, yo...
+      preview_generated: Work through focused C and C++ examples on an Arm Linux system
+        to understand how integer and floating-point conversions behave, and how mistakes
+        can occur. You will review data type ranges, examine ex...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
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-      generated_at_before: '2026-05-06T17:17:53Z'
-      generated_at_after: '2026-05-06T17:17:53Z'
+      generated_at_before: '2026-06-01T21:07:38Z'
+      generated_at_after: '2026-06-02T21:41:57Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - Which compiler should I use and are any specific flags required?
+        - How do I know the golden_ratio.c program worked?
+        - What should I check if I see unexpected truncation or loss of precision?
+        - Which Arm platforms and operating system does this target?
+        removed_questions:
+        - What do I need before starting?
+        - Which conversion topics are covered?
+        - What example programs will I work with?
+        - How do I verify that the steps worked?
+        - What platform and architectures does this target?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - Which compiler should I use and are any specific flags required?
+        - How do I know the golden_ratio.c program worked?
+        - What should I check if I see unexpected truncation or loss of precision?
+        - Which Arm platforms and operating system does this target?
+        removed_questions:
+        - What do I need before starting?
+        - Which conversion topics are covered?
+        - What example programs will I work with?
+        - How do I verify that the steps worked?
+        - What platform and architectures does this target?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/intrinsics/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
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+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -199841,51 +1937,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:53Z'
-      generated_at_after: '2026-05-06T17:17:53Z'
-      preview_before: Learn how to port architecture-specific intrinsics to Arm processors. It is designed
-        for software developers interested in porting architecture specific intrinsics to Arm processors.
-        By the end, you w...
-      preview_after: Learn how to port architecture-specific intrinsics to Arm processors. It is designed
-        for software developers interested in porting architecture specific intrinsics to Arm processors.
-        By the end, you w...
-      preview_generated: Learn how to port architecture-specific intrinsics to Arm processors. It is designed
-        for software developers interested in porting architecture specific intrinsics to Arm processors.
-        By the end, you w...
+      generated_at_before: '2026-06-01T21:08:20Z'
+      generated_at_after: '2026-06-01T21:08:20Z'
+      preview_before: This advanced Learning Path shows how to migrate C/C++ code
+        that relies on architecture-specific intrinsics from x64 to Arm. You will
+        learn how to identify intrinsics in your source, understand how co...
+      preview_after: This advanced Learning Path shows how to migrate C/C++ code that
+        relies on architecture-specific intrinsics from x64 to Arm. You will learn
+        how to identify intrinsics in your source, understand how co...
+      preview_generated: This advanced Learning Path shows how to port architecture-specific
+        intrinsics in C/C++ from x64 to Arm on Ubuntu Linux. You will learn to identify
+        intrinsics in code, then choose practical header-onl...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: ecf51d9a9085f95dedda9c0cfbfa4d6350d0f68d81b61f9461bc51070abd0b69
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-      generated_at_after: '2026-05-06T17:17:53Z'
+      generated_at_before: '2026-06-01T21:08:20Z'
+      generated_at_after: '2026-06-02T21:42:32Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - How do I find architecture-specific intrinsics in a large code base?
+        - 'Which option should I use to port x86 intrinsics: sse2neon or SIMDe?'
+        - What changes are required when porting with sse2neon?
+        - What are the high-level steps to use SIMD Everywhere (SIMDe)?
+        removed_questions:
+        - What environment and prerequisites are required?
+        - When should I choose sse2neon versus SIMD Everywhere (SIMDe)?
+        - What changes are needed to port code with sse2neon?
+        - How can I find intrinsics in a large codebase before porting?
+        - What is the expected outcome and how long does it take?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - How do I find architecture-specific intrinsics in a large code base?
+        - 'Which option should I use to port x86 intrinsics: sse2neon or SIMDe?'
+        - What changes are required when porting with sse2neon?
+        - What are the high-level steps to use SIMD Everywhere (SIMDe)?
+        removed_questions:
+        - What environment and prerequisites are required?
+        - When should I choose sse2neon versus SIMD Everywhere (SIMDe)?
+        - What changes are needed to port code with sse2neon?
+        - How can I find intrinsics in a large codebase before porting?
+        - What is the expected outcome and how long does it take?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/ipexplorer/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -199895,51 +2016,78 @@ history:
       source_hash_before: 47cd598f6e33c12d3729c319a1d6a7afcd748de7acd6faea639f2e8600a085ed
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-      generated_at_before: '2026-05-06T17:17:53Z'
-      generated_at_after: '2026-05-06T17:17:53Z'
-      preview_before: Learn how to run custom software benchmarks on IP Explorer simulation platforms
-        and compare performance across Arm Cortex-M processors using cycle count analysis. It is designed
-        for IP Explorer users ...
-      preview_after: Learn how to run custom software benchmarks on IP Explorer simulation platforms and
-        compare performance across Arm Cortex-M processors using cycle count analysis. It is designed
-        for IP Explorer users ...
-      preview_generated: Learn how to run custom software benchmarks on IP Explorer simulation platforms
-        and compare performance across Arm Cortex-M processors using cycle count analysis. It is designed
-        for IP Explorer users ...
+      generated_at_before: '2026-06-01T21:08:49Z'
+      generated_at_after: '2026-06-01T21:08:49Z'
+      preview_before: "This introductory path shows how to use Arm IP Explorer\u2019\
+        s cloud simulation platforms to run and compare custom bare-metal software\
+        \ benchmarks on Arm Cortex-M processors using cycle count analysis. You..."
+      preview_after: "This introductory path shows how to use Arm IP Explorer\u2019\
+        s cloud simulation platforms to run and compare custom bare-metal software\
+        \ benchmarks on Arm Cortex-M processors using cycle count analysis. You..."
+      preview_generated: Use Arm IP Explorer to run and compare bare-metal software
+        benchmarks on cloud-hosted simulation platforms for Arm Cortex-M processors.
+        You will start with a pre-installed example, then create a custo...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
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-        added_questions: []
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+        added_questions:
+        - What do I need before running the steps?
+        - How do I create and edit the custom benchmark code?
+        - Where do I upload my custom software in IP Explorer, and what file should
+          I select?
+        - How do I compare performance across different Cortex-M processors?
+        - What should I check if my Cortex-M instances are not listed?
+        removed_questions:
+        - What do I need before starting?
+        - What code template or samples are provided for the custom benchmark?
+        - How do I package and upload my benchmark to IP Explorer?
+        - Which compiler should I select when running in IP Explorer?
+        - How do I compare results across Cortex-M processors and know it worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
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-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I create and edit the custom benchmark code?
+        - Where do I upload my custom software in IP Explorer, and what file should
+          I select?
+        - How do I compare performance across different Cortex-M processors?
+        - What should I check if my Cortex-M instances are not listed?
+        removed_questions:
+        - What do I need before starting?
+        - What code template or samples are provided for the custom benchmark?
+        - How do I package and upload my benchmark to IP Explorer?
+        - Which compiler should I select when running in IP Explorer?
+        - How do I compare results across Cortex-M processors and know it worked?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/kleidiai-explainer/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
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-    template_version_before: summary-faq-v1
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+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
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+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
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@@ -199949,51 +2097,87 @@ history:
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-      preview_before: Learn how to use KleidiAI micro-kernels to accelerate AI inference performance through
-        optimized matrix multiplication on Arm processors with architecture features like i8mm. It is
-        designed for develo...
-      preview_after: Learn how to use KleidiAI micro-kernels to accelerate AI inference performance through
-        optimized matrix multiplication on Arm processors with architecture features like i8mm. It is
-        designed for develo...
-      preview_generated: Learn how to use KleidiAI micro-kernels to accelerate AI inference performance
-        through optimized matrix multiplication on Arm processors with architecture features like i8mm.
-        It is designed for develo...
+      generated_at_before: '2026-06-01T21:09:31Z'
+      generated_at_after: '2026-06-01T21:09:31Z'
+      preview_before: This introductory path shows how KleidiAI micro-kernels accelerate
+        Generative AI inference on Arm CPUs by optimizing matrix multiplication using
+        architecture features such as Int8 Matrix Multiplicatio...
+      preview_after: This introductory path shows how KleidiAI micro-kernels accelerate
+        Generative AI inference on Arm CPUs by optimizing matrix multiplication using
+        architecture features such as Int8 Matrix Multiplicatio...
+      preview_generated: Learn how to use KleidiAI micro-kernels to accelerate Generative
+        AI inference on Arm processors by focusing on optimized matrix multiplication
+        with the i8mm architecture feature. You will explore the ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
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       change_details:
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-        added_questions: []
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+        added_questions:
+        - What do I need before running the example?
+        - How do I know if my ML framework will use KleidiAI automatically?
+        - Where do I find the relevant micro-kernels in the KleidiAI repository?
+        - What should I expect when I run the C++ matrix multiplication example?
+        - Do I need to modify my ML stack or write assembly to use KleidiAI?
+        removed_questions:
+        - What environment do I need to complete this Learning Path?
+        - Do I need to integrate KleidiAI into my ML framework for this path?
+        - What code will I run and where does it come from?
+        - Which KleidiAI components will I examine in the repository?
+        - What background knowledge and time are expected?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
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-        updated_questions: []
+        added_questions:
+        - What do I need before running the example?
+        - How do I know if my ML framework will use KleidiAI automatically?
+        - Where do I find the relevant micro-kernels in the KleidiAI repository?
+        - What should I expect when I run the C++ matrix multiplication example?
+        - Do I need to modify my ML stack or write assembly to use KleidiAI?
+        removed_questions:
+        - What environment do I need to complete this Learning Path?
+        - Do I need to integrate KleidiAI into my ML framework for this path?
+        - What code will I run and where does it come from?
+        - Which KleidiAI components will I examine in the repository?
+        - What background knowledge and time are expected?
+        updated_questions: []
+    category: cross-platform
+  - path: content/learning-paths/cross-platform/llm-fine-tuning-for-web-applications/_index.md
+    status: skipped
+    skip_reason: draft
+    change_reasons:
+    - draft
+    ai_requested: false
+    summary:
+      action: skipped
+    faqs:
+      action: skipped
+    category: cross-platform
   - path: content/learning-paths/cross-platform/loop-reflowing/_index.md
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-    rerun_flags_reset: []
-    change_reasons: []
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+    status: updated
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+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
       missing_before: false
@@ -200003,51 +2187,76 @@ history:
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-      preview_before: Learn how to optimize C/C++ code using compiler autovectorization techniques including
-        loop modifications, restrict qualifiers, and conditional handling for Arm processors. It is designed
-        for C/C++ de...
-      preview_after: Learn how to optimize C/C++ code using compiler autovectorization techniques including
-        loop modifications, restrict qualifiers, and conditional handling for Arm processors. It is designed
-        for C/C++ de...
-      preview_generated: Learn how to optimize C/C++ code using compiler autovectorization techniques
-        including loop modifications, restrict qualifiers, and conditional handling for Arm processors.
-        It is designed for C/C++ de...
+      generated_at_before: '2026-06-01T21:10:12Z'
+      generated_at_after: '2026-06-01T21:10:12Z'
+      preview_before: This advanced, 45-minute Learning Path guides C/C++ developers
+        on Arm Linux through practical compiler autovectorization techniques on Arm
+        processors. You will compile small examples (such as addvec, ...
+      preview_after: This advanced, 45-minute Learning Path guides C/C++ developers
+        on Arm Linux through practical compiler autovectorization techniques on Arm
+        processors. You will compile small examples (such as addvec, ...
+      preview_generated: This advanced Learning Path teaches C/C++ developers how
+        to structure code so Arm compilers can autovectorize loops on Linux. Working
+        on an Arm system with GCC or Clang, you will modify loop-based exa...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:10:12Z'
+      generated_at_after: '2026-06-02T21:44:40Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - When should I use the restrict qualifier in the examples?
+        - Which commands does the path use to compile and inspect the code?
+        - How do I know if a loop is eligible for autovectorization?
+        - "What should I check if my loop has conditionals and isn\u2019t being vectorized?"
+        removed_questions:
+        - What setup do I need before starting?
+        - Can I follow the steps with Clang if the examples use GCC?
+        - What code will I compile and examine?
+        - How do I verify that autovectorization occurred?
+        - What kinds of loops can be autovectorized in this path?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - When should I use the restrict qualifier in the examples?
+        - Which commands does the path use to compile and inspect the code?
+        - How do I know if a loop is eligible for autovectorization?
+        - "What should I check if my loop has conditionals and isn\u2019t being vectorized?"
+        removed_questions:
+        - What setup do I need before starting?
+        - Can I follow the steps with Clang if the examples use GCC?
+        - What code will I compile and examine?
+        - How do I verify that autovectorization occurred?
+        - What kinds of loops can be autovectorized in this path?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/matrix/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -200057,51 +2266,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to develop and test a modern C++ library using CMake, GoogleTest, and
-        matrix processing as a practical example on Arm platforms. It is designed for developers who want
-        to learn how to develo...
-      preview_after: Learn how to develop and test a modern C++ library using CMake, GoogleTest, and matrix
-        processing as a practical example on Arm platforms. It is designed for developers who want to
-        learn how to develo...
-      preview_generated: Learn how to develop and test a modern C++ library using CMake, GoogleTest, and
-        matrix processing as a practical example on Arm platforms. It is designed for developers who want
-        to learn how to develo...
+      generated_at_before: '2026-06-01T21:10:50Z'
+      generated_at_after: '2026-06-01T21:10:50Z'
+      preview_before: This Learning Path guides you through developing and testing
+        a modern C++ matrix-processing library on an Arm-based machine using CMake
+        and GoogleTest. You will prepare a C++17 toolchain (GCC or Clang...
+      preview_after: This Learning Path guides you through developing and testing
+        a modern C++ matrix-processing library on an Arm-based machine using CMake
+        and GoogleTest. You will prepare a C++17 toolchain (GCC or Clang...
+      preview_generated: Build and test a modern C++17 matrix-processing library on
+        an Arm-based machine using CMake and GoogleTest. You set up a cross-platform
+        development environment on Linux, macOS, or Windows with GCC or ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
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+      generated_at_before: '2026-06-01T21:10:50Z'
+      generated_at_after: '2026-06-02T21:45:23Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need on my Arm-based machine before starting?
+        - Which compiler, C++ standard, and build system should I use?
+        - How do I know my environment is set up correctly?
+        - What functionality will I implement in the Matrix library?
+        - How does this path address error handling in the library?
+        removed_questions:
+        - What hardware, OS, and tools do I need before starting?
+        - Which parts of the matrix library will I implement?
+        - How are tests used in this Learning Path?
+        - Does this path cover design and error-handling considerations?
+        - What skill level is assumed and how long will it take?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need on my Arm-based machine before starting?
+        - Which compiler, C++ standard, and build system should I use?
+        - How do I know my environment is set up correctly?
+        - What functionality will I implement in the Matrix library?
+        - How does this path address error handling in the library?
+        removed_questions:
+        - What hardware, OS, and tools do I need before starting?
+        - Which parts of the matrix library will I implement?
+        - How are tests used in this Learning Path?
+        - Does this path cover design and error-handling considerations?
+        - What skill level is assumed and how long will it take?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/mca-godbolt/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
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+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -200111,51 +2345,76 @@ history:
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-      preview_before: Learn how to use llvm-mca with Compiler Explorer to analyze Arm assembly performance,
-        estimate hardware resource pressure, and diagnose performance issues. It is designed for developers
-        who want to di...
-      preview_after: Learn how to use llvm-mca with Compiler Explorer to analyze Arm assembly performance,
-        estimate hardware resource pressure, and diagnose performance issues. It is designed for developers
-        who want to di...
-      preview_generated: Learn how to use llvm-mca with Compiler Explorer to analyze Arm assembly performance,
-        estimate hardware resource pressure, and diagnose performance issues. It is designed for developers
-        who want to di...
+      generated_at_before: '2026-06-01T21:11:17Z'
+      generated_at_after: '2026-06-01T21:11:17Z'
+      preview_before: This introductory Learning Path shows how to analyze Arm assembly
+        performance with LLVM Machine Code Analyzer (llvm-mca) and Compiler Explorer.
+        You will run llvm-mca on a small Arm assembly example th...
+      preview_after: This introductory Learning Path shows how to analyze Arm assembly
+        performance with LLVM Machine Code Analyzer (llvm-mca) and Compiler Explorer.
+        You will run llvm-mca on a small Arm assembly example th...
+      preview_generated: Learn to use LLVM Machine Code Analyzer (llvm-mca) to evaluate
+        Arm assembly performance by estimating cycle counts and hardware resource
+        pressure, and apply those estimates to diagnose potential perfo...
     faqs:
-      action: unchanged
+      action: rerun_requested
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:11:17Z'
+      generated_at_after: '2026-06-02T21:45:53Z'
       before_count: 5
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       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - Can I use llvm-mca without installing LLVM locally?
+        - What do I need to run llvm-mca on my machine?
+        - What source code does the path analyze?
+        - What output should I expect from llvm-mca, and how is it used?
+        - Which LLVM version includes support for Neoverse V2?
+        removed_questions:
+        - Do I need to install LLVM, or can I follow the path entirely in a browser?
+        - What are the prerequisites before I start?
+        - What will I analyze and produce during the path?
+        - Which Arm processors is this relevant to?
+        - Why is Compiler Explorer used here?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - Can I use llvm-mca without installing LLVM locally?
+        - What do I need to run llvm-mca on my machine?
+        - What source code does the path analyze?
+        - What output should I expect from llvm-mca, and how is it used?
+        - Which LLVM version includes support for Neoverse V2?
+        removed_questions:
+        - Do I need to install LLVM, or can I follow the path entirely in a browser?
+        - What are the prerequisites before I start?
+        - What will I analyze and produce during the path?
+        - Which Arm processors is this relevant to?
+        - Why is Compiler Explorer used here?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/mcp-ai-agent/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
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-    template_version_before: summary-faq-v1
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+    rerun_flags_reset:
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+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
       missing_before: false
@@ -200165,51 +2424,76 @@ history:
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-      preview_before: Learn how to deploy a Model Context Protocol server on Raspberry Pi 5 and use the
-        OpenAI Agent SDK to create AI agents with custom tools for local inference. It is designed for
-        LLM and IoT developers ...
-      preview_after: Learn how to deploy a Model Context Protocol server on Raspberry Pi 5 and use the
-        OpenAI Agent SDK to create AI agents with custom tools for local inference. It is designed for
-        LLM and IoT developers ...
-      preview_generated: Learn how to deploy a Model Context Protocol server on Raspberry Pi 5 and use
-        the OpenAI Agent SDK to create AI agents with custom tools for local inference. It is designed
-        for LLM and IoT developers ...
+      generated_at_before: '2026-06-01T21:11:51Z'
+      generated_at_after: '2026-06-01T21:11:51Z'
+      preview_before: This Learning Path shows how to deploy a lightweight Model Context
+        Protocol (MCP) server on a Raspberry Pi 5 and connect it to an AI agent built
+        with the OpenAI Agent SDK. You will use uv, a fast Pyth...
+      preview_after: This Learning Path shows how to deploy a lightweight Model Context
+        Protocol (MCP) server on a Raspberry Pi 5 and connect it to an AI agent built
+        with the OpenAI Agent SDK. You will use uv, a fast Pyth...
+      preview_generated: This Learning Path shows how to deploy a lightweight Model
+        Context Protocol (MCP) server on a Raspberry Pi 5, then build an AI agent
+        on a Linux Arm development machine and connect it to the Pi for loc...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
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       before_count: 5
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       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which machine hosts the MCP server and where does the agent run?
+        - How do I install uv and what project files should I see?
+        - How do I expose the MCP server running on my Raspberry Pi to the internet?
+        - What result should I expect to confirm the setup is working?
+        removed_questions:
+        - What hardware and OS are required?
+        - Where do I run the MCP server and where do I run the AI agent?
+        - What does uv add to the workflow?
+        - What capabilities does the example MCP server provide?
+        - How do I know the setup worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which machine hosts the MCP server and where does the agent run?
+        - How do I install uv and what project files should I see?
+        - How do I expose the MCP server running on my Raspberry Pi to the internet?
+        - What result should I expect to confirm the setup is working?
+        removed_questions:
+        - What hardware and OS are required?
+        - Where do I run the MCP server and where do I run the AI agent?
+        - What does uv add to the workflow?
+        - What capabilities does the example MCP server provide?
+        - How do I know the setup worked?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/memory-latency/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
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+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
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     summary:
       action: unchanged
       missing_before: false
@@ -200219,51 +2503,76 @@ history:
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-      preview_before: Learn how to reduce memory latency impact in applications using cache alignment
-        and prefetching techniques on Arm processors for improved performance. It is designed for Arm
-        developers who want to lea...
-      preview_after: Learn how to reduce memory latency impact in applications using cache alignment and
-        prefetching techniques on Arm processors for improved performance. It is designed for Arm developers
-        who want to lea...
-      preview_generated: Learn how to reduce memory latency impact in applications using cache alignment
-        and prefetching techniques on Arm processors for improved performance. It is designed for Arm
-        developers who want to lea...
+      generated_at_before: '2026-06-01T21:12:22Z'
+      generated_at_after: '2026-06-01T21:12:22Z'
+      preview_before: Learn practical ways to reduce the impact of memory latency
+        on Arm processors by experimenting with cache alignment and prefetching in
+        C. You will build and run an example, then create a second versio...
+      preview_after: Learn practical ways to reduce the impact of memory latency on
+        Arm processors by experimenting with cache alignment and prefetching in C.
+        You will build and run an example, then create a second versio...
+      preview_generated: This Learning Path shows how to reduce the impact of memory
+        latency in Arm-based Linux applications by experimenting with cache alignment
+        and prefetching. You will review what memory latency is, then ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
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+      generated_at_before: '2026-06-01T21:12:22Z'
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       before_count: 5
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       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - What should I expect after copying memory-latency1.c to memory-latency2.c?
+        - How do I know whether the cache alignment change had an effect?
+        - How far ahead should I prefetch in the loop?
+        - What should I check if my results differ from the sample output?
+        removed_questions:
+        - What hardware and OS do I need to complete this path?
+        - Which compilers can I use to build the examples?
+        - What code will I work on, and what changes will I make?
+        - How do I check whether the changes improved behavior?
+        - How much time and prior knowledge are expected?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - What should I expect after copying memory-latency1.c to memory-latency2.c?
+        - How do I know whether the cache alignment change had an effect?
+        - How far ahead should I prefetch in the loop?
+        - What should I check if my results differ from the sample output?
+        removed_questions:
+        - What hardware and OS do I need to complete this path?
+        - Which compilers can I use to build the examples?
+        - What code will I work on, and what changes will I make?
+        - How do I check whether the changes improved behavior?
+        - How much time and prior knowledge are expected?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
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+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
       missing_before: false
@@ -200273,51 +2582,78 @@ history:
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to build MNN on an Armv9 system, run text, vision, and audio prompts with
-        a multimodal Omni model, and combine image and audio inputs into a single-shot retail restock
-        ticket workflow. It is...
-      preview_after: Learn how to build MNN on an Armv9 system, run text, vision, and audio prompts with
-        a multimodal Omni model, and combine image and audio inputs into a single-shot retail restock
-        ticket workflow. It is...
-      preview_generated: Learn how to build MNN on an Armv9 system, run text, vision, and audio prompts
-        with a multimodal Omni model, and combine image and audio inputs into a single-shot retail restock
-        ticket workflow. It is...
+      generated_at_before: '2026-06-01T21:13:18Z'
+      generated_at_after: '2026-06-01T21:13:18Z'
+      preview_before: This advanced Learning Path shows how to build the MNN (Mobile
+        Neural Network) inference engine natively on an Armv9 Linux device and run
+        a CPU-only Omni multimodal model. You start by verifying a tex...
+      preview_after: This advanced Learning Path shows how to build the MNN (Mobile
+        Neural Network) inference engine natively on an Armv9 Linux device and run
+        a CPU-only Omni multimodal model. You start by verifying a tex...
+      preview_generated: This advanced path shows how to build MNN on an Armv9 Linux
+        system and run a CPU-only Omni multimodal model for a practical retail restocking
+        workflow. You will build MNN natively, validate a text-onl...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
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-      generated_at_after: '2026-05-06T17:17:54Z'
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       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - Do I need a GPU or accelerator to run the demos?
+        - What do I need before building MNN on my Armv9 device?
+        - How do I confirm my MNN build and model are ready?
+        - What result should I expect from the text-only baseline?
+        - What outputs should I expect from the vision and audio steps, and how do
+          they fit together?
+        removed_questions:
+        - What environment do I need to follow this Learning Path?
+        - Do I need a GPU or other accelerator to run the model?
+        - Which tools and languages are used in the steps?
+        - How do I verify that my build and model setup are correct?
+        - What outputs should I expect from the demos?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - Do I need a GPU or accelerator to run the demos?
+        - What do I need before building MNN on my Armv9 device?
+        - How do I confirm my MNN build and model are ready?
+        - What result should I expect from the text-only baseline?
+        - What outputs should I expect from the vision and audio steps, and how do
+          they fit together?
+        removed_questions:
+        - What environment do I need to follow this Learning Path?
+        - Do I need a GPU or other accelerator to run the model?
+        - Which tools and languages are used in the steps?
+        - How do I verify that my build and model setup are correct?
+        - What outputs should I expect from the demos?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
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+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -200327,51 +2663,87 @@ history:
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-      preview_before: Learn how to implement and optimize matrix multiplication using Arm's Scalable Matrix
-        Extension 2 (SME2) with assembly and intrinsics, including benchmarking and validation on Arm
-        hardware. It is desi...
-      preview_after: Learn how to implement and optimize matrix multiplication using Arm's Scalable Matrix
-        Extension 2 (SME2) with assembly and intrinsics, including benchmarking and validation on Arm
-        hardware. It is desi...
-      preview_generated: Learn how to implement and optimize matrix multiplication using Arm's Scalable
-        Matrix Extension 2 (SME2) with assembly and intrinsics, including benchmarking and validation
-        on Arm hardware. It is desi...
+      generated_at_before: '2026-06-01T21:13:59Z'
+      generated_at_after: '2026-06-01T21:13:59Z'
+      preview_before: "This advanced Learning Path shows how to implement, build,\
+        \ and evaluate matrix multiplication using Arm\u2019s Scalable Matrix Extension\
+        \ 2 (SME2) with both assembly and intrinsics. You will set up a develo..."
+      preview_after: "This advanced Learning Path shows how to implement, build, and\
+        \ evaluate matrix multiplication using Arm\u2019s Scalable Matrix Extension\
+        \ 2 (SME2) with both assembly and intrinsics. You will set up a develo..."
+      preview_generated: "This advanced Learning Path guides you through implementing\
+        \ and accelerating matrix multiplication using Arm\u2019s Scalable Matrix\
+        \ Extension 2 (SME2) with both assembly and intrinsics. You will build a ba..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
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-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:13:59Z'
+      generated_at_after: '2026-06-02T21:48:10Z'
       before_count: 5
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       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - Should I use native SME2 hardware or an emulator?
+        - How do I verify my SME2 toolchain and environment are set up correctly?
+        - How do I use streaming mode and handle ZA state in SME?
+        - How do I validate and benchmark the SME2-optimized matrix multiplication?
+        removed_questions:
+        - Can I follow this path without native SME2 hardware?
+        - What host OS and tools do I need to build the examples?
+        - How do I verify that my SME2 environment is configured correctly?
+        - What code will I implement and what artifacts should I expect?
+        - How is SME streaming mode and ZA state managed in the examples?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - Should I use native SME2 hardware or an emulator?
+        - How do I verify my SME2 toolchain and environment are set up correctly?
+        - How do I use streaming mode and handle ZA state in SME?
+        - How do I validate and benchmark the SME2-optimized matrix multiplication?
+        removed_questions:
+        - Can I follow this path without native SME2 hardware?
+        - What host OS and tools do I need to build the examples?
+        - How do I verify that my SME2 environment is configured correctly?
+        - What code will I implement and what artifacts should I expect?
+        - How is SME streaming mode and ZA state managed in the examples?
+        updated_questions: []
+    category: cross-platform
+  - path: content/learning-paths/cross-platform/psa-tfm/_index.md
+    status: skipped
+    skip_reason: draft
+    change_reasons:
+    - draft
+    ai_requested: false
+    summary:
+      action: skipped
+    faqs:
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+    category: cross-platform
   - path: content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
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+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
       missing_before: false
@@ -200381,51 +2753,76 @@ history:
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-      preview_before: Learn how to create and train a PyTorch neural network for MNIST digit classification,
-        optimize it with quantization and fusing, and deploy it in an Android application with performance
-        measurement. I...
-      preview_after: Learn how to create and train a PyTorch neural network for MNIST digit classification,
-        optimize it with quantization and fusing, and deploy it in an Android application with performance
-        measurement. I...
-      preview_generated: Learn how to create and train a PyTorch neural network for MNIST digit classification,
-        optimize it with quantization and fusing, and deploy it in an Android application with performance
-        measurement. I...
+      generated_at_before: '2026-06-01T21:14:41Z'
+      generated_at_after: '2026-06-01T21:14:41Z'
+      preview_before: This advanced Learning Path guides you through preparing a PyTorch
+        development environment, downloading and organizing the MNIST dataset, and
+        creating, training, and saving a feedforward neural networ...
+      preview_after: This advanced Learning Path guides you through preparing a PyTorch
+        development environment, downloading and organizing the MNIST dataset, and
+        creating, training, and saving a feedforward neural networ...
+      preview_generated: This Learning Path guides you through building and training
+        a feedforward PyTorch model for MNIST digit classification, then optimizing
+        and deploying it in an Android application with basic performanc...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
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+      generated_at_before: '2026-06-01T21:14:41Z'
+      generated_at_after: '2026-06-02T21:48:30Z'
       before_count: 5
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       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need installed before running the training and Android steps?
+        - How do I download MNIST and create DataLoaders in this path?
+        - How do I know the training step worked and the model is saved?
+        - During inference, how should I preprocess inputs so they match training?
+        - When do I apply quantization and fusing, and what gets deployed to Android?
+        removed_questions:
+        - What environment and tools do I need to follow this Learning Path?
+        - What will I build and measure by the end?
+        - Which dataset is used and how is it prepared?
+        - Does the Learning Path include model optimization and deployment steps?
+        - How can I tell that the workflow is working correctly?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need installed before running the training and Android steps?
+        - How do I download MNIST and create DataLoaders in this path?
+        - How do I know the training step worked and the model is saved?
+        - During inference, how should I preprocess inputs so they match training?
+        - When do I apply quantization and fusing, and what gets deployed to Android?
+        removed_questions:
+        - What environment and tools do I need to follow this Learning Path?
+        - What will I build and measure by the end?
+        - Which dataset is used and how is it prepared?
+        - Does the Learning Path include model optimization and deployment steps?
+        - How can I tell that the workflow is working correctly?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/remoteit/_index.md
-    status: unchanged
-    changed_on_disk: false
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-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -200435,51 +2832,80 @@ history:
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to install and configure Remote.It for secure remote device access using
-        SSH and other services, with proxy and peer-to-peer connection options. It is designed for software
-        developers who wa...
-      preview_after: Learn how to install and configure Remote.It for secure remote device access using
-        SSH and other services, with proxy and peer-to-peer connection options. It is designed for software
-        developers who wa...
-      preview_generated: Learn how to install and configure Remote.It for secure remote device access
-        using SSH and other services, with proxy and peer-to-peer connection options. It is designed for
-        software developers who wa...
+      generated_at_before: '2026-06-01T21:15:32Z'
+      generated_at_after: '2026-06-01T21:15:32Z'
+      preview_before: This introductory Learning Path shows how to install and configure
+        Remote.It to access remote devices using SSH and other services, and how to
+        choose between proxy and peer-to-peer connection options....
+      preview_after: This introductory Learning Path shows how to install and configure
+        Remote.It to access remote devices using SSH and other services, and how to
+        choose between proxy and peer-to-peer connection options....
+      preview_generated: This Learning Path shows how to install and configure Remote.It
+        on target devices to enable private remote access using SSH and other services.
+        You will set up the Remote.It device package on a target...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:15:32Z'
+      generated_at_after: '2026-06-02T21:49:07Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the setup?
+        - How do I install the Remote.It device package when I already have access
+          to the target?
+        - Do I need to install anything on the initiator computer to connect?
+        - Which connection type should I use, proxy or peer-to-peer?
+        - What result should I expect after completing the steps, and how do I know
+          it worked?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - Do I need to install software on the initiator computer?
+        - How do proxy and peer-to-peer connections differ in this path?
+        - How can I verify that my Remote.It setup works?
+        - Can I use this when my device lacks a public IP or is behind CGNAT/5G/Starlink?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the setup?
+        - How do I install the Remote.It device package when I already have access
+          to the target?
+        - Do I need to install anything on the initiator computer to connect?
+        - Which connection type should I use, proxy or peer-to-peer?
+        - What result should I expect after completing the steps, and how do I know
+          it worked?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - Do I need to install software on the initiator computer?
+        - How do proxy and peer-to-peer connections differ in this path?
+        - How can I verify that my Remote.It setup works?
+        - Can I use this when my device lacks a public IP or is behind CGNAT/5G/Starlink?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -200489,51 +2915,76 @@ history:
       source_hash_before: 9825df99004e981fadce5884b40b2152f4e43064cbba2cd2129fd90006090678
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to use the C99 restrict keyword to indicate non-overlapping memory regions
-        and enable better compiler optimizations for vectorization on Arm platforms. It is designed for
-        C developers who ar...
-      preview_after: Learn how to use the C99 restrict keyword to indicate non-overlapping memory regions
-        and enable better compiler optimizations for vectorization on Arm platforms. It is designed for
-        C developers who ar...
-      preview_generated: Learn how to use the C99 restrict keyword to indicate non-overlapping memory
-        regions and enable better compiler optimizations for vectorization on Arm platforms. It is designed
-        for C developers who ar...
+      generated_at_before: '2026-06-01T21:16:11Z'
+      generated_at_after: '2026-06-01T21:16:11Z'
+      preview_before: This Learning Path shows C developers on Arm Linux how to use
+        the C99 restrict keyword to indicate non-overlapping memory regions so compilers
+        can apply stronger optimizations, including vectorization...
+      preview_after: This Learning Path shows C developers on Arm Linux how to use
+        the C99 restrict keyword to indicate non-overlapping memory regions so compilers
+        can apply stronger optimizations, including vectorization...
+      preview_generated: Learn how to apply the C99 restrict keyword to indicate non-overlapping
+        memory regions so compilers can perform stronger vectorization on Arm platforms.
+        This path walks through a pointer-aliasing prob...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 9825df99004e981fadce5884b40b2152f4e43064cbba2cd2129fd90006090678
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:16:11Z'
+      generated_at_after: '2026-06-02T21:49:44Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - Which compiler and options are used in the SVE2 example?
+        - "How do I decide if I can add restrict to a function\u2019s pointer parameters?"
+        - How do I know that restrict enabled vectorization on Arm?
+        - What should I avoid when considering restrict?
+        removed_questions:
+        - What environment do I need to follow this path?
+        - Do I need SVE2 or a specific compiler version?
+        - How do I decide when it is safe to add restrict to pointers?
+        - What will I do in the steps?
+        - Can I use Clang, or should I use GCC for the examples?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - Which compiler and options are used in the SVE2 example?
+        - "How do I decide if I can add restrict to a function\u2019s pointer parameters?"
+        - How do I know that restrict enabled vectorization on Arm?
+        - What should I avoid when considering restrict?
+        removed_questions:
+        - What environment do I need to follow this path?
+        - Do I need SVE2 or a specific compiler version?
+        - How do I decide when it is safe to add restrict to pointers?
+        - What will I do in the steps?
+        - Can I use Clang, or should I use GCC for the examples?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/rust_armds/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -200543,51 +2994,74 @@ history:
       source_hash_before: f237cd239e5886b21bd5bee88dcd9f95d88eda9a5c2eea363cc9db252e0c6e9f
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to build an embedded Rust application for Arm processors, run it on a
-        Fixed Virtual Platform, and debug it using Arm Development Studio. It is designed for embedded
-        application developers to...
-      preview_after: Learn how to build an embedded Rust application for Arm processors, run it on a Fixed
-        Virtual Platform, and debug it using Arm Development Studio. It is designed for embedded application
-        developers to...
-      preview_generated: Learn how to build an embedded Rust application for Arm processors, run it on
-        a Fixed Virtual Platform, and debug it using Arm Development Studio. It is designed for embedded
-        application developers to...
+      generated_at_before: '2026-06-01T21:16:47Z'
+      generated_at_after: '2026-06-01T21:16:47Z'
+      preview_before: This introductory path guides you through building a bare-metal
+        embedded Rust application for Armv7-M, running it on a Fixed Virtual Platform,
+        and debugging with Arm Development Studio. You will insta...
+      preview_after: This introductory path guides you through building a bare-metal
+        embedded Rust application for Armv7-M, running it on a Fixed Virtual Platform,
+        and debugging with Arm Development Studio. You will insta...
+      preview_generated: Follow this introductory path to build a bare-metal embedded
+        Rust application for Arm processors, run it on a Fixed Virtual Platform (FVP),
+        and debug it with Arm Development Studio. You install Arm De...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: f237cd239e5886b21bd5bee88dcd9f95d88eda9a5c2eea363cc9db252e0c6e9f
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:16:47Z'
+      generated_at_after: '2026-06-02T21:50:10Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I run the built application on the FVP?
+        - How can I reduce the FVP start time?
+        - What result should I expect when the program runs on the FVP?
+        removed_questions:
+        - What do I need before starting?
+        - How do I run the built application and confirm it worked?
+        - Can I reduce FVP startup time?
+        - Does this path include debugging with Arm Development Studio?
+        updated_questions:
+        - Which Arm architecture and FVP model does the example use?
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I run the built application on the FVP?
+        - How can I reduce the FVP start time?
+        - What result should I expect when the program runs on the FVP?
+        removed_questions:
+        - What do I need before starting?
+        - How do I run the built application and confirm it worked?
+        - Can I reduce FVP startup time?
+        - Does this path include debugging with Arm Development Studio?
+        updated_questions:
+        - Which Arm architecture and FVP model does the example use?
+    category: cross-platform
   - path: content/learning-paths/cross-platform/simd-info-demo/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -200597,51 +3071,82 @@ history:
       source_hash_before: 7a40efa6d83b4629888f9622260e6e9aa9192db836b203fa4bf388cbe636b7e6
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to use SIMD.info to port SIMD intrinsics across Arm architectures, including
-        navigation, search, and comparison features for finding equivalent instructions. It is designed
-        for software deve...
-      preview_after: Learn how to use SIMD.info to port SIMD intrinsics across Arm architectures, including
-        navigation, search, and comparison features for finding equivalent instructions. It is designed
-        for software deve...
-      preview_generated: Learn how to use SIMD.info to port SIMD intrinsics across Arm architectures,
-        including navigation, search, and comparison features for finding equivalent instructions. It
-        is designed for software deve...
+      generated_at_before: '2026-06-01T21:17:19Z'
+      generated_at_after: '2026-06-01T21:17:19Z'
+      preview_before: Learn how to use SIMD.info to port SIMD intrinsics between architectures
+        with a practical, code-centric walkthrough. You will examine a short C example
+        that uses Intel SSE4.2 intrinsics on Linux, then...
+      preview_after: Learn how to use SIMD.info to port SIMD intrinsics between architectures
+        with a practical, code-centric walkthrough. You will examine a short C example
+        that uses Intel SSE4.2 intrinsics on Linux, then...
+      preview_generated: This Learning Path shows how to use SIMD.info to port SIMD
+        intrinsics across Arm architectures, using a concrete C example that starts
+        with Intel SSE4.2 on x86_64 Linux and then maps operations to Arm...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 7a40efa6d83b4629888f9622260e6e9aa9192db836b203fa4bf388cbe636b7e6
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       current_source_hash: 7a40efa6d83b4629888f9622260e6e9aa9192db836b203fa4bf388cbe636b7e6
-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:17:19Z'
+      generated_at_after: '2026-06-02T21:50:47Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the example and porting steps?
+        - How do I use SIMD.info to find Neon equivalents for the SSE4.2 intrinsics
+          in the example?
+        - Which intrinsics from the example should I look up on SIMD.info?
+        - How should vector initialization and storing change when moving from SSE4.2
+          to Neon?
+        - How do I verify my Neon port is correct, and should I focus on performance
+          now?
+        removed_questions:
+        - What prerequisites do I need before starting?
+        - Which architectures and intrinsics does the example focus on?
+        - How is SIMD.info used during the porting process?
+        - Where does the example start, and how do I validate the port?
+        - What outcome should I expect by the end of the path?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the example and porting steps?
+        - How do I use SIMD.info to find Neon equivalents for the SSE4.2 intrinsics
+          in the example?
+        - Which intrinsics from the example should I look up on SIMD.info?
+        - How should vector initialization and storing change when moving from SSE4.2
+          to Neon?
+        - How do I verify my Neon port is correct, and should I focus on performance
+          now?
+        removed_questions:
+        - What prerequisites do I need before starting?
+        - Which architectures and intrinsics does the example focus on?
+        - How is SIMD.info used during the porting process?
+        - Where does the example start, and how do I validate the port?
+        - What outcome should I expect by the end of the path?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/simd-loops/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -200651,51 +3156,80 @@ history:
       source_hash_before: b1c43e1bf971db4582ca358c98dab2c7e6e047d6c79bfcc0db148bc575f33679
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to write high-performance SIMD code using the SIMD Loops project, with
-        hands-on examples demonstrating SVE, SVE2, and SME2 features on Arm processors. It is designed
-        for software developers ...
-      preview_after: Learn how to write high-performance SIMD code using the SIMD Loops project, with
-        hands-on examples demonstrating SVE, SVE2, and SME2 features on Arm processors. It is designed
-        for software developers ...
-      preview_generated: Learn how to write high-performance SIMD code using the SIMD Loops project, with
-        hands-on examples demonstrating SVE, SVE2, and SME2 features on Arm processors. It is designed
-        for software developers ...
+      generated_at_before: '2026-06-01T21:18:06Z'
+      generated_at_after: '2026-06-01T21:18:06Z'
+      preview_before: "This advanced Learning Path shows how to use Arm\u2019s Scalable\
+        \ Vector Extension (SVE), SVE2, and Scalable Matrix Extension (SME/SME2) with\
+        \ the SIMD Loops project. You will clone the repository, explore h..."
+      preview_after: "This advanced Learning Path shows how to use Arm\u2019s Scalable\
+        \ Vector Extension (SVE), SVE2, and Scalable Matrix Extension (SME/SME2) with\
+        \ the SIMD Loops project. You will clone the repository, explore h..."
+      preview_generated: This advanced Learning Path uses the SIMD Loops project to
+        teach Scalable Vector Extension (SVE), SVE2, and Scalable Matrix Extension
+        (SME2) programming on Arm processors. On an AArch64 system running...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: b1c43e1bf971db4582ca358c98dab2c7e6e047d6c79bfcc0db148bc575f33679
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:18:06Z'
+      generated_at_after: '2026-06-02T21:51:17Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - How do I know my machine is Arm-based?
+        - Where are the loop kernels listed, and how are they organized?
+        - Which example does this path use to explain the project structure, and what
+          does it compute?
+        - How do I build, run, and validate a kernel implementation?
+        removed_questions:
+        - What hardware and operating system do I need to follow this path?
+        - Which compilers are recommended for SVE/SME development in this project?
+        - How do I confirm that I am running on an Arm system?
+        - How are the kernels organized in SIMD Loops, and which example does this
+          path use?
+        - What will I build and how do I validate that it worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - How do I know my machine is Arm-based?
+        - Where are the loop kernels listed, and how are they organized?
+        - Which example does this path use to explain the project structure, and what
+          does it compute?
+        - How do I build, run, and validate a kernel implementation?
+        removed_questions:
+        - What hardware and operating system do I need to follow this path?
+        - Which compilers are recommended for SVE/SME development in this project?
+        - How do I confirm that I am running on an Arm system?
+        - How are the kernels organized in SIMD Loops, and which example does this
+          path use?
+        - What will I build and how do I validate that it worked?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/simd-on-rust/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -200705,51 +3239,78 @@ history:
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to write SIMD code in Rust on Arm platforms using Neon intrinsics, portable
-        SIMD abstractions, and optimize performance with architecture-specific instructions. It is designed
-        for software d...
-      preview_after: Learn how to write SIMD code in Rust on Arm platforms using Neon intrinsics, portable
-        SIMD abstractions, and optimize performance with architecture-specific instructions. It is designed
-        for software d...
-      preview_generated: Learn how to write SIMD code in Rust on Arm platforms using Neon intrinsics,
-        portable SIMD abstractions, and optimize performance with architecture-specific instructions.
-        It is designed for software d...
+      generated_at_before: '2026-06-01T21:18:44Z'
+      generated_at_after: '2026-06-01T21:18:44Z'
+      preview_before: "This advanced path teaches you to write SIMD code on Arm using\
+        \ Rust on Linux. You will use Rust\u2019s std::arch Neon intrinsics and portable\
+        \ std::simd, apply feature detection and target attributes for ar..."
+      preview_after: "This advanced path teaches you to write SIMD code on Arm using\
+        \ Rust on Linux. You will use Rust\u2019s std::arch Neon intrinsics and portable\
+        \ std::simd, apply feature detection and target attributes for ar..."
+      preview_generated: This advanced Learning Path shows how to implement SIMD on
+        Arm using Rust on Linux. You will begin with C examples that use Arm Advanced
+        SIMD (Neon) intrinsics, then translate key operations into Rust...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
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-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:18:44Z'
+      generated_at_after: '2026-06-02T21:51:46Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - Which compiler should I use to build the C examples?
+        - Which source files will I create, and what do they demonstrate?
+        - When should I use std::simd versus Neon intrinsics in Rust?
+        - How do I know the SIMD code is working and producing the right instructions?
+        removed_questions:
+        - What hardware, OS, and tools are required?
+        - Which Arm architectures and SIMD technologies are covered?
+        - Does this Learning Path use both C and Rust, and what will I compare?
+        - Will I learn portable SIMD with std::simd and when to use architecture-specific
+          code?
+        - How do I validate that the steps worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - Which compiler should I use to build the C examples?
+        - Which source files will I create, and what do they demonstrate?
+        - When should I use std::simd versus Neon intrinsics in Rust?
+        - How do I know the SIMD code is working and producing the right instructions?
+        removed_questions:
+        - What hardware, OS, and tools are required?
+        - Which Arm architectures and SIMD technologies are covered?
+        - Does this Learning Path use both C and Rust, and what will I compare?
+        - Will I learn portable SIMD with std::simd and when to use architecture-specific
+          code?
+        - How do I validate that the steps worked?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/sme-executorch-profiling/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -200759,51 +3320,78 @@ history:
       source_hash_before: 36f7dfe13c8c940abbaad2b61620b3b1c6d97f580de15e573b45ef7760753797
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to profile and optimize ExecuTorch models using SME2 acceleration on Arm
-        platforms, including operator-level analysis and performance bottleneck identification. It is
-        designed for developers...
-      preview_after: Learn how to profile and optimize ExecuTorch models using SME2 acceleration on Arm
-        platforms, including operator-level analysis and performance bottleneck identification. It is
-        designed for developers...
-      preview_generated: Learn how to profile and optimize ExecuTorch models using SME2 acceleration on
-        Arm platforms, including operator-level analysis and performance bottleneck identification. It
-        is designed for developers...
+      generated_at_before: '2026-06-01T21:19:22Z'
+      generated_at_after: '2026-06-01T21:19:22Z'
+      preview_before: This advanced Learning Path shows how to profile ExecuTorch
+        models on Arm with SME2 acceleration in approximately 90 minutes. You will
+        set up a reusable Apple Silicon macOS workspace (Python 3.9+ and ...
+      preview_after: This advanced Learning Path shows how to profile ExecuTorch models
+        on Arm with SME2 acceleration in approximately 90 minutes. You will set up
+        a reusable Apple Silicon macOS workspace (Python 3.9+ and ...
+      preview_generated: This advanced Learning Path shows how to profile ExecuTorch
+        models on Arm with SME2 acceleration and make evidence-based decisions from
+        operator-level results. You set up a reusable profiling pipeline...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 36f7dfe13c8c940abbaad2b61620b3b1c6d97f580de15e573b45ef7760753797
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:19:22Z'
+      generated_at_after: '2026-06-02T21:52:19Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need on my host machine before starting the setup?
+        - Do I need an Android device, and how should it be configured if I use one?
+        - Which model format should I export, and is the profiling pipeline model-specific?
+        - How do I collect profiling data for comparison?
+        - What result should I expect when enabling SME2, and how do I interpret the
+          profiles?
+        removed_questions:
+        - What are the prerequisites and host environment requirements?
+        - Do I need an Android device to follow this Learning Path?
+        - What model format does the workflow use, and how do I onboard a model?
+        - What artifacts will I create and how do I validate the results?
+        - Can I automate the profiling workflow?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need on my host machine before starting the setup?
+        - Do I need an Android device, and how should it be configured if I use one?
+        - Which model format should I export, and is the profiling pipeline model-specific?
+        - How do I collect profiling data for comparison?
+        - What result should I expect when enabling SME2, and how do I interpret the
+          profiles?
+        removed_questions:
+        - What are the prerequisites and host environment requirements?
+        - Do I need an Android device to follow this Learning Path?
+        - What model format does the workflow use, and how do I onboard a model?
+        - What artifacts will I create and how do I validate the results?
+        - Can I automate the profiling workflow?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -200813,51 +3401,76 @@ history:
       source_hash_before: 09142517ecc64fba821062e1af4db3ac9d72f9adcd3f10c12d6fd5a4cf3daaf4
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to deploy Tinkerblox UltraEdge HPC-I for edge AI and mixed workloads on
-        Arm platforms, including installation and configuration on Debian, Ubuntu, and Yocto systems.
-        It is designed for busin...
-      preview_after: Learn how to deploy Tinkerblox UltraEdge HPC-I for edge AI and mixed workloads on
-        Arm platforms, including installation and configuration on Debian, Ubuntu, and Yocto systems.
-        It is designed for busin...
-      preview_generated: Learn how to deploy Tinkerblox UltraEdge HPC-I for edge AI and mixed workloads
-        on Arm platforms, including installation and configuration on Debian, Ubuntu, and Yocto systems.
-        It is designed for busin...
+      generated_at_before: '2026-06-01T21:20:11Z'
+      generated_at_after: '2026-06-01T21:20:11Z'
+      preview_before: This Learning Path shows how to deploy Tinkerblox UltraEdge
+        HPC-I on Arm for AI and mixed workloads. You start by understanding the UltraEdge
+        layered architecture (core, boost, prime), then provision ...
+      preview_after: This Learning Path shows how to deploy Tinkerblox UltraEdge HPC-I
+        on Arm for AI and mixed workloads. You start by understanding the UltraEdge
+        layered architecture (core, boost, prime), then provision ...
+      preview_generated: This advanced Learning Path shows how to deploy Tinkerblox
+        UltraEdge HPC-I for AI and mixed workloads on Arm-based Linux systems, and
+        how to build a Yocto image for the NXP S32G-VNP-GLDBOX3 using a Go...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:20:11Z'
+      generated_at_after: '2026-06-02T21:52:57Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the Yocto image build steps?
+        - Which Ubuntu releases are supported as Yocto build hosts right now?
+        - How do I register a Debian or Ubuntu device for UltraEdge?
+        - How do I deploy and validate a sample microservice on UltraEdge?
+        - Do I need Docker or Kubernetes to run workloads in this Learning Path?
+        removed_questions:
+        - Do I need Docker or Kubernetes for this deployment?
+        - Which platforms and operating systems are targeted?
+        - What Google Cloud VM configuration should I provision for Yocto builds?
+        - Which host operating systems are supported for Yocto image builds?
+        - How do I validate that UltraEdge is installed and a microservice is running?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the Yocto image build steps?
+        - Which Ubuntu releases are supported as Yocto build hosts right now?
+        - How do I register a Debian or Ubuntu device for UltraEdge?
+        - How do I deploy and validate a sample microservice on UltraEdge?
+        - Do I need Docker or Kubernetes to run workloads in this Learning Path?
+        removed_questions:
+        - Do I need Docker or Kubernetes for this deployment?
+        - Which platforms and operating systems are targeted?
+        - What Google Cloud VM configuration should I provision for Yocto builds?
+        - Which host operating systems are supported for Yocto image builds?
+        - How do I validate that UltraEdge is installed and a microservice is running?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/topdown-compare/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -200867,51 +3480,80 @@ history:
       source_hash_before: 5f4b05e3d962476c67c4a4400c8fa71f04412f3f0354d5c3123f38af57791304
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to compare Arm Neoverse and Intel x86 top-down performance analysis methodologies
-        using PMU counters, Linux Perf, and topdown-tool to identify bottlenecks across architectures.
-        It is designe...
-      preview_after: Learn how to compare Arm Neoverse and Intel x86 top-down performance analysis methodologies
-        using PMU counters, Linux Perf, and topdown-tool to identify bottlenecks across architectures.
-        It is designe...
-      preview_generated: Learn how to compare Arm Neoverse and Intel x86 top-down performance analysis
-        methodologies using PMU counters, Linux Perf, and topdown-tool to identify bottlenecks across
-        architectures. It is designe...
+      generated_at_before: '2026-06-01T21:20:45Z'
+      generated_at_after: '2026-06-01T21:20:45Z'
+      preview_before: "This advanced Learning Path shows how to compare Arm Neoverse\
+        \ and Intel x86 top-down performance analysis on Linux using PMU counters.\
+        \ You will review Intel\u2019s multilevel hierarchical model and Arm\u2019\
+        s t..."
+      preview_after: "This advanced Learning Path shows how to compare Arm Neoverse\
+        \ and Intel x86 top-down performance analysis on Linux using PMU counters.\
+        \ You will review Intel\u2019s multilevel hierarchical model and Arm\u2019\
+        s t..."
+      preview_generated: "This advanced Learning Path shows how to compare Arm Neoverse\
+        \ and Intel x86 top-down performance analysis using PMU counters on Linux.\
+        \ You will examine Intel\u2019s multilevel hierarchical methodology (TMA..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 5f4b05e3d962476c67c4a4400c8fa71f04412f3f0354d5c3123f38af57791304
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:20:45Z'
+      generated_at_after: '2026-06-02T21:53:36Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the cross-platform example?
+        - Which tools should I install on each platform?
+        - How do I build and run the provided benchmark?
+        - What result should I expect when I run the benchmark?
+        - How should I compare results across Arm and Intel given different counters
+          and slot models?
+        removed_questions:
+        - What systems and operating system are required?
+        - What prior knowledge is assumed?
+        - Which tools do I need and how do I install them?
+        - What benchmark will I build and how do I run it?
+        - How are results compared and what output should I expect?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the cross-platform example?
+        - Which tools should I install on each platform?
+        - How do I build and run the provided benchmark?
+        - What result should I expect when I run the benchmark?
+        - How should I compare results across Arm and Intel given different counters
+          and slot models?
+        removed_questions:
+        - What systems and operating system are required?
+        - What prior knowledge is assumed?
+        - Which tools do I need and how do I install them?
+        - What benchmark will I build and how do I run it?
+        - How are results compared and what output should I expect?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/vectorization-comparison/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -200921,51 +3563,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to migrate x86-64 SIMD code to Arm64 by mapping Intel SSE/AVX to Arm Neon,
-        SVE, and SME, with code examples and migration strategies using autovectorization or intrinsics.
-        It is designed for...
-      preview_after: Learn how to migrate x86-64 SIMD code to Arm64 by mapping Intel SSE/AVX to Arm Neon,
-        SVE, and SME, with code examples and migration strategies using autovectorization or intrinsics.
-        It is designed for...
-      preview_generated: Learn how to migrate x86-64 SIMD code to Arm64 by mapping Intel SSE/AVX to Arm
-        Neon, SVE, and SME, with code examples and migration strategies using autovectorization or intrinsics.
-        It is designed for...
+      generated_at_before: '2026-06-01T21:21:21Z'
+      generated_at_after: '2026-06-01T21:21:21Z'
+      preview_before: This advanced Learning Path shows how to migrate x86-64 SIMD
+        code to Arm64 by mapping Intel SSE/AVX/AMX to Arm Neon, SVE, and SME. You
+        review migration strategies using autovectorization, intrinsics, ...
+      preview_after: This advanced Learning Path shows how to migrate x86-64 SIMD
+        code to Arm64 by mapping Intel SSE/AVX/AMX to Arm Neon, SVE, and SME. You
+        review migration strategies using autovectorization, intrinsics, ...
+      preview_generated: This advanced Learning Path shows how to migrate x86-64 SIMD
+        code to Arm64 on Linux by mapping Intel SSE/AVX/AMX to Arm Neon, SVE, and
+        SME. Using GCC or Clang, you will build and run a SAXPY kernel wr...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
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-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:21:21Z'
+      generated_at_after: '2026-06-02T21:54:01Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - Which compiler should I use to build the code?
+        - How do I map x86 SIMD intrinsics to Arm equivalents?
+        - What result should I expect when I run the SAXPY variants?
+        - When should I use a library instead of writing intrinsics?
+        removed_questions:
+        - What setup and tools do I need to follow this Learning Path?
+        - What prior knowledge is expected?
+        - What code will I build and run?
+        - Which migration strategies are covered?
+        - How do I know the steps worked on my system?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - Which compiler should I use to build the code?
+        - How do I map x86 SIMD intrinsics to Arm equivalents?
+        - What result should I expect when I run the SAXPY variants?
+        - When should I use a library instead of writing intrinsics?
+        removed_questions:
+        - What setup and tools do I need to follow this Learning Path?
+        - What prior knowledge is expected?
+        - What code will I build and run?
+        - Which migration strategies are covered?
+        - How do I know the steps worked on my system?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -200975,51 +3642,76 @@ history:
       source_hash_before: 14a22194d3fc73ebebb62db9c7cfbeabe0bd9acdaf340b2df52072be65d98655
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to optimize SIMD performance on Arm by restructuring data layouts from
-        Array-of-Structures to Structure-of-Arrays, with practical examples using Neon and SVE intrinsics.
-        It is designed for C...
-      preview_after: Learn how to optimize SIMD performance on Arm by restructuring data layouts from
-        Array-of-Structures to Structure-of-Arrays, with practical examples using Neon and SVE intrinsics.
-        It is designed for C...
-      preview_generated: Learn how to optimize SIMD performance on Arm by restructuring data layouts from
-        Array-of-Structures to Structure-of-Arrays, with practical examples using Neon and SVE intrinsics.
-        It is designed for C...
+      generated_at_before: '2026-06-01T21:21:48Z'
+      generated_at_after: '2026-06-01T21:21:48Z'
+      preview_before: This advanced Learning Path guides C/C++ developers on Arm Linux
+        through restructuring data from Array-of-Structures to Structure-of-Arrays
+        to make SIMD vectorization more effective. You will study da...
+      preview_after: This advanced Learning Path guides C/C++ developers on Arm Linux
+        through restructuring data from Array-of-Structures to Structure-of-Arrays
+        to make SIMD vectorization more effective. You will study da...
+      preview_generated: This advanced Learning Path guides C/C++ developers on Arm
+        Linux through restructuring data layouts to make SIMD code more effective,
+        moving from Array-of-Structures to Structure-of-Arrays with alignm...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:21:48Z'
+      generated_at_after: '2026-06-02T21:54:37Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - How do I know if my current data layout is blocking vectorization?
+        - Which files do I edit and in what order?
+        - When should I switch to hand-written intrinsics, and which ones are used?
+        - Does this Learning Path cover both Neon and SVE intrinsics?
+        removed_questions:
+        - What hardware and software do I need before starting?
+        - Which SIMD technologies are used in the examples?
+        - What code artifacts will I create or modify?
+        - How will I know the data layout changes are moving in the right direction?
+        - Which Arm CPU families is this relevant to?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - How do I know if my current data layout is blocking vectorization?
+        - Which files do I edit and in what order?
+        - When should I switch to hand-written intrinsics, and which ones are used?
+        - Does this Learning Path cover both Neon and SVE intrinsics?
+        removed_questions:
+        - What hardware and software do I need before starting?
+        - Which SIMD technologies are used in the examples?
+        - What code artifacts will I create or modify?
+        - How will I know the data layout changes are moving in the right direction?
+        - Which Arm CPU families is this relevant to?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -201029,51 +3721,78 @@ history:
       source_hash_before: bf887ef2481b51cf6c32e49e3eb1d5577346b7b9b1e4d6a604c559597b5306f0
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to sample and profile CPU instructions using WindowsPerf with Arm Statistical
-        Profiling Extension (SPE) on Windows on Arm, demonstrated with CPython workload analysis. It is
-        designed for dev...
-      preview_after: Learn how to sample and profile CPU instructions using WindowsPerf with Arm Statistical
-        Profiling Extension (SPE) on Windows on Arm, demonstrated with CPython workload analysis. It is
-        designed for dev...
-      preview_generated: Learn how to sample and profile CPU instructions using WindowsPerf with Arm Statistical
-        Profiling Extension (SPE) on Windows on Arm, demonstrated with CPython workload analysis. It is
-        designed for dev...
+      generated_at_before: '2026-06-01T21:22:34Z'
+      generated_at_after: '2026-06-01T21:22:34Z'
+      preview_before: This introductory path shows how to sample and profile CPU instructions
+        on Windows on Arm using WindowsPerf with the Arm Statistical Profiling Extension
+        (SPE), demonstrated on a CPython workload. You ...
+      preview_after: This introductory path shows how to sample and profile CPU instructions
+        on Windows on Arm using WindowsPerf with the Arm Statistical Profiling Extension
+        (SPE), demonstrated on a CPython workload. You ...
+      preview_generated: This Learning Path shows how to sample and profile CPU instructions
+        on Windows on Arm using WindowsPerf with the Arm Statistical Profiling Extension
+        (SPE), demonstrated with a CPython workload. You wi...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:22:34Z'
+      generated_at_after: '2026-06-02T21:55:45Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - How do I check if my Arm CPU supports SPE?
+        - Which WindowsPerf build should I use for SPE?
+        - What workload is used to exercise CPython during sampling?
+        - "In the wperf record example, what does the \u201C--\u201D mean and what\
+          \ data is captured?"
+        removed_questions:
+        - What hardware and software are required before starting?
+        - How do I get a WindowsPerf build with SPE support?
+        - Do I need to build CPython from source, and which binary is used?
+        - Which WindowsPerf commands and options will I use during the exercises?
+        - How do I check whether my Arm CPU supports SPE?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - How do I check if my Arm CPU supports SPE?
+        - Which WindowsPerf build should I use for SPE?
+        - What workload is used to exercise CPython during sampling?
+        - "In the wperf record example, what does the \u201C--\u201D mean and what\
+          \ data is captured?"
+        removed_questions:
+        - What hardware and software are required before starting?
+        - How do I get a WindowsPerf build with SPE support?
+        - Do I need to build CPython from source, and which binary is used?
+        - Which WindowsPerf commands and options will I use during the exercises?
+        - How do I check whether my Arm CPU supports SPE?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/woa_azure/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -201083,51 +3802,76 @@ history:
       source_hash_before: 367566dfec0a76685b1504c2c1a996e50c55e67b2f4c5515057c0f0f1384ef98
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to create and connect to a Windows on Arm virtual machine in Microsoft
-        Azure using the Azure Marketplace and RDP. It is designed for software developers interested using
-        Windows on Arm in th...
-      preview_after: Learn how to create and connect to a Windows on Arm virtual machine in Microsoft
-        Azure using the Azure Marketplace and RDP. It is designed for software developers interested using
-        Windows on Arm in th...
-      preview_generated: Learn how to create and connect to a Windows on Arm virtual machine in Microsoft
-        Azure using the Azure Marketplace and RDP. It is designed for software developers interested using
-        Windows on Arm in th...
+      generated_at_before: '2026-06-01T21:23:14Z'
+      generated_at_after: '2026-06-01T21:23:14Z'
+      preview_before: Learn how to deploy a Windows on Arm virtual machine in Microsoft
+        Azure and connect to it using Remote Desktop. This introductory path guides
+        you through signing in to Azure, using the Azure Marketpla...
+      preview_after: Learn how to deploy a Windows on Arm virtual machine in Microsoft
+        Azure and connect to it using Remote Desktop. This introductory path guides
+        you through signing in to Azure, using the Azure Marketpla...
+      preview_generated: This introductory Learning Path shows how to deploy a Windows
+        on Arm virtual machine on Microsoft Azure using the Azure Marketplace, then
+        connect to it with a Remote Desktop Protocol (RDP) client. You...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:23:14Z'
+      generated_at_after: '2026-06-02T21:56:12Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Where do I start creating the Windows on Arm VM in Azure?
+        - How do I discover Arm-based image offerings in Azure?
+        - How do I connect to the VM after it is created?
+        - Can I use the same instructions to deploy a Linux image on Arm?
+        removed_questions:
+        - What are the prerequisites to follow this path?
+        - Can I use a personal Azure subscription, or do I need an organization account?
+        - How do I find Windows on Arm and other Arm-based images in Azure?
+        - Can I use these steps to deploy a Linux Arm VM instead of Windows?
+        - How do I know the VM deployment worked, and how long should it take?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Where do I start creating the Windows on Arm VM in Azure?
+        - How do I discover Arm-based image offerings in Azure?
+        - How do I connect to the VM after it is created?
+        - Can I use the same instructions to deploy a Linux image on Arm?
+        removed_questions:
+        - What are the prerequisites to follow this path?
+        - Can I use a personal Azure subscription, or do I need an organization account?
+        - How do I find Windows on Arm and other Arm-based images in Azure?
+        - Can I use these steps to deploy a Linux Arm VM instead of Windows?
+        - How do I know the VM deployment worked, and how long should it take?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -201137,51 +3881,76 @@ history:
       source_hash_before: a56dce27c6a846bcf35b6e9505142f1445b22ff71530f1189700049d7b14e994
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to build and deploy distributed Zenoh systems on Arm devices like Raspberry
-        Pi, using pub/sub, storage, and queryable models for scalable robotics and IoT applications. It
-        is designed for ro...
-      preview_after: Learn how to build and deploy distributed Zenoh systems on Arm devices like Raspberry
-        Pi, using pub/sub, storage, and queryable models for scalable robotics and IoT applications. It
-        is designed for ro...
-      preview_generated: Learn how to build and deploy distributed Zenoh systems on Arm devices like Raspberry
-        Pi, using pub/sub, storage, and queryable models for scalable robotics and IoT applications. It
-        is designed for ro...
+      generated_at_before: '2026-06-01T21:23:51Z'
+      generated_at_after: '2026-06-01T21:23:51Z'
+      preview_before: Learn to build and deploy a distributed Eclipse Zenoh system
+        on Arm Linux devices, including Raspberry Pi 4/5 and Arm servers or cloud
+        instances. You will install the Rust-based Zenoh stack, build cor...
+      preview_after: Learn to build and deploy a distributed Eclipse Zenoh system
+        on Arm Linux devices, including Raspberry Pi 4/5 and Arm servers or cloud
+        instances. You will install the Rust-based Zenoh stack, build cor...
+      preview_generated: Learn to build and deploy a multi-node Eclipse Zenoh system
+        on Arm-based Linux devices, including Raspberry Pi 4 or 5. You will install
+        the Rust toolchain, build Zenoh and its example binaries, and th...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:23:51Z'
+      generated_at_after: '2026-06-02T21:56:40Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Do I have to use Docker to deploy across multiple devices?
+        - How do I know the Zenoh build on Raspberry Pi completed correctly?
+        - What network setup and topics are used in the pub/sub example?
+        - How do I validate the storage and query example is working?
+        removed_questions:
+        - What hardware and OS do I need to follow this path?
+        - Is Docker required to deploy across multiple devices?
+        - Do I need ROS 2 to complete the examples?
+        - How do I know the pub/sub example is working?
+        - How can I validate the storage and query example?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Do I have to use Docker to deploy across multiple devices?
+        - How do I know the Zenoh build on Raspberry Pi completed correctly?
+        - What network setup and topics are used in the pub/sub example?
+        - How do I validate the storage and query example is working?
+        removed_questions:
+        - What hardware and OS do I need to follow this path?
+        - Is Docker required to deploy across multiple devices?
+        - Do I need ROS 2 to complete the examples?
+        - How do I know the pub/sub example is working?
+        - How can I validate the storage and query example?
+        updated_questions: []
+    category: cross-platform
   - path: content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
       missing_before: false
@@ -201191,51 +3960,78 @@ history:
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-      preview_before: Learn how to design and integrate a custom AXI-Lite peripheral with a Cortex-A9
-        processor on the Zybo Z7-10 board using Vivado, configuring GPIOs to control LEDs based on switch
-        inputs. It is designed...
-      preview_after: Learn how to design and integrate a custom AXI-Lite peripheral with a Cortex-A9 processor
-        on the Zybo Z7-10 board using Vivado, configuring GPIOs to control LEDs based on switch inputs.
-        It is designed...
-      preview_generated: Learn how to design and integrate a custom AXI-Lite peripheral with a Cortex-A9
-        processor on the Zybo Z7-10 board using Vivado, configuring GPIOs to control LEDs based on switch
-        inputs. It is designed...
+      generated_at_before: '2026-06-01T21:24:18Z'
+      generated_at_after: '2026-06-01T21:24:18Z'
+      preview_before: This Learning Path guides you through designing and integrating
+        a custom AXI-Lite peripheral with the Cortex-A9 Processing System on a Zybo
+        Z7-10 board using Xilinx Vivado, then generating a bitstream...
+      preview_after: This Learning Path guides you through designing and integrating
+        a custom AXI-Lite peripheral with the Cortex-A9 Processing System on a Zybo
+        Z7-10 board using Xilinx Vivado, then generating a bitstream...
+      preview_generated: Build a simple bare-metal system on the Zybo Z7-10 that uses
+        a custom AXI-Lite peripheral with a Cortex-A9 to read switch inputs and drive
+        LEDs. You will set up a Vivado workspace on Windows, create a...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
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       change_details:
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-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before starting this Learning Path?
+        - What project setup should I use in Vivado?
+        - Which option should I use to create the custom AXI-Lite peripheral?
+        - How do I expose LEDs and switches from the custom peripheral?
+        - What steps complete the design and what should I expect when running the
+          application?
+        removed_questions:
+        - What platform and tools does this path use?
+        - What prerequisites do I need before starting?
+        - What will I build and what artifacts should I expect to produce?
+        - How do I know the design works after I program the board?
+        - Are there any important setup details to avoid common issues?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before starting this Learning Path?
+        - What project setup should I use in Vivado?
+        - Which option should I use to create the custom AXI-Lite peripheral?
+        - How do I expose LEDs and switches from the custom peripheral?
+        - What steps complete the design and what should I expect when running the
+          application?
+        removed_questions:
+        - What platform and tools does this path use?
+        - What prerequisites do I need before starting?
+        - What will I build and what artifacts should I expect to produce?
+        - How do I know the design works after I program the board?
+        - Are there any important setup details to avoid common issues?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
-    status: unchanged
-    changed_on_disk: false
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-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
       missing_before: false
@@ -201245,51 +4041,77 @@ history:
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit and
-        run inference on the Ethos-U85 NPU. It is designed for embedded developers who want to deploy
-        a neural network model t...
-      preview_after: Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit and
-        run inference on the Ethos-U85 NPU. It is designed for embedded developers who want to deploy
-        a neural network model t...
-      preview_generated: Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit
-        and run inference on the Ethos-U85 NPU. It is designed for embedded developers who want to deploy
-        a neural network model t...
+      generated_at_before: '2026-06-01T21:24:49Z'
+      generated_at_after: '2026-06-01T21:24:49Z'
+      preview_before: "This advanced Learning Path guides you through deploying a\
+        \ MobileNetV2 image classification model to the Alif Ensemble E8 DevKit and\
+        \ running inference on the Ethos\u2011U85 NPU from the Cortex\u2011M55 High\u2011\
+        Per..."
+      preview_after: "This advanced Learning Path guides you through deploying a MobileNetV2\
+        \ image classification model to the Alif Ensemble E8 DevKit and running inference\
+        \ on the Ethos\u2011U85 NPU from the Cortex\u2011M55 High\u2011Per..."
+      preview_generated: This advanced path guides you through deploying a MobileNetV2
+        image classification model to an Alif Ensemble E8 DevKit, using the Cortex-M55
+        High-Performance core to orchestrate inference on the Ethos...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
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-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:24:49Z'
+      generated_at_after: '2026-06-02T21:57:47Z'
       before_count: 5
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       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Why should I build on an Arm-based cloud instance instead of my local host?
+        - When creating the firmware project, which components must be included?
+        - How should I configure memory and linker settings for this workload?
+        - What result should I expect after flashing, and how do I verify it?
+        removed_questions:
+        - Do I need an Arm-based cloud instance for model compilation?
+        - What prerequisites and tools are assumed before starting?
+        - What do I build on the firmware side?
+        - What memory and linker changes are required for this workload?
+        - How do I validate that everything worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Why should I build on an Arm-based cloud instance instead of my local host?
+        - When creating the firmware project, which components must be included?
+        - How should I configure memory and linker settings for this workload?
+        - What result should I expect after flashing, and how do I verify it?
+        removed_questions:
+        - Do I need an Arm-based cloud instance for model compilation?
+        - What prerequisites and tools are assumed before starting?
+        - What do I build on the firmware side?
+        - What memory and linker changes are required for this workload?
+        - How do I validate that everything worked?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -201299,51 +4121,76 @@ history:
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-      preview_before: Learn how to build a motion-detection device with Raspberry Pi Pico (RP2040 Cortex-M0+)
-        using Arduino IDE, PIR sensors, and interrupt-driven programming on baremetal. It is designed
-        for software devel...
-      preview_after: Learn how to build a motion-detection device with Raspberry Pi Pico (RP2040 Cortex-M0+)
-        using Arduino IDE, PIR sensors, and interrupt-driven programming on baremetal. It is designed
-        for software devel...
-      preview_generated: Learn how to build a motion-detection device with Raspberry Pi Pico (RP2040 Cortex-M0+)
-        using Arduino IDE, PIR sensors, and interrupt-driven programming on baremetal. It is designed
-        for software devel...
+      generated_at_before: '2026-06-01T21:25:21Z'
+      generated_at_after: '2026-06-01T21:25:21Z'
+      preview_before: "Build a motion-detection device on a Raspberry Pi Pico (RP2040\
+        \ Cortex\u2011M0+) using the Arduino IDE on baremetal. This introductory Learning\
+        \ Path explains the differences between application and embedded..."
+      preview_after: "Build a motion-detection device on a Raspberry Pi Pico (RP2040\
+        \ Cortex\u2011M0+) using the Arduino IDE on baremetal. This introductory Learning\
+        \ Path explains the differences between application and embedded..."
+      preview_generated: Build a motion-detection device on a Raspberry Pi Pico (RP2040,
+        Arm Cortex-M0+) using the Arduino IDE. You will compare application and embedded
+        stacks, then write and run a bare-metal embedded applic...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
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-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:25:21Z'
+      generated_at_after: '2026-06-02T21:58:43Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I know the Arduino IDE is ready for RP2040 development?
+        - Is an RTOS used, or is this bare-metal Arduino on RP2040?
+        - What result should I expect when I run the program on the Pico?
+        - "What should I check if the buzzer doesn\u2019t sound when motion is detected?"
+        removed_questions:
+        - What hardware and software do I need before starting?
+        - Does this project use an RTOS or run baremetal?
+        - Which Arduino board support package should I install?
+        - Why use a Raspberry Pi Pico for an Arduino-based workflow?
+        - What will I build and how do I know it worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I know the Arduino IDE is ready for RP2040 development?
+        - Is an RTOS used, or is this bare-metal Arduino on RP2040?
+        - What result should I expect when I run the program on the Pico?
+        - "What should I check if the buzzer doesn\u2019t sound when motion is detected?"
+        removed_questions:
+        - What hardware and software do I need before starting?
+        - Does this project use an RTOS or run baremetal?
+        - Which Arduino board support package should I install?
+        - Why use a Raspberry Pi Pico for an Arduino-based workflow?
+        - What will I build and how do I know it worked?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/armds/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -201353,51 +4200,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to import and build example projects in Arm Development Studio and debug
-        embedded applications using Fixed Virtual Platforms (FVPs) or hardware with DSTREAM debug probes.
-        It is designed for ...
-      preview_after: Learn how to import and build example projects in Arm Development Studio and debug
-        embedded applications using Fixed Virtual Platforms (FVPs) or hardware with DSTREAM debug probes.
-        It is designed for ...
-      preview_generated: Learn how to import and build example projects in Arm Development Studio and
-        debug embedded applications using Fixed Virtual Platforms (FVPs) or hardware with DSTREAM debug
-        probes. It is designed for ...
+      generated_at_before: '2026-06-01T21:25:49Z'
+      generated_at_after: '2026-06-01T21:25:49Z'
+      preview_before: Learn how to get productive with Arm Development Studio by importing
+        and building an example bare-metal project, then debugging it on a Fixed Virtual
+        Platform (FVP) or on hardware using a DSTREAM debu...
+      preview_after: Learn how to get productive with Arm Development Studio by importing
+        and building an example bare-metal project, then debugging it on a Fixed Virtual
+        Platform (FVP) or on hardware using a DSTREAM debu...
+      preview_generated: Learn to import and build an example project in Arm Development
+        Studio and debug it on a Fixed Virtual Platform (FVP) or on a board using
+        a DSTREAM probe. You will launch the IDE, initialize a workspa...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
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-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:25:49Z'
+      generated_at_after: '2026-06-02T22:00:01Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I launch the IDE and set up the workspace?
+        - Can I run the example without hardware, and which FVP does it target?
+        - Where is the FVP debug configuration and how do I use it?
+        - How do I select a different Arm Compiler for Embedded version for my project?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - Can I complete the steps without any target hardware?
+        - What target and debug configuration does the example use?
+        - How do I change the Arm Compiler for Embedded version used by the project?
+        - How long does this Learning Path take to complete?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I launch the IDE and set up the workspace?
+        - Can I run the example without hardware, and which FVP does it target?
+        - Where is the FVP debug configuration and how do I use it?
+        - How do I select a different Arm Compiler for Embedded version for my project?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - Can I complete the steps without any target hardware?
+        - What target and debug configuration does the example use?
+        - How do I change the Arm Compiler for Embedded version used by the project?
+        - How long does this Learning Path take to complete?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/asm/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -201407,51 +4279,79 @@ history:
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to write mixed C and assembly programs for Cortex-M microcontrollers using
-        Keil MDK, following Arm Procedure Call Standard conventions. It is designed for software developers
-        who are interes...
-      preview_after: Learn how to write mixed C and assembly programs for Cortex-M microcontrollers using
-        Keil MDK, following Arm Procedure Call Standard conventions. It is designed for software developers
-        who are interes...
-      preview_generated: Learn how to write mixed C and assembly programs for Cortex-M microcontrollers
-        using Keil MDK, following Arm Procedure Call Standard conventions. It is designed for software
-        developers who are interes...
+      generated_at_before: '2026-06-01T21:26:11Z'
+      generated_at_after: '2026-06-01T21:26:11Z'
+      preview_before: Learn to write mixed C and assembly for Cortex-M microcontrollers
+        using Keil MDK, following the Arm Procedure Call Standard. You will set up
+        a bare-metal Cortex-M4 project either in Keil Studio (VS Co...
+      preview_after: Learn to write mixed C and assembly for Cortex-M microcontrollers
+        using Keil MDK, following the Arm Procedure Call Standard. You will set up
+        a bare-metal Cortex-M4 project either in Keil Studio (VS Co...
+      preview_generated: "This Learning Path guides you through writing mixed C and\
+        \ Arm assembler for Cortex-M microcontrollers using Keil MDK on a bare\u2011\
+        metal setup. You will create a Cortex\u2011M4 project either in Keil Studio\
+        \ (V..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:26:11Z'
+      generated_at_after: '2026-06-02T22:01:17Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - Which Keil environment should I use, and what setup steps differ?
+        - How do I run the example on a Cortex-M4 Fixed Virtual Platform instead of
+          hardware?
+        - How do I add the main C file in each environment?
+        - What assembly functions do I implement and how are they called?
+        - What calling convention should the assembly subroutines follow?
+        removed_questions:
+        - Do I need a physical microcontroller board to follow this path?
+        - "Which IDE should I use: Keil Studio (VS Code) or \u03BCVision?"
+        - "What project components and debugger settings are required in \u03BCVision?"
+        - What code will I implement and how do I validate it?
+        - What prerequisites and time commitment are expected?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - Which Keil environment should I use, and what setup steps differ?
+        - How do I run the example on a Cortex-M4 Fixed Virtual Platform instead of
+          hardware?
+        - How do I add the main C file in each environment?
+        - What assembly functions do I implement and how are they called?
+        - What calling convention should the assembly subroutines follow?
+        removed_questions:
+        - Do I need a physical microcontroller board to follow this path?
+        - "Which IDE should I use: Keil Studio (VS Code) or \u03BCVision?"
+        - "What project components and debugger settings are required in \u03BCVision?"
+        - What code will I implement and how do I validate it?
+        - What prerequisites and time commitment are expected?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -201461,51 +4361,78 @@ history:
       source_hash_before: 983afd3fcc565266c8f12588086e36d736540e23d0e748c94b5d2a45ab53d6ab
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to create a custom Balena OS image, run it on Arm Virtual Hardware, and
-        deploy IoT applications to a virtual Raspberry Pi 4 device. It is designed for embedded software
-        developers interested...
-      preview_after: Learn how to create a custom Balena OS image, run it on Arm Virtual Hardware, and
-        deploy IoT applications to a virtual Raspberry Pi 4 device. It is designed for embedded software
-        developers interested...
-      preview_generated: Learn how to create a custom Balena OS image, run it on Arm Virtual Hardware,
-        and deploy IoT applications to a virtual Raspberry Pi 4 device. It is designed for embedded software
-        developers interested...
+      generated_at_before: '2026-06-01T21:26:34Z'
+      generated_at_after: '2026-06-01T21:26:34Z'
+      preview_before: This introductory Learning Path shows how to prepare a custom
+        Balena OS image, run it on Arm Virtual Hardware as a virtual Raspberry Pi
+        4, and deploy a pre-built IoT application from Balena Hub. Worki...
+      preview_after: This introductory Learning Path shows how to prepare a custom
+        Balena OS image, run it on Arm Virtual Hardware as a virtual Raspberry Pi
+        4, and deploy a pre-built IoT application from Balena Hub. Worki...
+      preview_generated: This Learning Path walks you through creating a custom Balena
+        OS image, running it on Arm Virtual Hardware as a virtual Raspberry Pi 4,
+        and deploying a pre-built IoT application from Balena Hub. You w...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 983afd3fcc565266c8f12588086e36d736540e23d0e748c94b5d2a45ab53d6ab
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:26:34Z'
+      generated_at_after: '2026-06-02T22:02:28Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before I start?
+        - When and why do I create a fleet in Balena Cloud?
+        - In Arm Virtual Hardware, which device should I select and how do I provide
+          the OS image?
+        - How do I open Balena Hub and which example application should I deploy?
+        - Can I follow this path without using the hosted Balena Cloud service?
+        removed_questions:
+        - What prerequisites do I need before starting?
+        - Which device and platform does this path target?
+        - How do I provide the Balena OS image to Arm Virtual Hardware?
+        - What application is deployed, and how can I tell it worked?
+        - Do I need a paid plan to complete this Learning Path?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before I start?
+        - When and why do I create a fleet in Balena Cloud?
+        - In Arm Virtual Hardware, which device should I select and how do I provide
+          the OS image?
+        - How do I open Balena Hub and which example application should I deploy?
+        - Can I follow this path without using the hosted Balena Cloud service?
+        removed_questions:
+        - What prerequisites do I need before starting?
+        - Which device and platform does this path target?
+        - How do I provide the Balena OS image to Arm Virtual Hardware?
+        - What application is deployed, and how can I tell it worked?
+        - Do I need a paid plan to complete this Learning Path?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -201515,51 +4442,76 @@ history:
       source_hash_before: 85845fd4a47960bca053aed8d87c1d1442a7f5de0be5caff349fc92c6980b07e
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to set up AWS IoT Greengrass Core on Arm Virtual Hardware and deploy AWS
-        Greengrass components to a virtual Raspberry Pi 4 device. It is designed for embedded software
-        developers interested ...
-      preview_after: Learn how to set up AWS IoT Greengrass Core on Arm Virtual Hardware and deploy AWS
-        Greengrass components to a virtual Raspberry Pi 4 device. It is designed for embedded software
-        developers interested ...
-      preview_generated: Learn how to set up AWS IoT Greengrass Core on Arm Virtual Hardware and deploy
-        AWS Greengrass components to a virtual Raspberry Pi 4 device. It is designed for embedded software
-        developers interested ...
+      generated_at_before: '2026-06-01T21:27:06Z'
+      generated_at_after: '2026-06-01T21:27:06Z'
+      preview_before: This introductory Learning Path guides embedded Linux developers
+        through running a virtual Raspberry Pi 4 on Arm Virtual Hardware and deploying
+        AWS IoT Greengrass components to it. You will create or ...
+      preview_after: This introductory Learning Path guides embedded Linux developers
+        through running a virtual Raspberry Pi 4 on Arm Virtual Hardware and deploying
+        AWS IoT Greengrass components to it. You will create or ...
+      preview_generated: This Learning Path shows how to set up AWS IoT Greengrass
+        Core on Arm Virtual Hardware and deploy pre-built Greengrass components to
+        a virtual Raspberry Pi 4 device. You will start a Raspberry Pi Arm ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 85845fd4a47960bca053aed8d87c1d1442a7f5de0be5caff349fc92c6980b07e
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:27:06Z'
+      generated_at_after: '2026-06-02T22:03:50Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Will I be charged by AWS or Arm Virtual Hardware during this tutorial?
+        - Which virtual device does this Learning Path use?
+        - Where do I create the AWS IoT Greengrass deployment?
+        - How do I change what runs on the device after deployment?
+        removed_questions:
+        - Do I need a physical Raspberry Pi for this Learning Path?
+        - What accounts and prerequisites are required?
+        - Are there any costs to complete this Learning Path?
+        - How do I create and start a Greengrass deployment?
+        - Which platform and architecture does this target?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Will I be charged by AWS or Arm Virtual Hardware during this tutorial?
+        - Which virtual device does this Learning Path use?
+        - Where do I create the AWS IoT Greengrass deployment?
+        - How do I change what runs on the device after deployment?
+        removed_questions:
+        - Do I need a physical Raspberry Pi for this Learning Path?
+        - What accounts and prerequisites are required?
+        - Are there any costs to complete this Learning Path?
+        - How do I create and start a Greengrass deployment?
+        - Which platform and architecture does this target?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -201569,51 +4521,78 @@ history:
       source_hash_before: bd39d50c93219201ead5de5da627447a91a82ea9c65e232350facd0c3516bedc
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to build Matter reference examples on Arm Virtual Hardware, demonstrate
-        device communication, and automate testing with GitHub Actions CI/CD workflows. It is designed
-        for embedded software d...
-      preview_after: Learn how to build Matter reference examples on Arm Virtual Hardware, demonstrate
-        device communication, and automate testing with GitHub Actions CI/CD workflows. It is designed
-        for embedded software d...
-      preview_generated: Learn how to build Matter reference examples on Arm Virtual Hardware, demonstrate
-        device communication, and automate testing with GitHub Actions CI/CD workflows. It is designed
-        for embedded software d...
+      generated_at_before: '2026-06-01T21:27:33Z'
+      generated_at_after: '2026-06-01T21:27:33Z'
+      preview_before: This introductory Learning Path guides embedded developers through
+        building and running Matter reference examples on Arm Virtual Hardware, demonstrating
+        communication between two Raspberry Pi 4 virtua...
+      preview_after: This introductory Learning Path guides embedded developers through
+        building and running Matter reference examples on Arm Virtual Hardware, demonstrating
+        communication between two Raspberry Pi 4 virtua...
+      preview_generated: This introductory Learning Path shows how to build and run
+        Matter reference examples on Arm Virtual Hardware using Linux. You will instantiate
+        Raspberry Pi 4 AVH instances, build the Matter lighting a...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: bd39d50c93219201ead5de5da627447a91a82ea9c65e232350facd0c3516bedc
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:27:33Z'
+      generated_at_after: '2026-06-02T22:04:34Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Arm Virtual Hardware targets should I create, and how many?
+        - How do I get the Matter sources into my AVH instances?
+        - What should I do before configuring GitHub Actions in the repository?
+        - How do I enable API-based control of AVH in the workflow, and what result
+          should I expect?
+        removed_questions:
+        - What accounts and permissions do I need before starting?
+        - Which targets and operating system are used in the steps?
+        - Why do I need to fork the Matter repository?
+        - How is CI/CD configured with GitHub Actions in this path?
+        - How is the Arm Virtual Hardware API integrated into the workflow?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Arm Virtual Hardware targets should I create, and how many?
+        - How do I get the Matter sources into my AVH instances?
+        - What should I do before configuring GitHub Actions in the repository?
+        - How do I enable API-based control of AVH in the workflow, and what result
+          should I expect?
+        removed_questions:
+        - What accounts and permissions do I need before starting?
+        - Which targets and operating system are used in the steps?
+        - Why do I need to fork the Matter repository?
+        - How is CI/CD configured with GitHub Actions in this path?
+        - How is the Arm Virtual Hardware API integrated into the workflow?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -201623,51 +4602,78 @@ history:
       source_hash_before: 398077be1406d0787b0a5d8dc254472da0d125b51b0907769cd4a3516ce9111b
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to export and compile a PaddleOCR text recognition model using TVMC and
-        deploy it on the Arm Corstone-300 FVP with Cortex-M55 processors. It is designed for software
-        developers interested in...
-      preview_after: Learn how to export and compile a PaddleOCR text recognition model using TVMC and
-        deploy it on the Arm Corstone-300 FVP with Cortex-M55 processors. It is designed for software
-        developers interested in...
-      preview_generated: Learn how to export and compile a PaddleOCR text recognition model using TVMC
-        and deploy it on the Arm Corstone-300 FVP with Cortex-M55 processors. It is designed for software
-        developers interested in...
+      generated_at_before: '2026-06-01T21:28:14Z'
+      generated_at_after: '2026-06-01T21:28:14Z'
+      preview_before: This introductory Learning Path shows how to export a PaddlePaddle
+        inference model for text recognition, compile it with TVMC, and deploy it
+        on the Arm Corstone-300 Fixed Virtual Platform (FVP) with C...
+      preview_after: This introductory Learning Path shows how to export a PaddlePaddle
+        inference model for text recognition, compile it with TVMC, and deploy it
+        on the Arm Corstone-300 Fixed Virtual Platform (FVP) with C...
+      preview_generated: This Learning Path walks you through exporting a PaddlePaddle
+        inference model for text recognition (PaddleOCR), compiling it with TVMC,
+        and deploying it to the Arm Corstone-300 Fixed Virtual Platform ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 398077be1406d0787b0a5d8dc254472da0d125b51b0907769cd4a3516ce9111b
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       current_source_hash: 398077be1406d0787b0a5d8dc254472da0d125b51b0907769cd4a3516ce9111b
-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:28:14Z'
+      generated_at_after: '2026-06-02T22:05:16Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the workflow?
+        - Do I need to train a model, or does this use a pre-trained PaddlePaddle
+          model?
+        - Which Arm platform and runtime does this target?
+        - How do I start the environment on AWS?
+        - What result should I expect after completing the steps?
+        removed_questions:
+        - What platform and operating system does this Learning Path target?
+        - What prerequisites do I need before starting?
+        - Which model and tools are used in the workflow?
+        - How do I know the deployment worked?
+        - Does the path include background on OCR or just deployment steps?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the workflow?
+        - Do I need to train a model, or does this use a pre-trained PaddlePaddle
+          model?
+        - Which Arm platform and runtime does this target?
+        - How do I start the environment on AWS?
+        - What result should I expect after completing the steps?
+        removed_questions:
+        - What platform and operating system does this Learning Path target?
+        - What prerequisites do I need before starting?
+        - Which model and tools are used in the workflow?
+        - How do I know the deployment worked?
+        - Does the path include background on OCR or just deployment steps?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -201677,51 +4683,76 @@ history:
       source_hash_before: aaff31b8917320c53825f3e7410b703bc7c7e273b1963fd80727b6b874f50566
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to create and integrate a virtual LED peripheral using the Virtual IO
-        interface of Arm Virtual Hardware to simulate real-world peripherals. It is designed for software
-        developers new to Arm ...
-      preview_after: Learn how to create and integrate a virtual LED peripheral using the Virtual IO interface
-        of Arm Virtual Hardware to simulate real-world peripherals. It is designed for software developers
-        new to Arm ...
-      preview_generated: Learn how to create and integrate a virtual LED peripheral using the Virtual
-        IO interface of Arm Virtual Hardware to simulate real-world peripherals. It is designed for software
-        developers new to Arm ...
+      generated_at_before: '2026-06-01T21:28:41Z'
+      generated_at_after: '2026-06-01T21:28:41Z'
+      preview_before: This introductory Learning Path guides you to create and integrate
+        a virtual LED peripheral using the Virtual IO (VIO) interface in Arm Virtual
+        Hardware (AVH) to simulate real-world peripherals. You w...
+      preview_after: This introductory Learning Path guides you to create and integrate
+        a virtual LED peripheral using the Virtual IO (VIO) interface in Arm Virtual
+        Hardware (AVH) to simulate real-world peripherals. You w...
+      preview_generated: This introductory Learning Path shows how to create and integrate
+        a virtual LED peripheral using the Virtual IO (VIO) interface of Arm Virtual
+        Hardware (AVH). You will work in a bare-metal environment...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
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       drift_detected: false
       source_hash_before: aaff31b8917320c53825f3e7410b703bc7c7e273b1963fd80727b6b874f50566
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-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:28:41Z'
+      generated_at_after: '2026-06-02T22:06:31Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the example?
+        - How do I launch the environment used in this Learning Path?
+        - How do I install the Tkinter dependency in the AVH instance?
+        - How do I obtain the example project files?
+        - Do I need physical hardware to test the LED peripheral?
+        removed_questions:
+        - How do I set up the environment to follow this Learning Path?
+        - What additional software needs to be installed in the AVH environment?
+        - Where do I get the example code and how do I start?
+        - What exactly will I implement, and what targets is it relevant to?
+        - What prerequisites and skill level are expected, and how long will it take?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the example?
+        - How do I launch the environment used in this Learning Path?
+        - How do I install the Tkinter dependency in the AVH instance?
+        - How do I obtain the example project files?
+        - Do I need physical hardware to test the LED peripheral?
+        removed_questions:
+        - How do I set up the environment to follow this Learning Path?
+        - What additional software needs to be installed in the AVH environment?
+        - Where do I get the example code and how do I start?
+        - What exactly will I implement, and what targets is it relevant to?
+        - What prerequisites and skill level are expected, and how long will it take?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -201731,51 +4762,78 @@ history:
       source_hash_before: 0e653bdbf5e5b08a6a00ba68e5ed215a0082e22c634d7d632d088851d48bb01c
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to build a complete IoT solution in Azure that streams, stores, monitors,
-        aggregates, and visualizes telemetry data from Arm devices using IoT Hub, Stream Analytics, Cosmos
-        DB, and Azure Fun...
-      preview_after: Learn how to build a complete IoT solution in Azure that streams, stores, monitors,
-        aggregates, and visualizes telemetry data from Arm devices using IoT Hub, Stream Analytics, Cosmos
-        DB, and Azure Fun...
-      preview_generated: Learn how to build a complete IoT solution in Azure that streams, stores, monitors,
-        aggregates, and visualizes telemetry data from Arm devices using IoT Hub, Stream Analytics, Cosmos
-        DB, and Azure Fun...
+      generated_at_before: '2026-06-01T21:29:13Z'
+      generated_at_after: '2026-06-01T21:29:13Z'
+      preview_before: This advanced Learning Path guides you through building an end-to-end
+        IoT solution in Azure for Arm devices using Python and Visual Studio Code.
+        You will set up Azure IoT Hub, register a device, and s...
+      preview_after: This advanced Learning Path guides you through building an end-to-end
+        IoT solution in Azure for Arm devices using Python and Visual Studio Code.
+        You will set up Azure IoT Hub, register a device, and s...
+      preview_generated: Build a complete Azure-based IoT pipeline for Arm devices
+        by connecting simulated sensor data to cloud services and producing usable
+        outputs. You will provision Azure IoT Hub, implement a Python-based...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 0e653bdbf5e5b08a6a00ba68e5ed215a0082e22c634d7d632d088851d48bb01c
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:29:13Z'
+      generated_at_after: '2026-06-02T22:07:49Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Do I need physical Arm hardware, or can I simulate device telemetry?
+        - Which Azure services will I create and how are they used in the workflow?
+        - How do I know my simulator is successfully sending data to Azure IoT Hub?
+        - What outcome should I expect after configuring Stream Analytics and Cosmos
+          DB?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - Do I need a physical Arm device, or can I simulate telemetry?
+        - Which Azure services will I configure and what gets created?
+        - How do I verify that data is flowing end-to-end?
+        - How long will this take and what skill level is expected?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Do I need physical Arm hardware, or can I simulate device telemetry?
+        - Which Azure services will I create and how are they used in the workflow?
+        - How do I know my simulator is successfully sending data to Azure IoT Hub?
+        - What outcome should I expect after configuring Stream Analytics and Cosmos
+          DB?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - Do I need a physical Arm device, or can I simulate telemetry?
+        - Which Azure services will I configure and what gets created?
+        - How do I verify that data is flowing end-to-end?
+        - How long will this take and what skill level is expected?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -201785,51 +4843,77 @@ history:
       source_hash_before: 6521f17d1a10ab8871d13917923f7f64320f69301b4c0c5f5b528163d3cb43c6
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to create, build, and run a bare-metal embedded application for Armv8-A
-        processors using Arm Compiler for Embedded and Fixed Virtual Platforms, including basic exception
-        handling. It is desi...
-      preview_after: Learn how to create, build, and run a bare-metal embedded application for Armv8-A
-        processors using Arm Compiler for Embedded and Fixed Virtual Platforms, including basic exception
-        handling. It is desi...
-      preview_generated: Learn how to create, build, and run a bare-metal embedded application for Armv8-A
-        processors using Arm Compiler for Embedded and Fixed Virtual Platforms, including basic exception
-        handling. It is desi...
+      generated_at_before: '2026-06-01T21:29:37Z'
+      generated_at_after: '2026-06-01T21:29:37Z'
+      preview_before: "Build and run a bare-metal Armv8-A \u201CHello World\u201D\
+        \ on a Fixed Virtual Platform, then extend it with minimal boot code, UART\
+        \ output, and basic exception handling. You will use Arm Development Studio\
+        \ or t..."
+      preview_after: "Build and run a bare-metal Armv8-A \u201CHello World\u201D on\
+        \ a Fixed Virtual Platform, then extend it with minimal boot code, UART output,\
+        \ and basic exception handling. You will use Arm Development Studio or t..."
+      preview_generated: Build and run a bare-metal Armv8-A embedded application using
+        Arm Compiler for Embedded and Arm Fixed Virtual Platforms. You start with
+        a simple project, then add minimal boot code by writing a reset ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 6521f17d1a10ab8871d13917923f7f64320f69301b4c0c5f5b528163d3cb43c6
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       current_source_hash: 6521f17d1a10ab8871d13917923f7f64320f69301b4c0c5f5b528163d3cb43c6
-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:29:37Z'
+      generated_at_after: '2026-06-02T22:08:50Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What tools do I need before starting?
+        - Which Fixed Virtual Platform should I use to run the example?
+        - How do I ensure the application runs on a single core after reset?
+        - How do I know if printf is using semihosting and how do I redirect output?
+        - How are interrupts configured in the event-driven example?
+        removed_questions:
+        - What tools do I need and how do I set them up?
+        - What target platform is used to run the application?
+        - What does the reset handler do in this example?
+        - How is printf output handled, and how do I avoid semihosting?
+        - How are exceptions and interrupts configured in this path?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What tools do I need before starting?
+        - Which Fixed Virtual Platform should I use to run the example?
+        - How do I ensure the application runs on a single core after reset?
+        - How do I know if printf is using semihosting and how do I redirect output?
+        - How are interrupts configured in the event-driven example?
+        removed_questions:
+        - What tools do I need and how do I set them up?
+        - What target platform is used to run the application?
+        - What does the reset handler do in this example?
+        - How is printf output handled, and how do I avoid semihosting?
+        - How are exceptions and interrupts configured in this path?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -201839,51 +4923,78 @@ history:
       source_hash_before: 3394bf994039e441d57dced2abb64d725077d4672e0e68dc71f1de50e58f0408
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to deploy containerized embedded applications and firmware onto an Arm
-        Cortex-M core from a Cortex-A core using containerd, K3s, and the hybrid-runtime on Arm Virtual
-        Hardware. It is designe...
-      preview_after: Learn how to deploy containerized embedded applications and firmware onto an Arm
-        Cortex-M core from a Cortex-A core using containerd, K3s, and the hybrid-runtime on Arm Virtual
-        Hardware. It is designe...
-      preview_generated: Learn how to deploy containerized embedded applications and firmware onto an
-        Arm Cortex-M core from a Cortex-A core using containerd, K3s, and the hybrid-runtime on Arm Virtual
-        Hardware. It is designe...
+      generated_at_before: '2026-06-01T21:29:58Z'
+      generated_at_after: '2026-06-01T21:29:58Z'
+      preview_before: This introductory path shows how to deploy containerized embedded
+        applications and firmware to a Cortex-M core from a Linux-based Cortex-A application
+        core using the OCI-compatible hybrid-runtime with...
+      preview_after: This introductory path shows how to deploy containerized embedded
+        applications and firmware to a Cortex-M core from a Linux-based Cortex-A application
+        core using the OCI-compatible hybrid-runtime with...
+      preview_generated: This path shows how to deploy containerized embedded applications
+        and firmware to a Cortex-M core from a Cortex-A application core using the
+        hybrid-runtime with containerd and K3s on Arm Virtual Hardw...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 3394bf994039e441d57dced2abb64d725077d4672e0e68dc71f1de50e58f0408
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:29:58Z'
+      generated_at_after: '2026-06-02T22:09:54Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which Arm Virtual Hardware device should I create?
+        - Which runtime should I specify when starting a container with containerd?
+        - How do I verify that the container started correctly with containerd?
+        - How should I install and configure K3s for this demo?
+        removed_questions:
+        - What do I need before I start?
+        - Which processors and operating systems does this workflow target?
+        - How do I confirm that the container deployed correctly with containerd?
+        - What K3s configuration is used in this path?
+        - Do I have to build the hybrid-runtime and firmware image, and what tools
+          are involved?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which Arm Virtual Hardware device should I create?
+        - Which runtime should I specify when starting a container with containerd?
+        - How do I verify that the container started correctly with containerd?
+        - How should I install and configure K3s for this demo?
+        removed_questions:
+        - What do I need before I start?
+        - Which processors and operating systems does this workflow target?
+        - How do I confirm that the container deployed correctly with containerd?
+        - What K3s configuration is used in this path?
+        - Do I have to build the hybrid-runtime and firmware image, and what tools
+          are involved?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -201893,51 +5004,82 @@ history:
       source_hash_before: d8c73d658fa7a8051131276b8567cbd7376aac3b8f6e87c8ecdf224443c3ef99
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to create, build, and debug an RTX5 RTOS-based application using Keil
-        μVision with CMSIS-RTOS2 API and Event Recorder for embedded Cortex-M development. It is designed
-        for software developer...
-      preview_after: Learn how to create, build, and debug an RTX5 RTOS-based application using Keil μVision
-        with CMSIS-RTOS2 API and Event Recorder for embedded Cortex-M development. It is designed for
-        software developer...
-      preview_generated: Learn how to create, build, and debug an RTX5 RTOS-based application using Keil
-        μVision with CMSIS-RTOS2 API and Event Recorder for embedded Cortex-M development. It is designed
-        for software developer...
+      generated_at_before: '2026-06-01T21:30:25Z'
+      generated_at_after: '2026-06-01T21:30:25Z'
+      preview_before: "Learn how to create, build, and debug a basic RTX5 RTOS application\
+        \ using Keil \u03BCVision in Keil MDK. You will install or update CMSIS packs,\
+        \ initialize RTX5 via the CMSIS-RTOS2 API (including SysTick s..."
+      preview_after: "Learn how to create, build, and debug a basic RTX5 RTOS application\
+        \ using Keil \u03BCVision in Keil MDK. You will install or update CMSIS packs,\
+        \ initialize RTX5 via the CMSIS-RTOS2 API (including SysTick s..."
+      preview_generated: "This introductory path guides you through creating, building,\
+        \ and debugging a basic RTX5 RTOS application for Arm Cortex-M using Keil\
+        \ \u03BCVision (Keil MDK). You install the latest CMSIS packs, configure ..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: d8c73d658fa7a8051131276b8567cbd7376aac3b8f6e87c8ecdf224443c3ef99
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       current_source_hash: d8c73d658fa7a8051131276b8567cbd7376aac3b8f6e87c8ecdf224443c3ef99
-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:30:25Z'
+      generated_at_after: '2026-06-02T22:10:56Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need installed before running the steps, and which IDE should
+          I use?
+        - How do I install the required CMSIS components for the project?
+        - Which source files do I create, and where do I add them in the project?
+        - How do I build, start the FVP, and observe the RTOS during debug in Keil
+          MDK?
+        - How do I enable Event Recorder for printf output in Keil MDK, and when should
+          I use it?
+        removed_questions:
+        - Which tools should I use to follow this Learning Path?
+        - What are the prerequisites before I start?
+        - What does the example application implement?
+        - How do I build, run, and verify the application in Keil MDK?
+        - How do I get printf output if semihosting is not available?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need installed before running the steps, and which IDE should
+          I use?
+        - How do I install the required CMSIS components for the project?
+        - Which source files do I create, and where do I add them in the project?
+        - How do I build, start the FVP, and observe the RTOS during debug in Keil
+          MDK?
+        - How do I enable Event Recorder for printf output in Keil MDK, and when should
+          I use it?
+        removed_questions:
+        - Which tools should I use to follow this Learning Path?
+        - What are the prerequisites before I start?
+        - What does the example application implement?
+        - How do I build, run, and verify the application in Keil MDK?
+        - How do I get printf output if semihosting is not available?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
       missing_before: false
@@ -201947,51 +5089,78 @@ history:
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to create, configure, and debug an RTX5 RTOS application using Keil Studio
-        for VS Code with CMSIS-RTOS2 API for embedded Cortex-M development. It is designed for software
-        developers new to R...
-      preview_after: Learn how to create, configure, and debug an RTX5 RTOS application using Keil Studio
-        for VS Code with CMSIS-RTOS2 API for embedded Cortex-M development. It is designed for software
-        developers new to R...
-      preview_generated: Learn how to create, configure, and debug an RTX5 RTOS application using Keil
-        Studio for VS Code with CMSIS-RTOS2 API for embedded Cortex-M development. It is designed for
-        software developers new to R...
+      generated_at_before: '2026-06-01T21:30:42Z'
+      generated_at_after: '2026-06-01T21:30:42Z'
+      preview_before: Learn to create, configure, and debug a basic RTX5 RTOS application
+        for Arm Cortex-M using Keil Studio for VS Code and the CMSIS-RTOS2 API. You
+        will set up a new csolution project, configure the Run-T...
+      preview_after: Learn to create, configure, and debug a basic RTX5 RTOS application
+        for Arm Cortex-M using Keil Studio for VS Code and the CMSIS-RTOS2 API. You
+        will set up a new csolution project, configure the Run-T...
+      preview_generated: This Learning Path shows how to create, configure, and debug
+        a basic RTX5 RTOS application for Arm Cortex-M using Keil Studio for VS Code
+        and the CMSIS-RTOS2 API. You will start a csolution project fo...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
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       drift_detected: false
       source_hash_before: f4d1ab3448590c5654e2479937c9eec3e378d05229b29a45b8675139f40ac177
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:30:42Z'
+      generated_at_after: '2026-06-02T22:11:43Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which target does this use, and can I run it on other hardware?
+        - What project should I create and what initial setup is required?
+        - How do I set up the OS and create threads?
+        - How do I build, debug, and verify that it works?
+        removed_questions:
+        - What are the prerequisites to start?
+        - What platform does this use, and do I need hardware?
+        - "Can I follow this with \u03BCVision or Arm Development Studio instead of\
+          \ Keil Studio for VS Code?"
+        - What will I implement in the application?
+        - How do I build, run, and verify the application?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which target does this use, and can I run it on other hardware?
+        - What project should I create and what initial setup is required?
+        - How do I set up the OS and create threads?
+        - How do I build, debug, and verify that it works?
+        removed_questions:
+        - What are the prerequisites to start?
+        - What platform does this use, and do I need hardware?
+        - "Can I follow this with \u03BCVision or Arm Development Studio instead of\
+          \ Keil Studio for VS Code?"
+        - What will I implement in the application?
+        - How do I build, run, and verify the application?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -202001,51 +5170,80 @@ history:
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to prototype and port DSP algorithms using the CMSIS-DSP Python package,
-        mapping Python code to efficient C implementations for embedded Cortex-M and Cortex-A platforms.
-        It is designed for d...
-      preview_after: Learn how to prototype and port DSP algorithms using the CMSIS-DSP Python package,
-        mapping Python code to efficient C implementations for embedded Cortex-M and Cortex-A platforms.
-        It is designed for d...
-      preview_generated: Learn how to prototype and port DSP algorithms using the CMSIS-DSP Python package,
-        mapping Python code to efficient C implementations for embedded Cortex-M and Cortex-A platforms.
-        It is designed for d...
+      generated_at_before: '2026-06-01T21:31:12Z'
+      generated_at_after: '2026-06-01T21:31:12Z'
+      preview_before: This advanced Learning Path shows how to prototype DSP algorithms
+        in Python using the CMSIS-DSP Python package and understand how the Python
+        API maps to the CMSIS-DSP C implementation for Arm Cortex-M...
+      preview_after: This advanced Learning Path shows how to prototype DSP algorithms
+        in Python using the CMSIS-DSP Python package and understand how the Python
+        API maps to the CMSIS-DSP C implementation for Arm Cortex-M...
+      preview_generated: Prototype and port DSP algorithms using the CMSIS-DSP Python
+        package in a Jupyter notebook on Linux, Windows, or macOS. You will set up
+        a Python virtual environment, install cmsisdsp (which brings in ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 69526ee8b840c8f5d77d49349d2f0cc7ff4594fec5e4311984ef72a0672d3462
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:31:12Z'
+      generated_at_after: '2026-06-02T22:13:13Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the notebook?
+        - Should I create a Python virtual environment and which packages do I install?
+        - Where does the sample audio come from and how is it used?
+        - How do I know my VAD and noise suppression steps are working?
+        - How does the Python code relate to the CMSIS-DSP C implementation on Arm
+          cores?
+        removed_questions:
+        - What do I need installed before starting?
+        - Which operating systems and Arm targets are addressed?
+        - What will I implement in the notebook, and what data is used?
+        - Does this path include porting the Python prototype to C and building on
+          hardware?
+        - How can I tell my setup is working?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the notebook?
+        - Should I create a Python virtual environment and which packages do I install?
+        - Where does the sample audio come from and how is it used?
+        - How do I know my VAD and noise suppression steps are working?
+        - How does the Python code relate to the CMSIS-DSP C implementation on Arm
+          cores?
+        removed_questions:
+        - What do I need installed before starting?
+        - Which operating systems and Arm targets are addressed?
+        - What will I implement in the notebook, and what data is used?
+        - Does this path include porting the Python prototype to C and building on
+          hardware?
+        - How can I tell my setup is working?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -202055,51 +5253,76 @@ history:
       source_hash_before: 0b7a5895a87de83903c223215845b6c840602663838c0682f46e50d139d69c59
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to implement context switching operations on Arm Cortex-M processors using
-        the Memory Protection Unit and SysTick exception in a bare-metal environment. It is designed for
-        software developer...
-      preview_after: Learn how to implement context switching operations on Arm Cortex-M processors using
-        the Memory Protection Unit and SysTick exception in a bare-metal environment. It is designed for
-        software developer...
-      preview_generated: Learn how to implement context switching operations on Arm Cortex-M processors
-        using the Memory Protection Unit and SysTick exception in a bare-metal environment. It is designed
-        for software developer...
+      generated_at_before: '2026-06-01T21:31:37Z'
+      generated_at_after: '2026-06-01T21:31:37Z'
+      preview_before: This introductory path shows how to implement context switching
+        on Arm Cortex-M processors in a bare-metal environment using the Memory Protection
+        Unit (MPU) and the SysTick exception. You will build ...
+      preview_after: This introductory path shows how to implement context switching
+        on Arm Cortex-M processors in a bare-metal environment using the Memory Protection
+        Unit (MPU) and the SysTick exception. You will build ...
+      preview_generated: This Learning Path introduces context switching on Arm Cortex-M
+        processors in a bare-metal environment. You will program the Memory Protection
+        Unit (MPU), use the SysTick exception with context switch...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 0b7a5895a87de83903c223215845b6c840602663838c0682f46e50d139d69c59
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:31:37Z'
+      generated_at_after: '2026-06-02T22:14:04Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - Where do I get the example project used in this Learning Path?
+        - Which tool versions should I use to build and run the example?
+        - Where is the example intended to run?
+        - How do MPU and SysTick feature in the example?
+        - What should I check if the project does not build or run as expected?
+        removed_questions:
+        - Which tools and versions are required to follow this path?
+        - Do I need physical Cortex-M hardware to run the example?
+        - Where does the example project come from and what does it demonstrate?
+        - What prior knowledge is expected before starting?
+        - How do I know the path worked after completing the steps?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - Where do I get the example project used in this Learning Path?
+        - Which tool versions should I use to build and run the example?
+        - Where is the example intended to run?
+        - How do MPU and SysTick feature in the example?
+        - What should I check if the project does not build or run as expected?
+        removed_questions:
+        - Which tools and versions are required to follow this path?
+        - Do I need physical Cortex-M hardware to run the example?
+        - Where does the example project come from and what does it demonstrate?
+        - What prior knowledge is expected before starting?
+        - How do I know the path worked after completing the steps?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -202109,51 +5332,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to set up and use code coverage in Keil MDK with FVPs to verify that your
-        embedded application tests execute all code paths. It is designed for embedded software developers
-        new to the code-c...
-      preview_after: Learn how to set up and use code coverage in Keil MDK with FVPs to verify that your
-        embedded application tests execute all code paths. It is designed for embedded software developers
-        new to the code-c...
-      preview_generated: Learn how to set up and use code coverage in Keil MDK with FVPs to verify that
-        your embedded application tests execute all code paths. It is designed for embedded software developers
-        new to the code-c...
+      generated_at_before: '2026-06-01T21:31:57Z'
+      generated_at_after: '2026-06-01T21:31:57Z'
+      preview_before: Learn to configure and run code coverage in Keil MDK using Fixed
+        Virtual Platforms (FVPs) for Cortex-M targets. You will import and build the
+        CMSIS-RTOS2 Blinky (uVision Simulator) example for ARMCM3 ...
+      preview_after: Learn to configure and run code coverage in Keil MDK using Fixed
+        Virtual Platforms (FVPs) for Cortex-M targets. You will import and build the
+        CMSIS-RTOS2 Blinky (uVision Simulator) example for ARMCM3 ...
+      preview_generated: This introductory Learning Path shows how to set up and use
+        code coverage in Keil MDK with Arm Fixed Virtual Platforms (FVPs) to verify
+        that your embedded application tests exercise intended code path...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: f989929b11c48df42c2701dc71516cec0b8637b4c639c3dbbf6ac5e7440c8a56
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:31:57Z'
+      generated_at_after: '2026-06-02T22:15:46Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - Do I need real target hardware to follow this path?
+        - What do I need before I start?
+        - Which device and example should I select in the Pack Installer?
+        - Can I use a different project instead of the CMSIS-RTOS2 Blinky example?
+        - What should I look for in the Code Coverage report?
+        removed_questions:
+        - What do I need before starting?
+        - Which example project is used and how do I import it?
+        - Do I need physical hardware to follow this Learning Path?
+        - What targets and operating systems does this apply to?
+        - How do I verify that code coverage is working?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - Do I need real target hardware to follow this path?
+        - What do I need before I start?
+        - Which device and example should I select in the Pack Installer?
+        - Can I use a different project instead of the CMSIS-RTOS2 Blinky example?
+        - What should I look for in the Code Coverage report?
+        removed_questions:
+        - What do I need before starting?
+        - Which example project is used and how do I import it?
+        - Do I need physical hardware to follow this Learning Path?
+        - What targets and operating systems does this apply to?
+        - How do I verify that code coverage is working?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -202163,51 +5411,76 @@ history:
       source_hash_before: 9d9e4c170fa318a9875c888c2c812ceb27c59f56c4b0dd7e0ae87dd31bb5783c
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Device-to-Device communication with Device Connect walks you through an end-to-end
-        Arm software workflow. It is designed for developers wiring up heterogeneous edge fleets, where
-        devices need a shared...
-      preview_after: Device-to-Device communication with Device Connect walks you through an end-to-end
-        Arm software workflow. It is designed for developers wiring up heterogeneous edge fleets, where
-        devices need a shared...
-      preview_generated: Device-to-Device communication with Device Connect walks you through an end-to-end
-        Arm software workflow. It is designed for developers wiring up heterogeneous edge fleets, where
-        devices need a shared...
+      generated_at_before: '2026-06-01T21:38:11Z'
+      generated_at_after: '2026-06-01T21:38:11Z'
+      preview_before: Learn how to establish peer-to-peer device-to-device communication
+        at the edge using the Device Connect Edge SDK in a Python environment, with
+        no hardware required. You will build two simulated device...
+      preview_after: Learn how to establish peer-to-peer device-to-device communication
+        at the edge using the Device Connect Edge SDK in a Python environment, with
+        no hardware required. You will build two simulated device...
+      preview_generated: Learn how to stand up peer-to-peer device-to-device communication
+        using the Device Connect Edge SDK with no hardware required. You will set
+        up a Python environment (managed with uv) on Linux, macOS, o...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:38:11Z'
+      generated_at_after: '2026-06-02T22:16:41Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Do I need a broker or cloud service to complete the device-to-device setup?
+        - Which tool is used to manage the Python project and dependencies?
+        - How are devices defined and brought online with Device Connect?
+        - How do I know the two simulated devices are discoverable and callable?
+        removed_questions:
+        - Do I need any physical devices or special hardware?
+        - Which operating systems can I use for this walkthrough?
+        - What will I build during the path?
+        - What tools do I need to install to follow the steps?
+        - How do I verify that the setup works?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Do I need a broker or cloud service to complete the device-to-device setup?
+        - Which tool is used to manage the Python project and dependencies?
+        - How are devices defined and brought online with Device Connect?
+        - How do I know the two simulated devices are discoverable and callable?
+        removed_questions:
+        - Do I need any physical devices or special hardware?
+        - Which operating systems can I use for this walkthrough?
+        - What will I build during the path?
+        - What tools do I need to install to follow the steps?
+        - How do I verify that the setup works?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -202217,51 +5490,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to connect AI agents to Arm-based edge devices using Device Connect for
-        structured device access and Strands for agent orchestration, with examples for both simulated
-        and physical robots. It...
-      preview_after: Learn how to connect AI agents to Arm-based edge devices using Device Connect for
-        structured device access and Strands for agent orchestration, with examples for both simulated
-        and physical robots. It...
-      preview_generated: Learn how to connect AI agents to Arm-based edge devices using Device Connect
-        for structured device access and Strands for agent orchestration, with examples for both simulated
-        and physical robots. It...
+      generated_at_before: '2026-06-01T21:38:36Z'
+      generated_at_after: '2026-06-01T21:38:36Z'
+      preview_before: Learn to connect AI agents to Arm-based edge devices using Device
+        Connect for structured device access and Strands for agent orchestration.
+        You will set up a Python environment from source by cloning ...
+      preview_after: Learn to connect AI agents to Arm-based edge devices using Device
+        Connect for structured device access and Strands for agent orchestration.
+        You will set up a Python environment from source by cloning ...
+      preview_generated: This introductory Learning Path shows how to connect AI agents
+        to Arm-based edge devices using Device Connect for structured device access
+        and Strands for agent orchestration. You will clone the robot...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: c31d398d3ce965a4193fca45cd025182d788a2fc603fbe8c828dfbd5a1c599ba
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-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:38:36Z'
+      generated_at_after: '2026-06-02T22:17:42Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before cloning the repository?
+        - How do I set up the Python environment?
+        - Which option should I choose for device discovery and control?
+        - How do I know the agent discovered the robot?
+        - What changes when I run with the full Device Connect infrastructure?
+        removed_questions:
+        - What platforms and tools do I need to follow this path?
+        - Do I need physical hardware, or can I run everything locally?
+        - Which repository do I clone, and what does it provide?
+        - How do I verify that the basic example worked?
+        - What does the optional infrastructure step add?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before cloning the repository?
+        - How do I set up the Python environment?
+        - Which option should I choose for device discovery and control?
+        - How do I know the agent discovered the robot?
+        - What changes when I run with the full Device Connect infrastructure?
+        removed_questions:
+        - What platforms and tools do I need to follow this path?
+        - Do I need physical hardware, or can I run everything locally?
+        - Which repository do I clone, and what does it provide?
+        - How do I verify that the basic example worked?
+        - What does the optional infrastructure step add?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/docker/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -202271,51 +5569,76 @@ history:
       source_hash_before: a4b522c46c68d3c6f5c4af7c76b00c7bde48bb638147c201376bc19a4de79cca
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to create a Dockerfile, build a Docker image with Arm Compiler for Embedded
-        and Fixed Virtual Platforms, and test the containerized Arm development environment. It is designed
-        for embedded s...
-      preview_after: Learn how to create a Dockerfile, build a Docker image with Arm Compiler for Embedded
-        and Fixed Virtual Platforms, and test the containerized Arm development environment. It is designed
-        for embedded s...
-      preview_generated: Learn how to create a Dockerfile, build a Docker image with Arm Compiler for
-        Embedded and Fixed Virtual Platforms, and test the containerized Arm development environment.
-        It is designed for embedded s...
+      generated_at_before: '2026-06-01T21:39:09Z'
+      generated_at_after: '2026-06-01T21:39:09Z'
+      preview_before: Build a containerized Arm embedded development environment by
+        creating a Dockerfile, constructing an Ubuntu-based Docker image that includes
+        Arm Compiler for Embedded and a library of Fixed Virtual Pl...
+      preview_after: Build a containerized Arm embedded development environment by
+        creating a Dockerfile, constructing an Ubuntu-based Docker image that includes
+        Arm Compiler for Embedded and a library of Fixed Virtual Pl...
+      preview_generated: This introductory Learning Path shows embedded developers
+        how to create a Dockerfile, build an Ubuntu-based Docker image that includes
+        Arm Compiler for Embedded and a library of Fixed Virtual Platform...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: a4b522c46c68d3c6f5c4af7c76b00c7bde48bb638147c201376bc19a4de79cca
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:39:09Z'
+      generated_at_after: '2026-06-02T22:19:02Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running docker build?
+        - Which host operating systems can I use to follow this path?
+        - What base operating system does the container use?
+        - What will the resulting Docker image contain?
+        - How do I know the image works after the build?
+        removed_questions:
+        - What do I need before I start?
+        - Which operating systems are used for the host and the container?
+        - Will I need sudo to run Docker commands on Linux?
+        - What exactly will I build and test in this Learning Path?
+        - How long does this take and what level of experience is assumed?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running docker build?
+        - Which host operating systems can I use to follow this path?
+        - What base operating system does the container use?
+        - What will the resulting Docker image contain?
+        - How do I know the image works after the build?
+        removed_questions:
+        - What do I need before I start?
+        - Which operating systems are used for the host and the container?
+        - Will I need sudo to run Docker commands on Linux?
+        - What exactly will I build and test in this Learning Path?
+        - How long does this take and what level of experience is assumed?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/edge/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -202325,51 +5648,87 @@ history:
       source_hash_before: 8a7ec29d10a89649662ebb0fa4f14712984786902b6e7343a195abb1f3cbc00b
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to collect and preprocess audio data using Edge Impulse, train an audio
-        classification model, and deploy it to the Arduino Nano RP2040 to control LEDs based on voice
-        commands. It is designed...
-      preview_after: Learn how to collect and preprocess audio data using Edge Impulse, train an audio
-        classification model, and deploy it to the Arduino Nano RP2040 to control LEDs based on voice
-        commands. It is designed...
-      preview_generated: Learn how to collect and preprocess audio data using Edge Impulse, train an audio
-        classification model, and deploy it to the Arduino Nano RP2040 to control LEDs based on voice
-        commands. It is designed...
+      generated_at_before: '2026-06-01T21:39:32Z'
+      generated_at_after: '2026-06-01T21:39:32Z'
+      preview_before: This introductory Learning Path guides you through building
+        a TinyML audio command demo on the Arduino Nano RP2040 Connect. You will use
+        Edge Impulse to collect and preprocess audio data, train a simp...
+      preview_after: This introductory Learning Path guides you through building a
+        TinyML audio command demo on the Arduino Nano RP2040 Connect. You will use
+        Edge Impulse to collect and preprocess audio data, train a simp...
+      preview_generated: This introductory Learning Path guides you through building
+        a TinyML voice-command demo on the Arduino Nano RP2040 Connect. You will use
+        Edge Impulse to collect and preprocess audio data, train a simp...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 8a7ec29d10a89649662ebb0fa4f14712984786902b6e7343a195abb1f3cbc00b
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:39:32Z'
+      generated_at_after: '2026-06-02T22:20:08Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which platform and tools does this project use?
+        - How do I get the Edge Impulse model into my Arduino sketch?
+        - What result should I expect after deployment?
+        - What should I check if the LED does not react to voice commands?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - Is this suitable for beginners, and what prior knowledge is assumed?
+        - How do I build and train the audio model?
+        - How is the model deployed to the Arduino Nano RP2040 Connect?
+        - How do I verify that everything is working?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which platform and tools does this project use?
+        - How do I get the Edge Impulse model into my Arduino sketch?
+        - What result should I expect after deployment?
+        - What should I check if the LED does not react to voice commands?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - Is this suitable for beginners, and what prior knowledge is assumed?
+        - How do I build and train the audio model?
+        - How is the model deployed to the Arduino Nano RP2040 Connect?
+        - How do I verify that everything is working?
+        updated_questions: []
+    category: embedded-and-microcontrollers
+  - path: content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/_index.md
+    status: skipped
+    skip_reason: draft
+    change_reasons:
+    - draft
+    ai_requested: false
+    summary:
+      action: skipped
+    faqs:
+      action: skipped
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -202379,51 +5738,76 @@ history:
       source_hash_before: 66024a2e3da90dcda298bf66b5de07952ed1e355d22172bc2812c46ad4fcda7f
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Develop a image classification neural network model and deploy it on an STM32 B-L475E-IOT01A2
-        board. It is designed for embedded software developers interested in building neural network models
-        for mi...
-      preview_after: Develop a image classification neural network model and deploy it on an STM32 B-L475E-IOT01A2
-        board. It is designed for embedded software developers interested in building neural network models
-        for mi...
-      preview_generated: Develop a image classification neural network model and deploy it on an STM32
-        B-L475E-IOT01A2 board. It is designed for embedded software developers interested in building
-        neural network models for mi...
+      generated_at_before: '2026-06-01T21:40:03Z'
+      generated_at_after: '2026-06-01T21:40:03Z'
+      preview_before: This Learning Path walks you through building a convolutional
+        neural network for image classification using the CIFAR-10 dataset in a Jupyter
+        Notebook environment set up with Anaconda, then deploying ...
+      preview_after: This Learning Path walks you through building a convolutional
+        neural network for image classification using the CIFAR-10 dataset in a Jupyter
+        Notebook environment set up with Anaconda, then deploying ...
+      preview_generated: Build and train a convolutional neural network for image
+        classification using TensorFlow and the CIFAR-10 dataset, then deploy it to
+        an STM32 B-L475E-IOT01A2 (Arm Cortex-M) board. You set up Anaconda,...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 66024a2e3da90dcda298bf66b5de07952ed1e355d22172bc2812c46ad4fcda7f
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:40:03Z'
+      generated_at_after: '2026-06-02T22:21:11Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - How do I open and run the training notebook?
+        - Which dataset and model are used for training?
+        - Which STM32Cube tools and versions should I use during deployment?
+        - How do I run the testing tool and what if the board is not detected?
+        removed_questions:
+        - What hardware and skills do I need before starting?
+        - How is the model built and what dataset is used?
+        - What software environment is used for training?
+        - How do I deploy the trained model to the STM32 board?
+        - How do I run and test the model on the board?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - How do I open and run the training notebook?
+        - Which dataset and model are used for training?
+        - Which STM32Cube tools and versions should I use during deployment?
+        - How do I run the testing tool and what if the board is not detected?
+        removed_questions:
+        - What hardware and skills do I need before starting?
+        - How is the model built and what dataset is used?
+        - What software environment is used for training?
+        - How do I deploy the trained model to the STM32 board?
+        - How do I run and test the model on the board?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/intro/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -202433,51 +5817,76 @@ history:
       source_hash_before: ac69e979101f6befe55abee1429299c163c70b9a886d87a8f64779babd9fe5af
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn where Arm architecture is used in microcontrollers and discover microcontroller
-        hardware options for software development on Arm Cortex-M processors. It is designed for software
-        developers worki...
-      preview_after: Learn where Arm architecture is used in microcontrollers and discover microcontroller
-        hardware options for software development on Arm Cortex-M processors. It is designed for software
-        developers worki...
-      preview_generated: Learn where Arm architecture is used in microcontrollers and discover microcontroller
-        hardware options for software development on Arm Cortex-M processors. It is designed for software
-        developers worki...
+      generated_at_before: '2026-06-01T21:40:30Z'
+      generated_at_after: '2026-06-01T21:40:30Z'
+      preview_before: This introductory Learning Path explains where Arm architecture
+        appears in microcontrollers and helps you identify hardware options for software
+        development on Arm Cortex-M processors. You will review...
+      preview_after: This introductory Learning Path explains where Arm architecture
+        appears in microcontrollers and helps you identify hardware options for software
+        development on Arm Cortex-M processors. You will review...
+      preview_generated: "This introductory Learning Path explains where Arm architecture\
+        \ appears in microcontrollers and how to find hardware for software development\
+        \ on Arm Cortex\u2011M processors. In about 10 minutes, you revie..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:40:30Z'
+      generated_at_after: '2026-06-02T22:22:16Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - Do I need any prerequisites or hardware to start this Learning Path?
+        - How do evaluation boards differ from edge computing boards or SBCs?
+        - Which operating environments are in scope for the examples and guidance?
+        - "Will this help if I\u2019m migrating an application from another architecture?"
+        - Where can I find additional learning resources after finishing?
+        removed_questions:
+        - Do I need any prerequisites before starting?
+        - How long will this Learning Path take to complete?
+        - Which operating systems or environments does this path consider?
+        - What kinds of hardware does this path help me evaluate?
+        - Does this path include tool installation or coding steps?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - Do I need any prerequisites or hardware to start this Learning Path?
+        - How do evaluation boards differ from edge computing boards or SBCs?
+        - Which operating environments are in scope for the examples and guidance?
+        - "Will this help if I\u2019m migrating an application from another architecture?"
+        - Where can I find additional learning resources after finishing?
+        removed_questions:
+        - Do I need any prerequisites before starting?
+        - How long will this Learning Path take to complete?
+        - Which operating systems or environments does this path consider?
+        - What kinds of hardware does this path help me evaluate?
+        - Does this path include tool installation or coding steps?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -202487,51 +5896,76 @@ history:
       source_hash_before: aa49e4a1651367965e79d36c5f6af16e9b341c392d48cf051f24cbb21070e972
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       current_source_hash: aa49e4a1651367965e79d36c5f6af16e9b341c392d48cf051f24cbb21070e972
-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn what differentiates TinyML from other AI domains, explore Arm-based edge devices
-        for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed Virtual
-        Platform. It is ...
-      preview_after: Learn what differentiates TinyML from other AI domains, explore Arm-based edge devices
-        for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed Virtual
-        Platform. It is ...
-      preview_generated: Learn what differentiates TinyML from other AI domains, explore Arm-based edge
-        devices for TinyML, and set up a development environment using ExecuTorch and Corstone-320 Fixed
-        Virtual Platform. It is ...
+      generated_at_before: '2026-06-01T21:40:55Z'
+      generated_at_after: '2026-06-01T21:40:55Z'
+      preview_before: This introductory path explains what differentiates TinyML from
+        other AI domains and why Arm-based edge devices are a good fit. You set up
+        a Linux-hosted TinyML environment using PyTorch, ExecuTorch, ...
+      preview_after: This introductory path explains what differentiates TinyML from
+        other AI domains and why Arm-based edge devices are a good fit. You set up
+        a Linux-hosted TinyML environment using PyTorch, ExecuTorch, ...
+      preview_generated: This introductory path explains what differentiates TinyML
+        from other AI domains, highlights Arm-based edge devices, and guides you through
+        setting up a TinyML development environment on Linux using P...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: aa49e4a1651367965e79d36c5f6af16e9b341c392d48cf051f24cbb21070e972
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:40:55Z'
+      generated_at_after: '2026-06-02T22:23:20Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the setup?
+        - Do I need physical Arm hardware to complete this path?
+        - What does the Corstone-320 FVP provide for this workflow?
+        - How do I validate that ExecuTorch and the environment are installed correctly?
+        - What code artifact will I create in the modeling step?
+        removed_questions:
+        - What skills and system requirements are assumed?
+        - Which tools and components are used in this path?
+        - Do I need physical hardware to follow the steps?
+        - What will I create or verify by the end?
+        - Which Arm architectures or accelerators are covered?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the setup?
+        - Do I need physical Arm hardware to complete this path?
+        - What does the Corstone-320 FVP provide for this workflow?
+        - How do I validate that ExecuTorch and the environment are installed correctly?
+        - What code artifact will I create in the modeling step?
+        removed_questions:
+        - What skills and system requirements are assumed?
+        - Which tools and components are used in this path?
+        - Do I need physical hardware to follow the steps?
+        - What will I create or verify by the end?
+        - Which Arm architectures or accelerators are covered?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -202541,51 +5975,76 @@ history:
       source_hash_before: 11fc64eb1a0595b1d8e625e465be9fa87a4de04f59492004204ccbe61a92a5b7
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       current_source_hash: 11fc64eb1a0595b1d8e625e465be9fa87a4de04f59492004204ccbe61a92a5b7
-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300 virtual
-        hardware to understand complete IoT software stack construction. It is designed for embedded software
-        developers ...
-      preview_after: Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300 virtual
-        hardware to understand complete IoT software stack construction. It is designed for embedded software
-        developers ...
-      preview_generated: Learn how to build examples from the Open-IoT-SDK and run them on Corstone-300
-        virtual hardware to understand complete IoT software stack construction. It is designed for embedded
-        software developers ...
+      generated_at_before: '2026-06-01T21:41:45Z'
+      generated_at_after: '2026-06-01T21:41:45Z'
+      preview_before: This introductory path shows how to build Open-IoT-SDK examples
+        and run them on Corstone-300 virtual hardware using Arm Virtual Hardware.
+        You set up an AVH instance, install the required Python enviro...
+      preview_after: This introductory path shows how to build Open-IoT-SDK examples
+        and run them on Corstone-300 virtual hardware using Arm Virtual Hardware.
+        You set up an AVH instance, install the required Python enviro...
+      preview_generated: Build and run Open-IoT-SDK examples on Arm Virtual Hardware
+        to explore how Arm Total Solutions for IoT assemble a complete IoT software
+        stack for Corstone-300. You will set up an AVH instance, install...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 11fc64eb1a0595b1d8e625e465be9fa87a4de04f59492004204ccbe61a92a5b7
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       current_source_hash: 11fc64eb1a0595b1d8e625e465be9fa87a4de04f59492004204ccbe61a92a5b7
-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:41:45Z'
+      generated_at_after: '2026-06-02T22:25:19Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - How do I set up Arm Virtual Hardware and install the required software?
+        - How do I build and run the keyword example?
+        - What result should I expect in the terminal when the example runs successfully?
+        - How is AWS connectivity used in the examples, and what should I configure?
+        removed_questions:
+        - What do I need before starting?
+        - Which platform and tools does this path use?
+        - How do I set up Arm Virtual Hardware for this path?
+        - How do I build and run an example, and what should I see?
+        - How is AWS connectivity used in the examples?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - How do I set up Arm Virtual Hardware and install the required software?
+        - How do I build and run the keyword example?
+        - What result should I expect in the terminal when the example runs successfully?
+        - How is AWS connectivity used in the examples, and what should I configure?
+        removed_questions:
+        - What do I need before starting?
+        - Which platform and tools does this path use?
+        - How do I set up Arm Virtual Hardware for this path?
+        - How do I build and run an example, and what should I see?
+        - How is AWS connectivity used in the examples?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -202595,51 +6054,76 @@ history:
       source_hash_before: fdf582f1b54f372768a2c896e1da152c50d22c3bd238848253cdbe18cc68222d
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       current_source_hash: fdf582f1b54f372768a2c896e1da152c50d22c3bd238848253cdbe18cc68222d
-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to set up a Jetson Orin Nano with a MIPI CSI-2 camera and perform real-time
-        object detection from live video and image files using DetectNet and TensorRT. It is designed
-        for developers inter...
-      preview_after: Learn how to set up a Jetson Orin Nano with a MIPI CSI-2 camera and perform real-time
-        object detection from live video and image files using DetectNet and TensorRT. It is designed
-        for developers inter...
-      preview_generated: Learn how to set up a Jetson Orin Nano with a MIPI CSI-2 camera and perform real-time
-        object detection from live video and image files using DetectNet and TensorRT. It is designed
-        for developers inter...
+      generated_at_before: '2026-06-01T21:42:08Z'
+      generated_at_after: '2026-06-01T21:42:08Z'
+      preview_before: This introductory path shows how to bring up a Jetson Orin Nano
+        on Linux with a MIPI CSI-2 camera and run real-time object detection using
+        DetectNet and TensorRT. You will download the latest Jetson O...
+      preview_after: This introductory path shows how to bring up a Jetson Orin Nano
+        on Linux with a MIPI CSI-2 camera and run real-time object detection using
+        DetectNet and TensorRT. You will download the latest Jetson O...
+      preview_generated: Set up a Jetson Orin Nano on Linux with a MIPI CSI-2 camera
+        to run real-time object detection using DetectNet with TensorRT. You will
+        flash the NVIDIA developer kit image to a microSD card using balen...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: fdf582f1b54f372768a2c896e1da152c50d22c3bd238848253cdbe18cc68222d
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       current_source_hash: fdf582f1b54f372768a2c896e1da152c50d22c3bd238848253cdbe18cc68222d
-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:42:08Z'
+      generated_at_after: '2026-06-02T22:25:46Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before starting the setup?
+        - How do I write the Jetson image to the microSD card?
+        - How do I download and start the jetson-inference Docker container?
+        - How do I check that the Docker container is running and find its ID?
+        - How do I run DetectNet on the live camera and adjust sensitivity?
+        removed_questions:
+        - What hardware do I need before starting?
+        - How do I prepare the Jetson Orin Nano software image?
+        - How do I launch the Docker environment used in this path?
+        - How do I run object detection from the camera and adjust sensitivity?
+        - How do I know the setup worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before starting the setup?
+        - How do I write the Jetson image to the microSD card?
+        - How do I download and start the jetson-inference Docker container?
+        - How do I check that the Docker container is running and find its ID?
+        - How do I run DetectNet on the live camera and adjust sensitivity?
+        removed_questions:
+        - What hardware do I need before starting?
+        - How do I prepare the Jetson Orin Nano software image?
+        - How do I launch the Docker environment used in this path?
+        - How do I run object detection from the camera and adjust sensitivity?
+        - How do I know the setup worked?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -202649,51 +6133,78 @@ history:
       source_hash_before: f3a01adf18ad93027b6ab61ceb5b0c470e6b5c298f9d4f944989a96ff64eec81
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to import, build, and debug your first Keil Studio Cloud project. It is
-        designed for embedded software developers new to Keil Studio Cloud. By the end, you will be able
-        to import and build a...
-      preview_after: Learn how to import, build, and debug your first Keil Studio Cloud project. It is
-        designed for embedded software developers new to Keil Studio Cloud. By the end, you will be able
-        to import and build a...
-      preview_generated: Learn how to import, build, and debug your first Keil Studio Cloud project. It
-        is designed for embedded software developers new to Keil Studio Cloud. By the end, you will be
-        able to import and build a...
+      generated_at_before: '2026-06-01T21:42:40Z'
+      generated_at_after: '2026-06-01T21:42:40Z'
+      preview_before: This introductory Learning Path shows how to import and build
+        an example Cortex-M project in Keil Studio Cloud and run it on Arm Virtual
+        Hardware. Using the browser-based IDE with Arm Compiler for Emb...
+      preview_after: This introductory Learning Path shows how to import and build
+        an example Cortex-M project in Keil Studio Cloud and run it on Arm Virtual
+        Hardware. Using the browser-based IDE with Arm Compiler for Emb...
+      preview_generated: This introductory path shows how to import, build, and debug
+        a first project in Keil Studio Cloud and run it on Arm Virtual Hardware. You
+        work with Cortex-M targets and examples that can be bare-metal...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: f3a01adf18ad93027b6ab61ceb5b0c470e6b5c298f9d4f944989a96ff64eec81
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       current_source_hash: f3a01adf18ad93027b6ab61ceb5b0c470e6b5c298f9d4f944989a96ff64eec81
-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:42:40Z'
+      generated_at_after: '2026-06-02T22:27:21Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before I can access Keil Studio Cloud?
+        - Which browser should I use, especially if I plan to connect a board over
+          USB?
+        - Can I complete this Learning Path without physical hardware?
+        - How do I check if my development board is supported by Keil Studio Cloud?
+        - What targets and tools are used in the example project?
+        removed_questions:
+        - What do I need before I start?
+        - Do I need a physical development board to follow this path?
+        - Which browsers can I use, and when is WebUSB required?
+        - What Arm targets and software components are involved?
+        - What will I accomplish and how long will it take?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before I can access Keil Studio Cloud?
+        - Which browser should I use, especially if I plan to connect a board over
+          USB?
+        - Can I complete this Learning Path without physical hardware?
+        - How do I check if my development board is supported by Keil Studio Cloud?
+        - What targets and tools are used in the example project?
+        removed_questions:
+        - What do I need before I start?
+        - Do I need a physical development board to follow this path?
+        - Which browsers can I use, and when is WebUSB required?
+        - What Arm targets and software components are involved?
+        - What will I accomplish and how long will it take?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -202703,51 +6214,76 @@ history:
       source_hash_before: 567387e8e513458a360f74c14d3f4d02c4af392bc573620e605c93976e4d8b4f
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       current_source_hash: 567387e8e513458a360f74c14d3f4d02c4af392bc573620e605c93976e4d8b4f
-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to boot and configure the NXP FRDM i.MX 93 Arm board with Linux, create
-        a user with sudo access, connect to WiFi using ConnMan, and transfer files over the network. It
-        is designed for embedd...
-      preview_after: Learn how to boot and configure the NXP FRDM i.MX 93 Arm board with Linux, create
-        a user with sudo access, connect to WiFi using ConnMan, and transfer files over the network. It
-        is designed for embedd...
-      preview_generated: Learn how to boot and configure the NXP FRDM i.MX 93 Arm board with Linux, create
-        a user with sudo access, connect to WiFi using ConnMan, and transfer files over the network. It
-        is designed for embedd...
+      generated_at_before: '2026-06-01T21:43:12Z'
+      generated_at_after: '2026-06-01T21:43:12Z'
+      preview_before: This Learning Path shows how to bring up Linux on the NXP FRDM
+        i.MX 93 board and prepare it for on-device development. You will boot and
+        log in over the DBG serial console, create a non-root user with...
+      preview_after: This Learning Path shows how to bring up Linux on the NXP FRDM
+        i.MX 93 board and prepare it for on-device development. You will boot and
+        log in over the DBG serial console, create a non-root user with...
+      preview_generated: This Learning Path shows how to boot and configure an NXP
+        FRDM i.MX 93 Arm board running Linux, then prepare it for day-to-day development.
+        You will log in over a serial console from a Linux or macOS ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 567387e8e513458a360f74c14d3f4d02c4af392bc573620e605c93976e4d8b4f
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-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:43:12Z'
+      generated_at_after: '2026-06-02T22:28:35Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before powering the board?
+        - How do I access the Linux console on the board?
+        - Which tool should I use to connect to WiFi, and how do I verify it worked?
+        - How do I transfer files to the board during development?
+        - What should I check if WiFi does not reconnect after a reboot?
+        removed_questions:
+        - How do I access the board console for the first login?
+        - Why create a non-root user, and how is sudo enabled?
+        - How do I connect the board to WiFi and verify it worked?
+        - How can I transfer files to the board?
+        - "What if WiFi doesn\u2019t reconnect after a reboot?"
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before powering the board?
+        - How do I access the Linux console on the board?
+        - Which tool should I use to connect to WiFi, and how do I verify it worked?
+        - How do I transfer files to the board during development?
+        - What should I check if WiFi does not reconnect after a reboot?
+        removed_questions:
+        - How do I access the board console for the first login?
+        - Why create a non-root user, and how is sudo enabled?
+        - How do I connect the board to WiFi and verify it worked?
+        - How can I transfer files to the board?
+        - "What if WiFi doesn\u2019t reconnect after a reboot?"
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -202757,51 +6293,76 @@ history:
       source_hash_before: 5943d817827bef274f175f7c14249cad769b2160094b3c65d7476aca6cbf0a1d
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to boot a Linux software stack on Arm Fixed Virtual Platforms (FVPs),
-        then debug Trusted Firmware-A and the Linux kernel using Arm Development Studio. It is designed
-        for developers who want ...
-      preview_after: Learn how to boot a Linux software stack on Arm Fixed Virtual Platforms (FVPs), then
-        debug Trusted Firmware-A and the Linux kernel using Arm Development Studio. It is designed for
-        developers who want ...
-      preview_generated: Learn how to boot a Linux software stack on Arm Fixed Virtual Platforms (FVPs),
-        then debug Trusted Firmware-A and the Linux kernel using Arm Development Studio. It is designed
-        for developers who want ...
+      generated_at_before: '2026-06-01T21:43:52Z'
+      generated_at_after: '2026-06-01T21:43:52Z'
+      preview_before: This introductory Learning Path shows how to boot a Linux software
+        stack on Arm Fixed Virtual Platforms (FVPs) and then debug Trusted Firmware-A
+        (TF-A) and the Linux kernel using Arm Development Studi...
+      preview_after: This introductory Learning Path shows how to boot a Linux software
+        stack on Arm Fixed Virtual Platforms (FVPs) and then debug Trusted Firmware-A
+        (TF-A) and the Linux kernel using Arm Development Studi...
+      preview_generated: This Learning Path shows how to boot a Linux software stack
+        on Arm Fixed Virtual Platforms (FVPs) and then debug Trusted Firmware-A (TF-A)
+        and the Linux kernel using Arm Development Studio. You will c...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 5943d817827bef274f175f7c14249cad769b2160094b3c65d7476aca6cbf0a1d
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:43:52Z'
+      generated_at_after: '2026-06-02T22:29:22Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How should I modify the device tree for CPU FVPs?
+        - How do I confirm that cpu_ops is enabled in my TF-A build?
+        - What result should I expect from the build output?
+        - How do I run and debug the software stack on an FVP?
+        removed_questions:
+        - What host system and skills are required to follow this Learning Path?
+        - Which Arm Development Studio version should I use for debugging?
+        - Why do I need cpu_ops support in Trusted Firmware-A for this workflow?
+        - What device tree changes are required for CPU FVPs?
+        - How do I verify that my build is ready to run on the FVP?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How should I modify the device tree for CPU FVPs?
+        - How do I confirm that cpu_ops is enabled in my TF-A build?
+        - What result should I expect from the build output?
+        - How do I run and debug the software stack on an FVP?
+        removed_questions:
+        - What host system and skills are required to follow this Learning Path?
+        - Which Arm Development Studio version should I use for debugging?
+        - Why do I need cpu_ops support in Trusted Firmware-A for this workflow?
+        - What device tree changes are required for CPU FVPs?
+        - How do I verify that my build is ready to run on the FVP?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -202811,51 +6372,80 @@ history:
       source_hash_before: aa6252610c0603acfdfc8d4555bb7da93cbdd5b9d92ba140c5dc5f63d23e2b07
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to install the Python version of llama.cpp on a Raspberry Pi 5, download
-        an LLM from Hugging Face, assess memory and performance, and run the model using Python bindings.
-        It is designed for ...
-      preview_after: Learn how to install the Python version of llama.cpp on a Raspberry Pi 5, download
-        an LLM from Hugging Face, assess memory and performance, and run the model using Python bindings.
-        It is designed for ...
-      preview_generated: Learn how to install the Python version of llama.cpp on a Raspberry Pi 5, download
-        an LLM from Hugging Face, assess memory and performance, and run the model using Python bindings.
-        It is designed for ...
+      generated_at_before: '2026-06-01T21:44:40Z'
+      generated_at_after: '2026-06-01T21:44:40Z'
+      preview_before: This introductory Learning Path guides you through running a
+        local LLM chatbot on a Raspberry Pi 5. You install the Python version of llama.cpp
+        on Raspberry Pi OS (64-bit), download a model from Huggi...
+      preview_after: This introductory Learning Path guides you through running a
+        local LLM chatbot on a Raspberry Pi 5. You install the Python version of llama.cpp
+        on Raspberry Pi OS (64-bit), download a model from Huggi...
+      preview_generated: This introductory Learning Path shows how to run a local
+        Large Language Model chatbot on a Raspberry Pi 5. You will install the Python
+        version of llama.cpp, download an LLM from Hugging Face, assess m...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: aa6252610c0603acfdfc8d4555bb7da93cbdd5b9d92ba140c5dc5f63d23e2b07
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       current_source_hash: aa6252610c0603acfdfc8d4555bb7da93cbdd5b9d92ba140c5dc5f63d23e2b07
-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:44:40Z'
+      generated_at_after: '2026-06-02T22:30:54Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - How should I prepare the SD card and which Raspberry Pi OS build should
+          I choose?
+        - Do I need the 8GB RAM Raspberry Pi 5 model?
+        - Can I follow these steps on another Arm Linux computer?
+        - Where do I obtain the model and how is it executed?
+        - What result should I expect after completing the steps, and how long will
+          it take?
+        removed_questions:
+        - What hardware and OS do I need before starting?
+        - How do I set up Raspberry Pi OS before installing the LLM tooling?
+        - What software stack does this path use to run the model?
+        - Can I follow these steps on other Arm Linux systems?
+        - "How do I know I\u2019ve completed the path successfully?"
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - How should I prepare the SD card and which Raspberry Pi OS build should
+          I choose?
+        - Do I need the 8GB RAM Raspberry Pi 5 model?
+        - Can I follow these steps on another Arm Linux computer?
+        - Where do I obtain the model and how is it executed?
+        - What result should I expect after completing the steps, and how long will
+          it take?
+        removed_questions:
+        - What hardware and OS do I need before starting?
+        - How do I set up Raspberry Pi OS before installing the LLM tooling?
+        - What software stack does this path use to run the model?
+        - Can I follow these steps on other Arm Linux systems?
+        - "How do I know I\u2019ve completed the path successfully?"
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/migration/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -202865,51 +6455,76 @@ history:
       source_hash_before: 81a15ff0d5579be9529a8c9c444be57521ebc48bbf5234f042f5a47a8a709b5c
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn the software migration methodology for porting Linux workloads from x86_64
-        to aarch64, including using Arm compilers, porting compiler intrinsics, and deploying applications
-        in containers. It is...
-      preview_after: Learn the software migration methodology for porting Linux workloads from x86_64
-        to aarch64, including using Arm compilers, porting compiler intrinsics, and deploying applications
-        in containers. It is...
-      preview_generated: Learn the software migration methodology for porting Linux workloads from x86_64
-        to aarch64, including using Arm compilers, porting compiler intrinsics, and deploying applications
-        in containers. It is...
+      generated_at_before: '2026-06-01T21:45:31Z'
+      generated_at_after: '2026-06-01T21:45:31Z'
+      preview_before: This advanced Learning Path guides you through migrating an
+        x86_64 Linux application to aarch64 using a practical porting methodology.
+        You will set up an aarch64 GCC development environment in a Docke...
+      preview_after: This advanced Learning Path guides you through migrating an x86_64
+        Linux application to aarch64 using a practical porting methodology. You will
+        set up an aarch64 GCC development environment in a Docke...
+      preview_generated: Follow a practical migration methodology to port a Linux
+        x86_64 workload to aarch64 using a Sobel filter example with non-SIMD C++,
+        x86_64 intrinsics, and OpenCV variants. You will analyze the origina...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 81a15ff0d5579be9529a8c9c444be57521ebc48bbf5234f042f5a47a8a709b5c
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       current_source_hash: 81a15ff0d5579be9529a8c9c444be57521ebc48bbf5234f042f5a47a8a709b5c
-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:45:31Z'
+      generated_at_after: '2026-06-02T22:32:21Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Can I complete this Learning Path without physical Arm hardware?
+        - Which compiler and environment should I use for the port?
+        - How should I handle x86 SIMD intrinsics during the port?
+        - What result should I expect when I run the ported application?
+        removed_questions:
+        - Do I need physical Arm hardware to complete this Learning Path?
+        - What development environment does the path use?
+        - Which compilers and libraries are involved?
+        - How are x86_64 intrinsics handled when porting to Arm?
+        - How do I validate that the port worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Can I complete this Learning Path without physical Arm hardware?
+        - Which compiler and environment should I use for the port?
+        - How should I handle x86 SIMD intrinsics during the port?
+        - What result should I expect when I run the ported application?
+        removed_questions:
+        - Do I need physical Arm hardware to complete this Learning Path?
+        - What development environment does the path use?
+        - Which compilers and libraries are involved?
+        - How are x86_64 intrinsics handled when porting to Arm?
+        - How do I validate that the port worked?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/mlek/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -202919,51 +6534,76 @@ history:
       source_hash_before: acf43df3c6f344b55bb413b7483d60e26d0e137489ac148bca64500e443140ab
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to build examples from the Machine Learning Evaluation Kit (MLEK) and
-        run them on the Arm Ecosystem FVP for machine learning application development on microcontrollers.
-        It is designed for e...
-      preview_after: Learn how to build examples from the Machine Learning Evaluation Kit (MLEK) and run
-        them on the Arm Ecosystem FVP for machine learning application development on microcontrollers.
-        It is designed for e...
-      preview_generated: Learn how to build examples from the Machine Learning Evaluation Kit (MLEK) and
-        run them on the Arm Ecosystem FVP for machine learning application development on microcontrollers.
-        It is designed for e...
+      generated_at_before: '2026-06-01T21:46:02Z'
+      generated_at_after: '2026-06-01T21:46:02Z'
+      preview_before: This Learning Path shows how to build sample applications from
+        the Arm Machine Learning Evaluation Kit (MLEK) and run them on an Arm Ecosystem
+        Fixed Virtual Platform (FVP) for bare-metal ML developmen...
+      preview_after: This Learning Path shows how to build sample applications from
+        the Arm Machine Learning Evaluation Kit (MLEK) and run them on an Arm Ecosystem
+        Fixed Virtual Platform (FVP) for bare-metal ML developmen...
+      preview_generated: Learn how to build examples from the Arm Machine Learning
+        Evaluation Kit (MLEK) and run them on an Arm Ecosystem Fixed Virtual Platform
+        (FVP) for microcontroller ML development. You will compile sampl...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: acf43df3c6f344b55bb413b7483d60e26d0e137489ac148bca64500e443140ab
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       current_source_hash: acf43df3c6f344b55bb413b7483d60e26d0e137489ac148bca64500e443140ab
-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:46:02Z'
+      generated_at_after: '2026-06-02T22:33:38Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need on my host machine before running the steps?
+        - Which FVP should I install to run the examples?
+        - Where will the built binaries be located after compiling MLEK?
+        - How do I choose and run a specific example on the FVP?
+        - What Arm IP and reference system do these examples target?
+        removed_questions:
+        - What host setup is recommended to follow this Learning Path?
+        - What prior knowledge do I need?
+        - Which virtual platform should I install, and can I use a different one?
+        - Where will the built binaries be located and what format are they?
+        - How do I run an example on the FVP and configure the Ethos-U?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need on my host machine before running the steps?
+        - Which FVP should I install to run the examples?
+        - Where will the built binaries be located after compiling MLEK?
+        - How do I choose and run a specific example on the FVP?
+        - What Arm IP and reference system do these examples target?
+        removed_questions:
+        - What host setup is recommended to follow this Learning Path?
+        - What prior knowledge do I need?
+        - Which virtual platform should I install, and can I use a different one?
+        - Where will the built binaries be located and what format are they?
+        - How do I run an example on the FVP and configure the Ethos-U?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -202973,51 +6613,80 @@ history:
       source_hash_before: 0cfaa21be515b980097232ed38092b80d046df308120887e5503513a3384a1d7
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to understand and select physical and virtual hardware targets for ML
-        application development with Cortex-M and Ethos-U, identify software tools, and find example applications.
-        It is designe...
-      preview_after: Learn how to understand and select physical and virtual hardware targets for ML application
-        development with Cortex-M and Ethos-U, identify software tools, and find example applications.
-        It is designe...
-      preview_generated: Learn how to understand and select physical and virtual hardware targets for
-        ML application development with Cortex-M and Ethos-U, identify software tools, and find example
-        applications. It is designe...
+      generated_at_before: '2026-06-01T21:46:50Z'
+      generated_at_after: '2026-06-01T21:46:50Z'
+      preview_before: This introductory path helps embedded developers plan Machine
+        Learning workflows on Arm Cortex-M with Ethos-U by choosing suitable physical
+        and virtual targets, identifying core tools, and locating ex...
+      preview_after: This introductory path helps embedded developers plan Machine
+        Learning workflows on Arm Cortex-M with Ethos-U by choosing suitable physical
+        and virtual targets, identifying core tools, and locating ex...
+      preview_generated: This introductory path guides embedded developers through
+        selecting and using hardware and virtual platforms for machine learning on
+        Cortex-M with Ethos-U. You learn how Corstone reference systems and...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 0cfaa21be515b980097232ed38092b80d046df308120887e5503513a3384a1d7
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:46:50Z'
+      generated_at_after: '2026-06-02T22:35:12Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - "I don\u2019t have an Ethos-U board\u2014what platform should I start with?"
+        - Can I follow this path on Windows, or do I need Linux?
+        - Which compilers can I use to build ML applications for Cortex-M and Ethos-U?
+        - What physical hardware options exist today for Ethos-U development?
+        - Does this path assume bare-metal or an RTOS, and what prior experience is
+          needed?
+        removed_questions:
+        - Which development host operating system should I use?
+        - Do I need physical hardware to get started?
+        - What hardware platforms are discussed for Ethos-U development?
+        - Which compilers and tools are relevant for building ML applications on Cortex-M
+          with Ethos-U?
+        - What experience is expected and what operating context is assumed?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - "I don\u2019t have an Ethos-U board\u2014what platform should I start with?"
+        - Can I follow this path on Windows, or do I need Linux?
+        - Which compilers can I use to build ML applications for Cortex-M and Ethos-U?
+        - What physical hardware options exist today for Ethos-U development?
+        - Does this path assume bare-metal or an RTOS, and what prior experience is
+          needed?
+        removed_questions:
+        - Which development host operating system should I use?
+        - Do I need physical hardware to get started?
+        - What hardware platforms are discussed for Ethos-U development?
+        - Which compilers and tools are relevant for building ML applications on Cortex-M
+          with Ethos-U?
+        - What experience is expected and what operating context is assumed?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -203027,51 +6696,78 @@ history:
       source_hash_before: d2c403c7fd1001dbd985697c9eb018951a955358c2be39c727183a87a3dfdf51
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to create debug configurations for virtual platforms and development boards
-        in Arm Development Studio, including setting up connections for Fast Models and DSTREAM debug
-        probes. It is design...
-      preview_after: Learn how to create debug configurations for virtual platforms and development boards
-        in Arm Development Studio, including setting up connections for Fast Models and DSTREAM debug
-        probes. It is design...
-      preview_generated: Learn how to create debug configurations for virtual platforms and development
-        boards in Arm Development Studio, including setting up connections for Fast Models and DSTREAM
-        debug probes. It is design...
+      generated_at_before: '2026-06-01T21:47:16Z'
+      generated_at_after: '2026-06-01T21:47:16Z'
+      preview_before: This introductory Learning Path shows how to add new debug targets
+        in Arm Development Studio for both virtual platforms and physical development
+        boards. You will create debugger connections to Arm Fas...
+      preview_after: This introductory Learning Path shows how to add new debug targets
+        in Arm Development Studio for both virtual platforms and physical development
+        boards. You will create debugger connections to Arm Fas...
+      preview_generated: This Learning Path shows how to add new debug targets in
+        Arm Development Studio by creating configurations for both virtual platforms
+        using Arm Fast Models and physical development boards using Arm DS...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: d2c403c7fd1001dbd985697c9eb018951a955358c2be39c727183a87a3dfdf51
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:47:16Z'
+      generated_at_after: '2026-06-02T22:35:33Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need installed before creating a Fast Models debug connection
+          in Arm Development Studio?
+        - Do I need a physical development board to follow this path?
+        - Which DSTREAM probe should I choose for my board?
+        - Should I connect DSTREAM to my host over USB or Ethernet?
+        - What result should I expect after creating each debug configuration?
+        removed_questions:
+        - What do I need installed before starting?
+        - Can I follow this Learning Path without a physical board?
+        - Which DSTREAM probe should I choose?
+        - How does the host connect to the DSTREAM probe?
+        - What targets and software context does this cover?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need installed before creating a Fast Models debug connection
+          in Arm Development Studio?
+        - Do I need a physical development board to follow this path?
+        - Which DSTREAM probe should I choose for my board?
+        - Should I connect DSTREAM to my host over USB or Ethernet?
+        - What result should I expect after creating each debug configuration?
+        removed_questions:
+        - What do I need installed before starting?
+        - Can I follow this Learning Path without a physical board?
+        - Which DSTREAM probe should I choose?
+        - How does the host connect to the DSTREAM probe?
+        - What targets and software context does this cover?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -203081,51 +6777,76 @@ history:
       source_hash_before: be2b3571d4d2ed75a16bc97589abc22c970d83be868b7e94bb60962a4ba853da
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to bring up ExecuTorch executor_runner firmware on the NXP FRDM i.MX 93
-        Cortex-M33 using Linux RemoteProc, compile .pte models for Ethos-U65, and run inference with NPU
-        acceleration. It is d...
-      preview_after: Learn how to bring up ExecuTorch executor_runner firmware on the NXP FRDM i.MX 93
-        Cortex-M33 using Linux RemoteProc, compile .pte models for Ethos-U65, and run inference with NPU
-        acceleration. It is d...
-      preview_generated: Learn how to bring up ExecuTorch executor_runner firmware on the NXP FRDM i.MX
-        93 Cortex-M33 using Linux RemoteProc, compile .pte models for Ethos-U65, and run inference with
-        NPU acceleration. It is d...
+      generated_at_before: '2026-06-01T21:47:49Z'
+      generated_at_after: '2026-06-01T21:47:49Z'
+      preview_before: This Learning Path guides you through deploying ExecuTorch on
+        the NXP FRDM i.MX 93 to accelerate inference with the Arm Ethos-U65. You will
+        bring up a custom executor_runner firmware on the Cortex-M33...
+      preview_after: This Learning Path guides you through deploying ExecuTorch on
+        the NXP FRDM i.MX 93 to accelerate inference with the Arm Ethos-U65. You will
+        bring up a custom executor_runner firmware on the Cortex-M33...
+      preview_generated: This Learning Path shows how to bring up a custom ExecuTorch
+        executor_runner firmware on the NXP FRDM i.MX 93 Cortex-M33 using Linux RemoteProc,
+        compile ExecuTorch .pte models for Ethos-U65, and run i...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: be2b3571d4d2ed75a16bc97589abc22c970d83be868b7e94bb60962a4ba853da
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:47:49Z'
+      generated_at_after: '2026-06-02T22:36:25Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps on the FRDM i.MX 93?
+        - How should I set up the ExecuTorch build environment on macOS?
+        - "How do I connect to the board\u2019s serial console, especially on macOS?"
+        - How can I verify that ExecuTorch installed correctly in my environment?
+        - Which artifacts do I deploy, and how do they run on this heterogeneous system?
+        removed_questions:
+        - What do I need before starting?
+        - Which host operating systems are supported, and how is macOS handled?
+        - How do I connect to and boot the board?
+        - What artifacts will I build and deploy on the device?
+        - What is the expected outcome and how long will it take?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps on the FRDM i.MX 93?
+        - How should I set up the ExecuTorch build environment on macOS?
+        - "How do I connect to the board\u2019s serial console, especially on macOS?"
+        - How can I verify that ExecuTorch installed correctly in my environment?
+        - Which artifacts do I deploy, and how do they run on this heterogeneous system?
+        removed_questions:
+        - What do I need before starting?
+        - Which host operating systems are supported, and how is macOS handled?
+        - How do I connect to and boot the board?
+        - What artifacts will I build and deploy on the device?
+        - What is the expected outcome and how long will it take?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -203135,51 +6856,78 @@ history:
       source_hash_before: 8039812773c6c1ac45cceb4039cee801e627d67871b625283c1bb5b3fa2960b3
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to migrate a CMSIS v5-based CMSIS-Pack with device support to CMSIS v6
-        and update example projects for compatibility with the new CMSIS version. It is designed for maintainers
-        of CMSIS-Packs...
-      preview_after: Learn how to migrate a CMSIS v5-based CMSIS-Pack with device support to CMSIS v6
-        and update example projects for compatibility with the new CMSIS version. It is designed for maintainers
-        of CMSIS-Packs...
-      preview_generated: Learn how to migrate a CMSIS v5-based CMSIS-Pack with device support to CMSIS
-        v6 and update example projects for compatibility with the new CMSIS version. It is designed for
-        maintainers of CMSIS-Packs...
+      generated_at_before: '2026-06-01T21:48:37Z'
+      generated_at_after: '2026-06-01T21:48:37Z'
+      preview_before: This path shows maintainers how to migrate a CMSIS v5-based
+        CMSIS-Pack with device support to CMSIS v6 and update example projects for
+        compatibility. You will update device support by switching from a...
+      preview_after: This path shows maintainers how to migrate a CMSIS v5-based CMSIS-Pack
+        with device support to CMSIS v6 and update example projects for compatibility.
+        You will update device support by switching from a...
+      preview_generated: This advanced Learning Path guides maintainers of CMSIS v5-based
+        CMSIS-Packs with device support through migrating to CMSIS v6 and updating
+        example projects. You will update device support by switchin...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 8039812773c6c1ac45cceb4039cee801e627d67871b625283c1bb5b3fa2960b3
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:48:37Z'
+      generated_at_after: '2026-06-02T22:37:02Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - Which toolchains can I use for CMSIS v6, and which one is used in this path?
+        - What do I need before running the migration steps?
+        - What changes are required in device support when moving to CMSIS v6?
+        - My example projects use Arm Compiler 5. What should I do first?
+        - When can I convert projects to the CMSIS-Toolbox csolution/cproject format?
+        removed_questions:
+        - What do I need before starting this migration?
+        - Which toolchains are supported by CMSIS v6, and which one is used in this
+          path?
+        - What device support changes are required during migration?
+        - How do I migrate example projects that still use Arm Compiler 5?
+        - What is the expected outcome after completing the steps?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - Which toolchains can I use for CMSIS v6, and which one is used in this path?
+        - What do I need before running the migration steps?
+        - What changes are required in device support when moving to CMSIS v6?
+        - My example projects use Arm Compiler 5. What should I do first?
+        - When can I convert projects to the CMSIS-Toolbox csolution/cproject format?
+        removed_questions:
+        - What do I need before starting this migration?
+        - Which toolchains are supported by CMSIS v6, and which one is used in this
+          path?
+        - What device support changes are required during migration?
+        - How do I migrate example projects that still use Arm Compiler 5?
+        - What is the expected outcome after completing the steps?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -203189,51 +6937,78 @@ history:
       source_hash_before: 7a192506fc8a15423d1257fe92e7655053e38be85aad8310dae29602e483fbba
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to migrate CMSIS v5 projects to CMSIS v6 by identifying supported toolchains,
-        installing required CMSIS-Packs, and selecting the necessary software components. It is designed
-        for embedded de...
-      preview_after: Learn how to migrate CMSIS v5 projects to CMSIS v6 by identifying supported toolchains,
-        installing required CMSIS-Packs, and selecting the necessary software components. It is designed
-        for embedded de...
-      preview_generated: Learn how to migrate CMSIS v5 projects to CMSIS v6 by identifying supported toolchains,
-        installing required CMSIS-Packs, and selecting the necessary software components. It is designed
-        for embedded de...
+      generated_at_before: '2026-06-01T21:48:57Z'
+      generated_at_after: '2026-06-01T21:48:57Z'
+      preview_before: Learn how to migrate CMSIS v5 projects to CMSIS v6 for Cortex-M
+        targets on bare-metal or RTOS. Verify your toolchain (Arm Compiler for Embedded
+        v6+, Arm GNU Toolchain v12+, LLVM v16+, or IAR Embedded ...
+      preview_after: Learn how to migrate CMSIS v5 projects to CMSIS v6 for Cortex-M
+        targets on bare-metal or RTOS. Verify your toolchain (Arm Compiler for Embedded
+        v6+, Arm GNU Toolchain v12+, LLVM v16+, or IAR Embedded ...
+      preview_generated: This advanced path guides embedded developers through migrating
+        CMSIS v5 projects to CMSIS v6 for Cortex-M, targeting bare-metal and RTOS
+        environments. You will confirm supported toolchains (Arm Compi...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:48:57Z'
+      generated_at_after: '2026-06-02T22:37:32Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - Which toolchain versions are supported for CMSIS v6?
+        - Which CMSIS-Packs do I need when migrating from CMSIS v5 to v6?
+        - I used the Keil.ARM_Compiler pack. Which packs replace it for CMSIS v6?
+        - How do I map my CMSIS v5 device to the Cortex_DFP pack?
+        - "What should I do if I\u2019m using a Keil MDK v5 uvprojx project?"
+        removed_questions:
+        - Which toolchains and versions can I use with CMSIS v6?
+        - Which CMSIS-Packs are required when migrating from CMSIS v5?
+        - My project depends on Keil.ARM_Compiler. What should I install for CMSIS
+          v6?
+        - How do I update device selection after moving to CMSIS v6?
+        - What problems does the troubleshooting section cover?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - Which toolchain versions are supported for CMSIS v6?
+        - Which CMSIS-Packs do I need when migrating from CMSIS v5 to v6?
+        - I used the Keil.ARM_Compiler pack. Which packs replace it for CMSIS v6?
+        - How do I map my CMSIS v5 device to the Cortex_DFP pack?
+        - "What should I do if I\u2019m using a Keil MDK v5 uvprojx project?"
+        removed_questions:
+        - Which toolchains and versions can I use with CMSIS v6?
+        - Which CMSIS-Packs are required when migrating from CMSIS v5?
+        - My project depends on Keil.ARM_Compiler. What should I install for CMSIS
+          v6?
+        - How do I update device selection after moving to CMSIS v6?
+        - What problems does the troubleshooting section cover?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -203243,51 +7018,78 @@ history:
       source_hash_before: a0145c77b3d4a8bb25a32c62adaa3ad378e65fccbe6db88d9a46a569897d238a
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to run large language models locally on the Raspberry Pi 5 using Ollama,
-        control GPIO-connected devices, and deploy a privacy-first web-based smart home assistant without
-        cloud services. It ...
-      preview_after: Learn how to run large language models locally on the Raspberry Pi 5 using Ollama,
-        control GPIO-connected devices, and deploy a privacy-first web-based smart home assistant without
-        cloud services. It ...
-      preview_generated: Learn how to run large language models locally on the Raspberry Pi 5 using Ollama,
-        control GPIO-connected devices, and deploy a privacy-first web-based smart home assistant without
-        cloud services. It ...
+      generated_at_before: '2026-06-01T21:50:06Z'
+      generated_at_after: '2026-06-01T21:50:06Z'
+      preview_before: This introductory Learning Path guides you through building
+        a fully local, privacy-first smart home assistant on Raspberry Pi 5 with an
+        Arm Cortex-A76 CPU. You install Python and required libraries, s...
+      preview_after: This introductory Learning Path guides you through building a
+        fully local, privacy-first smart home assistant on Raspberry Pi 5 with an
+        Arm Cortex-A76 CPU. You install Python and required libraries, s...
+      preview_generated: Follow this Learning Path to build a privacy-first smart
+        home assistant that runs entirely on a Raspberry Pi 5 (Arm Cortex-A76) with
+        no cloud services. You will install Python, required libraries, and...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: a0145c77b3d4a8bb25a32c62adaa3ad378e65fccbe6db88d9a46a569897d238a
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:50:06Z'
+      generated_at_after: '2026-06-02T22:37:58Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the setup?
+        - How should I connect to my Raspberry Pi 5 to install dependencies?
+        - How do I wire and verify the GPIO LED test?
+        - Where do I get the assistant code and what does the main script do?
+        - How do I interact with the assistant and what behavior should I expect from
+          the LLM?
+        removed_questions:
+        - What hardware and skills are required before starting?
+        - Does this project rely on cloud services or an internet connection?
+        - Which operating system and environment are assumed?
+        - How do I wire and verify the GPIO test circuit?
+        - How do I run and interact with the smart home assistant?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the setup?
+        - How should I connect to my Raspberry Pi 5 to install dependencies?
+        - How do I wire and verify the GPIO LED test?
+        - Where do I get the assistant code and what does the main script do?
+        - How do I interact with the assistant and what behavior should I expect from
+          the LLM?
+        removed_questions:
+        - What hardware and skills are required before starting?
+        - Does this project rely on cloud services or an internet connection?
+        - Which operating system and environment are assumed?
+        - How do I wire and verify the GPIO test circuit?
+        - How do I run and interact with the smart home assistant?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -203297,51 +7099,77 @@ history:
       source_hash_before: ed2034f5c95c4148fca355f6d545219c320f2e73267a0725934c792ebc4c0c59
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to build a voice-controlled bot on a Raspberry Pi that listens for a wake
-        word, converts speech to text using Google Speech Recognition, sends requests to ChatGPT's API,
-        and plays audio resp...
-      preview_after: Learn how to build a voice-controlled bot on a Raspberry Pi that listens for a wake
-        word, converts speech to text using Google Speech Recognition, sends requests to ChatGPT's API,
-        and plays audio resp...
-      preview_generated: Learn how to build a voice-controlled bot on a Raspberry Pi that listens for
-        a wake word, converts speech to text using Google Speech Recognition, sends requests to ChatGPT's
-        API, and plays audio resp...
+      generated_at_before: '2026-06-01T21:50:55Z'
+      generated_at_after: '2026-06-01T21:50:55Z'
+      preview_before: This introductory Learning Path guides you through building
+        and running a voice-controlled ChatGPT bot on a Raspberry Pi 4 or 5 using
+        Raspberry Pi OS (64-bit, Linux). You will install the OS with Rasp...
+      preview_after: This introductory Learning Path guides you through building and
+        running a voice-controlled ChatGPT bot on a Raspberry Pi 4 or 5 using Raspberry
+        Pi OS (64-bit, Linux). You will install the OS with Rasp...
+      preview_generated: "Build a voice-controlled bot on a Raspberry Pi that wakes\
+        \ on a keyword, converts your speech to text with Google Speech Recognition,\
+        \ sends the text to ChatGPT\u2019s gpt-4-turbo-preview via API, and plays\
+        \ ..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: ed2034f5c95c4148fca355f6d545219c320f2e73267a0725934c792ebc4c0c59
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:50:55Z'
+      generated_at_after: '2026-06-02T22:38:37Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What Raspberry Pi hardware and OS do I need before starting?
+        - How do I verify my microphone and speakers are set up correctly?
+        - Which Python version and packages does the application use?
+        - How do I run and stop the bot?
+        - What behavior should I expect when I say the wake word?
+        removed_questions:
+        - Which Raspberry Pi models and OS does this path use?
+        - What hardware peripherals do I need, and how do I verify audio is working?
+        - What Python environment and packages are used?
+        - How does the bot process voice, and which wake word and models are used?
+        - "How do I run and stop the bot, and what indicates it\u2019s working?"
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What Raspberry Pi hardware and OS do I need before starting?
+        - How do I verify my microphone and speakers are set up correctly?
+        - Which Python version and packages does the application use?
+        - How do I run and stop the bot?
+        - What behavior should I expect when I say the wake word?
+        removed_questions:
+        - Which Raspberry Pi models and OS does this path use?
+        - What hardware peripherals do I need, and how do I verify audio is working?
+        - What Python environment and packages are used?
+        - How does the bot process voice, and which wake word and models are used?
+        - "How do I run and stop the bot, and what indicates it\u2019s working?"
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/rpi/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -203351,51 +7179,78 @@ history:
       source_hash_before: 5e5fad1563b67ed38d1cf3399d8e0d62162e76cae8d6848c3be7638f67cd037c
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to build and run multiple software examples on the Raspberry Pi 4, including
-        TensorFlow and Docker applications, and compare its performance to Arm cloud servers. It is designed
-        for software...
-      preview_after: Learn how to build and run multiple software examples on the Raspberry Pi 4, including
-        TensorFlow and Docker applications, and compare its performance to Arm cloud servers. It is designed
-        for software...
-      preview_generated: Learn how to build and run multiple software examples on the Raspberry Pi 4,
-        including TensorFlow and Docker applications, and compare its performance to Arm cloud servers.
-        It is designed for software...
+      generated_at_before: '2026-06-01T21:51:21Z'
+      generated_at_after: '2026-06-01T21:51:21Z'
+      preview_before: This introductory Learning Path walks you through setting up
+        a Raspberry Pi 4 with 64-bit Raspberry Pi OS and an Arm-based cloud instance,
+        then running comparable software examples on both to understa...
+      preview_after: This introductory Learning Path walks you through setting up
+        a Raspberry Pi 4 with 64-bit Raspberry Pi OS and an Arm-based cloud instance,
+        then running comparable software examples on both to understa...
+      preview_generated: Follow this introductory path to set up a Raspberry Pi 4
+        with 64-bit Raspberry Pi OS, provision an Arm-based cloud instance, and run
+        comparable software on both to observe performance differences. You...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 5e5fad1563b67ed38d1cf3399d8e0d62162e76cae8d6848c3be7638f67cd037c
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       current_source_hash: 5e5fad1563b67ed38d1cf3399d8e0d62162e76cae8d6848c3be7638f67cd037c
-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:51:21Z'
+      generated_at_after: '2026-06-02T22:39:04Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Raspberry Pi OS should I install and how?
+        - How do I verify that both systems are 64-bit Arm and running Linux?
+        - How do I install and test TensorFlow in this path?
+        - What result should I expect from the Linux kernel compile comparison?
+        removed_questions:
+        - What hardware and cloud resources do I need before starting?
+        - How do I choose and prepare the Arm-based cloud instance?
+        - How can I verify both systems are running the expected 64-bit Arm environment?
+        - What examples will I run to compare performance between the Raspberry Pi
+          4 and the cloud server?
+        - How do I install and run TensorFlow in this path?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Raspberry Pi OS should I install and how?
+        - How do I verify that both systems are 64-bit Arm and running Linux?
+        - How do I install and test TensorFlow in this path?
+        - What result should I expect from the Linux kernel compile comparison?
+        removed_questions:
+        - What hardware and cloud resources do I need before starting?
+        - How do I choose and prepare the Arm-based cloud instance?
+        - How can I verify both systems are running the expected 64-bit Arm environment?
+        - What examples will I run to compare performance between the Raspberry Pi
+          4 and the cloud server?
+        - How do I install and run TensorFlow in this path?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -203405,51 +7260,78 @@ history:
       source_hash_before: 9cd2c3082c4874dc596e1b7b59c3dc2143aaf1ce3e66948ab8b6af190ffa3776
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       current_source_hash: 9cd2c3082c4874dc596e1b7b59c3dc2143aaf1ce3e66948ab8b6af190ffa3776
-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to compile the Llama 3 large language model using ExecuTorch, deploy it
-        to a Raspberry Pi 5, and understand techniques for running LLMs in embedded environments. It is
-        designed for anyone in...
-      preview_after: Learn how to compile the Llama 3 large language model using ExecuTorch, deploy it
-        to a Raspberry Pi 5, and understand techniques for running LLMs in embedded environments. It is
-        designed for anyone in...
-      preview_generated: Learn how to compile the Llama 3 large language model using ExecuTorch, deploy
-        it to a Raspberry Pi 5, and understand techniques for running LLMs in embedded environments. It
-        is designed for anyone in...
+      generated_at_before: '2026-06-01T21:51:49Z'
+      generated_at_after: '2026-06-01T21:51:49Z'
+      preview_before: This introductory Learning Path shows how to compile the Llama
+        3 large language model with ExecuTorch using a Docker container that runs
+        Raspberry Pi OS on an Arm Linux machine or Arm cloud instance, ...
+      preview_after: This introductory Learning Path shows how to compile the Llama
+        3 large language model with ExecuTorch using a Docker container that runs
+        Raspberry Pi OS on an Arm Linux machine or Arm cloud instance, ...
+      preview_generated: This introductory Learning Path shows how to prepare and
+        deploy the Llama 3 large language model to a Raspberry Pi 5 using ExecuTorch.
+        You will use Docker to run Raspberry Pi OS on an Arm Linux machin...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 9cd2c3082c4874dc596e1b7b59c3dc2143aaf1ce3e66948ab8b6af190ffa3776
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       current_source_hash: 9cd2c3082c4874dc596e1b7b59c3dc2143aaf1ce3e66948ab8b6af190ffa3776
-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:51:49Z'
+      generated_at_after: '2026-06-02T22:39:40Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Where do I build the binaries for deployment?
+        - Which Raspberry Pi OS should I install on the device?
+        - Do I need to quantize the Llama 3 model for the Raspberry Pi 5?
+        - How do I validate that the model is running correctly on the Raspberry Pi
+          5?
+        removed_questions:
+        - What hardware and host environment do I need before starting?
+        - How is the development environment set up?
+        - How do I install ExecuTorch for this workflow?
+        - What artifacts will I create and deploy to the Raspberry Pi 5?
+        - How do I verify that the deployment worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Where do I build the binaries for deployment?
+        - Which Raspberry Pi OS should I install on the device?
+        - Do I need to quantize the Llama 3 model for the Raspberry Pi 5?
+        - How do I validate that the model is running correctly on the Raspberry Pi
+          5?
+        removed_questions:
+        - What hardware and host environment do I need before starting?
+        - How is the development environment set up?
+        - How do I install ExecuTorch for this workflow?
+        - What artifacts will I create and deploy to the Raspberry Pi 5?
+        - How do I verify that the deployment worked?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -203459,51 +7341,78 @@ history:
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-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to reduce compile time for embedded Linux projects by installing a Raspberry
-        Pi OS file system on an Arm server, building the MXNet machine learning framework, and transferring
-        it to a Raspb...
-      preview_after: Learn how to reduce compile time for embedded Linux projects by installing a Raspberry
-        Pi OS file system on an Arm server, building the MXNet machine learning framework, and transferring
-        it to a Raspb...
-      preview_generated: Learn how to reduce compile time for embedded Linux projects by installing a
-        Raspberry Pi OS file system on an Arm server, building the MXNet machine learning framework, and
-        transferring it to a Raspb...
+      generated_at_before: '2026-06-01T21:52:22Z'
+      generated_at_after: '2026-06-01T21:52:22Z'
+      preview_before: This advanced Learning Path shows how to cut compile time for
+        embedded Linux work by building the MXNet machine learning framework on an
+        Arm Linux server using a Raspberry Pi OS file system, then depl...
+      preview_after: This advanced Learning Path shows how to cut compile time for
+        embedded Linux work by building the MXNet machine learning framework on an
+        Arm Linux server using a Raspberry Pi OS file system, then depl...
+      preview_generated: Learn how to reduce compile time for embedded Linux projects
+        by building the MXNet machine learning framework inside a Raspberry Pi OS
+        file system hosted on an Arm Linux server or cloud instance. You ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:52:22Z'
+      generated_at_after: '2026-06-02T22:40:08Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need on the Arm server before starting?
+        - How do I know I am inside the Raspberry Pi OS file system before installing
+          dependencies?
+        - Which user should compile MXNet, and where should I run the build?
+        - Which packages are required to build MXNet in this path?
+        - How do I transfer the built image and deploy it on a Raspberry Pi?
+        removed_questions:
+        - What environment do I need to start?
+        - Do I need a Raspberry Pi to complete the Learning Path?
+        - Which user account should I use when building MXNet?
+        - What is the expected build output?
+        - How do I deploy the result to the Raspberry Pi?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need on the Arm server before starting?
+        - How do I know I am inside the Raspberry Pi OS file system before installing
+          dependencies?
+        - Which user should compile MXNet, and where should I run the build?
+        - Which packages are required to build MXNet in this path?
+        - How do I transfer the built image and deploy it on a Raspberry Pi?
+        removed_questions:
+        - What environment do I need to start?
+        - Do I need a Raspberry Pi to complete the Learning Path?
+        - Which user account should I use when building MXNet?
+        - What is the expected build output?
+        - How do I deploy the result to the Raspberry Pi?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -203513,51 +7422,74 @@ history:
       source_hash_before: d9fe3cfc8f7a7092f40786763fc28e371697e4b57dba99b2c9b191dae4273911
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Setup tools and start programming with Raspberry Pi Pico. It is designed for embedded
-        software developers new to Raspberry Pi Pico. By the end, you will be able to install the Raspberry
-        Pi Pico SDK, r...
-      preview_after: Setup tools and start programming with Raspberry Pi Pico. It is designed for embedded
-        software developers new to Raspberry Pi Pico. By the end, you will be able to install the Raspberry
-        Pi Pico SDK, r...
-      preview_generated: Setup tools and start programming with Raspberry Pi Pico. It is designed for
-        embedded software developers new to Raspberry Pi Pico. By the end, you will be able to install
-        the Raspberry Pi Pico SDK, r...
+      generated_at_before: '2026-06-01T21:52:41Z'
+      generated_at_after: '2026-06-01T21:52:41Z'
+      preview_before: This introductory path shows how to set up the Raspberry Pi
+        Pico C/C++ SDK on a Raspberry Pi development computer and write bare-metal
+        applications for the Arm Cortex-M0+ on the Pico. You will install...
+      preview_after: This introductory path shows how to set up the Raspberry Pi Pico
+        C/C++ SDK on a Raspberry Pi development computer and write bare-metal applications
+        for the Arm Cortex-M0+ on the Pico. You will install...
+      preview_generated: Set up a Raspberry Pi Pico development environment and complete
+        a first bare-metal workflow on Arm Cortex-M0+. You will install the Raspberry
+        Pi Pico SDK (via the pico_setup.sh script from GitHub), bu...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: d9fe3cfc8f7a7092f40786763fc28e371697e4b57dba99b2c9b191dae4273911
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-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:52:41Z'
+      generated_at_after: '2026-06-02T22:41:08Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which tools does the Pico SDK use to build applications?
+        - How can I measure the number of cycles a code section takes on the Pico?
+        - How can I load and debug without pressing the BOOTSEL button each time?
+        removed_questions:
+        - What hardware is required to follow this Learning Path?
+        - What tools and languages are used to build applications?
+        - How is application performance measured in this path?
+        - How are programs loaded and debugged without using the BOOTSEL button?
+        updated_questions:
+        - How do I know the Hello World example worked?
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which tools does the Pico SDK use to build applications?
+        - How can I measure the number of cycles a code section takes on the Pico?
+        - How can I load and debug without pressing the BOOTSEL button each time?
+        removed_questions:
+        - What hardware is required to follow this Learning Path?
+        - What tools and languages are used to build applications?
+        - How is application performance measured in this path?
+        - How are programs loaded and debugged without using the BOOTSEL button?
+        updated_questions:
+        - How do I know the Hello World example worked?
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -203567,51 +7499,80 @@ history:
       source_hash_before: 0b8de63a15d3d77b9c9972103b1317d6c48907875ac625c3d1c1b8db5360879a
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       current_source_hash: 0b8de63a15d3d77b9c9972103b1317d6c48907875ac625c3d1c1b8db5360879a
-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
-      preview_before: Learn how to profile Linux kernel modules using Arm Streamline to identify performance
-        bottlenecks, analyze both out-of-tree and in-tree modules, and use Statistical Profiling Extension
-        (SPE) for deep...
-      preview_after: Learn how to profile Linux kernel modules using Arm Streamline to identify performance
-        bottlenecks, analyze both out-of-tree and in-tree modules, and use Statistical Profiling Extension
-        (SPE) for deep...
-      preview_generated: Learn how to profile Linux kernel modules using Arm Streamline to identify performance
-        bottlenecks, analyze both out-of-tree and in-tree modules, and use Statistical Profiling Extension
-        (SPE) for deep...
+      generated_at_before: '2026-06-01T21:53:04Z'
+      generated_at_after: '2026-06-01T21:53:04Z'
+      preview_before: This advanced Learning Path shows how to profile Linux kernel
+        modules on Arm-based systems using Arm Streamline, part of Arm Performance
+        Studio. You will prepare a Buildroot-based environment, impleme...
+      preview_after: This advanced Learning Path shows how to profile Linux kernel
+        modules on Arm-based systems using Arm Streamline, part of Arm Performance
+        Studio. You will prepare a Buildroot-based environment, impleme...
+      preview_generated: This advanced path shows how to profile Linux kernel modules
+        on Arm-based systems using Arm Streamline from Arm Performance Studio. You
+        will prepare an AArch64-based Linux host with Buildroot prerequi...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 0b8de63a15d3d77b9c9972103b1317d6c48907875ac625c3d1c1b8db5360879a
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       current_source_hash: 0b8de63a15d3d77b9c9972103b1317d6c48907875ac625c3d1c1b8db5360879a
-      generated_at_before: '2026-05-06T17:17:54Z'
-      generated_at_after: '2026-05-06T17:17:54Z'
+      generated_at_before: '2026-06-01T21:53:04Z'
+      generated_at_after: '2026-06-02T22:41:38Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps on hardware?
+        - Which system should I use to install Buildroot prerequisites and run the
+          build steps?
+        - How does the example kernel module create measurable behavior for profiling?
+        - What should I add in Streamline to profile an in-tree driver with kernel
+          symbols?
+        - How is the Statistical Profiling Extension (SPE) used in this path?
+        removed_questions:
+        - What do I need before starting?
+        - Will I build both out-of-tree and in-tree kernel modules?
+        - Do I need to prepare a Linux image, and how is it done?
+        - What profiling data does Arm Streamline provide in this path?
+        - How can I tell that profiling worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps on hardware?
+        - Which system should I use to install Buildroot prerequisites and run the
+          build steps?
+        - How does the example kernel module create measurable behavior for profiling?
+        - What should I add in Streamline to profile an in-tree driver with kernel
+          symbols?
+        - How is the Statistical Profiling Extension (SPE) used in this path?
+        removed_questions:
+        - What do I need before starting?
+        - Will I build both out-of-tree and in-tree kernel modules?
+        - Do I need to prepare a Linux image, and how is it done?
+        - What profiling data does Arm Streamline provide in this path?
+        - How can I tell that profiling worked?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -203621,51 +7582,80 @@ history:
       source_hash_before: b5714494f00fff407c39e6f2f7bf37de94cde61d7569ba147412fec1b2f537c2
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Build a letter recognition neural network model using TensorFlow and deploy it on
-        an STM32 B-L475E-IOT01A2 board. It is designed for software developers interested in building
-        network models for micro...
-      preview_after: Build a letter recognition neural network model using TensorFlow and deploy it on
-        an STM32 B-L475E-IOT01A2 board. It is designed for software developers interested in building
-        network models for micro...
-      preview_generated: Build a letter recognition neural network model using TensorFlow and deploy it
-        on an STM32 B-L475E-IOT01A2 board. It is designed for software developers interested in building
-        network models for micro...
+      generated_at_before: '2026-06-01T21:53:37Z'
+      generated_at_after: '2026-06-01T21:53:37Z'
+      preview_before: This Learning Path guides you through building a letter recognition
+        neural network in TensorFlow using accelerometer data from an STM32 B-L475E-IOT01A2
+        board, then deploying it to the device with STM3...
+      preview_after: This Learning Path guides you through building a letter recognition
+        neural network in TensorFlow using accelerometer data from an STM32 B-L475E-IOT01A2
+        board, then deploying it to the device with STM3...
+      preview_generated: Build and deploy a letter recognition neural network on the
+        STM32 B-L475E-IOT01A2 (Arm Cortex-M4). You will set up a Python environment
+        with Anaconda, work in a Jupyter notebook to collect acceleromet...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: b5714494f00fff407c39e6f2f7bf37de94cde61d7569ba147412fec1b2f537c2
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T21:53:37Z'
+      generated_at_after: '2026-06-02T22:42:04Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - How should I run the Jupyter notebook steps, and how do I know each cell
+          finished?
+        - What data do I train on, and how is it prepared?
+        - Which model architecture should I define in TensorFlow?
+        - Which option should I use in STM32CubeMX to target the board and import
+          the model?
+        removed_questions:
+        - What hardware and target environment does this path use?
+        - What prior knowledge is required before starting?
+        - Which tools and frameworks are used throughout the steps?
+        - How is the training data obtained and what features are used?
+        - What artifacts should I expect by the end, and how do I know it worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - How should I run the Jupyter notebook steps, and how do I know each cell
+          finished?
+        - What data do I train on, and how is it prepared?
+        - Which model architecture should I define in TensorFlow?
+        - Which option should I use in STM32CubeMX to target the board and import
+          the model?
+        removed_questions:
+        - What hardware and target environment does this path use?
+        - What prior knowledge is required before starting?
+        - Which tools and frameworks are used throughout the steps?
+        - How is the training data obtained and what features are used?
+        - What artifacts should I expect by the end, and how do I know it worked?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/tfm/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -203675,51 +7665,78 @@ history:
       source_hash_before: 8d8f9df559ff1b1570dc98baf640fc2d4c4d225d69f22529e1881b62d4017752
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to build and run the reference Trusted Firmware-M tests and example application
-        on Arm Fixed Virtual Platforms for secure microcontroller development. It is designed for software
-        developers ...
-      preview_after: Learn how to build and run the reference Trusted Firmware-M tests and example application
-        on Arm Fixed Virtual Platforms for secure microcontroller development. It is designed for software
-        developers ...
-      preview_generated: Learn how to build and run the reference Trusted Firmware-M tests and example
-        application on Arm Fixed Virtual Platforms for secure microcontroller development. It is designed
-        for software developers ...
+      generated_at_before: '2026-06-01T21:54:11Z'
+      generated_at_after: '2026-06-01T21:54:11Z'
+      preview_before: This introductory Learning Path shows how to build and run the
+        reference Trusted Firmware-M (TF-M) tests and example application on the Corstone-300
+        Fixed Virtual Platform (FVP). Working in a bare-met...
+      preview_after: This introductory Learning Path shows how to build and run the
+        reference Trusted Firmware-M (TF-M) tests and example application on the Corstone-300
+        Fixed Virtual Platform (FVP). Working in a bare-met...
+      preview_generated: Build and run the reference Trusted Firmware-M (TF-M) tests
+        and example application on the Corstone-300 Fixed Virtual Platform (FVP) to
+        get started with secure microcontroller development. This introd...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 8d8f9df559ff1b1570dc98baf640fc2d4c4d225d69f22529e1881b62d4017752
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       current_source_hash: 8d8f9df559ff1b1570dc98baf640fc2d4c4d225d69f22529e1881b62d4017752
-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T21:54:11Z'
+      generated_at_after: '2026-06-02T22:43:17Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which platform should I use to run the TF-M tests and example?
+        - Is an RTOS required or is this a bare-metal setup?
+        - Which Ubuntu version is assumed, and what initial setup step should I run?
+        - What result should I expect after completing the steps, and how long will
+          it take?
+        removed_questions:
+        - What environment and prerequisites are expected?
+        - Which platform and tools will I use?
+        - Where do I obtain the Corstone-300 FVP?
+        - What will I build and run in this path?
+        - How do I know the steps were successful?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which platform should I use to run the TF-M tests and example?
+        - Is an RTOS required or is this a bare-metal setup?
+        - Which Ubuntu version is assumed, and what initial setup step should I run?
+        - What result should I expect after completing the steps, and how long will
+          it take?
+        removed_questions:
+        - What environment and prerequisites are expected?
+        - Which platform and tools will I use?
+        - Where do I obtain the Corstone-300 FVP?
+        - What will I build and run in this path?
+        - How do I know the steps were successful?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -203729,51 +7746,76 @@ history:
       source_hash_before: 20c500fd5174c9901058676d4619981ee1c8a8cf8e331affea8c0292112651c9
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       current_source_hash: 20c500fd5174c9901058676d4619981ee1c8a8cf8e331affea8c0292112651c9
-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to train a CNN image classification model using PyTorch, convert it to
-        ExecuTorch format, and run it as an interactive mini-game on Arm-based edge devices. It is designed
-        for machine learnin...
-      preview_after: Learn how to train a CNN image classification model using PyTorch, convert it to
-        ExecuTorch format, and run it as an interactive mini-game on Arm-based edge devices. It is designed
-        for machine learnin...
-      preview_generated: Learn how to train a CNN image classification model using PyTorch, convert it
-        to ExecuTorch format, and run it as an interactive mini-game on Arm-based edge devices. It is
-        designed for machine learnin...
+      generated_at_before: '2026-06-01T21:54:57Z'
+      generated_at_after: '2026-06-01T21:54:57Z'
+      preview_before: This Learning Path walks you through training a small CNN in
+        PyTorch to classify images of the letters R, P, and S into rock, paper, or
+        scissors, exporting the model to an ExecuTorch program (.pte), a...
+      preview_after: This Learning Path walks you through training a small CNN in
+        PyTorch to classify images of the letters R, P, and S into rock, paper, or
+        scissors, exporting the model to an ExecuTorch program (.pte), a...
+      preview_generated: Build a tiny rock-paper-scissors classifier with PyTorch,
+        export it to an ExecuTorch program (.pte), and run it both as a local CLI
+        mini-game and on the Corstone-320 Fixed Virtual Platform (FVP). You ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 20c500fd5174c9901058676d4619981ee1c8a8cf8e331affea8c0292112651c9
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       current_source_hash: 20c500fd5174c9901058676d4619981ee1c8a8cf8e331affea8c0292112651c9
-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T21:54:57Z'
+      generated_at_after: '2026-06-02T22:43:51Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Where should I create the script and start training?
+        - Do I need a real image dataset to train the model?
+        - What artifact should I expect after exporting the model?
+        - What should I expect when I run the mini-game or the FVP build?
+        removed_questions:
+        - What prerequisites and environment do I need to follow this Learning Path?
+        - What will I build and run by the end of the steps?
+        - Where do I place the example code and how do I run it?
+        - Do I need a real dataset for training the model?
+        - Do I need Arm hardware to test deployment?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Where should I create the script and start training?
+        - Do I need a real image dataset to train the model?
+        - What artifact should I expect after exporting the model?
+        - What should I expect when I run the mini-game or the FVP build?
+        removed_questions:
+        - What prerequisites and environment do I need to follow this Learning Path?
+        - What will I build and run by the end of the steps?
+        - Where do I place the example code and how do I run it?
+        - Do I need a real dataset for training the model?
+        - Do I need Arm hardware to test deployment?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -203783,51 +7825,76 @@ history:
       source_hash_before: 6c81f3c9595df1128ca6f4f89446f093826d2242fa789ffa2d37465854be1f8e
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       current_source_hash: 6c81f3c9595df1128ca6f4f89446f093826d2242fa789ffa2d37465854be1f8e
-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to install Keil MDK Tools, run a TrustZone hello world example on the
-        NXP LPCXpresso55S69 board, and understand security state switching and secure function calls.
-        It is designed for softwar...
-      preview_after: Learn how to install Keil MDK Tools, run a TrustZone hello world example on the NXP
-        LPCXpresso55S69 board, and understand security state switching and secure function calls. It is
-        designed for softwar...
-      preview_generated: Learn how to install Keil MDK Tools, run a TrustZone hello world example on the
-        NXP LPCXpresso55S69 board, and understand security state switching and secure function calls.
-        It is designed for softwar...
+      generated_at_before: '2026-06-01T21:55:22Z'
+      generated_at_after: '2026-06-01T21:55:22Z'
+      preview_before: This introductory path shows how to set up Keil MDK with Arm
+        Compiler for Embedded on Windows and run a bare-metal TrustZone hello world
+        on the NXP LPCXpresso55S69. You will obtain the example using t...
+      preview_after: This introductory path shows how to set up Keil MDK with Arm
+        Compiler for Embedded on Windows and run a bare-metal TrustZone hello world
+        on the NXP LPCXpresso55S69. You will obtain the example using t...
+      preview_generated: This Learning Path guides you through installing Keil MDK
+        Tools and Arm Compiler for Embedded on Windows, connecting the NXP LPCXpresso55S69
+        board, and running a TrustZone hello world example on bare ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 6c81f3c9595df1128ca6f4f89446f093826d2242fa789ffa2d37465854be1f8e
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       current_source_hash: 6c81f3c9595df1128ca6f4f89446f093826d2242fa789ffa2d37465854be1f8e
-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T21:55:22Z'
+      generated_at_after: '2026-06-02T22:45:23Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - "How do I obtain the TrustZone hello world example in Keil \u03BCVision?"
+        - Which project should I open to build and run the example?
+        - What result should I expect when starting a debug session?
+        - How do I explore security state switching and secure function calls?
+        removed_questions:
+        - What setup is required before I start?
+        - "How do I get the TrustZone hello world example in Keil \xB5Vision?"
+        - Which project should I open to begin building and debugging?
+        - What TrustZone concepts will I observe during debugging?
+        - What prerequisites and time commitment are expected?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - "How do I obtain the TrustZone hello world example in Keil \u03BCVision?"
+        - Which project should I open to build and run the example?
+        - What result should I expect when starting a debug session?
+        - How do I explore security state switching and secure function calls?
+        removed_questions:
+        - What setup is required before I start?
+        - "How do I get the TrustZone hello world example in Keil \xB5Vision?"
+        - Which project should I open to begin building and debugging?
+        - What TrustZone concepts will I observe during debugging?
+        - What prerequisites and time commitment are expected?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -203837,51 +7904,77 @@ history:
       source_hash_before: 57e05139cdea1de2f4a4ce239d701f0cef634b6ac11fd437cba47cc071e14098
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       current_source_hash: 57e05139cdea1de2f4a4ce239d701f0cef634b6ac11fd437cba47cc071e14098
-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to acquire and print materials, assemble a universal SBC rack mount system
-        in a 4U chassis, and install single board computers in the racks using 3D-printed parts. It is
-        designed for softwar...
-      preview_after: Learn how to acquire and print materials, assemble a universal SBC rack mount system
-        in a 4U chassis, and install single board computers in the racks using 3D-printed parts. It is
-        designed for softwar...
-      preview_generated: Learn how to acquire and print materials, assemble a universal SBC rack mount
-        system in a 4U chassis, and install single board computers in the racks using 3D-printed parts.
-        It is designed for softwar...
+      generated_at_before: '2026-06-01T21:55:43Z'
+      generated_at_after: '2026-06-01T21:55:43Z'
+      preview_before: This Learning Path shows you how to 3D print parts and assemble
+        a universal rack mount system for single board computers in a 4U chassis.
+        You will print bay bodies and covers using PETG, cut and prepa...
+      preview_after: This Learning Path shows you how to 3D print parts and assemble
+        a universal rack mount system for single board computers in a 4U chassis.
+        You will print bay bodies and covers using PETG, cut and prepa...
+      preview_generated: "Build a universal rack mount system to house Arm Cortex-A\
+        \ single board computers in a 4U chassis using 3D\u2011printed parts. You\
+        \ will print bay bodies, bay covers, and spacers\u2014preferably in PETG\u2014\
+        then asse..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 57e05139cdea1de2f4a4ce239d701f0cef634b6ac11fd437cba47cc071e14098
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       current_source_hash: 57e05139cdea1de2f4a4ce239d701f0cef634b6ac11fd437cba47cc071e14098
-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T21:55:43Z'
+      generated_at_after: '2026-06-02T22:46:11Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before I start printing and assembling the rack?
+        - Which filament should I use for the printed parts and why?
+        - How many printed parts do I need per bay?
+        - How should I prepare and assemble the chassis bays?
+        - How do I mount an SBC to a card plate and check orientation?
+        removed_questions:
+        - What materials and tools are required before I start?
+        - How long does this build take and what skill level is assumed?
+        - Why is PETG recommended for the printed parts, and can I use other filaments?
+        - What chassis and hardware dimensions does the design assume?
+        - How many printed parts do I need per bay, and how do I confirm the fit?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before I start printing and assembling the rack?
+        - Which filament should I use for the printed parts and why?
+        - How many printed parts do I need per bay?
+        - How should I prepare and assemble the chassis bays?
+        - How do I mount an SBC to a card plate and check orientation?
+        removed_questions:
+        - What materials and tools are required before I start?
+        - How long does this build take and what skill level is assumed?
+        - Why is PETG recommended for the printed parts, and can I use other filaments?
+        - What chassis and hardware dimensions does the design assume?
+        - How many printed parts do I need per bay, and how do I confirm the fit?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -203891,51 +7984,77 @@ history:
       source_hash_before: 27ced3e9f69dbe51e75dcf13999ae1745a994137f31c86d6f17d4de341c7fa2a
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       current_source_hash: 27ced3e9f69dbe51e75dcf13999ae1745a994137f31c86d6f17d4de341c7fa2a
-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to debug microcontrollers using µVision with basic run/stop debug, advanced
-        techniques using Event Recorder and Serial Wire Viewer, ETM Trace for performance analysis, and
-        power measurement ...
-      preview_after: Learn how to debug microcontrollers using µVision with basic run/stop debug, advanced
-        techniques using Event Recorder and Serial Wire Viewer, ETM Trace for performance analysis, and
-        power measurement ...
-      preview_generated: Learn how to debug microcontrollers using µVision with basic run/stop debug,
-        advanced techniques using Event Recorder and Serial Wire Viewer, ETM Trace for performance analysis,
-        and power measurement ...
+      generated_at_before: '2026-06-01T21:56:36Z'
+      generated_at_after: '2026-06-01T21:56:36Z'
+      preview_before: "This advanced Learning Path guides you through debugging Cortex-M\
+        \ software in Arm Keil \xB5Vision using a Blinky example on the Corstone-300\
+        \ Ecosystem FVP. You will build the project, start a debug sessi..."
+      preview_after: "This advanced Learning Path guides you through debugging Cortex-M\
+        \ software in Arm Keil \xB5Vision using a Blinky example on the Corstone-300\
+        \ Ecosystem FVP. You will build the project, start a debug sessi..."
+      preview_generated: "This advanced Learning Path shows how to debug Cortex-M\
+        \ software with Arm Keil MDK\u2019s \xB5Vision, starting with basic run/stop\
+        \ debugging and moving to advanced techniques. You work with a Blinky example\
+        \ p..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 27ced3e9f69dbe51e75dcf13999ae1745a994137f31c86d6f17d4de341c7fa2a
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T21:56:36Z'
+      generated_at_after: '2026-06-02T22:47:15Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How can I print debug text without a UART?
+        - What should I check if Serial Wire Viewer (SWV) shows no data?
+        - When should I enable ETM Trace, and what results should I expect?
+        - How do I measure power with ULINKplus and configure it?
+        removed_questions:
+        - What do I need installed before starting?
+        - Can I follow this path without physical hardware?
+        - "Which example project is used and how do I open it in \xB5Vision?"
+        - How can I view application output without a UART?
+        - What advanced analysis features are covered beyond basic run/stop?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How can I print debug text without a UART?
+        - What should I check if Serial Wire Viewer (SWV) shows no data?
+        - When should I enable ETM Trace, and what results should I expect?
+        - How do I measure power with ULINKplus and configure it?
+        removed_questions:
+        - What do I need installed before starting?
+        - Can I follow this path without physical hardware?
+        - "Which example project is used and how do I open it in \xB5Vision?"
+        - How can I view application output without a UART?
+        - What advanced analysis features are covered beyond basic run/stop?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -203945,51 +8064,76 @@ history:
       source_hash_before: 179b0318bbef9cef31ca6bb82de853597a609f61d6868aaaa85cfb40a27a051a
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       current_source_hash: 179b0318bbef9cef31ca6bb82de853597a609f61d6868aaaa85cfb40a27a051a
-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to import, convert, and build uvprojx-based projects to csolution format
-        using Keil Studio, µVision, and command-line tools for CMSIS-Toolbox compatibility. It is designed
-        for This is a topi...
-      preview_after: Learn how to import, convert, and build uvprojx-based projects to csolution format
-        using Keil Studio, µVision, and command-line tools for CMSIS-Toolbox compatibility. It is designed
-        for This is a topi...
-      preview_generated: Learn how to import, convert, and build uvprojx-based projects to csolution format
-        using Keil Studio, µVision, and command-line tools for CMSIS-Toolbox compatibility. It is designed
-        for This is a topi...
+      generated_at_before: '2026-06-01T21:57:17Z'
+      generated_at_after: '2026-06-01T21:57:17Z'
+      preview_before: "This Learning Path shows how to migrate existing \xB5Vision\
+        \ uvprojx-based Cortex-M projects to the csolution format required by CMSIS-Toolbox.\
+        \ You will convert projects using three workflows: Keil Studio..."
+      preview_after: "This Learning Path shows how to migrate existing \xB5Vision\
+        \ uvprojx-based Cortex-M projects to the csolution format required by CMSIS-Toolbox.\
+        \ You will convert projects using three workflows: Keil Studio..."
+      preview_generated: "This Learning Path shows how to migrate existing \xB5Vision\
+        \ (uvprojx) projects to the CMSIS-Toolbox csolution format using Keil Studio,\
+        \ \xB5Vision, or the uv2csolution command-line tool. You will open a uvp..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 179b0318bbef9cef31ca6bb82de853597a609f61d6868aaaa85cfb40a27a051a
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T21:57:17Z'
+      generated_at_after: '2026-06-02T22:48:13Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need installed before running the conversion?
+        - How do I start and verify the conversion in Keil Studio?
+        - What files should I expect after a successful conversion?
+        - "How do I export from \xB5Vision and confirm it worked?"
+        - What should I check if my project currently uses Arm Compiler 5?
+        removed_questions:
+        - What tools and requirements do I need before starting?
+        - Which operating systems can I use for this conversion?
+        - What conversion methods are covered?
+        - How do I know the conversion worked and what files should I expect?
+        - What can I do with the project after conversion?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need installed before running the conversion?
+        - How do I start and verify the conversion in Keil Studio?
+        - What files should I expect after a successful conversion?
+        - "How do I export from \xB5Vision and confirm it worked?"
+        - What should I check if my project currently uses Arm Compiler 5?
+        removed_questions:
+        - What tools and requirements do I need before starting?
+        - Which operating systems can I use for this conversion?
+        - What conversion methods are covered?
+        - How do I know the conversion worked and what files should I expect?
+        - What can I do with the project after conversion?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -203999,51 +8143,80 @@ history:
       source_hash_before: 0c3422e1cd571e6abff676c28ec32c2ec69a626a406167f9166e57daa6829252
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to install vcpkg, initialize it, create vcpkg-configuration.json files,
-        use vcpkg for tool management, activate tool licensing, and remove vcpkg for reproducible command-line
-        tool installati...
-      preview_after: Learn how to install vcpkg, initialize it, create vcpkg-configuration.json files,
-        use vcpkg for tool management, activate tool licensing, and remove vcpkg for reproducible command-line
-        tool installati...
-      preview_generated: Learn how to install vcpkg, initialize it, create vcpkg-configuration.json files,
-        use vcpkg for tool management, activate tool licensing, and remove vcpkg for reproducible command-line
-        tool installati...
+      generated_at_before: '2026-06-01T21:57:53Z'
+      generated_at_after: '2026-06-01T21:57:53Z'
+      preview_before: Use vcpkg on Linux, Windows, or macOS to create reproducible
+        command-line installations of tools used in Arm Cortex-M development. You
+        will install and initialize vcpkg in each new terminal session, c...
+      preview_after: Use vcpkg on Linux, Windows, or macOS to create reproducible
+        command-line installations of tools used in Arm Cortex-M development. You
+        will install and initialize vcpkg in each new terminal session, c...
+      preview_generated: Use vcpkg to create reproducible command-line installations
+        of Arm development tools across Linux, Windows, and macOS. You will install
+        vcpkg, initialize it in each new shell, and create a vcpkg-confi...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 0c3422e1cd571e6abff676c28ec32c2ec69a626a406167f9166e57daa6829252
       source_hash_after: 0c3422e1cd571e6abff676c28ec32c2ec69a626a406167f9166e57daa6829252
       current_source_hash: 0c3422e1cd571e6abff676c28ec32c2ec69a626a406167f9166e57daa6829252
-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T21:57:53Z'
+      generated_at_after: '2026-06-02T22:49:26Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which initialization command should I use on my OS, and when should I run
+          it?
+        - What is the purpose of vcpkg-configuration.json?
+        - How do I activate the tools and confirm activation worked?
+        - When do I need to activate a license, and how can I verify it?
+        removed_questions:
+        - Which operating systems and shells are covered, and how do I initialize
+          vcpkg?
+        - Why do I need a vcpkg-configuration.json, and when should I create it?
+        - How do I activate the tools defined by my configuration and know it worked?
+        - How do I activate and verify an Arm tool license?
+        - "Can I remove vcpkg after I\u2019m done?"
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which initialization command should I use on my OS, and when should I run
+          it?
+        - What is the purpose of vcpkg-configuration.json?
+        - How do I activate the tools and confirm activation worked?
+        - When do I need to activate a license, and how can I verify it?
+        removed_questions:
+        - Which operating systems and shells are covered, and how do I initialize
+          vcpkg?
+        - Why do I need a vcpkg-configuration.json, and when should I create it?
+        - How do I activate the tools defined by my configuration and know it worked?
+        - How do I activate and verify an Arm tool license?
+        - "Can I remove vcpkg after I\u2019m done?"
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -204053,51 +8226,78 @@ history:
       source_hash_before: dcdbc4cecc376ed4c0df9377d13cb4fe792ad7c68acfe0610d75cf41898804ff
       source_hash_after: dcdbc4cecc376ed4c0df9377d13cb4fe792ad7c68acfe0610d75cf41898804ff
       current_source_hash: dcdbc4cecc376ed4c0df9377d13cb4fe792ad7c68acfe0610d75cf41898804ff
-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to identify Arm-based targets for TinyML, install Fixed Virtual Platforms,
-        deploy ExecuTorch models on Corstone-320 FVP, and visualize model execution using the FVP graphical
-        interface. It i...
-      preview_after: Learn how to identify Arm-based targets for TinyML, install Fixed Virtual Platforms,
-        deploy ExecuTorch models on Corstone-320 FVP, and visualize model execution using the FVP graphical
-        interface. It i...
-      preview_generated: Learn how to identify Arm-based targets for TinyML, install Fixed Virtual Platforms,
-        deploy ExecuTorch models on Corstone-320 FVP, and visualize model execution using the FVP graphical
-        interface. It i...
+      generated_at_before: '2026-06-01T21:59:12Z'
+      generated_at_after: '2026-06-01T21:59:12Z'
+      preview_before: This introductory Learning Path shows how to evaluate TinyML
+        workloads on Arm virtual hardware before physical boards are available. You
+        will set up an ExecuTorch development environment on Linux or m...
+      preview_after: This introductory Learning Path shows how to evaluate TinyML
+        workloads on Arm virtual hardware before physical boards are available. You
+        will set up an ExecuTorch development environment on Linux or m...
+      preview_generated: This introductory Learning Path walks you through evaluating
+        TinyML workloads on Arm virtual hardware using ExecuTorch and the Corstone-320
+        Fixed Virtual Platform (FVP). You will identify Arm-based ta...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: dcdbc4cecc376ed4c0df9377d13cb4fe792ad7c68acfe0610d75cf41898804ff
       source_hash_after: dcdbc4cecc376ed4c0df9377d13cb4fe792ad7c68acfe0610d75cf41898804ff
       current_source_hash: dcdbc4cecc376ed4c0df9377d13cb4fe792ad7c68acfe0610d75cf41898804ff
-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T21:59:12Z'
+      generated_at_after: '2026-06-02T22:50:53Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - "I\u2019m using macOS\u2014are there extra steps to run the FVP?"
+        - Where is the example model and how do I run it?
+        - How do I know the FVP and ExecuTorch setup worked?
+        - Do I need physical hardware to test Ethos-U NPU performance?
+        removed_questions:
+        - What host operating systems are supported, and are there any special setup
+          notes?
+        - Do I need Arm hardware to follow this path?
+        - What does this path teach about the ExecuTorch workflow?
+        - Which model is deployed, and how is it run?
+        - How do I verify that everything worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - "I\u2019m using macOS\u2014are there extra steps to run the FVP?"
+        - Where is the example model and how do I run it?
+        - How do I know the FVP and ExecuTorch setup worked?
+        - Do I need physical hardware to test Ethos-U NPU performance?
+        removed_questions:
+        - What host operating systems are supported, and are there any special setup
+          notes?
+        - Do I need Arm hardware to follow this path?
+        - What does this path teach about the ExecuTorch workflow?
+        - Which model is deployed, and how is it run?
+        - How do I verify that everything worked?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -204107,51 +8307,76 @@ history:
       source_hash_before: 3bcfc51edbab56e3c8416f27045a61b069333e6f0cd130e8689b9a4d9fde1dd6
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       current_source_hash: 3bcfc51edbab56e3c8416f27045a61b069333e6f0cd130e8689b9a4d9fde1dd6
-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Introduction to building a minimal Yocto Linux image and running it on 64-bit Qemu
-        Arm target. It is designed for software developers interested in learning the basics of building
-        Yocto Linux for embe...
-      preview_after: Introduction to building a minimal Yocto Linux image and running it on 64-bit Qemu
-        Arm target. It is designed for software developers interested in learning the basics of building
-        Yocto Linux for embe...
-      preview_generated: Introduction to building a minimal Yocto Linux image and running it on 64-bit
-        Qemu Arm target. It is designed for software developers interested in learning the basics of building
-        Yocto Linux for embe...
+      generated_at_before: '2026-06-01T21:59:52Z'
+      generated_at_after: '2026-06-01T21:59:52Z'
+      preview_before: Learn how to build a minimal Yocto Linux image for a generic
+        64-bit Arm (Cortex-A class) target and run it under QEMU. Working on a Linux
+        host (Ubuntu 22.04) with at least 60 GB of disk space, you use...
+      preview_after: Learn how to build a minimal Yocto Linux image for a generic
+        64-bit Arm (Cortex-A class) target and run it under QEMU. Working on a Linux
+        host (Ubuntu 22.04) with at least 60 GB of disk space, you use...
+      preview_generated: This introductory Learning Path shows how to build a minimal
+        Yocto Linux image for a generic 64-bit Arm target and run it under QEMU. You
+        will use the Yocto Project with the Poky reference distributio...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 3bcfc51edbab56e3c8416f27045a61b069333e6f0cd130e8689b9a4d9fde1dd6
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       current_source_hash: 3bcfc51edbab56e3c8416f27045a61b069333e6f0cd130e8689b9a4d9fde1dd6
-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T21:59:52Z'
+      generated_at_after: '2026-06-02T22:52:18Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Yocto distribution should I use to start the build?
+        - Do I need physical Arm hardware to complete this Learning Path?
+        - Which target architecture is used when running under QEMU?
+        - What result should I expect after the build, and how do I run it?
+        removed_questions:
+        - What host setup do I need before starting?
+        - Do I need physical Arm hardware to complete this path?
+        - Which Yocto distribution or components are used?
+        - What will I produce and how do I know it worked?
+        - How long does this Learning Path take to complete?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Yocto distribution should I use to start the build?
+        - Do I need physical Arm hardware to complete this Learning Path?
+        - Which target architecture is used when running under QEMU?
+        - What result should I expect after the build, and how do I run it?
+        removed_questions:
+        - What host setup do I need before starting?
+        - Do I need physical Arm hardware to complete this path?
+        - Which Yocto distribution or components are used?
+        - What will I produce and how do I know it worked?
+        - How long does this Learning Path take to complete?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -204161,51 +8386,76 @@ history:
       source_hash_before: b0cb917bdcfb601c054e6cac93b2d04aca3ffe84b5a1963288fd7b89dd9bad3b
       source_hash_after: b0cb917bdcfb601c054e6cac93b2d04aca3ffe84b5a1963288fd7b89dd9bad3b
       current_source_hash: b0cb917bdcfb601c054e6cac93b2d04aca3ffe84b5a1963288fd7b89dd9bad3b
-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to run a YOLO object detection model on the Himax WiseEye2 module, build
-        the Himax SDK, update firmware, and connect to the Grove Vision AI module for computer vision
-        applications. It is des...
-      preview_after: Learn how to run a YOLO object detection model on the Himax WiseEye2 module, build
-        the Himax SDK, update firmware, and connect to the Grove Vision AI module for computer vision
-        applications. It is des...
-      preview_generated: Learn how to run a YOLO object detection model on the Himax WiseEye2 module,
-        build the Himax SDK, update firmware, and connect to the Grove Vision AI module for computer vision
-        applications. It is des...
+      generated_at_before: '2026-06-01T22:00:17Z'
+      generated_at_after: '2026-06-01T22:00:17Z'
+      preview_before: Build and deploy a YOLO object detection application on the
+        Himax WiseEye2 platform (Arm Cortex-M55 with Ethos-U55) using the Seeed Grove
+        Vision AI Module V2. You will prepare a Linux or macOS host, i...
+      preview_after: Build and deploy a YOLO object detection application on the Himax
+        WiseEye2 platform (Arm Cortex-M55 with Ethos-U55) using the Seeed Grove Vision
+        AI Module V2. You will prepare a Linux or macOS host, i...
+      preview_generated: Follow this introductory Learning Path to run a YOLO object
+        detection model on the Himax WiseEye2 microcontroller using the Seeed Grove
+        Vision AI Module V2. You will set up a Linux or macOS host, inst...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: b0cb917bdcfb601c054e6cac93b2d04aca3ffe84b5a1963288fd7b89dd9bad3b
       source_hash_after: b0cb917bdcfb601c054e6cac93b2d04aca3ffe84b5a1963288fd7b89dd9bad3b
       current_source_hash: b0cb917bdcfb601c054e6cac93b2d04aca3ffe84b5a1963288fd7b89dd9bad3b
-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T22:00:17Z'
+      generated_at_after: '2026-06-02T22:54:16Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which operating systems are supported, and can I use Windows?
+        - How do I clone the Himax project with all required submodules?
+        - How do I install Xmodem for flashing the firmware?
+        - How do I select and run different models, such as YOLO object detection?
+        removed_questions:
+        - What hardware and host system do I need?
+        - Which operating systems are supported for the host machine?
+        - How do I obtain the code and build the firmware image?
+        - How do I flash the board and run the application?
+        - Can I switch models or run YOLO specifically?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which operating systems are supported, and can I use Windows?
+        - How do I clone the Himax project with all required submodules?
+        - How do I install Xmodem for flashing the firmware?
+        - How do I select and run different models, such as YOLO object detection?
+        removed_questions:
+        - What hardware and host system do I need?
+        - Which operating systems are supported for the host machine?
+        - How do I obtain the code and build the firmware image?
+        - How do I flash the board and run the application?
+        - Can I switch models or run YOLO specifically?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -204215,51 +8465,78 @@ history:
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-      preview_before: Learn how to build and run Zephyr RTOS applications on the Arm Corstone-300 Fixed
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-        Virtual Platform using Arm Virtual Hardware. It is designed for software developers getting started
-        with the Zephyr RT...
-      preview_generated: Learn how to build and run Zephyr RTOS applications on the Arm Corstone-300 Fixed
-        Virtual Platform using Arm Virtual Hardware. It is designed for software developers getting started
-        with the Zephyr RT...
+      generated_at_before: '2026-06-01T22:00:54Z'
+      generated_at_after: '2026-06-01T22:00:54Z'
+      preview_before: This Learning Path shows how to build and run Zephyr RTOS applications
+        on the Arm Corstone-300 Fixed Virtual Platform (FVP) using Arm Virtual Hardware.
+        You will obtain the Zephyr source, install the Z...
+      preview_after: This Learning Path shows how to build and run Zephyr RTOS applications
+        on the Arm Corstone-300 Fixed Virtual Platform (FVP) using Arm Virtual Hardware.
+        You will obtain the Zephyr source, install the Z...
+      preview_generated: This introductory path shows how to build and run Zephyr
+        RTOS applications on the Arm Corstone-300 Fixed Virtual Platform (FVP) using
+        Arm Virtual Hardware. You will obtain the Zephyr source, install t...
     faqs:
-      action: unchanged
+      action: rerun_requested
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-      rerun_requested: false
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       change_details:
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         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Do I need physical hardware for this Learning Path?
+        - 'Which environment should I use: local Ubuntu or Arm Virtual Hardware on
+          AWS?'
+        - What will I build and run in this path?
+        - How do I know the application ran correctly on the Corstone-300 FVP?
+        removed_questions:
+        - What prerequisites are required before starting?
+        - Which Arm platform does this Learning Path target?
+        - What tools or software will I use during the steps?
+        - Do I need an AWS account, or can I run this locally?
+        - How do I know the process worked and what should I expect as output?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Do I need physical hardware for this Learning Path?
+        - 'Which environment should I use: local Ubuntu or Arm Virtual Hardware on
+          AWS?'
+        - What will I build and run in this path?
+        - How do I know the application ran correctly on the Corstone-300 FVP?
+        removed_questions:
+        - What prerequisites are required before starting?
+        - Which Arm platform does this Learning Path target?
+        - What tools or software will I use during the steps?
+        - Do I need an AWS account, or can I run this locally?
+        - How do I know the process worked and what should I expect as output?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
       missing_before: false
@@ -204269,51 +8546,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:55Z'
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-      preview_before: Learn how to install Workbench for Zephyr extension in VS Code, set up the complete
-        Zephyr development environment, create and build Zephyr applications, debug embedded systems,
-        and perform memory usa...
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-        Zephyr development environment, create and build Zephyr applications, debug embedded systems,
-        and perform memory usa...
-      preview_generated: Learn how to install Workbench for Zephyr extension in VS Code, set up the complete
-        Zephyr development environment, create and build Zephyr applications, debug embedded systems,
-        and perform memory usa...
+      generated_at_before: '2026-06-01T22:01:28Z'
+      generated_at_after: '2026-06-01T22:01:28Z'
+      preview_before: This introductory path shows how to install and configure the
+        Workbench for Zephyr extension in Visual Studio Code, set up the Zephyr SDK
+        and toolchain, and create, build, and debug Zephyr RTOS applic...
+      preview_after: This introductory path shows how to install and configure the
+        Workbench for Zephyr extension in Visual Studio Code, set up the Zephyr SDK
+        and toolchain, and create, build, and debug Zephyr RTOS applic...
+      preview_generated: Learn how to set up Zephyr RTOS development in Visual Studio
+        Code using the open-source Workbench for Zephyr extension. This introductory
+        path guides you to install and configure the extension, provis...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
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       drift_detected: false
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       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
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         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I know if my Arm Cortex-M board will work for this path?
+        - Which debug runner should I use for my board?
+        - What result should I expect after I build the sample application in Workbench?
+        - What should I check if the build or debug setup fails?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - Do I need a specific development board to follow the steps?
+        - What does the Workbench for Zephyr extension provide?
+        - How do I verify that my environment is correctly set up?
+        - How long will this Learning Path take and what is the skill level?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I know if my Arm Cortex-M board will work for this path?
+        - Which debug runner should I use for my board?
+        - What result should I expect after I build the sample application in Workbench?
+        - What should I check if the build or debug setup fails?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - Do I need a specific development board to follow the steps?
+        - What does the Workbench for Zephyr extension provide?
+        - How do I verify that my environment is correctly set up?
+        - How long will this Learning Path take and what is the skill level?
+        updated_questions: []
+    category: embedded-and-microcontrollers
   - path: content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -204323,51 +8625,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to create and run Ubuntu containers on ChromeOS Crostini using LXC with
-        file sharing and GUI application support on Arm-based Chromebooks. It is designed for software
-        developers who want to ...
-      preview_after: Learn how to create and run Ubuntu containers on ChromeOS Crostini using LXC with
-        file sharing and GUI application support on Arm-based Chromebooks. It is designed for software
-        developers who want to ...
-      preview_generated: Learn how to create and run Ubuntu containers on ChromeOS Crostini using LXC
-        with file sharing and GUI application support on Arm-based Chromebooks. It is designed for software
-        developers who want to ...
+      generated_at_before: '2026-06-01T22:01:54Z'
+      generated_at_after: '2026-06-01T22:01:54Z'
+      preview_before: This introductory Learning Path shows how to create and run
+        an Ubuntu 24.04 LXC container on ChromeOS (Crostini) from the Termina shell
+        on an Arm-based Chromebook. You will set up ChromeOS integration...
+      preview_after: This introductory Learning Path shows how to create and run an
+        Ubuntu 24.04 LXC container on ChromeOS (Crostini) from the Termina shell on
+        an Arm-based Chromebook. You will set up ChromeOS integration...
+      preview_generated: This Learning Path shows how to create and run an Ubuntu
+        24.04 Linux container on ChromeOS Crostini using LXC from the Termina shell
+        on an Arm-based Chromebook. You will set up ChromeOS integration fo...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
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-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T22:01:54Z'
+      generated_at_after: '2026-06-02T22:58:53Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Where do I run the LXC and setup commands on ChromeOS?
+        - How do I start, stop, and access my Ubuntu container, and check its status?
+        - How do I share folders between ChromeOS and the Ubuntu container?
+        - How do I enable and test Linux GUI applications from the container?
+        removed_questions:
+        - What hardware and setup do I need before starting?
+        - Which Ubuntu version does the container use?
+        - How do I share files between ChromeOS and the Ubuntu container?
+        - How are Linux GUI applications enabled in the container?
+        - How do I manage and verify the container from the Termina shell?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Where do I run the LXC and setup commands on ChromeOS?
+        - How do I start, stop, and access my Ubuntu container, and check its status?
+        - How do I share folders between ChromeOS and the Ubuntu container?
+        - How do I enable and test Linux GUI applications from the container?
+        removed_questions:
+        - What hardware and setup do I need before starting?
+        - Which Ubuntu version does the container use?
+        - How do I share files between ChromeOS and the Ubuntu container?
+        - How are Linux GUI applications enabled in the container?
+        - How do I manage and verify the container from the Termina shell?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -204377,51 +8704,77 @@ history:
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-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to build and deploy high-fidelity robotic simulations and reinforcement
-        learning pipelines using Isaac Sim and Isaac Lab on Arm-based NVIDIA DGX Spark with Grace-Blackwell
-        architecture. It i...
-      preview_after: Learn how to build and deploy high-fidelity robotic simulations and reinforcement
-        learning pipelines using Isaac Sim and Isaac Lab on Arm-based NVIDIA DGX Spark with Grace-Blackwell
-        architecture. It i...
-      preview_generated: Learn how to build and deploy high-fidelity robotic simulations and reinforcement
-        learning pipelines using Isaac Sim and Isaac Lab on Arm-based NVIDIA DGX Spark with Grace-Blackwell
-        architecture. It i...
+      generated_at_before: '2026-06-01T22:02:33Z'
+      generated_at_after: '2026-06-01T22:02:33Z'
+      preview_before: "This advanced Learning Path shows how to build, configure,\
+        \ and run NVIDIA Isaac Sim and Isaac Lab on an Arm-based NVIDIA DGX Spark\
+        \ system powered by the Grace\u2013Blackwell (GB10) architecture. You will\
+        \ v..."
+      preview_after: "This advanced Learning Path shows how to build, configure, and\
+        \ run NVIDIA Isaac Sim and Isaac Lab on an Arm-based NVIDIA DGX Spark system\
+        \ powered by the Grace\u2013Blackwell (GB10) architecture. You will v..."
+      preview_generated: "This advanced Learning Path shows how to build and run NVIDIA\
+        \ Isaac Sim and Isaac Lab on an Arm-based NVIDIA DGX Spark system powered\
+        \ by the Grace\u2013Blackwell (GB10) architecture running Linux. You will..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
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-      generated_at_after: '2026-05-06T17:17:55Z'
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       before_count: 5
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       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How long does installation usually take, and how much storage is required?
+        - How are Isaac Sim and Isaac Lab arranged in the environment?
+        - Which simulation do I run first, and how do I confirm it worked?
+        - Which RL framework and algorithm are used for training the humanoid policy?
+        removed_questions:
+        - What hardware and operating system are required?
+        - What prior skills or knowledge are expected?
+        - What does the setup phase include and how long does it take?
+        - What simulation example will I run to validate the environment?
+        - Which reinforcement learning workflow is covered?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How long does installation usually take, and how much storage is required?
+        - How are Isaac Sim and Isaac Lab arranged in the environment?
+        - Which simulation do I run first, and how do I confirm it worked?
+        - Which RL framework and algorithm are used for training the humanoid policy?
+        removed_questions:
+        - What hardware and operating system are required?
+        - What prior skills or knowledge are expected?
+        - What does the setup phase include and how long does it take?
+        - What simulation example will I run to validate the environment?
+        - Which reinforcement learning workflow is covered?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -204431,51 +8784,76 @@ history:
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-      preview_before: Learn how to build and optimize quantized LLMs using llama.cpp on NVIDIA DGX Spark
-        with Grace-Blackwell architecture, leveraging Armv9 SIMD acceleration. It is designed for AI practitioners,
-        performan...
-      preview_after: Learn how to build and optimize quantized LLMs using llama.cpp on NVIDIA DGX Spark
-        with Grace-Blackwell architecture, leveraging Armv9 SIMD acceleration. It is designed for AI practitioners,
-        performan...
-      preview_generated: Learn how to build and optimize quantized LLMs using llama.cpp on NVIDIA DGX
-        Spark with Grace-Blackwell architecture, leveraging Armv9 SIMD acceleration. It is designed for
-        AI practitioners, performan...
+      generated_at_before: '2026-06-01T22:03:05Z'
+      generated_at_after: '2026-06-01T22:03:05Z'
+      preview_before: "This Learning Path shows how to build and validate both CUDA-enabled\
+        \ and CPU-only versions of llama.cpp on an Arm-based NVIDIA DGX Spark system\
+        \ with the Grace\u2013Blackwell (GB10) architecture running Lin..."
+      preview_after: "This Learning Path shows how to build and validate both CUDA-enabled\
+        \ and CPU-only versions of llama.cpp on an Arm-based NVIDIA DGX Spark system\
+        \ with the Grace\u2013Blackwell (GB10) architecture running Lin..."
+      preview_generated: "This Learning Path shows how to build and validate quantized\
+        \ LLM inference with llama.cpp on NVIDIA DGX Spark systems powered by the\
+        \ Grace\u2013Blackwell (GB10) architecture. You will verify system readine..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
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-      generated_at_after: '2026-05-06T17:17:55Z'
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       before_count: 5
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       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps on DGX Spark?
+        - How do I confirm my DGX Spark is ready for building llama.cpp?
+        - 'Which llama.cpp build should I use on GB10: GPU or CPU-only?'
+        - What result should I expect after completing the builds?
+        - How do I analyze the Armv9 instruction mix during CPU inference?
+        removed_questions:
+        - What environment and skills do I need before starting?
+        - How should I prepare the system for the GPU build of llama.cpp?
+        - What will I build and how is success validated?
+        - Can I follow this path without using the GPU?
+        - How do I analyze which Arm instructions run during inference?
         updated_questions: []
       generated_diff:
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         after_count: 5
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+        added_questions:
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+        - How do I confirm my DGX Spark is ready for building llama.cpp?
+        - 'Which llama.cpp build should I use on GB10: GPU or CPU-only?'
+        - What result should I expect after completing the builds?
+        - How do I analyze the Armv9 instruction mix during CPU inference?
+        removed_questions:
+        - What environment and skills do I need before starting?
+        - How should I prepare the system for the GPU build of llama.cpp?
+        - What will I build and how is success validated?
+        - Can I follow this path without using the GPU?
+        - How do I analyze which Arm instructions run during inference?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md
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-      preview_before: Learn how to build a Retrieval-Augmented Generation (RAG) pipeline on NVIDIA DGX
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-        DGX Spark combining Arm Grace CPU orchestration with Blackwell GPU-accelerated inference using
-        llama.cpp. It is designed fo...
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+      preview_after: "This advanced Learning Path guides you through building a hybrid\
+        \ Retrieval-Augmented Generation (RAG) pipeline on Arm-based NVIDIA DGX Spark\
+        \ (Grace\u2013Blackwell/GB10). You will set up a Python environmen..."
+      preview_generated: "This advanced Learning Path guides you through building\
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+        \ Spark (Grace\u2013Blackwell/GB10) systems. You will set up a Python environme..."
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+        - What platform and resources are required to follow the steps?
+        - Which models and libraries does the RAG pipeline use?
+        - How should I set up the Python environment for this project?
+        - How do I verify the pipeline is working and monitor performance?
+        removed_questions:
+        - What hardware and operating system are required?
+        - Which models and libraries are used in the RAG pipeline?
+        - How are tasks split between the Arm Grace CPUs and Blackwell GPUs?
+        - Is there any recommended preparation before starting?
+        - How can I validate that the pipeline is working?
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       generated_diff:
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+        added_questions:
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+        - What platform and resources are required to follow the steps?
+        - Which models and libraries does the RAG pipeline use?
+        - How should I set up the Python environment for this project?
+        - How do I verify the pipeline is working and monitor performance?
+        removed_questions:
+        - What hardware and operating system are required?
+        - Which models and libraries are used in the RAG pipeline?
+        - How are tasks split between the Arm Grace CPUs and Blackwell GPUs?
+        - Is there any recommended preparation before starting?
+        - How can I validate that the pipeline is working?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md
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-      preview_after: Learn how to build an offline voice assistant combining speech-to-text via faster-whisper
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-        and text generation via vLLM on Arm-based DGX Spark for privacy-focused deployments. It is designed
-        for develo...
+      generated_at_before: '2026-06-01T22:04:24Z'
+      generated_at_after: '2026-06-01T22:04:24Z'
+      preview_before: This advanced Learning Path guides you through building a private,
+        offline voice chatbot on Arm-based DGX Spark running Linux. You will capture
+        real-time audio from a USB microphone using PyAudio with...
+      preview_after: This advanced Learning Path guides you through building a private,
+        offline voice chatbot on Arm-based DGX Spark running Linux. You will capture
+        real-time audio from a USB microphone using PyAudio with...
+      preview_generated: Build a privacy-focused, offline voice assistant by combining
+        faster-whisper for speech-to-text with vLLM for local text generation on an
+        Arm-based DGX Spark system running Linux. You will capture rea...
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+        - Which components run on CPU versus GPU in this workflow?
+        - How do I verify that faster-whisper is installed correctly?
+        - How is audio captured and segmented for transcription?
+        - What result should I expect when the full pipeline is running?
+        removed_questions:
+        - What hardware and input devices do I need before starting?
+        - What software and frameworks are used in this path?
+        - Does the pipeline run entirely offline for privacy-focused use cases?
+        - Is a GPU required, or can I run the pipeline on CPU only?
+        - How can I tell the setup is working at each stage?
         updated_questions: []
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+        - Which components run on CPU versus GPU in this workflow?
+        - How do I verify that faster-whisper is installed correctly?
+        - How is audio captured and segmented for transcription?
+        - What result should I expect when the full pipeline is running?
+        removed_questions:
+        - What hardware and input devices do I need before starting?
+        - What software and frameworks are used in this path?
+        - Does the pipeline run entirely offline for privacy-focused use cases?
+        - Is a GPU required, or can I run the pipeline on CPU only?
+        - How can I tell the setup is working at each stage?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/docker-models/_index.md
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-      preview_before: Learn how to run pre-trained AI models locally using Docker Model Runner and build
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-        and AI enthusias...
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-        and AI enthusias...
-      preview_generated: Learn how to run pre-trained AI models locally using Docker Model Runner and
-        build containerized applications integrating large language models. It is designed for software
-        developers and AI enthusias...
+      generated_at_before: '2026-06-01T22:04:54Z'
+      generated_at_after: '2026-06-01T22:04:54Z'
+      preview_before: This introductory path shows how to run pre-trained large language
+        models locally on Windows or macOS using Docker Model Runner, an official
+        Docker extension that leverages llama.cpp without requiring...
+      preview_after: This introductory path shows how to run pre-trained large language
+        models locally on Windows or macOS using Docker Model Runner, an official
+        Docker extension that leverages llama.cpp without requiring...
+      preview_generated: This Learning Path shows how to run pre-trained large language
+        models locally with Docker Model Runner and then deploy a simple containerized
+        AI chat application. Working on Windows or macOS with Dock...
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+        added_questions:
+        - What do I need before running the steps?
+        - Do I need to install any LLM frameworks or toolchains locally?
+        - Will this work on Arm-based systems?
+        - Which models can I try with the example chat app?
+        - What result should I expect after deploying with Docker Compose?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - Do I need to install or build any LLM frameworks manually?
+        - What will I deploy by the end of the path?
+        - Which AI models can the example chat app use?
+        - "Will this work on Arm-based systems, and how do I know it\u2019s working?"
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       generated_diff:
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+        - What do I need before running the steps?
+        - Do I need to install any LLM frameworks or toolchains locally?
+        - Will this work on Arm-based systems?
+        - Which models can I try with the example chat app?
+        - What result should I expect after deploying with Docker Compose?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - Do I need to install or build any LLM frameworks manually?
+        - What will I deploy by the end of the path?
+        - Which AI models can the example chat app use?
+        - "Will this work on Arm-based systems, and how do I know it\u2019s working?"
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/electron/_index.md
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-        cross-platform...
-      preview_generated: Learn how to develop and build cross-platform desktop applications using the
-        Electron Framework on Windows on Arm devices. It is designed for developers who want to learn
-        how to develop cross-platform...
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+      generated_at_after: '2026-06-01T22:05:20Z'
+      preview_before: This Learning Path shows how to develop a simple Electron desktop
+        application on Windows on Arm (Arm64) and build it for multiple architectures.
+        You will set up a Windows on Arm device or virtual mach...
+      preview_after: This Learning Path shows how to develop a simple Electron desktop
+        application on Windows on Arm (Arm64) and build it for multiple architectures.
+        You will set up a Windows on Arm device or virtual mach...
+      preview_generated: Learn how to develop and build a cross-platform desktop application
+        with the Electron framework on Windows on Arm. You will create a sample Electron
+        app on a Windows on Arm64 system, then configure a ...
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+        - How long should I plan to spend on this Learning Path?
+        - How do I add Electron Builder to my project?
+        - Where do I configure the project for cross-platform builds?
+        - Which architectures will the final build target?
+        removed_questions:
+        - What hardware and software do I need before starting?
+        - Can I complete this Learning Path in a virtual machine?
+        - What will I build and run by the end?
+        - How are cross-platform builds produced in this path?
+        - How much time will this take and what skill level is assumed?
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+        - How long should I plan to spend on this Learning Path?
+        - How do I add Electron Builder to my project?
+        - Where do I configure the project for cross-platform builds?
+        - Which architectures will the final build target?
+        removed_questions:
+        - What hardware and software do I need before starting?
+        - Can I complete this Learning Path in a virtual machine?
+        - What will I build and run by the end?
+        - How are cross-platform builds produced in this path?
+        - How much time will this take and what skill level is assumed?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md
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-        Arm-hosted runners and GitHub Actions workflows. It is designed for This introductory tutorial
-        is for software develop...
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+      preview_before: This introductory Learning Path shows how to automate Windows
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+        GitHub Actions. You will learn what Arm-hosted Windows runners ...
+      preview_after: This introductory Learning Path shows how to automate Windows
+        application builds on Arm architecture using GitHub Arm-hosted runners and
+        GitHub Actions. You will learn what Arm-hosted Windows runners ...
+      preview_generated: This introductory Learning Path shows how to automate Windows
+        application builds on Arm architecture using GitHub Arm-hosted Windows runners
+        and GitHub Actions. You will learn what Arm-hosted Windows ...
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       generated_count: 5
       change_details:
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         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I target a GitHub Arm-hosted Windows runner in my workflow?
+        - Do I need to provide my own server or a self-hosted runner?
+        - Which application is used as the example, and where are the detailed build
+          instructions?
+        - Can I configure a larger runner if my build needs more resources?
+        removed_questions:
+        - What do I need before starting?
+        - What environment do the workflows run on?
+        - What will I configure or build in this Learning Path?
+        - Do I need to provision my own runner?
+        - Does this cover detailed build steps for Visual Studio, MSBuild, or Arm
+          Performance Libraries?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I target a GitHub Arm-hosted Windows runner in my workflow?
+        - Do I need to provide my own server or a self-hosted runner?
+        - Which application is used as the example, and where are the detailed build
+          instructions?
+        - Can I configure a larger runner if my build needs more resources?
+        removed_questions:
+        - What do I need before starting?
+        - What environment do the workflows run on?
+        - What will I configure or build in this Learning Path?
+        - Do I need to provision my own runner?
+        - Does this cover detailed build steps for Visual Studio, MSBuild, or Arm
+          Performance Libraries?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/hyper-v/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
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+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -204755,51 +9262,74 @@ history:
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-      generated_at_before: '2026-05-06T17:17:55Z'
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-      preview_before: Learn how to create and manage Arm-based Linux virtual machines using Hyper-V on
-        Windows on Arm devices. It is designed for software developers who want to use Linux virtual machines
-        with Windows on A...
-      preview_after: Learn how to create and manage Arm-based Linux virtual machines using Hyper-V on
-        Windows on Arm devices. It is designed for software developers who want to use Linux virtual machines
-        with Windows on A...
-      preview_generated: Learn how to create and manage Arm-based Linux virtual machines using Hyper-V
-        on Windows on Arm devices. It is designed for software developers who want to use Linux virtual
-        machines with Windows on A...
+      generated_at_before: '2026-06-01T22:06:03Z'
+      generated_at_after: '2026-06-01T22:06:03Z'
+      preview_before: This introductory Learning Path shows how to create and manage
+        Arm-based Linux virtual machines using Hyper-V on Windows on Arm devices.
+        Working on Windows 11 version 22H2 or newer with Hyper-V instal...
+      preview_after: This introductory Learning Path shows how to create and manage
+        Arm-based Linux virtual machines using Hyper-V on Windows on Arm devices.
+        Working on Windows 11 version 22H2 or newer with Hyper-V instal...
+      preview_generated: Learn how to create Arm-based Linux virtual machines on Windows
+        on Arm using Hyper-V. The path uses Ubuntu 24.04 as the example distribution
+        and requires the Arm ISO image. You need a Windows on Arm c...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
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       before_count: 5
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       generated_count: 5
       change_details:
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         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Ubuntu image should I download for this setup?
+        - How do I proceed if I want a different Linux distribution?
+        - How long will this take and what result should I expect?
+        removed_questions:
+        - What are the prerequisites to start this Learning Path?
+        - Which Linux distribution and image are used in the example?
+        - Can I follow these steps for other Linux distributions?
+        - How long will this take and what is the expected outcome?
+        updated_questions:
+        - Can I use Hyper-V Quick Create on Windows on Arm?
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Ubuntu image should I download for this setup?
+        - How do I proceed if I want a different Linux distribution?
+        - How long will this take and what result should I expect?
+        removed_questions:
+        - What are the prerequisites to start this Learning Path?
+        - Which Linux distribution and image are used in the example?
+        - Can I follow these steps for other Linux distributions?
+        - How long will this take and what is the expected outcome?
+        updated_questions:
+        - Can I use Hyper-V Quick Create on Windows on Arm?
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/intro/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -204809,51 +9339,78 @@ history:
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-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn where the Arm architecture is used in desktop and laptop computers and find
-        hardware for software development on Arm platforms. It is designed for developers working on laptops
-        and desktops and ...
-      preview_after: Learn where the Arm architecture is used in desktop and laptop computers and find
-        hardware for software development on Arm platforms. It is designed for developers working on laptops
-        and desktops and ...
-      preview_generated: Learn where the Arm architecture is used in desktop and laptop computers and
-        find hardware for software development on Arm platforms. It is designed for developers working
-        on laptops and desktops and ...
+      generated_at_before: '2026-06-01T22:06:27Z'
+      generated_at_after: '2026-06-01T22:06:27Z'
+      preview_before: This introductory Learning Path explains where Arm architecture
+        is used in modern laptops and desktops and helps you identify hardware suitable
+        for software development. You will review platform choic...
+      preview_after: This introductory Learning Path explains where Arm architecture
+        is used in modern laptops and desktops and helps you identify hardware suitable
+        for software development. You will review platform choic...
+      preview_generated: This introductory Learning Path explains where Arm architecture
+        appears in laptops and desktops and helps you identify suitable Arm-based
+        hardware for software development. You will review options acr...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
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       drift_detected: false
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-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T22:06:27Z'
+      generated_at_after: '2026-06-02T23:07:34Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which operating systems are covered for Arm laptops and desktops?
+        - Which processor vendors are mentioned for Arm-based laptops and desktops?
+        - What Chromebook models are highlighted as Arm-based options?
+        - How does this path help me align my local machine with my server or cloud
+          architecture?
+        removed_questions:
+        - Do I need any prerequisites or tools, and how long will this take?
+        - Which operating systems and device types are discussed?
+        - Which processor vendors are mentioned for Arm laptops and desktops?
+        - Are there example devices I can consider for development?
+        - How does this help if I also target servers or cloud instances?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which operating systems are covered for Arm laptops and desktops?
+        - Which processor vendors are mentioned for Arm-based laptops and desktops?
+        - What Chromebook models are highlighted as Arm-based options?
+        - How does this path help me align my local machine with my server or cloud
+          architecture?
+        removed_questions:
+        - Do I need any prerequisites or tools, and how long will this take?
+        - Which operating systems and device types are discussed?
+        - Which processor vendors are mentioned for Arm laptops and desktops?
+        - Are there example devices I can consider for development?
+        - How does this help if I also target servers or cloud instances?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -204863,51 +9420,76 @@ history:
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-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to build, test, and verify KleidiCV with Scalable Matrix Extensions (SME)
-        on Apple Silicon Macs for accelerated computer vision performance. It is designed for software
-        developers who want t...
-      preview_after: Learn how to build, test, and verify KleidiCV with Scalable Matrix Extensions (SME)
-        on Apple Silicon Macs for accelerated computer vision performance. It is designed for software
-        developers who want t...
-      preview_generated: Learn how to build, test, and verify KleidiCV with Scalable Matrix Extensions
-        (SME) on Apple Silicon Macs for accelerated computer vision performance. It is designed for software
-        developers who want t...
+      generated_at_before: '2026-06-01T22:06:52Z'
+      generated_at_after: '2026-06-01T22:06:52Z'
+      preview_before: This introductory path shows how to download, build, and test
+        Arm KleidiCV on macOS using an Apple Silicon Mac (M4 generation or newer).
+        You will compile the library, run its API tests, and verify Sca...
+      preview_after: This introductory path shows how to download, build, and test
+        Arm KleidiCV on macOS using an Apple Silicon Mac (M4 generation or newer).
+        You will compile the library, run its API tests, and verify Sca...
+      preview_generated: This Learning Path shows how to build, test, and validate
+        Arm KleidiCV on macOS using Apple Silicon (M4 generation or newer). You will
+        install and compile the library, run the provided API tests, and ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 3e93048b46c24514a89ccc2f277b53111a419c049b53662e1461be38a22e83f7
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-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T22:06:52Z'
+      generated_at_after: '2026-06-02T23:07:54Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the build steps?
+        - How do I run the KleidiCV API test and what result should I expect?
+        - How do I verify that the SME backend is enabled and see its impact?
+        - Do I need to change my code to use Neon, SVE2, or SME2 with KleidiCV?
+        - Do I need a specific computer vision framework to complete this path?
+        removed_questions:
+        - What hardware and tools do I need before starting?
+        - Do I need to modify my CV code to use Neon, SVE2, or SME2?
+        - How do I confirm the build succeeded and SME support is enabled?
+        - What tests will I run and what do they validate?
+        - How long will this take and what should I have at the end?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the build steps?
+        - How do I run the KleidiCV API test and what result should I expect?
+        - How do I verify that the SME backend is enabled and see its impact?
+        - Do I need to change my code to use Neon, SVE2, or SME2 with KleidiCV?
+        - Do I need a specific computer vision framework to complete this path?
+        removed_questions:
+        - What hardware and tools do I need before starting?
+        - Do I need to modify my CV code to use Neon, SVE2, or SME2?
+        - How do I confirm the build succeeded and SME support is enabled?
+        - What tests will I run and what do they validate?
+        - How long will this take and what should I have at the end?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/llvm_putty/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -204917,51 +9499,74 @@ history:
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to configure the LLVM toolchain with Visual Studio to build native Windows
-        on Arm applications using the open-source PuTTY project. It is designed for software developers
-        doing native develo...
-      preview_after: Learn how to configure the LLVM toolchain with Visual Studio to build native Windows
-        on Arm applications using the open-source PuTTY project. It is designed for software developers
-        doing native develo...
-      preview_generated: Learn how to configure the LLVM toolchain with Visual Studio to build native
-        Windows on Arm applications using the open-source PuTTY project. It is designed for software developers
-        doing native develo...
+      generated_at_before: '2026-06-01T22:07:27Z'
+      generated_at_after: '2026-06-01T22:07:27Z'
+      preview_before: This introductory Learning Path shows how to configure the native
+        LLVM toolchain in Visual Studio to compile a Windows on Arm application, using
+        the open-source PuTTY project as the example. You will ...
+      preview_after: This introductory Learning Path shows how to configure the native
+        LLVM toolchain in Visual Studio to compile a Windows on Arm application, using
+        the open-source PuTTY project as the example. You will ...
+      preview_generated: Follow this Learning Path to configure the native LLVM toolchain
+        in Visual Studio and use Clang to build the open-source PuTTY application
+        for Windows on Arm. You will install Visual Studio 2022 or la...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
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       drift_detected: false
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T22:07:27Z'
+      generated_at_after: '2026-06-02T23:09:11Z'
       before_count: 5
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       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - Do I need Arm hardware, or can I use a virtual machine?
+        - Which version of Visual Studio and components are required?
+        - Which compiler and build system are used to compile PuTTY?
+        - What result should I expect after the build completes?
+        removed_questions:
+        - What hardware or environment do I need, and can I use a VM?
+        - Which development tools must be installed before I start?
+        - What will I build, and which compiler does the path use?
+        - How long will this take and what is the expected outcome?
+        updated_questions:
+        - Which Strawberry Perl package should I install on Windows on Arm?
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - Do I need Arm hardware, or can I use a virtual machine?
+        - Which version of Visual Studio and components are required?
+        - Which compiler and build system are used to compile PuTTY?
+        - What result should I expect after the build completes?
+        removed_questions:
+        - What hardware or environment do I need, and can I use a VM?
+        - Which development tools must be installed before I start?
+        - What will I build, and which compiler does the path use?
+        - How long will this take and what is the expected outcome?
+        updated_questions:
+        - Which Strawberry Perl package should I install on Windows on Arm?
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
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+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -204971,51 +9576,76 @@ history:
       source_hash_before: 87d4afe4ce7f0cef113cd61fd712fde073cca0eaafbe86a2066b76a117328d11
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-      preview_before: Learn how to apply Arm Memory Tagging Extension (MTE) to protect dynamic memory
-        allocations and prevent common memory use errors. It is designed for software developers who want
-        to learn how to use th...
-      preview_after: Learn how to apply Arm Memory Tagging Extension (MTE) to protect dynamic memory allocations
-        and prevent common memory use errors. It is designed for software developers who want to learn
-        how to use th...
-      preview_generated: Learn how to apply Arm Memory Tagging Extension (MTE) to protect dynamic memory
-        allocations and prevent common memory use errors. It is designed for software developers who want
-        to learn how to use th...
+      generated_at_before: '2026-06-01T22:08:16Z'
+      generated_at_after: '2026-06-01T22:08:16Z'
+      preview_before: This advanced Learning Path shows how to add Arm Memory Tagging
+        Extension (MTE) to a C dynamic memory allocator on Linux. Using the provided
+        project (CMakeLists.txt, heap.c/.h, mte_utils.c/.h, and mai...
+      preview_after: This advanced Learning Path shows how to add Arm Memory Tagging
+        Extension (MTE) to a C dynamic memory allocator on Linux. Using the provided
+        project (CMakeLists.txt, heap.c/.h, mte_utils.c/.h, and mai...
+      preview_generated: This advanced Learning Path shows how to add Arm Memory Tagging
+        Extension (MTE) to a dynamic memory allocator on Linux. You will examine a
+        small C project (CMakeLists.txt, heap.c/.h, mte_utils.c/.h, m...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T22:08:16Z'
+      generated_at_after: '2026-06-02T23:09:52Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the code in this Learning Path?
+        - Which source files contain the allocator and MTE-specific logic?
+        - How is MTE enabled and memory with tag storage requested in the allocator?
+        - How do I exercise the examples and what result should I expect?
+        - Is the allocator implementation intended for production use?
+        removed_questions:
+        - What prerequisites and skills are expected?
+        - What project files are included and what parts are explained?
+        - How is MTE configured in this project?
+        - How do I verify that MTE is working with the allocator?
+        - Is the provided allocator suitable for production use?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the code in this Learning Path?
+        - Which source files contain the allocator and MTE-specific logic?
+        - How is MTE enabled and memory with tag storage requested in the allocator?
+        - How do I exercise the examples and what result should I expect?
+        - Is the allocator implementation intended for production use?
+        removed_questions:
+        - What prerequisites and skills are expected?
+        - What project files are included and what parts are explained?
+        - How is MTE configured in this project?
+        - How do I verify that MTE is working with the allocator?
+        - Is the provided allocator suitable for production use?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -205025,51 +9655,80 @@ history:
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to install and configure Arch Linux for Arm with the i3 window manager
-        and Neovim editor on the Pinebook Pro laptop. It is designed for developers who want to use the
-        Pinebook Pro as an Arm ...
-      preview_after: Learn how to install and configure Arch Linux for Arm with the i3 window manager
-        and Neovim editor on the Pinebook Pro laptop. It is designed for developers who want to use the
-        Pinebook Pro as an Arm ...
-      preview_generated: Learn how to install and configure Arch Linux for Arm with the i3 window manager
-        and Neovim editor on the Pinebook Pro laptop. It is designed for developers who want to use the
-        Pinebook Pro as an Arm ...
+      generated_at_before: '2026-06-01T22:08:50Z'
+      generated_at_after: '2026-06-01T22:08:50Z'
+      preview_before: This advanced Learning Path shows how to install and configure
+        Arch Linux for Arm on a Pinebook Pro, then set up the i3 window manager and
+        optionally configure Neovim for development. You will prepare...
+      preview_after: This advanced Learning Path shows how to install and configure
+        Arch Linux for Arm on a Pinebook Pro, then set up the i3 window manager and
+        optionally configure Neovim for development. You will prepare...
+      preview_generated: This Learning Path guides you through installing Arch Linux
+        for Arm on a Pinebook Pro, then configuring the i3 window manager and an optional
+        Neovim-based developer setup. You will prepare a microSD c...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: e1befed4cafab0eaee29a31c2f259a6a90a14d9f8230c570b6c10a9840b761d5
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T22:08:50Z'
+      generated_at_after: '2026-06-02T23:10:51Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - Do I need a second computer to prepare the microSD card, and which OS is
+          covered?
+        - What hardware do I need before starting?
+        - Which account should I use when installing and running the i3 window manager?
+        - How do I set the Pinebook Pro display to maximum brightness under i3?
+        - Is the Neovim setup required, and what should I expect the first time I
+          open it?
+        removed_questions:
+        - What hardware and setup do I need before starting?
+        - What software will I install and configure in this path?
+        - Which account should I use to install and run i3?
+        - How can I adjust the Pinebook Pro display brightness under i3?
+        - Is the Neovim configuration required, and what does it provide?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - Do I need a second computer to prepare the microSD card, and which OS is
+          covered?
+        - What hardware do I need before starting?
+        - Which account should I use when installing and running the i3 window manager?
+        - How do I set the Pinebook Pro display to maximum brightness under i3?
+        - Is the Neovim setup required, and what should I expect the first time I
+          open it?
+        removed_questions:
+        - What hardware and setup do I need before starting?
+        - What software will I install and configure in this path?
+        - Which account should I use to install and run i3?
+        - How can I adjust the Pinebook Pro display brightness under i3?
+        - Is the Neovim configuration required, and what does it provide?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -205079,51 +9738,74 @@ history:
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to fine-tune large language models using PyTorch and Hugging Face on NVIDIA
-        DGX Spark to improve domain-specific accuracy. It is designed for AI developers and ML engineers
-        who want to fine-...
-      preview_after: Learn how to fine-tune large language models using PyTorch and Hugging Face on NVIDIA
-        DGX Spark to improve domain-specific accuracy. It is designed for AI developers and ML engineers
-        who want to fine-...
-      preview_generated: Learn how to fine-tune large language models using PyTorch and Hugging Face on
-        NVIDIA DGX Spark to improve domain-specific accuracy. It is designed for AI developers and ML
-        engineers who want to fine-...
+      generated_at_before: '2026-06-01T22:09:20Z'
+      generated_at_after: '2026-06-01T22:09:20Z'
+      preview_before: Learn how to fine-tune the Llama 3.2 3B language model on domain
+        data using PyTorch and Hugging Face on an NVIDIA DGX Spark with an Arm-based
+        Grace CPU and a Blackwell GPU. You will configure Docker o...
+      preview_after: Learn how to fine-tune the Llama 3.2 3B language model on domain
+        data using PyTorch and Hugging Face on an NVIDIA DGX Spark with an Arm-based
+        Grace CPU and a Blackwell GPU. You will configure Docker o...
+      preview_generated: This Learning Path shows how to fine-tune a large language
+        model on an NVIDIA DGX Spark, which combines an Arm-based Grace CPU with a
+        Blackwell GPU. You will configure Docker, pull a pre-built PyTorch...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: aa2a78baf3e52172e37506c3f75254968d775b4eb516f9696a0a6998aba50e97
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-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T22:09:20Z'
+      generated_at_after: '2026-06-02T23:11:51Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Do I need to install Docker on DGX Spark?
+        - Which containers are used for training and serving?
+        - How do I know the fine-tuned model improved factual accuracy?
+        removed_questions:
+        - What environment and hardware does this Learning Path target?
+        - What prerequisites do I need before starting?
+        - How is the training executed on the DGX Spark?
+        - How do I validate that the fine-tuned model improves factual accuracy?
+        updated_questions:
+        - Which model and dataset format are used for fine-tuning?
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Do I need to install Docker on DGX Spark?
+        - Which containers are used for training and serving?
+        - How do I know the fine-tuned model improved factual accuracy?
+        removed_questions:
+        - What environment and hardware does this Learning Path target?
+        - What prerequisites do I need before starting?
+        - How is the training executed on the DGX Spark?
+        - How do I validate that the fine-tuned model improves factual accuracy?
+        updated_questions:
+        - Which model and dataset format are used for fine-tuning?
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -205133,51 +9815,78 @@ history:
       source_hash_before: a851b2f81b6aac54aef84acf7537a1cc6b99f66ce31ffd26631d6a966401e4ed
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to create a CI/CD pipeline in GitHub using self-hosted Arm64 runners to
-        build and push Docker images to DockerHub. It is designed for software developers and IT practitioners
-        who want to lea...
-      preview_after: Learn how to create a CI/CD pipeline in GitHub using self-hosted Arm64 runners to
-        build and push Docker images to DockerHub. It is designed for software developers and IT practitioners
-        who want to lea...
-      preview_generated: Learn how to create a CI/CD pipeline in GitHub using self-hosted Arm64 runners
-        to build and push Docker images to DockerHub. It is designed for software developers and IT practitioners
-        who want to lea...
+      generated_at_before: '2026-06-01T22:09:45Z'
+      generated_at_after: '2026-06-01T22:09:45Z'
+      preview_before: This introductory Learning Path shows how to build a GitHub
+        Actions CI/CD pipeline that uses a self-hosted Arm64 runner to compile a .NET
+        application and publish an Arm64 Docker image to DockerHub. Yo...
+      preview_after: This introductory Learning Path shows how to build a GitHub Actions
+        CI/CD pipeline that uses a self-hosted Arm64 runner to compile a .NET application
+        and publish an Arm64 Docker image to DockerHub. Yo...
+      preview_generated: Learn how to create a GitHub Actions CI/CD pipeline that
+        runs on a self-hosted Arm64 Linux runner to build a .NET application, package
+        it as an Arm64 Docker image, and push the image to DockerHub. You...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: a851b2f81b6aac54aef84acf7537a1cc6b99f66ce31ffd26631d6a966401e4ed
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T22:09:45Z'
+      generated_at_after: '2026-06-02T23:12:53Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which DockerHub repository settings should I use, and what push command
+          will I see?
+        - How do I bring the sample application into my GitHub account?
+        - Which secrets should I add to the GitHub repository?
+        - What software must be installed on the self-hosted Arm64 runner?
+        removed_questions:
+        - What environment do I need for the self-hosted runner?
+        - Which accounts are required before starting?
+        - How do I get the starter code for this pipeline?
+        - What secrets do I need to configure in GitHub?
+        - What does the pipeline produce and how do I confirm it worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which DockerHub repository settings should I use, and what push command
+          will I see?
+        - How do I bring the sample application into my GitHub account?
+        - Which secrets should I add to the GitHub repository?
+        - What software must be installed on the self-hosted Arm64 runner?
+        removed_questions:
+        - What environment do I need for the self-hosted runner?
+        - Which accounts are required before starting?
+        - How do I get the starter code for this pipeline?
+        - What secrets do I need to configure in GitHub?
+        - What does the pipeline produce and how do I confirm it worked?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/win-opencv/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -205187,51 +9896,76 @@ history:
       source_hash_before: d24bf30aef690451942789bb66df63b7a3483ecc3cd2c4b3e60ce15fad90cb91
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to build the OpenCV library for Windows on Arm devices and develop computer
-        vision applications using OpenCV. It is designed for software developers who want to build and
-        develop application...
-      preview_after: Learn how to build the OpenCV library for Windows on Arm devices and develop computer
-        vision applications using OpenCV. It is designed for software developers who want to build and
-        develop application...
-      preview_generated: Learn how to build the OpenCV library for Windows on Arm devices and develop
-        computer vision applications using OpenCV. It is designed for software developers who want to
-        build and develop application...
+      generated_at_before: '2026-06-01T22:10:09Z'
+      generated_at_after: '2026-06-01T22:10:09Z'
+      preview_before: This Learning Path shows how to build the OpenCV library from
+        source on Windows on Arm and create a small test application using either
+        MSVC or Clang. You will work on a Windows on Arm machine or an A...
+      preview_after: This Learning Path shows how to build the OpenCV library from
+        source on Windows on Arm and create a small test application using either
+        MSVC or Clang. You will work on a Windows on Arm machine or an A...
+      preview_generated: Build the OpenCV library on Windows on Arm and create a small
+        C++ test application that uses it. You will work in Windows PowerShell, use
+        Git to clone the OpenCV source, configure the build with CMake...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: d24bf30aef690451942789bb66df63b7a3483ecc3cd2c4b3e60ce15fad90cb91
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       current_source_hash: d24bf30aef690451942789bb66df63b7a3483ecc3cd2c4b3e60ce15fad90cb91
-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T22:10:09Z'
+      generated_at_after: '2026-06-02T23:13:24Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before building OpenCV on Windows on Arm?
+        - Which compiler should I use, MSVC or Clang?
+        - Where do I run the commands to fetch and configure OpenCV?
+        - Can I use a newer OpenCV version than 4.10.0?
+        - What result should I expect after completing the steps?
+        removed_questions:
+        - What hardware or environment do I need to follow this path?
+        - What tools should I install before starting, and which versions were tested?
+        - 'Which compiler should I use: MSVC or Clang?'
+        - Which OpenCV version do the steps use?
+        - What will I build, and how do I verify that it worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before building OpenCV on Windows on Arm?
+        - Which compiler should I use, MSVC or Clang?
+        - Where do I run the commands to fetch and configure OpenCV?
+        - Can I use a newer OpenCV version than 4.10.0?
+        - What result should I expect after completing the steps?
+        removed_questions:
+        - What hardware or environment do I need to follow this path?
+        - What tools should I install before starting, and which versions were tested?
+        - 'Which compiler should I use: MSVC or Clang?'
+        - Which OpenCV version do the steps use?
+        - What will I build, and how do I verify that it worked?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -205241,51 +9975,78 @@ history:
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to measure application resource usage, benchmark video encoding tasks,
-        and monitor CPU, memory, and power consumption on Windows on Arm using FFmpeg and PowerShell.
-        It is designed for develo...
-      preview_after: Learn how to measure application resource usage, benchmark video encoding tasks,
-        and monitor CPU, memory, and power consumption on Windows on Arm using FFmpeg and PowerShell.
-        It is designed for develo...
-      preview_generated: Learn how to measure application resource usage, benchmark video encoding tasks,
-        and monitor CPU, memory, and power consumption on Windows on Arm using FFmpeg and PowerShell.
-        It is designed for develo...
+      generated_at_before: '2026-06-01T22:10:37Z'
+      generated_at_after: '2026-06-01T22:10:37Z'
+      preview_before: Learn how to measure application resource and power usage on
+        Windows on Arm using FFmpeg and PowerShell. You will set up FFmpeg, encode
+        a test video, and run a decoding workload while PowerShell scrip...
+      preview_after: Learn how to measure application resource and power usage on
+        Windows on Arm using FFmpeg and PowerShell. You will set up FFmpeg, encode
+        a test video, and run a decoding workload while PowerShell scrip...
+      preview_generated: This Learning Path shows how to measure application resource
+        and power usage on Windows on Arm using FFmpeg and PowerShell. You will set
+        up FFmpeg, encode a test video, and then run a sample video dec...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: fc0d79605640cc8e7a36070a044323d6e278335484949921d0aa4e9cd163d4bd
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T22:10:37Z'
+      generated_at_after: '2026-06-02T23:14:27Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the scripts?
+        - Which FFmpeg binaries should I use for the tests?
+        - How do I capture CPU and memory usage during decoding, and what output should
+          I expect?
+        - How is power usage measured without extra hardware?
+        - How should I compare results between Arm64 and x86_64 runs?
+        removed_questions:
+        - What hardware and software do I need before starting?
+        - What workloads and metrics are covered in this path?
+        - How are the results recorded, and what artifacts should I expect?
+        - Can I compare x86_64 emulated and Arm64 native FFmpeg runs?
+        - Do I need external equipment to measure power consumption?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the scripts?
+        - Which FFmpeg binaries should I use for the tests?
+        - How do I capture CPU and memory usage during decoding, and what output should
+          I expect?
+        - How is power usage measured without extra hardware?
+        - How should I compare results between Arm64 and x86_64 runs?
+        removed_questions:
+        - What hardware and software do I need before starting?
+        - What workloads and metrics are covered in this path?
+        - How are the results recorded, and what artifacts should I expect?
+        - Can I compare x86_64 emulated and Arm64 native FFmpeg runs?
+        - Do I need external equipment to measure power consumption?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -205295,51 +10056,78 @@ history:
       source_hash_before: 915f6eb5e95bd42ed09b727b4599855d965125e67a8761fbd48a124e9e74b1bc
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to automate Windows on Arm VM creation on Arm Linux systems using QEMU,
-        KVM, and Bash scripts for development and testing. It is designed for developers and system administrators
-        who want to...
-      preview_after: Learn how to automate Windows on Arm VM creation on Arm Linux systems using QEMU,
-        KVM, and Bash scripts for development and testing. It is designed for developers and system administrators
-        who want to...
-      preview_generated: Learn how to automate Windows on Arm VM creation on Arm Linux systems using QEMU,
-        KVM, and Bash scripts for development and testing. It is designed for developers and system administrators
-        who want to...
+      generated_at_before: '2026-06-01T22:11:02Z'
+      generated_at_after: '2026-06-01T22:11:02Z'
+      preview_before: This introductory path shows how to install and run Windows
+        11 on Arm virtual machines on an Arm Linux system using QEMU, KVM, and two
+        Bash automation scripts. You will clone a GitHub project, underst...
+      preview_after: This introductory path shows how to install and run Windows 11
+        on Arm virtual machines on an Arm Linux system using QEMU, KVM, and two Bash
+        automation scripts. You will clone a GitHub project, underst...
+      preview_generated: This Learning Path shows how to automate creating and running
+        a Windows on Arm virtual machine on an Arm Linux host using QEMU, KVM, and
+        Bash scripts. You will clone a GitHub project that provides two...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 915f6eb5e95bd42ed09b727b4599855d965125e67a8761fbd48a124e9e74b1bc
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T22:11:02Z'
+      generated_at_after: '2026-06-02T23:15:47Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the VM automation scripts?
+        - How do I get the automation scripts onto my Arm Linux system?
+        - Which command should I use to create a new Windows on Arm VM quickly?
+        - How do I start and connect to the VM after it is created?
+        - What should I check if VM creation or startup fails?
+        removed_questions:
+        - What host system do I need before starting?
+        - Where do I get the automation scripts and how do I begin?
+        - What is the fastest way to create a Windows on Arm VM and choose its storage
+          location?
+        - How do I start the VM and verify that it is running?
+        - Does the path cover customization and troubleshooting?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the VM automation scripts?
+        - How do I get the automation scripts onto my Arm Linux system?
+        - Which command should I use to create a new Windows on Arm VM quickly?
+        - How do I start and connect to the VM after it is created?
+        - What should I check if VM creation or startup fails?
+        removed_questions:
+        - What host system do I need before starting?
+        - Where do I get the automation scripts and how do I begin?
+        - What is the fastest way to create a Windows on Arm VM and choose its storage
+          location?
+        - How do I start the VM and verify that it is running?
+        - Does the path cover customization and troubleshooting?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -205349,51 +10137,76 @@ history:
       source_hash_before: 4069c5bce1ce4b689a7a67d740fc077dc55c9b0bfbab392f5984ba0bdd9e59c3
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to build native Arm applications and migrate x86/x64 applications to Arm
-        using Arm64EC on Windows on Arm devices. It is designed for software developers who want to use
-        Arm64EC with Windows ...
-      preview_after: Learn how to build native Arm applications and migrate x86/x64 applications to Arm
-        using Arm64EC on Windows on Arm devices. It is designed for software developers who want to use
-        Arm64EC with Windows ...
-      preview_generated: Learn how to build native Arm applications and migrate x86/x64 applications to
-        Arm using Arm64EC on Windows on Arm devices. It is designed for software developers who want to
-        use Arm64EC with Windows ...
+      generated_at_before: '2026-06-01T22:11:42Z'
+      generated_at_after: '2026-06-01T22:11:42Z'
+      preview_before: This Learning Path shows how to use Arm64EC on Windows 11 on
+        Arm to build native Arm applications and begin migrating existing x86 or x64
+        code. Working on a Windows on Arm computer (for example, a Len...
+      preview_after: This Learning Path shows how to use Arm64EC on Windows 11 on
+        Arm to build native Arm applications and begin migrating existing x86 or x64
+        code. Working on a Windows on Arm computer (for example, a Len...
+      preview_generated: This Learning Path shows how to use Arm64EC on Windows 11
+        on Arm to build native Arm applications and migrate existing x86 or x64 applications.
+        Working in Visual Studio (2022 or higher), you create bu...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 4069c5bce1ce4b689a7a67d740fc077dc55c9b0bfbab392f5984ba0bdd9e59c3
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T22:11:42Z'
+      generated_at_after: '2026-06-02T23:17:05Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which option should I use to migrate an existing x86 or x64 application?
+        - What should I check if I do not see Arm64EC options in Visual Studio?
+        - How do I compare performance across build configurations?
+        - How do I verify that my build was successful?
+        removed_questions:
+        - What hardware and OS do I need to follow this Learning Path?
+        - Which tools and versions are required?
+        - Can I complete this Learning Path on an x86 or x64 Windows PC?
+        - What is Arm64EC and why is it used here?
+        - What should I expect to build or verify by the end?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which option should I use to migrate an existing x86 or x64 application?
+        - What should I check if I do not see Arm64EC options in Visual Studio?
+        - How do I compare performance across build configurations?
+        - How do I verify that my build was successful?
+        removed_questions:
+        - What hardware and OS do I need to follow this Learning Path?
+        - Which tools and versions are required?
+        - Can I complete this Learning Path on an x86 or x64 Windows PC?
+        - What is Arm64EC and why is it used here?
+        - What should I expect to build or verify by the end?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -205403,51 +10216,76 @@ history:
       source_hash_before: 3b3ecd451ed8b634c7fbe194248cdf1d33432633efbcbef5de713495041ff425
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to port Qt-based Python desktop applications with C/C++ dependencies to
-        Arm64 using Arm64EC on Windows on Arm. It is designed for developers who want to learn how to
-        port their applications ...
-      preview_after: Learn how to port Qt-based Python desktop applications with C/C++ dependencies to
-        Arm64 using Arm64EC on Windows on Arm. It is designed for developers who want to learn how to
-        port their applications ...
-      preview_generated: Learn how to port Qt-based Python desktop applications with C/C++ dependencies
-        to Arm64 using Arm64EC on Windows on Arm. It is designed for developers who want to learn how
-        to port their applications ...
+      generated_at_before: '2026-06-01T22:12:11Z'
+      generated_at_after: '2026-06-01T22:12:11Z'
+      preview_before: This Learning Path shows how to port a Qt-based Python desktop
+        application with C/C++ dependencies to Arm64 on Windows using Arm64EC. You
+        will build the app, create C/C++ DLLs, and port each DLL to Ar...
+      preview_after: This Learning Path shows how to port a Qt-based Python desktop
+        application with C/C++ dependencies to Arm64 on Windows using Arm64EC. You
+        will build the app, create C/C++ DLLs, and port each DLL to Ar...
+      preview_generated: Follow this introductory Windows on Arm path to port a Qt-based
+        Python desktop application with C/C++ DLL dependencies to Arm64 using Arm64EC.
+        You will build the app, create C/C++ dependencies, and th...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 3b3ecd451ed8b634c7fbe194248cdf1d33432633efbcbef5de713495041ff425
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T22:12:11Z'
+      generated_at_after: '2026-06-02T23:18:19Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - 'Which option should I use to port DLLs: CMake or MSBuild?'
+        - How do I enable Arm64EC for a CMake project in this path?
+        - How do I set up an MSBuild project for Arm64EC in Visual Studio 2022?
+        - What result should I expect after building with Arm64EC?
+        removed_questions:
+        - What hardware and software do I need before starting?
+        - Can I use a virtual machine instead of physical Windows on Arm hardware?
+        - Do I need to port all my dependencies to Arm64 immediately?
+        - Which build systems are covered and what changes will I make?
+        - What will I produce by the end of this Learning Path?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - 'Which option should I use to port DLLs: CMake or MSBuild?'
+        - How do I enable Arm64EC for a CMake project in this path?
+        - How do I set up an MSBuild project for Arm64EC in Visual Studio 2022?
+        - What result should I expect after building with Arm64EC?
+        removed_questions:
+        - What hardware and software do I need before starting?
+        - Can I use a virtual machine instead of physical Windows on Arm hardware?
+        - Do I need to port all my dependencies to Arm64 immediately?
+        - Which build systems are covered and what changes will I make?
+        - What will I produce by the end of this Learning Path?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -205457,51 +10295,76 @@ history:
       source_hash_before: 63389742eced4df89f85bdf56a01e489e52a9702d446b557f8f55312f9d31f20
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to build and run Qt-based desktop applications on Windows on Arm and investigate
-        native Arm64 performance improvements. It is designed for software developers who want to use
-        the native perf...
-      preview_after: Learn how to build and run Qt-based desktop applications on Windows on Arm and investigate
-        native Arm64 performance improvements. It is designed for software developers who want to use
-        the native perf...
-      preview_generated: Learn how to build and run Qt-based desktop applications on Windows on Arm and
-        investigate native Arm64 performance improvements. It is designed for software developers who
-        want to use the native perf...
+      generated_at_before: '2026-06-01T22:12:37Z'
+      generated_at_after: '2026-06-01T22:12:37Z'
+      preview_before: This Learning Path shows how to build and run a Qt-based desktop
+        application on Windows on Arm (WoA) and investigate native Arm64 performance
+        characteristics. You work on a WoA device such as a Lenovo...
+      preview_after: This Learning Path shows how to build and run a Qt-based desktop
+        application on Windows on Arm (WoA) and investigate native Arm64 performance
+        characteristics. You work on a WoA device such as a Lenovo...
+      preview_generated: Build and run a Qt-based desktop application on Windows on
+        Arm (WoA), then investigate the performance improvements of running natively
+        on Arm64. This introductory path targets developers using C/C++ ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 63389742eced4df89f85bdf56a01e489e52a9702d446b557f8f55312f9d31f20
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       current_source_hash: 63389742eced4df89f85bdf56a01e489e52a9702d446b557f8f55312f9d31f20
-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T22:12:37Z'
+      generated_at_after: '2026-06-02T23:20:12Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Qt package or version should I install for Windows on Arm?
+        - Can I use a virtual machine instead of physical hardware?
+        - Do I need to use Qt Creator for this Learning Path?
+        - What result should I expect and how long will it take?
+        removed_questions:
+        - What platform does this Learning Path target, and what hardware can I use?
+        - What software do I need before I start?
+        - Do I need a specific Qt version or IDE?
+        - "What will I build, and how do I verify it\u2019s running natively on Arm64?"
+        - How long does this take and what skill level is assumed?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Qt package or version should I install for Windows on Arm?
+        - Can I use a virtual machine instead of physical hardware?
+        - Do I need to use Qt Creator for this Learning Path?
+        - What result should I expect and how long will it take?
+        removed_questions:
+        - What platform does this Learning Path target, and what hardware can I use?
+        - What software do I need before I start?
+        - Do I need a specific Qt version or IDE?
+        - "What will I build, and how do I verify it\u2019s running natively on Arm64?"
+        - How long does this take and what skill level is assumed?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -205511,51 +10374,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to build and run an ASP.NET Core 8 web server application with Web API
-        and dependency injection services on Windows on Arm. It is designed for developers who are interested
-        in building a web...
-      preview_after: Learn how to build and run an ASP.NET Core 8 web server application with Web API
-        and dependency injection services on Windows on Arm. It is designed for developers who are interested
-        in building a web...
-      preview_generated: Learn how to build and run an ASP.NET Core 8 web server application with Web
-        API and dependency injection services on Windows on Arm. It is designed for developers who are
-        interested in building a web...
+      generated_at_before: '2026-06-01T22:13:07Z'
+      generated_at_after: '2026-06-01T22:13:07Z'
+      preview_before: Follow this advanced, approximately 30-minute Learning Path
+        to build and run an ASP.NET Core 8 Web API on Windows on Arm (Arm64). You
+        will create a project that uses dependency injection for services,...
+      preview_after: Follow this advanced, approximately 30-minute Learning Path to
+        build and run an ASP.NET Core 8 Web API on Windows on Arm (Arm64). You will
+        create a project that uses dependency injection for services,...
+      preview_generated: Learn to build and run an ASP.NET Core 8 Web API on Windows
+        on Arm for headless IoT scenarios. You will create a project (for example,
+        Arm64.HeadlessIoT), implement and consume services using dependen...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: e60ce02e9d1872da06bc5bfeb18c7ec65f2bc17defb2b75292f930fb3ad41711
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T22:13:07Z'
+      generated_at_after: '2026-06-02T23:20:57Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - How do I create and run the ASP.NET Core Web API project on Windows on Arm?
+        - What result should I expect when the server starts successfully?
+        - "What should I check if dotnet run doesn\u2019t show a listening address?"
+        - How are dependency injection services used in this path?
+        removed_questions:
+        - What hardware or VM setup do I need?
+        - What software must be installed before I start?
+        - What will I build in this Learning Path?
+        - How do I run and verify the server is working?
+        - Does this Learning Path cover containerization or deployment beyond localhost?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - How do I create and run the ASP.NET Core Web API project on Windows on Arm?
+        - What result should I expect when the server starts successfully?
+        - "What should I check if dotnet run doesn\u2019t show a listening address?"
+        - How are dependency injection services used in this path?
+        removed_questions:
+        - What hardware or VM setup do I need?
+        - What software must be installed before I start?
+        - What will I build in this Learning Path?
+        - How do I run and verify the server is working?
+        - Does this Learning Path cover containerization or deployment beyond localhost?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -205565,51 +10453,76 @@ history:
       source_hash_before: cddfb7b83e82f0daa513558b1e7ee09b55c63e2ff95675d67be7d4408d391aa4
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to create Node.js IoT applications that stream sensor data from Windows
-        on Arm devices to AWS IoT Core using MQTT. It is designed for developers who want to learn how
-        to create IoT applicati...
-      preview_after: Learn how to create Node.js IoT applications that stream sensor data from Windows
-        on Arm devices to AWS IoT Core using MQTT. It is designed for developers who want to learn how
-        to create IoT applicati...
-      preview_generated: Learn how to create Node.js IoT applications that stream sensor data from Windows
-        on Arm devices to AWS IoT Core using MQTT. It is designed for developers who want to learn how
-        to create IoT applicati...
+      generated_at_before: '2026-06-01T22:13:29Z'
+      generated_at_after: '2026-06-01T22:13:29Z'
+      preview_before: "This Learning Path shows how to build a Node.js IoT application\
+        \ on Windows on Arm that streams synthesized sensor data to AWS IoT Core over\
+        \ MQTT. You will register a device using the AWS IoT Core \u201CCon..."
+      preview_after: "This Learning Path shows how to build a Node.js IoT application\
+        \ on Windows on Arm that streams synthesized sensor data to AWS IoT Core over\
+        \ MQTT. You will register a device using the AWS IoT Core \u201CCon..."
+      preview_generated: Build a Node.js IoT application on Windows on Arm that streams
+        synthesized sensor data to AWS IoT Core using MQTT. You will register and
+        secure a device in AWS IoT Core using the Connect one device wi...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: cddfb7b83e82f0daa513558b1e7ee09b55c63e2ff95675d67be7d4408d391aa4
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T22:13:29Z'
+      generated_at_after: '2026-06-02T23:21:33Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Where do I register and connect the device in AWS IoT Core?
+        - How do I check network connectivity to AWS IoT Core before sending data?
+        - Which MQTT topic should I subscribe to in the test client to view messages?
+        - How do I know the data stream from the emulator is working?
+        removed_questions:
+        - What environment and tools do I need before starting?
+        - Do I need access to AWS services?
+        - Is physical sensor hardware required?
+        - How do I register and connect the device or emulator in AWS IoT Core?
+        - How do I verify that data is reaching AWS IoT Core?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Where do I register and connect the device in AWS IoT Core?
+        - How do I check network connectivity to AWS IoT Core before sending data?
+        - Which MQTT topic should I subscribe to in the test client to view messages?
+        - How do I know the data stream from the emulator is working?
+        removed_questions:
+        - What environment and tools do I need before starting?
+        - Do I need access to AWS services?
+        - Is physical sensor hardware required?
+        - How do I register and connect the device or emulator in AWS IoT Core?
+        - How do I verify that data is reaching AWS IoT Core?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -205619,51 +10532,76 @@ history:
       source_hash_before: f25597c6c9e69e09e9a86fa7c02d0ace9c347f293a21a11c00a6d3498300052c
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to configure AWS IoT Core rules to parse MQTT messages and store IoT data
-        in Amazon DynamoDB from Windows on Arm devices. It is designed for developers who are interested
-        in using Amazon Dyn...
-      preview_after: Learn how to configure AWS IoT Core rules to parse MQTT messages and store IoT data
-        in Amazon DynamoDB from Windows on Arm devices. It is designed for developers who are interested
-        in using Amazon Dyn...
-      preview_generated: Learn how to configure AWS IoT Core rules to parse MQTT messages and store IoT
-        data in Amazon DynamoDB from Windows on Arm devices. It is designed for developers who are interested
-        in using Amazon Dyn...
+      generated_at_before: '2026-06-01T22:13:59Z'
+      generated_at_after: '2026-06-01T22:13:59Z'
+      preview_before: This Learning Path guides you through configuring AWS IoT Core
+        to parse MQTT messages and store IoT data in Amazon DynamoDB from a Windows
+        on Arm environment. Building on the previously completed weat...
+      preview_after: This Learning Path guides you through configuring AWS IoT Core
+        to parse MQTT messages and store IoT data in Amazon DynamoDB from a Windows
+        on Arm environment. Building on the previously completed weat...
+      preview_generated: This Learning Path shows how to configure AWS IoT Core rules
+        to parse MQTT messages and store IoT data in Amazon DynamoDB from a Windows
+        on Arm device. You will reuse the IoT application from the prer...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: f25597c6c9e69e09e9a86fa7c02d0ace9c347f293a21a11c00a6d3498300052c
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T22:13:59Z'
+      generated_at_after: '2026-06-02T23:22:02Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running these steps?
+        - Where do I create the AWS IoT Core rule?
+        - What should I name the rule?
+        - Do I need to modify or rebuild the IoT application for this path?
+        - What result should I expect after completing the configuration?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - What will I configure in AWS IoT Core?
+        - How is the data stream for this path generated?
+        - Which platform and tools are used during the steps?
+        - How will I know the configuration worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running these steps?
+        - Where do I create the AWS IoT Core rule?
+        - What should I name the rule?
+        - Do I need to modify or rebuild the IoT application for this path?
+        - What result should I expect after completing the configuration?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - What will I configure in AWS IoT Core?
+        - How is the data stream for this path generated?
+        - Which platform and tools are used during the steps?
+        - How will I know the configuration worked?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -205673,51 +10611,78 @@ history:
       source_hash_before: f685f45be9e5fc05b278e28590f9d421a920d43cc36ccb572545de4eaf4a799a
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to process IoT data using AWS Lambda functions triggered by AWS IoT Core
-        messages from Windows on Arm devices. It is designed for developers who are interested in using
-        AWS Lambda for proces...
-      preview_after: Learn how to process IoT data using AWS Lambda functions triggered by AWS IoT Core
-        messages from Windows on Arm devices. It is designed for developers who are interested in using
-        AWS Lambda for proces...
-      preview_generated: Learn how to process IoT data using AWS Lambda functions triggered by AWS IoT
-        Core messages from Windows on Arm devices. It is designed for developers who are interested in
-        using AWS Lambda for proces...
+      generated_at_before: '2026-06-01T22:14:31Z'
+      generated_at_after: '2026-06-01T22:14:31Z'
+      preview_before: This Learning Path shows how to process IoT data on Arm64 by
+        connecting AWS IoT Core to an AWS Lambda function from a Windows on Arm device.
+        You will reuse the weather-station IoT emulator from the pr...
+      preview_after: This Learning Path shows how to process IoT data on Arm64 by
+        connecting AWS IoT Core to an AWS Lambda function from a Windows on Arm device.
+        You will reuse the weather-station IoT emulator from the pr...
+      preview_generated: "This advanced Learning Path shows how to process IoT data\
+        \ on Arm64 by connecting AWS IoT Core messages to an AWS Lambda function from\
+        \ a Windows on Arm device. Building on the \u201CCreate IoT applications ..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: f685f45be9e5fc05b278e28590f9d421a920d43cc36ccb572545de4eaf4a799a
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T22:14:31Z'
+      generated_at_after: '2026-06-02T23:23:16Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Where do I create the AWS IoT Core rule that triggers the Lambda function?
+        - Which AWS services are used and how do they interact in this path?
+        - How do I know the Lambda trigger and notifications are working?
+        - What should I check if I do not receive an email after sending a high temperature
+          reading?
+        removed_questions:
+        - What do I need before starting?
+        - Do I need a physical IoT device to follow this path?
+        - Which AWS services are used and what are their roles?
+        - How is the Lambda function triggered and how do I know it worked?
+        - What tools or languages are assumed for development?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Where do I create the AWS IoT Core rule that triggers the Lambda function?
+        - Which AWS services are used and how do they interact in this path?
+        - How do I know the Lambda trigger and notifications are working?
+        - What should I check if I do not receive an email after sending a high temperature
+          reading?
+        removed_questions:
+        - What do I need before starting?
+        - Do I need a physical IoT device to follow this path?
+        - Which AWS services are used and what are their roles?
+        - How is the Lambda function triggered and how do I know it worked?
+        - What tools or languages are assumed for development?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -205727,51 +10692,76 @@ history:
       source_hash_before: f5a93346a0fd55659b7c0a6df501db97742ec71755b6443051b654f3ce871cdf
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       current_source_hash: f5a93346a0fd55659b7c0a6df501db97742ec71755b6443051b654f3ce871cdf
-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to implement AWS Lambda functions that process and aggregate IoT data
-        stored in DynamoDB tables from Windows on Arm devices. It is designed for developers who are interested
-        in using AWS Lam...
-      preview_after: Learn how to implement AWS Lambda functions that process and aggregate IoT data stored
-        in DynamoDB tables from Windows on Arm devices. It is designed for developers who are interested
-        in using AWS Lam...
-      preview_generated: Learn how to implement AWS Lambda functions that process and aggregate IoT data
-        stored in DynamoDB tables from Windows on Arm devices. It is designed for developers who are interested
-        in using AWS Lam...
+      generated_at_before: '2026-06-01T22:14:53Z'
+      generated_at_after: '2026-06-01T22:14:53Z'
+      preview_before: This Learning Path shows how to implement and test an AWS Lambda
+        function on Windows on Arm that scans and aggregates IoT data stored in Amazon
+        DynamoDB. You will create a Lambda function in the AWS c...
+      preview_after: This Learning Path shows how to implement and test an AWS Lambda
+        function on Windows on Arm that scans and aggregates IoT data stored in Amazon
+        DynamoDB. You will create a Lambda function in the AWS c...
+      preview_generated: Build a serverless data processing step for your IoT workload
+        on a Windows on Arm device by implementing an AWS Lambda function in Node.js
+        that scans a DynamoDB table and returns an aggregate (average...
     faqs:
-      action: unchanged
+      action: rerun_requested
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-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T22:14:53Z'
+      generated_at_after: '2026-06-02T23:23:55Z'
       before_count: 5
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       generated_count: 5
       change_details:
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         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running these steps?
+        - Which options should I choose when creating the Lambda function?
+        - Where do I add the code and what file name should I use?
+        - How do I populate data and test the function?
+        - What should I check if the function returns no average or errors?
+        removed_questions:
+        - What prerequisites do I need before starting?
+        - What will I implement in this Learning Path?
+        - Do I need existing data in DynamoDB to follow the steps?
+        - How do I test that the Lambda function works?
+        - Which region, table, and attribute names does the example use?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running these steps?
+        - Which options should I choose when creating the Lambda function?
+        - Where do I add the code and what file name should I use?
+        - How do I populate data and test the function?
+        - What should I check if the function returns no average or errors?
+        removed_questions:
+        - What prerequisites do I need before starting?
+        - What will I implement in this Learning Path?
+        - Do I need existing data in DynamoDB to follow the steps?
+        - How do I test that the Lambda function works?
+        - Which region, table, and attribute names does the example use?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
       missing_before: false
@@ -205781,51 +10771,78 @@ history:
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-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to create a static website hosted on Amazon S3 that interacts with AWS
-        Lambda functions to display IoT data from Windows on Arm devices. It is designed for developers
-        who are interested in u...
-      preview_after: Learn how to create a static website hosted on Amazon S3 that interacts with AWS
-        Lambda functions to display IoT data from Windows on Arm devices. It is designed for developers
-        who are interested in u...
-      preview_generated: Learn how to create a static website hosted on Amazon S3 that interacts with
-        AWS Lambda functions to display IoT data from Windows on Arm devices. It is designed for developers
-        who are interested in u...
+      generated_at_before: '2026-06-01T22:15:20Z'
+      generated_at_after: '2026-06-01T22:15:20Z'
+      preview_before: This Learning Path guides you through hosting a static IoT website
+        on Amazon S3 from a Windows on Arm environment. You will create a simple site
+        (index.html, styles.css, index.js), connect it to an ex...
+      preview_after: This Learning Path guides you through hosting a static IoT website
+        on Amazon S3 from a Windows on Arm environment. You will create a simple site
+        (index.html, styles.css, index.js), connect it to an ex...
+      preview_generated: This Learning Path shows how to build and deploy a static
+        website to Amazon S3 that calls AWS Lambda to display IoT data from Windows
+        on Arm devices. You will create a simple site (index.html, styles....
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
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-      generated_at_after: '2026-05-06T17:17:55Z'
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+      generated_at_after: '2026-06-02T23:24:33Z'
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       generated_count: 5
       change_details:
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         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How should I structure the static website files, and what does each file
+          do?
+        - Where do I find the AWS Lambda Function URL to use in my website?
+        - How do I set up AWS CLI to deploy to Amazon S3?
+        - How do I know the website is working after deployment?
+        removed_questions:
+        - What environment and tools do I need before starting?
+        - Do I need to complete another Learning Path first?
+        - What will I build in this Learning Path?
+        - How do I connect the website to my AWS Lambda function?
+        - How do I deploy and verify the site on Amazon S3?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How should I structure the static website files, and what does each file
+          do?
+        - Where do I find the AWS Lambda Function URL to use in my website?
+        - How do I set up AWS CLI to deploy to Amazon S3?
+        - How do I know the website is working after deployment?
+        removed_questions:
+        - What environment and tools do I need before starting?
+        - Do I need to complete another Learning Path first?
+        - What will I build in this Learning Path?
+        - How do I connect the website to my AWS Lambda function?
+        - How do I deploy and verify the site on Amazon S3?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/win_cef/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
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+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -205835,51 +10852,78 @@ history:
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-      preview_before: Learn how to create and build Chromium Embedded Framework desktop applications using
-        CMake and web technologies on Windows on Arm. It is designed for developers who want to learn
-        how to use web techno...
-      preview_after: Learn how to create and build Chromium Embedded Framework desktop applications using
-        CMake and web technologies on Windows on Arm. It is designed for developers who want to learn
-        how to use web techno...
-      preview_generated: Learn how to create and build Chromium Embedded Framework desktop applications
-        using CMake and web technologies on Windows on Arm. It is designed for developers who want to
-        learn how to use web techno...
+      generated_at_before: '2026-06-01T22:15:54Z'
+      generated_at_after: '2026-06-01T22:15:54Z'
+      preview_before: This introductory Learning Path guides you through creating
+        and building a Chromium Embedded Framework (CEF) desktop application on Windows
+        on Arm using CMake. Working in Visual Studio 2022 on a Windo...
+      preview_after: This introductory Learning Path guides you through creating and
+        building a Chromium Embedded Framework (CEF) desktop application on Windows
+        on Arm using CMake. Working in Visual Studio 2022 on a Windo...
+      preview_generated: Build a Chromium Embedded Framework (CEF) desktop application
+        on Windows on Arm using CMake, C++, and web technologies. Working on a Windows
+        11 on Arm device or a Windows on Arm virtual machine, you w...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T22:15:54Z'
+      generated_at_after: '2026-06-02T23:25:17Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before starting this Learning Path?
+        - Which tools and languages will I use to build the application?
+        - What environment does the resulting application target?
+        - What result should I expect when I finish the steps?
+        - Is this suitable if I am new to CEF or Windows on Arm, and how long will
+          it take?
+        removed_questions:
+        - What hardware and software do I need before starting?
+        - Can I use a virtual machine instead of a physical device?
+        - Which tools and languages are used in this path?
+        - What will I produce by the end of the path?
+        - How long does it take and what level is assumed?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before starting this Learning Path?
+        - Which tools and languages will I use to build the application?
+        - What environment does the resulting application target?
+        - What result should I expect when I finish the steps?
+        - Is this suitable if I am new to CEF or Windows on Arm, and how long will
+          it take?
+        removed_questions:
+        - What hardware and software do I need before starting?
+        - Can I use a virtual machine instead of a physical device?
+        - Which tools and languages are used in this path?
+        - What will I produce by the end of the path?
+        - How long does it take and what level is assumed?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/win_forms/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -205889,51 +10933,76 @@ history:
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-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to create and build Windows Forms applications and measure code execution
-        performance on Arm64. It is designed for developers who want to learn how to create Windows Forms
-        applications on Wi...
-      preview_after: Learn how to create and build Windows Forms applications and measure code execution
-        performance on Arm64. It is designed for developers who want to learn how to create Windows Forms
-        applications on Wi...
-      preview_generated: Learn how to create and build Windows Forms applications and measure code execution
-        performance on Arm64. It is designed for developers who want to learn how to create Windows Forms
-        applications on Wi...
+      generated_at_before: '2026-06-01T22:16:20Z'
+      generated_at_after: '2026-06-01T22:16:20Z'
+      preview_before: This introductory path shows how to create and build a Windows
+        Forms desktop application in C#/.NET on Windows on Arm using Visual Studio
+        2022. You will configure build settings, including creating an...
+      preview_after: This introductory path shows how to create and build a Windows
+        Forms desktop application in C#/.NET on Windows on Arm using Visual Studio
+        2022. You will configure build settings, including creating an...
+      preview_generated: "Build a simple Windows Forms application in C# with Visual\
+        \ Studio 2022 on Windows on Arm, then change the project\u2019s build configuration\
+        \ to run natively on ARM64. You will use Visual Studio\u2019s Configura..."
     faqs:
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       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T22:16:20Z'
+      generated_at_after: '2026-06-02T23:25:52Z'
       before_count: 5
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       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which language and framework does the sample use?
+        - How do I switch the project to build for ARM64 in Visual Studio?
+        - "How do I confirm I\u2019m building and running the ARM64 configuration?"
+        - What result should I expect when comparing performance settings?
+        removed_questions:
+        - What hardware and software do I need to follow this Learning Path?
+        - Can I complete this Learning Path on a virtual machine?
+        - Does this path show how to target Arm64 in Visual Studio?
+        - How is performance measured in the example application?
+        - How long does this Learning Path take and what prior knowledge is assumed?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which language and framework does the sample use?
+        - How do I switch the project to build for ARM64 in Visual Studio?
+        - "How do I confirm I\u2019m building and running the ARM64 configuration?"
+        - What result should I expect when comparing performance settings?
+        removed_questions:
+        - What hardware and software do I need to follow this Learning Path?
+        - Can I complete this Learning Path on a virtual machine?
+        - Does this path show how to target Arm64 in Visual Studio?
+        - How is performance measured in the example application?
+        - How long does this Learning Path take and what prior knowledge is assumed?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/win_net/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -205943,51 +11012,80 @@ history:
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-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to build and run a .NET 6 Windows Presentation Foundation (WPF) application
-        on Windows on Arm machines. It is designed for software developers doing native development on
-        Windows on Arm comp...
-      preview_after: Learn how to build and run a .NET 6 Windows Presentation Foundation (WPF) application
-        on Windows on Arm machines. It is designed for software developers doing native development on
-        Windows on Arm comp...
-      preview_generated: Learn how to build and run a .NET 6 Windows Presentation Foundation (WPF) application
-        on Windows on Arm machines. It is designed for software developers doing native development on
-        Windows on Arm comp...
+      generated_at_before: '2026-06-01T22:16:47Z'
+      generated_at_after: '2026-06-01T22:16:47Z'
+      preview_before: This introductory Learning Path shows how to build and run a
+        native .NET 6 Windows Presentation Foundation (WPF) application on a Windows
+        on Arm system. You will prepare your environment by installing...
+      preview_after: This introductory Learning Path shows how to build and run a
+        native .NET 6 Windows Presentation Foundation (WPF) application on a Windows
+        on Arm system. You will prepare your environment by installing...
+      preview_generated: Learn how to build and run a native .NET 6 Windows Presentation
+        Foundation (WPF) application on Windows on Arm using Visual Studio 2022 or
+        later. You will prepare a Windows on Arm computer or virtual ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
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-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T22:16:47Z'
+      generated_at_after: '2026-06-02T23:26:32Z'
       before_count: 5
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       generated_count: 5
       change_details:
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         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Visual Studio components should I install?
+        - How do I add the .NET desktop development workload to an existing Visual
+          Studio installation?
+        - Can I use a Windows on Arm virtual machine instead of physical hardware?
+        - What result should I expect after completing the steps, and how long will
+          it take?
+        removed_questions:
+        - What hardware or VM do I need to follow this path?
+        - Which version of Visual Studio and components are required?
+        - What will I build and run in this Learning Path?
+        - How long does this take and what experience level is assumed?
+        - How do I verify my setup and results?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Visual Studio components should I install?
+        - How do I add the .NET desktop development workload to an existing Visual
+          Studio installation?
+        - Can I use a Windows on Arm virtual machine instead of physical hardware?
+        - What result should I expect after completing the steps, and how long will
+          it take?
+        removed_questions:
+        - What hardware or VM do I need to follow this path?
+        - Which version of Visual Studio and components are required?
+        - What will I build and run in this Learning Path?
+        - How long does this take and what experience level is assumed?
+        - How do I verify my setup and results?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/win_net8/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -205997,51 +11095,76 @@ history:
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-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to build, run, and benchmark .NET 8 Console applications to measure performance
-        on Windows on Arm devices. It is designed for developers who want to benchmark the performance
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   - path: content/learning-paths/laptops-and-desktops/win_net_maui/_index.md
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+        MAUI application on Windows on Arm and measure code execution performance
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+      preview_generated: This Learning Path shows how to create and build a cross-platform
+        .NET MAUI application on Windows on Arm and measure code execution performance
+        on Arm64. Using Visual Studio 2022, you will start a ne...
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+        removed_questions:
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+        - How do I know the performance measurement part worked?
+        removed_questions:
+        - What hardware and operating system do I need?
+        - Which Visual Studio components are required?
+        - What will I build and what code will I write?
+        - Can I use a virtual machine instead of a physical device?
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+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md
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+        model with KleidiAI acceleration. You will clone and build ONNX ...
+      preview_after: This Learning Path shows how to build ONNX Runtime with the Generate()
+        API on Windows on Arm and run inference on the Phi-3 Mini (3.3B) model with
+        KleidiAI acceleration. You will clone and build ONNX ...
+      preview_generated: This advanced Learning Path guides you through building ONNX
+        Runtime and enabling the Generate() API on Windows on Arm, then running Phi-3
+        Mini (3.3B) inference with KleidiAI acceleration. You will cl...
     faqs:
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+        - How do I know the build and run were successful?
+        - Do I need extra configuration to use KleidiAI acceleration?
+        removed_questions:
+        - What hardware or platform do I need before starting?
+        - Which tools and languages are used in the steps?
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+        - How do I know the process worked?
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+        added_questions:
+        - What do I need before running the steps?
+        - Which Phi-3 model variant should I use in this path?
+        - How is the ONNX Runtime Generate() API used here?
+        - How do I know the build and run were successful?
+        - Do I need extra configuration to use KleidiAI acceleration?
+        removed_questions:
+        - What hardware or platform do I need before starting?
+        - Which tools and languages are used in the steps?
+        - Which Phi-3 model variant is used, and in what format?
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+        - How do I know the process worked?
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+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md
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-      preview_generated: Learn how to apply Profile-Guided Optimization (PGO) to build performance-tuned
-        C++ binaries and measure improvements using Google Benchmark on Windows on Arm. It is designed
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+      preview_before: This Learning Path guides you through applying Profile-Guided
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+        Optimization (PGO) to C++ code and measuring the impact with Google Benchmark
+        on Windows on Arm. You start by understanding PGO fundamenta...
+      preview_generated: This Learning Path shows how to measure and improve C++ performance
+        on Windows on Arm using Profile-Guided Optimization (PGO) with MSVC and Google
+        Benchmark. You will create a baseline microbenchmark ...
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+        - What does the baseline benchmark measure, and why was it chosen?
+        - How do I apply PGO here, and how do I know it worked?
+        - Do I need to install Google Benchmark before starting?
+        removed_questions:
+        - What platform and tools does this Learning Path use?
+        - What will I build and measure during the steps?
+        - How is PGO applied in this workflow?
+        - What are the prerequisites before starting?
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+        added_questions:
+        - What do I need before running the steps?
+        - Which build environment should I use on Windows on Arm?
+        - What does the baseline benchmark measure, and why was it chosen?
+        - How do I apply PGO here, and how do I know it worked?
+        - Do I need to install Google Benchmark before starting?
+        removed_questions:
+        - What platform and tools does this Learning Path use?
+        - What will I build and measure during the steps?
+        - How is PGO applied in this workflow?
+        - What are the prerequisites before starting?
+        - How do I confirm that the PGO process worked?
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+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/win_python/_index.md
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-        performance for platform-dependent packages. It is designed for developers who are interested
-        in building Python appl...
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-        in building Python appl...
-      preview_generated: Learn how to build Python applications on Windows on Arm and leverage native
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+      generated_at_before: '2026-06-01T22:18:32Z'
+      generated_at_after: '2026-06-01T22:18:32Z'
+      preview_before: This introductory path shows how to build native Python applications
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+        a Windows on Arm PC or virtual machine, a code edito...
+      preview_after: This introductory path shows how to build native Python applications
+        on Windows on Arm and work with platform-dependent packages using Arm64. Using
+        a Windows on Arm PC or virtual machine, a code edito...
+      preview_generated: This introductory Learning Path shows how to build and run
+        a native Arm64 Python application on Windows on Arm, with a focus on platform-dependent
+        packages. You will create a small NumPy-based program...
     faqs:
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+        added_questions:
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+        - Do I need Visual Studio 2022 if I plan to edit code in VS Code?
+        - What should I create and what does the sample application do?
+        - Where can I find the complete sample code?
+        removed_questions:
+        - What hardware and software do I need to start?
+        - Can I complete this Learning Path on a Windows on Arm virtual machine?
+        - What will I build and how do I verify it works?
+        - Do I need Visual Studio Code, Visual Studio, or both?
+        - What level of experience is assumed and how long does it take?
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+        - Can I use a Windows on Arm virtual machine instead of physical hardware?
+        - Do I need Visual Studio 2022 if I plan to edit code in VS Code?
+        - What should I create and what does the sample application do?
+        - Where can I find the complete sample code?
+        removed_questions:
+        - What hardware and software do I need to start?
+        - Can I complete this Learning Path on a Windows on Arm virtual machine?
+        - What will I build and how do I verify it works?
+        - Do I need Visual Studio Code, Visual Studio, or both?
+        - What level of experience is assumed and how long does it take?
+        updated_questions: []
+    category: laptops-and-desktops
+  - path: content/learning-paths/laptops-and-desktops/win_python_onnx/_index.md
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-      preview_generated: Learn how to configure Windows Sandbox as a self-hosted GitHub Actions runner
-        to build and run .NET 8 WPF applications in CI/CD workflows. It is designed for software developers
-        who are developing app...
+      generated_at_before: '2026-06-01T22:19:03Z'
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+      preview_before: This Learning Path shows how to use Windows Sandbox on a Windows
+        on Arm PC as a self-hosted Arm64 GitHub Actions runner, then run a CI/CD workflow
+        that builds and runs a .NET 8 Windows Presentation Fo...
+      preview_after: This Learning Path shows how to use Windows Sandbox on a Windows
+        on Arm PC as a self-hosted Arm64 GitHub Actions runner, then run a CI/CD workflow
+        that builds and runs a .NET 8 Windows Presentation Fo...
+      preview_generated: This Learning Path shows how to configure Windows Sandbox
+        as a self-hosted Arm64 GitHub Actions runner to build and run a .NET 8 Windows
+        Presentation Foundation (WPF) sample application in a CI/CD wor...
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+        - Where is the workflow file located and how is it triggered?
+        - What result should I expect when I run the pipeline?
+        - What should I check if my jobs are queued and do not run in Windows Sandbox?
+        removed_questions:
+        - What do I need before starting?
+        - Does this Learning Path set up an Arm64 runner?
+        - What does the workflow build and run?
+        - Where is the GitHub Actions workflow defined and how is it triggered?
+        - How do I verify that the setup worked?
         updated_questions: []
       generated_diff:
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+        - Where is the workflow file located and how is it triggered?
+        - What result should I expect when I run the pipeline?
+        - What should I check if my jobs are queued and do not run in Windows Sandbox?
+        removed_questions:
+        - What do I need before starting?
+        - Does this Learning Path set up an Arm64 runner?
+        - What does the workflow build and run?
+        - Where is the GitHub Actions workflow defined and how is it triggered?
+        - How do I verify that the setup worked?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md
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-        to Arm64. By t...
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-        to Arm64. By t...
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-        console applications. It is designed for developers who want to learn how to port their Win32
-        applications to Arm64. By t...
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+      preview_before: This introductory Learning Path shows how to create a C/C++
+        Win32 DLL, use it from a Windows console application, and port the library
+        to Arm64 for Windows on Arm. You work on a Windows on Arm device ...
+      preview_after: This introductory Learning Path shows how to create a C/C++ Win32
+        DLL, use it from a Windows console application, and port the library to Arm64
+        for Windows on Arm. You work on a Windows on Arm device ...
+      preview_generated: This introductory Learning Path shows how to create a C/C++
+        Win32 DLL and use it from a Windows console application, then port both to
+        Arm64. You will work on Windows on Arm hardware such as a Lenovo ...
     faqs:
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+        - How do I choose the correct build target for Arm64?
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+        removed_questions:
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+        - Do I need an existing Win32 DLL before starting?
+        - How do I verify that the port to Arm64 worked?
+        - How long does it take and what is the difficulty level?
+        updated_questions:
+        - Can I complete this on a virtual machine instead of physical hardware?
       generated_diff:
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-        added_questions: []
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+        added_questions:
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+        - How do I choose the correct build target for Arm64?
+        - What should I check if my Arm64 build fails or the app cannot load the DLL?
+        removed_questions:
+        - What environment and tools do I need to follow this Learning Path?
+        - Do I need an existing Win32 DLL before starting?
+        - How do I verify that the port to Arm64 worked?
+        - How long does it take and what is the difficulty level?
+        updated_questions:
+        - Can I complete this on a virtual machine instead of physical hardware?
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/win_winui3/_index.md
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-    rerun_flags_reset: []
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-        code execution performance on Arm64. It is designed for developers who want to learn how to create
-        cross-platform appl...
-      preview_after: Learn how to create and build Windows UI Library (WinUI) applications and measure
-        code execution performance on Arm64. It is designed for developers who want to learn how to create
-        cross-platform appl...
-      preview_generated: Learn how to create and build Windows UI Library (WinUI) applications and measure
-        code execution performance on Arm64. It is designed for developers who want to learn how to create
-        cross-platform appl...
+      generated_at_before: '2026-06-01T22:20:00Z'
+      generated_at_after: '2026-06-01T22:20:00Z'
+      preview_before: This Learning Path shows how to create and build a Windows UI
+        Library (WinUI 3) application in C#/.NET using Visual Studio 2022 on Windows
+        on Arm, then compare code execution performance on Arm64 vers...
+      preview_after: This Learning Path shows how to create and build a Windows UI
+        Library (WinUI 3) application in C#/.NET using Visual Studio 2022 on Windows
+        on Arm, then compare code execution performance on Arm64 vers...
+      preview_generated: This introductory path shows how to create and build a Windows
+        UI Library (WinUI 3) application in Visual Studio on Windows on Arm, then
+        compare code execution performance by timing matrix multiplicat...
     faqs:
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+        - What do I need before running the steps?
+        - Which Visual Studio settings should I use to build and run for each architecture?
+        - How do I run the performance comparison between x64 and ARM64?
+        - How do I confirm I built the app for ARM64?
+        - Can I complete this Learning Path without a physical Arm device?
+        removed_questions:
+        - What do I need before starting?
+        - Which architectures can I build and how do I select them in Visual Studio?
+        - What does the sample application measure?
+        - How do I confirm that I am running the correct configuration for performance
+          comparison?
+        - Who is this Learning Path for and how long does it take?
         updated_questions: []
       generated_diff:
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+        - What do I need before running the steps?
+        - Which Visual Studio settings should I use to build and run for each architecture?
+        - How do I run the performance comparison between x64 and ARM64?
+        - How do I confirm I built the app for ARM64?
+        - Can I complete this Learning Path without a physical Arm device?
+        removed_questions:
+        - What do I need before starting?
+        - Which architectures can I build and how do I select them in Visual Studio?
+        - What does the sample application measure?
+        - How do I confirm that I am running the correct configuration for performance
+          comparison?
+        - Who is this Learning Path for and how long does it take?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/win_wpf/_index.md
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-        and measure code execution performance uplift on Arm64. It is designed for developers who want
-        to learn how to create d...
-      preview_after: Learn how to create and build Windows Presentation Foundation (WPF) applications
-        and measure code execution performance uplift on Arm64. It is designed for developers who want
-        to learn how to create d...
-      preview_generated: Learn how to create and build Windows Presentation Foundation (WPF) applications
-        and measure code execution performance uplift on Arm64. It is designed for developers who want
-        to learn how to create d...
+      generated_at_before: '2026-06-01T22:20:26Z'
+      generated_at_after: '2026-06-01T22:20:26Z'
+      preview_before: This Learning Path shows how to create and build a Windows Presentation
+        Foundation (WPF) desktop application on Windows on Arm and compare execution
+        times between ARM64 and x86_64 builds using Visual ...
+      preview_after: This Learning Path shows how to create and build a Windows Presentation
+        Foundation (WPF) desktop application on Windows on Arm and compare execution
+        times between ARM64 and x86_64 builds using Visual ...
+      preview_generated: This Learning Path shows how to create and build a Windows
+        Presentation Foundation (WPF) desktop application in C# using Visual Studio
+        2022 on Windows on Arm, then run it under different build configu...
     faqs:
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+        - Which Visual Studio option do I use to target ARM64?
+        - Do I also need an x86_64 configuration for comparison?
+        - How do I run the app to compare execution times across configurations?
+        - How do I know the app is running as ARM64 rather than x86_64?
+        removed_questions:
+        - What hardware or virtual machine do I need to follow this path?
+        - Which software must be installed before I start?
+        - How do I add an ARM64 build configuration in Visual Studio?
+        - What will I build and what will I measure?
+        - How long does this Learning Path take and what skill level is assumed?
         updated_questions: []
       generated_diff:
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-        added_questions: []
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+        added_questions:
+        - What do I need before running the steps?
+        - Which Visual Studio option do I use to target ARM64?
+        - Do I also need an x86_64 configuration for comparison?
+        - How do I run the app to compare execution times across configurations?
+        - How do I know the app is running as ARM64 rather than x86_64?
+        removed_questions:
+        - What hardware or virtual machine do I need to follow this path?
+        - Which software must be installed before I start?
+        - How do I add an ARM64 build configuration in Visual Studio?
+        - What will I build and what will I measure?
+        - How long does this Learning Path take and what skill level is assumed?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md
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@@ -206483,51 +11817,80 @@ history:
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-      preview_before: Learn how to create and build Xamarin Forms applications using the MVVM pattern
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-      preview_generated: Learn how to create and build Xamarin Forms applications using the MVVM pattern
-        and measure code execution performance uplift on Arm64. It is designed for developers who want
-        to learn how to create cr...
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+      generated_at_after: '2026-06-01T22:20:57Z'
+      preview_before: This introductory Learning Path shows how to create and build
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+        will apply the Model-View-ViewModel (MVVM) pattern by adding a...
+      preview_after: This introductory Learning Path shows how to create and build
+        a Xamarin Forms application on Windows on Arm using Visual Studio 2022. You
+        will apply the Model-View-ViewModel (MVVM) pattern by adding a...
+      preview_generated: Build and run a Xamarin Forms application on Windows on Arm
+        using the MVVM pattern, then measure code execution performance uplift on
+        Arm64. Working in Visual Studio 2022, you create the project, add ...
     faqs:
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       missing_before: false
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+        added_questions:
+        - What do I need installed before starting on Windows on Arm?
+        - Can I complete this Learning Path using a virtual machine instead of physical
+          hardware?
+        - Which Visual Studio workloads should I select for this Xamarin Forms project?
+        - Where should I place the DataPoint2d model when implementing MVVM?
+        - How will I measure code execution performance uplift on Arm64 in this path?
+        removed_questions:
+        - What hardware and software do I need before starting?
+        - Which environment does this path target, and what platforms does Xamarin
+          Forms support?
+        - What does the MVVM implementation include in this path?
+        - How long will this take and what skill level is assumed?
+        - How do I know the path worked, including the performance measurement?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need installed before starting on Windows on Arm?
+        - Can I complete this Learning Path using a virtual machine instead of physical
+          hardware?
+        - Which Visual Studio workloads should I select for this Xamarin Forms project?
+        - Where should I place the DataPoint2d model when implementing MVVM?
+        - How will I measure code execution performance uplift on Arm64 in this path?
+        removed_questions:
+        - What hardware and software do I need before starting?
+        - Which environment does this path target, and what platforms does Xamarin
+          Forms support?
+        - What does the MVVM implementation include in this path?
+        - How long will this take and what skill level is assumed?
+        - How do I know the path worked, including the performance measurement?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/windows_armpl/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
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-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -206537,51 +11900,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to develop Windows on Arm applications using Visual Studio and optimize
-        performance with Arm Performance Libraries. It is designed for software developers who want to
-        improve the performance...
-      preview_after: Learn how to develop Windows on Arm applications using Visual Studio and optimize
-        performance with Arm Performance Libraries. It is designed for software developers who want to
-        improve the performance...
-      preview_generated: Learn how to develop Windows on Arm applications using Visual Studio and optimize
-        performance with Arm Performance Libraries. It is designed for software developers who want to
-        improve the performance...
+      generated_at_before: '2026-06-01T22:21:28Z'
+      generated_at_after: '2026-06-01T22:21:28Z'
+      preview_before: This introductory path guides you through setting up Visual
+        Studio 2022 on a Windows on Arm device, creating and running a simple console
+        application, and then building and profiling a sample that ren...
+      preview_after: This introductory path guides you through setting up Visual Studio
+        2022 on a Windows on Arm device, creating and running a simple console application,
+        and then building and profiling a sample that ren...
+      preview_generated: This introductory Learning Path shows how to develop and
+        evaluate Windows on Arm applications using Microsoft Visual Studio and Arm
+        Performance Libraries. You start by creating and running a simple co...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: f058f628cefb7766ef0728e594c9ef5b57bde97c1850395786d54cc591271503
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-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T22:21:28Z'
+      generated_at_after: '2026-06-02T23:34:43Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - Which Visual Studio edition should I install on Windows on Arm?
+        - How do I create the initial Windows on Arm project in Visual Studio?
+        - How do I get the SpinTheCubeInGDI example used in this path?
+        - How do I open and run the spinning cube example in Visual Studio?
+        - How do I use Arm Performance Libraries with this example?
+        removed_questions:
+        - What hardware do I need to follow this Learning Path?
+        - Which tools will I use during the steps?
+        - Which Visual Studio edition should I install?
+        - What example project is used, and how do I get it?
+        - How will I know the setup worked and what do I do with Arm Performance Libraries?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - Which Visual Studio edition should I install on Windows on Arm?
+        - How do I create the initial Windows on Arm project in Visual Studio?
+        - How do I get the SpinTheCubeInGDI example used in this path?
+        - How do I open and run the spinning cube example in Visual Studio?
+        - How do I use Arm Performance Libraries with this example?
+        removed_questions:
+        - What hardware do I need to follow this Learning Path?
+        - Which tools will I use during the steps?
+        - Which Visual Studio edition should I install?
+        - What example project is used, and how do I get it?
+        - How will I know the setup worked and what do I do with Arm Performance Libraries?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -206591,51 +11979,80 @@ history:
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Get started with GitHub CI/CD development flow on a Windows on Arm machine (or virtual
-        machine). It is designed for software developers interested in running their CI flows on Windows
-        on Arm machines....
-      preview_after: Get started with GitHub CI/CD development flow on a Windows on Arm machine (or virtual
-        machine). It is designed for software developers interested in running their CI flows on Windows
-        on Arm machines....
-      preview_generated: Get started with GitHub CI/CD development flow on a Windows on Arm machine (or
-        virtual machine). It is designed for software developers interested in running their CI flows
-        on Windows on Arm machines....
+      generated_at_before: '2026-06-01T22:22:06Z'
+      generated_at_after: '2026-06-01T22:22:06Z'
+      preview_before: Set up a GitHub self-hosted runner on a Windows on Arm machine
+        or cloud instance and run a minimal GitHub Actions workflow to validate a
+        basic CI/CD flow on this platform. You will create a new GitHub...
+      preview_after: Set up a GitHub self-hosted runner on a Windows on Arm machine
+        or cloud instance and run a minimal GitHub Actions workflow to validate a
+        basic CI/CD flow on this platform. You will create a new GitHub...
+      preview_generated: This introductory Learning Path shows how to set up a GitHub
+        Actions CI/CD flow using a Windows on Arm machine or cloud instance as a self-hosted
+        runner. You will create a new GitHub repository, prepa...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 828e4022f387698d2736d94010de5d93efa9f38ffd3a2e4f15252410e9ccedb2
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-01T22:22:06Z'
+      generated_at_after: '2026-06-02T23:35:23Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Can I use a virtual machine instead of physical Windows on Arm hardware?
+        - How do I create the repository used for testing the workflow?
+        - How do I set up the Windows on Arm self-hosted runner, and what does it
+          do?
+        - How do I create and run the sample GitHub Actions workflow, and what file
+          should I expect?
+        removed_questions:
+        - Can I use a virtual machine instead of a physical Windows on Arm device?
+        - What do I need before starting?
+        - Do I need to create a new GitHub repository for this path?
+        - What workflow file is created and where is it located?
+        - What is the expected outcome after completing the steps?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Can I use a virtual machine instead of physical Windows on Arm hardware?
+        - How do I create the repository used for testing the workflow?
+        - How do I set up the Windows on Arm self-hosted runner, and what does it
+          do?
+        - How do I create and run the sample GitHub Actions workflow, and what file
+          should I expect?
+        removed_questions:
+        - Can I use a virtual machine instead of a physical Windows on Arm device?
+        - What do I need before starting?
+        - Do I need to create a new GitHub repository for this path?
+        - What workflow file is created and where is it located?
+        - What is the expected outcome after completing the steps?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/windowsperf/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -206645,51 +12062,76 @@ history:
       source_hash_before: 61a6390d42ce6bfc37a3bb981710dc23b8566070733f7979f9ec37bc63ab7d78
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to install WindowsPerf on Windows on Arm machines and generate sample
-        performance reports for CPU profiling. It is designed for software developers working on laptops
-        and desktops and new to...
-      preview_after: Learn how to install WindowsPerf on Windows on Arm machines and generate sample performance
-        reports for CPU profiling. It is designed for software developers working on laptops and desktops
-        and new to...
-      preview_generated: Learn how to install WindowsPerf on Windows on Arm machines and generate sample
-        performance reports for CPU profiling. It is designed for software developers working on laptops
-        and desktops and new to...
+      generated_at_before: '2026-06-02T02:36:31Z'
+      generated_at_after: '2026-06-02T02:36:31Z'
+      preview_before: This introductory Learning Path shows how to install WindowsPerf
+        on a Windows on Arm desktop or development machine and generate sample CPU
+        profiling reports. You will use the wperf command-line inter...
+      preview_after: This introductory Learning Path shows how to install WindowsPerf
+        on a Windows on Arm desktop or development machine and generate sample CPU
+        profiling reports. You will use the wperf command-line inter...
+      preview_generated: This introductory path shows how to install WindowsPerf on
+        a Windows on Arm machine and generate a sample CPU profiling report. You will
+        learn the basics of using the wperf command-line interface with...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 61a6390d42ce6bfc37a3bb981710dc23b8566070733f7979f9ec37bc63ab7d78
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-02T02:36:31Z'
+      generated_at_after: '2026-06-02T23:36:06Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which wperf command should I use for counting versus sampling?
+        - How do I limit a count to a specific core and time window?
+        - What result should I expect from counting and sampling runs?
+        - Where can I find example PMU events and metrics to try?
+        removed_questions:
+        - What do I need before starting?
+        - What tool will I install and use?
+        - Which profiling modes does this cover?
+        - What kinds of events or metrics can I measure?
+        - How do I verify that installation and profiling worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which wperf command should I use for counting versus sampling?
+        - How do I limit a count to a specific core and time window?
+        - What result should I expect from counting and sampling runs?
+        - Where can I find example PMU events and metrics to try?
+        removed_questions:
+        - What do I need before starting?
+        - What tool will I install and use?
+        - Which profiling modes does this cover?
+        - What kinds of events or metrics can I measure?
+        - How do I verify that installation and profiling worked?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -206699,51 +12141,76 @@ history:
       source_hash_before: 1dd709b14537e06c80b9cdee58729cfa7066f44f0de4ceabe5450b33288731aa
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to install and use the WindowsPerf Visual Studio extension to generate
-        counting and sampling reports and analyze performance data in Windows Performance Analyzer. It
-        is designed for software...
-      preview_after: Learn how to install and use the WindowsPerf Visual Studio extension to generate
-        counting and sampling reports and analyze performance data in Windows Performance Analyzer. It
-        is designed for software...
-      preview_generated: Learn how to install and use the WindowsPerf Visual Studio extension to generate
-        counting and sampling reports and analyze performance data in Windows Performance Analyzer. It
-        is designed for software...
+      generated_at_before: '2026-06-02T02:37:03Z'
+      generated_at_after: '2026-06-02T02:37:03Z'
+      preview_before: This introductory Learning Path shows how to install and use
+        the WindowsPerf Visual Studio extension on Windows on Arm to generate counting
+        and sampling reports and analyze performance data in Windows...
+      preview_after: This introductory Learning Path shows how to install and use
+        the WindowsPerf Visual Studio extension on Windows on Arm to generate counting
+        and sampling reports and analyze performance data in Windows...
+      preview_generated: This Learning Path shows how to install and use the WindowsPerf
+        Visual Studio extension on Windows on Arm to generate and inspect performance
+        data. You will configure the required tools, produce count...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 1dd709b14537e06c80b9cdee58729cfa7066f44f0de4ceabe5450b33288731aa
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-02T02:37:03Z'
+      generated_at_after: '2026-06-02T23:36:56Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need installed before I start?
+        - How do I open and configure the counting settings in Visual Studio?
+        - How do I generate a counting report and review it in WPA?
+        - Where do I find the sampling tools and set sampling preferences?
+        - What should I check if the SPE feature does not work on my system?
+        removed_questions:
+        - What are the prerequisites to follow this Learning Path?
+        - Which development tools do I need and where do I find setup guidance?
+        - How do I access the counting and sampling features in Visual Studio?
+        - What outputs will I create, and how do I verify results?
+        - Is SPE required, and what hardware support is needed?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need installed before I start?
+        - How do I open and configure the counting settings in Visual Studio?
+        - How do I generate a counting report and review it in WPA?
+        - Where do I find the sampling tools and set sampling preferences?
+        - What should I check if the SPE feature does not work on my system?
+        removed_questions:
+        - What are the prerequisites to follow this Learning Path?
+        - Which development tools do I need and where do I find setup guidance?
+        - How do I access the counting and sampling features in Visual Studio?
+        - What outputs will I create, and how do I verify results?
+        - Is SPE required, and what hardware support is needed?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -206753,51 +12220,78 @@ history:
       source_hash_before: e23de84e7e05d771072ab799f5cc802476fd5e3c23da41a202c9c50fe9980e25
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to use WindowsPerf for performance sampling on Windows on Arm, build CPython
-        from sources, and analyze native workload performance. It is designed for developers keen to understand
-        sampling ...
-      preview_after: Learn how to use WindowsPerf for performance sampling on Windows on Arm, build CPython
-        from sources, and analyze native workload performance. It is designed for developers keen to understand
-        sampling ...
-      preview_generated: Learn how to use WindowsPerf for performance sampling on Windows on Arm, build
-        CPython from sources, and analyze native workload performance. It is designed for developers keen
-        to understand sampling ...
+      generated_at_before: '2026-06-02T02:37:39Z'
+      generated_at_after: '2026-06-02T02:37:39Z'
+      preview_before: This Learning Path shows how to use WindowsPerf to sample a
+        native Windows on Arm workload by building CPython from sources for the ARM64
+        target and analyzing its runtime. You will create a debug buil...
+      preview_after: This Learning Path shows how to use WindowsPerf to sample a native
+        Windows on Arm workload by building CPython from sources for the ARM64 target
+        and analyzing its runtime. You will create a debug buil...
+      preview_generated: Follow a concise, hands-on path to build a debug CPython
+        for the Windows on Arm ARM64 target and analyze its performance with WindowsPerf.
+        You will run an interactive Python workload (such as a Googol...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-02T02:37:39Z'
+      generated_at_after: '2026-06-02T23:37:32Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - Which CPython build should I use during the sampling exercises?
+        - Which WindowsPerf command should I use to spawn and pin CPython to a core?
+        - How do I pass command-line arguments to my program when using WindowsPerf?
+        - What result should I expect when I run counting and sampling on the Googolplex
+          workload?
+        removed_questions:
+        - What environment and tools do I need before starting?
+        - Which CPython build is used for sampling?
+        - What workload is sampled to generate activity?
+        - How do I pin the CPython process to a CPU core?
+        - How do I validate that sampling worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - Which CPython build should I use during the sampling exercises?
+        - Which WindowsPerf command should I use to spawn and pin CPython to a core?
+        - How do I pass command-line arguments to my program when using WindowsPerf?
+        - What result should I expect when I run counting and sampling on the Googolplex
+          workload?
+        removed_questions:
+        - What environment and tools do I need before starting?
+        - Which CPython build is used for sampling?
+        - What workload is sampled to generate activity?
+        - How do I pin the CPython process to a CPU core?
+        - How do I validate that sampling worked?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -206807,51 +12301,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to import WindowsPerf data in Windows Performance Analyzer (WPA) and visualize
-        timeline and telemetry data using the WPA plugin. It is designed for software developers interested
-        in using th...
-      preview_after: Learn how to import WindowsPerf data in Windows Performance Analyzer (WPA) and visualize
-        timeline and telemetry data using the WPA plugin. It is designed for software developers interested
-        in using th...
-      preview_generated: Learn how to import WindowsPerf data in Windows Performance Analyzer (WPA) and
-        visualize timeline and telemetry data using the WPA plugin. It is designed for software developers
-        interested in using th...
+      generated_at_before: '2026-06-02T02:38:21Z'
+      generated_at_after: '2026-06-02T02:38:21Z'
+      preview_before: This Learning Path shows how to take performance data collected
+        with WindowsPerf on a Windows on Arm laptop and analyze it in Windows Performance
+        Analyzer (WPA) using the WPA plugin. You will generate...
+      preview_after: This Learning Path shows how to take performance data collected
+        with WindowsPerf on a Windows on Arm laptop and analyze it in Windows Performance
+        Analyzer (WPA) using the WPA plugin. You will generate...
+      preview_generated: Learn how to bring WindowsPerf measurements into Windows
+        Performance Analyzer (WPA) using the WPA plugin and view them as timeline
+        and telemetry data. You will generate a .json report from the Windows...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-02T02:38:21Z'
+      generated_at_after: '2026-06-02T23:38:01Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I create the .json file that WPA will import?
+        - Where should I run the wperf stat command?
+        - How do I know the import into WPA worked?
+        - What should I check if I do not see the plugin views in WPA?
+        removed_questions:
+        - What software must be installed before I start?
+        - Which platform does this Learning Path target?
+        - How do I generate the data file to import into WPA?
+        - What file format do I import into WPA with the plugin?
+        - How will I know the import and plugin are working?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I create the .json file that WPA will import?
+        - Where should I run the wperf stat command?
+        - How do I know the import into WPA worked?
+        - What should I check if I do not see the plugin views in WPA?
+        removed_questions:
+        - What software must be installed before I start?
+        - Which platform does this Learning Path target?
+        - How do I generate the data file to import into WPA?
+        - What file format do I import into WPA with the plugin?
+        - How will I know the import and plugin are working?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/laptops-and-desktops/wsl2/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
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+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -206861,51 +12380,76 @@ history:
       source_hash_before: 03d816ff419e6e5b1533f95e9d4ffa087b9c0c9aefeee112f67b70223c8aedc3
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to configure and run WSL with Linux distributions, graphical applications,
-        remote desktop, and development tools on Windows on Arm computers. It is designed for Software
-        developers with Wind...
-      preview_after: Learn how to configure and run WSL with Linux distributions, graphical applications,
-        remote desktop, and development tools on Windows on Arm computers. It is designed for Software
-        developers with Wind...
-      preview_generated: Learn how to configure and run WSL with Linux distributions, graphical applications,
-        remote desktop, and development tools on Windows on Arm computers. It is designed for Software
-        developers with Wind...
+      generated_at_before: '2026-06-02T02:38:49Z'
+      generated_at_after: '2026-06-02T02:38:49Z'
+      preview_before: This Learning Path shows how to configure and run Windows Subsystem
+        for Linux (WSL) on Windows on Arm computers to support Linux and cloud-native
+        development. You will set up WSL with various Linux di...
+      preview_after: This Learning Path shows how to configure and run Windows Subsystem
+        for Linux (WSL) on Windows on Arm computers to support Linux and cloud-native
+        development. You will set up WSL with various Linux di...
+      preview_generated: This Learning Path shows how to set up and use Windows Subsystem
+        for Linux (WSL) on Windows on Arm to support Linux and cloud native development.
+        You will configure WSL with Linux distributions, enabl...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 03d816ff419e6e5b1533f95e9d4ffa087b9c0c9aefeee112f67b70223c8aedc3
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-02T02:38:49Z'
+      generated_at_after: '2026-06-02T23:38:39Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - How do I know systemd is enabled and running in my WSL distribution?
+        - How can I run and verify a graphical Linux application on Windows 11?
+        - Do I need SSH to move files between Windows and WSL on the same machine?
+        - What should I check if RDP does not display the Linux desktop?
+        removed_questions:
+        - What do I need before starting, and which tools does this path use?
+        - How do I enable systemd in my WSL distribution?
+        - How do I run graphical Linux applications on Windows 11?
+        - When should I use SSH, and how do I move files between Windows and WSL?
+        - How do I set up and verify remote desktop access to a Linux desktop in WSL?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - How do I know systemd is enabled and running in my WSL distribution?
+        - How can I run and verify a graphical Linux application on Windows 11?
+        - Do I need SSH to move files between Windows and WSL on the same machine?
+        - What should I check if RDP does not display the Linux desktop?
+        removed_questions:
+        - What do I need before starting, and which tools does this path use?
+        - How do I enable systemd in my WSL distribution?
+        - How do I run graphical Linux applications on Windows 11?
+        - When should I use SSH, and how do I move files between Windows and WSL?
+        - How do I set up and verify remote desktop access to a Linux desktop in WSL?
+        updated_questions: []
+    category: laptops-and-desktops
   - path: content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -206915,51 +12459,78 @@ history:
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to enable and verify Arm Fixed Rate Compression in Vulkan applications
-        on Android devices to reduce memory footprint and bandwidth. It is designed for Software developers
-        of Android applicat...
-      preview_after: Learn how to enable and verify Arm Fixed Rate Compression in Vulkan applications
-        on Android devices to reduce memory footprint and bandwidth. It is designed for Software developers
-        of Android applicat...
-      preview_generated: Learn how to enable and verify Arm Fixed Rate Compression in Vulkan applications
-        on Android devices to reduce memory footprint and bandwidth. It is designed for Software developers
-        of Android applicat...
+      generated_at_before: '2026-06-02T02:39:32Z'
+      generated_at_after: '2026-06-02T02:39:32Z'
+      preview_before: This Learning Path shows how to enable and verify Arm Fixed
+        Rate Compression (AFRC) in Vulkan applications on Android. You will check
+        for VK_EXT_image_compression_control support (and VK_EXT_image_com...
+      preview_after: This Learning Path shows how to enable and verify Arm Fixed Rate
+        Compression (AFRC) in Vulkan applications on Android. You will check for VK_EXT_image_compression_control
+        support (and VK_EXT_image_com...
+      preview_generated: This Learning Path shows how to enable and verify Arm Fixed
+        Rate Compression (AFRC) in Vulkan applications targeting Android to reduce
+        memory footprint and bandwidth. You will check device support for...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: e4512ad20b372f616409199c51a38d95cb116ec6cf49d9004348d6bb8ee4014d
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-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-02T02:39:32Z'
+      generated_at_after: '2026-06-02T23:39:11Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - How do I know if my Android device supports the required Vulkan extensions
+          for AFRC?
+        - Where do I enable the Vulkan extensions in my application?
+        - How do I query whether a specific image setup supports fixed-rate compression?
+        - How do I request fixed-rate compression at image creation time?
+        - What result should I expect when verifying that compression was applied?
+        removed_questions:
+        - What prerequisites do I need before starting?
+        - Can I complete this path without an existing Vulkan application?
+        - Which Vulkan extensions are required and how do I enable them?
+        - How do I check whether a specific image supports AFRC before creating it?
+        - How do I request and verify fixed-rate compression in my app?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - How do I know if my Android device supports the required Vulkan extensions
+          for AFRC?
+        - Where do I enable the Vulkan extensions in my application?
+        - How do I query whether a specific image setup supports fixed-rate compression?
+        - How do I request fixed-rate compression at image creation time?
+        - What result should I expect when verifying that compression was applied?
+        removed_questions:
+        - What prerequisites do I need before starting?
+        - Can I complete this path without an existing Vulkan application?
+        - Which Vulkan extensions are required and how do I enable them?
+        - How do I check whether a specific image supports AFRC before creating it?
+        - How do I request and verify fixed-rate compression in my app?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -206969,51 +12540,76 @@ history:
       source_hash_before: dee181248ecc8cb40e3ad76642fdb216cbf1e7610dde2f2605ba04f111b8926a
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
-      preview_before: Learn how to build and optimize AI-powered camera pipeline applications on Arm Linux
-        using KleidiAI, KleidiCV, and SME2 to accelerate denoising, background blur, and low-light effects.
-        It is designed ...
-      preview_after: Learn how to build and optimize AI-powered camera pipeline applications on Arm Linux
-        using KleidiAI, KleidiCV, and SME2 to accelerate denoising, background blur, and low-light effects.
-        It is designed ...
-      preview_generated: Learn how to build and optimize AI-powered camera pipeline applications on Arm
-        Linux using KleidiAI, KleidiCV, and SME2 to accelerate denoising, background blur, and low-light
-        effects. It is designed ...
+      generated_at_before: '2026-06-02T02:40:04Z'
+      generated_at_after: '2026-06-02T02:40:04Z'
+      preview_before: Build and run AI-powered camera pipeline applications on Arm
+        using SME2 with KleidiAI and KleidiCV. You will clone the ai-camera-pipelines
+        repository with Git LFS, build a Docker container, compile th...
+      preview_after: Build and run AI-powered camera pipeline applications on Arm
+        using SME2 with KleidiAI and KleidiCV. You will clone the ai-camera-pipelines
+        repository with Git LFS, build a Docker container, compile th...
+      preview_generated: Build and run AI-powered camera pipelines on Arm to accelerate
+        background blur, denoising, and low-light effects using the Scalable Matrix
+        Extension 2 (SME2), KleidiAI, and KleidiCV. You will clone th...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: dee181248ecc8cb40e3ad76642fdb216cbf1e7610dde2f2605ba04f111b8926a
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-      generated_at_before: '2026-05-06T17:17:55Z'
-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-02T02:40:04Z'
+      generated_at_after: '2026-06-02T23:39:57Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which repository do I clone and why is Git LFS required?
+        - How do I build the container used to compile the pipelines?
+        - How do I run a background blur or other effect and verify success?
+        - How do I run benchmarks and what result should I expect?
+        removed_questions:
+        - Do I need Arm64 hardware with SME2, and which OS is recommended?
+        - What tools should I install before starting?
+        - How do I get the source code and large model assets?
+        - What do I build and how do I run the pipelines?
+        - How do I benchmark the pipelines and what should I expect?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which repository do I clone and why is Git LFS required?
+        - How do I build the container used to compile the pipelines?
+        - How do I run a background blur or other effect and verify success?
+        - How do I run benchmarks and what result should I expect?
+        removed_questions:
+        - Do I need Arm64 hardware with SME2, and which OS is recommended?
+        - What tools should I install before starting?
+        - How do I get the source code and large model assets?
+        - What do I build and how do I run the pipelines?
+        - How do I benchmark the pipelines and what should I expect?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -207023,51 +12619,76 @@ history:
       source_hash_before: 3d1a743c1b3ee617f52191fc9c9fd33a9d44454ac079f00b6f532e2865103377
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-      generated_at_before: '2026-05-06T17:17:55Z'
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-      preview_before: Learn how to use each of the tools supplied with Arm Performance Studio (formerly
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-        Arm Performance Studio...
-      preview_generated: Learn how to use each of the tools supplied with Arm Performance Studio (formerly
-        known as Arm Mobile Studio). It is designed for Android application and games developers new to
-        Arm Performance Studio...
+      generated_at_before: '2026-06-02T02:40:50Z'
+      generated_at_after: '2026-06-02T02:40:50Z'
+      preview_before: This introductory path shows Android developers how to start
+        profiling apps on devices with Mali-based GPUs using Arm Performance Studio.
+        You will install the tools, connect an Android device over adb...
+      preview_after: This introductory path shows Android developers how to start
+        profiling apps on devices with Mali-based GPUs using Arm Performance Studio.
+        You will install the tools, connect an Android device over adb...
+      preview_generated: This introductory path shows how to start profiling Android
+        applications with Arm Performance Studio on devices with Mali-based GPUs.
+        You will install and launch the tools, import and review an exampl...
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+        added_questions:
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+        - Which graphics APIs and Android versions are supported?
+        - How do I connect my Android device in Streamline?
+        - How do I open the example Streamline capture?
+        - How do I generate a Performance Advisor report from a Streamline capture?
+        removed_questions:
+        - What do I need before starting?
+        - How do I connect my device in Streamline?
+        - Is there a sample capture I can use before profiling my own app?
+        - How do I generate a Performance Advisor report?
+        - Does this path cover platforms other than Android?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
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+        - Which graphics APIs and Android versions are supported?
+        - How do I connect my Android device in Streamline?
+        - How do I open the example Streamline capture?
+        - How do I generate a Performance Advisor report from a Streamline capture?
+        removed_questions:
+        - What do I need before starting?
+        - How do I connect my device in Streamline?
+        - Is there a sample capture I can use before profiling my own app?
+        - How do I generate a Performance Advisor report?
+        - Does this path cover platforms other than Android?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md
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@@ -207077,51 +12698,76 @@ history:
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-      preview_before: Learn how to capture frame data from Android applications and analyze performance
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-        developers who wa...
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-        inefficiencies using Frame Advisor in Arm Performance Studio. It is designed for Android application
-        developers who wa...
-      preview_generated: Learn how to capture frame data from Android applications and analyze performance
-        inefficiencies using Frame Advisor in Arm Performance Studio. It is designed for Android application
-        developers who wa...
+      generated_at_before: '2026-06-02T02:41:30Z'
+      generated_at_after: '2026-06-02T02:41:30Z'
+      preview_before: This introductory Learning Path shows how to use Frame Advisor
+        in Arm Performance Studio to capture a significant frame from an Android application
+        and analyze where time is spent. You will connect a ...
+      preview_after: This introductory Learning Path shows how to use Frame Advisor
+        in Arm Performance Studio to capture a significant frame from an Android application
+        and analyze where time is spent. You will connect a ...
+      preview_generated: This Learning Path shows how to use Frame Advisor, part of
+        Arm Performance Studio, to capture and analyze a significant frame from an
+        Android application. You will start a trace from a connected devic...
     faqs:
-      action: unchanged
+      action: rerun_requested
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+        added_questions:
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+        - How do I start a capture trace from my device?
+        - How do I know the capture and analysis worked?
+        - Which view helps me find unused render passes or attachments?
+        - How can I locate the most complex meshes in my scene?
+        removed_questions:
+        - What do I need on my host machine before starting?
+        - Which Android devices and graphics APIs are supported?
+        - Do I need a specific build of my app?
+        - How do I start a trace capture for my app?
+        - How do I verify the capture and what will I analyze?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
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+        added_questions:
+        - What do I need before running Frame Advisor?
+        - How do I start a capture trace from my device?
+        - How do I know the capture and analysis worked?
+        - Which view helps me find unused render passes or attachments?
+        - How can I locate the most complex meshes in my scene?
+        removed_questions:
+        - What do I need on my host machine before starting?
+        - Which Android devices and graphics APIs are supported?
+        - Do I need a specific build of my app?
+        - How do I start a trace capture for my app?
+        - How do I verify the capture and what will I analyze?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md
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-    rerun_flags_reset: []
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     summary:
       action: unchanged
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@@ -207131,51 +12777,80 @@ history:
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-      preview_before: Learn how to build an Android chatbot app using Arm's AI Chat library to run GGUF
-        models on-device with optimized performance on Arm CPUs. It is designed for developers who want
-        to add a local, on-dev...
-      preview_after: Learn how to build an Android chatbot app using Arm's AI Chat library to run GGUF
-        models on-device with optimized performance on Arm CPUs. It is designed for developers who want
-        to add a local, on-dev...
-      preview_generated: Learn how to build an Android chatbot app using Arm's AI Chat library to run
-        GGUF models on-device with optimized performance on Arm CPUs. It is designed for developers who
-        want to add a local, on-dev...
+      generated_at_before: '2026-06-02T02:41:57Z'
+      generated_at_after: '2026-06-02T02:41:57Z'
+      preview_before: "Build a simple Android chatbot app that runs a local LLM on-device\
+        \ using Arm\u2019s AI Chat library. You will create a new Android Studio project,\
+        \ verify google() and mavenCentral() repositories, add the l..."
+      preview_after: "Build a simple Android chatbot app that runs a local LLM on-device\
+        \ using Arm\u2019s AI Chat library. You will create a new Android Studio project,\
+        \ verify google() and mavenCentral() repositories, add the l..."
+      preview_generated: "Build a simple Android chatbot that runs a local LLM on-device\
+        \ using Arm\u2019s AI Chat library. You will create a new Android Studio project,\
+        \ verify google() and mavenCentral() repositories, and add the l..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
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       drift_detected: false
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-      generated_at_after: '2026-05-06T17:17:55Z'
+      generated_at_before: '2026-06-02T02:41:57Z'
+      generated_at_after: '2026-06-02T23:42:41Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which repositories should be in settings.gradle.kts to resolve the AI Chat
+          library?
+        - Where do I add the AI Chat dependency and what is the coordinate?
+        - How do I choose a mobile-compatible GGUF model, and is there an example?
+        - "What result should I expect when I run the app, and how do I know it\u2019\
+          s working?"
+        removed_questions:
+        - What are the prerequisites to follow this Learning Path?
+        - How do I add the Arm AI Chat library to my project?
+        - Which project files will I modify?
+        - What GGUF model should I use on a phone?
+        - How do I confirm the app is working?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which repositories should be in settings.gradle.kts to resolve the AI Chat
+          library?
+        - Where do I add the AI Chat dependency and what is the coordinate?
+        - How do I choose a mobile-compatible GGUF model, and is there an example?
+        - "What result should I expect when I run the app, and how do I know it\u2019\
+          s working?"
+        removed_questions:
+        - What are the prerequisites to follow this Learning Path?
+        - How do I add the Arm AI Chat library to my project?
+        - Which project files will I modify?
+        - What GGUF model should I use on a phone?
+        - How do I confirm the app is working?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
-    status: unchanged
-    changed_on_disk: false
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-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
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+    status: updated
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+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
       missing_before: false
@@ -207185,51 +12860,89 @@ history:
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-      preview_before: Learn how to build real-time image processing pipelines using Halide on Android,
-        combining operations for improved performance in Kotlin applications. It is designed for developers
-        interested in learn...
-      preview_after: Learn how to build real-time image processing pipelines using Halide on Android,
-        combining operations for improved performance in Kotlin applications. It is designed for developers
-        interested in learn...
-      preview_generated: Learn how to build real-time image processing pipelines using Halide on Android,
-        combining operations for improved performance in Kotlin applications. It is designed for developers
-        interested in learn...
+      generated_at_before: '2026-06-02T02:42:22Z'
+      generated_at_after: '2026-06-02T02:42:22Z'
+      preview_before: This introductory Learning Path shows how to build and integrate
+        real-time image processing pipelines with Halide on Android. You start by
+        installing and configuring Halide, then build a camera pipeli...
+      preview_after: This introductory Learning Path shows how to build and integrate
+        real-time image processing pipelines with Halide on Android. You start by
+        installing and configuring Halide, then build a camera pipeli...
+      preview_generated: This Learning Path shows how to build and deploy a real-time
+        image processing pipeline on Arm-based Android devices using Halide. You will
+        set up Halide, implement a camera pipeline that captures fram...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
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       before_count: 5
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       change_details:
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-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - What result should I expect from the initial pipeline, and how do I confirm
+          it worked?
+        - Which Halide scheduling options will I use, and how can I inspect the schedule?
+        - When should I use operator fusion versus materializing intermediates?
+        - Where does Android compilation happen, and what target should I build for?
+        removed_questions:
+        - What are the prerequisites to begin this Learning Path?
+        - What will I build before integrating with Android?
+        - How does this path address performance in Halide pipelines?
+        - How are Halide pipelines compiled for Android in this path?
+        - How do I use the Halide pipeline from an Android Kotlin app?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - What result should I expect from the initial pipeline, and how do I confirm
+          it worked?
+        - Which Halide scheduling options will I use, and how can I inspect the schedule?
+        - When should I use operator fusion versus materializing intermediates?
+        - Where does Android compilation happen, and what target should I build for?
+        removed_questions:
+        - What are the prerequisites to begin this Learning Path?
+        - What will I build before integrating with Android?
+        - How does this path address performance in Halide pipelines?
+        - How are Halide pipelines compiled for Android in this path?
+        - How do I use the Halide pipeline from an Android Kotlin app?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
+  - path: content/learning-paths/mobile-graphics-and-gaming/android_neon/_index.md
+    status: skipped
+    skip_reason: draft
+    change_reasons:
+    - draft
+    ai_requested: false
+    summary:
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+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md
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-    rerun_flags_reset: []
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-    template_version_before: summary-faq-v1
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     summary:
       action: unchanged
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@@ -207239,51 +12952,76 @@ history:
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-      preview_before: Learn how to create and configure an Android project with OpenCV support to process
-        camera images for computer vision applications. It is designed for developers who are interested
-        in creating Compute...
-      preview_after: Learn how to create and configure an Android project with OpenCV support to process
-        camera images for computer vision applications. It is designed for developers who are interested
-        in creating Compute...
-      preview_generated: Learn how to create and configure an Android project with OpenCV support to process
-        camera images for computer vision applications. It is designed for developers who are interested
-        in creating Compute...
+      generated_at_before: '2026-06-02T02:42:50Z'
+      generated_at_after: '2026-06-02T02:42:50Z'
+      preview_before: Build an introductory Android camera app that uses OpenCV to
+        process images on an Arm-based smartphone. Working in Android Studio on Windows,
+        you create a Kotlin project, integrate the OpenCV library,...
+      preview_after: Build an introductory Android camera app that uses OpenCV to
+        process images on an Arm-based smartphone. Working in Android Studio on Windows,
+        you create a Kotlin project, integrate the OpenCV library,...
+      preview_generated: This Learning Path shows how to build a simple computer vision
+        app on Android that captures camera frames and processes them with OpenCV.
+        Using Android Studio on a Windows development machine, you cre...
     faqs:
-      action: unchanged
+      action: rerun_requested
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       before_count: 5
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       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - Which Android Studio version should I use for this path?
+        - Do I need to develop on Windows to follow the steps?
+        - Should I use Kotlin or Java for the project?
+        - How do I know OpenCV is integrated correctly?
+        - What result should I expect when I run the app on my phone?
+        removed_questions:
+        - What do I need before starting?
+        - Which development environment and language does the path use?
+        - How is OpenCV used in the app?
+        - How do I capture and view camera frames?
+        - How do I know the Learning Path worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - Which Android Studio version should I use for this path?
+        - Do I need to develop on Windows to follow the steps?
+        - Should I use Kotlin or Java for the project?
+        - How do I know OpenCV is integrated correctly?
+        - What result should I expect when I run the app on my phone?
+        removed_questions:
+        - What do I need before starting?
+        - Which development environment and language does the path use?
+        - How is OpenCV used in the app?
+        - How do I capture and view camera frames?
+        - How do I know the Learning Path worked?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md
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+    - rerun_flags_reset
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-      preview_before: Learn how to implement face detection on Android devices using OpenCV, camera frame
-        retrieval, and Haar cascade classifiers. It is designed for developers who are interested in creating
-        Computer Visio...
-      preview_after: Learn how to implement face detection on Android devices using OpenCV, camera frame
-        retrieval, and Haar cascade classifiers. It is designed for developers who are interested in creating
-        Computer Visio...
-      preview_generated: Learn how to implement face detection on Android devices using OpenCV, camera
-        frame retrieval, and Haar cascade classifiers. It is designed for developers who are interested
-        in creating Computer Visio...
+      generated_at_before: '2026-06-02T02:43:15Z'
+      generated_at_after: '2026-06-02T02:43:15Z'
+      preview_before: Build an introductory Android app that detects faces in real
+        time using OpenCV. Working in Android Studio on Windows or macOS, you will
+        create a Kotlin project, add OpenCV, retrieve camera frames, and...
+      preview_after: Build an introductory Android app that detects faces in real
+        time using OpenCV. Working in Android Studio on Windows or macOS, you will
+        create a Kotlin project, add OpenCV, retrieve camera frames, and...
+      preview_generated: This introductory Learning Path shows how to implement face
+        detection on Android devices using OpenCV. You will create an Android Studio
+        project in Kotlin, add OpenCV, retrieve camera frames, and appl...
     faqs:
-      action: unchanged
+      action: rerun_requested
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       before_count: 5
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       change_details:
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-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Do I need a specific version of Android Studio?
+        - Which Haar cascade file should I use and how is it included?
+        - How do I know OpenCV is correctly added and camera frames are being read?
+        - What should I check if faces are not being detected?
+        removed_questions:
+        - What setup do I need before starting?
+        - Which operating systems and Android Studio version are used in the examples?
+        - What tools and languages does the project use?
+        - How are faces detected in this Learning Path?
+        - Do I need a physical Android device, or can I use an emulator?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
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+        added_questions:
+        - What do I need before running the steps?
+        - Do I need a specific version of Android Studio?
+        - Which Haar cascade file should I use and how is it included?
+        - How do I know OpenCV is correctly added and camera frames are being read?
+        - What should I check if faces are not being detected?
+        removed_questions:
+        - What setup do I need before starting?
+        - Which operating systems and Android Studio version are used in the examples?
+        - What tools and languages does the project use?
+        - How are faces detected in this Learning Path?
+        - Do I need a physical Android device, or can I use an emulator?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
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-    rerun_flags_reset: []
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+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
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@@ -207347,51 +13110,76 @@ history:
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-      preview_before: Learn how to accelerate OpenCV-based Android applications using KleidiCV for enhanced
-        computer vision performance. It is designed for developers who are interested in creating Computer
-        Vision applicat...
-      preview_after: Learn how to accelerate OpenCV-based Android applications using KleidiCV for enhanced
-        computer vision performance. It is designed for developers who are interested in creating Computer
-        Vision applicat...
-      preview_generated: Learn how to accelerate OpenCV-based Android applications using KleidiCV for
-        enhanced computer vision performance. It is designed for developers who are interested in creating
-        Computer Vision applicat...
+      generated_at_before: '2026-06-02T02:43:45Z'
+      generated_at_after: '2026-06-02T02:43:45Z'
+      preview_before: This introductory Android Learning Path shows how to build an
+        OpenCV-based app accelerated with KleidiCV. You will create a new Android
+        Studio project, add OpenCV with KleidiCV support, define a simpl...
+      preview_after: This introductory Android Learning Path shows how to build an
+        OpenCV-based app accelerated with KleidiCV. You will create a new Android
+        Studio project, add OpenCV with KleidiCV support, define a simpl...
+      preview_generated: This Learning Path guides you through creating an OpenCV-based
+        Android app accelerated with KleidiCV on Arm Cortex-A devices. Using Android
+        Studio, you will create a new project, integrate OpenCV with...
     faqs:
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+        added_questions:
+        - What do I need before running through the steps?
+        - Which Android Studio version is referenced in the example?
+        - Where should I place the test image, and does it have to be PNG?
+        - Which files do I edit to define the UI and application logic?
+        - What result should I expect when I run the app on my device?
+        removed_questions:
+        - What are the prerequisites to follow this Learning Path?
+        - Do I need a physical Android device, or can I use an emulator?
+        - Which Android Studio setup does the path use to start the project?
+        - What functionality does the sample application implement?
+        - How do I provide an input image and verify the app works?
         updated_questions: []
       generated_diff:
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         after_count: 5
-        added_questions: []
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-        updated_questions: []
+        added_questions:
+        - What do I need before running through the steps?
+        - Which Android Studio version is referenced in the example?
+        - Where should I place the test image, and does it have to be PNG?
+        - Which files do I edit to define the UI and application logic?
+        - What result should I expect when I run the app on my device?
+        removed_questions:
+        - What are the prerequisites to follow this Learning Path?
+        - Do I need a physical Android device, or can I use an emulator?
+        - Which Android Studio setup does the path use to start the project?
+        - What functionality does the sample application implement?
+        - How do I provide an input image and verify the app works?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md
-    status: unchanged
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-    rerun_flags_reset: []
-    change_reasons: []
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+    rerun_flags_reset:
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+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
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@@ -207401,51 +13189,76 @@ history:
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-      preview_before: Get started with Scalable Vector Extension 2 (SVE2) on Android walks you through
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-        how to use the Scala...
-      preview_after: Get started with Scalable Vector Extension 2 (SVE2) on Android walks you through
-        an end-to-end Arm software workflow. It is designed for software developers interested in learning
-        how to use the Scala...
-      preview_generated: Get started with Scalable Vector Extension 2 (SVE2) on Android walks you through
-        an end-to-end Arm software workflow. It is designed for software developers interested in learning
-        how to use the Scala...
+      generated_at_before: '2026-06-02T02:44:16Z'
+      generated_at_after: '2026-06-02T02:44:16Z'
+      preview_before: This Learning Path guides you through enabling Scalable Vector
+        Extension 2 (SVE2) in Android Studio and implementing a native Android NDK
+        example that computes vector fused multiply-add (a * b + c) us...
+      preview_after: This Learning Path guides you through enabling Scalable Vector
+        Extension 2 (SVE2) in Android Studio and implementing a native Android NDK
+        example that computes vector fused multiply-add (a * b + c) us...
+      preview_generated: Learn how to use Scalable Vector Extension 2 (SVE2) on Arm-powered
+        Android devices with an introductory, hands-on path. You will enable SVE2
+        support in Android Studio, implement a fused multiply-add (...
     faqs:
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       missing_before: false
-      rerun_requested: false
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-        added_questions: []
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+        added_questions:
+        - What do I need before running the steps?
+        - How do I enable SVE2 support in Android Studio for this project?
+        - Which source file do I modify to add the FMA and timing code?
+        - How is performance measured, and what result should I expect to see?
+        - Can I complete this path without a physical Arm-based Android device?
+        removed_questions:
+        - What hardware and software do I need before starting?
+        - Does this Learning Path use the Android NDK and C++?
+        - Where do I add or modify the native code?
+        - How do I enable SVE2 support in Android Studio?
+        - How will I verify that SVE2 makes a difference?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
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-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I enable SVE2 support in Android Studio for this project?
+        - Which source file do I modify to add the FMA and timing code?
+        - How is performance measured, and what result should I expect to see?
+        - Can I complete this path without a physical Arm-based Android device?
+        removed_questions:
+        - What hardware and software do I need before starting?
+        - Does this Learning Path use the Android NDK and C++?
+        - Where do I add or modify the native code?
+        - How do I enable SVE2 support in Android Studio?
+        - How will I verify that SVE2 makes a difference?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
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+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
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@@ -207455,51 +13268,76 @@ history:
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-      preview_before: Learn how to integrate Dawn WebGPU in an Android application, render 3D objects,
-        and profile the application using Streamline. It is designed for developers who are building GPU-based
-        Android applicat...
-      preview_after: Learn how to integrate Dawn WebGPU in an Android application, render 3D objects,
-        and profile the application using Streamline. It is designed for developers who are building GPU-based
-        Android applicat...
-      preview_generated: Learn how to integrate Dawn WebGPU in an Android application, render 3D objects,
-        and profile the application using Streamline. It is designed for developers who are building GPU-based
-        Android applicat...
+      generated_at_before: '2026-06-02T02:44:35Z'
+      generated_at_after: '2026-06-02T02:44:35Z'
+      preview_before: This Learning Path shows how to integrate Dawn WebGPU into a
+        C++-based Android Game Activity, render a simple 3D object using WebGPU APIs,
+        and profile the application with Arm Streamline. You will set...
+      preview_after: This Learning Path shows how to integrate Dawn WebGPU into a
+        C++-based Android Game Activity, render a simple 3D object using WebGPU APIs,
+        and profile the application with Arm Streamline. You will set...
+      preview_generated: This Learning Path guides you through integrating Dawn WebGPU
+        into a C++-based Android Game Activity project, rendering a simple 3D object,
+        and profiling the running application with Arm Streamline. Y...
     faqs:
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-        added_questions: []
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+        added_questions:
+        - What do I need before running the steps?
+        - Which Android Studio project template should I start with?
+        - How should I set up the Android SDK and NDK for this project?
+        - After integrating Dawn, which project files do I keep or add?
+        - When should I profile the app with Streamline and what is the expected outcome?
+        removed_questions:
+        - What prerequisites and tools do I need before starting?
+        - Which host operating systems are supported for development?
+        - How should I configure Android Studio and the SDK?
+        - What Android project template and files does the path use?
+        - What will I build and how do I validate success?
         updated_questions: []
       generated_diff:
         before_count: 5
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+        added_questions:
+        - What do I need before running the steps?
+        - Which Android Studio project template should I start with?
+        - How should I set up the Android SDK and NDK for this project?
+        - After integrating Dawn, which project files do I keep or add?
+        - When should I profile the app with Streamline and what is the expected outcome?
+        removed_questions:
+        - What prerequisites and tools do I need before starting?
+        - Which host operating systems are supported for development?
+        - How should I configure Android Studio and the SDK?
+        - What Android project template and files does the path use?
+        - What will I build and how do I validate success?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
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+    - rerun_flags_reset
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     summary:
       action: unchanged
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@@ -207509,51 +13347,76 @@ history:
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-      preview_before: Learn how to optimize hardware ray tracing with Lumen on Android devices powered
-        by Arm Mali GPUs to maximize performance. It is designed for Unreal Engine developers interested
-        in optimizing hardware...
-      preview_after: Learn how to optimize hardware ray tracing with Lumen on Android devices powered
-        by Arm Mali GPUs to maximize performance. It is designed for Unreal Engine developers interested
-        in optimizing hardware...
-      preview_generated: Learn how to optimize hardware ray tracing with Lumen on Android devices powered
-        by Arm Mali GPUs to maximize performance. It is designed for Unreal Engine developers interested
-        in optimizing hardware...
+      generated_at_before: '2026-06-02T02:45:10Z'
+      generated_at_after: '2026-06-02T02:45:10Z'
+      preview_before: This introductory Learning Path shows Unreal Engine developers
+        how to improve hardware ray tracing with Lumen on Android devices powered
+        by Arm Mali GPUs, including Immortalis series. In approximately...
+      preview_after: This introductory Learning Path shows Unreal Engine developers
+        how to improve hardware ray tracing with Lumen on Android devices powered
+        by Arm Mali GPUs, including Immortalis series. In approximately...
+      preview_generated: This Learning Path shows Unreal Engine developers how to
+        improve hardware ray tracing with Lumen on Android devices powered by Arm
+        Mali GPUs, including Immortalis-G715 and Immortalis-G720. You will re...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
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-        added_questions: []
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+        added_questions:
+        - What do I need before running the steps?
+        - Should I enable Lumen hardware ray tracing before following these optimizations?
+        - "How do I exclude actors that don\u2019t help lighting from ray tracing?"
+        - How can I check and use instancing to improve efficiency?
+        - How do I identify and reduce mesh overlap in the acceleration structure?
+        removed_questions:
+        - What hardware and software do I need before starting?
+        - Which Android GPUs is this guidance aimed at?
+        - Do I need to enable Lumen hardware ray tracing first?
+        - How do I remove unnecessary geometry from ray tracing in Unreal Engine?
+        - How do I check instancing and mesh overlap while optimizing?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Should I enable Lumen hardware ray tracing before following these optimizations?
+        - "How do I exclude actors that don\u2019t help lighting from ray tracing?"
+        - How can I check and use instancing to improve efficiency?
+        - How do I identify and reduce mesh overlap in the acceleration structure?
+        removed_questions:
+        - What hardware and software do I need before starting?
+        - Which Android GPUs is this guidance aimed at?
+        - Do I need to enable Lumen hardware ray tracing first?
+        - How do I remove unnecessary geometry from ray tracing in Unreal Engine?
+        - How do I check instancing and mesh overlap while optimizing?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -207563,51 +13426,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
-      preview_before: Learn how to build ONNX Runtime and the generate() API for Android to run a Phi-3
-        model on Arm-based smartphones. It is designed for software developers interested in learning
-        how to build an Android ...
-      preview_after: Learn how to build ONNX Runtime and the generate() API for Android to run a Phi-3
-        model on Arm-based smartphones. It is designed for software developers interested in learning
-        how to build an Android ...
-      preview_generated: Learn how to build ONNX Runtime and the generate() API for Android to run a Phi-3
-        model on Arm-based smartphones. It is designed for software developers interested in learning
-        how to build an Android ...
+      generated_at_before: '2026-06-02T02:45:40Z'
+      generated_at_after: '2026-06-02T02:45:40Z'
+      preview_before: This advanced Learning Path guides you through cross-compiling
+        ONNX Runtime and its generate() API for Android on a Windows x86_64 host,
+        then running a Phi-3 model on an Arm-based (Cortex-A) smartphon...
+      preview_after: This advanced Learning Path guides you through cross-compiling
+        ONNX Runtime and its generate() API for Android on a Windows x86_64 host,
+        then running a Phi-3 model on an Arm-based (Cortex-A) smartphon...
+      preview_generated: This Learning Path shows how to build and cross-compile ONNX
+        Runtime and its generate() API for Android, then run a Phi-3-mini model on
+        an Arm-based smartphone and build a simple chat app. Working on ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: deeb745d72457138cb81252c421205cd8dfb43b5e29ceee3a15c5842cc90cc33
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-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-06-02T02:45:40Z'
+      generated_at_after: '2026-06-02T23:48:15Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which software versions should I install for the build environment?
+        - What is the build target for ONNX Runtime and the generate() API?
+        - Where should the CMake toolchain file point when building the model runner?
+        - What result should I expect when running the benchmark on the phone?
+        removed_questions:
+        - What host and device do I need to complete this Learning Path?
+        - Which software and versions should I install before building?
+        - Which repositories are used, and is a specific commit required?
+        - What will I build and run on the device?
+        - How do I validate that the setup worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which software versions should I install for the build environment?
+        - What is the build target for ONNX Runtime and the generate() API?
+        - Where should the CMake toolchain file point when building the model runner?
+        - What result should I expect when running the benchmark on the phone?
+        removed_questions:
+        - What host and device do I need to complete this Learning Path?
+        - Which software and versions should I install before building?
+        - Which repositories are used, and is a specific commit required?
+        - What will I build and run on the device?
+        - How do I validate that the setup worked?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -207617,51 +13505,76 @@ history:
       source_hash_before: 13ff69755e51c6d7c355616f95703b3f2cefaedcf1f8dbeab31fe2e3db9aec74
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
-      preview_before: Learn how to build a hands-free selfie Android application using MediaPipe multimodal
-        AI, Kotlin flows, CameraX, and MVVM architecture. It is designed for mobile application developers
-        interested in l...
-      preview_after: Learn how to build a hands-free selfie Android application using MediaPipe multimodal
-        AI, Kotlin flows, CameraX, and MVVM architecture. It is designed for mobile application developers
-        interested in l...
-      preview_generated: Learn how to build a hands-free selfie Android application using MediaPipe multimodal
-        AI, Kotlin flows, CameraX, and MVVM architecture. It is designed for mobile application developers
-        interested in l...
+      generated_at_before: '2026-06-02T02:46:24Z'
+      generated_at_after: '2026-06-02T02:46:24Z'
+      preview_before: Build a hands-free selfie Android app that runs on a recent
+        Arm-powered Android phone using MediaPipe multimodal AI, Kotlin Flows, CameraX,
+        and an MVVM architecture. You will set up Android Studio, co...
+      preview_after: Build a hands-free selfie Android app that runs on a recent Arm-powered
+        Android phone using MediaPipe multimodal AI, Kotlin Flows, CameraX, and an
+        MVVM architecture. You will set up Android Studio, co...
+      preview_generated: Build a hands-free selfie Android app on an Arm-powered phone
+        using MediaPipe multimodal AI, CameraX, Kotlin Flows, and an MVVM architecture.
+        You will install and configure Android Studio, connect an ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 13ff69755e51c6d7c355616f95703b3f2cefaedcf1f8dbeab31fe2e3db9aec74
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-06-02T02:46:24Z'
+      generated_at_after: '2026-06-02T23:49:17Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the app on a device?
+        - How do I know my Android Studio setup is complete before coding?
+        - How do I set up and verify device debugging over USB?
+        - Which option should I use to access the camera in this app?
+        - How do I add MediaPipe and handle UI state and events?
+        removed_questions:
+        - Do I need a physical Android device for this Learning Path?
+        - Which host operating systems can I use for development?
+        - What tools and skills are required before starting?
+        - How is MediaPipe integrated and what ML features are used?
+        - How are camera access, UI state, and events handled in the app?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the app on a device?
+        - How do I know my Android Studio setup is complete before coding?
+        - How do I set up and verify device debugging over USB?
+        - Which option should I use to access the camera in this app?
+        - How do I add MediaPipe and handle UI state and events?
+        removed_questions:
+        - Do I need a physical Android device for this Learning Path?
+        - Which host operating systems can I use for development?
+        - What tools and skills are required before starting?
+        - How is MediaPipe integrated and what ML features are used?
+        - How are camera access, UI state, and events handled in the app?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -207671,51 +13584,80 @@ history:
       source_hash_before: 688b5b6eddc0480fa732308802d225696371c22a468bb6b140c6b74908222bbc
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
-      preview_before: Learn how to build an Android chat application with Llama models using ExecuTorch,
-        XNNPACK, and KleidiAI for accelerated performance on Arm smartphones. It is designed for software
-        developers interest...
-      preview_after: Learn how to build an Android chat application with Llama models using ExecuTorch,
-        XNNPACK, and KleidiAI for accelerated performance on Arm smartphones. It is designed for software
-        developers interest...
-      preview_generated: Learn how to build an Android chat application with Llama models using ExecuTorch,
-        XNNPACK, and KleidiAI for accelerated performance on Arm smartphones. It is designed for software
-        developers interest...
+      generated_at_before: '2026-06-02T02:46:55Z'
+      generated_at_after: '2026-06-02T02:46:55Z'
+      preview_before: Learn how to build and deploy a simple LLM-based Android chat
+        app using ExecuTorch with XNNPACK and KleidiAI on Arm smartphones. You will
+        set up an ExecuTorch development environment, prepare the Llam...
+      preview_after: Learn how to build and deploy a simple LLM-based Android chat
+        app using ExecuTorch with XNNPACK and KleidiAI on Arm smartphones. You will
+        set up an ExecuTorch development environment, prepare the Llam...
+      preview_generated: Build and deploy a simple LLM-based Android chat app using
+        Llama models with ExecuTorch, XNNPACK, and KleidiAI on Arm Cortex-A smartphones.
+        You will set up an ExecuTorch development environment, learn...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 688b5b6eddc0480fa732308802d225696371c22a468bb6b140c6b74908222bbc
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-06-02T02:46:55Z'
+      generated_at_after: '2026-06-02T23:49:55Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - Do I need macOS or Linux for the host, and what resources are required?
+        - What Android device requirements should I confirm before starting?
+        - When setting up ExecuTorch, should I use a Python virtual environment or
+          Conda?
+        - How do I obtain and prepare the Llama model used in this path?
+        - What should I set before cross-compiling the Llama runner for Android, and
+          what outputs should I expect?
+        removed_questions:
+        - What hardware, OS, and tools do I need before starting?
+        - How do I obtain and prepare the Llama model for ExecuTorch?
+        - How should I set up the ExecuTorch Python environment?
+        - What Android build steps are required to run on the device?
+        - What artifacts will I build, and how do I know the path worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - Do I need macOS or Linux for the host, and what resources are required?
+        - What Android device requirements should I confirm before starting?
+        - When setting up ExecuTorch, should I use a Python virtual environment or
+          Conda?
+        - How do I obtain and prepare the Llama model used in this path?
+        - What should I set before cross-compiling the Llama runner for Android, and
+          what outputs should I expect?
+        removed_questions:
+        - What hardware, OS, and tools do I need before starting?
+        - How do I obtain and prepare the Llama model for ExecuTorch?
+        - How should I set up the ExecuTorch Python environment?
+        - What Android build steps are required to run on the device?
+        - What artifacts will I build, and how do I know the path worked?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -207725,51 +13667,78 @@ history:
       source_hash_before: 9ccf2cdd6406694d85c553204479d7ff1f688512056a3c76d4c85266cdc418b1
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
-      preview_before: Learn how to build a customer support chatbot for Android using Llama 3.2, ExecuTorch,
-        and KleidiAI to run on-device inference on Arm platforms. It is designed for software developers
-        interested in bu...
-      preview_after: Learn how to build a customer support chatbot for Android using Llama 3.2, ExecuTorch,
-        and KleidiAI to run on-device inference on Arm platforms. It is designed for software developers
-        interested in bu...
-      preview_generated: Learn how to build a customer support chatbot for Android using Llama 3.2, ExecuTorch,
-        and KleidiAI to run on-device inference on Arm platforms. It is designed for software developers
-        interested in bu...
+      generated_at_before: '2026-06-02T02:47:30Z'
+      generated_at_after: '2026-06-02T02:47:30Z'
+      preview_before: "Learn to build and deploy an on-device customer support chatbot\
+        \ for Android using Meta\u2019s Llama 3.2 and the ExecuTorch runtime with\
+        \ KleidiAI integrated through XNNPACK on Arm. You set up a development ..."
+      preview_after: "Learn to build and deploy an on-device customer support chatbot\
+        \ for Android using Meta\u2019s Llama 3.2 and the ExecuTorch runtime with\
+        \ KleidiAI integrated through XNNPACK on Arm. You set up a development ..."
+      preview_generated: "Build an on-device customer support chatbot for Android\
+        \ using Meta\u2019s Llama 3.2 with the ExecuTorch runtime and KleidiAI acceleration\
+        \ on Arm. You will prepare a macOS or Linux development environment, ..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 9ccf2cdd6406694d85c553204479d7ff1f688512056a3c76d4c85266cdc418b1
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       current_source_hash: 9ccf2cdd6406694d85c553204479d7ff1f688512056a3c76d4c85266cdc418b1
-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-06-02T02:47:30Z'
+      generated_at_after: '2026-06-02T23:50:22Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Should I use a Python virtual environment for ExecuTorch, and which Python
+          version is required?
+        - How do I obtain and prepare the Llama model for ExecuTorch?
+        - Which Llama model variant does this path use, and can I try others?
+        - How do I build and run the chatbot on Android, and how do I confirm it works?
+        removed_questions:
+        - What hardware do I need on the host and target devices?
+        - What software should be installed before starting?
+        - Which Llama model is used and how do I obtain it?
+        - How is ExecuTorch configured to use KleidiAI on Arm?
+        - What will I build and how do I validate it on Android?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Should I use a Python virtual environment for ExecuTorch, and which Python
+          version is required?
+        - How do I obtain and prepare the Llama model for ExecuTorch?
+        - Which Llama model variant does this path use, and can I try others?
+        - How do I build and run the chatbot on Android, and how do I confirm it works?
+        removed_questions:
+        - What hardware do I need on the host and target devices?
+        - What software should be installed before starting?
+        - Which Llama model is used and how do I obtain it?
+        - How is ExecuTorch configured to use KleidiAI on Arm?
+        - What will I build and how do I validate it on Android?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -207779,51 +13748,87 @@ history:
       source_hash_before: 95d4fb855552939d1a0e9cccfe46333692ea291ee85dd08b816321db29ceebdc
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
-      preview_before: Learn how to detect and debug memory safety bugs in Android applications using Arm
-        Memory Tagging Extension (MTE) on a Google Pixel 8 smartphone. It is designed for developers interested
-        in learning h...
-      preview_after: Learn how to detect and debug memory safety bugs in Android applications using Arm
-        Memory Tagging Extension (MTE) on a Google Pixel 8 smartphone. It is designed for developers interested
-        in learning h...
-      preview_generated: Learn how to detect and debug memory safety bugs in Android applications using
-        Arm Memory Tagging Extension (MTE) on a Google Pixel 8 smartphone. It is designed for developers
-        interested in learning h...
+      generated_at_before: '2026-06-02T02:48:04Z'
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+      preview_before: This Learning Path shows how to detect and debug memory safety
+        bugs in Android applications using Arm Memory Tagging Extension (MTE) on a
+        Google Pixel 8. You will clone an Android MTE Test app from Gi...
+      preview_after: This Learning Path shows how to detect and debug memory safety
+        bugs in Android applications using Arm Memory Tagging Extension (MTE) on a
+        Google Pixel 8. You will clone an Android MTE Test app from Gi...
+      preview_generated: This Learning Path shows how to detect and debug memory safety
+        bugs in Android applications using the Arm Memory Tagging Extension (MTE)
+        on a Google Pixel 8. You will clone an Android MTE Test app fro...
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+        - How do I get the MTE Test app project into Android Studio?
+        - Which file do I edit to enable or disable MTE?
+        - How do I run and debug the app on my Pixel 8?
+        - What should I check if my Pixel 8 does not appear in Android Studio?
+        removed_questions:
+        - What hardware and tools do I need before starting?
+        - Where do I get the MTE Test app and how do I open it?
+        - How do I enable or disable MTE for the app?
+        - How do I run and debug the app on the Pixel 8 and verify it is working?
+        - Can I use a device other than a Google Pixel 8?
         updated_questions: []
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         before_count: 5
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-        added_questions: []
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+        - How do I get the MTE Test app project into Android Studio?
+        - Which file do I edit to enable or disable MTE?
+        - How do I run and debug the app on my Pixel 8?
+        - What should I check if my Pixel 8 does not appear in Android Studio?
+        removed_questions:
+        - What hardware and tools do I need before starting?
+        - Where do I get the MTE Test app and how do I open it?
+        - How do I enable or disable MTE for the app?
+        - How do I run and debug the app on the Pixel 8 and verify it is working?
+        - Can I use a device other than a Google Pixel 8?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
+  - path: content/learning-paths/mobile-graphics-and-gaming/gemma4-kleidiai-sme2/_index.md
+    status: skipped
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+    change_reasons:
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+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md
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     summary:
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@@ -207833,51 +13838,77 @@ history:
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-      preview_before: Get started with Arm Accuracy Super Resolution (Arm ASR) walks you through an end-to-end
-        Arm software workflow. It is designed for mobile, gaming, and graphics developers who want to
-        install and confi...
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-      preview_generated: Get started with Arm Accuracy Super Resolution (Arm ASR) walks you through an
-        end-to-end Arm software workflow. It is designed for mobile, gaming, and graphics developers who
-        want to install and confi...
+      generated_at_before: '2026-06-02T02:48:29Z'
+      generated_at_after: '2026-06-02T02:48:29Z'
+      preview_before: "Learn how to install and integrate Arm Accuracy Super Resolution\
+        \ (Arm ASR)\u2014a mobile-optimized temporal upscaling technique derived from\
+        \ AMD Fidelity Super Resolution 2 v2.2.2\u2014into Android game project..."
+      preview_after: "Learn how to install and integrate Arm Accuracy Super Resolution\
+        \ (Arm ASR)\u2014a mobile-optimized temporal upscaling technique derived from\
+        \ AMD Fidelity Super Resolution 2 v2.2.2\u2014into Android game project..."
+      preview_generated: "This Learning Path shows how to install and configure Arm\
+        \ Accuracy Super Resolution (Arm ASR) for mobile games on Android. You will\
+        \ use an example Unreal Engine project (recommended versions 5.3\u20135.5)\
+        \ ..."
     faqs:
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       change_details:
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-        added_questions: []
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+        - Which Unreal Engine versions should I use for this Learning Path?
+        - What do I need before running the steps?
+        - "I\u2019m not using Unreal Engine\u2014how can I integrate Arm ASR?"
+        - What configuration areas will I manage when integrating ASR?
+        - How is Arm ASR related to AMD FSR2?
+        removed_questions:
+        - Which Unreal Engine versions does this Learning Path support?
+        - What are the prerequisites to follow this Learning Path?
+        - Can I use Arm ASR outside Unreal Engine?
+        - What configuration tasks will I perform after integrating Arm ASR?
+        - Is there Unity support for Arm ASR?
         updated_questions: []
       generated_diff:
         before_count: 5
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-        added_questions: []
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+        added_questions:
+        - Which Unreal Engine versions should I use for this Learning Path?
+        - What do I need before running the steps?
+        - "I\u2019m not using Unreal Engine\u2014how can I integrate Arm ASR?"
+        - What configuration areas will I manage when integrating ASR?
+        - How is Arm ASR related to AMD FSR2?
+        removed_questions:
+        - Which Unreal Engine versions does this Learning Path support?
+        - What are the prerequisites to follow this Learning Path?
+        - Can I use Arm ASR outside Unreal Engine?
+        - What configuration tasks will I perform after integrating Arm ASR?
+        - Is there Unity support for Arm ASR?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md
-    status: unchanged
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     summary:
       action: unchanged
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@@ -207887,51 +13918,76 @@ history:
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-      preview_before: Get started with Unity on Android walks you through an end-to-end Arm software workflow.
-        It is designed for Unity developers who want to target Android devices. By the end, you will be
-        able to set up ...
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-        It is designed for Unity developers who want to target Android devices. By the end, you will be
-        able to set up ...
-      preview_generated: Get started with Unity on Android walks you through an end-to-end Arm software
-        workflow. It is designed for Unity developers who want to target Android devices. By the end,
-        you will be able to set up ...
+      generated_at_before: '2026-06-02T02:48:56Z'
+      generated_at_after: '2026-06-02T02:48:56Z'
+      preview_before: This introductory path shows how to set up Unity for Android,
+        build and deploy a simple sample to a real device, and begin investigating
+        performance with the Unity Profiler. You will install the lates...
+      preview_after: This introductory path shows how to set up Unity for Android,
+        build and deploy a simple sample to a real device, and begin investigating
+        performance with the Unity Profiler. You will install the lates...
+      preview_generated: This introductory path guides Unity developers through building
+        and profiling a simple Android app. You will install the latest Unity with
+        Android Build Support, open a provided sample project (a spin...
     faqs:
-      action: unchanged
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       missing_before: false
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+      generated_at_before: '2026-06-02T02:48:56Z'
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       before_count: 5
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       generated_count: 5
       change_details:
         before_count: 5
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-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which Unity components should I install to target Android?
+        - How do I open and inspect the sample project and scene?
+        - How do I switch the project to Android and build for my device?
+        - Should I profile in the editor or on my Android device?
+        removed_questions:
+        - What are the prerequisites to start this Learning Path?
+        - Which Unity version and components should I install?
+        - What sample project will I use, and what does it demonstrate?
+        - Which desktop operating systems can I use for development?
+        - How do I profile the app and verify it is collecting data?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
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+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which Unity components should I install to target Android?
+        - How do I open and inspect the sample project and scene?
+        - How do I switch the project to Android and build for my device?
+        - Should I profile in the editor or on my Android device?
+        removed_questions:
+        - What are the prerequisites to start this Learning Path?
+        - Which Unity version and components should I install?
+        - What sample project will I use, and what does it demonstrate?
+        - Which desktop operating systems can I use for development?
+        - How do I profile the app and verify it is collecting data?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md
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-    rerun_flags_reset: []
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     summary:
       action: unchanged
       missing_before: false
@@ -207941,51 +13997,76 @@ history:
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-      preview_before: Profile Android game performance in Godot with Arm Performance Studio walks you
-        through an end-to-end Arm software workflow. It is designed for Godot developers targeting Android
-        devices who want to o...
-      preview_after: Profile Android game performance in Godot with Arm Performance Studio walks you through
-        an end-to-end Arm software workflow. It is designed for Godot developers targeting Android devices
-        who want to o...
-      preview_generated: Profile Android game performance in Godot with Arm Performance Studio walks you
-        through an end-to-end Arm software workflow. It is designed for Godot developers targeting Android
-        devices who want to o...
+      generated_at_before: '2026-06-02T02:49:25Z'
+      generated_at_after: '2026-06-02T02:49:25Z'
+      preview_before: Learn how to profile Android games built with Godot using Arm
+        Performance Studio. You install the Arm Performance Studio Integration extension
+        from the Godot Asset Library, then add annotations in GDS...
+      preview_after: Learn how to profile Android games built with Godot using Arm
+        Performance Studio. You install the Arm Performance Studio Integration extension
+        from the Godot Asset Library, then add annotations in GDS...
+      preview_generated: Learn how to instrument and profile your Godot Android game
+        on Arm-based devices using Arm Performance Studio. You will install the Arm
+        Performance Studio Integration from the Godot Asset Library, the...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
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       before_count: 5
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       generated_count: 5
       change_details:
         before_count: 5
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-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - Which Godot versions support the Arm Performance Studio extension?
+        - How do I install the Arm Performance Studio Integration in my Godot project?
+        - How do I add a basic marker and where will I see it?
+        - How do I define a performance region and how is it reported?
+        - When should I use channels, and what do they capture?
+        removed_questions:
+        - What skills or tools do I need before starting?
+        - Which Godot versions and platforms does this cover?
+        - How do I install the Arm Performance Studio extension in Godot?
+        - How do I add annotations to my Godot game?
+        - How do I verify that my annotations are captured?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - Which Godot versions support the Arm Performance Studio extension?
+        - How do I install the Arm Performance Studio Integration in my Godot project?
+        - How do I add a basic marker and where will I see it?
+        - How do I define a performance region and how is it reported?
+        - When should I use channels, and what do they capture?
+        removed_questions:
+        - What skills or tools do I need before starting?
+        - Which Godot versions and platforms does this cover?
+        - How do I install the Arm Performance Studio extension in Godot?
+        - How do I add annotations to my Godot game?
+        - How do I verify that my annotations are captured?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md
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-    changed_on_disk: false
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     summary:
       action: unchanged
       missing_before: false
@@ -207995,51 +14076,78 @@ history:
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-      preview_before: How to Enable Hardware Ray Tracing on Lumen for Android Devices walks you through
-        an end-to-end Arm software workflow. It is designed for Unreal Engine developers interested in
-        using hardware ray trac...
-      preview_after: How to Enable Hardware Ray Tracing on Lumen for Android Devices walks you through
-        an end-to-end Arm software workflow. It is designed for Unreal Engine developers interested in
-        using hardware ray trac...
-      preview_generated: How to Enable Hardware Ray Tracing on Lumen for Android Devices walks you through
-        an end-to-end Arm software workflow. It is designed for Unreal Engine developers interested in
-        using hardware ray trac...
+      generated_at_before: '2026-06-02T02:49:43Z'
+      generated_at_after: '2026-06-02T02:49:43Z'
+      preview_before: This introductory Learning Path guides Unreal Engine developers
+        through enabling hardware ray tracing for Lumen on Android devices with Arm
+        Mali GPUs, including those based on Immortalis-G715 or G720....
+      preview_after: This introductory Learning Path guides Unreal Engine developers
+        through enabling hardware ray tracing for Lumen on Android devices with Arm
+        Mali GPUs, including those based on Immortalis-G715 or G720....
+      preview_generated: This Learning Path guides Unreal Engine developers through
+        enabling hardware ray tracing for Lumen on Android devices with Mali GPUs.
+        You will review what Lumen and global illumination are, then confi...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
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       before_count: 5
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       generated_count: 5
       change_details:
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-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Where do I enable Lumen for Global Illumination and Reflections?
+        - Can I enable Lumen per scene instead of project-wide?
+        - How do I enable the SM5 shader format for Android?
+        - Which shading path should I choose when using Lumen?
+        removed_questions:
+        - What do I need before starting?
+        - Which Android GPUs or devices are targeted?
+        - How do I enable Lumen in my Unreal project?
+        - What settings are required to enable hardware ray tracing with Lumen on
+          Android?
+        - How can I tell if Lumen with hardware ray tracing is active?
         updated_questions: []
       generated_diff:
         before_count: 5
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-        added_questions: []
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+        added_questions:
+        - What do I need before running the steps?
+        - Where do I enable Lumen for Global Illumination and Reflections?
+        - Can I enable Lumen per scene instead of project-wide?
+        - How do I enable the SM5 shader format for Android?
+        - Which shading path should I choose when using Lumen?
+        removed_questions:
+        - What do I need before starting?
+        - Which Android GPUs or devices are targeted?
+        - How do I enable Lumen in my Unreal project?
+        - What settings are required to enable hardware ray tracing with Lumen on
+          Android?
+        - How can I tell if Lumen with hardware ray tracing is active?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/intro/_index.md
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-      preview_generated: Get started with Arm hardware walks you through an end-to-end Arm software workflow.
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-        the end, you will be ...
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+      preview_generated: "This short, introductory path helps you identify Android\
+        \ smartphones with Arm hardware that are suitable for mobile software development.\
+        \ You will learn what to check in device specifications\u2014specific..."
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+        - How does Arm Performance Studio for Mobile fit into this path?
+        - What platform does this target, and how long will it take?
+        removed_questions:
+        - What types of devices should I look for?
+        - Which operating system and Arm IPs does this path focus on?
+        - Are there any prerequisites or required tools?
+        - How do I check if a device is suitable for performance analysis?
+        - Do all smartphones provide the same level of performance analysis?
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+        - How does Arm Performance Studio for Mobile fit into this path?
+        - What platform does this target, and how long will it take?
+        removed_questions:
+        - What types of devices should I look for?
+        - Which operating system and Arm IPs does this path focus on?
+        - Are there any prerequisites or required tools?
+        - How do I check if a device is suitable for performance analysis?
+        - Do all smartphones provide the same level of performance analysis?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md
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@@ -208103,51 +14236,82 @@ history:
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-      preview_generated: Understand KleidiAI SME2 matmul microkernels walks you through an end-to-end
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-        By the end, you...
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+      preview_before: This advanced Learning Path explains how KleidiAI implements
+        matrix multiplication microkernels for quantized inference on Arm CPUs using
+        SME2 INT8 MOPA instructions. You will decode a specific SME2 m...
+      preview_after: This advanced Learning Path explains how KleidiAI implements
+        matrix multiplication microkernels for quantized inference on Arm CPUs using
+        SME2 INT8 MOPA instructions. You will decode a specific SME2 m...
+      preview_generated: This advanced Learning Path explains how KleidiAI SME2 matmul
+        microkernels implement quantized matrix multiplication on Arm CPUs. You will
+        decode a specific microkernel, see how SME2 INT8 MOPA (matrix...
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+        - Do I need a device with SME2 support to follow this Learning Path?
+        - How do I verify that SME2 INT8 MOPA instructions are used in the microkernel?
+        - Which llama.cpp operations route through the SME2 matmul microkernel in
+          this context?
+        - Which tiling and packing parameters should I pay attention to?
+        - What SVL and matrix sizes does the example assume, and how do I interpret
+          1vlx4vl?
+        removed_questions:
+        - Do I need an Arm CPU with SME2 to follow this Learning Path?
+        - Which platforms and tools are used in the examples?
+        - What example matrix shapes and data types are used?
+        - How can I confirm that SME2 INT8 MOPA instructions are used in the inner
+          loop?
+        - What tiling and packing concepts will I work with?
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+        - How do I verify that SME2 INT8 MOPA instructions are used in the microkernel?
+        - Which llama.cpp operations route through the SME2 matmul microkernel in
+          this context?
+        - Which tiling and packing parameters should I pay attention to?
+        - What SVL and matrix sizes does the example assume, and how do I interpret
+          1vlx4vl?
+        removed_questions:
+        - Do I need an Arm CPU with SME2 to follow this Learning Path?
+        - Which platforms and tools are used in the examples?
+        - What example matrix shapes and data types are used?
+        - How can I confirm that SME2 INT8 MOPA instructions are used in the inner
+          loop?
+        - What tiling and packing concepts will I work with?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md
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-      preview_generated: Learn how to run LLM inference on Android devices using MediaPipe with KleidiAI-enhanced
-        Arm i8mm features to benchmark the Gemma 2B model. It is designed for Android developers who want
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+      preview_before: "Learn to cross-compile and run LLM inference on Android using\
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+        \ Google AI Edge\u2019s MediaPipe with XNNPACK and KleidiAI-enhanced Arm i8mm.\
+        \ Starting from an x86_64 Ubuntu host (or a provided Docker setup), ..."
+      preview_generated: "This Learning Path shows how to run and benchmark the Gemma\
+        \ 2B LLM on an Android device using Google AI Edge\u2019s MediaPipe with XNNPACK\
+        \ and KleidiAI-enhanced Arm i8mm features. You will set up dependenc..."
     faqs:
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+        - Should I install dependencies with Docker or directly on Ubuntu?
+        - Which Bazel options target Android Arm64 and enable i8mm?
+        - How do I confirm the inference engine built correctly?
+        - What result should I expect when running inference and benchmarking?
+        removed_questions:
+        - What hardware and OS do I need to follow this Learning Path?
+        - Can I use Docker instead of installing dependencies locally?
+        - What exactly do I build, and how do I verify the build?
+        - "How do I enable or disable KleidiAI\u2019s i8mm path during benchmarking?"
+        - Which model and frameworks are used, and what is the goal?
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+        - Should I install dependencies with Docker or directly on Ubuntu?
+        - Which Bazel options target Android Arm64 and enable i8mm?
+        - How do I confirm the inference engine built correctly?
+        - What result should I expect when running inference and benchmarking?
+        removed_questions:
+        - What hardware and OS do I need to follow this Learning Path?
+        - Can I use Docker instead of installing dependencies locally?
+        - What exactly do I build, and how do I verify the build?
+        - "How do I enable or disable KleidiAI\u2019s i8mm path during benchmarking?"
+        - Which model and frameworks are used, and what is the goal?
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+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md
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-      preview_generated: Learn how to build the libGPUInfo library using Android NDK and query configuration
-        details of Arm Mali or Immortalis GPUs on Android devices. It is designed for Android developers
-        who want to adjust ...
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+      generated_at_after: '2026-06-02T02:51:15Z'
+      preview_before: Learn to build the libGPUInfo C++ library with the Android NDK
+        and run an example application on an Android device to query configuration
+        details of Arm Mali or Arm Immortalis GPUs. Working from a Deb...
+      preview_after: Learn to build the libGPUInfo C++ library with the Android NDK
+        and run an example application on an Android device to query configuration
+        details of Arm Mali or Arm Immortalis GPUs. Working from a Deb...
+      preview_generated: This introductory Learning Path shows how to build the libGPUInfo
+        C++ library with the Android NDK and run an example application on an Android
+        device to query configuration details of Arm Mali or Arm...
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+        - Which Android GPUs and devices does this target?
+        - Does this Learning Path include installing the Android NDK and using adb?
+        - What result should I expect from the example application?
+        - How would I use libGPUInfo in my own application?
+        removed_questions:
+        - What do I need before starting?
+        - Which tools are used in this Learning Path?
+        - What will I build and run?
+        - How do I know the example worked?
+        - Who is this for and how long will it take?
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+        - Which Android GPUs and devices does this target?
+        - Does this Learning Path include installing the Android NDK and using adb?
+        - What result should I expect from the example application?
+        - How would I use libGPUInfo in my own application?
+        removed_questions:
+        - What do I need before starting?
+        - Which tools are used in this Learning Path?
+        - What will I build and run?
+        - How do I know the example worked?
+        - Who is this for and how long will it take?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md
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-        SME2 instructions and validate performance with the benchmark tool. It is designed for developers
-        looking to leverage Arm'...
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+      preview_before: This advanced Learning Path shows how to accelerate LiteRT (Lite
+        Runtime) model inference on Android by enabling KleidiAI with Scalable Matrix
+        Extension 2 (SME2) via XNNPACK, then validating the resul...
+      preview_after: This advanced Learning Path shows how to accelerate LiteRT (Lite
+        Runtime) model inference on Android by enabling KleidiAI with Scalable Matrix
+        Extension 2 (SME2) via XNNPACK, then validating the resul...
+      preview_generated: "This advanced Learning Path shows how to accelerate LiteRT\
+        \ (Lite Runtime, formerly TensorFlow Lite) model inference on Android using\
+        \ KleidiAI micro-kernels with Arm\u2019s Scalable Matrix Extension 2 (SME2..."
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+        added_questions:
+        - What do I need before building and benchmarking?
+        - How do I check if my Android device supports SME2?
+        - Which parts of my LiteRT model are accelerated through KleidiAI SME2?
+        - Why build two versions of the benchmark_model tool?
+        - What should I check if my benchmark does not reflect SME2 acceleration?
+        removed_questions:
+        - What hardware and OS do I need before starting?
+        - How can I verify that my Android device supports SME2?
+        - What do I build and run in this Learning Path?
+        - Which LiteRT operators are accelerated by KleidiAI with SME2?
+        - How do I validate that SME2 acceleration is being used?
         updated_questions: []
       generated_diff:
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+        - How do I check if my Android device supports SME2?
+        - Which parts of my LiteRT model are accelerated through KleidiAI SME2?
+        - Why build two versions of the benchmark_model tool?
+        - What should I check if my benchmark does not reflect SME2 acceleration?
+        removed_questions:
+        - What hardware and OS do I need before starting?
+        - How can I verify that my Android device supports SME2?
+        - What do I build and run in this Learning Path?
+        - Which LiteRT operators are accelerated by KleidiAI with SME2?
+        - How do I validate that SME2 acceleration is being used?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md
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     summary:
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@@ -208319,51 +14558,76 @@ history:
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-      preview_before: Learn how to benchmark KleidiAI micro-kernels in ExecuTorch using SME/SME2 instructions
-        on Arm64 platforms with ETDump profiling and analysis. It is designed for developers, performance
-        engineers, and...
-      preview_after: Learn how to benchmark KleidiAI micro-kernels in ExecuTorch using SME/SME2 instructions
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-        engineers, and...
-      preview_generated: Learn how to benchmark KleidiAI micro-kernels in ExecuTorch using SME/SME2 instructions
-        on Arm64 platforms with ETDump profiling and analysis. It is designed for developers, performance
-        engineers, and...
+      generated_at_before: '2026-06-02T02:51:58Z'
+      generated_at_after: '2026-06-02T02:51:58Z'
+      preview_before: This Learning Path shows how to benchmark KleidiAI micro-kernels
+        in ExecuTorch on Arm64 platforms that support SME or SME2. You will set up
+        an isolated Python environment on an x86_64 Ubuntu host, cro...
+      preview_after: This Learning Path shows how to benchmark KleidiAI micro-kernels
+        in ExecuTorch on Arm64 platforms that support SME or SME2. You will set up
+        an isolated Python environment on an x86_64 Ubuntu host, cro...
+      preview_generated: This Learning Path shows how to benchmark KleidiAI micro-kernels
+        in ExecuTorch on Arm64 platforms that support SME or SME2. You will set up
+        an isolated Python environment on an x86_64 Ubuntu host, cro...
     faqs:
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-      rerun_requested: false
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       before_count: 5
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       generated_count: 5
       change_details:
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-        added_questions: []
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+        added_questions:
+        - What do I need before running the steps?
+        - Should I use a Python virtual environment, and how long should it stay active?
+        - Which toolchain should I install to cross-compile ExecuTorch for AArch64?
+        - How do I know if KleidiAI micro-kernels are being used for my operators?
+        - What results should I expect after running executor_runner?
+        removed_questions:
+        - What systems and hardware do I need to complete this path?
+        - Should I use a Python virtual environment?
+        - How do I build ExecuTorch for the Arm64 target and where do I run the benchmarks?
+        - Which operators and variants are benchmarked in this path?
+        - What outputs are produced and how do I analyze them?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
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-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Should I use a Python virtual environment, and how long should it stay active?
+        - Which toolchain should I install to cross-compile ExecuTorch for AArch64?
+        - How do I know if KleidiAI micro-kernels are being used for my operators?
+        - What results should I expect after running executor_runner?
+        removed_questions:
+        - What systems and hardware do I need to complete this path?
+        - Should I use a Python virtual environment?
+        - How do I build ExecuTorch for the Arm64 target and where do I run the benchmarks?
+        - Which operators and variants are benchmarked in this path?
+        - What outputs are produced and how do I analyze them?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
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+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
       missing_before: false
@@ -208373,51 +14637,76 @@ history:
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-      preview_before: Learn how to fine-tune and evaluate Neural Super Sampling (NSS) models using PyTorch
-        and Arm's Model Gym API with hardware-aware optimization. It is designed for developers exploring
-        neural graphics a...
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-        and Arm's Model Gym API with hardware-aware optimization. It is designed for developers exploring
-        neural graphics a...
-      preview_generated: Learn how to fine-tune and evaluate Neural Super Sampling (NSS) models using
-        PyTorch and Arm's Model Gym API with hardware-aware optimization. It is designed for developers
-        exploring neural graphics a...
+      generated_at_before: '2026-06-02T02:52:24Z'
+      generated_at_after: '2026-06-02T02:52:24Z'
+      preview_before: "This advanced Learning Path shows how to fine-tune and evaluate\
+        \ a Neural Super Sampling (NSS) upscaler using PyTorch with Arm\u2019s Model\
+        \ Gym API and CLI on Ubuntu 22.04. You will set up a Python 3.10+ en..."
+      preview_after: "This advanced Learning Path shows how to fine-tune and evaluate\
+        \ a Neural Super Sampling (NSS) upscaler using PyTorch with Arm\u2019s Model\
+        \ Gym API and CLI on Ubuntu 22.04. You will set up a Python 3.10+ en..."
+      preview_generated: "This advanced Learning Path guides you through fine-tuning\
+        \ and evaluating Neural Super Sampling (NSS) models using PyTorch and Arm\u2019\
+        s Model Gym API and CLI on Ubuntu 22.04. You will set up a Python 3.1..."
     faqs:
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       before_count: 5
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       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the notebooks?
+        - How do I set up Python and system dependencies on Ubuntu?
+        - How do I get the example notebooks used in this Learning Path?
+        - What result should I expect after training the NSS model?
+        - Can I integrate my own model into Model Gym?
+        removed_questions:
+        - What system requirements do I need to follow this Learning Path?
+        - How do I prepare the environment and obtain the example notebooks?
+        - How is the NSS model fine-tuned and evaluated in this path?
+        - What outputs should I expect and how can I inspect them?
+        - Can I use my own model with Model Gym?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the notebooks?
+        - How do I set up Python and system dependencies on Ubuntu?
+        - How do I get the example notebooks used in this Learning Path?
+        - What result should I expect after training the NSS model?
+        - Can I integrate my own model into Model Gym?
+        removed_questions:
+        - What system requirements do I need to follow this Learning Path?
+        - How do I prepare the environment and obtain the example notebooks?
+        - How is the NSS model fine-tuned and evaluated in this path?
+        - What outputs should I expect and how can I inspect them?
+        - Can I use my own model with Model Gym?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/mte/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
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+    status: updated
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+    rerun_flags_reset:
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+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
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@@ -208427,51 +14716,78 @@ history:
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-        of the Arm Memory Tagging Extension (MTE). It is designed for developers who want to gain some
-        experience wit...
-      preview_after: Learn how to run example C programs on AArch64 Linux to gain an introductory understanding
-        of the Arm Memory Tagging Extension (MTE). It is designed for developers who want to gain some
-        experience wit...
-      preview_generated: Learn how to run example C programs on AArch64 Linux to gain an introductory
-        understanding of the Arm Memory Tagging Extension (MTE). It is designed for developers who want
-        to gain some experience wit...
+      generated_at_before: '2026-06-02T02:52:58Z'
+      generated_at_after: '2026-06-02T02:52:58Z'
+      preview_before: Learn how to build and run a small C program on AArch64 Linux
+        to explore the Arm Memory Tagging Extension (MTE). MTE, available in Armv8.5-A
+        and Armv9-A processors, helps detect memory safety issues s...
+      preview_after: Learn how to build and run a small C program on AArch64 Linux
+        to explore the Arm Memory Tagging Extension (MTE). MTE, available in Armv8.5-A
+        and Armv9-A processors, helps detect memory safety issues s...
+      preview_generated: Build and run a small C program on AArch64 Linux to get an
+        introductory, hands-on view of the Arm Memory Tagging Extension (MTE). MTE,
+        available in Armv8.5-A and Armv9-A processors, detects common mem...
     faqs:
-      action: unchanged
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       before_count: 5
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       generated_count: 5
       change_details:
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-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the example C program?
+        - Can I use a cloud-based AArch64 instance for this path?
+        - Is QEMU required for this Learning Path?
+        - How do I know if my environment supports MTE?
+        - What result should I expect when I build and run the example?
+        removed_questions:
+        - What environment do I need to follow this Learning Path?
+        - Do I need a processor that supports MTE, and which Arm architectures include
+          it?
+        - What will I build and run?
+        - Is QEMU required, or can I use physical or cloud hardware?
+        - How long does it take, and what is the skill level?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
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-        updated_questions: []
+        added_questions:
+        - What do I need before running the example C program?
+        - Can I use a cloud-based AArch64 instance for this path?
+        - Is QEMU required for this Learning Path?
+        - How do I know if my environment supports MTE?
+        - What result should I expect when I build and run the example?
+        removed_questions:
+        - What environment do I need to follow this Learning Path?
+        - Do I need a processor that supports MTE, and which Arm architectures include
+          it?
+        - What will I build and run?
+        - Is QEMU required, or can I use physical or cloud hardware?
+        - How long does it take, and what is the skill level?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md
-    status: unchanged
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-    rerun_flags_reset: []
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-    template_version_before: summary-faq-v1
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+    status: updated
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+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
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@@ -208481,51 +14797,78 @@ history:
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-      preview_before: Learn how to enable Arm Memory Tagging Extension (MTE) on a Google Pixel 8 smartphone,
-        trigger memory bug crashes, and interpret bug reports. It is designed for developers interested
-        in learning how t...
-      preview_after: Learn how to enable Arm Memory Tagging Extension (MTE) on a Google Pixel 8 smartphone,
-        trigger memory bug crashes, and interpret bug reports. It is designed for developers interested
-        in learning how t...
-      preview_generated: Learn how to enable Arm Memory Tagging Extension (MTE) on a Google Pixel 8 smartphone,
-        trigger memory bug crashes, and interpret bug reports. It is designed for developers interested
-        in learning how t...
+      generated_at_before: '2026-06-02T02:53:24Z'
+      generated_at_after: '2026-06-02T02:53:24Z'
+      preview_before: "This Learning Path shows how to enable Arm\u2019s Memory Tagging\
+        \ Extension (MTE) on a Google Pixel 8, trigger memory-bug crashes using a\
+        \ test app, and examine the resulting Android bug report. You will ena..."
+      preview_after: "This Learning Path shows how to enable Arm\u2019s Memory Tagging\
+        \ Extension (MTE) on a Google Pixel 8, trigger memory-bug crashes using a\
+        \ test app, and examine the resulting Android bug report. You will ena..."
+      preview_generated: This introductory Learning Path shows how to enable Arm Memory
+        Tagging Extension (MTE) on a Google Pixel 8 running Android, trigger reproducible
+        memory-bug crashes using a provided test APK, and inter...
     faqs:
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       before_count: 5
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       generated_count: 5
       change_details:
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-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before enabling MTE on my Pixel 8?
+        - How do I turn on Developer options to access MTE settings?
+        - How do I confirm that MTE is active after I enable it?
+        - How do I capture a bug report after the test app crashes?
+        - Which files should I inspect in the bug report, and why might the filename
+          include 'husky'?
+        removed_questions:
+        - What do I need before starting?
+        - How do I enable MTE on the Pixel 8?
+        - How can I verify that MTE is working?
+        - How do I capture and access the bug report?
+        - How long does this take and what skill level is assumed?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before enabling MTE on my Pixel 8?
+        - How do I turn on Developer options to access MTE settings?
+        - How do I confirm that MTE is active after I enable it?
+        - How do I capture a bug report after the test app crashes?
+        - Which files should I inspect in the bug report, and why might the filename
+          include 'husky'?
+        removed_questions:
+        - What do I need before starting?
+        - How do I enable MTE on the Pixel 8?
+        - How can I verify that MTE is working?
+        - How do I capture and access the bug report?
+        - How long does this take and what skill level is assumed?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md
-    status: unchanged
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-    rerun_flags_reset: []
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-    template_version_before: summary-faq-v1
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+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
       missing_before: false
@@ -208535,51 +14878,78 @@ history:
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-      preview_before: Learn how to capture high-quality frame datasets from Unreal Engine 5.5 gameplay
-        for training and evaluating neural graphics models like Neural Super Sampling. It is designed
-        for Unreal Engine develop...
-      preview_after: Learn how to capture high-quality frame datasets from Unreal Engine 5.5 gameplay
-        for training and evaluating neural graphics models like Neural Super Sampling. It is designed
-        for Unreal Engine develop...
-      preview_generated: Learn how to capture high-quality frame datasets from Unreal Engine 5.5 gameplay
-        for training and evaluating neural graphics models like Neural Super Sampling. It is designed
-        for Unreal Engine develop...
+      generated_at_before: '2026-06-02T02:53:51Z'
+      generated_at_after: '2026-06-02T02:53:51Z'
+      preview_before: Learn how to capture high-quality frame datasets from Unreal
+        Engine 5.5 gameplay using the Neural Graphics Data Capture plugin on Windows.
+        You will install and enable the plugin in a C++ Unreal projec...
+      preview_after: Learn how to capture high-quality frame datasets from Unreal
+        Engine 5.5 gameplay using the Neural Graphics Data Capture plugin on Windows.
+        You will install and enable the plugin in a C++ Unreal projec...
+      preview_generated: Use the Neural Graphics Data Capture plugin to generate structured
+        frame datasets from Unreal Engine 5.5 gameplay on Windows. You will clone
+        the plugin from GitHub, add it to a C++ Unreal project, bui...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
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-        added_questions: []
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+        added_questions:
+        - What do I need before running the capture workflow?
+        - How do I install and enable the Neural Graphics Data Capture plugin in my
+          project?
+        - How do I set up hotkeys to start and stop capture?
+        - Where can I configure capture parameters and output locations?
+        - What should I check if my captured frame dimensions look wrong?
+        removed_questions:
+        - What are the prerequisites and supported versions?
+        - How do I install and enable the Neural Graphics Data Capture plugin?
+        - How do I configure capture controls in my level?
+        - Which play mode should I use to record frames?
+        - Where are captured datasets saved and how do I verify success?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the capture workflow?
+        - How do I install and enable the Neural Graphics Data Capture plugin in my
+          project?
+        - How do I set up hotkeys to start and stop capture?
+        - Where can I configure capture parameters and output locations?
+        - What should I check if my captured frame dimensions look wrong?
+        removed_questions:
+        - What are the prerequisites and supported versions?
+        - How do I install and enable the Neural Graphics Data Capture plugin?
+        - How do I configure capture controls in my level?
+        - Which play mode should I use to record frames?
+        - Where are captured datasets saved and how do I verify success?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md
-    status: unchanged
-    changed_on_disk: false
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-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
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+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
       missing_before: false
@@ -208589,51 +14959,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:56Z'
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-      preview_before: Learn how to configure ML Extensions for Vulkan emulation and enable Neural Super
-        Sampling (NSS) in Unreal Engine for real-time upscaling. It is designed for developers experimenting
-        with neural graph...
-      preview_after: Learn how to configure ML Extensions for Vulkan emulation and enable Neural Super
-        Sampling (NSS) in Unreal Engine for real-time upscaling. It is designed for developers experimenting
-        with neural graph...
-      preview_generated: Learn how to configure ML Extensions for Vulkan emulation and enable Neural Super
-        Sampling (NSS) in Unreal Engine for real-time upscaling. It is designed for developers experimenting
-        with neural graph...
+      generated_at_before: '2026-06-02T02:54:18Z'
+      generated_at_after: '2026-06-02T02:54:18Z'
+      preview_before: This Learning Path shows how to configure ML Extensions for
+        Vulkan emulation and enable Arm Neural Super Sampling (NSS) in Unreal Engine
+        on Windows 11. You will install the Vulkan SDK and activate the...
+      preview_after: This Learning Path shows how to configure ML Extensions for Vulkan
+        emulation and enable Arm Neural Super Sampling (NSS) in Unreal Engine on Windows
+        11. You will install the Vulkan SDK and activate the...
+      preview_generated: This Learning Path shows how to configure ML Extensions for
+        Vulkan emulation and enable Arm Neural Super Sampling (NSS) in Unreal Engine
+        for real-time upscaling on Windows. You install the Vulkan SDK ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - Which Unreal Engine versions should I use for this path?
+        - Do I need the Vulkan SDK, and how are the ML emulation layers enabled?
+        - Where do I get the NSS plugin and what does it include?
+        - How do I verify that NSS is active and view its output in Unreal?
+        - When should I use RenderDoc during this workflow?
+        removed_questions:
+        - What are the prerequisites and supported versions?
+        - Do I need a neural accelerator or specific GPU to run NSS in this path?
+        - What components do I install and configure for Vulkan ML emulation?
+        - How do I get the NSS plugin and set up an example project?
+        - How do I verify NSS is running and inspect its output?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - Which Unreal Engine versions should I use for this path?
+        - Do I need the Vulkan SDK, and how are the ML emulation layers enabled?
+        - Where do I get the NSS plugin and what does it include?
+        - How do I verify that NSS is active and view its output in Unreal?
+        - When should I use RenderDoc during this workflow?
+        removed_questions:
+        - What are the prerequisites and supported versions?
+        - Do I need a neural accelerator or specific GPU to run NSS in this path?
+        - What components do I install and configure for Vulkan ML emulation?
+        - How do I get the NSS plugin and set up an example project?
+        - How do I verify NSS is running and inspect its output?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md
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-    changed_on_disk: false
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-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
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+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
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+    rerun_flags_reset:
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+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
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@@ -208643,51 +15038,82 @@ history:
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-      preview_before: Learn how to build, optimize, and deploy machine learning models using ONNX Runtime
-        on Arm64 platforms, including Raspberry Pi, cloud instances, and Android devices. It is designed
-        for developers who ...
-      preview_after: Learn how to build, optimize, and deploy machine learning models using ONNX Runtime
-        on Arm64 platforms, including Raspberry Pi, cloud instances, and Android devices. It is designed
-        for developers who ...
-      preview_generated: Learn how to build, optimize, and deploy machine learning models using ONNX Runtime
-        on Arm64 platforms, including Raspberry Pi, cloud instances, and Android devices. It is designed
-        for developers who ...
+      generated_at_before: '2026-06-02T02:54:49Z'
+      generated_at_after: '2026-06-02T02:54:49Z'
+      preview_before: This advanced Learning Path shows how to build, optimize, and
+        deploy ONNX models for Arm64 platforms using ONNX Runtime. You will create
+        a small digit-recognition CNN in Python, export it to ONNX, val...
+      preview_after: This advanced Learning Path shows how to build, optimize, and
+        deploy ONNX models for Arm64 platforms using ONNX Runtime. You will create
+        a small digit-recognition CNN in Python, export it to ONNX, val...
+      preview_generated: Follow an end-to-end workflow to build, optimize, and deploy
+        an ONNX-based ML model on Arm64 platforms. You will install Python, ONNX,
+        and ONNX Runtime; verify execution providers; generate a syntheti...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
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+      generated_at_before: '2026-06-02T02:54:49Z'
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       before_count: 5
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       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - Which Python version should I install for this Learning Path?
+        - Which Arm64 hardware can I use, and can I develop on macOS or Windows on
+          Arm?
+        - How do I know ONNX Runtime is using the expected execution provider on my
+          device?
+        - Do I need to prepare a dataset before training the digit recognizer?
+        - What artifacts should I expect after training and evaluation, and when is
+          the model ready for Android deployment?
+        removed_questions:
+        - Which Python versions are supported for this Learning Path?
+        - What hardware and operating systems can I use?
+        - What will I build and deploy by completing the steps?
+        - Do I need Android Studio, and when is it required?
+        - How do I verify that my ONNX model and runtime setup are correct?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - Which Python version should I install for this Learning Path?
+        - Which Arm64 hardware can I use, and can I develop on macOS or Windows on
+          Arm?
+        - How do I know ONNX Runtime is using the expected execution provider on my
+          device?
+        - Do I need to prepare a dataset before training the digit recognizer?
+        - What artifacts should I expect after training and evaluation, and when is
+          the model ready for Android deployment?
+        removed_questions:
+        - Which Python versions are supported for this Learning Path?
+        - What hardware and operating systems can I use?
+        - What will I build and deploy by completing the steps?
+        - Do I need Android Studio, and when is it required?
+        - How do I verify that my ONNX model and runtime setup are correct?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
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+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
       missing_before: false
@@ -208697,51 +15123,74 @@ history:
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-      preview_before: Learn how to optimize vertex representations and analyze Vertex Memory Efficiency
-        using Arm Frame Advisor for improved GPU performance on Android. It is designed for Android graphics
-        application devel...
-      preview_after: Learn how to optimize vertex representations and analyze Vertex Memory Efficiency
-        using Arm Frame Advisor for improved GPU performance on Android. It is designed for Android graphics
-        application devel...
-      preview_generated: Learn how to optimize vertex representations and analyze Vertex Memory Efficiency
-        using Arm Frame Advisor for improved GPU performance on Android. It is designed for Android graphics
-        application devel...
+      generated_at_before: '2026-06-02T02:55:06Z'
+      generated_at_after: '2026-06-02T02:55:06Z'
+      preview_before: This advanced Learning Path guides Android graphics developers
+        through diagnosing and improving vertex data efficiency on Arm GPUs. Using
+        Arm Frame Advisor (part of Arm Performance Studio), you will p...
+      preview_after: This advanced Learning Path guides Android graphics developers
+        through diagnosing and improving vertex data efficiency on Arm GPUs. Using
+        Arm Frame Advisor (part of Arm Performance Studio), you will p...
+      preview_generated: This advanced Learning Path shows Android graphics developers
+        how to diagnose and address poor Vertex Memory Efficiency (VME) on Arm GPUs
+        using Arm Frame Advisor (part of Arm Performance Studio). You ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
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-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-06-02T02:55:06Z'
+      generated_at_after: '2026-06-03T00:02:15Z'
       before_count: 5
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       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - How do I know if Vertex Memory Efficiency is low in my frame?
+        - What should I check if the shadow map draw calls report low VME?
+        - What do I need before running the steps in this path?
+        - Which platforms and GPUs does this apply to?
+        removed_questions:
+        - What skills or knowledge are expected before starting?
+        - Which platforms and GPUs does this Learning Path target?
+        - What tool and metric will I use to analyze vertex efficiency?
+        - Does this path cover installing or configuring Arm Frame Advisor?
+        updated_questions:
+        - How do I validate that my changes improved vertex efficiency?
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - How do I know if Vertex Memory Efficiency is low in my frame?
+        - What should I check if the shadow map draw calls report low VME?
+        - What do I need before running the steps in this path?
+        - Which platforms and GPUs does this apply to?
+        removed_questions:
+        - What skills or knowledge are expected before starting?
+        - Which platforms and GPUs does this Learning Path target?
+        - What tool and metric will I use to analyze vertex efficiency?
+        - Does this path cover installing or configuring Arm Frame Advisor?
+        updated_questions:
+        - How do I validate that my changes improved vertex efficiency?
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
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+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
       missing_before: false
@@ -208751,51 +15200,76 @@ history:
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-      preview_before: Learn how to build llama.cpp with KleidiAI and SME2 support to profile and accelerate
-        LLM inference performance on Android devices. It is designed for software developers, performance
-        engineers, and A...
-      preview_after: Learn how to build llama.cpp with KleidiAI and SME2 support to profile and accelerate
-        LLM inference performance on Android devices. It is designed for software developers, performance
-        engineers, and A...
-      preview_generated: Learn how to build llama.cpp with KleidiAI and SME2 support to profile and accelerate
-        LLM inference performance on Android devices. It is designed for software developers, performance
-        engineers, and A...
+      generated_at_before: '2026-06-02T02:55:26Z'
+      generated_at_after: '2026-06-02T02:55:26Z'
+      preview_before: This advanced Learning Path shows you how to build a statically
+        linked llama.cpp (llama-cli) with Arm KleidiAI and Scalable Matrix Extension
+        2 (SME2) to measure LLM inference performance on Android. Y...
+      preview_after: This advanced Learning Path shows you how to build a statically
+        linked llama.cpp (llama-cli) with Arm KleidiAI and Scalable Matrix Extension
+        2 (SME2) to measure LLM inference performance on Android. Y...
+      preview_generated: "This Learning Path shows how to build, run, and measure\
+        \ LLM inference on an SME2\u2011capable Android device using llama.cpp accelerated\
+        \ by Arm KleidiAI. Working from a Linux host, you set up the Linux\u2011hos..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
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+      generated_at_before: '2026-06-02T02:55:26Z'
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       before_count: 5
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       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before building and running this path?
+        - Which compiler and target should I use to enable SME2 in llama.cpp?
+        - How do I put the model and binary onto the Android device?
+        - How do I verify that SME2 microkernels are being used during inference?
+        - What should I check if SME2 is not selected at runtime?
+        removed_questions:
+        - What hardware and software do I need before starting?
+        - Can I build from macOS or Windows?
+        - What exactly will I build and with which toolchain?
+        - Which model file is used and how do I run it on the device?
+        - How do I verify SME2 acceleration and measure results?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before building and running this path?
+        - Which compiler and target should I use to enable SME2 in llama.cpp?
+        - How do I put the model and binary onto the Android device?
+        - How do I verify that SME2 microkernels are being used during inference?
+        - What should I check if SME2 is not selected at runtime?
+        removed_questions:
+        - What hardware and software do I need before starting?
+        - Can I build from macOS or Windows?
+        - What exactly will I build and with which toolchain?
+        - Which model file is used and how do I run it on the device?
+        - How do I verify SME2 acceleration and measure results?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md
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@@ -208805,51 +15279,76 @@ history:
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-        performance ...
-      preview_generated: Learn how to build ONNX Runtime with KleidiAI and SME2 support for Android and
-        profile ONNX model performance to compare acceleration improvements. It is designed for software
-        developers, performance ...
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+      preview_before: This Learning Path shows how to build ONNX Runtime for Android
+        with KleidiAI micro-kernels and Arm Scalable Matrix Extension 2 (SME2) support,
+        then profile model performance to assess acceleration. Yo...
+      preview_after: This Learning Path shows how to build ONNX Runtime for Android
+        with KleidiAI micro-kernels and Arm Scalable Matrix Extension 2 (SME2) support,
+        then profile model performance to assess acceleration. Yo...
+      preview_generated: This advanced Learning Path shows how to build ONNX Runtime
+        (v1.23.2) for Android with KleidiAI and Arm Scalable Matrix Extension 2 (SME2)
+        support, then profile ONNX model performance to compare stand...
     faqs:
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+        - Which ONNX Runtime version is used and how do I check it out?
+        - How does ONNX Runtime select KleidiAI SME2 kernels at runtime?
+        - How do I prepare the example model on the device for profiling?
+        - "What should I check if I don\u2019t observe SME2-optimized execution?"
+        removed_questions:
+        - What environment and prerequisites are required?
+        - Which toolchain versions are used in the build?
+        - How does ONNX Runtime use KleidiAI with SME2?
+        - What model and benchmarking tool are used for profiling?
+        - How do I know the SME2-optimized path is active and working?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
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+        - What do I need before building ONNX Runtime for Android in this path?
+        - Which ONNX Runtime version is used and how do I check it out?
+        - How does ONNX Runtime select KleidiAI SME2 kernels at runtime?
+        - How do I prepare the example model on the device for profiling?
+        - "What should I check if I don\u2019t observe SME2-optimized execution?"
+        removed_questions:
+        - What environment and prerequisites are required?
+        - Which toolchain versions are used in the build?
+        - How does ONNX Runtime use KleidiAI with SME2?
+        - What model and benchmarking tool are used for profiling?
+        - How do I know the SME2-optimized path is active and working?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md
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-    rerun_flags_reset: []
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     summary:
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@@ -208859,51 +15358,78 @@ history:
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-      preview_before: Learn how to profile ML model execution times and application performance on Arm
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-        developers who wa...
-      preview_after: Learn how to profile ML model execution times and application performance on Arm
-        Android devices using Arm Performance Studio and Android Studio Profiler. It is designed for software
-        developers who wa...
-      preview_generated: Learn how to profile ML model execution times and application performance on
-        Arm Android devices using Arm Performance Studio and Android Studio Profiler. It is designed for
-        software developers who wa...
+      generated_at_before: '2026-06-02T02:56:03Z'
+      generated_at_after: '2026-06-02T02:56:03Z'
+      preview_before: Learn how to profile ML model execution times and end-to-end
+        application behavior on Arm-powered Android devices using Arm Performance
+        Studio (Streamline), Android Studio Profiler, and framework-level...
+      preview_after: Learn how to profile ML model execution times and end-to-end
+        application behavior on Arm-powered Android devices using Arm Performance
+        Studio (Streamline), Android Studio Profiler, and framework-level...
+      preview_generated: This introductory Learning Path shows how to profile ML model
+        execution times and Android application performance on Arm-powered mobile
+        devices. You will use Arm Performance Studio with Streamline to ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
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       before_count: 5
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       generated_count: 5
       change_details:
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         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the profiling steps?
+        - How do I set up Android Studio Profiler to examine memory?
+        - Which profiler should I use for system behavior versus memory analysis?
+        - What output should I expect from Arm NN ExecuteNetwork when profiling a
+          LiteRT model?
+        - Which performance metrics does Streamline provide during sampling?
+        removed_questions:
+        - What do I need before starting?
+        - Which profilers are used for the application, and what data do they capture?
+        - How do I profile memory usage of my Android ML app?
+        - How can I profile per-layer execution inside the neural network?
+        - What outputs should I expect to validate that profiling worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
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-        updated_questions: []
+        added_questions:
+        - What do I need before running the profiling steps?
+        - How do I set up Android Studio Profiler to examine memory?
+        - Which profiler should I use for system behavior versus memory analysis?
+        - What output should I expect from Arm NN ExecuteNetwork when profiling a
+          LiteRT model?
+        - Which performance metrics does Streamline provide during sampling?
+        removed_questions:
+        - What do I need before starting?
+        - Which profilers are used for the application, and what data do they capture?
+        - How do I profile memory usage of my Android ML app?
+        - How can I profile per-layer execution inside the neural network?
+        - What outputs should I expect to validate that profiling worked?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md
-    status: unchanged
-    changed_on_disk: false
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-    rerun_flags_reset: []
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     summary:
       action: unchanged
       missing_before: false
@@ -208913,51 +15439,76 @@ history:
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-      preview_before: Learn how to deploy Unity applications to Android, profile code running on Arm devices,
-        and analyze performance data for optimization. It is designed for Unity developers wanting to
-        analyze the perfor...
-      preview_after: Learn how to deploy Unity applications to Android, profile code running on Arm devices,
-        and analyze performance data for optimization. It is designed for Unity developers wanting to
-        analyze the perfor...
-      preview_generated: Learn how to deploy Unity applications to Android, profile code running on Arm
-        devices, and analyze performance data for optimization. It is designed for Unity developers wanting
-        to analyze the perfor...
+      generated_at_before: '2026-06-02T02:56:23Z'
+      generated_at_after: '2026-06-02T02:56:23Z'
+      preview_before: This introductory Learning Path guides Unity developers through
+        deploying a sample app to an Android device, collecting frame-level performance
+        data with the Unity Profiler, and comparing captures in ...
+      preview_after: This introductory Learning Path guides Unity developers through
+        deploying a sample app to an Android device, collecting frame-level performance
+        data with the Unity Profiler, and comparing captures in ...
+      preview_generated: This introductory path shows how to profile a Unity sample
+        app on an Android device and compare performance across code variants. You
+        will create a blank Unity project using the 3D (URP) Core template...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
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-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-06-02T02:56:23Z'
+      generated_at_after: '2026-06-03T00:04:52Z'
       before_count: 5
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       generated_count: 5
       change_details:
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         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which Unity project template should I use when creating the project?
+        - How are the Profiler and Profile Analyzer used differently in this path?
+        - Which sample modes should I run, and what do they represent?
+        - How should I run the sample on the device during data collection?
+        removed_questions:
+        - What hardware and software do I need before starting?
+        - Do I need to complete another Learning Path first?
+        - How should I set up the Unity project used in this path?
+        - Which tools are used to gather and analyze performance data?
+        - How do I validate that the profiling workflow is working?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which Unity project template should I use when creating the project?
+        - How are the Profiler and Profile Analyzer used differently in this path?
+        - Which sample modes should I run, and what do they represent?
+        - How should I run the sample on the device during data collection?
+        removed_questions:
+        - What hardware and software do I need before starting?
+        - Do I need to complete another Learning Path first?
+        - How should I set up the Unity project used in this path?
+        - Which tools are used to gather and analyze performance data?
+        - How do I validate that the profiling workflow is working?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md
-    status: unchanged
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-    rerun_flags_reset: []
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+    rerun_flags_reset:
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     summary:
       action: unchanged
       missing_before: false
@@ -208967,51 +15518,76 @@ history:
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-      preview_before: Learn how to apply post-training quantization to PyTorch models using TorchAO and
-        export INT8 models to .vgf format with the ExecuTorch Arm backend. It is designed for ML developers
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-      preview_after: Learn how to apply post-training quantization to PyTorch models using TorchAO and
-        export INT8 models to .vgf format with the ExecuTorch Arm backend. It is designed for ML developers
-        who want to reduce...
-      preview_generated: Learn how to apply post-training quantization to PyTorch models using TorchAO
-        and export INT8 models to .vgf format with the ExecuTorch Arm backend. It is designed for ML developers
-        who want to reduce...
+      generated_at_before: '2026-06-02T02:56:51Z'
+      generated_at_after: '2026-06-02T02:56:51Z'
+      preview_before: This advanced Learning Path guides ML developers through applying
+        post-training quantization (PTQ) and quantization-aware training (QAT) to
+        PyTorch models using TorchAO PT2E APIs, then exporting INT8 ...
+      preview_after: This advanced Learning Path guides ML developers through applying
+        post-training quantization (PTQ) and quantization-aware training (QAT) to
+        PyTorch models using TorchAO PT2E APIs, then exporting INT8 ...
+      preview_generated: This advanced Learning Path shows how to apply post-training
+        quantization (PTQ) and quantization-aware training (QAT) to PyTorch models
+        using TorchAO, then export INT8 models to the .vgf format with t...
     faqs:
-      action: unchanged
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       missing_before: false
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       before_count: 5
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       generated_count: 5
       change_details:
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         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Should I start with PTQ or QAT in this workflow?
+        - Where will the .vgf files be generated, and what result should I expect?
+        - How do I inspect the exported graph and what should I look for?
+        - Can I apply this quantization and export flow to my own model?
+        removed_questions:
+        - What are the prerequisites and supported operating systems?
+        - What will I run and what artifacts are produced?
+        - How do I validate that the export worked?
+        - How should I choose between PTQ and QAT in this workflow?
+        - Can I reuse my existing environment or apply this to my own model?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Should I start with PTQ or QAT in this workflow?
+        - Where will the .vgf files be generated, and what result should I expect?
+        - How do I inspect the exported graph and what should I look for?
+        - Can I apply this quantization and export flow to my own model?
+        removed_questions:
+        - What are the prerequisites and supported operating systems?
+        - What will I run and what artifacts are produced?
+        - How do I validate that the export worked?
+        - How should I choose between PTQ and QAT in this workflow?
+        - Can I reuse my existing environment or apply this to my own model?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
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-    template_version_before: summary-faq-v1
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+    status: updated
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+    - rerun_flags_reset
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     summary:
       action: unchanged
       missing_before: false
@@ -209021,51 +15597,76 @@ history:
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-      preview_before: Learn how to use the Vulkan ray tracing API to implement realistic shadows, reflections,
-        and refractions in Android applications. It is designed for Vulkan developers who are familiar
-        with rendering a...
-      preview_after: Learn how to use the Vulkan ray tracing API to implement realistic shadows, reflections,
-        and refractions in Android applications. It is designed for Vulkan developers who are familiar
-        with rendering a...
-      preview_generated: Learn how to use the Vulkan ray tracing API to implement realistic shadows, reflections,
-        and refractions in Android applications. It is designed for Vulkan developers who are familiar
-        with rendering a...
+      generated_at_before: '2026-06-02T02:57:13Z'
+      generated_at_after: '2026-06-02T02:57:13Z'
+      preview_before: Learn how to add ray tracing to Android renderers using the
+        Vulkan ray tracing API. This Learning Path explains core concepts, compares
+        the ray tracing pipeline and ray query approaches, shows how to ...
+      preview_after: Learn how to add ray tracing to Android renderers using the Vulkan
+        ray tracing API. This Learning Path explains core concepts, compares the ray
+        tracing pipeline and ray query approaches, shows how to ...
+      preview_generated: This Learning Path shows how to add basic ray-traced shadows,
+        reflections, and refractions to an Android Vulkan renderer using the Vulkan
+        ray tracing API. You will enable the necessary ray tracing fea...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
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       before_count: 5
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       generated_count: 5
       change_details:
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-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I know if my Android device or GPU supports Vulkan ray tracing?
+        - Which Vulkan approach should I use to launch rays?
+        - What acceleration structures will I build for ray tracing?
+        - Are bindless materials required for the examples?
+        removed_questions:
+        - What Android hardware do I need to follow this path?
+        - Do I need prior Vulkan experience or an existing renderer?
+        - Which Vulkan features or extensions are used in this Learning Path?
+        - Can I prototype on a PC and then run on Android?
+        - How do I know the implementation worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I know if my Android device or GPU supports Vulkan ray tracing?
+        - Which Vulkan approach should I use to launch rays?
+        - What acceleration structures will I build for ray tracing?
+        - Are bindless materials required for the examples?
+        removed_questions:
+        - What Android hardware do I need to follow this path?
+        - Do I need prior Vulkan experience or an existing renderer?
+        - Which Vulkan features or extensions are used in this Learning Path?
+        - Can I prototype on a PC and then run on Android?
+        - How do I know the implementation worked?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
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+    managed_block_updated: true
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+    rerun_flags_reset:
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+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
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     summary:
       action: unchanged
       missing_before: false
@@ -209075,51 +15676,78 @@ history:
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
-      preview_before: Learn how to use Frame Advisor's Render Graph view to identify and resolve graphics
-        performance issues in Android applications. It is designed for Mobile application developers who
-        wish to improve gra...
-      preview_after: Learn how to use Frame Advisor's Render Graph view to identify and resolve graphics
-        performance issues in Android applications. It is designed for Mobile application developers who
-        wish to improve gra...
-      preview_generated: Learn how to use Frame Advisor's Render Graph view to identify and resolve graphics
-        performance issues in Android applications. It is designed for Mobile application developers who
-        wish to improve gra...
+      generated_at_before: '2026-06-02T02:57:43Z'
+      generated_at_after: '2026-06-02T02:57:43Z'
+      preview_before: "Learn to analyze Android graphics workloads using Frame Advisor\u2019\
+        s Render Graph view in Arm Performance Studio. You will capture GPU data with\
+        \ Streamline Performance Analyzer, then inspect the directed..."
+      preview_after: "Learn to analyze Android graphics workloads using Frame Advisor\u2019\
+        s Render Graph view in Arm Performance Studio. You will capture GPU data with\
+        \ Streamline Performance Analyzer, then inspect the directed..."
+      preview_generated: "This Learning Path shows how to use Frame Advisor\u2019\
+        s Render Graph view in Arm Performance Studio to visualize and diagnose GPU\
+        \ performance issues in Android applications. You will generate a render gra..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
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       drift_detected: false
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-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-06-02T02:57:43Z'
+      generated_at_after: '2026-06-03T00:06:38Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which Streamline capture settings should I use to record GPU data for the
+          render graph?
+        - What result should I expect from the Render Graph view?
+        - What should I check if the graph shows resources that are never consumed?
+        - How do I decide whether an execution node can be removed?
+        removed_questions:
+        - What do I need installed before starting?
+        - Do I need an Android device to follow this path?
+        - Which operating systems and graphics APIs does this path cover?
+        - How do I generate a render graph for my application?
+        - What kinds of issues can I identify and what actions might I take?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which Streamline capture settings should I use to record GPU data for the
+          render graph?
+        - What result should I expect from the Render Graph view?
+        - What should I check if the graph shows resources that are never consumed?
+        - How do I decide whether an execution node can be removed?
+        removed_questions:
+        - What do I need installed before starting?
+        - Do I need an Android device to follow this path?
+        - Which operating systems and graphics APIs does this path cover?
+        - How do I generate a render graph for my application?
+        - What kinds of issues can I identify and what actions might I take?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md
-    status: unchanged
-    changed_on_disk: false
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-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -209129,51 +15757,78 @@ history:
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
-      preview_before: Learn how to convert and deploy the Stable Audio Open Small text-to-audio model
-        to LiteRT format for audio generation on Android devices and macOS. It is designed for developers
-        looking to deploy the ...
-      preview_after: Learn how to convert and deploy the Stable Audio Open Small text-to-audio model to
-        LiteRT format for audio generation on Android devices and macOS. It is designed for developers
-        looking to deploy the ...
-      preview_generated: Learn how to convert and deploy the Stable Audio Open Small text-to-audio model
-        to LiteRT format for audio generation on Android devices and macOS. It is designed for developers
-        looking to deploy the ...
+      generated_at_before: '2026-06-02T02:58:12Z'
+      generated_at_after: '2026-06-02T02:58:12Z'
+      preview_before: This Learning Path shows how to take the Stable Audio Open Small
+        text-to-audio model from Hugging Face, convert its submodules to LiteRT (.tflite),
+        build LiteRT from the TensorFlow repository using Ba...
+      preview_after: This Learning Path shows how to take the Stable Audio Open Small
+        text-to-audio model from Hugging Face, convert its submodules to LiteRT (.tflite),
+        build LiteRT from the TensorFlow repository using Ba...
+      preview_generated: This Learning Path shows how to convert and deploy the Stable
+        Audio Open Small text-to-audio model to LiteRT (.tflite) and generate audio
+        on Arm-based Android devices. You will set up a Linux or macOS...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 388cab0dcb284c29fe2ad82f2b2934d9243c6356b2885d26db0a97c1a1d4d393
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-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-06-02T02:58:12Z'
+      generated_at_after: '2026-06-03T00:07:29Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which model files do I download from Hugging Face, and how do I verify them?
+        - Which tool versions are required for the environment setup?
+        - How are the model components converted to LiteRT format?
+        - What result should I expect when running the Android app, and how do I configure
+          the build?
+        removed_questions:
+        - What development environment and hardware do I need?
+        - Which software versions are required before I start?
+        - How do I obtain the Stable Audio Open Small model files?
+        - What will I build and what is the expected output?
+        - Does this Learning Path include macOS deployment steps?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which model files do I download from Hugging Face, and how do I verify them?
+        - Which tool versions are required for the environment setup?
+        - How are the model components converted to LiteRT format?
+        - What result should I expect when running the Android app, and how do I configure
+          the build?
+        removed_questions:
+        - What development environment and hardware do I need?
+        - Which software versions are required before I start?
+        - How do I obtain the Stable Audio Open Small model files?
+        - What will I build and what is the expected output?
+        - Does this Learning Path include macOS deployment steps?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -209183,51 +15838,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
-      preview_before: Learn how to convert the Stable Audio Open Small model to ExecuTorch format and
-        build an audio generation application for Android or macOS. It is designed for developers who
-        want to deploy the Stable ...
-      preview_after: Learn how to convert the Stable Audio Open Small model to ExecuTorch format and build
-        an audio generation application for Android or macOS. It is designed for developers who want to
-        deploy the Stable ...
-      preview_generated: Learn how to convert the Stable Audio Open Small model to ExecuTorch format and
-        build an audio generation application for Android or macOS. It is designed for developers who
-        want to deploy the Stable ...
+      generated_at_before: '2026-06-02T02:58:48Z'
+      generated_at_after: '2026-06-02T02:58:48Z'
+      preview_before: This Learning Path shows how to download the Stable Audio Open
+        Small model from Hugging Face, convert it to ExecuTorch (.pte), and build
+        an audio generation application targeting Arm CPUs. You will se...
+      preview_after: This Learning Path shows how to download the Stable Audio Open
+        Small model from Hugging Face, convert it to ExecuTorch (.pte), and build
+        an audio generation application targeting Arm CPUs. You will se...
+      preview_generated: Follow this introductory path to download the Stable Audio
+        Open Small model from Hugging Face, convert it to ExecuTorch (.pte), and build
+        a text-to-audio generation application targeting Arm CPUs on A...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-06-02T02:58:48Z'
+      generated_at_after: '2026-06-03T00:08:09Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the conversion and build steps?
+        - Which ExecuTorch installation option should I use?
+        - How should I set up the Python environment for conversion?
+        - How do I know the model conversion to ExecuTorch succeeded?
+        - What should I check if the Android build or run fails?
+        removed_questions:
+        - What development machine and accounts do I need before starting?
+        - What are the Android device requirements?
+        - Which software tools and versions are expected?
+        - How is the Stable Audio Open Small model obtained and prepared?
+        - How do I verify that everything worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the conversion and build steps?
+        - Which ExecuTorch installation option should I use?
+        - How should I set up the Python environment for conversion?
+        - How do I know the model conversion to ExecuTorch succeeded?
+        - What should I check if the Android build or run fails?
+        removed_questions:
+        - What development machine and accounts do I need before starting?
+        - What are the Android device requirements?
+        - Which software tools and versions are expected?
+        - How is the Stable Audio Open Small model obtained and prepared?
+        - How do I verify that everything worked?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -209237,51 +15917,76 @@ history:
       source_hash_before: dea8c3e15cca703f075c8fa9ba3daf873a90e3eb2ee9975dd05b973dad744b54
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
-      preview_before: Learn how to build and install a Unity game on an Orange Pi 5 single-board computer
-        running Droid OS. It is designed for software developers who want to build and run a Unity game
-        on an Arm-based sing...
-      preview_after: Learn how to build and install a Unity game on an Orange Pi 5 single-board computer
-        running Droid OS. It is designed for software developers who want to build and run a Unity game
-        on an Arm-based sing...
-      preview_generated: Learn how to build and install a Unity game on an Orange Pi 5 single-board computer
-        running Droid OS. It is designed for software developers who want to build and run a Unity game
-        on an Arm-based sing...
+      generated_at_before: '2026-06-02T02:59:30Z'
+      generated_at_after: '2026-06-02T02:59:30Z'
+      preview_before: This introductory Learning Path shows how to install Droid OS
+        on an Arm-based Orange Pi 5, build a Unity game for Android, and deploy the
+        resulting APK to the board. You will use a Windows PC to obtai...
+      preview_after: This introductory Learning Path shows how to install Droid OS
+        on an Arm-based Orange Pi 5, build a Unity game for Android, and deploy the
+        resulting APK to the board. You will use a Windows PC to obtai...
+      preview_generated: This Learning Path shows how to build and install a Unity
+        game on an Arm-based Orange Pi 5 running Droid OS. You will prepare a bootable
+        microSD card with the Droid OS image using SDDiskTool on a Wind...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: dea8c3e15cca703f075c8fa9ba3daf873a90e3eb2ee9975dd05b973dad744b54
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-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-06-02T02:59:30Z'
+      generated_at_after: '2026-06-03T00:08:53Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - Do I need a Windows PC to flash Droid OS to the microSD card?
+        - Where do I download the correct Droid OS image for Orange Pi 5?
+        - Which Unity components are required to build for the Orange Pi 5?
+        - What microSD card should I use for Droid OS on the Orange Pi 5?
+        - How can I move my Unity APK onto the Orange Pi 5?
+        removed_questions:
+        - What hardware and software do I need before starting?
+        - Where do I download the Droid OS image and imaging tool?
+        - Which Unity settings are required to build for the Orange Pi 5?
+        - How do I transfer the APK to the Orange Pi 5 running Droid OS?
+        - How do I know the steps worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - Do I need a Windows PC to flash Droid OS to the microSD card?
+        - Where do I download the correct Droid OS image for Orange Pi 5?
+        - Which Unity components are required to build for the Orange Pi 5?
+        - What microSD card should I use for Droid OS on the Orange Pi 5?
+        - How can I move my Unity APK onto the Orange Pi 5?
+        removed_questions:
+        - What hardware and software do I need before starting?
+        - Where do I download the Droid OS image and imaging tool?
+        - Which Unity settings are required to build for the Orange Pi 5?
+        - How do I transfer the APK to the Orange Pi 5 running Droid OS?
+        - How do I know the steps worked?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -209291,51 +15996,78 @@ history:
       source_hash_before: 4a990e957658b03bac9306d5f8e6955734cc1d3b063f43c38ac525ed1bf95b79
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
-      preview_before: Learn how to install Arm integration packages in Unity to view GPU metrics in Unity
-        Profiler and annotate games with markers for Arm Performance Studio. It is designed for Unity
-        developers who are tar...
-      preview_after: Learn how to install Arm integration packages in Unity to view GPU metrics in Unity
-        Profiler and annotate games with markers for Arm Performance Studio. It is designed for Unity
-        developers who are tar...
-      preview_generated: Learn how to install Arm integration packages in Unity to view GPU metrics in
-        Unity Profiler and annotate games with markers for Arm Performance Studio. It is designed for
-        Unity developers who are tar...
+      generated_at_before: '2026-06-02T02:59:56Z'
+      generated_at_after: '2026-06-02T02:59:56Z'
+      preview_before: Learn how to install Arm integration packages in Unity to profile
+        games targeting Android devices with Arm CPUs and GPUs. In about 20 minutes,
+        you add the System Metrics Mali package to enable Arm GPU...
+      preview_after: Learn how to install Arm integration packages in Unity to profile
+        games targeting Android devices with Arm CPUs and GPUs. In about 20 minutes,
+        you add the System Metrics Mali package to enable Arm GPU...
+      preview_generated: This Learning Path shows how to install Arm integration packages
+        in Unity so you can capture Arm GPU hardware counters in the Unity Profiler
+        and add annotations that provide context in Arm Performance...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
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+        - What changes in the Unity Profiler after installing the GPU metrics package?
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+        - What does the Arm Performance Studio Unity integration provide?
+        - What prerequisites and platforms are assumed for this Learning Path?
+        updated_questions:
+        - How do I install the System Metrics Mali package in Unity?
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md
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+        intrinsics in Unity C# scripts for Android, compiled with the Unity Burst
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+      preview_generated: Learn to apply Arm Neon intrinsics in Unity C# scripts on
+        Android using the Unity Burst compiler, then measure and compare results with
+        Unity Profiler and Analyzer. You will set up Unity with Android ...
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+        - How do I enable the Burst package in my Unity project?
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+        - How do I enable the Burst compiler in my Unity project?
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+          modes?
+        - How do I validate that the performance comparison worked?
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+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md
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+      preview_before: "This Learning Path shows how to use Unity\u2019s Machine Learning\
+        \ Agents toolkit inside a Unity project that can be deployed to Arm-powered\
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+      preview_after: "This Learning Path shows how to use Unity\u2019s Machine Learning\
+        \ Agents toolkit inside a Unity project that can be deployed to Arm-powered\
+        \ Android devices. You will install Unity (via Unity Hub), open the..."
+      preview_generated: "This Learning Path guides you through integrating Unity\u2019\
+        s Machine Learning Agents (ML-Agents) into a game that targets Arm-powered\
+        \ Android devices. Using the Dr Arm sample project, you configure scene..."
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+        - Which scene should I open in the Dr Arm project to follow the steps?
+        - What Android device requirements should I check before proceeding?
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+        - Which tools should I install to get started?
+        - Do I need Python installed before starting?
+        - Does this Learning Path include Android deployment or profiling steps?
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+        - Which scene should I open in the Dr Arm project to follow the steps?
+        - What Android device requirements should I check before proceeding?
+        - Does this Learning Path include Android deployment and profiling steps?
+        removed_questions:
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+        - Which tools should I install to get started?
+        - Do I need Python installed before starting?
+        - Does this Learning Path include Android deployment or profiling steps?
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+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md
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+        (ViT) inference on Android using the Mobile Neural Network (MNN) framework
+        and KleidiAI micro-kernels. You will download a Vision LLM f...
+      preview_generated: This Learning Path guides you through running Vision LLM/ViT
+        inference on Android using the Mobile Neural Network (MNN) framework with
+        KleidiAI micro-kernels. You will download a vision model from Hug...
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+        - How is the model prepared for use with MNN?
+        - How do I run the benchmark and what input image should I use?
+        removed_questions:
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+        - Which model and framework are used in this path?
+        - How do I build the Android demo application?
+        - Is there a command-line demo, and how do I provide input images?
+        - How do I verify that KleidiAI is used and compare performance?
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+        - Where do I get the source code for the Android demo app?
+        - How is the model prepared for use with MNN?
+        - How do I run the benchmark and what input image should I use?
+        removed_questions:
+        - What hardware and software do I need before starting?
+        - Which model and framework are used in this path?
+        - How do I build the Android demo application?
+        - Is there a command-line demo, and how do I provide input images?
+        - How do I verify that KleidiAI is used and compare performance?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md
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-      preview_generated: Learn how to build and optimize a multimodal Voice Assistant application on Android
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-        implement a multimoda...
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+      preview_before: Build and run a multimodal Voice Assistant on Android and explore
+        how KleidiAI and SME2 can accelerate its performance. You will set up Android
+        Studio and supporting command-line tools (cmake, python3...
+      preview_after: Build and run a multimodal Voice Assistant on Android and explore
+        how KleidiAI and SME2 can accelerate its performance. You will set up Android
+        Studio and supporting command-line tools (cmake, python3...
+      preview_generated: Build and run a multimodal Voice Assistant on Android and
+        learn where KleidiAI and SME2 accelerate the pipeline. You will set up a development
+        machine with Android Studio plus cmake, python3, git, and...
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+        - Which command-line tools should I install and why?
+        - How do I build the app in Android Studio?
+        - How do I install and run the app on my phone?
+        - How are KleidiAI, SME2, and Llama.cpp used in this application?
+        removed_questions:
+        - What hardware do I need to follow this Learning Path?
+        - Which tools should I install before building the application?
+        - How do I obtain the source code and build the app?
+        - How do I deploy and run the app on my Android device?
+        - What does the Voice Assistant pipeline include, and how do KleidiAI and
+          SME2 fit in?
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+        - Which command-line tools should I install and why?
+        - How do I build the app in Android Studio?
+        - How do I install and run the app on my phone?
+        - How are KleidiAI, SME2, and Llama.cpp used in this application?
+        removed_questions:
+        - What hardware do I need to follow this Learning Path?
+        - Which tools should I install before building the application?
+        - How do I obtain the source code and build the app?
+        - How do I deploy and run the app on my Android device?
+        - What does the Voice Assistant pipeline include, and how do KleidiAI and
+          SME2 fit in?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md
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-      preview_after: Build an end-to-end, on-device voice assistant that understands both speech and emotion
-        using Whisper, HuBERT, ONNX Runtime, and a local LLM with llama.cpp on Arm. It is designed for
-        developers, ML pr...
-      preview_generated: Build an end-to-end, on-device voice assistant that understands both speech and
-        emotion using Whisper, HuBERT, ONNX Runtime, and a local LLM with llama.cpp on Arm. It is designed
-        for developers, ML pr...
+      generated_at_before: '2026-06-02T03:02:45Z'
+      generated_at_after: '2026-06-02T03:02:45Z'
+      preview_before: Build an end-to-end, on-device voice assistant on Arm that understands
+        both speech and emotion. You will set up an isolated Python environment (Linux,
+        Windows, or macOS), install dependencies includin...
+      preview_after: Build an end-to-end, on-device voice assistant on Arm that understands
+        both speech and emotion. You will set up an isolated Python environment (Linux,
+        Windows, or macOS), install dependencies includin...
+      preview_generated: This Learning Path guides you through building an on-device,
+        sentiment-aware voice assistant on Arm using Whisper, HuBERT, ONNX Runtime,
+        and a local LLM with llama.cpp. You will set up an isolated env...
     faqs:
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+        added_questions:
+        - What do I need before running the steps?
+        - Which operating systems are supported, and how should I set up the environment?
+        - What result should I expect when the baseline voice-to-LLM pipeline is working?
+        - Which dataset and sentiment labels are used for training the classifier?
+        - How do I verify the ONNX conversion and quantization steps?
+        removed_questions:
+        - What platforms and prerequisites are required?
+        - Which tools and libraries are used?
+        - What does the baseline pipeline include and how do I verify it?
+        - How is the sentiment model trained and what dataset is used?
+        - What outputs should I expect and how are they used on device?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which operating systems are supported, and how should I set up the environment?
+        - What result should I expect when the baseline voice-to-LLM pipeline is working?
+        - Which dataset and sentiment labels are used for training the classifier?
+        - How do I verify the ONNX conversion and quantization steps?
+        removed_questions:
+        - What platforms and prerequisites are required?
+        - Which tools and libraries are used?
+        - What does the baseline pipeline include and how do I verify it?
+        - How is the sentiment model trained and what dataset is used?
+        - What outputs should I expect and how are they used on device?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
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+    rerun_flags_reset:
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+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
       missing_before: false
@@ -209615,51 +16476,76 @@ history:
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-      generated_at_after: '2026-05-06T17:17:56Z'
-      preview_before: Learn how to set up ML Emulation Layers for Vulkan, run sample applications using
-        ML extensions, and debug the flow with RenderDoc. It is designed for engine developers interested
-        in learning about ne...
-      preview_after: Learn how to set up ML Emulation Layers for Vulkan, run sample applications using
-        ML extensions, and debug the flow with RenderDoc. It is designed for engine developers interested
-        in learning about ne...
-      preview_generated: Learn how to set up ML Emulation Layers for Vulkan, run sample applications using
-        ML extensions, and debug the flow with RenderDoc. It is designed for engine developers interested
-        in learning about ne...
+      generated_at_before: '2026-06-02T03:03:08Z'
+      generated_at_after: '2026-06-02T03:03:08Z'
+      preview_before: This Learning Path shows how to enable neural graphics workflows
+        on Windows by using ML Extensions for Vulkan. You install the ML Emulation
+        Layers to simulate VK_ARM_data_graph and VK_ARM_tensors, set...
+      preview_after: This Learning Path shows how to enable neural graphics workflows
+        on Windows by using ML Extensions for Vulkan. You install the ML Emulation
+        Layers to simulate VK_ARM_data_graph and VK_ARM_tensors, set...
+      preview_generated: This advanced path guides engine developers on Windows 11
+        through enabling neural graphics in Vulkan using the VK_ARM_data_graph and
+        VK_ARM_tensors ML extensions. You install development tools, add th...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-06-02T03:03:08Z'
+      generated_at_after: '2026-06-03T00:13:13Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need installed before building and running the samples?
+        - Which Vulkan ML extensions does this path use, and how are they enabled?
+        - How do I get and build the first sample?
+        - How do I run a complete inference test beyond the simple sample?
+        - When should I use RenderDoc with these samples, and what can I inspect?
+        removed_questions:
+        - What environment and prerequisites do I need before starting?
+        - How are the ML Extensions for Vulkan enabled on my system?
+        - What will I build and run during this Learning Path?
+        - How do I verify that my setup is working correctly?
+        - How is RenderDoc used in this Learning Path?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need installed before building and running the samples?
+        - Which Vulkan ML extensions does this path use, and how are they enabled?
+        - How do I get and build the first sample?
+        - How do I run a complete inference test beyond the simple sample?
+        - When should I use RenderDoc with these samples, and what can I inspect?
+        removed_questions:
+        - What environment and prerequisites do I need before starting?
+        - How are the ML Extensions for Vulkan enabled on my system?
+        - What will I build and run during this Learning Path?
+        - How do I verify that my setup is working correctly?
+        - How is RenderDoc used in this Learning Path?
+        updated_questions: []
+    category: mobile-graphics-and-gaming
   - path: content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -209669,51 +16555,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
-      preview_before: Learn how to build and deploy an AI agent application on Arm servers using llama.cpp
-        and llama-cpp-agent with KleidiAI optimization for efficient LLM inference and function calling.
-        It is designed for...
-      preview_after: Learn how to build and deploy an AI agent application on Arm servers using llama.cpp
-        and llama-cpp-agent with KleidiAI optimization for efficient LLM inference and function calling.
-        It is designed for...
-      preview_generated: Learn how to build and deploy an AI agent application on Arm servers using llama.cpp
-        and llama-cpp-agent with KleidiAI optimization for efficient LLM inference and function calling.
-        It is designed for...
+      generated_at_before: '2026-06-02T03:03:34Z'
+      generated_at_after: '2026-06-02T03:03:34Z'
+      preview_before: This Learning Path shows how to build and deploy an AI agent
+        application on Arm servers using llama.cpp, llama-cpp-python, and llama-cpp-agent
+        with KleidiAI optimization. You will configure an Arm-opt...
+      preview_after: This Learning Path shows how to build and deploy an AI agent
+        application on Arm servers using llama.cpp, llama-cpp-python, and llama-cpp-agent
+        with KleidiAI optimization. You will configure an Arm-opt...
+      preview_generated: Build and deploy an AI agent application on Arm servers using
+        llama.cpp and llama-cpp-agent with KleidiAI optimization. You will set up
+        llama-cpp-python optimized for Arm, download and run an open-sou...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-06-02T03:03:34Z'
+      generated_at_after: '2026-06-03T00:14:25Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which environment or instance type is assumed?
+        - Which model is used in the example and how is it referenced?
+        - Do I need special configuration to use KleidiAI optimizations?
+        - How do I know the AI agent is working after I create agent.py?
+        removed_questions:
+        - What environment and resources do I need to follow this Learning Path?
+        - Can I run this on different cloud providers or on-premises?
+        - Which models and libraries are used, and how are they optimized for Arm?
+        - What will I build and what artifacts should I expect?
+        - How do I know the agent is working, and how long will this take?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which environment or instance type is assumed?
+        - Which model is used in the example and how is it referenced?
+        - Do I need special configuration to use KleidiAI optimizations?
+        - How do I know the AI agent is working after I create agent.py?
+        removed_questions:
+        - What environment and resources do I need to follow this Learning Path?
+        - Can I run this on different cloud providers or on-premises?
+        - Which models and libraries are used, and how are they optimized for Arm?
+        - What will I build and what artifacts should I expect?
+        - How do I know the agent is working, and how long will this take?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/aks/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -209723,51 +16634,76 @@ history:
       source_hash_before: 01c5cc8930650a8d81d67d4c48b62e0f9d7b1ebfc2d09a552e11046fb982fc52
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
-      preview_before: Learn how to automate the deployment of an Arm-based Kubernetes cluster on Azure
-        AKS using Terraform and deploy a sample WordPress application as a workload. It is designed for
-        software developers who...
-      preview_after: Learn how to automate the deployment of an Arm-based Kubernetes cluster on Azure
-        AKS using Terraform and deploy a sample WordPress application as a workload. It is designed for
-        software developers who...
-      preview_generated: Learn how to automate the deployment of an Arm-based Kubernetes cluster on Azure
-        AKS using Terraform and deploy a sample WordPress application as a workload. It is designed for
-        software developers who...
+      generated_at_before: '2026-06-02T03:03:59Z'
+      generated_at_after: '2026-06-02T03:03:59Z'
+      preview_before: This Learning Path shows how to automate the creation of an
+        Arm-based Azure Kubernetes Service (AKS) cluster using Terraform and then
+        deploy a WordPress example workload backed by MySQL. You will targ...
+      preview_after: This Learning Path shows how to automate the creation of an Arm-based
+        Azure Kubernetes Service (AKS) cluster using Terraform and then deploy a WordPress
+        example workload backed by MySQL. You will targ...
+      preview_generated: This Learning Path shows how to automate the creation of
+        an Arm-based Azure Kubernetes Service (AKS) cluster using Terraform, then
+        deploy a sample WordPress workload backed by MySQL. You will target A...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-06-02T03:03:59Z'
+      generated_at_after: '2026-06-03T00:15:11Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the Terraform deployment?
+        - Which Azure VM series is used for Arm-based AKS nodes in this path?
+        - Can I run the setup steps from my local computer or a virtual machine?
+        - What files do I create to deploy the WordPress example?
+        - "How do I know I\u2019m ready to deploy WordPress to the cluster?"
+        removed_questions:
+        - What do I need before starting?
+        - Which Azure compute is used for the Arm-based AKS nodes?
+        - Can I run the setup from my local computer?
+        - When and how is WordPress deployed?
+        - What will I have at the end and how long will it take?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the Terraform deployment?
+        - Which Azure VM series is used for Arm-based AKS nodes in this path?
+        - Can I run the setup steps from my local computer or a virtual machine?
+        - What files do I create to deploy the WordPress example?
+        - "How do I know I\u2019m ready to deploy WordPress to the cluster?"
+        removed_questions:
+        - What do I need before starting?
+        - Which Azure compute is used for the Arm-based AKS nodes?
+        - Can I run the setup from my local computer?
+        - When and how is WordPress deployed?
+        - What will I have at the end and how long will it take?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -209777,51 +16713,78 @@ history:
       source_hash_before: 90b638385267e085ea07a70a1c23f16578aa3caaeabeca1f651bcdcac5bf38fb
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
-      preview_before: Learn how to deploy Apache Arrow and Arrow Flight on Google Cloud Axion C4A processors
-        for high-throughput columnar data processing and low-latency data transport with MinIO integration.
-        It is designe...
-      preview_after: Learn how to deploy Apache Arrow and Arrow Flight on Google Cloud Axion C4A processors
-        for high-throughput columnar data processing and low-latency data transport with MinIO integration.
-        It is designe...
-      preview_generated: Learn how to deploy Apache Arrow and Arrow Flight on Google Cloud Axion C4A processors
-        for high-throughput columnar data processing and low-latency data transport with MinIO integration.
-        It is designe...
+      generated_at_before: '2026-06-02T03:04:20Z'
+      generated_at_after: '2026-06-02T03:04:20Z'
+      preview_before: This Learning Path shows how to deploy Apache Arrow and Arrow
+        Flight on Arm-based Google Cloud C4A Axion instances for high-throughput columnar
+        analytics and low-latency data transport. You will provi...
+      preview_after: This Learning Path shows how to deploy Apache Arrow and Arrow
+        Flight on Arm-based Google Cloud C4A Axion instances for high-throughput columnar
+        analytics and low-latency data transport. You will provi...
+      preview_generated: This Learning Path walks you through deploying Apache Arrow
+        and Arrow Flight on Arm-based Google Cloud Axion C4A instances to build a
+        high-throughput, cloud-native analytics stack with MinIO for objec...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 90b638385267e085ea07a70a1c23f16578aa3caaeabeca1f651bcdcac5bf38fb
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-06-02T03:04:20Z'
+      generated_at_after: '2026-06-03T00:15:38Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Google Cloud machine type and operating system are used?
+        - Which firewall ports should I open for MinIO and Arrow Flight?
+        - How is MinIO used, and how does Apache Arrow access data?
+        - What result should I expect after the analysis and Arrow Flight steps, and
+          how can I validate success?
+        removed_questions:
+        - What Google Cloud setup do I need before starting?
+        - Which operating system and architecture does the path use?
+        - Which firewall ports must be opened?
+        - What will I deploy or run by the end of the path?
+        - How do I verify the setup is working?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Google Cloud machine type and operating system are used?
+        - Which firewall ports should I open for MinIO and Arrow Flight?
+        - How is MinIO used, and how does Apache Arrow access data?
+        - What result should I expect after the analysis and Arrow Flight steps, and
+          how can I validate success?
+        removed_questions:
+        - What Google Cloud setup do I need before starting?
+        - Which operating system and architecture does the path use?
+        - Which firewall ports must be opened?
+        - What will I deploy or run by the end of the path?
+        - How do I verify the setup is working?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
-    status: unchanged
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-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
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+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -209831,51 +16794,76 @@ history:
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-      generated_at_after: '2026-05-06T17:17:56Z'
-      preview_before: Learn how to build llama.cpp, quantize the Arcee AFM-4.5B model, and run optimized
-        inference on AWS Graviton4 instances with perplexity-based quality evaluation. It is designed
-        for developers and ML e...
-      preview_after: Learn how to build llama.cpp, quantize the Arcee AFM-4.5B model, and run optimized
-        inference on AWS Graviton4 instances with perplexity-based quality evaluation. It is designed
-        for developers and ML e...
-      preview_generated: Learn how to build llama.cpp, quantize the Arcee AFM-4.5B model, and run optimized
-        inference on AWS Graviton4 instances with perplexity-based quality evaluation. It is designed
-        for developers and ML e...
+      generated_at_before: '2026-06-02T03:04:42Z'
+      generated_at_after: '2026-06-02T03:04:42Z'
+      preview_before: "Follow a concise workflow to deploy Arcee\u2019s AFM-4.5B small\
+        \ language model on Arm-based AWS Graviton4 using Llama.cpp. You will launch\
+        \ a Graviton4 EC2 instance (c8g.4xlarge or larger), configure a Linu..."
+      preview_after: "Follow a concise workflow to deploy Arcee\u2019s AFM-4.5B small\
+        \ language model on Arm-based AWS Graviton4 using Llama.cpp. You will launch\
+        \ a Graviton4 EC2 instance (c8g.4xlarge or larger), configure a Linu..."
+      preview_generated: "This Learning Path walks you through deploying Arcee\u2019\
+        s AFM-4.5B small language model on Arm-based AWS Graviton4 instances using\
+        \ Llama.cpp. You will launch a Graviton4 EC2 instance (c8g.4xlarge or larg..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
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-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-06-02T03:04:42Z'
+      generated_at_after: '2026-06-03T00:16:17Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - Do I need specific AWS access or resources before starting?
+        - Which EC2 instance type should I launch for this workflow?
+        - How do I connect to the EC2 instance?
+        - Which Llama.cpp repository should I use for AFM-4.5B?
+        - What are the main steps after provisioning the instance?
+        removed_questions:
+        - What AWS resources do I need to start?
+        - Do I need a custom fork of Llama.cpp for AFM-4.5B?
+        - What setup steps are covered before running the model?
+        - How do I obtain and prepare the AFM-4.5B model?
+        - How do I verify that the deployment worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - Do I need specific AWS access or resources before starting?
+        - Which EC2 instance type should I launch for this workflow?
+        - How do I connect to the EC2 instance?
+        - Which Llama.cpp repository should I use for AFM-4.5B?
+        - What are the main steps after provisioning the instance?
+        removed_questions:
+        - What AWS resources do I need to start?
+        - Do I need a custom fork of Llama.cpp for AFM-4.5B?
+        - What setup steps are covered before running the model?
+        - How do I obtain and prepare the AFM-4.5B model?
+        - How do I verify that the deployment worked?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -209885,51 +16873,78 @@ history:
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
-      preview_before: Learn how to build llama.cpp, quantize the Arcee AFM-4.5B model, and run optimized
-        inference on Google Cloud Axion instances with perplexity-based quality evaluation. It is designed
-        for developers and...
-      preview_after: Learn how to build llama.cpp, quantize the Arcee AFM-4.5B model, and run optimized
-        inference on Google Cloud Axion instances with perplexity-based quality evaluation. It is designed
-        for developers and...
-      preview_generated: Learn how to build llama.cpp, quantize the Arcee AFM-4.5B model, and run optimized
-        inference on Google Cloud Axion instances with perplexity-based quality evaluation. It is designed
-        for developers and...
+      generated_at_before: '2026-06-02T03:05:06Z'
+      generated_at_after: '2026-06-02T03:05:06Z'
+      preview_before: "This Learning Path guides you through deploying Arcee\u2019\
+        s AFM-4.5B small language model on Arm-based Google Cloud Axion instances\
+        \ using Llama.cpp. You will provision a Linux Compute Engine VM (c4a-stand..."
+      preview_after: "This Learning Path guides you through deploying Arcee\u2019\
+        s AFM-4.5B small language model on Arm-based Google Cloud Axion instances\
+        \ using Llama.cpp. You will provision a Linux Compute Engine VM (c4a-stand..."
+      preview_generated: "This Learning Path guides you through deploying Arcee\u2019\
+        s AFM-4.5B small language model on Arm-based Google Cloud Axion (Arm64) using\
+        \ Llama.cpp. You will provision a Compute Engine VM (c4a-standard-16 o..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: b2f60d2c31ef380e6e6ef3028671ad9242dd51c98f4a192f2da32bb05b94e185
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-06-02T03:05:06Z'
+      generated_at_after: '2026-06-03T00:16:57Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need in my Google Cloud project before launching the VM?
+        - Which Llama.cpp repository should I clone for AFM-4.5B support?
+        - Do I need a Hugging Face account or token to download AFM-4.5B?
+        - Why create a Python virtual environment for Llama.cpp, and how is it set
+          up here?
+        - What result should I expect after completing the steps?
+        removed_questions:
+        - What Google Cloud resources and permissions are required?
+        - What environment does this path use?
+        - Do I need a custom Llama.cpp fork for AFM-4.5B?
+        - How is the AFM-4.5B model obtained and prepared?
+        - How do I verify that the deployment worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need in my Google Cloud project before launching the VM?
+        - Which Llama.cpp repository should I clone for AFM-4.5B support?
+        - Do I need a Hugging Face account or token to download AFM-4.5B?
+        - Why create a Python virtual environment for Llama.cpp, and how is it set
+          up here?
+        - What result should I expect after completing the steps?
+        removed_questions:
+        - What Google Cloud resources and permissions are required?
+        - What environment does this path use?
+        - Do I need a custom Llama.cpp fork for AFM-4.5B?
+        - How is the AFM-4.5B model obtained and prepared?
+        - How do I verify that the deployment worked?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -209939,51 +16954,76 @@ history:
       source_hash_before: c6106f21859f5a8e04bbd579db47c7177bb5371d4c7f306054e56e81ed9e89a1
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
-      preview_before: Learn how to deploy and manage applications on Google Cloud GKE Arm64 clusters using
-        Argo CD GitOps workflows with automated sync and self-healing on Axion processors. It is designed
-        for developers an...
-      preview_after: Learn how to deploy and manage applications on Google Cloud GKE Arm64 clusters using
-        Argo CD GitOps workflows with automated sync and self-healing on Axion processors. It is designed
-        for developers an...
-      preview_generated: Learn how to deploy and manage applications on Google Cloud GKE Arm64 clusters
-        using Argo CD GitOps workflows with automated sync and self-healing on Axion processors. It is
-        designed for developers an...
+      generated_at_before: '2026-06-02T03:05:29Z'
+      generated_at_after: '2026-06-02T03:05:29Z'
+      preview_before: Learn how to deploy and manage applications on Arm-based Google
+        Kubernetes Engine (GKE) using Argo CD and GitOps. You will provision an Arm-based
+        SUSE Linux Enterprise Server VM on a Google Axion C4A ...
+      preview_after: Learn how to deploy and manage applications on Arm-based Google
+        Kubernetes Engine (GKE) using Argo CD and GitOps. You will provision an Arm-based
+        SUSE Linux Enterprise Server VM on a Google Axion C4A ...
+      preview_generated: This Learning Path guides you through deploying and managing
+        applications on Arm-based Google Kubernetes Engine (GKE) clusters using Argo
+        CD and GitOps. You will provision a SUSE Linux Enterprise Serv...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: c6106f21859f5a8e04bbd579db47c7177bb5371d4c7f306054e56e81ed9e89a1
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-06-02T03:05:29Z'
+      generated_at_after: '2026-06-03T00:17:35Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Google Cloud VM and OS are used for the setup host?
+        - What type of GKE cluster should I create for this path?
+        - How do I know Argo CD is installed and accessible?
+        - What repository do I need for the GitOps deployment?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - Do I need an existing Kubernetes cluster?
+        - Where do I run the setup and management commands?
+        - How is Argo CD installed and accessed in this path?
+        - What gets deployed, and how do I confirm it worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Google Cloud VM and OS are used for the setup host?
+        - What type of GKE cluster should I create for this path?
+        - How do I know Argo CD is installed and accessible?
+        - What repository do I need for the GitOps deployment?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - Do I need an existing Kubernetes cluster?
+        - Where do I run the setup and management commands?
+        - How is Argo CD installed and accessed in this path?
+        - What gets deployed, and how do I confirm it worked?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -209993,51 +17033,76 @@ history:
       source_hash_before: 3a4bc8b2ab548be507b6d528844b0593b27f2dab3ab3c9649e807abdf4887ce0
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
-      preview_before: Learn how to write correct concurrent C++ code when porting applications from x86
-        to Arm by understanding memory ordering differences and using best practices to avoid race conditions.
-        It is designed ...
-      preview_after: Learn how to write correct concurrent C++ code when porting applications from x86
-        to Arm by understanding memory ordering differences and using best practices to avoid race conditions.
-        It is designed ...
-      preview_generated: Learn how to write correct concurrent C++ code when porting applications from
-        x86 to Arm by understanding memory ordering differences and using best practices to avoid race
-        conditions. It is designed ...
+      generated_at_before: '2026-06-02T03:06:00Z'
+      generated_at_after: '2026-06-02T03:06:00Z'
+      preview_before: This Learning Path helps experienced C++ developers port concurrent
+        code from x86 to Arm by explaining the C++ memory model, highlighting key
+        memory ordering differences, and demonstrating how subtle ...
+      preview_after: This Learning Path helps experienced C++ developers port concurrent
+        code from x86 to Arm by explaining the C++ memory model, highlighting key
+        memory ordering differences, and demonstrating how subtle ...
+      preview_generated: This Learning Path explains the C++ memory model and how
+        differences between x86 and Arm memory ordering affect concurrent code when
+        porting to Arm. You will walk through a simple race-condition examp...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 3a4bc8b2ab548be507b6d528844b0593b27f2dab3ab3c9649e807abdf4887ce0
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-06-02T03:06:00Z'
+      generated_at_after: '2026-06-03T00:18:10Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the example?
+        - Which Arm instance and OS are used in the walkthrough?
+        - Which compiler/toolchain should I use for ThreadSanitizer (TSan)?
+        - How do I know if the race condition has been reproduced?
+        - What operating system is assumed for this Learning Path?
+        removed_questions:
+        - What prerequisites do I need before starting?
+        - What platforms and operating system are used in the example?
+        - What will I actually do in this Learning Path?
+        - How do I detect and analyze race conditions here?
+        - How long will this take, and how do I know I succeeded?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the example?
+        - Which Arm instance and OS are used in the walkthrough?
+        - Which compiler/toolchain should I use for ThreadSanitizer (TSan)?
+        - How do I know if the race condition has been reproduced?
+        - What operating system is assumed for this Learning Path?
+        removed_questions:
+        - What prerequisites do I need before starting?
+        - What platforms and operating system are used in the example?
+        - What will I actually do in this Learning Path?
+        - How do I detect and analyze race conditions here?
+        - How long will this take, and how do I know I succeeded?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -210047,51 +17112,76 @@ history:
       source_hash_before: b870ac5160d35ddb51955ca8379493e172787ced8125cde8ae79f7700e653a87
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
-      preview_before: Learn how to automate x86-to-Arm application migration using the Arm MCP Server,
-        with AI-assisted compatibility checks, C++ code refactoring, and Docker-based validation on Arm
-        cloud platforms. It is ...
-      preview_after: Learn how to automate x86-to-Arm application migration using the Arm MCP Server,
-        with AI-assisted compatibility checks, C++ code refactoring, and Docker-based validation on Arm
-        cloud platforms. It is ...
-      preview_generated: Learn how to automate x86-to-Arm application migration using the Arm MCP Server,
-        with AI-assisted compatibility checks, C++ code refactoring, and Docker-based validation on Arm
-        cloud platforms. It is ...
+      generated_at_before: '2026-06-02T03:06:30Z'
+      generated_at_after: '2026-06-02T03:06:30Z'
+      preview_before: This Learning Path guides you through automating x86-to-Arm
+        application migration using the Arm MCP Server. You will connect an AI-powered
+        IDE or agent to the MCP Server to run AI-assisted checks on D...
+      preview_after: This Learning Path guides you through automating x86-to-Arm application
+        migration using the Arm MCP Server. You will connect an AI-powered IDE or
+        agent to the MCP Server to run AI-assisted checks on D...
+      preview_generated: This Learning Path shows how to automate x86-to-Arm application
+        migration using the Arm MCP Server as a bridge between AI coding assistants
+        and Arm-specific migration tools. You will use AI-assisted c...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-06-02T03:06:30Z'
+      generated_at_after: '2026-06-03T00:18:38Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - How do I check if a Docker base image supports arm64 during migration?
+        - "I\u2019m not using GitHub Copilot\u2014how do I follow the migration workflow?"
+        - What should I do if my C++ code uses x86 SIMD intrinsics?
+        - How do I validate the migrated C++ application on Arm?
+        removed_questions:
+        - What do I need before starting?
+        - How is the Arm MCP Server used in this workflow?
+        - Do I have to use GitHub Copilot?
+        - Does this path address SIMD intrinsics during migration?
+        - What will I produce and how do I validate success?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - How do I check if a Docker base image supports arm64 during migration?
+        - "I\u2019m not using GitHub Copilot\u2014how do I follow the migration workflow?"
+        - What should I do if my C++ code uses x86 SIMD intrinsics?
+        - How do I validate the migrated C++ application on Arm?
+        removed_questions:
+        - What do I need before starting?
+        - How is the Arm MCP Server used in this workflow?
+        - Do I have to use GitHub Copilot?
+        - Does this path address SIMD intrinsics during migration?
+        - What will I produce and how do I validate success?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -210101,51 +17191,76 @@ history:
       source_hash_before: 49b3f2b1464efb20f0c8fbcff46e9f30910d856567ca01c01dc348aa1c5740d1
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
-      preview_before: Learn how to migrate C applications between Arm platforms using Kiro's AI-assisted
-        tooling to identify hardware dependencies and implement abstraction layers for cross-platform
-        compatibility. It is de...
-      preview_after: Learn how to migrate C applications between Arm platforms using Kiro's AI-assisted
-        tooling to identify hardware dependencies and implement abstraction layers for cross-platform
-        compatibility. It is de...
-      preview_generated: Learn how to migrate C applications between Arm platforms using Kiro's AI-assisted
-        tooling to identify hardware dependencies and implement abstraction layers for cross-platform
-        compatibility. It is de...
+      generated_at_before: '2026-06-02T03:06:57Z'
+      generated_at_after: '2026-06-02T03:06:57Z'
+      preview_before: This advanced Learning Path shows how to migrate a C application
+        between Arm platforms using Kiro Arm SoC Migration Power. You install Kiro
+        IDE on your local machine, enable the Migration Power, and u...
+      preview_after: This advanced Learning Path shows how to migrate a C application
+        between Arm platforms using Kiro Arm SoC Migration Power. You install Kiro
+        IDE on your local machine, enable the Migration Power, and u...
+      preview_generated: This Learning Path shows how to migrate a C application between
+        Arm platforms using Kiro Arm SoC Migration Power. You install Kiro IDE locally,
+        enable the Migration Power, and run the Arm MCP server a...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 49b3f2b1464efb20f0c8fbcff46e9f30910d856567ca01c01dc348aa1c5740d1
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-06-02T03:06:57Z'
+      generated_at_after: '2026-06-03T00:19:04Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the migration workflow?
+        - How do I set up Kiro and the required backend services?
+        - Which application and platforms are used in the example?
+        - How do I know the analysis phase is working during migration?
+        - What should I check to confirm the migration is successful?
+        removed_questions:
+        - What do I need before starting?
+        - Which platforms are used in the example, and can I apply this to others?
+        - How is Kiro set up for this workflow?
+        - What will I build or modify during the migration?
+        - How do I verify that the migration worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the migration workflow?
+        - How do I set up Kiro and the required backend services?
+        - Which application and platforms are used in the example?
+        - How do I know the analysis phase is working during migration?
+        - What should I check to confirm the migration is successful?
+        removed_questions:
+        - What do I need before starting?
+        - Which platforms are used in the example, and can I apply this to others?
+        - How is Kiro set up for this workflow?
+        - What will I build or modify during the migration?
+        - How do I verify that the migration worked?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -210155,51 +17270,76 @@ history:
       source_hash_before: 67004eb9a75926144eb6f75124104972b3cf20a57a22b698c9359d20db3eca02
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
-      preview_before: Learn how to install and configure a Linux kernel with 64K page size support on
-        Arm systems to improve memory efficiency and performance for memory-intensive workloads. It is
-        designed for developers w...
-      preview_after: Learn how to install and configure a Linux kernel with 64K page size support on Arm
-        systems to improve memory efficiency and performance for memory-intensive workloads. It is designed
-        for developers w...
-      preview_generated: Learn how to install and configure a Linux kernel with 64K page size support
-        on Arm systems to improve memory efficiency and performance for memory-intensive workloads. It
-        is designed for developers w...
+      generated_at_before: '2026-06-02T03:07:24Z'
+      generated_at_after: '2026-06-02T03:07:24Z'
+      preview_before: "This Learning Path shows how to install and boot a Linux kernel\
+        \ configured for 64K page size on Arm-based systems to improve memory efficiency\
+        \ and performance for memory\u2011intensive workloads. You will ..."
+      preview_after: "This Learning Path shows how to install and boot a Linux kernel\
+        \ configured for 64K page size on Arm-based systems to improve memory efficiency\
+        \ and performance for memory\u2011intensive workloads. You will ..."
+      preview_generated: This Learning Path shows how to install and boot a Linux
+        kernel configured with a 64K page size on Arm-based systems to improve memory
+        efficiency and performance for memory-intensive workloads. You wi...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 67004eb9a75926144eb6f75124104972b3cf20a57a22b698c9359d20db3eca02
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       current_source_hash: 67004eb9a75926144eb6f75124104972b3cf20a57a22b698c9359d20db3eca02
-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-06-02T03:07:24Z'
+      generated_at_after: '2026-06-03T00:19:30Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Linux distributions and versions are covered?
+        - How do I check my current memory page size and kernel?
+        - On Debian, do I need to compile a 64K kernel and which source should I use?
+        - How do I verify the 64K page size is active, and can I revert to 4K?
+        removed_questions:
+        - Which Linux distributions and versions does this cover?
+        - What do I need before starting?
+        - How do I check my current memory page size?
+        - Will I need to compile a kernel to get 64K page size?
+        - How do I verify the change worked and can I revert it?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Linux distributions and versions are covered?
+        - How do I check my current memory page size and kernel?
+        - On Debian, do I need to compile a 64K kernel and which source should I use?
+        - How do I verify the 64K page size is active, and can I revert to 4K?
+        removed_questions:
+        - Which Linux distributions and versions does this cover?
+        - What do I need before starting?
+        - How do I check my current memory page size?
+        - Will I need to compile a kernel to get 64K page size?
+        - How do I verify the change worked and can I revert it?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -210209,51 +17349,76 @@ history:
       source_hash_before: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
-      preview_before: Learn how to access and use Arm hardware performance counters and the system counter
-        from user space using PAPI, perf_event_open, and assembly code for performance instrumentation.
-        It is designed for ...
-      preview_after: Learn how to access and use Arm hardware performance counters and the system counter
-        from user space using PAPI, perf_event_open, and assembly code for performance instrumentation.
-        It is designed for ...
-      preview_generated: Learn how to access and use Arm hardware performance counters and the system
-        counter from user space using PAPI, perf_event_open, and assembly code for performance instrumentation.
-        It is designed for ...
+      generated_at_before: '2026-06-02T03:07:54Z'
+      generated_at_after: '2026-06-02T03:07:54Z'
+      preview_before: Learn how to access Arm hardware performance counters (PMU)
+        and the system counter from user space on Linux. You will measure time using
+        the system counter with small assembly snippets (MRS/MSR), inst...
+      preview_after: Learn how to access Arm hardware performance counters (PMU) and
+        the system counter from user space on Linux. You will measure time using the
+        system counter with small assembly snippets (MRS/MSR), inst...
+      preview_generated: 'This advanced Learning Path shows how to access Arm hardware
+        performance counters and the system counter from user space on Linux. You
+        will use three options: inline assembly with MRS/MSR to read the ...'
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
       source_hash_after: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
       current_source_hash: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-06-02T03:07:54Z'
+      generated_at_after: '2026-06-03T00:20:03Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - How do I decide between using the system counter, PAPI, or perf_event_open?
+        - Which environment variables and permissions are required for the PAPI steps?
+        - What does the perf_event_open section demonstrate, and does it support multiplexing?
+        - What should I check if I cannot access certain hardware counters?
+        removed_questions:
+        - What hardware and OS setup do I need?
+        - Do I need elevated privileges to access counters from user space?
+        - How do I install and configure PAPI for the examples?
+        - Can I measure time or cycles without using PAPI?
+        - What does the perf_event_open section cover, and are multiple counters supported?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - How do I decide between using the system counter, PAPI, or perf_event_open?
+        - Which environment variables and permissions are required for the PAPI steps?
+        - What does the perf_event_open section demonstrate, and does it support multiplexing?
+        - What should I check if I cannot access certain hardware counters?
+        removed_questions:
+        - What hardware and OS setup do I need?
+        - Do I need elevated privileges to access counters from user space?
+        - How do I install and configure PAPI for the examples?
+        - Can I measure time or cycles without using PAPI?
+        - What does the perf_event_open section cover, and are multiple counters supported?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -210263,51 +17428,78 @@ history:
       source_hash_before: ced3882cf8be11a55304d2697f450a2c862a81d87317ab42298148755e9f272c
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
-      preview_before: Learn how to package multi-architecture container applications and deploy them on
-        AWS Fargate with Graviton processors using the AWS Copilot CLI. It is designed for software developers
-        who want to lea...
-      preview_after: Learn how to package multi-architecture container applications and deploy them on
-        AWS Fargate with Graviton processors using the AWS Copilot CLI. It is designed for software developers
-        who want to lea...
-      preview_generated: Learn how to package multi-architecture container applications and deploy them
-        on AWS Fargate with Graviton processors using the AWS Copilot CLI. It is designed for software
-        developers who want to lea...
+      generated_at_before: '2026-06-02T03:08:36Z'
+      generated_at_after: '2026-06-02T03:08:36Z'
+      preview_before: This Learning Path shows how to package a multi-architecture
+        container and deploy it to AWS Fargate using the AWS Copilot CLI, configured
+        to run on AWS Graviton processors. You will containerize an ex...
+      preview_after: This Learning Path shows how to package a multi-architecture
+        container and deploy it to AWS Fargate using the AWS Copilot CLI, configured
+        to run on AWS Graviton processors. You will containerize an ex...
+      preview_generated: Learn to package a multi-architecture container and deploy
+        it to AWS Fargate on Graviton using the AWS Copilot CLI. You will build from
+        a Dockerfile, initialize a Copilot application as a Load Balance...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: ced3882cf8be11a55304d2697f450a2c862a81d87317ab42298148755e9f272c
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-06-02T03:08:36Z'
+      generated_at_after: '2026-06-03T00:20:41Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - What architecture does Copilot use by default, and how does this affect
+          deploying on Graviton?
+        - How do I deploy the sample service with Copilot?
+        - Can I use an existing container image instead of building from a Dockerfile?
+        - What result should I expect after a successful deployment?
+        removed_questions:
+        - What do I need before starting?
+        - Can I use an existing container image instead of a Dockerfile?
+        - How does this Learning Path target AWS Graviton on Fargate?
+        - What does the copilot init command do during deployment?
+        - How long will this take and what is the skill level?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - What architecture does Copilot use by default, and how does this affect
+          deploying on Graviton?
+        - How do I deploy the sample service with Copilot?
+        - Can I use an existing container image instead of building from a Dockerfile?
+        - What result should I expect after a successful deployment?
+        removed_questions:
+        - What do I need before starting?
+        - Can I use an existing container image instead of a Dockerfile?
+        - How does this Learning Path target AWS Graviton on Fargate?
+        - What does the copilot init command do during deployment?
+        - How long will this take and what is the skill level?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -210317,51 +17509,76 @@ history:
       source_hash_before: 087a4d56914e5b53cec5ad17e8d682e72d04ba6b394237c081efe4a3f939e04e
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
-      preview_before: Learn how to automate the creation and deployment of AWS Graviton instances using
-        Terraform with jump server access for secure infrastructure management. It is designed for software
-        developers who are...
-      preview_after: Learn how to automate the creation and deployment of AWS Graviton instances using
-        Terraform with jump server access for secure infrastructure management. It is designed for software
-        developers who are...
-      preview_generated: Learn how to automate the creation and deployment of AWS Graviton instances using
-        Terraform with jump server access for secure infrastructure management. It is designed for software
-        developers who are...
+      generated_at_before: '2026-06-02T03:09:04Z'
+      generated_at_after: '2026-06-02T03:09:04Z'
+      preview_before: This Learning Path shows how to automate the provisioning of
+        Arm-based AWS Graviton instances using Terraform, with access provided through
+        a Jump Server (bastion) for secure infrastructure management...
+      preview_after: This Learning Path shows how to automate the provisioning of
+        Arm-based AWS Graviton instances using Terraform, with access provided through
+        a Jump Server (bastion) for secure infrastructure management...
+      preview_generated: Follow this Learning Path to automate the creation and deployment
+        of AWS Graviton (Arm) EC2 instances using Terraform. You will use Terraform
+        Cloud to provision infrastructure and set up a Jump Server...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 087a4d56914e5b53cec5ad17e8d682e72d04ba6b394237c081efe4a3f939e04e
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-06-02T03:09:04Z'
+      generated_at_after: '2026-06-03T00:21:25Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the Terraform steps?
+        - Does this path use Terraform Cloud or local Terraform?
+        - What infrastructure gets created by the configuration?
+        - How do I access the deployed instances?
+        - Can I reuse or modify the Terraform files for other Learning Paths?
+        removed_questions:
+        - What do I need before starting?
+        - Does this Learning Path use Terraform Cloud or local Terraform?
+        - What infrastructure does the Terraform configuration create?
+        - How do I validate that the deployment worked?
+        - Can I reuse these Terraform files for other Learning Paths?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the Terraform steps?
+        - Does this path use Terraform Cloud or local Terraform?
+        - What infrastructure gets created by the configuration?
+        - How do I access the deployed instances?
+        - Can I reuse or modify the Terraform files for other Learning Paths?
+        removed_questions:
+        - What do I need before starting?
+        - Does this Learning Path use Terraform Cloud or local Terraform?
+        - What infrastructure does the Terraform configuration create?
+        - How do I validate that the deployment worked?
+        - Can I reuse these Terraform files for other Learning Paths?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -210371,51 +17588,76 @@ history:
       source_hash_before: 1682c0527bb49c417263286e2aba7c82fb9a27fa315ecc6b9ca10978b69cc236
       source_hash_after: 1682c0527bb49c417263286e2aba7c82fb9a27fa315ecc6b9ca10978b69cc236
       current_source_hash: 1682c0527bb49c417263286e2aba7c82fb9a27fa315ecc6b9ca10978b69cc236
-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
-      preview_before: Learn how to create and deploy Azure Resource Manager templates to provision Arm64-based
-        Cobalt 100 virtual machines on Azure using the Azure CLI. It is designed for developers and DevOps
-        engineers wh...
-      preview_after: Learn how to create and deploy Azure Resource Manager templates to provision Arm64-based
-        Cobalt 100 virtual machines on Azure using the Azure CLI. It is designed for developers and DevOps
-        engineers wh...
-      preview_generated: Learn how to create and deploy Azure Resource Manager templates to provision
-        Arm64-based Cobalt 100 virtual machines on Azure using the Azure CLI. It is designed for developers
-        and DevOps engineers wh...
+      generated_at_before: '2026-06-02T03:09:38Z'
+      generated_at_after: '2026-06-02T03:09:38Z'
+      preview_before: Learn how to automate the provisioning of Arm64-based Azure
+        Cobalt 100 virtual machines using Azure Resource Manager (ARM) templates and
+        the Azure CLI. You will author a JSON template with parameters,...
+      preview_after: Learn how to automate the provisioning of Arm64-based Azure Cobalt
+        100 virtual machines using Azure Resource Manager (ARM) templates and the
+        Azure CLI. You will author a JSON template with parameters,...
+      preview_generated: This Learning Path shows how to create and deploy an Azure
+        Resource Manager (ARM) template to provision a Linux virtual machine on Microsoft
+        Azure using Arm-based Cobalt 100 processors. You will struc...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 1682c0527bb49c417263286e2aba7c82fb9a27fa315ecc6b9ca10978b69cc236
       source_hash_after: 1682c0527bb49c417263286e2aba7c82fb9a27fa315ecc6b9ca10978b69cc236
       current_source_hash: 1682c0527bb49c417263286e2aba7c82fb9a27fa315ecc6b9ca10978b69cc236
-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-06-02T03:09:38Z'
+      generated_at_after: '2026-06-03T00:21:51Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the template?
+        - Which Azure region and VM size should I use for Cobalt 100?
+        - How is the ARM template structured and how do I customize it?
+        - "How do I get the VM\u2019s public IP to connect over SSH?"
+        - What result should I expect after deployment, and how do I verify Arm64?
+        removed_questions:
+        - What do I need before starting?
+        - How do I choose a region and confirm Cobalt 100 availability?
+        - What does the ARM template contain and what file do I create?
+        - Which Azure resources get created and where are they placed?
+        - How do I verify the deployment and Arm64 architecture?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the template?
+        - Which Azure region and VM size should I use for Cobalt 100?
+        - How is the ARM template structured and how do I customize it?
+        - "How do I get the VM\u2019s public IP to connect over SSH?"
+        - What result should I expect after deployment, and how do I verify Arm64?
+        removed_questions:
+        - What do I need before starting?
+        - How do I choose a region and confirm Cobalt 100 availability?
+        - What does the ARM template contain and what file do I create?
+        - Which Azure resources get created and where are they placed?
+        - How do I verify the deployment and Arm64 architecture?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -210425,51 +17667,74 @@ history:
       source_hash_before: b5bd3abde8d9dd3146e0f11f4819ac510417ad7f707b4d0970c02fd63801b358
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-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
-      preview_before: Learn how to configure a self-hosted GitHub runner on Azure Cobalt 100, create an
-        AKS cluster with Terraform, and deploy a .NET application using GitHub Actions CI/CD. It is designed
-        for software deve...
-      preview_after: Learn how to configure a self-hosted GitHub runner on Azure Cobalt 100, create an
-        AKS cluster with Terraform, and deploy a .NET application using GitHub Actions CI/CD. It is designed
-        for software deve...
-      preview_generated: Learn how to configure a self-hosted GitHub runner on Azure Cobalt 100, create
-        an AKS cluster with Terraform, and deploy a .NET application using GitHub Actions CI/CD. It is
-        designed for software deve...
+      generated_at_before: '2026-06-02T03:10:03Z'
+      generated_at_after: '2026-06-02T03:10:03Z'
+      preview_before: This Learning Path shows how to configure a self-hosted GitHub
+        Actions Arm64 runner on an Azure Cobalt 100 VM, create an Arm-based Azure
+        Kubernetes Service (AKS) cluster with Terraform, and deploy a ....
+      preview_after: This Learning Path shows how to configure a self-hosted GitHub
+        Actions Arm64 runner on an Azure Cobalt 100 VM, create an Arm-based Azure
+        Kubernetes Service (AKS) cluster with Terraform, and deploy a ....
+      preview_generated: Learn how to configure a self-hosted GitHub Actions Arm64
+        runner on an Azure Cobalt 100 VM, create an Arm-based AKS cluster with Terraform,
+        and use CI/CD to build and deploy a .NET 8 web application. ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: b5bd3abde8d9dd3146e0f11f4819ac510417ad7f707b4d0970c02fd63801b358
       source_hash_after: b5bd3abde8d9dd3146e0f11f4819ac510417ad7f707b4d0970c02fd63801b358
       current_source_hash: b5bd3abde8d9dd3146e0f11f4819ac510417ad7f707b4d0970c02fd63801b358
-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-06-02T03:10:03Z'
+      generated_at_after: '2026-06-03T00:22:28Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which Azure Cobalt 100 VM series should I use for the runner or AKS nodes?
+        - What does the Terraform configuration create?
+        - What should I expect after the GitHub Actions workflow runs?
+        removed_questions:
+        - What accounts and local tools do I need before starting?
+        - What operating system and architecture does this path target?
+        - What will I create and deploy by the end of the Learning Path?
+        - Which Azure VM series are available for Cobalt 100?
+        updated_questions:
+        - Can I use GitHub-hosted Arm64 runners instead of a self-hosted runner?
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which Azure Cobalt 100 VM series should I use for the runner or AKS nodes?
+        - What does the Terraform configuration create?
+        - What should I expect after the GitHub Actions workflow runs?
+        removed_questions:
+        - What accounts and local tools do I need before starting?
+        - What operating system and architecture does this path target?
+        - What will I create and deploy by the end of the Learning Path?
+        - Which Azure VM series are available for Cobalt 100?
+        updated_questions:
+        - Can I use GitHub-hosted Arm64 runners instead of a self-hosted runner?
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -210479,51 +17744,76 @@ history:
       source_hash_before: 6713af3d70887933cf54c7fa36bdc88d71a51fa34fb29a4ce90636ff1de624c7
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       current_source_hash: 6713af3d70887933cf54c7fa36bdc88d71a51fa34fb29a4ce90636ff1de624c7
-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
-      preview_before: Learn how to automate the creation of Azure Arm virtual machines using Terraform.
-        It is designed for software developers who are new to deploying Arm instances on Azure using Terraform.
-        By the end, yo...
-      preview_after: Learn how to automate the creation of Azure Arm virtual machines using Terraform.
-        It is designed for software developers who are new to deploying Arm instances on Azure using Terraform.
-        By the end, yo...
-      preview_generated: Learn how to automate the creation of Azure Arm virtual machines using Terraform.
-        It is designed for software developers who are new to deploying Arm instances on Azure using Terraform.
-        By the end, yo...
+      generated_at_before: '2026-06-02T03:10:40Z'
+      generated_at_after: '2026-06-02T03:10:40Z'
+      preview_before: This Learning Path shows how to automate the creation of Arm-based
+        virtual machines on Microsoft Azure using Terraform and Terraform Cloud. You
+        will deploy Azure Arm VMs (Neoverse) and configure acces...
+      preview_after: This Learning Path shows how to automate the creation of Arm-based
+        virtual machines on Microsoft Azure using Terraform and Terraform Cloud. You
+        will deploy Azure Arm VMs (Neoverse) and configure acces...
+      preview_generated: This Learning Path shows how to use Terraform Cloud to automate
+        the creation of Arm virtual machines on Microsoft Azure and configure secure
+        access through a Jump Server (bastion host). It is intended...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 6713af3d70887933cf54c7fa36bdc88d71a51fa34fb29a4ce90636ff1de624c7
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       current_source_hash: 6713af3d70887933cf54c7fa36bdc88d71a51fa34fb29a4ce90636ff1de624c7
-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-06-02T03:10:40Z'
+      generated_at_after: '2026-06-03T00:23:24Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the Terraform steps?
+        - Which Terraform workflow does this Learning Path use?
+        - Can I deploy Linux or Windows on Arm with these instructions?
+        - How is access to the deployed VMs provided?
+        - What should I expect to have at the end of this Learning Path?
+        removed_questions:
+        - What do I need before starting?
+        - Does this Learning Path use Terraform Cloud or local Terraform execution?
+        - What gets created when I complete the steps?
+        - How do I access the deployed VMs?
+        - Can I reuse the Terraform code from this Learning Path?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the Terraform steps?
+        - Which Terraform workflow does this Learning Path use?
+        - Can I deploy Linux or Windows on Arm with these instructions?
+        - How is access to the deployed VMs provided?
+        - What should I expect to have at the end of this Learning Path?
+        removed_questions:
+        - What do I need before starting?
+        - Does this Learning Path use Terraform Cloud or local Terraform execution?
+        - What gets created when I complete the steps?
+        - How do I access the deployed VMs?
+        - Can I reuse the Terraform code from this Learning Path?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -210533,51 +17823,76 @@ history:
       source_hash_before: 413467e581e3561425229d0f6118975dbb596d6a9494544a54f0b9ab22c1b89d
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       current_source_hash: 413467e581e3561425229d0f6118975dbb596d6a9494544a54f0b9ab22c1b89d
-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
-      preview_before: Learn how to create a custom Azure Linux 3.0 VM image using QEMU, upload it to Azure
-        Shared Image Gallery, and deploy it on Arm-based Cobalt 100 processors. It is designed for developers
-        who want to r...
-      preview_after: Learn how to create a custom Azure Linux 3.0 VM image using QEMU, upload it to Azure
-        Shared Image Gallery, and deploy it on Arm-based Cobalt 100 processors. It is designed for developers
-        who want to r...
-      preview_generated: Learn how to create a custom Azure Linux 3.0 VM image using QEMU, upload it to
-        Azure Shared Image Gallery, and deploy it on Arm-based Cobalt 100 processors. It is designed for
-        developers who want to r...
+      generated_at_before: '2026-06-02T03:11:18Z'
+      generated_at_after: '2026-06-02T03:11:18Z'
+      preview_before: This advanced Learning Path guides you through building a custom
+        Azure Linux 3.0 image for Arm and deploying it on Microsoft Azure Cobalt 100
+        processors. You will use QEMU on a Linux host to create a ...
+      preview_after: This advanced Learning Path guides you through building a custom
+        Azure Linux 3.0 image for Arm and deploying it on Microsoft Azure Cobalt 100
+        processors. You will use QEMU on a Linux host to create a ...
+      preview_generated: This Learning Path shows how to build and deploy a custom
+        Azure Linux 3.0 image for Arm on Microsoft Azure. You will use QEMU on a Linux
+        host to create a raw disk, boot an AArch64 ISO, and install Azu...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 413467e581e3561425229d0f6118975dbb596d6a9494544a54f0b9ab22c1b89d
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       current_source_hash: 413467e581e3561425229d0f6118975dbb596d6a9494544a54f0b9ab22c1b89d
-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-06-02T03:11:18Z'
+      generated_at_after: '2026-06-03T00:24:06Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running these steps?
+        - Which Azure Linux ISO and architecture should I use with QEMU?
+        - What artifacts should I have before uploading to Azure?
+        - How is the VHD registered so I can reuse it to create VMs?
+        - How do I launch a VM on Cobalt 100 using my custom image?
+        removed_questions:
+        - What do I need before I start?
+        - Which ISO and architecture should I use to install Azure Linux 3.0 in QEMU?
+        - What artifacts will I create and where are they used?
+        - How do I know the custom image works?
+        - How long does the Learning Path take to complete?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running these steps?
+        - Which Azure Linux ISO and architecture should I use with QEMU?
+        - What artifacts should I have before uploading to Azure?
+        - How is the VHD registered so I can reuse it to create VMs?
+        - How do I launch a VM on Cobalt 100 using my custom image?
+        removed_questions:
+        - What do I need before I start?
+        - Which ISO and architecture should I use to install Azure Linux 3.0 in QEMU?
+        - What artifacts will I create and where are they used?
+        - How do I know the custom image works?
+        - How long does the Learning Path take to complete?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
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     summary:
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@@ -210587,51 +17902,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:56Z'
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-      preview_before: Learn how to deploy and accelerate PyTorch NLP sentiment analysis models from Hugging
-        Face on Arm servers with BFloat16 fast math kernel optimization on Graviton3 processors. It is
-        designed for softwa...
-      preview_after: Learn how to deploy and accelerate PyTorch NLP sentiment analysis models from Hugging
-        Face on Arm servers with BFloat16 fast math kernel optimization on Graviton3 processors. It is
-        designed for softwa...
-      preview_generated: Learn how to deploy and accelerate PyTorch NLP sentiment analysis models from
-        Hugging Face on Arm servers with BFloat16 fast math kernel optimization on Graviton3 processors.
-        It is designed for softwa...
+      generated_at_before: '2026-06-02T03:11:52Z'
+      generated_at_after: '2026-06-02T03:11:52Z'
+      preview_before: Learn to deploy and evaluate Hugging Face Sentiment Analysis
+        models with PyTorch on Arm servers running Linux. You will run three NLP models
+        using the Sentiment Analysis pipeline, then enable BFloat16...
+      preview_after: Learn to deploy and evaluate Hugging Face Sentiment Analysis
+        models with PyTorch on Arm servers running Linux. You will run three NLP models
+        using the Sentiment Analysis pipeline, then enable BFloat16...
+      preview_generated: This Learning Path shows how to deploy and accelerate PyTorch
+        NLP sentiment analysis models from Hugging Face on Arm servers. You will install
+        PyTorch, run the Hugging Face Sentiment Analysis pipeline...
     faqs:
-      action: unchanged
+      action: rerun_requested
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-      rerun_requested: false
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       before_count: 5
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       generated_count: 5
       change_details:
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         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - Which platforms can I use for this path?
+        - What should I install first to follow the steps?
+        - How do I know the sentiment analysis models ran successfully?
+        - How do I enable and validate BFloat16 fast math kernels on Graviton3?
+        removed_questions:
+        - What kind of environment do I need to follow this Learning Path?
+        - Do I need to use AWS Graviton3 specifically?
+        - Which tools and frameworks are used?
+        - What will I build and measure by the end?
+        - How can I verify that everything worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - Which platforms can I use for this path?
+        - What should I install first to follow the steps?
+        - How do I know the sentiment analysis models ran successfully?
+        - How do I enable and validate BFloat16 fast math kernels on Graviton3?
+        removed_questions:
+        - What kind of environment do I need to follow this Learning Path?
+        - Do I need to use AWS Graviton3 specifically?
+        - Which tools and frameworks are used?
+        - What will I build and measure by the end?
+        - How can I verify that everything worked?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
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+    status: updated
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+    ai_requested: true
+    rerun_flags_reset:
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+    change_reasons:
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+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
       missing_before: false
@@ -210641,51 +17981,76 @@ history:
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-      preview_before: Learn how to implement and benchmark bitmap scanning operations for database workloads
-        using scalar, Neon, and SVE instructions on Arm-based cloud instances. It is designed for database
-        developers, pe...
-      preview_after: Learn how to implement and benchmark bitmap scanning operations for database workloads
-        using scalar, Neon, and SVE instructions on Arm-based cloud instances. It is designed for database
-        developers, pe...
-      preview_generated: Learn how to implement and benchmark bitmap scanning operations for database
-        workloads using scalar, Neon, and SVE instructions on Arm-based cloud instances. It is designed
-        for database developers, pe...
+      generated_at_before: '2026-06-02T03:12:13Z'
+      generated_at_after: '2026-06-02T03:12:13Z'
+      preview_before: Learn how to implement and benchmark bitmap scanning for database
+        workloads on Arm-based cloud instances running Linux. You will build a simple
+        bitmap data structure and multiple scanning routines in ...
+      preview_after: Learn how to implement and benchmark bitmap scanning for database
+        workloads on Arm-based cloud instances running Linux. You will build a simple
+        bitmap data structure and multiple scanning routines in ...
+      preview_generated: This Learning Path shows how to implement and benchmark bitmap
+        scanning for database-style workloads on Arm-based cloud instances running
+        Linux. You will build a simple bit vector in C, add multiple s...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
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-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Where do I put the code for this Learning Path?
+        - Which bitmap scanning implementations will I build and compare?
+        - What results should I expect from the benchmarking step?
+        - How do I validate that all implementations are correct?
+        removed_questions:
+        - What environment do I need to run this Learning Path?
+        - Which implementations will I build and compare?
+        - Do I need to use AWS, or can I use other cloud providers?
+        - How do I validate that my code works?
+        - How long does this take and what experience level is expected?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Where do I put the code for this Learning Path?
+        - Which bitmap scanning implementations will I build and compare?
+        - What results should I expect from the benchmarking step?
+        - How do I validate that all implementations are correct?
+        removed_questions:
+        - What environment do I need to run this Learning Path?
+        - Which implementations will I build and compare?
+        - Do I need to use AWS, or can I use other cloud providers?
+        - How do I validate that my code works?
+        - How long does this take and what experience level is expected?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/bolt/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
       missing_before: false
@@ -210695,51 +18060,76 @@ history:
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-      preview_before: Learn how to build, profile, and optimize Arm executables using BOLT post-link binary
-        optimization to improve application performance through code layout improvements. It is designed
-        for software deve...
-      preview_after: Learn how to build, profile, and optimize Arm executables using BOLT post-link binary
-        optimization to improve application performance through code layout improvements. It is designed
-        for software deve...
-      preview_generated: Learn how to build, profile, and optimize Arm executables using BOLT post-link
-        binary optimization to improve application performance through code layout improvements. It is
-        designed for software deve...
+      generated_at_before: '2026-06-02T03:12:45Z'
+      generated_at_after: '2026-06-02T03:12:45Z'
+      preview_before: This Learning Path shows how to build, profile, and post-link
+        optimize an Arm Linux executable with BOLT. You will collect runtime profiles
+        on an Arm-based target using Linux Perf (via samples, ETM, o...
+      preview_after: This Learning Path shows how to build, profile, and post-link
+        optimize an Arm Linux executable with BOLT. You will collect runtime profiles
+        on an Arm-based target using Linux Perf (via samples, ETM, o...
+      preview_generated: This Learning Path shows how to build, profile, and optimize
+        an Arm Linux executable using BOLT. You will run your application on an Arm-based
+        Linux target to collect a performance profile with Linux ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
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-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-06-02T03:12:45Z'
+      generated_at_after: '2026-06-03T00:25:57Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - Do I need one or two Linux systems for this workflow?
+        - 'Which profiling option should I choose: samples, ETM, or SPE?'
+        - What versions of Linux kernel and Perf are required before I start?
+        - How do I collect the performance profile and verify that it worked?
+        - What does BOLT produce after profiling, and how is it used?
+        removed_questions:
+        - Do I need one or two Linux systems to complete the steps?
+        - What operating system and tool prerequisites are required?
+        - Which profiling methods are covered and what does each produce?
+        - Are there any special version checks for SPE?
+        - What is the expected output and how do I know it worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - Do I need one or two Linux systems for this workflow?
+        - 'Which profiling option should I choose: samples, ETM, or SPE?'
+        - What versions of Linux kernel and Perf are required before I start?
+        - How do I collect the performance profile and verify that it worked?
+        - What does BOLT produce after profiling, and how is it used?
+        removed_questions:
+        - Do I need one or two Linux systems to complete the steps?
+        - What operating system and tool prerequisites are required?
+        - Which profiling methods are covered and what does each produce?
+        - Are there any special version checks for SPE?
+        - What is the expected output and how do I know it worked?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -210749,51 +18139,80 @@ history:
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-      preview_before: Learn how to identify optimization candidates and apply LLVM BOLT post-link optimization
-        to AArch64 binaries using BRBE, SPE, instrumentation, and PMU profiling techniques. It is designed
-        for develope...
-      preview_after: Learn how to identify optimization candidates and apply LLVM BOLT post-link optimization
-        to AArch64 binaries using BRBE, SPE, instrumentation, and PMU profiling techniques. It is designed
-        for develope...
-      preview_generated: Learn how to identify optimization candidates and apply LLVM BOLT post-link optimization
-        to AArch64 binaries using BRBE, SPE, instrumentation, and PMU profiling techniques. It is designed
-        for develope...
+      generated_at_before: '2026-06-02T03:13:32Z'
+      generated_at_after: '2026-06-02T03:13:32Z'
+      preview_before: This introductory Learning Path shows how to assess AArch64
+        programs for code layout optimization and apply LLVM BOLT to a deliberately
+        inefficient, BubbleSort-based example on Linux. You install LLVM...
+      preview_after: This introductory Learning Path shows how to assess AArch64 programs
+        for code layout optimization and apply LLVM BOLT to a deliberately inefficient,
+        BubbleSort-based example on Linux. You install LLVM...
+      preview_generated: This introductory Learning Path shows how to evaluate and
+        apply LLVM BOLT post-link optimization to AArch64 Linux applications with
+        poor instruction locality. You will install a specific BOLT release ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
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-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-06-02T03:13:32Z'
+      generated_at_after: '2026-06-03T00:26:35Z'
       before_count: 5
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       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which BOLT version should I install, and what if my package manager provides
+          an older one?
+        - How should I set up the example and organize outputs?
+        - How do I know if my application is a good candidate for BOLT?
+        - What does BRBE profiling capture and why is it useful here?
+        removed_questions:
+        - What system and software do I need before starting?
+        - Which LLVM BOLT version is required and how is it installed?
+        - Can I follow this Learning Path in a virtual machine?
+        - What example program is used and what artifacts will be created?
+        - How do I decide if my program is a good candidate for BOLT, and how is profiling
+          performed?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which BOLT version should I install, and what if my package manager provides
+          an older one?
+        - How should I set up the example and organize outputs?
+        - How do I know if my application is a good candidate for BOLT?
+        - What does BRBE profiling capture and why is it useful here?
+        removed_questions:
+        - What system and software do I need before starting?
+        - Which LLVM BOLT version is required and how is it installed?
+        - Can I follow this Learning Path in a virtual machine?
+        - What example program is used and what artifacts will be created?
+        - How do I decide if my program is a good candidate for BOLT, and how is profiling
+          performed?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -210803,51 +18222,76 @@ history:
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-      generated_at_after: '2026-05-06T17:17:56Z'
-      preview_before: Learn how to optimize Arm application binaries and shared libraries using BOLT profile
-        instrumentation, merge multiple profiles for improved coverage, and integrate optimized libraries.
-        It is designed...
-      preview_after: Learn how to optimize Arm application binaries and shared libraries using BOLT profile
-        instrumentation, merge multiple profiles for improved coverage, and integrate optimized libraries.
-        It is designed...
-      preview_generated: Learn how to optimize Arm application binaries and shared libraries using BOLT
-        profile instrumentation, merge multiple profiles for improved coverage, and integrate optimized
-        libraries. It is designed...
+      generated_at_before: '2026-06-02T03:14:28Z'
+      generated_at_after: '2026-06-02T03:14:28Z'
+      preview_before: This advanced path shows how to instrument and optimize Arm
+        application binaries and shared libraries on Linux using BOLT and Linux perf.
+        You will build the MySQL server (mysqld) from source, create a...
+      preview_after: This advanced path shows how to instrument and optimize Arm application
+        binaries and shared libraries on Linux using BOLT and Linux perf. You will
+        build the MySQL server (mysqld) from source, create a...
+      preview_generated: This advanced Learning Path shows how to use BOLT on an Arm-based
+        Linux system to instrument and optimize both an application binary and its
+        shared libraries using real workload profiles. You will bui...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-06-02T03:14:28Z'
+      generated_at_after: '2026-06-03T00:27:14Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I generate profiles for BOLT to use with mysqld?
+        - When should I merge profiles, and what does that produce?
+        - What should I do if libssl.so or libcrypto.so are stripped and lack relocations?
+        - How do I compare baseline and BOLT-optimized results?
+        removed_questions:
+        - What environment and tools are required to follow this path?
+        - What will I build or modify during the steps?
+        - How are profiles collected and merged for optimization?
+        - What if my system OpenSSL libraries are stripped and lack symbols?
+        - How do I validate the results of the optimization?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I generate profiles for BOLT to use with mysqld?
+        - When should I merge profiles, and what does that produce?
+        - What should I do if libssl.so or libcrypto.so are stripped and lack relocations?
+        - How do I compare baseline and BOLT-optimized results?
+        removed_questions:
+        - What environment and tools are required to follow this path?
+        - What will I build or modify during the steps?
+        - How are profiles collected and merged for optimization?
+        - What if my system OpenSSL libraries are stripped and lack symbols?
+        - How do I validate the results of the optimization?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -210857,51 +18301,76 @@ history:
       source_hash_before: 4bda206717eef380430009f859826d9bcf820442d13492cd3c22a114561e2917
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       current_source_hash: 4bda206717eef380430009f859826d9bcf820442d13492cd3c22a114561e2917
-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
-      preview_before: Learn how to configure Buildkite agents on Google Axion C4A VMs to build and publish
-        multi-architecture Docker images using Docker Buildx for Arm and x86 platforms. It is designed
-        for developers who w...
-      preview_after: Learn how to configure Buildkite agents on Google Axion C4A VMs to build and publish
-        multi-architecture Docker images using Docker Buildx for Arm and x86 platforms. It is designed
-        for developers who w...
-      preview_generated: Learn how to configure Buildkite agents on Google Axion C4A VMs to build and
-        publish multi-architecture Docker images using Docker Buildx for Arm and x86 platforms. It is
-        designed for developers who w...
+      generated_at_before: '2026-06-02T03:15:44Z'
+      generated_at_after: '2026-06-02T03:15:44Z'
+      preview_before: This Learning Path shows how to use Buildkite on Arm-based Google
+        Axion C4A virtual machines to build and publish multi-architecture Docker
+        images. You will provision a c4a-standard-4 VM on Google Clo...
+      preview_after: This Learning Path shows how to use Buildkite on Arm-based Google
+        Axion C4A virtual machines to build and publish multi-architecture Docker
+        images. You will provision a c4a-standard-4 VM on Google Clo...
+      preview_generated: Configure a Buildkite CI/CD agent on Arm-based Google Axion
+        C4A virtual machines in Google Cloud to build and publish multi-architecture
+        Docker images. You will provision a c4a-standard-4 instance (4 ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 4bda206717eef380430009f859826d9bcf820442d13492cd3c22a114561e2917
       source_hash_after: 4bda206717eef380430009f859826d9bcf820442d13492cd3c22a114561e2917
       current_source_hash: 4bda206717eef380430009f859826d9bcf820442d13492cd3c22a114561e2917
-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-06-02T03:15:44Z'
+      generated_at_after: '2026-06-03T00:27:49Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before provisioning the Google Axion C4A VM?
+        - Which instance type and operating systems does this path use?
+        - How do I install the Buildkite agent on the C4A VM?
+        - How do I know my Buildkite agent is ready to run jobs?
+        - What does the pipeline build and where is it published?
+        removed_questions:
+        - Which Google Cloud resources and operating systems does this path use?
+        - What accounts and skills are required before starting?
+        - What will I build and publish in the pipeline?
+        - How do I set up and validate the Buildkite agent on the VM?
+        - How do I know the Learning Path worked end to end?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before provisioning the Google Axion C4A VM?
+        - Which instance type and operating systems does this path use?
+        - How do I install the Buildkite agent on the C4A VM?
+        - How do I know my Buildkite agent is ready to run jobs?
+        - What does the pipeline build and where is it published?
+        removed_questions:
+        - Which Google Cloud resources and operating systems does this path use?
+        - What accounts and skills are required before starting?
+        - What will I build and publish in the pipeline?
+        - How do I set up and validate the Buildkite agent on the VM?
+        - How do I know the Learning Path worked end to end?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -210911,51 +18380,76 @@ history:
       source_hash_before: b8268d4df3b62aeb9943b750b436a936134d47745fa71db6cc08db8c84eb37be
       source_hash_after: b8268d4df3b62aeb9943b750b436a936134d47745fa71db6cc08db8c84eb37be
       current_source_hash: b8268d4df3b62aeb9943b750b436a936134d47745fa71db6cc08db8c84eb37be
-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
-      preview_before: Learn how to install and configure Apache Cassandra on Google Cloud Axion C4A Arm64
-        instances and benchmark read/write performance using cassandra-stress. It is designed for software
-        developers migrat...
-      preview_after: Learn how to install and configure Apache Cassandra on Google Cloud Axion C4A Arm64
-        instances and benchmark read/write performance using cassandra-stress. It is designed for software
-        developers migrat...
-      preview_generated: Learn how to install and configure Apache Cassandra on Google Cloud Axion C4A
-        Arm64 instances and benchmark read/write performance using cassandra-stress. It is designed for
-        software developers migrat...
+      generated_at_before: '2026-06-02T03:16:24Z'
+      generated_at_after: '2026-06-02T03:16:24Z'
+      preview_before: Follow this introductory path to provision a Google Cloud Axion
+        C4A Arm64 virtual machine, install Apache Cassandra with Java 17 on SUSE or
+        Ubuntu, validate basic database operations, and benchmark re...
+      preview_after: Follow this introductory path to provision a Google Cloud Axion
+        C4A Arm64 virtual machine, install Apache Cassandra with Java 17 on SUSE or
+        Ubuntu, validate basic database operations, and benchmark re...
+      preview_generated: This Learning Path walks you through deploying Apache Cassandra
+        on Arm-based Google Cloud Axion C4A virtual machines built on Arm Neoverse-V2
+        cores. You will provision a c4a-standard-4 instance from t...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: b8268d4df3b62aeb9943b750b436a936134d47745fa71db6cc08db8c84eb37be
       source_hash_after: b8268d4df3b62aeb9943b750b436a936134d47745fa71db6cc08db8c84eb37be
       current_source_hash: b8268d4df3b62aeb9943b750b436a936134d47745fa71db6cc08db8c84eb37be
-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-06-02T03:16:24Z'
+      generated_at_after: '2026-06-03T00:28:19Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before provisioning the VM on Google Cloud?
+        - Which Google Cloud machine type is used in this guide?
+        - Which Linux distributions does the installation cover?
+        - How do I verify that Cassandra started correctly?
+        - How do I confirm cassandra-stress is available and what does it test?
+        removed_questions:
+        - What do I need before I start?
+        - Which OS and instance type are used in the steps?
+        - Is this a single-node or multi-node Cassandra setup?
+        - How do I verify that Cassandra is running correctly?
+        - How is benchmarking performed and where is cassandra-stress located?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before provisioning the VM on Google Cloud?
+        - Which Google Cloud machine type is used in this guide?
+        - Which Linux distributions does the installation cover?
+        - How do I verify that Cassandra started correctly?
+        - How do I confirm cassandra-stress is available and what does it test?
+        removed_questions:
+        - What do I need before I start?
+        - Which OS and instance type are used in the steps?
+        - Is this a single-node or multi-node Cassandra setup?
+        - How do I verify that Cassandra is running correctly?
+        - How is benchmarking performed and where is cassandra-stress located?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -210965,51 +18459,76 @@ history:
       source_hash_before: 3947ebbac742399e70001e41ac5face92819bed0deb55aba3e2414f98432023e
       source_hash_after: 3947ebbac742399e70001e41ac5face92819bed0deb55aba3e2414f98432023e
       current_source_hash: 3947ebbac742399e70001e41ac5face92819bed0deb55aba3e2414f98432023e
-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
-      preview_before: Learn how to run the Arm CCA reference software stack on an FVP with RME support,
-        create a Realm virtual machine, and obtain attestation tokens for confidential computing. It is
-        designed for software ...
-      preview_after: Learn how to run the Arm CCA reference software stack on an FVP with RME support,
-        create a Realm virtual machine, and obtain attestation tokens for confidential computing. It is
-        designed for software ...
-      preview_generated: Learn how to run the Arm CCA reference software stack on an FVP with RME support,
-        create a Realm virtual machine, and obtain attestation tokens for confidential computing. It is
-        designed for software ...
+      generated_at_before: '2026-06-02T03:16:50Z'
+      generated_at_after: '2026-06-02T03:16:50Z'
+      preview_before: This Learning Path shows how to bring up the Arm Confidential
+        Compute Architecture (CCA) reference software stack on an Armv-A AEM Fixed
+        Virtual Platform (FVP) with Realm Management Extension (RME) su...
+      preview_after: This Learning Path shows how to bring up the Arm Confidential
+        Compute Architecture (CCA) reference software stack on an Armv-A AEM Fixed
+        Virtual Platform (FVP) with Realm Management Extension (RME) su...
+      preview_generated: "This Learning Path shows how to run the Arm Confidential\
+        \ Compute Architecture (CCA) reference software stack on an Armv\u2011A AEM\
+        \ Base FVP with Realm Management Extension (RME) support, create a Realm vir..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 3947ebbac742399e70001e41ac5face92819bed0deb55aba3e2414f98432023e
       source_hash_after: 3947ebbac742399e70001e41ac5face92819bed0deb55aba3e2414f98432023e
       current_source_hash: 3947ebbac742399e70001e41ac5face92819bed0deb55aba3e2414f98432023e
-      generated_at_before: '2026-05-06T17:17:56Z'
-      generated_at_after: '2026-05-06T17:17:56Z'
+      generated_at_before: '2026-06-02T03:16:50Z'
+      generated_at_after: '2026-06-03T00:28:52Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Docker image should I pull, and how do I verify it downloaded?
+        - What runs inside the Realm, and what result should I expect regarding attestation?
+        - How do I run my own application inside the Realm in this example?
+        - When do I use Memory Encryption Contexts (MEC), and what does it change?
+        removed_questions:
+        - What host system do I need to follow this Learning Path?
+        - Do I need to build the CCA stack or FVP myself?
+        - How do I verify that the required Docker image is available locally?
+        - What will I run inside the Realm and how is it protected?
+        - Does this Learning Path cover attestation and memory encryption features?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Docker image should I pull, and how do I verify it downloaded?
+        - What runs inside the Realm, and what result should I expect regarding attestation?
+        - How do I run my own application inside the Realm in this example?
+        - When do I use Memory Encryption Contexts (MEC), and what does it change?
+        removed_questions:
+        - What host system do I need to follow this Learning Path?
+        - Do I need to build the CCA stack or FVP myself?
+        - How do I verify that the required Docker image is available locally?
+        - What will I run inside the Realm and how is it protected?
+        - Does this Learning Path cover attestation and memory encryption features?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -211019,51 +18538,78 @@ history:
       source_hash_before: e21f51feb101ad90245ecceab95fe13e5eb61958faf24dff818eddd11f484b9a
       source_hash_after: e21f51feb101ad90245ecceab95fe13e5eb61958faf24dff818eddd11f484b9a
       current_source_hash: e21f51feb101ad90245ecceab95fe13e5eb61958faf24dff818eddd11f484b9a
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how Arm CCA Realms interact with I/O devices using VirtIO paravirtualization,
-        SWIOTLB bounce buffers, and PCIe-TDISP secure device attach mechanisms with attestation. It is
-        designed for develope...
-      preview_after: Learn how Arm CCA Realms interact with I/O devices using VirtIO paravirtualization,
-        SWIOTLB bounce buffers, and PCIe-TDISP secure device attach mechanisms with attestation. It is
-        designed for develope...
-      preview_generated: Learn how Arm CCA Realms interact with I/O devices using VirtIO paravirtualization,
-        SWIOTLB bounce buffers, and PCIe-TDISP secure device attach mechanisms with attestation. It is
-        designed for develope...
+      generated_at_before: '2026-06-02T03:17:31Z'
+      generated_at_after: '2026-06-02T03:17:31Z'
+      preview_before: This advanced Learning Path explains how Arm CCA Realms interact
+        with I/O devices, contrasting VirtIO paravirtualized attach with secure physical
+        device attach. You will review what a Realm is, how th...
+      preview_after: This advanced Learning Path explains how Arm CCA Realms interact
+        with I/O devices, contrasting VirtIO paravirtualized attach with secure physical
+        device attach. You will review what a Realm is, how th...
+      preview_generated: This advanced Learning Path explains how Arm Confidential
+        Computing Architecture (CCA) Realms interact with I/O, from paravirtualized
+        device access to secure physical device attach. You will review Re...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: e21f51feb101ad90245ecceab95fe13e5eb61958faf24dff818eddd11f484b9a
       source_hash_after: e21f51feb101ad90245ecceab95fe13e5eb61958faf24dff818eddd11f484b9a
       current_source_hash: e21f51feb101ad90245ecceab95fe13e5eb61958faf24dff818eddd11f484b9a
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:17:31Z'
+      generated_at_after: '2026-06-03T00:29:35Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the exercise?
+        - How is attestation covered when discussing secure physical device attach?
+        - How do I start the Key Broker server (KBS) used in the exercise?
+        - How do I confirm that SWIOTLB bounce buffers are being used inside the Realm?
+        - How can I check network interfaces during the exercise?
+        removed_questions:
+        - What environment and tools do I need to follow this Learning Path?
+        - What prior knowledge or prerequisites are required?
+        - What will I run during the hands-on exercise, and what will I observe?
+        - How do I know I completed the exercise successfully?
+        - "Does this Learning Path configure PCIe\u2011TDISP and PCIe\u2011IDE, or\
+          \ describe them conceptually?"
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the exercise?
+        - How is attestation covered when discussing secure physical device attach?
+        - How do I start the Key Broker server (KBS) used in the exercise?
+        - How do I confirm that SWIOTLB bounce buffers are being used inside the Realm?
+        - How can I check network interfaces during the exercise?
+        removed_questions:
+        - What environment and tools do I need to follow this Learning Path?
+        - What prior knowledge or prerequisites are required?
+        - What will I run during the hands-on exercise, and what will I observe?
+        - How do I know I completed the exercise successfully?
+        - "Does this Learning Path configure PCIe\u2011TDISP and PCIe\u2011IDE, or\
+          \ describe them conceptually?"
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -211073,51 +18619,76 @@ history:
       source_hash_before: b032debbdfe82cbd017812cf671907520b146fd8684afce0c45c91e7f2287e18
       source_hash_after: b032debbdfe82cbd017812cf671907520b146fd8684afce0c45c91e7f2287e18
       current_source_hash: b032debbdfe82cbd017812cf671907520b146fd8684afce0c45c91e7f2287e18
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to deploy a CCA realm on an FVP with RME support and connect it with attestation
-        services to create an end-to-end confidential computing workflow. It is designed for software
-        developers who ...
-      preview_after: Learn how to deploy a CCA realm on an FVP with RME support and connect it with attestation
-        services to create an end-to-end confidential computing workflow. It is designed for software
-        developers who ...
-      preview_generated: Learn how to deploy a CCA realm on an FVP with RME support and connect it with
-        attestation services to create an end-to-end confidential computing workflow. It is designed for
-        software developers who ...
+      generated_at_before: '2026-06-02T03:18:08Z'
+      generated_at_after: '2026-06-02T03:18:08Z'
+      preview_before: "This advanced Learning Path guides you through running an end-to-end\
+        \ attestation flow with Arm\u2019s Confidential Computing Architecture (CCA).\
+        \ You will deploy a simple workload inside a confidential Linu..."
+      preview_after: "This advanced Learning Path guides you through running an end-to-end\
+        \ attestation flow with Arm\u2019s Confidential Computing Architecture (CCA).\
+        \ You will deploy a simple workload inside a confidential Linu..."
+      preview_generated: "This advanced Learning Path shows how to deploy a simple\
+        \ workload inside a Linux realm using Arm\u2019s Confidential Computing Architecture\
+        \ (CCA) on the Armv9-A AEM Base Fixed Virtual Platform (FVP) with R..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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       change_details:
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-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which FVP and Arm features does the example require?
+        - How do I run the Key Broker Server (KBS) used in this path?
+        - What result should I expect when attestation succeeds?
+        - How long does this take and which tools will I use?
+        removed_questions:
+        - What host system do I need to follow this Learning Path?
+        - What should I complete before starting?
+        - What will I set up and run during the Learning Path?
+        - Do I need Docker, and what does it run here?
+        - How long will it take and how do I know it worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which FVP and Arm features does the example require?
+        - How do I run the Key Broker Server (KBS) used in this path?
+        - What result should I expect when attestation succeeds?
+        - How long does this take and which tools will I use?
+        removed_questions:
+        - What host system do I need to follow this Learning Path?
+        - What should I complete before starting?
+        - What will I set up and run during the Learning Path?
+        - Do I need Docker, and what does it run here?
+        - How long will it take and how do I know it worked?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -211127,51 +18698,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to deploy Confidential Containers from encrypted images inside Arm CCA
-        Realms using Trustee services for attestation-based authorization on an FVP with RME support.
-        It is designed for develo...
-      preview_after: Learn how to deploy Confidential Containers from encrypted images inside Arm CCA
-        Realms using Trustee services for attestation-based authorization on an FVP with RME support.
-        It is designed for develo...
-      preview_generated: Learn how to deploy Confidential Containers from encrypted images inside Arm
-        CCA Realms using Trustee services for attestation-based authorization on an FVP with RME support.
-        It is designed for develo...
+      generated_at_before: '2026-06-02T03:19:07Z'
+      generated_at_after: '2026-06-02T03:19:07Z'
+      preview_before: Learn to deploy a Confidential Container from an encrypted image
+        inside an Arm CCA Realm using Trustee for attestation-based authorization.
+        Working on the Armv9-A AEM Base Fixed Virtual Platform (FVP)...
+      preview_after: Learn to deploy a Confidential Container from an encrypted image
+        inside an Arm CCA Realm using Trustee for attestation-based authorization.
+        Working on the Armv9-A AEM Base Fixed Virtual Platform (FVP)...
+      preview_generated: This Learning Path shows how to deploy a Confidential Container
+        from an encrypted image inside an Arm CCA Realm using Trustee services for
+        attestation-based authorization on an Armv9-A AEM Base Fixed ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:19:07Z'
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       before_count: 5
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       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which platform does the container run on in this workflow?
+        - Which services must be started before launching the confidential container?
+        - How do I create and publish the encrypted container image?
+        - How do I know the container is running inside an Arm CCA Realm?
+        removed_questions:
+        - What environment and platform does this Learning Path use?
+        - What are the prerequisites before starting?
+        - Which services and components will I start during setup?
+        - What artifacts are created and deployed?
+        - How do I verify the deployment worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which platform does the container run on in this workflow?
+        - Which services must be started before launching the confidential container?
+        - How do I create and publish the encrypted container image?
+        - How do I know the container is running inside an Arm CCA Realm?
+        removed_questions:
+        - What environment and platform does this Learning Path use?
+        - What are the prerequisites before starting?
+        - Which services and components will I start during setup?
+        - What artifacts are created and deployed?
+        - How do I verify the deployment worked?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -211181,51 +18777,76 @@ history:
       source_hash_before: 563456f5d151c7ef59bf458f6ac971c321077475a0a78d3b5a3885b97157ba9e
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to deploy a CCA realm workload on an FVP and connect it with Trustee services
-        to enable attestation-based confidential data processing. It is designed for software developers
-        who want to run...
-      preview_after: Learn how to deploy a CCA realm workload on an FVP and connect it with Trustee services
-        to enable attestation-based confidential data processing. It is designed for software developers
-        who want to run...
-      preview_generated: Learn how to deploy a CCA realm workload on an FVP and connect it with Trustee
-        services to enable attestation-based confidential data processing. It is designed for software
-        developers who want to run...
+      generated_at_before: '2026-06-02T03:20:03Z'
+      generated_at_after: '2026-06-02T03:20:03Z'
+      preview_before: This Learning Path shows how to run an end-to-end attestation
+        flow using Arm Confidential Computing Architecture (CCA) and Trustee services.
+        On a Linux or macOS host (AArch64 or x86_64), you will use ...
+      preview_after: This Learning Path shows how to run an end-to-end attestation
+        flow using Arm Confidential Computing Architecture (CCA) and Trustee services.
+        On a Linux or macOS host (AArch64 or x86_64), you will use ...
+      preview_generated: This advanced Learning Path shows how to run an end-to-end
+        attestation flow using Arm Confidential Computing Architecture (CCA) and Trustee
+        services. You will launch a Linux realm on the Armv9-A AEM B...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 563456f5d151c7ef59bf458f6ac971c321077475a0a78d3b5a3885b97157ba9e
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:20:03Z'
+      generated_at_after: '2026-06-03T00:31:41Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the exercises?
+        - Can I use a cloud instance as the host machine?
+        - Which FVP and realm environment does this path use?
+        - Which Trustee components are started during the flow?
+        - What result should I expect when I request a secret?
+        removed_questions:
+        - What host system do I need to follow this Learning Path?
+        - Do I need to complete any other Learning Paths first?
+        - Which components and tools are used in the flow?
+        - How is the attestation policy validated during the steps?
+        - What will I have achieved by the end?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the exercises?
+        - Can I use a cloud instance as the host machine?
+        - Which FVP and realm environment does this path use?
+        - Which Trustee components are started during the flow?
+        - What result should I expect when I request a secret?
+        removed_questions:
+        - What host system do I need to follow this Learning Path?
+        - Do I need to complete any other Learning Paths first?
+        - Which components and tools are used in the flow?
+        - How is the attestation policy validated during the steps?
+        - What will I have achieved by the end?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -211235,51 +18856,78 @@ history:
       source_hash_before: 9e6dee3aef79c1c65cc1a1f4fe3528b9c5542d8046f0b059ef703079ef964d77
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to inspect and verify Arm CCA attestation tokens using command-line tools
-        and the open-source Veraison attestation verification service. It is designed for developers who
-        would like to learn...
-      preview_after: Learn how to inspect and verify Arm CCA attestation tokens using command-line tools
-        and the open-source Veraison attestation verification service. It is designed for developers who
-        would like to learn...
-      preview_generated: Learn how to inspect and verify Arm CCA attestation tokens using command-line
-        tools and the open-source Veraison attestation verification service. It is designed for developers
-        who would like to learn...
+      generated_at_before: '2026-06-02T03:20:26Z'
+      generated_at_after: '2026-06-02T03:20:26Z'
+      preview_before: Learn how to work with Arm Confidential Computing Architecture
+        (CCA) attestation by obtaining an example CCA attestation token, inspecting
+        its contents with command-line tools on Ubuntu, and evaluatin...
+      preview_after: Learn how to work with Arm Confidential Computing Architecture
+        (CCA) attestation by obtaining an example CCA attestation token, inspecting
+        its contents with command-line tools on Ubuntu, and evaluatin...
+      preview_generated: This introductory Learning Path shows how to inspect and
+        verify Arm CCA attestation tokens on Ubuntu using command-line tools and the
+        open-source Veraison attestation verification service. You will in...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 9e6dee3aef79c1c65cc1a1f4fe3528b9c5542d8046f0b059ef703079ef964d77
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:20:26Z'
+      generated_at_after: '2026-06-03T00:32:10Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I install Go for this Learning Path?
+        - What is Veraison used for here?
+        - How do I obtain and inspect the example CCA attestation token?
+        - Which service should I use to verify the token, and what tokens does it
+          support?
+        removed_questions:
+        - What system do I need to follow this Learning Path?
+        - Do I need access to Arm CCA hardware or an FVP to complete the steps?
+        - What software will I install or configure?
+        - How do I know the attestation workflow worked?
+        - What is Veraison and how is it used here?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I install Go for this Learning Path?
+        - What is Veraison used for here?
+        - How do I obtain and inspect the example CCA attestation token?
+        - Which service should I use to verify the token, and what tokens does it
+          support?
+        removed_questions:
+        - What system do I need to follow this Learning Path?
+        - Do I need access to Arm CCA hardware or an FVP to complete the steps?
+        - What software will I install or configure?
+        - How do I know the attestation workflow worked?
+        - What is Veraison and how is it used here?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -211289,51 +18937,76 @@ history:
       source_hash_before: 55b8032ceaf735d53b1157102a3a1da5aa5cb686e9ec51cf4896ded66d9bf263
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to deploy a scalable Arm CCA attestation verifier service on AWS using
-        Veraison components with platform endorsement provisioning. It is designed for developers familiar
-        with CCA attestation...
-      preview_after: Learn how to deploy a scalable Arm CCA attestation verifier service on AWS using
-        Veraison components with platform endorsement provisioning. It is designed for developers familiar
-        with CCA attestation...
-      preview_generated: Learn how to deploy a scalable Arm CCA attestation verifier service on AWS using
-        Veraison components with platform endorsement provisioning. It is designed for developers familiar
-        with CCA attestation...
+      generated_at_before: '2026-06-02T03:21:13Z'
+      generated_at_after: '2026-06-02T03:21:13Z'
+      preview_before: This advanced Learning Path shows how to build and deploy a
+        scalable Arm CCA attestation verifier on AWS using Veraison. You will prepare
+        your AWS account, install and authenticate the AWS CLI, create...
+      preview_after: This advanced Learning Path shows how to build and deploy a scalable
+        Arm CCA attestation verifier on AWS using Veraison. You will prepare your
+        AWS account, install and authenticate the AWS CLI, create...
+      preview_generated: This Learning Path guides you through deploying a scalable
+        Arm CCA attestation verifier on AWS using Veraison. You prepare your AWS account
+        (administrator privileges are assumed), install and authenti...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 55b8032ceaf735d53b1157102a3a1da5aa5cb686e9ec51cf4896ded66d9bf263
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:21:13Z'
+      generated_at_after: '2026-06-03T00:32:36Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before starting the deployment?
+        - How should I authenticate the AWS CLI before deploying Veraison?
+        - Do I need a public domain, and how is it used?
+        - What should I expect when running the Veraison deployment?
+        - How do I add CCA platform endorsements so the verifier can process tokens?
+        removed_questions:
+        - What do I need before starting?
+        - Do I need a public domain and TLS certificate?
+        - How do I deploy the Veraison verifier on AWS, and how long does it take?
+        - How are Arm CCA platform endorsements provisioned?
+        - How can I confirm the deployment worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before starting the deployment?
+        - How should I authenticate the AWS CLI before deploying Veraison?
+        - Do I need a public domain, and how is it used?
+        - What should I expect when running the Veraison deployment?
+        - How do I add CCA platform endorsements so the verifier can process tokens?
+        removed_questions:
+        - What do I need before starting?
+        - Do I need a public domain and TLS certificate?
+        - How do I deploy the Veraison verifier on AWS, and how long does it take?
+        - How are Arm CCA platform endorsements provisioned?
+        - How can I confirm the deployment worked?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -211343,51 +19016,76 @@ history:
       source_hash_before: ec9cdca7aa9a5670f54ea3646f965149460d8e66d9d5d904e1b6dcf09867df39
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to set up CircleCI self-hosted machine runners on Google Cloud Axion C4A
-        SUSE VMs and execute Arm-native CI/CD workflows using custom resource classes. It is designed
-        for developers and DevO...
-      preview_after: Learn how to set up CircleCI self-hosted machine runners on Google Cloud Axion C4A
-        SUSE VMs and execute Arm-native CI/CD workflows using custom resource classes. It is designed
-        for developers and DevO...
-      preview_generated: Learn how to set up CircleCI self-hosted machine runners on Google Cloud Axion
-        C4A SUSE VMs and execute Arm-native CI/CD workflows using custom resource classes. It is designed
-        for developers and DevO...
+      generated_at_before: '2026-06-02T03:22:01Z'
+      generated_at_after: '2026-06-02T03:22:01Z'
+      preview_before: Set up a SUSE Linux Arm64 virtual machine on Google Cloud C4A
+        with Axion processors and run CircleCI Arm-native CI/CD workflows using self-hosted
+        machine runners. You will provision a c4a instance via...
+      preview_after: Set up a SUSE Linux Arm64 virtual machine on Google Cloud C4A
+        with Axion processors and run CircleCI Arm-native CI/CD workflows using self-hosted
+        machine runners. You will provision a c4a instance via...
+      preview_generated: This Learning Path shows how to run CircleCI Arm-native CI/CD
+        workloads on Google Cloud Axion C4A using a SUSE Linux Arm64 virtual machine.
+        You will provision a c4a-standard-4 VM, install the CircleCI...
     faqs:
-      action: unchanged
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       change_details:
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-        added_questions: []
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+        added_questions:
+        - What do I need before running the steps?
+        - Which Google Cloud VM type and OS should I use for the self-hosted runner?
+        - How is the CircleCI CLI used in this path?
+        - How do resource classes direct jobs to my Arm VM?
+        - How do I know the self-hosted runner is working with my Node.js demo workflow?
+        removed_questions:
+        - What prerequisites do I need before starting?
+        - Which Google Cloud instance and operating system does this path use?
+        - How do CircleCI jobs target the self-hosted Arm runner?
+        - What gets installed on the SUSE VM to enable Arm-native workflows?
+        - How can I verify that the setup works end-to-end?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Google Cloud VM type and OS should I use for the self-hosted runner?
+        - How is the CircleCI CLI used in this path?
+        - How do resource classes direct jobs to my Arm VM?
+        - How do I know the self-hosted runner is working with my Node.js demo workflow?
+        removed_questions:
+        - What prerequisites do I need before starting?
+        - Which Google Cloud instance and operating system does this path use?
+        - How do CircleCI jobs target the self-hosted Arm runner?
+        - What gets installed on the SUSE VM to enable Arm-native workflows?
+        - How can I verify that the setup works end-to-end?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
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-    template_version_before: summary-faq-v1
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+    status: updated
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+    rerun_flags_reset:
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+    change_reasons:
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+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
       missing_before: false
@@ -211397,51 +19095,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:57Z'
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-      preview_before: Learn how to install and configure CircleCI self-hosted machine runners on AWS Graviton
-        Arm64 instances to execute CI/CD workflows natively on Arm. It is designed for developers and
-        DevOps engineers w...
-      preview_after: Learn how to install and configure CircleCI self-hosted machine runners on AWS Graviton
-        Arm64 instances to execute CI/CD workflows natively on Arm. It is designed for developers and
-        DevOps engineers w...
-      preview_generated: Learn how to install and configure CircleCI self-hosted machine runners on AWS
-        Graviton Arm64 instances to execute CI/CD workflows natively on Arm. It is designed for developers
-        and DevOps engineers w...
+      generated_at_before: '2026-06-02T03:22:34Z'
+      generated_at_after: '2026-06-02T03:22:34Z'
+      preview_before: Learn how to set up CircleCI self-hosted machine runners on
+        AWS EC2 Graviton (Arm64) to execute CI/CD jobs natively on Arm. You will create
+        a Linux Arm64 VM on an m6g.large instance, install the Circl...
+      preview_after: Learn how to set up CircleCI self-hosted machine runners on AWS
+        EC2 Graviton (Arm64) to execute CI/CD jobs natively on Arm. You will create
+        a Linux Arm64 VM on an m6g.large instance, install the Circl...
+      preview_generated: This Learning Path shows how to run CircleCI workflows natively
+        on Arm by installing and configuring a self-hosted machine runner on an AWS
+        EC2 Graviton (Arm64) instance. You will create an m6g.large ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
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-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:22:34Z'
+      generated_at_after: '2026-06-03T00:33:29Z'
       before_count: 5
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       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - Which EC2 instance type and OS should I use for this setup?
+        - What do I need before launching the instance and configuring CircleCI?
+        - How do I install the CircleCI CLI on the Graviton instance?
+        - How do I register and link a self-hosted runner to my CircleCI organization?
+        - How is the CircleCI machine runner installed on the EC2 instance?
+        removed_questions:
+        - What do I need before starting?
+        - What AWS instance and operating system does this use?
+        - Why install the CircleCI CLI on the instance?
+        - How is the self-hosted runner linked to my CircleCI organization?
+        - How do I verify the runner is working on Arm?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - Which EC2 instance type and OS should I use for this setup?
+        - What do I need before launching the instance and configuring CircleCI?
+        - How do I install the CircleCI CLI on the Graviton instance?
+        - How do I register and link a self-hosted runner to my CircleCI organization?
+        - How is the CircleCI machine runner installed on the EC2 instance?
+        removed_questions:
+        - What do I need before starting?
+        - What AWS instance and operating system does this use?
+        - Why install the CircleCI CLI on the instance?
+        - How is the self-hosted runner linked to my CircleCI organization?
+        - How do I verify the runner is working on Arm?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/clair/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
       missing_before: false
@@ -211451,51 +19174,74 @@ history:
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to install and run Clair on Arm servers using combined and distributed
-        deployment models to scan container images and generate vulnerability reports. It is designed
-        for software developers i...
-      preview_after: Learn how to install and run Clair on Arm servers using combined and distributed
-        deployment models to scan container images and generate vulnerability reports. It is designed
-        for software developers i...
-      preview_generated: Learn how to install and run Clair on Arm servers using combined and distributed
-        deployment models to scan container images and generate vulnerability reports. It is designed
-        for software developers i...
+      generated_at_before: '2026-06-02T03:23:30Z'
+      generated_at_after: '2026-06-02T03:23:30Z'
+      preview_before: This Learning Path shows how to install and run Clair on Arm-based
+        Linux servers to scan container images and generate vulnerability reports.
+        You will deploy Clair using both combined (single-process)...
+      preview_after: This Learning Path shows how to install and run Clair on Arm-based
+        Linux servers to scan container images and generate vulnerability reports.
+        You will deploy Clair using both combined (single-process)...
+      preview_generated: This Learning Path shows how to install and run Clair on
+        Arm-based Linux servers to scan container images and generate vulnerability
+        reports. You will deploy Clair in either a combined model (all serv...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
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-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:23:30Z'
+      generated_at_after: '2026-06-03T00:33:54Z'
       before_count: 5
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       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Clair deployment model should I use?
+        - How do I know when Clair is ready to scan images?
+        - What result should I expect after submitting a manifest?
+        removed_questions:
+        - What environment and prerequisites are required?
+        - Which Clair deployment model should I start with?
+        - How do I know Clair is ready to return accurate vulnerability results?
+        - Does Clair run my container images during analysis?
+        updated_questions:
+        - How do I submit a container image for scanning?
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Clair deployment model should I use?
+        - How do I know when Clair is ready to scan images?
+        - What result should I expect after submitting a manifest?
+        removed_questions:
+        - What environment and prerequisites are required?
+        - Which Clair deployment model should I start with?
+        - How do I know Clair is ready to return accurate vulnerability results?
+        - Does Clair run my container images during analysis?
+        updated_questions:
+        - How do I submit a container image for scanning?
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -211505,51 +19251,76 @@ history:
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-      preview_before: Learn how to install ClickHouse on Arm-based cloud instances and measure database
-        performance using ClickBench to determine appropriate instance configurations. It is designed
-        for software developers ...
-      preview_after: Learn how to install ClickHouse on Arm-based cloud instances and measure database
-        performance using ClickBench to determine appropriate instance configurations. It is designed
-        for software developers ...
-      preview_generated: Learn how to install ClickHouse on Arm-based cloud instances and measure database
-        performance using ClickBench to determine appropriate instance configurations. It is designed
-        for software developers ...
+      generated_at_before: '2026-06-02T03:24:05Z'
+      generated_at_after: '2026-06-02T03:24:05Z'
+      preview_before: "This Learning Path shows how to install ClickHouse on an Arm-based\
+        \ cloud instance or Arm server running Ubuntu for Arm, then measure query\
+        \ latency with ClickBench using a web\u2011analytics dataset. It is ..."
+      preview_after: "This Learning Path shows how to install ClickHouse on an Arm-based\
+        \ cloud instance or Arm server running Ubuntu for Arm, then measure query\
+        \ latency with ClickBench using a web\u2011analytics dataset. It is ..."
+      preview_generated: Follow this introductory path to install ClickHouse on an
+        Arm-based server and measure its performance with ClickBench. You will use
+        a Linux environment running a recent Ubuntu for Arm to execute the ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
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-      generated_at_after: '2026-05-06T17:17:57Z'
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       before_count: 5
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       change_details:
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         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which cloud platforms can I use for the Arm instance?
+        - Which operating system should I run on the instance?
+        - What result should I expect after running ClickBench?
+        - What should I check if the benchmark fails or seems unusually slow?
+        removed_questions:
+        - What are the prerequisites before starting?
+        - Which cloud providers or platforms can I use?
+        - How long does this Learning Path take to complete?
+        - What will I install and measure in this path?
+        - How do I know the setup worked and what should I expect as output?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which cloud platforms can I use for the Arm instance?
+        - Which operating system should I run on the instance?
+        - What result should I expect after running ClickBench?
+        - What should I check if the benchmark fails or seems unusually slow?
+        removed_questions:
+        - What are the prerequisites before starting?
+        - Which cloud providers or platforms can I use?
+        - How long does this Learning Path take to complete?
+        - What will I install and measure in this path?
+        - How do I know the setup worked and what should I expect as output?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -211559,51 +19330,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to deploy ClickHouse on Google Cloud Axion C4A processors and build a
-        streaming ETL pipeline using Apache Beam, Dataflow, and Pub/Sub for real-time analytics. It is
-        designed for developers d...
-      preview_after: Learn how to deploy ClickHouse on Google Cloud Axion C4A processors and build a streaming
-        ETL pipeline using Apache Beam, Dataflow, and Pub/Sub for real-time analytics. It is designed
-        for developers d...
-      preview_generated: Learn how to deploy ClickHouse on Google Cloud Axion C4A processors and build
-        a streaming ETL pipeline using Apache Beam, Dataflow, and Pub/Sub for real-time analytics. It
-        is designed for developers d...
+      generated_at_before: '2026-06-02T03:24:37Z'
+      generated_at_after: '2026-06-02T03:24:37Z'
+      preview_before: Follow this introductory Learning Path to deploy ClickHouse
+        on Arm-based Google Cloud Axion C4A virtual machines and build a real-time
+        analytics pipeline. You will provision a SUSE Linux (Arm64) VM us...
+      preview_after: Follow this introductory Learning Path to deploy ClickHouse on
+        Arm-based Google Cloud Axion C4A virtual machines and build a real-time analytics
+        pipeline. You will provision a SUSE Linux (Arm64) VM us...
+      preview_generated: Deploy ClickHouse on Arm-based Google Cloud Axion C4A virtual
+        machines and build a streaming ETL pipeline for real-time analytics. You will
+        provision a SUSE SLES Arm64 VM (C4A), open a firewall rule f...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
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-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:24:37Z'
+      generated_at_after: '2026-06-03T00:34:45Z'
       before_count: 5
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       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which VM type and OS should I use on Google Cloud?
+        - Which network port must be opened for this setup?
+        - How should I configure Pub/Sub for ingestion?
+        - What outcome should I expect after deployment and configuration?
+        removed_questions:
+        - What are the prerequisites before starting?
+        - Which Google Cloud resources will I create and configure?
+        - Do I use the Google Cloud Console or command line in this path?
+        - Which software and language versions are used on the VM?
+        - How do I validate success and what results should I capture?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which VM type and OS should I use on Google Cloud?
+        - Which network port must be opened for this setup?
+        - How should I configure Pub/Sub for ingestion?
+        - What outcome should I expect after deployment and configuration?
+        removed_questions:
+        - What are the prerequisites before starting?
+        - Which Google Cloud resources will I create and configure?
+        - Do I use the Google Cloud Console or command line in this path?
+        - Which software and language versions are used on the VM?
+        - How do I validate success and what results should I capture?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -211613,51 +19409,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to deploy an Arm-based Cobalt 100 virtual machine on Azure, connect via
-        SSH, and configure network security group rules for external connectivity. It is designed for
-        developers and DevOps en...
-      preview_after: Learn how to deploy an Arm-based Cobalt 100 virtual machine on Azure, connect via
-        SSH, and configure network security group rules for external connectivity. It is designed for
-        developers and DevOps en...
-      preview_generated: Learn how to deploy an Arm-based Cobalt 100 virtual machine on Azure, connect
-        via SSH, and configure network security group rules for external connectivity. It is designed
-        for developers and DevOps en...
+      generated_at_before: '2026-06-02T03:25:26Z'
+      generated_at_after: '2026-06-02T03:25:26Z'
+      preview_before: This Learning Path walks you through deploying a Linux-based
+        Cobalt 100 virtual machine on Microsoft Azure, connecting via SSH, and configuring
+        Network Security Group (NSG) rules to expose an applicat...
+      preview_after: This Learning Path walks you through deploying a Linux-based
+        Cobalt 100 virtual machine on Microsoft Azure, connecting via SSH, and configuring
+        Network Security Group (NSG) rules to expose an applicat...
+      preview_generated: This introductory Learning Path shows how to deploy an Arm-based
+        Cobalt 100 virtual machine on Microsoft Azure, connect to it over SSH, and
+        expose an application port for testing. You will use the Azu...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: e4eb84292a669a8d73bff4b63d2fe3f70382dd873d023fc8ff24db5ad6178ab8
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:25:26Z'
+      generated_at_after: '2026-06-03T00:35:17Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Cobalt 100 VM series should I choose during creation?
+        - How do I find the public IP to SSH into the VM?
+        - What SSH command and username should I use to connect?
+        - How do I open and test an application port like 8080?
+        removed_questions:
+        - What Azure prerequisites do I need before starting?
+        - Which Azure VM series support Cobalt 100, and how should I choose?
+        - Do the steps use the Azure Portal or the Azure CLI?
+        - How do I connect to the VM via SSH?
+        - How do I open and verify an inbound application port?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Cobalt 100 VM series should I choose during creation?
+        - How do I find the public IP to SSH into the VM?
+        - What SSH command and username should I use to connect?
+        - How do I open and test an application port like 8080?
+        removed_questions:
+        - What Azure prerequisites do I need before starting?
+        - Which Azure VM series support Cobalt 100, and how should I choose?
+        - Do the steps use the Azure Portal or the Azure CLI?
+        - How do I connect to the VM via SSH?
+        - How do I open and verify an inbound application port?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -211667,51 +19488,76 @@ history:
       source_hash_before: e7ea48aaea0e25f624ea1d77c6252b0e537fdaeca3aacca560d7bc9d103bbbb3
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to automate Docker image creation for Arm using AWS CodeBuild with GitHub
-        integration and run the images on any Arm system with Docker installed. It is designed for software
-        developers inter...
-      preview_after: Learn how to automate Docker image creation for Arm using AWS CodeBuild with GitHub
-        integration and run the images on any Arm system with Docker installed. It is designed for software
-        developers inter...
-      preview_generated: Learn how to automate Docker image creation for Arm using AWS CodeBuild with
-        GitHub integration and run the images on any Arm system with Docker installed. It is designed
-        for software developers inter...
+      generated_at_before: '2026-06-02T03:25:50Z'
+      generated_at_after: '2026-06-02T03:25:50Z'
+      preview_before: Automate building Arm AArch64 Docker images with AWS CodeBuild
+        using a GitHub project, then publish them to Docker Hub and the Amazon ECR
+        Public Gallery and run them on any Arm system with Docker inst...
+      preview_after: Automate building Arm AArch64 Docker images with AWS CodeBuild
+        using a GitHub project, then publish them to Docker Hub and the Amazon ECR
+        Public Gallery and run them on any Arm system with Docker inst...
+      preview_generated: This advanced Learning Path shows how to use AWS CodeBuild
+        with GitHub to automate building AArch64 Docker images for Arm and share them
+        through Amazon ECR Public Gallery and Docker Hub. You configure...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: e7ea48aaea0e25f624ea1d77c6252b0e537fdaeca3aacca560d7bc9d103bbbb3
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       current_source_hash: e7ea48aaea0e25f624ea1d77c6252b0e537fdaeca3aacca560d7bc9d103bbbb3
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:25:50Z'
+      generated_at_after: '2026-06-03T00:35:39Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I verify that my machine is Arm AArch64 before running the images?
+        - Where will the built Docker images be published?
+        - When should I pull and run the images on my Arm machine?
+        - Do I need a GitHub repository to follow this path?
+        removed_questions:
+        - What AWS service and source control integration does this path use?
+        - What are the prerequisites to follow this Learning Path?
+        - How do I confirm my system is Arm AArch64 before running the images?
+        - Where are the built images published and how do I use them?
+        - How long will this take and what experience level is expected?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I verify that my machine is Arm AArch64 before running the images?
+        - Where will the built Docker images be published?
+        - When should I pull and run the images on my Arm machine?
+        - Do I need a GitHub repository to follow this path?
+        removed_questions:
+        - What AWS service and source control integration does this path use?
+        - What are the prerequisites to follow this Learning Path?
+        - How do I confirm my system is Arm AArch64 before running the images?
+        - Where are the built images published and how do I use them?
+        - How long will this take and what experience level is expected?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/codec/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -211721,51 +19567,76 @@ history:
       source_hash_before: 908a750f2a891a0c4c9d1183c2130ac5ac0fdcfb4a45185cef6ed6da47c9aaa9
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       current_source_hash: 908a750f2a891a0c4c9d1183c2130ac5ac0fdcfb4a45185cef6ed6da47c9aaa9
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to build and run the x265 H.265 codec on Arm servers with performance
-        benchmarking across various video resolutions and encoding presets. It is designed for software
-        developers who want to b...
-      preview_after: Learn how to build and run the x265 H.265 codec on Arm servers with performance benchmarking
-        across various video resolutions and encoding presets. It is designed for software developers
-        who want to b...
-      preview_generated: Learn how to build and run the x265 H.265 codec on Arm servers with performance
-        benchmarking across various video resolutions and encoding presets. It is designed for software
-        developers who want to b...
+      generated_at_before: '2026-06-02T03:26:25Z'
+      generated_at_after: '2026-06-02T03:26:25Z'
+      preview_before: "Build and run the x265 H.265 encoder on Arm servers and benchmark\
+        \ its performance across different video resolutions and encoding presets.\
+        \ You will use an Arm-based cloud instance\u2014verified on AWS EC2 ..."
+      preview_after: "Build and run the x265 H.265 encoder on Arm servers and benchmark\
+        \ its performance across different video resolutions and encoding presets.\
+        \ You will use an Arm-based cloud instance\u2014verified on AWS EC2 ..."
+      preview_generated: Build and run the open-source x265 (H.265/HEVC) encoder on
+        Arm servers, then compare encoding performance across different video resolutions
+        and presets. You will install GCC, CMake, and supporting pa...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 908a750f2a891a0c4c9d1183c2130ac5ac0fdcfb4a45185cef6ed6da47c9aaa9
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       current_source_hash: 908a750f2a891a0c4c9d1183c2130ac5ac0fdcfb4a45185cef6ed6da47c9aaa9
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:26:25Z'
+      generated_at_after: '2026-06-03T00:36:00Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which packages should I install to build x265 on Ubuntu?
+        - Where do the Arm optimizations for x265 come from?
+        - How will I measure the performance impact of different settings?
+        - Which operating systems and platforms are validated for these steps?
+        removed_questions:
+        - What environment do I need to start?
+        - Which software packages are installed during setup?
+        - Does this use Arm Neoverse-specific optimizations for x265?
+        - What will I build and how do I validate it works?
+        - What input video or dataset is required?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which packages should I install to build x265 on Ubuntu?
+        - Where do the Arm optimizations for x265 come from?
+        - How will I measure the performance impact of different settings?
+        - Which operating systems and platforms are validated for these steps?
+        removed_questions:
+        - What environment do I need to start?
+        - Which software packages are installed during setup?
+        - Does this use Arm Neoverse-specific optimizations for x265?
+        - What will I build and how do I validate it works?
+        - What input video or dataset is required?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/codec1/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -211775,51 +19646,76 @@ history:
       source_hash_before: c0643a788cdb0b3e33fe645fbb61d99a1899806e3ee197541c1eb8134b2876c1
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       current_source_hash: c0643a788cdb0b3e33fe645fbb61d99a1899806e3ee197541c1eb8134b2876c1
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to build and run the AV1 and VP9 video codecs on Arm Linux systems with
-        performance benchmarking across various resolutions and encoding configurations. It is designed
-        for software developer...
-      preview_after: Learn how to build and run the AV1 and VP9 video codecs on Arm Linux systems with
-        performance benchmarking across various resolutions and encoding configurations. It is designed
-        for software developer...
-      preview_generated: Learn how to build and run the AV1 and VP9 video codecs on Arm Linux systems
-        with performance benchmarking across various resolutions and encoding configurations. It is designed
-        for software developer...
+      generated_at_before: '2026-06-02T03:27:16Z'
+      generated_at_after: '2026-06-02T03:27:16Z'
+      preview_before: Learn how to build and run the AV1 (libaom) and VP9 (libvpx)
+        video codecs on Arm Linux, then benchmark them on example videos using multiple
+        resolutions and encoding configurations. You will install b...
+      preview_after: Learn how to build and run the AV1 (libaom) and VP9 (libvpx)
+        video codecs on Arm Linux, then benchmark them on example videos using multiple
+        resolutions and encoding configurations. You will install b...
+      preview_generated: Build and run the AV1 and VP9 video codecs on Arm Linux,
+        then benchmark them across resolutions and encoding configurations. You will
+        compile the AV1 reference implementation (libxaom) and the VP9 ref...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: c0643a788cdb0b3e33fe645fbb61d99a1899806e3ee197541c1eb8134b2876c1
       source_hash_after: c0643a788cdb0b3e33fe645fbb61d99a1899806e3ee197541c1eb8134b2876c1
       current_source_hash: c0643a788cdb0b3e33fe645fbb61d99a1899806e3ee197541c1eb8134b2876c1
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:27:16Z'
+      generated_at_after: '2026-06-03T00:36:28Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which codecs and libraries are used in this path?
+        - Which development tools do I need to install to build the codecs?
+        - Where do I obtain the source code for the codecs?
+        - What results should I expect after completing the path?
+        removed_questions:
+        - What environment and tools do I need to complete this path?
+        - Which codecs and libraries are built in this path?
+        - How do I obtain the source code for the codecs?
+        - What will I run, and how can I validate that everything worked?
+        - How much time should I plan for, and what skill level is assumed?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which codecs and libraries are used in this path?
+        - Which development tools do I need to install to build the codecs?
+        - Where do I obtain the source code for the codecs?
+        - What results should I expect after completing the path?
+        removed_questions:
+        - What environment and tools do I need to complete this path?
+        - Which codecs and libraries are built in this path?
+        - How do I obtain the source code for the codecs?
+        - What will I run, and how can I validate that everything worked?
+        - How much time should I plan for, and what skill level is assumed?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -211829,51 +19725,76 @@ history:
       source_hash_before: 0a7d76b8c944a50073c7524813acf83948155c7c61d9c92f9202eae1192c9600
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       current_source_hash: 0a7d76b8c944a50073c7524813acf83948155c7c61d9c92f9202eae1192c9600
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to install and configure Couchbase on Google Cloud Axion C4A Arm64 instances
-        and benchmark read/write performance using YCSB workloads. It is designed for developers deploying
-        Couchbase work...
-      preview_after: Learn how to install and configure Couchbase on Google Cloud Axion C4A Arm64 instances
-        and benchmark read/write performance using YCSB workloads. It is designed for developers deploying
-        Couchbase work...
-      preview_generated: Learn how to install and configure Couchbase on Google Cloud Axion C4A Arm64
-        instances and benchmark read/write performance using YCSB workloads. It is designed for developers
-        deploying Couchbase work...
+      generated_at_before: '2026-06-02T03:28:12Z'
+      generated_at_after: '2026-06-02T03:28:12Z'
+      preview_before: Follow this introductory path to deploy Couchbase Server on
+        Arm-based Google Cloud Axion C4A virtual machines and run basic performance
+        checks. You will provision a SUSE Linux Enterprise Server (SLES)...
+      preview_after: Follow this introductory path to deploy Couchbase Server on Arm-based
+        Google Cloud Axion C4A virtual machines and run basic performance checks.
+        You will provision a SUSE Linux Enterprise Server (SLES)...
+      preview_generated: Follow this introductory Learning Path to deploy Couchbase
+        Server on Arm-based Google Cloud C4A virtual machines powered by Axion processors.
+        You will provision a SUSE Linux Enterprise Server (Arm64) ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 0a7d76b8c944a50073c7524813acf83948155c7c61d9c92f9202eae1192c9600
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       current_source_hash: 0a7d76b8c944a50073c7524813acf83948155c7c61d9c92f9202eae1192c9600
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:28:12Z'
+      generated_at_after: '2026-06-03T00:36:56Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Google Cloud VM type and OS should I use?
+        - How do I allow and test access to the Couchbase Web Console?
+        - How do I know Couchbase installed correctly on the VM?
+        - What should I capture when running the YCSB benchmarks?
+        removed_questions:
+        - Do I need a Google Cloud account or specific permissions?
+        - What VM type and operating system does this path use?
+        - How do I access the Couchbase Web Console?
+        - What should be in place before running benchmarks?
+        - What benchmarks are run and how do I know they worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Google Cloud VM type and OS should I use?
+        - How do I allow and test access to the Couchbase Web Console?
+        - How do I know Couchbase installed correctly on the VM?
+        - What should I capture when running the YCSB benchmarks?
+        removed_questions:
+        - Do I need a Google Cloud account or specific permissions?
+        - What VM type and operating system does this path use?
+        - How do I access the Couchbase Web Console?
+        - What should be in place before running benchmarks?
+        - What benchmarks are run and how do I know they worked?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -211883,51 +19804,76 @@ history:
       source_hash_before: 22f845b8ea4dbb9ffc63fafb76c17c79aedde14a28417d49e1ab0833bbbc1eba
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       current_source_hash: 22f845b8ea4dbb9ffc63fafb76c17c79aedde14a28417d49e1ab0833bbbc1eba
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to apply g++ compiler optimization techniques and flags to improve C++
-        application performance on Arm systems with hands-on examples. It is designed for beginner C++
-        developers who are looki...
-      preview_after: Learn how to apply g++ compiler optimization techniques and flags to improve C++
-        application performance on Arm systems with hands-on examples. It is designed for beginner C++
-        developers who are looki...
-      preview_generated: Learn how to apply g++ compiler optimization techniques and flags to improve
-        C++ application performance on Arm systems with hands-on examples. It is designed for beginner
-        C++ developers who are looki...
+      generated_at_before: '2026-06-02T03:28:45Z'
+      generated_at_after: '2026-06-02T03:28:45Z'
+      preview_before: Learn how to apply g++ compiler optimization flags when building
+        C++ applications for Arm-based servers. You will provision and connect to
+        an AWS Graviton4 (r8g.xlarge) instance running Ubuntu 24.04 L...
+      preview_after: Learn how to apply g++ compiler optimization flags when building
+        C++ applications for Arm-based servers. You will provision and connect to
+        an AWS Graviton4 (r8g.xlarge) instance running Ubuntu 24.04 L...
+      preview_generated: This introductory path shows how to use the g++ compiler
+        to apply optimization flags when building a C++ program for Arm targets. You
+        will provision an AWS Graviton4 (r8g.xlarge) instance running Ubun...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 22f845b8ea4dbb9ffc63fafb76c17c79aedde14a28417d49e1ab0833bbbc1eba
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       current_source_hash: 22f845b8ea4dbb9ffc63fafb76c17c79aedde14a28417d49e1ab0833bbbc1eba
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:28:45Z'
+      generated_at_after: '2026-06-03T00:37:16Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which -march value should I use for my build?
+        - How do I know my environment and compiler are ready?
+        - What result should I expect after I build and run the example?
+        - Can I follow this on other Arm-based cloud instances?
+        removed_questions:
+        - What infrastructure do I need to follow this Learning Path?
+        - What skills or prerequisites are assumed?
+        - Which compiler and flags are used, and how do I choose them?
+        - What will I build or produce by the end?
+        - Can I use other Arm-based cloud providers for this path?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which -march value should I use for my build?
+        - How do I know my environment and compiler are ready?
+        - What result should I expect after I build and run the example?
+        - Can I follow this on other Arm-based cloud instances?
+        removed_questions:
+        - What infrastructure do I need to follow this Learning Path?
+        - What skills or prerequisites are assumed?
+        - Which compiler and flags are used, and how do I choose them?
+        - What will I build or produce by the end?
+        - Can I use other Arm-based cloud providers for this path?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -211937,51 +19883,76 @@ history:
       source_hash_before: 4e7de348514d0b5a742fa47bb85a2a5814fa9bd587478e7ef08365d16273f6bf
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to apply profile-guided optimization to C++ applications on Arm systems
-        and measure performance improvements using Google Benchmark. It is designed for Developers looking
-        to optimize C++ per...
-      preview_after: Learn how to apply profile-guided optimization to C++ applications on Arm systems
-        and measure performance improvements using Google Benchmark. It is designed for Developers looking
-        to optimize C++ per...
-      preview_generated: Learn how to apply profile-guided optimization to C++ applications on Arm systems
-        and measure performance improvements using Google Benchmark. It is designed for Developers looking
-        to optimize C++ per...
+      generated_at_before: '2026-06-02T03:29:36Z'
+      generated_at_after: '2026-06-02T03:29:36Z'
+      preview_before: Learn to measure and tune C++ code on Arm-based Linux systems
+        using Profile-Guided Optimization (PGO) and Google Benchmark. You will compile
+        an instrumented binary with GCC/G++ using -fprofile-generat...
+      preview_after: Learn to measure and tune C++ code on Arm-based Linux systems
+        using Profile-Guided Optimization (PGO) and Google Benchmark. You will compile
+        an instrumented binary with GCC/G++ using -fprofile-generat...
+      preview_generated: Learn how to microbenchmark C++ code on Arm-based Linux systems
+        and apply profile-guided optimization (PGO) with GCC/G++ and Google Benchmark.
+        You will build an instrumented binary with -fprofile-gene...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 4e7de348514d0b5a742fa47bb85a2a5814fa9bd587478e7ef08365d16273f6bf
       source_hash_after: 4e7de348514d0b5a742fa47bb85a2a5814fa9bd587478e7ef08365d16273f6bf
       current_source_hash: 4e7de348514d0b5a742fa47bb85a2a5814fa9bd587478e7ef08365d16273f6bf
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:29:36Z'
+      generated_at_after: '2026-06-03T00:37:40Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which compiler options should I use for PGO with GCC/G++ and in what order?
+        - How do I know the profiling run succeeded and where are the files?
+        - What will I benchmark in this path and why that example?
+        - When should I apply PGO in my project or CI workflow?
+        removed_questions:
+        - What environment and prerequisites do I need?
+        - Which compiler and flags are used for PGO in this path?
+        - What will I benchmark, and why was it chosen?
+        - How do I verify that profile data was collected and used?
+        - Can I use PGO in CI, and what trade-offs should I expect?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which compiler options should I use for PGO with GCC/G++ and in what order?
+        - How do I know the profiling run succeeded and where are the files?
+        - What will I benchmark in this path and why that example?
+        - When should I apply PGO in my project or CI workflow?
+        removed_questions:
+        - What environment and prerequisites do I need?
+        - Which compiler and flags are used for PGO in this path?
+        - What will I benchmark, and why was it chosen?
+        - How do I verify that profile data was collected and used?
+        - Can I use PGO in CI, and what trade-offs should I expect?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -211991,51 +19962,81 @@ history:
       source_hash_before: 58dba071f70b4f85b87b3bd27b3a7ba3ff985f80079a42e05b797a998aeaf104
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       current_source_hash: 58dba071f70b4f85b87b3bd27b3a7ba3ff985f80079a42e05b797a998aeaf104
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to profile and identify CPU hotspots in C++ applications on Arm Neoverse
-        using Arm Performix flame graphs to guide optimization. It is designed for software developers
-        and performance engine...
-      preview_after: Learn how to profile and identify CPU hotspots in C++ applications on Arm Neoverse
-        using Arm Performix flame graphs to guide optimization. It is designed for software developers
-        and performance engine...
-      preview_generated: Learn how to profile and identify CPU hotspots in C++ applications on Arm Neoverse
-        using Arm Performix flame graphs to guide optimization. It is designed for software developers
-        and performance engine...
+      generated_at_before: '2026-06-02T03:30:14Z'
+      generated_at_after: '2026-06-02T03:30:14Z'
+      preview_before: This Learning Path shows how to find code hotspots in C++ applications
+        running on Arm Linux systems using Arm Performix on Arm Neoverse. You will
+        build and run a C++11 Mandelbrot example that generate...
+      preview_after: This Learning Path shows how to find code hotspots in C++ applications
+        running on Arm Linux systems using Arm Performix on Arm Neoverse. You will
+        build and run a C++11 Mandelbrot example that generate...
+      preview_generated: "This Learning Path shows how to use Arm Performix on Arm\
+        \ Neoverse to quickly locate CPU hotspots in a C++ application running on\
+        \ Linux. You will build a C++11 Mandelbrot example that renders a 1920\xD7\
+        10..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 58dba071f70b4f85b87b3bd27b3a7ba3ff985f80079a42e05b797a998aeaf104
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       current_source_hash: 58dba071f70b4f85b87b3bd27b3a7ba3ff985f80079a42e05b797a998aeaf104
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:30:14Z'
+      generated_at_after: '2026-06-03T00:38:16Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - Which Arm Performix feature should I run to find hotspots?
+        - What do I need before running the steps?
+        - What do I build and what output should I expect from the example?
+        - How do I know profiling worked?
+        - What should I check if the image file is missing when profiling under Arm
+          Performix?
+        removed_questions:
+        - What do I need before starting?
+        - What will I build and profile?
+        - How are hotspots collected and presented?
+        - "Why doesn\u2019t the output image appear where I expect when launched from\
+          \ Arm Performix?"
+        - What kind of optimization guidance does the example provide?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - Which Arm Performix feature should I run to find hotspots?
+        - What do I need before running the steps?
+        - What do I build and what output should I expect from the example?
+        - How do I know profiling worked?
+        - What should I check if the image file is missing when profiling under Arm
+          Performix?
+        removed_questions:
+        - What do I need before starting?
+        - What will I build and profile?
+        - How are hotspots collected and presented?
+        - "Why doesn\u2019t the output image appear where I expect when launched from\
+          \ Arm Performix?"
+        - What kind of optimization guidance does the example provide?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/csp/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -212045,51 +20046,74 @@ history:
       source_hash_before: 9e94b69eabf35677c48db812bb85ea9cef184efc078a826b96954f540a45e915
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       current_source_hash: 9e94b69eabf35677c48db812bb85ea9cef184efc078a826b96954f540a45e915
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to start an Arm-based virtual machine instance from major cloud service
-        providers and verify the Arm architecture is being used. It is designed for software developers
-        who are new to Arm-bas...
-      preview_after: Learn how to start an Arm-based virtual machine instance from major cloud service
-        providers and verify the Arm architecture is being used. It is designed for software developers
-        who are new to Arm-bas...
-      preview_generated: Learn how to start an Arm-based virtual machine instance from major cloud service
-        providers and verify the Arm architecture is being used. It is designed for software developers
-        who are new to Arm-bas...
+      generated_at_before: '2026-06-02T03:30:48Z'
+      generated_at_after: '2026-06-02T03:30:48Z'
+      preview_before: This introductory Learning Path shows how to launch a Linux
+        virtual machine on Arm-based instances from major cloud providers and confirm
+        that it is running on Arm architecture. You will use each prov...
+      preview_after: This introductory Learning Path shows how to launch a Linux virtual
+        machine on Arm-based instances from major cloud providers and confirm that
+        it is running on Arm architecture. You will use each prov...
+      preview_generated: This introductory Learning Path shows how to launch a Linux
+        virtual machine on major cloud providers using Arm-based CPU instances and
+        verify that the instance is running on Arm. You will provision Ar...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 9e94b69eabf35677c48db812bb85ea9cef184efc078a826b96954f540a45e915
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       current_source_hash: 9e94b69eabf35677c48db812bb85ea9cef184efc078a826b96954f540a45e915
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:30:48Z'
+      generated_at_after: '2026-06-03T00:38:48Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - Which instance types should I choose to get an Arm VM on each cloud?
+        - Which operating system is used in the examples?
+        - "How do I verify that the VM is Arm-based once it\u2019s running?"
+        - What result should I expect after completing the steps?
+        removed_questions:
+        - Which cloud platforms does this path cover?
+        - What operating system is used in the examples?
+        - How do I verify that my instance is Arm-based?
+        - Are there specific instance types or sizes I should choose?
+        updated_questions:
+        - What do I need before starting?
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - Which instance types should I choose to get an Arm VM on each cloud?
+        - Which operating system is used in the examples?
+        - "How do I verify that the VM is Arm-based once it\u2019s running?"
+        - What result should I expect after completing the steps?
+        removed_questions:
+        - Which cloud platforms does this path cover?
+        - What operating system is used in the examples?
+        - How do I verify that my instance is Arm-based?
+        - Are there specific instance types or sizes I should choose?
+        updated_questions:
+        - What do I need before starting?
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -212099,51 +20123,76 @@ history:
       source_hash_before: f600fcba0adea12ff1b8b092e75de553577d940dc3bf632e9a247cec22d364a4
       source_hash_after: f600fcba0adea12ff1b8b092e75de553577d940dc3bf632e9a247cec22d364a4
       current_source_hash: f600fcba0adea12ff1b8b092e75de553577d940dc3bf632e9a247cec22d364a4
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to deploy and run the DeepSeek-R1 language model on Arm servers using
-        llama.cpp with quantization for efficient CPU inference. It is designed for developers who want
-        to run DeepSeek-R1 on Ar...
-      preview_after: Learn how to deploy and run the DeepSeek-R1 language model on Arm servers using llama.cpp
-        with quantization for efficient CPU inference. It is designed for developers who want to run DeepSeek-R1
-        on Ar...
-      preview_generated: Learn how to deploy and run the DeepSeek-R1 language model on Arm servers using
-        llama.cpp with quantization for efficient CPU inference. It is designed for developers who want
-        to run DeepSeek-R1 on Ar...
+      generated_at_before: '2026-06-02T03:31:44Z'
+      generated_at_after: '2026-06-02T03:31:44Z'
+      preview_before: This Learning Path shows how to deploy and run the DeepSeek-R1
+        671B language model on Arm-based servers using llama.cpp with quantization
+        for CPU inference. You will clone and build llama.cpp, downloa...
+      preview_after: This Learning Path shows how to deploy and run the DeepSeek-R1
+        671B language model on Arm-based servers using llama.cpp with quantization
+        for CPU inference. You will clone and build llama.cpp, downloa...
+      preview_generated: This Learning Path shows how to deploy the DeepSeek-R1 671B
+        language model on Arm-based servers using llama.cpp with a pre-quantized model
+        for CPU inference. You will clone and build llama.cpp on Ubun...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: f600fcba0adea12ff1b8b092e75de553577d940dc3bf632e9a247cec22d364a4
       source_hash_after: f600fcba0adea12ff1b8b092e75de553577d940dc3bf632e9a247cec22d364a4
       current_source_hash: f600fcba0adea12ff1b8b092e75de553577d940dc3bf632e9a247cec22d364a4
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:31:44Z'
+      generated_at_after: '2026-06-03T00:39:18Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Where do I get the DeepSeek-R1 model and what format is expected?
+        - How do I start and access the model server during this Learning Path?
+        - Do I need any extra tools to query or work with the API responses?
+        - What should I check if the llama.cpp server binary is missing?
+        removed_questions:
+        - What server specs and OS are required to follow this path?
+        - Which platforms can I use for the Arm instance?
+        - How do I obtain the DeepSeek-R1 model used here?
+        - How is the model served and accessed by applications?
+        - What additional software or build steps are included?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Where do I get the DeepSeek-R1 model and what format is expected?
+        - How do I start and access the model server during this Learning Path?
+        - Do I need any extra tools to query or work with the API responses?
+        - What should I check if the llama.cpp server binary is missing?
+        removed_questions:
+        - What server specs and OS are required to follow this path?
+        - Which platforms can I use for the Arm instance?
+        - How do I obtain the DeepSeek-R1 model used here?
+        - How is the model served and accessed by applications?
+        - What additional software or build steps are included?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -212153,51 +20202,76 @@ history:
       source_hash_before: a1ec216948e7cfd4fc52815196bb3b99ab4e76c9c756aa9e9a8e3216ef5e7ce4
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to use fio to microbenchmark storage performance on Arm systems and monitor
-        storage using iostat, iotop, and pidstat to identify bottlenecks. It is designed for developers
-        looking to optimiz...
-      preview_after: Learn how to use fio to microbenchmark storage performance on Arm systems and monitor
-        storage using iostat, iotop, and pidstat to identify bottlenecks. It is designed for developers
-        looking to optimiz...
-      preview_generated: Learn how to use fio to microbenchmark storage performance on Arm systems and
-        monitor storage using iostat, iotop, and pidstat to identify bottlenecks. It is designed for developers
-        looking to optimiz...
+      generated_at_before: '2026-06-02T03:32:18Z'
+      generated_at_after: '2026-06-02T03:32:18Z'
+      preview_before: This introductory Learning Path shows how to monitor and microbenchmark
+        storage on Arm-based Linux systems. You will review storage fundamentals and
+        key workload attributes (IOPS, I/O size, throughput...
+      preview_after: This introductory Learning Path shows how to monitor and microbenchmark
+        storage on Arm-based Linux systems. You will review storage fundamentals and
+        key workload attributes (IOPS, I/O size, throughput...
+      preview_generated: Learn to characterize and microbenchmark storage performance
+        on Arm-based Linux systems using fio and observe behavior with iostat, iotop,
+        and pidstat. You will review storage fundamentals and workloa...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: a1ec216948e7cfd4fc52815196bb3b99ab4e76c9c756aa9e9a8e3216ef5e7ce4
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       current_source_hash: a1ec216948e7cfd4fc52815196bb3b99ab4e76c9c756aa9e9a8e3216ef5e7ce4
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:32:18Z'
+      generated_at_after: '2026-06-03T00:39:53Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Can I use a cloud provider other than AWS?
+        - Which instance type and example workload are used in the path?
+        - Which block storage devices are benchmarked and how are they created?
+        - How should I monitor and validate storage behavior while running fio?
+        removed_questions:
+        - What environment do I need to follow this Learning Path?
+        - Is AWS required, and which instance/storage does the example use?
+        - Which tools will I use during the steps?
+        - What storage metrics are examined or measured?
+        - How do I know I completed the path successfully?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Can I use a cloud provider other than AWS?
+        - Which instance type and example workload are used in the path?
+        - Which block storage devices are benchmarked and how are they created?
+        - How should I monitor and validate storage behavior while running fio?
+        removed_questions:
+        - What environment do I need to follow this Learning Path?
+        - Is AWS required, and which instance/storage does the example use?
+        - Which tools will I use during the steps?
+        - What storage metrics are examined or measured?
+        - How do I know I completed the path successfully?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -212207,51 +20281,76 @@ history:
       source_hash_before: 6ac0c7cf1ab4b3efa680acbf1e349b858bee5dc7992baf6689e1f293a83060a4
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       current_source_hash: 6ac0c7cf1ab4b3efa680acbf1e349b858bee5dc7992baf6689e1f293a83060a4
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Run distributed LLM inference with llama.cpp across multiple AWS Graviton4 instances,
-        covering multi-node setup, coordination, and performance trade-offs. It is designed for This introductory
-        topic is...
-      preview_after: Run distributed LLM inference with llama.cpp across multiple AWS Graviton4 instances,
-        covering multi-node setup, coordination, and performance trade-offs. It is designed for This introductory
-        topic is...
-      preview_generated: Run distributed LLM inference with llama.cpp across multiple AWS Graviton4 instances,
-        covering multi-node setup, coordination, and performance trade-offs. It is designed for This introductory
-        topic is...
+      generated_at_before: '2026-06-02T03:33:12Z'
+      generated_at_after: '2026-06-02T03:33:12Z'
+      preview_before: Learn to run distributed LLM inference with llama.cpp across
+        multiple Arm-based AWS Graviton4 instances on Linux. You will set up a master
+        (main) host and worker nodes, download a Meta Llama 3.1 model...
+      preview_after: Learn to run distributed LLM inference with llama.cpp across
+        multiple Arm-based AWS Graviton4 instances on Linux. You will set up a master
+        (main) host and worker nodes, download a Meta Llama 3.1 model...
+      preview_generated: "This Learning Path shows how to run distributed LLM inference\
+        \ with llama.cpp on Arm-based AWS Graviton4 instances. You will build llama.cpp\
+        \ on Linux, download a Llama 3.1 model, convert Meta\u2019s safeten..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 6ac0c7cf1ab4b3efa680acbf1e349b858bee5dc7992baf6689e1f293a83060a4
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       current_source_hash: 6ac0c7cf1ab4b3efa680acbf1e349b858bee5dc7992baf6689e1f293a83060a4
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:33:12Z'
+      generated_at_after: '2026-06-03T00:40:36Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What AWS resources do I need before starting?
+        - Which model is used and how is it prepared?
+        - How do I register worker nodes on the master node?
+        - How do I verify that the master can reach a worker node?
+        - What access and prior knowledge do I need to download and run the model?
+        removed_questions:
+        - What AWS resources and software do I need before starting?
+        - Which model formats and artifacts are produced in this path?
+        - How are the nodes organized for distributed inference?
+        - How do I verify that the master can reach the workers?
+        - What level of experience and time commitment are expected?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What AWS resources do I need before starting?
+        - Which model is used and how is it prepared?
+        - How do I register worker nodes on the master node?
+        - How do I verify that the master can reach a worker node?
+        - What access and prior knowledge do I need to download and run the model?
+        removed_questions:
+        - What AWS resources and software do I need before starting?
+        - Which model formats and artifacts are produced in this path?
+        - How are the nodes organized for distributed inference?
+        - How do I verify that the master can reach the workers?
+        - What level of experience and time commitment are expected?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/django/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -212261,51 +20360,76 @@ history:
       source_hash_before: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
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       current_source_hash: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to create a simple Django web application and deploy it on Arm machines
-        using Nginx and PostgreSQL. It is designed for engineers who want to deploy a Django based application
-        on Arm machines...
-      preview_after: Learn how to create a simple Django web application and deploy it on Arm machines
-        using Nginx and PostgreSQL. It is designed for engineers who want to deploy a Django based application
-        on Arm machines...
-      preview_generated: Learn how to create a simple Django web application and deploy it on Arm machines
-        using Nginx and PostgreSQL. It is designed for engineers who want to deploy a Django based application
-        on Arm machines...
+      generated_at_before: '2026-06-02T03:33:56Z'
+      generated_at_after: '2026-06-02T03:33:56Z'
+      preview_before: Build and deploy a simple Django web application on Arm-based
+        Linux machines using Nginx and PostgreSQL. This introductory path uses Ubuntu
+        22.04 LTS and walks you through creating a Django project, c...
+      preview_after: Build and deploy a simple Django web application on Arm-based
+        Linux machines using Nginx and PostgreSQL. This introductory path uses Ubuntu
+        22.04 LTS and walks you through creating a Django project, c...
+      preview_generated: "This introductory path walks you through creating a simple\
+        \ Django application and deploying it on an Arm-based Linux machine using\
+        \ Nginx and PostgreSQL. You\u2019ll connect to an Arm server or VM (the step..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:33:56Z'
+      generated_at_after: '2026-06-03T00:41:18Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What environment do I need to run this?
+        - Do I need a specific Python version or a virtual environment?
+        - Do I need to install Nginx and PostgreSQL before deploying?
+        - How do I know the Django project was created correctly?
+        - Which PostgreSQL settings should I use and how do I create the database?
+        removed_questions:
+        - What machines and OS can I use for this deployment?
+        - What privileges and skills are required?
+        - Which Python version should I use?
+        - What will I create and configure during the path?
+        - How do I configure the PostgreSQL connection in Django?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What environment do I need to run this?
+        - Do I need a specific Python version or a virtual environment?
+        - Do I need to install Nginx and PostgreSQL before deploying?
+        - How do I know the Django project was created correctly?
+        - Which PostgreSQL settings should I use and how do I create the database?
+        removed_questions:
+        - What machines and OS can I use for this deployment?
+        - What privileges and skills are required?
+        - Which Python version should I use?
+        - What will I create and configure during the path?
+        - How do I configure the PostgreSQL connection in Django?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -212315,51 +20439,78 @@ history:
       source_hash_before: 6675df4c91126b157dcbf39c96a773130f1a95e2f5680913979da96f6f6c97cd
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       current_source_hash: 6675df4c91126b157dcbf39c96a773130f1a95e2f5680913979da96f6f6c97cd
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to deploy a production-grade Django REST API on Google Kubernetes Engine
-        with Arm64 Axion node pools integrated with Google Cloud managed data services. It is designed
-        for DevOps engineers a...
-      preview_after: Learn how to deploy a production-grade Django REST API on Google Kubernetes Engine
-        with Arm64 Axion node pools integrated with Google Cloud managed data services. It is designed
-        for DevOps engineers a...
-      preview_generated: Learn how to deploy a production-grade Django REST API on Google Kubernetes Engine
-        with Arm64 Axion node pools integrated with Google Cloud managed data services. It is designed
-        for DevOps engineers a...
+      generated_at_before: '2026-06-02T03:34:24Z'
+      generated_at_after: '2026-06-02T03:34:24Z'
+      preview_before: This Learning Path shows how to deploy a production-grade Django
+        REST API on Google Cloud using Arm-based Axion compute. You will provision
+        Arm64 Axion C4A virtual machines and GKE node pools, package...
+      preview_after: This Learning Path shows how to deploy a production-grade Django
+        REST API on Google Cloud using Arm-based Axion compute. You will provision
+        Arm64 Axion C4A virtual machines and GKE node pools, package...
+      preview_generated: This Learning Path walks you through deploying a production-grade
+        Django REST API on Google Kubernetes Engine using Arm64 Axion node pools on
+        Google Cloud. You will provision Arm-based Axion compute (...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 6675df4c91126b157dcbf39c96a773130f1a95e2f5680913979da96f6f6c97cd
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       current_source_hash: 6675df4c91126b157dcbf39c96a773130f1a95e2f5680913979da96f6f6c97cd
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:34:24Z'
+      generated_at_after: '2026-06-03T00:41:54Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I run and reach the Django development server on the Axion VM?
+        - Which container and registry steps are included before deploying to GKE?
+        - Which Kubernetes resources and exposure method are used on GKE?
+        - How does the app connect to managed data services and how is performance
+          evaluated?
+        removed_questions:
+        - What prerequisites do I need before starting?
+        - Which Google Cloud resources and services are used in this path?
+        - What operating system and instance type are used for the VM steps?
+        - How do I verify the Django development server is reachable?
+        - How is application performance measured in this Learning Path?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I run and reach the Django development server on the Axion VM?
+        - Which container and registry steps are included before deploying to GKE?
+        - Which Kubernetes resources and exposure method are used on GKE?
+        - How does the app connect to managed data services and how is performance
+          evaluated?
+        removed_questions:
+        - What prerequisites do I need before starting?
+        - Which Google Cloud resources and services are used in this path?
+        - What operating system and instance type are used for the VM steps?
+        - How do I verify the Django development server is reachable?
+        - How is application performance measured in this Learning Path?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -212369,51 +20520,76 @@ history:
       source_hash_before: 82716c2de19d18a154c85d03b7f2ec01839284262914f7bb3ad04b18a105379d
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       current_source_hash: 82716c2de19d18a154c85d03b7f2ec01839284262914f7bb3ad04b18a105379d
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to build and benchmark the Deep Learning Recommendation Model using PyTorch
-        and MLPerf on Arm Neoverse V2 processors. It is designed for software developers who want to set
-        up a pipeline in ...
-      preview_after: Learn how to build and benchmark the Deep Learning Recommendation Model using PyTorch
-        and MLPerf on Arm Neoverse V2 processors. It is designed for software developers who want to set
-        up a pipeline in ...
-      preview_generated: Learn how to build and benchmark the Deep Learning Recommendation Model using
-        PyTorch and MLPerf on Arm Neoverse V2 processors. It is designed for software developers who want
-        to set up a pipeline in ...
+      generated_at_before: '2026-06-02T03:35:11Z'
+      generated_at_after: '2026-06-02T03:35:11Z'
+      preview_before: This Learning Path shows how to build and benchmark the Deep
+        Learning Recommendation Model (DLRM) on Arm Neoverse V2 processors using PyTorch
+        and MLPerf. You will prepare a Linux Arm-based cloud insta...
+      preview_after: This Learning Path shows how to build and benchmark the Deep
+        Learning Recommendation Model (DLRM) on Arm Neoverse V2 processors using PyTorch
+        and MLPerf. You will prepare a Linux Arm-based cloud insta...
+      preview_generated: Follow this Learning Path to build and benchmark the Deep
+        Learning Recommendation Model (DLRM) on Arm Neoverse V2 processors using MLPerf
+        and PyTorch on Linux. You will prepare storage locations for d...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 82716c2de19d18a154c85d03b7f2ec01839284262914f7bb3ad04b18a105379d
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-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:35:11Z'
+      generated_at_after: '2026-06-03T00:42:37Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which operating system and processors does this target?
+        - How do I download the DLRM data and model weights?
+        - Which frameworks and versions are used to run the benchmark?
+        - How do I run the benchmark and confirm it completed successfully?
+        removed_questions:
+        - What hardware and operating system are required?
+        - Which cloud providers can I use for this Learning Path?
+        - What software stack is used to run the benchmark?
+        - How do I obtain the DLRM data and model weights?
+        - How do I know the benchmark ran correctly and what outputs should I expect?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which operating system and processors does this target?
+        - How do I download the DLRM data and model weights?
+        - Which frameworks and versions are used to run the benchmark?
+        - How do I run the benchmark and confirm it completed successfully?
+        removed_questions:
+        - What hardware and operating system are required?
+        - Which cloud providers can I use for this Learning Path?
+        - What software stack is used to run the benchmark?
+        - How do I obtain the DLRM data and model weights?
+        - How do I know the benchmark ran correctly and what outputs should I expect?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -212423,51 +20599,78 @@ history:
       source_hash_before: 80785022032bf4e3c65da682e698940a212ee1ee77386698889a9fafbed9f823
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to use the Docker MCP Toolkit with the Arm MCP Server and GitHub Copilot
-        to automate container and code migration from x86 to Arm64. Through a hands-on example, migrate
-        a legacy C++ applicat...
-      preview_after: Learn how to use the Docker MCP Toolkit with the Arm MCP Server and GitHub Copilot
-        to automate container and code migration from x86 to Arm64. Through a hands-on example, migrate
-        a legacy C++ applicat...
-      preview_generated: Learn how to use the Docker MCP Toolkit with the Arm MCP Server and GitHub Copilot
-        to automate container and code migration from x86 to Arm64. Through a hands-on example, migrate
-        a legacy C++ applicat...
+      generated_at_before: '2026-06-02T03:37:07Z'
+      generated_at_after: '2026-06-02T03:37:07Z'
+      preview_before: This advanced path shows how to use the Docker MCP Toolkit with
+        the Arm MCP Server and GitHub Copilot in VS Code to automate migration of
+        a containerized C++ app from x86 AVX2 intrinsics to Arm64 Neon...
+      preview_after: This advanced path shows how to use the Docker MCP Toolkit with
+        the Arm MCP Server and GitHub Copilot in VS Code to automate migration of
+        a containerized C++ app from x86 AVX2 intrinsics to Arm64 Neon...
+      preview_generated: Learn to automate migration of containerized x86 code to
+        Arm64 using the Docker MCP Toolkit with the Arm MCP Server and GitHub Copilot
+        in VS Code. You will configure the Arm, GitHub, and Sequential Th...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 80785022032bf4e3c65da682e698940a212ee1ee77386698889a9fafbed9f823
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:37:07Z'
+      generated_at_after: '2026-06-03T00:43:28Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before starting the migration steps?
+        - Which MCP servers should I configure, and how do I make them available to
+          Copilot in VS Code?
+        - Where do I get the demo application and open it in VS Code?
+        - How do I direct GitHub Copilot to perform the x86-to-Arm64 migration?
+        - What result should I expect after building and running the Arm64 container?
+        removed_questions:
+        - What tools and accounts do I need before starting?
+        - Do I need an Arm-based machine or a cloud instance to complete this path?
+        - What codebase is used and what gets migrated?
+        - Which MCP servers and integrations are configured?
+        - How do I validate that the migration worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before starting the migration steps?
+        - Which MCP servers should I configure, and how do I make them available to
+          Copilot in VS Code?
+        - Where do I get the demo application and open it in VS Code?
+        - How do I direct GitHub Copilot to perform the x86-to-Arm64 migration?
+        - What result should I expect after building and running the Arm64 container?
+        removed_questions:
+        - What tools and accounts do I need before starting?
+        - Do I need an Arm-based machine or a cloud instance to complete this path?
+        - What codebase is used and what gets migrated?
+        - Which MCP servers and integrations are configured?
+        - How do I validate that the migration worked?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -212477,51 +20680,76 @@ history:
       source_hash_before: 08d8f0c86625ef41476d3a8b24bad9b0a0820797022ef847bf9bb17a976726a7
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to build and run an OrchardCore CMS .NET application on Azure Cobalt 100
-        processors, covering AnyCPU configuration and shared C library integration. It is designed for
-        .NET developers who wa...
-      preview_after: Learn how to build and run an OrchardCore CMS .NET application on Azure Cobalt 100
-        processors, covering AnyCPU configuration and shared C library integration. It is designed for
-        .NET developers who wa...
-      preview_generated: Learn how to build and run an OrchardCore CMS .NET application on Azure Cobalt
-        100 processors, covering AnyCPU configuration and shared C library integration. It is designed
-        for .NET developers who wa...
+      generated_at_before: '2026-06-02T03:38:20Z'
+      generated_at_after: '2026-06-02T03:38:20Z'
+      preview_before: Learn how to migrate and run an OrchardCore CMS .NET application
+        on Azure Cobalt 100 Arm-based virtual machines. You will build and run the
+        app on Ubuntu 24.04 with port 8080 open, integrate a simple ...
+      preview_after: Learn how to migrate and run an OrchardCore CMS .NET application
+        on Azure Cobalt 100 Arm-based virtual machines. You will build and run the
+        app on Ubuntu 24.04 with port 8080 open, integrate a simple ...
+      preview_generated: This Learning Path shows how to migrate a .NET OrchardCore
+        CMS app to Arm-based Azure Cobalt 100 virtual machines on Linux. You will
+        launch an Ubuntu 24.04 instance, open port 8080, install the .NET S...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 08d8f0c86625ef41476d3a8b24bad9b0a0820797022ef847bf9bb17a976726a7
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:38:20Z'
+      generated_at_after: '2026-06-03T00:44:01Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need in Azure before I start?
+        - Which VM image and network settings should I use for the OrchardCore app?
+        - What tools and project setup are required on the VM?
+        - How do I build the C shared library and verify it is called from .NET?
+        - How do I run the same build on both Arm and x86 machines?
+        removed_questions:
+        - What are the prerequisites to follow this Learning Path?
+        - What VM and operating system setup does this path use?
+        - How do I integrate a C shared library into the .NET OrchardCore app?
+        - How can I run the same build on both Arm and x86?
+        - Which .NET versions are considered for performance and support?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need in Azure before I start?
+        - Which VM image and network settings should I use for the OrchardCore app?
+        - What tools and project setup are required on the VM?
+        - How do I build the C shared library and verify it is called from .NET?
+        - How do I run the same build on both Arm and x86 machines?
+        removed_questions:
+        - What are the prerequisites to follow this Learning Path?
+        - What VM and operating system setup does this path use?
+        - How do I integrate a C shared library into the .NET OrchardCore app?
+        - How can I run the same build on both Arm and x86?
+        - Which .NET versions are considered for performance and support?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -212531,51 +20759,76 @@ history:
       source_hash_before: 4ef29931bf19dc95bb586440796381725c271ef1b953dcf46d00ad9617eabbb1
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to deploy Dynatrace OneAgent on Azure Cobalt 100 Arm64 virtual machines
-        and configure ActiveGate for secure infrastructure and application monitoring. It is designed
-        for developers, DevOps e...
-      preview_after: Learn how to deploy Dynatrace OneAgent on Azure Cobalt 100 Arm64 virtual machines
-        and configure ActiveGate for secure infrastructure and application monitoring. It is designed
-        for developers, DevOps e...
-      preview_generated: Learn how to deploy Dynatrace OneAgent on Azure Cobalt 100 Arm64 virtual machines
-        and configure ActiveGate for secure infrastructure and application monitoring. It is designed
-        for developers, DevOps e...
+      generated_at_before: '2026-06-02T03:39:49Z'
+      generated_at_after: '2026-06-02T03:39:49Z'
+      preview_before: This Learning Path shows how to monitor Azure Cobalt 100 Arm64
+        virtual machines with Dynatrace. You will create an Azure VM in the Dpsv6
+        series, install Dynatrace OneAgent on Ubuntu 24.04 LTS Arm64, a...
+      preview_after: This Learning Path shows how to monitor Azure Cobalt 100 Arm64
+        virtual machines with Dynatrace. You will create an Azure VM in the Dpsv6
+        series, install Dynatrace OneAgent on Ubuntu 24.04 LTS Arm64, a...
+      preview_generated: This Learning Path shows how to deploy Dynatrace OneAgent
+        on Microsoft Azure Cobalt 100 Arm64 virtual machines and configure Dynatrace
+        ActiveGate for secure communication. You will create a general-pu...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 4ef29931bf19dc95bb586440796381725c271ef1b953dcf46d00ad9617eabbb1
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:39:49Z'
+      generated_at_after: '2026-06-03T00:44:58Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Azure VM type and operating system should I use?
+        - How do I allow Dynatrace ActiveGate traffic to the VM?
+        - How do I know if OneAgent and ActiveGate are installed correctly?
+        - What result should I expect when validating with the sample NGINX workload?
+        removed_questions:
+        - What Azure VM and OS image does this path use?
+        - What network configuration is required for Dynatrace ActiveGate?
+        - What gets installed and what does it do?
+        - Does this solution run natively on Arm64?
+        - How do I validate that monitoring works?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Azure VM type and operating system should I use?
+        - How do I allow Dynatrace ActiveGate traffic to the VM?
+        - How do I know if OneAgent and ActiveGate are installed correctly?
+        - What result should I expect when validating with the sample NGINX workload?
+        removed_questions:
+        - What Azure VM and OS image does this path use?
+        - What network configuration is required for Dynatrace ActiveGate?
+        - What gets installed and what does it do?
+        - Does this solution run natively on Arm64?
+        - How do I validate that monitoring works?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/ecs/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -212585,51 +20838,76 @@ history:
       source_hash_before: ef5f9e7c8844b20b9044b43f4758bc1d74374521093d7738a7f8832d21f1dcac
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to create an AWS ECS cluster with Fargate and AWS Graviton processors,
-        then create and run containerized tasks on Arm infrastructure. It is designed for developers who
-        want to use AWS Gravit...
-      preview_after: Learn how to create an AWS ECS cluster with Fargate and AWS Graviton processors,
-        then create and run containerized tasks on Arm infrastructure. It is designed for developers who
-        want to use AWS Gravit...
-      preview_generated: Learn how to create an AWS ECS cluster with Fargate and AWS Graviton processors,
-        then create and run containerized tasks on Arm infrastructure. It is designed for developers who
-        want to use AWS Gravit...
+      generated_at_before: '2026-06-02T03:40:50Z'
+      generated_at_after: '2026-06-02T03:40:50Z'
+      preview_before: Learn to deploy containerized applications on Amazon Elastic
+        Container Service (ECS) using Fargate with AWS Graviton processors. You will
+        create an ECS cluster, configure required identity settings, a...
+      preview_after: Learn to deploy containerized applications on Amazon Elastic
+        Container Service (ECS) using Fargate with AWS Graviton processors. You will
+        create an ECS cluster, configure required identity settings, a...
+      preview_generated: This Learning Path shows how to deploy a containerized application
+        on Amazon Elastic Container Service (ECS) using the Fargate launch type on
+        AWS Graviton processors (Arm-based). You will create an EC...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: ef5f9e7c8844b20b9044b43f4758bc1d74374521093d7738a7f8832d21f1dcac
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       current_source_hash: ef5f9e7c8844b20b9044b43f4758bc1d74374521093d7738a7f8832d21f1dcac
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:40:50Z'
+      generated_at_after: '2026-06-03T00:45:33Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Do I need to manage EC2 instances for this deployment?
+        - Which architecture should my container image target to run on AWS Graviton?
+        - Where will I store and pull my container images in this workflow?
+        - What result should I expect after completing the Terraform section?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - Do I need to provision or manage EC2 instances for ECS?
+        - What will I deploy and where does it run?
+        - Is Terraform required, and what does it automate here?
+        - Will I configure identity or permissions as part of this path?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Do I need to manage EC2 instances for this deployment?
+        - Which architecture should my container image target to run on AWS Graviton?
+        - Where will I store and pull my container images in this workflow?
+        - What result should I expect after completing the Terraform section?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - Do I need to provision or manage EC2 instances for ECS?
+        - What will I deploy and where does it run?
+        - Is Terraform required, and what does it automate here?
+        - Will I configure identity or permissions as part of this path?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/eks/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -212639,51 +20917,80 @@ history:
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to provision an Amazon EKS cluster on Arm-based Graviton instances and
-        deploy a WordPress application with MySQL database. It is designed for software developers new
-        to Kubernetes on AWS who...
-      preview_after: Learn how to provision an Amazon EKS cluster on Arm-based Graviton instances and
-        deploy a WordPress application with MySQL database. It is designed for software developers new
-        to Kubernetes on AWS who...
-      preview_generated: Learn how to provision an Amazon EKS cluster on Arm-based Graviton instances
-        and deploy a WordPress application with MySQL database. It is designed for software developers
-        new to Kubernetes on AWS who...
+      generated_at_before: '2026-06-02T03:42:15Z'
+      generated_at_after: '2026-06-02T03:42:15Z'
+      preview_before: Provision an Amazon EKS cluster on Arm-based Graviton instances
+        and deploy a WordPress application with a MySQL database. Working from a machine
+        with the AWS CLI, EKS CLI, and Kubernetes CLI installed...
+      preview_after: Provision an Amazon EKS cluster on Arm-based Graviton instances
+        and deploy a WordPress application with a MySQL database. Working from a machine
+        with the AWS CLI, EKS CLI, and Kubernetes CLI installed...
+      preview_generated: Provision an Amazon EKS cluster on Arm-based Graviton instances
+        and deploy a WordPress application with a MySQL backend. You will install
+        and verify AWS CLI, EKS CLI, and kubectl, configure AWS creden...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 4f1c448eef66300e024bda27c420f9746047c0c4b76e8556d3d8693382206055
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:42:15Z'
+      generated_at_after: '2026-06-03T00:45:59Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which machine can I use to run the setup?
+        - How do I create an EKS cluster on Arm-based instances?
+        - Which files are required to deploy WordPress and where do I set the MySQL
+          password?
+        - How do I apply the deployment and know it targets my EKS cluster?
+        removed_questions:
+        - What do I need before starting?
+        - Which platform and architecture does this target?
+        - What kind of computer can I use to follow the steps?
+        - Which files do I create to deploy WordPress and how is the database password
+          set?
+        - What is the expected outcome and how long does it take?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which machine can I use to run the setup?
+        - How do I create an EKS cluster on Arm-based instances?
+        - Which files are required to deploy WordPress and where do I set the MySQL
+          password?
+        - How do I apply the deployment and know it targets my EKS cluster?
+        removed_questions:
+        - What do I need before starting?
+        - Which platform and architecture does this target?
+        - What kind of computer can I use to follow the steps?
+        - Which files do I create to deploy WordPress and how is the database password
+          set?
+        - What is the expected outcome and how long does it take?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -212693,51 +21000,78 @@ history:
       source_hash_before: e32bdb090c422d1fb5bb1f9bd3af56c55fdf8989e6a7fe6a101e90dcb6f3eadd
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       current_source_hash: e32bdb090c422d1fb5bb1f9bd3af56c55fdf8989e6a7fe6a101e90dcb6f3eadd
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to use docker buildx and docker manifest to build and deploy multi-architecture
-        container images with x86/amd64 and arm64 support on Amazon EKS. It is designed for software developers
-        who wa...
-      preview_after: Learn how to use docker buildx and docker manifest to build and deploy multi-architecture
-        container images with x86/amd64 and arm64 support on Amazon EKS. It is designed for software developers
-        who wa...
-      preview_generated: Learn how to use docker buildx and docker manifest to build and deploy multi-architecture
-        container images with x86/amd64 and arm64 support on Amazon EKS. It is designed for software developers
-        who wa...
+      generated_at_before: '2026-06-02T03:43:01Z'
+      generated_at_after: '2026-06-02T03:43:01Z'
+      preview_before: This Learning Path shows how to build and deploy a multi-architecture
+        container application for x86/amd64 and arm64 on Amazon EKS using docker buildx
+        and docker manifest. You will create a hybrid EKS ...
+      preview_after: This Learning Path shows how to build and deploy a multi-architecture
+        container application for x86/amd64 and arm64 on Amazon EKS using docker buildx
+        and docker manifest. You will create a hybrid EKS ...
+      preview_generated: This advanced Learning Path shows how to build x86/amd64
+        and arm64 container images using docker buildx and docker manifest, then deploy
+        a single multi-architecture application to a hybrid Amazon EKS ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: e32bdb090c422d1fb5bb1f9bd3af56c55fdf8989e6a7fe6a101e90dcb6f3eadd
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:43:01Z'
+      generated_at_after: '2026-06-03T00:46:43Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which tools are used to build multi-architecture images, and where do I
+          run them?
+        - How is the Amazon EKS cluster set up for multiple architectures?
+        - What result should I expect after deployment?
+        - What should I check if the application only runs on one node type?
+        removed_questions:
+        - What setup do I need before starting?
+        - Which architectures and nodes does this path target?
+        - Does this path cover creating a hybrid EKS cluster?
+        - How are the multi-architecture images built?
+        - How do I know I completed the path successfully?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which tools are used to build multi-architecture images, and where do I
+          run them?
+        - How is the Amazon EKS cluster set up for multiple architectures?
+        - What result should I expect after deployment?
+        - What should I check if the application only runs on one node type?
+        removed_questions:
+        - What setup do I need before starting?
+        - Which architectures and nodes does this path target?
+        - Does this path cover creating a hybrid EKS cluster?
+        - How are the multi-architecture images built?
+        - How do I know I completed the path successfully?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/envoy/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -212747,51 +21081,74 @@ history:
       source_hash_before: d492b6dce11b7d8f6591ff3b3ce9aa2c382a6a7a749add228c3ac1f6ae57e218
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to build, install, and run Envoy proxy on Arm servers and configure it
-        as a web server for traffic management. It is designed for engineers who want to use Envoy on
-        Arm. By the end, you will...
-      preview_after: Learn how to build, install, and run Envoy proxy on Arm servers and configure it
-        as a web server for traffic management. It is designed for engineers who want to use Envoy on
-        Arm. By the end, you will...
-      preview_generated: Learn how to build, install, and run Envoy proxy on Arm servers and configure
-        it as a web server for traffic management. It is designed for engineers who want to use Envoy
-        on Arm. By the end, you will...
+      generated_at_before: '2026-06-02T03:43:48Z'
+      generated_at_after: '2026-06-02T03:43:48Z'
+      preview_before: This Learning Path shows how to build, install, and run Envoy
+        on Arm-based Linux servers and configure it as a basic web server for traffic
+        management. You will provision an Arm instance in the cloud ...
+      preview_after: This Learning Path shows how to build, install, and run Envoy
+        on Arm-based Linux servers and configure it as a basic web server for traffic
+        management. You will provision an Arm instance in the cloud ...
+      preview_generated: This introductory Learning Path shows how to build, install,
+        and run the Envoy proxy on Arm-based Linux servers, then configure it as a
+        simple web server for traffic management. You will work on an Ar...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: d492b6dce11b7d8f6591ff3b3ce9aa2c382a6a7a749add228c3ac1f6ae57e218
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       current_source_hash: d492b6dce11b7d8f6591ff3b3ce9aa2c382a6a7a749add228c3ac1f6ae57e218
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:43:48Z'
+      generated_at_after: '2026-06-03T00:47:28Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which platforms can I use for the Arm-based instance?
+        - Which operating system do the steps target?
+        - What should I check if I cannot reach the Envoy web server?
+        removed_questions:
+        - What infrastructure and access do I need to follow this Learning Path?
+        - What will I install and configure during the steps?
+        - Are there additional prerequisites or specific skills required?
+        - How do I verify that Envoy is working correctly at the end?
+        updated_questions:
+        - How do I run Envoy as a service in this path?
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which platforms can I use for the Arm-based instance?
+        - Which operating system do the steps target?
+        - What should I check if I cannot reach the Envoy web server?
+        removed_questions:
+        - What infrastructure and access do I need to follow this Learning Path?
+        - What will I install and configure during the steps?
+        - Are there additional prerequisites or specific skills required?
+        - How do I verify that Envoy is working correctly at the end?
+        updated_questions:
+        - How do I run Envoy as a service in this path?
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -212801,51 +21158,76 @@ history:
       source_hash_before: f36b1e9a45b6ec29d8041b70997550d917322cf9cdd456db26083169d21175ed
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to install and configure Envoy proxy on Google Cloud Axion C4A Arm64 instances
-        and benchmark HTTP proxy performance with load testing. It is designed for This introductory topic
-        for software...
-      preview_after: Learn how to install and configure Envoy proxy on Google Cloud Axion C4A Arm64 instances
-        and benchmark HTTP proxy performance with load testing. It is designed for This introductory topic
-        for software...
-      preview_generated: Learn how to install and configure Envoy proxy on Google Cloud Axion C4A Arm64
-        instances and benchmark HTTP proxy performance with load testing. It is designed for This introductory
-        topic for software...
+      generated_at_before: '2026-06-02T03:44:25Z'
+      generated_at_after: '2026-06-02T03:44:25Z'
+      preview_before: This Learning Path shows how to deploy Envoy Proxy on Google
+        Cloud Axion C4A Arm64 virtual machines built on Arm Neoverse V2 cores, then
+        validate and benchmark it. You will provision a c4a-standard-4 ...
+      preview_after: This Learning Path shows how to deploy Envoy Proxy on Google
+        Cloud Axion C4A Arm64 virtual machines built on Arm Neoverse V2 cores, then
+        validate and benchmark it. You will provision a c4a-standard-4 ...
+      preview_generated: Learn to deploy and evaluate Envoy Proxy on Google Cloud
+        Axion C4A Arm64 instances built on Arm Neoverse V2. You will provision a c4a-standard-4
+        VM in the Google Cloud Console, install dependencies on...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: f36b1e9a45b6ec29d8041b70997550d917322cf9cdd456db26083169d21175ed
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       current_source_hash: f36b1e9a45b6ec29d8041b70997550d917322cf9cdd456db26083169d21175ed
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:44:25Z'
+      generated_at_after: '2026-06-03T00:48:02Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before provisioning the C4A VM on GCP?
+        - Which C4A machine type is used, and where do I create it?
+        - What Envoy build is installed on the C4A instance?
+        - How do I validate Envoy after installation, and what result should I expect?
+        - How do I run the benchmarks and what metrics does Siege report?
+        removed_questions:
+        - What do I need before starting on Google Cloud?
+        - Which VM configuration and operating system does this path use?
+        - Which Envoy version is installed and how?
+        - How do I validate that Envoy is running correctly?
+        - How are benchmarks performed and what should I measure?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before provisioning the C4A VM on GCP?
+        - Which C4A machine type is used, and where do I create it?
+        - What Envoy build is installed on the C4A instance?
+        - How do I validate Envoy after installation, and what result should I expect?
+        - How do I run the benchmarks and what metrics does Siege report?
+        removed_questions:
+        - What do I need before starting on Google Cloud?
+        - Which VM configuration and operating system does this path use?
+        - Which Envoy version is installed and how?
+        - How do I validate that Envoy is running correctly?
+        - How are benchmarks performed and what should I measure?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -212855,51 +21237,76 @@ history:
       source_hash_before: cbbcc8fa33f864e422f8db7d58fba32c26f6070be2846b246837c63ccf37e1c7
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       current_source_hash: cbbcc8fa33f864e422f8db7d58fba32c26f6070be2846b246837c63ccf37e1c7
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to optimize Envoy proxy performance on Arm servers using Transparent Huge
-        Pages and Profile-Guided Optimization techniques. It is designed for software developers who want
-        to use Envoy on Ar...
-      preview_after: Learn how to optimize Envoy proxy performance on Arm servers using Transparent Huge
-        Pages and Profile-Guided Optimization techniques. It is designed for software developers who want
-        to use Envoy on Ar...
-      preview_generated: Learn how to optimize Envoy proxy performance on Arm servers using Transparent
-        Huge Pages and Profile-Guided Optimization techniques. It is designed for software developers
-        who want to use Envoy on Ar...
+      generated_at_before: '2026-06-02T03:45:17Z'
+      generated_at_after: '2026-06-02T03:45:17Z'
+      preview_before: "Learn how to tune Envoy on Arm servers running Linux\u2014\
+        on bare metal or Arm instances from AWS, Microsoft Azure, Google Cloud, or\
+        \ Oracle\u2014using Transparent Huge Pages (THP) and Profile-Guided Optimizatio..."
+      preview_after: "Learn how to tune Envoy on Arm servers running Linux\u2014on\
+        \ bare metal or Arm instances from AWS, Microsoft Azure, Google Cloud, or\
+        \ Oracle\u2014using Transparent Huge Pages (THP) and Profile-Guided Optimizatio..."
+      preview_generated: This advanced Learning Path shows how to tune Envoy on Arm
+        servers running Linux using Transparent Huge Pages (THP) and Profile-Guided
+        Optimization (PGO). You will review kernel parameters that affect...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: cbbcc8fa33f864e422f8db7d58fba32c26f6070be2846b246837c63ccf37e1c7
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       current_source_hash: cbbcc8fa33f864e422f8db7d58fba32c26f6070be2846b246837c63ccf37e1c7
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:45:17Z'
+      generated_at_after: '2026-06-03T00:48:49Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running these tuning steps?
+        - Which environments does this Learning Path target?
+        - How do I check my Linux kernel configuration for THP on Ubuntu?
+        - Which toolchain should I use to build Envoy with PGO?
+        - What performance improvement should I expect from THP or PGO?
+        removed_questions:
+        - Do I need an existing Envoy deployment before starting?
+        - What environment does this Learning Path target?
+        - Will I need to rebuild Envoy for PGO, and what toolchain is used?
+        - How is THP addressed in this path?
+        - What outputs should I expect after completing the steps?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running these tuning steps?
+        - Which environments does this Learning Path target?
+        - How do I check my Linux kernel configuration for THP on Ubuntu?
+        - Which toolchain should I use to build Envoy with PGO?
+        - What performance improvement should I expect from THP or PGO?
+        removed_questions:
+        - Do I need an existing Envoy deployment before starting?
+        - What environment does this Learning Path target?
+        - Will I need to rebuild Envoy for PGO, and what toolchain is used?
+        - How is THP addressed in this path?
+        - What outputs should I expect after completing the steps?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -212909,51 +21316,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how stack buffer overflow exploits work on AArch64 by analyzing stack frame
-        layouts, redirecting control flow, and understanding defense mechanisms. It is designed for software
-        developers intere...
-      preview_after: Learn how stack buffer overflow exploits work on AArch64 by analyzing stack frame
-        layouts, redirecting control flow, and understanding defense mechanisms. It is designed for software
-        developers intere...
-      preview_generated: Learn how stack buffer overflow exploits work on AArch64 by analyzing stack frame
-        layouts, redirecting control flow, and understanding defense mechanisms. It is designed for software
-        developers intere...
+      generated_at_before: '2026-06-02T03:46:23Z'
+      generated_at_after: '2026-06-02T03:46:23Z'
+      preview_before: This advanced Learning Path shows how stack buffer overflow
+        exploits work on AArch64 Linux by building and analyzing small, controlled
+        examples. You will create a Docker-based lab on an Arm machine us...
+      preview_after: This advanced Learning Path shows how stack buffer overflow exploits
+        work on AArch64 Linux by building and analyzing small, controlled examples.
+        You will create a Docker-based lab on an Arm machine us...
+      preview_generated: This advanced path walks you through how stack buffer overflow
+        exploits work on AArch64 by examining stack frame layouts, observing how user
+        input can overwrite a saved return address, and redirecting...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 02eedcebf59a8ab506d4ebbf5bbe20ead367cf3c0700950db5a9059d2f671853
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-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:46:23Z'
+      generated_at_after: '2026-06-03T00:49:39Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the exercises?
+        - Why does the Dockerfile disable ASLR, and what happens if I skip that step?
+        - Where should I save the example source files when using Docker?
+        - Which tools will I use inside the container to build and inspect the examples?
+        - How do I know if the control-flow redirection worked?
+        removed_questions:
+        - What environment do I need to run the exercises?
+        - Which tools and languages are used?
+        - What prior knowledge is expected before starting?
+        - Why is ASLR disabled in the setup?
+        - How do I know the Learning Path worked for me?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the exercises?
+        - Why does the Dockerfile disable ASLR, and what happens if I skip that step?
+        - Where should I save the example source files when using Docker?
+        - Which tools will I use inside the container to build and inspect the examples?
+        - How do I know if the control-flow redirection worked?
+        removed_questions:
+        - What environment do I need to run the exercises?
+        - Which tools and languages are used?
+        - What prior knowledge is expected before starting?
+        - Why is ASLR disabled in the setup?
+        - How do I know the Learning Path worked for me?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -212963,51 +21395,76 @@ history:
       source_hash_before: dde32f5d14a877313a8b0aafec58acb09cd1e77f4fc9138b9e1bd6d9fedf250a
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to identify and fix false sharing issues using Perf C2C cache line analysis
-        and Arm Statistical Profiling Extension on Arm-based cloud systems. It is designed for performance-oriented
-        develo...
-      preview_after: Learn how to identify and fix false sharing issues using Perf C2C cache line analysis
-        and Arm Statistical Profiling Extension on Arm-based cloud systems. It is designed for performance-oriented
-        develo...
-      preview_generated: Learn how to identify and fix false sharing issues using Perf C2C cache line
-        analysis and Arm Statistical Profiling Extension on Arm-based cloud systems. It is designed for
-        performance-oriented develo...
+      generated_at_before: '2026-06-02T03:47:24Z'
+      generated_at_after: '2026-06-02T03:47:24Z'
+      preview_before: Learn how to detect and address false sharing on Arm-based cloud
+        systems using Linux perf C2C and the Arm Statistical Profiling Extension (SPE).
+        You will set up a Linux environment on an Arm Neoverse-...
+      preview_after: Learn how to detect and address false sharing on Arm-based cloud
+        systems using Linux perf C2C and the Arm Statistical Profiling Extension (SPE).
+        You will set up a Linux environment on an Arm Neoverse-...
+      preview_generated: This Learning Path shows how to use Linux perf, including
+        perf c2c and the Arm Statistical Profiling Extension (SPE), to identify and
+        fix false sharing on Arm-based cloud systems. You will prepare an ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: dde32f5d14a877313a8b0aafec58acb09cd1e77f4fc9138b9e1bd6d9fedf250a
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-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:47:24Z'
+      generated_at_after: '2026-06-03T00:50:12Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - How do I know if my cloud instance supports Arm SPE?
+        - Which cloud platforms can I use for this path?
+        - Which perf commands will I use during the analysis?
+        - What result should I expect from the false sharing example?
+        - What should I check if perf c2c does not show the expected events?
+        removed_questions:
+        - What environment do I need to follow this Learning Path?
+        - What prior knowledge is expected?
+        - Which tools will I use?
+        - What code or artifacts will I create?
+        - How do I know the setup and analysis worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - How do I know if my cloud instance supports Arm SPE?
+        - Which cloud platforms can I use for this path?
+        - Which perf commands will I use during the analysis?
+        - What result should I expect from the false sharing example?
+        - What should I check if perf c2c does not show the expected events?
+        removed_questions:
+        - What environment do I need to follow this Learning Path?
+        - What prior knowledge is expected?
+        - Which tools will I use?
+        - What code or artifacts will I create?
+        - How do I know the setup and analysis worked?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/fastpath/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -213017,51 +21474,78 @@ history:
       source_hash_before: 978d3da8668e38758c138313c31809f3de5a8cefd1b7c2b47a536c0ee364b692
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to build custom Linux kernels using tuxmake and Fastpath, then benchmark
-        and compare kernel versions on Arm-based EC2 instances. It is designed for software developers
-        and performance engine...
-      preview_after: Learn how to build custom Linux kernels using tuxmake and Fastpath, then benchmark
-        and compare kernel versions on Arm-based EC2 instances. It is designed for software developers
-        and performance engine...
-      preview_generated: Learn how to build custom Linux kernels using tuxmake and Fastpath, then benchmark
-        and compare kernel versions on Arm-based EC2 instances. It is designed for software developers
-        and performance engine...
+      generated_at_before: '2026-06-02T03:48:10Z'
+      generated_at_after: '2026-06-02T03:48:10Z'
+      preview_before: This advanced Learning Path guides you through building custom
+        Linux kernels with tuxmake, provisioning Arm-based AWS EC2 instances, and
+        benchmarking multiple kernel versions using Fastpath. You will ...
+      preview_after: This advanced Learning Path guides you through building custom
+        Linux kernels with tuxmake, provisioning Arm-based AWS EC2 instances, and
+        benchmarking multiple kernel versions using Fastpath. You will ...
+      preview_generated: This advanced Learning Path guides you through building custom
+        Linux kernels with tuxmake, provisioning Arm-based AWS EC2 instances, and
+        using Fastpath to benchmark and compare kernel versions. You cr...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 978d3da8668e38758c138313c31809f3de5a8cefd1b7c2b47a536c0ee364b692
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:48:10Z'
+      generated_at_after: '2026-06-03T00:50:51Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before provisioning the EC2 instances?
+        - Which EC2 instance types and images are used for each role?
+        - Can I use the AWS Management Console or the AWS CLI to create the instances?
+        - Where are kernels built and which tools are used?
+        - How do I generate and run the Fastpath benchmark plan, and what should I
+          expect?
+        removed_questions:
+        - What AWS resources do I need to follow this Learning Path?
+        - Which tools are used and what is their role?
+        - Can I provision EC2 instances with either the AWS Console or the AWS CLI?
+        - What artifacts should I expect to produce?
+        - How do I validate that the setup is ready before running benchmarks?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before provisioning the EC2 instances?
+        - Which EC2 instance types and images are used for each role?
+        - Can I use the AWS Management Console or the AWS CLI to create the instances?
+        - Where are kernels built and which tools are used?
+        - How do I generate and run the Fastpath benchmark plan, and what should I
+          expect?
+        removed_questions:
+        - What AWS resources do I need to follow this Learning Path?
+        - Which tools are used and what is their role?
+        - Can I provision EC2 instances with either the AWS Console or the AWS CLI?
+        - What artifacts should I expect to produce?
+        - How do I validate that the setup is ready before running benchmarks?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/fexpa/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -213071,51 +21555,78 @@ history:
       source_hash_before: a67c552b76df6323eccc331c9146d0fcbdb8ad222fe81dbf2e1c2339c7609b61
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to implement exponential functions using Arm SVE intrinsics with the FEXPA
-        instruction for hardware-accelerated computations on Neoverse processors. It is designed for developers
-        interested ...
-      preview_after: Learn how to implement exponential functions using Arm SVE intrinsics with the FEXPA
-        instruction for hardware-accelerated computations on Neoverse processors. It is designed for developers
-        interested ...
-      preview_generated: Learn how to implement exponential functions using Arm SVE intrinsics with the
-        FEXPA instruction for hardware-accelerated computations on Neoverse processors. It is designed
-        for developers interested ...
+      generated_at_before: '2026-06-02T03:49:04Z'
+      generated_at_after: '2026-06-02T03:49:04Z'
+      preview_before: Learn how to implement the exponential function on Arm Neoverse
+        processors using SVE intrinsics and then refine it with the FEXPA instruction.
+        You will review range reduction and polynomial approximat...
+      preview_after: Learn how to implement the exponential function on Arm Neoverse
+        processors using SVE intrinsics and then refine it with the FEXPA instruction.
+        You will review range reduction and polynomial approximat...
+      preview_generated: Learn to implement and accelerate the exponential function
+        on Arm Neoverse processors using SVE intrinsics and the FEXPA instruction.
+        You will start with range reduction and polynomial approximation, ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: a67c552b76df6323eccc331c9146d0fcbdb8ad222fe81dbf2e1c2339c7609b61
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:49:04Z'
+      generated_at_after: '2026-06-03T00:51:30Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the example?
+        - Which instance type should I pick, and what was used to validate the steps?
+        - How do I set up the build environment and source file?
+        - What changes when I enable FEXPA compared to the initial SVE implementation?
+        - "I\u2019m on macOS\u2014what should I do if the Linux package commands don\u2019\
+          t work?"
+        removed_questions:
+        - What do I need before starting?
+        - Which Arm platforms and instances does this target?
+        - What will I implement and which tools or languages are used?
+        - How is FEXPA used in this Learning Path?
+        - How long does it take and what skill level is assumed?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the example?
+        - Which instance type should I pick, and what was used to validate the steps?
+        - How do I set up the build environment and source file?
+        - What changes when I enable FEXPA compared to the initial SVE implementation?
+        - "I\u2019m on macOS\u2014what should I do if the Linux package commands don\u2019\
+          t work?"
+        removed_questions:
+        - What do I need before starting?
+        - Which Arm platforms and instances does this target?
+        - What will I implement and which tools or languages are used?
+        - How is FEXPA used in this Learning Path?
+        - How long does it take and what skill level is assumed?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/flink/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -213125,51 +21636,76 @@ history:
       source_hash_before: 974d71b1ee968e3aeabe900bfdd52ae4fbfdd0ca7dea420e6c1fc01f5475e8c1
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to install and run Apache Flink on Arm servers and benchmark stream processing
-        performance using the Nexmark benchmark suite. It is designed for software developers using Flink
-        as their stre...
-      preview_after: Learn how to install and run Apache Flink on Arm servers and benchmark stream processing
-        performance using the Nexmark benchmark suite. It is designed for software developers using Flink
-        as their stre...
-      preview_generated: Learn how to install and run Apache Flink on Arm servers and benchmark stream
-        processing performance using the Nexmark benchmark suite. It is designed for software developers
-        using Flink as their stre...
+      generated_at_before: '2026-06-02T03:50:28Z'
+      generated_at_after: '2026-06-02T03:50:28Z'
+      preview_before: This Learning Path shows how to install and run Apache Flink
+        on an Arm-based Linux server and benchmark its stream processing performance
+        using the Nexmark suite. You will set up Java, configure a Fli...
+      preview_after: This Learning Path shows how to install and run Apache Flink
+        on an Arm-based Linux server and benchmark its stream processing performance
+        using the Nexmark suite. You will set up Java, configure a Fli...
+      preview_generated: This Learning Path shows how to install and run Apache Flink
+        on an Arm-based Linux server and benchmark stream processing performance with
+        the Nexmark suite. You will set up a Java runtime (JDK 11), c...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:50:28Z'
+      generated_at_after: '2026-06-03T00:52:15Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Java version should I install for this setup?
+        - What are the Nexmark setup requirements I must have in place?
+        - Where do I run the commands to start Flink and the benchmark?
+        - What should I check if the Nexmark scripts fail to start components?
+        removed_questions:
+        - What do I need before starting?
+        - Which Java and other tools are required?
+        - Does this use a Flink Standalone Cluster, and how is it started?
+        - How do I run the Nexmark benchmark and add more queries?
+        - How long will this take and how do I know it worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Java version should I install for this setup?
+        - What are the Nexmark setup requirements I must have in place?
+        - Where do I run the commands to start Flink and the benchmark?
+        - What should I check if the Nexmark scripts fail to start components?
+        removed_questions:
+        - What do I need before starting?
+        - Which Java and other tools are required?
+        - Does this use a Flink Standalone Cluster, and how is it started?
+        - How do I run the Nexmark benchmark and add more queries?
+        - How long will this take and how do I know it worked?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -213179,51 +21715,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to install and configure Apache Flink on Google Cloud Axion C4A Arm64
-        instances and benchmark stream processing performance with Nexmark. It is designed for developers
-        deploying and optimizi...
-      preview_after: Learn how to install and configure Apache Flink on Google Cloud Axion C4A Arm64 instances
-        and benchmark stream processing performance with Nexmark. It is designed for developers deploying
-        and optimizi...
-      preview_generated: Learn how to install and configure Apache Flink on Google Cloud Axion C4A Arm64
-        instances and benchmark stream processing performance with Nexmark. It is designed for developers
-        deploying and optimizi...
+      generated_at_before: '2026-06-02T03:51:50Z'
+      generated_at_after: '2026-06-02T03:51:50Z'
+      preview_before: Learn how to deploy Apache Flink on Google Cloud C4A virtual
+        machines powered by Axion processors (Arm Neoverse-V2) using a SUSE Linux
+        Arm64 environment. You will provision a c4a-standard-4 VM through...
+      preview_after: Learn how to deploy Apache Flink on Google Cloud C4A virtual
+        machines powered by Axion processors (Arm Neoverse-V2) using a SUSE Linux
+        Arm64 environment. You will provision a c4a-standard-4 VM through...
+      preview_generated: Follow a concise, hands-on workflow to deploy Apache Flink
+        on Google Cloud C4A virtual machines powered by Axion processors (Arm Neoverse-V2).
+        You will provision a SUSE SLES Arm64 VM (for example, c4a...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: adcf2a1b8a4a77e5834e14a40e46e27b0cbe5e440fbf732a4366ce486d7fafb7
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:51:50Z'
+      generated_at_after: '2026-06-03T00:53:07Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which Google Cloud VM and OS should I create for the exercises?
+        - Which Java version is required on the VM?
+        - Where should I install Flink and how do I confirm it works?
+        - Which benchmarks will I run and how are they executed?
+        removed_questions:
+        - What do I need before starting?
+        - Which Google Cloud VM and operating system does this path use?
+        - What software is installed on the VM to run Flink jobs?
+        - How do I verify that my Flink installation works?
+        - What benchmarks are run, and what results should I expect?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which Google Cloud VM and OS should I create for the exercises?
+        - Which Java version is required on the VM?
+        - Where should I install Flink and how do I confirm it works?
+        - Which benchmarks will I run and how are they executed?
+        removed_questions:
+        - What do I need before starting?
+        - Which Google Cloud VM and operating system does this path use?
+        - What software is installed on the VM to run Flink jobs?
+        - How do I verify that my Flink installation works?
+        - What benchmarks are run, and what results should I expect?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -213233,51 +21794,76 @@ history:
       source_hash_before: 85359b9210812675169f149c86bf11a1736ab838c011d18e876b246463d297ee
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to build scalable machine learning workflow pipelines on Google Cloud
-        C4A Axion processors using Flyte for workflow orchestration and gRPC for distributed service communication.
-        It is design...
-      preview_after: Learn how to build scalable machine learning workflow pipelines on Google Cloud C4A
-        Axion processors using Flyte for workflow orchestration and gRPC for distributed service communication.
-        It is design...
-      preview_generated: Learn how to build scalable machine learning workflow pipelines on Google Cloud
-        C4A Axion processors using Flyte for workflow orchestration and gRPC for distributed service communication.
-        It is design...
+      generated_at_before: '2026-06-02T03:53:08Z'
+      generated_at_after: '2026-06-02T03:53:08Z'
+      preview_before: This Learning Path shows how to build and run an introductory
+        machine learning workflow on Arm-based Google Cloud C4A Axion processors using
+        Flyte for orchestration and gRPC for distributed service co...
+      preview_after: This Learning Path shows how to build and run an introductory
+        machine learning workflow on Arm-based Google Cloud C4A Axion processors using
+        Flyte for orchestration and gRPC for distributed service co...
+      preview_generated: This introductory Learning Path shows how to build and run
+        a machine learning workflow on Arm-based Google Cloud C4A Axion processors
+        using Flyte for orchestration and gRPC for distributed feature eng...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 85359b9210812675169f149c86bf11a1736ab838c011d18e876b246463d297ee
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       current_source_hash: 85359b9210812675169f149c86bf11a1736ab838c011d18e876b246463d297ee
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:53:08Z'
+      generated_at_after: '2026-06-03T00:53:45Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which Google Cloud VM type should I create for the exercises?
+        - Which operating system and architecture are used on the VM?
+        - How does the Flyte workflow interact with the gRPC feature engineering service?
+        - What result should I expect after running the workflow?
+        removed_questions:
+        - What are the prerequisites to follow this Learning Path?
+        - Which Google Cloud instance and configuration will I use?
+        - What environment and tools do I set up on the VM?
+        - What will I build and run during the path?
+        - How do I know the workflow and service are working together?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which Google Cloud VM type should I create for the exercises?
+        - Which operating system and architecture are used on the VM?
+        - How does the Flyte workflow interact with the gRPC feature engineering service?
+        - What result should I expect after running the workflow?
+        removed_questions:
+        - What are the prerequisites to follow this Learning Path?
+        - Which Google Cloud instance and configuration will I use?
+        - What environment and tools do I set up on the VM?
+        - What will I build and run during the path?
+        - How do I know the workflow and service are working together?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -213287,51 +21873,76 @@ history:
       source_hash_before: 94866800acca2c5f9cd89f76f972af1a343aad5777b6844d49fac09eb764f580
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to create an Arm64 Azure VM, install .NET SDK, containerize .NET applications,
-        and push Docker images to Azure Container Registry. It is designed for software developers interested
-        in learni...
-      preview_after: Learn how to create an Arm64 Azure VM, install .NET SDK, containerize .NET applications,
-        and push Docker images to Azure Container Registry. It is designed for software developers interested
-        in learni...
-      preview_generated: Learn how to create an Arm64 Azure VM, install .NET SDK, containerize .NET applications,
-        and push Docker images to Azure Container Registry. It is designed for software developers interested
-        in learni...
+      generated_at_before: '2026-06-02T03:53:58Z'
+      generated_at_after: '2026-06-02T03:53:58Z'
+      preview_before: This introductory path shows how to deploy a .NET application
+        on Arm64 in Microsoft Azure. You will create a Linux Arm64 virtual machine,
+        connect over SSH using Azure Cloud Shell, install the .NET 7 S...
+      preview_after: This introductory path shows how to deploy a .NET application
+        on Arm64 in Microsoft Azure. You will create a Linux Arm64 virtual machine,
+        connect over SSH using Azure Cloud Shell, install the .NET 7 S...
+      preview_generated: Follow this path to deploy a .NET application on an Arm64
+        Linux virtual machine in Microsoft Azure, then containerize it and publish
+        the image to Azure Container Registry. You will create the VM throu...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 94866800acca2c5f9cd89f76f972af1a343aad5777b6844d49fac09eb764f580
       source_hash_after: 94866800acca2c5f9cd89f76f972af1a343aad5777b6844d49fac09eb764f580
       current_source_hash: 94866800acca2c5f9cd89f76f972af1a343aad5777b6844d49fac09eb764f580
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:53:58Z'
+      generated_at_after: '2026-06-03T00:54:23Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I connect to the VM and which IP address should I use?
+        - Which SDK and tools are installed on the VM to build the app?
+        - How will the application be accessible from the internet?
+        - Where should I build the Docker image and how is it published to Azure?
+        removed_questions:
+        - What Azure resources will I create in this path?
+        - What prerequisites do I need before starting?
+        - How do I connect to the Azure VM during the exercises?
+        - What software is installed on the VM to build and run the app?
+        - How do I containerize and publish the application image?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I connect to the VM and which IP address should I use?
+        - Which SDK and tools are installed on the VM to build the app?
+        - How will the application be accessible from the internet?
+        - Where should I build the Docker image and how is it published to Azure?
+        removed_questions:
+        - What Azure resources will I create in this path?
+        - What prerequisites do I need before starting?
+        - How do I connect to the Azure VM during the exercises?
+        - What software is installed on the VM to build and run the app?
+        - How do I containerize and publish the application image?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -213341,51 +21952,80 @@ history:
       source_hash_before: 3823ad3df8ae868acfd59b5b5b541240624572fe739aaf2275185d5cd3578032
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       current_source_hash: 3823ad3df8ae868acfd59b5b5b541240624572fe739aaf2275185d5cd3578032
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to create and run Docker containers on Azure Container Instances for Arm64-based
-        containerized application deployment. It is designed for developers interested in learning how
-        to create and ...
-      preview_after: Learn how to create and run Docker containers on Azure Container Instances for Arm64-based
-        containerized application deployment. It is designed for developers interested in learning how
-        to create and ...
-      preview_generated: Learn how to create and run Docker containers on Azure Container Instances for
-        Arm64-based containerized application deployment. It is designed for developers interested in
-        learning how to create and ...
+      generated_at_before: '2026-06-02T03:54:18Z'
+      generated_at_after: '2026-06-02T03:54:18Z'
+      preview_before: This introductory Learning Path shows how to create an Azure
+        Container Instance (ACI) and run a Docker container on Microsoft Azure. You
+        will provision ACI through the Azure Portal and Cloud Shell, en...
+      preview_after: This introductory Learning Path shows how to create an Azure
+        Container Instance (ACI) and run a Docker container on Microsoft Azure. You
+        will provision ACI through the Azure Portal and Cloud Shell, en...
+      preview_generated: This introductory path shows how to create an Azure Container
+        Instance, run a Docker container, and make the application reachable via the
+        assigned public IP and port 8080. You will enable the Admin a...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 3823ad3df8ae868acfd59b5b5b541240624572fe739aaf2275185d5cd3578032
       source_hash_after: 3823ad3df8ae868acfd59b5b5b541240624572fe739aaf2275185d5cd3578032
       current_source_hash: 3823ad3df8ae868acfd59b5b5b541240624572fe739aaf2275185d5cd3578032
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:54:18Z'
+      generated_at_after: '2026-06-03T00:55:15Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which container image should I use for Azure Container Instances in this
+          path?
+        - Where do I run the Azure CLI commands shown in the steps?
+        - How do I enable and verify the Azure Container Registry Admin account?
+        - How do I access the running application and what port should I use?
+        removed_questions:
+        - What do I need before starting?
+        - Which container image is used in the example steps?
+        - Can I deploy Arm64 images to Azure Container Instances in this path?
+        - How do I validate that the containerized application is running?
+        - Why do I need to enable the Admin account in Azure Container Registry and
+          how do I check it?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which container image should I use for Azure Container Instances in this
+          path?
+        - Where do I run the Azure CLI commands shown in the steps?
+        - How do I enable and verify the Azure Container Registry Admin account?
+        - How do I access the running application and what port should I use?
+        removed_questions:
+        - What do I need before starting?
+        - Which container image is used in the example steps?
+        - Can I deploy Arm64 images to Azure Container Instances in this path?
+        - How do I validate that the containerized application is running?
+        - Why do I need to enable the Admin account in Azure Container Registry and
+          how do I check it?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -213395,51 +22035,76 @@ history:
       source_hash_before: a3347a62c3322d88b80fc7848fa5ee1d92ee86333c2a21891b958286f8b70935
       source_hash_after: a3347a62c3322d88b80fc7848fa5ee1d92ee86333c2a21891b958286f8b70935
       current_source_hash: a3347a62c3322d88b80fc7848fa5ee1d92ee86333c2a21891b958286f8b70935
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to create an Azure Kubernetes Service cluster with Arm64 virtual machines
-        and deploy a containerized application to AKS. It is designed for This learning path is dedicated
-        to developers inte...
-      preview_after: Learn how to create an Azure Kubernetes Service cluster with Arm64 virtual machines
-        and deploy a containerized application to AKS. It is designed for This learning path is dedicated
-        to developers inte...
-      preview_generated: Learn how to create an Azure Kubernetes Service cluster with Arm64 virtual machines
-        and deploy a containerized application to AKS. It is designed for This learning path is dedicated
-        to developers inte...
+      generated_at_before: '2026-06-02T03:55:07Z'
+      generated_at_after: '2026-06-02T03:55:07Z'
+      preview_before: This introductory Learning Path shows how to create an Azure
+        Kubernetes Service (AKS) cluster backed by arm64-based virtual machines, connect
+        to it, and deploy a containerized application. You will pr...
+      preview_after: This introductory Learning Path shows how to create an Azure
+        Kubernetes Service (AKS) cluster backed by arm64-based virtual machines, connect
+        to it, and deploy a containerized application. You will pr...
+      preview_generated: This Learning Path shows how to provision an Azure Kubernetes
+        Service (AKS) cluster on Arm64 virtual machines and deploy a containerized
+        application. You will create a managed Kubernetes cluster integ...
     faqs:
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+      action: rerun_requested
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       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - "How do I connect to the AKS cluster once it\u2019s created?"
+        - Where do the container images for deployment come from?
+        - What result should I expect after applying the Kubernetes YAML?
+        - What should I check if kubectl commands fail after connecting?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - How is the AKS cluster created and what architecture does it use?
+        - How do I connect to the AKS cluster after creation?
+        - What exactly do I deploy to the cluster?
+        - How can I verify that the deployment worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - "How do I connect to the AKS cluster once it\u2019s created?"
+        - Where do the container images for deployment come from?
+        - What result should I expect after applying the Kubernetes YAML?
+        - What should I check if kubectl commands fail after connecting?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - How is the AKS cluster created and what architecture does it use?
+        - How do I connect to the AKS cluster after creation?
+        - What exactly do I deploy to the cluster?
+        - How can I verify that the deployment worked?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md
-    status: unchanged
-    changed_on_disk: false
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-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
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+    rerun_flags_reset:
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+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
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@@ -213449,51 +22114,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:57Z'
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-      preview_before: Learn how to automate Azure resource deployment using Infrastructure as Code with
-        Pulumi to provision Azure Container Instances for containerized applications. It is designed for
-        developers interested...
-      preview_after: Learn how to automate Azure resource deployment using Infrastructure as Code with
-        Pulumi to provision Azure Container Instances for containerized applications. It is designed for
-        developers interested...
-      preview_generated: Learn how to automate Azure resource deployment using Infrastructure as Code
-        with Pulumi to provision Azure Container Instances for containerized applications. It is designed
-        for developers interested...
+      generated_at_before: '2026-06-02T03:56:05Z'
+      generated_at_after: '2026-06-02T03:56:05Z'
+      preview_before: Learn how to use Infrastructure as Code with Pulumi to automate
+        Azure resource deployment on Windows. You will install and configure Node.js,
+        the Pulumi CLI, and the Azure CLI, then create a Pulumi Ty...
+      preview_after: Learn how to use Infrastructure as Code with Pulumi to automate
+        Azure resource deployment on Windows. You will install and configure Node.js,
+        the Pulumi CLI, and the Azure CLI, then create a Pulumi Ty...
+      preview_generated: Learn how to use Infrastructure as Code with Pulumi to provision
+        Azure resources for a containerized application on Windows. You will set up
+        Pulumi (with a free Pulumi account and CLI), Node.js for Ar...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
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-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:56:05Z'
+      generated_at_after: '2026-06-03T00:56:53Z'
       before_count: 5
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       generated_count: 5
       change_details:
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         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which installers should I use on Windows?
+        - Which Pulumi runtime and language does this path use?
+        - After creating the Pulumi app, what should I see in the project?
+        - What result should I expect after updating index.ts and deploying?
+        removed_questions:
+        - What do I need before starting?
+        - What operating system and language does this path use?
+        - What Azure resources are created by the example?
+        - Is Docker required to follow this path?
+        - How do I know the deployment worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which installers should I use on Windows?
+        - Which Pulumi runtime and language does this path use?
+        - After creating the Pulumi app, what should I see in the project?
+        - What result should I expect after updating index.ts and deploying?
+        removed_questions:
+        - What do I need before starting?
+        - What operating system and language does this path use?
+        - What Azure resources are created by the example?
+        - Is Docker required to follow this path?
+        - How do I know the deployment worked?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/funasr/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
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+    status: updated
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+    ai_requested: true
+    rerun_flags_reset:
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+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
       missing_before: false
@@ -213503,51 +22193,76 @@ history:
       source_hash_before: 410ec28d257ce7aa1308f11a741aa87baea80daf1acb113c4149761144269022
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to deploy the ModelScope FunASR Chinese automatic speech recognition model
-        on Arm-based servers with real-time transcription and sentiment analysis. It is designed for developers
-        interested ...
-      preview_after: Learn how to deploy the ModelScope FunASR Chinese automatic speech recognition model
-        on Arm-based servers with real-time transcription and sentiment analysis. It is designed for developers
-        interested ...
-      preview_generated: Learn how to deploy the ModelScope FunASR Chinese automatic speech recognition
-        model on Arm-based servers with real-time transcription and sentiment analysis. It is designed
-        for developers interested ...
+      generated_at_before: '2026-06-02T03:56:56Z'
+      generated_at_after: '2026-06-02T03:56:56Z'
+      preview_before: Deploy the ModelScope FunASR Chinese ASR model on Arm-based
+        Linux servers to enable real-time transcription, punctuation restoration,
+        and sentiment analysis. This introductory path walks you through t...
+      preview_after: Deploy the ModelScope FunASR Chinese ASR model on Arm-based Linux
+        servers to enable real-time transcription, punctuation restoration, and sentiment
+        analysis. This introductory path walks you through t...
+      preview_generated: Follow a practical, introductory workflow to deploy the ModelScope
+        FunASR Chinese automatic speech recognition model on Arm-based Linux servers
+        for real-time transcription with punctuation restoration...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
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-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:56:56Z'
+      generated_at_after: '2026-06-03T00:57:43Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which FunASR version should I install and how?
+        - Can I run this on a cloud provider and which ones are suitable?
+        - How do I know FunASR is working correctly after installation?
+        - What output should I expect from the deployment?
+        removed_questions:
+        - What environment do I need to complete this Learning Path?
+        - Which cloud providers are suitable for the Arm instance?
+        - What tools and versions are used for ASR?
+        - What capabilities will I deploy with FunASR and ModelScope?
+        - How do I know the deployment is working?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which FunASR version should I install and how?
+        - Can I run this on a cloud provider and which ones are suitable?
+        - How do I know FunASR is working correctly after installation?
+        - What output should I expect from the deployment?
+        removed_questions:
+        - What environment do I need to complete this Learning Path?
+        - Which cloud providers are suitable for the Arm instance?
+        - What tools and versions are used for ASR?
+        - What capabilities will I deploy with FunASR and ModelScope?
+        - How do I know the deployment is working?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -213557,51 +22272,78 @@ history:
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-      preview_before: Learn how to install and configure Gardener Kubernetes management platform on Google
-        Cloud Axion C4A SUSE Arm64 instances and deploy workload clusters. It is designed for software
-        developers deploying...
-      preview_after: Learn how to install and configure Gardener Kubernetes management platform on Google
-        Cloud Axion C4A SUSE Arm64 instances and deploy workload clusters. It is designed for software
-        developers deploying...
-      preview_generated: Learn how to install and configure Gardener Kubernetes management platform on
-        Google Cloud Axion C4A SUSE Arm64 instances and deploy workload clusters. It is designed for software
-        developers deploying...
+      generated_at_before: '2026-06-02T03:57:32Z'
+      generated_at_after: '2026-06-02T03:57:32Z'
+      preview_before: Learn how to provision a Google Cloud C4A virtual machine powered
+        by Axion (Arm Neoverse-V2) and install Gardener on SUSE Linux Enterprise Server
+        (Arm64). You will set up Gardener Local, deploy Garden...
+      preview_after: Learn how to provision a Google Cloud C4A virtual machine powered
+        by Axion (Arm Neoverse-V2) and install Gardener on SUSE Linux Enterprise Server
+        (Arm64). You will set up Gardener Local, deploy Garden...
+      preview_generated: This Learning Path walks you through installing and configuring
+        Gardener on a Google Cloud Axion C4A Arm-based SUSE Linux Enterprise Server
+        VM, then deploying and validating local Garden, Seed, and Sh...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:57:32Z'
+      generated_at_after: '2026-06-03T00:58:45Z'
       before_count: 5
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       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before creating the Axion C4A VM on Google Cloud?
+        - Which VM type and operating system does this path use for Gardener?
+        - Do the Garden, Seed, and Shoot clusters run in the cloud or locally?
+        - How do I point kubectl at the Gardener Local cluster to validate the setup?
+        - What should be ready before running kube-bench, and what output should I
+          expect?
+        removed_questions:
+        - What environment does this Learning Path use?
+        - What are the prerequisites before starting?
+        - What components are deployed with Gardener?
+        - How do I verify the Gardener setup is working?
+        - What security checks are included in this path?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before creating the Axion C4A VM on Google Cloud?
+        - Which VM type and operating system does this path use for Gardener?
+        - Do the Garden, Seed, and Shoot clusters run in the cloud or locally?
+        - How do I point kubectl at the Gardener Local cluster to validate the setup?
+        - What should be ready before running kube-bench, and what output should I
+          expect?
+        removed_questions:
+        - What environment does this Learning Path use?
+        - What are the prerequisites before starting?
+        - What components are deployed with Gardener?
+        - How do I verify the Gardener setup is working?
+        - What security checks are included in this path?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -213611,51 +22353,76 @@ history:
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-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to apply link-time optimization with the GCC toolchain to improve application
-        performance by optimizing across compilation units. It is designed for developers who want to
-        improve applicatio...
-      preview_after: Learn how to apply link-time optimization with the GCC toolchain to improve application
-        performance by optimizing across compilation units. It is designed for developers who want to
-        improve applicatio...
-      preview_generated: Learn how to apply link-time optimization with the GCC toolchain to improve application
-        performance by optimizing across compilation units. It is designed for developers who want to
-        improve applicatio...
+      generated_at_before: '2026-06-02T03:57:53Z'
+      generated_at_after: '2026-06-02T03:57:53Z'
+      preview_before: This introductory Learning Path shows how to enable and use
+        GCC link-time optimization (LTO) on an Arm Linux system to improve application
+        performance by optimizing across compilation units. You will ...
+      preview_after: This introductory Learning Path shows how to enable and use GCC
+        link-time optimization (LTO) on an Arm Linux system to improve application
+        performance by optimizing across compilation units. You will ...
+      preview_generated: This introductory path shows how to enable and apply GCC
+        Link-Time Optimization (LTO) on an Arm Linux system to optimize across compilation
+        units. You will learn what LTO does, when to use it, and how...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:57:53Z'
+      generated_at_after: '2026-06-03T00:59:35Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which GCC flags do I use to enable LTO?
+        - Do I need to compile every translation unit with -flto?
+        - Can I build a small program with a single gcc command?
+        - How should I evaluate the impact of LTO on my workload?
+        removed_questions:
+        - What do I need before starting?
+        - How do I enable LTO with GCC?
+        - Can I build small programs with LTO in a single command?
+        - How should I evaluate the impact of LTO?
+        - Is this Learning Path specific to GCC on Linux for Arm targets?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which GCC flags do I use to enable LTO?
+        - Do I need to compile every translation unit with -flto?
+        - Can I build a small program with a single gcc command?
+        - How should I evaluate the impact of LTO on my workload?
+        removed_questions:
+        - What do I need before starting?
+        - How do I enable LTO with GCC?
+        - Can I build small programs with LTO in a single command?
+        - How should I evaluate the impact of LTO?
+        - Is this Learning Path specific to GCC on Linux for Arm targets?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/gcp/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -213665,51 +22432,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to automate the creation of Arm virtual machines on Google Cloud Platform
-        using Terraform with jump server access configuration. It is designed for anyone new to using
-        Arm virtual machines i...
-      preview_after: Learn how to automate the creation of Arm virtual machines on Google Cloud Platform
-        using Terraform with jump server access configuration. It is designed for anyone new to using
-        Arm virtual machines i...
-      preview_generated: Learn how to automate the creation of Arm virtual machines on Google Cloud Platform
-        using Terraform with jump server access configuration. It is designed for anyone new to using
-        Arm virtual machines i...
+      generated_at_before: '2026-06-02T03:58:46Z'
+      generated_at_after: '2026-06-02T03:58:46Z'
+      preview_before: Learn to automate the deployment of Arm-based virtual machines
+        on Google Cloud Platform using Terraform, with secure access configured through
+        a Jump Server (bastion host). You will generate an SSH ke...
+      preview_after: Learn to automate the deployment of Arm-based virtual machines
+        on Google Cloud Platform using Terraform, with secure access configured through
+        a Jump Server (bastion host). You will generate an SSH ke...
+      preview_generated: This introductory path shows how to automate the creation
+        of Arm-based virtual machines on Google Cloud Platform using Terraform, with
+        access provided through a Jump Server (bastion). You will generat...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 24e2ffcf9b7cda919e6e864f18bb9424c0c328242c92f491c1b284e317ed7e1c
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-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:58:46Z'
+      generated_at_after: '2026-06-03T01:00:09Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the Terraform steps?
+        - Do I need to generate a new SSH key pair, and where should it be located?
+        - How do I authenticate Terraform with my Google Cloud project?
+        - What gets created when I apply the Terraform configuration?
+        - How do I access the deployed Arm instances after provisioning?
+        removed_questions:
+        - What do I need before starting?
+        - What infrastructure does the Terraform configuration create?
+        - How do I authenticate Terraform with GCP?
+        - How will I access the provisioned Arm VMs?
+        - Can I reuse or modify the Terraform files for other Learning Paths?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the Terraform steps?
+        - Do I need to generate a new SSH key pair, and where should it be located?
+        - How do I authenticate Terraform with my Google Cloud project?
+        - What gets created when I apply the Terraform configuration?
+        - How do I access the deployed Arm instances after provisioning?
+        removed_questions:
+        - What do I need before starting?
+        - What infrastructure does the Terraform configuration create?
+        - How do I authenticate Terraform with GCP?
+        - How will I access the provisioned Arm VMs?
+        - Can I reuse or modify the Terraform files for other Learning Paths?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -213719,51 +22511,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Run Geekbench on Arm systems to benchmark CPU performance, interpret the results,
-        and compare different Arm configurations. It is designed for software developers interested in
-        comparing the performan...
-      preview_after: Run Geekbench on Arm systems to benchmark CPU performance, interpret the results,
-        and compare different Arm configurations. It is designed for software developers interested in
-        comparing the performan...
-      preview_generated: Run Geekbench on Arm systems to benchmark CPU performance, interpret the results,
-        and compare different Arm configurations. It is designed for software developers interested in
-        comparing the performan...
+      generated_at_before: '2026-06-02T03:59:40Z'
+      generated_at_after: '2026-06-02T03:59:40Z'
+      preview_before: This introductory Learning Path shows how to download and run
+        Geekbench on Arm Linux systems to benchmark CPU performance. You will install
+        and execute Geekbench, obtain single-core and multi-core sco...
+      preview_after: This introductory Learning Path shows how to download and run
+        Geekbench on Arm Linux systems to benchmark CPU performance. You will install
+        and execute Geekbench, obtain single-core and multi-core sco...
+      preview_generated: This introductory path shows how to download and run Geekbench
+        on an Arm Linux system to measure CPU performance and compare Arm configurations
+        for your workload. You will execute the benchmark and re...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 85a0a44bde6f217bdfc7fd0660ed6a86279b24c7ede302307ef6efb2626d83d9
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T03:59:40Z'
+      generated_at_after: '2026-06-03T01:01:02Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this benchmark?
+        - Which Geekbench package should I download for Arm Linux?
+        - What result should I expect after a successful run?
+        - How should I compare different Arm systems using Geekbench?
+        - Can I use an operating system other than Linux for this path?
+        removed_questions:
+        - What do I need before starting?
+        - Which operating systems are covered?
+        - Where do I get Geekbench for Arm Linux?
+        - What results will I get and how are they used?
+        - How long does this take and what skill level is assumed?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this benchmark?
+        - Which Geekbench package should I download for Arm Linux?
+        - What result should I expect after a successful run?
+        - How should I compare different Arm systems using Geekbench?
+        - Can I use an operating system other than Linux for this path?
+        removed_questions:
+        - What do I need before starting?
+        - Which operating systems are covered?
+        - Where do I get Geekbench for Arm Linux?
+        - What results will I get and how are they used?
+        - How long does this take and what skill level is assumed?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -213773,51 +22590,78 @@ history:
       source_hash_before: 642b67e7ca3717f499ab73efedd924eeab29a0055fad81c2d60fda993dcea195
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to set up Arm-hosted GitHub runners and train PyTorch ML models using
-        the German Traffic Sign Recognition Benchmark dataset with automated workflows. It is designed
-        for software developers i...
-      preview_after: Learn how to set up Arm-hosted GitHub runners and train PyTorch ML models using the
-        German Traffic Sign Recognition Benchmark dataset with automated workflows. It is designed for
-        software developers i...
-      preview_generated: Learn how to set up Arm-hosted GitHub runners and train PyTorch ML models using
-        the German Traffic Sign Recognition Benchmark dataset with automated workflows. It is designed
-        for software developers i...
+      generated_at_before: '2026-06-02T04:00:17Z'
+      generated_at_after: '2026-06-02T04:00:17Z'
+      preview_before: This Learning Path shows how to automate an end-to-end MLOps
+        workflow on Linux using Arm-hosted GitHub runners and GitHub Actions. You
+        will fork an example repository, set up workflows to train and te...
+      preview_after: This Learning Path shows how to automate an end-to-end MLOps
+        workflow on Linux using Arm-hosted GitHub runners and GitHub Actions. You
+        will fork an example repository, set up workflows to train and te...
+      preview_generated: 'Automate an end-to-end MLOps workflow on Arm-hosted GitHub
+        runners: fork an example repository, train and test a PyTorch model on the
+        German Traffic Sign Recognition Benchmark (GTSRB) dataset, compare...'
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 642b67e7ca3717f499ab73efedd924eeab29a0055fad81c2d60fda993dcea195
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T04:00:17Z'
+      generated_at_after: '2026-06-03T01:01:51Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the workflows?
+        - Where should I fork the example repository, and what if the name conflicts?
+        - Which workflow trains the model and what should I expect as output?
+        - How do I compare inference performance across PyTorch backends?
+        - How do I containerize and publish the trained model, and how is deployment
+          validated?
+        removed_questions:
+        - What accounts and access do I need before starting?
+        - How do I get the example project into my environment?
+        - How are training and testing automated in this path?
+        - How do I compare PyTorch backends for inference performance?
+        - What is produced during deployment and how can I access the model?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the workflows?
+        - Where should I fork the example repository, and what if the name conflicts?
+        - Which workflow trains the model and what should I expect as output?
+        - How do I compare inference performance across PyTorch backends?
+        - How do I containerize and publish the trained model, and how is deployment
+          validated?
+        removed_questions:
+        - What accounts and access do I need before starting?
+        - How do I get the example project into my environment?
+        - How are training and testing automated in this path?
+        - How do I compare PyTorch backends for inference performance?
+        - What is produced during deployment and how can I access the model?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -213827,51 +22671,74 @@ history:
       source_hash_before: cc72ef1fe1fde9f4f9ea0769c0e04d749731b8073b2efc0e65d7f608665abc2d
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to install RunsOn self-hosted runner manager in your AWS account to execute
-        GitHub Actions workflows on Arm runners. It is designed for developers who want to use Arm runners
-        offered by AWS ...
-      preview_after: Learn how to install RunsOn self-hosted runner manager in your AWS account to execute
-        GitHub Actions workflows on Arm runners. It is designed for developers who want to use Arm runners
-        offered by AWS ...
-      preview_generated: Learn how to install RunsOn self-hosted runner manager in your AWS account to
-        execute GitHub Actions workflows on Arm runners. It is designed for developers who want to use
-        Arm runners offered by AWS ...
+      generated_at_before: '2026-06-02T04:01:04Z'
+      generated_at_after: '2026-06-02T04:01:04Z'
+      preview_before: This Learning Path shows how to install RunsOn, a self-hosted
+        runner manager, in your AWS account to run GitHub Actions on Arm-based AWS
+        EC2 instances. You will set up RunsOn using AWS CloudFormation ...
+      preview_after: This Learning Path shows how to install RunsOn, a self-hosted
+        runner manager, in your AWS account to run GitHub Actions on Arm-based AWS
+        EC2 instances. You will set up RunsOn using AWS CloudFormation ...
+      preview_generated: Learn to install RunsOn, a self-hosted runner manager, in
+        your AWS account and execute GitHub Actions workflows on Arm-based AWS Graviton
+        instances. You will sign in to the AWS console, use the offici...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: cc72ef1fe1fde9f4f9ea0769c0e04d749731b8073b2efc0e65d7f608665abc2d
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T04:01:04Z'
+      generated_at_after: '2026-06-03T01:02:59Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the installation?
+        - Which EC2 instance types and Arm processors can I use for runners?
+        - How do I change my GitHub Actions workflow to target an Arm runner?
+        - What outcome and timing should I expect after triggering a workflow?
+        removed_questions:
+        - What accounts do I need before starting?
+        - Do I need a license key to use RunsOn?
+        - How do I target Arm runners in my GitHub Actions workflows?
+        - How can I verify that the setup worked?
+        updated_questions:
+        - How do I install RunsOn in my AWS account?
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the installation?
+        - Which EC2 instance types and Arm processors can I use for runners?
+        - How do I change my GitHub Actions workflow to target an Arm runner?
+        - What outcome and timing should I expect after triggering a workflow?
+        removed_questions:
+        - What accounts do I need before starting?
+        - Do I need a license key to use RunsOn?
+        - How do I target Arm runners in my GitHub Actions workflows?
+        - How can I verify that the setup worked?
+        updated_questions:
+        - How do I install RunsOn in my AWS account?
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -213881,51 +22748,76 @@ history:
       source_hash_before: d5df467355a7792f7a04eafc4a6bbd2410353aca000cd2b1aec74a7ed93a9270
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to provision a Google Axion C4A Arm virtual machine and set up a GitHub
-        Actions self-hosted runner for CI/CD workflows. It is designed for developers who want to deploy
-        a GitHub Actions self...
-      preview_after: Learn how to provision a Google Axion C4A Arm virtual machine and set up a GitHub
-        Actions self-hosted runner for CI/CD workflows. It is designed for developers who want to deploy
-        a GitHub Actions self...
-      preview_generated: Learn how to provision a Google Axion C4A Arm virtual machine and set up a GitHub
-        Actions self-hosted runner for CI/CD workflows. It is designed for developers who want to deploy
-        a GitHub Actions self...
+      generated_at_before: '2026-06-02T04:01:33Z'
+      generated_at_after: '2026-06-02T04:01:33Z'
+      preview_before: This Learning Path shows how to provision a Google Axion C4A
+        Arm virtual machine on Google Cloud and use it as a self-hosted runner for
+        GitHub Actions. You will create a c4a-standard-4 instance from t...
+      preview_after: This Learning Path shows how to provision a Google Axion C4A
+        Arm virtual machine on Google Cloud and use it as a self-hosted runner for
+        GitHub Actions. You will create a c4a-standard-4 instance from t...
+      preview_generated: Provision a Google Axion C4A Arm virtual machine on Google
+        Cloud and use it as a GitHub Actions self-hosted runner to execute CI/CD jobs
+        on Arm. This introductory path walks you through creating a c4a...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: d5df467355a7792f7a04eafc4a6bbd2410353aca000cd2b1aec74a7ed93a9270
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T04:01:33Z'
+      generated_at_after: '2026-06-03T01:03:50Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before creating the VM and runner?
+        - Which Google Cloud machine type is used in the steps?
+        - Which operating system is assumed on the VM?
+        - How do I set up the self-hosted runner on the VM?
+        - How do I verify that the workflow executed on the Arm runner?
+        removed_questions:
+        - What accounts or prerequisites do I need before starting?
+        - How do I create the compute environment used in this path?
+        - Which tools are installed on the VM to set up the self-hosted runner?
+        - How do I confirm the self-hosted runner is working?
+        - What is the expected outcome and how long will it take?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before creating the VM and runner?
+        - Which Google Cloud machine type is used in the steps?
+        - Which operating system is assumed on the VM?
+        - How do I set up the self-hosted runner on the VM?
+        - How do I verify that the workflow executed on the Arm runner?
+        removed_questions:
+        - What accounts or prerequisites do I need before starting?
+        - How do I create the compute environment used in this path?
+        - Which tools are installed on the VM to set up the self-hosted runner?
+        - How do I confirm the self-hosted runner is working?
+        - What is the expected outcome and how long will it take?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/gke/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -213935,51 +22827,76 @@ history:
       source_hash_before: 7c3d37545c93db584d6e0ce88d8feaf0bf690e0c8786b664a6643be713d0336f
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       current_source_hash: 7c3d37545c93db584d6e0ce88d8feaf0bf690e0c8786b664a6643be713d0336f
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to automate the deployment of an Arm-based Google Kubernetes Engine cluster
-        using Terraform for container orchestration. It is designed for software developers who want to
-        deploy an Arm-base...
-      preview_after: Learn how to automate the deployment of an Arm-based Google Kubernetes Engine cluster
-        using Terraform for container orchestration. It is designed for software developers who want to
-        deploy an Arm-base...
-      preview_generated: Learn how to automate the deployment of an Arm-based Google Kubernetes Engine
-        cluster using Terraform for container orchestration. It is designed for software developers who
-        want to deploy an Arm-base...
+      generated_at_before: '2026-06-02T04:01:56Z'
+      generated_at_after: '2026-06-02T04:01:56Z'
+      preview_before: Automate the creation of an Arm-based Kubernetes cluster on
+        Google Cloud using Terraform. This advanced Learning Path focuses on deploying
+        Google Kubernetes Engine (GKE) on Tau T2A virtual machines po...
+      preview_after: Automate the creation of an Arm-based Kubernetes cluster on Google
+        Cloud using Terraform. This advanced Learning Path focuses on deploying Google
+        Kubernetes Engine (GKE) on Tau T2A virtual machines po...
+      preview_generated: Automate the provisioning of an Arm-based Kubernetes cluster
+        on Google Cloud using Terraform. This Learning Path guides you through deploying
+        Google Kubernetes Engine (GKE) nodes on the Tau T2A VM fam...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 7c3d37545c93db584d6e0ce88d8feaf0bf690e0c8786b664a6643be713d0336f
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       current_source_hash: 7c3d37545c93db584d6e0ce88d8feaf0bf690e0c8786b664a6643be713d0336f
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T04:01:56Z'
+      generated_at_after: '2026-06-03T01:04:26Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the Terraform configuration?
+        - How do I ensure the GKE nodes are Arm-based?
+        - Will I create a new Google Cloud project or use an existing one?
+        - What result should I expect when the Terraform apply completes?
+        - Does this Learning Path cover deploying workloads or only cluster creation?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - Which Google Cloud resources are used to provide Arm-based nodes?
+        - Does this Learning Path require Linux?
+        - Will I create a new Google Cloud project as part of the steps?
+        - What is the expected result after completing the steps?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the Terraform configuration?
+        - How do I ensure the GKE nodes are Arm-based?
+        - Will I create a new Google Cloud project or use an existing one?
+        - What result should I expect when the Terraform apply completes?
+        - Does this Learning Path cover deploying workloads or only cluster creation?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - Which Google Cloud resources are used to provide Arm-based nodes?
+        - Does this Learning Path require Linux?
+        - Will I create a new Google Cloud project as part of the steps?
+        - What is the expected result after completing the steps?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -213989,51 +22906,76 @@ history:
       source_hash_before: 50715640292b60ea4216ee2211140c4212592a27356d628d945e83d9deb7fdcc
       source_hash_after: 50715640292b60ea4216ee2211140c4212592a27356d628d945e83d9deb7fdcc
       current_source_hash: 50715640292b60ea4216ee2211140c4212592a27356d628d945e83d9deb7fdcc
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to add Arm-based Google Axion nodes to an existing x86 GKE cluster and
-        rebuild applications for multi-architecture support. It is designed for software developers who
-        are looking to migrate ...
-      preview_after: Learn how to add Arm-based Google Axion nodes to an existing x86 GKE cluster and
-        rebuild applications for multi-architecture support. It is designed for software developers who
-        are looking to migrate ...
-      preview_generated: Learn how to add Arm-based Google Axion nodes to an existing x86 GKE cluster
-        and rebuild applications for multi-architecture support. It is designed for software developers
-        who are looking to migrate ...
+      generated_at_before: '2026-06-02T04:02:44Z'
+      generated_at_after: '2026-06-02T04:02:44Z'
+      preview_before: This Learning Path shows how to extend an existing x86-based
+        Google Kubernetes Engine (GKE) cluster with Arm-based Google Axion nodes and
+        rebuild an x86 application for multi-architecture support. You...
+      preview_after: This Learning Path shows how to extend an existing x86-based
+        Google Kubernetes Engine (GKE) cluster with Arm-based Google Axion nodes and
+        rebuild an x86 application for multi-architecture support. You...
+      preview_generated: This advanced path shows how to extend an existing x86-based
+        Google Kubernetes Engine (GKE) cluster with Arm-based Google Axion nodes and
+        run your application across both architectures. You will add a...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 50715640292b60ea4216ee2211140c4212592a27356d628d945e83d9deb7fdcc
       source_hash_after: 50715640292b60ea4216ee2211140c4212592a27356d628d945e83d9deb7fdcc
       current_source_hash: 50715640292b60ea4216ee2211140c4212592a27356d628d945e83d9deb7fdcc
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T04:02:44Z'
+      generated_at_after: '2026-06-03T01:05:07Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which VM type should I use for the Arm-based node pool?
+        - How do I rebuild my existing x86 application for multi-architecture?
+        - How will I control which pods run on Arm versus x86 nodes?
+        - How do I know the application is running on the intended architecture?
+        removed_questions:
+        - Do I need a new GKE cluster for this Learning Path?
+        - What tools and accounts are required before starting?
+        - Which Arm node types are used and what Arm technology do they rely on?
+        - What will I build or configure during the path?
+        - How do I know the migration worked and how long will it take?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which VM type should I use for the Arm-based node pool?
+        - How do I rebuild my existing x86 application for multi-architecture?
+        - How will I control which pods run on Arm versus x86 nodes?
+        - How do I know the application is running on the intended architecture?
+        removed_questions:
+        - Do I need a new GKE cluster for this Learning Path?
+        - What tools and accounts are required before starting?
+        - Which Arm node types are used and what Arm technology do they rely on?
+        - What will I build or configure during the path?
+        - How do I know the migration worked and how long will it take?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -214043,51 +22985,78 @@ history:
       source_hash_before: cf981c67553824c7c57b6dda9c2953ac80926750676ac101e939f6bbff655ab5
       source_hash_after: cf981c67553824c7c57b6dda9c2953ac80926750676ac101e939f6bbff655ab5
       current_source_hash: cf981c67553824c7c57b6dda9c2953ac80926750676ac101e939f6bbff655ab5
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to create dual-architecture GKE clusters with arm64 and amd64 node pools,
-        build multi-architecture Docker images, and migrate services to Google Axion processors. It is
-        designed for cloud, p...
-      preview_after: Learn how to create dual-architecture GKE clusters with arm64 and amd64 node pools,
-        build multi-architecture Docker images, and migrate services to Google Axion processors. It is
-        designed for cloud, p...
-      preview_generated: Learn how to create dual-architecture GKE clusters with arm64 and amd64 node
-        pools, build multi-architecture Docker images, and migrate services to Google Axion processors.
-        It is designed for cloud, p...
+      generated_at_before: '2026-06-02T04:03:32Z'
+      generated_at_after: '2026-06-02T04:03:32Z'
+      preview_before: This advanced Learning Path walks you through migrating a microservices
+        application from x86 to Arm on Google Kubernetes Engine using multi-architecture
+        container images and Google Axion processors. Y...
+      preview_after: This advanced Learning Path walks you through migrating a microservices
+        application from x86 to Arm on Google Kubernetes Engine using multi-architecture
+        container images and Google Axion processors. Y...
+      preview_generated: This advanced Learning Path guides you through migrating
+        a Kubernetes microservices application from x86 to Arm on Google Kubernetes
+        Engine using Google Axion processors. You will modify Dockerfiles f...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: cf981c67553824c7c57b6dda9c2953ac80926750676ac101e939f6bbff655ab5
       source_hash_after: cf981c67553824c7c57b6dda9c2953ac80926750676ac101e939f6bbff655ab5
       current_source_hash: cf981c67553824c7c57b6dda9c2953ac80926750676ac101e939f6bbff655ab5
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T04:03:32Z'
+      generated_at_after: '2026-06-03T01:05:58Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which GKE cluster configuration and networking are used?
+        - Which Online Boutique services require Dockerfile changes for multi-architecture
+          builds?
+        - How are the multi-architecture images built and published?
+        - How do I deploy on amd64 first and then migrate to Arm?
+        removed_questions:
+        - What do I need before starting?
+        - Do I need to change the application code to migrate to Arm?
+        - Which Online Boutique services require Dockerfile updates?
+        - How are the multi-architecture images built and stored?
+        - How do I deploy to x86 first and then migrate to Arm in GKE?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which GKE cluster configuration and networking are used?
+        - Which Online Boutique services require Dockerfile changes for multi-architecture
+          builds?
+        - How are the multi-architecture images built and published?
+        - How do I deploy on amd64 first and then migrate to Arm?
+        removed_questions:
+        - What do I need before starting?
+        - Do I need to change the application code to migrate to Arm?
+        - Which Online Boutique services require Dockerfile updates?
+        - How are the multi-architecture images built and stored?
+        - How do I deploy to x86 first and then migrate to Arm in GKE?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -214097,51 +23066,76 @@ history:
       source_hash_before: 74952d68380c7540306543b0a7520f6960f673dd55ad5f164365fcfe1470380e
       source_hash_after: 74952d68380c7540306543b0a7520f6960f673dd55ad5f164365fcfe1470380e
       current_source_hash: 74952d68380c7540306543b0a7520f6960f673dd55ad5f164365fcfe1470380e
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Rebuild and benchmark glibc with LSE atomics on Arm servers, then evaluate scalability
-        using MongoDB workloads and guidance on when LSE delivers a measurable uplift. It is designed
-        for software develo...
-      preview_after: Rebuild and benchmark glibc with LSE atomics on Arm servers, then evaluate scalability
-        using MongoDB workloads and guidance on when LSE delivers a measurable uplift. It is designed
-        for software develo...
-      preview_generated: Rebuild and benchmark glibc with LSE atomics on Arm servers, then evaluate scalability
-        using MongoDB workloads and guidance on when LSE delivers a measurable uplift. It is designed
-        for software develo...
+      generated_at_before: '2026-06-02T04:04:16Z'
+      generated_at_after: '2026-06-02T04:04:16Z'
+      preview_before: This advanced path shows how to rebuild and install glibc with
+        Armv8-A Large System Extensions (LSE) on an Arm server running Linux, then
+        benchmark the impact on MongoDB. You will build MongoDB 5.3.2 ...
+      preview_after: This advanced path shows how to rebuild and install glibc with
+        Armv8-A Large System Extensions (LSE) on an Arm server running Linux, then
+        benchmark the impact on MongoDB. You will build MongoDB 5.3.2 ...
+      preview_generated: This advanced path guides you through rebuilding and installing
+        glibc with Large System Extensions (LSE) on an Arm server running Linux, then
+        assessing its impact using MongoDB workloads. You will com...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 74952d68380c7540306543b0a7520f6960f673dd55ad5f164365fcfe1470380e
       source_hash_after: 74952d68380c7540306543b0a7520f6960f673dd55ad5f164365fcfe1470380e
       current_source_hash: 74952d68380c7540306543b0a7520f6960f673dd55ad5f164365fcfe1470380e
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T04:04:16Z'
+      generated_at_after: '2026-06-03T01:06:30Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Do I need to rebuild glibc on the instance, and why?
+        - Which MongoDB version is used and how is it installed?
+        - How do I run and validate the benchmarks with and without LSE?
+        - What result should I expect from the No-LSE baseline?
+        removed_questions:
+        - What environment and prerequisites are required?
+        - What will I build and configure during the path?
+        - How are the benchmarks executed?
+        - How do I know the setup worked and what to look for in results?
+        - What is LSE and why is it relevant here?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Do I need to rebuild glibc on the instance, and why?
+        - Which MongoDB version is used and how is it installed?
+        - How do I run and validate the benchmarks with and without LSE?
+        - What result should I expect from the No-LSE baseline?
+        removed_questions:
+        - What environment and prerequisites are required?
+        - What will I build and configure during the path?
+        - How are the benchmarks executed?
+        - How do I know the setup worked and what to look for in results?
+        - What is LSE and why is it relevant here?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -214151,51 +23145,78 @@ history:
       source_hash_before: aa78434138f08b424352302e92a1cd40d8297459bc65202715dcb41c40de6057
       source_hash_after: aa78434138f08b424352302e92a1cd40d8297459bc65202715dcb41c40de6057
       current_source_hash: aa78434138f08b424352302e92a1cd40d8297459bc65202715dcb41c40de6057
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to provision Arm64 and x86_64 VM instances on Google Cloud, then install
-        and use Sweet and Benchstat to measure and compare Go application performance. It is designed
-        for This introductory t...
-      preview_after: Learn how to provision Arm64 and x86_64 VM instances on Google Cloud, then install
-        and use Sweet and Benchstat to measure and compare Go application performance. It is designed
-        for This introductory t...
-      preview_generated: Learn how to provision Arm64 and x86_64 VM instances on Google Cloud, then install
-        and use Sweet and Benchstat to measure and compare Go application performance. It is designed
-        for This introductory t...
+      generated_at_before: '2026-06-02T04:05:11Z'
+      generated_at_after: '2026-06-02T04:05:11Z'
+      preview_before: Provision Arm64 and x86_64 Linux VM instances on Google Cloud
+        and use Go benchmarking tools to compare performance across architectures.
+        You will create an Arm-based c4a-standard-4 and an Intel Emeral...
+      preview_after: Provision Arm64 and x86_64 Linux VM instances on Google Cloud
+        and use Go benchmarking tools to compare performance across architectures.
+        You will create an Arm-based c4a-standard-4 and an Intel Emeral...
+      preview_generated: Provision comparable Arm64 and x86_64 Linux VM instances
+        on Google Cloud, then install Go along with the Sweet benchmark runner and
+        Benchstat to measure and compare Go application performance across a...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: aa78434138f08b424352302e92a1cd40d8297459bc65202715dcb41c40de6057
       source_hash_after: aa78434138f08b424352302e92a1cd40d8297459bc65202715dcb41c40de6057
       current_source_hash: aa78434138f08b424352302e92a1cd40d8297459bc65202715dcb41c40de6057
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T04:05:11Z'
+      generated_at_after: '2026-06-03T01:07:15Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which VM types should I create for the comparison?
+        - Do I install Go, Sweet, and Benchstat on both VMs, and where should I run
+          the install?
+        - How do I execute and compare the benchmarks?
+        - What output should I expect from Benchstat?
+        removed_questions:
+        - What do I need before starting?
+        - Which VM instances does this path use?
+        - Where are the benchmarking tools installed?
+        - How do I run and compare benchmarks?
+        - How do I verify that everything worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which VM types should I create for the comparison?
+        - Do I install Go, Sweet, and Benchstat on both VMs, and where should I run
+          the install?
+        - How do I execute and compare the benchmarks?
+        - What output should I expect from Benchstat?
+        removed_questions:
+        - What do I need before starting?
+        - Which VM instances does this path use?
+        - Where are the benchmarking tools installed?
+        - How do I run and compare benchmarks?
+        - How do I verify that everything worked?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -214205,51 +23226,78 @@ history:
       source_hash_before: 46a0a3df799ac473f56d3820390af405b3301758cf15685a250a04c4854aba9e
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       current_source_hash: 46a0a3df799ac473f56d3820390af405b3301758cf15685a250a04c4854aba9e
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to provision Azure Cobalt 100 Arm64 virtual machines and deploy Golang
-        applications with performance benchmarking on Arm architecture. It is designed for software developers,
-        DevOps engineer...
-      preview_after: Learn how to provision Azure Cobalt 100 Arm64 virtual machines and deploy Golang
-        applications with performance benchmarking on Arm architecture. It is designed for software developers,
-        DevOps engineer...
-      preview_generated: Learn how to provision Azure Cobalt 100 Arm64 virtual machines and deploy Golang
-        applications with performance benchmarking on Arm architecture. It is designed for software developers,
-        DevOps engineer...
+      generated_at_before: '2026-06-02T04:05:37Z'
+      generated_at_after: '2026-06-02T04:05:37Z'
+      preview_before: This introductory Learning Path guides you through provisioning
+        an Arm64 Azure Cobalt 100 (Dpsv6-series) virtual machine using the Azure portal
+        with Ubuntu Pro 24.04 LTS, installing the Go toolchain, ...
+      preview_after: This introductory Learning Path guides you through provisioning
+        an Arm64 Azure Cobalt 100 (Dpsv6-series) virtual machine using the Azure portal
+        with Ubuntu Pro 24.04 LTS, installing the Go toolchain, ...
+      preview_generated: This Learning Path shows how to provision an Arm64 virtual
+        machine on Microsoft Azure using Cobalt 100 processors (Arm Neoverse N2),
+        install the Go toolchain on Ubuntu Pro 24.04 LTS, and validate the ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 46a0a3df799ac473f56d3820390af405b3301758cf15685a250a04c4854aba9e
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       current_source_hash: 46a0a3df799ac473f56d3820390af405b3301758cf15685a250a04c4854aba9e
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T04:05:37Z'
+      generated_at_after: '2026-06-03T01:07:53Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which VM series and operating system image should I choose?
+        - Which Go distribution should I install on the Arm64 VM?
+        - What result should I expect from the baseline Go web server test?
+        - How do I run and interpret the performance benchmarks, and compare with
+          x86_64?
+        removed_questions:
+        - What Azure access do I need before starting?
+        - Which operating system image should I select for the VM?
+        - Can I create the VM with the Azure CLI or an IaC tool instead of the portal?
+        - How do I verify that Go is correctly installed on the Arm64 VM?
+        - What benchmarks will I run and how do I interpret them?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which VM series and operating system image should I choose?
+        - Which Go distribution should I install on the Arm64 VM?
+        - What result should I expect from the baseline Go web server test?
+        - How do I run and interpret the performance benchmarks, and compare with
+          x86_64?
+        removed_questions:
+        - What Azure access do I need before starting?
+        - Which operating system image should I select for the VM?
+        - Can I create the VM with the Azure CLI or an IaC tool instead of the portal?
+        - How do I verify that Go is correctly installed on the Arm64 VM?
+        - What benchmarks will I run and how do I interpret them?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -214259,51 +23307,76 @@ history:
       source_hash_before: 87e9d25d5fb1f45d126aa4ca2fa1e13d6a470f2bcdc70968aa4622790106e629
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       current_source_hash: 87e9d25d5fb1f45d126aa4ca2fa1e13d6a470f2bcdc70968aa4622790106e629
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn how to install Helm on Google Cloud Axion C4A SUSE VMs and deploy applications
-        like NGINX, PostgreSQL, and Redis using Helm charts. It is designed for This is an introductory
-        topic intended for ...
-      preview_after: Learn how to install Helm on Google Cloud Axion C4A SUSE VMs and deploy applications
-        like NGINX, PostgreSQL, and Redis using Helm charts. It is designed for This is an introductory
-        topic intended for ...
-      preview_generated: Learn how to install Helm on Google Cloud Axion C4A SUSE VMs and deploy applications
-        like NGINX, PostgreSQL, and Redis using Helm charts. It is designed for This is an introductory
-        topic intended for ...
+      generated_at_before: '2026-06-02T04:06:17Z'
+      generated_at_after: '2026-06-02T04:06:17Z'
+      preview_before: Follow this introductory, hands-on path to install and validate
+        Helm on Arm-based Google Cloud Axion C4A virtual machines running SUSE Linux
+        Enterprise Server. You will provision a C4A instance, insta...
+      preview_after: Follow this introductory, hands-on path to install and validate
+        Helm on Arm-based Google Cloud Axion C4A virtual machines running SUSE Linux
+        Enterprise Server. You will provision a C4A instance, insta...
+      preview_generated: This Learning Path guides you through installing and validating
+        Helm on Google Cloud Axion C4A Arm-based SUSE Linux VMs and deploying services
+        on Google Kubernetes Engine (GKE). You provision a SLES V...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 87e9d25d5fb1f45d126aa4ca2fa1e13d6a470f2bcdc70968aa4622790106e629
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       current_source_hash: 87e9d25d5fb1f45d126aa4ca2fa1e13d6a470f2bcdc70968aa4622790106e629
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T04:06:17Z'
+      generated_at_after: '2026-06-03T01:08:45Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Google Cloud machine type is used for the C4A VM in this path?
+        - Which tools are installed on the SUSE Arm64 VM to prepare for Helm testing?
+        - How do I confirm that Helm and the chart repository are set up correctly?
+        - What is deployed to GKE, and how does that differ from the local KinD cluster?
+        removed_questions:
+        - What are the prerequisites to start this Learning Path?
+        - Which VM type and operating system are used on Google Cloud?
+        - Do I need a Kubernetes cluster, and which ones are used?
+        - How do I verify that Helm and kubectl are working correctly?
+        - What outcomes should I expect after completing the steps?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Google Cloud machine type is used for the C4A VM in this path?
+        - Which tools are installed on the SUSE Arm64 VM to prepare for Helm testing?
+        - How do I confirm that Helm and the chart repository are set up correctly?
+        - What is deployed to GKE, and how does that differ from the local KinD cluster?
+        removed_questions:
+        - What are the prerequisites to start this Learning Path?
+        - Which VM type and operating system are used on Google Cloud?
+        - Do I need a Kubernetes cluster, and which ones are used?
+        - How do I verify that Helm and kubectl are working correctly?
+        - What outcomes should I expect after completing the steps?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/intro/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -214313,51 +23386,76 @@ history:
       source_hash_before: d2786f42b505823253ae16c647ca2e03c154f371b7c326210fde8523b12a8188
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       current_source_hash: d2786f42b505823253ae16c647ca2e03c154f371b7c326210fde8523b12a8188
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Learn where Arm architecture is used in servers and cloud computing, and find Arm-based
-        hardware platforms for software development. It is designed for software developers working on
-        server and cloud ...
-      preview_after: Learn where Arm architecture is used in servers and cloud computing, and find Arm-based
-        hardware platforms for software development. It is designed for software developers working on
-        server and cloud ...
-      preview_generated: Learn where Arm architecture is used in servers and cloud computing, and find
-        Arm-based hardware platforms for software development. It is designed for software developers
-        working on server and cloud ...
+      generated_at_before: '2026-06-02T04:07:02Z'
+      generated_at_after: '2026-06-02T04:07:02Z'
+      preview_before: This short, introductory Learning Path helps software developers
+        new to Arm understand where Arm architecture appears in servers and cloud
+        computing and how to find Arm-based hardware for development....
+      preview_after: This short, introductory Learning Path helps software developers
+        new to Arm understand where Arm architecture appears in servers and cloud
+        computing and how to find Arm-based hardware for development....
+      preview_generated: Use this introductory path to understand where Arm architecture,
+        specifically Arm Neoverse processors, fits in servers and cloud computing
+        and to find Arm-based hardware for software development. You ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: d2786f42b505823253ae16c647ca2e03c154f371b7c326210fde8523b12a8188
       source_hash_after: d2786f42b505823253ae16c647ca2e03c154f371b7c326210fde8523b12a8188
       current_source_hash: d2786f42b505823253ae16c647ca2e03c154f371b7c326210fde8523b12a8188
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T04:07:02Z'
+      generated_at_after: '2026-06-03T01:09:37Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - Do I need my own Arm server to follow this path?
+        - Which operating system does this path assume?
+        - How do I choose an Arm-based instance in the cloud?
+        - Does this path include step-by-step migration or tuning guidance?
+        - What outcome should I expect, and how long will it take?
+        removed_questions:
+        - Do I need existing access to Arm hardware to follow this path?
+        - What operating system and tools does this path assume?
+        - Will I provision cloud instances or perform migrations in this path?
+        - What Arm technology is emphasized here?
+        - How will I know I achieved the learning objectives?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - Do I need my own Arm server to follow this path?
+        - Which operating system does this path assume?
+        - How do I choose an Arm-based instance in the cloud?
+        - Does this path include step-by-step migration or tuning guidance?
+        - What outcome should I expect, and how long will it take?
+        removed_questions:
+        - Do I need existing access to Arm hardware to follow this path?
+        - What operating system and tools does this path assume?
+        - Will I provision cloud instances or perform migrations in this path?
+        - What Arm technology is emphasized here?
+        - How will I know I achieved the learning objectives?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -214367,51 +23465,76 @@ history:
       source_hash_before: 6fc608d45c4fdf85a77bddd4f8c26b05a7950d1086e578a8be0dd637feeb5d79
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       current_source_hash: 6fc608d45c4fdf85a77bddd4f8c26b05a7950d1086e578a8be0dd637feeb5d79
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Analyze and optimize interrupt request (IRQ) patterns on Arm Linux servers to improve
-        network workload performance through IRQ distribution strategies. It is designed for developers
-        and performance en...
-      preview_after: Analyze and optimize interrupt request (IRQ) patterns on Arm Linux servers to improve
-        network workload performance through IRQ distribution strategies. It is designed for developers
-        and performance en...
-      preview_generated: Analyze and optimize interrupt request (IRQ) patterns on Arm Linux servers to
-        improve network workload performance through IRQ distribution strategies. It is designed for developers
-        and performance en...
+      generated_at_before: '2026-06-02T04:07:50Z'
+      generated_at_after: '2026-06-02T04:07:50Z'
+      preview_before: Learn how to analyze and adjust network interrupt (IRQ) distribution
+        on Arm Linux servers to improve network workload performance. You will inspect
+        the current IRQ layout, experiment with different IR...
+      preview_after: Learn how to analyze and adjust network interrupt (IRQ) distribution
+        on Arm Linux servers to improve network workload performance. You will inspect
+        the current IRQ layout, experiment with different IR...
+      preview_generated: Learn how to analyze and tune network interrupt request (IRQ)
+        handling on Arm Linux servers to improve network workload behavior. This introductory
+        Learning Path guides you to inspect your current IRQ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 6fc608d45c4fdf85a77bddd4f8c26b05a7950d1086e578a8be0dd637feeb5d79
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       current_source_hash: 6fc608d45c4fdf85a77bddd4f8c26b05a7950d1086e578a8be0dd637feeb5d79
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T04:07:50Z'
+      generated_at_after: '2026-06-03T01:10:27Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - How do I know how my NIC IRQs are currently distributed?
+        - Which IRQ distribution strategies can I try, and how are they applied?
+        - How should I choose a strategy for my system size or workload?
+        - How do I make my IRQ configuration persistent and confirm it worked?
+        removed_questions:
+        - What environment and skills do I need before starting?
+        - What will I configure or change during this Learning Path?
+        - Is this guidance tied to a specific Arm CPU or cloud provider?
+        - How do I know if the changes improved my workload?
+        - Are there specific recommendations for smaller systems?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - How do I know how my NIC IRQs are currently distributed?
+        - Which IRQ distribution strategies can I try, and how are they applied?
+        - How should I choose a strategy for my system size or workload?
+        - How do I make my IRQ configuration persistent and confirm it worked?
+        removed_questions:
+        - What environment and skills do I need before starting?
+        - What will I configure or change during this Learning Path?
+        - Is this guidance tied to a specific Arm CPU or cloud provider?
+        - How do I know if the changes improved my workload?
+        - Are there specific recommendations for smaller systems?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -214421,51 +23544,76 @@ history:
       source_hash_before: f57dd99bad280f64f53071373519e2d3ba8ae50b94fe1ad0a9cc40d02b458b9b
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       current_source_hash: f57dd99bad280f64f53071373519e2d3ba8ae50b94fe1ad0a9cc40d02b458b9b
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Monitor, interpret, and optimize Java Garbage Collector performance on Arm servers
-        by comparing different GCs and tuning parameters for your workload. It is designed for Java developers
-        aiming to opti...
-      preview_after: Monitor, interpret, and optimize Java Garbage Collector performance on Arm servers
-        by comparing different GCs and tuning parameters for your workload. It is designed for Java developers
-        aiming to opti...
-      preview_generated: Monitor, interpret, and optimize Java Garbage Collector performance on Arm servers
-        by comparing different GCs and tuning parameters for your workload. It is designed for Java developers
-        aiming to opti...
+      generated_at_before: '2026-06-02T04:08:34Z'
+      generated_at_after: '2026-06-02T04:08:34Z'
+      preview_before: "Learn to monitor, interpret, and tune Java Garbage Collection\
+        \ on Arm-based Linux servers. Using an Arm instance on AWS, Microsoft Azure,\
+        \ Google Cloud, Oracle, or an on\u2011premise Arm server, you will ver..."
+      preview_after: "Learn to monitor, interpret, and tune Java Garbage Collection\
+        \ on Arm-based Linux servers. Using an Arm instance on AWS, Microsoft Azure,\
+        \ Google Cloud, Oracle, or an on\u2011premise Arm server, you will ver..."
+      preview_generated: Learn how to monitor, interpret, and tune Java Garbage Collector
+        behavior on Arm-based Linux servers. You will verify your JDK version, identify
+        which collectors your JDK provides, compare key product...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: f57dd99bad280f64f53071373519e2d3ba8ae50b94fe1ad0a9cc40d02b458b9b
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T04:08:34Z'
+      generated_at_after: '2026-06-03T01:11:10Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - How do I check which JDK version I am using?
+        - How do I find which Garbage Collectors are available with my JDK?
+        - How do I use the example application to observe GC behavior?
+        - "What should I do if I\u2019m on an older JDK release?"
+        removed_questions:
+        - What environment do I need to complete this Learning Path?
+        - What are the Java prerequisites and how do I install Java?
+        - How do I check which JDK I am using, and is a specific version recommended?
+        - Which Garbage Collectors are discussed and how should I choose among them?
+        - What will I run to observe GC behavior and confirm my tuning changes?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - How do I check which JDK version I am using?
+        - How do I find which Garbage Collectors are available with my JDK?
+        - How do I use the example application to observe GC behavior?
+        - "What should I do if I\u2019m on an older JDK release?"
+        removed_questions:
+        - What environment do I need to complete this Learning Path?
+        - What are the Java prerequisites and how do I install Java?
+        - How do I check which JDK I am using, and is a specific version recommended?
+        - Which Garbage Collectors are discussed and how should I choose among them?
+        - What will I run to observe GC behavior and confirm my tuning changes?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -214475,51 +23623,78 @@ history:
       source_hash_before: e6f73fbca45a1be1644f79bbcfbaaec964e31f1aab236e9a872c72a6fd5e4b66
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Deploy and optimize Java applications on Google Cloud Axion processors by testing
-        JDK versions and performance optimization flags. It is designed for software developers who want
-        to learn how to run t...
-      preview_after: Deploy and optimize Java applications on Google Cloud Axion processors by testing
-        JDK versions and performance optimization flags. It is designed for software developers who want
-        to learn how to run t...
-      preview_generated: Deploy and optimize Java applications on Google Cloud Axion processors by testing
-        JDK versions and performance optimization flags. It is designed for software developers who want
-        to learn how to run t...
+      generated_at_before: '2026-06-02T04:09:25Z'
+      generated_at_after: '2026-06-02T04:09:25Z'
+      preview_before: Learn how to deploy and evaluate a Java workload on Google Cloud
+        Axion instances built on Armv9 Neoverse V2. You will create an Arm-based VM
+        using the gcloud CLI, install Java on Ubuntu 24.04, and bui...
+      preview_after: Learn how to deploy and evaluate a Java workload on Google Cloud
+        Axion instances built on Armv9 Neoverse V2. You will create an Arm-based VM
+        using the gcloud CLI, install Java on Ubuntu 24.04, and bui...
+      preview_generated: This Learning Path shows how to create an Arm-based VM on
+        Google Cloud using Axion processors (Armv9 Neoverse V2), install Java on Ubuntu
+        24.04, deploy a Java application, and evaluate runtime choices...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: e6f73fbca45a1be1644f79bbcfbaaec964e31f1aab236e9a872c72a6fd5e4b66
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-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T04:09:25Z'
+      generated_at_after: '2026-06-03T01:12:09Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before creating the VM?
+        - Which method should I use to create the Axion VM?
+        - How do I connect to the instance, and which OS is used?
+        - Which Java package should I install and how do I verify it?
+        - What application and tool are used for performance testing, and how should
+          I run the tests?
+        removed_questions:
+        - What access do I need on Google Cloud to follow this path?
+        - How is the Axion VM created in this guide, and are there other options?
+        - What operating system and Java setup are used, and how do I verify it?
+        - What workload and tools are used for performance testing?
+        - Will my existing Java application need changes to run on Axion?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before creating the VM?
+        - Which method should I use to create the Axion VM?
+        - How do I connect to the instance, and which OS is used?
+        - Which Java package should I install and how do I verify it?
+        - What application and tool are used for performance testing, and how should
+          I run the tests?
+        removed_questions:
+        - What access do I need on Google Cloud to follow this path?
+        - How is the Axion VM created in this guide, and are there other options?
+        - What operating system and Java setup are used, and how do I verify it?
+        - What workload and tools are used for performance testing?
+        - Will my existing Java application need changes to run on Axion?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -214529,51 +23704,78 @@ history:
       source_hash_before: 58b0bb9f6e4190029d7edc65bdb04a597c7539de55a5e51ed59e88b174e527c3
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       current_source_hash: 58b0bb9f6e4190029d7edc65bdb04a597c7539de55a5e51ed59e88b174e527c3
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Deploy Java on Azure Cobalt 100 Arm virtual machines and benchmark application performance
-        with JMH microbenchmarks. It is designed for This is an introductory topic about Java deployment
-        and benchmar...
-      preview_after: Deploy Java on Azure Cobalt 100 Arm virtual machines and benchmark application performance
-        with JMH microbenchmarks. It is designed for This is an introductory topic about Java deployment
-        and benchmar...
-      preview_generated: Deploy Java on Azure Cobalt 100 Arm virtual machines and benchmark application
-        performance with JMH microbenchmarks. It is designed for This is an introductory topic about Java
-        deployment and benchmar...
+      generated_at_before: '2026-06-02T04:09:50Z'
+      generated_at_after: '2026-06-02T04:09:50Z'
+      preview_before: Provision an Arm-based Azure Cobalt 100 virtual machine using
+        the Azure portal, install Java on Ubuntu Pro 24.04 LTS (Arm64), and measure
+        application performance with JVM-aware microbenchmarks. This i...
+      preview_after: Provision an Arm-based Azure Cobalt 100 virtual machine using
+        the Azure portal, install Java on Ubuntu Pro 24.04 LTS (Arm64), and measure
+        application performance with JVM-aware microbenchmarks. This i...
+      preview_generated: This Learning Path guides you through deploying Java on Microsoft
+        Azure Cobalt 100 Arm-based virtual machines and benchmarking with JMH. You
+        will use the Azure portal to provision an Arm64 VM backed b...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 58b0bb9f6e4190029d7edc65bdb04a597c7539de55a5e51ed59e88b174e527c3
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       current_source_hash: 58b0bb9f6e4190029d7edc65bdb04a597c7539de55a5e51ed59e88b174e527c3
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T04:09:50Z'
+      generated_at_after: '2026-06-03T01:13:27Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How should I create the VM and which OS image should I choose?
+        - Which Java package should I install on Ubuntu Pro 24.04 LTS (Arm64)?
+        - Why start with a Tomcat-like baseline instead of deploying a full Tomcat
+          server?
+        - How will I benchmark the Java code and what results should I look for?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - Which VM type and OS image does this path use?
+        - How is Java installed on the VM?
+        - Do I need to deploy Apache Tomcat?
+        - How are benchmarks performed in this path?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How should I create the VM and which OS image should I choose?
+        - Which Java package should I install on Ubuntu Pro 24.04 LTS (Arm64)?
+        - Why start with a Tomcat-like baseline instead of deploying a full Tomcat
+          server?
+        - How will I benchmark the Java code and what results should I look for?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - Which VM type and OS image does this path use?
+        - How is Java installed on the VM?
+        - Do I need to deploy Apache Tomcat?
+        - How are benchmarks performed in this path?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -214583,51 +23785,76 @@ history:
       source_hash_before: 655180d91bb9b9cf571840b59d8943c1d2c73d8f8aea647db57dc2704ede3af8
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       current_source_hash: 655180d91bb9b9cf571840b59d8943c1d2c73d8f8aea647db57dc2704ede3af8
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Profile Java applications on Arm Neoverse servers using flame graphs generated with
-        async-profiler and Java agents to identify performance bottlenecks. It is designed for developers
-        who want to analyz...
-      preview_after: Profile Java applications on Arm Neoverse servers using flame graphs generated with
-        async-profiler and Java agents to identify performance bottlenecks. It is designed for developers
-        who want to analyz...
-      preview_generated: Profile Java applications on Arm Neoverse servers using flame graphs generated
-        with async-profiler and Java agents to identify performance bottlenecks. It is designed for developers
-        who want to analyz...
+      generated_at_before: '2026-06-02T04:10:23Z'
+      generated_at_after: '2026-06-02T04:10:23Z'
+      preview_before: Learn how to analyze Java application performance on Arm Neoverse-based
+        Linux servers by benchmarking a Tomcat deployment and generating flame graphs.
+        You will set up Apache Tomcat, drive HTTP load wi...
+      preview_after: Learn how to analyze Java application performance on Arm Neoverse-based
+        Linux servers by benchmarking a Tomcat deployment and generating flame graphs.
+        You will set up Apache Tomcat, drive HTTP load wi...
+      preview_generated: Follow this Learning Path to profile Java applications on
+        Arm Neoverse-based Linux servers by generating flame graphs with two practical
+        methods. You will set up a Tomcat benchmarking environment and ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 655180d91bb9b9cf571840b59d8943c1d2c73d8f8aea647db57dc2704ede3af8
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       current_source_hash: 655180d91bb9b9cf571840b59d8943c1d2c73d8f8aea647db57dc2704ede3af8
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T04:10:23Z'
+      generated_at_after: '2026-06-03T01:14:15Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Can I perform the steps on an x86 server?
+        - Where should I run async-profiler relative to Tomcat?
+        - How are flame graphs generated with the Java agent approach?
+        - Do I need to generate load during profiling, and how should I do that?
+        removed_questions:
+        - What hardware and operating system do I need, and can I use cloud instances?
+        - Which software and tools are used in this Learning Path?
+        - Where should I run async-profiler for accurate results?
+        - Why add a Java agent when profiling with perf?
+        - What will I set up and what outputs should I expect?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Can I perform the steps on an x86 server?
+        - Where should I run async-profiler relative to Tomcat?
+        - How are flame graphs generated with the Java agent approach?
+        - Do I need to generate load during profiling, and how should I do that?
+        removed_questions:
+        - What hardware and operating system do I need, and can I use cloud instances?
+        - Which software and tools are used in this Learning Path?
+        - Where should I run async-profiler for accurate results?
+        - Why add a Java agent when profiling with perf?
+        - What will I set up and what outputs should I expect?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -214637,51 +23864,76 @@ history:
       source_hash_before: 525d893a796e8e4eb5cc7a9d5996bd7d55a35f8fe413f87b9a275a214d77626f
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       current_source_hash: 525d893a796e8e4eb5cc7a9d5996bd7d55a35f8fe413f87b9a275a214d77626f
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
-      preview_before: Deploy Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate installation,
-        and execute Arm-native CI/CD pipelines including Docker workflows. It is designed for software
-        developers d...
-      preview_after: Deploy Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate installation,
-        and execute Arm-native CI/CD pipelines including Docker workflows. It is designed for software
-        developers d...
-      preview_generated: Deploy Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate
-        installation, and execute Arm-native CI/CD pipelines including Docker workflows. It is designed
-        for software developers d...
+      generated_at_before: '2026-06-02T04:11:14Z'
+      generated_at_after: '2026-06-02T04:11:14Z'
+      preview_before: This Learning Path guides you through deploying Jenkins LTS
+        on Arm-based cloud servers and validating Arm-native CI/CD pipelines. You
+        provision an Azure Cobalt 100 (Dpsv6) virtual machine using the Az...
+      preview_after: This Learning Path guides you through deploying Jenkins LTS on
+        Arm-based cloud servers and validating Arm-native CI/CD pipelines. You provision
+        an Azure Cobalt 100 (Dpsv6) virtual machine using the Az...
+      preview_generated: This Learning Path shows how to deploy and validate Jenkins
+        on Arm-based cloud servers using Microsoft Azure Cobalt 100 (Dpsv6) and Google
+        Cloud C4A instances powered by Axion processors. You will pro...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 525d893a796e8e4eb5cc7a9d5996bd7d55a35f8fe413f87b9a275a214d77626f
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       current_source_hash: 525d893a796e8e4eb5cc7a9d5996bd7d55a35f8fe413f87b9a275a214d77626f
-      generated_at_before: '2026-05-06T17:17:57Z'
-      generated_at_after: '2026-05-06T17:17:57Z'
+      generated_at_before: '2026-06-02T04:11:14Z'
+      generated_at_after: '2026-06-03T01:14:54Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which VM types and operating systems are used in this path?
+        - How do I expose the Jenkins web UI to my browser?
+        - How do I validate that Jenkins installed correctly on the Azure VM?
+        - What should I check if I plan to run Docker-based pipelines?
+        removed_questions:
+        - What accounts or access do I need before starting?
+        - Which VM types and operating systems are used?
+        - How is Jenkins exposed for browser access?
+        - Which Jenkins and Java versions are used?
+        - How do I know Jenkins is correctly installed on Arm?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which VM types and operating systems are used in this path?
+        - How do I expose the Jenkins web UI to my browser?
+        - How do I validate that Jenkins installed correctly on the Azure VM?
+        - What should I check if I plan to run Docker-based pipelines?
+        removed_questions:
+        - What accounts or access do I need before starting?
+        - Which VM types and operating systems are used?
+        - How is Jenkins exposed for browser access?
+        - Which Jenkins and Java versions are used?
+        - How do I know Jenkins is correctly installed on Arm?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/kafka/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -214691,51 +23943,76 @@ history:
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-      preview_before: Deploy and configure a Kafka cluster with Zookeeper on Arm servers, test event streaming,
-        and automate deployment on AWS and Google Cloud. It is designed for software developers who want
-        to learn how ...
-      preview_after: Deploy and configure a Kafka cluster with Zookeeper on Arm servers, test event streaming,
-        and automate deployment on AWS and Google Cloud. It is designed for software developers who want
-        to learn how ...
-      preview_generated: Deploy and configure a Kafka cluster with Zookeeper on Arm servers, test event
-        streaming, and automate deployment on AWS and Google Cloud. It is designed for software developers
-        who want to learn how ...
+      generated_at_before: '2026-06-02T04:11:38Z'
+      generated_at_after: '2026-06-02T04:11:38Z'
+      preview_before: This advanced Learning Path guides you through deploying a production-style
+        Kafka event streaming cluster on Arm-based Linux servers. You will install
+        and configure a three-node ZooKeeper ensemble and...
+      preview_after: This advanced Learning Path guides you through deploying a production-style
+        Kafka event streaming cluster on Arm-based Linux servers. You will install
+        and configure a three-node ZooKeeper ensemble and...
+      preview_generated: This Learning Path guides you through deploying Apache Kafka
+        with ZooKeeper on Arm-based Linux machines, then validating event streaming
+        end to end. You will install and configure a three-node ZooKeep...
     faqs:
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-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the setup?
+        - How should I assign roles to the seven machines?
+        - Which configuration values do I change on Kafka nodes to connect to ZooKeeper?
+        - Where do I run the validation and what result should I expect?
+        - Which options are available for automated deployment on cloud platforms?
+        removed_questions:
+        - What infrastructure do I need before starting?
+        - Which network ports must be opened?
+        - How are the ZooKeeper and Kafka clusters structured?
+        - How do I verify that the Kafka cluster is working?
+        - Can I automate deployment in the cloud, and what tools are used?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
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+        added_questions:
+        - What do I need before running the setup?
+        - How should I assign roles to the seven machines?
+        - Which configuration values do I change on Kafka nodes to connect to ZooKeeper?
+        - Where do I run the validation and what result should I expect?
+        - Which options are available for automated deployment on cloud platforms?
+        removed_questions:
+        - What infrastructure do I need before starting?
+        - Which network ports must be opened?
+        - How are the ZooKeeper and Kafka clusters structured?
+        - How do I verify that the Kafka cluster is working?
+        - Can I automate deployment in the cloud, and what tools are used?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
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       action: unchanged
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@@ -214745,51 +24022,76 @@ history:
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-      preview_before: Deploy Apache Kafka on Azure Cobalt 100 Arm virtual machines and benchmark message
-        throughput performance. It is designed for developers looking to migrate their Apache Kafka workloads
-        from x86_64 to ...
-      preview_after: Deploy Apache Kafka on Azure Cobalt 100 Arm virtual machines and benchmark message
-        throughput performance. It is designed for developers looking to migrate their Apache Kafka workloads
-        from x86_64 to ...
-      preview_generated: Deploy Apache Kafka on Azure Cobalt 100 Arm virtual machines and benchmark message
-        throughput performance. It is designed for developers looking to migrate their Apache Kafka workloads
-        from x86_64 to ...
+      generated_at_before: '2026-06-02T04:12:27Z'
+      generated_at_after: '2026-06-02T04:12:27Z'
+      preview_before: Provision an Arm-based Microsoft Azure Cobalt 100 (Dpsv6) virtual
+        machine using the Azure portal, install Apache Kafka on Ubuntu Pro 24.04 LTS
+        (arm64), and validate end-to-end messaging before running...
+      preview_after: Provision an Arm-based Microsoft Azure Cobalt 100 (Dpsv6) virtual
+        machine using the Azure portal, install Apache Kafka on Ubuntu Pro 24.04 LTS
+        (arm64), and validate end-to-end messaging before running...
+      preview_generated: This Learning Path guides you through deploying Apache Kafka
+        on Arm-based Microsoft Azure Cobalt 100 virtual machines. You will provision
+        an Arm64 VM in the Azure portal using Ubuntu Pro 24.04 LTS, in...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
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       generated_count: 5
       change_details:
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-        added_questions: []
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+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which Azure VM size and OS image should I select?
+        - Do I need ZooKeeper for this Kafka setup?
+        - How do I know the baseline test worked?
+        - Which tools are used for benchmarking and what should be running first?
+        removed_questions:
+        - What do I need before starting?
+        - How is the virtual machine created and which image is used?
+        - Do I need Java installed before setting up Kafka?
+        - Does this deployment use ZooKeeper or KRaft, and what topology is covered?
+        - How do I verify and benchmark the deployment?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which Azure VM size and OS image should I select?
+        - Do I need ZooKeeper for this Kafka setup?
+        - How do I know the baseline test worked?
+        - Which tools are used for benchmarking and what should be running first?
+        removed_questions:
+        - What do I need before starting?
+        - How is the virtual machine created and which image is used?
+        - Do I need Java installed before setting up Kafka?
+        - Does this deployment use ZooKeeper or KRaft, and what topology is covered?
+        - How do I verify and benchmark the deployment?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
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-    rerun_flags_reset: []
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+    - rerun_flags_reset
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     summary:
       action: unchanged
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@@ -214799,51 +24101,76 @@ history:
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-      preview_before: Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing Kedify
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-        workloads on Kuber...
-      preview_after: Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing Kedify
-        and KEDA with Helm and testing autoscaling behavior. It is designed for developers running HTTP
-        workloads on Kuber...
-      preview_generated: Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing
-        Kedify and KEDA with Helm and testing autoscaling behavior. It is designed for developers running
-        HTTP workloads on Kuber...
+      generated_at_before: '2026-06-02T04:12:58Z'
+      generated_at_after: '2026-06-02T04:12:58Z'
+      preview_before: "This Learning Path shows how to enable event-driven autoscaling\
+        \ for HTTP workloads on Kubernetes using KEDA and Kedify. You will use Helm\
+        \ to add the Kedify chart repository and install three charts\u2014th..."
+      preview_after: "This Learning Path shows how to enable event-driven autoscaling\
+        \ for HTTP workloads on Kubernetes using KEDA and Kedify. You will use Helm\
+        \ to add the Kedify chart repository and install three charts\u2014th..."
+      preview_generated: "This Learning Path shows how to enable event-driven autoscaling\
+        \ for HTTP workloads on Kubernetes using Kedify and KEDA on Linux. You will\
+        \ add the Kedify Helm repository, install three charts\u2014KEDA (Ked..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
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-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:12:58Z'
+      generated_at_after: '2026-06-03T01:17:17Z'
       before_count: 5
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       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before I start the installation?
+        - Do I need an ingress controller, and which one is used here?
+        - Which Helm charts are installed to enable HTTP autoscaling?
+        - How do I know Kedify and KEDA are running correctly?
+        - What behavior should I expect when testing the sample HTTP app?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - Which Kubernetes environments and architectures are supported here?
+        - Which components are installed with Helm?
+        - Do I need an ingress controller for HTTP autoscaling?
+        - How do I verify that autoscaling is working?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before I start the installation?
+        - Do I need an ingress controller, and which one is used here?
+        - Which Helm charts are installed to enable HTTP autoscaling?
+        - How do I know Kedify and KEDA are running correctly?
+        - What behavior should I expect when testing the sample HTTP app?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - Which Kubernetes environments and architectures are supported here?
+        - Which components are installed with Helm?
+        - Do I need an ingress controller for HTTP autoscaling?
+        - How do I verify that autoscaling is working?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
-    status: unchanged
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-    rerun_flags_reset: []
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-    template_version_before: summary-faq-v1
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+    ai_requested: true
+    rerun_flags_reset:
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+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -214853,51 +24180,78 @@ history:
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-      preview_before: Create, train, and evaluate a neural network model on Arm servers using Keras Core
-        with TensorFlow, PyTorch, and JAX backends. It is designed for engineers who want to create a
-        neural network model on...
-      preview_after: Create, train, and evaluate a neural network model on Arm servers using Keras Core
-        with TensorFlow, PyTorch, and JAX backends. It is designed for engineers who want to create a
-        neural network model on...
-      preview_generated: Create, train, and evaluate a neural network model on Arm servers using Keras
-        Core with TensorFlow, PyTorch, and JAX backends. It is designed for engineers who want to create
-        a neural network model on...
+      generated_at_before: '2026-06-02T04:13:36Z'
+      generated_at_after: '2026-06-02T04:13:36Z'
+      preview_before: This introductory Learning Path shows how to create, train,
+        and evaluate a simple neural network on Arm servers using Keras Core with
+        TensorFlow, PyTorch, and JAX backends. You work on Ubuntu 22.04 LT...
+      preview_after: This introductory Learning Path shows how to create, train, and
+        evaluate a simple neural network on Arm servers using Keras Core with TensorFlow,
+        PyTorch, and JAX backends. You work on Ubuntu 22.04 LT...
+      preview_generated: Follow a short, introductory workflow to build, train, evaluate,
+        and generate predictions from a simple neural network using Keras Core on
+        Arm machines. You will work on Ubuntu 22.04 LTS running on an...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
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-        added_questions: []
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+        added_questions:
+        - What environment should I prepare before starting?
+        - Which Python version should I use on Ubuntu 22.04, and do I need pip and
+          venv?
+        - How do I switch between TensorFlow, PyTorch, and JAX backends in Keras Core?
+        - What script do I run, and what should I expect as output?
+        - What input shape and data type does the example model expect?
+        removed_questions:
+        - What environment do I need to follow this Learning Path?
+        - What skills are assumed before I start?
+        - Which Keras Core backends are used in this path?
+        - Do I need a specific Python version?
+        - How do I know the steps worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What environment should I prepare before starting?
+        - Which Python version should I use on Ubuntu 22.04, and do I need pip and
+          venv?
+        - How do I switch between TensorFlow, PyTorch, and JAX backends in Keras Core?
+        - What script do I run, and what should I expect as output?
+        - What input shape and data type does the example model expect?
+        removed_questions:
+        - What environment do I need to follow this Learning Path?
+        - What skills are assumed before I start?
+        - Which Keras Core backends are used in this path?
+        - Do I need a specific Python version?
+        - How do I know the steps worked?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
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-    rerun_flags_reset: []
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+    status: updated
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+    rerun_flags_reset:
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+    - rerun_flags_reset
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     summary:
       action: unchanged
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@@ -214907,51 +24261,76 @@ history:
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-      preview_before: Compile and install custom Linux kernels on Arm cloud instances using TuxMake with
-        configurations for 64 KB page sizes and Fastpath testing. It is designed for software developers
-        building custom Linu...
-      preview_after: Compile and install custom Linux kernels on Arm cloud instances using TuxMake with
-        configurations for 64 KB page sizes and Fastpath testing. It is designed for software developers
-        building custom Linu...
-      preview_generated: Compile and install custom Linux kernels on Arm cloud instances using TuxMake
-        with configurations for 64 KB page sizes and Fastpath testing. It is designed for software developers
-        building custom Linu...
+      generated_at_before: '2026-06-02T04:14:17Z'
+      generated_at_after: '2026-06-02T04:14:17Z'
+      preview_before: Learn how to build and install custom Linux kernels on Arm cloud
+        instances using TuxMake. You will provision an Ubuntu 24.04 LTS Arm server
+        (minimum 24 vCPUs and 200 GB free storage), configure a buil...
+      preview_after: Learn how to build and install custom Linux kernels on Arm cloud
+        instances using TuxMake. You will provision an Ubuntu 24.04 LTS Arm server
+        (minimum 24 vCPUs and 200 GB free storage), configure a buil...
+      preview_generated: Learn how to build, install, and verify custom Linux kernels
+        on Arm cloud instances using TuxMake. You will set up an Ubuntu 24.04 LTS
+        Arm instance, run standard build workflows for direct installatio...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
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-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need on my Arm cloud instance before starting?
+        - How do I choose which Linux kernel version to build with TuxMake?
+        - What result should I expect from a standard TuxMake build workflow?
+        - What is the correct workflow for Fastpath builds?
+        - What should I check if compilation is very slow or runs out of memory?
+        removed_questions:
+        - What infrastructure do I need to follow this Learning Path?
+        - Which tool is used to build the kernels and what configurations are covered?
+        - Can I choose which Linux kernel version to build?
+        - How do Fastpath builds differ from standard kernel builds?
+        - What outputs should I expect and how do I validate success?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need on my Arm cloud instance before starting?
+        - How do I choose which Linux kernel version to build with TuxMake?
+        - What result should I expect from a standard TuxMake build workflow?
+        - What is the correct workflow for Fastpath builds?
+        - What should I check if compilation is very slow or runs out of memory?
+        removed_questions:
+        - What infrastructure do I need to follow this Learning Path?
+        - Which tool is used to build the kernels and what configurations are covered?
+        - Can I choose which Linux kernel version to build?
+        - How do Fastpath builds differ from standard kernel builds?
+        - What outputs should I expect and how do I validate success?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -214961,51 +24340,78 @@ history:
       source_hash_before: 43e4e28b88b9721ca94457418f3b4887d0099017578a54da1db5fcdc11f4a122
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Identify and migrate container images in a Kubernetes cluster to Arm-compatible
-        versions using KubeArchInspect reports. It is designed for software developers who want to ensure
-        containers running in ...
-      preview_after: Identify and migrate container images in a Kubernetes cluster to Arm-compatible versions
-        using KubeArchInspect reports. It is designed for software developers who want to ensure containers
-        running in ...
-      preview_generated: Identify and migrate container images in a Kubernetes cluster to Arm-compatible
-        versions using KubeArchInspect reports. It is designed for software developers who want to ensure
-        containers running in ...
+      generated_at_before: '2026-06-02T04:14:53Z'
+      generated_at_after: '2026-06-02T04:14:53Z'
+      preview_before: Learn how to assess and migrate Kubernetes container images
+        to Arm-compatible versions using KubeArchInspect. You will install KubeArchInspect
+        on Linux, ensure kubectl is configured to your cluster, r...
+      preview_after: Learn how to assess and migrate Kubernetes container images to
+        Arm-compatible versions using KubeArchInspect. You will install KubeArchInspect
+        on Linux, ensure kubectl is configured to your cluster, r...
+      preview_generated: Use KubeArchInspect to quickly assess Arm architecture support
+        for container images running in your Kubernetes cluster. After installing
+        the tool and ensuring kubectl is configured, you run a single c...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 43e4e28b88b9721ca94457418f3b4887d0099017578a54da1db5fcdc11f4a122
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:14:53Z'
+      generated_at_after: '2026-06-03T01:19:42Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running KubeArchInspect?
+        - Which command should I use to generate the image report?
+        - How does KubeArchInspect determine whether an image supports Arm?
+        - How do I interpret the output symbols in the report?
+        - What should I do after running the report?
+        removed_questions:
+        - What do I need before I start?
+        - How do I run KubeArchInspect and what does it check?
+        - How do I interpret the KubeArchInspect report?
+        - What should I do when an image lacks Arm support or a newer version adds
+          it?
+        - How can I verify that my migration to Arm-compatible images worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running KubeArchInspect?
+        - Which command should I use to generate the image report?
+        - How does KubeArchInspect determine whether an image supports Arm?
+        - How do I interpret the output symbols in the report?
+        - What should I do after running the report?
+        removed_questions:
+        - What do I need before I start?
+        - How do I run KubeArchInspect and what does it check?
+        - How do I interpret the KubeArchInspect report?
+        - What should I do when an image lacks Arm support or a newer version adds
+          it?
+        - How can I verify that my migration to Arm-compatible images worked?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -215015,51 +24421,76 @@ history:
       source_hash_before: 22fa96e29143f2e0dc0c204919422914b3f70d63ddf833cf1067fbf67b525aa0
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Deploy AWS Lambda functions on Graviton processors using Terraform for Python and
-        Node.js runtimes. It is designed for software developers who want to learn how to deploy Lambda
-        functions on AWS Gravi...
-      preview_after: Deploy AWS Lambda functions on Graviton processors using Terraform for Python and
-        Node.js runtimes. It is designed for software developers who want to learn how to deploy Lambda
-        functions on AWS Gravi...
-      preview_generated: Deploy AWS Lambda functions on Graviton processors using Terraform for Python
-        and Node.js runtimes. It is designed for software developers who want to learn how to deploy Lambda
-        functions on AWS Gravi...
+      generated_at_before: '2026-06-02T04:15:30Z'
+      generated_at_after: '2026-06-02T04:15:30Z'
+      preview_before: This introductory Learning Path shows how to deploy AWS Lambda
+        functions on AWS Graviton processors using Terraform. From a Linux host with
+        Terraform and the AWS CLI installed, you will provision Lamb...
+      preview_after: This introductory Learning Path shows how to deploy AWS Lambda
+        functions on AWS Graviton processors using Terraform. From a Linux host with
+        Terraform and the AWS CLI installed, you will provision Lamb...
+      preview_generated: This introductory path shows how to deploy AWS Lambda functions
+        on Arm-based Graviton processors using Terraform. You will configure Lambda
+        to use the arm64 architecture and apply the workflow to both...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 22fa96e29143f2e0dc0c204919422914b3f70d63ddf833cf1067fbf67b525aa0
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       current_source_hash: 22fa96e29143f2e0dc0c204919422914b3f70d63ddf833cf1067fbf67b525aa0
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:15:30Z'
+      generated_at_after: '2026-06-03T01:20:35Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - Which architecture should I select in Terraform to run the function on Graviton?
+        - What do I need before running the steps?
+        - Can I reuse the same deployment approach for Python and Node.js?
+        - How do I know the sample Python function is behaving as expected?
+        - What should I check if Terraform deployment does not work as expected?
+        removed_questions:
+        - What do I need installed before starting?
+        - Which Lambda runtimes are covered?
+        - How do I target AWS Graviton processors?
+        - What environment should I use to follow the steps?
+        - What will I create by following this path?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - Which architecture should I select in Terraform to run the function on Graviton?
+        - What do I need before running the steps?
+        - Can I reuse the same deployment approach for Python and Node.js?
+        - How do I know the sample Python function is behaving as expected?
+        - What should I check if Terraform deployment does not work as expected?
+        removed_questions:
+        - What do I need installed before starting?
+        - Which Lambda runtimes are covered?
+        - How do I target AWS Graviton processors?
+        - What environment should I use to follow the steps?
+        - What will I create by following this path?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -215069,51 +24500,76 @@ history:
       source_hash_before: 66d13edff1371295f0e88a618e924ca67b85bcc8aa72034d1e582a0b267f0d05
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Enable and measure libhugetlbfs performance improvements for MySQL and other workloads
-        on Arm Linux servers. It is designed for engineers looking for ways to increase performance on
-        Arm servers. By th...
-      preview_after: Enable and measure libhugetlbfs performance improvements for MySQL and other workloads
-        on Arm Linux servers. It is designed for engineers looking for ways to increase performance on
-        Arm servers. By th...
-      preview_generated: Enable and measure libhugetlbfs performance improvements for MySQL and other
-        workloads on Arm Linux servers. It is designed for engineers looking for ways to increase performance
-        on Arm servers. By th...
+      generated_at_before: '2026-06-02T04:16:07Z'
+      generated_at_after: '2026-06-02T04:16:07Z'
+      preview_before: This Learning Path shows how to enable libhugetlbfs on an Arm
+        server running Ubuntu Linux and measure its impact on memory-intensive workloads.
+        You will configure hugepages so application text, data, ...
+      preview_after: This Learning Path shows how to enable libhugetlbfs on an Arm
+        server running Ubuntu Linux and measure its impact on memory-intensive workloads.
+        You will configure hugepages so application text, data, ...
+      preview_generated: Learn how to enable libhugetlbfs on an Arm Linux server (Ubuntu)
+        and evaluate its impact on memory-intensive workloads such as MySQL. You will
+        install the required Ubuntu packages, configure applicati...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 66d13edff1371295f0e88a618e924ca67b85bcc8aa72034d1e582a0b267f0d05
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       current_source_hash: 66d13edff1371295f0e88a618e924ca67b85bcc8aa72034d1e582a0b267f0d05
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:16:07Z'
+      generated_at_after: '2026-06-03T01:21:05Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Can I use a cloud VM for this Learning Path?
+        - Where do I add libhugetlbfs build options when compiling MySQL?
+        - Do I need to change both build and run settings for MySQL?
+        - How should I evaluate the effect of enabling libhugetlbfs?
+        removed_questions:
+        - What environment do I need to follow this path?
+        - What prior knowledge is required?
+        - Does this path include complete MySQL build and setup instructions?
+        - How is libhugetlbfs enabled for MySQL in this Learning Path?
+        - How do I validate that libhugetlbfs made a difference?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Can I use a cloud VM for this Learning Path?
+        - Where do I add libhugetlbfs build options when compiling MySQL?
+        - Do I need to change both build and run settings for MySQL?
+        - How should I evaluate the effect of enabling libhugetlbfs?
+        removed_questions:
+        - What environment do I need to follow this path?
+        - What prior knowledge is required?
+        - Does this path include complete MySQL build and setup instructions?
+        - How is libhugetlbfs enabled for MySQL in this Learning Path?
+        - How do I validate that libhugetlbfs made a difference?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -215123,51 +24579,78 @@ history:
       source_hash_before: d82aa4faeb599a3a1ac12d477204ebe0a4ddb99564bedced86ca0fc4851e17b9
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Serve the llama.cpp chatbot through an OpenAI-compatible API, enabling existing
-        OpenAI-style clients and applications to run against a persistent Arm-hosted LLM. It is designed
-        for developers interest...
-      preview_after: Serve the llama.cpp chatbot through an OpenAI-compatible API, enabling existing OpenAI-style
-        clients and applications to run against a persistent Arm-hosted LLM. It is designed for developers
-        interest...
-      preview_generated: Serve the llama.cpp chatbot through an OpenAI-compatible API, enabling existing
-        OpenAI-style clients and applications to run against a persistent Arm-hosted LLM. It is designed
-        for developers interest...
+      generated_at_before: '2026-06-02T04:16:47Z'
+      generated_at_after: '2026-06-02T04:16:47Z'
+      preview_before: "Deploy a pre-quantized Llama\u20113.1\u20118B chatbot on an\
+        \ Arm server using llama.cpp with KleidiAI, and expose it through an OpenAI\u2011\
+        compatible API. You will download and build llama.cpp, fetch the pre\u2011\
+        quantiz..."
+      preview_after: "Deploy a pre-quantized Llama\u20113.1\u20118B chatbot on an\
+        \ Arm server using llama.cpp with KleidiAI, and expose it through an OpenAI\u2011\
+        compatible API. You will download and build llama.cpp, fetch the pre\u2011\
+        quantiz..."
+      preview_generated: Build and run a Llama 3.1-based chatbot on Arm servers using
+        llama.cpp (with KleidiAI) and expose it through an OpenAI-compatible API.
+        You will compile llama.cpp on Ubuntu 24.04 LTS, download a pre-qu...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: d82aa4faeb599a3a1ac12d477204ebe0a4ddb99564bedced86ca0fc4851e17b9
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       current_source_hash: d82aa4faeb599a3a1ac12d477204ebe0a4ddb99564bedced86ca0fc4851e17b9
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:16:47Z'
+      generated_at_after: '2026-06-03T01:21:51Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which LLM model should I download for this setup?
+        - How do I start and access the OpenAI-compatible server?
+        - Is any extra package required to interact with the API responses?
+        - Can I measure performance during inference, and how is it covered?
+        removed_questions:
+        - What environment do I need to follow this Learning Path?
+        - Which model is deployed and where do I get it?
+        - How is the chatbot served and how do clients connect?
+        - What software is built or installed during the steps?
+        - How do I validate the deployment and assess performance?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which LLM model should I download for this setup?
+        - How do I start and access the OpenAI-compatible server?
+        - Is any extra package required to interact with the API responses?
+        - Can I measure performance during inference, and how is it covered?
+        removed_questions:
+        - What environment do I need to follow this Learning Path?
+        - Which model is deployed and where do I get it?
+        - How is the chatbot served and how do clients connect?
+        - What software is built or installed during the steps?
+        - How do I validate the deployment and assess performance?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -215177,51 +24660,77 @@ history:
       source_hash_before: 3e8307a5daf435c21f3cecff635ac51724a63ff9ea4307082d869601563b4204
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Build a production-ready vision chatbot on Google Axion using Streamlit, PyTorch,
-        and Hugging Face Transformers with a quantized Llama 3.2-Vision model. It is designed for software
-        developers and ML e...
-      preview_after: Build a production-ready vision chatbot on Google Axion using Streamlit, PyTorch,
-        and Hugging Face Transformers with a quantized Llama 3.2-Vision model. It is designed for software
-        developers and ML e...
-      preview_generated: Build a production-ready vision chatbot on Google Axion using Streamlit, PyTorch,
-        and Hugging Face Transformers with a quantized Llama 3.2-Vision model. It is designed for software
-        developers and ML e...
+      generated_at_before: '2026-06-02T04:17:37Z'
+      generated_at_after: '2026-06-02T04:17:37Z'
+      preview_before: "This Learning Path shows how to deploy a production-ready,\
+        \ vision-enabled chatbot on Arm-based servers using Google Cloud Axion. You\
+        \ will build a Flask backend that downloads a Llama 3.2\u2011Vision model\
+        \ ..."
+      preview_after: "This Learning Path shows how to deploy a production-ready, vision-enabled\
+        \ chatbot on Arm-based servers using Google Cloud Axion. You will build a\
+        \ Flask backend that downloads a Llama 3.2\u2011Vision model ..."
+      preview_generated: "Build and deploy a vision-enabled chatbot on Arm-based Google\
+        \ Cloud Axion using Python, PyTorch, Hugging Face Transformers, and Streamlit.\
+        \ You will create a Flask backend that downloads the Llama 3.2\u2011..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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       before_count: 5
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       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which environment is targeted and what instance was used for testing?
+        - Which model is used and how is it prepared for inference?
+        - How do I access the web application once the services are running?
+        - What result should I expect to validate that inference is working?
+        removed_questions:
+        - What infrastructure do I need to follow this Learning Path?
+        - Which model and libraries are used for inference?
+        - What components will I build in this path?
+        - How do I access the web application after deployment?
+        - What skills are assumed and how long will it take?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which environment is targeted and what instance was used for testing?
+        - Which model is used and how is it prepared for inference?
+        - How do I access the web application once the services are running?
+        - What result should I expect to validate that inference is working?
+        removed_questions:
+        - What infrastructure do I need to follow this Learning Path?
+        - Which model and libraries are used for inference?
+        - What components will I build in this path?
+        - How do I access the web application after deployment?
+        - What skills are assumed and how long will it take?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -215231,51 +24740,76 @@ history:
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-      preview_before: Optimize llama.cpp on Arm CPUs by integrating Streamline Annotations to profile
-        Prefill and Decode stages, analyze operators, and evaluate multi-core execution. It is designed
-        for software developers,...
-      preview_after: Optimize llama.cpp on Arm CPUs by integrating Streamline Annotations to profile Prefill
-        and Decode stages, analyze operators, and evaluate multi-core execution. It is designed for software
-        developers,...
-      preview_generated: Optimize llama.cpp on Arm CPUs by integrating Streamline Annotations to profile
-        Prefill and Decode stages, analyze operators, and evaluate multi-core execution. It is designed
-        for software developers,...
+      generated_at_before: '2026-06-02T04:18:16Z'
+      generated_at_after: '2026-06-02T04:18:16Z'
+      preview_before: Learn how to profile llama.cpp inference on Arm CPUs using Arm
+        Streamline. This advanced path guides you to integrate Streamline Annotation
+        Markers and Annotation Channels into the llama.cpp codebase ...
+      preview_after: Learn how to profile llama.cpp inference on Arm CPUs using Arm
+        Streamline. This advanced path guides you to integrate Streamline Annotation
+        Markers and Annotation Channels into the llama.cpp codebase ...
+      preview_generated: This advanced Learning Path shows how to instrument and profile
+        llama.cpp inference on Arm Neoverse or Cortex-A CPUs running Linux or Android
+        using Arm Streamline. You will add Streamline Annotation M...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:18:16Z'
+      generated_at_after: '2026-06-03T01:23:50Z'
       before_count: 5
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       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the profiling steps?
+        - Which option should I use to visualize the Prefill and Decode stages?
+        - How can I analyze operator-level performance during token generation?
+        - How do I evaluate multi-core or multi-thread execution in this path?
+        - What should I check if Streamline is not collecting data from my target?
+        removed_questions:
+        - What hardware and operating systems are supported?
+        - What skills or knowledge are required before starting?
+        - What code changes are made to llama.cpp in this path?
+        - What needs to be set up on the target system to capture profiling data?
+        - How do I verify that profiling is working and what results should I expect?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the profiling steps?
+        - Which option should I use to visualize the Prefill and Decode stages?
+        - How can I analyze operator-level performance during token generation?
+        - How do I evaluate multi-core or multi-thread execution in this path?
+        - What should I check if Streamline is not collecting data from my target?
+        removed_questions:
+        - What hardware and operating systems are supported?
+        - What skills or knowledge are required before starting?
+        - What code changes are made to llama.cpp in this path?
+        - What needs to be set up on the target system to capture profiling data?
+        - How do I verify that profiling is working and what results should I expect?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/lse/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -215285,51 +24819,76 @@ history:
       source_hash_before: 9610291239b88c67e21055f94cd03fe7cf80f7d875d53ebe0d8de7837ce99fa7
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Understand Large System Extensions (LSE) for Arm processors and verify whether applications
-        use LSE for improved atomic operation performance. It is designed for software developers who
-        want to learn ...
-      preview_after: Understand Large System Extensions (LSE) for Arm processors and verify whether applications
-        use LSE for improved atomic operation performance. It is designed for software developers who
-        want to learn ...
-      preview_generated: Understand Large System Extensions (LSE) for Arm processors and verify whether
-        applications use LSE for improved atomic operation performance. It is designed for software developers
-        who want to learn ...
+      generated_at_before: '2026-06-02T04:18:56Z'
+      generated_at_after: '2026-06-02T04:18:56Z'
+      preview_before: This Learning Path introduces Large System Extensions (LSE)
+        on Arm processors and shows how to check whether your application and toolchain
+        use LSE for atomic operations. You will build and run a shor...
+      preview_after: This Learning Path introduces Large System Extensions (LSE) on
+        Arm processors and shows how to check whether your application and toolchain
+        use LSE for atomic operations. You will build and run a shor...
+      preview_generated: Use this Learning Path to understand Large System Extensions
+        (LSE) on Arm and verify whether your applications use them for atomic operations
+        on many-core systems. You will build and run a small C pro...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:18:56Z'
+      generated_at_after: '2026-06-03T01:24:38Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the example?
+        - Which compiler should I use to build the example program?
+        - How do I know if my build is using Large System Extensions?
+        - Can I complete this Learning Path without an AWS account?
+        - What result should I expect after running the example program?
+        removed_questions:
+        - What environment do I need to follow this Learning Path?
+        - Which tools will I use to build the example?
+        - What will I build or run during the steps?
+        - How do I validate that LSE is being used?
+        - How long will it take and what experience level is assumed?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the example?
+        - Which compiler should I use to build the example program?
+        - How do I know if my build is using Large System Extensions?
+        - Can I complete this Learning Path without an AWS account?
+        - What result should I expect after running the example program?
+        removed_questions:
+        - What environment do I need to follow this Learning Path?
+        - Which tools will I use to build the example?
+        - What will I build or run during the steps?
+        - How do I validate that LSE is being used?
+        - How long will it take and what experience level is assumed?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -215339,51 +24898,76 @@ history:
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-      preview_before: Deploy MariaDB on Arm cloud instances across AWS, Azure, and Google Cloud using
-        Docker, Amazon RDS, and automation with Terraform and Ansible. It is designed for software developers
-        who want to deploy...
-      preview_after: Deploy MariaDB on Arm cloud instances across AWS, Azure, and Google Cloud using Docker,
-        Amazon RDS, and automation with Terraform and Ansible. It is designed for software developers
-        who want to deploy...
-      preview_generated: Deploy MariaDB on Arm cloud instances across AWS, Azure, and Google Cloud using
-        Docker, Amazon RDS, and automation with Terraform and Ansible. It is designed for software developers
-        who want to deploy...
+      generated_at_before: '2026-06-02T04:19:21Z'
+      generated_at_after: '2026-06-02T04:19:21Z'
+      preview_before: Learn how to deploy MariaDB on Arm-based cloud infrastructure
+        across AWS, Microsoft Azure, and Google Cloud using Terraform, Ansible, Docker,
+        and Amazon RDS. You will provision single virtual machines...
+      preview_after: Learn how to deploy MariaDB on Arm-based cloud infrastructure
+        across AWS, Microsoft Azure, and Google Cloud using Terraform, Ansible, Docker,
+        and Amazon RDS. You will provision single virtual machines...
+      preview_generated: This Learning Path shows how to deploy MariaDB on Arm-based
+        cloud servers across AWS, Microsoft Azure, and Google Cloud using practical
+        automation. You will provision single instances on each provider...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: db4ce1c59ad10bd127b4990c82ce876311d7dc7e1ad5d39ec132daf82ceb8b79
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-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:19:21Z'
+      generated_at_after: '2026-06-03T01:25:42Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need installed locally before starting?
+        - Can I follow only the sections for the cloud provider I use?
+        - Which tools does each deployment method use?
+        - What additional setup is required for the Docker-based deployment?
+        - What credentials are required for the Amazon RDS section?
+        removed_questions:
+        - Which platforms and deployment options does this Learning Path cover?
+        - Do I need accounts for all cloud providers to follow this path?
+        - What tools do I need installed locally, and where can I run them?
+        - Do I need prior experience with Terraform or Ansible?
+        - What will be created, and how do I know the deployment worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need installed locally before starting?
+        - Can I follow only the sections for the cloud provider I use?
+        - Which tools does each deployment method use?
+        - What additional setup is required for the Docker-based deployment?
+        - What credentials are required for the Amazon RDS section?
+        removed_questions:
+        - Which platforms and deployment options does this Learning Path cover?
+        - Do I need accounts for all cloud providers to follow this path?
+        - What tools do I need installed locally, and where can I run them?
+        - Do I need prior experience with Terraform or Ansible?
+        - What will be created, and how do I know the deployment worked?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/memcached/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -215393,51 +24977,76 @@ history:
       source_hash_before: b42ca5cc8f4db6ba6019f3720a5ef46119aa403a2baaef5362e874f898ce0419
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Install memcached on Arm cloud servers and benchmark in-memory key-value store performance
-        using open-source tools. It is designed for developers who want to use memcached as their in-memory
-        key-value...
-      preview_after: Install memcached on Arm cloud servers and benchmark in-memory key-value store performance
-        using open-source tools. It is designed for developers who want to use memcached as their in-memory
-        key-value...
-      preview_generated: Install memcached on Arm cloud servers and benchmark in-memory key-value store
-        performance using open-source tools. It is designed for developers who want to use memcached as
-        their in-memory key-value...
+      generated_at_before: '2026-06-02T04:20:04Z'
+      generated_at_after: '2026-06-02T04:20:04Z'
+      preview_before: This introductory Learning Path shows how to install and run
+        Memcached on an Arm-based Ubuntu Linux cloud instance and measure its performance
+        with the open-source memtier_benchmark tool. You will pro...
+      preview_after: This introductory Learning Path shows how to install and run
+        Memcached on an Arm-based Ubuntu Linux cloud instance and measure its performance
+        with the open-source memtier_benchmark tool. You will pro...
+      preview_generated: This Learning Path shows how to install and run memcached
+        on an Arm-based Ubuntu Linux cloud instance and measure its performance with
+        an open-source benchmark. You will provision an Arm server (teste...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: b42ca5cc8f4db6ba6019f3720a5ef46119aa403a2baaef5362e874f898ce0419
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:20:04Z'
+      generated_at_after: '2026-06-03T01:26:50Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which cloud platforms are referenced in this Learning Path?
+        - Which packages should I install to prepare for memcached and the benchmark?
+        - Which benchmark tool is used to measure memcached performance?
+        - How do I know the setup worked?
+        removed_questions:
+        - What are the prerequisites and environment assumptions?
+        - Which cloud platforms does this Learning Path target?
+        - What software and packages will I install?
+        - What benchmark tool is used and what will I measure?
+        - How long will this take and what skill level is assumed?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which cloud platforms are referenced in this Learning Path?
+        - Which packages should I install to prepare for memcached and the benchmark?
+        - Which benchmark tool is used to measure memcached performance?
+        - How do I know the setup worked?
+        removed_questions:
+        - What are the prerequisites and environment assumptions?
+        - Which cloud platforms does this Learning Path target?
+        - What software and packages will I install?
+        - What benchmark tool is used and what will I measure?
+        - How long will this take and what skill level is assumed?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -215447,51 +25056,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Deploy Memcached as a cache for MySQL and PostgreSQL on Arm servers. It is designed
-        for developers who want to use memcached as their in-memory key-value store. By the end, you will
-        be able to deploy ...
-      preview_after: Deploy Memcached as a cache for MySQL and PostgreSQL on Arm servers. It is designed
-        for developers who want to use memcached as their in-memory key-value store. By the end, you will
-        be able to deploy ...
-      preview_generated: Deploy Memcached as a cache for MySQL and PostgreSQL on Arm servers. It is designed
-        for developers who want to use memcached as their in-memory key-value store. By the end, you will
-        be able to deploy ...
+      generated_at_before: '2026-06-02T04:20:49Z'
+      generated_at_after: '2026-06-02T04:20:49Z'
+      preview_before: Learn how to deploy Memcached as a cache for MySQL and PostgreSQL
+        on Arm-based cloud instances using Terraform and Ansible. You will provision
+        Linux instances on AWS, Microsoft Azure, and Google Cloud...
+      preview_after: Learn how to deploy Memcached as a cache for MySQL and PostgreSQL
+        on Arm-based cloud instances using Terraform and Ansible. You will provision
+        Linux instances on AWS, Microsoft Azure, and Google Cloud...
+      preview_generated: This Learning Path shows how to deploy Memcached as a cache
+        for MySQL and PostgreSQL on Arm-based Linux servers across AWS, Microsoft
+        Azure, and Google Cloud. Using Terraform and Ansible, you provisio...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 4efe46aaf4580a6b8f263b69e8f072211bfd24fcf7f7a885689c927fc05a4c63
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:20:49Z'
+      generated_at_after: '2026-06-03T01:27:30Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which database and cloud combinations are covered in the sections?
+        - Where do I run Terraform and Ansible from?
+        - "I'm new to Terraform\u2014what should I read first?"
+        - What result should I expect after completing a section?
+        removed_questions:
+        - Which cloud platforms and environments does this Learning Path target?
+        - What tools and accounts do I need before starting?
+        - Do I need prior Terraform experience?
+        - Which databases and providers are covered in the steps?
+        - What is the expected outcome and how long will it take?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which database and cloud combinations are covered in the sections?
+        - Where do I run Terraform and Ansible from?
+        - "I'm new to Terraform\u2014what should I read first?"
+        - What result should I expect after completing a section?
+        removed_questions:
+        - Which cloud platforms and environments does this Learning Path target?
+        - What tools and accounts do I need before starting?
+        - Do I need prior Terraform experience?
+        - Which databases and providers are covered in the steps?
+        - What is the expected outcome and how long will it take?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -215501,51 +25135,76 @@ history:
       source_hash_before: d61c9ddcef1c7ffe12b3ee818852fb3f0a62a90cec451692c1186cf2c01de625
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       current_source_hash: d61c9ddcef1c7ffe12b3ee818852fb3f0a62a90cec451692c1186cf2c01de625
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Use ASCT to measure cache latency, streaming bandwidth, and coherency latency on
-        Arm Neoverse systems, and compare results across Graviton generations. It is designed for software
-        developers and perfo...
-      preview_after: Use ASCT to measure cache latency, streaming bandwidth, and coherency latency on
-        Arm Neoverse systems, and compare results across Graviton generations. It is designed for software
-        developers and perfo...
-      preview_generated: Use ASCT to measure cache latency, streaming bandwidth, and coherency latency
-        on Arm Neoverse systems, and compare results across Graviton generations. It is designed for software
-        developers and perfo...
+      generated_at_before: '2026-06-02T04:21:33Z'
+      generated_at_after: '2026-06-02T04:21:33Z'
+      preview_before: This advanced Learning Path shows how to characterize the CPU-side
+        memory subsystem on Arm Neoverse-based Linux systems using the Arm System
+        Characterization Tool (ASCT). You will identify CPU topolog...
+      preview_after: This advanced Learning Path shows how to characterize the CPU-side
+        memory subsystem on Arm Neoverse-based Linux systems using the Arm System
+        Characterization Tool (ASCT). You will identify CPU topolog...
+      preview_generated: Use the Arm System Characterization Tool (ASCT) on Arm Neoverse
+        Linux systems to characterize the CPU-side memory subsystem. You will identify
+        core topology, cluster layout, and cache hierarchy with s...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: d61c9ddcef1c7ffe12b3ee818852fb3f0a62a90cec451692c1186cf2c01de625
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       current_source_hash: d61c9ddcef1c7ffe12b3ee818852fb3f0a62a90cec451692c1186cf2c01de625
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:21:33Z'
+      generated_at_after: '2026-06-03T01:28:09Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running these tests?
+        - How do I identify core, cache, and NUMA topology on my system?
+        - Which ASCT benchmarks should I run to measure latency and bandwidth?
+        - How do I know the latency and bandwidth measurements are reasonable?
+        - How should I compare results across systems like Graviton2 and Graviton4?
+        removed_questions:
+        - What hardware and access are required to follow this Learning Path?
+        - What software should be installed before starting?
+        - How are latency and bandwidth measured with ASCT in this path?
+        - What outputs should I expect, and how can I tell the results are sensible?
+        - What background knowledge is expected, and how long will it take?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running these tests?
+        - How do I identify core, cache, and NUMA topology on my system?
+        - Which ASCT benchmarks should I run to measure latency and bandwidth?
+        - How do I know the latency and bandwidth measurements are reasonable?
+        - How should I compare results across systems like Graviton2 and Graviton4?
+        removed_questions:
+        - What hardware and access are required to follow this Learning Path?
+        - What software should be installed before starting?
+        - How are latency and bandwidth measured with ASCT in this path?
+        - What outputs should I expect, and how can I tell the results are sensible?
+        - What background knowledge is expected, and how long will it take?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -215555,51 +25214,76 @@ history:
       source_hash_before: 6aa61de638be961339d958345225d696ff2b83b8f9cab22bf956f9bf2d15c1aa
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Test and validate thread synchronization approaches in the Arm memory model using
-        Herd7, Litmus7, and Arm hardware with assembly snippets. It is designed for developers seeking
-        practical ways to test ...
-      preview_after: Test and validate thread synchronization approaches in the Arm memory model using
-        Herd7, Litmus7, and Arm hardware with assembly snippets. It is designed for developers seeking
-        practical ways to test ...
-      preview_generated: Test and validate thread synchronization approaches in the Arm memory model using
-        Herd7, Litmus7, and Arm hardware with assembly snippets. It is designed for developers seeking
-        practical ways to test ...
+      generated_at_before: '2026-06-02T04:22:41Z'
+      generated_at_after: '2026-06-02T04:22:41Z'
+      preview_before: This advanced Learning Path guides you through testing and validating
+        thread synchronization in the Arm memory model on Linux using Herd7, Litmus7,
+        and Arm hardware. You will create and run litmus tes...
+      preview_after: This advanced Learning Path guides you through testing and validating
+        thread synchronization in the Arm memory model on Linux using Herd7, Litmus7,
+        and Arm hardware. You will create and run litmus tes...
+      preview_generated: This advanced Learning Path guides you through testing and
+        validating thread synchronization in the Arm memory model on Linux using Herd7
+        and Litmus7 with Arm assembly snippets. You will write and run...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 6aa61de638be961339d958345225d696ff2b83b8f9cab22bf956f9bf2d15c1aa
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       current_source_hash: 6aa61de638be961339d958345225d696ff2b83b8f9cab22bf956f9bf2d15c1aa
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:22:41Z'
+      generated_at_after: '2026-06-03T01:28:34Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - Do I need access to Arm hardware, and what operating system is used?
+        - Which tools should I use for modeling versus running on hardware?
+        - How do I start with a litmus test in this path?
+        - Which Arm synchronization instructions are covered in the examples?
+        - What results should I expect to compare when I finish?
+        removed_questions:
+        - What tools and environment does this Learning Path use?
+        - What skills are required before I start?
+        - Will I run tests on real Arm hardware?
+        - Which Arm instructions and ordering semantics are covered?
+        - How do I know the tests worked and what outcomes should I expect?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - Do I need access to Arm hardware, and what operating system is used?
+        - Which tools should I use for modeling versus running on hardware?
+        - How do I start with a litmus test in this path?
+        - Which Arm synchronization instructions are covered in the examples?
+        - What results should I expect to compare when I finish?
+        removed_questions:
+        - What tools and environment does this Learning Path use?
+        - What skills are required before I start?
+        - Will I run tests on real Arm hardware?
+        - Which Arm instructions and ordering semantics are covered?
+        - How do I know the tests worked and what outcomes should I expect?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -215609,51 +25293,78 @@ history:
       source_hash_before: a67d36fb650b77c170c15f193049490286a3f097801d0c79d701d3f5610fe1dc
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Microbenchmark and tune network performance with iPerf3 and Linux traffic control
-        walks you through an end-to-end Arm software workflow. It is designed for performance engineers,
-        Linux system administ...
-      preview_after: Microbenchmark and tune network performance with iPerf3 and Linux traffic control
-        walks you through an end-to-end Arm software workflow. It is designed for performance engineers,
-        Linux system administ...
-      preview_generated: Microbenchmark and tune network performance with iPerf3 and Linux traffic control
-        walks you through an end-to-end Arm software workflow. It is designed for performance engineers,
-        Linux system administ...
+      generated_at_before: '2026-06-02T04:23:38Z'
+      generated_at_after: '2026-06-02T04:23:38Z'
+      preview_before: "Learn to microbenchmark and tune network performance on Arm-based\
+        \ Linux systems using iPerf3 and Linux traffic control (tc). You will provision\
+        \ two Arm-based instances\u2014such as AWS EC2 with Graviton wi..."
+      preview_after: "Learn to microbenchmark and tune network performance on Arm-based\
+        \ Linux systems using iPerf3 and Linux traffic control (tc). You will provision\
+        \ two Arm-based instances\u2014such as AWS EC2 with Graviton wi..."
+      preview_generated: "Learn how to measure and tune network performance between\
+        \ Arm-based Linux systems using iPerf3 and Linux traffic control (tc). You\
+        \ will set up two Arm-based cloud instances\u2014such as AWS EC2 with Gravit..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: a67d36fb650b77c170c15f193049490286a3f097801d0c79d701d3f5610fe1dc
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:23:38Z'
+      generated_at_after: '2026-06-03T01:29:10Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the tests?
+        - "How do I start the iPerf3 server and confirm it\u2019s ready?"
+        - Can I use a cloud provider other than AWS for this Learning Path?
+        - How do I simulate latency or packet loss with tc and which interface should
+          I modify?
+        - What should I check if a local-to-cloud test cannot connect?
+        removed_questions:
+        - What environment do I need to complete this Learning Path?
+        - What tools and features will I use?
+        - How do I start the iPerf3 test and which port is used?
+        - Where do I apply traffic control (tc) settings?
+        - How do I validate a local-to-cloud test setup?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the tests?
+        - "How do I start the iPerf3 server and confirm it\u2019s ready?"
+        - Can I use a cloud provider other than AWS for this Learning Path?
+        - How do I simulate latency or packet loss with tc and which interface should
+          I modify?
+        - What should I check if a local-to-cloud test cannot connect?
+        removed_questions:
+        - What environment do I need to complete this Learning Path?
+        - What tools and features will I use?
+        - How do I start the iPerf3 test and which port is used?
+        - Where do I apply traffic control (tc) settings?
+        - How do I validate a local-to-cloud test setup?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -215663,51 +25374,78 @@ history:
       source_hash_before: 56a38d1d742dd301d48e9ad61ff00e07048559f3f646815e22937113243adcdb
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Scan source code for architecture-specific portability issues using migrate-ease
-        to identify and resolve AArch64 porting challenges before migration. It is designed for developers
-        looking to migrate a...
-      preview_after: Scan source code for architecture-specific portability issues using migrate-ease
-        to identify and resolve AArch64 porting challenges before migration. It is designed for developers
-        looking to migrate a...
-      preview_generated: Scan source code for architecture-specific portability issues using migrate-ease
-        to identify and resolve AArch64 porting challenges before migration. It is designed for developers
-        looking to migrate a...
+      generated_at_before: '2026-06-02T04:24:02Z'
+      generated_at_after: '2026-06-02T04:24:02Z'
+      preview_before: Use migrate-ease to scan your source code for architecture-specific
+        issues before migrating applications to Arm-based servers. This introductory,
+        Linux-focused path shows how to set up dependencies, c...
+      preview_after: Use migrate-ease to scan your source code for architecture-specific
+        issues before migrating applications to Arm-based servers. This introductory,
+        Linux-focused path shows how to set up dependencies, c...
+      preview_generated: Use migrate-ease to scan your application's source code for
+        architecture-specific issues before migrating to Arm-based servers. This path
+        guides you through setting up a Linux environment, cloning the...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 56a38d1d742dd301d48e9ad61ff00e07048559f3f646815e22937113243adcdb
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       current_source_hash: 56a38d1d742dd301d48e9ad61ff00e07048559f3f646815e22937113243adcdb
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:24:02Z'
+      generated_at_after: '2026-06-03T01:29:38Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running migrate-ease?
+        - Can I run migrate-ease on x86_64, or do I need an Arm machine?
+        - Which packages should I install on my distro before cloning the tool?
+        - Which command does the path use to scan the Protobuf v2.5.0 source and write
+          a report?
+        - What result should I expect, and how do I verify it?
+        removed_questions:
+        - What operating systems and platforms can I use to run migrate-ease?
+        - What are the prerequisites before starting?
+        - Does migrate-ease modify my source code?
+        - What kind of projects does migrate-ease target?
+        - What output should I expect and how do I verify success?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running migrate-ease?
+        - Can I run migrate-ease on x86_64, or do I need an Arm machine?
+        - Which packages should I install on my distro before cloning the tool?
+        - Which command does the path use to scan the Protobuf v2.5.0 source and write
+          a report?
+        - What result should I expect, and how do I verify it?
+        removed_questions:
+        - What operating systems and platforms can I use to run migrate-ease?
+        - What are the prerequisites before starting?
+        - Does migrate-ease modify my source code?
+        - What kind of projects does migrate-ease target?
+        - What output should I expect and how do I verify success?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/migration/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -215717,51 +25455,78 @@ history:
       source_hash_before: 6b2b985c39579c4d0855c8c28b610ba558e25b451a6b755475ff4393e2810670
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Set up an Arm development environment, analyze dependencies, and understand common
-        challenges and scenarios for migrating applications to Arm servers. It is designed for software
-        developers looking to...
-      preview_after: Set up an Arm development environment, analyze dependencies, and understand common
-        challenges and scenarios for migrating applications to Arm servers. It is designed for software
-        developers looking to...
-      preview_generated: Set up an Arm development environment, analyze dependencies, and understand common
-        challenges and scenarios for migrating applications to Arm servers. It is designed for software
-        developers looking to...
+      generated_at_before: '2026-06-02T04:24:49Z'
+      generated_at_after: '2026-06-02T04:24:49Z'
+      preview_before: Learn the essentials of migrating applications to Arm servers
+        on Linux. This introductory path guides you to set up an Arm-based development
+        machine (typically a cloud instance), analyze application d...
+      preview_after: Learn the essentials of migrating applications to Arm servers
+        on Linux. This introductory path guides you to set up an Arm-based development
+        machine (typically a cloud instance), analyze application d...
+      preview_generated: Learn the essentials for migrating applications to Arm servers
+        by setting up a Linux-based Arm development machine, analyzing dependencies,
+        and reviewing common migration challenges and scenarios. Thi...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 6b2b985c39579c4d0855c8c28b610ba558e25b451a6b755475ff4393e2810670
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:24:49Z'
+      generated_at_after: '2026-06-03T01:30:21Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which C/C++ compiler versions should I use on Arm Neoverse?
+        - How should I install Java on Arm Linux, and are there JVM options to consider?
+        - Which Go version should I install for Arm servers?
+        - "Where can I check if my application\u2019s dependencies or ISV software\
+          \ support Arm?"
+        removed_questions:
+        - What do I need before starting?
+        - Which cloud platforms can I use for the development machine?
+        - Does this path include step-by-step migration examples or tuning guides?
+        - What languages and toolchains are discussed?
+        - How can I confirm software and ISV support on Arm?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which C/C++ compiler versions should I use on Arm Neoverse?
+        - How should I install Java on Arm Linux, and are there JVM options to consider?
+        - Which Go version should I install for Arm servers?
+        - "Where can I check if my application\u2019s dependencies or ISV software\
+          \ support Arm?"
+        removed_questions:
+        - What do I need before starting?
+        - Which cloud platforms can I use for the development machine?
+        - Does this path include step-by-step migration examples or tuning guides?
+        - What languages and toolchains are discussed?
+        - How can I confirm software and ISV support on Arm?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -215771,51 +25536,78 @@ history:
       source_hash_before: 2eb252b19b7535fbb776a3153bfc9f70b6217088e5b4b55deb56d01393abaf3b
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Build a Retrieval-Augmented Generation (RAG) application on Arm servers using Zilliz
-        Cloud for vector search and llama.cpp for LLM inference. It is designed for software developers
-        who want to create ...
-      preview_after: Build a Retrieval-Augmented Generation (RAG) application on Arm servers using Zilliz
-        Cloud for vector search and llama.cpp for LLM inference. It is designed for software developers
-        who want to create ...
-      preview_generated: Build a Retrieval-Augmented Generation (RAG) application on Arm servers using
-        Zilliz Cloud for vector search and llama.cpp for LLM inference. It is designed for software developers
-        who want to create ...
+      generated_at_before: '2026-06-02T04:25:25Z'
+      generated_at_after: '2026-06-02T04:25:25Z'
+      preview_before: Build a Retrieval-Augmented Generation application on Arm-based
+        servers using Zilliz Cloud for vector search and llama.cpp for LLM inference.
+        You will create a Dedicated Zilliz Cloud cluster on AWS us...
+      preview_after: Build a Retrieval-Augmented Generation application on Arm-based
+        servers using Zilliz Cloud for vector search and llama.cpp for LLM inference.
+        You will create a Dedicated Zilliz Cloud cluster on AWS us...
+      preview_generated: Learn how to assemble a simple Retrieval-Augmented Generation
+        (RAG) workflow on Arm-based servers using Zilliz Cloud for vector search and
+        llama.cpp for local LLM inference. You will create a Dedicate...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 2eb252b19b7535fbb776a3153bfc9f70b6217088e5b4b55deb56d01393abaf3b
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:25:25Z'
+      generated_at_after: '2026-06-03T01:30:54Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Zilliz Cloud cluster should I create for this path?
+        - Do I need to request access to the Llama 3.1 model before launching llama.cpp?
+        - Do I need an OpenAI API key when using the OpenAI SDK with the local llama.cpp
+          server?
+        - What output should I see when I test the embedding model in the Python script?
+        removed_questions:
+        - What do I need before starting?
+        - How is vector storage set up, and can I self-host?
+        - Which LLM and serving approach are used?
+        - Do I need an API key to call the LLM from Python?
+        - How do I verify that everything is working?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Zilliz Cloud cluster should I create for this path?
+        - Do I need to request access to the Llama 3.1 model before launching llama.cpp?
+        - Do I need an OpenAI API key when using the OpenAI SDK with the local llama.cpp
+          server?
+        - What output should I see when I test the embedding model in the Python script?
+        removed_questions:
+        - What do I need before starting?
+        - How is vector storage set up, and can I self-host?
+        - Which LLM and serving approach are used?
+        - Do I need an API key to call the LLM from Python?
+        - How do I verify that everything is working?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -215825,51 +25617,78 @@ history:
       source_hash_before: 6c438573edec90b3aa4135ace38cd3ae604c58658c1949117ca80dbdd21394bd
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Learn how to deploy and configure MinIO on an Azure Cobalt 100 virtual machine,
-        benchmark object storage throughput, and validate S3 compatibility using the boto3 Python SDK.
-        It is designed for develo...
-      preview_after: Learn how to deploy and configure MinIO on an Azure Cobalt 100 virtual machine, benchmark
-        object storage throughput, and validate S3 compatibility using the boto3 Python SDK. It is designed
-        for develo...
-      preview_generated: Learn how to deploy and configure MinIO on an Azure Cobalt 100 virtual machine,
-        benchmark object storage throughput, and validate S3 compatibility using the boto3 Python SDK.
-        It is designed for develo...
+      generated_at_before: '2026-06-02T04:26:22Z'
+      generated_at_after: '2026-06-02T04:26:22Z'
+      preview_before: This Learning Path shows how to deploy a single-node, S3-compatible
+        MinIO server on an Arm-based Azure Cobalt 100 virtual machine and verify it
+        end to end. You will provision a Dpsv6 instance (Ubuntu ...
+      preview_after: This Learning Path shows how to deploy a single-node, S3-compatible
+        MinIO server on an Arm-based Azure Cobalt 100 virtual machine and verify it
+        end to end. You will provision a Dpsv6 instance (Ubuntu ...
+      preview_generated: Deploy a single-node MinIO object storage server on an Arm-based
+        Azure Cobalt 100 virtual machine and validate it for AI/ML data workflows.
+        You will use the Azure Portal to provision a Dpsv6 VM runnin...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 6c438573edec90b3aa4135ace38cd3ae604c58658c1949117ca80dbdd21394bd
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       current_source_hash: 6c438573edec90b3aa4135ace38cd3ae604c58658c1949117ca80dbdd21394bd
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:26:22Z'
+      generated_at_after: '2026-06-03T01:31:21Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - Which provisioning method, VM size, and OS are used in this path?
+        - Which network ports must I open for MinIO, and where do I configure them?
+        - How do I connect to the Azure Cobalt 100 VM?
+        - How do I run the throughput benchmark and what result should I expect to
+          see?
+        - How is S3 API compatibility validated in this path?
+        removed_questions:
+        - What are the prerequisites to start this Learning Path?
+        - Which Azure VM type and operating system are used in the steps?
+        - Do I have to use the Azure Portal to create the VM?
+        - Which network ports must be opened for MinIO on Azure?
+        - How do I know the deployment and tests worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - Which provisioning method, VM size, and OS are used in this path?
+        - Which network ports must I open for MinIO, and where do I configure them?
+        - How do I connect to the Azure Cobalt 100 VM?
+        - How do I run the throughput benchmark and what result should I expect to
+          see?
+        - How is S3 API compatibility validated in this path?
+        removed_questions:
+        - What are the prerequisites to start this Learning Path?
+        - Which Azure VM type and operating system are used in the steps?
+        - Do I have to use the Azure Portal to create the VM?
+        - Which network ports must be opened for MinIO on Azure?
+        - How do I know the deployment and tests worked?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -215879,51 +25698,76 @@ history:
       source_hash_before: 973ba6c1e00fc67e1702606412124d8172e071b9b677a1df53c3be66210bf704
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Benchmark machine learning inference performance on Arm servers using TensorFlow
-        and the MLPerf Inference benchmark suite from MLCommons. It is designed for software developers
-        interested in benchmark...
-      preview_after: Benchmark machine learning inference performance on Arm servers using TensorFlow
-        and the MLPerf Inference benchmark suite from MLCommons. It is designed for software developers
-        interested in benchmark...
-      preview_generated: Benchmark machine learning inference performance on Arm servers using TensorFlow
-        and the MLPerf Inference benchmark suite from MLCommons. It is designed for software developers
-        interested in benchmark...
+      generated_at_before: '2026-06-02T04:26:55Z'
+      generated_at_after: '2026-06-02T04:26:55Z'
+      preview_before: Set up an Arm-based Linux server and benchmark machine learning
+        inference using TensorFlow and the MLPerf Inference benchmark suite from MLCommons.
+        You will launch an Arm instance running Ubuntu 20.04...
+      preview_after: Set up an Arm-based Linux server and benchmark machine learning
+        inference using TensorFlow and the MLPerf Inference benchmark suite from MLCommons.
+        You will launch an Arm instance running Ubuntu 20.04...
+      preview_generated: This introductory path shows how to measure machine learning
+        inference performance on Arm-based servers using TensorFlow and the MLPerf
+        Inference benchmark suite from MLCommons. You will launch an Arm...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 973ba6c1e00fc67e1702606412124d8172e071b9b677a1df53c3be66210bf704
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:26:55Z'
+      generated_at_after: '2026-06-03T01:31:54Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the benchmarks?
+        - Which Ubuntu version should I choose for this path?
+        - Which packages do I install to prepare the environment?
+        - How are TensorFlow and MLPerf Inference used here?
+        - How long will this take and what result should I expect?
+        removed_questions:
+        - Which environment and operating system should I use?
+        - What are the prerequisites before starting?
+        - What software and packages will be installed?
+        - How do I verify the setup and benchmarks are working?
+        - How long does this Learning Path take and what is the difficulty level?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the benchmarks?
+        - Which Ubuntu version should I choose for this path?
+        - Which packages do I install to prepare the environment?
+        - How are TensorFlow and MLPerf Inference used here?
+        - How long will this take and what result should I expect?
+        removed_questions:
+        - Which environment and operating system should I use?
+        - What are the prerequisites before starting?
+        - What software and packages will be installed?
+        - How do I verify the setup and benchmarks are working?
+        - How long does this Learning Path take and what is the difficulty level?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -215933,51 +25777,84 @@ history:
       source_hash_before: b52a1b60a74e19f2a3b60b8a77199ad5369c1ccd3b4d5ce0252a169d9f6123fd
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Install MongoDB on Arm servers and benchmark database performance using Yahoo Cloud
-        Serving Benchmark (YCSB) to compare against other architectures. It is designed for software developers
-        who want to ...
-      preview_after: Install MongoDB on Arm servers and benchmark database performance using Yahoo Cloud
-        Serving Benchmark (YCSB) to compare against other architectures. It is designed for software developers
-        who want to ...
-      preview_generated: Install MongoDB on Arm servers and benchmark database performance using Yahoo
-        Cloud Serving Benchmark (YCSB) to compare against other architectures. It is designed for software
-        developers who want to ...
+      generated_at_before: '2026-06-02T04:27:23Z'
+      generated_at_after: '2026-06-02T04:27:23Z'
+      preview_before: Learn how to install MongoDB Community Edition 8.0 on Arm-based
+        Linux servers and evaluate database performance using Yahoo Cloud Serving
+        Benchmark (YCSB). You will provision an Arm instance from a cl...
+      preview_after: Learn how to install MongoDB Community Edition 8.0 on Arm-based
+        Linux servers and evaluate database performance using Yahoo Cloud Serving
+        Benchmark (YCSB). You will provision an Arm instance from a cl...
+      preview_generated: This Learning Path guides you to install MongoDB Community
+        Edition 8.0 on Arm-based Linux instances and evaluate database performance
+        using Yahoo Cloud Serving Benchmark (YCSB). You will deploy MongoD...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: b52a1b60a74e19f2a3b60b8a77199ad5369c1ccd3b4d5ce0252a169d9f6123fd
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       current_source_hash: b52a1b60a74e19f2a3b60b8a77199ad5369c1ccd3b4d5ce0252a169d9f6123fd
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:27:23Z'
+      generated_at_after: '2026-06-03T01:32:28Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - Which Linux distributions are supported for installing MongoDB Community
+          Edition 8.0 in this path?
+        - How should I structure the MongoDB environment for testing with YCSB?
+        - What additional packages are required to run YCSB, and how do I install
+          them on Ubuntu?
+        - Which YCSB workloads should I run, for how long, and how do I know the system
+          is exercised enough?
+        - Is there an alternative to YCSB for testing MongoDB performance in this
+          path?
+        removed_questions:
+        - What do I need before I start?
+        - Which operating systems and MongoDB version are covered?
+        - How should I set up the test topology for MongoDB and YCSB?
+        - What workloads and runtime settings should I use with YCSB?
+        - Is there an alternative to YCSB in this Learning Path?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - Which Linux distributions are supported for installing MongoDB Community
+          Edition 8.0 in this path?
+        - How should I structure the MongoDB environment for testing with YCSB?
+        - What additional packages are required to run YCSB, and how do I install
+          them on Ubuntu?
+        - Which YCSB workloads should I run, for how long, and how do I know the system
+          is exercised enough?
+        - Is there an alternative to YCSB for testing MongoDB performance in this
+          path?
+        removed_questions:
+        - What do I need before I start?
+        - Which operating systems and MongoDB version are covered?
+        - How should I set up the test topology for MongoDB and YCSB?
+        - What workloads and runtime settings should I use with YCSB?
+        - Is there an alternative to YCSB in this Learning Path?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -215987,51 +25864,78 @@ history:
       source_hash_before: f270cfc90245c0090685fabf5cbebf62a714edbf34e1ea86c3df0bfbb4699ea4
       source_hash_after: f270cfc90245c0090685fabf5cbebf62a714edbf34e1ea86c3df0bfbb4699ea4
       current_source_hash: f270cfc90245c0090685fabf5cbebf62a714edbf34e1ea86c3df0bfbb4699ea4
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Deploy MongoDB on Azure Cobalt 100 Arm virtual machines and benchmark database performance
-        using mongotop and mongostat monitoring tools. It is designed for software developers who want
-        to migrate Mon...
-      preview_after: Deploy MongoDB on Azure Cobalt 100 Arm virtual machines and benchmark database performance
-        using mongotop and mongostat monitoring tools. It is designed for software developers who want
-        to migrate Mon...
-      preview_generated: Deploy MongoDB on Azure Cobalt 100 Arm virtual machines and benchmark database
-        performance using mongotop and mongostat monitoring tools. It is designed for software developers
-        who want to migrate Mon...
+      generated_at_before: '2026-06-02T04:28:12Z'
+      generated_at_after: '2026-06-02T04:28:12Z'
+      preview_before: This Learning Path shows how to run MongoDB on Arm-based Microsoft
+        Azure Cobalt 100 virtual machines. You will provision a Dpsv6 instance using
+        the Azure console with Ubuntu Pro 24.04 LTS (Arm64), ins...
+      preview_after: This Learning Path shows how to run MongoDB on Arm-based Microsoft
+        Azure Cobalt 100 virtual machines. You will provision a Dpsv6 instance using
+        the Azure console with Ubuntu Pro 24.04 LTS (Arm64), ins...
+      preview_generated: Provision an Arm64 Azure Cobalt 100 (Dpsv6) virtual machine
+        and deploy MongoDB on Ubuntu Pro 24.04 LTS. You will install MongoDB and mongosh,
+        create data and log directories, and start the server loca...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: f270cfc90245c0090685fabf5cbebf62a714edbf34e1ea86c3df0bfbb4699ea4
       source_hash_after: f270cfc90245c0090685fabf5cbebf62a714edbf34e1ea86c3df0bfbb4699ea4
       current_source_hash: f270cfc90245c0090685fabf5cbebf62a714edbf34e1ea86c3df0bfbb4699ea4
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:28:12Z'
+      generated_at_after: '2026-06-03T01:33:04Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before creating the Azure VM?
+        - Which Azure VM series and OS image should I select?
+        - How do I verify that MongoDB was installed and is working?
+        - How is access control handled during the exercises and how can I enable
+          remote access later?
+        - How do I monitor MongoDB activity and what should be running first?
+        removed_questions:
+        - What Azure resources do I need before starting?
+        - Which operating system and architecture does the VM use?
+        - How is MongoDB installed and initially configured?
+        - How do I generate load and monitor MongoDB performance?
+        - How do I verify that everything worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before creating the Azure VM?
+        - Which Azure VM series and OS image should I select?
+        - How do I verify that MongoDB was installed and is working?
+        - How is access control handled during the exercises and how can I enable
+          remote access later?
+        - How do I monitor MongoDB activity and what should be running first?
+        removed_questions:
+        - What Azure resources do I need before starting?
+        - Which operating system and architecture does the VM use?
+        - How is MongoDB installed and initially configured?
+        - How do I generate load and monitor MongoDB performance?
+        - How do I verify that everything worked?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -216041,51 +25945,78 @@ history:
       source_hash_before: abbdde22271151fb1485c54bafe463e7797a0b913c7d4c99245d2914a3f9bb82
       source_hash_after: abbdde22271151fb1485c54bafe463e7797a0b913c7d4c99245d2914a3f9bb82
       current_source_hash: abbdde22271151fb1485c54bafe463e7797a0b913c7d4c99245d2914a3f9bb82
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Deploy MongoDB on Google Cloud Axion C4A virtual machines and benchmark database
-        performance with Yahoo Cloud Serving Benchmark (YCSB). It is designed for This introductory topic
-        is for software devel...
-      preview_after: Deploy MongoDB on Google Cloud Axion C4A virtual machines and benchmark database
-        performance with Yahoo Cloud Serving Benchmark (YCSB). It is designed for This introductory topic
-        is for software devel...
-      preview_generated: Deploy MongoDB on Google Cloud Axion C4A virtual machines and benchmark database
-        performance with Yahoo Cloud Serving Benchmark (YCSB). It is designed for This introductory topic
-        is for software devel...
+      generated_at_before: '2026-06-02T04:28:40Z'
+      generated_at_after: '2026-06-02T04:28:40Z'
+      preview_before: Learn how to deploy MongoDB on Arm-based Google Axion C4A virtual
+        machines and benchmark it with the Yahoo Cloud Serving Benchmark (YCSB). You
+        will create a c4a-standard-4 VM in Google Cloud using the...
+      preview_after: Learn how to deploy MongoDB on Arm-based Google Axion C4A virtual
+        machines and benchmark it with the Yahoo Cloud Serving Benchmark (YCSB). You
+        will create a c4a-standard-4 VM in Google Cloud using the...
+      preview_generated: Follow this path to deploy MongoDB on an Arm-based Google
+        Cloud Axion C4A virtual machine and benchmark it with Yahoo Cloud Serving
+        Benchmark (YCSB). You will create a C4A instance in the Google Cloud...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: abbdde22271151fb1485c54bafe463e7797a0b913c7d4c99245d2914a3f9bb82
       source_hash_after: abbdde22271151fb1485c54bafe463e7797a0b913c7d4c99245d2914a3f9bb82
       current_source_hash: abbdde22271151fb1485c54bafe463e7797a0b913c7d4c99245d2914a3f9bb82
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:28:40Z'
+      generated_at_after: '2026-06-03T01:33:34Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before creating the VM on Google Cloud?
+        - Which VM configuration does this path use for Axion C4A?
+        - Which operating system and MongoDB package are assumed?
+        - How do I verify that MongoDB is running correctly?
+        - How do I install and run YCSB for MongoDB, and what data size is loaded
+          initially?
+        removed_questions:
+        - What do I need before starting?
+        - Which VM configuration and OS does this path use?
+        - How do I confirm that MongoDB is running correctly?
+        - How is YCSB installed and what dataset size is loaded by default?
+        - Does this cover migrating an existing x86_64 MongoDB deployment to Arm?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before creating the VM on Google Cloud?
+        - Which VM configuration does this path use for Axion C4A?
+        - Which operating system and MongoDB package are assumed?
+        - How do I verify that MongoDB is running correctly?
+        - How do I install and run YCSB for MongoDB, and what data size is loaded
+          initially?
+        removed_questions:
+        - What do I need before starting?
+        - Which VM configuration and OS does this path use?
+        - How do I confirm that MongoDB is running correctly?
+        - How is YCSB installed and what dataset size is loaded by default?
+        - Does this cover migrating an existing x86_64 MongoDB deployment to Arm?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/mpi/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -216095,51 +26026,76 @@ history:
       source_hash_before: 300794f4010658d945212c74c5257635d620cbb6230cac0de92bf23e4e9e29fe
       source_hash_after: 300794f4010658d945212c74c5257635d620cbb6230cac0de92bf23e4e9e29fe
       current_source_hash: 300794f4010658d945212c74c5257635d620cbb6230cac0de92bf23e4e9e29fe
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Debug, profile, and optimize MPI parallel applications on Arm servers using Linaro
-        Forge, gdb, and Arm Performance Libraries. It is designed for HPC software developers writing
-        MPI applications. By th...
-      preview_after: Debug, profile, and optimize MPI parallel applications on Arm servers using Linaro
-        Forge, gdb, and Arm Performance Libraries. It is designed for HPC software developers writing
-        MPI applications. By th...
-      preview_generated: Debug, profile, and optimize MPI parallel applications on Arm servers using Linaro
-        Forge, gdb, and Arm Performance Libraries. It is designed for HPC software developers writing
-        MPI applications. By th...
+      generated_at_before: '2026-06-02T04:29:07Z'
+      generated_at_after: '2026-06-02T04:29:07Z'
+      preview_before: This advanced Learning Path is for HPC developers building MPI
+        applications on Arm-based Linux servers or cloud instances. You will install
+        and validate Linaro Forge, then build, debug, and profile a ...
+      preview_after: This advanced Learning Path is for HPC developers building MPI
+        applications on Arm-based Linux servers or cloud instances. You will install
+        and validate Linaro Forge, then build, debug, and profile a ...
+      preview_generated: This advanced path shows how to debug, profile, and improve
+        an MPI-based parallel matrix multiplication application on Arm-based Linux
+        servers. You will provision an Arm system (local or via AWS, Micr...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 300794f4010658d945212c74c5257635d620cbb6230cac0de92bf23e4e9e29fe
       source_hash_after: 300794f4010658d945212c74c5257635d620cbb6230cac0de92bf23e4e9e29fe
       current_source_hash: 300794f4010658d945212c74c5257635d620cbb6230cac0de92bf23e4e9e29fe
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:29:07Z'
+      generated_at_after: '2026-06-03T01:34:00Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I verify that Linaro Forge installed correctly?
+        - Where is the example application and which languages are available?
+        - Which build flags should I use for debugging and where do I set them?
+        - How should I approach profiling and comparing alternatives?
+        removed_questions:
+        - Can I run this on a cloud instance, and which providers are suitable?
+        - What operating system is expected?
+        - What software do I need to install and how do I verify it?
+        - What application will I work on, and how do I build it for debugging?
+        - How do I approach profiling and using optimized routines?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I verify that Linaro Forge installed correctly?
+        - Where is the example application and which languages are available?
+        - Which build flags should I use for debugging and where do I set them?
+        - How should I approach profiling and comparing alternatives?
+        removed_questions:
+        - Can I run this on a cloud instance, and which providers are suitable?
+        - What operating system is expected?
+        - What software do I need to install and how do I verify it?
+        - What application will I work on, and how do I build it for debugging?
+        - How do I approach profiling and using optimized routines?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -216149,51 +26105,76 @@ history:
       source_hash_before: a10683fdd14359e93056dc4ba24581856833765c64915979d0466a06887e0755
       source_hash_after: a10683fdd14359e93056dc4ba24581856833765c64915979d0466a06887e0755
       current_source_hash: a10683fdd14359e93056dc4ba24581856833765c64915979d0466a06887e0755
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Select and apply accuracy modes for vectorized math functions in Libamath to balance
-        performance and precision for your application. It is designed for developers who want to use
-        the different accurac...
-      preview_after: Select and apply accuracy modes for vectorized math functions in Libamath to balance
-        performance and precision for your application. It is designed for developers who want to use
-        the different accurac...
-      preview_generated: Select and apply accuracy modes for vectorized math functions in Libamath to
-        balance performance and precision for your application. It is designed for developers who want
-        to use the different accurac...
+      generated_at_before: '2026-06-02T04:29:42Z'
+      generated_at_after: '2026-06-02T04:29:42Z'
+      preview_before: Learn how to control floating-point accuracy for vectorized
+        math functions in Libamath, a component of Arm Performance Libraries, on Linux.
+        This path introduces IEEE-754 representation, Units in the L...
+      preview_after: Learn how to control floating-point accuracy for vectorized math
+        functions in Libamath, a component of Arm Performance Libraries, on Linux.
+        This path introduces IEEE-754 representation, Units in the L...
+      preview_generated: Learn how to choose and apply floating-point accuracy modes
+        for vectorized math functions in Libamath, part of Arm Performance Libraries.
+        After a short review of IEEE-754 floating-point and Units in t...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: a10683fdd14359e93056dc4ba24581856833765c64915979d0466a06887e0755
       source_hash_after: a10683fdd14359e93056dc4ba24581856833765c64915979d0466a06887e0755
       current_source_hash: a10683fdd14359e93056dc4ba24581856833765c64915979d0466a06887e0755
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:29:42Z'
+      generated_at_after: '2026-06-03T01:34:28Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the example code?
+        - How do I select a specific Libamath accuracy mode in my code?
+        - How is ULP error computed when checking results?
+        - What files should I have to build the example?
+        - What should I check if the build fails with missing headers or vector types?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - How do I select and identify accuracy modes in Libamath?
+        - How is function accuracy measured in this path?
+        - What does the provided code example demonstrate?
+        - How do I verify that the chosen accuracy mode is working as expected?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the example code?
+        - How do I select a specific Libamath accuracy mode in my code?
+        - How is ULP error computed when checking results?
+        - What files should I have to build the example?
+        - What should I check if the build fails with missing headers or vector types?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - How do I select and identify accuracy modes in Libamath?
+        - How is function accuracy measured in this path?
+        - What does the provided code example demonstrate?
+        - How do I verify that the chosen accuracy mode is working as expected?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -216203,51 +26184,78 @@ history:
       source_hash_before: d765afadfe5155f0ecd5bd08373fa12464b2ee5ebb1f8c7831039eb07eba8831
       source_hash_after: d765afadfe5155f0ecd5bd08373fa12464b2ee5ebb1f8c7831039eb07eba8831
       current_source_hash: d765afadfe5155f0ecd5bd08373fa12464b2ee5ebb1f8c7831039eb07eba8831
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Build a multi-architecture Kubernetes cluster running nginx on Azure AKS walks you
-        through an end-to-end Arm software workflow. It is designed for developers who want to deploy
-        multi-architecture Kube...
-      preview_after: Build a multi-architecture Kubernetes cluster running nginx on Azure AKS walks you
-        through an end-to-end Arm software workflow. It is designed for developers who want to deploy
-        multi-architecture Kube...
-      preview_generated: Build a multi-architecture Kubernetes cluster running nginx on Azure AKS walks
-        you through an end-to-end Arm software workflow. It is designed for developers who want to deploy
-        multi-architecture Kube...
+      generated_at_before: '2026-06-02T04:31:12Z'
+      generated_at_after: '2026-06-02T04:31:12Z'
+      preview_before: This Learning Path walks you through building a hybrid Azure
+        Kubernetes Service (AKS) cluster with both Arm-based and x86 node pools on
+        Linux, then deploying nginx using a multi-architecture image to ...
+      preview_after: This Learning Path walks you through building a hybrid Azure
+        Kubernetes Service (AKS) cluster with both Arm-based and x86 node pools on
+        Linux, then deploying nginx using a multi-architecture image to ...
+      preview_generated: Follow this introductory path to build a hybrid Azure Kubernetes
+        Service (AKS) cluster with both x86 and Arm64 nodes, then deploy nginx as
+        a multi-architecture workload on each architecture. You will ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: d765afadfe5155f0ecd5bd08373fa12464b2ee5ebb1f8c7831039eb07eba8831
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:31:12Z'
+      generated_at_after: '2026-06-03T01:35:04Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the setup?
+        - How do I know my AKS cluster includes both x86 and Arm nodes?
+        - Which files set up nginx on Intel, and what should I expect after applying
+          them?
+        - How is the Arm nginx deployment created and exposed?
+        - How do I compare performance between the x86 and Arm nginx instances?
+        removed_questions:
+        - What do I need before starting?
+        - What infrastructure does this path create in Azure?
+        - Which container image and Kubernetes objects are deployed for nginx?
+        - How do I confirm that nginx is running on the intended architecture?
+        - How do I compare nginx behavior across architectures?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the setup?
+        - How do I know my AKS cluster includes both x86 and Arm nodes?
+        - Which files set up nginx on Intel, and what should I expect after applying
+          them?
+        - How is the Arm nginx deployment created and exposed?
+        - How do I compare performance between the x86 and Arm nginx instances?
+        removed_questions:
+        - What do I need before starting?
+        - What infrastructure does this path create in Azure?
+        - Which container image and Kubernetes objects are deployed for nginx?
+        - How do I confirm that nginx is running on the intended architecture?
+        - How do I compare nginx behavior across architectures?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -216257,51 +26265,78 @@ history:
       source_hash_before: cd31c217eaeab6faad263728c446ee188cd9d542904d87ae7bfd5120713dfaa4
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       current_source_hash: cd31c217eaeab6faad263728c446ee188cd9d542904d87ae7bfd5120713dfaa4
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Add Arm nodes to your GKE cluster using a multi-architecture Ollama container image
-        walks you through an end-to-end Arm software workflow. It is designed for developers who want
-        to compare the perform...
-      preview_after: Add Arm nodes to your GKE cluster using a multi-architecture Ollama container image
-        walks you through an end-to-end Arm software workflow. It is designed for developers who want
-        to compare the perform...
-      preview_generated: Add Arm nodes to your GKE cluster using a multi-architecture Ollama container
-        image walks you through an end-to-end Arm software workflow. It is designed for developers who
-        want to compare the perform...
+      generated_at_before: '2026-06-02T04:32:13Z'
+      generated_at_after: '2026-06-02T04:32:13Z'
+      preview_before: This Learning Path shows how to extend a Google Kubernetes Engine
+        (GKE) cluster with Arm-based nodes and deploy Ollama using a single multi-architecture
+        container image. You begin with an amd64 node r...
+      preview_after: This Learning Path shows how to extend a Google Kubernetes Engine
+        (GKE) cluster with Arm-based nodes and deploy Ollama using a single multi-architecture
+        container image. You begin with an amd64 node r...
+      preview_generated: This Learning Path shows how to create a hybrid Google Kubernetes
+        Engine (GKE) cluster with both amd64 and arm64 nodes and deploy Ollama using
+        a single multi-architecture container image. You begin wi...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: cd31c217eaeab6faad263728c446ee188cd9d542904d87ae7bfd5120713dfaa4
       source_hash_after: cd31c217eaeab6faad263728c446ee188cd9d542904d87ae7bfd5120713dfaa4
       current_source_hash: cd31c217eaeab6faad263728c446ee188cd9d542904d87ae7bfd5120713dfaa4
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:32:13Z'
+      generated_at_after: '2026-06-03T01:35:29Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How is the initial amd64 deployment organized in Kubernetes?
+        - What settings should I use when adding the Arm node pool?
+        - How do I verify that requests can reach either architecture in the hybrid
+          cluster?
+        - How do I compare amd64 and arm64 behavior and performance in this setup?
+        removed_questions:
+        - What are the prerequisites to follow this Learning Path?
+        - What Kubernetes resources are created during the steps?
+        - How do I add Arm nodes to the existing GKE cluster?
+        - How do I verify that both architectures are serving requests?
+        - Do I need separate container images for amd64 and arm64?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How is the initial amd64 deployment organized in Kubernetes?
+        - What settings should I use when adding the Arm node pool?
+        - How do I verify that requests can reach either architecture in the hybrid
+          cluster?
+        - How do I compare amd64 and arm64 behavior and performance in this setup?
+        removed_questions:
+        - What are the prerequisites to follow this Learning Path?
+        - What Kubernetes resources are created during the steps?
+        - How do I add Arm nodes to the existing GKE cluster?
+        - How do I verify that both architectures are serving requests?
+        - Do I need separate container images for amd64 and arm64?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/mysql/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -216311,51 +26346,76 @@ history:
       source_hash_before: b9b2ed7f58611c9bc7e1867fe06700f3197eb095ddc62d91c00814021967cf72
       source_hash_after: b9b2ed7f58611c9bc7e1867fe06700f3197eb095ddc62d91c00814021967cf72
       current_source_hash: b9b2ed7f58611c9bc7e1867fe06700f3197eb095ddc62d91c00814021967cf72
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Learn how to deploy MySQL walks you through an end-to-end Arm software workflow.
-        It is designed for software developers who want to deploy MySQL on Arm. By the end, you will be
-        able to learn about the...
-      preview_after: Learn how to deploy MySQL walks you through an end-to-end Arm software workflow.
-        It is designed for software developers who want to deploy MySQL on Arm. By the end, you will be
-        able to learn about the...
-      preview_generated: Learn how to deploy MySQL walks you through an end-to-end Arm software workflow.
-        It is designed for software developers who want to deploy MySQL on Arm. By the end, you will be
-        able to learn about the...
+      generated_at_before: '2026-06-02T04:33:29Z'
+      generated_at_after: '2026-06-02T04:33:29Z'
+      preview_before: This introductory Learning Path shows how to deploy MySQL on
+        Arm-based Linux systems and interact with it using the MySQL client CLI. You
+        will review common deployment options on Arm, including bare m...
+      preview_after: This introductory Learning Path shows how to deploy MySQL on
+        Arm-based Linux systems and interact with it using the MySQL client CLI. You
+        will review common deployment options on Arm, including bare m...
+      preview_generated: This introductory path shows how to deploy MySQL on Arm-based
+        servers and cloud VMs running Linux, with a focus on Arm Neoverse platforms.
+        You will review common deployment options (bare metal, cloud ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: b9b2ed7f58611c9bc7e1867fe06700f3197eb095ddc62d91c00814021967cf72
       source_hash_after: b9b2ed7f58611c9bc7e1867fe06700f3197eb095ddc62d91c00814021967cf72
       current_source_hash: b9b2ed7f58611c9bc7e1867fe06700f3197eb095ddc62d91c00814021967cf72
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:33:29Z'
+      generated_at_after: '2026-06-03T01:35:55Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - "I don\u2019t have an Arm node\u2014what should I do?"
+        - Which deployment approach should I choose for MySQL on Arm?
+        - How do I know the installation worked?
+        - Does this path cover performance tuning?
+        removed_questions:
+        - What do I need before I start?
+        - Which deployment environments are covered?
+        - What will I set up or learn to use?
+        - How do I verify the deployment is working?
+        - Is this the right path if I already know how to deploy MySQL?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - "I don\u2019t have an Arm node\u2014what should I do?"
+        - Which deployment approach should I choose for MySQL on Arm?
+        - How do I know the installation worked?
+        - Does this path cover performance tuning?
+        removed_questions:
+        - What do I need before I start?
+        - Which deployment environments are covered?
+        - What will I set up or learn to use?
+        - How do I verify the deployment is working?
+        - Is this the right path if I already know how to deploy MySQL?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -216365,51 +26425,87 @@ history:
       source_hash_before: b9bc1d1f965e4fdeeb9552d853330c201e00597829420c8a0397fa425c3231c4
       source_hash_after: b9bc1d1f965e4fdeeb9552d853330c201e00597829420c8a0397fa425c3231c4
       current_source_hash: b9bc1d1f965e4fdeeb9552d853330c201e00597829420c8a0397fa425c3231c4
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Deploy MySQL on Microsoft Azure Cobalt 100 processors walks you through an end-to-end
-        Arm software workflow. It is designed for developers migrating MySQL applications from x86_64
-        to Arm. By the end, ...
-      preview_after: Deploy MySQL on Microsoft Azure Cobalt 100 processors walks you through an end-to-end
-        Arm software workflow. It is designed for developers migrating MySQL applications from x86_64
-        to Arm. By the end, ...
-      preview_generated: Deploy MySQL on Microsoft Azure Cobalt 100 processors walks you through an end-to-end
-        Arm software workflow. It is designed for developers migrating MySQL applications from x86_64
-        to Arm. By the end, ...
+      generated_at_before: '2026-06-02T04:33:49Z'
+      generated_at_after: '2026-06-02T04:33:49Z'
+      preview_before: Learn how to provision an Arm64 virtual machine on Microsoft
+        Azure Cobalt 100 (Neoverse-N2) using the Azure Portal with Ubuntu Pro 24.04
+        LTS, deploy and secure MySQL, validate the service, and run bas...
+      preview_after: Learn how to provision an Arm64 virtual machine on Microsoft
+        Azure Cobalt 100 (Neoverse-N2) using the Azure Portal with Ubuntu Pro 24.04
+        LTS, deploy and secure MySQL, validate the service, and run bas...
+      preview_generated: Follow this introductory path to provision an Arm64 virtual
+        machine on Microsoft Azure Cobalt 100 (Dpsv6) using the Azure Portal and Ubuntu
+        Pro 24.04 LTS, then deploy and validate MySQL. You will inst...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: b9bc1d1f965e4fdeeb9552d853330c201e00597829420c8a0397fa425c3231c4
       source_hash_after: b9bc1d1f965e4fdeeb9552d853330c201e00597829420c8a0397fa425c3231c4
       current_source_hash: b9bc1d1f965e4fdeeb9552d853330c201e00597829420c8a0397fa425c3231c4
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:33:49Z'
+      generated_at_after: '2026-06-03T01:36:24Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - Which Azure VM size and base image should I use?
+        - Can I create the VM with Azure CLI or IaC instead of the Azure Portal?
+        - What do I need before running the steps?
+        - How do I know MySQL started and is ready for use?
+        - How do I benchmark MySQL in this setup, and what does mysqlslap measure?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - Which Azure VM series and operating system image are used?
+        - How do I create the Azure Arm64 VM?
+        - How do I confirm that MySQL is running correctly on the VM?
+        - What tool is used for benchmarking and what does it evaluate?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - Which Azure VM size and base image should I use?
+        - Can I create the VM with Azure CLI or IaC instead of the Azure Portal?
+        - What do I need before running the steps?
+        - How do I know MySQL started and is ready for use?
+        - How do I benchmark MySQL in this setup, and what does mysqlslap measure?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - Which Azure VM series and operating system image are used?
+        - How do I create the Azure Arm64 VM?
+        - How do I confirm that MySQL is running correctly on the VM?
+        - What tool is used for benchmarking and what does it evaluate?
+        updated_questions: []
+    category: servers-and-cloud-computing
+  - path: content/learning-paths/servers-and-cloud-computing/mysql-lns-azure/_index.md
+    status: skipped
+    skip_reason: draft
+    change_reasons:
+    - draft
+    ai_requested: false
+    summary:
+      action: skipped
+    faqs:
+      action: skipped
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -216419,51 +26515,76 @@ history:
       source_hash_before: e473c0724855bbbc59481822130f7dc5e1684593527a6b5688939f84cd32963d
       source_hash_after: e473c0724855bbbc59481822130f7dc5e1684593527a6b5688939f84cd32963d
       current_source_hash: e473c0724855bbbc59481822130f7dc5e1684593527a6b5688939f84cd32963d
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Benchmarking MySQL with Sysbench walks you through an end-to-end Arm software workflow.
-        It is designed for performance engineers who want to benchmark MySQL using Sysbench and optimize
-        performance on ...
-      preview_after: Benchmarking MySQL with Sysbench walks you through an end-to-end Arm software workflow.
-        It is designed for performance engineers who want to benchmark MySQL using Sysbench and optimize
-        performance on ...
-      preview_generated: Benchmarking MySQL with Sysbench walks you through an end-to-end Arm software
-        workflow. It is designed for performance engineers who want to benchmark MySQL using Sysbench
-        and optimize performance on ...
+      generated_at_before: '2026-06-02T04:34:09Z'
+      generated_at_after: '2026-06-02T04:34:09Z'
+      preview_before: This Learning Path shows how to benchmark MySQL on Arm Linux
+        using Sysbench and apply profile-guided optimization (PGO) with GCC. You will
+        build, configure, and run a MySQL server on one Arm server ru...
+      preview_after: This Learning Path shows how to benchmark MySQL on Arm Linux
+        using Sysbench and apply profile-guided optimization (PGO) with GCC. You will
+        build, configure, and run a MySQL server on one Arm server ru...
+      preview_generated: This Learning Path shows you how to benchmark MySQL on Arm
+        Linux systems using Sysbench and apply profile-guided optimization (PGO) with
+        GCC. You will build, configure, and run a MySQL server on one A...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: e473c0724855bbbc59481822130f7dc5e1684593527a6b5688939f84cd32963d
       source_hash_after: e473c0724855bbbc59481822130f7dc5e1684593527a6b5688939f84cd32963d
       current_source_hash: e473c0724855bbbc59481822130f7dc5e1684593527a6b5688939f84cd32963d
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:34:09Z'
+      generated_at_after: '2026-06-03T01:37:09Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which packages should I install to build MySQL on Ubuntu 22.04?
+        - Why do I need to build MySQL on the Sysbench client as well?
+        - Can I use a different Linux distribution or Ubuntu version?
+        - How is PGO applied to MySQL in this path, and which compiler is used?
+        removed_questions:
+        - What hardware and OS do I need before starting?
+        - Do I have to use Ubuntu 22.04, or can other Linux distributions work?
+        - Why do I need to build MySQL on the Sysbench client?
+        - What artifacts will I have at the end of the path?
+        - How is PGO applied to MySQL in this path?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which packages should I install to build MySQL on Ubuntu 22.04?
+        - Why do I need to build MySQL on the Sysbench client as well?
+        - Can I use a different Linux distribution or Ubuntu version?
+        - How is PGO applied to MySQL in this path, and which compiler is used?
+        removed_questions:
+        - What hardware and OS do I need before starting?
+        - Do I have to use Ubuntu 22.04, or can other Linux distributions work?
+        - Why do I need to build MySQL on the Sysbench client?
+        - What artifacts will I have at the end of the path?
+        - How is PGO applied to MySQL in this path?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -216473,51 +26594,78 @@ history:
       source_hash_before: faa914d636e03296c6b65ba146745c543326955ec457577f047eec51aa6a3733
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       current_source_hash: faa914d636e03296c6b65ba146745c543326955ec457577f047eec51aa6a3733
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Learn how to Tune MySQL walks you through an end-to-end Arm software workflow. It
-        is designed for software developers and DevOps professionals interested in optimizing MySQL performance
-        on Arm-based V...
-      preview_after: Learn how to Tune MySQL walks you through an end-to-end Arm software workflow. It
-        is designed for software developers and DevOps professionals interested in optimizing MySQL performance
-        on Arm-based V...
-      preview_generated: Learn how to Tune MySQL walks you through an end-to-end Arm software workflow.
-        It is designed for software developers and DevOps professionals interested in optimizing MySQL
-        performance on Arm-based V...
+      generated_at_before: '2026-06-02T04:34:44Z'
+      generated_at_after: '2026-06-02T04:34:44Z'
+      preview_before: This advanced Learning Path guides you through tuning MySQL
+        for better performance on Arm-based (Neoverse) cloud VMs running Linux. You
+        will review system-level considerations such as storage technolo...
+      preview_after: This advanced Learning Path guides you through tuning MySQL for
+        better performance on Arm-based (Neoverse) cloud VMs running Linux. You will
+        review system-level considerations such as storage technolo...
+      preview_generated: This advanced Learning Path provides practical guidance for
+        tuning MySQL on Arm-based VMs (Neoverse) running Linux in major clouds, including
+        AWS, Microsoft Azure, Google Cloud, and Oracle. You will r...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:34:44Z'
+      generated_at_after: '2026-06-03T01:37:44Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which platforms and Arm targets does this path focus on?
+        - How should I choose storage and filesystem for MySQL?
+        - Where should I place MySQL tuning parameters, and can I use command-line
+          options?
+        - Should I change many MySQL settings at once?
+        removed_questions:
+        - What do I need before starting?
+        - Which environment does this target?
+        - How do I apply the MySQL tuning settings?
+        - What system-level choices should I evaluate for performance?
+        - How do I validate that the tuning is effective?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which platforms and Arm targets does this path focus on?
+        - How should I choose storage and filesystem for MySQL?
+        - Where should I place MySQL tuning parameters, and can I use command-line
+          options?
+        - Should I change many MySQL settings at once?
+        removed_questions:
+        - What do I need before starting?
+        - Which environment does this target?
+        - How do I apply the MySQL tuning settings?
+        - What system-level choices should I evaluate for performance?
+        - How do I validate that the tuning is effective?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -216527,51 +26675,78 @@ history:
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Develop and Validate Firmware Pre-Silicon on Arm Neoverse CSS V3 walks you through
-        an end-to-end Arm software workflow. It is designed for This advanced topic is for firmware developers,
-        system archit...
-      preview_after: Develop and Validate Firmware Pre-Silicon on Arm Neoverse CSS V3 walks you through
-        an end-to-end Arm software workflow. It is designed for This advanced topic is for firmware developers,
-        system archit...
-      preview_generated: Develop and Validate Firmware Pre-Silicon on Arm Neoverse CSS V3 walks you through
-        an end-to-end Arm software workflow. It is designed for This advanced topic is for firmware developers,
-        system archit...
+      generated_at_before: '2026-06-02T04:35:16Z'
+      generated_at_after: '2026-06-02T04:35:16Z'
+      preview_before: "This advanced Learning Path shows how to develop and validate\
+        \ firmware pre-silicon for Arm Neoverse CSS\u2011V3 using the RD\u2011V3 reference\
+        \ design and Arm Fixed Virtual Platforms (FVPs). You will examine the..."
+      preview_after: "This advanced Learning Path shows how to develop and validate\
+        \ firmware pre-silicon for Arm Neoverse CSS\u2011V3 using the RD\u2011V3 reference\
+        \ design and Arm Fixed Virtual Platforms (FVPs). You will examine the..."
+      preview_generated: "This advanced Learning Path guides you through validating\
+        \ the Arm Neoverse CSS\u2011V3 firmware stack pre\u2011silicon using the RD\u2011\
+        V3 Fixed Virtual Platform (FVP). You will set up a containerized build environ..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 65336a8f8d1d11c4b127ea13982a581afffa94cbfd1af10a9e4df2becbc34b5a
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:35:16Z'
+      generated_at_after: '2026-06-03T01:38:16Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the build and simulation steps?
+        - "Which FVP model version should I use with my RD\u2011V3 release tag?"
+        - What result should I expect when the FVP simulation completes successfully?
+        - How do I diagnose issues if the boot sequence stalls?
+        - "What is different about running the dual\u2011chip RD\u2011V3\u2011R1 simulation,\
+          \ and what should I verify?"
+        removed_questions:
+        - What are the prerequisites and recommended environment?
+        - Can I complete this on a cloud instance?
+        - What tools and source workflow does the path use?
+        - How do I choose the correct FVP model for my build?
+        - What outputs should I expect, and how do I validate success?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the build and simulation steps?
+        - "Which FVP model version should I use with my RD\u2011V3 release tag?"
+        - What result should I expect when the FVP simulation completes successfully?
+        - How do I diagnose issues if the boot sequence stalls?
+        - "What is different about running the dual\u2011chip RD\u2011V3\u2011R1 simulation,\
+          \ and what should I verify?"
+        removed_questions:
+        - What are the prerequisites and recommended environment?
+        - Can I complete this on a cloud instance?
+        - What tools and source workflow does the path use?
+        - How do I choose the correct FVP model for my build?
+        - What outputs should I expect, and how do I validate success?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -216581,51 +26756,74 @@ history:
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Run a .NET Aspire application on Arm-based VMs on AWS and GCP walks you through
-        an end-to-end Arm software workflow. It is designed for software developers interested in learning
-        how to deploy .NET As...
-      preview_after: Run a .NET Aspire application on Arm-based VMs on AWS and GCP walks you through an
-        end-to-end Arm software workflow. It is designed for software developers interested in learning
-        how to deploy .NET As...
-      preview_generated: Run a .NET Aspire application on Arm-based VMs on AWS and GCP walks you through
-        an end-to-end Arm software workflow. It is designed for software developers interested in learning
-        how to deploy .NET As...
+      generated_at_before: '2026-06-02T04:36:04Z'
+      generated_at_after: '2026-06-02T04:36:04Z'
+      preview_before: This introductory Learning Path guides you through creating,
+        running, modifying, and deploying a .NET Aspire application using a Windows
+        on Arm development machine and Arm-based virtual machines on AW...
+      preview_after: This introductory Learning Path guides you through creating,
+        running, modifying, and deploying a .NET Aspire application using a Windows
+        on Arm development machine and Arm-based virtual machines on AW...
+      preview_generated: This Learning Path guides you through creating, running,
+        modifying, and deploying a .NET Aspire application from a Windows on Arm development
+        machine to Arm-based virtual machines on AWS and Google Cl...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 5a5fd9b66a09de71009ccb098c7ef08a848463a8a7956643020ab1fb1db22fbb
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:36:04Z'
+      generated_at_after: '2026-06-03T01:38:53Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I run the application locally and confirm it started correctly?
+        - Where do I add the computational code, and what does it do?
+        - Which cloud targets are supported, and how do I begin with AWS?
+        removed_questions:
+        - What do I need before starting?
+        - How do I run the application locally and handle HTTPS certificates?
+        - What code changes will I make in the sample application?
+        - Where will I deploy the application in the cloud?
+        updated_questions:
+        - How do I check my .NET version and install the Aspire workload?
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I run the application locally and confirm it started correctly?
+        - Where do I add the computational code, and what does it do?
+        - Which cloud targets are supported, and how do I begin with AWS?
+        removed_questions:
+        - What do I need before starting?
+        - How do I run the application locally and handle HTTPS certificates?
+        - What code changes will I make in the sample application?
+        - Where will I deploy the application in the cloud?
+        updated_questions:
+        - How do I check my .NET version and install the Aspire workload?
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/nginx/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -216635,51 +26833,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Learn how to deploy Nginx walks you through an end-to-end Arm software workflow.
-        It is designed for engineers who want to use Nginx on Arm. By the end, you will be able to install
-        and run Nginx on Arm...
-      preview_after: Learn how to deploy Nginx walks you through an end-to-end Arm software workflow.
-        It is designed for engineers who want to use Nginx on Arm. By the end, you will be able to install
-        and run Nginx on Arm...
-      preview_generated: Learn how to deploy Nginx walks you through an end-to-end Arm software workflow.
-        It is designed for engineers who want to use Nginx on Arm. By the end, you will be able to install
-        and run Nginx on Arm...
+      generated_at_before: '2026-06-02T04:36:50Z'
+      generated_at_after: '2026-06-02T04:36:50Z'
+      preview_before: Deploy the open source Nginx on Arm-based Linux servers and
+        configure it as a minimal HTTPS static file server and as a reverse proxy
+        and API gateway. You will first install Nginx using a package mana...
+      preview_after: Deploy the open source Nginx on Arm-based Linux servers and configure
+        it as a minimal HTTPS static file server and as a reverse proxy and API gateway.
+        You will first install Nginx using a package mana...
+      preview_generated: Follow this path to install and run the open source Nginx
+        on Arm-based Linux servers, then configure it for two common roles. You will
+        first install Nginx from a package manager and review its build c...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: c5e077458808373c8ce9660235716b5bb55e4e7eb8b6300c162c041ef1c96cb0
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:36:50Z'
+      generated_at_after: '2026-06-03T01:39:16Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - Which Nginx edition does this path use?
+        - How many Arm-based instances do I need to complete the exercises?
+        - Should I install Nginx from a package manager or build from source?
+        - What network settings should I configure before starting?
+        - What should be ready before configuring the reverse proxy and API gateway?
+        removed_questions:
+        - What environment do I need to follow this Learning Path?
+        - How many servers are required for each scenario?
+        - Does this path use open source Nginx or Nginx Plus?
+        - Should I install Nginx from a package or build from source?
+        - What will I have working by the end, and how do I learn more?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - Which Nginx edition does this path use?
+        - How many Arm-based instances do I need to complete the exercises?
+        - Should I install Nginx from a package manager or build from source?
+        - What network settings should I configure before starting?
+        - What should be ready before configuring the reverse proxy and API gateway?
+        removed_questions:
+        - What environment do I need to follow this Learning Path?
+        - How many servers are required for each scenario?
+        - Does this path use open source Nginx or Nginx Plus?
+        - Should I install Nginx from a package or build from source?
+        - What will I have working by the end, and how do I learn more?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -216689,51 +26912,76 @@ history:
       source_hash_before: e6f6f4d843b4974d1c1d67a35009b4428d08bb356d26c08a441f82726e002fb2
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Deploy NGINX on Azure Cobalt 100 Arm-based virtual machines walks you through an
-        end-to-end Arm software workflow. It is designed for system administrators and developers who
-        want to learn how to depl...
-      preview_after: Deploy NGINX on Azure Cobalt 100 Arm-based virtual machines walks you through an
-        end-to-end Arm software workflow. It is designed for system administrators and developers who
-        want to learn how to depl...
-      preview_generated: Deploy NGINX on Azure Cobalt 100 Arm-based virtual machines walks you through
-        an end-to-end Arm software workflow. It is designed for system administrators and developers who
-        want to learn how to depl...
+      generated_at_before: '2026-06-02T04:37:18Z'
+      generated_at_after: '2026-06-02T04:37:18Z'
+      preview_before: This Learning Path shows how to deploy and validate NGINX on
+        an Arm-based Microsoft Azure Cobalt 100 virtual machine. Using the Azure portal,
+        you create a general-purpose Dpsv6 Arm64 VM with Ubuntu Pr...
+      preview_after: This Learning Path shows how to deploy and validate NGINX on
+        an Arm-based Microsoft Azure Cobalt 100 virtual machine. Using the Azure portal,
+        you create a general-purpose Dpsv6 Arm64 VM with Ubuntu Pr...
+      preview_generated: This Learning Path guides you through deploying NGINX on
+        Microsoft Azure Cobalt 100 Arm-based virtual machines. Using the Azure portal,
+        you will create an Arm64 VM in the Dpsv6 size series with Ubuntu...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: e6f6f4d843b4974d1c1d67a35009b4428d08bb356d26c08a441f82726e002fb2
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:37:18Z'
+      generated_at_after: '2026-06-03T01:39:34Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before I start creating the VM on Azure?
+        - Which Azure VM series and OS image should I select?
+        - Can I use Azure CLI or IaC instead of the portal to create the VM?
+        - How do I know NGINX is installed and serving content?
+        - How do I install and verify ApacheBench (ab) on Ubuntu Pro 24.04 LTS?
+        removed_questions:
+        - What do I need before starting?
+        - Which Azure VM type and OS image are used?
+        - Do I need the Azure CLI or an IaC tool to follow the steps?
+        - How do I confirm that NGINX is installed and serving my content?
+        - How is NGINX benchmarked in this path?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before I start creating the VM on Azure?
+        - Which Azure VM series and OS image should I select?
+        - Can I use Azure CLI or IaC instead of the portal to create the VM?
+        - How do I know NGINX is installed and serving content?
+        - How do I install and verify ApacheBench (ab) on Ubuntu Pro 24.04 LTS?
+        removed_questions:
+        - What do I need before starting?
+        - Which Azure VM type and OS image are used?
+        - Do I need the Azure CLI or an IaC tool to follow the steps?
+        - How do I confirm that NGINX is installed and serving my content?
+        - How is NGINX benchmarked in this path?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -216743,51 +26991,78 @@ history:
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Learn how to tune Nginx walks you through an end-to-end Arm software workflow. It
-        is designed for software developers who want to use Nginx on Arm. By the end, you will be able
-        to describe how kernel ...
-      preview_after: Learn how to tune Nginx walks you through an end-to-end Arm software workflow. It
-        is designed for software developers who want to use Nginx on Arm. By the end, you will be able
-        to describe how kernel ...
-      preview_generated: Learn how to tune Nginx walks you through an end-to-end Arm software workflow.
-        It is designed for software developers who want to use Nginx on Arm. By the end, you will be able
-        to describe how kernel ...
+      generated_at_before: '2026-06-02T04:38:02Z'
+      generated_at_after: '2026-06-02T04:38:02Z'
+      preview_before: This advanced Learning Path shows how to tune Nginx on Arm-based
+        Linux servers in about 60 minutes. You will review how Linux kernel parameters,
+        compiler and library choices, and Nginx configuration a...
+      preview_after: This advanced Learning Path shows how to tune Nginx on Arm-based
+        Linux servers in about 60 minutes. You will review how Linux kernel parameters,
+        compiler and library choices, and Nginx configuration a...
+      preview_generated: This advanced Learning Path shows how to tune Nginx for Arm-based
+        deployments on Linux, focusing on changes that can improve behavior without
+        scaling your infrastructure up or out. You will examine ho...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
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       drift_detected: false
       source_hash_before: 10f13dee3459577fcadf5966cf80d990f3396321189f638432a3308699e773a8
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-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:38:02Z'
+      generated_at_after: '2026-06-03T01:40:13Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I list and change the Linux kernel networking parameters mentioned
+          in the tuning guidance?
+        - Which Nginx configuration files will I tune?
+        - Do I have to use wrk2 for performance testing?
+        - What result should I expect after tuning, and how do I validate it?
+        removed_questions:
+        - What setup do I need before starting?
+        - Which operating system and hardware does this target?
+        - Which configuration files and parameters will I modify?
+        - Does this provide a one-size-fits-all tuning recipe?
+        - How do I test and validate the impact of my changes?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I list and change the Linux kernel networking parameters mentioned
+          in the tuning guidance?
+        - Which Nginx configuration files will I tune?
+        - Do I have to use wrk2 for performance testing?
+        - What result should I expect after tuning, and how do I validate it?
+        removed_questions:
+        - What setup do I need before starting?
+        - Which operating system and hardware does this target?
+        - Which configuration files and parameters will I modify?
+        - Does this provide a one-size-fits-all tuning recipe?
+        - How do I test and validate the impact of my changes?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
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+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -216797,51 +27072,76 @@ history:
       source_hash_before: 81bdee16a18b09da91a7514eb70368771b251ed3f8ec657ec105ca10ecead038
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-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Run a Natural Language Processing (NLP) model from Hugging Face on Arm servers walks
-        you through an end-to-end Arm software workflow. It is designed for software developers who want
-        to learn how to ru...
-      preview_after: Run a Natural Language Processing (NLP) model from Hugging Face on Arm servers walks
-        you through an end-to-end Arm software workflow. It is designed for software developers who want
-        to learn how to ru...
-      preview_generated: Run a Natural Language Processing (NLP) model from Hugging Face on Arm servers
-        walks you through an end-to-end Arm software workflow. It is designed for software developers
-        who want to learn how to ru...
+      generated_at_before: '2026-06-02T04:38:40Z'
+      generated_at_after: '2026-06-02T04:38:40Z'
+      preview_before: Learn how to run a Hugging Face Natural Language Processing
+        (NLP) model with PyTorch on Arm servers. Using an Arm-based cloud instance
+        or on-prem Arm server running Ubuntu 22.04 LTS, you will install ...
+      preview_after: Learn how to run a Hugging Face Natural Language Processing (NLP)
+        model with PyTorch on Arm servers. Using an Arm-based cloud instance or on-prem
+        Arm server running Ubuntu 22.04 LTS, you will install ...
+      preview_generated: Follow this introductory path to deploy and run a Hugging
+        Face Natural Language Processing (NLP) model with PyTorch on an Arm AArch64
+        CPU. The instructions target an Arm server running Ubuntu 22.04 LT...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:38:40Z'
+      generated_at_after: '2026-06-03T01:40:53Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which operating system should my server use?
+        - Can I use AWS, Microsoft Azure, Google Cloud, or Oracle Cloud for this?
+        - Do I need a GPU to run the model?
+        - How do I know the deployment and profiling worked?
+        removed_questions:
+        - What hardware and operating system do I need?
+        - Which cloud providers can I use to get an Arm instance?
+        - What tools and languages are used in this Learning Path?
+        - Do I need a specific NLP model from Hugging Face?
+        - How do I know the setup worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which operating system should my server use?
+        - Can I use AWS, Microsoft Azure, Google Cloud, or Oracle Cloud for this?
+        - Do I need a GPU to run the model?
+        - How do I know the deployment and profiling worked?
+        removed_questions:
+        - What hardware and operating system do I need?
+        - Which cloud providers can I use to get an Arm instance?
+        - What tools and languages are used in this Learning Path?
+        - Do I need a specific NLP model from Hugging Face?
+        - How do I know the setup worked?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -216851,51 +27151,78 @@ history:
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Deploy Node.js on Google Cloud C4A Arm-based Axion VMs walks you through an end-to-end
-        Arm software workflow. It is designed for software developers migrating Node.js workloads from
-        x86_64 to Arm-base...
-      preview_after: Deploy Node.js on Google Cloud C4A Arm-based Axion VMs walks you through an end-to-end
-        Arm software workflow. It is designed for software developers migrating Node.js workloads from
-        x86_64 to Arm-base...
-      preview_generated: Deploy Node.js on Google Cloud C4A Arm-based Axion VMs walks you through an end-to-end
-        Arm software workflow. It is designed for software developers migrating Node.js workloads from
-        x86_64 to Arm-base...
+      generated_at_before: '2026-06-02T04:39:16Z'
+      generated_at_after: '2026-06-02T04:39:16Z'
+      preview_before: Learn how to deploy and evaluate Node.js on Google Cloud C4A
+        virtual machines powered by Axion processors built on Arm Neoverse-V2 cores.
+        You will provision a SUSE Linux Enterprise Server VM (for exam...
+      preview_after: Learn how to deploy and evaluate Node.js on Google Cloud C4A
+        virtual machines powered by Axion processors built on Arm Neoverse-V2 cores.
+        You will provision a SUSE Linux Enterprise Server VM (for exam...
+      preview_generated: "This introductory Learning Path shows how to deploy and\
+        \ test Node.js on Arm-based Google Cloud C4A virtual machines powered by Google\u2019\
+        s Axion processors (Arm Neoverse\u2011V2). You will provision a SUSE Li..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:39:16Z'
+      generated_at_after: '2026-06-03T01:41:19Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before provisioning the VM?
+        - Which Google Cloud instance type and OS image are used in the steps?
+        - How do I install Node.js on the Arm VM?
+        - How do I confirm the Node.js setup before benchmarking?
+        - What should I expect from the Autocannon benchmark, and what should I check
+          if it fails?
+        removed_questions:
+        - Which Google Cloud instance type does this path use?
+        - What operating system and architecture are targeted?
+        - What are the prerequisites before starting?
+        - How is Node.js installed and how can I validate it?
+        - What does the benchmarking step measure and what should I expect?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before provisioning the VM?
+        - Which Google Cloud instance type and OS image are used in the steps?
+        - How do I install Node.js on the Arm VM?
+        - How do I confirm the Node.js setup before benchmarking?
+        - What should I expect from the Autocannon benchmark, and what should I check
+          if it fails?
+        removed_questions:
+        - Which Google Cloud instance type does this path use?
+        - What operating system and architecture are targeted?
+        - What are the prerequisites before starting?
+        - How is Node.js installed and how can I validate it?
+        - What does the benchmarking step measure and what should I expect?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -216905,51 +27232,76 @@ history:
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-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Deploy Arm Instances on Oracle Cloud Infrastructure (OCI) using Terraform walks
-        you through an end-to-end Arm software workflow. It is designed for software developers who are
-        new to deploying Arm ins...
-      preview_after: Deploy Arm Instances on Oracle Cloud Infrastructure (OCI) using Terraform walks you
-        through an end-to-end Arm software workflow. It is designed for software developers who are new
-        to deploying Arm ins...
-      preview_generated: Deploy Arm Instances on Oracle Cloud Infrastructure (OCI) using Terraform walks
-        you through an end-to-end Arm software workflow. It is designed for software developers who are
-        new to deploying Arm ins...
+      generated_at_before: '2026-06-02T04:39:57Z'
+      generated_at_after: '2026-06-02T04:39:57Z'
+      preview_before: Learn how to automate the creation of Arm (Neoverse) virtual
+        machine instances on Oracle Cloud Infrastructure (OCI) using Terraform. This
+        Learning Path is aimed at developers new to deploying Arm inst...
+      preview_after: Learn how to automate the creation of Arm (Neoverse) virtual
+        machine instances on Oracle Cloud Infrastructure (OCI) using Terraform. This
+        Learning Path is aimed at developers new to deploying Arm inst...
+      preview_generated: Learn how to automate the creation of Arm virtual machine
+        instances on Oracle Cloud Infrastructure (OCI) using Terraform. In about 60
+        minutes, you will use Terraform from a local environment (command ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
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-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:39:57Z'
+      generated_at_after: '2026-06-03T01:41:50Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Do I have to use Linux to follow the commands?
+        - Is there anything I should review before starting with OCI?
+        - How long does this take and what experience level is expected?
+        - What result should I expect after completing the steps?
+        removed_questions:
+        - What are the prerequisites to start this Learning Path?
+        - What will I deploy by following the steps?
+        - What system should I use to run the commands?
+        - Does this path cover setting up my OCI environment?
+        - How do I know the deployment worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Do I have to use Linux to follow the commands?
+        - Is there anything I should review before starting with OCI?
+        - How long does this take and what experience level is expected?
+        - What result should I expect after completing the steps?
+        removed_questions:
+        - What are the prerequisites to start this Learning Path?
+        - What will I deploy by following the steps?
+        - What system should I use to run the commands?
+        - Does this path cover setting up my OCI environment?
+        - How do I know the deployment worked?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/onnx/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -216959,51 +27311,76 @@ history:
       source_hash_before: a9e547c4a9f99e1c7bfbfe582032ede11eb8ff5a74dbb7a93bada275ac435220
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Deploy Phi-4-mini model with ONNX Runtime on Azure Cobalt 100 walks you through
-        an end-to-end Arm software workflow. It is designed for developers, ML engineers, and cloud practitioners
-        looking to dep...
-      preview_after: Deploy Phi-4-mini model with ONNX Runtime on Azure Cobalt 100 walks you through an
-        end-to-end Arm software workflow. It is designed for developers, ML engineers, and cloud practitioners
-        looking to dep...
-      preview_generated: Deploy Phi-4-mini model with ONNX Runtime on Azure Cobalt 100 walks you through
-        an end-to-end Arm software workflow. It is designed for developers, ML engineers, and cloud practitioners
-        looking to dep...
+      generated_at_before: '2026-06-02T04:40:35Z'
+      generated_at_after: '2026-06-02T04:40:35Z'
+      preview_before: "This advanced Learning Path guides you through quantizing and\
+        \ deploying Microsoft\u2019s Phi-4-mini model with ONNX Runtime on Arm-based\
+        \ Azure Cobalt 100 virtual machines running Ubuntu 24.04 LTS. You will..."
+      preview_after: "This advanced Learning Path guides you through quantizing and\
+        \ deploying Microsoft\u2019s Phi-4-mini model with ONNX Runtime on Arm-based\
+        \ Azure Cobalt 100 virtual machines running Ubuntu 24.04 LTS. You will..."
+      preview_generated: "This advanced Learning Path guides you through deploying\
+        \ Microsoft\u2019s Phi-4-mini model with ONNX Runtime on Arm-based Azure Cobalt\
+        \ 100 (Neoverse N2) virtual machines. You will build and configure ONNX ..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
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       drift_detected: false
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-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:40:35Z'
+      generated_at_after: '2026-06-03T01:42:17Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What kind of Azure instance should I use to follow this path?
+        - Which operating system and environment are the instructions written for?
+        - Do I need to quantize the Phi-4-mini model before running inference?
+        - How do I run the chatbot server and which arguments matter?
+        - How do I know the deployment worked and what results should I expect?
+        removed_questions:
+        - What environment does this Learning Path target?
+        - What prerequisites do I need before starting?
+        - What will I build and run during the path?
+        - How do I validate that the model is working?
+        - How long does it take and what VM specs were used for testing?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What kind of Azure instance should I use to follow this path?
+        - Which operating system and environment are the instructions written for?
+        - Do I need to quantize the Phi-4-mini model before running inference?
+        - How do I run the chatbot server and which arguments matter?
+        - How do I know the deployment worked and what results should I expect?
+        removed_questions:
+        - What environment does this Learning Path target?
+        - What prerequisites do I need before starting?
+        - What will I build and run during the path?
+        - How do I validate that the model is working?
+        - How long does it take and what VM specs were used for testing?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -217013,51 +27390,76 @@ history:
       source_hash_before: 84ba887426f3ca45b698c79028023d80cbf0a06e6bc2fb71fcbe924943078dd8
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       current_source_hash: 84ba887426f3ca45b698c79028023d80cbf0a06e6bc2fb71fcbe924943078dd8
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Deploy SqueezeNet 1.0 INT8 model with ONNX Runtime on Azure Cobalt 100 walks you
-        through an end-to-end Arm software workflow. It is designed for developers deploying ONNX-based
-        applications on Arm-bas...
-      preview_after: Deploy SqueezeNet 1.0 INT8 model with ONNX Runtime on Azure Cobalt 100 walks you
-        through an end-to-end Arm software workflow. It is designed for developers deploying ONNX-based
-        applications on Arm-bas...
-      preview_generated: Deploy SqueezeNet 1.0 INT8 model with ONNX Runtime on Azure Cobalt 100 walks
-        you through an end-to-end Arm software workflow. It is designed for developers deploying ONNX-based
-        applications on Arm-bas...
+      generated_at_before: '2026-06-02T04:41:17Z'
+      generated_at_after: '2026-06-02T04:41:17Z'
+      preview_before: Provision an Arm-based Azure Cobalt 100 (Dpsv6) virtual machine
+        using the Azure portal and Ubuntu Pro 24.04 LTS, then set up a clean Python
+        environment to run ONNX Runtime with a SqueezeNet 1.0 INT8 m...
+      preview_after: Provision an Arm-based Azure Cobalt 100 (Dpsv6) virtual machine
+        using the Azure portal and Ubuntu Pro 24.04 LTS, then set up a clean Python
+        environment to run ONNX Runtime with a SqueezeNet 1.0 INT8 m...
+      preview_generated: Follow a practical workflow to deploy and evaluate an ONNX
+        model on Arm-based Azure Cobalt 100 instances. You will provision a Dpsv6
+        Arm64 virtual machine via the Azure portal using Ubuntu Pro 24.04 L...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 84ba887426f3ca45b698c79028023d80cbf0a06e6bc2fb71fcbe924943078dd8
       source_hash_after: 84ba887426f3ca45b698c79028023d80cbf0a06e6bc2fb71fcbe924943078dd8
       current_source_hash: 84ba887426f3ca45b698c79028023d80cbf0a06e6bc2fb71fcbe924943078dd8
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:41:17Z'
+      generated_at_after: '2026-06-03T01:43:11Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before provisioning the VM?
+        - When creating the VM, which size series and OS image should I choose?
+        - Can I use the Azure CLI or IaC to create the VM instead of the portal?
+        - How should I prepare the Python environment for ONNX Runtime on the VM?
+        - How do I run and validate the SqueezeNet INT8 baseline and benchmark?
+        removed_questions:
+        - Which Azure VM and OS image does this Learning Path use?
+        - What are the prerequisites before starting?
+        - How do I prepare the Python environment for ONNX Runtime?
+        - How do I validate that ONNX Runtime is working on the VM?
+        - Where do I get the SqueezeNet INT8 model and how is benchmarking performed?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before provisioning the VM?
+        - When creating the VM, which size series and OS image should I choose?
+        - Can I use the Azure CLI or IaC to create the VM instead of the portal?
+        - How should I prepare the Python environment for ONNX Runtime on the VM?
+        - How do I run and validate the SqueezeNet INT8 baseline and benchmark?
+        removed_questions:
+        - Which Azure VM and OS image does this Learning Path use?
+        - What are the prerequisites before starting?
+        - How do I prepare the Python environment for ONNX Runtime?
+        - How do I validate that ONNX Runtime is working on the VM?
+        - Where do I get the SqueezeNet INT8 model and how is benchmarking performed?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -217067,51 +27469,74 @@ history:
       source_hash_before: dec913a7a15e82ebf129e2ac64cba8d9140694840f8f9e817fb091b0c82c4cab
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Simulate OpenBMC and UEFI pre-silicon on Neoverse RD-V3 walks you through an end-to-end
-        Arm software workflow. It is designed for This advanced topic is for firmware developers, platform
-        software engi...
-      preview_after: Simulate OpenBMC and UEFI pre-silicon on Neoverse RD-V3 walks you through an end-to-end
-        Arm software workflow. It is designed for This advanced topic is for firmware developers, platform
-        software engi...
-      preview_generated: Simulate OpenBMC and UEFI pre-silicon on Neoverse RD-V3 walks you through an
-        end-to-end Arm software workflow. It is designed for This advanced topic is for firmware developers,
-        platform software engi...
+      generated_at_before: '2026-06-02T04:41:47Z'
+      generated_at_after: '2026-06-02T04:41:47Z'
+      preview_before: This advanced Learning Path shows how to build and simulate
+        OpenBMC and UEFI firmware pre-silicon on the Arm Neoverse RD-V3 r1 Fixed Virtual
+        Platform (FVP). You will set up a Docker-based build enviro...
+      preview_after: This advanced Learning Path shows how to build and simulate OpenBMC
+        and UEFI firmware pre-silicon on the Arm Neoverse RD-V3 r1 Fixed Virtual Platform
+        (FVP). You will set up a Docker-based build enviro...
+      preview_generated: "This advanced Learning Path guides you through simulating\
+        \ the Arm server firmware flow pre-silicon on the Neoverse RD\u2011V3 r1 Fixed\
+        \ Virtual Platform (FVP). You will set up a Docker-based workspace, buil..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: dec913a7a15e82ebf129e2ac64cba8d9140694840f8f9e817fb091b0c82c4cab
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       current_source_hash: dec913a7a15e82ebf129e2ac64cba8d9140694840f8f9e817fb091b0c82c4cab
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:41:47Z'
+      generated_at_after: '2026-06-03T01:43:38Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the builds?
+        - How do I know the RD-V3 FVP booted OpenBMC and UEFI correctly?
+        - What should I check if the UART console windows do not appear?
+        - How do I add and validate a custom IPMI command in OpenBMC?
+        removed_questions:
+        - What host machine and skills do I need before starting?
+        - Which components are built and simulated in this path?
+        - Can I run the RD-V3 FVP simulation over SSH only?
+        - How do I know the simulation and custom IPMI command worked?
+        updated_questions:
+        - How do I access the host console through OpenBMC?
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the builds?
+        - How do I know the RD-V3 FVP booted OpenBMC and UEFI correctly?
+        - What should I check if the UART console windows do not appear?
+        - How do I add and validate a custom IPMI command in OpenBMC?
+        removed_questions:
+        - What host machine and skills do I need before starting?
+        - Which components are built and simulated in this path?
+        - Can I run the RD-V3 FVP simulation over SSH only?
+        - How do I know the simulation and custom IPMI command worked?
+        updated_questions:
+        - How do I access the host console through OpenBMC?
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -217121,51 +27546,80 @@ history:
       source_hash_before: 778ac2a8b8ad9ffd665f6f0be545541090465f355094c354cc693e784d27559a
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Learn how to profile an example C++ data-processing workload on Arm Linux with Arm
-        Performix, then accelerate random number generation using OpenRNG and Arm Performance Libraries.
-        It is designed for C...
-      preview_after: Learn how to profile an example C++ data-processing workload on Arm Linux with Arm
-        Performix, then accelerate random number generation using OpenRNG and Arm Performance Libraries.
-        It is designed for C...
-      preview_generated: Learn how to profile an example C++ data-processing workload on Arm Linux with
-        Arm Performix, then accelerate random number generation using OpenRNG and Arm Performance Libraries.
-        It is designed for C...
+      generated_at_before: '2026-06-02T04:42:23Z'
+      generated_at_after: '2026-06-02T04:42:23Z'
+      preview_before: Learn to profile and accelerate a C++ data-processing workload
+        on Arm Linux (aarch64) using Arm Performix and OpenRNG from Arm Performance
+        Libraries. You will build and run a baseline application, use...
+      preview_after: Learn to profile and accelerate a C++ data-processing workload
+        on Arm Linux (aarch64) using Arm Performix and OpenRNG from Arm Performance
+        Libraries. You will build and run a baseline application, use...
+      preview_generated: "This Learning Path guides you through building and profiling\
+        \ a baseline C++ data\u2011processing workload on Arm Linux (aarch64), then\
+        \ accelerating its random number generation using OpenRNG from Arm Perfo..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 778ac2a8b8ad9ffd665f6f0be545541090465f355094c354cc693e784d27559a
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       current_source_hash: 778ac2a8b8ad9ffd665f6f0be545541090465f355094c354cc693e784d27559a
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:42:23Z'
+      generated_at_after: '2026-06-03T01:44:04Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - "Which packages should I install, and what if I\u2019m not using Amazon\
+          \ Linux?"
+        - How do I decide which function to optimize after running the baseline?
+        - When integrating OpenRNG, which API should I use and what changes am I making?
+        - What result should I expect from the microbenchmark sweep, and how do I
+          compare builds?
+        removed_questions:
+        - What environment and prerequisites do I need?
+        - Can I use a different Linux distribution, and how do I install dependencies?
+        - What does the baseline workload implement?
+        - How will I use Arm Performix in this path?
+        - How is OpenRNG integrated, and how do I verify the improvement?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - "Which packages should I install, and what if I\u2019m not using Amazon\
+          \ Linux?"
+        - How do I decide which function to optimize after running the baseline?
+        - When integrating OpenRNG, which API should I use and what changes am I making?
+        - What result should I expect from the microbenchmark sweep, and how do I
+          compare builds?
+        removed_questions:
+        - What environment and prerequisites do I need?
+        - Can I use a different Linux distribution, and how do I install dependencies?
+        - What does the baseline workload implement?
+        - How will I use Arm Performix in this path?
+        - How is OpenRNG integrated, and how do I verify the improvement?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/openshift/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -217175,51 +27629,78 @@ history:
       source_hash_before: 7972fb230273ab1809c6f0917c4bbc49934f495feb2649da665d67bf71a45a3f
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Build multi-architecture applications with Red Hat OpenShift Pipelines on AWS walks
-        you through an end-to-end Arm software workflow. It is designed for OpenShift administrators who
-        want to migrate the...
-      preview_after: Build multi-architecture applications with Red Hat OpenShift Pipelines on AWS walks
-        you through an end-to-end Arm software workflow. It is designed for OpenShift administrators who
-        want to migrate the...
-      preview_generated: Build multi-architecture applications with Red Hat OpenShift Pipelines on AWS
-        walks you through an end-to-end Arm software workflow. It is designed for OpenShift administrators
-        who want to migrate the...
+      generated_at_before: '2026-06-02T04:43:07Z'
+      generated_at_after: '2026-06-02T04:43:07Z'
+      preview_before: Learn how to use Red Hat OpenShift Pipelines (Tekton) on AWS
+        to migrate existing OpenShift applications from x86 compute nodes to Arm 64-bit
+        (arm64) nodes and build multi-architecture container images...
+      preview_after: Learn how to use Red Hat OpenShift Pipelines (Tekton) on AWS
+        to migrate existing OpenShift applications from x86 compute nodes to Arm 64-bit
+        (arm64) nodes and build multi-architecture container images...
+      preview_generated: This Learning Path guides OpenShift administrators on AWS
+        through migrating existing x86-based workloads to Arm 64-bit (arm64) nodes
+        using Red Hat OpenShift Pipelines (Tekton). Starting from an OpenSh...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 7972fb230273ab1809c6f0917c4bbc49934f495feb2649da665d67bf71a45a3f
       source_hash_after: 7972fb230273ab1809c6f0917c4bbc49934f495feb2649da665d67bf71a45a3f
       current_source_hash: 7972fb230273ab1809c6f0917c4bbc49934f495feb2649da665d67bf71a45a3f
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:43:07Z'
+      generated_at_after: '2026-06-03T01:44:23Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which environment does the example start from?
+        - Do I need Arm64 worker nodes already available?
+        - How do I know my application can run on Arm (arm64)?
+        - What result should I expect after completing the steps?
+        removed_questions:
+        - What are the prerequisites to follow this Learning Path?
+        - What starting environment does the example assume?
+        - Does this Learning Path show how to enable multi-architecture support and
+          configure Arm64 nodes?
+        - Will I need to rebuild my container images?
+        - How do I know the migration was successful?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which environment does the example start from?
+        - Do I need Arm64 worker nodes already available?
+        - How do I know my application can run on Arm (arm64)?
+        - What result should I expect after completing the steps?
+        removed_questions:
+        - What are the prerequisites to follow this Learning Path?
+        - What starting environment does the example assume?
+        - Does this Learning Path show how to enable multi-architecture support and
+          configure Arm64 nodes?
+        - Will I need to rebuild my container images?
+        - How do I know the migration was successful?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -217229,51 +27710,76 @@ history:
       source_hash_before: 42628afa923ce6e89693bdfc72469ac0803610c3decb6cd4f36bd1a003874d0d
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Deploy OpenStack on Azure Cobalt 100 Arm64 virtual machines using DevStack for development
-        and Kolla-Ansible for containerized production deployments. It is designed for This learning path
-        is designed...
-      preview_after: Deploy OpenStack on Azure Cobalt 100 Arm64 virtual machines using DevStack for development
-        and Kolla-Ansible for containerized production deployments. It is designed for This learning path
-        is designed...
-      preview_generated: Deploy OpenStack on Azure Cobalt 100 Arm64 virtual machines using DevStack for
-        development and Kolla-Ansible for containerized production deployments. It is designed for This
-        learning path is designed...
+      generated_at_before: '2026-06-02T04:43:52Z'
+      generated_at_after: '2026-06-02T04:43:52Z'
+      preview_before: Learn how to deploy OpenStack on Arm-based Microsoft Azure Cobalt
+        100 (Arm64) virtual machines. You will provision an Azure Dpsv6 series VM
+        and use DevStack to bring up a single-node development envir...
+      preview_after: Learn how to deploy OpenStack on Arm-based Microsoft Azure Cobalt
+        100 (Arm64) virtual machines. You will provision an Azure Dpsv6 series VM
+        and use DevStack to bring up a single-node development envir...
+      preview_generated: 'Learn how to deploy OpenStack on Microsoft Azure Cobalt
+        100 Arm64 virtual machines using two approaches: DevStack for a single-node
+        development setup and Kolla-Ansible for a containerized deployment. ...'
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 42628afa923ce6e89693bdfc72469ac0803610c3decb6cd4f36bd1a003874d0d
       source_hash_after: 42628afa923ce6e89693bdfc72469ac0803610c3decb6cd4f36bd1a003874d0d
       current_source_hash: 42628afa923ce6e89693bdfc72469ac0803610c3decb6cd4f36bd1a003874d0d
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:43:52Z'
+      generated_at_after: '2026-06-03T01:44:49Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before I start?
+        - Which Azure VM size and disk setup should I use for the DevStack deployment?
+        - What specifications and OS are required for the Kolla-Ansible host?
+        - After deployment, how do I access OpenStack and what should I expect to
+          be running?
+        removed_questions:
+        - What do I need before starting?
+        - Which Azure VM configuration is used for DevStack and Kolla-Ansible?
+        - Which OpenStack services are deployed and how do I access them?
+        - How do I verify the deployment worked?
+        updated_questions:
+        - Can I run DevStack and Kolla-Ansible on the same VM?
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before I start?
+        - Which Azure VM size and disk setup should I use for the DevStack deployment?
+        - What specifications and OS are required for the Kolla-Ansible host?
+        - After deployment, how do I access OpenStack and what should I expect to
+          be running?
+        removed_questions:
+        - What do I need before starting?
+        - Which Azure VM configuration is used for DevStack and Kolla-Ansible?
+        - Which OpenStack services are deployed and how do I access them?
+        - How do I verify the deployment worked?
+        updated_questions:
+        - Can I run DevStack and Kolla-Ansible on the same VM?
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -217283,51 +27789,76 @@ history:
       source_hash_before: cb4227a3f8375d6ae63801891703d6e615bdc60a01fca099a2f76453775ef460
       source_hash_after: cb4227a3f8375d6ae63801891703d6e615bdc60a01fca099a2f76453775ef460
       current_source_hash: cb4227a3f8375d6ae63801891703d6e615bdc60a01fca099a2f76453775ef460
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Deploy OpenTelemetry on Google Cloud C4A Axion processors walks you through an end-to-end
-        Arm software workflow. It is designed for DevOps engineers, platform engineers, and software developers
-        who wa...
-      preview_after: Deploy OpenTelemetry on Google Cloud C4A Axion processors walks you through an end-to-end
-        Arm software workflow. It is designed for DevOps engineers, platform engineers, and software developers
-        who wa...
-      preview_generated: Deploy OpenTelemetry on Google Cloud C4A Axion processors walks you through an
-        end-to-end Arm software workflow. It is designed for DevOps engineers, platform engineers, and
-        software developers who wa...
+      generated_at_before: '2026-06-02T04:44:29Z'
+      generated_at_after: '2026-06-02T04:44:29Z'
+      preview_before: This Learning Path guides you through deploying and observing
+        a Python Flask microservice on Arm64-based Google Cloud C4A Axion processors.
+        You will provision a c4a-standard-4 VM running SUSE Linux, c...
+      preview_after: This Learning Path guides you through deploying and observing
+        a Python Flask microservice on Arm64-based Google Cloud C4A Axion processors.
+        You will provision a c4a-standard-4 VM running SUSE Linux, c...
+      preview_generated: Follow this introductory path to deploy an instrumented Python
+        Flask microservice on an Arm64 Google Cloud C4A Axion virtual machine and
+        stand up an end-to-end observability pipeline with OpenTelemetr...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: cb4227a3f8375d6ae63801891703d6e615bdc60a01fca099a2f76453775ef460
       source_hash_after: cb4227a3f8375d6ae63801891703d6e615bdc60a01fca099a2f76453775ef460
       current_source_hash: cb4227a3f8375d6ae63801891703d6e615bdc60a01fca099a2f76453775ef460
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:44:29Z'
+      generated_at_after: '2026-06-03T01:45:22Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Google Cloud VM and operating system does this path use?
+        - Which firewall ports should I open and why?
+        - How are the telemetry components connected in this setup?
+        - How do I validate that telemetry is flowing end-to-end?
+        removed_questions:
+        - What Google Cloud resources will I create?
+        - Which firewall ports must be opened on GCP?
+        - What components are deployed and how do they connect?
+        - What are the prerequisites before starting?
+        - How do I verify that telemetry is being collected?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Google Cloud VM and operating system does this path use?
+        - Which firewall ports should I open and why?
+        - How are the telemetry components connected in this setup?
+        - How do I validate that telemetry is flowing end-to-end?
+        removed_questions:
+        - What Google Cloud resources will I create?
+        - Which firewall ports must be opened on GCP?
+        - What components are deployed and how do they connect?
+        - What are the prerequisites before starting?
+        - How do I verify that telemetry is being collected?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/pac/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -217337,51 +27868,78 @@ history:
       source_hash_before: fa699cafa5c0d998a63de306371bfbe47680c7453b28fc4b35d4fe2671902b23
       source_hash_after: fa699cafa5c0d998a63de306371bfbe47680c7453b28fc4b35d4fe2671902b23
       current_source_hash: fa699cafa5c0d998a63de306371bfbe47680c7453b28fc4b35d4fe2671902b23
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Understand Arm Pointer Authentication walks you through an end-to-end Arm software
-        workflow. It is designed for software developers interested in understanding Arm Pointer Authentication.
-        By the end, ...
-      preview_after: Understand Arm Pointer Authentication walks you through an end-to-end Arm software
-        workflow. It is designed for software developers interested in understanding Arm Pointer Authentication.
-        By the end, ...
-      preview_generated: Understand Arm Pointer Authentication walks you through an end-to-end Arm software
-        workflow. It is designed for software developers interested in understanding Arm Pointer Authentication.
-        By the end, ...
+      generated_at_before: '2026-06-02T04:45:16Z'
+      generated_at_after: '2026-06-02T04:45:16Z'
+      preview_before: Use a Linux Arm server to explore Arm Pointer Authentication
+        (PAC) by building and analyzing a small, vulnerable C program. You will compile
+        the application with and without PAC, inspect the generated...
+      preview_after: Use a Linux Arm server to explore Arm Pointer Authentication
+        (PAC) by building and analyzing a small, vulnerable C program. You will compile
+        the application with and without PAC, inspect the generated...
+      preview_generated: This Learning Path provides a hands-on introduction to Arm
+        Pointer Authentication on a Linux Arm server. You will create a small vulnerable
+        C program, build it with and without Pointer Authentication,...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: fa699cafa5c0d998a63de306371bfbe47680c7453b28fc4b35d4fe2671902b23
       source_hash_after: fa699cafa5c0d998a63de306371bfbe47680c7453b28fc4b35d4fe2671902b23
       current_source_hash: fa699cafa5c0d998a63de306371bfbe47680c7453b28fc4b35d4fe2671902b23
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:45:16Z'
+      generated_at_after: '2026-06-03T01:45:44Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Can I use any cloud provider for the Arm instance?
+        - Which tools do I install to run the exploit code?
+        - Which binary should I target when running the exploit?
+        - What result should I expect when the exploit works, and how do I compare
+          with Pointer Authentication enabled?
+        removed_questions:
+        - What setup do I need before starting?
+        - Can I use my preferred cloud provider?
+        - What will I build and test?
+        - What tools do I need to install?
+        - How do I validate that the path worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Can I use any cloud provider for the Arm instance?
+        - Which tools do I install to run the exploit code?
+        - Which binary should I target when running the exploit?
+        - What result should I expect when the exploit works, and how do I compare
+          with Pointer Authentication enabled?
+        removed_questions:
+        - What setup do I need before starting?
+        - Can I use my preferred cloud provider?
+        - What will I build and test?
+        - What tools do I need to install?
+        - How do I validate that the path worked?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -217391,51 +27949,76 @@ history:
       source_hash_before: 0599665da13e9e1a8b017b0dac87c63f323ef769b1a5be62a60a32a36bc82696
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       current_source_hash: 0599665da13e9e1a8b017b0dac87c63f323ef769b1a5be62a60a32a36bc82696
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Learn how to use an AI agent and the Performix tool through the Arm MCP Server to
-        run the Code Hotspots recipe on a C++ application, interpret flame graph results, and apply targeted
-        optimizations on ...
-      preview_after: Learn how to use an AI agent and the Performix tool through the Arm MCP Server to
-        run the Code Hotspots recipe on a C++ application, interpret flame graph results, and apply targeted
-        optimizations on ...
-      preview_generated: Learn how to use an AI agent and the Performix tool through the Arm MCP Server
-        to run the Code Hotspots recipe on a C++ application, interpret flame graph results, and apply
-        targeted optimizations on ...
+      generated_at_before: '2026-06-02T04:46:21Z'
+      generated_at_after: '2026-06-02T04:46:21Z'
+      preview_before: Use an AI coding assistant with the Arm MCP Server to run Arm
+        Performix Code Hotspots on a C++ application and act on the results on Arm
+        Neoverse. You configure a GitHub Copilot prompt file to launch ...
+      preview_after: Use an AI coding assistant with the Arm MCP Server to run Arm
+        Performix Code Hotspots on a C++ application and act on the results on Arm
+        Neoverse. You configure a GitHub Copilot prompt file to launch ...
+      preview_generated: This advanced Learning Path shows how to drive Arm Performix
+        profiling through the Arm MCP Server using an AI coding assistant to find
+        and address C++ code hotspots on Arm Neoverse. You will build a s...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 0599665da13e9e1a8b017b0dac87c63f323ef769b1a5be62a60a32a36bc82696
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       current_source_hash: 0599665da13e9e1a8b017b0dac87c63f323ef769b1a5be62a60a32a36bc82696
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:46:21Z'
+      generated_at_after: '2026-06-03T01:46:30Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Do I have to use Visual Studio Code and GitHub Copilot?
+        - Which prompt file should I use to run the Code Hotspots recipe?
+        - How do I know Arm Performix can reach my remote Arm target?
+        - What result should I expect, and what optimizations are applied?
+        removed_questions:
+        - Do I need prior experience with the Arm MCP Server before starting?
+        - What environment and tools does this Learning Path use?
+        - What application is profiled and why was it chosen?
+        - How is profiling executed and what results should I expect?
+        - What optimizations does the agent suggest and what is the expected outcome?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Do I have to use Visual Studio Code and GitHub Copilot?
+        - Which prompt file should I use to run the Code Hotspots recipe?
+        - How do I know Arm Performix can reach my remote Arm target?
+        - What result should I expect, and what optimizations are applied?
+        removed_questions:
+        - Do I need prior experience with the Arm MCP Server before starting?
+        - What environment and tools does this Learning Path use?
+        - What application is profiled and why was it chosen?
+        - How is profiling executed and what results should I expect?
+        - What optimizations does the agent suggest and what is the expected outcome?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -217445,51 +28028,89 @@ history:
       source_hash_before: ec027f6022cc86a644a71a7d2016bf4601e2c4ac41570e861e9f124468b228ae
       source_hash_after: ec027f6022cc86a644a71a7d2016bf4601e2c4ac41570e861e9f124468b228ae
       current_source_hash: ec027f6022cc86a644a71a7d2016bf4601e2c4ac41570e861e9f124468b228ae
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Optimize application performance using Arm Performix CPU microarchitecture analysis
-        walks you through an end-to-end Arm software workflow. It is designed for software developers
-        who want to learn perf...
-      preview_after: Optimize application performance using Arm Performix CPU microarchitecture analysis
-        walks you through an end-to-end Arm software workflow. It is designed for software developers
-        who want to learn perf...
-      preview_generated: Optimize application performance using Arm Performix CPU microarchitecture analysis
-        walks you through an end-to-end Arm software workflow. It is designed for software developers
-        who want to learn perf...
+      generated_at_before: '2026-06-02T04:47:05Z'
+      generated_at_after: '2026-06-02T04:47:05Z'
+      preview_before: "Analyze and improve a Linux application\u2019s performance\
+        \ on Arm Neoverse-based servers using Arm Performix Runbook. You will configure\
+        \ a Performix connection, build a C Mandelbrot set generator, then run..."
+      preview_after: "Analyze and improve a Linux application\u2019s performance on\
+        \ Arm Neoverse-based servers using Arm Performix Runbook. You will configure\
+        \ a Performix connection, build a C Mandelbrot set generator, then run..."
+      preview_generated: Learn to analyze and improve a Linux application on Arm Neoverse-based
+        servers using Arm Performix. You will configure a Performix connection, build
+        a Mandelbrot set generator, and run the CPU Microar...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: ec027f6022cc86a644a71a7d2016bf4601e2c4ac41570e861e9f124468b228ae
       source_hash_after: ec027f6022cc86a644a71a7d2016bf4601e2c4ac41570e861e9f124468b228ae
       current_source_hash: ec027f6022cc86a644a71a7d2016bf4601e2c4ac41570e861e9f124468b228ae
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:47:05Z'
+      generated_at_after: '2026-06-03T01:47:08Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - How do I know the sample Mandelbrot application built and runs correctly?
+        - Which option should I select for the Instruction Mix recipe?
+        - What should I look for in the CPU Microarchitecture recipe results?
+        - "How do I confirm whether my workload is using SIMD, and what if it isn\u2019\
+          t?"
+        removed_questions:
+        - What hardware and OS do I need to follow this path?
+        - What sample application will I build, and what does it produce?
+        - Which Arm Performix recipes are used and why?
+        - Which analysis mode should I select for the Instruction Mix recipe?
+        - How do I verify that my optimizations had an effect?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - How do I know the sample Mandelbrot application built and runs correctly?
+        - Which option should I select for the Instruction Mix recipe?
+        - What should I look for in the CPU Microarchitecture recipe results?
+        - "How do I confirm whether my workload is using SIMD, and what if it isn\u2019\
+          t?"
+        removed_questions:
+        - What hardware and OS do I need to follow this path?
+        - What sample application will I build, and what does it produce?
+        - Which Arm Performix recipes are used and why?
+        - Which analysis mode should I select for the Instruction Mix recipe?
+        - How do I verify that my optimizations had an effect?
+        updated_questions: []
+    category: servers-and-cloud-computing
+  - path: content/learning-paths/servers-and-cloud-computing/performix-system-characterization/_index.md
+    status: skipped
+    skip_reason: draft
+    change_reasons:
+    - draft
+    ai_requested: false
+    summary:
+      action: skipped
+    faqs:
+      action: skipped
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -217499,51 +28120,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Deploy PHP on Google Cloud C4A Arm-based Axion VMs walks you through an end-to-end
-        Arm software workflow. It is designed for developers migrating Hypertext Preprocessor (PHP) workloads
-        from x86_64 to ...
-      preview_after: Deploy PHP on Google Cloud C4A Arm-based Axion VMs walks you through an end-to-end
-        Arm software workflow. It is designed for developers migrating Hypertext Preprocessor (PHP) workloads
-        from x86_64 to ...
-      preview_generated: Deploy PHP on Google Cloud C4A Arm-based Axion VMs walks you through an end-to-end
-        Arm software workflow. It is designed for developers migrating Hypertext Preprocessor (PHP) workloads
-        from x86_64 to ...
+      generated_at_before: '2026-06-02T04:47:26Z'
+      generated_at_after: '2026-06-02T04:47:26Z'
+      preview_before: Follow this introductory path to deploy and validate a PHP stack
+        on Arm-based Google Cloud C4A virtual machines built on Axion processors.
+        You will provision a SUSE Linux Enterprise Server instance (c...
+      preview_after: Follow this introductory path to deploy and validate a PHP stack
+        on Arm-based Google Cloud C4A virtual machines built on Axion processors.
+        You will provision a SUSE Linux Enterprise Server instance (c...
+      preview_generated: Follow this introductory path to deploy and validate a PHP
+        stack on Google Cloud C4A Arm-based Axion virtual machines using SUSE Linux
+        Enterprise Server. You will provision a c4a-standard-4 instance (...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 719ec8966a776cc5ee97782b7ef7cc2b989a8616d14cb36b9847c9cbd2f16446
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:47:26Z'
+      generated_at_after: '2026-06-03T01:48:24Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before provisioning the instance on Google Cloud?
+        - Which Google Cloud VM configuration does this path use?
+        - Which operating system and architecture are targeted?
+        - How do I install the PHP stack on the SUSE instance?
+        - How do I validate the setup and what should I look for in benchmarks?
+        removed_questions:
+        - What do I need before starting?
+        - Which VM type and operating system are used?
+        - What software will I install and configure?
+        - How do I validate that PHP is working on the Arm VM?
+        - What benchmarking is included and what metrics will I see?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before provisioning the instance on Google Cloud?
+        - Which Google Cloud VM configuration does this path use?
+        - Which operating system and architecture are targeted?
+        - How do I install the PHP stack on the SUSE instance?
+        - How do I validate the setup and what should I look for in benchmarks?
+        removed_questions:
+        - What do I need before starting?
+        - Which VM type and operating system are used?
+        - What software will I install and configure?
+        - How do I validate that PHP is working on the Arm VM?
+        - What benchmarking is included and what metrics will I see?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -217553,51 +28199,78 @@ history:
       source_hash_before: 2fdb45e72a36eb114250486b88d603cc8687b9f6b44bb5bb656e25c37d9795a9
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Optimize application performance with CPU affinity walks you through an end-to-end
-        Arm software workflow. It is designed for developers, performance engineers, and system administrators
-        looking to fin...
-      preview_after: Optimize application performance with CPU affinity walks you through an end-to-end
-        Arm software workflow. It is designed for developers, performance engineers, and system administrators
-        looking to fin...
-      preview_generated: Optimize application performance with CPU affinity walks you through an end-to-end
-        Arm software workflow. It is designed for developers, performance engineers, and system administrators
-        looking to fin...
+      generated_at_before: '2026-06-02T04:47:41Z'
+      generated_at_after: '2026-06-02T04:47:41Z'
+      preview_before: This advanced Learning Path teaches you how to control where
+        your workloads run on many-core Arm-based Linux systems by setting CPU affinity
+        for processes and threads. You will pin threads to specific...
+      preview_after: This advanced Learning Path teaches you how to control where
+        your workloads run on many-core Arm-based Linux systems by setting CPU affinity
+        for processes and threads. You will pin threads to specific...
+      preview_generated: Learn how to control CPU scheduling on Arm-based Linux systems
+        by pinning processes and threads to specific cores. You will create a single-threaded
+        Python benchmark, use taskset to constrain executio...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 2fdb45e72a36eb114250486b88d603cc8687b9f6b44bb5bb656e25c37d9795a9
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       current_source_hash: 2fdb45e72a36eb114250486b88d603cc8687b9f6b44bb5bb656e25c37d9795a9
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:47:41Z'
+      generated_at_after: '2026-06-03T01:49:12Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Do I have to use the AWS Graviton3 instance mentioned in the setup?
+        - How do I check whether my system has a single NUMA node before choosing
+          cores?
+        - How do I validate that thread pinning changed behavior?
+        - When is thread pinning most useful in this Learning Path?
+        removed_questions:
+        - What system do I need to follow this Learning Path?
+        - How do I verify the NUMA characteristics of my instance?
+        - Which tools and languages are used in the steps?
+        - What will I implement and how do I validate results?
+        - What background knowledge is assumed?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Do I have to use the AWS Graviton3 instance mentioned in the setup?
+        - How do I check whether my system has a single NUMA node before choosing
+          cores?
+        - How do I validate that thread pinning changed behavior?
+        - When is thread pinning most useful in this Learning Path?
+        removed_questions:
+        - What system do I need to follow this Learning Path?
+        - How do I verify the NUMA characteristics of my instance?
+        - Which tools and languages are used in the steps?
+        - What will I implement and how do I validate results?
+        - What background knowledge is assumed?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -217607,51 +28280,76 @@ history:
       source_hash_before: 3ec6ae40daff557dd05cd092ff7d6b5a63954a1618605030a443f56fe5ffe490
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Implement Code level Performance Analysis using the PMUv3 plugin walks you through
-        an end-to-end Arm software workflow. It is designed for Engineers who want to carry out C/C++
-        performance analysis by...
-      preview_after: Implement Code level Performance Analysis using the PMUv3 plugin walks you through
-        an end-to-end Arm software workflow. It is designed for Engineers who want to carry out C/C++
-        performance analysis by...
-      preview_generated: Implement Code level Performance Analysis using the PMUv3 plugin walks you through
-        an end-to-end Arm software workflow. It is designed for Engineers who want to carry out C/C++
-        performance analysis by...
+      generated_at_before: '2026-06-02T04:48:11Z'
+      generated_at_after: '2026-06-02T04:48:11Z'
+      preview_before: This Learning Path shows how to instrument C/C++ applications
+        on Arm-based Linux systems for precise, code-level performance analysis using
+        the PMUv3 plugin. You will prepare the plugin, enable user-s...
+      preview_after: This Learning Path shows how to instrument C/C++ applications
+        on Arm-based Linux systems for precise, code-level performance analysis using
+        the PMUv3 plugin. You will prepare the plugin, enable user-s...
+      preview_generated: This Learning Path shows how to implement code-level performance
+        analysis on Arm Linux using the PMUv3 plugin. You will instrument C/C++ functions
+        or code blocks to capture precise measurements based ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 3ec6ae40daff557dd05cd092ff7d6b5a63954a1618605030a443f56fe5ffe490
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:48:11Z'
+      generated_at_after: '2026-06-03T01:50:24Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - How do I enable and verify userspace access to the PMU counters?
+        - How should I organize my directories before instrumenting code?
+        - Which events and metrics can I collect in a single run?
+        - How do I instrument multiple sections of code in C?
+        - How do I set up the Python environment to plot and analyze results?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - How do I enable and verify user-space access to performance counters?
+        - How should I organize the source and test directories?
+        - How do I instrument one or multiple code sections?
+        - What data is collected and how do I visualize results?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - How do I enable and verify userspace access to the PMU counters?
+        - How should I organize my directories before instrumenting code?
+        - Which events and metrics can I collect in a single run?
+        - How do I instrument multiple sections of code in C?
+        - How do I set up the Python environment to plot and analyze results?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - How do I enable and verify user-space access to performance counters?
+        - How should I organize the source and test directories?
+        - How do I instrument one or multiple code sections?
+        - What data is collected and how do I visualize results?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/postgresql/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -217661,51 +28359,76 @@ history:
       source_hash_before: a60cc2148a83f919467076e9d5a49fc4c9cec85670a919c7ba044cba72bfe219
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Learn how to deploy PostgreSQL walks you through an end-to-end Arm software workflow.
-        It is designed for software developers who want to deploy PostgreSQL on Arm. By the end, you will
-        be able to learn...
-      preview_after: Learn how to deploy PostgreSQL walks you through an end-to-end Arm software workflow.
-        It is designed for software developers who want to deploy PostgreSQL on Arm. By the end, you will
-        be able to learn...
-      preview_generated: Learn how to deploy PostgreSQL walks you through an end-to-end Arm software workflow.
-        It is designed for software developers who want to deploy PostgreSQL on Arm. By the end, you will
-        be able to learn...
+      generated_at_before: '2026-06-02T04:48:33Z'
+      generated_at_after: '2026-06-02T04:48:33Z'
+      preview_before: This introductory Learning Path shows how to deploy PostgreSQL
+        on Arm-based infrastructure running Linux. In about 30 minutes, you will review
+        deployment choices on Arm, including bare metal, cloud VM...
+      preview_after: This introductory Learning Path shows how to deploy PostgreSQL
+        on Arm-based infrastructure running Linux. In about 30 minutes, you will review
+        deployment choices on Arm, including bare metal, cloud VM...
+      preview_generated: "This introductory Learning Path shows how to deploy PostgreSQL\
+        \ on Arm-based Linux systems in about 30 minutes. You will review deployment\
+        \ choices\u2014bare metal, cloud virtual machines, and managed SQL se..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: a60cc2148a83f919467076e9d5a49fc4c9cec85670a919c7ba044cba72bfe219
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:48:33Z'
+      generated_at_after: '2026-06-03T01:51:17Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which Arm deployment options does this path cover?
+        - Will I use the psql client, and for what?
+        - How do I know my PostgreSQL installation is working?
+        - Can I skip any sections if I already have experience or hardware?
+        removed_questions:
+        - What environments can I use to follow this Learning Path?
+        - Do I need access to an Arm server before starting?
+        - Which tools will I use to interact with the database?
+        - How long does this Learning Path take and what skill level is it?
+        - What if I already know how to deploy PostgreSQL?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which Arm deployment options does this path cover?
+        - Will I use the psql client, and for what?
+        - How do I know my PostgreSQL installation is working?
+        - Can I skip any sections if I already have experience or hardware?
+        removed_questions:
+        - What environments can I use to follow this Learning Path?
+        - Do I need access to an Arm server before starting?
+        - Which tools will I use to interact with the database?
+        - How long does this Learning Path take and what skill level is it?
+        - What if I already know how to deploy PostgreSQL?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -217715,51 +28438,80 @@ history:
       source_hash_before: 57827f45473867b3b5039a9cbca04d2c6d8a93e177ca0ab053999517389014b5
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       current_source_hash: 57827f45473867b3b5039a9cbca04d2c6d8a93e177ca0ab053999517389014b5
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Deploy PostgreSQL on Azure Cobalt 100 Arm64 virtual machines, load a relational
-        schema with transactional data, and benchmark and optimize query performance using pgbench and
-        pg_stat_statements. It is...
-      preview_after: Deploy PostgreSQL on Azure Cobalt 100 Arm64 virtual machines, load a relational schema
-        with transactional data, and benchmark and optimize query performance using pgbench and pg_stat_statements.
-        It is...
-      preview_generated: Deploy PostgreSQL on Azure Cobalt 100 Arm64 virtual machines, load a relational
-        schema with transactional data, and benchmark and optimize query performance using pgbench and
-        pg_stat_statements. It is...
+      generated_at_before: '2026-06-02T04:48:52Z'
+      generated_at_after: '2026-06-02T04:48:52Z'
+      preview_before: Deploy PostgreSQL on Arm-based Microsoft Azure Cobalt 100 virtual
+        machines and validate it for transactional and analytical workloads in about
+        30 minutes. You will provision a Dpsv6 VM, install Postgr...
+      preview_after: Deploy PostgreSQL on Arm-based Microsoft Azure Cobalt 100 virtual
+        machines and validate it for transactional and analytical workloads in about
+        30 minutes. You will provision a Dpsv6 VM, install Postgr...
+      preview_generated: Deploy PostgreSQL on Arm-based Azure Cobalt 100 (Dpsv6) virtual
+        machines running Ubuntu 24.04 Pro Arm64. Following this path, you will provision
+        a VM in the Azure Portal, install and configure Postgre...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 57827f45473867b3b5039a9cbca04d2c6d8a93e177ca0ab053999517389014b5
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       current_source_hash: 57827f45473867b3b5039a9cbca04d2c6d8a93e177ca0ab053999517389014b5
-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:48:52Z'
+      generated_at_after: '2026-06-03T01:52:07Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need in Azure before creating the VM?
+        - Which option should I use to provision the Cobalt 100 VM?
+        - How do I confirm PostgreSQL is installed and ready for connections?
+        - What schema and data are created before running queries?
+        - What should I expect after running the pgbench initialization, and how do
+          I monitor queries?
+        removed_questions:
+        - What Azure resources do I need to follow this Learning Path?
+        - How is the virtual machine created, and can I use tools other than the Azure
+          Portal?
+        - Which operating system and PostgreSQL components are installed?
+        - What database objects and data are created, and how do I validate success?
+        - How does the Learning Path approach performance measurement and tuning?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need in Azure before creating the VM?
+        - Which option should I use to provision the Cobalt 100 VM?
+        - How do I confirm PostgreSQL is installed and ready for connections?
+        - What schema and data are created before running queries?
+        - What should I expect after running the pgbench initialization, and how do
+          I monitor queries?
+        removed_questions:
+        - What Azure resources do I need to follow this Learning Path?
+        - How is the virtual machine created, and can I use tools other than the Azure
+          Portal?
+        - Which operating system and PostgreSQL components are installed?
+        - What database objects and data are created, and how do I validate success?
+        - How does the Learning Path approach performance measurement and tuning?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md
-    status: unchanged
-    changed_on_disk: false
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-    rerun_flags_reset: []
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+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
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@@ -217769,51 +28521,76 @@ history:
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-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Learn how to Tune PostgreSQL walks you through an end-to-end Arm software workflow.
-        It is designed for software developers and DevOps professionals interested in optimizing PostgreSQL
-        performance. By ...
-      preview_after: Learn how to Tune PostgreSQL walks you through an end-to-end Arm software workflow.
-        It is designed for software developers and DevOps professionals interested in optimizing PostgreSQL
-        performance. By ...
-      preview_generated: Learn how to Tune PostgreSQL walks you through an end-to-end Arm software workflow.
-        It is designed for software developers and DevOps professionals interested in optimizing PostgreSQL
-        performance. By ...
+      generated_at_before: '2026-06-02T04:49:21Z'
+      generated_at_after: '2026-06-02T04:49:21Z'
+      preview_before: This advanced Learning Path guides developers and DevOps engineers
+        through tuning PostgreSQL on Linux, with relevance to Arm Neoverse-based servers
+        and common cloud providers. You will review system c...
+      preview_after: This advanced Learning Path guides developers and DevOps engineers
+        through tuning PostgreSQL on Linux, with relevance to Arm Neoverse-based servers
+        and common cloud providers. You will review system c...
+      preview_generated: This advanced Learning Path guides you through tuning PostgreSQL
+        on Linux for Arm Neoverse-based servers and cloud instances by adjusting configuration
+        and validating changes with a repeatable benchma...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
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+      generated_at_before: '2026-06-02T04:49:21Z'
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       before_count: 5
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       generated_count: 5
       change_details:
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         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the tuning and tests?
+        - How should I apply the provided PostgreSQL configuration parameters?
+        - Which storage and file system options should I start with?
+        - Do I need to use HammerDB if I already have a performance test?
+        - Should I increase max_connections or max_prepared_transactions?
+        removed_questions:
+        - What setup is required before starting?
+        - Which environments and platforms does this apply to?
+        - What PostgreSQL settings will I tune, and where are they changed?
+        - How do I validate the impact of tuning changes?
+        - Are there system-level considerations I should evaluate?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the tuning and tests?
+        - How should I apply the provided PostgreSQL configuration parameters?
+        - Which storage and file system options should I start with?
+        - Do I need to use HammerDB if I already have a performance test?
+        - Should I increase max_connections or max_prepared_transactions?
+        removed_questions:
+        - What setup is required before starting?
+        - Which environments and platforms does this apply to?
+        - What PostgreSQL settings will I tune, and where are they changed?
+        - How do I validate the impact of tuning changes?
+        - Are there system-level considerations I should evaluate?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -217823,51 +28600,76 @@ history:
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-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Run Process watch on your Arm machine walks you through an end-to-end Arm software
-        workflow. It is designed for software developers who want to build and run the Process Watch tool
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-      preview_after: Run Process watch on your Arm machine walks you through an end-to-end Arm software
-        workflow. It is designed for software developers who want to build and run the Process Watch tool
-        on an Arm-based mac...
-      preview_generated: Run Process watch on your Arm machine walks you through an end-to-end Arm software
-        workflow. It is designed for software developers who want to build and run the Process Watch tool
-        on an Arm-based mac...
+      generated_at_before: '2026-06-02T04:49:56Z'
+      generated_at_after: '2026-06-02T04:49:56Z'
+      preview_before: This Learning Path shows you how to build and run the Process
+        Watch tool on an Arm-based Linux machine to monitor, in real time, whether
+        workloads use specific Arm instructions and features. You will ...
+      preview_after: This Learning Path shows you how to build and run the Process
+        Watch tool on an Arm-based Linux machine to monitor, in real time, whether
+        workloads use specific Arm instructions and features. You will ...
+      preview_generated: This introductory Learning Path shows you how to build and
+        run the Process Watch tool on an Arm-based Linux machine. You will install
+        required build dependencies (CMake, Clang/LLVM, libelf), clone the...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
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-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:49:56Z'
+      generated_at_after: '2026-06-03T01:53:34Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps in this Learning Path?
+        - Which packages should I install on Ubuntu 20.04 or later?
+        - How should I clone the Process Watch repository to include all submodules?
+        - Should I run Process Watch as root, or can I enable it for non-root users?
+        - How do I run Process Watch and interpret its output for NEON or SVE usage?
+        removed_questions:
+        - What hardware and OS do I need before starting?
+        - Which dependencies must be installed and are Ubuntu commands provided?
+        - Do I need to run Process Watch as root, and how can non-root users run it?
+        - How do I run Process Watch and view available options?
+        - How do I know Process Watch is working and what does the output show?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps in this Learning Path?
+        - Which packages should I install on Ubuntu 20.04 or later?
+        - How should I clone the Process Watch repository to include all submodules?
+        - Should I run Process Watch as root, or can I enable it for non-root users?
+        - How do I run Process Watch and interpret its output for NEON or SVE usage?
+        removed_questions:
+        - What hardware and OS do I need before starting?
+        - Which dependencies must be installed and are Ubuntu commands provided?
+        - Do I need to run Process Watch as root, and how can non-root users run it?
+        - How do I run Process Watch and view available options?
+        - How do I know Process Watch is working and what does the output show?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -217877,51 +28679,77 @@ history:
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-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Profiling for Neoverse with Streamline CLI Tools walks you through an end-to-end
-        Arm software workflow. It is designed for This is an introductory guide for developers who want
-        to measure and optimize...
-      preview_after: Profiling for Neoverse with Streamline CLI Tools walks you through an end-to-end
-        Arm software workflow. It is designed for This is an introductory guide for developers who want
-        to measure and optimize...
-      preview_generated: Profiling for Neoverse with Streamline CLI Tools walks you through an end-to-end
-        Arm software workflow. It is designed for This is an introductory guide for developers who want
-        to measure and optimize...
+      generated_at_before: '2026-06-02T04:50:40Z'
+      generated_at_after: '2026-06-02T04:50:40Z'
+      preview_before: "This introductory Learning Path shows how to profile applications\
+        \ on Arm Neoverse-based Linux servers using Streamline CLI tools and Arm\u2019\
+        s top-down performance methodology. You begin by checking hardw..."
+      preview_after: "This introductory Learning Path shows how to profile applications\
+        \ on Arm Neoverse-based Linux servers using Streamline CLI tools and Arm\u2019\
+        s top-down performance methodology. You begin by checking hardw..."
+      preview_generated: "This introductory path shows how to profile applications\
+        \ on Arm Neoverse\u2013based Linux servers using the Streamline CLI tools.\
+        \ You start by checking system support for hardware-assisted profiling with\
+        \ A..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
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+      generated_at_before: '2026-06-02T04:50:40Z'
+      generated_at_after: '2026-06-03T01:54:25Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the profiling steps?
+        - How do I know if my system supports hardware-assisted profiling?
+        - Do I need to rebuild my application before profiling?
+        - Which Streamline CLI tools should I run and in what order?
+        - What result should I expect, and how do I interpret low Retiring%?
+        removed_questions:
+        - What hardware and operating system do I need to follow this path?
+        - How do I check if my system supports hardware-assisted profiling?
+        - What should I do before capturing a profile with Streamline CLI tools?
+        - Which Streamline CLI tools are used and in what order?
+        - What output should I expect, and how do I interpret it?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the profiling steps?
+        - How do I know if my system supports hardware-assisted profiling?
+        - Do I need to rebuild my application before profiling?
+        - Which Streamline CLI tools should I run and in what order?
+        - What result should I expect, and how do I interpret low Retiring%?
+        removed_questions:
+        - What hardware and operating system do I need to follow this path?
+        - How do I check if my system supports hardware-assisted profiling?
+        - What should I do before capturing a profile with Streamline CLI tools?
+        - Which Streamline CLI tools are used and in what order?
+        - What output should I expect, and how do I interpret it?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md
-    status: unchanged
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-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -217931,51 +28759,76 @@ history:
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-      preview_before: Deploy Puppet on Google Cloud C4A walks you through an end-to-end Arm software workflow.
-        It is designed for developers deploying and optimizing Puppet workloads on Arm Linux environments,
-        specifically...
-      preview_after: Deploy Puppet on Google Cloud C4A walks you through an end-to-end Arm software workflow.
-        It is designed for developers deploying and optimizing Puppet workloads on Arm Linux environments,
-        specifically...
-      preview_generated: Deploy Puppet on Google Cloud C4A walks you through an end-to-end Arm software
-        workflow. It is designed for developers deploying and optimizing Puppet workloads on Arm Linux
-        environments, specifically...
+      generated_at_before: '2026-06-02T04:51:08Z'
+      generated_at_after: '2026-06-02T04:51:08Z'
+      preview_before: Learn how to deploy and validate Puppet on Arm-based Google
+        Cloud C4A virtual machines powered by Axion processors. You will provision
+        a SUSE Linux Arm64 VM (c4a-standard-4), install Puppet by setting...
+      preview_after: Learn how to deploy and validate Puppet on Arm-based Google Cloud
+        C4A virtual machines powered by Axion processors. You will provision a SUSE
+        Linux Arm64 VM (c4a-standard-4), install Puppet by setting...
+      preview_generated: This Learning Path shows how to deploy Puppet on Arm-based
+        Google Cloud C4A virtual machines powered by Axion processors using SUSE Linux
+        Enterprise Server (Arm64). You will provision a c4a-standard-4...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
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       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before provisioning the VM?
+        - Which Google Cloud machine type and OS should I select?
+        - Do I need to build Ruby, and which version is used?
+        - How do I verify that Puppet installed correctly?
+        - Does the benchmark require a Puppet Master, and what does it measure?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - Which VM type and operating system are used?
+        - How is Puppet installed on the SUSE Arm64 VM?
+        - How do I verify that Puppet is working correctly?
+        - Do I need a Puppet Master for the benchmark and what does it measure?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before provisioning the VM?
+        - Which Google Cloud machine type and OS should I select?
+        - Do I need to build Ruby, and which version is used?
+        - How do I verify that Puppet installed correctly?
+        - Does the benchmark require a Puppet Master, and what does it measure?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - Which VM type and operating system are used?
+        - How is Puppet installed on the SUSE Arm64 VM?
+        - How do I verify that Puppet is working correctly?
+        - Do I need a Puppet Master for the benchmark and what does it measure?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
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+    status: updated
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+    ai_requested: true
+    rerun_flags_reset:
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+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -217985,51 +28838,78 @@ history:
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-      preview_before: Run a Large Language Model (LLM) chatbot with PyTorch using KleidiAI on Arm servers
-        walks you through an end-to-end Arm software workflow. It is designed for software developers
-        interested in running ...
-      preview_after: Run a Large Language Model (LLM) chatbot with PyTorch using KleidiAI on Arm servers
-        walks you through an end-to-end Arm software workflow. It is designed for software developers
-        interested in running ...
-      preview_generated: Run a Large Language Model (LLM) chatbot with PyTorch using KleidiAI on Arm servers
-        walks you through an end-to-end Arm software workflow. It is designed for software developers
-        interested in running ...
+      generated_at_before: '2026-06-02T04:51:34Z'
+      generated_at_after: '2026-06-02T04:51:34Z'
+      preview_before: Learn how to run a Meta Llama 3.1 LLM chatbot on Arm-based servers
+        using PyTorch and KleidiAI INT4 kernels. You will use an Ubuntu 24.04 LTS
+        Arm instance with at least 16 cores, 64 GB RAM, and 50 GB d...
+      preview_after: Learn how to run a Meta Llama 3.1 LLM chatbot on Arm-based servers
+        using PyTorch and KleidiAI INT4 kernels. You will use an Ubuntu 24.04 LTS
+        Arm instance with at least 16 cores, 64 GB RAM, and 50 GB d...
+      preview_generated: Set up and run a Large Language Model chatbot on Arm-based
+        servers using PyTorch and KleidiAI. You will download the Meta Llama 3.1 model
+        from the Meta Hugging Face repository, 4-bit quantize it with ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:51:34Z'
+      generated_at_after: '2026-06-03T01:55:09Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What infrastructure and OS should I use to follow this path?
+        - Do I need a GPU to run the example?
+        - Where do I obtain the model used in the example?
+        - How is quantization performed, and what role does KleidiAI play?
+        - Which packages are required for the frontend, and how do I avoid HTTP client
+          issues?
+        removed_questions:
+        - What system resources and OS are required?
+        - Which LLM and source repository are used?
+        - How is the model prepared and executed on Arm?
+        - What does the deployed chatbot consist of and how do I use it?
+        - Are there any specific Python packages or versions noted?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What infrastructure and OS should I use to follow this path?
+        - Do I need a GPU to run the example?
+        - Where do I obtain the model used in the example?
+        - How is quantization performed, and what role does KleidiAI play?
+        - Which packages are required for the frontend, and how do I avoid HTTP client
+          issues?
+        removed_questions:
+        - What system resources and OS are required?
+        - Which LLM and source repository are used?
+        - How is the model prepared and executed on Arm?
+        - What does the deployed chatbot consist of and how do I use it?
+        - Are there any specific Python packages or versions noted?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -218039,51 +28919,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Learn how to deploy Qdrant on Google Cloud C4A Axion processors, generate vector
-        embeddings with Sentence Transformers, and build a semantic search and chatbot retrieval system
-        on Arm-based infrastruc...
-      preview_after: Learn how to deploy Qdrant on Google Cloud C4A Axion processors, generate vector
-        embeddings with Sentence Transformers, and build a semantic search and chatbot retrieval system
-        on Arm-based infrastruc...
-      preview_generated: Learn how to deploy Qdrant on Google Cloud C4A Axion processors, generate vector
-        embeddings with Sentence Transformers, and build a semantic search and chatbot retrieval system
-        on Arm-based infrastruc...
+      generated_at_before: '2026-06-02T04:52:49Z'
+      generated_at_after: '2026-06-02T04:52:49Z'
+      preview_before: This Learning Path shows how to deploy the Qdrant vector database
+        on Arm-based Google Cloud C4A Axion processors, generate text embeddings with
+        Sentence Transformers in Python, and run semantic simila...
+      preview_after: This Learning Path shows how to deploy the Qdrant vector database
+        on Arm-based Google Cloud C4A Axion processors, generate text embeddings with
+        Sentence Transformers in Python, and run semantic simila...
+      preview_generated: This Learning Path shows how to deploy the Qdrant vector
+        database on Google Cloud C4A Axion Arm-based instances and build a basic semantic
+        search and chatbot retrieval workflow. You will provision a c...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:52:49Z'
+      generated_at_after: '2026-06-03T01:55:47Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - Do I need anything set up in Google Cloud before I start?
+        - Which Google Cloud instance and operating system should I create?
+        - How do I confirm that Qdrant is installed and running on the VM?
+        - Which Sentence Transformers model should I use to generate embeddings?
+        - What result should I expect when I run a semantic similarity query?
+        removed_questions:
+        - Which Google Cloud VM type is used in this path?
+        - What operating system and architecture are assumed?
+        - What prerequisites do I need before starting?
+        - Which tools and libraries will I use?
+        - How do I know the deployment worked and what should I expect to build?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - Do I need anything set up in Google Cloud before I start?
+        - Which Google Cloud instance and operating system should I create?
+        - How do I confirm that Qdrant is installed and running on the VM?
+        - Which Sentence Transformers model should I use to generate embeddings?
+        - What result should I expect when I run a semantic similarity query?
+        removed_questions:
+        - Which Google Cloud VM type is used in this path?
+        - What operating system and architecture are assumed?
+        - What prerequisites do I need before starting?
+        - Which tools and libraries will I use?
+        - How do I know the deployment worked and what should I expect to build?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -218093,51 +28998,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Deploy RabbitMQ on Arm64 Cloud Platforms (Azure and GCP) walks you through an end-to-end
-        Arm software workflow. It is designed for software engineers and platform engineers migrating
-        messaging and eve...
-      preview_after: Deploy RabbitMQ on Arm64 Cloud Platforms (Azure and GCP) walks you through an end-to-end
-        Arm software workflow. It is designed for software engineers and platform engineers migrating
-        messaging and eve...
-      preview_generated: Deploy RabbitMQ on Arm64 Cloud Platforms (Azure and GCP) walks you through an
-        end-to-end Arm software workflow. It is designed for software engineers and platform engineers
-        migrating messaging and eve...
+      generated_at_before: '2026-06-02T04:53:31Z'
+      generated_at_after: '2026-06-02T04:53:31Z'
+      preview_before: Learn how to deploy RabbitMQ on Arm64 infrastructure across
+        Microsoft Azure and Google Cloud. You will provision Arm-based Linux virtual
+        machines on Azure Cobalt 100 (Dpsv6) and Google Cloud C4A with ...
+      preview_after: Learn how to deploy RabbitMQ on Arm64 infrastructure across Microsoft
+        Azure and Google Cloud. You will provision Arm-based Linux virtual machines
+        on Azure Cobalt 100 (Dpsv6) and Google Cloud C4A with ...
+      preview_generated: Learn how to deploy and validate RabbitMQ on Arm-based cloud
+        infrastructure using Microsoft Azure Cobalt 100 and Google Cloud C4A instances
+        powered by Axion processors. You will provision Arm64 Linux ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:53:31Z'
+      generated_at_after: '2026-06-03T01:56:11Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Azure VM series and creation method does this path use?
+        - How do I verify RabbitMQ and Erlang after installation on Azure?
+        - How do I expose the RabbitMQ management interface on GCP?
+        - What should I check if baseline validation fails?
+        removed_questions:
+        - Which Arm-based instances and operating systems are used in this path?
+        - How do I create the Azure virtual machine in this guide?
+        - What RabbitMQ and Erlang versions are installed, and how do I validate them?
+        - How do I enable access to the RabbitMQ management interface on GCP?
+        - What prerequisites are required before starting?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Azure VM series and creation method does this path use?
+        - How do I verify RabbitMQ and Erlang after installation on Azure?
+        - How do I expose the RabbitMQ management interface on GCP?
+        - What should I check if baseline validation fails?
+        removed_questions:
+        - Which Arm-based instances and operating systems are used in this path?
+        - How do I create the Azure virtual machine in this guide?
+        - What RabbitMQ and Erlang versions are installed, and how do I validate them?
+        - How do I enable access to the RabbitMQ management interface on GCP?
+        - What prerequisites are required before starting?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/rag/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -218147,51 +29077,76 @@ history:
       source_hash_before: d9435cc1dc1fe65432d83934e69993853dd3f294f66f98e9bcfb5f81e6ac6d5f
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Deploy a RAG-based Chatbot with llama-cpp-python using KleidiAI on Google Axion
-        processors walks you through an end-to-end Arm software workflow. It is designed for software
-        developers, ML engineers, ...
-      preview_after: Deploy a RAG-based Chatbot with llama-cpp-python using KleidiAI on Google Axion processors
-        walks you through an end-to-end Arm software workflow. It is designed for software developers,
-        ML engineers, ...
-      preview_generated: Deploy a RAG-based Chatbot with llama-cpp-python using KleidiAI on Google Axion
-        processors walks you through an end-to-end Arm software workflow. It is designed for software
-        developers, ML engineers, ...
+      generated_at_before: '2026-06-02T04:54:26Z'
+      generated_at_after: '2026-06-02T04:54:26Z'
+      preview_before: Build and deploy a Retrieval Augmented Generation (RAG) chatbot
+        on Arm-based Google Cloud Axion processors using llama-cpp-python with KleidiAI.
+        You will provision an Arm server running Ubuntu 22.04 L...
+      preview_after: Build and deploy a Retrieval Augmented Generation (RAG) chatbot
+        on Arm-based Google Cloud Axion processors using llama-cpp-python with KleidiAI.
+        You will provision an Arm server running Ubuntu 22.04 L...
+      preview_generated: This advanced Learning Path shows how to deploy a Retrieval
+        Augmented Generation (RAG) chatbot on Arm-based Google Cloud Axion processors
+        using llama-cpp-python with KleidiAI. You will work on Linux (...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
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       drift_detected: false
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-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:54:26Z'
+      generated_at_after: '2026-06-03T01:56:43Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this on Google Cloud Axion?
+        - Which ports and URLs are used by the backend and frontend?
+        - How do I know the RAG pipeline is working after I start the servers?
+        - How is model performance addressed in this Learning Path?
+        - Do I need a specific LLM or a GPU to complete the steps?
+        removed_questions:
+        - What environment and resources do I need to follow this Learning Path?
+        - What prior knowledge is expected?
+        - Which software components and architecture are used?
+        - What will I build and what are the key artifacts?
+        - How do I access the web app and verify it is working?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this on Google Cloud Axion?
+        - Which ports and URLs are used by the backend and frontend?
+        - How do I know the RAG pipeline is working after I start the servers?
+        - How is model performance addressed in this Learning Path?
+        - Do I need a specific LLM or a GPU to complete the steps?
+        removed_questions:
+        - What environment and resources do I need to follow this Learning Path?
+        - What prior knowledge is expected?
+        - Which software components and architecture are used?
+        - What will I build and what are the key artifacts?
+        - How do I access the web app and verify it is working?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/ran/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -218201,51 +29156,76 @@ history:
       source_hash_before: 2b329c566f7fea90010c52f1ce1cb11bccf9d32cf604aaa720bfca5ef1f85fae
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Get started with the Arm 5G RAN Acceleration Library (ArmRAL) walks you through
-        an end-to-end Arm software workflow. It is designed for software developers new to the Arm RAN
-        Acceleration Library (Arm...
-      preview_after: Get started with the Arm 5G RAN Acceleration Library (ArmRAL) walks you through an
-        end-to-end Arm software workflow. It is designed for software developers new to the Arm RAN Acceleration
-        Library (Arm...
-      preview_generated: Get started with the Arm 5G RAN Acceleration Library (ArmRAL) walks you through
-        an end-to-end Arm software workflow. It is designed for software developers new to the Arm RAN
-        Acceleration Library (Arm...
+      generated_at_before: '2026-06-02T04:55:40Z'
+      generated_at_after: '2026-06-02T04:55:40Z'
+      preview_before: "This introductory Learning Path shows how to build and install\
+        \ the Arm RAN Acceleration Library (ArmRAL) on an Arm-based Linux system and\
+        \ then exercise it to test your platform\u2019s capabilities. You wil..."
+      preview_after: "This introductory Learning Path shows how to build and install\
+        \ the Arm RAN Acceleration Library (ArmRAL) on an Arm-based Linux system and\
+        \ then exercise it to test your platform\u2019s capabilities. You wil..."
+      preview_generated: Learn how to build and install the Arm RAN Acceleration Library
+        (ArmRAL) on an Arm-based Linux system and run quick checks to understand how
+        your platform handles ArmRAL functions for telecommunicatio...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
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-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:55:40Z'
+      generated_at_after: '2026-06-03T01:57:23Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Can I use an Arm-based cloud instance instead of local hardware?
+        - Which operating system do the instructions target?
+        - Which compiler is used to build ArmRAL in this path?
+        - What result should I expect after completing the steps?
+        removed_questions:
+        - What hardware and OS do I need to complete this Learning Path?
+        - Can I follow this path on an Arm-based cloud server?
+        - Which tools are used to build ArmRAL in this path?
+        - What are the expected outputs when I finish?
+        - Is ArmRAL open-source, and what is the license?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Can I use an Arm-based cloud instance instead of local hardware?
+        - Which operating system do the instructions target?
+        - Which compiler is used to build ArmRAL in this path?
+        - What result should I expect after completing the steps?
+        removed_questions:
+        - What hardware and OS do I need to complete this Learning Path?
+        - Can I follow this path on an Arm-based cloud server?
+        - Which tools are used to build ArmRAL in this path?
+        - What are the expected outputs when I finish?
+        - Is ArmRAL open-source, and what is the license?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -218255,51 +29235,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:58Z'
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-      preview_before: Deploy and run distributed AI workloads using Ray on Google Cloud Axion C4A Arm-based
-        VMs, covering parallel tasks, hyperparameter tuning, and model serving with Ray Core, Train, Tune,
-        and Serve. It i...
-      preview_after: Deploy and run distributed AI workloads using Ray on Google Cloud Axion C4A Arm-based
-        VMs, covering parallel tasks, hyperparameter tuning, and model serving with Ray Core, Train, Tune,
-        and Serve. It i...
-      preview_generated: Deploy and run distributed AI workloads using Ray on Google Cloud Axion C4A Arm-based
-        VMs, covering parallel tasks, hyperparameter tuning, and model serving with Ray Core, Train, Tune,
-        and Serve. It i...
+      generated_at_before: '2026-06-02T04:56:29Z'
+      generated_at_after: '2026-06-02T04:56:29Z'
+      preview_before: This Learning Path shows how to deploy and run distributed AI
+        workloads with Ray on Google Cloud Axion C4A Arm-based VMs. You will provision
+        a c4a-standard-4 instance (4 vCPUs, 16 GB) running SUSE Lin...
+      preview_after: This Learning Path shows how to deploy and run distributed AI
+        workloads with Ray on Google Cloud Axion C4A Arm-based VMs. You will provision
+        a c4a-standard-4 instance (4 vCPUs, 16 GB) running SUSE Lin...
+      preview_generated: Learn how to deploy and run distributed AI workloads with
+        Ray on Google Cloud Axion C4A Arm-based virtual machines using SUSE Linux
+        Enterprise Server (SLES) Arm64. You will provision a c4a-standard-4 ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:56:29Z'
+      generated_at_after: '2026-06-03T01:57:59Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which VM type should I create for this path?
+        - Which Ray components will I use, and for what?
+        - How do I expose the Ray Dashboard and Ray Serve endpoints?
+        - How do I verify that Ray is set up correctly?
+        removed_questions:
+        - What do I need before starting?
+        - Which VM and operating system are used?
+        - Is this a single-node or multi-node Ray setup?
+        - How are the Ray Dashboard and Serve endpoints exposed?
+        - How do I verify that Ray is running correctly?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which VM type should I create for this path?
+        - Which Ray components will I use, and for what?
+        - How do I expose the Ray Dashboard and Ray Serve endpoints?
+        - How do I verify that Ray is set up correctly?
+        removed_questions:
+        - What do I need before starting?
+        - Which VM and operating system are used?
+        - Is this a single-node or multi-node Ray setup?
+        - How are the Ray Dashboard and Serve endpoints exposed?
+        - How do I verify that Ray is running correctly?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/redis/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -218309,51 +29314,78 @@ history:
       source_hash_before: 7b9906a3c16ebd3c6b41618be7db76d7a97cc4b16c4da927257fb39a66753e12
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
-      preview_before: Deploy Redis on Arm walks you through an end-to-end Arm software workflow. It is
-        designed for developers who want to deploy Redis on Arm based virtual machines. By the end, you
-        will be able to underst...
-      preview_after: Deploy Redis on Arm walks you through an end-to-end Arm software workflow. It is
-        designed for developers who want to deploy Redis on Arm based virtual machines. By the end, you
-        will be able to underst...
-      preview_generated: Deploy Redis on Arm walks you through an end-to-end Arm software workflow. It
-        is designed for developers who want to deploy Redis on Arm based virtual machines. By the end,
-        you will be able to underst...
+      generated_at_before: '2026-06-02T04:57:15Z'
+      generated_at_after: '2026-06-02T04:57:15Z'
+      preview_before: Deploy Redis on Arm is an introductory, 30-minute path that
+        guides you through installing, configuring, and connecting to Redis on an
+        Arm-based Linux instance. You will learn about Redis deployment co...
+      preview_after: Deploy Redis on Arm is an introductory, 30-minute path that guides
+        you through installing, configuring, and connecting to Redis on an Arm-based
+        Linux instance. You will learn about Redis deployment co...
+      preview_generated: Learn how to deploy Redis on an Arm-based Linux instance
+        and bring up a working single-node server in about 30 minutes. This introductory
+        path covers installation, basic configuration, and how to conn...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 7b9906a3c16ebd3c6b41618be7db76d7a97cc4b16c4da927257fb39a66753e12
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-      generated_at_before: '2026-05-06T17:17:58Z'
-      generated_at_after: '2026-05-06T17:17:58Z'
+      generated_at_before: '2026-06-02T04:57:15Z'
+      generated_at_after: '2026-06-03T01:58:39Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which cloud providers can I use for the Arm instance?
+        - How do I enable remote access to my single-node Redis server?
+        - What port does Redis use in this setup?
+        - What should I do after I have Redis running with the default configuration?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - Which cloud platforms can I use for the Arm instance?
+        - What operating system does this path target?
+        - How is Redis configured for a single-node deployment that accepts remote
+          connections?
+        - How long will this take and what is the expected outcome?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which cloud providers can I use for the Arm instance?
+        - How do I enable remote access to my single-node Redis server?
+        - What port does Redis use in this setup?
+        - What should I do after I have Redis running with the default configuration?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - Which cloud platforms can I use for the Arm instance?
+        - What operating system does this path target?
+        - How is Redis configured for a single-node deployment that accepts remote
+          connections?
+        - How long will this take and what is the expected outcome?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -218363,51 +29395,76 @@ history:
       source_hash_before: 9b306bc318491834d0d74dd75221b24081acc20dac7993ccdbd1cb40ba6158ac
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-      generated_at_before: '2026-05-06T17:17:59Z'
-      generated_at_after: '2026-05-06T17:17:59Z'
-      preview_before: Learn how to install and configure Redis on an Azure Cobalt 100 Arm64 virtual machine,
-        implement real-time messaging with Pub/Sub and Streams, and benchmark throughput and latency on
-        Arm infrastructur...
-      preview_after: Learn how to install and configure Redis on an Azure Cobalt 100 Arm64 virtual machine,
-        implement real-time messaging with Pub/Sub and Streams, and benchmark throughput and latency on
-        Arm infrastructur...
-      preview_generated: Learn how to install and configure Redis on an Azure Cobalt 100 Arm64 virtual
-        machine, implement real-time messaging with Pub/Sub and Streams, and benchmark throughput and
-        latency on Arm infrastructur...
+      generated_at_before: '2026-06-02T04:58:08Z'
+      generated_at_after: '2026-06-02T04:58:08Z'
+      preview_before: Learn how to deploy Redis on Azure Cobalt 100 Arm64 virtual
+        machines running Linux, then build and validate real-time messaging and event-driven
+        processing on Arm. You will provision a Cobalt 100 VM i...
+      preview_after: Learn how to deploy Redis on Azure Cobalt 100 Arm64 virtual machines
+        running Linux, then build and validate real-time messaging and event-driven
+        processing on Arm. You will provision a Cobalt 100 VM i...
+      preview_generated: This Learning Path shows how to deploy Redis on Microsoft
+        Azure Cobalt 100 Arm64 virtual machines running Linux, then implement real-time
+        messaging and event-driven processing with Redis Pub/Sub and S...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 9b306bc318491834d0d74dd75221b24081acc20dac7993ccdbd1cb40ba6158ac
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-      generated_at_before: '2026-05-06T17:17:59Z'
-      generated_at_after: '2026-05-06T17:17:59Z'
+      generated_at_before: '2026-06-02T04:58:08Z'
+      generated_at_after: '2026-06-03T01:59:01Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Azure VM type and creation method should I use?
+        - "How do I confirm I\u2019m using an Arm-based Cobalt 100 VM?"
+        - Do I need Python, and where is it used?
+        - What result should I expect after completing the examples and benchmarks?
+        removed_questions:
+        - What are the prerequisites to start this Learning Path?
+        - Which Azure VM type and provisioning method are used?
+        - What operating system and tools are used?
+        - What will I build and how do I validate it works?
+        - How advanced is this path and how long will it take?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Azure VM type and creation method should I use?
+        - "How do I confirm I\u2019m using an Arm-based Cobalt 100 VM?"
+        - Do I need Python, and where is it used?
+        - What result should I expect after completing the examples and benchmarks?
+        removed_questions:
+        - What are the prerequisites to start this Learning Path?
+        - Which Azure VM type and provisioning method are used?
+        - What operating system and tools are used?
+        - What will I build and how do I validate it works?
+        - How advanced is this path and how long will it take?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -218417,51 +29474,76 @@ history:
       source_hash_before: eb008543ea4248b231be5e6345546164533edf2bc149613693095812bbbba3d9
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-      generated_at_before: '2026-05-06T17:17:59Z'
-      generated_at_after: '2026-05-06T17:17:59Z'
-      preview_before: Deploy Redis for data searching on Google Cloud C4A walks you through an end-to-end
-        Arm software workflow. It is designed for developers deploying and optimizing Redis-based data
-        searching workloads o...
-      preview_after: Deploy Redis for data searching on Google Cloud C4A walks you through an end-to-end
-        Arm software workflow. It is designed for developers deploying and optimizing Redis-based data
-        searching workloads o...
-      preview_generated: Deploy Redis for data searching on Google Cloud C4A walks you through an end-to-end
-        Arm software workflow. It is designed for developers deploying and optimizing Redis-based data
-        searching workloads o...
+      generated_at_before: '2026-06-02T04:58:42Z'
+      generated_at_after: '2026-06-02T04:58:42Z'
+      preview_before: This Learning Path guides you through deploying Redis for data
+        searching on Google Cloud C4A virtual machines powered by Axion processors
+        (Arm Neoverse-V2 cores). You will provision a SUSE Linux (SLES...
+      preview_after: This Learning Path guides you through deploying Redis for data
+        searching on Google Cloud C4A virtual machines powered by Axion processors
+        (Arm Neoverse-V2 cores). You will provision a SUSE Linux (SLES...
+      preview_generated: Follow this Learning Path to deploy and evaluate Redis on
+        Arm-based Google Cloud C4A instances powered by Axion processors. You will
+        provision a SUSE SLES Arm64 virtual machine (for example, c4a-stand...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: eb008543ea4248b231be5e6345546164533edf2bc149613693095812bbbba3d9
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-      generated_at_before: '2026-05-06T17:17:59Z'
-      generated_at_after: '2026-05-06T17:17:59Z'
+      generated_at_before: '2026-06-02T04:58:42Z'
+      generated_at_after: '2026-06-03T01:59:24Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which Google Cloud instance and OS should I use?
+        - How is Redis installed on the SUSE Arm64 VM?
+        - How do I start Redis and confirm it is running?
+        - How do I benchmark Redis and what results should I look for?
+        removed_questions:
+        - What do I need before starting?
+        - Which instance type and architecture does this path use?
+        - How is Redis installed on the VM?
+        - How do I verify that Redis is running before testing?
+        - What performance measurements will I collect?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path?
+        - Which Google Cloud instance and OS should I use?
+        - How is Redis installed on the SUSE Arm64 VM?
+        - How do I start Redis and confirm it is running?
+        - How do I benchmark Redis and what results should I look for?
+        removed_questions:
+        - What do I need before starting?
+        - Which instance type and architecture does this path use?
+        - How is Redis installed on the VM?
+        - How do I verify that Redis is running before testing?
+        - What performance measurements will I collect?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -218471,51 +29553,78 @@ history:
       source_hash_before: 9146285feb38ee45171ac234f605eee08467bbf675a77df231a6dc63c38282f2
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-      generated_at_before: '2026-05-06T17:17:59Z'
-      generated_at_after: '2026-05-06T17:17:59Z'
-      preview_before: Deploy Redis as a cache for MySQL and PostgreSQL on Arm servers walks you through
-        an end-to-end Arm software workflow. It is designed for developers who want to deploy Redis as
-        a cache on Arm based vi...
-      preview_after: Deploy Redis as a cache for MySQL and PostgreSQL on Arm servers walks you through
-        an end-to-end Arm software workflow. It is designed for developers who want to deploy Redis as
-        a cache on Arm based vi...
-      preview_generated: Deploy Redis as a cache for MySQL and PostgreSQL on Arm servers walks you through
-        an end-to-end Arm software workflow. It is designed for developers who want to deploy Redis as
-        a cache on Arm based vi...
+      generated_at_before: '2026-06-02T04:59:18Z'
+      generated_at_after: '2026-06-02T04:59:18Z'
+      preview_before: Deploy Redis as a cache for MySQL and PostgreSQL on Arm Neoverse-based
+        Linux virtual machines across AWS, Microsoft Azure, and Google Cloud. Using
+        Terraform and Ansible, you will provision cloud insta...
+      preview_after: Deploy Redis as a cache for MySQL and PostgreSQL on Arm Neoverse-based
+        Linux virtual machines across AWS, Microsoft Azure, and Google Cloud. Using
+        Terraform and Ansible, you will provision cloud insta...
+      preview_generated: Deploy Redis as a cache for MySQL and PostgreSQL on Arm-based
+        Linux instances across major clouds using Terraform and Ansible. This advanced
+        Learning Path guides you through provisioning on AWS, Azure...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 9146285feb38ee45171ac234f605eee08467bbf675a77df231a6dc63c38282f2
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-      generated_at_before: '2026-05-06T17:17:59Z'
-      generated_at_after: '2026-05-06T17:17:59Z'
+      generated_at_before: '2026-06-02T04:59:18Z'
+      generated_at_after: '2026-06-03T01:59:56Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the deployment steps?
+        - Which section should I follow for my database and cloud provider?
+        - "I am new to Terraform\u2014what should I read before starting?"
+        - What result should I expect, and how long will it take?
+        - Is there a section for deploying Redis as a cache for PostgreSQL on Google
+          Cloud?
+        removed_questions:
+        - What accounts and tools do I need before starting?
+        - Which cloud platforms and databases are covered?
+        - Do I need prior Terraform experience?
+        - Where do I run the Terraform and Ansible commands from?
+        - How long will this take and what skill level is expected?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the deployment steps?
+        - Which section should I follow for my database and cloud provider?
+        - "I am new to Terraform\u2014what should I read before starting?"
+        - What result should I expect, and how long will it take?
+        - Is there a section for deploying Redis as a cache for PostgreSQL on Google
+          Cloud?
+        removed_questions:
+        - What accounts and tools do I need before starting?
+        - Which cloud platforms and databases are covered?
+        - Do I need prior Terraform experience?
+        - Where do I run the Terraform and Ansible commands from?
+        - How long will this take and what skill level is expected?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md
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-    rerun_flags_reset: []
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-    template_version_before: summary-faq-v1
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+    status: updated
+    changed_on_disk: true
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+    ai_requested: true
+    rerun_flags_reset:
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+    change_reasons:
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+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
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@@ -218525,51 +29634,76 @@ history:
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-      generated_at_after: '2026-05-06T17:17:59Z'
-      preview_before: Learn how to tune Redis walks you through an end-to-end Arm software workflow. It
-        is designed for software developers who want to deploy Redis on Arm-based servers and follow best
-        practices to get per...
-      preview_after: Learn how to tune Redis walks you through an end-to-end Arm software workflow. It
-        is designed for software developers who want to deploy Redis on Arm-based servers and follow best
-        practices to get per...
-      preview_generated: Learn how to tune Redis walks you through an end-to-end Arm software workflow.
-        It is designed for software developers who want to deploy Redis on Arm-based servers and follow
-        best practices to get per...
+      generated_at_before: '2026-06-02T05:00:17Z'
+      generated_at_after: '2026-06-02T05:00:17Z'
+      preview_before: This advanced Learning Path shows how to tune Redis on Arm-based
+        servers built on Neoverse, running Linux in the cloud (AWS, Microsoft Azure,
+        Google Cloud, Oracle) or on bare metal. You will review Li...
+      preview_after: This advanced Learning Path shows how to tune Redis on Arm-based
+        servers built on Neoverse, running Linux in the cloud (AWS, Microsoft Azure,
+        Google Cloud, Oracle) or on bare metal. You will review Li...
+      preview_generated: This advanced Learning Path shows how to tune Redis on Arm-based
+        Linux servers built on Arm Neoverse, whether running in the cloud (AWS, Microsoft
+        Azure, Google Cloud, or Oracle) or on bare metal. You...
     faqs:
-      action: unchanged
+      action: rerun_requested
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       before_count: 5
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       generated_count: 5
       change_details:
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         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the tuning steps?
+        - Where do I change Linux memory-related kernel parameters during this path?
+        - How should I decide which kernel, compiler, and OpenSSL settings to use?
+        - Which Redis configuration does this path focus on?
+        - Can I follow these steps on my preferred cloud provider?
+        removed_questions:
+        - What do I need before I start?
+        - Which platforms and operating systems does this Learning Path target?
+        - What components will I tune or configure?
+        - Does this Learning Path prescribe a single set of tuning parameters?
+        - How advanced is this content and how long will it take?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the tuning steps?
+        - Where do I change Linux memory-related kernel parameters during this path?
+        - How should I decide which kernel, compiler, and OpenSSL settings to use?
+        - Which Redis configuration does this path focus on?
+        - Can I follow these steps on my preferred cloud provider?
+        removed_questions:
+        - What do I need before I start?
+        - Which platforms and operating systems does this Learning Path target?
+        - What components will I tune or configure?
+        - Does this Learning Path prescribe a single set of tuning parameters?
+        - How advanced is this content and how long will it take?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
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+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -218579,51 +29713,80 @@ history:
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-      generated_at_before: '2026-05-06T17:17:59Z'
-      generated_at_after: '2026-05-06T17:17:59Z'
-      preview_before: Debug Neoverse N2 Reference Design with Arm Development Studio walks you through
-        an end-to-end Arm software workflow. It is designed for software developers who are interested
-        in debugging the Arm Neo...
-      preview_after: Debug Neoverse N2 Reference Design with Arm Development Studio walks you through
-        an end-to-end Arm software workflow. It is designed for software developers who are interested
-        in debugging the Arm Neo...
-      preview_generated: Debug Neoverse N2 Reference Design with Arm Development Studio walks you through
-        an end-to-end Arm software workflow. It is designed for software developers who are interested
-        in debugging the Arm Neo...
+      generated_at_before: '2026-06-02T05:00:55Z'
+      generated_at_after: '2026-06-02T05:00:55Z'
+      preview_before: Learn how to debug the Neoverse N2 Reference Design firmware
+        stack using Arm Development Studio on Linux. This path shows how to create
+        a debug connection to an associated Fixed Virtual Platform (FVP)...
+      preview_after: Learn how to debug the Neoverse N2 Reference Design firmware
+        stack using Arm Development Studio on Linux. This path shows how to create
+        a debug connection to an associated Fixed Virtual Platform (FVP)...
+      preview_generated: Follow this advanced, Linux-based path to debug the Arm Neoverse
+        N2 Reference Design firmware stack using Arm Development Studio. You will
+        create a debug connection, step through SCP/LCP/RSE firmware,...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
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-      generated_at_after: '2026-05-06T17:17:59Z'
+      generated_at_before: '2026-06-02T05:00:55Z'
+      generated_at_after: '2026-06-03T02:00:58Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the debug steps?
+        - Which optimization flag should I use for SCP firmware debug, and how do
+          I change it?
+        - "Why can\u2019t I start the debugger at BL1, and what\u2019s the workaround?"
+        - How do I set a breakpoint for BL31?
+        - How do I add symbols to debug BL33/UEFI?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - How do I create the initial debug connection in Arm Development Studio?
+        - How should I build SCP/LCP/RSE firmware for easier debugging?
+        - "I cannot attach to BL1 because the AP cores are powered off. What\u2019\
+          s the workaround?"
+        - How do I set and verify a breakpoint in BL31?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the debug steps?
+        - Which optimization flag should I use for SCP firmware debug, and how do
+          I change it?
+        - "Why can\u2019t I start the debugger at BL1, and what\u2019s the workaround?"
+        - How do I set a breakpoint for BL31?
+        - How do I add symbols to debug BL33/UEFI?
+        removed_questions:
+        - What do I need before starting this Learning Path?
+        - How do I create the initial debug connection in Arm Development Studio?
+        - How should I build SCP/LCP/RSE firmware for easier debugging?
+        - "I cannot attach to BL1 because the AP cores are powered off. What\u2019\
+          s the workaround?"
+        - How do I set and verify a breakpoint in BL31?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -218633,51 +29796,78 @@ history:
       source_hash_before: 8f2515b7c416a821bd397a77297adc263397a8b3cb86e9bb34e646735a21f854
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-      generated_at_after: '2026-05-06T17:17:59Z'
-      preview_before: Get started with the Neoverse Reference Design software stack walks you through
-        an end-to-end Arm software workflow. It is designed for software developers interested in testing
-        the Neoverse Reference...
-      preview_after: Get started with the Neoverse Reference Design software stack walks you through an
-        end-to-end Arm software workflow. It is designed for software developers interested in testing
-        the Neoverse Reference...
-      preview_generated: Get started with the Neoverse Reference Design software stack walks you through
-        an end-to-end Arm software workflow. It is designed for software developers interested in testing
-        the Neoverse Reference...
+      generated_at_before: '2026-06-02T05:02:22Z'
+      generated_at_after: '2026-06-02T05:02:22Z'
+      preview_before: Learn how to set up a Linux host, build, and test the Neoverse
+        Reference Design (RD-N2) firmware stack using containers and an Arm Ecosystem
+        FVP. You will prepare an Ubuntu 22.04 AArch64 or x86_64 mac...
+      preview_after: Learn how to set up a Linux host, build, and test the Neoverse
+        Reference Design (RD-N2) firmware stack using containers and an Arm Ecosystem
+        FVP. You will prepare an Ubuntu 22.04 AArch64 or x86_64 mac...
+      preview_generated: "This introductory path shows how to set up an Ubuntu 22.04\
+        \ host (AArch64 or x86_64), build the Neoverse Reference Design (RD\u2011\
+        N2) firmware stack, and validate it on an Arm Ecosystem Fixed Virtual Platf..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
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-      generated_at_after: '2026-05-06T17:17:59Z'
+      generated_at_before: '2026-06-02T05:02:22Z'
+      generated_at_after: '2026-06-03T02:01:19Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - Which host platforms and OS versions can I use?
+        - How much disk space and memory do I need to sync and build the software
+          stack?
+        - How do I launch the build environment and start the build?
+        - Which FVP should I download for testing, and how do I install it?
+        - What result should I expect when I test the firmware on the FVP?
+        removed_questions:
+        - What host system do I need to follow this Learning Path?
+        - What do I build and test during the steps?
+        - Is Docker required for the build?
+        - How do I obtain and configure the RD-N2 FVP?
+        - How long will this take and how do I verify success?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - Which host platforms and OS versions can I use?
+        - How much disk space and memory do I need to sync and build the software
+          stack?
+        - How do I launch the build environment and start the build?
+        - Which FVP should I download for testing, and how do I install it?
+        - What result should I expect when I test the firmware on the FVP?
+        removed_questions:
+        - What host system do I need to follow this Learning Path?
+        - What do I build and test during the steps?
+        - Is Docker required for the build?
+        - How do I obtain and configure the RD-N2 FVP?
+        - How long will this take and how do I verify success?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md
-    status: unchanged
-    changed_on_disk: false
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-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -218687,51 +29877,78 @@ history:
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-      preview_before: Enable reproducible math functions across vector extensions with Arm Performance
-        Libraries walks you through an end-to-end Arm software workflow. It is designed for developers
-        who want to produce repr...
-      preview_after: Enable reproducible math functions across vector extensions with Arm Performance
-        Libraries walks you through an end-to-end Arm software workflow. It is designed for developers
-        who want to produce repr...
-      preview_generated: Enable reproducible math functions across vector extensions with Arm Performance
-        Libraries walks you through an end-to-end Arm software workflow. It is designed for developers
-        who want to produce repr...
+      generated_at_before: '2026-06-02T05:03:04Z'
+      generated_at_after: '2026-06-02T05:03:04Z'
+      preview_before: This Learning Path shows you how to enable and use reproducible
+        math functions in Libamath, a component of Arm Performance Libraries, on Linux-based
+        Arm systems. You will learn what numerical reproduc...
+      preview_after: This Learning Path shows you how to enable and use reproducible
+        math functions in Libamath, a component of Arm Performance Libraries, on Linux-based
+        Arm systems. You will learn what numerical reproduc...
+      preview_generated: Learn how to produce bitwise-reproducible floating-point
+        results across scalar, Neon (AdvSIMD), and SVE implementations of selected
+        math functions using Libamath in Arm Performance Libraries on Linux....
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
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-      generated_at_after: '2026-05-06T17:17:59Z'
+      generated_at_before: '2026-06-02T05:03:04Z'
+      generated_at_after: '2026-06-03T02:01:52Z'
       before_count: 5
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       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the example?
+        - Which vector extensions are covered by reproducibility in this path?
+        - Which math functions are reproducible in Libamath?
+        - How do I compile and link the example against Arm Performance Libraries?
+        - What result should I expect when verifying reproducibility?
+        removed_questions:
+        - What hardware and software prerequisites are required?
+        - Which vector extensions and operating systems does this reproducibility
+          apply to?
+        - What math functions are included, and how is accuracy defined?
+        - How do I enable and verify reproducibility in my application?
+        - Why is reproducibility important when compilers auto-vectorize code?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the example?
+        - Which vector extensions are covered by reproducibility in this path?
+        - Which math functions are reproducible in Libamath?
+        - How do I compile and link the example against Arm Performance Libraries?
+        - What result should I expect when verifying reproducibility?
+        removed_questions:
+        - What hardware and software prerequisites are required?
+        - Which vector extensions and operating systems does this reproducibility
+          apply to?
+        - What math functions are included, and how is accuracy defined?
+        - How do I enable and verify reproducibility in my application?
+        - Why is reproducibility important when compilers auto-vectorize code?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -218741,51 +29958,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:59Z'
-      generated_at_after: '2026-05-06T17:17:59Z'
-      preview_before: Learn how to create a virtual machine in a Realm using Arm Confidential Compute
-        Architecture (CCA) walks you through an end-to-end Arm software workflow. It is designed for software
-        developers who wan...
-      preview_after: Learn how to create a virtual machine in a Realm using Arm Confidential Compute Architecture
-        (CCA) walks you through an end-to-end Arm software workflow. It is designed for software developers
-        who wan...
-      preview_generated: Learn how to create a virtual machine in a Realm using Arm Confidential Compute
-        Architecture (CCA) walks you through an end-to-end Arm software workflow. It is designed for software
-        developers who wan...
+      generated_at_before: '2026-06-02T05:04:08Z'
+      generated_at_after: '2026-06-02T05:04:08Z'
+      preview_before: Build and run the Arm Confidential Compute Architecture (CCA)
+        reference software stack on an Armv-A AEM Base FVP with RME support, then
+        create a guest Linux virtual machine inside a Realm. This introd...
+      preview_after: Build and run the Arm Confidential Compute Architecture (CCA)
+        reference software stack on an Armv-A AEM Base FVP with RME support, then
+        create a guest Linux virtual machine inside a Realm. This introd...
+      preview_generated: This Learning Path shows how to build and run the Arm Confidential
+        Compute Architecture (CCA) reference software stack on the Armv-A AEM Base
+        FVP with Realm Management Extension (RME) support, then cr...
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+        - Which FVP should I use to run the CCA stack?
+        - Can I complete this Learning Path on a cloud instance?
+        - Do I need to enable X11 forwarding?
+        - What outcome should I expect when everything runs correctly?
+        removed_questions:
+        - What host system do I need and how much storage should I allocate?
+        - Can I complete this Learning Path on a cloud VM?
+        - Which platform and Arm features does the path target?
+        - What tools and packages are required before I start building?
+        - What should I expect to have working at the end?
         updated_questions: []
       generated_diff:
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+        - Which FVP should I use to run the CCA stack?
+        - Can I complete this Learning Path on a cloud instance?
+        - Do I need to enable X11 forwarding?
+        - What outcome should I expect when everything runs correctly?
+        removed_questions:
+        - What host system do I need and how much storage should I allocate?
+        - Can I complete this Learning Path on a cloud VM?
+        - Which platform and Arm features does the path target?
+        - What tools and packages are required before I start building?
+        - What should I expect to have working at the end?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md
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-        Model (LLM) wit...
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+      preview_before: This introductory Learning Path guides you through running a
+        Large Language Model (LLM) chatbot on an Arm-based CPU using rtp-llm. You
+        will build rtp-llm, set up Python 3.10 with micromamba, install B...
+      preview_after: This introductory Learning Path guides you through running a
+        Large Language Model (LLM) chatbot on an Arm-based CPU using rtp-llm. You
+        will build rtp-llm, set up Python 3.10 with micromamba, install B...
+      preview_generated: Learn how to build and run a small LLM chatbot on Arm-based
+        servers using rtp-llm. You will set up dependencies on Ubuntu 22.04 LTS, including
+        micromamba to provide Python 3.10 at /opt/conda310 and ba...
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+        - Which Python version and location does the rtp-llm build expect?
+        - Which tools do I need to build rtp-llm?
+        - Which model will I run and how is it obtained?
+        - How do I interact with the model after starting the server?
+        removed_questions:
+        - What hardware and OS do I need before starting?
+        - Which model does this Learning Path use and where does it come from?
+        - What software and tools will be installed during the steps?
+        - How do I run and access the chatbot once rtp-llm is built?
+        - How can I verify that the setup worked?
         updated_questions: []
       generated_diff:
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+        - Which Python version and location does the rtp-llm build expect?
+        - Which tools do I need to build rtp-llm?
+        - Which model will I run and how is it obtained?
+        - How do I interact with the model after starting the server?
+        removed_questions:
+        - What hardware and OS do I need before starting?
+        - Which model does this Learning Path use and where does it come from?
+        - What software and tools will be installed during the steps?
+        - How do I run and access the chatbot once rtp-llm is built?
+        - How can I verify that the setup worked?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md
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     summary:
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-      preview_generated: Deploy Ruby on Rails on Arm-based Google Cloud C4A virtual machines walks you
-        through an end-to-end Arm software workflow. It is designed for developers deploying and optimizing
-        Ruby on Rails workload...
+      generated_at_before: '2026-06-02T05:06:21Z'
+      generated_at_after: '2026-06-02T05:06:21Z'
+      preview_before: "This Learning Path guides you through deploying Ruby on Rails\
+        \ on Arm-based Google Cloud C4A virtual machines powered by Axion processors.\
+        \ You will provision a SUSE Linux Enterprise Server instance\u2014ill..."
+      preview_after: "This Learning Path guides you through deploying Ruby on Rails\
+        \ on Arm-based Google Cloud C4A virtual machines powered by Axion processors.\
+        \ You will provision a SUSE Linux Enterprise Server instance\u2014ill..."
+      preview_generated: This Learning Path shows how to deploy a Ruby on Rails application
+        on Arm-based Google Cloud C4A virtual machines running SUSE Linux Enterprise
+        Server. You will provision a c4a-standard-4 instance in ...
     faqs:
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+        - Which Google Cloud machine type and OS does this path use?
+        - Where in Google Cloud Console do I create the C4A instance?
+        - How should I prepare SUSE SLES for installing Ruby on Rails?
+        - Which PostgreSQL packages are needed for Rails on SUSE SLES?
+        removed_questions:
+        - What do I need before I start?
+        - Which VM type and operating system does this use?
+        - What software is installed on the VM?
+        - How do I verify Rails and PostgreSQL are working together?
+        - What benchmarks will I run and where do I execute them?
         updated_questions: []
       generated_diff:
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+        - Which Google Cloud machine type and OS does this path use?
+        - Where in Google Cloud Console do I create the C4A instance?
+        - How should I prepare SUSE SLES for installing Ruby on Rails?
+        - Which PostgreSQL packages are needed for Rails on SUSE SLES?
+        removed_questions:
+        - What do I need before I start?
+        - Which VM type and operating system does this use?
+        - What software is installed on the VM?
+        - How do I verify Rails and PostgreSQL are working together?
+        - What benchmarks will I run and where do I execute them?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md
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     summary:
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@@ -218903,51 +30195,74 @@ history:
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-      preview_generated: Learn to deploy and benchmark Rust applications on Google Cloud C4A virtual machines
-        powered by Arm-based Axion processors. It is designed for developers deploying and optimizing
-        Rust workloads on Lin...
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+      preview_before: This introductory Learning Path shows how to deploy and benchmark
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+        (Arm Neoverse-V2 cores). You will provision a SUSE SLE...
+      preview_after: This introductory Learning Path shows how to deploy and benchmark
+        Rust on Google Cloud C4A virtual machines powered by Arm-based Axion processors
+        (Arm Neoverse-V2 cores). You will provision a SUSE SLE...
+      preview_generated: Follow this Learning Path to provision a Google Cloud C4A
+        virtual machine powered by Arm-based Axion processors (Arm Neoverse-V2 cores),
+        install Rust on a SUSE SLES Arm64 environment, validate the too...
     faqs:
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+        added_questions:
+        - What do I need before running the steps?
+        - Which VM type and OS should I create on Google Cloud?
+        - How do I install Rust and build tools on the SUSE Arm64 VM?
+        - How do I set up and run benchmarks with Criterion?
+        removed_questions:
+        - What environment does this Learning Path use on Google Cloud?
+        - What prerequisites do I need before starting?
+        - How do I install Rust on the SUSE Arm64 VM?
+        - How are benchmarks set up and executed?
+        updated_questions:
+        - How do I verify that the Rust toolchain is working?
       generated_diff:
         before_count: 5
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-        added_questions: []
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+        added_questions:
+        - What do I need before running the steps?
+        - Which VM type and OS should I create on Google Cloud?
+        - How do I install Rust and build tools on the SUSE Arm64 VM?
+        - How do I set up and run benchmarks with Criterion?
+        removed_questions:
+        - What environment does this Learning Path use on Google Cloud?
+        - What prerequisites do I need before starting?
+        - How do I install Rust on the SUSE Arm64 VM?
+        - How are benchmarks set up and executed?
+        updated_questions:
+        - How do I verify that the Rust toolchain is working?
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md
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+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
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@@ -218957,51 +30272,76 @@ history:
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-      preview_generated: Perform Sentiment Analysis on X on Arm-based EKS clusters walks you through an
-        end-to-end Arm software workflow. It is designed for software developers who want to build an
-        end-to-end ML sentiment ana...
+      generated_at_before: '2026-06-02T05:07:57Z'
+      generated_at_after: '2026-06-02T05:07:57Z'
+      preview_before: Build an end-to-end sentiment analysis workflow on an Arm-based
+        Amazon EKS cluster. You will deploy a text classification model with Apache
+        Spark, index and analyze posts from X using Elasticsearch, a...
+      preview_after: Build an end-to-end sentiment analysis workflow on an Arm-based
+        Amazon EKS cluster. You will deploy a text classification model with Apache
+        Spark, index and analyze posts from X using Elasticsearch, a...
+      preview_generated: Build an end-to-end sentiment analysis workflow on an Arm-based
+        Amazon EKS cluster. You will deploy a text classification model with Apache
+        Spark, analyze posts on X using Elasticsearch with a Kibana ...
     faqs:
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+        - What do I need before running the setup commands?
+        - How do I provide AWS credentials for the deployment tools?
+        - Where do I get the code and configurations used in this path?
+        - Which dashboards will I use and what data should I expect to see?
+        - How do I know the deployment succeeded?
+        removed_questions:
+        - What do I need before starting?
+        - What will I deploy in this Learning Path?
+        - Which platform and architecture does this target?
+        - How do I validate that the solution is working?
+        - Where do the code and configurations come from?
         updated_questions: []
       generated_diff:
         before_count: 5
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-        added_questions: []
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+        added_questions:
+        - What do I need before running the setup commands?
+        - How do I provide AWS credentials for the deployment tools?
+        - Where do I get the code and configurations used in this path?
+        - Which dashboards will I use and what data should I expect to see?
+        - How do I know the deployment succeeded?
+        removed_questions:
+        - What do I need before starting?
+        - What will I deploy in this Learning Path?
+        - Which platform and architecture does this target?
+        - How do I validate that the solution is working?
+        - Where do the code and configurations come from?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md
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     summary:
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@@ -219011,51 +30351,76 @@ history:
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+      preview_after: Learn to set up the Serverless Framework on a Windows on Arm
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+      preview_generated: Set up the Serverless Framework on Windows on Arm and deploy
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+        - How do I start creating the project and choose the correct template?
+        - What does the wizard generate for me?
+        - How do I know my AWS credentials are ready for deployment?
+        removed_questions:
+        - What environment does this Learning Path target?
+        - What AWS setup do I need?
+        - Which tools and versions should I install?
+        - How do I create the project and choose the runtime template?
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+        - How do I install the Serverless Framework on Windows on Arm?
+        - How do I start creating the project and choose the correct template?
+        - What does the wizard generate for me?
+        - How do I know my AWS credentials are ready for deployment?
+        removed_questions:
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+        - What AWS setup do I need?
+        - Which tools and versions should I install?
+        - How do I create the project and choose the runtime template?
+        - What will I have at the end of the Learning Path and how long will it take?
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+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md
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+        will declare a service that provisions a DynamoDB table for s...
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+        that integrates AWS Lambda with DynamoDB using the Serverless Framework. You
+        will declare a service that provisions a DynamoDB table for s...
+      preview_generated: Learn how to declare and deploy a small serverless application
+        on AWS using the Serverless Framework. You will define a multi-resource service
+        that includes a DynamoDB table for hypothetical sensor da...
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+        - What result should I expect after running the deploy command?
+        - What should I check if deployment fails?
+        removed_questions:
+        - What do I need before starting?
+        - Which cloud provider and tools does this Learning Path use?
+        - What AWS resources are created?
+        - How do I deploy the service?
+        - How can I confirm the deployment succeeded?
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+        - Which AWS resources does this service create?
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+        - What result should I expect after running the deploy command?
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+        removed_questions:
+        - What do I need before starting?
+        - Which cloud provider and tools does this Learning Path use?
+        - What AWS resources are created?
+        - How do I deploy the service?
+        - How can I confirm the deployment succeeded?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md
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-      preview_generated: Deploy a static website to Amazon S3 and integrate with AWS Lambda and DynamoDB
-        using the Serverless Framework walks you through an end-to-end Arm software workflow. It is designed
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+        using the Serverless Framework. You will declare a service that provisions
+        an Amazon S3 bucket to host a static website, a DynamoDB tabl...
+      preview_generated: Build and deploy a small serverless web application on AWS
+        using the Serverless Framework. You will declare a service that provisions
+        a DynamoDB table for timestamped temperature samples, two AWS Lamb...
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+        - Which AWS resources does the service declare and deploy?
+        - From which directory and with which commands do I deploy?
+        - What result should I expect after deployment?
+        removed_questions:
+        - Do I need to complete another Learning Path before starting this one?
+        - What environment and tools are required?
+        - Which AWS resources are created by the service declaration?
+        - Where should I place the website files and what is the key file?
+        - How do I deploy the solution and verify it worked?
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+        - Where should I create the website files?
+        - Which AWS resources does the service declare and deploy?
+        - From which directory and with which commands do I deploy?
+        - What result should I expect after deployment?
+        removed_questions:
+        - Do I need to complete another Learning Path before starting this one?
+        - What environment and tools are required?
+        - Which AWS resources are created by the service declaration?
+        - Where should I place the website files and what is the key file?
+        - How do I deploy the solution and verify it worked?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/snappy/_index.md
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     summary:
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-      preview_generated: Measure performance of compression libraries on Arm servers walks you through
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+      generated_at_before: '2026-06-02T05:10:41Z'
+      generated_at_after: '2026-06-02T05:10:41Z'
+      preview_before: This Learning Path guides you through installing and running
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+        on Arm servers. It targets Linux and has been tested on AWS EC...
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+        lzbench with Snappy and Zstandard to measure compression library performance
+        on Arm servers. It targets Linux and has been tested on AWS EC...
+      preview_generated: This Learning Path shows how to install and use lzbench to
+        benchmark Snappy and Zstandard on Arm-based cloud servers. You will set up
+        required packages (GNU gcc/g++, make, unzip) on Linux and then run...
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+        added_questions:
+        - What do I need before running the steps?
+        - Which Linux distributions are supported for Snappy and Zstandard in this
+          path?
+        - Which packages should I install on the instance before building or running
+          lzbench?
+        - Which compression libraries are benchmarked and how are they executed?
+        - What result should I expect after running the benchmarks?
+        removed_questions:
+        - What environment do I need to follow this Learning Path?
+        - Which Linux distributions support Snappy and Zstandard in this context?
+        - What packages should I install before using lzbench?
+        - Which tools and libraries are used to measure compression performance?
+        - How do I know the steps worked?
         updated_questions: []
       generated_diff:
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-        added_questions: []
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-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Linux distributions are supported for Snappy and Zstandard in this
+          path?
+        - Which packages should I install on the instance before building or running
+          lzbench?
+        - Which compression libraries are benchmarked and how are they executed?
+        - What result should I expect after running the benchmarks?
+        removed_questions:
+        - What environment do I need to follow this Learning Path?
+        - Which Linux distributions support Snappy and Zstandard in this context?
+        - What packages should I install before using lzbench?
+        - Which tools and libraries are used to measure compression performance?
+        - How do I know the steps worked?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md
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+      preview_before: "Learn how to install Snort 3 on an Arm-based Linux server and\
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+        \ configure it to use multithreading for processing capture files. You will\
+        \ adjust Snort\u2019s Lua configuration to set the number of packet-pro..."
+      preview_generated: "This Learning Path guides you through installing Snort 3\
+        \ on an Arm-based Linux system and enabling multithreading to handle network\
+        \ capture files. You will configure Snort\u2019s Lua files to set the numbe..."
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+        added_questions:
+        - What do I need before running the steps?
+        - Which platforms and services can I use for the Arm instance?
+        - How do I enable multithreading in Snort 3?
+        - How do I configure CPU affinity and memory settings before testing?
+        - What should I expect when processing PCAP files with multithreading enabled?
+        removed_questions:
+        - What environment and OS do I need to follow this Learning Path?
+        - What prerequisites or skills are assumed?
+        - Which tools and software are used?
+        - What system configuration is required before testing multithreading?
+        - How do I enable multithreading and validate the results?
         updated_questions: []
       generated_diff:
         before_count: 5
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-        added_questions: []
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-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which platforms and services can I use for the Arm instance?
+        - How do I enable multithreading in Snort 3?
+        - How do I configure CPU affinity and memory settings before testing?
+        - What should I expect when processing PCAP files with multithreading enabled?
+        removed_questions:
+        - What environment and OS do I need to follow this Learning Path?
+        - What prerequisites or skills are assumed?
+        - Which tools and software are used?
+        - What system configuration is required before testing multithreading?
+        - How do I enable multithreading and validate the results?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/spark/_index.md
-    status: unchanged
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-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
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+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
       missing_before: false
@@ -219281,51 +30750,76 @@ history:
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-      generated_at_before: '2026-05-06T17:17:59Z'
-      generated_at_after: '2026-05-06T17:17:59Z'
-      preview_before: Learn how to deploy Spark on AWS Graviton2 walks you through an end-to-end Arm software
-        workflow. It is designed for anyone who wants to deploy Spark on AWS Graviton2. By the end, you
-        will be able to ...
-      preview_after: Learn how to deploy Spark on AWS Graviton2 walks you through an end-to-end Arm software
-        workflow. It is designed for anyone who wants to deploy Spark on AWS Graviton2. By the end, you
-        will be able to ...
-      preview_generated: Learn how to deploy Spark on AWS Graviton2 walks you through an end-to-end Arm
-        software workflow. It is designed for anyone who wants to deploy Spark on AWS Graviton2. By the
-        end, you will be able to ...
+      generated_at_before: '2026-06-02T05:12:06Z'
+      generated_at_after: '2026-06-02T05:12:06Z'
+      preview_before: Deploy a single-node Apache Spark environment on an AWS Graviton2
+        EC2 instance using Terraform and Ansible on Linux. This Learning Path focuses
+        on automating instance creation with Terraform and confi...
+      preview_after: Deploy a single-node Apache Spark environment on an AWS Graviton2
+        EC2 instance using Terraform and Ansible on Linux. This Learning Path focuses
+        on automating instance creation with Terraform and confi...
+      preview_generated: This Learning Path shows how to automate the deployment of
+        a single-node Apache Spark instance on an AWS EC2 instance powered by AWS
+        Graviton2 (Arm Neoverse). You will use Terraform and Ansible to pro...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
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-      generated_at_after: '2026-05-06T17:17:59Z'
+      generated_at_before: '2026-06-02T05:12:06Z'
+      generated_at_after: '2026-06-03T02:07:05Z'
       before_count: 5
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       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the deployment?
+        - Do I need prior Terraform experience to follow this path?
+        - What result should I expect after completing the steps?
+        - Which operating system and platform does this deployment target?
+        - Do I need to choose a specific AWS instance type or region?
+        removed_questions:
+        - What will this Learning Path deploy on AWS?
+        - Which tools are used and for what steps?
+        - What are the prerequisites I must have set up?
+        - Do I need prior Terraform experience?
+        - How long does it take and what is the expected outcome?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the deployment?
+        - Do I need prior Terraform experience to follow this path?
+        - What result should I expect after completing the steps?
+        - Which operating system and platform does this deployment target?
+        - Do I need to choose a specific AWS instance type or region?
+        removed_questions:
+        - What will this Learning Path deploy on AWS?
+        - Which tools are used and for what steps?
+        - What are the prerequisites I must have set up?
+        - Do I need prior Terraform experience?
+        - How long does it take and what is the expected outcome?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -219335,51 +30829,80 @@ history:
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-      generated_at_before: '2026-05-06T17:17:59Z'
-      generated_at_after: '2026-05-06T17:17:59Z'
-      preview_before: Run Spark applications on Microsoft Azure Cobalt 100 processors walks you through
-        an end-to-end Arm software workflow. It is designed for This is an advanced topic that introduces
-        Spark deployment on ...
-      preview_after: Run Spark applications on Microsoft Azure Cobalt 100 processors walks you through
-        an end-to-end Arm software workflow. It is designed for This is an advanced topic that introduces
-        Spark deployment on ...
-      preview_generated: Run Spark applications on Microsoft Azure Cobalt 100 processors walks you through
-        an end-to-end Arm software workflow. It is designed for This is an advanced topic that introduces
-        Spark deployment on ...
+      generated_at_before: '2026-06-02T05:12:43Z'
+      generated_at_after: '2026-06-02T05:12:43Z'
+      preview_before: Learn how to deploy and validate Apache Spark on Microsoft Azure
+        Cobalt 100 (Arm-based) virtual machines using Azure Linux 3.0. You will provision
+        an Arm64 VM via the Azure portal, choose between runn...
+      preview_after: Learn how to deploy and validate Apache Spark on Microsoft Azure
+        Cobalt 100 (Arm-based) virtual machines using Azure Linux 3.0. You will provision
+        an Arm64 VM via the Azure portal, choose between runn...
+      preview_generated: This Learning Path guides you through running Apache Spark
+        on Microsoft Azure Cobalt 100 (Arm-based) virtual machines using Azure Linux
+        3.0. You will provision an Arm64 VM in the Azure portal, choose ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
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-      generated_at_after: '2026-05-06T17:17:59Z'
+      generated_at_before: '2026-06-02T05:12:43Z'
+      generated_at_after: '2026-06-03T02:07:30Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - "How do I make sure I\u2019m creating the correct Arm64 VM in Azure?"
+        - Should I deploy Spark in an Azure Linux 3.0 Docker container or on a custom-image
+          VM?
+        - Which packages do I install before setting up Spark, and how do I verify
+          Java?
+        - How do I validate that Spark is working after installation?
+        removed_questions:
+        - What prerequisites do I need before starting?
+        - How do I provision the target environment on Azure?
+        - Do I have to use Docker, or can I run Spark directly on a VM?
+        - How do I verify that Spark is installed and working?
+        - What is the expected outcome after completing the steps?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - "How do I make sure I\u2019m creating the correct Arm64 VM in Azure?"
+        - Should I deploy Spark in an Azure Linux 3.0 Docker container or on a custom-image
+          VM?
+        - Which packages do I install before setting up Spark, and how do I verify
+          Java?
+        - How do I validate that Spark is working after installation?
+        removed_questions:
+        - What prerequisites do I need before starting?
+        - How do I provision the target environment on Azure?
+        - Do I have to use Docker, or can I run Spark directly on a VM?
+        - How do I verify that Spark is installed and working?
+        - What is the expected outcome after completing the steps?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -219389,51 +30912,87 @@ history:
       source_hash_before: a4ac73af7e8959a546a66ab776c0bca8b60957e6f1729aa00fd78783e6594c0b
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-      generated_at_after: '2026-05-06T17:17:59Z'
-      preview_before: Deploy Apache Spark on Google Axion processors walks you through an end-to-end Arm
-        software workflow. It is designed for This introductory topic is for software developers interested
-        in migrating thei...
-      preview_after: Deploy Apache Spark on Google Axion processors walks you through an end-to-end Arm
-        software workflow. It is designed for This introductory topic is for software developers interested
-        in migrating thei...
-      preview_generated: Deploy Apache Spark on Google Axion processors walks you through an end-to-end
-        Arm software workflow. It is designed for This introductory topic is for software developers interested
-        in migrating thei...
+      generated_at_before: '2026-06-02T05:13:16Z'
+      generated_at_after: '2026-06-02T05:13:16Z'
+      preview_before: Learn how to deploy Apache Spark on Arm-based Google Axion C4A
+        virtual machines in Google Cloud. You will provision a c4a-standard-4 instance
+        with RHEL 9, install Java, Scala, Maven, and Spark, then v...
+      preview_after: Learn how to deploy Apache Spark on Arm-based Google Axion C4A
+        virtual machines in Google Cloud. You will provision a c4a-standard-4 instance
+        with RHEL 9, install Java, Scala, Maven, and Spark, then v...
+      preview_generated: "Follow this Learning Path to provision a Google Cloud C4A\
+        \ virtual machine based on Google Axion processors (Arm Neoverse\u2011V2),\
+        \ install Apache Spark on Red Hat Enterprise Linux 9, validate the setup wit..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: a4ac73af7e8959a546a66ab776c0bca8b60957e6f1729aa00fd78783e6594c0b
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-      generated_at_before: '2026-05-06T17:17:59Z'
-      generated_at_after: '2026-05-06T17:17:59Z'
+      generated_at_before: '2026-06-02T05:13:16Z'
+      generated_at_after: '2026-06-03T02:07:54Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before creating the VM?
+        - Which VM configuration and OS image should I use on GCP?
+        - How do I access the instance to install Spark and its dependencies?
+        - How do I confirm that my Spark installation works on the C4A VM?
+        - How are the performance benchmarks run and what do they measure?
+        removed_questions:
+        - What accounts and skills are required before starting?
+        - Which Google Cloud VM and OS image does this path use?
+        - What software is installed to set up Spark on the VM?
+        - How do I validate that my Spark installation is working on Arm?
+        - How are performance benchmarks run and what can I compare?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before creating the VM?
+        - Which VM configuration and OS image should I use on GCP?
+        - How do I access the instance to install Spark and its dependencies?
+        - How do I confirm that my Spark installation works on the C4A VM?
+        - How are the performance benchmarks run and what do they measure?
+        removed_questions:
+        - What accounts and skills are required before starting?
+        - Which Google Cloud VM and OS image does this path use?
+        - What software is installed to set up Spark on the VM?
+        - How do I validate that my Spark installation is working on Arm?
+        - How are performance benchmarks run and what can I compare?
+        updated_questions: []
+    category: servers-and-cloud-computing
+  - path: content/learning-paths/servers-and-cloud-computing/spark-velox-cobalt/_index.md
+    status: skipped
+    skip_reason: draft
+    change_reasons:
+    - draft
+    ai_requested: false
+    summary:
+      action: skipped
+    faqs:
+      action: skipped
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/supervisord/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -219443,51 +31002,80 @@ history:
       source_hash_before: d3fa3b409ed7cf2ecb521fa4d19af8411718909881861ef3885214824031b541
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-      generated_at_after: '2026-05-06T17:17:59Z'
-      preview_before: Access running containers using Supervisor, SSH, and Remote.It walks you through
-        an end-to-end Arm software workflow. It is designed for software developers who want to learn
-        how to run multiple servi...
-      preview_after: Access running containers using Supervisor, SSH, and Remote.It walks you through
-        an end-to-end Arm software workflow. It is designed for software developers who want to learn
-        how to run multiple servi...
-      preview_generated: Access running containers using Supervisor, SSH, and Remote.It walks you through
-        an end-to-end Arm software workflow. It is designed for software developers who want to learn
-        how to run multiple servi...
+      generated_at_before: '2026-06-02T05:13:51Z'
+      generated_at_after: '2026-06-02T05:13:51Z'
+      preview_before: Learn how to run multiple services in a single container with
+        Supervisor and access that container for debugging and testing without opening
+        SSH ports or changing AWS security groups. You will update ...
+      preview_after: Learn how to run multiple services in a single container with
+        Supervisor and access that container for debugging and testing without opening
+        SSH ports or changing AWS security groups. You will update ...
+      preview_generated: Learn how to run multiple services in a single container
+        with Supervisor and securely access that container for debug and test using
+        SSH and Remote.It. You will update a Dockerfile (based on Ubuntu 24...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: d3fa3b409ed7cf2ecb521fa4d19af8411718909881861ef3885214824031b541
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-      generated_at_before: '2026-05-06T17:17:59Z'
-      generated_at_after: '2026-05-06T17:17:59Z'
+      generated_at_before: '2026-06-02T05:13:51Z'
+      generated_at_after: '2026-06-03T02:08:16Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which changes should I make in the Dockerfile to run multiple services and
+          enable access?
+        - How do I access a container running in AWS Fargate without changing security
+          groups?
+        - How do I know the container is ready to accept SSH via Remote.It?
+        - Can I adapt this approach to other container runtimes besides AWS Fargate?
+        removed_questions:
+        - What do I need before starting?
+        - What changes will I make to the container image?
+        - How do I access a container running on AWS without changing security groups?
+        - Can this approach be adapted to other container runtimes?
+        - How do I verify that the setup worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which changes should I make in the Dockerfile to run multiple services and
+          enable access?
+        - How do I access a container running in AWS Fargate without changing security
+          groups?
+        - How do I know the container is ready to accept SSH via Remote.It?
+        - Can I adapt this approach to other container runtimes besides AWS Fargate?
+        removed_questions:
+        - What do I need before starting?
+        - What changes will I make to the container image?
+        - How do I access a container running on AWS without changing security groups?
+        - Can this approach be adapted to other container runtimes?
+        - How do I verify that the setup worked?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/sve/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
+    - rerun_faqs
+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -219497,51 +31085,76 @@ history:
       source_hash_before: 7b2074e39243a5cd0ccb6a01317c700a4797d8c66353f39aa4197237b6672027
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-      generated_at_before: '2026-05-06T17:17:59Z'
-      generated_at_after: '2026-05-06T17:17:59Z'
-      preview_before: Port Code to Arm Scalable Vector Extension (SVE) walks you through an end-to-end
-        Arm software workflow. It is designed for software developers using SIMD instructions for High-Performance
-        Computing, M...
-      preview_after: Port Code to Arm Scalable Vector Extension (SVE) walks you through an end-to-end
-        Arm software workflow. It is designed for software developers using SIMD instructions for High-Performance
-        Computing, M...
-      preview_generated: Port Code to Arm Scalable Vector Extension (SVE) walks you through an end-to-end
-        Arm software workflow. It is designed for software developers using SIMD instructions for High-Performance
-        Computing, M...
+      generated_at_before: '2026-06-02T05:14:53Z'
+      generated_at_after: '2026-06-02T05:14:53Z'
+      preview_before: This introductory Learning Path shows how to port SIMD code
+        to Arm Scalable Vector Extension (SVE) on Linux. You will compare Neon and
+        SVE to understand how SVE reduces fixed-length vector constraints...
+      preview_after: This introductory Learning Path shows how to port SIMD code to
+        Arm Scalable Vector Extension (SVE) on Linux. You will compare Neon and SVE
+        to understand how SVE reduces fixed-length vector constraints...
+      preview_generated: "This introductory path shows how to port SIMD code from\
+        \ Arm Neon to the Arm Scalable Vector Extension (SVE) on Linux-based Armv8-A\
+        \ systems. You will review key differences between Neon\u2019s fixed 128-bit..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
       drift_detected: false
       source_hash_before: 7b2074e39243a5cd0ccb6a01317c700a4797d8c66353f39aa4197237b6672027
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-        added_questions: []
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+        added_questions:
+        - What do I need before running the steps?
+        - Which GCC options enable SVE for my build?
+        - How can I run SVE instructions if my system lacks SVE hardware?
+        - How do I know if the compiler vectorized my code?
+        - What should I consider when moving from Neon to SVE?
+        removed_questions:
+        - What environment do I need to follow this Learning Path?
+        - Can I run SVE code without SVE-capable hardware?
+        - Which compilers and tools are used to build SVE code?
+        - What example program will I build, and what does it show?
+        - How do I validate that the steps worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which GCC options enable SVE for my build?
+        - How can I run SVE instructions if my system lacks SVE hardware?
+        - How do I know if the compiler vectorized my code?
+        - What should I consider when moving from Neon to SVE?
+        removed_questions:
+        - What environment do I need to follow this Learning Path?
+        - Can I run SVE code without SVE-capable hardware?
+        - Which compilers and tools are used to build SVE code?
+        - What example program will I build, and what does it show?
+        - How do I validate that the steps worked?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md
-    status: unchanged
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-    rerun_flags_reset: []
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+    - rerun_flags_reset
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     summary:
       action: unchanged
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@@ -219551,51 +31164,76 @@ history:
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-      preview_before: Accelerate search performance with SVE2 MATCH on Arm servers walks you through an
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-        and anyone optimizing...
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-        end-to-end Arm software workflow. It is designed for database developers, performance engineers,
-        and anyone optimizing...
-      preview_generated: Accelerate search performance with SVE2 MATCH on Arm servers walks you through
-        an end-to-end Arm software workflow. It is designed for database developers, performance engineers,
-        and anyone optimizing...
+      generated_at_before: '2026-06-02T05:15:37Z'
+      generated_at_after: '2026-06-02T05:15:37Z'
+      preview_before: "Implement and benchmark scalar and SVE2 MATCH-based search\
+        \ functions on Arm Neoverse servers to evaluate vectorized search performance\
+        \ on Linux. Working on a cloud VM with SVE2 support\u2014AWS Graviton4, ..."
+      preview_after: "Implement and benchmark scalar and SVE2 MATCH-based search functions\
+        \ on Arm Neoverse servers to evaluate vectorized search performance on Linux.\
+        \ Working on a cloud VM with SVE2 support\u2014AWS Graviton4, ..."
+      preview_generated: Learn how to accelerate array search workloads on Arm Neoverse-based
+        servers by implementing and benchmarking scalar and SVE2 MATCH versions of
+        a search function on Linux. Working on an AWS Graviton4,...
     faqs:
-      action: unchanged
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       before_count: 5
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       change_details:
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-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the exercises?
+        - Which cloud instance should I choose to use SVE2 MATCH?
+        - What will I implement and benchmark during the path?
+        - How do I know my results are correct or meaningful?
+        - Is Neon or Runbook required, or is the focus only on SVE2 MATCH?
+        removed_questions:
+        - What environment do I need to complete this Learning Path?
+        - Which cloud instance should I choose?
+        - What will I implement and benchmark?
+        - Do I need prior experience with SVE2 or Neon?
+        - How do I validate that the vectorized approach is working?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the exercises?
+        - Which cloud instance should I choose to use SVE2 MATCH?
+        - What will I implement and benchmark during the path?
+        - How do I know my results are correct or meaningful?
+        - Is Neon or Runbook required, or is the focus only on SVE2 MATCH?
+        removed_questions:
+        - What environment do I need to complete this Learning Path?
+        - Which cloud instance should I choose?
+        - What will I implement and benchmark?
+        - Do I need prior experience with SVE2 or Neon?
+        - How do I validate that the vectorized approach is working?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/sysreport/_index.md
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-    changed_on_disk: false
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-    rerun_flags_reset: []
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-    template_version_before: summary-faq-v1
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+    status: updated
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+    rerun_flags_reset:
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+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
       missing_before: false
@@ -219605,51 +31243,76 @@ history:
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-      preview_before: Get ready for performance analysis with Sysreport walks you through an end-to-end
-        Arm software workflow. It is designed for software developers who want to use the system capability
-        reporting tool, Sy...
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-        Arm software workflow. It is designed for software developers who want to use the system capability
-        reporting tool, Sy...
-      preview_generated: Get ready for performance analysis with Sysreport walks you through an end-to-end
-        Arm software workflow. It is designed for software developers who want to use the system capability
-        reporting tool, Sy...
+      generated_at_before: '2026-06-02T05:16:32Z'
+      generated_at_after: '2026-06-02T05:16:32Z'
+      preview_before: Use Sysreport to quickly assess the performance-related capabilities
+        of an Arm Linux system and decide what to configure before profiling. This
+        introductory path walks you through running the command-...
+      preview_after: Use Sysreport to quickly assess the performance-related capabilities
+        of an Arm Linux system and decide what to configure before profiling. This
+        introductory path walks you through running the command-...
+      preview_generated: This introductory path shows how to prepare an Arm Linux
+        system for performance analysis using Sysreport. You will verify access to
+        the target via SSH or a local console, confirm Python is available, ...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
+      changed: true
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-      generated_at_after: '2026-05-06T17:17:59Z'
+      generated_at_before: '2026-06-02T05:16:32Z'
+      generated_at_after: '2026-06-03T02:09:35Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running Sysreport on my Arm system?
+        - Which Python command should I use for the steps?
+        - How do I confirm Python is installed?
+        - What result should I expect after running Sysreport?
+        - What should I check if a feature I expected is missing in the report?
+        removed_questions:
+        - What do I need before I start?
+        - Do I need Python or Git for this Learning Path?
+        - What does Sysreport produce and how do I know it worked?
+        - Which platforms and Arm CPUs are covered?
+        - How do I use the results to prepare for performance analysis?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running Sysreport on my Arm system?
+        - Which Python command should I use for the steps?
+        - How do I confirm Python is installed?
+        - What result should I expect after running Sysreport?
+        - What should I check if a feature I expected is missing in the report?
+        removed_questions:
+        - What do I need before I start?
+        - Do I need Python or Git for this Learning Path?
+        - What does Sysreport produce and how do I know it worked?
+        - Which platforms and Arm CPUs are covered?
+        - How do I use the results to prepare for performance analysis?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
-    status: unchanged
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-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
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+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -219659,51 +31322,82 @@ history:
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-      preview_before: Deploy TensorFlow on Google Cloud C4A (Arm-based Axion VMs) walks you through an
-        end-to-end Arm software workflow. It is designed for software developers deploying and optimizing
-        TensorFlow workloads ...
-      preview_after: Deploy TensorFlow on Google Cloud C4A (Arm-based Axion VMs) walks you through an
-        end-to-end Arm software workflow. It is designed for software developers deploying and optimizing
-        TensorFlow workloads ...
-      preview_generated: Deploy TensorFlow on Google Cloud C4A (Arm-based Axion VMs) walks you through
-        an end-to-end Arm software workflow. It is designed for software developers deploying and optimizing
-        TensorFlow workloads ...
+      generated_at_before: '2026-06-02T05:17:09Z'
+      generated_at_after: '2026-06-02T05:17:09Z'
+      preview_before: Provision an Arm-based SUSE Linux Enterprise Server (SLES) VM
+        on Google Cloud C4A (Axion, Neoverse-V2) and set up a working TensorFlow environment
+        on Arm64. You will create a c4a-standard-4 instance, ...
+      preview_after: Provision an Arm-based SUSE Linux Enterprise Server (SLES) VM
+        on Google Cloud C4A (Axion, Neoverse-V2) and set up a working TensorFlow environment
+        on Arm64. You will create a c4a-standard-4 instance, ...
+      preview_generated: This Learning Path walks you through deploying TensorFlow
+        on Arm-based Google Cloud C4A virtual machines powered by Axion processors.
+        You will provision a SUSE Linux Enterprise Server (SLES) Arm64 VM,...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
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       change_details:
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-        added_questions: []
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+        added_questions:
+        - What do I need before running this Learning Path, and how long will it take?
+        - Which VM configuration and OS should I select on Google Cloud to match the
+          steps?
+        - Which Python version is used and how do I install the prerequisites for
+          TensorFlow?
+        - How do I verify that TensorFlow is correctly installed and recognizes the
+          hardware?
+        - What models are benchmarked and what metrics are collected in this path?
+        removed_questions:
+        - What do I need before starting?
+        - Which VM type and operating system does this path use?
+        - Which Python version and tools are installed for TensorFlow?
+        - Do I need a GPU to follow this Learning Path?
+        - How do I validate the setup and what benchmarks will I run?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running this Learning Path, and how long will it take?
+        - Which VM configuration and OS should I select on Google Cloud to match the
+          steps?
+        - Which Python version is used and how do I install the prerequisites for
+          TensorFlow?
+        - How do I verify that TensorFlow is correctly installed and recognizes the
+          hardware?
+        - What models are benchmarked and what metrics are collected in this path?
+        removed_questions:
+        - What do I need before starting?
+        - Which VM type and operating system does this path use?
+        - Which Python version and tools are installed for TensorFlow?
+        - Do I need a GPU to follow this Learning Path?
+        - How do I validate the setup and what benchmarks will I run?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md
-    status: unchanged
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-    rerun_flags_reset: []
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-    template_version_before: summary-faq-v1
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+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
       missing_before: false
@@ -219713,51 +31407,76 @@ history:
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-      preview_before: Run Text Classification with ThirdAI on Arm servers walks you through an end-to-end
-        Arm software workflow. It is designed for software developers who want to learn how to run text
-        classification tasks...
-      preview_after: Run Text Classification with ThirdAI on Arm servers walks you through an end-to-end
-        Arm software workflow. It is designed for software developers who want to learn how to run text
-        classification tasks...
-      preview_generated: Run Text Classification with ThirdAI on Arm servers walks you through an end-to-end
-        Arm software workflow. It is designed for software developers who want to learn how to run text
-        classification tasks...
+      generated_at_before: '2026-06-02T05:17:40Z'
+      generated_at_after: '2026-06-02T05:17:40Z'
+      preview_before: Learn how to run a text classification workflow with ThirdAI
+        on Arm servers running Linux. You will provision an Arm-based instance in
+        the cloud (AWS, Microsoft Azure, Google Cloud, or Oracle) or use ...
+      preview_after: Learn how to run a text classification workflow with ThirdAI
+        on Arm servers running Linux. You will provision an Arm-based instance in
+        the cloud (AWS, Microsoft Azure, Google Cloud, or Oracle) or use ...
+      preview_generated: This introductory Learning Path shows how to run a text classification
+        workflow with ThirdAI on Arm-based Linux servers. You will install Python
+        and pip on Ubuntu, create a virtual environment, and in...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
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+      generated_at_after: '2026-06-03T02:10:25Z'
       before_count: 5
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       generated_count: 5
       change_details:
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-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Can I follow the instructions on Linux distributions other than Ubuntu?
+        - Which setup commands prepare Python and an isolated environment?
+        - How do I install and activate ThirdAI for this example?
+        - How do I evaluate the trained model and what result should I expect?
+        removed_questions:
+        - What infrastructure do I need to follow this Learning Path?
+        - Which tools and languages are used?
+        - What will I build and how do I verify it worked?
+        - How long does this take and what skill level is assumed?
+        - Which ThirdAI APIs are demonstrated?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Can I follow the instructions on Linux distributions other than Ubuntu?
+        - Which setup commands prepare Python and an isolated environment?
+        - How do I install and activate ThirdAI for this example?
+        - How do I evaluate the trained model and what result should I expect?
+        removed_questions:
+        - What infrastructure do I need to follow this Learning Path?
+        - Which tools and languages are used?
+        - What will I build and how do I verify it worked?
+        - How long does this take and what skill level is assumed?
+        - Which ThirdAI APIs are demonstrated?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md
-    status: unchanged
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-    rerun_flags_reset: []
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-    template_version_before: summary-faq-v1
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+    status: updated
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+    rerun_flags_reset:
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+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
       missing_before: false
@@ -219767,51 +31486,78 @@ history:
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-      preview_before: Deploy a live sensor dashboard with TimescaleDB and Grafana on Google Cloud C4A
-        walks you through an end-to-end Arm software workflow. It is designed for DevOps engineers, database
-        engineers, and soft...
-      preview_after: Deploy a live sensor dashboard with TimescaleDB and Grafana on Google Cloud C4A walks
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-        walks you through an end-to-end Arm software workflow. It is designed for DevOps engineers, database
-        engineers, and soft...
+      generated_at_before: '2026-06-02T05:18:38Z'
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+      preview_before: Deploy a live sensor dashboard on Google Cloud Axion C4A Arm
+        instances by provisioning a c4a-standard-4 VM running SUSE Linux Enterprise
+        Server (SLES) Arm64, building PostgreSQL 15 with the TimescaleD...
+      preview_after: Deploy a live sensor dashboard on Google Cloud Axion C4A Arm
+        instances by provisioning a c4a-standard-4 VM running SUSE Linux Enterprise
+        Server (SLES) Arm64, building PostgreSQL 15 with the TimescaleD...
+      preview_generated: Provision an Arm-based Google Cloud C4A Axion VM and build
+        TimescaleDB from source on SUSE Linux Enterprise Server (Arm64) to ingest
+        and visualize live sensor data. You will create a c4a-standard-4 in...
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+        added_questions:
+        - What do I need before provisioning the VM on Google Cloud?
+        - Which Google Cloud VM and operating system are used in this path?
+        - Why does the path build TimescaleDB from source on Arm64, and which versions
+          are used?
+        - Which firewall port should I open, and what is it for?
+        - How do I know the ingestion and visualization are working?
+        removed_questions:
+        - Which Google Cloud resources and machine type should I provision?
+        - What operating system and database stack does this target?
+        - What firewall configuration is required for Grafana?
+        - What do I need before starting this Learning Path?
+        - How do I validate that data flows end to end?
         updated_questions: []
       generated_diff:
         before_count: 5
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-        added_questions: []
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+        added_questions:
+        - What do I need before provisioning the VM on Google Cloud?
+        - Which Google Cloud VM and operating system are used in this path?
+        - Why does the path build TimescaleDB from source on Arm64, and which versions
+          are used?
+        - Which firewall port should I open, and what is it for?
+        - How do I know the ingestion and visualization are working?
+        removed_questions:
+        - Which Google Cloud resources and machine type should I provision?
+        - What operating system and database stack does this target?
+        - What firewall configuration is required for Grafana?
+        - What do I need before starting this Learning Path?
+        - How do I validate that data flows end to end?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md
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-      preview_generated: Learn the Arm Neoverse N1 performance analysis methodology walks you through
-        an end-to-end Arm software workflow. It is designed for software developers who want to learn
-        about performance analysis me...
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+      generated_at_after: '2026-06-02T05:19:33Z'
+      preview_before: Learn how to analyze Linux application performance on Arm Neoverse
+        N1 using the Arm Telemetry Solution and Linux perf. You will build a slightly
+        modified DynamoRIO stride benchmark, collect sampling a...
+      preview_after: Learn how to analyze Linux application performance on Arm Neoverse
+        N1 using the Arm Telemetry Solution and Linux perf. You will build a slightly
+        modified DynamoRIO stride benchmark, collect sampling a...
+      preview_generated: This introductory Learning Path guides you through performance
+        analysis on Arm Neoverse N1 systems running Linux using the Arm Telemetry
+        Solution and Linux perf. You will build a modified DynamoRIO st...
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       change_details:
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+        added_questions:
+        - Do I need a bare-metal Neoverse N1 system, or can I use a VM?
+        - Which tools must be installed before I build and profile the example?
+        - What application is used as the example, and what does it measure?
+        - Can I run this Learning Path on hardware other than the N1SDP, and how will
+          results differ?
+        - How do I enable and tune software prefetching in the sample application?
+        removed_questions:
+        - What hardware and operating system do I need?
+        - Can I run this on other Arm hardware besides Neoverse N1?
+        - Which tools are used and how do I install them?
+        - What example application will I build?
+        - How do I validate that my analysis and optimization steps worked?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
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-        updated_questions: []
+        added_questions:
+        - Do I need a bare-metal Neoverse N1 system, or can I use a VM?
+        - Which tools must be installed before I build and profile the example?
+        - What application is used as the example, and what does it measure?
+        - Can I run this Learning Path on hardware other than the N1SDP, and how will
+          results differ?
+        - How do I enable and tune software prefetching in the sample application?
+        removed_questions:
+        - What hardware and operating system do I need?
+        - Can I run this on other Arm hardware besides Neoverse N1?
+        - Which tools are used and how do I install them?
+        - What example application will I build?
+        - How do I validate that my analysis and optimization steps worked?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/torchbench/_index.md
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-    rerun_flags_reset: []
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+    status: updated
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+    rerun_flags_reset:
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+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
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@@ -219875,51 +31648,76 @@ history:
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-      preview_before: Measure and accelerate PyTorch Inference on Arm servers walks you through an end-to-end
-        Arm software workflow. It is designed for software developers who want to learn how to measure
-        and accelerate th...
-      preview_after: Measure and accelerate PyTorch Inference on Arm servers walks you through an end-to-end
-        Arm software workflow. It is designed for software developers who want to learn how to measure
-        and accelerate th...
-      preview_generated: Measure and accelerate PyTorch Inference on Arm servers walks you through an
-        end-to-end Arm software workflow. It is designed for software developers who want to learn how
-        to measure and accelerate th...
+      generated_at_before: '2026-06-02T05:20:50Z'
+      generated_at_after: '2026-06-02T05:20:50Z'
+      preview_before: Learn to measure PyTorch inference on Arm-based servers using
+        the PyTorch Benchmarks suite. You will install the benchmarks on Ubuntu 22.04
+        LTS, run model inference tests with Python and PyTorch, and ...
+      preview_after: Learn to measure PyTorch inference on Arm-based servers using
+        the PyTorch Benchmarks suite. You will install the benchmarks on Ubuntu 22.04
+        LTS, run model inference tests with Python and PyTorch, and ...
+      preview_generated: This introductory Learning Path guides you to download and
+        install the PyTorch Benchmarks suite, then measure and compare the inference
+        performance of PyTorch NLP, vision, and recommender models on an...
     faqs:
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       before_count: 5
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       change_details:
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-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the benchmarks?
+        - Can I run this on AWS, Azure, Google Cloud, or Oracle Cloud?
+        - How do I know the PyTorch Benchmarks suite installed correctly?
+        - Which PyTorch execution modes should I compare?
+        - What results should I expect to collect and for which model types?
+        removed_questions:
+        - What are the prerequisites and minimum system specs?
+        - Which cloud platforms are suitable, and what instance was used for testing?
+        - What software will I install and use during the path?
+        - What tasks will I perform and what results should I expect?
+        - How long does this Learning Path take to complete?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
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-        updated_questions: []
+        added_questions:
+        - What do I need before running the benchmarks?
+        - Can I run this on AWS, Azure, Google Cloud, or Oracle Cloud?
+        - How do I know the PyTorch Benchmarks suite installed correctly?
+        - Which PyTorch execution modes should I compare?
+        - What results should I expect to collect and for which model types?
+        removed_questions:
+        - What are the prerequisites and minimum system specs?
+        - Which cloud platforms are suitable, and what instance was used for testing?
+        - What software will I install and use during the path?
+        - What tasks will I perform and what results should I expect?
+        - How long does this Learning Path take to complete?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md
-    status: unchanged
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-    rerun_flags_reset: []
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+    status: updated
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+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
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@@ -219929,51 +31727,76 @@ history:
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-      preview_before: Learn about Neoverse Cache PMU Events using C and Assembly Language walks you through
-        an end-to-end Arm software workflow. It is designed for software and hardware engineers who want
-        to learn about th...
-      preview_after: Learn about Neoverse Cache PMU Events using C and Assembly Language walks you through
-        an end-to-end Arm software workflow. It is designed for software and hardware engineers who want
-        to learn about th...
-      preview_generated: Learn about Neoverse Cache PMU Events using C and Assembly Language walks you
-        through an end-to-end Arm software workflow. It is designed for software and hardware engineers
-        who want to learn about th...
+      generated_at_before: '2026-06-02T05:21:26Z'
+      generated_at_after: '2026-06-02T05:21:26Z'
+      preview_before: This advanced Learning Path shows how simple C and assembly
+        code patterns trigger common cache Performance Monitoring Unit (PMU) events
+        on Arm Neoverse, with a focus on the Neoverse N2 core, in a Linu...
+      preview_after: This advanced Learning Path shows how simple C and assembly code
+        patterns trigger common cache Performance Monitoring Unit (PMU) events on
+        Arm Neoverse, with a focus on the Neoverse N2 core, in a Linu...
+      preview_generated: This advanced Learning Path guides you through understanding
+        cache-related Performance Monitoring Unit (PMU) events on Arm Neoverse, with
+        a focus on the Neoverse N2 core on Linux. Using concise C and ...
     faqs:
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       before_count: 5
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       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - Which PMU events are used to evaluate each cache level?
+        - How do the code samples trigger the intended cache PMU events?
+        - How do I know if my run matched the expected behavior?
+        - What should I check if LL cache events remain low or zero?
+        removed_questions:
+        - Which Arm core and operating system does this Learning Path focus on?
+        - What prerequisites do I need before starting?
+        - What tools or languages are used in the steps?
+        - What will I run and what gets measured?
+        - How do I verify that the PMU events were triggered as expected?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - Which PMU events are used to evaluate each cache level?
+        - How do the code samples trigger the intended cache PMU events?
+        - How do I know if my run matched the expected behavior?
+        - What should I check if LL cache events remain low or zero?
+        removed_questions:
+        - Which Arm core and operating system does this Learning Path focus on?
+        - What prerequisites do I need before starting?
+        - What tools or languages are used in the steps?
+        - What will I run and what gets measured?
+        - How do I verify that the PMU events were triggered as expected?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md
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+    - rerun_flags_reset
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     summary:
       action: unchanged
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@@ -219983,51 +31806,76 @@ history:
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-      preview_before: Learn about Neoverse Non-cache PMU events using C and Assembly Language walks you
-        through an end-to-end Arm software workflow. It is designed for software and hardware engineers
-        to learn about why com...
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-        through an end-to-end Arm software workflow. It is designed for software and hardware engineers
-        to learn about why com...
-      preview_generated: Learn about Neoverse Non-cache PMU events using C and Assembly Language walks
-        you through an end-to-end Arm software workflow. It is designed for software and hardware engineers
-        to learn about why com...
+      generated_at_before: '2026-06-02T05:21:57Z'
+      generated_at_after: '2026-06-02T05:21:57Z'
+      preview_before: This advanced Learning Path shows how to describe common non-cache
+        PMU events and understand why specific C and Arm assembly sequences trigger
+        them on the Arm Neoverse N2 core. You will run compact ex...
+      preview_after: This advanced Learning Path shows how to describe common non-cache
+        PMU events and understand why specific C and Arm assembly sequences trigger
+        them on the Arm Neoverse N2 core. You will run compact ex...
+      preview_generated: Work through targeted C and Arm assembly examples to trigger
+        and examine non-cache PMU events on the Arm Neoverse N2 core. You will use
+        short code snippets to exercise Topdown L1 metrics, TLB behavior...
     faqs:
-      action: unchanged
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       change_details:
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-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - Which execution environment should I use for the code?
+        - How do I know the ITLB-related events were exercised correctly?
+        - What result should I expect from the SIMD operation mix example?
+        - Where can I find the definitions and behavior of the PMU events used here?
+        removed_questions:
+        - What environment do I need to run the examples?
+        - What prior knowledge is expected?
+        - Which PMU metric groups are covered?
+        - How do I validate that the code is triggering the intended events?
+        - Will results be the same across systems or operating systems?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the examples?
+        - Which execution environment should I use for the code?
+        - How do I know the ITLB-related events were exercised correctly?
+        - What result should I expect from the SIMD operation mix example?
+        - Where can I find the definitions and behavior of the PMU events used here?
+        removed_questions:
+        - What environment do I need to run the examples?
+        - What prior knowledge is expected?
+        - Which PMU metric groups are covered?
+        - How do I validate that the code is triggering the intended events?
+        - Will results be the same across systems or operating systems?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md
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+    managed_block_updated: true
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+    rerun_flags_reset:
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     summary:
       action: unchanged
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@@ -220037,51 +31885,78 @@ history:
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-      preview_before: Scan multi-architecture containers with Trivy on Azure Cobalt 100 walks you through
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-        an end-to-end Arm software workflow. It is designed for developers and DevOps engineers who want
-        to integrate securi...
-      preview_generated: Scan multi-architecture containers with Trivy on Azure Cobalt 100 walks you through
-        an end-to-end Arm software workflow. It is designed for developers and DevOps engineers who want
-        to integrate securi...
+      generated_at_before: '2026-06-02T05:22:29Z'
+      generated_at_after: '2026-06-02T05:22:29Z'
+      preview_before: This Learning Path guides you through building and scanning
+        multi-architecture container images with Trivy on Microsoft Azure Cobalt 100
+        Arm64 virtual machines. You will provision a Dpsv6 series VM vi...
+      preview_after: This Learning Path guides you through building and scanning multi-architecture
+        container images with Trivy on Microsoft Azure Cobalt 100 Arm64 virtual machines.
+        You will provision a Dpsv6 series VM vi...
+      preview_generated: Learn how to build and scan multi-architecture container
+        images on an Arm-based Azure Cobalt 100 (Dpsv6) VM using Trivy and Docker.
+        You will configure Docker Buildx for multi-architecture builds, crea...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
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       change_details:
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-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before starting this Learning Path?
+        - Which Azure VM size and operating system should I use?
+        - Can I create the VM with Azure CLI or infrastructure as code instead of
+          the Portal?
+        - How do I build a multi-architecture container image on the VM?
+        - What should I expect from Trivy scanning and how is it used in CI?
+        removed_questions:
+        - Which method is used to create the Azure Cobalt 100 VM, and can I use others?
+        - What are the prerequisites before starting?
+        - What operating system and tools are used on the VM?
+        - What will I build, and how do I validate it?
+        - How does this Learning Path use GitHub Actions and security gates?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before starting this Learning Path?
+        - Which Azure VM size and operating system should I use?
+        - Can I create the VM with Azure CLI or infrastructure as code instead of
+          the Portal?
+        - How do I build a multi-architecture container image on the VM?
+        - What should I expect from Trivy scanning and how is it used in CI?
+        removed_questions:
+        - Which method is used to create the Azure Cobalt 100 VM, and can I use others?
+        - What are the prerequisites before starting?
+        - What operating system and tools are used on the VM?
+        - What will I build, and how do I validate it?
+        - How does this Learning Path use GitHub Actions and security gates?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md
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-    changed_on_disk: false
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-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
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+    status: updated
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+    managed_block_updated: true
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+    rerun_flags_reset:
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+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
       missing_before: false
@@ -220091,51 +31966,76 @@ history:
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-      preview_before: Tune network workloads on Arm-based bare-metal instances walks you through an end-to-end
-        Arm software workflow. It is designed for engineers who want to tune the performance of network
-        workloads on Ar...
-      preview_after: Tune network workloads on Arm-based bare-metal instances walks you through an end-to-end
-        Arm software workflow. It is designed for engineers who want to tune the performance of network
-        workloads on Ar...
-      preview_generated: Tune network workloads on Arm-based bare-metal instances walks you through an
-        end-to-end Arm software workflow. It is designed for engineers who want to tune the performance
-        of network workloads on Ar...
+      generated_at_before: '2026-06-02T05:23:06Z'
+      generated_at_after: '2026-06-02T05:23:06Z'
+      preview_before: "This advanced Learning Path shows how to benchmark and tune\
+        \ an HTTP network workload on Arm Neoverse-based bare\u2011metal servers using\
+        \ Apache Tomcat, wrk2, and OpenJDK 21 on Ubuntu 24.04. You will set up..."
+      preview_after: "This advanced Learning Path shows how to benchmark and tune\
+        \ an HTTP network workload on Arm Neoverse-based bare\u2011metal servers using\
+        \ Apache Tomcat, wrk2, and OpenJDK 21 on Ubuntu 24.04. You will set up..."
+      preview_generated: "This advanced Learning Path shows how to benchmark and tune\
+        \ a Tomcat-based HTTP workload on an Arm Neoverse bare\u2011metal server running\
+        \ Ubuntu 24.04, using OpenJDK 21 and wrk2 from an x86_64 client. You..."
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
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-      generated_at_after: '2026-05-06T17:17:59Z'
+      generated_at_before: '2026-06-02T05:23:06Z'
+      generated_at_after: '2026-06-03T02:13:37Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
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         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the benchmark?
+        - Do I need to raise file descriptor limits on both the client and server?
+        - How should I choose the NIC queue count during tuning?
+        - How do I decide where to place Tomcat for NUMA locality?
+        - How do I compare IOMMU strict mode with passthrough?
+        removed_questions:
+        - What hardware and operating systems do I need?
+        - Which software and tools are used in this path?
+        - What baseline configuration should I apply before tuning?
+        - How do NIC queues and NUMA affect results in this workflow?
+        - What IOMMU settings are compared, and how are they configured?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the benchmark?
+        - Do I need to raise file descriptor limits on both the client and server?
+        - How should I choose the NIC queue count during tuning?
+        - How do I decide where to place Tomcat for NUMA locality?
+        - How do I compare IOMMU strict mode with passthrough?
+        removed_questions:
+        - What hardware and operating systems do I need?
+        - Which software and tools are used in this path?
+        - What baseline configuration should I apply before tuning?
+        - How do NIC queues and NUMA affect results in this workflow?
+        - What IOMMU settings are compared, and how are they configured?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
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+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -220145,51 +32045,76 @@ history:
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-      preview_before: Deploy TypeScript on Google Cloud C4A virtual machines walks you through an end-to-end
-        Arm software workflow. It is designed for developers deploying and optimizing TypeScript workloads
-        on Arm64 Linux...
-      preview_after: Deploy TypeScript on Google Cloud C4A virtual machines walks you through an end-to-end
-        Arm software workflow. It is designed for developers deploying and optimizing TypeScript workloads
-        on Arm64 Linux...
-      preview_generated: Deploy TypeScript on Google Cloud C4A virtual machines walks you through an end-to-end
-        Arm software workflow. It is designed for developers deploying and optimizing TypeScript workloads
-        on Arm64 Linux...
+      generated_at_before: '2026-06-02T05:23:36Z'
+      generated_at_after: '2026-06-02T05:23:36Z'
+      preview_before: "Provision a SUSE Linux Enterprise Server (SLES) VM on Google\
+        \ Cloud\u2019s Arm-based C4A instances powered by Axion processors, install\
+        \ a TypeScript toolchain, validate it, and benchmark it. You will create..."
+      preview_after: "Provision a SUSE Linux Enterprise Server (SLES) VM on Google\
+        \ Cloud\u2019s Arm-based C4A instances powered by Axion processors, install\
+        \ a TypeScript toolchain, validate it, and benchmark it. You will create..."
+      preview_generated: This Learning Path guides you through deploying and benchmarking
+        TypeScript on Arm-based Google Cloud C4A virtual machines powered by Axion
+        processors. You will provision a SUSE Linux Enterprise Serve...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
+      rerun_requested: true
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-      generated_at_after: '2026-05-06T17:17:59Z'
+      generated_at_before: '2026-06-02T05:23:36Z'
+      generated_at_after: '2026-06-03T02:14:07Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before creating the VM on Google Cloud?
+        - Which machine type and OS should I use for the instance?
+        - Which packages are installed to run TypeScript on the SUSE Arm64 VM?
+        - How do I verify the TypeScript environment is working?
+        - What result should I expect from the benchmarking step?
+        removed_questions:
+        - What are the prerequisites to start this Learning Path?
+        - Which Google Cloud instance and operating system are used?
+        - What software do I install on the VM?
+        - How do I validate that TypeScript is working on the VM?
+        - How is TypeScript performance benchmarked in this path?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before creating the VM on Google Cloud?
+        - Which machine type and OS should I use for the instance?
+        - Which packages are installed to run TypeScript on the SUSE Arm64 VM?
+        - How do I verify the TypeScript environment is working?
+        - What result should I expect from the benchmarking step?
+        removed_questions:
+        - What are the prerequisites to start this Learning Path?
+        - Which Google Cloud instance and operating system are used?
+        - What software do I install on the VM?
+        - How do I validate that TypeScript is working on the VM?
+        - How is TypeScript performance benchmarked in this path?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
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     summary:
       action: unchanged
       missing_before: false
@@ -220199,51 +32124,87 @@ history:
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-      preview_before: Migrate applications that leverage performance libraries walks you through an end-to-end
-        Arm software workflow. It is designed for both C and C++ developers who want to migrate applications
-        that rely ...
-      preview_after: Migrate applications that leverage performance libraries walks you through an end-to-end
-        Arm software workflow. It is designed for both C and C++ developers who want to migrate applications
-        that rely ...
-      preview_generated: Migrate applications that leverage performance libraries walks you through an
-        end-to-end Arm software workflow. It is designed for both C and C++ developers who want to migrate
-        applications that rely ...
+      generated_at_before: '2026-06-02T05:24:08Z'
+      generated_at_after: '2026-06-02T05:24:08Z'
+      preview_before: This Learning Path guides C and C++ developers through migrating
+        applications that depend on optimized performance libraries from x86 to Arm
+        Architecture on Linux. You will compare the C++ standard li...
+      preview_after: This Learning Path guides C and C++ developers through migrating
+        applications that depend on optimized performance libraries from x86 to Arm
+        Architecture on Linux. You will compare the C++ standard li...
+      preview_generated: This Learning Path guides C and C++ developers through migrating
+        applications that depend on performance libraries from x86 to Arm Architecture
+        on Linux. You will review the differences between the C+...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
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+      generated_at_before: '2026-06-02T05:24:08Z'
+      generated_at_after: '2026-06-03T02:14:44Z'
       before_count: 5
       after_count: 5
       generated_count: 5
       change_details:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Arm instance and OS are used in the setup example?
+        - Which compiler should I use to build the examples?
+        - How do I install Arm Performance Libraries on the instance?
+        - How do I replace Intel Vector Statistics Library when migrating to AArch64?
+        removed_questions:
+        - What do I need before starting?
+        - What environment does the setup use?
+        - What software will I install and use in the exercises?
+        - How does the path handle code that depends on Intel VSL?
+        - How can I tell I completed the path successfully?
         updated_questions: []
       generated_diff:
         before_count: 5
         after_count: 5
-        added_questions: []
-        removed_questions: []
-        updated_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - Which Arm instance and OS are used in the setup example?
+        - Which compiler should I use to build the examples?
+        - How do I install Arm Performance Libraries on the instance?
+        - How do I replace Intel Vector Statistics Library when migrating to AArch64?
+        removed_questions:
+        - What do I need before starting?
+        - What environment does the setup use?
+        - What software will I install and use in the exercises?
+        - How does the path handle code that depends on Intel VSL?
+        - How can I tell I completed the path successfully?
+        updated_questions: []
+    category: servers-and-cloud-computing
+  - path: content/learning-paths/servers-and-cloud-computing/vLLM-quant/_index.md
+    status: skipped
+    skip_reason: draft
+    change_reasons:
+    - draft
+    ai_requested: false
+    summary:
+      action: skipped
+    faqs:
+      action: skipped
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md
-    status: unchanged
-    changed_on_disk: false
-    managed_block_updated: false
-    rerun_flags_reset: []
-    change_reasons: []
-    template_version_before: summary-faq-v1
-    template_version_after: summary-faq-v1
+    status: updated
+    changed_on_disk: true
+    managed_block_updated: true
+    ai_requested: true
+    rerun_flags_reset:
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+    change_reasons:
+    - rerun_faqs
+    - rerun_flags_reset
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
     summary:
       action: unchanged
       missing_before: false
@@ -220253,51 +32214,76 @@ history:
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-      preview_before: Install Vectorscan (Hyperscan on Arm) and use it with Snort 3 walks you through
-        an end-to-end Arm software workflow. It is designed for software developers using Hyperscan who
-        want to migrate to Arm. ...
-      preview_after: Install Vectorscan (Hyperscan on Arm) and use it with Snort 3 walks you through an
-        end-to-end Arm software workflow. It is designed for software developers using Hyperscan who want
-        to migrate to Arm. ...
-      preview_generated: Install Vectorscan (Hyperscan on Arm) and use it with Snort 3 walks you through
-        an end-to-end Arm software workflow. It is designed for software developers using Hyperscan who
-        want to migrate to Arm. ...
+      generated_at_before: '2026-06-02T05:24:57Z'
+      generated_at_after: '2026-06-02T05:24:57Z'
+      preview_before: Learn how to migrate regex-based workloads from Hyperscan to
+        Arm by installing and running Vectorscan on an Arm-based Ubuntu instance,
+        then integrating it with Snort 3. You will set up on Ubuntu 20.04...
+      preview_after: Learn how to migrate regex-based workloads from Hyperscan to
+        Arm by installing and running Vectorscan on an Arm-based Ubuntu instance,
+        then integrating it with Snort 3. You will set up on Ubuntu 20.04...
+      preview_generated: Learn how to migrate regex matching workloads from Hyperscan
+        to Arm by installing and running Vectorscan on an Arm-based Ubuntu 20.04 or
+        22.04 server, then installing Snort 3 and using it with Vectors...
     faqs:
-      action: unchanged
+      action: rerun_requested
       missing_before: false
-      rerun_requested: false
-      changed: false
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-      generated_at_after: '2026-05-06T17:17:59Z'
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+        - Should I install Hyperscan or Vectorscan on Arm?
+        - Can I use a cloud instance from AWS, Microsoft Azure, Google Cloud, or Oracle?
+        - Which Ubuntu versions are these steps intended for?
+        - What result should I expect after completing the steps?
+        removed_questions:
+        - What are the prerequisites to start this Learning Path?
+        - Can I use a cloud instance, and which providers are suitable?
+        - What will I install and run during the path?
+        - How is Vectorscan related to Hyperscan, and why is it used here?
+        - How do I know the setup worked?
         updated_questions: []
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+        - Should I install Hyperscan or Vectorscan on Arm?
+        - Can I use a cloud instance from AWS, Microsoft Azure, Google Cloud, or Oracle?
+        - Which Ubuntu versions are these steps intended for?
+        - What result should I expect after completing the steps?
+        removed_questions:
+        - What are the prerequisites to start this Learning Path?
+        - Can I use a cloud instance, and which providers are suitable?
+        - What will I install and run during the path?
+        - How is Vectorscan related to Hyperscan, and why is it used here?
+        - How do I know the setup worked?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/vllm/_index.md
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-      preview_generated: Build and Run vLLM on Arm Servers walks you through an end-to-end Arm software
-        workflow. It is designed for software developers and AI engineers interested in learning how to
-        use the vLLM library on A...
+      generated_at_before: '2026-06-02T05:25:21Z'
+      generated_at_after: '2026-06-02T05:25:21Z'
+      preview_before: Learn to build vLLM from source on an Arm-based Ubuntu 24.04
+        LTS server, verify BFloat16 support, and run both local batch inference and
+        an OpenAI-compatible server. The path uses a Qwen model from Hu...
+      preview_after: Learn to build vLLM from source on an Arm-based Ubuntu 24.04
+        LTS server, verify BFloat16 support, and run both local batch inference and
+        an OpenAI-compatible server. The path uses a Qwen model from Hu...
+      preview_generated: Follow this introductory Learning Path to build vLLM from
+        source on an Arm server, download a Qwen model from the Hugging Face Hub,
+        run local batch inference, and stand up an OpenAI-compatible server ...
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+        - Do I need to download the model from Hugging Face ahead of time?
+        - Which model is used in this Learning Path?
+        - When should I use batch inference versus the OpenAI-compatible server?
+        removed_questions:
+        - Can I use a cloud instance, or do I need local hardware?
+        - What system and OS requirements are needed?
+        - How do I check if my CPU supports BFloat16?
+        - Which model is used, and do I need to download it manually?
+        - What is the expected outcome and how do I validate it?
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       generated_diff:
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+        - How do I know if my Arm CPU supports BFloat16?
+        - Do I need to download the model from Hugging Face ahead of time?
+        - Which model is used in this Learning Path?
+        - When should I use batch inference versus the OpenAI-compatible server?
+        removed_questions:
+        - Can I use a cloud instance, or do I need local hardware?
+        - What system and OS requirements are needed?
+        - How do I check if my CPU supports BFloat16?
+        - Which model is used, and do I need to download it manually?
+        - What is the expected outcome and how do I validate it?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
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-      preview_generated: Run vLLM inference with INT4 quantization on Arm servers walks you through an
-        end-to-end Arm software workflow. It is designed for developers interested in building and optimizing
-        vLLM for Arm-based s...
+      generated_at_before: '2026-06-02T05:25:49Z'
+      generated_at_after: '2026-06-02T05:25:49Z'
+      preview_before: This Learning Path shows how to build an aarch64-optimized vLLM
+        with oneDNN and the Arm Compute Library on an Arm-based Linux server, set
+        up runtime dependencies (including PyTorch and llmcompressor),...
+      preview_after: This Learning Path shows how to build an aarch64-optimized vLLM
+        with oneDNN and the Arm Compute Library on an Arm-based Linux server, set
+        up runtime dependencies (including PyTorch and llmcompressor),...
+      preview_generated: This Learning Path walks you through building an Arm-optimized
+        vLLM for aarch64 with oneDNN and the Arm Compute Library, setting up runtime
+        dependencies (including PyTorch and llmcompressor), quantizi...
     faqs:
-      action: unchanged
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-        added_questions: []
-        removed_questions: []
+        added_questions:
+        - What do I need before running the steps?
+        - How do I build and verify vLLM is optimized for aarch64 with oneDNN and
+          ACL?
+        - Which packages do I install to quantize the model, and why are they needed?
+        - How should I set vLLM batch sizing parameters when serving the model?
+        - How do I evaluate accuracy for BF16 vs INT4 with LM Evaluation Harness?
+        removed_questions:
+        - What hardware and software prerequisites do I need?
+        - Which model and precisions are used in this path?
+        - What tools and libraries will I set up or build?
+        - How do I interact with the served model and what runtime limits matter?
+        - How do I validate the setup and measure quality?
         updated_questions: []
       generated_diff:
         before_count: 5
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-        added_questions: []
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+        added_questions:
+        - What do I need before running the steps?
+        - How do I build and verify vLLM is optimized for aarch64 with oneDNN and
+          ACL?
+        - Which packages do I install to quantize the model, and why are they needed?
+        - How should I set vLLM batch sizing parameters when serving the model?
+        - How do I evaluate accuracy for BF16 vs INT4 with LM Evaluation Harness?
+        removed_questions:
+        - What hardware and software prerequisites do I need?
+        - Which model and precisions are used in this path?
+        - What tools and libraries will I set up or build?
+        - How do I interact with the served model and what runtime limits matter?
+        - How do I validate the setup and measure quality?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
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-        Versatile Vide...
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-        software workflow. It is designed for software developers who want to build and run the VVenC®
-        (Fraunhofer Versatile Vide...
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+      generated_at_after: '2026-06-02T05:26:21Z'
+      preview_before: Learn how to build and run the open-source VVenC (vvenc) H.266/VVC
+        encoder on Arm-based Linux servers to encode a real 1080p video and measure
+        performance. This introductory path targets Arm Neoverse ...
+      preview_after: Learn how to build and run the open-source VVenC (vvenc) H.266/VVC
+        encoder on Arm-based Linux servers to encode a real 1080p video and measure
+        performance. This introductory path targets Arm Neoverse ...
+      preview_generated: This Learning Path shows how to build the open-source VVenC
+        (H.266/VVC) encoder on an Arm-based Linux server and run vvenc to encode a
+        real 1080p video, then measure performance. It targets Arm Neover...
     faqs:
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+        added_questions:
+        - What do I need before running the steps?
+        - Which cloud platforms can I use for the Arm instance?
+        - Where do I get the encoder source and which tool will I run?
+        - Do I need Neon or SVE/SVE2 to follow this path?
+        - What result should I expect after running vvenc on a 1080p video?
+        removed_questions:
+        - What environment do I need to complete this Learning Path?
+        - What will I build and run during the steps?
+        - Does vvenc include Arm-specific optimizations?
+        - Are there any explicit prerequisites beyond access to an Arm system?
+        - How do I know I completed the path successfully?
         updated_questions: []
       generated_diff:
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-        added_questions: []
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+        added_questions:
+        - What do I need before running the steps?
+        - Which cloud platforms can I use for the Arm instance?
+        - Where do I get the encoder source and which tool will I run?
+        - Do I need Neon or SVE/SVE2 to follow this path?
+        - What result should I expect after running vvenc on a 1080p video?
+        removed_questions:
+        - What environment do I need to complete this Learning Path?
+        - What will I build and run during the steps?
+        - Does vvenc include Arm-specific optimizations?
+        - Are there any explicit prerequisites beyond access to an Arm system?
+        - How do I know I completed the path successfully?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/whisper/_index.md
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-        Arm software workflow. It is designed for software developers familiar with basic machine learning
-        concepts and...
-      preview_generated: Accelerate Whisper on Arm with Hugging Face Transformers walks you through an
-        end-to-end Arm software workflow. It is designed for software developers familiar with basic machine
-        learning concepts and...
+      generated_at_before: '2026-06-02T05:26:50Z'
+      generated_at_after: '2026-06-02T05:26:50Z'
+      preview_before: This Learning Path shows how to run the OpenAI Whisper ASR model
+        on Arm-based cloud servers using Hugging Face Transformers. You will install
+        the required Python dependencies, configure environment va...
+      preview_after: This Learning Path shows how to run the OpenAI Whisper ASR model
+        on Arm-based cloud servers using Hugging Face Transformers. You will install
+        the required Python dependencies, configure environment va...
+      preview_generated: This introductory Learning Path shows how to install dependencies
+        and run the OpenAI Whisper automatic speech recognition model (whisper-large-v3-turbo)
+        on an Arm-based Ubuntu 24.04 LTS server using H...
     faqs:
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+        added_questions:
+        - What do I need before running the steps?
+        - Which Whisper model and libraries does this path use?
+        - Which settings will I change to improve performance on Arm CPUs?
+        - What result should I expect after running the demo?
+        - Where were these steps tested?
+        removed_questions:
+        - What hardware and operating system do I need?
+        - Which Whisper model and libraries are used?
+        - How does the Learning Path enable better performance on Arm CPUs?
+        - How do I know my setup is working?
+        - Which cloud providers can I use?
         updated_questions: []
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+        - What do I need before running the steps?
+        - Which Whisper model and libraries does this path use?
+        - Which settings will I change to improve performance on Arm CPUs?
+        - What result should I expect after running the demo?
+        - Where were these steps tested?
+        removed_questions:
+        - What hardware and operating system do I need?
+        - Which Whisper model and libraries are used?
+        - How does the Learning Path enable better performance on Arm CPUs?
+        - How do I know my setup is working?
+        - Which cloud providers can I use?
+        updated_questions: []
+    category: servers-and-cloud-computing
   - path: content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
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   - path: content/learning-paths/servers-and-cloud-computing/zlib/_index.md
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-      preview_generated: Learn how to build and use zlib-ng on Arm servers, using its Neon SIMD and ARMv8
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+      preview_after: Build and use zlib-ng on an Arm Linux server to take advantage
+        of Neon SIMD and ARMv8 CRC32 enhancements for compression-heavy workloads.
+        You will compile zlib-ng in zlib-compatible mode, run example ...
+      preview_generated: This Learning Path shows how to build and use zlib-ng on
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         updated_questions: []
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+    category: servers-and-cloud-computing
diff --git a/reports/generated-summary-faq/servers-and-cloud-computing.yml b/reports/generated-summary-faq/servers-and-cloud-computing.yml
new file mode 100644
index 0000000000..f0786a70d3
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+      preview_generated: This Learning Path shows how to deploy Arcee’s AFM-4.5B small language model
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+      preview_before: This Learning Path shows how to deploy Arcee’s AFM-4.5B model on Arm-based Google
+        Cloud Axion using Llama.cpp. You will launch a c4a-standard-16 (or larger) Compute Engine VM with
+        Ubuntu 24.04 LTS Min...
+      preview_after: This Learning Path shows how to deploy Arcee’s AFM-4.5B model on Arm-based Google
+        Cloud Axion using Llama.cpp. You will launch a c4a-standard-16 (or larger) Compute Engine VM with
+        Ubuntu 24.04 LTS Min...
+      preview_generated: This Learning Path shows how to deploy Arcee’s AFM-4.5B small language model
+        on Arm-based Google Cloud Axion using Llama.cpp. You will provision a Linux Compute Engine VM
+        (c4a-standard-16 or larger), ...
+    faqs:
+      action: drift_detected_preserved
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: true
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+      generated_at_before: '2026-05-18T17:04:00Z'
+      generated_at_after: '2026-05-18T17:04:00Z'
+      before_count: 5
+      after_count: 5
+      generated_count: 5
+      change_details:
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+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+      generated_diff:
+        before_count: 5
+        after_count: 5
+        added_questions:
+        - Who is this Learning Path for and how long does it take?
+        - What Google Cloud resources and permissions are required?
+        - How much storage should I provision on the VM?
+        - What software stack and operating system are used?
+        - Does AFM-4.5B require a custom Llama.cpp fork?
+        removed_questions:
+        - What are the prerequisites and expected duration?
+        - Which Google Cloud and OS settings are used?
+        - How do I obtain and prepare the AFM-4.5B model?
+        - How is Llama.cpp built and optimized for Axion?
+        - How do I run inference and evaluate results?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
+    status: drift_detected
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons:
+    - summary_drift_detected
+    - faq_drift_detected
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
+    summary:
+      action: drift_detected_preserved
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: true
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+      generated_at_before: '2026-05-18T17:04:39Z'
+      generated_at_after: '2026-05-18T17:04:39Z'
+      preview_before: This Learning Path guides you through provisioning an Arm-based SUSE Linux Enterprise
+        Server VM on Google Cloud C4A with Axion processors (Arm Neoverse V2), creating an Arm64 GKE cluster,
+        and installi...
+      preview_after: This Learning Path guides you through provisioning an Arm-based SUSE Linux Enterprise
+        Server VM on Google Cloud C4A with Axion processors (Arm Neoverse V2), creating an Arm64 GKE cluster,
+        and installi...
+      preview_generated: This Learning Path shows you how to deploy and manage applications on Arm-based
+        Google Kubernetes Engine (GKE) using GitOps with Argo CD. You will provision a SUSE Linux Enterprise
+        Server Arm64 VM on ...
+    faqs:
+      action: drift_detected_preserved
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: true
+      source_hash_before: c6106f21859f5a8e04bbd579db47c7177bb5371d4c7f306054e56e81ed9e89a1
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+      generated_at_before: '2026-05-18T17:04:39Z'
+      generated_at_after: '2026-05-18T17:04:39Z'
+      before_count: 5
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+      change_details:
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+        removed_questions: []
+        updated_questions: []
+      generated_diff:
+        before_count: 5
+        after_count: 5
+        added_questions:
+        - What will I build and deploy in this Learning Path?
+        - What prerequisites do I need before starting?
+        - Which Arm and Google Cloud technologies are used?
+        - How is Argo CD installed and accessed in the cluster?
+        - Do I need a Git repository, and what goes in it?
+        removed_questions:
+        - What will I build and configure in this Learning Path?
+        - What are the prerequisites?
+        - Which Arm and Google Cloud resources are used?
+        - How is Argo CD installed and accessed on the cluster?
+        - How is GitOps enforced and validated in this workflow?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
+    status: drift_detected
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons:
+    - summary_drift_detected
+    - faq_drift_detected
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
+    summary:
+      action: drift_detected_preserved
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: true
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+      generated_at_after: '2026-05-18T17:05:06Z'
+      preview_before: This Learning Path guides advanced C++ developers porting applications from x86
+        to Arm through the C++ memory model and its implications for concurrency on Linux. You will review
+        source, program, and ...
+      preview_after: This Learning Path guides advanced C++ developers porting applications from x86 to
+        Arm through the C++ memory model and its implications for concurrency on Linux. You will review
+        source, program, and ...
+      preview_generated: This Learning Path guides advanced C++ developers through writing correct concurrent
+        code when porting from x86 to Arm by focusing on the C++ memory model and hardware memory ordering.
+        You will revisi...
+    faqs:
+      action: drift_detected_preserved
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: true
+      source_hash_before: 3a4bc8b2ab548be507b6d528844b0593b27f2dab3ab3c9649e807abdf4887ce0
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+      generated_at_before: '2026-05-18T17:05:06Z'
+      generated_at_after: '2026-05-18T17:05:06Z'
+      before_count: 5
+      after_count: 5
+      generated_count: 5
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+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+      generated_diff:
+        before_count: 5
+        after_count: 5
+        added_questions:
+        - Who is this Learning Path for?
+        - What will I learn and practice?
+        - Which operating system and tools are used?
+        - Do I need a specific cloud instance type?
+        removed_questions:
+        - What will I learn about the C++ memory model in this path?
+        - Why can code that seems correct on x86 fail on Arm?
+        - What environment and tools are used in the exercises?
+        - How do I detect race conditions here, and what are TSan’s limitations?
+        updated_questions:
+        - What are the prerequisites?
+  - path: content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
+    status: drift_detected
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons:
+    - summary_drift_detected
+    - faq_drift_detected
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
+    summary:
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+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: true
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+      generated_at_before: '2026-05-18T17:05:58Z'
+      generated_at_after: '2026-05-18T17:05:58Z'
+      preview_before: Learn how to automate x86-to-Arm migration using the Arm MCP Server and AI-powered
+        IDEs. You will connect your assistant via the Model Context Protocol to run Arm-specific checks,
+        verify Docker base i...
+      preview_after: Learn how to automate x86-to-Arm migration using the Arm MCP Server and AI-powered
+        IDEs. You will connect your assistant via the Model Context Protocol to run Arm-specific checks,
+        verify Docker base i...
+      preview_generated: This Learning Path shows how to automate x86-to-Arm application migration using
+        the Arm MCP Server and the Model Context Protocol (MCP). You will connect an AI-powered IDE to
+        the server, use natural l...
+    faqs:
+      action: drift_detected_preserved
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: true
+      source_hash_before: b870ac5160d35ddb51955ca8379493e172787ced8125cde8ae79f7700e653a87
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+      generated_at_before: '2026-05-18T17:05:58Z'
+      generated_at_after: '2026-05-18T17:05:58Z'
+      before_count: 5
+      after_count: 5
+      generated_count: 5
+      change_details:
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+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+      generated_diff:
+        before_count: 5
+        after_count: 5
+        added_questions:
+        - What is the Arm MCP Server and how does it help with migration?
+        - What will I build and validate in this Learning Path?
+        - What prerequisites and environment are required?
+        - How do I check if my Docker base images support arm64?
+        - Do I have to use GitHub Copilot, or can I use other AI agents?
+        removed_questions:
+        - What is the Arm MCP Server and why is it used here?
+        - What are the prerequisites to follow this Learning Path?
+        - Do I have to use GitHub Copilot, or can I use other tools?
+        - How do I check whether a Docker image supports Arm?
+        - What code changes are covered and how are results validated?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
+    status: drift_detected
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons:
+    - summary_drift_detected
+    - faq_drift_detected
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
+    summary:
+      action: drift_detected_preserved
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: true
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+      generated_at_before: '2026-05-18T17:06:28Z'
+      generated_at_after: '2026-05-18T17:06:28Z'
+      preview_before: This Learning Path guides advanced C developers through migrating an application
+        between Arm platforms using Kiro Arm SoC Migration Power. You install Kiro IDE, enable the Migration
+        Power, and use its...
+      preview_after: This Learning Path guides advanced C developers through migrating an application
+        between Arm platforms using Kiro Arm SoC Migration Power. You install Kiro IDE, enable the Migration
+        Power, and use its...
+      preview_generated: This Learning Path shows how to migrate a C application between Arm platforms
+        using Kiro Arm SoC Migration Power. You install Kiro IDE, enable the Migration Power, and run
+        the Arm MCP server in a Dock...
+    faqs:
+      action: drift_detected_preserved
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: true
+      source_hash_before: 49b3f2b1464efb20f0c8fbcff46e9f30910d856567ca01c01dc348aa1c5740d1
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+      current_source_hash: 49b3f2b1464efb20f0c8fbcff46e9f30910d856567ca01c01dc348aa1c5740d1
+      generated_at_before: '2026-05-18T17:06:28Z'
+      generated_at_after: '2026-05-18T17:06:28Z'
+      before_count: 5
+      after_count: 5
+      generated_count: 5
+      change_details:
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+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+      generated_diff:
+        before_count: 5
+        after_count: 5
+        added_questions:
+        - What will I build or migrate in this Learning Path?
+        - How do I set up the environment?
+        - Does this workflow apply beyond the example platforms?
+        - How is the migration validated?
+        removed_questions:
+        - What will I build and verify in this Learning Path?
+        - Which tools and operating systems are used?
+        - Does the workflow apply beyond Graviton3 to Raspberry Pi 5?
+        - How long will it take and who should take it?
+        updated_questions:
+        - What are the prerequisites?
+  - path: content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
+    status: drift_detected
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons:
+    - summary_drift_detected
+    - faq_drift_detected
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
+    summary:
+      action: drift_detected_preserved
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: true
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+      generated_at_before: '2026-05-18T17:07:00Z'
+      generated_at_after: '2026-05-18T17:07:00Z'
+      preview_before: This Learning Path shows how to install and boot a Linux kernel configured with
+        64K memory pages on Arm-based systems to improve memory efficiency and performance for memory-intensive
+        workloads. You w...
+      preview_after: This Learning Path shows how to install and boot a Linux kernel configured with 64K
+        memory pages on Arm-based systems to improve memory efficiency and performance for memory-intensive
+        workloads. You w...
+      preview_generated: This Learning Path shows how to install and run a Linux kernel configured with
+        a 64K base page size on Arm systems to improve memory efficiency and benefit memory‑intensive
+        workloads. You will learn p...
+    faqs:
+      action: drift_detected_preserved
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: true
+      source_hash_before: 67004eb9a75926144eb6f75124104972b3cf20a57a22b698c9359d20db3eca02
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+      current_source_hash: 67004eb9a75926144eb6f75124104972b3cf20a57a22b698c9359d20db3eca02
+      generated_at_before: '2026-05-18T17:07:00Z'
+      generated_at_after: '2026-05-18T17:07:00Z'
+      before_count: 5
+      after_count: 5
+      generated_count: 5
+      change_details:
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+        added_questions: []
+        removed_questions: []
+        updated_questions: []
+      generated_diff:
+        before_count: 5
+        after_count: 5
+        added_questions:
+        - Which Linux distributions and versions does this Learning Path cover?
+        - What are the prerequisites to follow this path?
+        - How do I verify the current memory page size on my system?
+        - Does Debian provide a prebuilt 64K page size kernel?
+        - Can I switch back to the default 4K kernel after testing 64K?
+        removed_questions:
+        - Which Linux distributions and versions are covered?
+        - How do I verify the active page size and kernel version?
+        - Do I need to compile a custom kernel for 64K pages?
+        - Can I revert to the default 4K page size after testing?
+        - What are the prerequisites and expected effort?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
+    status: drift_detected
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons:
+    - summary_drift_detected
+    - faq_drift_detected
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
+    summary:
+      action: drift_detected_preserved
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: true
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+      generated_at_after: '2026-05-18T17:07:36Z'
+      preview_before: This advanced Learning Path shows how to access Arm Performance Monitoring Unit
+        (PMU) hardware event counters and the system counter from user space on Linux. You will read the
+        system counter using in...
+      preview_after: This advanced Learning Path shows how to access Arm Performance Monitoring Unit (PMU)
+        hardware event counters and the system counter from user space on Linux. You will read the system
+        counter using in...
+      preview_generated: This Learning Path shows how to access Arm hardware performance counters and
+        the system counter from Linux user space using assembly, PAPI, and the perf_event_open system
+        call. You will distinguish ha...
+    faqs:
+      action: drift_detected_preserved
+      missing_before: false
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+      changed: false
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+      generated_at_before: '2026-05-18T17:07:36Z'
+      generated_at_after: '2026-05-18T17:07:36Z'
+      before_count: 5
+      after_count: 5
+      generated_count: 5
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+        added_questions: []
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+        updated_questions: []
+      generated_diff:
+        before_count: 5
+        after_count: 5
+        added_questions:
+        - What does this Learning Path teach?
+        - What are the prerequisites and recommended platform?
+        - What will I build or run during the exercises?
+        - How are the Arm PMU and system counter used here?
+        - How do PAPI and perf_event_open differ, and is multiplexing supported?
+        removed_questions:
+        - What environment do I need to complete this Learning Path?
+        - Do I need root privileges to access counters from user space?
+        - How can I measure elapsed time in my code?
+        - How many hardware events can I count at once, and what about multiplexing?
+        - When should I use PAPI versus perf_event_open?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
+    status: drift_detected
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons:
+    - summary_drift_detected
+    - faq_drift_detected
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
+    summary:
+      action: drift_detected_preserved
+      missing_before: false
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+      changed: false
+      drift_detected: true
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+      generated_at_before: '2026-05-18T17:08:08Z'
+      generated_at_after: '2026-05-18T17:08:08Z'
+      preview_before: This introductory Learning Path shows how to package multi-architecture container
+        applications and deploy them on AWS Fargate with AWS Graviton processors using the AWS Copilot
+        CLI. You will container...
+      preview_after: This introductory Learning Path shows how to package multi-architecture container
+        applications and deploy them on AWS Fargate with AWS Graviton processors using the AWS Copilot
+        CLI. You will container...
+      preview_generated: This introductory Learning Path shows how to package a multi-architecture container
+        and deploy it to AWS Fargate on Arm-based AWS Graviton processors using the AWS Copilot CLI. You
+        will containerize a...
+    faqs:
+      action: drift_detected_preserved
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: true
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+        - What are the prerequisites before I start?
+        - Does Copilot default to Graviton or Arm architecture?
+        - Do I need a multi-architecture container image?
+        - Which AWS services and tools are used in the deployment?
+        removed_questions:
+        - What are the prerequisites?
+        - How do I ensure the service runs on AWS Graviton processors?
+        - Can I deploy an existing container image instead of building from a Dockerfile?
+        - Which AWS resources will Copilot create, and how do I check status?
+        updated_questions:
+        - What will I build and deploy in this Learning Path?
+  - path: content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
+    status: drift_detected
+    changed_on_disk: false
+    managed_block_updated: false
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+      preview_before: This Learning Path guides you through automating the deployment of Arm instances
+        on AWS, including Graviton, using Terraform and a secure jump server (bastion) pattern. You will
+        prepare AWS credential...
+      preview_after: This Learning Path guides you through automating the deployment of Arm instances
+        on AWS, including Graviton, using Terraform and a secure jump server (bastion) pattern. You will
+        prepare AWS credential...
+      preview_generated: Deploy Arm Instances on AWS using Terraform shows how to automate provisioning
+        of AWS Graviton (Arm Neoverse-based) EC2 instances and control access with a jump server (bastion).
+        You will define infra...
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+        - What are the prerequisites?
+        - Does this Learning Path use Terraform Cloud?
+        - How is access to the Arm instances secured?
+        - Can I adapt the Terraform configuration for other projects?
+        removed_questions:
+        - What will I build and deploy?
+        - What do I need before I start?
+        - Does this Learning Path use Terraform Cloud, and where do I run commands?
+        - How is access to private instances managed?
+        - What Terraform files will I work with, and can I reuse them?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
+    status: drift_detected
+    changed_on_disk: false
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+      preview_before: This introductory Learning Path guides you through creating and deploying an Azure
+        Resource Manager (ARM) template to provision a Linux virtual machine on Microsoft Azure using
+        Arm64-based Cobalt 100 ...
+      preview_after: This introductory Learning Path guides you through creating and deploying an Azure
+        Resource Manager (ARM) template to provision a Linux virtual machine on Microsoft Azure using
+        Arm64-based Cobalt 100 ...
+      preview_generated: Learn how to create and deploy an Azure Resource Manager (ARM) template that
+        provisions a Linux virtual machine on Microsoft Azure powered by Cobalt 100 processors. You will
+        structure a JSON template ...
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+        - How do I select a region and VM size that supports Azure Cobalt 100?
+        - How is the ARM template structured in this Learning Path?
+        - How do I verify the VM is running on Arm64 after deployment?
+        - Can I reuse this template in CI/CD pipelines?
+        removed_questions:
+        - Who is this Learning Path for and what will I build?
+        - What are the prerequisites to follow along?
+        - How do I select an Arm64 Cobalt 100 VM size in the template?
+        - How do I deploy the template with the Azure CLI?
+        - How do I verify the VM is running on Arm64 Cobalt 100 after deployment?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
+    status: added
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+      preview_before: ''
+      preview_after: This Learning Path shows how to deploy a .NET 8 web application on Microsoft Azure
+        Cobalt 100 Arm-based VMs. You will configure a Linux Azure Cobalt 100 VM as a self-hosted Arm64
+        GitHub Actions runner...
+      preview_generated: This Learning Path shows how to deploy a .NET 8 web application on Microsoft
+        Azure Cobalt 100 Arm-based VMs. You will configure a Linux Azure Cobalt 100 VM as a self-hosted
+        Arm64 GitHub Actions runner...
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+        - What will I build and deploy in this Learning Path?
+        - What are the prerequisites and supported environment?
+        - Can I use GitHub-hosted Arm64 runners instead of a self-hosted runner?
+        - What is Azure Cobalt 100 and which VM series are available?
+        - How long does this take and who is it for?
+        removed_questions: []
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+      generated_diff:
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+        - What are the prerequisites and supported environment?
+        - Can I use GitHub-hosted Arm64 runners instead of a self-hosted runner?
+        - What is Azure Cobalt 100 and which VM series are available?
+        - How long does this take and who is it for?
+        removed_questions: []
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
+    status: drift_detected
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
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+    - summary_drift_detected
+    - faq_drift_detected
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+      generated_at_after: '2026-05-18T17:11:00Z'
+      preview_before: This Learning Path shows how to automate deployment of Arm-based Linux virtual machines
+        on Microsoft Azure using Terraform and Terraform Cloud. You will prepare Azure authentication
+        with the Azure CLI...
+      preview_after: This Learning Path shows how to automate deployment of Arm-based Linux virtual machines
+        on Microsoft Azure using Terraform and Terraform Cloud. You will prepare Azure authentication
+        with the Azure CLI...
+      preview_generated: This Learning Path shows how to automate the creation of Arm Neoverse-based virtual
+        machines on Microsoft Azure using Terraform. You will define infrastructure as code, provision
+        Linux VMs, and enable...
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+        added_questions:
+        - What will I build in this Learning Path?
+        - What are the prerequisites to get started?
+        - Does the workflow use Terraform Cloud or only local Terraform?
+        - Which operating system is deployed on the VMs?
+        - Can I reuse the provided Terraform files for other projects?
+        removed_questions:
+        - What will I deploy in this Learning Path?
+        - What are the prerequisites?
+        - How do I choose the Azure VM image?
+        - How is secure access to the VMs provided?
+        - Who is this Learning Path for?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
+    status: drift_detected
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons:
+    - summary_drift_detected
+    - faq_drift_detected
+    template_version_before: summary-faq-v3
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+      generated_at_before: '2026-05-18T17:11:35Z'
+      generated_at_after: '2026-05-18T17:11:35Z'
+      preview_before: This Learning Path shows how to build and deploy a custom Azure Linux 3.0 image
+        on Arm-based Cobalt 100 processors in Microsoft Azure. You will use QEMU on a Linux host to create
+        a raw disk, boot from...
+      preview_after: This Learning Path shows how to build and deploy a custom Azure Linux 3.0 image on
+        Arm-based Cobalt 100 processors in Microsoft Azure. You will use QEMU on a Linux host to create
+        a raw disk, boot from...
+      preview_generated: This Learning Path guides you through building and deploying a custom Azure Linux
+        3.0 virtual machine image for Arm-based Cobalt 100 processors on Microsoft Azure. You will use
+        QEMU on a Linux host to...
+    faqs:
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+      before_count: 5
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+      generated_diff:
+        before_count: 5
+        after_count: 5
+        added_questions:
+        - What will I build and deploy in this Learning Path?
+        - What prerequisites are required?
+        - Why does the workflow use QEMU and an AArch64 ISO?
+        - How is the custom image registered for reuse in Azure?
+        - How long will it take and who should follow it?
+        removed_questions:
+        - Why do I need a custom Azure Linux 3.0 image for Arm on Azure?
+        - What prerequisites and tools are required?
+        - How is the Azure Linux 3.0 image built with QEMU?
+        - What disk format and size does Azure require?
+        - How do I deploy and verify the VM on Cobalt 100?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
+    status: drift_detected
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
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+    - faq_drift_detected
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+      generated_at_after: '2026-05-18T17:12:08Z'
+      preview_before: This Learning Path shows how to deploy and accelerate Hugging Face Sentiment Analysis
+        models with PyTorch on Arm servers running Linux. You will provision an Arm-based Ubuntu 22.04
+        LTS machine (at lea...
+      preview_after: This Learning Path shows how to deploy and accelerate Hugging Face Sentiment Analysis
+        models with PyTorch on Arm servers running Linux. You will provision an Arm-based Ubuntu 22.04
+        LTS machine (at lea...
+      preview_generated: This Learning Path shows how to deploy and accelerate PyTorch NLP sentiment analysis
+        models from Hugging Face on Arm servers. You will set up a Linux environment (tested on Ubuntu
+        22.04 LTS), run the ...
+    faqs:
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+      before_count: 5
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+      generated_diff:
+        before_count: 5
+        after_count: 5
+        added_questions:
+        - What will I build and measure in this Learning Path?
+        - What hardware and operating system do I need?
+        - Which cloud platforms and CPUs are covered or tested?
+        - Which tools and languages are used?
+        - How long does it take and who is it for?
+        removed_questions:
+        - What hardware and OS are assumed for this Learning Path?
+        - What exactly will I measure and compare?
+        - Are there specific prerequisites beyond access to an Arm server?
+        - Does this Learning Path cover training or fine-tuning models?
+        - Can I follow this on clouds other than AWS or on-premises?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
+    status: drift_detected
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons:
+    - summary_drift_detected
+    - faq_drift_detected
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+      generated_at_after: '2026-05-18T17:12:36Z'
+      preview_before: This Learning Path shows how to implement and benchmark bitmap scanning for database
+        workloads on Arm-based cloud instances running Linux. You will build a simple bit vector in C,
+        implement scalar, Ne...
+      preview_after: This Learning Path shows how to implement and benchmark bitmap scanning for database
+        workloads on Arm-based cloud instances running Linux. You will build a simple bit vector in C,
+        implement scalar, Ne...
+      preview_generated: This Learning Path shows how to implement and benchmark bitmap scanning for database
+        workloads on Arm-based cloud servers. You will build a simple bit vector in C, add scalar scanning
+        baselines, and t...
+    faqs:
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+        added_questions:
+        - What will I build and test in this Learning Path?
+        - What are the prerequisites to get started?
+        - Where can I run the exercises?
+        - How are Neon and SVE used in the examples?
+        - How will I measure and compare performance?
+        removed_questions:
+        - What will I build and measure in this Learning Path?
+        - What platforms and operating systems does this target?
+        - What are the prerequisites?
+        - How is this relevant to database systems?
+        - Which implementation should I use for different bit densities?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/bolt/_index.md
+    status: drift_detected
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons:
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+    - faq_drift_detected
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+      preview_before: Learn how to build, profile, and optimize Arm executables on Linux using BOLT post-link
+        optimization. You will run your application on an Arm Linux target, collect performance data with
+        Linux Perf usi...
+      preview_after: Learn how to build, profile, and optimize Arm executables on Linux using BOLT post-link
+        optimization. You will run your application on an Arm Linux target, collect performance data with
+        Linux Perf usi...
+      preview_generated: This Learning Path shows how to prepare, profile, and optimize an Arm Linux executable
+        using BOLT post-link optimization to improve performance through code layout changes. You will
+        decide on a single...
+    faqs:
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+        added_questions:
+        - What will I do in this Learning Path?
+        - What environment and versions are required?
+        - Can I use one or two machines for the workflow?
+        - Which profiling methods are covered, and how is the data used?
+        - Which Arm platforms is this relevant to, and how long will it take?
+        removed_questions:
+        - What systems and software do I need before starting?
+        - 'Which recording method should I use: Samples, ETM, or SPE?'
+        - Can I split profiling and optimization across two systems?
+        - How do I handle very large perf.data files from ETM?
+        - What if my executable is input-dependent?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
+    status: drift_detected
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons:
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+    - faq_drift_detected
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
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+      preview_before: Optimize AArch64 binaries with LLVM BOLT teaches you how to evaluate and improve
+        code layout on Arm Neoverse and Cortex-A systems running Linux. You install BOLT (LLVM 22.1.0+),
+        build a BubbleSort-bas...
+      preview_after: Optimize AArch64 binaries with LLVM BOLT teaches you how to evaluate and improve
+        code layout on Arm Neoverse and Cortex-A systems running Linux. You install BOLT (LLVM 22.1.0+),
+        build a BubbleSort-bas...
+      preview_generated: This Learning Path shows how to install and use LLVM BOLT on AArch64 Linux to
+        improve code layout for binaries with poor instruction locality. You will compile and run a BubbleSort-based
+        example, gath...
+    faqs:
+      action: drift_detected_preserved
+      missing_before: false
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+      changed: false
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+        - What will I build and measure in this Learning Path?
+        - What are the system and software prerequisites?
+        - Which LLVM BOLT version do I need and how do I install it?
+        - How do I decide if my program is a good candidate for BOLT?
+        - What profiling options are covered, and what is BRBE?
+        removed_questions:
+        - What will I accomplish in this Learning Path?
+        - What hardware and software do I need before starting?
+        - How do I know if my program is a good candidate for BOLT?
+        - Which profiling method should I choose?
+        - How do I install and verify the correct BOLT version?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
+    status: drift_detected
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons:
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+    - faq_drift_detected
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+    template_version_after: summary-faq-v3
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+      generated_at_after: '2026-05-18T17:14:36Z'
+      preview_before: This advanced Learning Path shows how to optimize Arm application binaries and shared
+        libraries with BOLT on Linux. You will build and instrument a MySQL server binary and its OpenSSL
+        dependencies (li...
+      preview_after: This advanced Learning Path shows how to optimize Arm application binaries and shared
+        libraries with BOLT on Linux. You will build and instrument a MySQL server binary and its OpenSSL
+        dependencies (li...
+      preview_generated: This advanced Learning Path shows how to optimize Arm application binaries and
+        shared libraries with BOLT on Linux, targeting Arm Neoverse and Cortex-A platforms. You will instrument
+        the MySQL server ...
+    faqs:
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+      missing_before: false
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+      changed: false
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+        - What will I do in this Learning Path?
+        - What are the prerequisites and target platforms?
+        - How are profiles collected and merged for BOLT optimization?
+        - Can I optimize shared libraries independently of the application?
+        - How is performance evaluated in this path?
+        removed_questions:
+        - Who is this Learning Path for?
+        - What do I need before I start?
+        - What will I build and optimize in the exercises?
+        - How are workload profiles produced and merged?
+        - How do I evaluate the impact of the optimizations?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
+    status: drift_detected
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons:
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+    - faq_drift_detected
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+    template_version_after: summary-faq-v3
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+      preview_before: This Learning Path shows how to use Buildkite on Arm-based Google Axion C4A virtual
+        machines to build and publish multi-architecture Docker images. You will provision a c4a-standard-4
+        instance on Goog...
+      preview_after: This Learning Path shows how to use Buildkite on Arm-based Google Axion C4A virtual
+        machines to build and publish multi-architecture Docker images. You will provision a c4a-standard-4
+        instance on Goog...
+      preview_generated: Create multi-architecture Docker images with Buildkite on Arm-based Google Cloud
+        C4A virtual machines powered by Google Axion processors. You will provision a c4a-standard-4 VM
+        running Ubuntu or SUSE ...
+    faqs:
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+      missing_before: false
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+      changed: false
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+        added_questions:
+        - Which Google Cloud resources and operating systems are used?
+        - What do I need before I start?
+        - What will I build and publish in this path?
+        - How is Buildkite set up on the VM?
+        - How long does it take and what is the skill level?
+        removed_questions:
+        - Which Google Cloud resources and OS images does this path use?
+        - What accounts and skills are required before starting?
+        - How do I install and connect a Buildkite agent on the C4A VM?
+        - How are multi-architecture Docker images built and published?
+        - How do I confirm the pipeline and application work correctly?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
+    status: drift_detected
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons:
+    - summary_drift_detected
+    - faq_drift_detected
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
+    summary:
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+      generated_at_after: '2026-05-18T17:16:01Z'
+      preview_before: This Learning Path shows how to deploy Apache Cassandra on Arm-based Google Cloud
+        Axion C4A virtual machines. You will provision a c4a-standard-4 instance in the Google Cloud Console,
+        choose an Arm64 ...
+      preview_after: This Learning Path shows how to deploy Apache Cassandra on Arm-based Google Cloud
+        Axion C4A virtual machines. You will provision a c4a-standard-4 instance in the Google Cloud Console,
+        choose an Arm64 ...
+      preview_generated: This Learning Path shows how to deploy Apache Cassandra on Arm-based Google Cloud
+        Axion C4A virtual machines built on Arm Neoverse-V2 cores. You will provision a C4A instance in
+        the Google Cloud Conso...
+    faqs:
+      action: drift_detected_preserved
+      missing_before: false
+      rerun_requested: false
+      changed: false
+      drift_detected: true
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+      before_count: 5
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+        added_questions:
+        - What do I need before starting?
+        - Which Google Cloud VM type and OS are used?
+        - What software is installed and configured on the VM?
+        - How do I verify that Cassandra is running correctly?
+        - How is performance benchmarking performed in this path?
+        removed_questions:
+        - Who is this Learning Path for?
+        - What will I set up and validate in this path?
+        - Which GCP instance type and operating systems are used?
+        - What are the prerequisites and expected duration?
+        - How do I run benchmarks with cassandra-stress?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
+    status: drift_detected
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons:
+    - summary_drift_detected
+    - faq_drift_detected
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
+    summary:
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+      generated_at_after: '2026-05-18T17:16:50Z'
+      preview_before: This introductory Learning Path shows how to run the Arm Confidential Compute Architecture
+        (CCA) reference software stack on an Armv-A AEM Base FVP with Realm Management Extension (RME)
+        support using ...
+      preview_after: This introductory Learning Path shows how to run the Arm Confidential Compute Architecture
+        (CCA) reference software stack on an Armv-A AEM Base FVP with Realm Management Extension (RME)
+        support using ...
+      preview_generated: This Learning Path shows how to run the Arm Confidential Compute Architecture
+        (CCA) reference software stack on the Armv‑A AEM Base FVP with Realm Management Extension (RME)
+        support using a pre-built ...
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+      generated_at_before: '2026-05-18T17:16:50Z'
+      generated_at_after: '2026-05-18T17:16:50Z'
+      before_count: 5
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+        added_questions:
+        - What will I build and run in this Learning Path?
+        - What are the prerequisites to get started?
+        - Which platforms and operating systems are used during the exercises?
+        - How is the application executed inside the Realm?
+        - Does this path cover attestation and memory encryption features?
+        removed_questions:
+        - What will I set up and run in this Learning Path?
+        - What host system and prerequisites are required?
+        - Do I need physical Arm hardware to complete this path?
+        - How do I run my own application inside a Realm?
+        - How are attestation and memory encryption addressed here?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
+    status: drift_detected
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons:
+    - summary_drift_detected
+    - faq_drift_detected
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
+    summary:
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+      generated_at_after: '2026-05-18T17:17:32Z'
+      preview_before: This Learning Path explains how Arm Confidential Computing Architecture (CCA) Realms
+        interact with I/O devices and what “secure device attach” means in practice. You will review how
+        the Realm Manageme...
+      preview_after: This Learning Path explains how Arm Confidential Computing Architecture (CCA) Realms
+        interact with I/O devices and what “secure device attach” means in practice. You will review how
+        the Realm Manageme...
+      preview_generated: This advanced Learning Path explains how Arm CCA Realms attach to I/O devices
+        using VirtIO paravirtualization, SWIOTLB bounce buffers, and secure physical device attach with
+        PCIe‑TDISP and PCIe‑IDE at...
+    faqs:
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+      generated_at_after: '2026-05-18T17:17:32Z'
+      before_count: 5
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+      generated_diff:
+        before_count: 5
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+        added_questions:
+        - What will I implement or verify in the exercise?
+        - What are the prerequisites to start this Learning Path?
+        - Which technologies and tools are used?
+        - What concepts will I understand by the end?
+        - Who should take this Learning Path and how long will it take?
+        removed_questions:
+        - What will I build or verify in this Learning Path?
+        - What are the prerequisites and environment requirements?
+        - How does VirtIO fit into device attach for Realms?
+        - When and why are SWIOTLB bounce buffers used in Realms?
+        - What does secure physical device attach with PCIe‑TDISP and PCIe‑IDE provide?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
+    status: drift_detected
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons:
+    - summary_drift_detected
+    - faq_drift_detected
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
+    summary:
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+        a confidential Linux...
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+        support and connec...
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+        realm on the Armv9-A AEM Base Fixed Virtual Platform (FVP) with Realm Management Extension (RME)
+        support and connec...
+      preview_generated: This advanced Learning Path guides you through running an end-to-end attestation
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+        simple workload in a Linu...
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+        - What host setup and prerequisites do I need?
+        - How is attestation policy enforced during the exercise?
+        - Which components and tools are used?
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+      preview_before: Get Started with CCA Attestation and Veraison is an introductory, 30‑minute Learning
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+        Architecture ...
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+        Path for developers who want a practical understanding of attestation in Arm’s Confidential Computing
+        Architecture ...
+      preview_generated: Get Started with CCA Attestation and Veraison introduces attestation for confidential
+        computing on Arm, focusing on Arm’s Confidential Computing Architecture (CCA) and the Realm Management
+        Extension (...
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+        - What environment do I need to follow the steps?
+        - Do I need access to CCA hardware?
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+        - What will I build and verify by the end?
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+  - path: content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
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+      preview_before: This Learning Path guides you through deploying a scalable Arm CCA attestation verifier
+        service on AWS using the Veraison project. You will prepare your AWS account and CLI authentication
+        (SSO recomme...
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+        service on AWS using the Veraison project. You will prepare your AWS account and CLI authentication
+        (SSO recomme...
+      preview_generated: Build a scalable Arm Confidential Compute Architecture (CCA) attestation verifier
+        on AWS using components from the Veraison project. You will prepare your AWS account and authentication
+        with the AWS C...
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+        - How are domains and certificates handled for the service?
+        - How do I provision CCA platform endorsements for Veraison?
+        - How much time and what experience are required?
+        removed_questions:
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+        - What prerequisites and environment are required?
+        - How do I authenticate to AWS during setup?
+        - How are the public domain and certificate handled?
+        - How do I add endorsements and test the verifier?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
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+      preview_before: This Learning Path shows how to run CircleCI Arm-native workflows on a SUSE Linux
+        Arm64 virtual machine on Google Cloud Axion C4A. You will provision a c4a-standard-4 instance,
+        install the CircleCI CL...
+      preview_after: This Learning Path shows how to run CircleCI Arm-native workflows on a SUSE Linux
+        Arm64 virtual machine on Google Cloud Axion C4A. You will provision a c4a-standard-4 instance,
+        install the CircleCI CL...
+      preview_generated: Learn how to run CircleCI Arm-native CI/CD workflows on Google Cloud Axion C4A
+        using a SUSE Linux Arm64 virtual machine. You will provision a c4a-standard-4 instance, install
+        the CircleCI CLI, define ...
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+        - What CircleCI components are installed and why?
+        - How does the custom resource class route jobs to the Arm runner?
+        - How is Docker used in the workflow on the Arm64 VM?
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+      preview_before: Deploy CircleCI Arm Native Workflows on AWS EC2 Graviton shows you how to run CI/CD
+        jobs natively on Arm64 using self-hosted machine runners. You will create an AWS EC2 Graviton
+        instance (Neoverse N1)...
+      preview_after: Deploy CircleCI Arm Native Workflows on AWS EC2 Graviton shows you how to run CI/CD
+        jobs natively on Arm64 using self-hosted machine runners. You will create an AWS EC2 Graviton
+        instance (Neoverse N1)...
+      preview_generated: This Learning Path shows how to deploy CircleCI Arm native workflows on AWS EC2
+        Graviton Arm64 instances built on Arm Neoverse N1 cores. You will create an EC2 instance from
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+        - How do I verify the runner is working correctly?
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+        - Who is this for?
+        - What are the prerequisites?
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+  - path: content/learning-paths/servers-and-cloud-computing/clair/_index.md
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+      preview_before: 'Learn to install and run Clair on Arm servers to statically scan container images
+        and generate vulnerability reports. Configure Clair’s Indexer, Matcher, and Notifier using two
+        deployment models: a si...'
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+        and generate vulnerability reports. Configure Clair’s Indexer, Matcher, and Notifier using two
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+        to scan container images and generate vulnerability reports. You will learn Clair’s architecture—Indexer,
+        Matcher, and...
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+        - Which operating systems and cloud platforms are covered?
+        - How does scanning work and when are results reliable?
+        removed_questions:
+        - What environment and prerequisites are required?
+        - What is the difference between combined and distributed deployments?
+        - How is PostgreSQL used and configured in this Learning Path?
+        - Do I need a load balancer?
+        - How do I submit an image and generate a vulnerability report?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
+    status: drift_detected
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+      preview_before: This Learning Path shows how to install ClickHouse on Arm-based cloud instances
+        and measure query latency with ClickBench to guide instance sizing for your workloads. You will
+        set up a Linux environme...
+      preview_after: This Learning Path shows how to install ClickHouse on Arm-based cloud instances and
+        measure query latency with ClickBench to guide instance sizing for your workloads. You will set
+        up a Linux environme...
+      preview_generated: This Learning Path shows how to install ClickHouse on Arm-based servers and measure
+        performance with ClickBench to choose suitable instance configurations. You will work on Linux,
+        with steps assuming ...
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+        - Which platforms and operating systems are covered?
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+        - What performance metrics will I measure and why?
+        removed_questions:
+        - What will I build or measure in this Learning Path?
+        - Which platforms and operating systems are supported?
+        - How long does it take to complete?
+        - Does this Learning Path include performance tuning?
+        updated_questions:
+        - What are the prerequisites?
+  - path: content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
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+      preview_before: This Learning Path guides you through deploying ClickHouse on Arm-based Google Cloud
+        Axion C4A virtual machines and building a real-time analytics pipeline. You will provision a SUSE
+        Linux Enterprise ...
+      preview_after: This Learning Path guides you through deploying ClickHouse on Arm-based Google Cloud
+        Axion C4A virtual machines and building a real-time analytics pipeline. You will provision a SUSE
+        Linux Enterprise ...
+      preview_generated: This Learning Path guides you through deploying ClickHouse on Arm-based Google
+        Cloud Axion C4A virtual machines and building a real-time analytics pipeline. You will provision
+        a SUSE Linux Arm64 VM wi...
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+        - Which instance type, OS, and Arm technology are used?
+        - What prerequisites do I need before starting?
+        - How do I configure network access and required tools on the VM?
+        - How does the streaming ETL pipeline ingest data into ClickHouse?
+        removed_questions:
+        - What will I build in this Learning Path?
+        - Who should take this and how long will it take?
+        - What prerequisites do I need?
+        - What environment and tools will I use?
+        - How are performance and correctness validated?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
+    status: drift_detected
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+      preview_before: This Learning Path shows how to deploy a Linux Cobalt 100 virtual machine on Microsoft
+        Azure using the Azure Portal, then secure and verify external access. You will select a Cobalt
+        100–backed size fr...
+      preview_after: This Learning Path shows how to deploy a Linux Cobalt 100 virtual machine on Microsoft
+        Azure using the Azure Portal, then secure and verify external access. You will select a Cobalt
+        100–backed size fr...
+      preview_generated: This Learning Path shows how to deploy an Arm-based Cobalt 100 virtual machine
+        on Microsoft Azure using the Azure Portal, connect via SSH, and expose an application port with
+        Network Security Group ru...
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+        - Which Azure VM series offer Cobalt 100 options?
+        - What prerequisites do I need before starting?
+        - How are ports and network access configured in this path?
+        - Can I use the Azure CLI instead of the Portal?
+        removed_questions:
+        - What are the prerequisites to complete this Learning Path?
+        - Which Azure VM series use Cobalt 100, and how do I choose a size?
+        - Why set Public inbound ports to None during VM creation?
+        - How do I connect to the VM over SSH and what if it fails?
+        - How do I verify external connectivity to port 8080, and can I use a different port?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
+    status: drift_detected
+    changed_on_disk: false
+    managed_block_updated: false
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+      preview_before: This Learning Path shows how to automate Arm AArch64 Docker image creation with
+        AWS CodeBuild using a GitHub project, and then run the images on any Arm system with Docker installed.
+        You will create a...
+      preview_after: This Learning Path shows how to automate Arm AArch64 Docker image creation with AWS
+        CodeBuild using a GitHub project, and then run the images on any Arm system with Docker installed.
+        You will create a...
+      preview_generated: This Learning Path shows how to automate creation of Arm AArch64 Docker images
+        using AWS CodeBuild with a GitHub project, then share and run those images on Arm systems with
+        Docker installed. You will...
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+        - What will I build in this Learning Path?
+        - Where are the images published?
+        - How do I verify my machine is compatible to run the images?
+        removed_questions:
+        - What will I build and run in this Learning Path?
+        - Where are images published, and how do I consume them?
+        - Does this Learning Path set up automatic build triggers from GitHub?
+        updated_questions:
+        - Which architectures and operating systems are targeted?
+        - What are the prerequisites?
+  - path: content/learning-paths/servers-and-cloud-computing/codec/_index.md
+    status: drift_detected
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+      generated_at_after: '2026-05-18T17:25:34Z'
+      preview_before: This Learning Path shows how to build and run the x265 (H.265/HEVC) encoder on Arm
+        servers and benchmark its performance. You will prepare an Ubuntu Linux 20.04 Arm instance (verified
+        on AWS EC2 and O...
+      preview_after: This Learning Path shows how to build and run the x265 (H.265/HEVC) encoder on Arm
+        servers and benchmark its performance. You will prepare an Ubuntu Linux 20.04 Arm instance (verified
+        on AWS EC2 and O...
+      preview_generated: Learn how to build and run the open-source x265 H.265 encoder on Arm-based cloud
+        servers and evaluate performance across video resolutions and encoding presets. You will install
+        GCC, CMake, and suppor...
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+        - What prerequisites and environment are required?
+        - Which tools and packages will I install?
+        - How are Arm Neoverse optimizations used with x265?
+        - How will I evaluate performance?
+        removed_questions:
+        - What will I build and measure in this Learning Path?
+        - What environment and operating system are verified?
+        - How do I build x265 on the Arm server?
+        - What inputs should I use for benchmarking and what variations should I test?
+        - How do I resolve an unknown -march value or ENABLE_NEON_I8MM build error?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/codec1/_index.md
+    status: drift_detected
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+    managed_block_updated: false
+    rerun_flags_reset: []
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+      generated_at_before: '2026-05-18T17:26:19Z'
+      generated_at_after: '2026-05-18T17:26:19Z'
+      preview_before: This Learning Path shows how to build and run the AV1 and VP9 codecs on Arm Linux
+        systems and measure their performance. You will clone and compile the libaom (AV1) and libvpx
+        (VP9) reference implemen...
+      preview_after: This Learning Path shows how to build and run the AV1 and VP9 codecs on Arm Linux
+        systems and measure their performance. You will clone and compile the libaom (AV1) and libvpx
+        (VP9) reference implemen...
+      preview_generated: This Learning Path shows how to build and run the AV1 and VP9 software codecs
+        on Arm Linux systems, then measure performance across different resolutions and encoding configurations.
+        You will compile ...
+    faqs:
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+      generated_at_after: '2026-05-18T17:26:19Z'
+      before_count: 5
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+        - What will I build and run?
+        - Which Arm platforms does this target?
+        - What tools and source repositories are used?
+        - Do these codecs use Arm Neon and SVE2 optimizations?
+        - What are the prerequisites and time to complete?
+        removed_questions:
+        - What will I build and run in this Learning Path?
+        - What are the prerequisites before starting?
+        - Which Arm-specific optimizations are used?
+        - Does this path cover unit testing for the codecs?
+        - How long does it take and what is the skill level?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
+    status: drift_detected
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons:
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+    - faq_drift_detected
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+      generated_at_after: '2026-05-18T17:26:50Z'
+      preview_before: This Learning Path shows how to deploy Couchbase Server on Google Cloud C4A Arm-based
+        virtual machines powered by Axion processors (Arm Neoverse-V2 cores). You will create a firewall
+        rule for TCP port...
+      preview_after: This Learning Path shows how to deploy Couchbase Server on Google Cloud C4A Arm-based
+        virtual machines powered by Axion processors (Arm Neoverse-V2 cores). You will create a firewall
+        rule for TCP port...
+      preview_generated: This Learning Path shows how to deploy Couchbase on Google Cloud C4A Arm64 instances
+        and validate performance. You will provision a SUSE Linux Enterprise Server VM on a Google Axion
+        C4A machine, open ...
+    faqs:
+      action: drift_detected_preserved
+      missing_before: false
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+      generated_at_before: '2026-05-18T17:26:50Z'
+      generated_at_after: '2026-05-18T17:26:50Z'
+      before_count: 5
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+      generated_diff:
+        before_count: 5
+        after_count: 5
+        added_questions:
+        - What are the prerequisites to follow this Learning Path?
+        - Which instance type and operating system are used?
+        - How do I make the Couchbase Web Console accessible?
+        - How do I verify the Couchbase deployment?
+        - How is performance benchmarking performed in this path?
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+        - What platform and instance type does this Learning Path use?
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+        on an AWS Graviton4 ...
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+        an AWS Graviton4 ...
+      preview_generated: This introductory Learning Path shows how to improve C++ application performance
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+        - Which environment and Arm platform are used?
+        - Which compiler flags are emphasized?
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+        Benchmark to measure a ...
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+        - How do I run the service and send requests to the model?
+        - Which cloud platforms can I use, and what configuration was tested?
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+        - Which model variant and file format are used?
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+        V2 processors using the MLPerf Inference Offline scenario. Working on Linux in approximately 90
+        minutes, you will fetc...
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+        Arm instance from a c...
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+        - What are the hardware and OS requirements?
+        - How do I obtain the dataset and model weights?
+        - What software stack and precision modes are used?
+        - How long does the end-to-end process take and what outputs should I expect?
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+  - path: content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
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+      preview_before: This advanced Learning Path shows how to automate migrating a containerized, x86-optimized
+        C++ application to Arm64 using the Docker MCP Toolkit, the Arm MCP Server, and GitHub Copilot
+        in VS Code. You...
+      preview_after: This advanced Learning Path shows how to automate migrating a containerized, x86-optimized
+        C++ application to Arm64 using the Docker MCP Toolkit, the Arm MCP Server, and GitHub Copilot
+        in VS Code. You...
+      preview_generated: This advanced Learning Path shows how to automate x86-to-Arm64 code and container
+        migration using the Docker MCP Toolkit with the Arm MCP Server and GitHub Copilot in VS Code.
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+        open port 8080, ins...
+      preview_generated: This Learning Path guides advanced .NET developers through migrating an OrchardCore
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+        100, built on Arm N...
+      preview_generated: This Learning Path shows how to monitor Azure Cobalt 100 Arm64 virtual machines
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+        Cobalt 100, built on Arm N...
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+      preview_before: This introductory Learning Path shows how to deploy a containerized application
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+        an ECS cluster, co...
+      preview_generated: This introductory Learning Path shows how to deploy a containerized application
+        to Amazon Elastic Container Service (ECS) with Fargate on AWS Graviton processors (Arm Neoverse).
+        You will create an ECS...
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+        - Is Terraform required, and what does it automate?
+        - Do my container images need to target Arm for Graviton?
+        removed_questions:
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+        - Do I need to manage EC2 instances to run the containers?
+        - How is Terraform used in this path?
+        - Do I need an Arm-based local machine to follow the steps?
+        updated_questions:
+        - What will I build in this Learning Path?
+  - path: content/learning-paths/servers-and-cloud-computing/eks/_index.md
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+      preview_before: This introductory Learning Path shows how to provision an Amazon Elastic Kubernetes
+        Service (EKS) cluster on Arm-based AWS Graviton instances and deploy a WordPress application with
+        a MySQL database. ...
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+        a MySQL database. ...
+      preview_generated: This Learning Path shows you how to provision an Amazon Elastic Kubernetes Service
+        (EKS) cluster on Arm-based AWS Graviton instances and deploy a WordPress application backed by
+        a MySQL database. You ...
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+        removed_questions:
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+        - What are the prerequisites and setup steps?
+        - Which Arm technology and instance type are used?
+        - Can I change the AWS region or instance type?
+        - How long does this Learning Path take and who is it for?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
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+      preview_before: This Learning Path shows advanced developers how to build and deploy a multi-architecture
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+        x86/amd64 and ar...
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+        container application on Amazon EKS. You will use docker buildx and docker manifest to create
+        x86/amd64 and ar...
+      preview_generated: Learn how to build and deploy a multi-architecture application on Amazon EKS
+        using docker buildx and docker manifest. You will create a hybrid Kubernetes cluster with x86/amd64
+        and Arm-based (Graviton...
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+        - Do I need separate clusters for each architecture?
+        removed_questions:
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+        - How is the EKS cluster configured for multiple architectures?
+        - Which tools are used to create and deploy the images?
+        - What operating system and time commitment should I expect?
+        updated_questions:
+        - What will I build and deploy in this Learning Path?
+  - path: content/learning-paths/servers-and-cloud-computing/envoy/_index.md
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+      preview_before: This introductory Learning Path shows how to build, install, and run Envoy on Arm-based
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+        an Arm deployment ...
+      preview_after: This introductory Learning Path shows how to build, install, and run Envoy on Arm-based
+        Linux servers and configure it as a simple web server for traffic management. You will choose
+        an Arm deployment ...
+      preview_generated: This introductory Learning Path explains how to build, install, and run Envoy
+        on Arm servers running Linux, and configure it as a basic web server for HTTP traffic management.
+        You will use an Arm-base...
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+        - Does this Learning Path cover performance tuning or advanced features?
+        removed_questions:
+        - What will I build and run in this Learning Path?
+        - What environment and prerequisites do I need?
+        - Do I have to build Envoy from source?
+        - How do I start Envoy with the provided configuration?
+        - How do I verify Envoy is working correctly?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md
+    status: added
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+      preview_before: ''
+      preview_after: This Learning Path guides you through deploying and benchmarking Envoy Proxy on Google
+        Cloud Axion C4A Arm64 virtual machines built on Arm Neoverse V2 cores. You will provision a c4a-standard-4
+        instan...
+      preview_generated: This Learning Path guides you through deploying and benchmarking Envoy Proxy
+        on Google Cloud Axion C4A Arm64 virtual machines built on Arm Neoverse V2 cores. You will provision
+        a c4a-standard-4 instan...
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+        - What are the prerequisites and who should take this?
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+  - path: content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md
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+      preview_before: This Advanced Learning Path shows how to tune Envoy on Arm Neoverse-based Linux
+        servers, including deployments on AWS, Microsoft Azure, Google Cloud, and Oracle. You will enable
+        Transparent Huge Pages...
+      preview_after: This Advanced Learning Path shows how to tune Envoy on Arm Neoverse-based Linux servers,
+        including deployments on AWS, Microsoft Azure, Google Cloud, and Oracle. You will enable Transparent
+        Huge Pages...
+      preview_generated: Learn how to tune Envoy on Arm servers by applying Transparent Huge Pages (THP)
+        and Profile-Guided Optimization (PGO). You will verify Linux kernel configuration, enable and
+        tune THP, and understand k...
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+        - What are the prerequisites?
+        - What will I do in this path?
+        - Which platforms and operating systems are relevant?
+        - What performance improvements are described?
+        removed_questions:
+        - Who should take this Learning Path and what are the prerequisites?
+        - What THP and hugetlbfs changes will I make?
+        - How do I build Envoy with PGO in this path?
+        - Which platforms and operating systems are covered?
+        - What performance gains and duration can I expect?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md
+    status: drift_detected
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+      preview_before: This advanced Learning Path shows how stack buffer overflows work on AArch64 Linux
+        and how they can redirect control flow. You will set up an AArch64 Ubuntu 22.04 Docker environment
+        with Clang and gdb...
+      preview_after: This advanced Learning Path shows how stack buffer overflows work on AArch64 Linux
+        and how they can redirect control flow. You will set up an AArch64 Ubuntu 22.04 Docker environment
+        with Clang and gdb...
+      preview_generated: This Learning Path explains the mechanics and impact of stack buffer overflows
+        on AArch64 Linux through hands-on, isolated experiments. You build and use a Docker container
+        (Ubuntu 22.04 with clang an...
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+        - Why does the Docker setup disable ASLR?
+        - What will I build or analyze during the exercises?
+        - How advanced is this content and what prior knowledge is expected?
+        - How long will it take and is it safe to run?
+        removed_questions:
+        - What will I build and learn in this Learning Path?
+        - What environment and tools are required?
+        - Why is ASLR disabled in the Docker setup?
+        - How will I determine which input bytes reach the return address?
+        - Who is this for and how long does it take?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
+    status: added
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+      preview_after: Analyze cache behavior with Perf C2C on Arm guides you through detecting and fixing
+        false sharing on Arm-based Linux systems using Linux perf, Perf C2C, and the Arm Statistical Profiling
+        Extension (SP...
+      preview_generated: Analyze cache behavior with Perf C2C on Arm guides you through detecting and
+        fixing false sharing on Arm-based Linux systems using Linux perf, Perf C2C, and the Arm Statistical
+        Profiling Extension (SP...
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+        for vector storag...
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+  - path: content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
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+        - What will I learn and practice?
+        - Which operating system and tools are used?
+        - Do I need a specific cloud instance type?
+        removed_questions:
+        - What will I learn about the C++ memory model in this path?
+        - Why can code that seems correct on x86 fail on Arm?
+        - What environment and tools are used in the exercises?
+        - How do I detect race conditions here, and what are TSan’s limitations?
+        updated_questions:
+        - What are the prerequisites?
+  - path: content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
+    status: drift_detected
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+      preview_before: Learn how to automate x86-to-Arm migration using the Arm MCP Server and AI-powered
+        IDEs. You will connect your assistant via the Model Context Protocol to run Arm-specific checks,
+        verify Docker base i...
+      preview_after: Learn how to automate x86-to-Arm migration using the Arm MCP Server and AI-powered
+        IDEs. You will connect your assistant via the Model Context Protocol to run Arm-specific checks,
+        verify Docker base i...
+      preview_generated: This Learning Path shows how to automate x86-to-Arm application migration using
+        the Arm MCP Server and the Model Context Protocol (MCP). You will connect an AI-powered IDE to
+        the server, use natural l...
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+        - What is the Arm MCP Server and how does it help with migration?
+        - What will I build and validate in this Learning Path?
+        - What prerequisites and environment are required?
+        - How do I check if my Docker base images support arm64?
+        - Do I have to use GitHub Copilot, or can I use other AI agents?
+        removed_questions:
+        - What is the Arm MCP Server and why is it used here?
+        - What are the prerequisites to follow this Learning Path?
+        - Do I have to use GitHub Copilot, or can I use other tools?
+        - How do I check whether a Docker image supports Arm?
+        - What code changes are covered and how are results validated?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
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+      preview_before: This Learning Path guides advanced C developers through migrating an application
+        between Arm platforms using Kiro Arm SoC Migration Power. You install Kiro IDE, enable the Migration
+        Power, and use its...
+      preview_after: This Learning Path guides advanced C developers through migrating an application
+        between Arm platforms using Kiro Arm SoC Migration Power. You install Kiro IDE, enable the Migration
+        Power, and use its...
+      preview_generated: This Learning Path shows how to migrate a C application between Arm platforms
+        using Kiro Arm SoC Migration Power. You install Kiro IDE, enable the Migration Power, and run
+        the Arm MCP server in a Dock...
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+        - How do I set up the environment?
+        - Does this workflow apply beyond the example platforms?
+        - How is the migration validated?
+        removed_questions:
+        - What will I build and verify in this Learning Path?
+        - Which tools and operating systems are used?
+        - Does the workflow apply beyond Graviton3 to Raspberry Pi 5?
+        - How long will it take and who should take it?
+        updated_questions:
+        - What are the prerequisites?
+  - path: content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
+    status: drift_detected
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+      preview_before: This Learning Path shows how to install and boot a Linux kernel configured with
+        64K memory pages on Arm-based systems to improve memory efficiency and performance for memory-intensive
+        workloads. You w...
+      preview_after: This Learning Path shows how to install and boot a Linux kernel configured with 64K
+        memory pages on Arm-based systems to improve memory efficiency and performance for memory-intensive
+        workloads. You w...
+      preview_generated: This Learning Path shows how to install and run a Linux kernel configured with
+        a 64K base page size on Arm systems to improve memory efficiency and benefit memory‑intensive
+        workloads. You will learn p...
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+        - Which Linux distributions and versions does this Learning Path cover?
+        - What are the prerequisites to follow this path?
+        - How do I verify the current memory page size on my system?
+        - Does Debian provide a prebuilt 64K page size kernel?
+        - Can I switch back to the default 4K kernel after testing 64K?
+        removed_questions:
+        - Which Linux distributions and versions are covered?
+        - How do I verify the active page size and kernel version?
+        - Do I need to compile a custom kernel for 64K pages?
+        - Can I revert to the default 4K page size after testing?
+        - What are the prerequisites and expected effort?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
+    status: drift_detected
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+      preview_before: This advanced Learning Path shows how to access Arm Performance Monitoring Unit
+        (PMU) hardware event counters and the system counter from user space on Linux. You will read the
+        system counter using in...
+      preview_after: This advanced Learning Path shows how to access Arm Performance Monitoring Unit (PMU)
+        hardware event counters and the system counter from user space on Linux. You will read the system
+        counter using in...
+      preview_generated: This Learning Path shows how to access Arm hardware performance counters and
+        the system counter from Linux user space using assembly, PAPI, and the perf_event_open system
+        call. You will distinguish ha...
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+        - What are the prerequisites and recommended platform?
+        - What will I build or run during the exercises?
+        - How are the Arm PMU and system counter used here?
+        - How do PAPI and perf_event_open differ, and is multiplexing supported?
+        removed_questions:
+        - What environment do I need to complete this Learning Path?
+        - Do I need root privileges to access counters from user space?
+        - How can I measure elapsed time in my code?
+        - How many hardware events can I count at once, and what about multiplexing?
+        - When should I use PAPI versus perf_event_open?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
+    status: drift_detected
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+      preview_before: This introductory Learning Path shows how to package multi-architecture container
+        applications and deploy them on AWS Fargate with AWS Graviton processors using the AWS Copilot
+        CLI. You will container...
+      preview_after: This introductory Learning Path shows how to package multi-architecture container
+        applications and deploy them on AWS Fargate with AWS Graviton processors using the AWS Copilot
+        CLI. You will container...
+      preview_generated: This introductory Learning Path shows how to package a multi-architecture container
+        and deploy it to AWS Fargate on Arm-based AWS Graviton processors using the AWS Copilot CLI. You
+        will containerize a...
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+        added_questions:
+        - What are the prerequisites before I start?
+        - Does Copilot default to Graviton or Arm architecture?
+        - Do I need a multi-architecture container image?
+        - Which AWS services and tools are used in the deployment?
+        removed_questions:
+        - What are the prerequisites?
+        - How do I ensure the service runs on AWS Graviton processors?
+        - Can I deploy an existing container image instead of building from a Dockerfile?
+        - Which AWS resources will Copilot create, and how do I check status?
+        updated_questions:
+        - What will I build and deploy in this Learning Path?
+  - path: content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
+    status: drift_detected
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+    managed_block_updated: false
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+      preview_before: This Learning Path guides you through automating the deployment of Arm instances
+        on AWS, including Graviton, using Terraform and a secure jump server (bastion) pattern. You will
+        prepare AWS credential...
+      preview_after: This Learning Path guides you through automating the deployment of Arm instances
+        on AWS, including Graviton, using Terraform and a secure jump server (bastion) pattern. You will
+        prepare AWS credential...
+      preview_generated: Deploy Arm Instances on AWS using Terraform shows how to automate provisioning
+        of AWS Graviton (Arm Neoverse-based) EC2 instances and control access with a jump server (bastion).
+        You will define infra...
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+        added_questions:
+        - What will I build in this Learning Path?
+        - What are the prerequisites?
+        - Does this Learning Path use Terraform Cloud?
+        - How is access to the Arm instances secured?
+        - Can I adapt the Terraform configuration for other projects?
+        removed_questions:
+        - What will I build and deploy?
+        - What do I need before I start?
+        - Does this Learning Path use Terraform Cloud, and where do I run commands?
+        - How is access to private instances managed?
+        - What Terraform files will I work with, and can I reuse them?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
+    status: drift_detected
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+      generated_at_after: '2026-05-18T17:09:18Z'
+      preview_before: This introductory Learning Path guides you through creating and deploying an Azure
+        Resource Manager (ARM) template to provision a Linux virtual machine on Microsoft Azure using
+        Arm64-based Cobalt 100 ...
+      preview_after: This introductory Learning Path guides you through creating and deploying an Azure
+        Resource Manager (ARM) template to provision a Linux virtual machine on Microsoft Azure using
+        Arm64-based Cobalt 100 ...
+      preview_generated: Learn how to create and deploy an Azure Resource Manager (ARM) template that
+        provisions a Linux virtual machine on Microsoft Azure powered by Cobalt 100 processors. You will
+        structure a JSON template ...
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+        added_questions:
+        - What are the prerequisites to complete this Learning Path?
+        - How do I select a region and VM size that supports Azure Cobalt 100?
+        - How is the ARM template structured in this Learning Path?
+        - How do I verify the VM is running on Arm64 after deployment?
+        - Can I reuse this template in CI/CD pipelines?
+        removed_questions:
+        - Who is this Learning Path for and what will I build?
+        - What are the prerequisites to follow along?
+        - How do I select an Arm64 Cobalt 100 VM size in the template?
+        - How do I deploy the template with the Azure CLI?
+        - How do I verify the VM is running on Arm64 Cobalt 100 after deployment?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
+    status: added
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+      preview_after: This Learning Path shows how to deploy a .NET 8 web application on Microsoft Azure
+        Cobalt 100 Arm-based VMs. You will configure a Linux Azure Cobalt 100 VM as a self-hosted Arm64
+        GitHub Actions runner...
+      preview_generated: This Learning Path shows how to deploy a .NET 8 web application on Microsoft
+        Azure Cobalt 100 Arm-based VMs. You will configure a Linux Azure Cobalt 100 VM as a self-hosted
+        Arm64 GitHub Actions runner...
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+        - How long does this take and who is it for?
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+        - What is Azure Cobalt 100 and which VM series are available?
+        - How long does this take and who is it for?
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+  - path: content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
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+      preview_before: This Learning Path shows how to automate deployment of Arm-based Linux virtual machines
+        on Microsoft Azure using Terraform and Terraform Cloud. You will prepare Azure authentication
+        with the Azure CLI...
+      preview_after: This Learning Path shows how to automate deployment of Arm-based Linux virtual machines
+        on Microsoft Azure using Terraform and Terraform Cloud. You will prepare Azure authentication
+        with the Azure CLI...
+      preview_generated: This Learning Path shows how to automate the creation of Arm Neoverse-based virtual
+        machines on Microsoft Azure using Terraform. You will define infrastructure as code, provision
+        Linux VMs, and enable...
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+        - Which operating system is deployed on the VMs?
+        - Can I reuse the provided Terraform files for other projects?
+        removed_questions:
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+        - What are the prerequisites?
+        - How do I choose the Azure VM image?
+        - How is secure access to the VMs provided?
+        - Who is this Learning Path for?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
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+      preview_before: This Learning Path shows how to build and deploy a custom Azure Linux 3.0 image
+        on Arm-based Cobalt 100 processors in Microsoft Azure. You will use QEMU on a Linux host to create
+        a raw disk, boot from...
+      preview_after: This Learning Path shows how to build and deploy a custom Azure Linux 3.0 image on
+        Arm-based Cobalt 100 processors in Microsoft Azure. You will use QEMU on a Linux host to create
+        a raw disk, boot from...
+      preview_generated: This Learning Path guides you through building and deploying a custom Azure Linux
+        3.0 virtual machine image for Arm-based Cobalt 100 processors on Microsoft Azure. You will use
+        QEMU on a Linux host to...
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+        - Why does the workflow use QEMU and an AArch64 ISO?
+        - How is the custom image registered for reuse in Azure?
+        - How long will it take and who should follow it?
+        removed_questions:
+        - Why do I need a custom Azure Linux 3.0 image for Arm on Azure?
+        - What prerequisites and tools are required?
+        - How is the Azure Linux 3.0 image built with QEMU?
+        - What disk format and size does Azure require?
+        - How do I deploy and verify the VM on Cobalt 100?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
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+      preview_before: This Learning Path shows how to deploy and accelerate Hugging Face Sentiment Analysis
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+        LTS machine (at lea...
+      preview_after: This Learning Path shows how to deploy and accelerate Hugging Face Sentiment Analysis
+        models with PyTorch on Arm servers running Linux. You will provision an Arm-based Ubuntu 22.04
+        LTS machine (at lea...
+      preview_generated: This Learning Path shows how to deploy and accelerate PyTorch NLP sentiment analysis
+        models from Hugging Face on Arm servers. You will set up a Linux environment (tested on Ubuntu
+        22.04 LTS), run the ...
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+        - What hardware and operating system do I need?
+        - Which cloud platforms and CPUs are covered or tested?
+        - Which tools and languages are used?
+        - How long does it take and who is it for?
+        removed_questions:
+        - What hardware and OS are assumed for this Learning Path?
+        - What exactly will I measure and compare?
+        - Are there specific prerequisites beyond access to an Arm server?
+        - Does this Learning Path cover training or fine-tuning models?
+        - Can I follow this on clouds other than AWS or on-premises?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
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+      preview_before: This Learning Path shows how to implement and benchmark bitmap scanning for database
+        workloads on Arm-based cloud instances running Linux. You will build a simple bit vector in C,
+        implement scalar, Ne...
+      preview_after: This Learning Path shows how to implement and benchmark bitmap scanning for database
+        workloads on Arm-based cloud instances running Linux. You will build a simple bit vector in C,
+        implement scalar, Ne...
+      preview_generated: This Learning Path shows how to implement and benchmark bitmap scanning for database
+        workloads on Arm-based cloud servers. You will build a simple bit vector in C, add scalar scanning
+        baselines, and t...
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+        - What are the prerequisites to get started?
+        - Where can I run the exercises?
+        - How are Neon and SVE used in the examples?
+        - How will I measure and compare performance?
+        removed_questions:
+        - What will I build and measure in this Learning Path?
+        - What platforms and operating systems does this target?
+        - What are the prerequisites?
+        - How is this relevant to database systems?
+        - Which implementation should I use for different bit densities?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/bolt/_index.md
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+      preview_before: Learn how to build, profile, and optimize Arm executables on Linux using BOLT post-link
+        optimization. You will run your application on an Arm Linux target, collect performance data with
+        Linux Perf usi...
+      preview_after: Learn how to build, profile, and optimize Arm executables on Linux using BOLT post-link
+        optimization. You will run your application on an Arm Linux target, collect performance data with
+        Linux Perf usi...
+      preview_generated: This Learning Path shows how to prepare, profile, and optimize an Arm Linux executable
+        using BOLT post-link optimization to improve performance through code layout changes. You will
+        decide on a single...
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+        - What will I do in this Learning Path?
+        - What environment and versions are required?
+        - Can I use one or two machines for the workflow?
+        - Which profiling methods are covered, and how is the data used?
+        - Which Arm platforms is this relevant to, and how long will it take?
+        removed_questions:
+        - What systems and software do I need before starting?
+        - 'Which recording method should I use: Samples, ETM, or SPE?'
+        - Can I split profiling and optimization across two systems?
+        - How do I handle very large perf.data files from ETM?
+        - What if my executable is input-dependent?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
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+      preview_before: Optimize AArch64 binaries with LLVM BOLT teaches you how to evaluate and improve
+        code layout on Arm Neoverse and Cortex-A systems running Linux. You install BOLT (LLVM 22.1.0+),
+        build a BubbleSort-bas...
+      preview_after: Optimize AArch64 binaries with LLVM BOLT teaches you how to evaluate and improve
+        code layout on Arm Neoverse and Cortex-A systems running Linux. You install BOLT (LLVM 22.1.0+),
+        build a BubbleSort-bas...
+      preview_generated: This Learning Path shows how to install and use LLVM BOLT on AArch64 Linux to
+        improve code layout for binaries with poor instruction locality. You will compile and run a BubbleSort-based
+        example, gath...
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+      generated_at_after: '2026-05-18T17:13:52Z'
+      before_count: 5
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+        - What will I build and measure in this Learning Path?
+        - What are the system and software prerequisites?
+        - Which LLVM BOLT version do I need and how do I install it?
+        - How do I decide if my program is a good candidate for BOLT?
+        - What profiling options are covered, and what is BRBE?
+        removed_questions:
+        - What will I accomplish in this Learning Path?
+        - What hardware and software do I need before starting?
+        - How do I know if my program is a good candidate for BOLT?
+        - Which profiling method should I choose?
+        - How do I install and verify the correct BOLT version?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
+    status: drift_detected
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+      preview_before: This advanced Learning Path shows how to optimize Arm application binaries and shared
+        libraries with BOLT on Linux. You will build and instrument a MySQL server binary and its OpenSSL
+        dependencies (li...
+      preview_after: This advanced Learning Path shows how to optimize Arm application binaries and shared
+        libraries with BOLT on Linux. You will build and instrument a MySQL server binary and its OpenSSL
+        dependencies (li...
+      preview_generated: This advanced Learning Path shows how to optimize Arm application binaries and
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+        the MySQL server ...
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+        - How are profiles collected and merged for BOLT optimization?
+        - Can I optimize shared libraries independently of the application?
+        - How is performance evaluated in this path?
+        removed_questions:
+        - Who is this Learning Path for?
+        - What do I need before I start?
+        - What will I build and optimize in the exercises?
+        - How are workload profiles produced and merged?
+        - How do I evaluate the impact of the optimizations?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
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+      preview_before: This Learning Path shows how to use Buildkite on Arm-based Google Axion C4A virtual
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+        instance on Goog...
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+        instance on Goog...
+      preview_generated: Create multi-architecture Docker images with Buildkite on Arm-based Google Cloud
+        C4A virtual machines powered by Google Axion processors. You will provision a c4a-standard-4 VM
+        running Ubuntu or SUSE ...
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+        - What will I build and publish in this path?
+        - How is Buildkite set up on the VM?
+        - How long does it take and what is the skill level?
+        removed_questions:
+        - Which Google Cloud resources and OS images does this path use?
+        - What accounts and skills are required before starting?
+        - How do I install and connect a Buildkite agent on the C4A VM?
+        - How are multi-architecture Docker images built and published?
+        - How do I confirm the pipeline and application work correctly?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
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+      preview_before: This Learning Path shows how to deploy Apache Cassandra on Arm-based Google Cloud
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+        choose an Arm64 ...
+      preview_after: This Learning Path shows how to deploy Apache Cassandra on Arm-based Google Cloud
+        Axion C4A virtual machines. You will provision a c4a-standard-4 instance in the Google Cloud Console,
+        choose an Arm64 ...
+      preview_generated: This Learning Path shows how to deploy Apache Cassandra on Arm-based Google Cloud
+        Axion C4A virtual machines built on Arm Neoverse-V2 cores. You will provision a C4A instance in
+        the Google Cloud Conso...
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+        - What software is installed and configured on the VM?
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+        - How is performance benchmarking performed in this path?
+        removed_questions:
+        - Who is this Learning Path for?
+        - What will I set up and validate in this path?
+        - Which GCP instance type and operating systems are used?
+        - What are the prerequisites and expected duration?
+        - How do I run benchmarks with cassandra-stress?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
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+        support using ...
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+        (CCA) reference software stack on an Armv-A AEM Base FVP with Realm Management Extension (RME)
+        support using ...
+      preview_generated: This Learning Path shows how to run the Arm Confidential Compute Architecture
+        (CCA) reference software stack on the Armv‑A AEM Base FVP with Realm Management Extension (RME)
+        support using a pre-built ...
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+        - How is the application executed inside the Realm?
+        - Does this path cover attestation and memory encryption features?
+        removed_questions:
+        - What will I set up and run in this Learning Path?
+        - What host system and prerequisites are required?
+        - Do I need physical Arm hardware to complete this path?
+        - How do I run my own application inside a Realm?
+        - How are attestation and memory encryption addressed here?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
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+      preview_before: This Learning Path explains how Arm Confidential Computing Architecture (CCA) Realms
+        interact with I/O devices and what “secure device attach” means in practice. You will review how
+        the Realm Manageme...
+      preview_after: This Learning Path explains how Arm Confidential Computing Architecture (CCA) Realms
+        interact with I/O devices and what “secure device attach” means in practice. You will review how
+        the Realm Manageme...
+      preview_generated: This advanced Learning Path explains how Arm CCA Realms attach to I/O devices
+        using VirtIO paravirtualization, SWIOTLB bounce buffers, and secure physical device attach with
+        PCIe‑TDISP and PCIe‑IDE at...
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+        - What are the prerequisites to start this Learning Path?
+        - Which technologies and tools are used?
+        - What concepts will I understand by the end?
+        - Who should take this Learning Path and how long will it take?
+        removed_questions:
+        - What will I build or verify in this Learning Path?
+        - What are the prerequisites and environment requirements?
+        - How does VirtIO fit into device attach for Realms?
+        - When and why are SWIOTLB bounce buffers used in Realms?
+        - What does secure physical device attach with PCIe‑TDISP and PCIe‑IDE provide?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
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+      preview_before: This Learning Path shows how to run an end-to-end attestation flow with Arm’s Confidential
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+        AEM Base Fixed...
+      preview_after: This Learning Path shows how to run an end-to-end attestation flow with Arm’s Confidential
+        Computing Architecture (CCA). You will deploy a simple workload in a Linux realm on an Armv9-A
+        AEM Base Fixed...
+      preview_generated: This advanced Learning Path shows how to run an end-to-end attestation flow with
+        Arm’s Confidential Computing Architecture (CCA) on Linux. You will deploy a simple workload inside
+        a confidential Linux...
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+        - Which platforms and tools are used?
+        - How is attestation used in this workflow?
+        - Is the included Key Broker Server suitable for production use?
+        removed_questions:
+        - What will I implement in this Learning Path?
+        - How does the attestation gating work in this example?
+        - Which tools and platforms are used?
+        - Is the provided Key Broker Server suitable for production?
+        updated_questions:
+        - What are the prerequisites?
+  - path: content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
+    status: drift_detected
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+      preview_before: This Learning Path shows how to deploy Confidential Containers from encrypted images
+        inside Arm CCA Realms using Trustee services for attestation-based authorization on an Armv9-A
+        AEM Base Fixed Virtu...
+      preview_after: This Learning Path shows how to deploy Confidential Containers from encrypted images
+        inside Arm CCA Realms using Trustee services for attestation-based authorization on an Armv9-A
+        AEM Base Fixed Virtu...
+      preview_generated: This Learning Path shows how to run a Confidential Container from an encrypted
+        image inside an Arm CCA Realm using Trustee services on an Armv9-A AEM Base Fixed Virtual Platform
+        (FVP) with RME support...
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+        - What prerequisites do I need before starting?
+        - Which host operating systems are supported?
+        - Which tools and Arm technologies are used?
+        - How are authorization and decryption of the image handled?
+        removed_questions:
+        - What environment and hardware do I need?
+        - Which software components are involved?
+        - Are there prerequisites before starting?
+        - How is confidentiality enforced and authorized?
+        updated_questions:
+        - What will I build and verify in this Learning Path?
+  - path: content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
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+      preview_before: Learn to deploy a Linux workload in an Arm Confidential Computing Architecture (CCA)
+        realm on the Armv9-A AEM Base Fixed Virtual Platform (FVP) with Realm Management Extension (RME)
+        support and connec...
+      preview_after: Learn to deploy a Linux workload in an Arm Confidential Computing Architecture (CCA)
+        realm on the Armv9-A AEM Base Fixed Virtual Platform (FVP) with Realm Management Extension (RME)
+        support and connec...
+      preview_generated: This advanced Learning Path guides you through running an end-to-end attestation
+        flow with Arm Confidential Compute Architecture (CCA) and Trustee services. You will deploy a
+        simple workload in a Linu...
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+      generated_at_after: '2026-05-18T17:23:55Z'
+      preview_before: This Learning Path shows how to deploy a Linux Cobalt 100 virtual machine on Microsoft
+        Azure using the Azure Portal, then secure and verify external access. You will select a Cobalt
+        100–backed size fr...
+      preview_after: This Learning Path shows how to deploy a Linux Cobalt 100 virtual machine on Microsoft
+        Azure using the Azure Portal, then secure and verify external access. You will select a Cobalt
+        100–backed size fr...
+      preview_generated: This Learning Path shows how to deploy an Arm-based Cobalt 100 virtual machine
+        on Microsoft Azure using the Azure Portal, connect via SSH, and expose an application port with
+        Network Security Group ru...
+    faqs:
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+      before_count: 5
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+      generated_diff:
+        before_count: 5
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+        added_questions:
+        - What is Cobalt 100 and which Arm architecture does it use?
+        - Which Azure VM series offer Cobalt 100 options?
+        - What prerequisites do I need before starting?
+        - How are ports and network access configured in this path?
+        - Can I use the Azure CLI instead of the Portal?
+        removed_questions:
+        - What are the prerequisites to complete this Learning Path?
+        - Which Azure VM series use Cobalt 100, and how do I choose a size?
+        - Why set Public inbound ports to None during VM creation?
+        - How do I connect to the VM over SSH and what if it fails?
+        - How do I verify external connectivity to port 8080, and can I use a different port?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
+    status: drift_detected
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons:
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+    - faq_drift_detected
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+      generated_at_before: '2026-05-18T17:24:46Z'
+      generated_at_after: '2026-05-18T17:24:46Z'
+      preview_before: This Learning Path shows how to automate Arm AArch64 Docker image creation with
+        AWS CodeBuild using a GitHub project, and then run the images on any Arm system with Docker installed.
+        You will create a...
+      preview_after: This Learning Path shows how to automate Arm AArch64 Docker image creation with AWS
+        CodeBuild using a GitHub project, and then run the images on any Arm system with Docker installed.
+        You will create a...
+      preview_generated: This Learning Path shows how to automate creation of Arm AArch64 Docker images
+        using AWS CodeBuild with a GitHub project, then share and run those images on Arm systems with
+        Docker installed. You will...
+    faqs:
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+        added_questions:
+        - What will I build in this Learning Path?
+        - Where are the images published?
+        - How do I verify my machine is compatible to run the images?
+        removed_questions:
+        - What will I build and run in this Learning Path?
+        - Where are images published, and how do I consume them?
+        - Does this Learning Path set up automatic build triggers from GitHub?
+        updated_questions:
+        - Which architectures and operating systems are targeted?
+        - What are the prerequisites?
+  - path: content/learning-paths/servers-and-cloud-computing/codec/_index.md
+    status: drift_detected
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons:
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+      generated_at_after: '2026-05-18T17:25:34Z'
+      preview_before: This Learning Path shows how to build and run the x265 (H.265/HEVC) encoder on Arm
+        servers and benchmark its performance. You will prepare an Ubuntu Linux 20.04 Arm instance (verified
+        on AWS EC2 and O...
+      preview_after: This Learning Path shows how to build and run the x265 (H.265/HEVC) encoder on Arm
+        servers and benchmark its performance. You will prepare an Ubuntu Linux 20.04 Arm instance (verified
+        on AWS EC2 and O...
+      preview_generated: Learn how to build and run the open-source x265 H.265 encoder on Arm-based cloud
+        servers and evaluate performance across video resolutions and encoding presets. You will install
+        GCC, CMake, and suppor...
+    faqs:
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+        added_questions:
+        - What will I do in this Learning Path?
+        - What prerequisites and environment are required?
+        - Which tools and packages will I install?
+        - How are Arm Neoverse optimizations used with x265?
+        - How will I evaluate performance?
+        removed_questions:
+        - What will I build and measure in this Learning Path?
+        - What environment and operating system are verified?
+        - How do I build x265 on the Arm server?
+        - What inputs should I use for benchmarking and what variations should I test?
+        - How do I resolve an unknown -march value or ENABLE_NEON_I8MM build error?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/codec1/_index.md
+    status: drift_detected
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons:
+    - summary_drift_detected
+    - faq_drift_detected
+    template_version_before: summary-faq-v3
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+      generated_at_after: '2026-05-18T17:26:19Z'
+      preview_before: This Learning Path shows how to build and run the AV1 and VP9 codecs on Arm Linux
+        systems and measure their performance. You will clone and compile the libaom (AV1) and libvpx
+        (VP9) reference implemen...
+      preview_after: This Learning Path shows how to build and run the AV1 and VP9 codecs on Arm Linux
+        systems and measure their performance. You will clone and compile the libaom (AV1) and libvpx
+        (VP9) reference implemen...
+      preview_generated: This Learning Path shows how to build and run the AV1 and VP9 software codecs
+        on Arm Linux systems, then measure performance across different resolutions and encoding configurations.
+        You will compile ...
+    faqs:
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+      missing_before: false
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+      drift_detected: true
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+      before_count: 5
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+        added_questions:
+        - What will I build and run?
+        - Which Arm platforms does this target?
+        - What tools and source repositories are used?
+        - Do these codecs use Arm Neon and SVE2 optimizations?
+        - What are the prerequisites and time to complete?
+        removed_questions:
+        - What will I build and run in this Learning Path?
+        - What are the prerequisites before starting?
+        - Which Arm-specific optimizations are used?
+        - Does this path cover unit testing for the codecs?
+        - How long does it take and what is the skill level?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
+    status: drift_detected
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons:
+    - summary_drift_detected
+    - faq_drift_detected
+    template_version_before: summary-faq-v3
+    template_version_after: summary-faq-v3
+    summary:
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+      generated_at_after: '2026-05-18T17:26:50Z'
+      preview_before: This Learning Path shows how to deploy Couchbase Server on Google Cloud C4A Arm-based
+        virtual machines powered by Axion processors (Arm Neoverse-V2 cores). You will create a firewall
+        rule for TCP port...
+      preview_after: This Learning Path shows how to deploy Couchbase Server on Google Cloud C4A Arm-based
+        virtual machines powered by Axion processors (Arm Neoverse-V2 cores). You will create a firewall
+        rule for TCP port...
+      preview_generated: This Learning Path shows how to deploy Couchbase on Google Cloud C4A Arm64 instances
+        and validate performance. You will provision a SUSE Linux Enterprise Server VM on a Google Axion
+        C4A machine, open ...
+    faqs:
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+      missing_before: false
+      rerun_requested: false
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+      generated_at_after: '2026-05-18T17:26:50Z'
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+      generated_diff:
+        before_count: 5
+        after_count: 5
+        added_questions:
+        - What are the prerequisites to follow this Learning Path?
+        - Which instance type and operating system are used?
+        - How do I make the Couchbase Web Console accessible?
+        - How do I verify the Couchbase deployment?
+        - How is performance benchmarking performed in this path?
+        removed_questions:
+        - What platform and instance type does this Learning Path use?
+        - What are the prerequisites and skill level?
+        - How is Couchbase installed and verified?
+        - How do I access the Couchbase Web Console on the VM?
+        - How is benchmarking performed and what metrics are captured?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
+    status: drift_detected
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons:
+    - summary_drift_detected
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+      generated_at_after: '2026-05-18T17:27:18Z'
+      preview_before: This introductory Learning Path shows how to use g++ optimization techniques to
+        improve C++ application performance on Arm systems. You will set up an Ubuntu 24.04 LTS environment
+        on an AWS Graviton4 ...
+      preview_after: This introductory Learning Path shows how to use g++ optimization techniques to improve
+        C++ application performance on Arm systems. You will set up an Ubuntu 24.04 LTS environment on
+        an AWS Graviton4 ...
+      preview_generated: This introductory Learning Path shows how to improve C++ application performance
+        on Arm by applying g++ compiler optimization techniques and flags on Linux. You will create and
+        connect to an AWS Gravi...
+    faqs:
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+      drift_detected: true
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+      generated_at_after: '2026-05-18T17:27:18Z'
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+        added_questions:
+        - What will I accomplish in this Learning Path?
+        - What environment and accounts are required?
+        - How do I choose the right -march= setting?
+        - When should I optimize for size instead of speed?
+        - How long does it take and what are the prerequisites?
+        removed_questions:
+        - What will I build and measure in this Learning Path?
+        - Which environment and Arm platform are used?
+        - Which compiler flags are emphasized?
+        - How do I inspect CPU architecture and features?
+        - What are the prerequisites and who is this for?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md
+    status: drift_detected
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
+    change_reasons:
+    - summary_drift_detected
+    - faq_drift_detected
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+      generated_at_after: '2026-05-18T17:27:52Z'
+      preview_before: This Learning Path shows how to microbenchmark and optimize C++ code on Arm-based
+        Linux systems using Google Benchmark and Profile-Guided Optimization (PGO). You will build an
+        instrumented binary with...
+      preview_after: This Learning Path shows how to microbenchmark and optimize C++ code on Arm-based
+        Linux systems using Google Benchmark and Profile-Guided Optimization (PGO). You will build an
+        instrumented binary with...
+      preview_generated: This Learning Path shows how to microbenchmark a C++ function on Arm-based Linux
+        systems and apply profile-guided optimization (PGO) to improve performance. You will use Google
+        Benchmark to measure a ...
+    faqs:
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+        added_questions:
+        - What will I build and measure in this Learning Path?
+        - What are the prerequisites to follow along?
+        - How does the PGO build process work with GCC/G++?
+        - Can I automate this with Make and CI systems?
+        - When should I apply PGO, and what are the trade-offs?
+        removed_questions:
+        - Who is this Learning Path for and what will I build?
+        - What environment and prerequisites do I need?
+        - How do I apply PGO with GCC/G++?
+        - How does Google Benchmark help and how do I prevent over-optimization?
+        - When should I use or avoid PGO?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
+    status: error
+    error: 'Could not reach AI endpoint: [Errno 8] nodename nor servname provided, or not known'
+  - path: content/learning-paths/servers-and-cloud-computing/csp/_index.md
+    status: error
+    error: 'Could not reach AI endpoint: [Errno 8] nodename nor servname provided, or not known'
+  - path: content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
+    status: drift_detected
+    changed_on_disk: false
+    managed_block_updated: false
+    rerun_flags_reset: []
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+        will clone and build lla...
+      preview_after: This Learning Path shows how to deploy the DeepSeek-R1 671B language model on Arm
+        servers using llama.cpp with quantized GGUF files for CPU inference. In about 30 minutes, you
+        will clone and build lla...
+      preview_generated: This Learning Path shows how to deploy a generative AI chatbot based on the DeepSeek-R1
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+        You will clone...
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+        - How do I run the service and send requests to the model?
+        - Which cloud platforms can I use, and what configuration was tested?
+        removed_questions:
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+        - Which operating system and platforms are supported in the instructions?
+        - Which model variant and file format are used?
+        - How do I start and access the model once deployed?
+        - Do I need a GPU for inference?
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+    status: error
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+      preview_before: Build and benchmark the Deep Learning Recommendation Model (DLRM) on Arm Neoverse
+        V2 processors using the MLPerf Inference Offline scenario. Working on Linux in approximately 90
+        minutes, you will fetc...
+      preview_after: Build and benchmark the Deep Learning Recommendation Model (DLRM) on Arm Neoverse
+        V2 processors using the MLPerf Inference Offline scenario. Working on Linux in approximately 90
+        minutes, you will fetc...
+      preview_generated: This introductory Learning Path shows how to build and benchmark the Deep Learning
+        Recommendation Model (DLRM) on Arm Neoverse V2. You will prepare a Linux-based Arm server or an
+        Arm instance from a c...
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+        - How long does this Learning Path take and what is the expected skill level?
+        - Can I run this on AWS or Google Cloud?
+        removed_questions:
+        - What will I build and benchmark in this Learning Path?
+        - What are the hardware and OS requirements?
+        - How do I obtain the dataset and model weights?
+        - What software stack and precision modes are used?
+        - How long does the end-to-end process take and what outputs should I expect?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
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+      preview_before: This advanced Learning Path shows how to automate migrating a containerized, x86-optimized
+        C++ application to Arm64 using the Docker MCP Toolkit, the Arm MCP Server, and GitHub Copilot
+        in VS Code. You...
+      preview_after: This advanced Learning Path shows how to automate migrating a containerized, x86-optimized
+        C++ application to Arm64 using the Docker MCP Toolkit, the Arm MCP Server, and GitHub Copilot
+        in VS Code. You...
+      preview_generated: This advanced Learning Path shows how to automate x86-to-Arm64 code and container
+        migration using the Docker MCP Toolkit with the Arm MCP Server and GitHub Copilot in VS Code.
+        You will set up MCP serv...
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+        - How is the migration validated and where can it run?
+        removed_questions:
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+        - Who is this for and what are the prerequisites?
+        - Which MCP servers and tools will I configure?
+        - How do I integrate MCP with VS Code and GitHub Copilot?
+        - Why consider migrating x86 containers to Arm?
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+  - path: content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md
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+      preview_after: This Learning Path guides advanced .NET developers through migrating an OrchardCore
+        CMS application to Azure Cobalt 100 Arm-based processors. You will provision an Ubuntu 24.04 VM,
+        open port 8080, ins...
+      preview_generated: This Learning Path guides advanced .NET developers through migrating an OrchardCore
+        CMS application to Azure Cobalt 100 Arm-based processors. You will provision an Ubuntu 24.04 VM,
+        open port 8080, ins...
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+        - What .NET versions are evaluated for performance on Arm?
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+  - path: content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
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+      preview_after: This Learning Path shows how to monitor Azure Cobalt 100 Arm64 virtual machines with
+        Dynatrace OneAgent and ActiveGate on Linux. You will create a Dpsv6 series VM on Azure’s Cobalt
+        100, built on Arm N...
+      preview_generated: This Learning Path shows how to monitor Azure Cobalt 100 Arm64 virtual machines
+        with Dynatrace OneAgent and ActiveGate on Linux. You will create a Dpsv6 series VM on Azure’s
+        Cobalt 100, built on Arm N...
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+        - What will I validate by the end, and who should follow this path?
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+        - How do Dynatrace OneAgent and ActiveGate operate in this setup?
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+  - path: content/learning-paths/servers-and-cloud-computing/ecs/_index.md
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+      preview_before: This introductory Learning Path shows how to deploy a containerized application
+        on Amazon Elastic Container Service (ECS) using Fargate with AWS Graviton processors. You will
+        create an ECS cluster, co...
+      preview_after: This introductory Learning Path shows how to deploy a containerized application on
+        Amazon Elastic Container Service (ECS) using Fargate with AWS Graviton processors. You will create
+        an ECS cluster, co...
+      preview_generated: This introductory Learning Path shows how to deploy a containerized application
+        to Amazon Elastic Container Service (ECS) with Fargate on AWS Graviton processors (Arm Neoverse).
+        You will create an ECS...
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+        - Do I need to manage EC2 instances for this deployment?
+        - What are the prerequisites to follow along?
+        - Is Terraform required, and what does it automate?
+        - Do my container images need to target Arm for Graviton?
+        removed_questions:
+        - What are the prerequisites?
+        - Do I need to manage EC2 instances to run the containers?
+        - How is Terraform used in this path?
+        - Do I need an Arm-based local machine to follow the steps?
+        updated_questions:
+        - What will I build in this Learning Path?
+  - path: content/learning-paths/servers-and-cloud-computing/eks/_index.md
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+      preview_before: This introductory Learning Path shows how to provision an Amazon Elastic Kubernetes
+        Service (EKS) cluster on Arm-based AWS Graviton instances and deploy a WordPress application with
+        a MySQL database. ...
+      preview_after: This introductory Learning Path shows how to provision an Amazon Elastic Kubernetes
+        Service (EKS) cluster on Arm-based AWS Graviton instances and deploy a WordPress application with
+        a MySQL database. ...
+      preview_generated: This Learning Path shows you how to provision an Amazon Elastic Kubernetes Service
+        (EKS) cluster on Arm-based AWS Graviton instances and deploy a WordPress application backed by
+        a MySQL database. You ...
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+        - Which operating system is assumed?
+        - How is the MySQL password configured?
+        - How does this relate to Arm technology?
+        removed_questions:
+        - What will I build and deploy?
+        - What are the prerequisites and setup steps?
+        - Which Arm technology and instance type are used?
+        - Can I change the AWS region or instance type?
+        - How long does this Learning Path take and who is it for?
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
+    status: drift_detected
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+      preview_before: This Learning Path shows advanced developers how to build and deploy a multi-architecture
+        container application on Amazon EKS. You will use docker buildx and docker manifest to create
+        x86/amd64 and ar...
+      preview_after: This Learning Path shows advanced developers how to build and deploy a multi-architecture
+        container application on Amazon EKS. You will use docker buildx and docker manifest to create
+        x86/amd64 and ar...
+      preview_generated: Learn how to build and deploy a multi-architecture application on Amazon EKS
+        using docker buildx and docker manifest. You will create a hybrid Kubernetes cluster with x86/amd64
+        and Arm-based (Graviton...
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+        - How is the EKS cluster configured for multiple architectures?
+        - Which tools are used to create and deploy the images?
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+        updated_questions:
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+  - path: content/learning-paths/servers-and-cloud-computing/envoy/_index.md
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+      preview_generated: This introductory Learning Path explains how to build, install, and run Envoy
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+        - How do I start Envoy with the provided configuration?
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+        Cloud Axion C4A Arm64 virtual machines built on Arm Neoverse V2 cores. You will provision a c4a-standard-4
+        instan...
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+        including deployments on AWS, Microsoft Azure, Google Cloud, and Oracle. You will enable Transparent
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+      preview_generated: Learn how to tune Envoy on Arm servers by applying Transparent Huge Pages (THP)
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+        tune THP, and understand k...
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+        - What THP and hugetlbfs changes will I make?
+        - How do I build Envoy with PGO in this path?
+        - Which platforms and operating systems are covered?
+        - What performance gains and duration can I expect?
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+      preview_before: This advanced Learning Path shows how stack buffer overflows work on AArch64 Linux
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+        with Clang and gdb...
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+        with Clang and gdb...
+      preview_generated: This Learning Path explains the mechanics and impact of stack buffer overflows
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+        (Ubuntu 22.04 with clang an...
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+        - How long will it take and is it safe to run?
+        removed_questions:
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+        - What environment and tools are required?
+        - Why is ASLR disabled in the Docker setup?
+        - How will I determine which input bytes reach the return address?
+        - Who is this for and how long does it take?
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+  - path: content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
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+        Extension (SP...
+      preview_generated: Analyze cache behavior with Perf C2C on Arm guides you through detecting and
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+      preview_before: 'Learn how to benchmark Linux kernel performance on Arm-based AWS servers using
+        Fastpath. You will provision three EC2 instances on Graviton: a kernel build host (m6g.12xlarge),
+        a Fastpath host (m6g.4x...'
+      preview_after: 'Learn how to benchmark Linux kernel performance on Arm-based AWS servers using Fastpath.
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+        host (m6g.4x...'
+      preview_generated: This Learning Path shows how to build, deploy, and benchmark custom Linux kernels
+        on Arm-based AWS EC2 instances using tuxmake and Fastpath. You will provision a kernel build host,
+        a Fastpath host, an...
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+        - Which tools are used and for what purpose?
+        - How are benchmark plans defined and executed?
+        removed_questions:
+        - What will I set up and accomplish in this Learning Path?
+        - What prerequisites do I need?
+        - Which instance types and operating systems are used in the examples?
+        - How are kernels built and moved into the test workflow?
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+      preview_after: This short Learning Path shows how to implement and optimize the exponential function
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+      preview_generated: This short Learning Path shows how to implement and optimize the exponential
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+      preview_after: This Learning Path guides you through installing and running Apache Flink on an Arm-based
+        Linux server and benchmarking stream processing performance using the Nexmark suite. You will
+        provision an Arm...
+      preview_generated: This Learning Path guides you through installing and running Apache Flink on
+        an Arm-based Linux server and benchmarking stream processing performance using the Nexmark suite.
+        You will provision an Arm...
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+        - What will I accomplish in this Learning Path?
+        - Why use Vectorscan instead of Hyperscan on Arm?
+        - What prerequisites do I need?
+        - How is performance evaluated in this path?
+        - Which platforms and operating systems are suitable?
+        removed_questions: []
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/vllm/_index.md
+    status: added
+    changed_on_disk: true
+    managed_block_updated: true
+    rerun_flags_reset: []
+    change_reasons:
+    - initial_generation
+    template_version_before: ''
+    template_version_after: summary-faq-v3
+    summary:
+      action: created
+      missing_before: false
+      rerun_requested: false
+      changed: true
+      drift_detected: false
+      source_hash_before: ''
+      source_hash_after: db5987ec7987375d9b51de843b0e1faf0e61731b14c5a563d19aaad26f50f6bb
+      current_source_hash: db5987ec7987375d9b51de843b0e1faf0e61731b14c5a563d19aaad26f50f6bb
+      generated_at_before: ''
+      generated_at_after: '2026-05-18T20:32:01Z'
+      preview_before: ''
+      preview_after: This Learning Path walks you through building the vLLM library from source on an
+        Arm Linux server and running a Qwen LLM locally. You will prepare an Arm-based environment (cloud
+        or on-prem) with at l...
+      preview_generated: This Learning Path walks you through building the vLLM library from source on
+        an Arm Linux server and running a Qwen LLM locally. You will prepare an Arm-based environment
+        (cloud or on-prem) with at l...
+    faqs:
+      action: created
+      missing_before: false
+      rerun_requested: false
+      changed: true
+      drift_detected: false
+      source_hash_before: ''
+      source_hash_after: db5987ec7987375d9b51de843b0e1faf0e61731b14c5a563d19aaad26f50f6bb
+      current_source_hash: db5987ec7987375d9b51de843b0e1faf0e61731b14c5a563d19aaad26f50f6bb
+      generated_at_before: ''
+      generated_at_after: '2026-05-18T20:32:01Z'
+      before_count: 0
+      after_count: 5
+      generated_count: 5
+      change_details:
+        before_count: 0
+        after_count: 5
+        added_questions:
+        - What hardware and OS do I need?
+        - Do I need to pre-download models from Hugging Face?
+        - What will I build and run by the end?
+        - Which platforms can I use to provision an Arm server?
+        - Why run an OpenAI-compatible server locally with vLLM?
+        removed_questions: []
+        updated_questions: []
+      generated_diff:
+        before_count: 0
+        after_count: 5
+        added_questions:
+        - What hardware and OS do I need?
+        - Do I need to pre-download models from Hugging Face?
+        - What will I build and run by the end?
+        - Which platforms can I use to provision an Arm server?
+        - Why run an OpenAI-compatible server locally with vLLM?
+        removed_questions: []
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
+    status: added
+    changed_on_disk: true
+    managed_block_updated: true
+    rerun_flags_reset: []
+    change_reasons:
+    - initial_generation
+    template_version_before: ''
+    template_version_after: summary-faq-v3
+    summary:
+      action: created
+      missing_before: false
+      rerun_requested: false
+      changed: true
+      drift_detected: false
+      source_hash_before: ''
+      source_hash_after: 487e9829c6e08798ad01be7a01f192e10e99cb06b71108575f1919c134eece02
+      current_source_hash: 487e9829c6e08798ad01be7a01f192e10e99cb06b71108575f1919c134eece02
+      generated_at_before: ''
+      generated_at_after: '2026-05-18T20:32:33Z'
+      preview_before: ''
+      preview_after: This Learning Path guides you through building and running vLLM on Arm-based Linux
+        servers to serve both BF16 and INT4-quantized large language models. You will build an aarch64-optimized
+        vLLM with on...
+      preview_generated: This Learning Path guides you through building and running vLLM on Arm-based
+        Linux servers to serve both BF16 and INT4-quantized large language models. You will build an aarch64-optimized
+        vLLM with on...
+    faqs:
+      action: created
+      missing_before: false
+      rerun_requested: false
+      changed: true
+      drift_detected: false
+      source_hash_before: ''
+      source_hash_after: 487e9829c6e08798ad01be7a01f192e10e99cb06b71108575f1919c134eece02
+      current_source_hash: 487e9829c6e08798ad01be7a01f192e10e99cb06b71108575f1919c134eece02
+      generated_at_before: ''
+      generated_at_after: '2026-05-18T20:32:33Z'
+      before_count: 0
+      after_count: 5
+      generated_count: 5
+      change_details:
+        before_count: 0
+        after_count: 5
+        added_questions:
+        - Who is this Learning Path for?
+        - What hardware and software prerequisites do I need?
+        - What will I build and run?
+        - How are requests served, and what limits should I tune?
+        - How is model accuracy evaluated?
+        removed_questions: []
+        updated_questions: []
+      generated_diff:
+        before_count: 0
+        after_count: 5
+        added_questions:
+        - Who is this Learning Path for?
+        - What hardware and software prerequisites do I need?
+        - What will I build and run?
+        - How are requests served, and what limits should I tune?
+        - How is model accuracy evaluated?
+        removed_questions: []
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
+    status: added
+    changed_on_disk: true
+    managed_block_updated: true
+    rerun_flags_reset: []
+    change_reasons:
+    - initial_generation
+    template_version_before: ''
+    template_version_after: summary-faq-v3
+    summary:
+      action: created
+      missing_before: false
+      rerun_requested: false
+      changed: true
+      drift_detected: false
+      source_hash_before: ''
+      source_hash_after: 4ed20056b2d82bd2c9ab1afe3d74657df9ac09808592d843c1b143626a8668ac
+      current_source_hash: 4ed20056b2d82bd2c9ab1afe3d74657df9ac09808592d843c1b143626a8668ac
+      generated_at_before: ''
+      generated_at_after: '2026-05-18T20:33:13Z'
+      preview_before: ''
+      preview_after: This Learning Path shows how to build and run the open-source VVenC (Fraunhofer Versatile
+        Video Encoder) H.266 encoder on Arm servers running Linux. You will install dependencies, compile
+        vvenc from s...
+      preview_generated: This Learning Path shows how to build and run the open-source VVenC (Fraunhofer
+        Versatile Video Encoder) H.266 encoder on Arm servers running Linux. You will install dependencies,
+        compile vvenc from s...
+    faqs:
+      action: created
+      missing_before: false
+      rerun_requested: false
+      changed: true
+      drift_detected: false
+      source_hash_before: ''
+      source_hash_after: 4ed20056b2d82bd2c9ab1afe3d74657df9ac09808592d843c1b143626a8668ac
+      current_source_hash: 4ed20056b2d82bd2c9ab1afe3d74657df9ac09808592d843c1b143626a8668ac
+      generated_at_before: ''
+      generated_at_after: '2026-05-18T20:33:13Z'
+      before_count: 0
+      after_count: 5
+      generated_count: 5
+      change_details:
+        before_count: 0
+        after_count: 5
+        added_questions:
+        - What will I build and run in this Learning Path?
+        - What are the prerequisites?
+        - Which cloud platforms can I use?
+        - Are there Arm-specific optimizations in vvenc?
+        - How long does it take and what skill level is required?
+        removed_questions: []
+        updated_questions: []
+      generated_diff:
+        before_count: 0
+        after_count: 5
+        added_questions:
+        - What will I build and run in this Learning Path?
+        - What are the prerequisites?
+        - Which cloud platforms can I use?
+        - Are there Arm-specific optimizations in vvenc?
+        - How long does it take and what skill level is required?
+        removed_questions: []
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/whisper/_index.md
+    status: added
+    changed_on_disk: true
+    managed_block_updated: true
+    rerun_flags_reset: []
+    change_reasons:
+    - initial_generation
+    template_version_before: ''
+    template_version_after: summary-faq-v3
+    summary:
+      action: created
+      missing_before: false
+      rerun_requested: false
+      changed: true
+      drift_detected: false
+      source_hash_before: ''
+      source_hash_after: e130630b5ae4626641779d0b66d3c1c132b90899cd3d6db07a46df8957c4da38
+      current_source_hash: e130630b5ae4626641779d0b66d3c1c132b90899cd3d6db07a46df8957c4da38
+      generated_at_before: ''
+      generated_at_after: '2026-05-18T20:34:09Z'
+      preview_before: ''
+      preview_after: Accelerate Whisper on Arm with Hugging Face Transformers guides you through installing
+        dependencies, running the whisper-large-v3-turbo model, configuring environment variables for
+        Arm CPU performance...
+      preview_generated: Accelerate Whisper on Arm with Hugging Face Transformers guides you through installing
+        dependencies, running the whisper-large-v3-turbo model, configuring environment variables for
+        Arm CPU performance...
+    faqs:
+      action: created
+      missing_before: false
+      rerun_requested: false
+      changed: true
+      drift_detected: false
+      source_hash_before: ''
+      source_hash_after: e130630b5ae4626641779d0b66d3c1c132b90899cd3d6db07a46df8957c4da38
+      current_source_hash: e130630b5ae4626641779d0b66d3c1c132b90899cd3d6db07a46df8957c4da38
+      generated_at_before: ''
+      generated_at_after: '2026-05-18T20:34:09Z'
+      before_count: 0
+      after_count: 5
+      generated_count: 5
+      change_details:
+        before_count: 0
+        after_count: 5
+        added_questions:
+        - What will I build in this Learning Path?
+        - What hardware and operating system are required?
+        - Which cloud platforms and instances are referenced?
+        - Do I need prior experience to follow this path?
+        - Does this Learning Path require a GPU?
+        removed_questions: []
+        updated_questions: []
+      generated_diff:
+        before_count: 0
+        after_count: 5
+        added_questions:
+        - What will I build in this Learning Path?
+        - What hardware and operating system are required?
+        - Which cloud platforms and instances are referenced?
+        - Do I need prior experience to follow this path?
+        - Does this Learning Path require a GPU?
+        removed_questions: []
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
+    status: added
+    changed_on_disk: true
+    managed_block_updated: true
+    rerun_flags_reset: []
+    change_reasons:
+    - initial_generation
+    template_version_before: ''
+    template_version_after: summary-faq-v3
+    summary:
+      action: created
+      missing_before: false
+      rerun_requested: false
+      changed: true
+      drift_detected: false
+      source_hash_before: ''
+      source_hash_after: 46f2645fb6ef298508af93569554315cfa2564be9891acabf1c874c94e52d70d
+      current_source_hash: 46f2645fb6ef298508af93569554315cfa2564be9891acabf1c874c94e52d70d
+      generated_at_before: ''
+      generated_at_after: '2026-05-18T20:34:53Z'
+      preview_before: ''
+      preview_after: This introductory Learning Path shows how to install MySQL Community Server and WordPress
+        on an Arm (Ampere) virtual machine running Oracle Linux in Oracle Cloud Infrastructure using the
+        always free t...
+      preview_generated: This introductory Learning Path shows how to install MySQL Community Server and
+        WordPress on an Arm (Ampere) virtual machine running Oracle Linux in Oracle Cloud Infrastructure
+        using the always free t...
+    faqs:
+      action: created
+      missing_before: false
+      rerun_requested: false
+      changed: true
+      drift_detected: false
+      source_hash_before: ''
+      source_hash_after: 46f2645fb6ef298508af93569554315cfa2564be9891acabf1c874c94e52d70d
+      current_source_hash: 46f2645fb6ef298508af93569554315cfa2564be9891acabf1c874c94e52d70d
+      generated_at_before: ''
+      generated_at_after: '2026-05-18T20:34:53Z'
+      before_count: 0
+      after_count: 5
+      generated_count: 5
+      change_details:
+        before_count: 0
+        after_count: 5
+        added_questions:
+        - What will I set up in this Learning Path?
+        - What are the prerequisites?
+        - Do I need to use Terraform to deploy the instance?
+        - How long does it take and what skill level is required?
+        - Which Arm and operating system technologies are used?
+        removed_questions: []
+        updated_questions: []
+      generated_diff:
+        before_count: 0
+        after_count: 5
+        added_questions:
+        - What will I set up in this Learning Path?
+        - What are the prerequisites?
+        - Do I need to use Terraform to deploy the instance?
+        - How long does it take and what skill level is required?
+        - Which Arm and operating system technologies are used?
+        removed_questions: []
+        updated_questions: []
+  - path: content/learning-paths/servers-and-cloud-computing/zlib/_index.md
+    status: added
+    changed_on_disk: true
+    managed_block_updated: true
+    rerun_flags_reset: []
+    change_reasons:
+    - initial_generation
+    template_version_before: ''
+    template_version_after: summary-faq-v3
+    summary:
+      action: created
+      missing_before: false
+      rerun_requested: false
+      changed: true
+      drift_detected: false
+      source_hash_before: ''
+      source_hash_after: 8916f83f621505dc5406f14e70d04e702ea304950e34b4b76dc1bbc987bcc4e3
+      current_source_hash: 8916f83f621505dc5406f14e70d04e702ea304950e34b4b76dc1bbc987bcc4e3
+      generated_at_before: ''
+      generated_at_after: '2026-05-18T20:35:23Z'
+      preview_before: ''
+      preview_after: This Learning Path shows how to build and use zlib-ng on Arm servers to improve data
+        compression performance over the system default zlib by enabling Arm-specific optimizations. You
+        will compile zlib-...
+      preview_generated: This Learning Path shows how to build and use zlib-ng on Arm servers to improve
+        data compression performance over the system default zlib by enabling Arm-specific optimizations.
+        You will compile zlib-...
+    faqs:
+      action: created
+      missing_before: false
+      rerun_requested: false
+      changed: true
+      drift_detected: false
+      source_hash_before: ''
+      source_hash_after: 8916f83f621505dc5406f14e70d04e702ea304950e34b4b76dc1bbc987bcc4e3
+      current_source_hash: 8916f83f621505dc5406f14e70d04e702ea304950e34b4b76dc1bbc987bcc4e3
+      generated_at_before: ''
+      generated_at_after: '2026-05-18T20:35:23Z'
+      before_count: 0
+      after_count: 5
+      generated_count: 5
+      change_details:
+        before_count: 0
+        after_count: 5
+        added_questions:
+        - What will I build and test in this Learning Path?
+        - What environment do I need to follow the steps?
+        - How does zlib-ng improve compression performance on Arm?
+        - Is zlib-ng API compatible with existing applications?
+        - How will I measure and analyze the performance impact?
+        removed_questions: []
+        updated_questions: []
+      generated_diff:
+        before_count: 0
+        after_count: 5
+        added_questions:
+        - What will I build and test in this Learning Path?
+        - What environment do I need to follow the steps?
+        - How does zlib-ng improve compression performance on Arm?
+        - Is zlib-ng API compatible with existing applications?
+        - How will I measure and analyze the performance impact?
+        removed_questions: []
+        updated_questions: []
diff --git a/themes/arm-design-system-hugo-theme/layouts/partials/learning-paths/generated-summary-faq.html b/themes/arm-design-system-hugo-theme/layouts/partials/learning-paths/generated-summary-faq.html
index 909e4bf8ed..aee93cec7d 100644
--- a/themes/arm-design-system-hugo-theme/layouts/partials/learning-paths/generated-summary-faq.html
+++ b/themes/arm-design-system-hugo-theme/layouts/partials/learning-paths/generated-summary-faq.html
@@ -18,44 +18,57 @@
 
 	{{ if or $summary $faqs }}
 		<div id="generated-summary-faq" class="u-margin-top-2">
-			{{ if $ai_assisted }}
-				<div class="generated-summary-faq__label u-margin-bottom-1">
-					<span class="generated-summary-faq__badge">AI-assisted</span>
-					<ads-modal id="ai-assisted-summary-faq-modal" modal-title="What AI-assisted means">
-						<div slot="body">
-							<p>
-								This summary and FAQ were drafted with an approved AI-assisted workflow and are reviewed by Arm contributors before publication.
-								Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.
-							</p>
-						</div>
-						<div slot="actions">
-							<ads-cta-button class="ai-assisted-summary-faq-close" level="primary" type="button" sr-label="close">Close</ads-cta-button>
-						</div>
-						<div slot="trigger">
-							<button class="generated-summary-faq__info" type="button" aria-label="Learn what AI-assisted means">?</button>
-						</div>
-					</ads-modal>
-				</div>
-				<script>
-					(() => {
-						const modal = document.getElementById('ai-assisted-summary-faq-modal');
-						if (!modal) return;
-						modal.querySelectorAll('.ai-assisted-summary-faq-close').forEach((button) => {
-							button.addEventListener('click', () => modal.close());
-						});
-					})();
-				</script>
-			{{ end }}
-
 			{{ with $summary }}
-				<h3>Summary</h3>
+				<div class="generated-summary-faq__heading">
+					<h3>Summary</h3>
+					{{ if $ai_assisted }}
+						<span class="generated-summary-faq__assist">
+							<ads-tag class="generated-summary-faq__ai-tag">AI-assisted</ads-tag>
+							<ads-modal id="ai-assisted-summary-modal" modal-title="What AI-assisted means">
+								<div slot="body">
+									<p>
+										This summary was drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication.
+										Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.
+									</p>
+								</div>
+								<div slot="actions">
+									<ads-cta-button class="ai-assisted-summary-faq-close" level="primary" type="button" sr-label="close">Close</ads-cta-button>
+								</div>
+								<div slot="trigger">
+									<ads-button class="generated-summary-faq__info" level="tertiary" size="small" is-circular="" type="button" sr-label="Learn what AI-assisted means">?</ads-button>
+								</div>
+							</ads-modal>
+						</span>
+					{{ end }}
+				</div>
 				<div class="u-margin-left-1">
 					{{ . | markdownify }}
-                </div>
-            {{ end }}
+				</div>
+			{{ end }}
 
 			{{ with $faqs }}
-				<h3 class="u-margin-top-2">Frequently asked questions</h3>
+				<div class="generated-summary-faq__heading u-margin-top-2">
+					<h3>Frequently asked questions</h3>
+					{{ if $ai_assisted }}
+						<span class="generated-summary-faq__assist">
+							<ads-tag class="generated-summary-faq__ai-tag">AI-assisted</ads-tag>
+							<ads-modal id="ai-assisted-faq-modal" modal-title="What AI-assisted means">
+								<div slot="body">
+									<p>
+										These FAQs were drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication.
+										Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.
+									</p>
+								</div>
+								<div slot="actions">
+									<ads-cta-button class="ai-assisted-summary-faq-close" level="primary" type="button" sr-label="close">Close</ads-cta-button>
+								</div>
+								<div slot="trigger">
+									<ads-button class="generated-summary-faq__info" level="tertiary" size="small" is-circular="" type="button" sr-label="Learn what AI-assisted means">?</ads-button>
+								</div>
+							</ads-modal>
+						</span>
+					{{ end }}
+				</div>
 				{{ range . }}
 					<ads-expansion-panel class="generated-summary-faq__panel u-margin-left-1 u-margin-bottom-1">
 						<span slot="toggle">{{ .question }}</span>
@@ -65,6 +78,16 @@ <h3 class="u-margin-top-2">Frequently asked questions</h3>
 					</ads-expansion-panel>
 				{{ end }}
 			{{ end }}
+
+			{{ if $ai_assisted }}
+				<script>
+					(() => {
+						document.querySelectorAll('.ai-assisted-summary-faq-close').forEach((button) => {
+							button.addEventListener('click', () => button.closest('ads-modal')?.close());
+						});
+					})();
+				</script>
+			{{ end }}
 		</div>
 	{{ end }}
 {{ end }}
diff --git a/tools/generate-summary-faq b/tools/generate-summary-faq
new file mode 100755
index 0000000000..922ca83430
--- /dev/null
+++ b/tools/generate-summary-faq
@@ -0,0 +1,644 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+DEFAULT_BASE_URL="https://openai-api-proxy.geo.arm.com/api/providers/openai/v1/responses/"
+DEFAULT_MODEL="gpt-5"
+DEFAULT_TIMEOUT="180"
+DEFAULT_RETRIES="3"
+DEFAULT_STEP_LIMIT="5"
+DEFAULT_EXCERPT_CHARS="600"
+DEFAULT_REPORTS_DIR="reports/generated-summary-faq"
+
+usage() {
+  cat <<'EOF'
+Generate AI-assisted summary and FAQ content for Arm Learning Paths.
+
+This is the team-facing wrapper around tools/generate_summary_faq.py. It always
+uses the configured LLM endpoint. There is no template/offline generation mode.
+
+Usage:
+  tools/generate-summary-faq --category CATEGORY [options]
+  tools/generate-summary-faq --path PATH [options]
+  tools/generate-summary-faq --all [options]
+  tools/generate-summary-faq --list-categories
+
+Target selection:
+  --category SLUG      Process one top-level Learning Path category.
+                       Example: servers-and-cloud-computing
+  --path PATH          Process one Learning Path directory, _index.md file, or
+                       a comma-separated list of paths.
+  --all                Process all eligible Learning Paths as one parent run,
+                       split internally by category.
+  --list-categories    Print available category slugs and exit.
+
+Run mode:
+  --dry-run            Generate report/log only. This is the default.
+  --write              Write generated content into matching _index.md files.
+  --allow-unflagged    Include paths even when generate_summary_faq is not true.
+
+LLM endpoint:
+  --model MODEL        Model exposed by the configured endpoint.
+                       Default: gpt-5, or $OPENAI_MODEL
+  --base-url URL       OpenAI-compatible Responses endpoint.
+                       Default: Arm OpenAI proxy, or $OPENAI_BASE_URL
+  --timeout SECONDS    Seconds to wait for each AI response before retrying.
+                       Default: 180, or $OPENAI_TIMEOUT
+  --retries COUNT      Retries for transient timeout/network errors.
+                       Default: 3, or $OPENAI_RETRIES
+  --ca-bundle FILE     Optional CA bundle for Python TLS verification.
+                       Default: $OPENAI_CA_BUNDLE or $SSL_CERT_FILE
+  --insecure           Skip TLS verification for local testing only.
+                       This wrapper also does this automatically when no
+                       CA bundle is configured.
+  --verify-tls         Require TLS verification even when no CA bundle is set.
+
+Prompt size:
+  --step-limit COUNT   Maximum step excerpts included in each prompt.
+                       Default: 5, or $SUMMARY_FAQ_PROMPT_STEP_LIMIT
+  --excerpt-chars N    Maximum characters per step excerpt.
+                       Default: 600, or $SUMMARY_FAQ_PROMPT_EXCERPT_CHARS
+
+Output:
+  --reports-dir DIR    Directory for logs/reports.
+                       Default: reports/generated-summary-faq
+  --run-name NAME      Base filename for category/path runs, or directory name
+                       for --all parent runs.
+                       Default: target slug + timestamp
+  --log-file FILE      Explicit text log path.
+  --report-file FILE   Explicit YAML report path.
+  --markdown-report FILE
+                       Explicit Markdown table report path.
+  --quiet-progress     Hide per-Learning Path progress output.
+
+Preflight:
+  --skip-preflight     Skip local checks before running generation.
+
+Required environment:
+  OPENAI_API_KEY       Arm proxy/OpenAI key. The script never prints it.
+
+Examples:
+  tools/generate-summary-faq --category automotive --dry-run
+  tools/generate-summary-faq --category servers-and-cloud-computing --write
+  tools/generate-summary-faq --all --write
+  tools/generate-summary-faq --path content/learning-paths/servers-and-cloud-computing/nginx_tune --write
+EOF
+}
+
+die() {
+  echo "ERROR: $*" >&2
+  exit 2
+}
+
+slugify() {
+  printf '%s' "$1" | tr '/,[:space:]' '---' | tr -cs 'A-Za-z0-9._-' '-' | sed 's/^-//; s/-$//'
+}
+
+timestamp() {
+  date -u +"%Y%m%d-%H%M%S"
+}
+
+list_categories() {
+  find content/learning-paths -mindepth 1 -maxdepth 1 -type d -exec basename {} \; | sort
+}
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+REPO_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)"
+cd "$REPO_ROOT"
+
+TARGET_KIND=""
+PATH_FILTER=""
+CATEGORY_FILTER=""
+MODE="--dry-run"
+ALLOW_UNFLAGGED="false"
+MODEL_VALUE="${OPENAI_MODEL:-$DEFAULT_MODEL}"
+BASE_URL_VALUE="${OPENAI_BASE_URL:-$DEFAULT_BASE_URL}"
+TIMEOUT_VALUE="${OPENAI_TIMEOUT:-$DEFAULT_TIMEOUT}"
+RETRIES_VALUE="${OPENAI_RETRIES:-$DEFAULT_RETRIES}"
+STEP_LIMIT_VALUE="${SUMMARY_FAQ_PROMPT_STEP_LIMIT:-$DEFAULT_STEP_LIMIT}"
+EXCERPT_CHARS_VALUE="${SUMMARY_FAQ_PROMPT_EXCERPT_CHARS:-$DEFAULT_EXCERPT_CHARS}"
+CA_BUNDLE_VALUE="${OPENAI_CA_BUNDLE:-${SSL_CERT_FILE:-}}"
+TLS_VERIFY_MODE="auto"
+REPORTS_DIR="$DEFAULT_REPORTS_DIR"
+RUN_NAME=""
+LOG_FILE=""
+REPORT_FILE=""
+MARKDOWN_REPORT_FILE=""
+PREFLIGHT="true"
+QUIET_PROGRESS="false"
+TLS_ARGS=()
+
+while [[ $# -gt 0 ]]; do
+  case "$1" in
+    --all)
+      [[ -z "$TARGET_KIND" ]] || die "Choose only one target: --all, --category, or --path."
+      TARGET_KIND="all"
+      shift
+      ;;
+    --category)
+      [[ -z "$TARGET_KIND" ]] || die "Choose only one target: --all, --category, or --path."
+      TARGET_KIND="category"
+      CATEGORY_FILTER="${2:-}"
+      [[ -n "$CATEGORY_FILTER" ]] || die "--category requires a value."
+      shift 2
+      ;;
+    --path)
+      [[ -z "$TARGET_KIND" ]] || die "Choose only one target: --all, --category, or --path."
+      TARGET_KIND="path"
+      PATH_FILTER="${2:-}"
+      [[ -n "$PATH_FILTER" ]] || die "--path requires a value."
+      shift 2
+      ;;
+    --list-categories)
+      list_categories
+      exit 0
+      ;;
+    --dry-run)
+      MODE="--dry-run"
+      shift
+      ;;
+    --write)
+      MODE="--write"
+      shift
+      ;;
+    --allow-unflagged)
+      ALLOW_UNFLAGGED="true"
+      shift
+      ;;
+    --model)
+      MODEL_VALUE="${2:-}"
+      [[ -n "$MODEL_VALUE" ]] || die "--model requires a value."
+      shift 2
+      ;;
+    --base-url)
+      BASE_URL_VALUE="${2:-}"
+      [[ -n "$BASE_URL_VALUE" ]] || die "--base-url requires a value."
+      shift 2
+      ;;
+    --timeout)
+      TIMEOUT_VALUE="${2:-}"
+      [[ -n "$TIMEOUT_VALUE" ]] || die "--timeout requires a value."
+      shift 2
+      ;;
+    --retries)
+      RETRIES_VALUE="${2:-}"
+      [[ -n "$RETRIES_VALUE" ]] || die "--retries requires a value."
+      shift 2
+      ;;
+    --step-limit)
+      STEP_LIMIT_VALUE="${2:-}"
+      [[ -n "$STEP_LIMIT_VALUE" ]] || die "--step-limit requires a value."
+      shift 2
+      ;;
+    --excerpt-chars)
+      EXCERPT_CHARS_VALUE="${2:-}"
+      [[ -n "$EXCERPT_CHARS_VALUE" ]] || die "--excerpt-chars requires a value."
+      shift 2
+      ;;
+    --ca-bundle)
+      CA_BUNDLE_VALUE="${2:-}"
+      [[ -n "$CA_BUNDLE_VALUE" ]] || die "--ca-bundle requires a value."
+      shift 2
+      ;;
+    --insecure)
+      TLS_VERIFY_MODE="insecure"
+      TLS_ARGS+=(--openai-insecure-skip-verify)
+      shift
+      ;;
+    --verify-tls)
+      TLS_VERIFY_MODE="verify"
+      shift
+      ;;
+    --reports-dir)
+      REPORTS_DIR="${2:-}"
+      [[ -n "$REPORTS_DIR" ]] || die "--reports-dir requires a value."
+      shift 2
+      ;;
+    --run-name)
+      RUN_NAME="${2:-}"
+      [[ -n "$RUN_NAME" ]] || die "--run-name requires a value."
+      shift 2
+      ;;
+    --log-file)
+      LOG_FILE="${2:-}"
+      [[ -n "$LOG_FILE" ]] || die "--log-file requires a value."
+      shift 2
+      ;;
+    --report-file)
+      REPORT_FILE="${2:-}"
+      [[ -n "$REPORT_FILE" ]] || die "--report-file requires a value."
+      shift 2
+      ;;
+    --markdown-report)
+      MARKDOWN_REPORT_FILE="${2:-}"
+      [[ -n "$MARKDOWN_REPORT_FILE" ]] || die "--markdown-report requires a value."
+      shift 2
+      ;;
+    --quiet-progress)
+      QUIET_PROGRESS="true"
+      shift
+      ;;
+    --skip-preflight)
+      PREFLIGHT="false"
+      shift
+      ;;
+    --help|-h)
+      usage
+      exit 0
+      ;;
+    *)
+      die "Unknown option: $1"
+      ;;
+  esac
+done
+
+[[ -n "$TARGET_KIND" ]] || die "Choose a target with --all, --category, or --path. Use --help for examples."
+[[ -n "${OPENAI_API_KEY:-}" ]] || die "OPENAI_API_KEY is required. Run: export OPENAI_API_KEY=\"...\""
+
+if [[ "$TARGET_KIND" == "category" && ! -d "content/learning-paths/$CATEGORY_FILTER" ]]; then
+  die "Unknown category: $CATEGORY_FILTER. Run: tools/generate-summary-faq --list-categories"
+fi
+
+if [[ -z "$RUN_NAME" ]]; then
+  case "$TARGET_KIND" in
+    all) RUN_NAME="all-learning-paths-$(timestamp)" ;;
+    category) RUN_NAME="$(slugify "$CATEGORY_FILTER")-$(timestamp)" ;;
+    path) RUN_NAME="$(slugify "$PATH_FILTER")-$(timestamp)" ;;
+  esac
+fi
+
+mkdir -p "$REPORTS_DIR"
+if [[ "$TARGET_KIND" == "all" ]]; then
+  RUN_DIR="$REPORTS_DIR/$RUN_NAME"
+  mkdir -p "$RUN_DIR"
+  [[ -n "$LOG_FILE" ]] || LOG_FILE="$RUN_DIR/run.txt"
+  [[ -n "$REPORT_FILE" ]] || REPORT_FILE="$RUN_DIR/run.yml"
+  [[ -n "$MARKDOWN_REPORT_FILE" ]] || MARKDOWN_REPORT_FILE="$RUN_DIR/run.md"
+else
+  RUN_DIR=""
+  [[ -n "$LOG_FILE" ]] || LOG_FILE="$REPORTS_DIR/$RUN_NAME.txt"
+  [[ -n "$REPORT_FILE" ]] || REPORT_FILE="$REPORTS_DIR/$RUN_NAME.yml"
+  [[ -n "$MARKDOWN_REPORT_FILE" ]] || MARKDOWN_REPORT_FILE="$REPORTS_DIR/$RUN_NAME.md"
+fi
+
+if [[ "$PREFLIGHT" == "true" ]]; then
+  command -v python3 >/dev/null 2>&1 || die "python3 was not found."
+  if ! python3 - <<'PY' >/dev/null
+import yaml
+PY
+  then
+    die "Python package PyYAML is required. Install it with: python3 -m pip install --user pyyaml"
+  fi
+  [[ -f "tools/generate_summary_faq.py" ]] || die "tools/generate_summary_faq.py was not found."
+  [[ -f "tools/prompts/summary_faq_system.md" ]] || die "tools/prompts/summary_faq_system.md was not found."
+  [[ -f "tools/prompts/summary_faq_user.md" ]] || die "tools/prompts/summary_faq_user.md was not found."
+  if [[ -n "$CA_BUNDLE_VALUE" && ! -f "$CA_BUNDLE_VALUE" ]]; then
+    die "CA bundle file not found: $CA_BUNDLE_VALUE"
+  fi
+fi
+
+if [[ "$TLS_VERIFY_MODE" == "auto" && -z "$CA_BUNDLE_VALUE" && "${OPENAI_INSECURE_SKIP_VERIFY:-}" != "true" ]]; then
+  TLS_ARGS+=(--openai-insecure-skip-verify)
+fi
+
+echo "Learning Path summary/FAQ generation"
+echo "  target: $TARGET_KIND"
+[[ -n "$CATEGORY_FILTER" ]] && echo "  category: $CATEGORY_FILTER"
+[[ -n "$PATH_FILTER" ]] && echo "  path: $PATH_FILTER"
+echo "  mode: ${MODE#--}"
+echo "  log_file: $LOG_FILE"
+echo "  report_file: $REPORT_FILE"
+echo "  markdown_report: $MARKDOWN_REPORT_FILE"
+[[ -n "$RUN_DIR" ]] && echo "  run_dir: $RUN_DIR"
+echo "  openai_base_url: $BASE_URL_VALUE"
+echo "  openai_model: $MODEL_VALUE"
+echo "  openai_timeout: $TIMEOUT_VALUE"
+echo "  openai_retries: $RETRIES_VALUE"
+echo "  prompt_step_limit: $STEP_LIMIT_VALUE"
+echo "  prompt_excerpt_chars: $EXCERPT_CHARS_VALUE"
+[[ -n "$CA_BUNDLE_VALUE" ]] && echo "  openai_ca_bundle: $CA_BUNDLE_VALUE"
+if [[ ${#TLS_ARGS[@]} -gt 0 || "${OPENAI_INSECURE_SKIP_VERIFY:-}" == "true" ]]; then
+  echo "  openai_tls_verify: disabled"
+  if [[ "$TLS_VERIFY_MODE" == "auto" ]]; then
+    echo "  openai_tls_note: no CA bundle configured; local wrapper defaulted to --insecure"
+  fi
+fi
+echo
+
+run_generator() {
+  local category="$1"
+  local path_filter="$2"
+  local log_file="$3"
+  local report_file="$4"
+  local markdown_report_file="$5"
+  local aggregate_log="${6:-}"
+
+  local cmd=(
+    python3 tools/generate_summary_faq.py
+    --path-filter "$path_filter"
+    --category "$category"
+    --openai-base-url "$BASE_URL_VALUE"
+    --openai-model "$MODEL_VALUE"
+    --openai-timeout "$TIMEOUT_VALUE"
+    --openai-retries "$RETRIES_VALUE"
+    --openai-ca-bundle "$CA_BUNDLE_VALUE"
+    --prompt-step-limit "$STEP_LIMIT_VALUE"
+    --prompt-excerpt-chars "$EXCERPT_CHARS_VALUE"
+    --report-file "$report_file"
+    --markdown-report-file "$markdown_report_file"
+    "$MODE"
+  )
+
+  if [[ -n "$log_file" ]]; then
+    cmd+=(--log-file "$log_file")
+  fi
+
+  if [[ "$ALLOW_UNFLAGGED" == "true" ]]; then
+    cmd+=(--allow-unflagged)
+  fi
+
+  if [[ "$QUIET_PROGRESS" == "true" ]]; then
+    cmd+=(--quiet-progress)
+  fi
+
+  if [[ ${#TLS_ARGS[@]} -gt 0 ]]; then
+    cmd+=("${TLS_ARGS[@]}")
+  fi
+
+  if [[ -n "$aggregate_log" ]]; then
+    set +e
+    "${cmd[@]}" 2>&1 | tee -a "$aggregate_log"
+    local status=${PIPESTATUS[0]}
+    set -e
+    return "$status"
+  fi
+
+  "${cmd[@]}"
+}
+
+refresh_latest_reports() {
+  local report_file="$1"
+  local markdown_report_file="$2"
+  local latest_yml="$REPORTS_DIR/latest-run.yml"
+  local latest_md="$REPORTS_DIR/latest-run.md"
+
+  if [[ -f "$report_file" && "$report_file" != "$latest_yml" ]]; then
+    cp "$report_file" "$latest_yml"
+  fi
+
+  if [[ -f "$markdown_report_file" && "$markdown_report_file" != "$latest_md" ]]; then
+    cp "$markdown_report_file" "$latest_md"
+  fi
+}
+
+write_aggregate_reports() {
+  local run_dir="$1"
+  local aggregate_yml="$2"
+  local aggregate_md="$3"
+  local aggregate_log="$4"
+  local run_name="$5"
+  local mode="$6"
+
+  python3 - "$run_dir" "$aggregate_yml" "$aggregate_md" "$aggregate_log" "$run_name" "$mode" <<'PY'
+from __future__ import annotations
+
+import sys
+from datetime import datetime, timezone
+from pathlib import Path
+
+import yaml
+
+run_dir = Path(sys.argv[1])
+aggregate_yml = Path(sys.argv[2])
+aggregate_md = Path(sys.argv[3])
+aggregate_log = Path(sys.argv[4])
+run_name = sys.argv[5]
+mode = sys.argv[6].lstrip("-")
+
+category_reports = []
+for report_path in sorted(run_dir.glob("*.yml")):
+    if report_path.resolve() == aggregate_yml.resolve():
+        continue
+    payload = yaml.safe_load(report_path.read_text(encoding="utf-8")) or {}
+    latest = payload.get("latest_run", {})
+    if latest:
+        category_reports.append((report_path.stem, report_path, latest))
+
+totals = {
+    "processed": 0,
+    "added": 0,
+    "updated": 0,
+    "unchanged": 0,
+    "drift_detected": 0,
+    "paths_with_drift": 0,
+    "skipped": 0,
+    "errors": 0,
+    "removed": 0,
+    "ai_requests": 0,
+    "summary_changed": 0,
+    "faq_changed": 0,
+    "rerun_flags_reset": 0,
+}
+section_totals = {"summary": {}, "faqs": {}}
+reason_totals = {}
+paths = []
+
+for category, report_path, latest in category_reports:
+    for key, value in latest.get("totals", {}).items():
+        totals[key] = totals.get(key, 0) + (value or 0)
+
+    for section in ("summary", "faqs"):
+        for action, count in latest.get("section_totals", {}).get(section, {}).items():
+            section_totals[section][action] = section_totals[section].get(action, 0) + (count or 0)
+
+    for reason, count in latest.get("reason_totals", {}).items():
+        reason_totals[reason] = reason_totals.get(reason, 0) + (count or 0)
+
+    for path_entry in latest.get("paths", []):
+        copied = dict(path_entry)
+        copied["category"] = category
+        paths.append(copied)
+
+timestamp = datetime.now(timezone.utc).replace(microsecond=0).isoformat().replace("+00:00", "Z")
+aggregate = {
+    "timestamp": timestamp,
+    "run_name": run_name,
+    "mode": mode,
+    "split_by_category": True,
+    "category_reports": [
+        {
+            "category": category,
+            "report_file": str(report_path),
+            "markdown_report_file": str(report_path.with_suffix(".md")),
+            "totals": latest.get("totals", {}),
+        }
+        for category, report_path, latest in category_reports
+    ],
+    "totals": totals,
+    "section_totals": section_totals,
+    "reason_totals": reason_totals,
+    "paths": paths,
+}
+
+aggregate_yml.write_text(yaml.safe_dump({"latest_run": aggregate}, sort_keys=False), encoding="utf-8")
+
+def esc(value):
+    return str(value if value is not None else "").replace("|", "\\|").replace("\n", "<br>")
+
+def row(label, value):
+    return f"| {esc(label)} | {esc(value)} |"
+
+metric_labels = {
+    "processed": "Processed",
+    "added": "Added",
+    "updated": "Updated",
+    "drift_detected": "Drift detected",
+    "paths_with_drift": "Paths with drift",
+    "skipped": "Skipped",
+    "unchanged": "Unchanged",
+    "errors": "Errors",
+    "ai_requests": "AI requests",
+    "summary_changed": "Summary changed",
+    "faq_changed": "FAQs changed",
+    "rerun_flags_reset": "Rerun flags reset",
+}
+
+lines = [
+    "# Generate Summary/FAQ Aggregate Run",
+    "",
+    f"Generated at: `{timestamp}`",
+    "",
+    "| Field | Value |",
+    "| --- | --- |",
+    row("Run name", run_name),
+    row("Mode", mode),
+    row("Split by category", True),
+    row("Category count", len(category_reports)),
+    "",
+    "## Run Overview",
+    "",
+    "| Metric | Count |",
+    "| --- | ---: |",
+]
+
+for key in (
+    "processed",
+    "added",
+    "updated",
+    "drift_detected",
+    "paths_with_drift",
+    "skipped",
+    "unchanged",
+    "errors",
+    "ai_requests",
+    "summary_changed",
+    "faq_changed",
+    "rerun_flags_reset",
+):
+    lines.append(row(metric_labels.get(key, key.replace("_", " ")), totals.get(key, 0)))
+
+lines.extend(["", "## Category Runs", "", "| Category | Processed | Added | Updated | Drift | Skipped | Unchanged | Errors | AI requests | Report |", "| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | --- |"])
+for category, report_path, latest in category_reports:
+    category_totals = latest.get("totals", {})
+    md_name = report_path.with_suffix(".md").name
+    lines.append(
+        "| {category} | {processed} | {added} | {updated} | {drift} | {skipped} | {unchanged} | {errors} | {ai_requests} | [{md}]({md}) |".format(
+            category=esc(category),
+            processed=category_totals.get("processed", 0),
+            added=category_totals.get("added", 0),
+            updated=category_totals.get("updated", 0),
+            drift=category_totals.get("drift_detected", 0),
+            skipped=category_totals.get("skipped", 0),
+            unchanged=category_totals.get("unchanged", 0),
+            errors=category_totals.get("errors", 0),
+            ai_requests=category_totals.get("ai_requests", 0),
+            md=esc(md_name),
+        )
+    )
+
+for section in ("summary", "faqs"):
+    title = "Summary Actions" if section == "summary" else "FAQ Actions"
+    lines.extend(["", f"## {title}", "", "| Action | Count |", "| --- | ---: |"])
+    for action, count in sorted(section_totals.get(section, {}).items()):
+        lines.append(row(action, count))
+
+nonzero_reasons = [(reason, count) for reason, count in reason_totals.items() if count]
+if nonzero_reasons:
+    lines.extend(["", "## Change Reasons", "", "| Reason | Count |", "| --- | ---: |"])
+    for reason, count in sorted(nonzero_reasons):
+        lines.append(row(reason, count))
+
+interesting = [entry for entry in paths if entry.get("status") != "unchanged" or entry.get("change_reasons") or entry.get("status") == "error"]
+if interesting:
+    lines.extend(["", "## Path Details", "", "| Category | Path | Status | Summary | FAQs | Reasons | Notes |", "| --- | --- | --- | --- | --- | --- | --- |"])
+    for entry in interesting:
+        summary = entry.get("summary", {})
+        faqs = entry.get("faqs", {})
+        reasons = ", ".join(entry.get("change_reasons", [])) or "none"
+        notes = entry.get("error", "") if entry.get("status") == "error" else ""
+        lines.append(
+            "| {category} | `{path}` | {status} | {summary} | {faqs} | {reasons} | {notes} |".format(
+                category=esc(entry.get("category", "")),
+                path=esc(entry.get("path", "")),
+                status=esc(entry.get("status", "")),
+                summary=esc(summary.get("action", "")),
+                faqs=esc(faqs.get("action", "")),
+                reasons=esc(reasons),
+                notes=esc(notes),
+            )
+        )
+
+aggregate_md.write_text("\n".join(lines).rstrip() + "\n", encoding="utf-8")
+with aggregate_log.open("a", encoding="utf-8") as log:
+    log.write(f"\nAggregate report: {aggregate_yml}\n")
+    log.write(f"Aggregate Markdown report: {aggregate_md}\n")
+PY
+}
+
+if [[ "$TARGET_KIND" == "all" ]]; then
+  : > "$LOG_FILE"
+  {
+    echo "Learning Path summary/FAQ aggregate run"
+    echo "run_name: $RUN_NAME"
+    echo "mode: ${MODE#--}"
+    echo "run_dir: $RUN_DIR"
+    echo
+  } | tee -a "$LOG_FILE"
+
+  failures=0
+  while IFS= read -r category; do
+    category_report="$RUN_DIR/$category.yml"
+    category_markdown="$RUN_DIR/$category.md"
+
+    {
+      echo
+      echo "=== Category: $category ==="
+      echo "report_file: $category_report"
+      echo "markdown_report: $category_markdown"
+      echo
+    } | tee -a "$LOG_FILE"
+
+    if ! run_generator "$category" "" "" "$category_report" "$category_markdown" "$LOG_FILE"; then
+      failures=$((failures + 1))
+      echo "Category failed: $category" | tee -a "$LOG_FILE"
+    fi
+  done < <(list_categories)
+
+  write_aggregate_reports "$RUN_DIR" "$REPORT_FILE" "$MARKDOWN_REPORT_FILE" "$LOG_FILE" "$RUN_NAME" "$MODE"
+  refresh_latest_reports "$REPORT_FILE" "$MARKDOWN_REPORT_FILE"
+  echo "Aggregate report written to $REPORT_FILE"
+  echo "Aggregate Markdown report written to $MARKDOWN_REPORT_FILE"
+  echo "Latest report snapshot written to $REPORTS_DIR/latest-run.yml"
+  echo "Latest Markdown snapshot written to $REPORTS_DIR/latest-run.md"
+
+  if [[ "$failures" -gt 0 ]]; then
+    exit 1
+  fi
+  exit 0
+fi
+
+set +e
+run_generator "$CATEGORY_FILTER" "$PATH_FILTER" "$LOG_FILE" "$REPORT_FILE" "$MARKDOWN_REPORT_FILE"
+status=$?
+set -e
+refresh_latest_reports "$REPORT_FILE" "$MARKDOWN_REPORT_FILE"
+exit "$status"
diff --git a/tools/generate-summary-faq.md b/tools/generate-summary-faq.md
new file mode 100644
index 0000000000..21266724e9
--- /dev/null
+++ b/tools/generate-summary-faq.md
@@ -0,0 +1,233 @@
+# Generate Summary/FAQ Tool
+
+Use `tools/generate-summary-faq` to generate AI-assisted summary and FAQ content for Learning Path `_index.md` files.
+
+The tool always uses the configured LLM endpoint. There is no template or offline generation mode.
+
+## Prerequisites
+
+Set your Arm OpenAI proxy key before running:
+
+```bash
+export OPENAI_API_KEY="..."
+```
+
+Optional endpoint configuration:
+
+```bash
+export OPENAI_BASE_URL="https://openai-api-proxy.geo.arm.com/api/providers/openai/v1/responses/"
+export OPENAI_MODEL="gpt-5"
+```
+
+If Python cannot verify the company TLS certificate locally, prefer a CA bundle:
+
+```bash
+export OPENAI_CA_BUNDLE="/path/to/arm-ca-bundle.pem"
+```
+
+For local convenience, `tools/generate-summary-faq` automatically bypasses TLS
+verification when no CA bundle is configured. This is intended only for local
+testing against the Arm proxy. If you want to force certificate verification,
+use:
+
+```bash
+tools/generate-summary-faq --all --dry-run --verify-tls
+```
+
+You can also make the bypass explicit:
+
+```bash
+export OPENAI_INSECURE_SKIP_VERIFY="true"
+```
+
+## Common Runs
+
+Default full generation for all opted-in Learning Paths:
+
+```bash
+./generate-summary-faq
+```
+
+The shortcut above is equivalent to:
+
+```bash
+tools/generate-summary-faq --all --write
+```
+
+Dry-run one category:
+
+```bash
+tools/generate-summary-faq --category automotive
+```
+
+Write generated content for one category:
+
+```bash
+tools/generate-summary-faq --category servers-and-cloud-computing --write
+```
+
+Write generated content for all eligible Learning Paths:
+
+```bash
+tools/generate-summary-faq --all --write
+```
+
+Process a single Learning Path:
+
+```bash
+tools/generate-summary-faq \
+  --path content/learning-paths/servers-and-cloud-computing/nginx_tune \
+  --write
+```
+
+List available categories:
+
+```bash
+tools/generate-summary-faq --list-categories
+```
+
+## Including or Excluding Learning Paths
+
+By default, the generator only processes Learning Paths with this front-matter flag:
+
+```yaml
+generate_summary_faq: true
+```
+
+Set it to `false` to leave a Learning Path out of generated summary/FAQ runs:
+
+```yaml
+generate_summary_faq: false
+```
+
+The `rerun_summary` and `rerun_faqs` fields are separate controls. For a
+Learning Path that already has generated summary/FAQ content, both fields can
+stay `false`; the tool will report the path as unchanged and will not send that
+Learning Path to the LLM again.
+
+Set one or both rerun flags to force regeneration for an existing generated
+section, then the tool resets the flag to `false` after a write run:
+
+```yaml
+rerun_summary: true
+rerun_faqs: true
+```
+
+The LLM is called only when at least one section needs work:
+
+```text
+generated_summary_faq is missing
+summary is missing
+FAQs are missing
+rerun_summary is true
+rerun_faqs is true
+existing generated content used a non-AI generator
+```
+
+## Output
+
+Each run writes three local artifacts under:
+
+```text
+reports/generated-summary-faq/
+```
+
+The files are:
+
+```text
+<run-name>.txt  progress log and terminal-style output
+<run-name>.yml  structured report with latest run and retained history
+<run-name>.md   local Markdown report with tables for review
+```
+
+When you run all Learning Paths, the tool still treats that as one parent run,
+but it splits the actual work by top-level category to reduce timeout/context
+risk. In that case, the artifacts are grouped in one run directory:
+
+```text
+reports/generated-summary-faq/<run-name>/
+run.txt          aggregate progress log for the whole run
+run.yml          aggregate structured report for the whole run
+run.md           aggregate Markdown report for the whole run
+automotive.yml   per-category structured report
+automotive.md    per-category Markdown report
+...
+```
+
+Use the aggregate `run.md` first. It links to the per-category Markdown reports
+when you need the deeper breakdown.
+
+Each run also refreshes these stable report snapshots:
+
+```text
+reports/generated-summary-faq/latest-run.yml
+reports/generated-summary-faq/latest-run.md
+```
+
+Those snapshots point at the most recent local run data, so you do not have to
+guess which timestamped folder was produced last. The per-command terminal log
+remains in the run folder as `run.txt`.
+
+Use `--run-name` to make output filenames predictable:
+
+```bash
+tools/generate-summary-faq \
+  --category servers-and-cloud-computing \
+  --run-name servers-and-cloud-computing \
+  --write
+```
+
+That creates:
+
+```text
+reports/generated-summary-faq/servers-and-cloud-computing.txt
+reports/generated-summary-faq/servers-and-cloud-computing.yml
+reports/generated-summary-faq/servers-and-cloud-computing.md
+```
+
+For all Learning Paths:
+
+```bash
+tools/generate-summary-faq \
+  --all \
+  --run-name all-learning-paths-test \
+  --write
+```
+
+That creates:
+
+```text
+reports/generated-summary-faq/all-learning-paths-test/run.txt
+reports/generated-summary-faq/all-learning-paths-test/run.yml
+reports/generated-summary-faq/all-learning-paths-test/run.md
+```
+
+Open the `.md` file locally to review the same table-style overview used by the GitHub Action summary:
+
+```bash
+open reports/generated-summary-faq/servers-and-cloud-computing.md
+```
+
+The Markdown report is intentionally plain Markdown, so it can also be copied into a page or wired into a local Hugo-only report page later.
+
+For example, to write the Markdown report somewhere else:
+
+```bash
+tools/generate-summary-faq \
+  --category automotive \
+  --markdown-report /tmp/automotive-summary-faq-report.md
+```
+
+## Timeout Tuning
+
+For larger categories or occasional read timeouts, use smaller prompts and more retries:
+
+```bash
+tools/generate-summary-faq \
+  --category servers-and-cloud-computing \
+  --timeout 180 \
+  --retries 3 \
+  --step-limit 5 \
+  --excerpt-chars 600 \
+  --write
+```
diff --git a/tools/generate_summary_faq.py b/tools/generate_summary_faq.py
index bef7b1916b..f2077cbd98 100644
--- a/tools/generate_summary_faq.py
+++ b/tools/generate_summary_faq.py
@@ -4,7 +4,6 @@
 Generate summary and FAQ content for Learning Path _index.md files.
 
 This script uses an OpenAI-compatible endpoint to generate AI-assisted content.
-It keeps a deterministic template fallback for local smoke tests. It:
 
 - selects eligible Learning Paths using a front-matter flag or explicit paths
 - manages a `generated_summary_faq` front-matter block
@@ -20,7 +19,7 @@
 
     # START generated_summary_faq
     generated_summary_faq:
-      template_version: summary-faq-v2
+      template_version: summary-faq-v3
       generated_at: "2026-05-06T19:40:00Z"
       generator: ai
       ai_assisted: true
@@ -51,12 +50,13 @@
 import re
 import ssl
 import sys
+import time
 import urllib.error
 import urllib.request
 from dataclasses import dataclass
 from datetime import datetime, timezone
 from pathlib import Path
-from typing import Any, Dict, Iterable, List, Sequence
+from typing import Any, Dict, List, Sequence
 
 import yaml
 
@@ -67,6 +67,10 @@
 DEFAULT_PROMPT_DIR = REPO_ROOT / "tools" / "prompts"
 DEFAULT_OPENAI_BASE_URL = "https://openai-api-proxy.geo.arm.com/api/providers/openai/v1/responses/"
 DEFAULT_OPENAI_MODEL = "gpt-4.1-mini"
+DEFAULT_OPENAI_TIMEOUT = 120
+DEFAULT_OPENAI_RETRIES = 2
+DEFAULT_PROMPT_STEP_LIMIT = 8
+DEFAULT_PROMPT_EXCERPT_CHARS = 900
 
 ENABLE_FLAG = "generate_summary_faq"
 RERUN_SUMMARY_FLAG = "rerun_summary"
@@ -172,12 +176,6 @@ def parse_args() -> argparse.Namespace:
         action="store_true",
         help=f"Process explicit or discovered Learning Paths even when `{ENABLE_FLAG}` is not true.",
     )
-    parser.add_argument(
-        "--generation-mode",
-        choices=("ai", "template"),
-        default=os.getenv("SUMMARY_FAQ_GENERATION_MODE", "ai"),
-        help="Use the Arm OpenAI-compatible endpoint, or the deterministic local template fallback.",
-    )
     parser.add_argument(
         "--openai-base-url",
         default=os.getenv("OPENAI_BASE_URL", DEFAULT_OPENAI_BASE_URL),
@@ -188,6 +186,18 @@ def parse_args() -> argparse.Namespace:
         default=os.getenv("OPENAI_MODEL", DEFAULT_OPENAI_MODEL),
         help="Model or deployment name exposed by the configured OpenAI-compatible endpoint.",
     )
+    parser.add_argument(
+        "--openai-timeout",
+        type=int,
+        default=int(os.getenv("OPENAI_TIMEOUT", DEFAULT_OPENAI_TIMEOUT)),
+        help="Seconds to wait for each AI endpoint response before retrying.",
+    )
+    parser.add_argument(
+        "--openai-retries",
+        type=int,
+        default=int(os.getenv("OPENAI_RETRIES", DEFAULT_OPENAI_RETRIES)),
+        help="Number of retries for transient AI endpoint timeout/network errors.",
+    )
     parser.add_argument(
         "--openai-ca-bundle",
         default=os.getenv("OPENAI_CA_BUNDLE", os.getenv("SSL_CERT_FILE", "")),
@@ -204,6 +214,18 @@ def parse_args() -> argparse.Namespace:
         default=str(DEFAULT_PROMPT_DIR),
         help="Directory containing summary/FAQ system and user prompt templates.",
     )
+    parser.add_argument(
+        "--prompt-step-limit",
+        type=int,
+        default=int(os.getenv("SUMMARY_FAQ_PROMPT_STEP_LIMIT", DEFAULT_PROMPT_STEP_LIMIT)),
+        help="Maximum number of Learning Path step excerpts included in each AI prompt.",
+    )
+    parser.add_argument(
+        "--prompt-excerpt-chars",
+        type=int,
+        default=int(os.getenv("SUMMARY_FAQ_PROMPT_EXCERPT_CHARS", DEFAULT_PROMPT_EXCERPT_CHARS)),
+        help="Maximum characters included from each step excerpt in each AI prompt.",
+    )
     parser.add_argument(
         "--write",
         action="store_true",
@@ -219,6 +241,11 @@ def parse_args() -> argparse.Namespace:
         default=str(DEFAULT_REPORT_PATH),
         help="Path to the central run report YAML file.",
     )
+    parser.add_argument(
+        "--markdown-report-file",
+        default="",
+        help="Optional Markdown report file with tables for local review.",
+    )
     parser.add_argument(
         "--log-file",
         default="",
@@ -284,9 +311,8 @@ def initialize_log(args: argparse.Namespace) -> None:
                 "Generate summary/FAQ local run",
                 f"timestamp: {current_timestamp()}",
                 f"mode: {'write' if args.write else 'dry-run'}",
-                f"generation_mode: {args.generation_mode}",
-                f"openai_base_url: {args.openai_base_url if args.generation_mode == 'ai' else ''}",
-                f"openai_model: {args.openai_model if args.generation_mode == 'ai' else ''}",
+                f"openai_base_url: {args.openai_base_url}",
+                f"openai_model: {args.openai_model}",
                 f"category: {args.category or ''}",
                 f"path_filter: {args.path_filter or ''}",
                 "",
@@ -424,15 +450,6 @@ def compact_whitespace(value: str) -> str:
     return re.sub(r"\s+", " ", value).strip()
 
 
-def ensure_sentence(value: str) -> str:
-    cleaned = compact_whitespace(value)
-    if not cleaned:
-        return ""
-    if cleaned[-1] not in ".!?":
-        cleaned += "."
-    return cleaned
-
-
 def strip_markdown_links(text: str) -> str:
     text = re.sub(r"\[([^\]]+)\]\([^)]+\)", r"\1", text)
     text = re.sub(r"`([^`]+)`", r"\1", text)
@@ -446,207 +463,6 @@ def preview_text(value: str, limit: int = 200) -> str:
     return preview
 
 
-def normalize_audience(value: str) -> str:
-    cleaned = compact_whitespace(value)
-    patterns = [
-        r"^This Learning Path is for\s+",
-        r"^This learning path is for\s+",
-        r"^This topic is for\s+",
-        r"^This is an? [^.]*? topic for\s+",
-        r"^This is for\s+",
-    ]
-    for pattern in patterns:
-        cleaned = re.sub(pattern, "", cleaned, flags=re.IGNORECASE)
-    return cleaned.rstrip(". ")
-
-
-def normalize_objective_for_sentence(objective: str) -> str:
-    cleaned = compact_whitespace(objective).rstrip(". ")
-    if not cleaned:
-        return ""
-    return cleaned[0].lower() + cleaned[1:] if len(cleaned) > 1 else cleaned.lower()
-
-
-def natural_join(items: Sequence[str], conjunction: str = "and") -> str:
-    cleaned = [compact_whitespace(str(item)) for item in items if compact_whitespace(str(item))]
-    if not cleaned:
-        return ""
-    if len(cleaned) == 1:
-        return cleaned[0]
-    if len(cleaned) == 2:
-        return f"{cleaned[0]} {conjunction} {cleaned[1]}"
-    return f"{', '.join(cleaned[:-1])}, {conjunction} {cleaned[-1]}"
-
-
-def semicolon_join(items: Sequence[str]) -> str:
-    cleaned = [compact_whitespace(str(item)) for item in items if compact_whitespace(str(item))]
-    return "; ".join(cleaned)
-
-
-def unique_strings(items: Iterable[Any]) -> List[str]:
-    seen = set()
-    ordered: List[str] = []
-    for item in items:
-        text = compact_whitespace(str(item))
-        if text and text not in seen:
-            ordered.append(text)
-            seen.add(text)
-    return ordered
-
-
-def as_list(value: Any) -> List[Any]:
-    if value is None:
-        return []
-    if isinstance(value, list):
-        return value
-    return [value]
-
-
-def build_platform_sentence(metadata: Dict[str, Any]) -> str:
-    tools = unique_strings(as_list(metadata.get("tools_software_languages")))
-    operating_systems = unique_strings(as_list(metadata.get("operatingsystems")))
-    arm_ips = [item for item in unique_strings(as_list(metadata.get("armips"))) if item.lower() != "all"]
-    cloud_providers = unique_strings(as_list(metadata.get("cloud_service_providers")))
-
-    parts: List[str] = []
-
-    if tools:
-        parts.append(f"tools and technologies such as {natural_join(tools[:5])}")
-    if operating_systems:
-        parts.append(f"{natural_join(operating_systems[:4])} environments")
-    if arm_ips:
-        parts.append(f"Arm platforms including {natural_join(arm_ips[:4])}")
-    if cloud_providers:
-        parts.append(f"cloud platforms such as {natural_join(cloud_providers[:4])}")
-
-    if not parts:
-        return ""
-
-    return ensure_sentence(f"It focuses on {natural_join(parts, conjunction='and')}")
-
-
-def build_step_sentence(steps: Sequence[StepPage]) -> str:
-    visible_titles = [
-        step.title
-        for step in steps
-        if step.path.name not in {"_index.md", "_next-steps.md", "_review.md", "_demo.md"}
-        and not as_bool(step.metadata.get("hide_from_navpane", False))
-        and step.title
-    ]
-    if not visible_titles:
-        return ""
-    selected = visible_titles[:5]
-    return ensure_sentence(f"The main steps cover {natural_join(selected)}")
-
-
-def build_summary(metadata: Dict[str, Any], steps: Sequence[StepPage]) -> str:
-    title = compact_whitespace(str(metadata.get("title", "This Learning Path")))
-    description = ensure_sentence(str(metadata.get("description", "")).strip())
-    audience = normalize_audience(str(metadata.get("who_is_this_for", "")).strip())
-    objectives = [normalize_objective_for_sentence(item) for item in as_list(metadata.get("learning_objectives"))]
-    objectives = [objective for objective in objectives if objective]
-
-    sentences: List[str] = []
-
-    if description:
-        sentences.append(description)
-    else:
-        sentences.append(ensure_sentence(f"{title} walks you through an end-to-end Arm software workflow"))
-
-    if audience:
-        sentences.append(ensure_sentence(f"It is designed for {audience}"))
-
-    if objectives:
-        sentences.append(ensure_sentence(f"By the end, you will be able to {natural_join(objectives[:3])}"))
-
-    platform_sentence = build_platform_sentence(metadata)
-    if platform_sentence:
-        sentences.append(platform_sentence)
-
-    step_sentence = build_step_sentence(steps)
-    if step_sentence:
-        sentences.append(step_sentence)
-
-    return " ".join(sentence for sentence in sentences if sentence)
-
-
-def build_faqs(metadata: Dict[str, Any], steps: Sequence[StepPage]) -> List[Dict[str, str]]:
-    description = ensure_sentence(str(metadata.get("description", "")).strip())
-    audience_raw = ensure_sentence(str(metadata.get("who_is_this_for", "")).strip())
-    prerequisites = [str(item).strip() for item in as_list(metadata.get("prerequisites")) if str(item).strip()]
-    objectives = [normalize_objective_for_sentence(item) for item in as_list(metadata.get("learning_objectives"))]
-    objectives = [objective for objective in objectives if objective]
-
-    tools = unique_strings(as_list(metadata.get("tools_software_languages")))
-    operating_systems = unique_strings(as_list(metadata.get("operatingsystems")))
-    arm_ips = [item for item in unique_strings(as_list(metadata.get("armips"))) if item.lower() != "all"]
-    cloud_providers = unique_strings(as_list(metadata.get("cloud_service_providers")))
-
-    visible_titles = [
-        step.title
-        for step in steps
-        if step.path.name not in {"_index.md", "_next-steps.md", "_review.md", "_demo.md"}
-        and not as_bool(step.metadata.get("hide_from_navpane", False))
-        and step.title
-    ]
-
-    accomplishment_parts: List[str] = []
-    if objectives:
-        accomplishment_parts.append(ensure_sentence(f"You will {natural_join(objectives[:3])}"))
-    if description:
-        accomplishment_parts.append(description)
-
-    prerequisites_answer = (
-        ensure_sentence(f"Before you start, make sure you have the following: {semicolon_join(prerequisites)}")
-        if prerequisites
-        else "There are no explicit prerequisites listed for this Learning Path."
-    )
-
-    coverage_parts: List[str] = []
-    if tools:
-        coverage_parts.append(f"tools and languages including {natural_join(tools[:5])}")
-    if operating_systems:
-        coverage_parts.append(f"{natural_join(operating_systems[:4])} environments")
-    if arm_ips:
-        coverage_parts.append(f"Arm platforms such as {natural_join(arm_ips[:4])}")
-    if cloud_providers:
-        coverage_parts.append(f"cloud platforms such as {natural_join(cloud_providers[:4])}")
-
-    structure_answer = (
-        ensure_sentence(f"The Learning Path is organized around {natural_join(visible_titles[:5])}")
-        if visible_titles
-        else "The Learning Path follows the standard introduction, guided steps, and next steps structure."
-    )
-
-    faqs = [
-        {
-            "question": "What will you accomplish in this Learning Path?",
-            "answer": " ".join(part for part in accomplishment_parts if part)
-            or "You will work through an Arm-focused workflow and finish with a concrete outcome.",
-        },
-        {
-            "question": "Who is this Learning Path for?",
-            "answer": audience_raw or "This Learning Path is written for Arm software developers.",
-        },
-        {
-            "question": "What do you need before you start?",
-            "answer": prerequisites_answer,
-        },
-        {
-            "question": "Which tools, languages, or platforms does it cover?",
-            "answer": ensure_sentence(f"It covers {natural_join(coverage_parts, conjunction='and')}")
-            if coverage_parts
-            else "It focuses on the tools, platforms, and steps listed in the Learning Path itself.",
-        },
-        {
-            "question": "How is the Learning Path structured?",
-            "answer": structure_answer,
-        },
-    ]
-
-    return faqs
-
-
 def prompt_metadata(metadata: Dict[str, Any]) -> Dict[str, Any]:
     keys = (
         "title",
@@ -665,8 +481,15 @@ def prompt_metadata(metadata: Dict[str, Any]) -> Dict[str, Any]:
     return {key: metadata.get(key) for key in keys if metadata.get(key) not in (None, "", [])}
 
 
-def prompt_steps(steps: Sequence[StepPage]) -> List[Dict[str, Any]]:
+def prompt_steps(
+    steps: Sequence[StepPage],
+    step_limit: int = DEFAULT_PROMPT_STEP_LIMIT,
+    excerpt_chars: int = DEFAULT_PROMPT_EXCERPT_CHARS,
+) -> List[Dict[str, Any]]:
     prompt_pages: List[Dict[str, Any]] = []
+    safe_step_limit = max(1, step_limit)
+    safe_excerpt_chars = max(200, excerpt_chars)
+
     for step in steps:
         if step.path.name in {"_index.md", "_next-steps.md", "_review.md", "_demo.md"}:
             continue
@@ -679,17 +502,25 @@ def prompt_steps(steps: Sequence[StepPage]) -> List[Dict[str, Any]]:
                 "file": step.path.name,
                 "title": step.title,
                 "weight": step.weight,
-                "excerpt": excerpt[:1600],
+                "excerpt": excerpt[:safe_excerpt_chars],
             }
         )
 
-    return prompt_pages[:12]
+    return prompt_pages[:safe_step_limit]
 
 
-def build_learning_path_prompt_context(metadata: Dict[str, Any], steps: Sequence[StepPage]) -> Dict[str, Any]:
+def build_learning_path_prompt_context(
+    metadata: Dict[str, Any],
+    steps: Sequence[StepPage],
+    args: argparse.Namespace,
+) -> Dict[str, Any]:
     return {
         "metadata": prompt_metadata(metadata),
-        "steps": prompt_steps(steps),
+        "steps": prompt_steps(
+            steps,
+            step_limit=args.prompt_step_limit,
+            excerpt_chars=args.prompt_excerpt_chars,
+        ),
         "output_requirements": {
             "summary": "One concise paragraph, approximately 70-120 words.",
             "faqs": "Exactly 5 FAQs. Each answer should be 1-3 sentences.",
@@ -804,6 +635,8 @@ def post_responses_request(
     payload: Dict[str, Any],
     ca_bundle: str = "",
     insecure_skip_verify: bool = False,
+    timeout: int = DEFAULT_OPENAI_TIMEOUT,
+    retries: int = DEFAULT_OPENAI_RETRIES,
 ) -> Dict[str, Any]:
     request = urllib.request.Request(
         endpoint,
@@ -816,23 +649,40 @@ def post_responses_request(
     )
 
     ssl_context = build_ssl_context(ca_bundle=ca_bundle, insecure_skip_verify=insecure_skip_verify)
+    max_attempts = max(1, retries + 1)
+    last_error: Exception | None = None
 
-    try:
-        with urllib.request.urlopen(request, timeout=60, context=ssl_context) as response:
-            body = response.read().decode("utf-8")
-    except urllib.error.HTTPError as exc:
-        error_body = exc.read().decode("utf-8", errors="replace")
-        raise RuntimeError(f"AI endpoint returned HTTP {exc.code}: {error_body}") from exc
-    except urllib.error.URLError as exc:
-        reason = str(exc.reason)
-        if "CERTIFICATE_VERIFY_FAILED" in reason:
-            raise RuntimeError(
-                "Could not verify the AI endpoint TLS certificate. "
-                "Set OPENAI_CA_BUNDLE to your Arm corporate CA bundle, or use "
-                "OPENAI_INSECURE_SKIP_VERIFY=true for local testing only. "
-                f"Original error: {reason}"
-            ) from exc
-        raise RuntimeError(f"Could not reach AI endpoint: {exc.reason}") from exc
+    for attempt in range(1, max_attempts + 1):
+        try:
+            with urllib.request.urlopen(request, timeout=timeout, context=ssl_context) as response:
+                body = response.read().decode("utf-8")
+            break
+        except urllib.error.HTTPError as exc:
+            error_body = exc.read().decode("utf-8", errors="replace")
+            if exc.code not in {408, 429, 500, 502, 503, 504} or attempt == max_attempts:
+                raise RuntimeError(f"AI endpoint returned HTTP {exc.code}: {error_body}") from exc
+            last_error = RuntimeError(f"AI endpoint returned HTTP {exc.code}: {error_body}")
+        except TimeoutError as exc:
+            if attempt == max_attempts:
+                raise RuntimeError(f"AI endpoint timed out after {timeout} seconds.") from exc
+            last_error = exc
+        except urllib.error.URLError as exc:
+            reason = str(exc.reason)
+            if "CERTIFICATE_VERIFY_FAILED" in reason:
+                raise RuntimeError(
+                    "Could not verify the AI endpoint TLS certificate. "
+                    "Set OPENAI_CA_BUNDLE to your Arm corporate CA bundle, or use "
+                    "OPENAI_INSECURE_SKIP_VERIFY=true for local testing only. "
+                    f"Original error: {reason}"
+                ) from exc
+            if attempt == max_attempts:
+                raise RuntimeError(f"Could not reach AI endpoint: {exc.reason}") from exc
+            last_error = exc
+
+        sleep_seconds = min(20, 2 ** (attempt - 1))
+        time.sleep(sleep_seconds)
+    else:
+        raise RuntimeError(f"AI endpoint failed after {max_attempts} attempts: {last_error}")
 
     try:
         parsed = json.loads(body)
@@ -848,14 +698,14 @@ def post_responses_request(
 def generate_ai_summary_faq(metadata: Dict[str, Any], steps: Sequence[StepPage], args: argparse.Namespace) -> Dict[str, Any]:
     api_key = os.getenv("OPENAI_API_KEY", "").strip()
     if not api_key:
-        raise RuntimeError("OPENAI_API_KEY is required when --generation-mode ai is used.")
+        raise RuntimeError("OPENAI_API_KEY is required for AI-assisted summary/FAQ generation.")
 
     prompt_dir = Path(args.prompt_dir)
     system_prompt = read_prompt_template(prompt_dir, "summary_faq_system.md")
     user_template = read_prompt_template(prompt_dir, "summary_faq_user.md")
     user_prompt = render_user_prompt(
         user_template,
-        build_learning_path_prompt_context(metadata, steps),
+        build_learning_path_prompt_context(metadata, steps, args),
     )
 
     prompt_input = (
@@ -873,6 +723,8 @@ def generate_ai_summary_faq(metadata: Dict[str, Any], steps: Sequence[StepPage],
         },
         ca_bundle=args.openai_ca_bundle,
         insecure_skip_verify=args.openai_insecure_skip_verify,
+        timeout=args.openai_timeout,
+        retries=args.openai_retries,
     )
 
     content = extract_response_text(response_payload)
@@ -884,16 +736,7 @@ def build_desired_summary_faq(
     steps: Sequence[StepPage],
     args: argparse.Namespace,
 ) -> Dict[str, Any]:
-    if args.generation_mode == "template":
-        return {
-            "summary": build_summary(metadata, steps),
-            "faqs": build_faqs(metadata, steps),
-            "generator": "template",
-        }
-
-    generated = generate_ai_summary_faq(metadata, steps, args)
-    generated["generator"] = "ai"
-    return generated
+    return generate_ai_summary_faq(metadata, steps, args)
 
 
 def build_source_hash(metadata: Dict[str, Any], steps: Sequence[StepPage]) -> str:
@@ -1139,7 +982,6 @@ def build_updated_generated_block(
     generated_at: str,
     summary_action: str,
     faq_action: str,
-    generator: str,
     model: str,
 ) -> Dict[str, Any]:
     summary_matches_current = not summaries_differ(summary_after, desired_summary)
@@ -1170,11 +1012,11 @@ def build_updated_generated_block(
     return {
         "template_version": TEMPLATE_VERSION,
         "generated_at": generated_at,
-        "generator": generator,
-        "ai_assisted": generator == "ai",
-        "ai_review_required": generator == "ai",
-        "model": model if generator == "ai" else "",
-        "prompt_template": PROMPT_TEMPLATE_VERSION if generator == "ai" else "",
+        "generator": "ai",
+        "ai_assisted": True,
+        "ai_review_required": True,
+        "model": model,
+        "prompt_template": PROMPT_TEMPLATE_VERSION,
         "source_hash": top_level_source_hash,
         SUMMARY_GENERATED_AT_KEY: summary_meta["generated_at"],
         SUMMARY_SOURCE_HASH_KEY: summary_meta["source_hash"],
@@ -1219,6 +1061,7 @@ def build_run_report(
         "skipped": 0,
         "errors": 0,
         "removed": 0,
+        "ai_requests": 0,
         "summary_changed": 0,
         "faq_changed": 0,
         "rerun_flags_reset": 0,
@@ -1252,6 +1095,9 @@ def build_run_report(
         if summary_result.get("drift_detected") or faq_result.get("drift_detected"):
             totals["paths_with_drift"] += 1
 
+        if result.get("ai_requested"):
+            totals["ai_requests"] += 1
+
         rerun_flags_reset = result.get("rerun_flags_reset", [])
         if rerun_flags_reset:
             totals["rerun_flags_reset"] += 1
@@ -1266,16 +1112,17 @@ def build_run_report(
         "mode": "write" if args.write else "dry-run",
         "require_enable_flag": not args.allow_unflagged,
         "path_filter": args.path_filter or "",
+        "category": args.category or "",
         "limit": args.limit,
         "run_url": args.run_url or "",
         "git_ref": args.git_ref or "",
         "git_sha": args.git_sha or "",
         "actor": args.actor or "",
         "template_version": TEMPLATE_VERSION,
-        "generation_mode": args.generation_mode,
-        "openai_base_url": args.openai_base_url if args.generation_mode == "ai" else "",
-        "openai_model": args.openai_model if args.generation_mode == "ai" else "",
-        "prompt_template": PROMPT_TEMPLATE_VERSION if args.generation_mode == "ai" else "",
+        "openai_base_url": args.openai_base_url,
+        "openai_model": args.openai_model,
+        "prompt_template": PROMPT_TEMPLATE_VERSION,
+        "markdown_report_file": args.markdown_report_file or "",
         "totals": totals,
         "section_totals": section_totals,
         "reason_totals": reason_totals,
@@ -1313,6 +1160,159 @@ def write_report(report_file: Path, run_report: Dict[str, Any], history_limit: i
     report_file.write_text(report_text, encoding="utf-8")
 
 
+def markdown_escape(value: Any) -> str:
+    text = str(value if value is not None else "")
+    return text.replace("|", "\\|").replace("\n", "<br>")
+
+
+def markdown_metric_row(label: str, value: Any) -> str:
+    return f"| {markdown_escape(label)} | {markdown_escape(value)} |"
+
+
+def render_markdown_report(run_report: Dict[str, Any]) -> str:
+    totals = run_report.get("totals", {})
+    section_totals = run_report.get("section_totals", {})
+    reason_totals = run_report.get("reason_totals", {})
+    paths = run_report.get("paths", [])
+
+    lines: List[str] = [
+        "# Generate Summary/FAQ Report",
+        "",
+        f"Generated at: `{markdown_escape(run_report.get('timestamp', ''))}`",
+        "",
+        "| Field | Value |",
+        "| --- | --- |",
+        markdown_metric_row("Mode", run_report.get("mode", "")),
+        markdown_metric_row("Require enable flag", run_report.get("require_enable_flag", "")),
+        markdown_metric_row("Category", run_report.get("category", "") or "all"),
+        markdown_metric_row("Path filter", run_report.get("path_filter", "") or "none"),
+        markdown_metric_row("Limit", run_report.get("limit", 0)),
+        markdown_metric_row("Model", run_report.get("openai_model", "")),
+        markdown_metric_row("Prompt template", run_report.get("prompt_template", "")),
+        "",
+        "## Run Overview",
+        "",
+        "| Metric | Count |",
+        "| --- | ---: |",
+        markdown_metric_row("Processed", totals.get("processed", 0)),
+        markdown_metric_row("Added", totals.get("added", 0)),
+        markdown_metric_row("Updated", totals.get("updated", 0)),
+        markdown_metric_row("Drift detected", totals.get("drift_detected", 0)),
+        markdown_metric_row("Paths with drift", totals.get("paths_with_drift", 0)),
+        markdown_metric_row("Skipped", totals.get("skipped", 0)),
+        markdown_metric_row("Unchanged", totals.get("unchanged", 0)),
+        markdown_metric_row("Errors", totals.get("errors", 0)),
+        markdown_metric_row("AI requests", totals.get("ai_requests", 0)),
+        markdown_metric_row("Summary changed", totals.get("summary_changed", 0)),
+        markdown_metric_row("FAQs changed", totals.get("faq_changed", 0)),
+        markdown_metric_row("Rerun flags reset", totals.get("rerun_flags_reset", 0)),
+        "",
+    ]
+
+    for section_name, title in (("summary", "Summary Actions"), ("faqs", "FAQ Actions")):
+        actions = section_totals.get(section_name, {})
+        lines.extend(
+            [
+                f"## {title}",
+                "",
+                "| Action | Count |",
+                "| --- | ---: |",
+            ]
+        )
+        for action in SUMMARY_ACTIONS:
+            lines.append(markdown_metric_row(action, actions.get(action, 0)))
+        lines.append("")
+
+    nonzero_reasons = [(reason, count) for reason, count in reason_totals.items() if count]
+    if nonzero_reasons:
+        lines.extend(["## Change Reasons", "", "| Reason | Count |", "| --- | ---: |"])
+        for reason, count in nonzero_reasons:
+            lines.append(markdown_metric_row(reason, count))
+        lines.append("")
+
+    interesting_paths = [
+        entry
+        for entry in paths
+        if entry.get("status") != "unchanged" or entry.get("change_reasons") or entry.get("status") == "error"
+    ]
+
+    if interesting_paths:
+        lines.extend(
+            [
+                "## Path Details",
+                "",
+                "| Path | Status | Summary | FAQs | Reasons | Notes |",
+                "| --- | --- | --- | --- | --- | --- |",
+            ]
+        )
+        for entry in interesting_paths:
+            summary = entry.get("summary", {})
+            faqs = entry.get("faqs", {})
+            reasons = ", ".join(entry.get("change_reasons", [])) or "none"
+            notes = ""
+            if entry.get("status") == "error":
+                notes = entry.get("error", "")
+            elif entry.get("status") == "skipped":
+                notes = entry.get("skip_reason", "")
+            elif faqs:
+                notes = f"FAQs {faqs.get('before_count', 0)} -> {faqs.get('after_count', 0)}"
+
+            lines.append(
+                "| `{path}` | {status} | {summary_action} | {faq_action} | {reasons} | {notes} |".format(
+                    path=markdown_escape(entry.get("path", "")),
+                    status=markdown_escape(entry.get("status", "")),
+                    summary_action=markdown_escape(summary.get("action", "")),
+                    faq_action=markdown_escape(faqs.get("action", "")),
+                    reasons=markdown_escape(reasons),
+                    notes=markdown_escape(notes),
+                )
+            )
+        lines.append("")
+    else:
+        lines.extend(["## Path Details", "", "All processed Learning Paths were unchanged.", ""])
+
+    changed_previews = [
+        entry
+        for entry in interesting_paths
+        if entry.get("summary", {}).get("changed") or entry.get("faqs", {}).get("changed")
+    ]
+    if changed_previews:
+        lines.extend(
+            [
+                "## Changed Content Preview",
+                "",
+                "| Path | Summary Preview | FAQ Change Details |",
+                "| --- | --- | --- |",
+            ]
+        )
+        for entry in changed_previews:
+            summary = entry.get("summary", {})
+            faqs = entry.get("faqs", {})
+            details = faqs.get("change_details", {})
+            faq_details = (
+                f"before={details.get('before_count', 0)}, "
+                f"after={details.get('after_count', 0)}, "
+                f"added={len(details.get('added_questions', []))}, "
+                f"removed={len(details.get('removed_questions', []))}, "
+                f"updated={len(details.get('updated_questions', []))}"
+            )
+            lines.append(
+                "| `{path}` | {summary_preview} | {faq_details} |".format(
+                    path=markdown_escape(entry.get("path", "")),
+                    summary_preview=markdown_escape(summary.get("preview_after", "")),
+                    faq_details=markdown_escape(faq_details),
+                )
+            )
+        lines.append("")
+
+    return "\n".join(lines).rstrip() + "\n"
+
+
+def write_markdown_report(markdown_report_file: Path, run_report: Dict[str, Any]) -> None:
+    markdown_report_file.parent.mkdir(parents=True, exist_ok=True)
+    markdown_report_file.write_text(render_markdown_report(run_report), encoding="utf-8")
+
+
 def print_result_summary(args: argparse.Namespace, run_report: Dict[str, Any]) -> None:
     totals = run_report["totals"]
     emit(
@@ -1320,7 +1320,7 @@ def print_result_summary(args: argparse.Namespace, run_report: Dict[str, Any]) -
         "Processed {processed} Learning Paths: "
         "{added} added, {updated} updated, {drift_detected} drift detected, "
         "{paths_with_drift} paths with drift, "
-        "{unchanged} unchanged, {errors} errors.".format(**totals)
+        "{skipped} skipped, {unchanged} unchanged, {errors} errors, {ai_requests} AI requests.".format(**totals)
     )
 
     summary_actions = run_report["section_totals"]["summary"]
@@ -1352,34 +1352,63 @@ def print_result_summary(args: argparse.Namespace, run_report: Dict[str, Any]) -
         line = f"- {status.upper():14s} {result['path']} | summary={summary_action} | faqs={faq_action} | reasons={reasons}"
         if status == "error":
             line += f" | error={result.get('error', 'Unknown error')}"
+        if status == "skipped":
+            line += f" | skip_reason={result.get('skip_reason', 'unknown')}"
         emit(args, line)
 
 
-def select_learning_paths(args: argparse.Namespace) -> List[Path]:
+def candidate_learning_paths(args: argparse.Namespace) -> tuple[List[Path], bool]:
     explicit_paths = normalize_path_filter(args.path_filter) if args.path_filter else []
     if explicit_paths:
-        selected = explicit_paths
+        return explicit_paths, True
     elif args.category:
-        selected = discover_category_indexes(args.category)
-    else:
-        selected = discover_learning_path_indexes()
-    filtered: List[Path] = []
+        return discover_category_indexes(args.category), False
+    return discover_learning_path_indexes(), False
+
+
+def skipped_result(index_path: Path, reason: str) -> Dict[str, Any]:
+    return {
+        "path": report_path_for_output(index_path),
+        "status": "skipped",
+        "skip_reason": reason,
+        "change_reasons": [reason],
+        "ai_requested": False,
+        "summary": {"action": "skipped"},
+        "faqs": {"action": "skipped"},
+    }
+
 
-    for index_path in selected:
+def selection_plan(args: argparse.Namespace) -> tuple[List[Path], List[Path], Dict[Path, Dict[str, Any]]]:
+    candidates, explicit = candidate_learning_paths(args)
+    selected: List[Path] = []
+    skipped: Dict[Path, Dict[str, Any]] = {}
+
+    for index_path in candidates:
         doc = read_markdown_document(index_path)
         if is_draft(doc):
+            skipped[index_path] = skipped_result(index_path, "draft")
             continue
         if not args.allow_unflagged and not has_enable_flag(doc):
+            skipped[index_path] = skipped_result(index_path, f"{ENABLE_FLAG}_false")
             continue
-        filtered.append(index_path)
+        selected.append(index_path)
 
-    if not explicit_paths and args.limit > 0:
-        filtered = filtered[: args.limit]
+    if not explicit and args.limit > 0 and len(selected) > args.limit:
+        limited = set(selected[: args.limit])
+        for index_path in selected[args.limit :]:
+            skipped[index_path] = skipped_result(index_path, "limit")
+        selected = [path for path in selected if path in limited]
 
-    return filtered
+    return candidates, selected, skipped
+
+
+def select_learning_paths(args: argparse.Namespace) -> List[Path]:
+    _, selected, _ = selection_plan(args)
+    return selected
 
 
 def process_learning_path(index_path: Path, args: argparse.Namespace) -> Dict[str, Any]:
+    ai_requested = False
     try:
         doc = read_markdown_document(index_path)
         steps = load_steps(index_path)
@@ -1393,18 +1422,35 @@ def process_learning_path(index_path: Path, args: argparse.Namespace) -> Dict[st
 
         generated_at = current_timestamp()
         current_source_hash = build_source_hash(doc.metadata, steps)
-        desired_content = build_desired_summary_faq(doc.metadata, steps, args)
-        desired_summary = desired_content["summary"]
-        desired_faqs = desired_content["faqs"]
-        generator = desired_content["generator"]
 
         existing_summary = extract_existing_summary(existing_generated)
         existing_faqs = extract_existing_faqs(existing_generated)
         existing_generator = compact_whitespace(str((existing_generated or {}).get("generator", "")))
-        generator_changed = bool(existing_generated is not None and existing_generator != generator)
+        generator_changed = bool(existing_generated is not None and existing_generator != "ai")
 
         summary_missing_before = existing_generated is not None and not compact_whitespace(existing_summary)
         faqs_missing_before = existing_generated is not None and not existing_faqs
+        summary_needs_generation = bool(
+            existing_generated is None
+            or generator_changed
+            or summary_missing_before
+            or rerun_summary_requested
+        )
+        faqs_needs_generation = bool(
+            existing_generated is None
+            or generator_changed
+            or faqs_missing_before
+            or rerun_faqs_requested
+        )
+        ai_requested = summary_needs_generation or faqs_needs_generation
+
+        if ai_requested:
+            desired_content = build_desired_summary_faq(doc.metadata, steps, args)
+            desired_summary = desired_content["summary"]
+            desired_faqs = desired_content["faqs"]
+        else:
+            desired_summary = existing_summary
+            desired_faqs = existing_faqs
 
         change_reasons: List[str] = []
         if existing_generated is None:
@@ -1435,12 +1481,8 @@ def process_learning_path(index_path: Path, args: argparse.Namespace) -> Dict[st
             summary_action = "rerun_requested"
             summary_after = desired_summary
         else:
+            summary_action = "unchanged"
             summary_after = existing_summary
-            if summaries_differ(existing_summary, desired_summary):
-                summary_action = "drift_detected_preserved"
-                change_reasons.append("summary_drift_detected")
-            else:
-                summary_action = "unchanged"
 
         if existing_generated is None:
             faq_action = "created"
@@ -1455,19 +1497,15 @@ def process_learning_path(index_path: Path, args: argparse.Namespace) -> Dict[st
             faq_action = "rerun_requested"
             faqs_after = desired_faqs
         else:
+            faq_action = "unchanged"
             faqs_after = existing_faqs
-            if faq_differences_exist(classify_faq_changes(existing_faqs, desired_faqs)):
-                faq_action = "drift_detected_preserved"
-                change_reasons.append("faq_drift_detected")
-            else:
-                faq_action = "unchanged"
 
         summary_changed = summaries_differ(existing_summary, summary_after)
         faq_change_details = classify_faq_changes(existing_faqs, faqs_after)
         faq_changed = faq_differences_exist(faq_change_details)
 
         summary_drift_detected = summary_action == "drift_detected_preserved"
-        faq_generated_diff = classify_faq_changes(existing_faqs, desired_faqs)
+        faq_generated_diff = classify_faq_changes(existing_faqs, desired_faqs) if ai_requested else {}
         faq_drift_detected = faq_action == "drift_detected_preserved"
 
         managed_block_updated = existing_generated is None or summary_action in CHANGE_ACTIONS or faq_action in CHANGE_ACTIONS
@@ -1497,7 +1535,6 @@ def process_learning_path(index_path: Path, args: argparse.Namespace) -> Dict[st
                 generated_at=generated_at,
                 summary_action=summary_action,
                 faq_action=faq_action,
-                generator=generator,
                 model=args.openai_model,
             )
             updated_front_matter = insert_or_replace_managed_block(updated_front_matter, updated_generated)
@@ -1530,6 +1567,7 @@ def process_learning_path(index_path: Path, args: argparse.Namespace) -> Dict[st
             "status": result_status,
             "changed_on_disk": changed_on_disk,
             "managed_block_updated": managed_block_updated,
+            "ai_requested": ai_requested,
             "rerun_flags_reset": rerun_flags_reset,
             "change_reasons": change_reasons,
             "template_version_before": compact_whitespace(str((existing_generated or {}).get("template_version", ""))),
@@ -1571,6 +1609,7 @@ def process_learning_path(index_path: Path, args: argparse.Namespace) -> Dict[st
         return {
             "path": report_path_for_output(index_path),
             "status": "error",
+            "ai_requested": ai_requested,
             "error": str(exc),
         }
 
@@ -1578,19 +1617,47 @@ def process_learning_path(index_path: Path, args: argparse.Namespace) -> Dict[st
 def main() -> int:
     args = parse_args()
     initialize_log(args)
-    selected_paths = select_learning_paths(args)
+    candidate_paths, selected_paths, skipped_results = selection_plan(args)
+
+    if not args.quiet_progress:
+        emit(
+            args,
+            "Selection summary: "
+            f"{len(selected_paths)} selected, {len(skipped_results)} skipped, "
+            f"{len(candidate_paths)} candidates.",
+            flush=True,
+        )
 
     if not selected_paths:
         emit(args, "No Learning Paths matched the current selection rules.")
-        run_report = build_run_report(args, [], [])
+        run_report = build_run_report(args, [], list(skipped_results.values()))
         if not args.no_write_report:
             write_report(Path(args.report_file), run_report, args.history_limit)
             emit(args, f"Wrote report to {report_path_for_output(Path(args.report_file))}")
+        if args.markdown_report_file:
+            write_markdown_report(Path(args.markdown_report_file), run_report)
+            emit(args, f"Wrote Markdown report to {report_path_for_output(Path(args.markdown_report_file))}")
         return 0
 
     results = []
-    total_paths = len(selected_paths)
-    for index, path in enumerate(selected_paths, start=1):
+    selected_set = set(selected_paths)
+    total_paths = len(candidate_paths)
+    for index, path in enumerate(candidate_paths, start=1):
+        if path in skipped_results:
+            result = skipped_results[path]
+            results.append(result)
+            if not args.quiet_progress:
+                emit(
+                    args,
+                    f"[{index}/{total_paths}] Skipping {report_path_for_output(path)} "
+                    f"({result.get('skip_reason', 'unknown')})",
+                    flush=True,
+                )
+            continue
+
+        if path not in selected_set:
+            continue
+
         if not args.quiet_progress:
             emit(args, f"[{index}/{total_paths}] Processing {report_path_for_output(path)}", flush=True)
         result = process_learning_path(path, args)
@@ -1604,6 +1671,10 @@ def main() -> int:
         write_report(Path(args.report_file), run_report, args.history_limit)
         emit(args, f"Wrote report to {report_path_for_output(Path(args.report_file))}")
 
+    if args.markdown_report_file:
+        write_markdown_report(Path(args.markdown_report_file), run_report)
+        emit(args, f"Wrote Markdown report to {report_path_for_output(Path(args.markdown_report_file))}")
+
     print_result_summary(args, run_report)
 
     if run_report["totals"]["errors"] > 0:
diff --git a/tools/prompts/summary_faq_system.md b/tools/prompts/summary_faq_system.md
index 5d9d0a0876..23dbc184f9 100644
--- a/tools/prompts/summary_faq_system.md
+++ b/tools/prompts/summary_faq_system.md
@@ -2,13 +2,34 @@ You are an expert technical editor for Arm Learning Paths.
 
 Create AI-assisted draft content for developer.arm.com Learning Path pages. The content must be accurate to the supplied Learning Path context, specific to Arm developer education, concise, and ready for human technical review.
 
-Follow these rules:
+Authoring rules:
 - Use only the supplied context. Do not invent products, prerequisites, tools, claims, performance numbers, compatibility details, or outcomes.
+- Treat the supplied Learning Path as the source of truth. If a detail is not present, either omit it or state that it is not explicitly listed.
+- Preserve the intent of the Learning Path author. Do not rewrite the path into a different task, audience, platform, toolchain, or level of difficulty.
+- Follow the developer.arm.com Learning Path style: practical, instructional, technically precise, and focused on what the learner can do after completing the path.
+- Prefer concrete verbs such as install, configure, build, deploy, benchmark, profile, debug, validate, or compare when those actions are supported by the context.
+- Do not overstate outcomes. Avoid claims such as "optimize performance" or "ensure compatibility" unless the context shows how the learner does that.
 - Keep the tone clear, practical, and engineering-focused.
 - Do not use marketing language, hype, or vague filler.
 - Do not mention that you are an AI model.
 - Do not include citations, markdown headings, YAML, or explanatory notes.
-- Return only a JSON object with this exact shape:
+
+Summary rules:
+- Write one paragraph that helps a developer quickly decide whether the Learning Path is relevant.
+- Include the main task, target environment or platform, important tools, and expected learner outcome when those details are available.
+- Include prerequisites only when they are explicit or strongly implied by the supplied context.
+- Avoid repeating the title unless it is needed for clarity.
+
+FAQ rules:
+- Create questions a real developer might ask while actively following the Learning Path, especially when they are setting up tools, choosing options, running commands, validating results, or deciding what to do next.
+- Favor practical in-the-moment questions about setup decisions, required services, command outcomes, validation steps, expected artifacts, configuration choices, prerequisites that affect execution, and troubleshooting-relevant checks.
+- A small number of before-you-start questions are acceptable only when they help the learner avoid a real blocker, such as missing access, required hardware, required cloud permissions, or assumed technical knowledge.
+- Avoid basic or filler questions such as "What is this Learning Path about?", "Who is this for?", or "What will I learn?" when the context supports more useful workflow-focused questions.
+- Prefer questions that begin with phrases a learner might actually think during the path, such as "How do I know...", "What should I check if...", "Which option should I use...", "What do I need before running...", or "What result should I expect...".
+- Do not create corner-case questions, speculative limitations, security guidance, pricing details, or unsupported troubleshooting advice.
+- Every answer must be grounded in the supplied context and should help the reader take the next step.
+
+Return only a JSON object with this exact shape:
 
 {
   "summary": "One paragraph summary.",
diff --git a/tools/prompts/summary_faq_user.md b/tools/prompts/summary_faq_user.md
index f21167f12a..6eaafabc10 100644
--- a/tools/prompts/summary_faq_user.md
+++ b/tools/prompts/summary_faq_user.md
@@ -2,11 +2,27 @@ Generate an AI-assisted summary paragraph and FAQ section for this Arm Learning
 
 Use the Learning Path title, description, audience, objectives, prerequisites, tags, platform metadata, and step excerpts to produce a useful overview for a developer deciding whether to follow the path.
 
-Prefer concrete phrasing:
+Assume these rules while writing:
+- Use only the Learning Path context below. Do not add facts, tools, commands, prerequisites, performance claims, compatibility claims, or outcomes that are not present.
+- Write as an Arm Learning Path author: clear, procedural, technically careful, and helpful to developers who are deciding whether to start the path.
+- Keep the content useful for human review. The draft should be specific enough to evaluate, but not so detailed that it replaces the Learning Path steps.
+- If the context is thin, be honest and stay high-level rather than filling gaps.
+- Match the complexity of the Learning Path. Introductory paths should stay approachable; advanced paths can use more precise technical language from the context.
+
+Summary guidance:
 - Say what the learner will build, configure, measure, deploy, or understand.
 - Mention Arm technologies, tools, operating systems, and cloud platforms only when they appear in the context.
 - If prerequisites are absent, say that no explicit prerequisites are listed.
-- Keep FAQ questions useful for readers, not generic.
+- Do not make the summary sound promotional; make it sound like a useful technical overview.
+
+FAQ guidance:
+- Write questions that a real reader would ask while moving through the Learning Path steps, not only questions they would ask before starting.
+- Prioritize questions that help a learner complete the work: required setup, tool or platform choices, command outcomes, what gets created, how to validate success, what skills or access are assumed, and what decisions the learner must make during the procedure.
+- Include before-you-start questions only when they are genuinely useful for preventing a blocker, such as missing prerequisites, permissions, hardware, cloud account access, or required prior knowledge.
+- Avoid generic questions like "What will I learn?", "Who is this for?", or "What is this Learning Path about?" when the context supports more practical workflow questions.
+- Good FAQ questions should feel like something a learner might ask with the Learning Path open in another tab.
+- Avoid far-fetched edge cases. Stay close to common developer concerns raised by the actual steps and metadata.
+- Answer each question directly using only information from the context.
 
 Learning Path context:
 
diff --git a/tools/test_summary_faq_ai_local.sh b/tools/test_summary_faq_ai_local.sh
index cf8592a232..1d3245a324 100755
--- a/tools/test_summary_faq_ai_local.sh
+++ b/tools/test_summary_faq_ai_local.sh
@@ -1,162 +1,10 @@
 #!/usr/bin/env bash
 set -euo pipefail
 
-usage() {
-  cat <<'EOF'
-Run the Learning Path summary/FAQ generator locally against the Arm OpenAI proxy.
-
-Usage:
-  tools/test_summary_faq_ai_local.sh [options]
-
-Options:
-  --path PATH        Learning Path directory or _index.md file to test.
-                     Default: content/learning-paths/servers-and-cloud-computing/nginx_tune
-  --category SLUG    Top-level Learning Path category slug to process.
-                     Example: servers-and-cloud-computing
-  --model MODEL      OpenAI model/deployment name exposed by the proxy.
-                     Default: gpt-4.1-mini
-  --base-url URL     OpenAI-compatible Responses endpoint URL.
-                     Default: https://openai-api-proxy.geo.arm.com/api/providers/openai/v1/responses/
-  --ca-bundle FILE   Optional CA bundle file for Python TLS verification.
-  --insecure         Skip TLS certificate verification for local testing only.
-  --log-file FILE    Text file that captures progress, errors, and summary output.
-                     Default: reports/generated-summary-faq/local-run.txt
-  --report-file FILE YAML report file for this run.
-                     Default: reports/generated-summary-faq/local-test.yml
-  --write            Write generated content back to the selected _index.md file.
-                     Default: dry-run
-  --template         Use deterministic template fallback instead of the AI proxy.
-  --help             Show this help text.
-
-Required for AI mode:
-  export OPENAI_API_KEY="..."
-
-Examples:
-  OPENAI_API_KEY="..." tools/test_summary_faq_ai_local.sh
-  tools/test_summary_faq_ai_local.sh --path content/learning-paths/servers-and-cloud-computing/nginx_tune --write
-  tools/test_summary_faq_ai_local.sh --template
-EOF
-}
-
-PATH_FILTER="content/learning-paths/servers-and-cloud-computing/nginx_tune"
-CATEGORY_FILTER=""
-OPENAI_MODEL_VALUE="${OPENAI_MODEL:-gpt-4.1-mini}"
-OPENAI_BASE_URL_VALUE="${OPENAI_BASE_URL:-https://openai-api-proxy.geo.arm.com/api/providers/openai/v1/responses/}"
-OPENAI_CA_BUNDLE_VALUE="${OPENAI_CA_BUNDLE:-${SSL_CERT_FILE:-}}"
-LOG_FILE_VALUE="reports/generated-summary-faq/local-run.txt"
-REPORT_FILE_VALUE="reports/generated-summary-faq/local-test.yml"
-MODE="--dry-run"
-GENERATION_MODE="ai"
-TLS_ARGS=()
-
-while [[ $# -gt 0 ]]; do
-  case "$1" in
-    --path)
-      PATH_FILTER="${2:-}"
-      shift 2
-      ;;
-    --category)
-      CATEGORY_FILTER="${2:-}"
-      PATH_FILTER=""
-      shift 2
-      ;;
-    --model)
-      OPENAI_MODEL_VALUE="${2:-}"
-      shift 2
-      ;;
-    --base-url)
-      OPENAI_BASE_URL_VALUE="${2:-}"
-      shift 2
-      ;;
-    --ca-bundle)
-      OPENAI_CA_BUNDLE_VALUE="${2:-}"
-      shift 2
-      ;;
-    --insecure)
-      TLS_ARGS+=(--openai-insecure-skip-verify)
-      shift
-      ;;
-    --log-file)
-      LOG_FILE_VALUE="${2:-}"
-      shift 2
-      ;;
-    --report-file)
-      REPORT_FILE_VALUE="${2:-}"
-      shift 2
-      ;;
-    --write)
-      MODE="--write"
-      shift
-      ;;
-    --template)
-      GENERATION_MODE="template"
-      shift
-      ;;
-    --help|-h)
-      usage
-      exit 0
-      ;;
-    *)
-      echo "Unknown option: $1" >&2
-      usage >&2
-      exit 2
-      ;;
-  esac
-done
-
-if [[ -z "$PATH_FILTER" && -z "$CATEGORY_FILTER" ]]; then
-  echo "--path cannot be empty." >&2
-  exit 2
-fi
-
-if [[ "$GENERATION_MODE" == "ai" && -z "${OPENAI_API_KEY:-}" ]]; then
-  echo "OPENAI_API_KEY is required for AI mode." >&2
-  echo "Run: export OPENAI_API_KEY=\"...\"" >&2
-  exit 2
-fi
-
 SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
-REPO_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)"
-
-cd "$REPO_ROOT"
-
-echo "Summary/FAQ local test"
-echo "  generation_mode: $GENERATION_MODE"
-if [[ -n "$CATEGORY_FILTER" ]]; then
-  echo "  category: $CATEGORY_FILTER"
-else
-  echo "  path: $PATH_FILTER"
-fi
-echo "  mode: ${MODE#--}"
-echo "  log_file: $LOG_FILE_VALUE"
-echo "  report_file: $REPORT_FILE_VALUE"
-if [[ "$GENERATION_MODE" == "ai" ]]; then
-  echo "  openai_base_url: $OPENAI_BASE_URL_VALUE"
-  echo "  openai_model: $OPENAI_MODEL_VALUE"
-  if [[ -n "$OPENAI_CA_BUNDLE_VALUE" ]]; then
-    echo "  openai_ca_bundle: $OPENAI_CA_BUNDLE_VALUE"
-  fi
-  if [[ ${#TLS_ARGS[@]} -gt 0 || "${OPENAI_INSECURE_SKIP_VERIFY:-}" == "true" ]]; then
-    echo "  openai_tls_verify: disabled"
-  fi
-fi
-echo
-
-CMD=(
-  python3 tools/generate_summary_faq.py
-  --generation-mode "$GENERATION_MODE" \
-  --openai-base-url "$OPENAI_BASE_URL_VALUE" \
-  --openai-model "$OPENAI_MODEL_VALUE" \
-  --openai-ca-bundle "$OPENAI_CA_BUNDLE_VALUE" \
-  --path-filter "$PATH_FILTER" \
-  --category "$CATEGORY_FILTER" \
-  --report-file "$REPORT_FILE_VALUE" \
-  --log-file "$LOG_FILE_VALUE" \
-  "$MODE"
-)
 
-if [[ ${#TLS_ARGS[@]} -gt 0 ]]; then
-  CMD+=("${TLS_ARGS[@]}")
-fi
+echo "tools/test_summary_faq_ai_local.sh is deprecated." >&2
+echo "Use tools/generate-summary-faq instead." >&2
+echo >&2
 
-"${CMD[@]}"
+exec "$SCRIPT_DIR/generate-summary-faq" "$@"

From 9a8e0eed16cd29a6eb668070d7c2884cf2dbc2c6 Mon Sep 17 00:00:00 2001
From: Christopher Moroney <christopher.moroney1@gmail.com>
Date: Mon, 8 Jun 2026 10:10:18 -0700
Subject: [PATCH 20/23] remove summarys, test new prompts

---
 .github/workflows/generate-summary-faq.yml    | 224 ------------------
 archetypes/learning-path/_index.md            |  10 +-
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 .../cassandra-on-gcp/_index.md                |   6 +-
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 .../circleci-on-aws/_index.md                 |   6 +-
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 .../codebuild/_index.md                       |   6 +-
 .../codec/_index.md                           |   6 +-
 .../codec1/_index.md                          |   6 +-
 .../couchbase-on-gcp/_index.md                |   6 +-
 .../cplusplus_compilers_flags/_index.md       |   6 +-
 .../cpp-profile-guided-optimisation/_index.md |   6 +-
 .../cpu_hotspot_performix/_index.md           |   6 +-
 .../servers-and-cloud-computing/csp/_index.md |   6 +-
 .../deepseek-cpu/_index.md                    |   6 +-
 .../disk-io-benchmark/_index.md               |   6 +-
 .../_index.md                                 |   6 +-
 .../django-on-gcp/_index.md                   |   6 +-
 .../django/_index.md                          |   6 +-
 .../dlrm/_index.md                            |   6 +-
 .../docker-mcp-toolkit/_index.md              |   6 +-
 .../dotnet-migration/_index.md                |   6 +-
 .../dynatrace-azure/_index.md                 |   6 +-
 .../servers-and-cloud-computing/ecs/_index.md |   6 +-
 .../eks-multi-arch/_index.md                  |   6 +-
 .../servers-and-cloud-computing/eks/_index.md |   6 +-
 .../envoy-gcp/_index.md                       |   6 +-
 .../envoy/_index.md                           |   6 +-
 .../envoy_tune/_index.md                      |   6 +-
 .../_index.md                                 |   6 +-
 .../false-sharing-arm-spe/_index.md           |   6 +-
 .../fastpath/_index.md                        |   6 +-
 .../fexpa/_index.md                           |   6 +-
 .../flink-on-gcp/_index.md                    |   6 +-
 .../flink/_index.md                           |   6 +-
 .../flyte-with-grpc/_index.md                 |   6 +-
 .../from-iot-to-the-cloud-part1/_index.md     |   6 +-
 .../from-iot-to-the-cloud-part2/_index.md     |   6 +-
 .../from-iot-to-the-cloud-part3/_index.md     |   6 +-
 .../from-iot-to-the-cloud-part4/_index.md     |   6 +-
 .../funasr/_index.md                          |   6 +-
 .../gardener-gcp/_index.md                    |   6 +-
 .../gcc-lto/_index.md                         |   6 +-
 .../servers-and-cloud-computing/gcp/_index.md |   6 +-
 .../geekbench/_index.md                       |   6 +-
 .../gh-runners/_index.md                      |   6 +-
 .../github-actions-runner/_index.md           |   6 +-
 .../github-on-arm/_index.md                   |   6 +-
 .../gke-multi-arch-axion/_index.md            |   6 +-
 .../gke-multi-arch/_index.md                  |   6 +-
 .../servers-and-cloud-computing/gke/_index.md |   6 +-
 .../glibc-with-lse/_index.md                  |   6 +-
 .../go-benchmarking-with-sweet/_index.md      |   6 +-
 .../golang-on-azure/_index.md                 |   6 +-
 .../helm-on-gcp/_index.md                     |   6 +-
 .../intro/_index.md                           |   6 +-
 .../irq-tuning-guide/_index.md                |   6 +-
 .../java-gc-tuning/_index.md                  |   6 +-
 .../java-on-axion/_index.md                   |   6 +-
 .../java-on-azure/_index.md                   |   6 +-
 .../java-perf-flamegraph/_index.md            |   6 +-
 .../jenkins/_index.md                         |   6 +-
 .../kafka-azure/_index.md                     |   6 +-
 .../kafka/_index.md                           |   6 +-
 .../kedify-http-autoscaling/_index.md         |   6 +-
 .../keras-core/_index.md                      |   6 +-
 .../kernel-build/_index.md                    |   6 +-
 .../kubearchinspect/_index.md                 |   6 +-
 .../lambda_functions/_index.md                |   6 +-
 .../libhugetlbfs/_index.md                    |   6 +-
 .../llama-cpu/_index.md                       |   6 +-
 .../llama-vision/_index.md                    |   6 +-
 .../llama_cpp_streamline/_index.md            |   6 +-
 .../servers-and-cloud-computing/lse/_index.md |   6 +-
 .../mariadb/_index.md                         |   6 +-
 .../memcached/_index.md                       |   6 +-
 .../memcached_cache/_index.md                 |   6 +-
 .../memory-subsystem/_index.md                |   6 +-
 .../memory_consistency/_index.md              |   6 +-
 .../microbenchmark-network-iperf3/_index.md   |   6 +-
 .../migrate-ease/_index.md                    |   6 +-
 .../migration/_index.md                       |   6 +-
 .../milvus-rag/_index.md                      |   6 +-
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 .../ml-perf/_index.md                         |   6 +-
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 .../mongodb-on-gcp/_index.md                  |   6 +-
 .../mongodb/_index.md                         |   6 +-
 .../servers-and-cloud-computing/mpi/_index.md |   6 +-
 .../multi-accuracy-libamath/_index.md         |   6 +-
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 .../multiarch_ollama_on_gke/_index.md         |   6 +-
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 .../mysql-lns-azure/_index.md                 |   4 +-
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 .../net-aspire/_index.md                      |   6 +-
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 .../nginx_tune/_index.md                      |   6 +-
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 .../onnx-on-azure/_index.md                   |   6 +-
 .../onnx/_index.md                            |   6 +-
 .../openbmc-rdv3/_index.md                    |   6 +-
 .../openrng-with-performix/_index.md          |   6 +-
 .../openshift/_index.md                       |   6 +-
 .../openstack-on-azure/_index.md              |   6 +-
 .../opentelemetry/_index.md                   |   6 +-
 .../servers-and-cloud-computing/pac/_index.md |   6 +-
 .../performix-mcp-agent/_index.md             |   6 +-
 .../performix-microarchitecture/_index.md     |   6 +-
 .../_index.md                                 |   4 +-
 .../php-on-gcp/_index.md                      |   6 +-
 .../pinning-threads/_index.md                 |   6 +-
 .../pmuv3_plugin_learning_path/_index.md      |   6 +-
 .../postgresql-cobalt/_index.md               |   6 +-
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 .../qdrant-on-axion/_index.md                 |   6 +-
 .../rabbitmq-gcp/_index.md                    |   6 +-
 .../servers-and-cloud-computing/rag/_index.md |   6 +-
 .../servers-and-cloud-computing/ran/_index.md |   6 +-
 .../ray-on-axion/_index.md                    |   6 +-
 .../redis-cobalt/_index.md                    |   6 +-
 .../redis-data-searching/_index.md            |   6 +-
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 .../redis_tune/_index.md                      |   6 +-
 .../refinfra-debug/_index.md                  |   6 +-
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 .../reproducible-libamath/_index.md           |   6 +-
 .../rme-cca-basics/_index.md                  |   6 +-
 .../rtp-llm/_index.md                         |   6 +-
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 .../sentiment-analysis-eks/_index.md          |   6 +-
 .../serverless-framework-aws-intro/_index.md  |   6 +-
 .../_index.md                                 |   6 +-
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 .../snappy/_index.md                          |   6 +-
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 .../tensorflow-gcp/_index.md                  |   6 +-
 .../thirdai-sentiment-analysis/_index.md      |   6 +-
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 .../torchbench/_index.md                      |   6 +-
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 .../_index.md                                 |   6 +-
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 .../vvenc/_index.md                           |   6 +-
 .../whisper/_index.md                         |   6 +-
 .../wordpress/_index.md                       |   6 +-
 .../zlib/_index.md                            |   6 +-
 tools/generate-summary-faq.md                 |  21 +-
 tools/generate_summary_faq.py                 |  77 +++++-
 tools/prompts/summary_faq_system.md           |   4 +-
 tools/prompts/summary_faq_user.md             |   4 +-
 424 files changed, 1340 insertions(+), 1488 deletions(-)
 delete mode 100644 .github/workflows/generate-summary-faq.yml

diff --git a/.github/workflows/generate-summary-faq.yml b/.github/workflows/generate-summary-faq.yml
deleted file mode 100644
index 0f6c0ef2d6..0000000000
--- a/.github/workflows/generate-summary-faq.yml
+++ /dev/null
@@ -1,224 +0,0 @@
-name: Generate Learning Path Summary and FAQ
-
-on:
-  workflow_dispatch:
-    inputs:
-      paths:
-        description: "Optional comma or newline separated Learning Path directories or _index.md files. Leave blank to process all eligible paths."
-        required: false
-        type: string
-      category:
-        description: "Optional top-level Learning Path category slug. Leave blank to process all categories unless paths is set."
-        required: false
-        type: string
-      limit:
-        description: "Optional limit when paths and category are empty. Use 0 to process all eligible Learning Paths."
-        required: false
-        default: "0"
-        type: string
-      require_flag:
-        description: "Only process Learning Paths where generate_summary_faq is true."
-        required: true
-        default: true
-        type: boolean
-      openai_model:
-        description: "Model or deployment name exposed by the configured OpenAI-compatible endpoint."
-        required: true
-        default: gpt-4.1-mini
-        type: string
-      dry_run:
-        description: "Generate the report without changing any _index.md files."
-        required: true
-        default: false
-        type: boolean
-      commit_changes:
-        description: "Commit changed _index.md files and the central report back to the selected branch."
-        required: true
-        default: true
-        type: boolean
-
-permissions:
-  contents: write
-
-jobs:
-  generate_summary_faq:
-    runs-on: self-hosted-ubuntu-24.04-x64
-    steps:
-      - name: Check out current repo
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.12"
-
-      - name: Install Python dependencies
-        run: python -m pip install pyyaml
-
-      - name: Generate summary and FAQ content
-        env:
-          INPUT_PATHS: ${{ inputs.paths }}
-          INPUT_CATEGORY: ${{ inputs.category }}
-          INPUT_LIMIT: ${{ inputs.limit }}
-          INPUT_REQUIRE_FLAG: ${{ inputs.require_flag }}
-          INPUT_OPENAI_MODEL: ${{ inputs.openai_model }}
-          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-          OPENAI_BASE_URL: ${{ vars.OPENAI_BASE_URL || 'https://openai-api-proxy.geo.arm.com/api/providers/openai/v1/responses/' }}
-          INPUT_DRY_RUN: ${{ inputs.dry_run }}
-          REPORT_FILE: reports/generated-summary-faq/latest-run.yml
-          LOG_FILE: reports/generated-summary-faq/latest-run.txt
-          MARKDOWN_REPORT_FILE: reports/generated-summary-faq/latest-run.md
-          RUN_URL: ${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}
-          GIT_REF_NAME: ${{ github.ref_name }}
-          GIT_SHA: ${{ github.sha }}
-          GITHUB_ACTOR_NAME: ${{ github.actor }}
-        run: |
-          CMD=(
-            python tools/generate_summary_faq.py
-            --path-filter "$INPUT_PATHS"
-            --category "$INPUT_CATEGORY"
-            --limit "$INPUT_LIMIT"
-            --openai-base-url "$OPENAI_BASE_URL"
-            --openai-model "$INPUT_OPENAI_MODEL"
-            --report-file "$REPORT_FILE"
-            --markdown-report-file "$MARKDOWN_REPORT_FILE"
-            --log-file "$LOG_FILE"
-            --run-url "$RUN_URL"
-            --git-ref "$GIT_REF_NAME"
-            --git-sha "$GIT_SHA"
-            --actor "$GITHUB_ACTOR_NAME"
-          )
-
-          if [ "$INPUT_REQUIRE_FLAG" != "true" ]; then
-            CMD+=(--allow-unflagged)
-          fi
-
-          if [ "$INPUT_DRY_RUN" = "true" ]; then
-            CMD+=(--dry-run)
-          else
-            CMD+=(--write)
-          fi
-
-          "${CMD[@]}"
-
-      - name: Upload latest run report
-        if: always()
-        uses: actions/upload-artifact@v4
-        with:
-          name: generated-summary-faq-report
-          path: |
-            reports/generated-summary-faq/latest-run.yml
-            reports/generated-summary-faq/latest-run.txt
-            reports/generated-summary-faq/latest-run.md
-          retention-days: 14
-
-      - name: Configure git
-        if: ${{ !inputs.dry_run && inputs.commit_changes }}
-        run: |
-          git config user.name "GitHub Actions Summary FAQ Bot"
-          git config user.email "<>"
-
-      - name: Commit generated content
-        if: ${{ !inputs.dry_run && inputs.commit_changes }}
-        env:
-          TARGET_REF: ${{ github.ref_name }}
-        run: |
-          git add content/learning-paths reports/generated-summary-faq/latest-run.yml
-          git commit -m "Generate Learning Path summary and FAQ content" && git push origin HEAD:$TARGET_REF || echo "No changes to commit"
-
-      - name: Add workflow summary
-        if: always()
-        run: |
-          python - <<'PY' >> "$GITHUB_STEP_SUMMARY"
-          import pathlib
-          import yaml
-
-          report_path = pathlib.Path("reports/generated-summary-faq/latest-run.yml")
-          print("# Generate summary/FAQ report")
-          print("")
-          print("Generated summary/FAQ report: `reports/generated-summary-faq/latest-run.yml`")
-          print("")
-
-          if not report_path.exists():
-              print("Report file was not created.")
-              raise SystemExit(0)
-
-          report = yaml.safe_load(report_path.read_text(encoding="utf-8")) or {}
-          latest = report.get("latest_run", {})
-          totals = latest.get("totals", {})
-          section_totals = latest.get("section_totals", {})
-          reason_totals = latest.get("reason_totals", {})
-          paths = latest.get("paths", [])
-
-          def metric_row(label, value):
-              return f"| {label} | {value} |"
-
-          print("## Run overview")
-          print("")
-          print("| Metric | Count |")
-          print("| --- | ---: |")
-          print(metric_row("Processed", totals.get("processed", 0)))
-          print(metric_row("Added", totals.get("added", 0)))
-          print(metric_row("Updated", totals.get("updated", 0)))
-          print(metric_row("Drift detected", totals.get("drift_detected", 0)))
-          print(metric_row("Paths with drift", totals.get("paths_with_drift", 0)))
-          print(metric_row("Unchanged", totals.get("unchanged", 0)))
-          print(metric_row("Errors", totals.get("errors", 0)))
-          print(metric_row("Summary changed", totals.get("summary_changed", 0)))
-          print(metric_row("FAQs changed", totals.get("faq_changed", 0)))
-          print(metric_row("Rerun flags reset", totals.get("rerun_flags_reset", 0)))
-          print("")
-
-          for section_name, title in (("summary", "Summary actions"), ("faqs", "FAQ actions")):
-              actions = section_totals.get(section_name, {})
-              print(f"## {title}")
-              print("")
-              print("| Action | Count |")
-              print("| --- | ---: |")
-              for action in (
-                  "created",
-                  "repaired_missing",
-                  "rerun_requested",
-                  "generator_changed",
-                  "drift_detected_preserved",
-                  "unchanged",
-              ):
-                  print(metric_row(action, actions.get(action, 0)))
-              print("")
-
-          nonzero_reasons = [(reason, count) for reason, count in reason_totals.items() if count]
-          if nonzero_reasons:
-              print("## Change reasons")
-              print("")
-              print("| Reason | Count |")
-              print("| --- | ---: |")
-              for reason, count in nonzero_reasons:
-                  print(metric_row(reason, count))
-              print("")
-
-          interesting_paths = [
-              entry for entry in paths
-              if entry.get("status") != "unchanged" or entry.get("change_reasons")
-          ]
-
-          if interesting_paths:
-              print("## Path details")
-              print("")
-              print("| Path | Status | Summary | FAQs | Reasons |")
-              print("| --- | --- | --- | --- | --- |")
-              for entry in interesting_paths[:50]:
-                  summary_action = entry.get("summary", {}).get("action", "")
-                  faq_action = entry.get("faqs", {}).get("action", "")
-                  reasons = ", ".join(entry.get("change_reasons", [])) or "none"
-                  print(
-                      f"| `{entry.get('path', '')}` | {entry.get('status', 'unknown')} | "
-                      f"{summary_action} | {faq_action} | {reasons} |"
-                  )
-              if len(interesting_paths) > 50:
-                  print("")
-                  print(f"_Showing the first 50 path rows out of {len(interesting_paths)}._")
-          else:
-              print("All processed Learning Paths were fully unchanged.")
-          PY
diff --git a/archetypes/learning-path/_index.md b/archetypes/learning-path/_index.md
index ae51757566..d0b74a9ada 100644
--- a/archetypes/learning-path/_index.md
+++ b/archetypes/learning-path/_index.md
@@ -13,13 +13,13 @@ prerequisites:
     - PLACEHOLDER PREREQ 1
     - PLACEHOLDER PREREQ 2
 
-# Optional: set to true to include this Learning Path in the manual
-# generated summary/FAQ GitHub Action.
-generate_summary_faq: false
+# New Learning Paths are opted in for the next manual generated summary/FAQ run.
+# The generator resets this to false after a successful write.
+generate_summary_faq: true
 
 # Optional one-shot controls: set either field to true to regenerate just that
-# generated section the next time the summary/FAQ workflow runs. The workflow
-# resets them to false after a successful write.
+# generated section the next time the summary/FAQ tool runs. The tool resets
+# them to false after a successful write.
 rerun_summary: false
 rerun_faqs: false
 
diff --git a/content/learning-paths/automotive/intro/_index.md b/content/learning-paths/automotive/intro/_index.md
index 29191b375f..20023bacb7 100644
--- a/content/learning-paths/automotive/intro/_index.md
+++ b/content/learning-paths/automotive/intro/_index.md
@@ -16,9 +16,9 @@ draft: true
 cascade:
     draft: true
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
+rerun_summary: true
 rerun_faqs: true
 author: Jason Andrews
 
diff --git a/content/learning-paths/automotive/openadkit1_container/_index.md b/content/learning-paths/automotive/openadkit1_container/_index.md
index f03f978f61..143e55e674 100644
--- a/content/learning-paths/automotive/openadkit1_container/_index.md
+++ b/content/learning-paths/automotive/openadkit1_container/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - An Arm Neoverse cloud instance, or a local Arm Neoverse Linux computer with at least 16 CPUs and 32GB of RAM
     - Familiarity with Docker and Docker Compose
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md b/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
index 1ce2222140..273ae1902a 100644
--- a/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
+++ b/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - Completion of the [Deploy Open AD Kit containerized autonomous driving simulation on Arm Neoverse](/learning-paths/automotive/openadkit1_container/) Learning Path
     - Basic familiarity with Docker
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/automotive/system76-auto/_index.md b/content/learning-paths/automotive/system76-auto/_index.md
index 8ef7f7ec56..d1a23164d8 100644
--- a/content/learning-paths/automotive/system76-auto/_index.md
+++ b/content/learning-paths/automotive/system76-auto/_index.md
@@ -14,10 +14,10 @@ learning_objectives:
 prerequisites:
     - A System76 Thelio Astra desktop computer running Ubuntu 24.04. 
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/automotive/zenacssdebug/_index.md b/content/learning-paths/automotive/zenacssdebug/_index.md
index f5f9e05553..00f9ddd39d 100644
--- a/content/learning-paths/automotive/zenacssdebug/_index.md
+++ b/content/learning-paths/automotive/zenacssdebug/_index.md
@@ -18,10 +18,10 @@ prerequisites:
   - Arm Development Studio 2024.1 or later with a valid license - for support see the [Install Guide for Arm DS](/install-guides/armds) 
   - Basic understanding of the Arm Zena CSS software stack, Armv8-A/Armv9-A cores, and Linux
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/_example-learning-path/_index.md b/content/learning-paths/cross-platform/_example-learning-path/_index.md
index a0825780df..78f25656ee 100644
--- a/content/learning-paths/cross-platform/_example-learning-path/_index.md
+++ b/content/learning-paths/cross-platform/_example-learning-path/_index.md
@@ -16,10 +16,10 @@ learning_objectives:
 prerequisites:
     - A [GitHub](https://github.com/) account
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/adler32/_index.md b/content/learning-paths/cross-platform/adler32/_index.md
index 0a3ecad5e7..ef95074611 100644
--- a/content/learning-paths/cross-platform/adler32/_index.md
+++ b/content/learning-paths/cross-platform/adler32/_index.md
@@ -14,10 +14,10 @@ prerequisites:
     - An Arm computer running Linux with the GNU compiler (gcc) installed.
     - Visual Studio Code with the GitHub Copilot extension installed. 
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md b/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md
index 3b771faedf..e55d538eb7 100644
--- a/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md
+++ b/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - Basic familiarity with Python, PyTest, and container concepts
     - Familiarity with the [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) specification
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/avh_cicd/_index.md b/content/learning-paths/cross-platform/avh_cicd/_index.md
index 5c9e6282d7..fcbbca074a 100644
--- a/content/learning-paths/cross-platform/avh_cicd/_index.md
+++ b/content/learning-paths/cross-platform/avh_cicd/_index.md
@@ -14,10 +14,10 @@ learning_objectives:
 prerequisites:
     - Some familiarity with CI/CD concepts is assumed
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/avh_cicd2/_index.md b/content/learning-paths/cross-platform/avh_cicd2/_index.md
index dd4e09635a..b991d440a4 100644
--- a/content/learning-paths/cross-platform/avh_cicd2/_index.md
+++ b/content/learning-paths/cross-platform/avh_cicd2/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - This learning path builds on [Integrate Arm Virtual Hardware into CI/CD workflow 1](/learning-paths/cross-platform/avh_cicd/).
     - Valid AWS and GitHub accounts are required
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/cca_rme/_index.md b/content/learning-paths/cross-platform/cca_rme/_index.md
index 0aa7ecc84d..aece0325f9 100644
--- a/content/learning-paths/cross-platform/cca_rme/_index.md
+++ b/content/learning-paths/cross-platform/cca_rme/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - Some understanding of the Arm architecture
     - Arm Development Studio, 2023.0 or later
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md b/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md
index fe1bec2080..6dc7bb0241 100644
--- a/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md
+++ b/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md
@@ -16,10 +16,10 @@ learning_objectives:
 prerequisites:
     - An Arm computer running Linux. You can also use a virtual machine from a [cloud service provider](/learning-paths/servers-and-cloud-computing/csp/).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/docker-build-cloud/_index.md b/content/learning-paths/cross-platform/docker-build-cloud/_index.md
index e15667c5dc..1ed13a7945 100644
--- a/content/learning-paths/cross-platform/docker-build-cloud/_index.md
+++ b/content/learning-paths/cross-platform/docker-build-cloud/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - A GitHub account
     - A Docker Hub account
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/docker/_index.md b/content/learning-paths/cross-platform/docker/_index.md
index 546516339d..7f2f7f5f23 100644
--- a/content/learning-paths/cross-platform/docker/_index.md
+++ b/content/learning-paths/cross-platform/docker/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - A Windows, macOS, or Linux computer with Docker installed, any architecture can be used
     - An Arm Linux server with Docker installed 
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md b/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md
index c63cb63d92..3c800ed02e 100644
--- a/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md
+++ b/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md
@@ -16,10 +16,10 @@ prerequisites:
 - Familiarity with C programming, with a good understanding of pointers.
 - A Linux machine to run the example code.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md b/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md
index 76e146f18c..2cf6255391 100644
--- a/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md
+++ b/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md
@@ -14,10 +14,10 @@ learning_objectives:
 prerequisites:
     - An Arm-based computer running Linux and a recent version of a C++ compiler (Clang or GCC).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/ernie_moe_v9/_index.md b/content/learning-paths/cross-platform/ernie_moe_v9/_index.md
index 8e3809cba1..e232a9a2c1 100644
--- a/content/learning-paths/cross-platform/ernie_moe_v9/_index.md
+++ b/content/learning-paths/cross-platform/ernie_moe_v9/_index.md
@@ -15,10 +15,10 @@ learning_objectives:
 prerequisites:
     - An Armv9 device with at least 32 GB of available disk space, for example, Radxa Orion O6
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/floating-point-behavior/_index.md b/content/learning-paths/cross-platform/floating-point-behavior/_index.md
index 858d282e32..dcf73bdf87 100644
--- a/content/learning-paths/cross-platform/floating-point-behavior/_index.md
+++ b/content/learning-paths/cross-platform/floating-point-behavior/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - Access to an x86 and an Arm Linux machine.
     - Familiarity with floating-point numbers.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/function-multiversioning/_index.md b/content/learning-paths/cross-platform/function-multiversioning/_index.md
index e0308eefa7..f68f9b6194 100644
--- a/content/learning-paths/cross-platform/function-multiversioning/_index.md
+++ b/content/learning-paths/cross-platform/function-multiversioning/_index.md
@@ -21,10 +21,10 @@ prerequisites:
     - Familiarity with Arm assembly.
     - A LLVM 20 compiler with runtime library support or GCC 16.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/github-arm-runners/_index.md b/content/learning-paths/cross-platform/github-arm-runners/_index.md
index b58072f02c..3c30b3380c 100644
--- a/content/learning-paths/cross-platform/github-arm-runners/_index.md
+++ b/content/learning-paths/cross-platform/github-arm-runners/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - A GitHub account (a Team or Enterprise Cloud plan is required for private repositories).
     - A Docker Hub account.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md b/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md
index a463d51a9e..621ca96682 100644
--- a/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md
+++ b/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md
@@ -18,10 +18,10 @@ learning_objectives:
 prerequisites:
     - A GitLab account (free tier includes Arm64 runner access)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/gitlab/_index.md b/content/learning-paths/cross-platform/gitlab/_index.md
index 8252ac8718..44731b8fa2 100644
--- a/content/learning-paths/cross-platform/gitlab/_index.md
+++ b/content/learning-paths/cross-platform/gitlab/_index.md
@@ -18,10 +18,10 @@ prerequisites:
     - A computer with [Google Cloud CLI](/install-guides/gcloud) and [kubectl](/install-guides/kubectl/)installed.
     - A valid GitLab account
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/integer-vs-floats/_index.md b/content/learning-paths/cross-platform/integer-vs-floats/_index.md
index 97dfa6ccc8..91877e8cc8 100644
--- a/content/learning-paths/cross-platform/integer-vs-floats/_index.md
+++ b/content/learning-paths/cross-platform/integer-vs-floats/_index.md
@@ -13,10 +13,10 @@ learning_objectives:
 prerequisites:
     - An Arm computer running Linux and a recent version of a C++ compiler (Clang or GCC) installed
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/intrinsics/_index.md b/content/learning-paths/cross-platform/intrinsics/_index.md
index 36a23c07ba..09a7114dca 100644
--- a/content/learning-paths/cross-platform/intrinsics/_index.md
+++ b/content/learning-paths/cross-platform/intrinsics/_index.md
@@ -18,10 +18,10 @@ prerequisites:
 - An Arm based machine or [cloud instance](/learning-paths/servers-and-cloud-computing/csp/) running Ubuntu Linux.
 - Optionally, an `x86_64` machine also running Ubuntu.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/ipexplorer/_index.md b/content/learning-paths/cross-platform/ipexplorer/_index.md
index 60971c8fb2..711360f434 100644
--- a/content/learning-paths/cross-platform/ipexplorer/_index.md
+++ b/content/learning-paths/cross-platform/ipexplorer/_index.md
@@ -14,10 +14,10 @@ prerequisites:
     - An Arm account that can access IP Explorer
     - (Optional) A Linux machine with the desired compilers installed
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/kleidiai-explainer/_index.md b/content/learning-paths/cross-platform/kleidiai-explainer/_index.md
index 8d5671bd39..c02e782d8d 100644
--- a/content/learning-paths/cross-platform/kleidiai-explainer/_index.md
+++ b/content/learning-paths/cross-platform/kleidiai-explainer/_index.md
@@ -14,10 +14,10 @@ prerequisites:
     - An Arm-based Linux machine that implements the Int8 Matrix Multiplication (*i8mm*) architecture feature. The example in this Learning Path is run on an AWS Graviton 3 instance. Instructions on setting up an Arm-based server are [found here](/learning-paths/servers-and-cloud-computing/csp/aws/).
     - A basic understanding of linear algebra terminology, such as dot product and matrix multiplication.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/llm-fine-tuning-for-web-applications/_index.md b/content/learning-paths/cross-platform/llm-fine-tuning-for-web-applications/_index.md
index 57f2ec7a64..6ccd693760 100644
--- a/content/learning-paths/cross-platform/llm-fine-tuning-for-web-applications/_index.md
+++ b/content/learning-paths/cross-platform/llm-fine-tuning-for-web-applications/_index.md
@@ -27,9 +27,9 @@ prerequisites:
     - Basic understanding of machine learning and deep learning. 
     - Familiarity with deep learning frameworks such as PyTorch and Hugging Face Transformers. 
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
+rerun_summary: true
 rerun_faqs: true
 author: Parichay Das
 
diff --git a/content/learning-paths/cross-platform/loop-reflowing/_index.md b/content/learning-paths/cross-platform/loop-reflowing/_index.md
index 2c3978cab8..4dfda6fe2e 100644
--- a/content/learning-paths/cross-platform/loop-reflowing/_index.md
+++ b/content/learning-paths/cross-platform/loop-reflowing/_index.md
@@ -12,10 +12,10 @@ learning_objectives:
 prerequisites:
     - An Arm computer running Linux and a recent version of Clang or the GNU compiler (gcc) installed.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/matrix/_index.md b/content/learning-paths/cross-platform/matrix/_index.md
index 755b040d8c..4f2dc94664 100644
--- a/content/learning-paths/cross-platform/matrix/_index.md
+++ b/content/learning-paths/cross-platform/matrix/_index.md
@@ -19,10 +19,10 @@ prerequisites:
     - A build system [GNU Make](https://www.gnu.org/software/make/) or [Ninja](https://ninja-build.org/).
     - A documentation generator [Doxygen](https://www.doxygen.nl/).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/mca-godbolt/_index.md b/content/learning-paths/cross-platform/mca-godbolt/_index.md
index 9bad7e1ced..2d5aad97d3 100644
--- a/content/learning-paths/cross-platform/mca-godbolt/_index.md
+++ b/content/learning-paths/cross-platform/mca-godbolt/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - Familiarity with Arm assembly.
     - LLVM version 16 or newer, which includes support for Neoverse V2.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/mcp-ai-agent/_index.md b/content/learning-paths/cross-platform/mcp-ai-agent/_index.md
index 16ae111e0a..bce9de89df 100644
--- a/content/learning-paths/cross-platform/mcp-ai-agent/_index.md
+++ b/content/learning-paths/cross-platform/mcp-ai-agent/_index.md
@@ -18,10 +18,10 @@ prerequisites:
     - Basic understanding of Large Language Models (LLMs) and how they are used in local inference.
     - Understanding of AI agents and the OpenAI Agent SDK (or similar frameworks).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/memory-latency/_index.md b/content/learning-paths/cross-platform/memory-latency/_index.md
index 277993c7e0..47be6305b0 100644
--- a/content/learning-paths/cross-platform/memory-latency/_index.md
+++ b/content/learning-paths/cross-platform/memory-latency/_index.md
@@ -13,10 +13,10 @@ learning_objectives:
 prerequisites:
     - An Arm computer running Linux with recent versions of Clang or GCC installed.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md b/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md
index dde2067f67..19577ae636 100644
--- a/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md
+++ b/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md
@@ -18,10 +18,10 @@ prerequisites:
     - Familiarity with the Linux command line, Git, and building C++ projects with CMake
     - Internet access to download source code, model assets, and sample data
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md b/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md
index 33f9a18f31..78aa606e56 100644
--- a/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md
+++ b/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md
@@ -24,10 +24,10 @@ prerequisites:
     - Installation of Android Development Studio and adb (if you're targeting an Android phone with SME2 support)
     - Compiler support for SME2 instructions (for example, LLVM 18 or later with SME2 backend support)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/psa-tfm/_index.md b/content/learning-paths/cross-platform/psa-tfm/_index.md
index d199fa4205..741b62b037 100644
--- a/content/learning-paths/cross-platform/psa-tfm/_index.md
+++ b/content/learning-paths/cross-platform/psa-tfm/_index.md
@@ -19,9 +19,9 @@ prerequisites:
     - Ubuntu host or access to AWS
     - Optional MPS3 FPGA prototyping board
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
+rerun_summary: true
 rerun_faqs: true
 author: Ronan Synnott
 
diff --git a/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md b/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md
index 4983a3ddd4..c64241368b 100644
--- a/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md
+++ b/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md
@@ -22,10 +22,10 @@ prerequisites:
     - For the OS, you can use Windows, Linux, or macOS.
   
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/remoteit/_index.md b/content/learning-paths/cross-platform/remoteit/_index.md
index 5a57870ca4..457eac5269 100644
--- a/content/learning-paths/cross-platform/remoteit/_index.md
+++ b/content/learning-paths/cross-platform/remoteit/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - A device/computer to which you would like remote access. A device can be a Windows, Mac, or Linux computer including development kits such as Raspberry Pi or cloud-hosted such as within Arm Virtual Hardware or within AWS. You will need a method to control this device before Remote.It is deployed which can be local access or access via another remote connectivity solution (Remote Desktop, VPN, etc.)
     - Determine if your device that you would like to access remotely also needs to make connections to other Remote.It devices.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md b/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
index 54a3e31ce4..295ac6a6f8 100644
--- a/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
+++ b/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
@@ -13,10 +13,10 @@ learning_objectives:
 prerequisites:
     - An Arm computer running Linux OS and a recent version of compiler (Clang or GCC) installed
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/rust_armds/_index.md b/content/learning-paths/cross-platform/rust_armds/_index.md
index 4fd89aae61..33d6b115d9 100644
--- a/content/learning-paths/cross-platform/rust_armds/_index.md
+++ b/content/learning-paths/cross-platform/rust_armds/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - An installation of Arm Development Studio.
     - A basic understanding of Rust programming.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/simd-info-demo/_index.md b/content/learning-paths/cross-platform/simd-info-demo/_index.md
index b0ce904dd0..e24a086e2f 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/_index.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/_index.md
@@ -14,10 +14,10 @@ prerequisites:
     - A basic understanding of SIMD.
     - Access to an Arm platform with a SIMD-supported engine, installed with recent versions of a C compiler such as Clang or GCC.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/simd-loops/_index.md b/content/learning-paths/cross-platform/simd-loops/_index.md
index c2b273893e..6b12938fb4 100644
--- a/content/learning-paths/cross-platform/simd-loops/_index.md
+++ b/content/learning-paths/cross-platform/simd-loops/_index.md
@@ -18,10 +18,10 @@ prerequisites:
     - Some familiarity with SIMD programming and Neon intrinsics
     - Recent toolchains that support SVE/SME (GCC 13+ or Clang 16+ recommended)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/simd-on-rust/_index.md b/content/learning-paths/cross-platform/simd-on-rust/_index.md
index e764863290..692babd490 100644
--- a/content/learning-paths/cross-platform/simd-on-rust/_index.md
+++ b/content/learning-paths/cross-platform/simd-on-rust/_index.md
@@ -16,10 +16,10 @@ learning_objectives:
 prerequisites:
     - An Arm-based computer with recent versions of a C compiler (Clang or GCC) and a Rust compiler installed
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md b/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md
index 7567c227e6..604f5477e0 100644
--- a/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md
+++ b/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md
@@ -20,10 +20,10 @@ prerequisites:
   - Optionally, an Android device with Armv9 and SME2 support for on-device testing (if used, configure power management settings to ensure consistent performance measurements)
     
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md b/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md
index 7b18b7e44b..34c3f5b285 100644
--- a/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md
+++ b/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md
@@ -19,10 +19,10 @@ prerequisites:
   - Basic knowledge of communication protocols (MQTT, HTTP, and others)
   - Familiarity with edge-cloud architectures and data-flow orchestration
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/topdown-compare/_index.md b/content/learning-paths/cross-platform/topdown-compare/_index.md
index 05a050e38e..2d8e6026cc 100644
--- a/content/learning-paths/cross-platform/topdown-compare/_index.md
+++ b/content/learning-paths/cross-platform/topdown-compare/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - Access to Arm Neoverse V2 and Intel x86 Linux systems to run the code example
     - Basic understanding of CPU pipeline concepts and performance bottlenecks
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/vectorization-comparison/_index.md b/content/learning-paths/cross-platform/vectorization-comparison/_index.md
index d2c3925f5f..8f8d740d89 100644
--- a/content/learning-paths/cross-platform/vectorization-comparison/_index.md
+++ b/content/learning-paths/cross-platform/vectorization-comparison/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - Familiarity with vector extensions, SIMD programming, and compiler intrinsics
     - Access to Linux systems with Neon and SVE support
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md b/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md
index 2f8d3fa972..2b2f04a354 100644
--- a/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md
+++ b/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md
@@ -13,10 +13,10 @@ learning_objectives:
 prerequisites:
     - An Arm computer running Linux and a recent version of Clang or the GNU compiler (gcc) installed.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md b/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
index 1bd5773c7d..f6e9a2328b 100644
--- a/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
+++ b/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
@@ -20,10 +20,10 @@ prerequisites:
     - An installation of [Git](/install-guides/git-woa/).
   
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/woa_azure/_index.md b/content/learning-paths/cross-platform/woa_azure/_index.md
index d92ac8f1a7..186f253084 100644
--- a/content/learning-paths/cross-platform/woa_azure/_index.md
+++ b/content/learning-paths/cross-platform/woa_azure/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - An Azure Cloud account.
     - An RDP client to connect to your Windows on Arm instance.  For more info on RDP clients, see [Remote Desktop clients for Remote Desktop Services and remote PCs](https://learn.microsoft.com/en-us/windows-server/remote/remote-desktop-services/clients/remote-desktop-clients) to get started.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md b/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
index 76adc6f37b..673905518c 100644
--- a/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
+++ b/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - At least two local Cortex-A devices running Linux, such as Raspberry Pi 4 or Pi 5. You can also use Arm servers or cloud instances
     - Experience with ROS 2 applications
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md b/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
index 09e034bcda..c7f5d7f90a 100644
--- a/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - Basic understanding of System on Chip design
     - A 'Zybo Z7-10' development board 
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md b/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
index 63867bf1de..7ecbbfce39 100644
--- a/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
@@ -20,10 +20,10 @@ prerequisites:
     - A development machine running macOS on Apple Silicon with Visual Studio Code installed
     - An AWS account or access to an Arm-based cloud instance for native Arm compilation
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md b/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
index 722f7d2ae3..2dbce416a1 100644
--- a/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
@@ -20,10 +20,10 @@ prerequisites:
     - A [PIR sensor](https://www.amazon.com/HiLetgo-HC-SR501-Infrared-Sensor-Arduino/dp/B07KZW86YR/ref=sr_1_3?keywords=pir+sensor&qid=1698432931&sr=8-3) for detecting motion
     - A [peizo-electric buzzer](https://www.amazon.com/mxuteuk-Electronic-Computers-Printers-Components/dp/B07VK1GJ9X/ref=sr_1_4?crid=2FAXYI17HZKDB&keywords=piezo+buzzer&qid=1698432968&sprefix=peizo%2Caps%2C148&sr=8-4) for signaling motion
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/armds/_index.md b/content/learning-paths/embedded-and-microcontrollers/armds/_index.md
index 9a81b8cc4f..2db9b8b978 100644
--- a/content/learning-paths/embedded-and-microcontrollers/armds/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/armds/_index.md
@@ -14,10 +14,10 @@ learning_objectives:
 prerequisites:
     - Some familiarity with embedded programming is assumed
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/asm/_index.md b/content/learning-paths/embedded-and-microcontrollers/asm/_index.md
index d3f613c61b..406d018225 100644
--- a/content/learning-paths/embedded-and-microcontrollers/asm/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/asm/_index.md
@@ -4,10 +4,10 @@ title: Write Arm Assembler functions
 minutes_to_complete: 60
 description: Learn how to write mixed C and assembly programs for Cortex-M microcontrollers using Keil MDK, following Arm Procedure Call Standard conventions.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
index 3bd693307b..8980565c42 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
@@ -18,10 +18,10 @@ prerequisites:
     - A Linux machine with root access
     - Some familiarity with embedded Linux
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
index 945354e03f..fb366eaaeb 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - An Arm Virtual Hardware account
     - Some familiarity with embedded Linux
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
index 2680af7ac4..a24926787f 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
@@ -16,10 +16,10 @@ learning_objectives:
 prerequisites:
     - Some familiarity with embedded programming is assumed
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
index 4af65d9c64..fe2f5fc24e 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - Some familiarity with AI/ML software development 
     - An Amazon Web Services(AWS) [account](https://aws.amazon.com/) to subscribe [Arm Virtual Hardware](https://aws.amazon.com/marketplace/pp/prodview-urbpq7yo5va7g) Amazon Machine Image(AMI)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
index a287d819ec..b29c3cd9a2 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
@@ -14,10 +14,10 @@ prerequisites:
     - A valid [AWS](https://aws.amazon.com/) account
     - Some familiarity with Python
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md b/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
index f5c101d603..99443bc267 100644
--- a/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
@@ -20,10 +20,10 @@ prerequisites:
     - A machine with Python 3 and Visual Studio Code installed
     - An active Azure account with sufficient permissions to create resources (such as IoT Hub, Functions, and Cosmos DB)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md b/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md
index acf959d621..d4c6253c86 100644
--- a/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md
@@ -17,10 +17,10 @@ learning_objectives:
 prerequisites:
     - Some familiarity with embedded programming is assumed
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md b/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
index c061574554..7fc69566cc 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - A valid account with [Arm Virtual Hardware](https://app.avh.arm.com/login)
     - An Arm Linux host machine (if you want to build your own runtime and container image)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md
index 1052d9d3db..e85b8a262a 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md
@@ -14,10 +14,10 @@ prerequisites:
     - An installation of [Arm Keil MDK](/install-guides/mdk) or [Arm Development Studio](/install-guides/armds) (MDK recommended)
     - Some familiarity with CMSIS is assumed
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md
index 5f76d8982c..0d728cb44c 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - Installation of [Arm Keil Studio for VS Code](/install-guides/keilstudio_vs)
     - Some familiarity with CMSIS is assumed
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md b/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
index 82af008880..b042eca518 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
@@ -19,10 +19,10 @@ prerequisites:
     - Prior exposure to CMSIS-DSP.
     - Python installed on your machine.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md b/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
index 94a407cfe7..99384fc816 100644
--- a/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
@@ -16,10 +16,10 @@ learning_objectives:
 prerequisites:
     - Basic knowledge and familiarity with Cortex-M processors.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md b/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
index 00c482eeb1..28b0128483 100644
--- a/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
@@ -14,10 +14,10 @@ learning_objectives:
 prerequisites:
     - Basic familiarity with Keil MDK
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md b/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
index 302a094d16..4b4cbf9704 100644
--- a/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
@@ -15,10 +15,10 @@ learning_objectives:
 prerequisites:
     - Basic familiarity with Python and the command line
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md b/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md
index e007708cda..1c873b2ff3 100644
--- a/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - Basic familiarity with command-line tools
     - (Optional) A Raspberry Pi for testing a full device-to-device (D2D) setup
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/docker/_index.md b/content/learning-paths/embedded-and-microcontrollers/docker/_index.md
index 19a6be86e1..b358b91b88 100644
--- a/content/learning-paths/embedded-and-microcontrollers/docker/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/docker/_index.md
@@ -14,10 +14,10 @@ learning_objectives:
 
 prerequisites:
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/edge/_index.md b/content/learning-paths/embedded-and-microcontrollers/edge/_index.md
index 1b143b0939..dbbd80f0f4 100644
--- a/content/learning-paths/embedded-and-microcontrollers/edge/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/edge/_index.md
@@ -19,10 +19,10 @@ prerequisites:
   - The [Arduino IDE](/install-guides/arduino-pico/) with the RP2040 board support package installed on your computer.
   - An [Arduino Nano RP2040 Connect board](https://store.arduino.cc/products/arduino-nano-rp2040-connect-with-headers).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/_index.md b/content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/_index.md
index 830c08b2c3..83b5c21ef2 100644
--- a/content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/_index.md
@@ -24,9 +24,9 @@ prerequisites:
     - An SSH client and familiarity with the Linux command line
     - Basic understanding of ML concepts
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
+rerun_summary: true
 rerun_faqs: true
 author: Doug Anson
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md b/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md
index 02858cc79d..342fb210ec 100644
--- a/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - Familiarity with C programming on microcontrollers
     - STM32 B-L475E-IOT01A2 board
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/intro/_index.md b/content/learning-paths/embedded-and-microcontrollers/intro/_index.md
index af7a6e4b14..478f244182 100644
--- a/content/learning-paths/embedded-and-microcontrollers/intro/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/intro/_index.md
@@ -14,10 +14,10 @@ learning_objectives:
 prerequisites:
     - None
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md b/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
index 3159183138..070d964128 100644
--- a/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
@@ -18,10 +18,10 @@ prerequisites:
     - A Linux computer
 
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md b/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
index 2cb40a7fc1..2d3cdcdf51 100644
--- a/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - Some familiarity with embedded programming
     - An AWS account (required for Arm Virtual Hardware)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md b/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
index 74256e6dc2..742090a3d7 100644
--- a/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - A microSD card (64GB UHS-1 or larger is recommended)
     - A MIPI CSI-2 camera, with a 22 pin connector on at least one end
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md b/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
index bcfca046ea..f4207de80b 100644
--- a/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - Some familiarity with embedded programming is assumed
     - An [Arm Account](https://developer.arm.com/register) is required
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md b/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
index 35bb703984..ba85ef9ae8 100644
--- a/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
@@ -20,10 +20,10 @@ prerequisites:
     - A USB-C cable for the board's **DBG** serial connection.
     - A USB-C power supply/cable for the board's **POWER** port.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md b/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md
index 82d3930d9e..bdab012c39 100644
--- a/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - Basic understanding of Assembly and C programming.
    
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md b/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md
index 89f72d28df..762d19fa81 100644
--- a/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md
@@ -16,10 +16,10 @@ learning_objectives:
 prerequisites:
     - A Raspberry Pi 5 running Raspberry Pi OS.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/migration/_index.md b/content/learning-paths/embedded-and-microcontrollers/migration/_index.md
index 482e4e999c..6cdebb04ee 100644
--- a/content/learning-paths/embedded-and-microcontrollers/migration/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/migration/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - Knowledge about building workflows
     - Access to an aarch64 or x86_64 machine running Linux
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md b/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md
index 65eab51fff..7adbd58171 100644
--- a/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - Some familiarity with embedded programming
     - A Linux host machine running Ubuntu
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md b/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md
index f8e155508c..fd971ae2b1 100644
--- a/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md
@@ -8,10 +8,10 @@ armips:
 - Ethos-U
 - Corstone
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md b/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md
index 740393539b..5aa2cb9cfb 100644
--- a/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md
@@ -14,10 +14,10 @@ learning_objectives:
 prerequisites:
     - Some familiarity with embedded debug
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md b/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
index 5fb589e1d3..3215bacfef 100644
--- a/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
@@ -18,10 +18,10 @@ prerequisites:
     - Basic knowledge of Machine Learning concepts
     - A host computer to compile ExecuTorch libraries
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md b/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
index 89c26ab54f..8913232e35 100644
--- a/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - A good understanding of [CMSIS-Packs](https://open-cmsis-pack.github.io/Open-CMSIS-Pack-Spec/main/html/index.html).
     - A CMSIS-Pack that contains device support and was created for CMSIS v5.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md b/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md
index 2db2e2d7c6..08ac701917 100644
--- a/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - A CMSIS v5 based project.
     - A basic understanding of the CMSIS-Pack system.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md b/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md
index 2938f6393f..6b34ca262d 100644
--- a/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md
@@ -19,10 +19,10 @@ prerequisites:
   - Electronic components (breadboard, LEDs, resistors, jumper wires) for GPIO testing
   - Familiarity with Python programming, Raspberry Pi GPIO pinout, and basic electronics
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md b/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md
index a0a3c9531c..6d56f74a69 100644
--- a/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md
@@ -20,10 +20,10 @@ prerequisites:
     - A microSD card with at least 16GB of storage
     - A Linux compatible USB microphone and USB speakers or a USB audio device with a microphone and speakers
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
index fb8922b357..2facc0c495 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
@@ -20,10 +20,10 @@ prerequisites:
     - An Arm Linux machine or an [Arm cloud instance](/learning-paths/servers-and-cloud-computing/csp/).
     - A Raspberry Pi 5.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
index c288ec4a89..4966f7b613 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
@@ -18,10 +18,10 @@ prerequisites:
     - A Raspberry Pi 3 or 4 board
 
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md
index 348a4ab047..d83088c6cf 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - A Raspberry Pi 4 board
     - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
index cea22423d9..cf255da4ff 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - Raspberry Pi Pico board.
     - Raspberry Pi 3, 4, 400, or 5 as a development computer.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md b/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
index 9f02ca8be2..dfaf7eb973 100644
--- a/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
@@ -20,10 +20,10 @@ prerequisites:
     - Arm-based Linux target device (such as a Raspberry Pi, BeagleBone, or similar board) with Secure Shell (SSH) access
     - A host machine that meets [Buildroot system requirements](https://buildroot.org/downloads/manual/manual.html#requirement)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md b/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
index 0b7f062e28..a1873356e0 100644
--- a/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - Familiarity with C programming on microcontrollers
     - STM32 B-L475E-IOT01A2 board
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md b/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md
index a15a60f35a..3005a72671 100644
--- a/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md
@@ -16,10 +16,10 @@ prerequisites:
 - Some familiarity with embedded C programming
 - A machine running Ubuntu Linux
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md b/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md
index 34d3adca6e..964696941b 100644
--- a/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md
@@ -19,10 +19,10 @@ prerequisites:
   - Completion of the Learning Path [Introduction to TinyML on Arm using PyTorch and ExecuTorch](/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/)
   - An x86 Linux host machine or VM running Ubuntu 22.04 or later
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md b/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md
index ec5a95246e..817dfa9744 100644
--- a/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md
@@ -18,10 +18,10 @@ prerequisites:
     - Comfortable with Windows 
     - NXP LPCXpresso55S69 board
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md b/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
index 1d0351154d..32c25f0ce7 100644
--- a/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
@@ -25,10 +25,10 @@ prerequisites:
     - PETG filament. Others can work, but PETG allows some flex without the risk of snapping
     
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md b/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
index 7448650cf7..acdc2f59d4 100644
--- a/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
@@ -7,10 +7,10 @@ description: Learn how to debug microcontrollers using µVision with basic run/s
 minutes_to_complete: 90
 # Always measured in minutes. Should be an integer, to complete the learning path (not read it).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md b/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
index 9bd4f41202..e9c063c743 100644
--- a/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
@@ -18,10 +18,10 @@ prerequisites:
     - Install [uv2csolution](https://arm-software.github.io/MDK-Toolbox/01_installation/) for the command line flow.
     - The &micro;Vision project must use Arm Compiler 6 as the default toolchain. Arm Compiler 5 is not supported.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md b/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
index b181bf540b..71f4c88b6b 100644
--- a/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
@@ -19,10 +19,10 @@ prerequisites:
     - A basic understanding of the [development tools for Arm Cortex-M](https://developer.arm.com/Tools%20and%20Software/)
     - Command line access to your machine
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
@@ -54,7 +54,7 @@ generated_summary_faq:
       access to your machine. No other explicit prerequisites are listed.
   - question: Which initialization command should I use on my OS, and when should I run it?
     answer: >-
-      Run the vcpkg init command in every new Terminal window: Windows (cmd): %USERPROFILE%\.vcpkgcpkg-init.cmd,
+      Run the vcpkg init command in every new Terminal window: Windows (cmd): %USERPROFILE%\.vcpkg\vcpkg-init.cmd,
       Windows (PowerShell): . ~/.vcpkg/vcpkg-init.ps1, Linux/macOS: . ~/.vcpkg/vcpkg-init. This
       ensures your shell session is set up to use vcpkg.
   - question: What is the purpose of vcpkg-configuration.json?
diff --git a/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md b/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md
index 1cbfc24ed8..37efc5b417 100644
--- a/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md
@@ -18,10 +18,10 @@ prerequisites:
     - A Linux or macOS computer with Python 3 installed
 
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md b/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md
index 8d2f23b6c3..4f02e263cd 100644
--- a/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - Some familiarity with embedded Linux.
     - A linux machine running Ubuntu 22.04 with at least 60 GB of disk space.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md b/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md
index c7e62bc4ab..5d4e5bc326 100644
--- a/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md
@@ -20,10 +20,10 @@ prerequisites:
     - A USB-C cable.
     - An x86 Linux machine, or a Mac running macOS.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md b/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md
index 4afb8a851f..e82a925410 100644
--- a/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md
@@ -16,10 +16,10 @@ prerequisites:
 - Some familiarity with embedded C programming
 - A Linux machine running Ubuntu, or an AWS account to use [Arm Virtual Hardware](https://www.arm.com/products/development-tools/simulation/virtual-hardware)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md b/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md
index a3d8a25ce4..b1ca6dd7f3 100644
--- a/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md
@@ -20,10 +20,10 @@ prerequisites:
     - A Cortex-M development board
     - Windows 10+ (64-bit), macOS with Homebrew, or Linux (preferably Ubuntu 20.04+)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md b/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md
index 254caf4369..9b7976ab0f 100644
--- a/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md
+++ b/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md
@@ -18,10 +18,10 @@ prerequisites:
     - A ChromeOS device with the Linux development environment enabled. The Lenovo Chromebook Plus 14 is recommended. 
     - Basic knowledge of the Linux command line
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md
index 1074ada65c..2989da231c 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md
@@ -19,10 +19,10 @@ prerequisites:
     - Experience with Python scripting and virtual environments
     - Basic understanding of reinforcement learning concepts (rewards, policies, episodes)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md
index d517ce3e3b..2b8f859c69 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md
@@ -21,10 +21,10 @@ prerequisites:
     - Experience building software from source using CMake and make
 
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md
index cfa9bdd13c..6e21cd84df 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md
@@ -16,10 +16,10 @@ learning_objectives:
 prerequisites:
     - An NVIDIA DGX Spark system with at least 15 GB of available disk space
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md
index 9c146039bc..3b281b1c23 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md
@@ -18,10 +18,10 @@ prerequisites:
     - An NVIDIA DGX Spark system with at least 15 GB of available disk space
     - A USB microphone for audio input
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/docker-models/_index.md b/content/learning-paths/laptops-and-desktops/docker-models/_index.md
index a6a37243dc..33e4a92582 100644
--- a/content/learning-paths/laptops-and-desktops/docker-models/_index.md
+++ b/content/learning-paths/laptops-and-desktops/docker-models/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - Basic understanding of Docker CLI and concepts.
     - Familiarity with LLM concepts.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/electron/_index.md b/content/learning-paths/laptops-and-desktops/electron/_index.md
index e103089cd8..ef3f5a5286 100644
--- a/content/learning-paths/laptops-and-desktops/electron/_index.md
+++ b/content/learning-paths/laptops-and-desktops/electron/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - Node.js for Arm64. You can find the [Node.js installer](https://nodejs.org/dist/v20.10.0/node-v20.10.0-arm64.msi).
     - Any code editor; we recommend using [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md b/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md
index 4b6e9d31dc..7b6cd13fd5 100644
--- a/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md
+++ b/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - A GitHub account. 
     - Familiarity with GitHub Actions.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/hyper-v/_index.md b/content/learning-paths/laptops-and-desktops/hyper-v/_index.md
index 60b1be2476..5871d66c94 100644
--- a/content/learning-paths/laptops-and-desktops/hyper-v/_index.md
+++ b/content/learning-paths/laptops-and-desktops/hyper-v/_index.md
@@ -13,10 +13,10 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 with [Hyper-V](/install-guides/hyper-v/) installed.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/intro/_index.md b/content/learning-paths/laptops-and-desktops/intro/_index.md
index 6aa5f77642..239038956f 100644
--- a/content/learning-paths/laptops-and-desktops/intro/_index.md
+++ b/content/learning-paths/laptops-and-desktops/intro/_index.md
@@ -14,10 +14,10 @@ learning_objectives:
 prerequisites:
     - Nothing
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md b/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md
index 510e16f53c..db3f1424b3 100644
--- a/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md
+++ b/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md
@@ -17,10 +17,10 @@ prerequisites:
 - Xcode command line tools installed
 - Basic familiarity with using the Terminal and command-line tools
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md b/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md
index 176e969d28..2b804ab3c4 100644
--- a/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md
+++ b/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md
@@ -14,10 +14,10 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md b/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md
index 7885442862..c64ce37c64 100644
--- a/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md
+++ b/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md
@@ -16,10 +16,10 @@ prerequisites:
 - Basic knowledge of how MTE works. Refer to the [Learn about Memory Tagging Extension Learning Path](/learning-paths/mobile-graphics-and-gaming/mte/)
 - Knowledge of how a dynamic memory allocator can be implemented. Refer to [Write a Dynamic Memory Allocator Learning Path](/learning-paths/cross-platform/dynamic-memory-allocator/).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md b/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
index 1838a7baed..fbc3f8790b 100644
--- a/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
+++ b/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - A Pinebook Pro laptop
     - A microSD card (8GB or greater; class 10 or faster)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md b/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
index fa94dc3614..7ed4ee7090 100644
--- a/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
+++ b/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - Hugging Face account and access token
     - NVIDIA DGX Spark workstation
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md b/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
index 935967026b..633da793d3 100644
--- a/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
+++ b/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - A DockerHub account. You can [set up a free DockerHub account](https://hub.docker.com/signup).
     - A GitHub account. You can [sign up for GitHub](https://github.com/signup).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/_index.md b/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
index 41078980d7..ebaa500f7b 100644
--- a/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
@@ -14,10 +14,10 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm machine such as the Lenovo Thinkpad X13s, or an [Azure virtual machine](/learning-paths/cross-platform/woa_azure/).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md b/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md
index eaa2f92cf8..0f2df1653c 100644
--- a/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11
     - A code editor such as [Visual Studio Code for Windows on Arm](https://code.visualstudio.com/docs/?dv=win32arm64user) 
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md b/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
index e476bde3be..9c20570bc9 100644
--- a/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
@@ -16,10 +16,10 @@ learning_objectives:
 prerequisites:
     - An Arm Linux system with KVM support and a minimum of 8GB RAM and 50GB free disk space
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md b/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
index 8ad5b86777..25ab162b77 100644
--- a/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
@@ -14,10 +14,10 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md b/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
index 3d3629bd7a..3fadd21b61 100644
--- a/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
@@ -18,10 +18,10 @@ prerequisites:
     - Visual Studio 2022 with Arm build tools. [Refer to this guide for the installation steps](https://developer.arm.com/documentation/102528/0100/Install-Visual-Studio).
     
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md b/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md
index cd94ccad28..6d6b10c746 100644
--- a/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - '[Qt framework](https://www.qt.io/) or [Qt for Open Source Development](https://www.qt.io/download-open-source)'
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md b/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
index 8bc391b36e..0d00b962a1 100644
--- a/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - .NET 8 SDK for [arm64](https://dotnet.microsoft.com/en-us/download/dotnet/thank-you/sdk-8.0.100-windows-arm64-installer).
     - Any code editor, we recommend using [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
index 7d85d00e67..d5eceb11be 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - A Windows-on-Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows-on-Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - Any code editor. Visual Studio Code is suitable.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md
index b4bdfeab8a..6aad915998 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is suitable.
     - Completion of the [Create IoT applications with Windows on Arm and AWS IoT Core](/learning-paths/laptops-and-desktops/win_aws_iot/) Learning Path.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md
index 6db5268208..555a986caa 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md
@@ -18,10 +18,10 @@ prerequisites:
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is suitable.
     - Completion of the [Create IoT applications with Windows on Arm and AWS IoT Core](/learning-paths/laptops-and-desktops/win_aws_iot/) Learning Path.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md
index e856878b7e..b87d92abcc 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is suitable.
     - Completion of the [Create IoT applications with Windows on Arm and AWS IoT Core](/learning-paths/laptops-and-desktops/win_aws_iot/) Learning Path.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md
index f7dc9afaa3..2e32e0939a 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is suitable.    
     - Completion of the [Use AWS Lambda for IoT applications](/learning-paths/laptops-and-desktops/win_aws_iot_lambda/) Learning Path.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/win_cef/_index.md b/content/learning-paths/laptops-and-desktops/win_cef/_index.md
index 9af97f64e8..ff1b4f66f6 100644
--- a/content/learning-paths/laptops-and-desktops/win_cef/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_cef/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - Visual Studio 2022.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/win_forms/_index.md b/content/learning-paths/laptops-and-desktops/win_forms/_index.md
index de2c075cba..e540839522 100644
--- a/content/learning-paths/laptops-and-desktops/win_forms/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_forms/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - Visual Studio 2022 with .NET Desktop Development workload
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/win_net/_index.md b/content/learning-paths/laptops-and-desktops/win_net/_index.md
index 14008b025e..65a8b26486 100644
--- a/content/learning-paths/laptops-and-desktops/win_net/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_net/_index.md
@@ -13,10 +13,10 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/win_net8/_index.md b/content/learning-paths/laptops-and-desktops/win_net8/_index.md
index fea5a55b9d..0b788a76b5 100644
--- a/content/learning-paths/laptops-and-desktops/win_net8/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_net8/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - .NET 8 SDK for [x64](https://dotnet.microsoft.com/en-us/download/dotnet/thank-you/sdk-8.0.100-windows-x64-installer) and [arm64](https://dotnet.microsoft.com/en-us/download/dotnet/thank-you/sdk-8.0.100-windows-arm64-installer).
     - Any code editor, we recommend using [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md b/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md
index 1145392ae4..607a99a5c2 100644
--- a/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - Visual Studio 2022 with .NET Multi-platform App UI development and Universal Windows Platform development installed.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md b/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md
index ff01df5636..74e1f19ec6 100644
--- a/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md
@@ -13,10 +13,10 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as a Lenovo Thinkpad X13 running Windows 11, or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md b/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md
index 175fac5558..7bc307d10e 100644
--- a/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - Familiarity with C++ development and compiling programs from the command line
     - A Windows on Arm machine with [Visual Studio](/install-guides/vs-woa/) and the C++ desktop development tools installed
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/win_python/_index.md b/content/learning-paths/laptops-and-desktops/win_python/_index.md
index 40166cab32..6866468435 100644
--- a/content/learning-paths/laptops-and-desktops/win_python/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_python/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - Any code editor, we recommend using [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user).
     - Visual Studio 2022 with Arm build tools. [Refer to this guide for the installation steps](https://developer.arm.com/documentation/102528/0100/Install-Visual-Studio)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/win_python_onnx/_index.md b/content/learning-paths/laptops-and-desktops/win_python_onnx/_index.md
index d87dab085a..d4d9eb210b 100644
--- a/content/learning-paths/laptops-and-desktops/win_python_onnx/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_python_onnx/_index.md
@@ -19,9 +19,9 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/). 
     - Any code editor like [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user).    
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
+rerun_summary: true
 rerun_faqs: true
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md b/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md
index b0b691c59f..0c3f8e6e9c 100644
--- a/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - A valid [GitHub account](https://github.com/) to complete this Learning Path.
     
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md b/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md
index 5de376efb6..6c291bba2a 100644
--- a/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - Refer to [Visual Studio 2022 with Arm build tools](/install-guides/vs-woa).
     
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/win_winui3/_index.md b/content/learning-paths/laptops-and-desktops/win_winui3/_index.md
index a561363f19..1ae1f72408 100644
--- a/content/learning-paths/laptops-and-desktops/win_winui3/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_winui3/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - Visual Studio 2022 with .NET desktop development and Universal Windows Platform development installed.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/win_wpf/_index.md b/content/learning-paths/laptops-and-desktops/win_wpf/_index.md
index 9567ea8182..a49fc4b945 100644
--- a/content/learning-paths/laptops-and-desktops/win_wpf/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_wpf/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - Visual Studio 2022 with .NET desktop development installed.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md b/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md
index 0ee73389a6..4287c8b81e 100644
--- a/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - Visual Studio 2022 with .NET desktop development and Universal Windows Platform development installed.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md b/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md
index d2d34fa612..1fda2e8dc7 100644
--- a/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md
@@ -14,10 +14,10 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md b/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md
index cebcee3c65..a37b82338b 100644
--- a/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - Valid GitHub account
     - Microsoft Azure account (if using virtual machine)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md
index 2d7f7d3c6f..9971c94891 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md
@@ -17,10 +17,10 @@ prerequisites:
   - A desktop or laptop running Windows on Arm.
   - Visual Studio 2022 Community Edition, WindowsPerf, WindowsPerf Visual Studio extension, and Windows Performance Analyzer (WPA) installed. 
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf/_index.md
index 8daa49fb2c..f8953271fb 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf/_index.md
@@ -14,10 +14,10 @@ learning_objectives:
 prerequisites:
     - Windows on Arm desktop or development machine
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md
index 4ebb9cade2..776c13e4e7 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - Windows on Arm desktop or development machine with [WindowsPerf installed](/install-guides/wperf)
     - Windows x86_64 desktop machine with [Visual Studio 2022 Community Edition](https://visualstudio.microsoft.com/vs/) installed.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
index 8fbb724789..ce1e369a53 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
@@ -14,10 +14,10 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm laptop with WindowsPerf, Windows Performance Analyzer (WPA), and the WPA plugin installed.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/laptops-and-desktops/wsl2/_index.md b/content/learning-paths/laptops-and-desktops/wsl2/_index.md
index 20d5e9b576..97871cac32 100644
--- a/content/learning-paths/laptops-and-desktops/wsl2/_index.md
+++ b/content/learning-paths/laptops-and-desktops/wsl2/_index.md
@@ -19,10 +19,10 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11. 
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md b/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md
index 4ce1551c85..0f854b249f 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - Knowledge of the Vulkan API.
     - A Vulkan application that creates and uses images. This Learning Path shows how to use an API Sample in the [Khronos Vulkan Samples repository](https://github.com/KhronosGroup/Vulkan-Samples/blob/main/scripts/README.adoc#generate-api-sample) as an example.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md b/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md
index 484944a44c..57d4f4ded5 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md
@@ -14,10 +14,10 @@ learning_objectives:
 prerequisites:
     - A computer running Arm Linux or macOS with Docker installed
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md b/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
index 665d87c92f..5a92f75523 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
@@ -20,10 +20,10 @@ prerequisites:
     - Arm Performance Studio installed. Follow the [Arm Performance Studio install guide](/install-guides/ams) for instructions.
     - Android SDK Platform tools installed. Required for the Android Debug bridge (adb).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md b/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md
index 0147dfb24f..f1ceaabf0f 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md
@@ -18,10 +18,10 @@ prerequisites:
     - Download and install Arm Performance Studio from [Product Download Hub](https://developer.arm.com/downloads/view/MOBST-PRO0). It is supported on Windows, Linux, and macOS host platforms.
     - Download and install [Android SDK Platform tools](https://developer.android.com/studio/releases/platform-tools.html). Required for [Android Debug bridge (adb)](https://developer.android.com/studio/command-line/adb).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md
index edc6403385..ff4b577039 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - An Android phone for testing, in Developer Mode, with USB cable for connection
     - Basic familiarity with Kotlin and Android app development
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
index 1651156bc8..a1dba23d66 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - Basic C++ knowledge
     - Android Studio with Android Emulator
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_neon/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_neon/_index.md
index 6e6dce1bb7..ece58cd5b4 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_neon/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_neon/_index.md
@@ -20,9 +20,9 @@ prerequisites:
     - A x86_64 or Apple M1 development machine with Android Studio installed.
     - A 64-bit Arm powered smartphone running Android.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
+rerun_summary: true
 rerun_faqs: true
 author: Dawid Borycki
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md
index 3bc3cff8a6..bc39132807 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - A development machine with [Android Studio](https://developer.android.com/studio) installed. 
     - An Android smartphone.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md
index 3afcee7b85..e1fb83da93 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md
@@ -17,10 +17,10 @@ prerequisites:
    - An Android smartphone.
    - Familiarity with OpenCV, review [Create Computer Vision Applications with OpenCV on Android Devices](/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/) before starting.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
index ec75159169..4265973fcb 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - Familiarity with Android development concepts.
     - An Android smartphone.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md
index 24765ff8c0..fba89a4db4 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - Knowledge of [Neon](https://developer.arm.com/documentation/102474/latest)
     - Knowledge of [Scalable Vector Extension (SVE)](https://developer.arm.com/documentation/101726/4-0)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
index c37df66b1e..2c1953f42a 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
@@ -25,10 +25,10 @@ prerequisites:
     - Arm Performance Studio.
     - Python 3.10 or later.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md b/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
index f3bc862b76..29a275ec9e 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
@@ -16,10 +16,10 @@ prerequisites:
 - An Android mobile device that has a Mali GPU with hardware ray tracing support.
 - A USB cable to connect the mobile device to your computer.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md b/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md
index 489e39eda8..54011fc894 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - A Windows x86_64 development machine with at least 16GB of RAM.
     - An Android phone with at least 8GB of RAM. This learning path was tested on Samsung Galaxy S24.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md b/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md
index 79a2107b99..c3f7d6bca8 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md
@@ -20,10 +20,10 @@ prerequisites:
     - Basic knowledge of Modern Android Architecture. See [Modern Android App Architecture](https://developer.android.com/courses/pathways/android-architecture).
     - Basic knowledge of Kotlin programming language, including [Coroutines](https://kotlinlang.org/docs/coroutines-overview.html) and [Kotlin Flows](https://kotlinlang.org/docs/flow.html).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md b/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md
index 01318920d9..e5cf5bbb63 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md
@@ -22,10 +22,10 @@ prerequisites:
     - Java 17 JDK. Follow the steps in [Java 17 JDK](https://www.oracle.com/java/technologies/javase/jdk17-archive-downloads.html) to download and install JDK for host.
     - Python 3.10.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md b/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md
index 54787f0f5e..7edd6dd1c9 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md
@@ -22,10 +22,10 @@ prerequisites:
     - Python 3.10 or later
     - A [Hugging Face](https://huggingface.co/) account with access to Meta Llama models
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md b/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md
index ebece99ae9..1a086e62b4 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md
@@ -19,10 +19,10 @@ prerequisites:
     - A USB cable to connect your computer to your Google Pixel 8.
     - Android Debug Bridge (adb) installed on your device. If needed, follow the steps in the [Android Debug Bridge](https://developer.android.com/tools/adb) documentation.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/gemma4-kleidiai-sme2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/gemma4-kleidiai-sme2/_index.md
index 54c0d6378c..be3d875037 100755
--- a/content/learning-paths/mobile-graphics-and-gaming/gemma4-kleidiai-sme2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/gemma4-kleidiai-sme2/_index.md
@@ -19,9 +19,9 @@ prerequisites:
     - A SME2 device (macOS M4 on Apple Silicon)
     - Git, Homebrew, and Xcode Command Line Tools
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
+rerun_summary: true
 rerun_faqs: true
 author: Annie Tallund
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md
index 887908adcc..91ea0769cf 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md
@@ -14,10 +14,10 @@ prerequisites:
     - A game project that uses advanced rendering features (such as hardware ray tracing) that stretch the performance capabilities of everyday smartphones.
     - A development machine with Git installed.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md
index 0b7002cabd..640bd12433 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - Recent Android device, such as a mobile phone or tablet
     - Desktop computer capable of running Unity
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md
index 80777cc993..96ad9b3723 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md
@@ -13,10 +13,10 @@ prerequisites:
     - Familiarity with Godot
     - Familiarity with Arm Performance Studio tools
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md b/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md
index 24726aeb83..f814b6bfa6 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md
@@ -14,10 +14,10 @@ prerequisites:
 - An Android mobile device that has a Mali GPU with hardware ray tracing support.
 - A USB cable to connect the mobile device to your computer.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md b/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md
index 23ca58a0d4..6d2e83b0c4 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md
@@ -11,10 +11,10 @@ learning_objectives:
 prerequisites:
     - None
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md b/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md
index ef4624d68b..945191cabc 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - Basic understanding of quantization concepts for neural networks
     - (Optional) Access to an Arm CPU with SME2 support (Linux or Android) for hands-on verification steps
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md b/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md
index 32d45e56ec..13717362c5 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - An x86_64 Linux machine running Ubuntu with approximately 500 MB of free space, or a docker daemon that can build and run a provided x86_64 Dockerfile.
     - An Android phone with support for i8mm (tested on Google Pixel 8 Pro).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md b/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md
index f3f52aaf3d..b44485f77b 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md
@@ -14,10 +14,10 @@ prerequisites:
     - A development machine running Ubuntu or Debian Linux with `x86_64` architecture
     - An Android device with an Arm GPU
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md b/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md
index eac77a18d8..3a0104b91b 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md
@@ -16,10 +16,10 @@ prerequisites:
 - An Arm64 Linux development machine 
 - An Android device that supports Arm SME2 architecture features - see this [list of devices with SME2 support](/learning-paths/cross-platform/multiplying-matrices-with-sme2/1-get-started/#devices)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md b/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md
index 4fa8c7f11b..6f0c11498b 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md
@@ -16,10 +16,10 @@ prerequisites:
   - An x86_64 Linux host machine running Ubuntu, with at least 15 GB of free disk space
   - An Arm64 target system with support for SME or SME2 - see the Learning Path [Devices with native SME2 support](/learning-paths/cross-platform/multiplying-matrices-with-sme2/1-get-started/#devices)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md b/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md
index 78ec20af83..f1fe45cc2e 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - A development machine running Ubuntu 22.04, with a CUDA-capable NVIDIA® GPU
     - CUDA Toolkit version 11.8 or later
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md b/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md
index 59b5085353..410689944f 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md
@@ -12,10 +12,10 @@ learning_objectives:
 prerequisites:
     - An AArch64 Linux development machine. Cloud instances can be used, refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md b/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md
index 083c132cd7..54cccd5091 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md
@@ -18,10 +18,10 @@ prerequisites:
     - A USB cable to connect your Google Pixel 8 to your desktop machine
     - Android Debug Bridge (adb) installed on your device. Follow the steps in https://developer.android.com/tools/adb to install Android SDK Platform Tools. The adb tool is included in this package.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md b/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md
index 434fb8111f..c2409c6942 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md
@@ -18,10 +18,10 @@ prerequisites:
     - Visual Studio with C++ game development tools
     - A C++ Unreal project (such as the Third Person template)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md b/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md
index 2bfbc38afb..8f2faa0ec5 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md
@@ -20,10 +20,10 @@ prerequisites:
     - Visual Studio (with Desktop Development with C++ and .NET desktop build tools)
 
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md b/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md
index 9a5c8bd089..2f1f1dddc9 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md
@@ -19,10 +19,10 @@ prerequisites:
   - An Arm64 device such as a Raspberry Pi or Android smartphone
   - Android Studio (required only for the final deployment section)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md b/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md
index eebd9e3af8..6735630a04 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md
@@ -14,10 +14,10 @@ prerequisites:
     - Understanding of vertex attributes.
     - Familiarity with Arm Frame Advisor (part of Arm Performance Studio).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md
index 2dd6e70b29..d19582ea00 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - Git, CMake, and Android Debug Bridge (ADB) installed on your host machine
     - An Android device with Arm SME2 support for running and profiling the executable
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md
index 4345015604..1a00d03e95 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - Basic understanding of machine learning model inference
     - Familiarity with Android NDK and cross-compilation
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md b/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md
index ca768c04e7..8f57d6e34d 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - Android Studio Profiler.
   
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md b/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md
index f8ac62d8d7..ec2752ca47 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - Basic knowledge of Unity and programming concepts
     - The setup described in the Learning Path [Get started with Unity on Android](/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md b/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md
index 9b662c38b8..cb56ab0efa 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - Basic PyTorch model training and evaluation experience
     - A development machine with Python 3.10+ and PyTorch installed that runs ExecuTorch
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md b/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md
index e8195329f1..b4e3420ece 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - Knowledge of the Vulkan API.
     - A Vulkan renderer. Most code is generic and should be easy to incorporate into any deferred PBR renderer.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md b/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md
index eb9668f62b..14eb38a876 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - If you wish to analyze your own applications you will need a supported Android device.
     - Some basic familiarity with Frame Advisor. Review the [Frame Advisor](/learning-paths/mobile-graphics-and-gaming/ams/fa/) section in [Get started with Arm Performance Studio for mobile](/learning-paths/mobile-graphics-and-gaming/ams/).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md
index 27d27063d9..5b063184a2 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md
@@ -18,10 +18,10 @@ prerequisites:
     - A [HuggingFace](https://huggingface.co/) account.
     - An Android phone in [developer mode](https://developer.android.com/studio/debug/dev-options) and a cable to connect it to your development machine.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md
index c7240abae0..1abfbb16e0 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - A [Hugging Face](https://huggingface.co/) account
     - An Android phone in [developer mode](https://developer.android.com/studio/debug/dev-options) with at least 8 GB of RAM and a cable to connect it to your development machine
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md b/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md
index e2b6d26483..225a725d38 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - An ethernet connection
     - A mouse and keyboard connected to the Orange Pi
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md b/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md
index 6eb005285d..129ff4b844 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - Familiarity with Unity and the Unity Profiler
     - Familiarity with Arm Performance Studio tools
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md b/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md
index 55264d7dc5..5181b7679a 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md
@@ -18,10 +18,10 @@ prerequisites:
     - Desktop computer capable of running Unity
     - Unity version compatible with Unity Burst compiler 1.5 or later
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md b/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md
index 4105236f9c..b1d97c3a4d 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md
@@ -15,10 +15,10 @@ prerequisites:
 - An Android mobile device that has a 64-bit processor and supports at least Android 8.
 - A USB cable to connect the mobile device to your computer.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md b/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md
index a06cfa3ee5..f9be54e755 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - A development machine with [Android Studio](https://developer.android.com/studio) installed.
     - A smartphone running Android with support for `i8mm` and `dotprod` instructions.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md b/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md
index 95b06526b6..134dc239bd 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md
@@ -18,10 +18,10 @@ prerequisites:
     - This Learning Path was tested on a Vivo X300 Pro.
     - A development machine with [Android Studio](https://developer.android.com/studio) installed.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md b/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md
index f5fbc31e96..550cff1f2f 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md
@@ -19,10 +19,10 @@ prerequisites:
     - A working microphone for voice input.
     - Basic Python and command-line knowledge.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md
index c3f40fc995..79edf0a2f3 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md
@@ -19,10 +19,10 @@ prerequisites:
 
 
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md b/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
index 9d96e5cd66..a65cd16837 100644
--- a/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - Basic understanding of Python and prompt engineering.
     - Understanding of LLM fundamentals.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/aks/_index.md b/content/learning-paths/servers-and-cloud-computing/aks/_index.md
index db2052e293..ace34c3b2a 100644
--- a/content/learning-paths/servers-and-cloud-computing/aks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/aks/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - An Azure account
     - A machine with [Terraform](/install-guides/terraform/), [Azure CLI](/install-guides/azure-cli), and [Kubectl](/install-guides/kubectl/) installed
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md b/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
index 2f19161973..6aae02c36f 100644
--- a/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
@@ -19,10 +19,10 @@ prerequisites:
   - Basic understanding of data formats such as Parquet or ORC
   - Familiarity with Linux command-line operations
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
index aeaedde83c..555b14fc55 100644
--- a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - An [AWS account](https://aws.amazon.com/) with permission to launch Graviton4 (`c8g.4xlarge` or larger) instances
     - Basic familiarity with Linux and SSH
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
index 03aac5c90e..22ac19416c 100644
--- a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - A [Google Cloud account](https://console.cloud.google.com/) with permission to launch Axion (`c4a-standard-16` or larger) instances
     - Basic familiarity with Linux and SSH
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
index 58cf2954de..a8303065b5 100644
--- a/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
@@ -21,10 +21,10 @@ prerequisites:
   - Basic understanding of Git and GitHub workflows
   - Familiarity with basic Linux command-line usage
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md b/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
index 49dc756b1f..480f3c2d59 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - Access to an x86 and an Arm cloud instance (virtual machine).
     - Proficiency in C++ programming.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md b/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
index a27d363d57..25c1bfbdb0 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
@@ -18,10 +18,10 @@ prerequisites:
     - Basic familiarity with Docker and C/C++ development
     - Access to an Arm-based cloud instance or local Arm computer running Linux or macOS
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md b/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
index a8f4612678..84293a520b 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
@@ -19,10 +19,10 @@ prerequisites:
     - Familiarity with Linux development environments and basic embedded or cloud deployment concepts
     - Experience building applications with GCC and CMake
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md b/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
index ce78d097c7..5f016ad1f6 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
@@ -17,10 +17,10 @@ learning_objectives:
 prerequisites:
   - Access to an Arm-based Linux system running Ubuntu, Debian, or CentOS.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md b/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
index c5e27624fc..41b5d018f1 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
@@ -14,10 +14,10 @@ learning_objectives:
 prerequisites:
     - An Arm computer running Linux. A bare metal or cloud metal instance is best because they expose more counters. You can use a virtual machine (VM), but fewer counters may be available. These instructions have been tested on the `a1.metal` instance type.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md b/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
index 6f65c1e941..9e8aca2f20 100644
--- a/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - An Amazon Web Services (AWS) account
     - A local computer with Docker, AWS CLI, and AWS Copilot CLI installed
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md b/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
index f7585c1164..7dffcdc01f 100644
--- a/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - An Amazon Web Services (AWS) [account](https://aws.amazon.com/)
     - A computer with [Terraform](/install-guides/terraform) installed
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
index 2c5fba26a9..45e754df5e 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - Azure CLI installed on your local machine - see the [Azure CLI install guide](/install-guides/azure-cli/)
     - An SSH key pair for authentication
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
index cc4485c1ab..fee958b574 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - A GitHub account.
     - A machine with [Terraform](/install-guides/terraform/),[Azure CLI](/install-guides/azure-cli), and [Kubectl](/install-guides/kubectl/) installed.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
index 916aa26064..0ccbaf278f 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - An Azure account
     - A computer with Terraform installed
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
index ba8dd46620..17fd726456 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
@@ -20,10 +20,10 @@ prerequisites:
     - A Linux machine with [QEMU](https://www.qemu.org/download/) and the [Azure CLI](/install-guides/azure-cli/) installed and authenticated
     
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md b/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
index 080a7f6e10..7ddeddefad 100644
--- a/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
@@ -14,10 +14,10 @@ learning_objectives:
 prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an on-premise Arm server.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md b/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
index 6bc8ce56bf..2417a60dca 100644
--- a/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
@@ -17,10 +17,10 @@ prerequisites:
 - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from an appropriate
   cloud service provider.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md b/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
index 040e004910..f99c0ebc02 100644
--- a/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
@@ -23,10 +23,10 @@ prerequisites:
     - A system with with sufficient hardware performance counters to use the [TopDown](/install-guides/topdown-tool) methodology. This typically requires running on bare metal rather than a virtualized environment.
 
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md b/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
index 9ae75d13be..4bb678c1a1 100644
--- a/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
@@ -16,10 +16,10 @@ learning_objectives:
 prerequisites:
   - An Arm-based Linux system with [BOLT](/install-guides/bolt/) and [Linux Perf](/install-guides/perf/) installed
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/bolt/_index.md b/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
index e9e35460b7..b7cc59f217 100644
--- a/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
@@ -15,9 +15,9 @@ prerequisites:
     - An Arm based system running Linux with [BOLT](/install-guides/bolt/) and [Linux Perf](/install-guides/perf/) installed. The Linux kernel should be version 5.15 or later. Earlier kernel versions can be used, but some Linux Perf features may be limited or not available. For [SPE](./bolt-spe) the version should be 6.14 or later.
     - (Optional) A second, more powerful Linux system to build the software executable and run BOLT.
 
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
+generate_summary_faq: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
index a5e1c04c85..4a210b6589 100644
--- a/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
@@ -19,10 +19,10 @@ prerequisites:
   - Familiarity with [Docker](https://docs.docker.com/get-started/) and container concepts
   - A [GitHub account](https://github.com/join) to host your application repository
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
index 3f9d69074c..f32d3243d0 100644
--- a/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
@@ -17,10 +17,10 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Familiarity with Cassandra architecture, replication, and [Cassandra partitioning and event-driven I/O](https://cassandra.apache.org/doc/stable/cassandra/architecture/)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
index b8626fd8f5..311b020df5 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
@@ -16,10 +16,10 @@ learning_objectives:
 prerequisites:
     - An AArch64 or x86_64 computer running Linux or macOS. You can use cloud instances, refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
index eacf51b470..cc63a945b5 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
@@ -19,10 +19,10 @@ prerequisites:
     - Completion of the [Run an application in a Realm using the Arm Confidential Computing Architecture (CCA)](/learning-paths/servers-and-cloud-computing/cca-container/) Learning Path
     - Completion of the [Run an end-to-end Attestation Flow](/learning-paths/servers-and-cloud-computing/cca-essentials/) Learning Path
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
index 29037baa7c..b87d5f5cc9 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - Completion of [Get Started with CCA Attestation and Veraison](/learning-paths/servers-and-cloud-computing/cca-veraison) Learning Path.
     - Completion of the [Run an application in a Realm using the Arm Confidential Computing Architecture (CCA)](/learning-paths/servers-and-cloud-computing/cca-container/) Learning Path.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
index d5a55f84f9..8861248607 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
@@ -15,10 +15,10 @@ prerequisites:
   - An AArch64 or x86_64 computer running Linux or macOS. Cloud-based instances can also be used; see the [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/)
   - Completion of the [Run an end-to-end Attestation with Arm CCA and Trustee](/learning-paths/servers-and-cloud-computing/cca-trustee) Learning Path
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
index fcc0f4a497..68b0e4ec96 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
@@ -16,10 +16,10 @@ prerequisites:
   - Completion of the [Get started with CCA attestation and Veraison](/learning-paths/servers-and-cloud-computing/cca-veraison) Learning Path
   - Completion of the [Run an end-to-end attestation flow with Arm CCA](/learning-paths/servers-and-cloud-computing/cca-essentials/) Learning Path
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
index e59a3dd135..4ffd978bee 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
@@ -14,10 +14,10 @@ prerequisites:
     - An [AWS account](/learning-paths/servers-and-cloud-computing/csp/aws/) with access to AWS services.
     - An x86 computer running Ubuntu or Arch Linux, authorized for AWS access. If you're using another build environment, you'll need to configure the toolchains for cross-compilation.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md
index 40cf980532..f752b6f672 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md
@@ -19,10 +19,10 @@ prerequisites:
     - An Arm-based or x86 computer running Ubuntu. You can use a server instance from a cloud service provider of your choice.
 
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
index 771523dc82..3ccf786ad2 100644
--- a/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
@@ -23,10 +23,10 @@ prerequisites:
     [runners](https://circleci.com/docs/guides/execution-runner/runner-overview/)
 
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md b/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md
index f46bdc9232..59ac0471a5 100644
--- a/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md
@@ -16,10 +16,10 @@ prerequisites:
   - A CircleCI account
   - Basic understanding of CircleCI workflows, jobs and resource classes
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/clair/_index.md b/content/learning-paths/servers-and-cloud-computing/clair/_index.md
index 190d0f610f..7428ba7a62 100644
--- a/content/learning-paths/servers-and-cloud-computing/clair/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/clair/_index.md
@@ -14,10 +14,10 @@ learning_objectives:
 prerequisites:
     - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an Arm server with recent versions of Docker and Go installed.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
index e2ebe06676..a80760bb46 100644
--- a/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
@@ -21,10 +21,10 @@ prerequisites:
   - Basic familiarity with [ClickHouse](https://clickhouse.com/)
   - Basic understanding of databases and SQL
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md b/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
index 89305c6ea6..0eb6ef33c2 100644
--- a/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
@@ -13,10 +13,10 @@ learning_objectives:
 prerequisites:
     - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an on-premise Arm server.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
index 45933d462c..8472f87213 100644
--- a/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - A Microsoft Azure subscription with permissions to create virtual machines and networking resources
     - Basic familiarity with SSH
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md b/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
index 6244859b3c..e97794ea52 100644
--- a/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
@@ -14,10 +14,10 @@ prerequisites:
     - An [AWS account](/learning-paths/servers-and-cloud-computing/csp/aws/) for accessing AWS cloud services.
     - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or any Arm server, laptop, or single-board computer running [Docker](/install-guides/docker/) used to run the created images
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/codec/_index.md b/content/learning-paths/servers-and-cloud-computing/codec/_index.md
index 821a51ecec..58607bb771 100644
--- a/content/learning-paths/servers-and-cloud-computing/codec/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/codec/_index.md
@@ -17,10 +17,10 @@ prerequisites:
 - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from an appropriate
   cloud service provider. This Learning Path has been verified on AWS EC2 and Oracle cloud services, running `Ubuntu Linux 20.04.`
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/codec1/_index.md b/content/learning-paths/servers-and-cloud-computing/codec1/_index.md
index 736aa10d62..4aa435b4d3 100644
--- a/content/learning-paths/servers-and-cloud-computing/codec1/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/codec1/_index.md
@@ -2,10 +2,10 @@
 title: Run the AV1 and VP9 codecs on Arm Linux
 description: Learn how to build and run the AV1 and VP9 video codecs on Arm Linux systems with performance benchmarking across various resolutions and encoding configurations.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
index 235a03bf2d..9cd11f02ad 100644
--- a/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
@@ -17,10 +17,10 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled  
   - Basic familiarity with [Couchbase](https://www.couchbase.com/)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md b/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
index f33d4589a0..fc33141574 100644
--- a/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
@@ -14,10 +14,10 @@ prerequisites:
     - Basic understanding of C++.
     - Basic understanding of compilers.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md b/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md
index 6df9cb1509..d5107caf21 100644
--- a/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md
@@ -14,10 +14,10 @@ prerequisites:
     - Basic C++ understanding.
     - Access to an Arm-based Linux machine.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md b/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
index d25df95620..055f2f5c9d 100644
--- a/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
@@ -14,10 +14,10 @@ prerequisites:
     - Access to Arm Performix
     - Basic understanding of C++
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/csp/_index.md b/content/learning-paths/servers-and-cloud-computing/csp/_index.md
index dc35c9a54c..07fdd78c50 100644
--- a/content/learning-paths/servers-and-cloud-computing/csp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/csp/_index.md
@@ -2,10 +2,10 @@
 title: Get started with Arm-based cloud instances
 description: Learn how to start an Arm-based virtual machine instance from major cloud service providers and verify the Arm architecture is being used.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md b/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
index 2155d00854..3b00135d53 100644
--- a/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
@@ -14,10 +14,10 @@ learning_objectives:
 prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud provider or an on-premise Arm server. This Learning Path was tested on an AWS Graviton4 r8g.24xlarge instance.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md b/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md
index ffb8c01431..f8e246771f 100644
--- a/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an Arm Linux server.
     - Familiarity with Linux.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md
index 2314a7d91b..ce5081bcf8 100644
--- a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - Familiarity with the Learning Path [Deploy a Large Language Model (LLM) chatbot with llama.cpp using KleidiAI on Arm servers](/learning-paths/servers-and-cloud-computing/llama-cpu)
     - Familiarity with AWS
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md
index 1b1af3a5c7..d9a10fd157 100644
--- a/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md
@@ -21,10 +21,10 @@ prerequisites:
   - Basic familiarity with [Django](https://www.djangoproject.com/)
   - Basic understanding of containers and Kubernetes concepts
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/django/_index.md b/content/learning-paths/servers-and-cloud-computing/django/_index.md
index 20454e51a7..18f6a496d8 100644
--- a/content/learning-paths/servers-and-cloud-computing/django/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/django/_index.md
@@ -17,9 +17,9 @@ prerequisites:
     - Be comfortable with SSH/Linux terminal and basic system administration tasks.
     - To install both [Nginx](/learning-paths/servers-and-cloud-computing/nginx/) and [PostgreSQL](/learning-paths/servers-and-cloud-computing/postgresql/)
 
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
+generate_summary_faq: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md b/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
index 83eb1e09f3..364cb2400b 100644
--- a/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
@@ -14,10 +14,10 @@ learning_objectives:
 prerequisites:
     - Any [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider (CSP), or an on-premise Arm server with at least 400GB of RAM and 800 GB of disk space.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md b/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
index 380557e5a9..e3e4f04c66 100644
--- a/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
@@ -23,10 +23,10 @@ prerequisites:
     - A machine with at least 8 GB RAM (16 GB recommended)
     - Basic familiarity with Docker, C++, and SIMD intrinsics concepts
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md b/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md
index 6b0bb6ee4f..94c3ae8273 100644
--- a/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md
@@ -20,10 +20,10 @@ prerequisites:
     - GCC installed (Linux) or access to a cross-compiler
     - OrchardCore application created using the .NET CLI or Visual Studio
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
index 2c89371b19..4e565c064c 100644
--- a/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
@@ -18,10 +18,10 @@ prerequisites:
   - Familiarity with SSH and remote server access
   - Basic understanding of cloud infrastructure and monitoring concepts
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/ecs/_index.md b/content/learning-paths/servers-and-cloud-computing/ecs/_index.md
index e09f4b655e..0af006bfd9 100644
--- a/content/learning-paths/servers-and-cloud-computing/ecs/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ecs/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - An AWS account
     - A computer with Docker, AWS CLI, and Terraform installed
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md b/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
index dfd6eb3de0..286c79183b 100644
--- a/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - A computer with [Amazon eksctl CLI](/install-guides/eksctl) and [kubectl](/install-guides/kubectl/)installed.
     - Docker installed on local computer [Docker](/install-guides/docker)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/eks/_index.md b/content/learning-paths/servers-and-cloud-computing/eks/_index.md
index 02790ae4c9..dfc3ad5b43 100644
--- a/content/learning-paths/servers-and-cloud-computing/eks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/eks/_index.md
@@ -14,10 +14,10 @@ learning_objectives:
 prerequisites:
     - An Amazon Web Services (AWS) [account](https://aws.amazon.com/)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md
index f35811a4b3..1c2c0dad52 100644
--- a/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md
@@ -17,10 +17,10 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free?utm_source=google&hl=en) account with billing enabled
   - Familiarity with networking concepts and the [Envoy architecture](https://www.envoyproxy.io/docs/envoy/latest/)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/envoy/_index.md b/content/learning-paths/servers-and-cloud-computing/envoy/_index.md
index 1bacb9518a..c7d2d07a2a 100644
--- a/content/learning-paths/servers-and-cloud-computing/envoy/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/envoy/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - To run Envoy as a web server, you will need at least one [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an on-premises Arm server.
     - Network settings (firewalls and security groups) which allow communication on port 22 (SSH) and port 80 (HTTP).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md
index c45cb63ab4..8469d8e4eb 100644
--- a/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - Cloud or bare-metal installation of an Envoy service
     - Review [Learn how to deploy Envoy](/learning-paths/servers-and-cloud-computing/envoy/) if you do not already have an Envoy setup
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md b/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md
index 3011d6225d..2cb5d15260 100644
--- a/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md
@@ -19,10 +19,10 @@ prerequisites:
     - Some familiarity with running linux command line commands.
     - Some familiarity with using a gdb debugger.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md b/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
index 265a972ada..fa3323b42b 100644
--- a/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - A basic understanding of cache coherency and its impact on performance.
     - Familiarity with Linux Perf tools.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md b/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md
index 5cab4a9eaf..88cadb4d67 100644
--- a/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - An AWS account with permissions to create EC2 instances
     - Familiarity with basic Linux administration and SSH
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md b/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md
index 18db4105be..75f4ee49fb 100644
--- a/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - Access to an [AWS Graviton4, Google Axion, or Azure Cobalt 100 virtual machine from a cloud service provider](/learning-paths/servers-and-cloud-computing/csp/)
     - Some familiarity with SIMD programming and SVE intrinsics
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md
index a7c81bd46e..653829b889 100644
--- a/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md
@@ -16,10 +16,10 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with [Apache Flink](https://flink.apache.org/) and its runtime environment
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/flink/_index.md b/content/learning-paths/servers-and-cloud-computing/flink/_index.md
index 0023452ef7..d6d3647570 100644
--- a/content/learning-paths/servers-and-cloud-computing/flink/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/flink/_index.md
@@ -14,10 +14,10 @@ learning_objectives:
 prerequisites:
 - An Arm based instance server from a cloud service provider.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md b/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md
index 320db60cc6..42bcfcebf6 100644
--- a/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md
@@ -19,10 +19,10 @@ prerequisites:
   - Basic understanding of machine learning pipelines
 
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
index 736aad4157..72496fa1e2 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
@@ -21,10 +21,10 @@ prerequisites:
     - 'C# Extension for Visual Studio Code: https://marketplace.visualstudio.com/items?itemName=ms-dotnettools.csharp'
     - '[Install Docker on Arm64](/install-guides/docker/docker-desktop/)'
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md
index 47b6aedf64..f58c172606 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - 'Azure subscription. Use this link to sign up for a free account: https://azure.microsoft.com/en-us/free/.'
     - 'Complete the [first learning path](/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1) of this series.'  
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
index db0d175fa0..ba46b2d871 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - 'Complete the [first](/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1) and [second](/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2) learning paths of this series.'
   
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md
index 19366a8775..192fa0e02b 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md
@@ -18,10 +18,10 @@ prerequisites:
     - 'Node.js (details provided in this learning path)'
     - 'Azure CLI (details provided in this learning path)'
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/funasr/_index.md b/content/learning-paths/servers-and-cloud-computing/funasr/_index.md
index 0569db89f1..4624551be0 100644
--- a/content/learning-paths/servers-and-cloud-computing/funasr/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/funasr/_index.md
@@ -14,10 +14,10 @@ learning_objectives:
 prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider, or a local Arm Linux computer with at least 8 CPUs and 16GB of RAM.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
index de973da3e8..9e287b042f 100644
--- a/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
@@ -18,10 +18,10 @@ prerequisites:
   - Basic familiarity with [Kubernetes](https://kubernetes.io/)
   - Familiarity with container concepts ([Docker](https://www.docker.com/))
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md b/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
index 79cc9db07b..8d1c46ed26 100644
--- a/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - An Arm Linux system (cloud instance, on-premises hardware, or a virtual machine)
     - A recent version of the [GCC toolchain](/install-guides/gcc/)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/gcp/_index.md
index 01b3db3d41..d39099a9ce 100644
--- a/content/learning-paths/servers-and-cloud-computing/gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gcp/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - A computer with [Terraform](/install-guides/terraform) installed.
     - A computer with [Google Cloud CLI](/install-guides/gcloud) installed.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md b/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
index d24f7f27b4..ee1be45070 100644
--- a/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
@@ -13,10 +13,10 @@ learning_objectives:
 prerequisites:
 - An Arm computer running Linux. You can use a cloud instance, refer to [Get started with Arm-based cloud instances](/learning-paths/servers-and-cloud-computing/csp/).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md b/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
index 0100c279c6..9d58723f1a 100644
--- a/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
@@ -18,10 +18,10 @@ prerequisites:
     - A Docker Hub account for storing container images.
     - Familiarity with the concepts of ML and continuous integration and deployment (CI/CD).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md b/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
index 000dd569b2..a7666041a3 100644
--- a/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
@@ -14,10 +14,10 @@ prerequisites:
     - An [Amazon Web Services account](/learning-paths/servers-and-cloud-computing/csp/aws/).
     - A GitHub account (personal or organizational).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md b/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md
index eb53e37f5c..7e958f02a2 100644
--- a/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md
@@ -15,10 +15,10 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free?utm_source=google&hl=en) account with billing enabled
   - A GitHub account; you can [sign up for GitHub](https://github.com/signup)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
index 9cdbb9e305..e46388d2f7 100644
--- a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - A local Linux or macOS computer with Docker, Kubernetes CLI (kubectl), Google Cloud CLI (gcloud), and Git installed, or access to Google Cloud Shell
     - Basic familiarity with Docker, Kubernetes, and gcloud
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
index 1db623b923..697c4ddb00 100644
--- a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - A computer with [Google Cloud CLI](/install-guides/gcloud) and [kubectl](/install-guides/kubectl/)installed.
     - An existing Google Kubernetes Engine (GKE) cluster with x86-based nodes
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/gke/_index.md b/content/learning-paths/servers-and-cloud-computing/gke/_index.md
index 8a4b3c8db4..e74d53e216 100644
--- a/content/learning-paths/servers-and-cloud-computing/gke/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gke/_index.md
@@ -13,10 +13,10 @@ prerequisites:
     - A Google Cloud account
     - A computer with the following tools installed`:` Terraform, Google Cloud CLI (gcloud), Kubernetes CLI (kubectl)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md b/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
index 17a24b09c8..d12177dbfb 100644
--- a/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
@@ -15,10 +15,10 @@ prerequisites:
 - An Arm based instance from a cloud service provider.
 - Review the learning path on [LSE](/learning-paths/servers-and-cloud-computing/lse/)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md b/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
index d1f9beacce..be2e97f36f 100644
--- a/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - A [Google Cloud account](https://console.cloud.google.com/). This Learning Path can be run on any cloud provider or on-premises, but it focuses on Google Cloud’s Axion Arm64-based instances.
     - A local machine with [Google Cloud CLI](/install-guides/gcloud/) installed
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md
index c8eaddbe32..d1a3d157cd 100644
--- a/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - Basic familiarity with the [Go programming language](https://go.dev/) and cloud deployment practices
     - Understanding of Linux command line and virtual machine management
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md
index e45a8a5c29..c24cc8ba1d 100644
--- a/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md
@@ -23,10 +23,10 @@ prerequisites:
   - Familiarity with basic Linux command-line usage
     
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/intro/_index.md b/content/learning-paths/servers-and-cloud-computing/intro/_index.md
index f51225ac02..9fec89077a 100644
--- a/content/learning-paths/servers-and-cloud-computing/intro/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/intro/_index.md
@@ -13,10 +13,10 @@ learning_objectives:
 prerequisites:
     - None
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md b/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
index 0367f2eaf9..e388716528 100644
--- a/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - An Arm computer running Linux
     - Some familiarity with the Linux command line
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md b/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
index 761409b4a7..53887cb314 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - Basic understanding of Java.
     - An [installation of Java](/install-guides/java/) on your machine.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
index dd1372a6ad..8e5b95e561 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
@@ -14,10 +14,10 @@ learning_objectives:
 prerequisites:
     - A [Google Cloud](https://cloud.google.com/) account with access to Axion based instances (C4A).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
index 477394f2b3..283846d89a 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - A [Microsoft Azure](https://azure.microsoft.com/) account with access to Cobalt 100 based instances (Dpsv6)
 
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
index c9c471b2e8..56e9508e4c 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
@@ -15,10 +15,10 @@ prerequisites:
   - Access to both Arm-based and x86-based computers running Ubuntu (you can use cloud-based server instances)
   - Basic familiarity with Java applications and performance profiling using flame graphs
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md b/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
index a63b2303da..4730be8821 100644
--- a/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
@@ -21,10 +21,10 @@ prerequisites:
     - Basic understanding of Linux command line
     - Familiarity with CI/CD concepts and [Jenkins fundamentals](https://www.jenkins.io/doc/book/pipeline/)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
index 076b321260..0e37ec09ab 100644
--- a/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - Basic understanding of Linux command line
     - Familiarity with the [Apache Kafka architecture](https://kafka.apache.org/) and deployment practices on Arm64 platforms
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/kafka/_index.md b/content/learning-paths/servers-and-cloud-computing/kafka/_index.md
index d930f84991..ffae3df504 100644
--- a/content/learning-paths/servers-and-cloud-computing/kafka/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kafka/_index.md
@@ -17,10 +17,10 @@ learning_objectives:
 prerequisites:
     - Seven physical Arm machines or cloud instances with either Ubuntu or Debian installed. 
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md b/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
index 3e61458755..bb3f6a3c19 100644
--- a/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
@@ -17,10 +17,10 @@ prerequisites:
   - Kubectl and Helm installed 
   - Access to the Kedify Service dashboard to obtain your Organization ID and API key (sign up at [Kedify dashboard](https://dashboard.kedify.io/))
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md b/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
index b2bb2cf9c4..9ce127a2ab 100644
--- a/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider, an on-premises Arm server, or a Linux virtual machine on your Arm device. 
     - Familiarity with SSH, the Linux command line, and basic system administration tasks.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md b/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
index 9146bff7f0..d60204d94a 100644
--- a/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
@@ -18,10 +18,10 @@ prerequisites:
     - Understanding of kernel images and modules
     - Familiarity with GRUB bootloader and initramfs
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md b/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
index 84c3ff6855..81e9299b13 100644
--- a/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
@@ -16,10 +16,10 @@ learning_objectives:
 prerequisites:
     - A running Kubernetes cluster accessible with `kubectl`.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md b/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
index c7a415a25a..79cfd87410 100644
--- a/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
@@ -14,10 +14,10 @@ prerequisites:
     - A computer with [Terraform](/install-guides/terraform/) and the [AWS CLI](/install-guides/aws-cli/) installed. 
     
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md b/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
index 7c848b8085..d67daf8d8e 100644
--- a/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - An Arm server or virtual machine instance from a cloud service provider with Ubuntu installed
     - Knowledge of how to build a MySQL server and run the sysbench benchmark test
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md b/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
index ad2a650f0d..f4ff7aab1d 100644
--- a/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
@@ -14,10 +14,10 @@ learning_objectives:
 prerequisites:
     - An AWS Graviton4 r8g.16xlarge instance to test Arm performance optimizations, or any [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an on-premise Arm server.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md b/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
index 0a5f4a0a3a..3f01b20bed 100644
--- a/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
@@ -19,10 +19,10 @@ prerequisites:
     - A basic understanding of Streamlit.
     - A basic understanding of LLM fundamentals.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
index 515eaa12f2..3a0e0872c0 100644
--- a/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
@@ -20,10 +20,10 @@ prerequisites:
     - Knowledge of Arm Streamline usage
     - An Arm Neoverse or Cortex-A hardware platform running Linux or Android
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/lse/_index.md b/content/learning-paths/servers-and-cloud-computing/lse/_index.md
index 76a00521c7..7c12568933 100644
--- a/content/learning-paths/servers-and-cloud-computing/lse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/lse/_index.md
@@ -14,10 +14,10 @@ learning_objectives:
 prerequisites:
     - An [AWS account](/learning-paths/servers-and-cloud-computing/csp/aws/) to access instance types with different AWS Graviton processors. If you don't have an AWS account, you can substitute other Arm Linux computers.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md b/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
index 14d3976fa6..e78dc571cd 100644
--- a/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - Cloud service provider accounts for each service you want to use including AWS, Azure, and GCP
     - A local computer with [Docker](/install-guides/docker/), [Terraform](/install-guides/terraform/), [AWS CLI](/install-guides/aws-cli/), [Azure CLI](/install-guides/azure-cli/), [Google Cloud CLI](/install-guides/gcloud/), and [Ansible](/install-guides/ansible/) installed
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/memcached/_index.md b/content/learning-paths/servers-and-cloud-computing/memcached/_index.md
index 5c7032072e..fd08bd8c08 100644
--- a/content/learning-paths/servers-and-cloud-computing/memcached/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memcached/_index.md
@@ -14,10 +14,10 @@ learning_objectives:
 prerequisites:
 - An Arm based instance from an appropriate cloud service provider.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md b/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
index 460314a406..418ba36db9 100644
--- a/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
@@ -18,10 +18,10 @@ prerequisites:
 - A Google Cloud [account](https://console.cloud.google.com/)
 - A machine with [Terraform](/install-guides/terraform/), [AWS CLI](/install-guides/aws-cli), [Google Cloud CLI](/install-guides/gcloud), [Azure CLI](/install-guides/azure-cli), [AWS IAM authenticator](https://docs.aws.amazon.com/eks/latest/userguide/install-aws-iam-authenticator.html), and [Ansible](/install-guides/ansible/) installed
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md b/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
index 7aa8ce979b..4db10435b4 100644
--- a/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
@@ -19,10 +19,10 @@ prerequisites:
     - Arm System Characterization Tool (ASCT) installed on each system
     - A good understanding of CPU memory subsystems, including cache hierarchies, cache lines, and DRAM in the memory hierarchy
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md b/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
index fb24945d52..68361c3709 100644
--- a/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
@@ -19,10 +19,10 @@ prerequisites:
     - Familiarity with general-purpose registers.
     - Familiarity with memory barriers, including Acquire-Release Semantics.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md b/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
index 6fa4fd1775..59ba1c9499 100644
--- a/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
@@ -14,10 +14,10 @@ prerequisites:
     - Basic understanding of networking principles such as Transmission Control Protocol/Internet Protocol (TCP/IP) and User Datagram Protocol (UDP).
     - Access to two [Arm-based cloud instances](/learning-paths/servers-and-cloud-computing/csp/).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md b/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
index cec0d389c1..e6c765466a 100644
--- a/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
@@ -16,10 +16,10 @@ learning_objectives:
 prerequisites:
     - Access to an [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) for testing and validation.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/migration/_index.md b/content/learning-paths/servers-and-cloud-computing/migration/_index.md
index ca8ab8960a..10bae98514 100644
--- a/content/learning-paths/servers-and-cloud-computing/migration/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/migration/_index.md
@@ -16,10 +16,10 @@ learning_objectives:
 prerequisites:
     - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
index 98b29abdf0..4cab9758e5 100644
--- a/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - An AWS Graviton3 C7g.2xlarge instance, or any [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp) from a cloud service provider or an on-premise Arm server.
     - A [Zilliz account](https://zilliz.com/cloud?utm_source=partner&utm_medium=referral&utm_campaign=2024-10-24_web_arm-dev-hub-data-loading_arm), which you can sign up for with a free trial.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
index 26eb6776c2..2f65426b9c 100644
--- a/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
@@ -18,10 +18,10 @@ prerequisites:
   - Familiarity with SSH and remote server access
   - Basic understanding of cloud storage concepts
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md b/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
index dc778d7039..d4a30c2f97 100644
--- a/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
@@ -15,10 +15,10 @@ prerequisites:
 - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from an appropriate
   cloud service provider or an on-premise Arm server.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
index e148cbdae3..ada73fca78 100644
--- a/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - A [Microsoft Azure](https://azure.microsoft.com/) account with access to Cobalt 100 (Dpsv6) instances
     - Familiarity with the [MongoDB architecture](https://www.mongodb.com/) and deployment practices on Arm64 platforms
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
index 8ede273c4e..cfac2beeae 100644
--- a/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
@@ -15,10 +15,10 @@ learning_objectives:
 prerequisites:
      - A [Google Cloud Platform (GCP)](https://cloud.google.com/free?utm_source=google&hl=en) account with billing enabled
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md b/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
index 76be244668..692be7cce7 100644
--- a/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
@@ -1,10 +1,10 @@
 ---
 title: Analyze the performance of MongoDB on Arm servers
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/mpi/_index.md b/content/learning-paths/servers-and-cloud-computing/mpi/_index.md
index 55b5c6f186..10fc8181fc 100644
--- a/content/learning-paths/servers-and-cloud-computing/mpi/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mpi/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - Some understanding of C, Python, and Linux commands
     - An Arm computer running Linux. Cloud instances can be used, refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md b/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md
index 7afbde272b..929df63977 100644
--- a/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md
@@ -3,10 +3,10 @@ title: Control floating-point accuracy modes in Arm Performance Libraries
 
 minutes_to_complete: 20
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md b/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md
index d9bdafc6fa..6f74c330d4 100644
--- a/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md
@@ -18,10 +18,10 @@ prerequisites:
     - A local machine with [`jq`](https://jqlang.org/download/), [`curl`](https://curl.se/download.html), [`wrk`](https://github.com/wg/wrk), [Azure CLI](/install-guides/azure-cli/), and [`kubectl`](/install-guides/kubectl/) installed
    
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md b/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md
index 3a3f354a69..4154204734 100644
--- a/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - The [GKE Cloud Plugin](https://cloud.google.com/kubernetes-engine/docs/how-to/cluster-access-for-kubectl#gcloud) installed.
    
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md
index b247d4baba..f5f8694df3 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - A [Microsoft Azure](https://azure.microsoft.com/) account with access to Cobalt 100 based instances (Dpsv6)
     - Familiarity with relational databases and the basics of [MySQL](https://dev.mysql.com/doc/refman/8.0/en/introduction.html)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql-lns-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql-lns-azure/_index.md
index fe2ef8e769..1ad0e6879b 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql-lns-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql-lns-azure/_index.md
@@ -21,9 +21,9 @@ prerequisites:
     - Basic familiarity with SSH and MySQL command-line tools
 
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
+rerun_summary: true
 rerun_faqs: true
 author: Doug Anson
 
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql/_index.md
index 6eba9e6668..4c8d3b53ce 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql/_index.md
@@ -13,10 +13,10 @@ prerequisites:
     - An Arm based instance from a cloud service provider, or an on-premise Arm server.
     - If you do not have an Arm node, the next section discusses some options.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md
index 51100a1ca4..634412529a 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md
@@ -13,10 +13,10 @@ prerequisites:
     - Basic knowledge of [MySQL databases](https://www.mysql.com/)
     - Two Arm servers running Ubuntu 22.04, one for the MySQL server and the other for the Sysbench client
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md
index 2f6857c85a..eb2b4aaae3 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md
@@ -11,10 +11,10 @@ learning_objectives:
 prerequisites:
     - Bare-metal or cloud [installation of MySQL](/learning-paths/servers-and-cloud-computing/mysql/)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md b/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md
index 2b5962ac09..06405c6774 100644
--- a/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md
@@ -19,10 +19,10 @@ prerequisites:
     - Understanding of firmware boot stages and SoC-level architecture
     - Docker installed, or a GitHub Codespaces-compatible development environment
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md b/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md
index 7aa1c11463..b8e0fe8832 100644
--- a/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from AWS or GCP.
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is an example of a suitable editor.    
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md
index 8b23345645..66656b22ff 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md
@@ -15,10 +15,10 @@ learning_objectives:
 prerequisites:
     - A [Microsoft Azure](https://azure.microsoft.com/) account with access to Cobalt 100 based instances (Dpsv6)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx/_index.md
index 4fe85d0343..5b3cd197d8 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - To create a reverse proxy or API gateway you will need at least three Arm based instances from a cloud service provider or at least three on-premises Arm servers.
     - Network settings (firewalls and security groups) which allow communication on port 22 (SSH) and port 443 (HTTPS).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
index dcc7ec0cd2..4a493e4f34 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
@@ -16,9 +16,9 @@ prerequisites:
     - A cloud or bare-metal installation of a Nginx file server or load balancer.
     - If you do not already have a Nginx setup, a review of [Learn how to deploy Nginx](/learning-paths/servers-and-cloud-computing/nginx/).
 
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
+generate_summary_faq: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md b/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md
index d9bfe9247a..f227fa4099 100644
--- a/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md
@@ -12,10 +12,10 @@ learning_objectives:
 prerequisites:
     - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an on-premise Arm server.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md
index 0294f070d1..fe2466d6e0 100644
--- a/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md
@@ -19,10 +19,10 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Familiarity with networking concepts and [Node.js event-driven architecture](https://nodejs.org/en/docs/guides/event-loop-timers-and-nexttick)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md b/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md
index 6594009561..0376a8c7a7 100644
--- a/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md
@@ -12,10 +12,10 @@ prerequisites:
     - An OCI account
     - A computer with Terraform installed
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md
index a2e706276c..365ca25e09 100644
--- a/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - Basic understanding of Python and machine learning concepts
     - Familiarity with [ONNX Runtime](https://onnxruntime.ai/docs/) and Azure cloud services
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/onnx/_index.md b/content/learning-paths/servers-and-cloud-computing/onnx/_index.md
index 0fce1cd121..154783ed35 100644
--- a/content/learning-paths/servers-and-cloud-computing/onnx/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/onnx/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - Knowledge of Large Language Model (LLM) fundamentals.
 
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md b/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md
index 90666bdb39..0ac502909c 100644
--- a/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md
@@ -18,10 +18,10 @@ prerequisites:
   - Working knowledge of Docker, Git, and common Linux terminal tools
   - Basic understanding of the server firmware stack (such as UEFI, BMC, and TF-A)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md b/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md
index 120706ca79..f562ee9494 100644
--- a/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - An Arm Linux (aarch64) server, such as an AWS Graviton3 instance
     - Basic understanding of C++ and CMake
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/openshift/_index.md b/content/learning-paths/servers-and-cloud-computing/openshift/_index.md
index c515155947..5bcea5ed98 100644
--- a/content/learning-paths/servers-and-cloud-computing/openshift/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openshift/_index.md
@@ -14,10 +14,10 @@ prerequisites:
   - Familiarity with the `oc` CLI, container fundamentals, and basic Tekton concepts (Task, Pipeline, PipelineRun)
   - Cluster access with cluster-admin or equivalent permissions to configure nodes and pipelines
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md
index 6ca4afe920..27c6917cd9 100644
--- a/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md
@@ -21,10 +21,10 @@ prerequisites:
   - Familiarity with SSH and remote server access
   - Basic understanding of cloud computing and virtualization concepts
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md b/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md
index b7c83349cf..3c32b8a08e 100644
--- a/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md
@@ -16,10 +16,10 @@ prerequisites:
   - Basic familiarity with Python and Flask
   - Basic understanding of containers and Kubernetes concepts
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/pac/_index.md b/content/learning-paths/servers-and-cloud-computing/pac/_index.md
index 178f596184..9f85efca7d 100644
--- a/content/learning-paths/servers-and-cloud-computing/pac/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pac/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - If needed, review [Get started with Arm-based cloud instances](/learning-paths/servers-and-cloud-computing/csp/) to learn how to deploy Arm in the cloud. These learning paths also point to more advanced learning paths that show how to automate the deployment of Arm instances at different cloud providers.
     
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md b/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md
index 97caf0d6af..495e6fb009 100644
--- a/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md
@@ -19,10 +19,10 @@ prerequisites:
     - Access to Arm Performix configured with the remote Arm target. See the [Arm Performix install guide](/install-guides/performix/) for setup instructions
     - Basic understanding of C++
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md b/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md
index d06308cfd1..74313af587 100644
--- a/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - Familiarity with Linux command line
     - Basic understanding of CPU performance concepts
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/performix-system-characterization/_index.md b/content/learning-paths/servers-and-cloud-computing/performix-system-characterization/_index.md
index a3fb62fe10..676a9a13ef 100644
--- a/content/learning-paths/servers-and-cloud-computing/performix-system-characterization/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/performix-system-characterization/_index.md
@@ -19,9 +19,9 @@ learning_objectives:
 prerequisites:
     - A Arm Linux target machine accessible via SSH to characterize.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
+rerun_summary: true
 rerun_faqs: true
 author:
 - Brendan Long
diff --git a/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md
index 3c600d1fe3..dcabef896d 100644
--- a/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md
@@ -17,10 +17,10 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with web servers and PHP scripting
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md b/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md
index 9b52accce6..dfdd79e971 100644
--- a/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - Understanding of build systems and computer architecture concepts
     - Familiarity with Linux command-line tools
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md b/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md
index bb326211d5..b2482b0b53 100644
--- a/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - An Arm-based computer running Linux.
     - Some familiarity with Linux application performance analysis.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md
index 8c0445f7df..48d6c9d1fa 100644
--- a/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md
@@ -18,10 +18,10 @@ prerequisites:
   - Familiarity with SSH and remote server access
   - Basic understanding of databases and SQL
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md b/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md
index b307f0e9c2..446d231973 100644
--- a/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md
@@ -13,10 +13,10 @@ prerequisites:
     - An Arm based instance from a cloud service provider, or an on-premise Arm server.
     - If you do not have an Arm node, the next section discusses some options.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md
index 1d53d53ced..c3f973c681 100644
--- a/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md
@@ -11,10 +11,10 @@ learning_objectives:
 prerequisites:
     - Bare-metal or cloud [installation of PostgreSQL](/learning-paths/servers-and-cloud-computing/postgresql/)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md b/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
index 57b8a4474e..a0022257b7 100644
--- a/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
@@ -13,10 +13,10 @@ prerequisites:
     - An Arm-based system (bare metal server, cloud instance, or developer board) running Linux with kernel version 5.8.0 or later.
     - Root access, or the ability to run the sudo command.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md b/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md
index 858c9537a7..f46af0aa79 100644
--- a/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md
@@ -12,10 +12,10 @@ learning_objectives:
 prerequisites:
     - An Arm Neoverse-based (N1, N2 or V1) computer running Linux. For your host OS, you can use Amazon Linux 2023 or newer, Debian 10 or newer, RHEL 8 or newer, or Ubuntu 20.04 or newer.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md
index 888ea80b2c..e0d7990d46 100644
--- a/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md
@@ -11,10 +11,10 @@ learning_objectives:
   - Verify Puppet by applying a test manifest and confirming successful resource creation on Arm64  
   - Benchmark Puppet by measuring catalog compile time, apply speed, and resource usage on Arm64
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md b/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md
index 6d0c5ac397..1e570eed2e 100644
--- a/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md
@@ -15,10 +15,10 @@ learning_objectives:
 prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) with at least 16 CPUs from a cloud service provider or an on-premise Arm server.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md
index 7393d3b4b6..8e79c1e3c5 100644
--- a/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md
@@ -19,10 +19,10 @@ prerequisites:
   - Basic understanding of machine learning embeddings
   - Familiarity with Linux command-line operations
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md
index cfb1e007c0..87f0ac2755 100644
--- a/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md
@@ -19,10 +19,10 @@ prerequisites:
   - Basic understanding of message queues and messaging concepts (publishers, consumers)
   - Familiarity with Linux command-line operations
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/rag/_index.md b/content/learning-paths/servers-and-cloud-computing/rag/_index.md
index 035925c70d..741458a1ee 100644
--- a/content/learning-paths/servers-and-cloud-computing/rag/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rag/_index.md
@@ -19,10 +19,10 @@ prerequisites:
     - Basic knowledge of vector databases.
     - Understanding of LLM fundamentals.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/ran/_index.md b/content/learning-paths/servers-and-cloud-computing/ran/_index.md
index 3d0dd9a9ad..a375e8cf13 100644
--- a/content/learning-paths/servers-and-cloud-computing/ran/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ran/_index.md
@@ -15,10 +15,10 @@ prerequisites:
 - An Arm computer running Linux. Cloud instances can be used, refer to the list of
   [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md
index 59b861babb..6879dff642 100644
--- a/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md
@@ -16,10 +16,10 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with Python and distributed systems concepts
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md
index e4abc2e5d5..dd5461ea90 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md
@@ -18,10 +18,10 @@ prerequisites:
   - Familiarity with SSH and remote server access
   - Basic understanding of databases, caching, and messaging systems
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md b/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md
index f9f7864368..f43aa5c61a 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md
@@ -15,10 +15,10 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with [Redis](https://redis.io/) 
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/redis/_index.md b/content/learning-paths/servers-and-cloud-computing/redis/_index.md
index 0fddb8dd9c..a6b62bd0a9 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis/_index.md
@@ -14,10 +14,10 @@ prerequisites:
     - An Arm based instance from a cloud service provider, or an on-premise Arm server.
     - If you do not have an Arm node, the next section discusses some options.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md b/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md
index 54f2cb1c41..3c0e011d32 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - A Google Cloud [account](https://console.cloud.google.com/)
     - A machine with [Terraform](/install-guides/terraform/), [AWS CLI](/install-guides/aws-cli), [Google Cloud CLI](/install-guides/gcloud), [Azure CLI](/install-guides/azure-cli), [AWS IAM authenticator](https://docs.aws.amazon.com/eks/latest/userguide/install-aws-iam-authenticator.html), and [Ansible](/install-guides/ansible/) installed
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md
index f1de4fd8c0..ed0bfe7782 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md
@@ -14,10 +14,10 @@ prerequisites:
     - Cloud or bare-metal installation of an Redis file server
     - Review [Learn how to deploy Redis](/learning-paths/servers-and-cloud-computing/redis/) if you do not already have Redis setup
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md b/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md
index 08c0fb1f00..c349cdf04a 100644
--- a/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - A Fixed Virtual Platform (FVP).
     - A basic understanding of Neoverse Reference Design (RD) platform boot.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md b/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md
index 671032a54b..a1a7bb3b85 100644
--- a/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - Some understanding of the Linux command line.
     - Optionally a basic understanding of Docker and containers.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md b/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md
index 50ef8547c6..6b3b5d6c3a 100644
--- a/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md
@@ -3,10 +3,10 @@ title: Enable reproducible math functions across vector extensions with Arm Perf
 
 minutes_to_complete: 10
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md b/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md
index f1026074e7..4acb216984 100644
--- a/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md
@@ -14,10 +14,10 @@ prerequisites:
     - An aarch64 or x86_64 computer running Ubuntu 22.04. Cloud instances can be used, refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
     - If you use a client application to access your computer running Ubuntu, make sure that X11 forwarding is enabled.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md b/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md
index 59a0b1f0aa..79f98b396b 100644
--- a/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md
@@ -14,10 +14,10 @@ prerequisites:
     - Any Arm Neoverse N2-based or Arm Neoverse V2-based instance running Ubuntu 22.04 LTS from a cloud service provider or an on-premise Arm server. 
     - For the server, at least four cores and 16GB of RAM, with disk storage configured up to at least 32 GB. 
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md b/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md
index f36fac898d..b34974c5b3 100644
--- a/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md
@@ -16,10 +16,10 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with Ruby programming, the Rails framework, and the [PostgreSQL Relational Database](https://www.postgresql.org/)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md
index c1cd93d7ba..a370274b62 100644
--- a/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md
@@ -17,10 +17,10 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled  
   - Basic familiarity with [Rust](https://www.rust-lang.org/)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md b/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md
index 3d58d75bec..aa02f9ae8c 100644
--- a/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md
@@ -14,10 +14,10 @@ prerequisites:
     - An AWS account.
     - A computer with Docker, Terraform, the Amazon eksctl command-line interface, and kubectl installed.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md
index a4cc0c84f8..6ad39daf30 100644
--- a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md
@@ -13,10 +13,10 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).   
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is suitable.    
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md
index 834704c106..d7368f606a 100644
--- a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md
@@ -14,10 +14,10 @@ prerequisites:
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is suitable.
     - Completion of [Deploy AWS services using the Serverless Framework](/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md
index f52a6fac91..9c3333251a 100644
--- a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md
@@ -14,10 +14,10 @@ prerequisites:
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is suitable.
     - Completion of the Learning Path that shows you how to [Deploy AWS services using the Serverless Framework](/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/snappy/_index.md b/content/learning-paths/servers-and-cloud-computing/snappy/_index.md
index dd4fcc2ae2..5948dd8f82 100644
--- a/content/learning-paths/servers-and-cloud-computing/snappy/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/snappy/_index.md
@@ -15,10 +15,10 @@ prerequisites:
 - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from an appropriate
   cloud service provider.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md b/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md
index ce49107ebe..766a68dade 100644
--- a/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md
@@ -15,10 +15,10 @@ prerequisites:
     - A basic understanding of Snort's operation and configuration.
     
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md
index 4c025b5c10..6041f80d59 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md
@@ -16,10 +16,10 @@ prerequisites:
     - A machine with [Docker](/install-guides/docker/) installed
     - Familiarity with distributed computing concepts and the [Apache Spark architecture](https://spark.apache.org/docs/latest/)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md
index a0d3a4d602..0e7c0c3c37 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md
@@ -15,10 +15,10 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free?utm_source=google&hl=en) account with billing enabled
   - Familiarity with distributed computing concepts and the [Apache Spark architecture](https://spark.apache.org/docs/latest/)
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/spark-velox-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/spark-velox-cobalt/_index.md
index 0a9f182832..13cb5aa9d5 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark-velox-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark-velox-cobalt/_index.md
@@ -22,9 +22,9 @@ prerequisites:
   - Familiarity with SSH and remote server access
   - Basic understanding of distributed systems and Apache Spark
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
+rerun_summary: true
 rerun_faqs: true
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/spark/_index.md b/content/learning-paths/servers-and-cloud-computing/spark/_index.md
index 3d0d3dd8e5..144abf9ee5 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark/_index.md
@@ -13,10 +13,10 @@ prerequisites:
     - An Amazon Web Services (AWS) [account](https://aws.amazon.com/)
     - A machine with [Terraform](/install-guides/terraform/), [AWS CLI](/install-guides/aws-cli/), [AWS IAM authenticator](https://docs.aws.amazon.com/eks/latest/userguide/install-aws-iam-authenticator.html), and [Ansible](/install-guides/ansible/) installed 
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md b/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md
index a01f141db8..cc3b0f955d 100644
--- a/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md
@@ -14,10 +14,10 @@ prerequisites:
     - An AWS account
     - A Remote.It account
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/sve/_index.md b/content/learning-paths/servers-and-cloud-computing/sve/_index.md
index bca38e3f30..ea7b608988 100644
--- a/content/learning-paths/servers-and-cloud-computing/sve/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sve/_index.md
@@ -14,10 +14,10 @@ prerequisites:
     - General knowledge about SIMD processing, vectorization or Arm Neon.
     - An Arm computer running Linux. Cloud instances can be used, refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md b/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md
index c6609ed182..726646bd46 100644
--- a/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md
@@ -16,10 +16,10 @@ learning_objectives:
 prerequisites:
 - Access to an [AWS Graviton4, Google Axion, or Azure Cobalt 100 virtual machine](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md b/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md
index ae397f6521..0b96fccabd 100644
--- a/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md
@@ -13,10 +13,10 @@ learning_objectives:
 prerequisites:
     - An Arm-based system (bare metal server, cloud instance, developer board) running Linux 
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
index cb53ebab9b..8dcb92efe6 100644
--- a/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
@@ -14,9 +14,9 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with [TensorFlow](https://www.tensorflow.org/)
 
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
+generate_summary_faq: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md b/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md
index cfcab64e41..1443563bbb 100644
--- a/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md
@@ -12,10 +12,10 @@ learning_objectives:
 prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an on-premise Arm server.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md
index c3691845c5..b9812fa63d 100644
--- a/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md
@@ -15,10 +15,10 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with SQL, Python, and Grafana
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md b/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md
index 1e6d815c48..776116438b 100644
--- a/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md
@@ -14,10 +14,10 @@ learning_objectives:
 prerequisites:
     - An Arm Neoverse N1 computer running Linux. A bare metal or cloud metal instance is best because they expose more counters. You can use a virtual machine (VM), but it may offer fewer counters and some commands might not succeed. These instructions have been tested on the `a1.metal` instance type.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md b/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md
index 703f4ddd7b..e72f409c13 100644
--- a/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md
@@ -13,10 +13,10 @@ learning_objectives:
 prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an on-premise Arm server.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md
index ff6beb1819..51e124d22c 100644
--- a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md
@@ -13,10 +13,10 @@ prerequisites:
     - Some familiarity with performance analysis.
     - The ability to read Arm assembly code.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md
index 088f29af3a..44357354e3 100644
--- a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md
@@ -14,10 +14,10 @@ prerequisites:
     - Knowledge of performance analysis. 
     - The ability to read Arm assembly code.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md b/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md
index a7cc8cff4a..803f8a0aa6 100644
--- a/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md
@@ -17,10 +17,10 @@ prerequisites:
   - Basic knowledge of Linux command-line operations
   - Familiarity with GitHub Actions runners
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md b/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md
index 8eeb67d44a..988c9e96a1 100644
--- a/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - Access to an x86_64 bare-metal server running Ubuntu 24.04 to run `wrk2`
     - Basic familiarity with Java applications
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md
index 5138b15aa8..5421336c50 100644
--- a/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md
@@ -16,10 +16,10 @@ prerequisites:
   - Basic familiarity with [TypeScript](https://www.typescriptlang.org/) and Node.js runtime environment
 
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md b/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md
index 2deb32107f..fdfc3c7e9d 100644
--- a/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md
@@ -13,10 +13,10 @@ prerequisites:
     - Access to both an Arm and an x86-based cloud instance.
     - Intermediate understanding of C++, compilers, and Linux. 
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/vLLM-quant/_index.md b/content/learning-paths/servers-and-cloud-computing/vLLM-quant/_index.md
index a9e434c645..656e026c8b 100644
--- a/content/learning-paths/servers-and-cloud-computing/vLLM-quant/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vLLM-quant/_index.md
@@ -22,9 +22,9 @@ prerequisites:
     - Familiarity with Python and basic understanding of transformer models and quantization techniques.
     - An active Hugging Face account with access to the target model.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
+rerun_summary: true
 rerun_faqs: true
 author: 
    - Rani Chowdary Mandepudi
diff --git a/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md b/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md
index b7fed74a39..b0a302ceab 100644
--- a/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md
@@ -14,10 +14,10 @@ learning_objectives:
 prerequisites:
     - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an Arm server with Ubuntu 20.04 or Ubuntu 22.04 installed.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md b/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
index 7851256336..45b7d358cd 100644
--- a/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
@@ -17,10 +17,10 @@ prerequisites:
     - An Arm-based Linux server (Ubuntu 22.04+ recommended) with a minimum of 32 vCPUs, 64 GB RAM, and 64 GB free disk space
     - Python 3.12 and basic familiarity with Hugging Face Transformers and quantization
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/vllm/_index.md b/content/learning-paths/servers-and-cloud-computing/vllm/_index.md
index 4d1338a5bd..2f1d4bba34 100644
--- a/content/learning-paths/servers-and-cloud-computing/vllm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vllm/_index.md
@@ -14,10 +14,10 @@ learning_objectives:
 prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider, or a local Arm Linux computer with at least 8 CPUs and 16 GB RAM.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md b/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
index 528ef80f3c..276df168a4 100644
--- a/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
@@ -1,10 +1,10 @@
 ---
 title: Run the vvenc H.266 encoder on Arm servers
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/whisper/_index.md b/content/learning-paths/servers-and-cloud-computing/whisper/_index.md
index 28575b7e44..c0359a1576 100644
--- a/content/learning-paths/servers-and-cloud-computing/whisper/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/whisper/_index.md
@@ -18,10 +18,10 @@ prerequisites:
     - Familiarity with machine learning concepts.
     - Familiarity with the fundamentals of the Whisper ASR Model.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md b/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
index 449d80f668..565103b6dd 100644
--- a/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
@@ -7,10 +7,10 @@ prerequisites:
     - An OCI account
     - An Arm compute instance deployed on OCI with Oracle Linux
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/content/learning-paths/servers-and-cloud-computing/zlib/_index.md b/content/learning-paths/servers-and-cloud-computing/zlib/_index.md
index db5518ad9a..0357ddeb71 100644
--- a/content/learning-paths/servers-and-cloud-computing/zlib/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/zlib/_index.md
@@ -15,10 +15,10 @@ learning_objectives:
 prerequisites:
 - An Arm Linux computer or an [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider running Ubuntu 22.04 or Ubuntu 24.04.
 
-generate_summary_faq: true
+generate_summary_faq: false
 
-rerun_summary: false
-rerun_faqs: false
+rerun_summary: true
+rerun_faqs: true
 
 # START generated_summary_faq
 generated_summary_faq:
diff --git a/tools/generate-summary-faq.md b/tools/generate-summary-faq.md
index 21266724e9..fccd4fe7d5 100644
--- a/tools/generate-summary-faq.md
+++ b/tools/generate-summary-faq.md
@@ -100,19 +100,36 @@ Set it to `false` to leave a Learning Path out of generated summary/FAQ runs:
 generate_summary_faq: false
 ```
 
+After a successful write run, the tool resets `generate_summary_faq` to `false`
+for every processed Learning Path. This keeps future runs from reprocessing
+content unless a contributor intentionally opts the path in again.
+
 The `rerun_summary` and `rerun_faqs` fields are separate controls. For a
 Learning Path that already has generated summary/FAQ content, both fields can
 stay `false`; the tool will report the path as unchanged and will not send that
 Learning Path to the LLM again.
 
 Set one or both rerun flags to force regeneration for an existing generated
-section, then the tool resets the flag to `false` after a write run:
+section. After a successful write run, the tool resets both rerun flags to
+`false`:
 
 ```yaml
 rerun_summary: true
 rerun_faqs: true
 ```
 
+New Learning Paths scaffolded from `archetypes/learning-path/_index.md` start
+with:
+
+```yaml
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+```
+
+If you create a Learning Path by copying an existing folder, confirm these
+three fields manually in the copied `_index.md`.
+
 The LLM is called only when at least one section needs work:
 
 ```text
@@ -202,7 +219,7 @@ reports/generated-summary-faq/all-learning-paths-test/run.yml
 reports/generated-summary-faq/all-learning-paths-test/run.md
 ```
 
-Open the `.md` file locally to review the same table-style overview used by the GitHub Action summary:
+Open the `.md` file locally to review the table-style run overview:
 
 ```bash
 open reports/generated-summary-faq/servers-and-cloud-computing.md
diff --git a/tools/generate_summary_faq.py b/tools/generate_summary_faq.py
index f2077cbd98..9f85f5897c 100644
--- a/tools/generate_summary_faq.py
+++ b/tools/generate_summary_faq.py
@@ -65,12 +65,20 @@
 LEARNING_PATH_ROOT = REPO_ROOT / "content" / "learning-paths"
 DEFAULT_REPORT_PATH = REPO_ROOT / "reports" / "generated-summary-faq" / "latest-run.yml"
 DEFAULT_PROMPT_DIR = REPO_ROOT / "tools" / "prompts"
+COPILOT_INSTRUCTIONS_PATH = REPO_ROOT / ".github" / "copilot-instructions.md"
+LEARNING_PATH_AUTHORING_GUIDE_DIR = LEARNING_PATH_ROOT / "cross-platform" / "_example-learning-path"
 DEFAULT_OPENAI_BASE_URL = "https://openai-api-proxy.geo.arm.com/api/providers/openai/v1/responses/"
 DEFAULT_OPENAI_MODEL = "gpt-4.1-mini"
 DEFAULT_OPENAI_TIMEOUT = 120
 DEFAULT_OPENAI_RETRIES = 2
 DEFAULT_PROMPT_STEP_LIMIT = 8
 DEFAULT_PROMPT_EXCERPT_CHARS = 900
+AUTHORING_GUIDANCE_FILES = (
+    (COPILOT_INSTRUCTIONS_PATH, 3200),
+    (LEARNING_PATH_AUTHORING_GUIDE_DIR / "overview.md", 1800),
+    (LEARNING_PATH_AUTHORING_GUIDE_DIR / "write-2-metadata.md", 2200),
+    (LEARNING_PATH_AUTHORING_GUIDE_DIR / "appendix-2-writing-style.md", 2600),
+)
 
 ENABLE_FLAG = "generate_summary_faq"
 RERUN_SUMMARY_FLAG = "rerun_summary"
@@ -107,6 +115,7 @@
     "generator_changed",
     "summary_drift_detected",
     "faq_drift_detected",
+    "generation_flags_reset",
     "rerun_flags_reset",
 )
 CHANGE_ACTIONS = {"created", "repaired_missing", "rerun_requested", "generator_changed"}
@@ -262,10 +271,10 @@ def parse_args() -> argparse.Namespace:
         default=DEFAULT_HISTORY_LIMIT,
         help="Maximum number of historical run entries to retain in the central report file.",
     )
-    parser.add_argument("--run-url", default="", help="Optional GitHub Actions run URL to store in the report.")
+    parser.add_argument("--run-url", default="", help="Optional run URL to store in the report.")
     parser.add_argument("--git-ref", default="", help="Optional Git ref or branch name to store in the report.")
     parser.add_argument("--git-sha", default="", help="Optional commit SHA to store in the report.")
-    parser.add_argument("--actor", default="", help="Optional workflow actor to store in the report.")
+    parser.add_argument("--actor", default="", help="Optional run actor to store in the report.")
     parser.add_argument(
         "--quiet-progress",
         action="store_true",
@@ -509,12 +518,44 @@ def prompt_steps(
     return prompt_pages[:safe_step_limit]
 
 
+def strip_front_matter_for_guidance(text: str) -> str:
+    match = re.match(r"\A---\s*\n.*?\n---\s*\n?(.*)\Z", text, re.DOTALL)
+    return match.group(1) if match else text
+
+
+def guidance_excerpt(path: Path, char_limit: int) -> Dict[str, str]:
+    if not path.exists():
+        return {
+            "path": report_path_for_output(path),
+            "excerpt": "",
+        }
+
+    text = strip_front_matter_for_guidance(path.read_text(encoding="utf-8"))
+    text = re.sub(r"{{%[^%]*%}}", "", text)
+    excerpt = compact_whitespace(strip_markdown_links(text))
+    if len(excerpt) > char_limit:
+        excerpt = excerpt[:char_limit].rstrip() + "..."
+
+    return {
+        "path": report_path_for_output(path),
+        "excerpt": excerpt,
+    }
+
+
+def build_authoring_guidance_context() -> List[Dict[str, str]]:
+    return [
+        guidance_excerpt(path, char_limit)
+        for path, char_limit in AUTHORING_GUIDANCE_FILES
+    ]
+
+
 def build_learning_path_prompt_context(
     metadata: Dict[str, Any],
     steps: Sequence[StepPage],
     args: argparse.Namespace,
 ) -> Dict[str, Any]:
     return {
+        "authoring_guidance": build_authoring_guidance_context(),
         "metadata": prompt_metadata(metadata),
         "steps": prompt_steps(
             steps,
@@ -1064,6 +1105,7 @@ def build_run_report(
         "ai_requests": 0,
         "summary_changed": 0,
         "faq_changed": 0,
+        "generation_flags_reset": 0,
         "rerun_flags_reset": 0,
     }
     section_totals = {
@@ -1098,6 +1140,10 @@ def build_run_report(
         if result.get("ai_requested"):
             totals["ai_requests"] += 1
 
+        generation_flags_reset = result.get("generation_flags_reset", [])
+        if generation_flags_reset:
+            totals["generation_flags_reset"] += 1
+
         rerun_flags_reset = result.get("rerun_flags_reset", [])
         if rerun_flags_reset:
             totals["rerun_flags_reset"] += 1
@@ -1205,6 +1251,7 @@ def render_markdown_report(run_report: Dict[str, Any]) -> str:
         markdown_metric_row("AI requests", totals.get("ai_requests", 0)),
         markdown_metric_row("Summary changed", totals.get("summary_changed", 0)),
         markdown_metric_row("FAQs changed", totals.get("faq_changed", 0)),
+        markdown_metric_row("Generation flags reset", totals.get("generation_flags_reset", 0)),
         markdown_metric_row("Rerun flags reset", totals.get("rerun_flags_reset", 0)),
         "",
     ]
@@ -1343,6 +1390,12 @@ def print_result_summary(args: argparse.Namespace, run_report: Dict[str, Any]) -
         f"{faq_actions['generator_changed']} generator_changed, "
         f"{faq_actions['drift_detected_preserved']} drift_detected_preserved."
     )
+    emit(
+        args,
+        "Flag resets: "
+        f"{totals.get('generation_flags_reset', 0)} generation flag sets reset, "
+        f"{totals.get('rerun_flags_reset', 0)} rerun flag sets reset."
+    )
 
     for result in run_report["paths"]:
         status = result["status"]
@@ -1509,11 +1562,18 @@ def process_learning_path(index_path: Path, args: argparse.Namespace) -> Dict[st
         faq_drift_detected = faq_action == "drift_detected_preserved"
 
         managed_block_updated = existing_generated is None or summary_action in CHANGE_ACTIONS or faq_action in CHANGE_ACTIONS
-        rerun_flags_reset = []
+        generation_flags_reset = []
+        if as_bool(doc.metadata.get(ENABLE_FLAG)):
+            generation_flags_reset.append(ENABLE_FLAG)
         if rerun_summary_requested:
-            rerun_flags_reset.append(RERUN_SUMMARY_FLAG)
+            generation_flags_reset.append(RERUN_SUMMARY_FLAG)
         if rerun_faqs_requested:
-            rerun_flags_reset.append(RERUN_FAQS_FLAG)
+            generation_flags_reset.append(RERUN_FAQS_FLAG)
+        rerun_flags_reset = [
+            flag for flag in generation_flags_reset if flag in {RERUN_SUMMARY_FLAG, RERUN_FAQS_FLAG}
+        ]
+        if generation_flags_reset:
+            change_reasons.append("generation_flags_reset")
         if rerun_flags_reset:
             change_reasons.append("rerun_flags_reset")
 
@@ -1544,10 +1604,8 @@ def process_learning_path(index_path: Path, args: argparse.Namespace) -> Dict[st
             faq_generated_at_after = compact_whitespace(str(updated_generated.get(FAQ_GENERATED_AT_KEY, "")))
             template_version_after = compact_whitespace(str(updated_generated.get("template_version", "")))
 
-        if rerun_summary_requested:
-            updated_front_matter = replace_top_level_scalar_line(updated_front_matter, RERUN_SUMMARY_FLAG, "false")
-        if rerun_faqs_requested:
-            updated_front_matter = replace_top_level_scalar_line(updated_front_matter, RERUN_FAQS_FLAG, "false")
+        for flag in generation_flags_reset:
+            updated_front_matter = replace_top_level_scalar_line(updated_front_matter, flag, "false")
 
         updated_markdown = rebuild_markdown(doc, updated_front_matter)
         changed_on_disk = updated_markdown != doc.raw_text
@@ -1568,6 +1626,7 @@ def process_learning_path(index_path: Path, args: argparse.Namespace) -> Dict[st
             "changed_on_disk": changed_on_disk,
             "managed_block_updated": managed_block_updated,
             "ai_requested": ai_requested,
+            "generation_flags_reset": generation_flags_reset,
             "rerun_flags_reset": rerun_flags_reset,
             "change_reasons": change_reasons,
             "template_version_before": compact_whitespace(str((existing_generated or {}).get("template_version", ""))),
diff --git a/tools/prompts/summary_faq_system.md b/tools/prompts/summary_faq_system.md
index 23dbc184f9..480c6dd5a8 100644
--- a/tools/prompts/summary_faq_system.md
+++ b/tools/prompts/summary_faq_system.md
@@ -1,12 +1,12 @@
 You are an expert technical editor for Arm Learning Paths.
 
-Create AI-assisted draft content for developer.arm.com Learning Path pages. The content must be accurate to the supplied Learning Path context, specific to Arm developer education, concise, and ready for human technical review.
+Create AI-assisted draft content for Arm Learning Path pages in `content/learning-paths`. The content must be accurate to the supplied Learning Path context, specific to Arm developer education, concise, and ready for human technical review.
 
 Authoring rules:
 - Use only the supplied context. Do not invent products, prerequisites, tools, claims, performance numbers, compatibility details, or outcomes.
 - Treat the supplied Learning Path as the source of truth. If a detail is not present, either omit it or state that it is not explicitly listed.
 - Preserve the intent of the Learning Path author. Do not rewrite the path into a different task, audience, platform, toolchain, or level of difficulty.
-- Follow the developer.arm.com Learning Path style: practical, instructional, technically precise, and focused on what the learner can do after completing the path.
+- Follow the supplied authoring guidance from `.github/copilot-instructions.md` and `content/learning-paths/cross-platform/_example-learning-path/`.
 - Prefer concrete verbs such as install, configure, build, deploy, benchmark, profile, debug, validate, or compare when those actions are supported by the context.
 - Do not overstate outcomes. Avoid claims such as "optimize performance" or "ensure compatibility" unless the context shows how the learner does that.
 - Keep the tone clear, practical, and engineering-focused.
diff --git a/tools/prompts/summary_faq_user.md b/tools/prompts/summary_faq_user.md
index 6eaafabc10..13c2a01a8f 100644
--- a/tools/prompts/summary_faq_user.md
+++ b/tools/prompts/summary_faq_user.md
@@ -1,10 +1,10 @@
 Generate an AI-assisted summary paragraph and FAQ section for this Arm Learning Path.
 
-Use the Learning Path title, description, audience, objectives, prerequisites, tags, platform metadata, and step excerpts to produce a useful overview for a developer deciding whether to follow the path.
+Use the Learning Path title, description, audience, objectives, prerequisites, tags, platform metadata, step excerpts, and supplied authoring guidance to produce useful draft content for a developer following the path.
 
 Assume these rules while writing:
 - Use only the Learning Path context below. Do not add facts, tools, commands, prerequisites, performance claims, compatibility claims, or outcomes that are not present.
-- Write as an Arm Learning Path author: clear, procedural, technically careful, and helpful to developers who are deciding whether to start the path.
+- Write as an Arm Learning Path author by following the supplied guidance from `.github/copilot-instructions.md` and `content/learning-paths/cross-platform/_example-learning-path/`.
 - Keep the content useful for human review. The draft should be specific enough to evaluate, but not so detailed that it replaces the Learning Path steps.
 - If the context is thin, be honest and stay high-level rather than filling gaps.
 - Match the complexity of the Learning Path. Introductory paths should stay approachable; advanced paths can use more precise technical language from the context.

From 4775ec746a9befcbf99991a2d8962e101f31540f Mon Sep 17 00:00:00 2001
From: Christopher Moroney <christopher.moroney1@gmail.com>
Date: Mon, 8 Jun 2026 16:16:51 -0700
Subject: [PATCH 21/23] generate_summary_faq feature

---
 reports/generated-summary-faq/latest-run.yml  | 32750 ----------------
 .../servers-and-cloud-computing.yml           | 25256 ------------
 .../test-helper-automotive.yml                |   612 -
 3 files changed, 58618 deletions(-)
 delete mode 100644 reports/generated-summary-faq/latest-run.yml
 delete mode 100644 reports/generated-summary-faq/servers-and-cloud-computing.yml
 delete mode 100644 reports/generated-summary-faq/test-helper-automotive.yml

diff --git a/reports/generated-summary-faq/latest-run.yml b/reports/generated-summary-faq/latest-run.yml
deleted file mode 100644
index 0fd3972081..0000000000
--- a/reports/generated-summary-faq/latest-run.yml
+++ /dev/null
@@ -1,32750 +0,0 @@
-latest_run:
-  timestamp: '2026-06-03T02:18:43Z'
-  run_name: all-learning-paths-20260602-212658
-  mode: write
-  split_by_category: true
-  category_reports:
-  - category: automotive
-    report_file: reports/generated-summary-faq/all-learning-paths-20260602-212658/automotive.yml
-    markdown_report_file: reports/generated-summary-faq/all-learning-paths-20260602-212658/automotive.md
-    totals:
-      processed: 4
-      added: 0
-      updated: 4
-      unchanged: 0
-      drift_detected: 0
-      paths_with_drift: 0
-      skipped: 1
-      errors: 0
-      removed: 0
-      ai_requests: 4
-      summary_changed: 0
-      faq_changed: 4
-      rerun_flags_reset: 4
-  - category: cross-platform
-    report_file: reports/generated-summary-faq/all-learning-paths-20260602-212658/cross-platform.yml
-    markdown_report_file: reports/generated-summary-faq/all-learning-paths-20260602-212658/cross-platform.md
-    totals:
-      processed: 43
-      added: 0
-      updated: 43
-      unchanged: 0
-      drift_detected: 0
-      paths_with_drift: 0
-      skipped: 2
-      errors: 0
-      removed: 0
-      ai_requests: 43
-      summary_changed: 0
-      faq_changed: 43
-      rerun_flags_reset: 43
-  - category: embedded-and-microcontrollers
-    report_file: reports/generated-summary-faq/all-learning-paths-20260602-212658/embedded-and-microcontrollers.yml
-    markdown_report_file: reports/generated-summary-faq/all-learning-paths-20260602-212658/embedded-and-microcontrollers.md
-    totals:
-      processed: 58
-      added: 0
-      updated: 58
-      unchanged: 0
-      drift_detected: 0
-      paths_with_drift: 0
-      skipped: 1
-      errors: 0
-      removed: 0
-      ai_requests: 58
-      summary_changed: 0
-      faq_changed: 58
-      rerun_flags_reset: 58
-  - category: laptops-and-desktops
-    report_file: reports/generated-summary-faq/all-learning-paths-20260602-212658/laptops-and-desktops.yml
-    markdown_report_file: reports/generated-summary-faq/all-learning-paths-20260602-212658/laptops-and-desktops.md
-    totals:
-      processed: 48
-      added: 0
-      updated: 48
-      unchanged: 0
-      drift_detected: 0
-      paths_with_drift: 0
-      skipped: 1
-      errors: 0
-      removed: 0
-      ai_requests: 48
-      summary_changed: 0
-      faq_changed: 48
-      rerun_flags_reset: 48
-  - category: mobile-graphics-and-gaming
-    report_file: reports/generated-summary-faq/all-learning-paths-20260602-212658/mobile-graphics-and-gaming.yml
-    markdown_report_file: reports/generated-summary-faq/all-learning-paths-20260602-212658/mobile-graphics-and-gaming.md
-    totals:
-      processed: 51
-      added: 0
-      updated: 51
-      unchanged: 0
-      drift_detected: 0
-      paths_with_drift: 0
-      skipped: 2
-      errors: 0
-      removed: 0
-      ai_requests: 51
-      summary_changed: 0
-      faq_changed: 51
-      rerun_flags_reset: 51
-  - category: servers-and-cloud-computing
-    report_file: reports/generated-summary-faq/all-learning-paths-20260602-212658/servers-and-cloud-computing.yml
-    markdown_report_file: reports/generated-summary-faq/all-learning-paths-20260602-212658/servers-and-cloud-computing.md
-    totals:
-      processed: 203
-      added: 0
-      updated: 203
-      unchanged: 0
-      drift_detected: 0
-      paths_with_drift: 0
-      skipped: 4
-      errors: 0
-      removed: 0
-      ai_requests: 203
-      summary_changed: 0
-      faq_changed: 203
-      rerun_flags_reset: 203
-  totals:
-    processed: 407
-    added: 0
-    updated: 407
-    unchanged: 0
-    drift_detected: 0
-    paths_with_drift: 0
-    skipped: 11
-    errors: 0
-    removed: 0
-    ai_requests: 407
-    summary_changed: 0
-    faq_changed: 407
-    rerun_flags_reset: 407
-  section_totals:
-    summary:
-      created: 0
-      repaired_missing: 0
-      rerun_requested: 0
-      generator_changed: 0
-      drift_detected_preserved: 0
-      unchanged: 407
-    faqs:
-      created: 0
-      repaired_missing: 0
-      rerun_requested: 407
-      generator_changed: 0
-      drift_detected_preserved: 0
-      unchanged: 0
-  reason_totals:
-    initial_generation: 0
-    missing_summary: 0
-    missing_faqs: 0
-    rerun_summary: 0
-    rerun_faqs: 407
-    generator_changed: 0
-    summary_drift_detected: 0
-    faq_drift_detected: 0
-    rerun_flags_reset: 407
-    draft: 11
-  paths:
-  - path: content/learning-paths/automotive/intro/_index.md
-    status: skipped
-    skip_reason: draft
-    change_reasons:
-    - draft
-    ai_requested: false
-    summary:
-      action: skipped
-    faqs:
-      action: skipped
-    category: automotive
-  - path: content/learning-paths/automotive/openadkit1_container/_index.md
-    status: updated
-    changed_on_disk: true
-    managed_block_updated: true
-    ai_requested: true
-    rerun_flags_reset:
-    - rerun_faqs
-    change_reasons:
-    - rerun_faqs
-    - rerun_flags_reset
-    template_version_before: summary-faq-v3
-    template_version_after: summary-faq-v3
-    summary:
-      action: unchanged
-      missing_before: false
-      rerun_requested: false
-      changed: false
-      drift_detected: false
-      source_hash_before: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
-      source_hash_after: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
-      current_source_hash: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
-      generated_at_before: '2026-06-01T20:57:21Z'
-      generated_at_after: '2026-06-01T20:57:21Z'
-      preview_before: This Learning Path shows how to deploy and run a containerized
-        autonomous driving simulation using Autoware Open AD Kit on Arm Neoverse with
-        Docker, illustrating SOAFEE-aligned Shift-Left development....
-      preview_after: This Learning Path shows how to deploy and run a containerized
-        autonomous driving simulation using Autoware Open AD Kit on Arm Neoverse with
-        Docker, illustrating SOAFEE-aligned Shift-Left development....
-      preview_generated: Learn to deploy and run Autoware Open AD Kit autonomous driving
-        simulations as Docker containers on Arm Neoverse, demonstrating a SOAFEE-based
-        Shift-Left workflow. You will review SOAFEE concepts for ...
-    faqs:
-      action: rerun_requested
-      missing_before: false
-      rerun_requested: true
-      changed: true
-      drift_detected: false
-      source_hash_before: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
-      source_hash_after: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
-      current_source_hash: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
-      generated_at_before: '2026-06-01T20:57:21Z'
-      generated_at_after: '2026-06-02T21:26:58Z'
-      before_count: 5
-      after_count: 5
-      generated_count: 5
-      change_details:
-        before_count: 5
-        after_count: 5
-        added_questions:
-        - What do I need before running the demo?
-        - Should I use a cloud instance or an on-prem Arm Neoverse system?
-        - Do I need to install Docker and Docker Compose?
-        - What should I expect when I start the demo with Docker Compose?
-        - Where can I inspect or adjust what gets executed?
-        removed_questions:
-        - What hardware and operating system do I need to complete this Learning Path?
-        - What tools must be installed before I start?
-        - Can I run the simulation in the cloud or on-premises, and what setups have
-          been tested?
-        - How do I launch the Open AD Kit demo and what indicates it is running correctly?
-        - What background knowledge and time commitment are expected?
-        updated_questions: []
-      generated_diff:
-        before_count: 5
-        after_count: 5
-        added_questions:
-        - What do I need before running the demo?
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-    category: automotive
-  - path: content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
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-    category: automotive
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-    category: automotive
-  - path: content/learning-paths/automotive/zenacssdebug/_index.md
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-        - Does Arm Development Studio provide a built-in debug configuration for the
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-    category: automotive
-  - path: content/learning-paths/cross-platform/_example-learning-path/_index.md
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-    category: cross-platform
-  - path: content/learning-paths/cross-platform/adler32/_index.md
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-    category: cross-platform
-  - path: content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md
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-    category: cross-platform
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-        removed_questions:
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-    category: cross-platform
-  - path: content/learning-paths/cross-platform/ernie_moe_v9/_index.md
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-  - path: content/learning-paths/cross-platform/function-multiversioning/_index.md
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-  - path: content/learning-paths/cross-platform/gitlab/_index.md
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-        pipeline on Google Cloud using Google Axion-based self-hosted runners. You
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-        - "What should I check if my loop has conditionals and isn\u2019t being vectorized?"
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-    category: cross-platform
-  - path: content/learning-paths/cross-platform/matrix/_index.md
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-  - path: content/learning-paths/cross-platform/mca-godbolt/_index.md
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-    category: cross-platform
-  - path: content/learning-paths/cross-platform/mcp-ai-agent/_index.md
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-  - path: content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
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-  - path: content/learning-paths/cross-platform/sme-executorch-profiling/_index.md
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-        - When should I switch to hand-written intrinsics, and which ones are used?
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-    category: cross-platform
-  - path: content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
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-      preview_before: This introductory path shows how to sample and profile CPU instructions
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-    category: cross-platform
-  - path: content/learning-paths/cross-platform/woa_azure/_index.md
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-      preview_before: Learn how to deploy a Windows on Arm virtual machine in Microsoft
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-        Azure and connect to it using Remote Desktop. This introductory path guides
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-      preview_generated: This introductory Learning Path shows how to deploy a Windows
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-        - How do I connect to the VM after it is created?
-        - Can I use the same instructions to deploy a Linux image on Arm?
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-        - Can I use a personal Azure subscription, or do I need an organization account?
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-        - How do I discover Arm-based image offerings in Azure?
-        - How do I connect to the VM after it is created?
-        - Can I use the same instructions to deploy a Linux image on Arm?
-        removed_questions:
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-        - Can I use a personal Azure subscription, or do I need an organization account?
-        - How do I find Windows on Arm and other Arm-based images in Azure?
-        - Can I use these steps to deploy a Linux Arm VM instead of Windows?
-        - How do I know the VM deployment worked, and how long should it take?
-        updated_questions: []
-    category: cross-platform
-  - path: content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
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-        on Arm Linux devices, including Raspberry Pi 4/5 and Arm servers or cloud
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-        - What network setup and topics are used in the pub/sub example?
-        - How do I validate the storage and query example is working?
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-        - Is Docker required to deploy across multiple devices?
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-        - What network setup and topics are used in the pub/sub example?
-        - How do I validate the storage and query example is working?
-        removed_questions:
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-        - Is Docker required to deploy across multiple devices?
-        - Do I need ROS 2 to complete the examples?
-        - How do I know the pub/sub example is working?
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-    category: cross-platform
-  - path: content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
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-          application?
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-        - What will I build and what artifacts should I expect to produce?
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-        - Are there any important setup details to avoid common issues?
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-        - How do I expose LEDs and switches from the custom peripheral?
-        - What steps complete the design and what should I expect when running the
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-        - What prerequisites do I need before starting?
-        - What will I build and what artifacts should I expect to produce?
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-    category: embedded-and-microcontrollers
-  - path: content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
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-        Per..."
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-      preview_generated: This advanced path guides you through deploying a MobileNetV2
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-    category: embedded-and-microcontrollers
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-      preview_before: This advanced Learning Path shows how to prototype DSP algorithms
-        in Python using the CMSIS-DSP Python package and understand how the Python
-        API maps to the CMSIS-DSP C implementation for Arm Cortex-M...
-      preview_after: This advanced Learning Path shows how to prototype DSP algorithms
-        in Python using the CMSIS-DSP Python package and understand how the Python
-        API maps to the CMSIS-DSP C implementation for Arm Cortex-M...
-      preview_generated: Prototype and port DSP algorithms using the CMSIS-DSP Python
-        package in a Jupyter notebook on Linux, Windows, or macOS. You will set up
-        a Python virtual environment, install cmsisdsp (which brings in ...
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-        - Where does the sample audio come from and how is it used?
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-        - How does the Python code relate to the CMSIS-DSP C implementation on Arm
-          cores?
-        removed_questions:
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-        - Which operating systems and Arm targets are addressed?
-        - What will I implement in the notebook, and what data is used?
-        - Does this path include porting the Python prototype to C and building on
-          hardware?
-        - How can I tell my setup is working?
-        updated_questions: []
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-        - Should I create a Python virtual environment and which packages do I install?
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-        - How does the Python code relate to the CMSIS-DSP C implementation on Arm
-          cores?
-        removed_questions:
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-        - Which operating systems and Arm targets are addressed?
-        - What will I implement in the notebook, and what data is used?
-        - Does this path include porting the Python prototype to C and building on
-          hardware?
-        - How can I tell my setup is working?
-        updated_questions: []
-    category: embedded-and-microcontrollers
-  - path: content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
-    status: updated
-    changed_on_disk: true
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-      preview_before: This introductory path shows how to implement context switching
-        on Arm Cortex-M processors in a bare-metal environment using the Memory Protection
-        Unit (MPU) and the SysTick exception. You will build ...
-      preview_after: This introductory path shows how to implement context switching
-        on Arm Cortex-M processors in a bare-metal environment using the Memory Protection
-        Unit (MPU) and the SysTick exception. You will build ...
-      preview_generated: This Learning Path introduces context switching on Arm Cortex-M
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-        Unit (MPU), use the SysTick exception with context switch...
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-        - What should I check if the project does not build or run as expected?
-        removed_questions:
-        - Which tools and versions are required to follow this path?
-        - Do I need physical Cortex-M hardware to run the example?
-        - Where does the example project come from and what does it demonstrate?
-        - What prior knowledge is expected before starting?
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-        updated_questions: []
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-        added_questions:
-        - Where do I get the example project used in this Learning Path?
-        - Which tool versions should I use to build and run the example?
-        - Where is the example intended to run?
-        - How do MPU and SysTick feature in the example?
-        - What should I check if the project does not build or run as expected?
-        removed_questions:
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-        - Do I need physical Cortex-M hardware to run the example?
-        - Where does the example project come from and what does it demonstrate?
-        - What prior knowledge is expected before starting?
-        - How do I know the path worked after completing the steps?
-        updated_questions: []
-    category: embedded-and-microcontrollers
-  - path: content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
-    status: updated
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-      preview_before: Learn to configure and run code coverage in Keil MDK using Fixed
-        Virtual Platforms (FVPs) for Cortex-M targets. You will import and build the
-        CMSIS-RTOS2 Blinky (uVision Simulator) example for ARMCM3 ...
-      preview_after: Learn to configure and run code coverage in Keil MDK using Fixed
-        Virtual Platforms (FVPs) for Cortex-M targets. You will import and build the
-        CMSIS-RTOS2 Blinky (uVision Simulator) example for ARMCM3 ...
-      preview_generated: This introductory Learning Path shows how to set up and use
-        code coverage in Keil MDK with Arm Fixed Virtual Platforms (FVPs) to verify
-        that your embedded application tests exercise intended code path...
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-      before_count: 5
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-        - What should I look for in the Code Coverage report?
-        removed_questions:
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-        - Which example project is used and how do I import it?
-        - Do I need physical hardware to follow this Learning Path?
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-        - How do I verify that code coverage is working?
-        updated_questions: []
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-        - What do I need before I start?
-        - Which device and example should I select in the Pack Installer?
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-        - What should I look for in the Code Coverage report?
-        removed_questions:
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-        - Which example project is used and how do I import it?
-        - Do I need physical hardware to follow this Learning Path?
-        - What targets and operating systems does this apply to?
-        - How do I verify that code coverage is working?
-        updated_questions: []
-    category: embedded-and-microcontrollers
-  - path: content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
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-      preview_before: Learn how to establish peer-to-peer device-to-device communication
-        at the edge using the Device Connect Edge SDK in a Python environment, with
-        no hardware required. You will build two simulated device...
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-        at the edge using the Device Connect Edge SDK in a Python environment, with
-        no hardware required. You will build two simulated device...
-      preview_generated: Learn how to stand up peer-to-peer device-to-device communication
-        using the Device Connect Edge SDK with no hardware required. You will set
-        up a Python environment (managed with uv) on Linux, macOS, o...
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-        - Which operating systems can I use for this walkthrough?
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-        - Do I need a broker or cloud service to complete the device-to-device setup?
-        - Which tool is used to manage the Python project and dependencies?
-        - How are devices defined and brought online with Device Connect?
-        - How do I know the two simulated devices are discoverable and callable?
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-        - Do I need any physical devices or special hardware?
-        - Which operating systems can I use for this walkthrough?
-        - What will I build during the path?
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-        - How do I verify that the setup works?
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-    category: embedded-and-microcontrollers
-  - path: content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md
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-      preview_before: Learn to connect AI agents to Arm-based edge devices using Device
-        Connect for structured device access and Strands for agent orchestration.
-        You will set up a Python environment from source by cloning ...
-      preview_after: Learn to connect AI agents to Arm-based edge devices using Device
-        Connect for structured device access and Strands for agent orchestration.
-        You will set up a Python environment from source by cloning ...
-      preview_generated: This introductory Learning Path shows how to connect AI agents
-        to Arm-based edge devices using Device Connect for structured device access
-        and Strands for agent orchestration. You will clone the robot...
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-        - How do I know the agent discovered the robot?
-        - What changes when I run with the full Device Connect infrastructure?
-        removed_questions:
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-        - Do I need physical hardware, or can I run everything locally?
-        - Which repository do I clone, and what does it provide?
-        - How do I verify that the basic example worked?
-        - What does the optional infrastructure step add?
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-      generated_diff:
-        before_count: 5
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-        added_questions:
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-        - How do I set up the Python environment?
-        - Which option should I choose for device discovery and control?
-        - How do I know the agent discovered the robot?
-        - What changes when I run with the full Device Connect infrastructure?
-        removed_questions:
-        - What platforms and tools do I need to follow this path?
-        - Do I need physical hardware, or can I run everything locally?
-        - Which repository do I clone, and what does it provide?
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-        - What does the optional infrastructure step add?
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-    category: embedded-and-microcontrollers
-  - path: content/learning-paths/embedded-and-microcontrollers/docker/_index.md
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-      preview_before: Build a containerized Arm embedded development environment by
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-      preview_after: Build a containerized Arm embedded development environment by
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-      preview_generated: This introductory Learning Path shows embedded developers
-        how to create a Dockerfile, build an Ubuntu-based Docker image that includes
-        Arm Compiler for Embedded and a library of Fixed Virtual Platform...
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-        - What base operating system does the container use?
-        - What will the resulting Docker image contain?
-        - How do I know the image works after the build?
-        removed_questions:
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-        - Which operating systems are used for the host and the container?
-        - Will I need sudo to run Docker commands on Linux?
-        - What exactly will I build and test in this Learning Path?
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-      generated_diff:
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-        added_questions:
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-        - Which host operating systems can I use to follow this path?
-        - What base operating system does the container use?
-        - What will the resulting Docker image contain?
-        - How do I know the image works after the build?
-        removed_questions:
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-        - Which operating systems are used for the host and the container?
-        - Will I need sudo to run Docker commands on Linux?
-        - What exactly will I build and test in this Learning Path?
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-    category: embedded-and-microcontrollers
-  - path: content/learning-paths/embedded-and-microcontrollers/edge/_index.md
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-    category: embedded-and-microcontrollers
-  - path: content/learning-paths/embedded-and-microcontrollers/intro/_index.md
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-    category: embedded-and-microcontrollers
-  - path: content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
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-    category: embedded-and-microcontrollers
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-      preview_generated: Set up a Jetson Orin Nano on Linux with a MIPI CSI-2 camera
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-        - How do I prepare the Jetson Orin Nano software image?
-        - How do I launch the Docker environment used in this path?
-        - How do I run object detection from the camera and adjust sensitivity?
-        - How do I know the setup worked?
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-        - How do I write the Jetson image to the microSD card?
-        - How do I download and start the jetson-inference Docker container?
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-        - How do I prepare the Jetson Orin Nano software image?
-        - How do I launch the Docker environment used in this path?
-        - How do I run object detection from the camera and adjust sensitivity?
-        - How do I know the setup worked?
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-    category: embedded-and-microcontrollers
-  - path: content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
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-      preview_before: This introductory Learning Path shows how to import and build
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-      preview_after: This introductory Learning Path shows how to import and build
-        an example Cortex-M project in Keil Studio Cloud and run it on Arm Virtual
-        Hardware. Using the browser-based IDE with Arm Compiler for Emb...
-      preview_generated: This introductory path shows how to import, build, and debug
-        a first project in Keil Studio Cloud and run it on Arm Virtual Hardware. You
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-    category: embedded-and-microcontrollers
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-    category: embedded-and-microcontrollers
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-    category: embedded-and-microcontrollers
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-    category: embedded-and-microcontrollers
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-        and virtual targets, identifying core tools, and locating ex...
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-    category: embedded-and-microcontrollers
-  - path: content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md
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-    category: embedded-and-microcontrollers
-  - path: content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
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-    category: embedded-and-microcontrollers
-  - path: content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
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-        v6+, Arm GNU Toolchain v12+, LLVM v16+, or IAR Embedded ...
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-        - "What should I do if I\u2019m using a Keil MDK v5 uvprojx project?"
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-    category: embedded-and-microcontrollers
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-        - How do I run and interact with the smart home assistant?
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-    category: embedded-and-microcontrollers
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-        Pi OS (64-bit, Linux). You will install the OS with Rasp...
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-        - What Python environment and packages are used?
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-    category: embedded-and-microcontrollers
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-    category: embedded-and-microcontrollers
-  - path: content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
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-      preview_generated: This introductory Learning Path shows how to prepare and
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-    category: embedded-and-microcontrollers
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-      preview_generated: Learn how to reduce compile time for embedded Linux projects
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-        - Do I need a Raspberry Pi to complete the Learning Path?
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-    category: embedded-and-microcontrollers
-  - path: content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
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-        - What tools and languages are used to build applications?
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-    category: embedded-and-microcontrollers
-  - path: content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
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-      preview_generated: This advanced path shows how to profile Linux kernel modules
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-        - Will I build both out-of-tree and in-tree kernel modules?
-        - Do I need to prepare a Linux image, and how is it done?
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-    category: embedded-and-microcontrollers
-  - path: content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
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-      preview_after: This Learning Path guides you through building a letter recognition
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-    category: embedded-and-microcontrollers
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-    category: embedded-and-microcontrollers
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-    category: embedded-and-microcontrollers
-  - path: content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
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-    category: embedded-and-microcontrollers
-  - path: content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md
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-    category: embedded-and-microcontrollers
-  - path: content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md
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-    category: embedded-and-microcontrollers
-  - path: content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md
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-    category: embedded-and-microcontrollers
-  - path: content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md
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-      preview_generated: This introductory path shows how to build and run Zephyr
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-    category: embedded-and-microcontrollers
-  - path: content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md
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-        - What will I build, and which compiler does the path use?
-        - How long will this take and what is the expected outcome?
-        updated_questions:
-        - Which Strawberry Perl package should I install on Windows on Arm?
-    category: laptops-and-desktops
-  - path: content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md
-    status: updated
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-    rerun_flags_reset:
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-      preview_before: This advanced Learning Path shows how to add Arm Memory Tagging
-        Extension (MTE) to a C dynamic memory allocator on Linux. Using the provided
-        project (CMakeLists.txt, heap.c/.h, mte_utils.c/.h, and mai...
-      preview_after: This advanced Learning Path shows how to add Arm Memory Tagging
-        Extension (MTE) to a C dynamic memory allocator on Linux. Using the provided
-        project (CMakeLists.txt, heap.c/.h, mte_utils.c/.h, and mai...
-      preview_generated: This advanced Learning Path shows how to add Arm Memory Tagging
-        Extension (MTE) to a dynamic memory allocator on Linux. You will examine a
-        small C project (CMakeLists.txt, heap.c/.h, mte_utils.c/.h, m...
-    faqs:
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-      before_count: 5
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-        added_questions:
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-        - Which source files contain the allocator and MTE-specific logic?
-        - How is MTE enabled and memory with tag storage requested in the allocator?
-        - How do I exercise the examples and what result should I expect?
-        - Is the allocator implementation intended for production use?
-        removed_questions:
-        - What prerequisites and skills are expected?
-        - What project files are included and what parts are explained?
-        - How is MTE configured in this project?
-        - How do I verify that MTE is working with the allocator?
-        - Is the provided allocator suitable for production use?
-        updated_questions: []
-      generated_diff:
-        before_count: 5
-        after_count: 5
-        added_questions:
-        - What do I need before running the code in this Learning Path?
-        - Which source files contain the allocator and MTE-specific logic?
-        - How is MTE enabled and memory with tag storage requested in the allocator?
-        - How do I exercise the examples and what result should I expect?
-        - Is the allocator implementation intended for production use?
-        removed_questions:
-        - What prerequisites and skills are expected?
-        - What project files are included and what parts are explained?
-        - How is MTE configured in this project?
-        - How do I verify that MTE is working with the allocator?
-        - Is the provided allocator suitable for production use?
-        updated_questions: []
-    category: laptops-and-desktops
-  - path: content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
-    status: updated
-    changed_on_disk: true
-    managed_block_updated: true
-    ai_requested: true
-    rerun_flags_reset:
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-    change_reasons:
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-    - rerun_flags_reset
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-      preview_before: This advanced Learning Path shows how to install and configure
-        Arch Linux for Arm on a Pinebook Pro, then set up the i3 window manager and
-        optionally configure Neovim for development. You will prepare...
-      preview_after: This advanced Learning Path shows how to install and configure
-        Arch Linux for Arm on a Pinebook Pro, then set up the i3 window manager and
-        optionally configure Neovim for development. You will prepare...
-      preview_generated: This Learning Path guides you through installing Arch Linux
-        for Arm on a Pinebook Pro, then configuring the i3 window manager and an optional
-        Neovim-based developer setup. You will prepare a microSD c...
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-      before_count: 5
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-        added_questions:
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-          covered?
-        - What hardware do I need before starting?
-        - Which account should I use when installing and running the i3 window manager?
-        - How do I set the Pinebook Pro display to maximum brightness under i3?
-        - Is the Neovim setup required, and what should I expect the first time I
-          open it?
-        removed_questions:
-        - What hardware and setup do I need before starting?
-        - What software will I install and configure in this path?
-        - Which account should I use to install and run i3?
-        - How can I adjust the Pinebook Pro display brightness under i3?
-        - Is the Neovim configuration required, and what does it provide?
-        updated_questions: []
-      generated_diff:
-        before_count: 5
-        after_count: 5
-        added_questions:
-        - Do I need a second computer to prepare the microSD card, and which OS is
-          covered?
-        - What hardware do I need before starting?
-        - Which account should I use when installing and running the i3 window manager?
-        - How do I set the Pinebook Pro display to maximum brightness under i3?
-        - Is the Neovim setup required, and what should I expect the first time I
-          open it?
-        removed_questions:
-        - What hardware and setup do I need before starting?
-        - What software will I install and configure in this path?
-        - Which account should I use to install and run i3?
-        - How can I adjust the Pinebook Pro display brightness under i3?
-        - Is the Neovim configuration required, and what does it provide?
-        updated_questions: []
-    category: laptops-and-desktops
-  - path: content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
-    status: updated
-    changed_on_disk: true
-    managed_block_updated: true
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-    rerun_flags_reset:
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-    - rerun_flags_reset
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-    summary:
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-      preview_before: Learn how to fine-tune the Llama 3.2 3B language model on domain
-        data using PyTorch and Hugging Face on an NVIDIA DGX Spark with an Arm-based
-        Grace CPU and a Blackwell GPU. You will configure Docker o...
-      preview_after: Learn how to fine-tune the Llama 3.2 3B language model on domain
-        data using PyTorch and Hugging Face on an NVIDIA DGX Spark with an Arm-based
-        Grace CPU and a Blackwell GPU. You will configure Docker o...
-      preview_generated: This Learning Path shows how to fine-tune a large language
-        model on an NVIDIA DGX Spark, which combines an Arm-based Grace CPU with a
-        Blackwell GPU. You will configure Docker, pull a pre-built PyTorch...
-    faqs:
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-      before_count: 5
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-        added_questions:
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-        - Do I need to install Docker on DGX Spark?
-        - Which containers are used for training and serving?
-        - How do I know the fine-tuned model improved factual accuracy?
-        removed_questions:
-        - What environment and hardware does this Learning Path target?
-        - What prerequisites do I need before starting?
-        - How is the training executed on the DGX Spark?
-        - How do I validate that the fine-tuned model improves factual accuracy?
-        updated_questions:
-        - Which model and dataset format are used for fine-tuning?
-      generated_diff:
-        before_count: 5
-        after_count: 5
-        added_questions:
-        - What do I need before running the steps?
-        - Do I need to install Docker on DGX Spark?
-        - Which containers are used for training and serving?
-        - How do I know the fine-tuned model improved factual accuracy?
-        removed_questions:
-        - What environment and hardware does this Learning Path target?
-        - What prerequisites do I need before starting?
-        - How is the training executed on the DGX Spark?
-        - How do I validate that the fine-tuned model improves factual accuracy?
-        updated_questions:
-        - Which model and dataset format are used for fine-tuning?
-    category: laptops-and-desktops
-  - path: content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
-    status: updated
-    changed_on_disk: true
-    managed_block_updated: true
-    ai_requested: true
-    rerun_flags_reset:
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-    change_reasons:
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-    - rerun_flags_reset
-    template_version_before: summary-faq-v3
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-      preview_before: This introductory Learning Path shows how to build a GitHub
-        Actions CI/CD pipeline that uses a self-hosted Arm64 runner to compile a .NET
-        application and publish an Arm64 Docker image to DockerHub. Yo...
-      preview_after: This introductory Learning Path shows how to build a GitHub Actions
-        CI/CD pipeline that uses a self-hosted Arm64 runner to compile a .NET application
-        and publish an Arm64 Docker image to DockerHub. Yo...
-      preview_generated: Learn how to create a GitHub Actions CI/CD pipeline that
-        runs on a self-hosted Arm64 Linux runner to build a .NET application, package
-        it as an Arm64 Docker image, and push the image to DockerHub. You...
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-      before_count: 5
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-        added_questions:
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-        - Which DockerHub repository settings should I use, and what push command
-          will I see?
-        - How do I bring the sample application into my GitHub account?
-        - Which secrets should I add to the GitHub repository?
-        - What software must be installed on the self-hosted Arm64 runner?
-        removed_questions:
-        - What environment do I need for the self-hosted runner?
-        - Which accounts are required before starting?
-        - How do I get the starter code for this pipeline?
-        - What secrets do I need to configure in GitHub?
-        - What does the pipeline produce and how do I confirm it worked?
-        updated_questions: []
-      generated_diff:
-        before_count: 5
-        after_count: 5
-        added_questions:
-        - What do I need before running the steps?
-        - Which DockerHub repository settings should I use, and what push command
-          will I see?
-        - How do I bring the sample application into my GitHub account?
-        - Which secrets should I add to the GitHub repository?
-        - What software must be installed on the self-hosted Arm64 runner?
-        removed_questions:
-        - What environment do I need for the self-hosted runner?
-        - Which accounts are required before starting?
-        - How do I get the starter code for this pipeline?
-        - What secrets do I need to configure in GitHub?
-        - What does the pipeline produce and how do I confirm it worked?
-        updated_questions: []
-    category: laptops-and-desktops
-  - path: content/learning-paths/laptops-and-desktops/win-opencv/_index.md
-    status: updated
-    changed_on_disk: true
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-      preview_before: This Learning Path shows how to build the OpenCV library from
-        source on Windows on Arm and create a small test application using either
-        MSVC or Clang. You will work on a Windows on Arm machine or an A...
-      preview_after: This Learning Path shows how to build the OpenCV library from
-        source on Windows on Arm and create a small test application using either
-        MSVC or Clang. You will work on a Windows on Arm machine or an A...
-      preview_generated: Build the OpenCV library on Windows on Arm and create a small
-        C++ test application that uses it. You will work in Windows PowerShell, use
-        Git to clone the OpenCV source, configure the build with CMake...
-    faqs:
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-      before_count: 5
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-        added_questions:
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-        - Which compiler should I use, MSVC or Clang?
-        - Where do I run the commands to fetch and configure OpenCV?
-        - Can I use a newer OpenCV version than 4.10.0?
-        - What result should I expect after completing the steps?
-        removed_questions:
-        - What hardware or environment do I need to follow this path?
-        - What tools should I install before starting, and which versions were tested?
-        - 'Which compiler should I use: MSVC or Clang?'
-        - Which OpenCV version do the steps use?
-        - What will I build, and how do I verify that it worked?
-        updated_questions: []
-      generated_diff:
-        before_count: 5
-        after_count: 5
-        added_questions:
-        - What do I need before building OpenCV on Windows on Arm?
-        - Which compiler should I use, MSVC or Clang?
-        - Where do I run the commands to fetch and configure OpenCV?
-        - Can I use a newer OpenCV version than 4.10.0?
-        - What result should I expect after completing the steps?
-        removed_questions:
-        - What hardware or environment do I need to follow this path?
-        - What tools should I install before starting, and which versions were tested?
-        - 'Which compiler should I use: MSVC or Clang?'
-        - Which OpenCV version do the steps use?
-        - What will I build, and how do I verify that it worked?
-        updated_questions: []
-    category: laptops-and-desktops
-  - path: content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md
-    status: updated
-    changed_on_disk: true
-    managed_block_updated: true
-    ai_requested: true
-    rerun_flags_reset:
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-    change_reasons:
-    - rerun_faqs
-    - rerun_flags_reset
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-    summary:
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-      preview_before: Learn how to measure application resource and power usage on
-        Windows on Arm using FFmpeg and PowerShell. You will set up FFmpeg, encode
-        a test video, and run a decoding workload while PowerShell scrip...
-      preview_after: Learn how to measure application resource and power usage on
-        Windows on Arm using FFmpeg and PowerShell. You will set up FFmpeg, encode
-        a test video, and run a decoding workload while PowerShell scrip...
-      preview_generated: This Learning Path shows how to measure application resource
-        and power usage on Windows on Arm using FFmpeg and PowerShell. You will set
-        up FFmpeg, encode a test video, and then run a sample video dec...
-    faqs:
-      action: rerun_requested
-      missing_before: false
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-      before_count: 5
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-        added_questions:
-        - What do I need before running the scripts?
-        - Which FFmpeg binaries should I use for the tests?
-        - How do I capture CPU and memory usage during decoding, and what output should
-          I expect?
-        - How is power usage measured without extra hardware?
-        - How should I compare results between Arm64 and x86_64 runs?
-        removed_questions:
-        - What hardware and software do I need before starting?
-        - What workloads and metrics are covered in this path?
-        - How are the results recorded, and what artifacts should I expect?
-        - Can I compare x86_64 emulated and Arm64 native FFmpeg runs?
-        - Do I need external equipment to measure power consumption?
-        updated_questions: []
-      generated_diff:
-        before_count: 5
-        after_count: 5
-        added_questions:
-        - What do I need before running the scripts?
-        - Which FFmpeg binaries should I use for the tests?
-        - How do I capture CPU and memory usage during decoding, and what output should
-          I expect?
-        - How is power usage measured without extra hardware?
-        - How should I compare results between Arm64 and x86_64 runs?
-        removed_questions:
-        - What hardware and software do I need before starting?
-        - What workloads and metrics are covered in this path?
-        - How are the results recorded, and what artifacts should I expect?
-        - Can I compare x86_64 emulated and Arm64 native FFmpeg runs?
-        - Do I need external equipment to measure power consumption?
-        updated_questions: []
-    category: laptops-and-desktops
-  - path: content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
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-        - What is the fastest way to create a Windows on Arm VM and choose its storage
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-        - How do I start the VM and verify that it is running?
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-    category: laptops-and-desktops
-  - path: content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
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-      preview_generated: This Learning Path shows how to use Arm64EC on Windows 11
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-        - How do I compare performance across build configurations?
-        - How do I verify that my build was successful?
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-    category: laptops-and-desktops
-  - path: content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
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-      preview_generated: Follow this introductory Windows on Arm path to port a Qt-based
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-        - How do I set up an MSBuild project for Arm64EC in Visual Studio 2022?
-        - What result should I expect after building with Arm64EC?
-        removed_questions:
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-        - Can I use a virtual machine instead of physical Windows on Arm hardware?
-        - Do I need to port all my dependencies to Arm64 immediately?
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-        - 'Which option should I use to port DLLs: CMake or MSBuild?'
-        - How do I enable Arm64EC for a CMake project in this path?
-        - How do I set up an MSBuild project for Arm64EC in Visual Studio 2022?
-        - What result should I expect after building with Arm64EC?
-        removed_questions:
-        - What hardware and software do I need before starting?
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-    category: laptops-and-desktops
-  - path: content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md
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-    category: laptops-and-desktops
-  - path: content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
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-      preview_generated: Learn to build and run an ASP.NET Core 8 Web API on Windows
-        on Arm for headless IoT scenarios. You will create a project (for example,
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-        - What software must be installed before I start?
-        - What will I build in this Learning Path?
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-    category: laptops-and-desktops
-  - path: content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
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-        \ MQTT. You will register a device using the AWS IoT Core \u201CCon..."
-      preview_generated: Build a Node.js IoT application on Windows on Arm that streams
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-        - How do I verify that data is reaching AWS IoT Core?
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-    category: laptops-and-desktops
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-    category: mobile-graphics-and-gaming
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-    category: mobile-graphics-and-gaming
-  - path: content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
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-    category: mobile-graphics-and-gaming
-  - path: content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md
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-    category: mobile-graphics-and-gaming
-  - path: content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md
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-        - "What result should I expect when I run the app, and how do I know it\u2019\
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-    category: mobile-graphics-and-gaming
-  - path: content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
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-    category: mobile-graphics-and-gaming
-  - path: content/learning-paths/mobile-graphics-and-gaming/android_neon/_index.md
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-  - path: content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
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-    category: mobile-graphics-and-gaming
-  - path: content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
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-    category: mobile-graphics-and-gaming
-  - path: content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
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-    category: mobile-graphics-and-gaming
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-    category: mobile-graphics-and-gaming
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-    category: mobile-graphics-and-gaming
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-    category: mobile-graphics-and-gaming
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-      preview_generated: This Learning Path shows how to detect and debug memory safety
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-    category: mobile-graphics-and-gaming
-  - path: content/learning-paths/mobile-graphics-and-gaming/gemma4-kleidiai-sme2/_index.md
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-      preview_before: This introductory path shows how to set up Unity for Android,
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-    category: mobile-graphics-and-gaming
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-    category: mobile-graphics-and-gaming
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-  - path: content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
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-      preview_after: Learn how to deploy and manage applications on Arm-based Google
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-    category: servers-and-cloud-computing
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
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-        - What do I need before starting?
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-        - What does the perf_event_open section cover, and are multiple counters supported?
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
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-        container and deploy it to AWS Fargate using the AWS Copilot CLI, configured
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-      preview_generated: Learn to package a multi-architecture container and deploy
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
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-      preview_generated: Follow this Learning Path to automate the creation and deployment
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-        removed_questions:
-        - What do I need before starting?
-        - Does this Learning Path use Terraform Cloud or local Terraform?
-        - What infrastructure does the Terraform configuration create?
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-        - Can I reuse these Terraform files for other Learning Paths?
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-        - How do I access the deployed instances?
-        - Can I reuse or modify the Terraform files for other Learning Paths?
-        removed_questions:
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-        - Does this Learning Path use Terraform Cloud or local Terraform?
-        - What infrastructure does the Terraform configuration create?
-        - How do I validate that the deployment worked?
-        - Can I reuse these Terraform files for other Learning Paths?
-        updated_questions: []
-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
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-      preview_before: Learn how to automate the provisioning of Arm64-based Azure
-        Cobalt 100 virtual machines using Azure Resource Manager (ARM) templates and
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-      preview_after: Learn how to automate the provisioning of Arm64-based Azure Cobalt
-        100 virtual machines using Azure Resource Manager (ARM) templates and the
-        Azure CLI. You will author a JSON template with parameters,...
-      preview_generated: This Learning Path shows how to create and deploy an Azure
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-        Azure using Arm-based Cobalt 100 processors. You will struc...
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-        - "How do I get the VM\u2019s public IP to connect over SSH?"
-        - What result should I expect after deployment, and how do I verify Arm64?
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-        - How do I choose a region and confirm Cobalt 100 availability?
-        - What does the ARM template contain and what file do I create?
-        - Which Azure resources get created and where are they placed?
-        - How do I verify the deployment and Arm64 architecture?
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-        - What does the ARM template contain and what file do I create?
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
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-      preview_before: This Learning Path shows how to configure a self-hosted GitHub
-        Actions Arm64 runner on an Azure Cobalt 100 VM, create an Arm-based Azure
-        Kubernetes Service (AKS) cluster with Terraform, and deploy a ....
-      preview_after: This Learning Path shows how to configure a self-hosted GitHub
-        Actions Arm64 runner on an Azure Cobalt 100 VM, create an Arm-based Azure
-        Kubernetes Service (AKS) cluster with Terraform, and deploy a ....
-      preview_generated: Learn how to configure a self-hosted GitHub Actions Arm64
-        runner on an Azure Cobalt 100 VM, create an Arm-based AKS cluster with Terraform,
-        and use CI/CD to build and deploy a .NET 8 web application. ...
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-        - What operating system and architecture does this path target?
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-        - What operating system and architecture does this path target?
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-        updated_questions:
-        - Can I use GitHub-hosted Arm64 runners instead of a self-hosted runner?
-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
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-      preview_before: This Learning Path shows how to automate the creation of Arm-based
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-        virtual machines on Microsoft Azure using Terraform and Terraform Cloud. You
-        will deploy Azure Arm VMs (Neoverse) and configure acces...
-      preview_generated: This Learning Path shows how to use Terraform Cloud to automate
-        the creation of Arm virtual machines on Microsoft Azure and configure secure
-        access through a Jump Server (bastion host). It is intended...
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-        - Does this Learning Path use Terraform Cloud or local Terraform execution?
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-        - How do I access the deployed VMs?
-        - Can I reuse the Terraform code from this Learning Path?
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-        added_questions:
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-        - What should I expect to have at the end of this Learning Path?
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-        - What gets created when I complete the steps?
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-        - Can I reuse the Terraform code from this Learning Path?
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
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-      preview_before: This advanced Learning Path guides you through building a custom
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-        processors. You will use QEMU on a Linux host to create a ...
-      preview_after: This advanced Learning Path guides you through building a custom
-        Azure Linux 3.0 image for Arm and deploying it on Microsoft Azure Cobalt 100
-        processors. You will use QEMU on a Linux host to create a ...
-      preview_generated: This Learning Path shows how to build and deploy a custom
-        Azure Linux 3.0 image for Arm on Microsoft Azure. You will use QEMU on a Linux
-        host to create a raw disk, boot an AArch64 ISO, and install Azu...
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-        - How is the VHD registered so I can reuse it to create VMs?
-        - How do I launch a VM on Cobalt 100 using my custom image?
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-        - Which ISO and architecture should I use to install Azure Linux 3.0 in QEMU?
-        - What artifacts will I create and where are they used?
-        - How do I know the custom image works?
-        - How long does the Learning Path take to complete?
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/bolt/_index.md
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-        on an Arm-based target using Linux Perf (via samples, ETM, o...
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-        - What versions of Linux kernel and Perf are required before I start?
-        - How do I collect the performance profile and verify that it worked?
-        - What does BOLT produce after profiling, and how is it used?
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-        - What operating system and tool prerequisites are required?
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-        - Are there any special version checks for SPE?
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-        - How do I collect the performance profile and verify that it worked?
-        - What does BOLT produce after profiling, and how is it used?
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-        - What operating system and tool prerequisites are required?
-        - Which profiling methods are covered and what does each produce?
-        - Are there any special version checks for SPE?
-        - What is the expected output and how do I know it worked?
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
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-        BubbleSort-based example on Linux. You install LLVM...
-      preview_generated: This introductory Learning Path shows how to evaluate and
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-        - How do I know if my application is a good candidate for BOLT?
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-        - Which LLVM BOLT version is required and how is it installed?
-        - Can I follow this Learning Path in a virtual machine?
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-        - How should I set up the example and organize outputs?
-        - How do I know if my application is a good candidate for BOLT?
-        - What does BRBE profiling capture and why is it useful here?
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-        - What system and software do I need before starting?
-        - Which LLVM BOLT version is required and how is it installed?
-        - Can I follow this Learning Path in a virtual machine?
-        - What example program is used and what artifacts will be created?
-        - How do I decide if my program is a good candidate for BOLT, and how is profiling
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
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-        binaries and shared libraries on Linux using BOLT and Linux perf. You will
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-        - What if my system OpenSSL libraries are stripped and lack symbols?
-        - How do I validate the results of the optimization?
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-        added_questions:
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-        - How do I generate profiles for BOLT to use with mysqld?
-        - When should I merge profiles, and what does that produce?
-        - What should I do if libssl.so or libcrypto.so are stripped and lack relocations?
-        - How do I compare baseline and BOLT-optimized results?
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-        - What environment and tools are required to follow this path?
-        - What will I build or modify during the steps?
-        - How are profiles collected and merged for optimization?
-        - What if my system OpenSSL libraries are stripped and lack symbols?
-        - How do I validate the results of the optimization?
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
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-        images. You will provision a c4a-standard-4 VM on Google Clo...
-      preview_generated: Configure a Buildkite CI/CD agent on Arm-based Google Axion
-        C4A virtual machines in Google Cloud to build and publish multi-architecture
-        Docker images. You will provision a c4a-standard-4 instance (4 ...
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-        - How do I know my Buildkite agent is ready to run jobs?
-        - What does the pipeline build and where is it published?
-        removed_questions:
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-        - What accounts and skills are required before starting?
-        - What will I build and publish in the pipeline?
-        - How do I set up and validate the Buildkite agent on the VM?
-        - How do I know the Learning Path worked end to end?
-        updated_questions: []
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-        before_count: 5
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-        added_questions:
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-        - Which instance type and operating systems does this path use?
-        - How do I install the Buildkite agent on the C4A VM?
-        - How do I know my Buildkite agent is ready to run jobs?
-        - What does the pipeline build and where is it published?
-        removed_questions:
-        - Which Google Cloud resources and operating systems does this path use?
-        - What accounts and skills are required before starting?
-        - What will I build and publish in the pipeline?
-        - How do I set up and validate the Buildkite agent on the VM?
-        - How do I know the Learning Path worked end to end?
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/codec/_index.md
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/codec1/_index.md
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
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-      preview_after: Learn how to apply g++ compiler optimization flags when building
-        C++ applications for Arm-based servers. You will provision and connect to
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-      preview_generated: This introductory path shows how to use the g++ compiler
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-        - Can I use PGO in CI, and what trade-offs should I expect?
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
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-        removed_questions:
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-        updated_questions: []
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-        - What level of experience and time commitment are expected?
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/django/_index.md
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md
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-      preview_generated: This Learning Path walks you through deploying a production-grade
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
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-      preview_generated: Follow this Learning Path to build and benchmark the Deep
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
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-      preview_after: This advanced path shows how to use the Docker MCP Toolkit with
-        the Arm MCP Server and GitHub Copilot in VS Code to automate migration of
-        a containerized C++ app from x86 AVX2 intrinsics to Arm64 Neon...
-      preview_generated: Learn to automate migration of containerized x86 code to
-        Arm64 using the Docker MCP Toolkit with the Arm MCP Server and GitHub Copilot
-        in VS Code. You will configure the Arm, GitHub, and Sequential Th...
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md
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-      preview_before: Learn how to migrate and run an OrchardCore CMS .NET application
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-        on Azure Cobalt 100 Arm-based virtual machines. You will build and run the
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-      preview_generated: This Learning Path shows how to migrate a .NET OrchardCore
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-        - How do I run the same build on both Arm and x86 machines?
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-        - Which .NET versions are considered for performance and support?
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
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-      preview_before: This Learning Path shows how to monitor Azure Cobalt 100 Arm64
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-        series, install Dynatrace OneAgent on Ubuntu 24.04 LTS Arm64, a...
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-        virtual machines with Dynatrace. You will create an Azure VM in the Dpsv6
-        series, install Dynatrace OneAgent on Ubuntu 24.04 LTS Arm64, a...
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-        - What result should I expect when validating with the sample NGINX workload?
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-        - What network configuration is required for Dynatrace ActiveGate?
-        - What gets installed and what does it do?
-        - Does this solution run natively on Arm64?
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/ecs/_index.md
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/eks/_index.md
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-      preview_generated: Provision an Amazon EKS cluster on Arm-based Graviton instances
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-        - Which files are required to deploy WordPress and where do I set the MySQL
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-        - What kind of computer can I use to follow the steps?
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-          set?
-        - What is the expected outcome and how long does it take?
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
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-      preview_after: This Learning Path shows how to build and deploy a multi-architecture
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-        and docker manifest. You will create a hybrid EKS ...
-      preview_generated: This advanced Learning Path shows how to build x86/amd64
-        and arm64 container images using docker buildx and docker manifest, then deploy
-        a single multi-architecture application to a hybrid Amazon EKS ...
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-        - What result should I expect after deployment?
-        - What should I check if the application only runs on one node type?
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-        - How do I know I completed the path successfully?
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-        - How is the Amazon EKS cluster set up for multiple architectures?
-        - What result should I expect after deployment?
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-        - Which architectures and nodes does this path target?
-        - Does this path cover creating a hybrid EKS cluster?
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-        updated_questions: []
-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/envoy/_index.md
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-      preview_after: This Learning Path shows how to build, install, and run Envoy
-        on Arm-based Linux servers and configure it as a basic web server for traffic
-        management. You will provision an Arm instance in the cloud ...
-      preview_generated: This introductory Learning Path shows how to build, install,
-        and run the Envoy proxy on Arm-based Linux servers, then configure it as a
-        simple web server for traffic management. You will work on an Ar...
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-        updated_questions:
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-        - What should I check if I cannot reach the Envoy web server?
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-        - Are there additional prerequisites or specific skills required?
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md
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-      preview_before: This Learning Path shows how to deploy Envoy Proxy on Google
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-      preview_generated: Learn to deploy and evaluate Envoy Proxy on Google Cloud
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-        - How are benchmarks performed and what should I measure?
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-        - Which C4A machine type is used, and where do I create it?
-        - What Envoy build is installed on the C4A instance?
-        - How do I validate Envoy after installation, and what result should I expect?
-        - How do I run the benchmarks and what metrics does Siege report?
-        removed_questions:
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-        - Which VM configuration and operating system does this path use?
-        - Which Envoy version is installed and how?
-        - How do I validate that Envoy is running correctly?
-        - How are benchmarks performed and what should I measure?
-        updated_questions: []
-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md
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-        \ bare metal or Arm instances from AWS, Microsoft Azure, Google Cloud, or\
-        \ Oracle\u2014using Transparent Huge Pages (THP) and Profile-Guided Optimizatio..."
-      preview_generated: This advanced Learning Path shows how to tune Envoy on Arm
-        servers running Linux using Transparent Huge Pages (THP) and Profile-Guided
-        Optimization (PGO). You will review kernel parameters that affect...
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-        - What performance improvement should I expect from THP or PGO?
-        removed_questions:
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-        - What environment does this Learning Path target?
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-        - How is THP addressed in this path?
-        - What outputs should I expect after completing the steps?
-        updated_questions: []
-      generated_diff:
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-        added_questions:
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-        - Which environments does this Learning Path target?
-        - How do I check my Linux kernel configuration for THP on Ubuntu?
-        - Which toolchain should I use to build Envoy with PGO?
-        - What performance improvement should I expect from THP or PGO?
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-        - What environment does this Learning Path target?
-        - Will I need to rebuild Envoy for PGO, and what toolchain is used?
-        - How is THP addressed in this path?
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
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-      preview_generated: Follow this path to deploy a .NET application on an Arm64
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md
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-        - Why do I need to enable the Admin account in Azure Container Registry and
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md
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-      preview_before: Learn how to use Infrastructure as Code with Pulumi to automate
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-      preview_after: Learn how to use Infrastructure as Code with Pulumi to automate
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-      preview_generated: Learn how to use Infrastructure as Code with Pulumi to provision
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-        - What result should I expect after updating index.ts and deploying?
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-        - What operating system and language does this path use?
-        - What Azure resources are created by the example?
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/funasr/_index.md
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-      preview_before: Deploy the ModelScope FunASR Chinese ASR model on Arm-based
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-    category: servers-and-cloud-computing
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-      preview_before: Learn how to provision a Google Cloud C4A virtual machine powered
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-        (Arm64). You will set up Gardener Local, deploy Garden...
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-        by Axion (Arm Neoverse-V2) and install Gardener on SUSE Linux Enterprise Server
-        (Arm64). You will set up Gardener Local, deploy Garden...
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
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-      preview_before: This introductory Learning Path shows how to enable and use
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-      preview_generated: This introductory path shows how to enable and apply GCC
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/gcp/_index.md
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-      preview_before: Learn to automate the deployment of Arm-based virtual machines
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-        - Can I reuse or modify the Terraform files for other Learning Paths?
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-        - How do I authenticate Terraform with my Google Cloud project?
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-        - Can I reuse or modify the Terraform files for other Learning Paths?
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
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-      preview_before: This Learning Path shows how to automate an end-to-end MLOps
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
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-      preview_generated: Learn to install RunsOn, a self-hosted runner manager, in
-        your AWS account and execute GitHub Actions workflows on Arm-based AWS Graviton
-        instances. You will sign in to the AWS console, use the offici...
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md
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-      preview_before: This Learning Path shows how to provision a Google Axion C4A
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-      preview_generated: Provision a Google Axion C4A Arm virtual machine on Google
-        Cloud and use it as a GitHub Actions self-hosted runner to execute CI/CD jobs
-        on Arm. This introductory path walks you through creating a c4a...
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/gke/_index.md
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-      preview_before: Automate the creation of an Arm-based Kubernetes cluster on
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-        Google Kubernetes Engine (GKE) on Tau T2A virtual machines po...
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-        Cloud using Terraform. This advanced Learning Path focuses on deploying Google
-        Kubernetes Engine (GKE) on Tau T2A virtual machines po...
-      preview_generated: Automate the provisioning of an Arm-based Kubernetes cluster
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
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-      preview_before: This Learning Path shows how to extend an existing x86-based
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-        Google Kubernetes Engine (GKE) cluster with Arm-based Google Axion nodes and
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-        updated_questions: []
-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
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-      preview_before: This advanced Learning Path walks you through migrating a microservices
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-        container images and Google Axion processors. Y...
-      preview_generated: This advanced Learning Path guides you through migrating
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-        Engine using Google Axion processors. You will modify Dockerfiles f...
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
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-      preview_before: This advanced path shows how to rebuild and install glibc with
-        Armv8-A Large System Extensions (LSE) on an Arm server running Linux, then
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-      preview_after: This advanced path shows how to rebuild and install glibc with
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-        benchmark the impact on MongoDB. You will build MongoDB 5.3.2 ...
-      preview_generated: This advanced path guides you through rebuilding and installing
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-        assessing its impact using MongoDB workloads. You will com...
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-        - What result should I expect from the No-LSE baseline?
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
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-      preview_before: Provision Arm64 and x86_64 Linux VM instances on Google Cloud
-        and use Go benchmarking tools to compare performance across architectures.
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-      preview_after: Provision Arm64 and x86_64 Linux VM instances on Google Cloud
-        and use Go benchmarking tools to compare performance across architectures.
-        You will create an Arm-based c4a-standard-4 and an Intel Emeral...
-      preview_generated: Provision comparable Arm64 and x86_64 Linux VM instances
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-        Benchstat to measure and compare Go application performance across a...
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-        - How do I execute and compare the benchmarks?
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-        - Where are the benchmarking tools installed?
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-        - How do I verify that everything worked?
-        updated_questions: []
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-        - Which VM types should I create for the comparison?
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-          the install?
-        - How do I execute and compare the benchmarks?
-        - What output should I expect from Benchstat?
-        removed_questions:
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-        - Which VM instances does this path use?
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md
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-      preview_before: This introductory Learning Path guides you through provisioning
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-        with Ubuntu Pro 24.04 LTS, installing the Go toolchain, ...
-      preview_after: This introductory Learning Path guides you through provisioning
-        an Arm64 Azure Cobalt 100 (Dpsv6-series) virtual machine using the Azure portal
-        with Ubuntu Pro 24.04 LTS, installing the Go toolchain, ...
-      preview_generated: This Learning Path shows how to provision an Arm64 virtual
-        machine on Microsoft Azure using Cobalt 100 processors (Arm Neoverse N2),
-        install the Go toolchain on Ubuntu Pro 24.04 LTS, and validate the ...
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-        - Which operating system image should I select for the VM?
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-        - What benchmarks will I run and how do I interpret them?
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
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-      preview_before: Learn how to analyze Java application performance on Arm Neoverse-based
-        Linux servers by benchmarking a Tomcat deployment and generating flame graphs.
-        You will set up Apache Tomcat, drive HTTP load wi...
-      preview_after: Learn how to analyze Java application performance on Arm Neoverse-based
-        Linux servers by benchmarking a Tomcat deployment and generating flame graphs.
-        You will set up Apache Tomcat, drive HTTP load wi...
-      preview_generated: Follow this Learning Path to profile Java applications on
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-        removed_questions:
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-        - Which software and tools are used in this Learning Path?
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-        - Which software and tools are used in this Learning Path?
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-        updated_questions: []
-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
-    status: updated
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-      preview_before: This Learning Path guides you through deploying Jenkins LTS
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-      preview_after: This Learning Path guides you through deploying Jenkins LTS on
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-      preview_generated: This Learning Path shows how to deploy and validate Jenkins
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-        - What should I check if I plan to run Docker-based pipelines?
-        removed_questions:
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-        - Which VM types and operating systems are used?
-        - How is Jenkins exposed for browser access?
-        - Which Jenkins and Java versions are used?
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-        updated_questions: []
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-        added_questions:
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-        - Which VM types and operating systems are used in this path?
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-        - How do I validate that Jenkins installed correctly on the Azure VM?
-        - What should I check if I plan to run Docker-based pipelines?
-        removed_questions:
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-        - Which VM types and operating systems are used?
-        - How is Jenkins exposed for browser access?
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-        updated_questions: []
-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/kafka/_index.md
-    status: updated
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-      preview_before: This advanced Learning Path guides you through deploying a production-style
-        Kafka event streaming cluster on Arm-based Linux servers. You will install
-        and configure a three-node ZooKeeper ensemble and...
-      preview_after: This advanced Learning Path guides you through deploying a production-style
-        Kafka event streaming cluster on Arm-based Linux servers. You will install
-        and configure a three-node ZooKeeper ensemble and...
-      preview_generated: This Learning Path guides you through deploying Apache Kafka
-        with ZooKeeper on Arm-based Linux machines, then validating event streaming
-        end to end. You will install and configure a three-node ZooKeep...
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-        - Where do I run the validation and what result should I expect?
-        - Which options are available for automated deployment on cloud platforms?
-        removed_questions:
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-        - Which network ports must be opened?
-        - How are the ZooKeeper and Kafka clusters structured?
-        - How do I verify that the Kafka cluster is working?
-        - Can I automate deployment in the cloud, and what tools are used?
-        updated_questions: []
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-        - How should I assign roles to the seven machines?
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-        - Where do I run the validation and what result should I expect?
-        - Which options are available for automated deployment on cloud platforms?
-        removed_questions:
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-        - Which network ports must be opened?
-        - How are the ZooKeeper and Kafka clusters structured?
-        - How do I verify that the Kafka cluster is working?
-        - Can I automate deployment in the cloud, and what tools are used?
-        updated_questions: []
-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
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-      preview_before: Provision an Arm-based Microsoft Azure Cobalt 100 (Dpsv6) virtual
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-      preview_after: Provision an Arm-based Microsoft Azure Cobalt 100 (Dpsv6) virtual
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-        (arm64), and validate end-to-end messaging before running...
-      preview_generated: This Learning Path guides you through deploying Apache Kafka
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-        an Arm64 VM in the Azure portal using Ubuntu Pro 24.04 LTS, in...
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-        - How do I know the baseline test worked?
-        - Which tools are used for benchmarking and what should be running first?
-        removed_questions:
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-        - How is the virtual machine created and which image is used?
-        - Do I need Java installed before setting up Kafka?
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-        added_questions:
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-        - Which Azure VM size and OS image should I select?
-        - Do I need ZooKeeper for this Kafka setup?
-        - How do I know the baseline test worked?
-        - Which tools are used for benchmarking and what should be running first?
-        removed_questions:
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-        - How is the virtual machine created and which image is used?
-        - Do I need Java installed before setting up Kafka?
-        - Does this deployment use ZooKeeper or KRaft, and what topology is covered?
-        - How do I verify and benchmark the deployment?
-        updated_questions: []
-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
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-      preview_before: "This Learning Path shows how to enable event-driven autoscaling\
-        \ for HTTP workloads on Kubernetes using KEDA and Kedify. You will use Helm\
-        \ to add the Kedify chart repository and install three charts\u2014th..."
-      preview_after: "This Learning Path shows how to enable event-driven autoscaling\
-        \ for HTTP workloads on Kubernetes using KEDA and Kedify. You will use Helm\
-        \ to add the Kedify chart repository and install three charts\u2014th..."
-      preview_generated: "This Learning Path shows how to enable event-driven autoscaling\
-        \ for HTTP workloads on Kubernetes using Kedify and KEDA on Linux. You will\
-        \ add the Kedify Helm repository, install three charts\u2014KEDA (Ked..."
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-        - What behavior should I expect when testing the sample HTTP app?
-        removed_questions:
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-        - Which Kubernetes environments and architectures are supported here?
-        - Which components are installed with Helm?
-        - Do I need an ingress controller for HTTP autoscaling?
-        - How do I verify that autoscaling is working?
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-        added_questions:
-        - What do I need before I start the installation?
-        - Do I need an ingress controller, and which one is used here?
-        - Which Helm charts are installed to enable HTTP autoscaling?
-        - How do I know Kedify and KEDA are running correctly?
-        - What behavior should I expect when testing the sample HTTP app?
-        removed_questions:
-        - What do I need before starting this Learning Path?
-        - Which Kubernetes environments and architectures are supported here?
-        - Which components are installed with Helm?
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-        - How do I verify that autoscaling is working?
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
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-      preview_before: This introductory Learning Path shows how to create, train,
-        and evaluate a simple neural network on Arm servers using Keras Core with
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-      preview_after: This introductory Learning Path shows how to create, train, and
-        evaluate a simple neural network on Arm servers using Keras Core with TensorFlow,
-        PyTorch, and JAX backends. You work on Ubuntu 22.04 LT...
-      preview_generated: Follow a short, introductory workflow to build, train, evaluate,
-        and generate predictions from a simple neural network using Keras Core on
-        Arm machines. You will work on Ubuntu 22.04 LTS running on an...
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-        - How do I switch between TensorFlow, PyTorch, and JAX backends in Keras Core?
-        - What script do I run, and what should I expect as output?
-        - What input shape and data type does the example model expect?
-        removed_questions:
-        - What environment do I need to follow this Learning Path?
-        - What skills are assumed before I start?
-        - Which Keras Core backends are used in this path?
-        - Do I need a specific Python version?
-        - How do I know the steps worked?
-        updated_questions: []
-      generated_diff:
-        before_count: 5
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-        added_questions:
-        - What environment should I prepare before starting?
-        - Which Python version should I use on Ubuntu 22.04, and do I need pip and
-          venv?
-        - How do I switch between TensorFlow, PyTorch, and JAX backends in Keras Core?
-        - What script do I run, and what should I expect as output?
-        - What input shape and data type does the example model expect?
-        removed_questions:
-        - What environment do I need to follow this Learning Path?
-        - What skills are assumed before I start?
-        - Which Keras Core backends are used in this path?
-        - Do I need a specific Python version?
-        - How do I know the steps worked?
-        updated_questions: []
-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
-    status: updated
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-      preview_before: Learn how to build and install custom Linux kernels on Arm cloud
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-        (minimum 24 vCPUs and 200 GB free storage), configure a buil...
-      preview_after: Learn how to build and install custom Linux kernels on Arm cloud
-        instances using TuxMake. You will provision an Ubuntu 24.04 LTS Arm server
-        (minimum 24 vCPUs and 200 GB free storage), configure a buil...
-      preview_generated: Learn how to build, install, and verify custom Linux kernels
-        on Arm cloud instances using TuxMake. You will set up an Ubuntu 24.04 LTS
-        Arm instance, run standard build workflows for direct installatio...
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-        - What outputs should I expect and how do I validate success?
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-        - What outputs should I expect and how do I validate success?
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
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-      preview_before: Learn how to assess and migrate Kubernetes container images
-        to Arm-compatible versions using KubeArchInspect. You will install KubeArchInspect
-        on Linux, ensure kubectl is configured to your cluster, r...
-      preview_after: Learn how to assess and migrate Kubernetes container images to
-        Arm-compatible versions using KubeArchInspect. You will install KubeArchInspect
-        on Linux, ensure kubectl is configured to your cluster, r...
-      preview_generated: Use KubeArchInspect to quickly assess Arm architecture support
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-        - What should I do after running the report?
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-        - How do I run KubeArchInspect and what does it check?
-        - How do I interpret the KubeArchInspect report?
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-        - How do I interpret the output symbols in the report?
-        - What should I do after running the report?
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
-    status: updated
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-      preview_before: This introductory Learning Path shows how to deploy AWS Lambda
-        functions on AWS Graviton processors using Terraform. From a Linux host with
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-        to use the arm64 architecture and apply the workflow to both...
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
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-        \ ..."
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-        - Which model and libraries are used for inference?
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
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-      preview_before: Use migrate-ease to scan your source code for architecture-specific
-        issues before migrating applications to Arm-based servers. This introductory,
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-        - Which packages should I install on my distro before cloning the tool?
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-        - What result should I expect, and how do I verify it?
-        removed_questions:
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-        updated_questions: []
-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/migration/_index.md
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-      preview_before: Learn the essentials of migrating applications to Arm servers
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-        machine (typically a cloud instance), analyze application d...
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-        on Linux. This introductory path guides you to set up an Arm-based development
-        machine (typically a cloud instance), analyze application d...
-      preview_generated: Learn the essentials for migrating applications to Arm servers
-        by setting up a Linux-based Arm development machine, analyzing dependencies,
-        and reviewing common migration challenges and scenarios. Thi...
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-        - Which Go version should I install for Arm servers?
-        - "Where can I check if my application\u2019s dependencies or ISV software\
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-        - Which cloud platforms can I use for the development machine?
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
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-      preview_before: Build a Retrieval-Augmented Generation application on Arm-based
-        servers using Zilliz Cloud for vector search and llama.cpp for LLM inference.
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-      preview_after: Build a Retrieval-Augmented Generation application on Arm-based
-        servers using Zilliz Cloud for vector search and llama.cpp for LLM inference.
-        You will create a Dedicated Zilliz Cloud cluster on AWS us...
-      preview_generated: Learn how to assemble a simple Retrieval-Augmented Generation
-        (RAG) workflow on Arm-based servers using Zilliz Cloud for vector search and
-        llama.cpp for local LLM inference. You will create a Dedicate...
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-        - Do I need an OpenAI API key when using the OpenAI SDK with the local llama.cpp
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-        - How do I verify that everything is working?
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
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-      preview_before: This Learning Path shows how to deploy a single-node, S3-compatible
-        MinIO server on an Arm-based Azure Cobalt 100 virtual machine and verify it
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-        MinIO server on an Arm-based Azure Cobalt 100 virtual machine and verify it
-        end to end. You will provision a Dpsv6 instance (Ubuntu ...
-      preview_generated: Deploy a single-node MinIO object storage server on an Arm-based
-        Azure Cobalt 100 virtual machine and validate it for AI/ML data workflows.
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-        - Which Azure VM type and operating system are used in the steps?
-        - Do I have to use the Azure Portal to create the VM?
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-        - Which network ports must I open for MinIO, and where do I configure them?
-        - How do I connect to the Azure Cobalt 100 VM?
-        - How do I run the throughput benchmark and what result should I expect to
-          see?
-        - How is S3 API compatibility validated in this path?
-        removed_questions:
-        - What are the prerequisites to start this Learning Path?
-        - Which Azure VM type and operating system are used in the steps?
-        - Do I have to use the Azure Portal to create the VM?
-        - Which network ports must be opened for MinIO on Azure?
-        - How do I know the deployment and tests worked?
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
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-      preview_before: Set up an Arm-based Linux server and benchmark machine learning
-        inference using TensorFlow and the MLPerf Inference benchmark suite from MLCommons.
-        You will launch an Arm instance running Ubuntu 20.04...
-      preview_after: Set up an Arm-based Linux server and benchmark machine learning
-        inference using TensorFlow and the MLPerf Inference benchmark suite from MLCommons.
-        You will launch an Arm instance running Ubuntu 20.04...
-      preview_generated: This introductory path shows how to measure machine learning
-        inference performance on Arm-based servers using TensorFlow and the MLPerf
-        Inference benchmark suite from MLCommons. You will launch an Arm...
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
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-      preview_before: Learn how to install MongoDB Community Edition 8.0 on Arm-based
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-        - What additional packages are required to run YCSB, and how do I install
-          them on Ubuntu?
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-          is exercised enough?
-        - Is there an alternative to YCSB for testing MongoDB performance in this
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-        - Which operating systems and MongoDB version are covered?
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-        - Is there an alternative to YCSB in this Learning Path?
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
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-      preview_after: Learn how to deploy MongoDB on Arm-based Google Axion C4A virtual
-        machines and benchmark it with the Yahoo Cloud Serving Benchmark (YCSB). You
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-      preview_generated: Follow this path to deploy MongoDB on an Arm-based Google
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-        Benchmark (YCSB). You will create a C4A instance in the Google Cloud...
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-        - How do I verify that MongoDB is running correctly?
-        - How do I install and run YCSB for MongoDB, and what data size is loaded
-          initially?
-        removed_questions:
-        - What do I need before starting?
-        - Which VM configuration and OS does this path use?
-        - How do I confirm that MongoDB is running correctly?
-        - How is YCSB installed and what dataset size is loaded by default?
-        - Does this cover migrating an existing x86_64 MongoDB deployment to Arm?
-        updated_questions: []
-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/mpi/_index.md
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-      preview_before: This advanced Learning Path is for HPC developers building MPI
-        applications on Arm-based Linux servers or cloud instances. You will install
-        and validate Linaro Forge, then build, debug, and profile a ...
-      preview_after: This advanced Learning Path is for HPC developers building MPI
-        applications on Arm-based Linux servers or cloud instances. You will install
-        and validate Linaro Forge, then build, debug, and profile a ...
-      preview_generated: This advanced path shows how to debug, profile, and improve
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-        servers. You will provision an Arm system (local or via AWS, Micr...
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-        - How do I approach profiling and using optimized routines?
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-        - How should I approach profiling and comparing alternatives?
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md
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-      preview_before: Learn how to control floating-point accuracy for vectorized
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-        functions in Libamath, a component of Arm Performance Libraries, on Linux.
-        This path introduces IEEE-754 representation, Units in the L...
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-        - How do I select and identify accuracy modes in Libamath?
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-        - What does the provided code example demonstrate?
-        - How do I verify that the chosen accuracy mode is working as expected?
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md
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-      preview_before: This Learning Path walks you through building a hybrid Azure
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-        Linux, then deploying nginx using a multi-architecture image to ...
-      preview_after: This Learning Path walks you through building a hybrid Azure
-        Kubernetes Service (AKS) cluster with both Arm-based and x86 node pools on
-        Linux, then deploying nginx using a multi-architecture image to ...
-      preview_generated: Follow this introductory path to build a hybrid Azure Kubernetes
-        Service (AKS) cluster with both x86 and Arm64 nodes, then deploy nginx as
-        a multi-architecture workload on each architecture. You will ...
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-        - How do I know my AKS cluster includes both x86 and Arm nodes?
-        - Which files set up nginx on Intel, and what should I expect after applying
-          them?
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-        - How do I compare performance between the x86 and Arm nginx instances?
-        removed_questions:
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-        - What infrastructure does this path create in Azure?
-        - Which container image and Kubernetes objects are deployed for nginx?
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-        - How do I compare nginx behavior across architectures?
-        updated_questions: []
-      generated_diff:
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-        - How do I know my AKS cluster includes both x86 and Arm nodes?
-        - Which files set up nginx on Intel, and what should I expect after applying
-          them?
-        - How is the Arm nginx deployment created and exposed?
-        - How do I compare performance between the x86 and Arm nginx instances?
-        removed_questions:
-        - What do I need before starting?
-        - What infrastructure does this path create in Azure?
-        - Which container image and Kubernetes objects are deployed for nginx?
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-        - How do I compare nginx behavior across architectures?
-        updated_questions: []
-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md
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-      preview_before: This Learning Path shows how to extend a Google Kubernetes Engine
-        (GKE) cluster with Arm-based nodes and deploy Ollama using a single multi-architecture
-        container image. You begin with an amd64 node r...
-      preview_after: This Learning Path shows how to extend a Google Kubernetes Engine
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-        container image. You begin with an amd64 node r...
-      preview_generated: This Learning Path shows how to create a hybrid Google Kubernetes
-        Engine (GKE) cluster with both amd64 and arm64 nodes and deploy Ollama using
-        a single multi-architecture container image. You begin wi...
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-        added_questions:
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-        - How is the initial amd64 deployment organized in Kubernetes?
-        - What settings should I use when adding the Arm node pool?
-        - How do I verify that requests can reach either architecture in the hybrid
-          cluster?
-        - How do I compare amd64 and arm64 behavior and performance in this setup?
-        removed_questions:
-        - What are the prerequisites to follow this Learning Path?
-        - What Kubernetes resources are created during the steps?
-        - How do I add Arm nodes to the existing GKE cluster?
-        - How do I verify that both architectures are serving requests?
-        - Do I need separate container images for amd64 and arm64?
-        updated_questions: []
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-        added_questions:
-        - What do I need before running the steps?
-        - How is the initial amd64 deployment organized in Kubernetes?
-        - What settings should I use when adding the Arm node pool?
-        - How do I verify that requests can reach either architecture in the hybrid
-          cluster?
-        - How do I compare amd64 and arm64 behavior and performance in this setup?
-        removed_questions:
-        - What are the prerequisites to follow this Learning Path?
-        - What Kubernetes resources are created during the steps?
-        - How do I add Arm nodes to the existing GKE cluster?
-        - How do I verify that both architectures are serving requests?
-        - Do I need separate container images for amd64 and arm64?
-        updated_questions: []
-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/mysql/_index.md
-    status: updated
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-      preview_before: This introductory Learning Path shows how to deploy MySQL on
-        Arm-based Linux systems and interact with it using the MySQL client CLI. You
-        will review common deployment options on Arm, including bare m...
-      preview_after: This introductory Learning Path shows how to deploy MySQL on
-        Arm-based Linux systems and interact with it using the MySQL client CLI. You
-        will review common deployment options on Arm, including bare m...
-      preview_generated: This introductory path shows how to deploy MySQL on Arm-based
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-        You will review common deployment options (bare metal, cloud ...
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-        added_questions:
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-        - "I don\u2019t have an Arm node\u2014what should I do?"
-        - Which deployment approach should I choose for MySQL on Arm?
-        - How do I know the installation worked?
-        - Does this path cover performance tuning?
-        removed_questions:
-        - What do I need before I start?
-        - Which deployment environments are covered?
-        - What will I set up or learn to use?
-        - How do I verify the deployment is working?
-        - Is this the right path if I already know how to deploy MySQL?
-        updated_questions: []
-      generated_diff:
-        before_count: 5
-        after_count: 5
-        added_questions:
-        - What do I need before running the steps?
-        - "I don\u2019t have an Arm node\u2014what should I do?"
-        - Which deployment approach should I choose for MySQL on Arm?
-        - How do I know the installation worked?
-        - Does this path cover performance tuning?
-        removed_questions:
-        - What do I need before I start?
-        - Which deployment environments are covered?
-        - What will I set up or learn to use?
-        - How do I verify the deployment is working?
-        - Is this the right path if I already know how to deploy MySQL?
-        updated_questions: []
-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md
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-      preview_before: Learn how to provision an Arm64 virtual machine on Microsoft
-        Azure Cobalt 100 (Neoverse-N2) using the Azure Portal with Ubuntu Pro 24.04
-        LTS, deploy and secure MySQL, validate the service, and run bas...
-      preview_after: Learn how to provision an Arm64 virtual machine on Microsoft
-        Azure Cobalt 100 (Neoverse-N2) using the Azure Portal with Ubuntu Pro 24.04
-        LTS, deploy and secure MySQL, validate the service, and run bas...
-      preview_generated: Follow this introductory path to provision an Arm64 virtual
-        machine on Microsoft Azure Cobalt 100 (Dpsv6) using the Azure Portal and Ubuntu
-        Pro 24.04 LTS, then deploy and validate MySQL. You will inst...
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-        added_questions:
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-        - Can I create the VM with Azure CLI or IaC instead of the Azure Portal?
-        - What do I need before running the steps?
-        - How do I know MySQL started and is ready for use?
-        - How do I benchmark MySQL in this setup, and what does mysqlslap measure?
-        removed_questions:
-        - What do I need before starting this Learning Path?
-        - Which Azure VM series and operating system image are used?
-        - How do I create the Azure Arm64 VM?
-        - How do I confirm that MySQL is running correctly on the VM?
-        - What tool is used for benchmarking and what does it evaluate?
-        updated_questions: []
-      generated_diff:
-        before_count: 5
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-        added_questions:
-        - Which Azure VM size and base image should I use?
-        - Can I create the VM with Azure CLI or IaC instead of the Azure Portal?
-        - What do I need before running the steps?
-        - How do I know MySQL started and is ready for use?
-        - How do I benchmark MySQL in this setup, and what does mysqlslap measure?
-        removed_questions:
-        - What do I need before starting this Learning Path?
-        - Which Azure VM series and operating system image are used?
-        - How do I create the Azure Arm64 VM?
-        - How do I confirm that MySQL is running correctly on the VM?
-        - What tool is used for benchmarking and what does it evaluate?
-        updated_questions: []
-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/mysql-lns-azure/_index.md
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-  - path: content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md
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-      preview_before: This Learning Path shows how to benchmark MySQL on Arm Linux
-        using Sysbench and apply profile-guided optimization (PGO) with GCC. You will
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-      preview_after: This Learning Path shows how to benchmark MySQL on Arm Linux
-        using Sysbench and apply profile-guided optimization (PGO) with GCC. You will
-        build, configure, and run a MySQL server on one Arm server ru...
-      preview_generated: This Learning Path shows you how to benchmark MySQL on Arm
-        Linux systems using Sysbench and apply profile-guided optimization (PGO) with
-        GCC. You will build, configure, and run a MySQL server on one A...
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-        added_questions:
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-        - Which packages should I install to build MySQL on Ubuntu 22.04?
-        - Why do I need to build MySQL on the Sysbench client as well?
-        - Can I use a different Linux distribution or Ubuntu version?
-        - How is PGO applied to MySQL in this path, and which compiler is used?
-        removed_questions:
-        - What hardware and OS do I need before starting?
-        - Do I have to use Ubuntu 22.04, or can other Linux distributions work?
-        - Why do I need to build MySQL on the Sysbench client?
-        - What artifacts will I have at the end of the path?
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-        updated_questions: []
-      generated_diff:
-        before_count: 5
-        after_count: 5
-        added_questions:
-        - What do I need before running the steps?
-        - Which packages should I install to build MySQL on Ubuntu 22.04?
-        - Why do I need to build MySQL on the Sysbench client as well?
-        - Can I use a different Linux distribution or Ubuntu version?
-        - How is PGO applied to MySQL in this path, and which compiler is used?
-        removed_questions:
-        - What hardware and OS do I need before starting?
-        - Do I have to use Ubuntu 22.04, or can other Linux distributions work?
-        - Why do I need to build MySQL on the Sysbench client?
-        - What artifacts will I have at the end of the path?
-        - How is PGO applied to MySQL in this path?
-        updated_questions: []
-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md
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-      preview_before: This advanced Learning Path guides you through tuning MySQL
-        for better performance on Arm-based (Neoverse) cloud VMs running Linux. You
-        will review system-level considerations such as storage technolo...
-      preview_after: This advanced Learning Path guides you through tuning MySQL for
-        better performance on Arm-based (Neoverse) cloud VMs running Linux. You will
-        review system-level considerations such as storage technolo...
-      preview_generated: This advanced Learning Path provides practical guidance for
-        tuning MySQL on Arm-based VMs (Neoverse) running Linux in major clouds, including
-        AWS, Microsoft Azure, Google Cloud, and Oracle. You will r...
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-        - Where should I place MySQL tuning parameters, and can I use command-line
-          options?
-        - Should I change many MySQL settings at once?
-        removed_questions:
-        - What do I need before starting?
-        - Which environment does this target?
-        - How do I apply the MySQL tuning settings?
-        - What system-level choices should I evaluate for performance?
-        - How do I validate that the tuning is effective?
-        updated_questions: []
-      generated_diff:
-        before_count: 5
-        after_count: 5
-        added_questions:
-        - What do I need before running the steps?
-        - Which platforms and Arm targets does this path focus on?
-        - How should I choose storage and filesystem for MySQL?
-        - Where should I place MySQL tuning parameters, and can I use command-line
-          options?
-        - Should I change many MySQL settings at once?
-        removed_questions:
-        - What do I need before starting?
-        - Which environment does this target?
-        - How do I apply the MySQL tuning settings?
-        - What system-level choices should I evaluate for performance?
-        - How do I validate that the tuning is effective?
-        updated_questions: []
-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md
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-      preview_before: "This advanced Learning Path shows how to develop and validate\
-        \ firmware pre-silicon for Arm Neoverse CSS\u2011V3 using the RD\u2011V3 reference\
-        \ design and Arm Fixed Virtual Platforms (FVPs). You will examine the..."
-      preview_after: "This advanced Learning Path shows how to develop and validate\
-        \ firmware pre-silicon for Arm Neoverse CSS\u2011V3 using the RD\u2011V3 reference\
-        \ design and Arm Fixed Virtual Platforms (FVPs). You will examine the..."
-      preview_generated: "This advanced Learning Path guides you through validating\
-        \ the Arm Neoverse CSS\u2011V3 firmware stack pre\u2011silicon using the RD\u2011\
-        V3 Fixed Virtual Platform (FVP). You will set up a containerized build environ..."
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-        added_questions:
-        - What do I need before running the build and simulation steps?
-        - "Which FVP model version should I use with my RD\u2011V3 release tag?"
-        - What result should I expect when the FVP simulation completes successfully?
-        - How do I diagnose issues if the boot sequence stalls?
-        - "What is different about running the dual\u2011chip RD\u2011V3\u2011R1 simulation,\
-          \ and what should I verify?"
-        removed_questions:
-        - What are the prerequisites and recommended environment?
-        - Can I complete this on a cloud instance?
-        - What tools and source workflow does the path use?
-        - How do I choose the correct FVP model for my build?
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-        - "Which FVP model version should I use with my RD\u2011V3 release tag?"
-        - What result should I expect when the FVP simulation completes successfully?
-        - How do I diagnose issues if the boot sequence stalls?
-        - "What is different about running the dual\u2011chip RD\u2011V3\u2011R1 simulation,\
-          \ and what should I verify?"
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md
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-        updated_questions:
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/nginx/_index.md
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-        - What will I have working by the end, and how do I learn more?
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-        - What network settings should I configure before starting?
-        - What should be ready before configuring the reverse proxy and API gateway?
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md
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-      preview_before: This Learning Path shows how to deploy and validate NGINX on
-        an Arm-based Microsoft Azure Cobalt 100 virtual machine. Using the Azure portal,
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-      preview_after: This Learning Path shows how to deploy and validate NGINX on
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-      preview_generated: This Learning Path guides you through deploying NGINX on
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-        - How do I install and verify ApacheBench (ab) on Ubuntu Pro 24.04 LTS?
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-        - Which Azure VM type and OS image are used?
-        - Do I need the Azure CLI or an IaC tool to follow the steps?
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-        - How is NGINX benchmarked in this path?
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-        - Which Azure VM series and OS image should I select?
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-        - How do I know NGINX is installed and serving content?
-        - How do I install and verify ApacheBench (ab) on Ubuntu Pro 24.04 LTS?
-        removed_questions:
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-        - Which Azure VM type and OS image are used?
-        - Do I need the Azure CLI or an IaC tool to follow the steps?
-        - How do I confirm that NGINX is installed and serving my content?
-        - How is NGINX benchmarked in this path?
-        updated_questions: []
-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
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-      preview_before: This advanced Learning Path shows how to tune Nginx on Arm-based
-        Linux servers in about 60 minutes. You will review how Linux kernel parameters,
-        compiler and library choices, and Nginx configuration a...
-      preview_after: This advanced Learning Path shows how to tune Nginx on Arm-based
-        Linux servers in about 60 minutes. You will review how Linux kernel parameters,
-        compiler and library choices, and Nginx configuration a...
-      preview_generated: This advanced Learning Path shows how to tune Nginx for Arm-based
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-        scaling your infrastructure up or out. You will examine ho...
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-        - What result should I expect after tuning, and how do I validate it?
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-        - How do I list and change the Linux kernel networking parameters mentioned
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-        - Which Nginx configuration files will I tune?
-        - Do I have to use wrk2 for performance testing?
-        - What result should I expect after tuning, and how do I validate it?
-        removed_questions:
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-        - Which operating system and hardware does this target?
-        - Which configuration files and parameters will I modify?
-        - Does this provide a one-size-fits-all tuning recipe?
-        - How do I test and validate the impact of my changes?
-        updated_questions: []
-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md
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-      preview_before: Learn how to run a Hugging Face Natural Language Processing
-        (NLP) model with PyTorch on Arm servers. Using an Arm-based cloud instance
-        or on-prem Arm server running Ubuntu 22.04 LTS, you will install ...
-      preview_after: Learn how to run a Hugging Face Natural Language Processing (NLP)
-        model with PyTorch on Arm servers. Using an Arm-based cloud instance or on-prem
-        Arm server running Ubuntu 22.04 LTS, you will install ...
-      preview_generated: Follow this introductory path to deploy and run a Hugging
-        Face Natural Language Processing (NLP) model with PyTorch on an Arm AArch64
-        CPU. The instructions target an Arm server running Ubuntu 22.04 LT...
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-        - How do I know the setup worked?
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-        - Do I need a GPU to run the model?
-        - How do I know the deployment and profiling worked?
-        removed_questions:
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-        - What tools and languages are used in this Learning Path?
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md
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-      preview_before: Learn how to deploy and evaluate Node.js on Google Cloud C4A
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-      preview_after: Learn how to deploy and evaluate Node.js on Google Cloud C4A
-        virtual machines powered by Axion processors built on Arm Neoverse-V2 cores.
-        You will provision a SUSE Linux Enterprise Server VM (for exam...
-      preview_generated: "This introductory Learning Path shows how to deploy and\
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-        - What should I expect from the Autocannon benchmark, and what should I check
-          if it fails?
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-        - What are the prerequisites before starting?
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-      preview_before: Learn how to deploy OpenStack on Arm-based Microsoft Azure Cobalt
-        100 (Arm64) virtual machines. You will provision an Azure Dpsv6 series VM
-        and use DevStack to bring up a single-node development envir...
-      preview_after: Learn how to deploy OpenStack on Arm-based Microsoft Azure Cobalt
-        100 (Arm64) virtual machines. You will provision an Azure Dpsv6 series VM
-        and use DevStack to bring up a single-node development envir...
-      preview_generated: 'Learn how to deploy OpenStack on Microsoft Azure Cobalt
-        100 Arm64 virtual machines using two approaches: DevStack for a single-node
-        development setup and Kolla-Ansible for a containerized deployment. ...'
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-        - Which Azure VM size and disk setup should I use for the DevStack deployment?
-        - What specifications and OS are required for the Kolla-Ansible host?
-        - After deployment, how do I access OpenStack and what should I expect to
-          be running?
-        removed_questions:
-        - What do I need before starting?
-        - Which Azure VM configuration is used for DevStack and Kolla-Ansible?
-        - Which OpenStack services are deployed and how do I access them?
-        - How do I verify the deployment worked?
-        updated_questions:
-        - Can I run DevStack and Kolla-Ansible on the same VM?
-      generated_diff:
-        before_count: 5
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-        added_questions:
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-        - Which Azure VM size and disk setup should I use for the DevStack deployment?
-        - What specifications and OS are required for the Kolla-Ansible host?
-        - After deployment, how do I access OpenStack and what should I expect to
-          be running?
-        removed_questions:
-        - What do I need before starting?
-        - Which Azure VM configuration is used for DevStack and Kolla-Ansible?
-        - Which OpenStack services are deployed and how do I access them?
-        - How do I verify the deployment worked?
-        updated_questions:
-        - Can I run DevStack and Kolla-Ansible on the same VM?
-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md
-    status: updated
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-      preview_before: This Learning Path guides you through deploying and observing
-        a Python Flask microservice on Arm64-based Google Cloud C4A Axion processors.
-        You will provision a c4a-standard-4 VM running SUSE Linux, c...
-      preview_after: This Learning Path guides you through deploying and observing
-        a Python Flask microservice on Arm64-based Google Cloud C4A Axion processors.
-        You will provision a c4a-standard-4 VM running SUSE Linux, c...
-      preview_generated: Follow this introductory path to deploy an instrumented Python
-        Flask microservice on an Arm64 Google Cloud C4A Axion virtual machine and
-        stand up an end-to-end observability pipeline with OpenTelemetr...
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-        - Which Google Cloud VM and operating system does this path use?
-        - Which firewall ports should I open and why?
-        - How are the telemetry components connected in this setup?
-        - How do I validate that telemetry is flowing end-to-end?
-        removed_questions:
-        - What Google Cloud resources will I create?
-        - Which firewall ports must be opened on GCP?
-        - What components are deployed and how do they connect?
-        - What are the prerequisites before starting?
-        - How do I verify that telemetry is being collected?
-        updated_questions: []
-      generated_diff:
-        before_count: 5
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-        added_questions:
-        - What do I need before running the steps?
-        - Which Google Cloud VM and operating system does this path use?
-        - Which firewall ports should I open and why?
-        - How are the telemetry components connected in this setup?
-        - How do I validate that telemetry is flowing end-to-end?
-        removed_questions:
-        - What Google Cloud resources will I create?
-        - Which firewall ports must be opened on GCP?
-        - What components are deployed and how do they connect?
-        - What are the prerequisites before starting?
-        - How do I verify that telemetry is being collected?
-        updated_questions: []
-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/pac/_index.md
-    status: updated
-    changed_on_disk: true
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-      preview_before: Use a Linux Arm server to explore Arm Pointer Authentication
-        (PAC) by building and analyzing a small, vulnerable C program. You will compile
-        the application with and without PAC, inspect the generated...
-      preview_after: Use a Linux Arm server to explore Arm Pointer Authentication
-        (PAC) by building and analyzing a small, vulnerable C program. You will compile
-        the application with and without PAC, inspect the generated...
-      preview_generated: This Learning Path provides a hands-on introduction to Arm
-        Pointer Authentication on a Linux Arm server. You will create a small vulnerable
-        C program, build it with and without Pointer Authentication,...
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-      before_count: 5
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-        added_questions:
-        - What do I need before running the steps?
-        - Can I use any cloud provider for the Arm instance?
-        - Which tools do I install to run the exploit code?
-        - Which binary should I target when running the exploit?
-        - What result should I expect when the exploit works, and how do I compare
-          with Pointer Authentication enabled?
-        removed_questions:
-        - What setup do I need before starting?
-        - Can I use my preferred cloud provider?
-        - What will I build and test?
-        - What tools do I need to install?
-        - How do I validate that the path worked?
-        updated_questions: []
-      generated_diff:
-        before_count: 5
-        after_count: 5
-        added_questions:
-        - What do I need before running the steps?
-        - Can I use any cloud provider for the Arm instance?
-        - Which tools do I install to run the exploit code?
-        - Which binary should I target when running the exploit?
-        - What result should I expect when the exploit works, and how do I compare
-          with Pointer Authentication enabled?
-        removed_questions:
-        - What setup do I need before starting?
-        - Can I use my preferred cloud provider?
-        - What will I build and test?
-        - What tools do I need to install?
-        - How do I validate that the path worked?
-        updated_questions: []
-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md
-    status: updated
-    changed_on_disk: true
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-      preview_before: Use an AI coding assistant with the Arm MCP Server to run Arm
-        Performix Code Hotspots on a C++ application and act on the results on Arm
-        Neoverse. You configure a GitHub Copilot prompt file to launch ...
-      preview_after: Use an AI coding assistant with the Arm MCP Server to run Arm
-        Performix Code Hotspots on a C++ application and act on the results on Arm
-        Neoverse. You configure a GitHub Copilot prompt file to launch ...
-      preview_generated: This advanced Learning Path shows how to drive Arm Performix
-        profiling through the Arm MCP Server using an AI coding assistant to find
-        and address C++ code hotspots on Arm Neoverse. You will build a s...
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-      before_count: 5
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-        added_questions:
-        - What do I need before running this Learning Path?
-        - Do I have to use Visual Studio Code and GitHub Copilot?
-        - Which prompt file should I use to run the Code Hotspots recipe?
-        - How do I know Arm Performix can reach my remote Arm target?
-        - What result should I expect, and what optimizations are applied?
-        removed_questions:
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-        - What environment and tools does this Learning Path use?
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-        - How is profiling executed and what results should I expect?
-        - What optimizations does the agent suggest and what is the expected outcome?
-        updated_questions: []
-      generated_diff:
-        before_count: 5
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-        added_questions:
-        - What do I need before running this Learning Path?
-        - Do I have to use Visual Studio Code and GitHub Copilot?
-        - Which prompt file should I use to run the Code Hotspots recipe?
-        - How do I know Arm Performix can reach my remote Arm target?
-        - What result should I expect, and what optimizations are applied?
-        removed_questions:
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-        - What environment and tools does this Learning Path use?
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-        - How is profiling executed and what results should I expect?
-        - What optimizations does the agent suggest and what is the expected outcome?
-        updated_questions: []
-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md
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-      preview_before: "Analyze and improve a Linux application\u2019s performance\
-        \ on Arm Neoverse-based servers using Arm Performix Runbook. You will configure\
-        \ a Performix connection, build a C Mandelbrot set generator, then run..."
-      preview_after: "Analyze and improve a Linux application\u2019s performance on\
-        \ Arm Neoverse-based servers using Arm Performix Runbook. You will configure\
-        \ a Performix connection, build a C Mandelbrot set generator, then run..."
-      preview_generated: Learn to analyze and improve a Linux application on Arm Neoverse-based
-        servers using Arm Performix. You will configure a Performix connection, build
-        a Mandelbrot set generator, and run the CPU Microar...
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-      before_count: 5
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-        added_questions:
-        - What do I need before running this Learning Path?
-        - How do I know the sample Mandelbrot application built and runs correctly?
-        - Which option should I select for the Instruction Mix recipe?
-        - What should I look for in the CPU Microarchitecture recipe results?
-        - "How do I confirm whether my workload is using SIMD, and what if it isn\u2019\
-          t?"
-        removed_questions:
-        - What hardware and OS do I need to follow this path?
-        - What sample application will I build, and what does it produce?
-        - Which Arm Performix recipes are used and why?
-        - Which analysis mode should I select for the Instruction Mix recipe?
-        - How do I verify that my optimizations had an effect?
-        updated_questions: []
-      generated_diff:
-        before_count: 5
-        after_count: 5
-        added_questions:
-        - What do I need before running this Learning Path?
-        - How do I know the sample Mandelbrot application built and runs correctly?
-        - Which option should I select for the Instruction Mix recipe?
-        - What should I look for in the CPU Microarchitecture recipe results?
-        - "How do I confirm whether my workload is using SIMD, and what if it isn\u2019\
-          t?"
-        removed_questions:
-        - What hardware and OS do I need to follow this path?
-        - What sample application will I build, and what does it produce?
-        - Which Arm Performix recipes are used and why?
-        - Which analysis mode should I select for the Instruction Mix recipe?
-        - How do I verify that my optimizations had an effect?
-        updated_questions: []
-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/performix-system-characterization/_index.md
-    status: skipped
-    skip_reason: draft
-    change_reasons:
-    - draft
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-    summary:
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md
-    status: updated
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-      generated_at_before: '2026-06-02T04:47:26Z'
-      generated_at_after: '2026-06-02T04:47:26Z'
-      preview_before: Follow this introductory path to deploy and validate a PHP stack
-        on Arm-based Google Cloud C4A virtual machines built on Axion processors.
-        You will provision a SUSE Linux Enterprise Server instance (c...
-      preview_after: Follow this introductory path to deploy and validate a PHP stack
-        on Arm-based Google Cloud C4A virtual machines built on Axion processors.
-        You will provision a SUSE Linux Enterprise Server instance (c...
-      preview_generated: Follow this introductory path to deploy and validate a PHP
-        stack on Google Cloud C4A Arm-based Axion virtual machines using SUSE Linux
-        Enterprise Server. You will provision a c4a-standard-4 instance (...
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-        added_questions:
-        - What do I need before provisioning the instance on Google Cloud?
-        - Which Google Cloud VM configuration does this path use?
-        - Which operating system and architecture are targeted?
-        - How do I install the PHP stack on the SUSE instance?
-        - How do I validate the setup and what should I look for in benchmarks?
-        removed_questions:
-        - What do I need before starting?
-        - Which VM type and operating system are used?
-        - What software will I install and configure?
-        - How do I validate that PHP is working on the Arm VM?
-        - What benchmarking is included and what metrics will I see?
-        updated_questions: []
-      generated_diff:
-        before_count: 5
-        after_count: 5
-        added_questions:
-        - What do I need before provisioning the instance on Google Cloud?
-        - Which Google Cloud VM configuration does this path use?
-        - Which operating system and architecture are targeted?
-        - How do I install the PHP stack on the SUSE instance?
-        - How do I validate the setup and what should I look for in benchmarks?
-        removed_questions:
-        - What do I need before starting?
-        - Which VM type and operating system are used?
-        - What software will I install and configure?
-        - How do I validate that PHP is working on the Arm VM?
-        - What benchmarking is included and what metrics will I see?
-        updated_questions: []
-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md
-    status: updated
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md
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-      preview_before: This Learning Path shows how to instrument C/C++ applications
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/postgresql/_index.md
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-    category: servers-and-cloud-computing
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-      preview_generated: Deploy PostgreSQL on Arm-based Azure Cobalt 100 (Dpsv6) virtual
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
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-      preview_generated: This introductory Learning Path shows you how to build and
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md
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-        Arm instance with at least 16 cores, 64 GB RAM, and 50 GB d...
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md
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-      preview_before: Learn how to deploy RabbitMQ on Arm64 infrastructure across
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-        Azure and Google Cloud. You will provision Arm-based Linux virtual machines
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-      preview_generated: Learn how to deploy and validate RabbitMQ on Arm-based cloud
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-        powered by Axion processors. You will provision Arm64 Linux ...
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/rag/_index.md
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-        on Arm-based Google Cloud Axion processors using llama-cpp-python with KleidiAI.
-        You will provision an Arm server running Ubuntu 22.04 L...
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/ran/_index.md
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/snappy/_index.md
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-  - path: content/learning-paths/servers-and-cloud-computing/sve/_index.md
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/sysreport/_index.md
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
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-    category: servers-and-cloud-computing
-  - path: content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md
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-  - path: content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md
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-        modified DynamoRIO stride benchmark, collect sampling a...
-      preview_generated: This introductory Learning Path guides you through performance
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From c13bb404e6acdfb6f507aa77fdd4a7278d6d7a63 Mon Sep 17 00:00:00 2001
From: Christopher Moroney <christopher.moroney1@gmail.com>
Date: Mon, 8 Jun 2026 16:17:28 -0700
Subject: [PATCH 22/23] generate_summary_faq ai feature ready to commit

---
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 .../_index.md                                 |  59 +---
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 .../_index.md                                 |  57 +---
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 .../arcee-foundation-model-on-aws/_index.md   |  54 +---
 .../arcee-foundation-model-on-gcp/_index.md   |  56 +---
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 .../arm-mcp-server/_index.md                  |  59 +---
 .../arm-soc-migration-learning-path/_index.md |  60 +---
 .../arm_linux_page_size/_index.md             |  55 +---
 .../arm_pmu/_index.md                         |  59 +---
 .../aws-copilot/_index.md                     |  55 +---
 .../aws-terraform/_index.md                   |  54 +---
 .../azure-arm-template/_index.md              |  59 +---
 .../azure-cobalt-cicd-aks/_index.md           |  54 +---
 .../azure-terraform/_index.md                 |  57 +---
 .../azure-vm/_index.md                        |  57 +---
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 .../bitmap_scan_sve2/_index.md                |  58 +---
 .../bolt-demo/_index.md                       |  62 +---
 .../bolt-merge/_index.md                      |  55 +---
 .../bolt/_index.md                            |  58 +---
 .../buildkite-gcp/_index.md                   |  59 +---
 .../cassandra-on-gcp/_index.md                |  55 +---
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 .../_index.md                                 |  56 +---
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 .../gke-multi-arch/_index.md                  |  55 +---
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 .../golang-on-azure/_index.md                 |  59 +---
 .../helm-on-gcp/_index.md                     |  57 +---
 .../intro/_index.md                           |  55 +---
 .../irq-tuning-guide/_index.md                |  59 +---
 .../java-gc-tuning/_index.md                  |  55 +---
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 .../jenkins/_index.md                         |  56 +---
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 .../kubearchinspect/_index.md                 |  58 +---
 .../lambda_functions/_index.md                |  55 +---
 .../libhugetlbfs/_index.md                    |  55 +---
 .../llama-cpu/_index.md                       |  56 +---
 .../llama-vision/_index.md                    |  56 +---
 .../llama_cpp_streamline/_index.md            |  58 +---
 .../servers-and-cloud-computing/lse/_index.md |  54 +---
 .../mariadb/_index.md                         |  56 +---
 .../memcached/_index.md                       |  54 +---
 .../memcached_cache/_index.md                 |  58 +---
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 .../memory_consistency/_index.md              |  58 +---
 .../microbenchmark-network-iperf3/_index.md   |  56 +---
 .../migrate-ease/_index.md                    |  56 +---
 .../migration/_index.md                       |  56 +---
 .../milvus-rag/_index.md                      |  54 +---
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 .../ml-perf/_index.md                         |  54 +---
 .../mongodb-on-azure/_index.md                |  57 +---
 .../mongodb-on-gcp/_index.md                  |  55 +---
 .../mongodb/_index.md                         |  61 +---
 .../servers-and-cloud-computing/mpi/_index.md |  59 +---
 .../multi-accuracy-libamath/_index.md         |  58 +---
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 .../multiarch_ollama_on_gke/_index.md         |  58 +---
 .../mysql-azure/_index.md                     |  57 +---
 .../mysql-lns-azure/_index.md                 |   9 +-
 .../mysql/_index.md                           |  56 +---
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 .../net-aspire/_index.md                      |  59 +---
 .../nginx-on-azure/_index.md                  |  56 +---
 .../nginx/_index.md                           |  55 +---
 .../nginx_tune/_index.md                      |  59 +---
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 .../onnx-on-azure/_index.md                   |  57 +---
 .../onnx/_index.md                            |  57 +---
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 .../openshift/_index.md                       |  58 +---
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 .../servers-and-cloud-computing/pac/_index.md |  57 +---
 .../performix-mcp-agent/_index.md             |  61 +---
 .../performix-microarchitecture/_index.md     |  57 +---
 .../_index.md                                 |   8 +-
 .../php-on-gcp/_index.md                      |  56 +---
 .../pinning-threads/_index.md                 |  58 +---
 .../pmuv3_plugin_learning_path/_index.md      |  59 +---
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 .../profiling-for-neoverse/_index.md          |  60 +---
 .../puppet-on-gcp/_index.md                   |  56 +---
 .../pytorch-llama/_index.md                   |  54 +---
 .../qdrant-on-axion/_index.md                 |  56 +---
 .../rabbitmq-gcp/_index.md                    |  58 +---
 .../servers-and-cloud-computing/rag/_index.md |  58 +---
 .../servers-and-cloud-computing/ran/_index.md |  51 +---
 .../ray-on-axion/_index.md                    |  58 +---
 .../redis-cobalt/_index.md                    |  57 +---
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 .../rme-cca-basics/_index.md                  |  53 +---
 .../rtp-llm/_index.md                         |  53 +---
 .../ruby-on-rails/_index.md                   |  55 +---
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 .../serverless-framework-aws-intro/_index.md  |  57 +---
 .../_index.md                                 |  59 +---
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 .../spark-velox-cobalt/_index.md              |   8 +-
 .../spark/_index.md                           |  53 +---
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 .../tensorflow-gcp/_index.md                  |  55 +---
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 .../_index.md                                 |  58 +---
 .../typescript-on-gcp/_index.md               |  57 +---
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 .../vLLM-quant/_index.md                      |   8 +-
 .../vectorscan/_index.md                      |  54 +---
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 .../vvenc/_index.md                           |  56 +---
 .../whisper/_index.md                         |  56 +---
 .../wordpress/_index.md                       |  54 +---
 .../zlib/_index.md                            |  55 +---
 reports/generated-summary-faq/README.md       |  38 +++
 set-summary-faq-flags                         |   5 +
 tools/generate-summary-faq.md                 | 279 +++++++++++-------
 tools/generate_summary_faq.py                 |  23 +-
 tools/prompts/summary_faq_system.md           |  19 +-
 tools/prompts/summary_faq_user.md             |  21 +-
 tools/set-summary-faq-flags                   |   5 +
 tools/set_summary_faq_flags.py                | 206 +++++++++++++
 427 files changed, 1723 insertions(+), 22155 deletions(-)
 create mode 100644 reports/generated-summary-faq/README.md
 create mode 100755 set-summary-faq-flags
 create mode 100755 tools/set-summary-faq-flags
 create mode 100644 tools/set_summary_faq_flags.py

diff --git a/.gitignore b/.gitignore
index ed57c8fe4a..261203bea1 100644
--- a/.gitignore
+++ b/.gitignore
@@ -27,7 +27,3 @@ tags
 
 # Generated spell check config
 .spellcheck-non-draft.yml
-reports/generated-summary-faq/local-test.yml
-reports/generated-summary-faq/*.txt
-reports/generated-summary-faq/*.md
-reports/generated-summary-faq/*/
diff --git a/content/learning-paths/automotive/intro/_index.md b/content/learning-paths/automotive/intro/_index.md
index 20023bacb7..af1f7f9f52 100644
--- a/content/learning-paths/automotive/intro/_index.md
+++ b/content/learning-paths/automotive/intro/_index.md
@@ -15,11 +15,9 @@ prerequisites:
 draft: true
 cascade:
     draft: true
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 author: Jason Andrews
 
 ### Tags
diff --git a/content/learning-paths/automotive/openadkit1_container/_index.md b/content/learning-paths/automotive/openadkit1_container/_index.md
index 143e55e674..b690b9bf93 100644
--- a/content/learning-paths/automotive/openadkit1_container/_index.md
+++ b/content/learning-paths/automotive/openadkit1_container/_index.md
@@ -14,59 +14,9 @@ learning_objectives:
 prerequisites:
     - An Arm Neoverse cloud instance, or a local Arm Neoverse Linux computer with at least 16 CPUs and 32GB of RAM
     - Familiarity with Docker and Docker Compose
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:26:58Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
-  summary_generated_at: '2026-06-01T20:57:21Z'
-  summary_source_hash: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
-  faq_generated_at: '2026-06-02T21:26:58Z'
-  faq_source_hash: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
-  summary: >-
-    This Learning Path shows how to deploy and run a containerized autonomous driving simulation
-    using Autoware Open AD Kit on Arm Neoverse with Docker, illustrating SOAFEE-aligned Shift-Left
-    development. You will use a Linux Arm Neoverse instance—cloud or on‑prem—and Docker Compose
-    to launch the Open AD Kit demo, which starts a Visualizer and then runs Planning and Simulation
-    services defined in docker/docker-compose.yml. It introduces the SOAFEE architecture plus
-    the roles of ROS 2 and Open AD Kit. Prerequisites are an Arm Neoverse system with at least
-    16 CPUs and 32GB RAM, and familiarity with Docker and Docker Compose. Estimated time is 60
-    minutes; the example was tested on AWS EC2 and an Ampere Altra workstation.
-  faqs:
-  - question: What do I need before running the demo?
-    answer: >-
-      You need an Arm Neoverse cloud instance or a local Arm Neoverse Linux computer with at least
-      16 CPUs and 32GB of RAM. Familiarity with Docker and Docker Compose is also required.
-  - question: Should I use a cloud instance or an on-prem Arm Neoverse system?
-    answer: >-
-      You can use either. The example has been tested on AWS EC2 and an Ampere Altra workstation,
-      so choose the environment you have access to or that best fits your needs.
-  - question: Do I need to install Docker and Docker Compose?
-    answer: >-
-      Yes. Docker is required to run Open AD Kit, and the demo uses Docker Compose; refer to the
-      Docker install guide to set it up on Linux.
-  - question: What should I expect when I start the demo with Docker Compose?
-    answer: >-
-      The Visualizer service starts first in detached mode, followed by continuous execution of
-      the Planning and Simulation components. The ROS 2 commands and service definitions are specified
-      in docker/docker-compose.yml.
-  - question: Where can I inspect or adjust what gets executed?
-    answer: >-
-      Open the docker/docker-compose.yml file to review the service configuration, startup order,
-      and ROS command lines. You can use it as the basis for exploring advanced configurations
-      mentioned in the path.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Odin Shen
 
diff --git a/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md b/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
index 273ae1902a..33a092875a 100644
--- a/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
+++ b/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
@@ -15,64 +15,9 @@ prerequisites:
     - Access to two Arm-based Neoverse cloud instances, or a local Arm Neoverse Linux system with at least 16 CPUs and 32 GB of RAM
     - Completion of the [Deploy Open AD Kit containerized autonomous driving simulation on Arm Neoverse](/learning-paths/automotive/openadkit1_container/) Learning Path
     - Basic familiarity with Docker
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:27:35Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 92a6dac2b1674a44cd623a0c8c3189b38438124a6067ed7ca776999ff1d8b5bf
-  summary_generated_at: '2026-06-01T20:57:59Z'
-  summary_source_hash: 92a6dac2b1674a44cd623a0c8c3189b38438124a6067ed7ca776999ff1d8b5bf
-  faq_generated_at: '2026-06-02T21:27:35Z'
-  faq_source_hash: 92a6dac2b1674a44cd623a0c8c3189b38438124a6067ed7ca776999ff1d8b5bf
-  summary: >-
-    This advanced Learning Path shows automotive engineers how to prototype safety-critical isolation
-    for autonomous driving workloads on Arm Neoverse running Linux. You apply ISO 26262 concepts
-    (including ASIL and the V-model), use a safety island architectural approach, and separate
-    a simulation platform into independent, safety-isolated components. Communication between
-    components uses DDS in a publish-subscribe pattern, with containerized deployment and tooling
-    that includes Docker, ROS 2, and Python. Prerequisites include two Arm-based Neoverse cloud
-    instances or a local Arm Neoverse Linux system with at least 16 CPUs and 32 GB RAM, completion
-    of the “Deploy Open AD Kit containerized autonomous driving simulation on Arm Neoverse” Learning
-    Path, and basic Docker familiarity. Estimated time to complete is about 60 minutes.
-  faqs:
-  - question: What do I need before running this path?
-    answer: >-
-      You need either two Arm-based Neoverse cloud instances or a local Arm Neoverse Linux system
-      with at least 16 CPUs and 32 GB of RAM. You must also have completed the “Deploy Open AD
-      Kit containerized autonomous driving simulation on Arm Neoverse” Learning Path and be familiar
-      with Docker.
-  - question: Can I use a single local system instead of two cloud instances?
-    answer: >-
-      Yes. A local Arm Neoverse Linux system with at least 16 CPUs and 32 GB of RAM is listed
-      as an alternative to two Arm-based Neoverse cloud instances.
-  - question: Which technologies are used for communication and isolation?
-    answer: >-
-      The path uses DDS with a publish–subscribe architecture and containerized deployment to
-      separate components and communicate between them. Tools referenced include Docker, ROS 2,
-      DDS, and Python on Linux.
-  - question: How are ISO 26262 and ASIL levels applied here?
-    answer: >-
-      The path introduces the ISO 26262 safety lifecycle aligned with the V-model and explains
-      how ASIL levels guide design and testing. You apply prevention and detection principles
-      and plan safe-state behavior as part of the workflow.
-  - question: What result should I expect and how do I know I’m on track?
-    answer: >-
-      Expect to separate the simulation platform into independent, safety-isolated components
-      that communicate via DDS. You should be able to describe a safety island architecture versus
-      a non-safety ECU and relate requirements to verification activities consistent with ISO
-      26262.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: 
     - Odin Shen
diff --git a/content/learning-paths/automotive/system76-auto/_index.md b/content/learning-paths/automotive/system76-auto/_index.md
index d1a23164d8..287b2e9553 100644
--- a/content/learning-paths/automotive/system76-auto/_index.md
+++ b/content/learning-paths/automotive/system76-auto/_index.md
@@ -13,60 +13,9 @@ learning_objectives:
 
 prerequisites:
     - A System76 Thelio Astra desktop computer running Ubuntu 24.04. 
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:28:14Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 2b758d2dcf28a683ab164e28578a736d6d730b81dfbc01a6765619052fcdebd0
-  summary_generated_at: '2026-06-01T20:58:28Z'
-  summary_source_hash: 2b758d2dcf28a683ab164e28578a736d6d730b81dfbc01a6765619052fcdebd0
-  faq_generated_at: '2026-06-02T21:28:14Z'
-  faq_source_hash: 2b758d2dcf28a683ab164e28578a736d6d730b81dfbc01a6765619052fcdebd0
-  summary: >-
-    This Learning Path shows how to set up a local automotive software development environment
-    on the Arm-based System76 Thelio Astra and build the Arm Automotive Solutions Software Reference
-    Stack. You will install Multipass on Ubuntu 24.04, create an Ubuntu 20.04 virtual machine,
-    and use Yocto, Docker, and Git to build the stack from the VM. The path introduces the Arm
-    Reference Design-1 AE (RD-1 AE) target, modeled by a Fixed Virtual Platform, and includes
-    running example applications such as a Parsec-enabled TLS demo. By the end, you will have
-    built and run the stack locally in a VM on Thelio Astra; no additional prerequisites beyond
-    the host hardware are listed.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a System76 Thelio Astra desktop computer running Ubuntu 24.04. Before starting,
-      install Multipass using the Multipass install guide for Arm Linux. The path uses Multipass,
-      Yocto, Docker, and Git; no other prerequisites are explicitly listed.
-  - question: Which Ubuntu version should I use inside the Multipass VM?
-    answer: >-
-      The build steps use an Ubuntu 20.04 Multipass virtual machine. Multipass creates a cloud-style
-      VM on your desktop to isolate build and test tasks and split system resources.
-  - question: How do I begin the build of the Arm Automotive Solutions Software Reference Stack?
-    answer: >-
-      From the Ubuntu 20.04 Multipass VM, create a working directory and clone the repository
-      as shown in the steps. A successful clone without errors indicates the environment is ready
-      for the Yocto-based build process.
-  - question: Can I run the demos without RD-1 AE hardware?
-    answer: >-
-      Yes. The example applications demonstrate the software stack running on a Fixed Virtual
-      Platform that models the reference hardware system.
-  - question: What result should I expect from the Parsec demo?
-    answer: >-
-      The Parsec-enabled TLS demo illustrates an HTTPS session where a simple web page is transferred
-      over a TLS connection. This demonstrates use of Parsec’s common API to access security and
-      cryptographic services in the stack.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/automotive/zenacssdebug/_index.md b/content/learning-paths/automotive/zenacssdebug/_index.md
index 00f9ddd39d..de8ee1c5d7 100644
--- a/content/learning-paths/automotive/zenacssdebug/_index.md
+++ b/content/learning-paths/automotive/zenacssdebug/_index.md
@@ -17,65 +17,9 @@ prerequisites:
   - Ubuntu 22.04 host machine
   - Arm Development Studio 2024.1 or later with a valid license - for support see the [Install Guide for Arm DS](/install-guides/armds) 
   - Basic understanding of the Arm Zena CSS software stack, Armv8-A/Armv9-A cores, and Linux
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:28:53Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 8dccdbdd4d9727f3466987ce918d9df0ed9d4d4f28cd613162bacab01d9c18f3
-  summary_generated_at: '2026-06-01T20:59:08Z'
-  summary_source_hash: 8dccdbdd4d9727f3466987ce918d9df0ed9d4d4f28cd613162bacab01d9c18f3
-  faq_generated_at: '2026-06-02T21:28:53Z'
-  faq_source_hash: 8dccdbdd4d9727f3466987ce918d9df0ed9d4d4f28cd613162bacab01d9c18f3
-  summary: >-
-    This introductory Learning Path shows how to debug the Arm Zena Compute Subsystem (CSS) Reference
-    Software Stack on a Fixed Virtual Platform using Arm Development Studio. You will launch the
-    Zena CSS FVP with the Iris debug server, create and save a custom debug configuration, and
-    set up connections for its heterogeneous subsystems: the Runtime Security Engine (Cortex-M55),
-    the Safety Island (Cortex-R82AE), and the primary compute cores (Cortex-A720AE) running Linux.
-    You will step the RSE from reset with TF-M symbols, attach to SI firmware, and attach to the
-    Linux kernel to debug user space processes. Prerequisites are Ubuntu 22.04, Arm Development
-    Studio 2024.1 or later with a valid license, and basic familiarity with Zena CSS, Armv8-A/Armv9-A,
-    and Linux.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      Use an Ubuntu 22.04 host and Arm Development Studio 2024.1 or later with a valid license.
-      A basic understanding of the Arm Zena CSS software stack, Armv8‑A/Armv9‑A cores, and Linux
-      is assumed.
-  - question: Why can’t Arm Development Studio connect if I launch the FVP from the build environment
-      command?
-    answer: >-
-      Launching with the provided build command does not enable the Iris debug server, so the
-      model cannot be debugged from Arm Development Studio. Re‑launch the model with additional
-      command‑line options that enable Iris; see FVP_RD_Aspen --help and follow the options shown
-      in the Learning Path.
-  - question: Which connection method should I choose in Arm Development Studio for this target?
-    answer: >-
-      Use the Iris interface to create a debug configuration for the Zena CSS FVP. As of Arm Development
-      Studio 2025.0 there is no out‑of‑the‑box configuration, so you will create your own and
-      save the connections as .launch files.
-  - question: How do I hold the RSE at reset and step through early boot?
-    answer: >-
-      Start a new tmux session if needed, then launch the FVP with the Iris server enabled and
-      without running so it stays at reset. Connect from Arm Development Studio, load Trusted
-      Firmware‑M symbols, and step from reset through the early boot sequence.
-  - question: Can I connect to the Safety Island and the Linux kernel simultaneously?
-    answer: >-
-      Yes. Arm Development Studio supports heterogeneous systems like Zena CSS, so you can create
-      separate connections and attach to all processors at the same time, including the Safety
-      Island firmware and the Linux kernel on the primary compute cores.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/cross-platform/_example-learning-path/_index.md b/content/learning-paths/cross-platform/_example-learning-path/_index.md
index 78f25656ee..34b9b8580c 100644
--- a/content/learning-paths/cross-platform/_example-learning-path/_index.md
+++ b/content/learning-paths/cross-platform/_example-learning-path/_index.md
@@ -15,58 +15,9 @@ learning_objectives:
 
 prerequisites:
     - A [GitHub](https://github.com/) account
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:29:45Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: a58d5eb4c4256f3e4f4374344ef0e73836e8b51ac7223b0a5238b22137ec0819
-  summary_generated_at: '2026-06-01T20:59:38Z'
-  summary_source_hash: a58d5eb4c4256f3e4f4374344ef0e73836e8b51ac7223b0a5238b22137ec0819
-  faq_generated_at: '2026-06-02T21:29:45Z'
-  faq_source_hash: a58d5eb4c4256f3e4f4374344ef0e73836e8b51ac7223b0a5238b22137ec0819
-  summary: >-
-    This introductory path shows content creators and software developers how to create and contribute
-    a new Arm Learning Path in about 60 minutes. You will set up a text editor, Hugo, and Git;
-    fork the GitHub repository; write your tutorial in markdown; choose one of six site categories
-    based on where the software runs; and add required metadata in the _index.md file so pages
-    render consistently. You will use Hugo to review content locally, commit and push changes
-    to your fork, and submit a pull request for review. All Learning Paths are community-created
-    and published under the Creative Commons Attribution-ShareAlike 4.0 International License.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a GitHub account. Three tools are mandatory for authoring: a text editor, the Hugo
-      static site generator, and Git.
-  - question: How do I know whether my topic belongs in a Learning Path?
-    answer: >-
-      A Learning Path is a concise tutorial with detailed steps to complete a specific task. It
-      is not product documentation, marketing material, product or developer news, or a place
-      to embed or link to videos.
-  - question: Which category should I use when adding my Learning Path?
-    answer: >-
-      Choose the category closest to the environment where the software runs: servers-and-cloud-computing,
-      laptops-and-desktops, embedded-and-microcontrollers, iot, mobile-graphics-and-gaming, or
-      automotive. If you are unsure, ask on GitHub.
-  - question: Where do I set the Learning Path metadata, and are there naming rules?
-    answer: >-
-      Add metadata in the _index.md file; it is used by the site to keep Learning Paths consistent.
-      The title should start with a verb, avoid adjectives, and be as concise as possible.
-  - question: How do I contribute my Learning Path for review?
-    answer: >-
-      Commit your changes with Git and push them to your fork on GitHub, then open a pull request.
-      Only you can see changes made to your fork until you submit the pull request.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Zach Lasiuk
 
diff --git a/content/learning-paths/cross-platform/adler32/_index.md b/content/learning-paths/cross-platform/adler32/_index.md
index ef95074611..9ee971a0c6 100644
--- a/content/learning-paths/cross-platform/adler32/_index.md
+++ b/content/learning-paths/cross-platform/adler32/_index.md
@@ -13,59 +13,9 @@ learning_objectives:
 prerequisites:
     - An Arm computer running Linux with the GNU compiler (gcc) installed.
     - Visual Studio Code with the GitHub Copilot extension installed. 
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:30:35Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 37b19106fba70423ba8c58f82097d9251f0ae208e882c852f9c4531afcebb46c
-  summary_generated_at: '2026-06-01T21:00:08Z'
-  summary_source_hash: 37b19106fba70423ba8c58f82097d9251f0ae208e882c852f9c4531afcebb46c
-  faq_generated_at: '2026-06-02T21:30:35Z'
-  faq_source_hash: 37b19106fba70423ba8c58f82097d9251f0ae208e882c852f9c4531afcebb46c
-  summary: >-
-    This introductory Learning Path shows C/C++ developers on Arm Linux how to use GitHub Copilot
-    in Visual Studio Code to implement and accelerate the Adler32 checksum with Arm Neon intrinsics.
-    You will start by prompting Copilot to generate a baseline C implementation, create a test
-    program that validates correctness and measures runtime on random inputs from 1 KB to 10 MB,
-    and have Copilot produce a gcc Makefile optimized for Neoverse N1. You then build and run
-    the project to validate results and use Copilot to add Neon intrinsics, aiming for significant
-    speedups over the C baseline. Prerequisites: an Arm computer running Linux with gcc, and VS
-    Code with the GitHub Copilot extension.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an Arm computer running Linux with gcc installed, and Visual Studio Code with the
-      GitHub Copilot extension. No other prerequisites are explicitly listed.
-  - question: Which GitHub Copilot mode or model should I use?
-    answer: >-
-      Open GitHub Copilot, choose the Large Language Model you prefer, and select Agent mode.
-      Results vary by model; the example output shown in the path was produced with Claude 3.7
-      Sonnet.
-  - question: How is the project built, and which CPU is it tuned for?
-    answer: >-
-      Copilot generates a Makefile that builds the project with gcc and selects optimization flags
-      for the Neoverse N1. Use the provided Makefile targets to compile and run the tests.
-  - question: What should I verify when I run the test program?
-    answer: >-
-      Check that the checksum results are correct for all listed data sizes. The test program
-      also measures performance to provide a baseline before introducing Neon intrinsics.
-  - question: When do I implement Neon intrinsics for Adler32?
-    answer: >-
-      After establishing the baseline C implementation and test harness, the path guides you to
-      use GitHub Copilot to write Neon intrinsics for Adler32. This step focuses on leveraging
-      Arm Advanced SIMD to accelerate the algorithm.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md b/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md
index e55d538eb7..37f8243843 100644
--- a/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md
+++ b/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md
@@ -16,61 +16,9 @@ prerequisites:
     - A computer with [Docker](/install-guides/docker/) and Python 3.11 or later installed
     - Basic familiarity with Python, PyTest, and container concepts
     - Familiarity with the [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) specification
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:31:11Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 597877722e5f277e5558e29ecd33878ac2262b70a84a9769325b3c3b0ce06e58
-  summary_generated_at: '2026-06-01T21:01:02Z'
-  summary_source_hash: 597877722e5f277e5558e29ecd33878ac2262b70a84a9769325b3c3b0ce06e58
-  faq_generated_at: '2026-06-02T21:31:11Z'
-  faq_source_hash: 597877722e5f277e5558e29ecd33878ac2262b70a84a9769325b3c3b0ce06e58
-  summary: >-
-    This introductory path shows how to automate integration testing of Model Context Protocol
-    (MCP) servers using PyTest and Testcontainers, with local runs and CI on GitHub Actions. You
-    will set up a Python environment with Docker, run a basic Testcontainers example, and build
-    a minimal integration test suite that exercises MCP server behavior over JSON-RPC 2.0 via
-    standard input/output. You will also create a .github/workflows/integration-tests.yml workflow
-    to run tests on pushes and pull requests, with support for Arm64 runners. The path targets
-    Linux, macOS, and Windows, and expects Docker, Python 3.11 or later, Git, and familiarity
-    with Python, PyTest, containers, and the MCP specification.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      Install Docker and Python 3.11 or later with virtual environment support, and have Git available.
-      On Linux, ensure the python3-venv package is installed. You should also be familiar with
-      Python, PyTest, container concepts, and the MCP specification.
-  - question: How do I check if Docker is ready before I start?
-    answer: >-
-      Run the command docker info. If it fails or shows that the daemon is not running, start
-      the Docker daemon and try again.
-  - question: How do MCP servers communicate in these tests?
-    answer: >-
-      MCP uses JSON-RPC 2.0 over standard input and output. Your integration tests interact with
-      the server through this protocol to validate functionality.
-  - question: What result should I expect from the basic Testcontainers example?
-    answer: >-
-      The example starts an alpine:latest container running a long-lived sleep process and then
-      executes a command inside it. You should see the container start successfully and the command
-      complete while the container is alive.
-  - question: Which triggers and runners does the GitHub Actions workflow use, and where is it
-      defined?
-    answer: >-
-      The workflow is defined at .github/workflows/integration-tests.yml and runs on push and
-      pull_request events. It uses GitHub’s native Arm64 runners with Docker pre-installed, and
-      supports parallel execution.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Neethu Elizabeth Simon
 
diff --git a/content/learning-paths/cross-platform/avh_cicd/_index.md b/content/learning-paths/cross-platform/avh_cicd/_index.md
index fcbbca074a..da122fd716 100644
--- a/content/learning-paths/cross-platform/avh_cicd/_index.md
+++ b/content/learning-paths/cross-platform/avh_cicd/_index.md
@@ -13,59 +13,9 @@ learning_objectives:
 
 prerequisites:
     - Some familiarity with CI/CD concepts is assumed
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:32:03Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: b6a7439a701f6ae09ce8c61f02150a61fa6ecc1151050e903492e16794999676
-  summary_generated_at: '2026-06-01T21:01:24Z'
-  summary_source_hash: b6a7439a701f6ae09ce8c61f02150a61fa6ecc1151050e903492e16794999676
-  faq_generated_at: '2026-06-02T21:32:03Z'
-  faq_source_hash: b6a7439a701f6ae09ce8c61f02150a61fa6ecc1151050e903492e16794999676
-  summary: >-
-    This introductory path shows embedded developers how to integrate Arm Virtual Hardware (AVH)
-    into a GitHub Actions CI/CD workflow for automated testing and validation of bare‑metal Cortex‑M
-    software. You will prepare a GitHub repository, generate and scope a Personal Access Token
-    to update GitHub Actions workflows, and set up an AVH instance following the Arm Virtual Hardware
-    install guide. The steps cover enabling Actions in your fork and creating a Linux x64 self‑hosted
-    runner that matches your AWS instance. An AWS account and a GitHub account are required, and
-    some familiarity with CI/CD concepts is assumed. By the end, you will have AVH wired into
-    a GitHub Actions flow using a self‑hosted runner.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a GitHub account, an AWS account, and an Arm Virtual Hardware instance set up using
-      the Arm Virtual Hardware install guide. Some familiarity with CI/CD concepts is assumed.
-  - question: How do I create the required GitHub Personal Access Token?
-    answer: >-
-      In GitHub, go to Settings > Developer Settings > Personal access tokens, select Generate
-      new token, and enable the permission to Update GitHub Action workflows. Generate and save
-      the token locally for use during the setup.
-  - question: How do I enable GitHub Actions in my forked repository?
-    answer: >-
-      Open your fork, navigate to Actions, and if workflows are disabled, click the prompt I understand
-      my workflows, go ahead and enable them. This allows the repository’s workflows to run.
-  - question: Which options should I choose when creating the self-hosted runner?
-    answer: >-
-      In the repository, go to Settings > Actions > Runners and create a New self-hosted runner
-      with Runner image set to Linux and Architecture set to x64. These settings should match
-      your AWS instance.
-  - question: Where do I run the commands shown when adding the self-hosted runner?
-    answer: >-
-      Run the displayed registration commands on your AWS instance where Arm Virtual Hardware
-      is set up. These commands connect that instance as the self-hosted runner for your repository.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/cross-platform/avh_cicd2/_index.md b/content/learning-paths/cross-platform/avh_cicd2/_index.md
index b991d440a4..a1a7c65f44 100644
--- a/content/learning-paths/cross-platform/avh_cicd2/_index.md
+++ b/content/learning-paths/cross-platform/avh_cicd2/_index.md
@@ -14,58 +14,9 @@ learning_objectives:
 prerequisites:
     - This learning path builds on [Integrate Arm Virtual Hardware into CI/CD workflow 1](/learning-paths/cross-platform/avh_cicd/).
     - Valid AWS and GitHub accounts are required
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:32:39Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 251b6edf6a016f9fc73eb35216f56b2001465fbca8253bd7b6e6f9f4afcd25e6
-  summary_generated_at: '2026-06-01T21:01:44Z'
-  summary_source_hash: 251b6edf6a016f9fc73eb35216f56b2001465fbca8253bd7b6e6f9f4afcd25e6
-  faq_generated_at: '2026-06-02T21:32:39Z'
-  faq_source_hash: 251b6edf6a016f9fc73eb35216f56b2001465fbca8253bd7b6e6f9f4afcd25e6
-  summary: >-
-    This advanced Learning Path shows how to integrate Arm Virtual Hardware with AWS and GitHub
-    Actions to automate test and validation for bare-metal Cortex-M projects. You will fork the
-    ARM-software/AVH-GetStarted repository, use its included CloudFormation template to prepare
-    your AWS account, and configure repository secrets so GitHub Actions can run an automated
-    build-and-validation example on Arm Virtual Hardware. The steps focus on setting up AWS integration
-    (including region and subnet) and connecting the example CI workflow in your fork. It builds
-    on “Integrate Arm Virtual Hardware into CI/CD workflow 1” and requires valid AWS and GitHub
-    accounts. Estimated time to complete is approximately 30 minutes.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      It builds on “Integrate Arm Virtual Hardware into CI/CD workflow 1” and requires valid AWS
-      and GitHub accounts. No other prerequisites are explicitly listed.
-  - question: Which example repository should I use and where do I find it?
-    answer: >-
-      Fork the Arm example at https://github.com/ARM-software/AVH-GetStarted/fork. It includes
-      a CloudFormation template and documentation for the CI workflow.
-  - question: When is my AWS account ready to connect to GitHub Actions?
-    answer: >-
-      After completing the CloudFormation stack step in “Prepare AWS account for GitHub integration.”
-      Once that is done, proceed to define the GitHub repository secrets.
-  - question: Which GitHub Actions secrets must I create and how do I find their values?
-    answer: >-
-      Create the secrets exactly as named in the Learning Path. Set AWS_DEFAULT_REGION to the
-      same region where the CloudFormation stack was created, and set AWS_SUBNET_ID by selecting
-      any valid Subnet ID from AWS Console > VPC > Subnets.
-  - question: What result should I expect after configuring the workflow?
-    answer: >-
-      The GitHub Actions pipeline will automate build, test, and validation of the example on
-      Arm Virtual Hardware. You should see the example run under CI using your AWS configuration.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/cross-platform/cca_rme/_index.md b/content/learning-paths/cross-platform/cca_rme/_index.md
index aece0325f9..80852da801 100644
--- a/content/learning-paths/cross-platform/cca_rme/_index.md
+++ b/content/learning-paths/cross-platform/cca_rme/_index.md
@@ -14,59 +14,9 @@ learning_objectives:
 prerequisites:
     - Some understanding of the Arm architecture
     - Arm Development Studio, 2023.0 or later
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:33:15Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: a1685fb43efecb9690d03c7f8ee64ab20ae8aece91190e2223705106568b1038
-  summary_generated_at: '2026-06-01T21:02:16Z'
-  summary_source_hash: a1685fb43efecb9690d03c7f8ee64ab20ae8aece91190e2223705106568b1038
-  faq_generated_at: '2026-06-02T21:33:15Z'
-  faq_source_hash: a1685fb43efecb9690d03c7f8ee64ab20ae8aece91190e2223705106568b1038
-  summary: >-
-    This introductory Learning Path shows how to explore Arm Confidential Compute Architecture
-    (CCA) and the Realm Management Extension (RME) using Arm Development Studio. You will import
-    a simple bare-metal example provided with Development Studio (2023.0 or later), run it on
-    the Arm Architecture Envelope Model (AEM) Fixed Virtual Platform included with the tools,
-    and use Arm Debugger features to examine behavior relevant to CCA. The material explains the
-    CCA security states—Normal, Secure, Realm, and Root—and the role of a secure monitor in managing
-    transitions. Prerequisites are a basic understanding of Arm architecture and access to Arm
-    Development Studio. Estimated time to complete is about 30 minutes.
-  faqs:
-  - question: What do I need before running the example?
-    answer: >-
-      Install Arm Development Studio 2023.0 or later and have some understanding of the Arm architecture.
-      The AEM Fixed Virtual Platform and the full bare-metal example are supplied with Development
-      Studio.
-  - question: How do I import the bare-metal RME example into Arm Development Studio?
-    answer: >-
-      Open the IDE and choose File > Import. Select Arm Development Studio > Examples & Programming
-      Libraries, then locate and import the RME bare-metal example provided with the installation.
-  - question: Which target should I run the example on?
-    answer: >-
-      Run the example on the Arm Architecture Envelope Model (AEM) Fixed Virtual Platform, which
-      is supplied with Arm Development Studio.
-  - question: How does this example demonstrate CCA concepts?
-    answer: >-
-      It illustrates RME, the architectural feature needed to implement CCA, highlighting the
-      Realm world in addition to Normal, Secure, and Root worlds. A secure monitor in Root world
-      manages transitions between these states, which you can examine with the Arm Debugger.
-  - question: Do I need Linux or Android to follow this path?
-    answer: >-
-      No. The example is bare-metal and runs on the AEM FVP provided with Arm Development Studio,
-      so no operating system setup is required.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md b/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md
index 6dc7bb0241..a586d3bcf1 100644
--- a/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md
+++ b/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md
@@ -15,58 +15,9 @@ learning_objectives:
 
 prerequisites:
     - An Arm computer running Linux. You can also use a virtual machine from a [cloud service provider](/learning-paths/servers-and-cloud-computing/csp/).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:33:42Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: a639e60da034fd04e891c9236e9fe5dab47cca87f6174828275ef5a76c43c32e
-  summary_generated_at: '2026-06-01T21:02:47Z'
-  summary_source_hash: a639e60da034fd04e891c9236e9fe5dab47cca87f6174828275ef5a76c43c32e
-  faq_generated_at: '2026-06-02T21:33:42Z'
-  faq_source_hash: a639e60da034fd04e891c9236e9fe5dab47cca87f6174828275ef5a76c43c32e
-  summary: >-
-    Learn how to improve the runtime of C++ loops on Arm by conveying loop-size boundaries to
-    the compiler. You will start from a baseline program where the loop size is only known at
-    runtime, then modify the code to enforce a multiple-of-4 loop size using integer-division
-    truncation. This developer knowledge enables the compiler to generate better code, potentially
-    including SIMD vectorization, and lets you compare the performance impact on Arm systems.
-    The path runs on Linux and targets Arm CPUs such as Neoverse and Cortex-A. Prerequisite: an
-    Arm computer running Linux, or a Linux VM from a cloud service provider.
-  faqs:
-  - question: What do I need before running the code examples?
-    answer: >-
-      You need an Arm computer running Linux, or you can use a Linux virtual machine from a cloud
-      service provider. No other explicit prerequisites are listed.
-  - question: How is the loop size provided in the baseline program, and why does that matter?
-    answer: >-
-      The baseline program reads max_loop_size from user input at runtime. Because the compiler
-      does not know this bound at compile time, it must generate conservative code.
-  - question: Why does rewriting the loop bound as ((max_loop_size/4)*4) help the compiler?
-    answer: >-
-      Integer division truncates, so (max_loop_size/4)*4 is always divisible by 4. Communicating
-      this constraint can enable SIMD vectorization and better code generation for that specific
-      case.
-  - question: What result should I expect after applying the boundary information?
-    answer: >-
-      The loop will iterate up to the largest multiple of 4 that does not exceed the original
-      input size. You can then compare and analyze runtime behavior and performance impact against
-      the baseline.
-  - question: Do I need any specific tools or compiler options to follow this path?
-    answer: >-
-      The steps focus on C++ source changes using the provided examples. Specific compiler options
-      or additional tools are not explicitly listed.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/cross-platform/docker-build-cloud/_index.md b/content/learning-paths/cross-platform/docker-build-cloud/_index.md
index 1ed13a7945..b0e1dd209e 100644
--- a/content/learning-paths/cross-platform/docker-build-cloud/_index.md
+++ b/content/learning-paths/cross-platform/docker-build-cloud/_index.md
@@ -15,60 +15,9 @@ prerequisites:
     - A computer with Docker installed. This can be Windows, macOS, or Linux. Any architecture can be used. 
     - A GitHub account
     - A Docker Hub account
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:34:59Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 9a94219b689b8e22a175c6067c6bb6d139d6ad22f3aac77c78b6b38970d5a5eb
-  summary_generated_at: '2026-06-01T21:03:46Z'
-  summary_source_hash: 9a94219b689b8e22a175c6067c6bb6d139d6ad22f3aac77c78b6b38970d5a5eb
-  faq_generated_at: '2026-06-02T21:34:59Z'
-  faq_source_hash: 9a94219b689b8e22a175c6067c6bb6d139d6ad22f3aac77c78b6b38970d5a5eb
-  summary: >-
-    This Learning Path shows how to build multi-architecture Docker images for Arm and x86 using
-    Docker Build Cloud, and automate the process with GitHub Actions. You will set up Docker Build
-    Cloud as your builder, create Arm-only and multi-architecture images, and configure a GitHub
-    repository with the required secrets so builds run in the cloud without instruction emulation.
-    It is an introductory, hands-on path aimed at developers who need practical steps to produce
-    images for multiple CPU architectures. Prerequisites are a computer with Docker installed
-    (Windows, macOS, or Linux), a GitHub account, and a Docker Hub account. Estimated time to
-    complete is about 30 minutes.
-  faqs:
-  - question: Do I need an Arm machine to follow this path?
-    answer: >-
-      No. You can use any computer with Docker installed on Windows, macOS, or Linux, and build
-      for Arm and x86 using Docker Build Cloud without local emulation.
-  - question: What do I need before running the builds?
-    answer: >-
-      You need Docker installed on your computer, a GitHub account, and a Docker Hub account.
-      No other prerequisites are explicitly listed.
-  - question: Which method for multi-architecture builds is used here?
-    answer: >-
-      The path explains common methods and focuses on using Docker Build Cloud as the builder
-      to avoid instruction emulation. Emulation is noted as slow for complex builds, so the cloud
-      builder is used instead.
-  - question: How do I set up GitHub Actions for this build?
-    answer: >-
-      Create a new GitHub repository, add a workflow that uses Docker Build Cloud as the builder,
-      and configure the required GitHub secrets referenced by the workflow. The steps guide you
-      through creating the repository and setting up secrets.
-  - question: What should I check if my GitHub Actions workflow fails early?
-    answer: >-
-      Verify that the required secrets referenced by the workflow are defined in the repository
-      settings and that you are using the correct GitHub account. If you are also building locally,
-      ensure Docker is installed and running.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/cross-platform/docker/_index.md b/content/learning-paths/cross-platform/docker/_index.md
index 7f2f7f5f23..d16c94c448 100644
--- a/content/learning-paths/cross-platform/docker/_index.md
+++ b/content/learning-paths/cross-platform/docker/_index.md
@@ -16,60 +16,9 @@ learning_objectives:
 prerequisites:
     - A Windows, macOS, or Linux computer with Docker installed, any architecture can be used
     - An Arm Linux server with Docker installed 
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:34:19Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 058f779bc7dd9faef294a57f4524bf525046d757e21a287744825fb9b290935e
-  summary_generated_at: '2026-06-01T21:03:24Z'
-  summary_source_hash: 058f779bc7dd9faef294a57f4524bf525046d757e21a287744825fb9b290935e
-  faq_generated_at: '2026-06-02T21:34:19Z'
-  faq_source_hash: 058f779bc7dd9faef294a57f4524bf525046d757e21a287744825fb9b290935e
-  summary: >-
-    Follow this introductory path to build, run, and share Docker images that support both Arm
-    and x86. You will validate your Docker setup, perform multi-architecture builds with Docker
-    buildx, and use a remote Arm Linux server over SSH to offload Arm image builds when local
-    emulation is slow. The path also covers creating multi-architecture images using Docker manifest
-    (an experimental feature not recommended for production) and checking image architecture support
-    in container registries. You can use a Windows, macOS, or Linux computer with Docker installed,
-    plus access to an Arm Linux server with Docker. By the end, you will have practiced building
-    and publishing images that run on Arm-based systems.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      Use a Windows, macOS, or Linux computer with Docker installed. For remote builds, you also
-      need an Arm Linux server with Docker installed and reachable over SSH without a password.
-  - question: How do I verify my Docker setup before starting builds?
-    answer: >-
-      Run docker run hello-world to confirm Docker is working. Then run docker buildx --help and
-      expect a usage message beginning with 'Usage: docker buildx [OPTIONS] COMMAND'; if you see
-      other output, install the most recent Docker version.
-  - question: When should I use a remote Arm server for builds?
-    answer: >-
-      If building Arm images on a non-Arm machine is slow due to emulation, switch to a remote
-      Arm server. Use docker context to target an Arm machine that has Docker installed and is
-      accessible via passwordless SSH.
-  - question: When should I use docker manifest in this workflow?
-    answer: >-
-      Use docker manifest when you have built separate images for each architecture and want to
-      publish a single multi-architecture image. Note that docker manifest is an experimental
-      feature and is not recommended for production use.
-  - question: How do I check that an image is multi-architecture and supports Arm?
-    answer: >-
-      Inspect the image in your container registry. For example, Docker Hub shows supported OS/ARCH
-      entries, and AWS ECR Public also lists the architectures.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md b/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md
index 3c800ed02e..c733b0bba1 100644
--- a/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md
+++ b/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md
@@ -15,59 +15,9 @@ learning_objectives:
 prerequisites:
 - Familiarity with C programming, with a good understanding of pointers.
 - A Linux machine to run the example code.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:35:52Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: c59308676849aea9b7cfce8c48c7d6e1ca072ef6fb38fb193a34aa5b2597b9b9
-  summary_generated_at: '2026-06-01T21:04:25Z'
-  summary_source_hash: c59308676849aea9b7cfce8c48c7d6e1ca072ef6fb38fb193a34aa5b2597b9b9
-  faq_generated_at: '2026-06-02T21:35:52Z'
-  faq_source_hash: c59308676849aea9b7cfce8c48c7d6e1ca072ef6fb38fb193a34aa5b2597b9b9
-  summary: >-
-    This introductory Learning Path guides you through implementing a simple dynamic memory allocator
-    in C on Linux. You will design and code two functions, simple_malloc and simple_free, to understand
-    how heap allocation works and what malloc/free do under the hood, then build and run provided
-    examples to observe allocation behavior. The project uses a small CMake-based structure (heap.c,
-    heap.h, main.c, CMakeLists.txt) to configure and build the test program. Prerequisites are
-    familiarity with C pointers and access to a Linux machine. The material also highlights some
-    risks of heap allocation and is relevant to developers targeting Arm Cortex-A and Neoverse
-    software. Estimated time to complete is about 120 minutes.
-  faqs:
-  - question: What do I need before running the examples?
-    answer: >-
-      You need a Linux machine and familiarity with C programming, including pointers. No additional
-      prerequisites are explicitly listed.
-  - question: Which allocator functions am I expected to implement?
-    answer: >-
-      You will implement simple_malloc and simple_free. simple_malloc takes a size in bytes and
-      returns a pointer or NULL on failure, and simple_free releases previously allocated memory.
-  - question: How is the project organized in the implementation step?
-    answer: >-
-      The project includes CMakeLists.txt, heap.c (allocator implementation), heap.h (function
-      declarations), and main.c (a test program). Everything required to build and run example
-      allocations is provided.
-  - question: How do I build and run the code on Linux?
-    answer: >-
-      Use the provided CMakeLists.txt to configure and build the project as shown in the Learning
-      Path steps. Building produces a program that exercises simple_malloc and simple_free.
-  - question: How do I know my allocator works as intended?
-    answer: >-
-      Run the included test program and observe that allocations succeed and that simple_malloc
-      returns NULL when memory cannot be allocated. The examples demonstrate basic allocation
-      and freeing behavior.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: David Spickett
 
diff --git a/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md b/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md
index 2cf6255391..34c18dfcd0 100644
--- a/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md
+++ b/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md
@@ -13,62 +13,9 @@ learning_objectives:
 
 prerequisites:
     - An Arm-based computer running Linux and a recent version of a C++ compiler (Clang or GCC).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:36:20Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 39c5e146afbfc74c3076d2cf3f1a67ddc0e4715f39b2cff1ccf76b288415bdc0
-  summary_generated_at: '2026-06-01T21:04:54Z'
-  summary_source_hash: 39c5e146afbfc74c3076d2cf3f1a67ddc0e4715f39b2cff1ccf76b288415bdc0
-  faq_generated_at: '2026-06-02T21:36:20Z'
-  faq_source_hash: 39c5e146afbfc74c3076d2cf3f1a67ddc0e4715f39b2cff1ccf76b288415bdc0
-  summary: >-
-    Learn to use the Eigen C++ linear algebra library on Arm systems that support ASIMD (Neon)
-    and SVE, then build TensorFlow with SVE enabled. You will build and run compact Eigen examples
-    that exercise vectorized operations, including element-wise expressions on a 100×100 matrix
-    and repeated 512×512 matrix multiplications that use FMA. The path then guides you to install
-    TensorFlow build requirements and follow the upstream build-from-source process with slight
-    modifications to enable SVE, producing a build you can run. Target environment is an Arm-based
-    Linux system. Tools include GCC or Clang. Prerequisites: an Arm-based computer running Linux
-    with a recent C++ compiler. Outcome: use Eigen on Arm and produce an SVE-enabled TensorFlow
-    build.
-  faqs:
-  - question: What do I need before running the examples or building TensorFlow?
-    answer: >-
-      You need an Arm-based computer running Linux and a recent version of a C++ compiler (Clang
-      or GCC). No other prerequisites are explicitly listed.
-  - question: Which compiler should I use, and are special flags required for ASIMD or SVE?
-    answer: >-
-      You can use either GCC or Clang. The path demonstrates Eigen on Arm SIMD engines, and specific
-      compiler options for ASIMD or SVE are not explicitly listed.
-  - question: What code do I create and what results indicate the Eigen examples worked?
-    answer: >-
-      You will write small Eigen programs, including a 100×100 matrix example that returns the
-      sum of all elements and a 512×512 matrix multiplication example in a file named eigen-test3.cpp.
-      Successful runs print a numeric result, such as a summed value or a line like "C.norm():
-      <number>".
-  - question: How do I approach building TensorFlow with SVE in this path?
-    answer: >-
-      You follow TensorFlow’s build-from-source instructions with slight modifications. First
-      install the required build dependencies provided in the steps, then build and run the SVE-enabled
-      TensorFlow.
-  - question: What should I do if my Arm system does not support SVE?
-    answer: >-
-      The path covers Eigen on both ASIMD (Neon) and SVE, so you can still work through the Eigen
-      examples using ASIMD. The steps do not list an alternative workflow for building TensorFlow
-      without SVE.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Konstantinos Margaritis
 
diff --git a/content/learning-paths/cross-platform/ernie_moe_v9/_index.md b/content/learning-paths/cross-platform/ernie_moe_v9/_index.md
index e232a9a2c1..34a3d5c1df 100644
--- a/content/learning-paths/cross-platform/ernie_moe_v9/_index.md
+++ b/content/learning-paths/cross-platform/ernie_moe_v9/_index.md
@@ -14,61 +14,9 @@ learning_objectives:
 
 prerequisites:
     - An Armv9 device with at least 32 GB of available disk space, for example, Radxa Orion O6
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:37:05Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: e835a550c955b13805c7859ff64e3b8d7fdee68222569225c53a0f15db72f046
-  summary_generated_at: '2026-06-01T21:05:21Z'
-  summary_source_hash: e835a550c955b13805c7859ff64e3b8d7fdee68222569225c53a0f15db72f046
-  faq_generated_at: '2026-06-02T21:37:05Z'
-  faq_source_hash: e835a550c955b13805c7859ff64e3b8d7fdee68222569225c53a0f15db72f046
-  summary: >-
-    This advanced Learning Path shows how to deploy ERNIE-4.5 Mixture of Experts (MoE) models
-    on Armv9 devices using llama.cpp on Linux. You will set up an Armv9 development board (for
-    example, a Radxa Orion O6 with at least 32 GB of available disk space), run and verify inference,
-    and validate multilingual outputs with the ERNIE-4.5 Thinking variant. You then compare the
-    PT and Thinking models, inspect MoE expert routing, and benchmark a baseline CPU build against
-    an Armv9-optimized build that enables SVE, i8mm, and dotprod instructions to measure their
-    impact. The outcome is the ability to deploy, compare, and benchmark ERNIE-4.5 MoE models
-    on Armv9 in about 60 minutes.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      You need an Armv9 device with at least 32 GB of available disk space. The steps assume a
-      Linux environment and use a Radxa Orion O6 as an example platform.
-  - question: Which ERNIE-4.5 variants are used, and what will I compare?
-    answer: >-
-      You will work with the PT and Thinking variants of ERNIE-4.5. The path compares their inference
-      behavior on the same task and shows how to inspect internal MoE expert routing. Both share
-      the same MoE architecture and parameter count (around 21B total, about 3B active at runtime).
-  - question: How do I validate that my setup and model inference are working?
-    answer: >-
-      You verify inference on an Armv9 development board and validate multilingual outputs using
-      the ERNIE Thinking variant. Successful inference confirms the environment and model setup
-      are ready for the comparison and benchmarking steps.
-  - question: What Armv9 optimizations are benchmarked, and how are they tested?
-    answer: >-
-      You measure performance with and without Armv9 vector instruction optimizations. The comparison
-      is between a baseline regular CPU build and an Armv9-specific build with SVE, i8mm, and
-      dotprod enabled.
-  - question: How can I observe which MoE experts are used during generation?
-    answer: >-
-      The path includes steps to inspect internal MoE expert routing behavior while generating
-      outputs. You use this to understand how the PT and Thinking variants route tokens to experts
-      during inference.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Odin Shen
 
diff --git a/content/learning-paths/cross-platform/floating-point-behavior/_index.md b/content/learning-paths/cross-platform/floating-point-behavior/_index.md
index dcf73bdf87..af9793e16a 100644
--- a/content/learning-paths/cross-platform/floating-point-behavior/_index.md
+++ b/content/learning-paths/cross-platform/floating-point-behavior/_index.md
@@ -15,61 +15,9 @@ learning_objectives:
 prerequisites:
     - Access to an x86 and an Arm Linux machine.
     - Familiarity with floating-point numbers.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:37:38Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 6427d4466fcd34f7275d16820cbfa2afa3815e1f0306c02e5011b2a4a6afa984
-  summary_generated_at: '2026-06-01T21:05:41Z'
-  summary_source_hash: 6427d4466fcd34f7275d16820cbfa2afa3815e1f0306c02e5011b2a4a6afa984
-  faq_generated_at: '2026-06-02T21:37:38Z'
-  faq_source_hash: 6427d4466fcd34f7275d16820cbfa2afa3815e1f0306c02e5011b2a4a6afa984
-  summary: >-
-    This Learning Path examines IEEE 754 floating-point behavior across x86 and Arm on Linux using
-    C++ examples. You will verify that both architectures produce identical results for all well-defined
-    operations, and learn where differences can appear in edge cases explicitly left undefined
-    by the standard. The path highlights scenarios such as out-of-range floating-point to integer
-    conversions and precision effects related to fused multiply-add (FMAC) in single precision,
-    with an example you can run on both platforms. It is aimed at developers porting applications
-    from x86 to Arm Cortex-A or Neoverse. Prerequisites are access to both an x86 and an Arm Linux
-    machine and familiarity with floating-point numbers. Estimated time is about 30 minutes.
-  faqs:
-  - question: What do I need before running the examples?
-    answer: >-
-      You need access to both an x86 and an Arm Linux machine and familiarity with floating-point
-      numbers. The examples use C++.
-  - question: How do I know if a difference I see is permitted by IEEE 754?
-    answer: >-
-      Check whether your code triggers an undefined case, such as converting an out-of-range floating-point
-      value to an integer. Differences in these cases are allowed by the standard and are not
-      defects in either architecture.
-  - question: Why might two mathematically equivalent C++ functions produce slightly different
-      results across architectures?
-    answer: >-
-      Minor variations can arise from precision and instruction-level choices, including fused
-      multiply-add (FMAC) behavior in single precision. The Learning Path shows an example to
-      help you recognize and reason about these cases.
-  - question: What result should I expect when I run the same C++ code on x86 and Arm?
-    answer: >-
-      For well-defined IEEE 754 operations, results should be identical. Differences should only
-      appear in special undefined cases that the standard permits, which this Learning Path highlights.
-  - question: How should I validate results when comparing x86 and Arm runs?
-    answer: >-
-      Run the provided example on both machines and compare the outputs produced by the program.
-      Use the guidance in the steps to identify whether any differences stem from undefined cases
-      or from the precision topics discussed.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: 
     - Kieran Hejmadi
diff --git a/content/learning-paths/cross-platform/function-multiversioning/_index.md b/content/learning-paths/cross-platform/function-multiversioning/_index.md
index f68f9b6194..7b1d23ffde 100644
--- a/content/learning-paths/cross-platform/function-multiversioning/_index.md
+++ b/content/learning-paths/cross-platform/function-multiversioning/_index.md
@@ -20,62 +20,9 @@ prerequisites:
     - Basic knowledge of loop vectorization.
     - Familiarity with Arm assembly.
     - A LLVM 20 compiler with runtime library support or GCC 16.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:38:11Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 1c1d745bd1af535315d5edd1b2b9214c96419d46abde3af8fa75bdde299bb65d
-  summary_generated_at: '2026-06-01T21:06:01Z'
-  summary_source_hash: 1c1d745bd1af535315d5edd1b2b9214c96419d46abde3af8fa75bdde299bb65d
-  faq_generated_at: '2026-06-02T21:38:11Z'
-  faq_source_hash: 1c1d745bd1af535315d5edd1b2b9214c96419d46abde3af8fa75bdde299bb65d
-  summary: >-
-    This advanced Learning Path shows how to apply function multiversioning in C/C++ for Arm64
-    targets using GCC or LLVM so your binaries can select the most appropriate implementation
-    at runtime. You will annotate functions with target_version and target_clones, build example
-    programs on Linux, Android, or macOS, and observe how the compiler generates versions specialized
-    for features such as SVE, SVE2, and FEAT_MOPS, including cases using ACLE intrinsics and inline
-    assembly. A dedicated step covers compatibility with Arm streaming mode. By the end, you will
-    be able to create function-level variants that leverage hardware capabilities and reuse the
-    same binary across different Arm64 systems. Prerequisites include basic GNU attributes, ifuncs,
-    loop vectorization, Arm assembly, and LLVM 20 (with runtime support) or GCC 16.
-  faqs:
-  - question: What do I need before running the examples?
-    answer: >-
-      You need either an LLVM 20 compiler with runtime library support or GCC 16. The path assumes
-      basic knowledge of GNU function attributes, familiarity with indirect functions (ifuncs)
-      and loop vectorization, and some familiarity with Arm assembly.
-  - question: Which attribute should I use to define multiple function versions?
-    answer: >-
-      Use __attribute__((target_version("name"))) to define a version keyed to specific features,
-      or __attribute__((target_clones("name", ...))) to create multiple versions at once. The
-      "name" string lists architectural features separated by '+'.
-  - question: Does the order of features in target_clones affect runtime selection?
-    answer: >-
-      No. The examples note that the order in which versions are listed with target_clones does
-      not matter.
-  - question: How do I know which version ran at runtime?
-    answer: >-
-      One example prints a message such as "Running the sve version of dotProduct" when the SVE
-      path executes. In general, the runtime mechanism selects the most appropriate version automatically,
-      and the examples include output cues to validate this.
-  - question: Is multiversioning compatible with Arm streaming mode?
-    answer: >-
-      Yes, as long as all versions of a function use the same calling convention. The examples
-      demonstrate compatibility using attributes like __arm_streaming and a variant specialized
-      for sme2.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Alexandros Lamprineas
 
diff --git a/content/learning-paths/cross-platform/github-arm-runners/_index.md b/content/learning-paths/cross-platform/github-arm-runners/_index.md
index 3c30b3380c..ae66bc4e4a 100644
--- a/content/learning-paths/cross-platform/github-arm-runners/_index.md
+++ b/content/learning-paths/cross-platform/github-arm-runners/_index.md
@@ -14,63 +14,9 @@ learning_objectives:
 prerequisites:
     - A GitHub account (a Team or Enterprise Cloud plan is required for private repositories).
     - A Docker Hub account.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:38:44Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 3557d534c51f81839cc353ebbd600ec588a60197d2c27c9a58e97c25017d07e4
-  summary_generated_at: '2026-06-01T21:06:27Z'
-  summary_source_hash: 3557d534c51f81839cc353ebbd600ec588a60197d2c27c9a58e97c25017d07e4
-  faq_generated_at: '2026-06-02T21:38:44Z'
-  faq_source_hash: 3557d534c51f81839cc353ebbd600ec588a60197d2c27c9a58e97c25017d07e4
-  summary: >-
-    Learn to build and publish multi-architecture container images for arm64 and amd64 using GitHub
-    Actions with Arm-hosted runners. This introductory path walks you through creating a repository,
-    defining a workflow that runs on Arm-hosted runners, and configuring the secrets needed to
-    automate deployment to Docker Hub. It also explains common build approaches for multi-architecture
-    images, including instruction emulation and using a manifest across multiple machines, noting
-    the performance drawback of emulation for complex builds. By the end, you will be able to
-    build Arm images and multi-architecture images with GitHub Actions. A Linux environment, a
-    GitHub account (Team or Enterprise Cloud for private repositories), and a Docker Hub account
-    are required. Estimated time: 30 minutes.
-  faqs:
-  - question: What do I need before running the workflow?
-    answer: >-
-      You need a GitHub account and a Docker Hub account. For private repositories, a GitHub Team
-      or Enterprise Cloud plan is required. No other explicit prerequisites are listed.
-  - question: Do I need to provision my own machines to run Arm jobs?
-    answer: >-
-      No. Arm-hosted runners are managed by GitHub, so you do not need to provide a server. They
-      are available for public and private repositories, with public repos on free plans subject
-      to standard usage limits.
-  - question: Which approach should I use to build multi-architecture images?
-    answer: >-
-      You can use instruction emulation or a manifest with multiple computers. Emulation is straightforward
-      but can be slow for complex builds, while the manifest approach builds natively on each
-      architecture. This Learning Path uses GitHub Actions with Arm-hosted runners as part of
-      a multi-architecture workflow.
-  - question: Can I use Arm-hosted runners in private repositories, and what runner types exist?
-    answer: >-
-      Yes, you can use them in private repositories with a Team or Enterprise Cloud plan. GitHub-hosted
-      runners include standard and larger runners; larger runners let you adjust RAM, CPU count,
-      and disk space, and offer options like a static IP and runner groups.
-  - question: How do I run the workflow and publish images to Docker Hub?
-    answer: >-
-      Create a new GitHub repository, add a GitHub Actions workflow that targets Arm-hosted runners,
-      and configure the repository secrets required by the workflow. The process builds images
-      for arm64 and amd64 and automates deployment to Docker Hub.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md b/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md
index 621ca96682..94d39379e9 100644
--- a/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md
+++ b/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md
@@ -17,56 +17,9 @@ learning_objectives:
 
 prerequisites:
     - A GitLab account (free tier includes Arm64 runner access)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:40:28Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: a6ad703d9d894da06ed4aa3725fcc9006d70050cef3f0a3ef9a758bcf4523289
-  summary_generated_at: '2026-06-01T21:07:14Z'
-  summary_source_hash: a6ad703d9d894da06ed4aa3725fcc9006d70050cef3f0a3ef9a758bcf4523289
-  faq_generated_at: '2026-06-02T21:40:28Z'
-  faq_source_hash: a6ad703d9d894da06ed4aa3725fcc9006d70050cef3f0a3ef9a758bcf4523289
-  summary: >-
-    This introductory Learning Path shows how to build a GitLab CI/CD pipeline that runs on GitLab-hosted
-    Arm64 runners. You create or use a GitLab project, write a simple C program, and containerize
-    it for Arm64 with Docker. You configure .gitlab-ci.yml to select Arm64 runner tags, build
-    and push the image to GitLab Container Registry, and run the pipeline on managed Arm infrastructure.
-    You verify execution on Arm64 by reviewing job logs and using lscpu output. The target environment
-    is Linux and GitLab, and no runner provisioning is required. Prerequisite: a GitLab account;
-    the free tier includes access to Arm64 runners.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      You need a GitLab account. The free tier includes access to GitLab-hosted Arm64 runners;
-      no other prerequisites are explicitly listed.
-  - question: How do I configure my pipeline to use Arm64 runners?
-    answer: >-
-      Create a .gitlab-ci.yml in the project root and specify the Arm64 runner tag. GitLab-hosted
-      runners are available to your project without additional setup.
-  - question: Which executor should I use for the jobs in this path?
-    answer: >-
-      Use the Docker executor for containerized builds. The path containerizes a C application
-      and builds it for the Arm64 architecture.
-  - question: What artifact does the pipeline produce and where is it stored?
-    answer: >-
-      The pipeline builds a container image for Arm64 and pushes it to the GitLab Container Registry.
-      After a successful run, you can view the image in your project’s registry.
-  - question: How do I verify the jobs actually ran on Arm64?
-    answer: >-
-      Open the job logs after running the pipeline and check the architecture verification step.
-      The lscpu output should indicate an Arm64 (AArch64) environment.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Mohamed Ismail
 
diff --git a/content/learning-paths/cross-platform/gitlab/_index.md b/content/learning-paths/cross-platform/gitlab/_index.md
index 44731b8fa2..7880e5bd63 100644
--- a/content/learning-paths/cross-platform/gitlab/_index.md
+++ b/content/learning-paths/cross-platform/gitlab/_index.md
@@ -17,59 +17,9 @@ prerequisites:
     - A [Google Cloud account](https://console.cloud.google.com/). Create an account if needed.
     - A computer with [Google Cloud CLI](/install-guides/gcloud) and [kubectl](/install-guides/kubectl/)installed.
     - A valid GitLab account
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:39:28Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: af9eb1c426e1844e2fd20215b7012d95c1efaf737f1d7b5be828931528342316
-  summary_generated_at: '2026-06-01T21:06:50Z'
-  summary_source_hash: af9eb1c426e1844e2fd20215b7012d95c1efaf737f1d7b5be828931528342316
-  faq_generated_at: '2026-06-02T21:39:28Z'
-  faq_source_hash: af9eb1c426e1844e2fd20215b7012d95c1efaf737f1d7b5be828931528342316
-  summary: >-
-    This advanced Learning Path shows how to build a GitLab CI/CD pipeline on Google Cloud using
-    Google Axion-based self-hosted runners. You will create a GitLab runner on Axion (Arm Neoverse)
-    and pair it with a native x86 runner to build a multi-architecture application targeting arm64
-    and amd64. Using GitLab CI/CD with Docker and Kubernetes on Linux, you will configure jobs
-    that produce per-architecture images and combine them into a single multi-arch image with
-    docker manifest, then automate build and deployment. Prerequisites include a Google Cloud
-    account, Google Cloud CLI, kubectl, and a GitLab account. The estimated time to complete is
-    about 30 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Google Cloud account, Google Cloud CLI and kubectl installed on your computer,
-      and a valid GitLab account. The path targets Linux.
-  - question: How do I know my Google Axion self-hosted runner is ready to run jobs?
-    answer: >-
-      After you register the runner, verify it appears in your GitLab project or group runner
-      list with the executor you selected. Continue when it is listed and available for CI/CD
-      jobs.
-  - question: Which approach does the pipeline use to produce a multi-architecture image?
-    answer: >-
-      It uses docker manifest to join separate amd64 and arm64 images into a single multi-architecture
-      image.
-  - question: Do I need both x86 and Arm runners to build the images?
-    answer: >-
-      Yes. The objectives include building multi-architecture Docker images using native GitLab
-      runners on x86 and Arm.
-  - question: Where are the built images stored, and how can I validate the result?
-    answer: >-
-      You create a Docker repository in Google Artifact Registry and push images there as part
-      of the pipeline. Validate by confirming the repository contains amd64 and arm64 images referenced
-      by a manifest after a successful run.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pranay Bakre
 
diff --git a/content/learning-paths/cross-platform/integer-vs-floats/_index.md b/content/learning-paths/cross-platform/integer-vs-floats/_index.md
index 91877e8cc8..47ec00b317 100644
--- a/content/learning-paths/cross-platform/integer-vs-floats/_index.md
+++ b/content/learning-paths/cross-platform/integer-vs-floats/_index.md
@@ -12,60 +12,9 @@ learning_objectives:
 
 prerequisites:
     - An Arm computer running Linux and a recent version of a C++ compiler (Clang or GCC) installed
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:41:57Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 1895767d551ba0fa249c5002d3e2e471acacdec06ddb7c2f5314a2fa0df94f2e
-  summary_generated_at: '2026-06-01T21:07:38Z'
-  summary_source_hash: 1895767d551ba0fa249c5002d3e2e471acacdec06ddb7c2f5314a2fa0df94f2e
-  faq_generated_at: '2026-06-02T21:41:57Z'
-  faq_source_hash: 1895767d551ba0fa249c5002d3e2e471acacdec06ddb7c2f5314a2fa0df94f2e
-  summary: >-
-    This Learning Path teaches advanced C/C++ developers on Arm how to identify and fix issues
-    in integer and floating-point conversions. Using an Arm computer running Linux with a recent
-    GCC or Clang, you will review data type ranges, explore explicit and implicit conversions,
-    and examine data type demotions. You will implement concise examples—a Fibonacci-based golden
-    ratio calculator in C and a C++ demotion test—to see where conversions and narrowing can change
-    results. By the end, you will be able to recognize risky conversions and decide when to use
-    explicit casts or different types on AArch64 (Armv8-A/Armv9-A). The estimated time to complete
-    is about 30 minutes.
-  faqs:
-  - question: What do I need before running the examples?
-    answer: >-
-      You need an Arm computer running Linux and a recent version of a C++ compiler, either Clang
-      or GCC. No additional prerequisites are explicitly listed.
-  - question: Which compiler should I use and are any specific flags required?
-    answer: >-
-      You can use either GCC or Clang on Linux. The Learning Path does not specify compiler flags;
-      the focus is on understanding code behavior around conversions.
-  - question: How do I know the golden_ratio.c program worked?
-    answer: >-
-      The program computes the golden ratio from consecutive Fibonacci numbers, so the output
-      should approach 1.618033988749894 as N increases. Compare the printed results to this value
-      to gauge correctness.
-  - question: What should I check if I see unexpected truncation or loss of precision?
-    answer: >-
-      Look for demotions, such as assigning a wider type to a narrower one (for example, double
-      to float or 64-bit to 16-bit) or performing integer division where floating-point was intended.
-      Remember that demotions are not detected in C and only in a few cases in C++, so verify
-      variable types against the ranges reviewed earlier.
-  - question: Which Arm platforms and operating system does this target?
-    answer: >-
-      The path targets AArch64 on Armv8-A and Armv9-A and assumes a Linux environment. The examples
-      are designed to be compiled and run on this setup.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Konstantinos Margaritis
 
diff --git a/content/learning-paths/cross-platform/intrinsics/_index.md b/content/learning-paths/cross-platform/intrinsics/_index.md
index 09a7114dca..24f1ab1774 100644
--- a/content/learning-paths/cross-platform/intrinsics/_index.md
+++ b/content/learning-paths/cross-platform/intrinsics/_index.md
@@ -17,61 +17,9 @@ prerequisites:
 - Some understanding of SIMD concepts.
 - An Arm based machine or [cloud instance](/learning-paths/servers-and-cloud-computing/csp/) running Ubuntu Linux.
 - Optionally, an `x86_64` machine also running Ubuntu.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:42:32Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: ecf51d9a9085f95dedda9c0cfbfa4d6350d0f68d81b61f9461bc51070abd0b69
-  summary_generated_at: '2026-06-01T21:08:20Z'
-  summary_source_hash: ecf51d9a9085f95dedda9c0cfbfa4d6350d0f68d81b61f9461bc51070abd0b69
-  faq_generated_at: '2026-06-02T21:42:32Z'
-  faq_source_hash: ecf51d9a9085f95dedda9c0cfbfa4d6350d0f68d81b61f9461bc51070abd0b69
-  summary: >-
-    This advanced Learning Path shows how to migrate C/C++ code that relies on architecture-specific
-    intrinsics from x64 to Arm. You will learn how to identify intrinsics in your source, understand
-    how compilers expose them, and use header-only libraries to rebuild and run on Arm processors.
-    The path demonstrates two approaches: mapping SSE intrinsics to Neon with sse2neon, and using
-    SIMD Everywhere (SIMDe) for broader coverage, including AVX. It also introduces Porting Advisor
-    for Graviton to locate intrinsics in large codebases. The target environment is Ubuntu Linux
-    on an Arm-based machine or cloud instance; an x86_64 Ubuntu system is optional. By the end,
-    you will have code that compiles and runs on Arm.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      You need some understanding of SIMD concepts and access to an Arm-based machine or cloud
-      instance running Ubuntu Linux. Optionally, have an x86_64 Ubuntu machine available.
-  - question: How do I find architecture-specific intrinsics in a large code base?
-    answer: >-
-      Use the background in this path to spot intrinsics in source and run Porting Advisor for
-      Graviton to assess portability and locate intrinsics. Porting Advisor is a command line
-      tool available on Linux, Windows, and macOS, and the example assumes you run it as an executable
-      in your PATH.
-  - question: 'Which option should I use to port x86 intrinsics: sse2neon or SIMDe?'
-    answer: >-
-      If your code uses MMX or SSE, you can use either sse2neon or SIMDe. If it contains AVX,
-      use SIMDe.
-  - question: What changes are required when porting with sse2neon?
-    answer: >-
-      Adjust SSE-specific header usage for the Arm build, include sse2neon.h to map intrinsics
-      to Neon, and update your g++ compiler flags for the Arm architecture. This approach can
-      get many C/C++ applications compiling and running on an appropriate Arm platform.
-  - question: What are the high-level steps to use SIMD Everywhere (SIMDe)?
-    answer: >-
-      Select the correct SIMDe header using the SIMDEverywhere wiki table, define the required
-      SIMDe configuration macro as shown in the steps, and build for Arm. SIMDe is a header-only
-      library intended to make intrinsic-based code portable across architectures.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/cross-platform/ipexplorer/_index.md b/content/learning-paths/cross-platform/ipexplorer/_index.md
index 711360f434..de81bc3bd0 100644
--- a/content/learning-paths/cross-platform/ipexplorer/_index.md
+++ b/content/learning-paths/cross-platform/ipexplorer/_index.md
@@ -13,61 +13,9 @@ learning_objectives:
 prerequisites:
     - An Arm account that can access IP Explorer
     - (Optional) A Linux machine with the desired compilers installed
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:43:14Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 47cd598f6e33c12d3729c319a1d6a7afcd748de7acd6faea639f2e8600a085ed
-  summary_generated_at: '2026-06-01T21:08:49Z'
-  summary_source_hash: 47cd598f6e33c12d3729c319a1d6a7afcd748de7acd6faea639f2e8600a085ed
-  faq_generated_at: '2026-06-02T21:43:14Z'
-  faq_source_hash: 47cd598f6e33c12d3729c319a1d6a7afcd748de7acd6faea639f2e8600a085ed
-  summary: >-
-    This introductory path shows how to use Arm IP Explorer’s cloud simulation platforms to run
-    and compare custom bare-metal software benchmarks on Arm Cortex-M processors using cycle count
-    analysis. You will run a pre-installed example, then clone the provided software package to
-    create your own benchmark from sample C projects that highlight marked code regions. Optionally
-    build and test locally on Linux using Arm GNU Toolchain or Arm Compiler for Embedded. Next,
-    package your application (custom-software.tgz), upload it via the Simulate Processors workflow,
-    select AC6 in the UI, and run on Cortex-M instances (for example, Cortex-M0 and Cortex-M7).
-    Requires an Arm account with IP Explorer access.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an Arm account that can access IP Explorer. Optionally, have a Linux machine with
-      the desired compilers installed if you plan to build the custom benchmark locally.
-  - question: How do I create and edit the custom benchmark code?
-    answer: >-
-      Clone the software package repository referenced in the steps, which includes sample projects.
-      Use the provided C source file with a marked code region to add or modify the algorithm
-      you want to benchmark.
-  - question: Where do I upload my custom software in IP Explorer, and what file should I select?
-    answer: >-
-      In IP Explorer, go to Simulate Processors, open your Cortex-M instance, then Software Simulation,
-      and click +New. From Select/Upload Software choose +New, upload the custom-software.tgz
-      you created, then select the my_example project, choose AC6 (Arm Compiler for Embedded),
-      and run.
-  - question: How do I compare performance across different Cortex-M processors?
-    answer: >-
-      Run the same benchmark on multiple Cortex-M instances (for example, Cortex-M0 and Cortex-M7).
-      Use the cycle-accurate data produced by the simulation to compare results across cores.
-  - question: What should I check if my Cortex-M instances are not listed?
-    answer: >-
-      Ensure you previously created the instances under Simulate Processors in IP Explorer, as
-      the steps expect them to exist. If they are missing, create the required Cortex-M instances
-      before starting a new Software Simulation.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/cross-platform/kleidiai-explainer/_index.md b/content/learning-paths/cross-platform/kleidiai-explainer/_index.md
index c02e782d8d..98a60bb238 100644
--- a/content/learning-paths/cross-platform/kleidiai-explainer/_index.md
+++ b/content/learning-paths/cross-platform/kleidiai-explainer/_index.md
@@ -13,65 +13,9 @@ learning_objectives:
 prerequisites:
     - An Arm-based Linux machine that implements the Int8 Matrix Multiplication (*i8mm*) architecture feature. The example in this Learning Path is run on an AWS Graviton 3 instance. Instructions on setting up an Arm-based server are [found here](/learning-paths/servers-and-cloud-computing/csp/aws/).
     - A basic understanding of linear algebra terminology, such as dot product and matrix multiplication.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:44:03Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 907255b3c2c1086b421abe4a1e378b68533de4524a4f4dd81138545a2ff1a5b5
-  summary_generated_at: '2026-06-01T21:09:31Z'
-  summary_source_hash: 907255b3c2c1086b421abe4a1e378b68533de4524a4f4dd81138545a2ff1a5b5
-  faq_generated_at: '2026-06-02T21:44:03Z'
-  faq_source_hash: 907255b3c2c1086b421abe4a1e378b68533de4524a4f4dd81138545a2ff1a5b5
-  summary: >-
-    This introductory path shows how KleidiAI micro-kernels accelerate Generative AI inference
-    on Arm CPUs by optimizing matrix multiplication using architecture features such as Int8 Matrix
-    Multiplication (i8mm). You will explore the KleidiAI GitLab repository, review the organization
-    of quantizing/packing and matmul micro-kernels under /kai/ukernels/matmul, and run a basic
-    C++ matrix multiplication example that highlights the i8mm micro-kernel and its supporting
-    routines. The path also connects core linear algebra operations to how Large Language Models
-    execute. It targets Arm-based Linux systems with i8mm; the example is run on an AWS Graviton
-    3 instance. By the end, you can explain where KleidiAI fits in a software stack and demonstrate
-    its micro-kernel speedup using the provided example. Prerequisites include an Arm-based Linux
-    machine with i8mm and basic linear algebra knowledge.
-  faqs:
-  - question: What do I need before running the example?
-    answer: >-
-      You need an Arm-based Linux machine that implements the Int8 Matrix Multiplication (i8mm)
-      feature; the example is run on an AWS Graviton 3 instance. Instructions on setting up an
-      Arm-based server are found here: /learning-paths/servers-and-cloud-computing/csp/aws/. A
-      basic understanding of dot product and matrix multiplication is also required.
-  - question: How do I know if my ML framework will use KleidiAI automatically?
-    answer: >-
-      If your ML framework integrates KleidiAI and your hardware supports the required Arm instructions
-      for your inference, you will benefit from KleidiAI without any further action. Both conditions
-      must be met.
-  - question: Where do I find the relevant micro-kernels in the KleidiAI repository?
-    answer: >-
-      Navigate to the KleidiAI GitLab repository and go to /kai/ukernels/matmul. Quantizing/packing
-      routines are in the pack directory, and matrix multiplication routines are in the remaining
-      subdirectories there.
-  - question: What should I expect when I run the C++ matrix multiplication example?
-    answer: >-
-      The example highlights the i8mm matrix multiplication micro-kernel along with the enabling
-      quantizing/packing micro-kernels. It is designed to showcase KleidiAI micro-kernel performance
-      rather than require changes to your ML framework.
-  - question: Do I need to modify my ML stack or write assembly to use KleidiAI?
-    answer: >-
-      No. KleidiAI micro-kernels are hand-optimized in Arm assembly, but in practice your ML framework
-      will leverage them automatically if supported. This Learning Path uses a standalone example
-      to illustrate how the micro-kernels work.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Zach Lasiuk
 ### Tags
diff --git a/content/learning-paths/cross-platform/llm-fine-tuning-for-web-applications/_index.md b/content/learning-paths/cross-platform/llm-fine-tuning-for-web-applications/_index.md
index 6ccd693760..e8261c0d92 100644
--- a/content/learning-paths/cross-platform/llm-fine-tuning-for-web-applications/_index.md
+++ b/content/learning-paths/cross-platform/llm-fine-tuning-for-web-applications/_index.md
@@ -26,11 +26,9 @@ prerequisites:
     - An Android smartphone with the i8mm feature and 16GB of RAM.
     - Basic understanding of machine learning and deep learning. 
     - Familiarity with deep learning frameworks such as PyTorch and Hugging Face Transformers. 
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 author: Parichay Das
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/loop-reflowing/_index.md b/content/learning-paths/cross-platform/loop-reflowing/_index.md
index 4dfda6fe2e..f8729c215e 100644
--- a/content/learning-paths/cross-platform/loop-reflowing/_index.md
+++ b/content/learning-paths/cross-platform/loop-reflowing/_index.md
@@ -11,61 +11,9 @@ learning_objectives:
 
 prerequisites:
     - An Arm computer running Linux and a recent version of Clang or the GNU compiler (gcc) installed.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:44:40Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 4218b8b0c1dee3862bb9765a83dfed0cb38555d1da7425e791c5c16b17f98c21
-  summary_generated_at: '2026-06-01T21:10:12Z'
-  summary_source_hash: 4218b8b0c1dee3862bb9765a83dfed0cb38555d1da7425e791c5c16b17f98c21
-  faq_generated_at: '2026-06-02T21:44:40Z'
-  faq_source_hash: 4218b8b0c1dee3862bb9765a83dfed0cb38555d1da7425e791c5c16b17f98c21
-  summary: >-
-    This advanced, 45-minute Learning Path guides C/C++ developers on Arm Linux through practical
-    compiler autovectorization techniques on Arm processors. You will compile small examples (such
-    as addvec, addvec_neon, and dotprod) with GCC or Clang at -O2, generate and inspect assembly
-    with objdump, and learn how to structure loops so compilers can vectorize them. The steps
-    cover using the C99 restrict qualifier, recognizing limits like non-countable loops and branches,
-    and adapting conditionals to enable the vectorizer. Prerequisite: an Arm computer running
-    Linux with a recent GCC or Clang installed. By the end, you will be able to modify loops to
-    help mainstream compilers autovectorize on Arm.
-  faqs:
-  - question: What do I need before running the examples?
-    answer: >-
-      You need an Arm computer running Linux and a recent version of GCC or Clang. The examples
-      use gcc, and the path references the GNU compiler install guide if you need installation
-      help.
-  - question: When should I use the restrict qualifier in the examples?
-    answer: >-
-      The path shows a classic case where adding restrict to pointer parameters removes potential
-      aliasing and enables autovectorization. You will compile both restricted and non-restricted
-      versions and compare their generated assembly.
-  - question: Which commands does the path use to compile and inspect the code?
-    answer: >-
-      It compiles with gcc -O2 addvec.c -o addvec and gcc -O2 addvec_neon.c -o addvec_neon. To
-      view the generated assembly, it uses objdump -D addvec.
-  - question: How do I know if a loop is eligible for autovectorization?
-    answer: >-
-      The path explains that countable loops—where the number of iterations is known before entry—are
-      candidates for vectorization. Examples show that loops with unknown trip counts or early
-      breaks are not vectorized.
-  - question: What should I check if my loop has conditionals and isn’t being vectorized?
-    answer: >-
-      Branches inside loops can inhibit autovectorization. The steps demonstrate when you can
-      adapt or restructure the loop to enable the vectorizer and when an algorithm change or manually
-      optimized code may be required.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Konstantinos Margaritis
 
diff --git a/content/learning-paths/cross-platform/matrix/_index.md b/content/learning-paths/cross-platform/matrix/_index.md
index 4f2dc94664..f1c2522d8c 100644
--- a/content/learning-paths/cross-platform/matrix/_index.md
+++ b/content/learning-paths/cross-platform/matrix/_index.md
@@ -18,61 +18,9 @@ prerequisites:
     - A C++ compiler with C++17 support.
     - A build system [GNU Make](https://www.gnu.org/software/make/) or [Ninja](https://ninja-build.org/).
     - A documentation generator [Doxygen](https://www.doxygen.nl/).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:45:23Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 5611b6d1eebbea167d1860e3ac7f4f7910584561cbb18c3ed696aa27215edce9
-  summary_generated_at: '2026-06-01T21:10:50Z'
-  summary_source_hash: 5611b6d1eebbea167d1860e3ac7f4f7910584561cbb18c3ed696aa27215edce9
-  faq_generated_at: '2026-06-02T21:45:23Z'
-  faq_source_hash: 5611b6d1eebbea167d1860e3ac7f4f7910584561cbb18c3ed696aa27215edce9
-  summary: >-
-    This Learning Path guides you through developing and testing a modern C++ matrix-processing
-    library on an Arm-based machine using CMake and GoogleTest. You will prepare a C++17 toolchain
-    (GCC or Clang), select a build system (GNU Make or Ninja), and set up an IDE and a documentation
-    generator such as Doxygen. Starting from project boilerplate, you implement core matrix types
-    and operations (add, subtract, multiply), separate traversal from data processing, and write
-    unit tests to guard against regressions. The path also discusses practical error-handling
-    trade-offs. By the end, you have a buildable CMake project with a GoogleTest suite running
-    on Linux, macOS, or Windows on Arm. Prerequisites are listed, and the estimated time to complete
-    is about 120 minutes.
-  faqs:
-  - question: What do I need on my Arm-based machine before starting?
-    answer: >-
-      You need an Arm-based computer running Linux, macOS, or Windows; an IDE; CMake; a C++17-capable
-      compiler (GCC or Clang); a build system (GNU Make or Ninja); and Doxygen. The path provides
-      an example installation on Ubuntu using build-essential, clang, ninja-build, cmake, and
-      doxygen.
-  - question: Which compiler, C++ standard, and build system should I use?
-    answer: >-
-      Use GCC or Clang with C++17 support. Either GNU Make or Ninja is suitable as the build system,
-      driven by CMake.
-  - question: How do I know my environment is set up correctly?
-    answer: >-
-      After configuring the project with CMake and adding GoogleTest, build and run the unit tests
-      for the Matrix library. Successful compilation and passing tests indicate the setup is working.
-  - question: What functionality will I implement in the Matrix library?
-    answer: >-
-      You first add the core boilerplate for Matrix objects (construction, assignment, and dump-to-screen),
-      then implement add, subtract, and multiply. The design separates matrix traversal from the
-      data processing to make testing and extension straightforward.
-  - question: How does this path address error handling in the library?
-    answer: >-
-      It explains how to balance safety and security with performance depending on the use case.
-      The path discusses considerations rather than prescribing a single policy.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Arnaud de Grandmaison
 
diff --git a/content/learning-paths/cross-platform/mca-godbolt/_index.md b/content/learning-paths/cross-platform/mca-godbolt/_index.md
index 2d5aad97d3..194553fcd2 100644
--- a/content/learning-paths/cross-platform/mca-godbolt/_index.md
+++ b/content/learning-paths/cross-platform/mca-godbolt/_index.md
@@ -14,57 +14,9 @@ learning_objectives:
 prerequisites:
     - Familiarity with Arm assembly.
     - LLVM version 16 or newer, which includes support for Neoverse V2.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:45:53Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: c86322d497541344f92907315793ab990669aa08bef994b0ce9ff1e32a5ba055
-  summary_generated_at: '2026-06-01T21:11:17Z'
-  summary_source_hash: c86322d497541344f92907315793ab990669aa08bef994b0ce9ff1e32a5ba055
-  faq_generated_at: '2026-06-02T21:45:53Z'
-  faq_source_hash: c86322d497541344f92907315793ab990669aa08bef994b0ce9ff1e32a5ba055
-  summary: >-
-    This introductory Learning Path shows how to analyze Arm assembly performance with LLVM Machine
-    Code Analyzer (llvm-mca) and Compiler Explorer. You will run llvm-mca on a small Arm assembly
-    example that sums six values, interpret estimated cycles and hardware resource pressure, and
-    use those metrics to diagnose a possible performance issue and improve the snippet. A brief
-    background section introduces instruction scheduling and pipelines. The path is relevant to
-    Arm cores such as Cortex-A and Neoverse, and notes that LLVM 16 or newer includes support
-    for Neoverse V2. It can be followed on Linux, Windows, or macOS, and also via a browser using
-    Compiler Explorer. Familiarity with Arm assembly is expected; no other explicit prerequisites
-    are listed. Estimated time to complete is about 60 minutes.
-  faqs:
-  - question: Can I use llvm-mca without installing LLVM locally?
-    answer: >-
-      Yes. The path shows how to run llvm-mca in Compiler Explorer at godbolt.org, which provides
-      llvm-mca as an online tool.
-  - question: What do I need to run llvm-mca on my machine?
-    answer: >-
-      You need familiarity with Arm assembly and LLVM version 16 or newer. The path can be followed
-      on Linux, Windows, or macOS.
-  - question: What source code does the path analyze?
-    answer: >-
-      An Arm assembly snippet saved as sum_test1.s that computes the sum of six numbers using
-      add instructions.
-  - question: What output should I expect from llvm-mca, and how is it used?
-    answer: >-
-      Expect estimates of cycles and hardware resource pressure. The path explains the expected
-      output and how to use these metrics to identify a potential performance issue in the example.
-  - question: Which LLVM version includes support for Neoverse V2?
-    answer: >-
-      LLVM 16 or newer includes support for Neoverse V2, as noted in the prerequisites.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Asher Dobrescu
 
diff --git a/content/learning-paths/cross-platform/mcp-ai-agent/_index.md b/content/learning-paths/cross-platform/mcp-ai-agent/_index.md
index bce9de89df..23cfa4df0a 100644
--- a/content/learning-paths/cross-platform/mcp-ai-agent/_index.md
+++ b/content/learning-paths/cross-platform/mcp-ai-agent/_index.md
@@ -17,61 +17,9 @@ prerequisites:
     - Familiarity with Python programming and prompt engineering techniques.
     - Basic understanding of Large Language Models (LLMs) and how they are used in local inference.
     - Understanding of AI agents and the OpenAI Agent SDK (or similar frameworks).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:46:28Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 39d489081bfa22125a4046c17d5c00a32c0d7b298cba7b6a12373cf3cf6bac04
-  summary_generated_at: '2026-06-01T21:11:51Z'
-  summary_source_hash: 39d489081bfa22125a4046c17d5c00a32c0d7b298cba7b6a12373cf3cf6bac04
-  faq_generated_at: '2026-06-02T21:46:28Z'
-  faq_source_hash: 39d489081bfa22125a4046c17d5c00a32c0d7b298cba7b6a12373cf3cf6bac04
-  summary: >-
-    This Learning Path shows how to deploy a lightweight Model Context Protocol (MCP) server on
-    a Raspberry Pi 5 and connect it to an AI agent built with the OpenAI Agent SDK. You will use
-    uv, a fast Python package manager, to bootstrap a FastMCP server that reads CPU temperature
-    and searches weather data, and expose it to the internet with ngrok. On a Linux Arm development
-    machine, you will create the agent, register custom tools, and point it at the Pi’s MCP endpoint
-    for local inference. Prerequisites include a Raspberry Pi 5 with a Linux-based OS, familiarity
-    with Python and prompt engineering, and a basic understanding of LLMs and AI agents. Estimated
-    time to complete is about 30 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Raspberry Pi 5 with a Linux-based OS installed, familiarity with Python and prompt
-      engineering, a basic understanding of LLMs and local inference, and an understanding of
-      AI agents and the OpenAI Agent SDK (or similar frameworks).
-  - question: Which machine hosts the MCP server and where does the agent run?
-    answer: >-
-      The MCP server runs on the Raspberry Pi 5 (Raspberry Pi OS 64-bit). The AI agent is set
-      up on your development machine, with the commands tested on a Linux Arm system, and it connects
-      to the MCP server on the Pi.
-  - question: How do I install uv and what project files should I see?
-    answer: >-
-      Install uv by running: curl -LsSf https://astral.sh/uv/install.sh | sh. When you initialize
-      a project with uv init, it creates a .venv/ directory and a pyproject.toml file for the
-      project.
-  - question: How do I expose the MCP server running on my Raspberry Pi to the internet?
-    answer: >-
-      Use ngrok to create an HTTPS tunnel to your local MCP server. The steps show how to expose
-      the server so it can be reached remotely.
-  - question: What result should I expect to confirm the setup is working?
-    answer: >-
-      On the Raspberry Pi, the MCP server should be able to read CPU temperature and search weather
-      data. On your development machine, the agent should successfully connect to the Pi’s MCP
-      server, and uv should have created the .venv and pyproject.toml in your agent project.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Andrew Choi
 
diff --git a/content/learning-paths/cross-platform/memory-latency/_index.md b/content/learning-paths/cross-platform/memory-latency/_index.md
index 47be6305b0..d7e72b810b 100644
--- a/content/learning-paths/cross-platform/memory-latency/_index.md
+++ b/content/learning-paths/cross-platform/memory-latency/_index.md
@@ -12,57 +12,9 @@ learning_objectives:
 
 prerequisites:
     - An Arm computer running Linux with recent versions of Clang or GCC installed.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:47:00Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 72e5ffe5091311850d17f30ed3ab4bd7487cbbd862d0d8751cc73bd91fff00e2
-  summary_generated_at: '2026-06-01T21:12:22Z'
-  summary_source_hash: 72e5ffe5091311850d17f30ed3ab4bd7487cbbd862d0d8751cc73bd91fff00e2
-  faq_generated_at: '2026-06-02T21:47:00Z'
-  faq_source_hash: 72e5ffe5091311850d17f30ed3ab4bd7487cbbd862d0d8751cc73bd91fff00e2
-  summary: >-
-    Learn practical ways to reduce the impact of memory latency on Arm processors by experimenting
-    with cache alignment and prefetching in C. You will build and run an example, then create
-    a second version by copying memory-latency1.c to memory-latency2.c, introducing an allocator,
-    adjusting data structure alignment, and adding prefetching to observe effects on execution.
-    The path targets Linux on Arm systems, including Cortex-A and Neoverse, and uses GCC or Clang.
-    Results will vary by processor and system, which is expected and part of the learning. Prerequisite:
-    an Arm computer running Linux with a recent GCC or Clang. Estimated time to complete: about
-    40 minutes.
-  faqs:
-  - question: What do I need before running the examples?
-    answer: >-
-      You need an Arm computer running Linux with recent versions of Clang or GCC installed. No
-      other prerequisites are explicitly listed.
-  - question: What should I expect after copying memory-latency1.c to memory-latency2.c?
-    answer: >-
-      You will have a modified C program that introduces a simple allocator and related bookkeeping.
-      Use it to compare behavior against the original and observe how allocation affects latency.
-  - question: How do I know whether the cache alignment change had an effect?
-    answer: >-
-      Rebuild and run the updated program and compare results with the previous version. Focus
-      on relative differences on your system rather than exact numbers.
-  - question: How far ahead should I prefetch in the loop?
-    answer: >-
-      Prefetch a few iterations ahead; prefetching only the next iteration is not sufficient.
-      The path notes typical RAM latency around 100 ns, so bring data closer earlier.
-  - question: What should I check if my results differ from the sample output?
-    answer: >-
-      This is expected because results depend on the processor and system you use. Focus on the
-      trend between versions, and note that the learning applies to any Arm processor.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Konstantinos Margaritis
 
diff --git a/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md b/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md
index 19577ae636..fcb8db85c0 100644
--- a/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md
+++ b/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md
@@ -17,61 +17,9 @@ prerequisites:
     - An Armv9 Linux device with at least 32 GB of available disk space, for example a Radxa Orion O6
     - Familiarity with the Linux command line, Git, and building C++ projects with CMake
     - Internet access to download source code, model assets, and sample data
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:47:36Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: bf3183bd62b590d4930f0bcf9c9dc836bbc5206a991be7bec5e18e9c20942352
-  summary_generated_at: '2026-06-01T21:13:18Z'
-  summary_source_hash: bf3183bd62b590d4930f0bcf9c9dc836bbc5206a991be7bec5e18e9c20942352
-  faq_generated_at: '2026-06-02T21:47:36Z'
-  faq_source_hash: bf3183bd62b590d4930f0bcf9c9dc836bbc5206a991be7bec5e18e9c20942352
-  summary: >-
-    This advanced Learning Path shows how to build the MNN (Mobile Neural Network) inference engine
-    natively on an Armv9 Linux device and run a CPU-only Omni multimodal model. You start by verifying
-    a text-only baseline to confirm the core inference path, then run local vision reasoning on
-    retail shelf images to estimate coverage across top, middle, and bottom levels, identify the
-    most sparse priority zone with a short reason, or return NOT_SURE when images are unclear.
-    You also convert a spoken restock note into a single-line, semicolon-separated ticket and
-    combine image and audio inputs into a single-shot restock workflow. Prerequisites include
-    an Armv9 Linux system with 32 GB free space, command-line and CMake/Git experience, and internet
-    access. Estimated time is about 90 minutes.
-  faqs:
-  - question: Do I need a GPU or accelerator to run the demos?
-    answer: >-
-      No. This Learning Path uses a native CPU-only MNN build on an Armv9 Linux system by design.
-  - question: What do I need before building MNN on my Armv9 device?
-    answer: >-
-      You need an Armv9 Linux device with at least 32 GB of available disk space, internet access,
-      and familiarity with the Linux command line, Git, and building C++ projects with CMake.
-  - question: How do I confirm my MNN build and model are ready?
-    answer: >-
-      Verify that the llm_demo binary can load a prebuilt Omni MNN model package on your Armv9
-      system. This confirms the setup needed for the text, vision, and audio demos.
-  - question: What result should I expect from the text-only baseline?
-    answer: >-
-      A reproducible text-only inference run with a simple prompt and predictable output behavior.
-      This validates the MNN runtime, the prompt input path, and token generation before adding
-      vision and audio.
-  - question: What outputs should I expect from the vision and audio steps, and how do they fit
-      together?
-    answer: >-
-      The vision audit estimates shelf coverage for top, middle, and bottom levels, identifies
-      the most sparse priority zone, provides a short reason, and returns NOT_SURE when the image
-      is unclear. The audio step converts a spoken note into a one-line, semicolon-separated ticket.
-      Together, they form a single-shot restock workflow using local image and audio inputs.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Odin Shen
 
diff --git a/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md b/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md
index 78aa606e56..f0a73f8fdf 100644
--- a/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md
+++ b/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md
@@ -23,68 +23,9 @@ prerequisites:
     - Installation of Docker for SME2 emulation (if you don't have SME2 available)
     - Installation of Android Development Studio and adb (if you're targeting an Android phone with SME2 support)
     - Compiler support for SME2 instructions (for example, LLVM 18 or later with SME2 backend support)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:48:10Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: eb01b77f36323331c080615edcbddbf8cb56cf005f2249f1ea309ab1dbec8616
-  summary_generated_at: '2026-06-01T21:13:59Z'
-  summary_source_hash: eb01b77f36323331c080615edcbddbf8cb56cf005f2249f1ea309ab1dbec8616
-  faq_generated_at: '2026-06-02T21:48:10Z'
-  faq_source_hash: eb01b77f36323331c080615edcbddbf8cb56cf005f2249f1ea309ab1dbec8616
-  summary: >-
-    This advanced Learning Path shows how to implement, build, and evaluate matrix multiplication
-    using Arm’s Scalable Matrix Extension 2 (SME2) with both assembly and intrinsics. You will
-    set up a development environment on Linux, macOS, or Windows and choose either native SME2
-    hardware (demonstrated on macOS with an M4 chip or some Android phones) or a Linux-based emulation
-    flow. After verifying your toolchain with CMake/Ninja and Clang/LLVM (LLVM 18+), you will
-    create a vanilla C matmul as a correctness reference, then add SME2 intrinsics and assembly,
-    learn how streaming mode and ZA state are handled via ACLE annotations, and benchmark and
-    validate results. Prerequisites include working knowledge of SVE/SME2, intermediate C and
-    Armv9-A assembly, Git, CMake, Ninja, and optionally Docker or Android Development Studio and
-    adb.
-  faqs:
-  - question: What do I need before running the examples?
-    answer: >-
-      You need working knowledge of SVE and SME2, intermediate C and Armv9-A assembly skills,
-      and a computer running Linux, macOS, or Windows. Install Git, CMake, Ninja, and a compiler
-      with SME2 support (for example, LLVM 18+). For emulation, install Docker; for Android targets,
-      install Android Studio and adb, and use a phone with SME2 support.
-  - question: Should I use native SME2 hardware or an emulator?
-    answer: >-
-      Use native SME2 hardware when available for direct execution; this Learning Path demonstrates
-      macOS with an M4 chip and some Android phones with SME2 support. If you lack SME2 hardware,
-      use the Linux-based emulation option. iPhone and iPad are not covered by the instructions,
-      even though they have SME2 support.
-  - question: How do I verify my SME2 toolchain and environment are set up correctly?
-    answer: >-
-      Build the provided code examples with CMake to confirm the compiler, hardware (or emulator),
-      and tools are working. For native builds, you may need to tell CMake which Clang to use
-      if the system default is not suitable. A successful, error-free build indicates your environment
-      is ready.
-  - question: How do I use streaming mode and handle ZA state in SME?
-    answer: >-
-      Annotate the relevant functions to enable streaming mode as defined by the Arm C Language
-      Extensions (ACLE). The compiler manages saving and restoring state, including ZA storage,
-      when streaming-mode functions call each other. No manual state management is required.
-  - question: How do I validate and benchmark the SME2-optimized matrix multiplication?
-    answer: >-
-      First implement the vanilla C matrix multiplication as a correctness reference. Then compile
-      the SME2 intrinsics and assembly implementations and run benchmarks on SME2 hardware or
-      in a Linux-based emulation environment. Compare the performance metrics to the baseline
-      and confirm numerical results match.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Arnaud de Grandmaison
 
diff --git a/content/learning-paths/cross-platform/psa-tfm/_index.md b/content/learning-paths/cross-platform/psa-tfm/_index.md
index 741b62b037..400e8ba518 100644
--- a/content/learning-paths/cross-platform/psa-tfm/_index.md
+++ b/content/learning-paths/cross-platform/psa-tfm/_index.md
@@ -18,11 +18,9 @@ learning_objectives:
 prerequisites:
     - Ubuntu host or access to AWS
     - Optional MPS3 FPGA prototyping board
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 author: Ronan Synnott
 
 ### Tags
diff --git a/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md b/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md
index c64241368b..b5ef504fa4 100644
--- a/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md
+++ b/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md
@@ -20,60 +20,9 @@ learning_objectives:
 prerequisites:
     - A machine that can run Python3, Visual Studio Code, and Android Studio. 
     - For the OS, you can use Windows, Linux, or macOS.
-  
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:48:30Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 1251465f13b67ee80292b66ef22069a1b6cc2ca67bb602cdd5e393a278576ec8
-  summary_generated_at: '2026-06-01T21:14:41Z'
-  summary_source_hash: 1251465f13b67ee80292b66ef22069a1b6cc2ca67bb602cdd5e393a278576ec8
-  faq_generated_at: '2026-06-02T21:48:30Z'
-  faq_source_hash: 1251465f13b67ee80292b66ef22069a1b6cc2ca67bb602cdd5e393a278576ec8
-  summary: >-
-    This advanced Learning Path guides you through preparing a PyTorch development environment,
-    downloading and organizing the MNIST dataset, and creating, training, and saving a feedforward
-    neural network for digit classification. You then create an Android application that loads
-    the pre-trained model, prepares input data consistently with training, and measures inference
-    time. The path also shows how to apply quantization and fusing to optimize the network and
-    deploy the optimized model in the app. You need a machine capable of running Python3, Visual
-    Studio Code, and Android Studio on Windows, Linux, or macOS. Estimated time to complete is
-    about 160 minutes.
-  faqs:
-  - question: What do I need installed before running the training and Android steps?
-    answer: >-
-      You need a machine that can run Python3, Visual Studio Code, and Android Studio. You can
-      use Windows, Linux, or macOS.
-  - question: How do I download MNIST and create DataLoaders in this path?
-    answer: >-
-      Use torchvision.datasets.MNIST with download=True and transforms.ToTensor, then create DataLoader
-      objects for the training and test sets. The example shows a batch size of 32 and uses a
-      data/ folder as the root.
-  - question: How do I know the training step worked and the model is saved?
-    answer: >-
-      The training step saves the trained model to a file that you load later for inference. After
-      saving, proceed to the inference step to validate loading and predictions.
-  - question: During inference, how should I preprocess inputs so they match training?
-    answer: >-
-      Apply the same preprocessing used during training, such as tensor conversion and normalization.
-      Ensure inputs are formatted like MNIST (28x28) before feeding them to the model.
-  - question: When do I apply quantization and fusing, and what gets deployed to Android?
-    answer: >-
-      Apply quantization and fusing after training to produce an optimized model. The Learning
-      Path then deploys this optimized model in an Android application and measures inference
-      time.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/cross-platform/remoteit/_index.md b/content/learning-paths/cross-platform/remoteit/_index.md
index 457eac5269..cdbd179f1b 100644
--- a/content/learning-paths/cross-platform/remoteit/_index.md
+++ b/content/learning-paths/cross-platform/remoteit/_index.md
@@ -16,68 +16,9 @@ prerequisites:
     - A Windows, macOS, or Linux computer which you will use to configure your devices as well as connect to your remote devices.
     - A device/computer to which you would like remote access. A device can be a Windows, Mac, or Linux computer including development kits such as Raspberry Pi or cloud-hosted such as within Arm Virtual Hardware or within AWS. You will need a method to control this device before Remote.It is deployed which can be local access or access via another remote connectivity solution (Remote Desktop, VPN, etc.)
     - Determine if your device that you would like to access remotely also needs to make connections to other Remote.It devices.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:49:07Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 927cfebb8ebf9595922dad115c9a8d10900e43c4f80e73e6102a71e3e4ca2da1
-  summary_generated_at: '2026-06-01T21:15:32Z'
-  summary_source_hash: 927cfebb8ebf9595922dad115c9a8d10900e43c4f80e73e6102a71e3e4ca2da1
-  faq_generated_at: '2026-06-02T21:49:07Z'
-  faq_source_hash: 927cfebb8ebf9595922dad115c9a8d10900e43c4f80e73e6102a71e3e4ca2da1
-  summary: >-
-    This introductory Learning Path shows how to install and configure Remote.It to access remote
-    devices using SSH and other services, and how to choose between proxy and peer-to-peer connection
-    options. You will install the Remote.It device package on a target device, connect from an
-    initiator computer, and use the Web Dashboard or CLI to create connections. The path applies
-    to Windows, macOS, and Linux environments and supports devices ranging from laptops and Raspberry
-    Pi to cloud-hosted targets such as Arm Virtual Hardware or within AWS. Prerequisites include
-    a Windows, macOS, or Linux computer for setup, control access to the target before deploying
-    Remote.It, and a decision on whether the target must also connect to other Remote.It devices.
-  faqs:
-  - question: What do I need before running the setup?
-    answer: >-
-      Have a Windows, macOS, or Linux computer to configure and connect, plus a target device
-      (Windows, Mac, or Linux) you can control locally or through another remote solution before
-      deploying Remote.It. Targets can include development kits like Raspberry Pi or cloud-hosted
-      systems such as Arm Virtual Hardware or AWS. Also determine whether your target will need
-      to make connections to other Remote.It devices.
-  - question: How do I install the Remote.It device package when I already have access to the
-      target?
-    answer: >-
-      Use a local console or SSH to access the target and follow the steps to install the Remote.It
-      device package. If you need SSH on the target, refer to the SSH guidance referenced in the
-      path.
-  - question: Do I need to install anything on the initiator computer to connect?
-    answer: >-
-      If you use the Remote.It Web Dashboard, proxy connections require only standard tools like
-      SSH on the initiator and no additional Remote.It software. For headless use or automation,
-      install the Remote.It CLI; if you already installed the Desktop software, the CLI binary
-      is included. On Linux, ensure the CLI binary has execute permission.
-  - question: Which connection type should I use, proxy or peer-to-peer?
-    answer: >-
-      The Web Dashboard creates proxy connections and is the easiest to set up because only the
-      target needs Remote.It installed; all traffic is routed through a Remote.It server. Peer-to-peer
-      connections are direct between initiator and target. Choose proxy for the simplest setup,
-      or peer-to-peer when you want a direct connection.
-  - question: What result should I expect after completing the steps, and how do I know it worked?
-    answer: >-
-      You should be able to initiate an SSH session to your Remote.It-enabled target from another
-      location using the connection type you configured. For proxy connections, traffic will route
-      through a Remote.It server; for peer-to-peer, the link is direct. A successful SSH login
-      indicates the setup is working.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Brenda Strech
 
diff --git a/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md b/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
index 295ac6a6f8..ddbb51c69c 100644
--- a/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
+++ b/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
@@ -12,60 +12,9 @@ learning_objectives:
 
 prerequisites:
     - An Arm computer running Linux OS and a recent version of compiler (Clang or GCC) installed
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:49:44Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 9825df99004e981fadce5884b40b2152f4e43064cbba2cd2129fd90006090678
-  summary_generated_at: '2026-06-01T21:16:11Z'
-  summary_source_hash: 9825df99004e981fadce5884b40b2152f4e43064cbba2cd2129fd90006090678
-  faq_generated_at: '2026-06-02T21:49:44Z'
-  faq_source_hash: 9825df99004e981fadce5884b40b2152f4e43064cbba2cd2129fd90006090678
-  summary: >-
-    This Learning Path shows C developers on Arm Linux how to use the C99 restrict keyword to
-    indicate non-overlapping memory regions so compilers can apply stronger optimizations, including
-    vectorization on AArch64. You will examine a case where overlapping pointers limit optimization,
-    learn the rule-of-thumb for when restrict is valid, and study an SVE2 example with generated
-    code. The steps reference GCC 13 with -O3 -march=armv9-a and compare results with Clang. After
-    completing the path, you will know when and how to apply restrict safely in your own functions.
-    Prerequisites: an Arm computer running Linux with a recent GCC or Clang installed. Estimated
-    time: 30 minutes.
-  faqs:
-  - question: What do I need before running the examples?
-    answer: >-
-      Use an Arm computer running Linux with a recent version of GCC or Clang installed. No additional
-      prerequisites are explicitly listed.
-  - question: Which compiler and options are used in the SVE2 example?
-    answer: >-
-      The example shows output from gcc-13 built with -O3 -march=armv9-a. In this case, GCC 13
-      produced a better result than Clang for the demonstrated function.
-  - question: How do I decide if I can add restrict to a function’s pointer parameters?
-    answer: >-
-      Add restrict when you are certain the pointer arguments refer to non-overlapping memory
-      and those objects are not accessed by any other means inside the function. The path provides
-      a rule of thumb and a counterexample to guide this decision.
-  - question: How do I know that restrict enabled vectorization on Arm?
-    answer: >-
-      Inspect the compiler’s generated output and compare versions with and without restrict.
-      In the SVE2 example, vectorization appears as SVE2 instructions operating on z registers
-      (for example, ld1b and add on z registers).
-  - question: What should I avoid when considering restrict?
-    answer: >-
-      Do not use restrict if the memory regions referenced by pointer arguments may overlap or
-      if the objects can be accessed through other pointers within the function. The path includes
-      a counterexample illustrating when restrict is not appropriate.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Konstantinos Margaritis
 
diff --git a/content/learning-paths/cross-platform/rust_armds/_index.md b/content/learning-paths/cross-platform/rust_armds/_index.md
index 33d6b115d9..992f0b5bf9 100644
--- a/content/learning-paths/cross-platform/rust_armds/_index.md
+++ b/content/learning-paths/cross-platform/rust_armds/_index.md
@@ -14,59 +14,9 @@ learning_objectives:
 prerequisites:
     - An installation of Arm Development Studio.
     - A basic understanding of Rust programming.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:50:10Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: f237cd239e5886b21bd5bee88dcd9f95d88eda9a5c2eea363cc9db252e0c6e9f
-  summary_generated_at: '2026-06-01T21:16:47Z'
-  summary_source_hash: f237cd239e5886b21bd5bee88dcd9f95d88eda9a5c2eea363cc9db252e0c6e9f
-  faq_generated_at: '2026-06-02T21:50:10Z'
-  faq_source_hash: f237cd239e5886b21bd5bee88dcd9f95d88eda9a5c2eea363cc9db252e0c6e9f
-  summary: >-
-    This introductory path guides you through building a bare-metal embedded Rust application
-    for Armv7-M, running it on a Fixed Virtual Platform, and debugging with Arm Development Studio.
-    You will install the Rust compiler with cross-compilation support, build the example, and
-    run it on the FVP_MPS2_Cortex-M3 model included with Arm Development Studio. The steps show
-    how to launch the FVP with the built binary and verify the runtime output; an option is provided
-    to disable visualization to reduce startup time. Prerequisites are an installation of Arm
-    Development Studio (license-managed) and a basic understanding of Rust. The path is designed
-    to be completed in about 60 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an installation of Arm Development Studio and a basic understanding of Rust programming.
-      Arm Development Studio is license-managed.
-  - question: Which Arm architecture and FVP model does the example use?
-    answer: >-
-      The example targets Armv7-M and runs on the FVP_MPS2_Cortex-M3 model that comes with Arm
-      Development Studio. Cross-compilation support for the chosen Arm architecture is added following
-      the Rust for Embedded Applications Install Guide.
-  - question: How do I run the built application on the FVP?
-    answer: >-
-      Use the FVP provided by Arm Development Studio with the command: FVP_MPS2_Cortex-M3.exe
-      -a target/thumbv7m-none-eabi/debug/examples/armds. This launches the model and executes
-      the example to completion.
-  - question: How can I reduce the FVP start time?
-    answer: >-
-      Add the option -C fvp_mps2.mps2_visualisation.disable-visualisation=1 to disable visualization.
-      This reduces startup time and has no other effect on FVP behavior.
-  - question: What result should I expect when the program runs on the FVP?
-    answer: >-
-      The application should run to completion and print messages similar to "Total sum to 1 is
-      1" and "Calculated sum is 1." Seeing this output confirms the run succeeded.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/cross-platform/simd-info-demo/_index.md b/content/learning-paths/cross-platform/simd-info-demo/_index.md
index e24a086e2f..d320db9bc9 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/_index.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/_index.md
@@ -13,63 +13,9 @@ learning_objectives:
 prerequisites:
     - A basic understanding of SIMD.
     - Access to an Arm platform with a SIMD-supported engine, installed with recent versions of a C compiler such as Clang or GCC.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:50:47Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 7a40efa6d83b4629888f9622260e6e9aa9192db836b203fa4bf388cbe636b7e6
-  summary_generated_at: '2026-06-01T21:17:19Z'
-  summary_source_hash: 7a40efa6d83b4629888f9622260e6e9aa9192db836b203fa4bf388cbe636b7e6
-  faq_generated_at: '2026-06-02T21:50:47Z'
-  faq_source_hash: 7a40efa6d83b4629888f9622260e6e9aa9192db836b203fa4bf388cbe636b7e6
-  summary: >-
-    Learn how to use SIMD.info to port SIMD intrinsics between architectures with a practical,
-    code-centric walkthrough. You will examine a short C example that uses Intel SSE4.2 intrinsics
-    on Linux, then use SIMD.info’s navigation, search, and comparison features to identify Arm
-    Neon/ASIMD equivalents for operations such as compare, add, multiply, and square root. The
-    path highlights SIMD.info’s intrinsic metadata (Purpose, Result, Example) and emphasizes correctness
-    of results over performance. It targets AArch64 on Armv8-A/Armv9-A and assumes a basic understanding
-    of SIMD plus access to an Arm platform with a SIMD engine and a recent C compiler (GCC or
-    Clang). Estimated time to complete is 30 minutes.
-  faqs:
-  - question: What do I need before running the example and porting steps?
-    answer: >-
-      You need basic SIMD knowledge and access to an Arm platform with a SIMD-supported engine
-      and a recent C compiler such as Clang or GCC. The example starts on an x86_64 Linux development
-      machine before being ported to Arm Neon/ASIMD.
-  - question: How do I use SIMD.info to find Neon equivalents for the SSE4.2 intrinsics in the
-      example?
-    answer: >-
-      Use SIMD.info’s navigation, search, and comparison features to look up each SSE4.2 intrinsic.
-      Review the Purpose, Result, and Example sections to identify the corresponding Arm Neon/ASIMD
-      intrinsic and understand its behavior.
-  - question: Which intrinsics from the example should I look up on SIMD.info?
-    answer: >-
-      The example uses _mm_cmpgt_ps, _mm_add_ps, _mm_mul_ps, and _mm_sqrt_ps. Look up each of
-      these to find the Arm Neon/ASIMD equivalents that perform the same comparison, addition,
-      multiplication, and square root operations.
-  - question: How should vector initialization and storing change when moving from SSE4.2 to Neon?
-    answer: >-
-      Replace the SSE4.2 _mm_set_ps macro with Neon’s brace {} initialization for vectors. Also
-      update the store operations to follow Neon’s way of moving data from vectors to arrays,
-      as outlined in the step-by-step guidance.
-  - question: How do I verify my Neon port is correct, and should I focus on performance now?
-    answer: >-
-      Compare the results of your Arm Neon build with the outputs from the original SSE example
-      to validate correctness. In this path, the integrity and accuracy of calculations are the
-      primary focus; performance is a secondary concern.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: 
     - Georgios Mermigkis
diff --git a/content/learning-paths/cross-platform/simd-loops/_index.md b/content/learning-paths/cross-platform/simd-loops/_index.md
index 6b12938fb4..95b587e88e 100644
--- a/content/learning-paths/cross-platform/simd-loops/_index.md
+++ b/content/learning-paths/cross-platform/simd-loops/_index.md
@@ -17,61 +17,9 @@ prerequisites:
     - An AArch64 computer running Linux or macOS. You can use cloud instances, refer to [Get started with Arm-based cloud instances](/learning-paths/servers-and-cloud-computing/csp/) for a list of cloud service providers
     - Some familiarity with SIMD programming and Neon intrinsics
     - Recent toolchains that support SVE/SME (GCC 13+ or Clang 16+ recommended)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:51:17Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: b1c43e1bf971db4582ca358c98dab2c7e6e047d6c79bfcc0db148bc575f33679
-  summary_generated_at: '2026-06-01T21:18:06Z'
-  summary_source_hash: b1c43e1bf971db4582ca358c98dab2c7e6e047d6c79bfcc0db148bc575f33679
-  faq_generated_at: '2026-06-02T21:51:17Z'
-  faq_source_hash: b1c43e1bf971db4582ca358c98dab2c7e6e047d6c79bfcc0db148bc575f33679
-  summary: >-
-    This advanced Learning Path shows how to use Arm’s Scalable Vector Extension (SVE), SVE2,
-    and Scalable Matrix Extension (SME/SME2) with the SIMD Loops project. You will clone the repository,
-    explore how kernels are organized across scalar, Neon, SVE/SVE2, and SME2 variants, and study
-    loop 202, a single-precision matrix multiplication example that ties inner_loop_202 to matmul_fp32.
-    You then build and run selected kernels with the provided runner, validate results against
-    the C reference, and choose build targets to compare Neon, SVE/SVE2, and SME2 implementations.
-    The path targets AArch64 systems on Linux or macOS and expects recent GCC or Clang toolchains
-    with SVE/SME support.
-  faqs:
-  - question: What do I need before running the examples?
-    answer: >-
-      Use an AArch64 computer running Linux or macOS, with a recent toolchain that supports SVE/SME
-      (GCC 13+ or Clang 16+ recommended). Some familiarity with SIMD programming and Neon intrinsics
-      is expected. You can use Arm-based cloud instances if local hardware is not available.
-  - question: How do I know my machine is Arm-based?
-    answer: >-
-      Run uname -m. On Linux, the expected output is aarch64; on macOS, the expected output is
-      arm64.
-  - question: Where are the loop kernels listed, and how are they organized?
-    answer: >-
-      The source for loops is under the loops directory. The complete list of loops, with brief
-      descriptions, is documented in the loops.inc file.
-  - question: Which example does this path use to explain the project structure, and what does
-      it compute?
-    answer: >-
-      It uses loop 202, which implements single-precision floating-point matrix multiplication
-      C[M×N] = A[M×K] × B[K×N]. You will examine inner_loop_202() in loops/loop_202.c and the
-      matmul_fp32 routine in loops/matmul_fp32.c.
-  - question: How do I build, run, and validate a kernel implementation?
-    answer: >-
-      Build and run a selected kernel using the project's runner and validate correctness against
-      the C reference implementation. Choose the appropriate build target to compare Neon, SVE/SVE2,
-      and SME2 variants as demonstrated in the Learning Path.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author:
     - Alejandro Martinez Vicente
diff --git a/content/learning-paths/cross-platform/simd-on-rust/_index.md b/content/learning-paths/cross-platform/simd-on-rust/_index.md
index 692babd490..e8357aaf42 100644
--- a/content/learning-paths/cross-platform/simd-on-rust/_index.md
+++ b/content/learning-paths/cross-platform/simd-on-rust/_index.md
@@ -15,60 +15,9 @@ learning_objectives:
 
 prerequisites:
     - An Arm-based computer with recent versions of a C compiler (Clang or GCC) and a Rust compiler installed
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:51:46Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: a051c519c1a4969f30d5a81e46823e77f0c15f163011b3a38f4c05104d853249
-  summary_generated_at: '2026-06-01T21:18:44Z'
-  summary_source_hash: a051c519c1a4969f30d5a81e46823e77f0c15f163011b3a38f4c05104d853249
-  faq_generated_at: '2026-06-02T21:51:46Z'
-  faq_source_hash: a051c519c1a4969f30d5a81e46823e77f0c15f163011b3a38f4c05104d853249
-  summary: >-
-    This advanced path teaches you to write SIMD code on Arm using Rust on Linux. You will use
-    Rust’s std::arch Neon intrinsics and portable std::simd, apply feature detection and target
-    attributes for architecture-specific optimizations, and compare C and Rust implementations
-    and their disassembly. Hands-on steps include building and running examples for pairwise averaging,
-    a dot-product-based SAD using vdotq_u32, a 4x4 matrix transpose, and a DCT butterfly operation.
-    The target environment is an Arm-based Linux system with a recent C compiler (Clang or GCC)
-    and a Rust compiler installed. By the end, you can implement and assess SIMD routines for
-    Arm Cortex-A and Neoverse CPUs.
-  faqs:
-  - question: What do I need before running the examples?
-    answer: >-
-      Use an Arm-based computer running Linux with recent versions of a C compiler (GCC or Clang)
-      and a Rust compiler installed. No additional prerequisites are explicitly listed.
-  - question: Which compiler should I use to build the C examples?
-    answer: >-
-      You can use either GCC or Clang with a recent version on your Arm-based Linux system. A
-      Rust compiler is also required for the Rust portions.
-  - question: Which source files will I create, and what do they demonstrate?
-    answer: >-
-      You will create average_neon.c (pairwise averages), dotprod1.c (SAD using vdotq_u32), and
-      transpose1.c (4x4 uint16_t matrix transpose). You will also implement a DCT butterfly (fdct_round_shift),
-      with Rust equivalents introduced where appropriate.
-  - question: When should I use std::simd versus Neon intrinsics in Rust?
-    answer: >-
-      Use std::simd for portable SIMD across platforms and Neon intrinsics via std::arch for Arm-specific
-      code paths. The path shows how to combine this with feature detection and target attributes
-      for architecture-specific optimizations.
-  - question: How do I know the SIMD code is working and producing the right instructions?
-    answer: >-
-      The examples compute concrete results (averages, SAD, matrix transpose, and the butterfly
-      operation) that you can compare between C and Rust versions. You will also compare disassembly
-      output to examine the generated instructions.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Konstantinos Margaritis
 
diff --git a/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md b/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md
index 604f5477e0..c75b2bcefe 100644
--- a/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md
+++ b/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md
@@ -18,64 +18,9 @@ prerequisites:
   - An Apple Silicon macOS host with Python 3.9 or later and CMake 3.29 or later
   - Basic familiarity with ExecuTorch or PyTorch
   - Optionally, an Android device with Armv9 and SME2 support for on-device testing (if used, configure power management settings to ensure consistent performance measurements)
-    
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:52:19Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 36f7dfe13c8c940abbaad2b61620b3b1c6d97f580de15e573b45ef7760753797
-  summary_generated_at: '2026-06-01T21:19:22Z'
-  summary_source_hash: 36f7dfe13c8c940abbaad2b61620b3b1c6d97f580de15e573b45ef7760753797
-  faq_generated_at: '2026-06-02T21:52:19Z'
-  faq_source_hash: 36f7dfe13c8c940abbaad2b61620b3b1c6d97f580de15e573b45ef7760753797
-  summary: >-
-    This advanced Learning Path shows how to profile ExecuTorch models on Arm with SME2 acceleration
-    in approximately 90 minutes. You will set up a reusable Apple Silicon macOS workspace (Python
-    3.9+ and CMake 3.29+), build ExecuTorch runner binaries with SME2 enabled and disabled, export
-    PyTorch models to .pte, and run a two-pass analysis (timing-only and then trace-enabled).
-    The model-agnostic workflow produces operator-level and operator-category breakdowns (for
-    example, convolution, GEMM, data movement) so you can see how latency shifts when compute
-    speeds up under SME2. Optionally, you can run on an Android Armv9 device with SME2 after configuring
-    power management for consistent measurements. By the end, you can compare execution profiles
-    and make evidence-based optimization decisions.
-  faqs:
-  - question: What do I need on my host machine before starting the setup?
-    answer: >-
-      Use an Apple Silicon macOS system with Python 3.9 or later and CMake 3.29 or later. Basic
-      familiarity with ExecuTorch or PyTorch is expected.
-  - question: Do I need an Android device, and how should it be configured if I use one?
-    answer: >-
-      An Android device is optional and should have Armv9 with SME2 support for on-device testing.
-      If you use one, configure its power management settings to keep performance measurements
-      consistent.
-  - question: Which model format should I export, and is the profiling pipeline model-specific?
-    answer: >-
-      Export your model to ExecuTorch .pte format. After that, the same runners, scripts, and
-      analysis steps apply regardless of model architecture; see the EfficientSAM example in executorch/examples/models
-      for a concrete onboarding reference.
-  - question: How do I collect profiling data for comparison?
-    answer: >-
-      Build ExecuTorch runner binaries with SME2 enabled and disabled, then run the two-run analysis
-      consisting of a timing-only pass and a trace-enabled pass. The Learning Path also provides
-      structured agent skills that you can use to automate these actions in an AI assistant or
-      CI system.
-  - question: What result should I expect when enabling SME2, and how do I interpret the profiles?
-    answer: >-
-      Inference latency often improves significantly with SME2 enabled, which can shift execution
-      time to other parts of the model. Use the operator-level and operator-category breakdowns
-      to identify which operators benefit most and which become the new bottlenecks.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Zhu, Tyler Mullenbach, Damien Dooley 
 
diff --git a/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md b/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md
index 34c3f5b285..fe6174ff0f 100644
--- a/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md
+++ b/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md
@@ -18,60 +18,9 @@ prerequisites:
   - Understanding of container runtimes (containerd) and CNI networking
   - Basic knowledge of communication protocols (MQTT, HTTP, and others)
   - Familiarity with edge-cloud architectures and data-flow orchestration
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:52:57Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 09142517ecc64fba821062e1af4db3ac9d72f9adcd3f10c12d6fd5a4cf3daaf4
-  summary_generated_at: '2026-06-01T21:20:11Z'
-  summary_source_hash: 09142517ecc64fba821062e1af4db3ac9d72f9adcd3f10c12d6fd5a4cf3daaf4
-  faq_generated_at: '2026-06-02T21:52:57Z'
-  faq_source_hash: 09142517ecc64fba821062e1af4db3ac9d72f9adcd3f10c12d6fd5a4cf3daaf4
-  summary: >-
-    This Learning Path shows how to deploy Tinkerblox UltraEdge HPC-I on Arm for AI and mixed
-    workloads. You start by understanding the UltraEdge layered architecture (core, boost, prime),
-    then provision a Google Axion C4A VM on Google Cloud to build a Yocto image targeting the
-    NXP S32G‑VNP‑GLDBOX3. You install UltraEdge on Debian or Ubuntu by registering a device in
-    the Uncloud dashboard, and use the Tinkerblox CLI to deploy MicroPacs, inspect system state,
-    and observe runtime behavior. By the end, you can build with the UltraEdge MicroStack, deploy
-    MicroPacs on Linux-based compute, and prepare edge–cloud data flows. This advanced path assumes
-    Linux, container runtime/CNI, protocol, and edge‑cloud orchestration knowledge.
-  faqs:
-  - question: What do I need before running the Yocto image build steps?
-    answer: >-
-      Provision a Google Axion C4A VM on Google Cloud using the c4a-standard-32 type (16 vCPUs,
-      128 GB memory). An Ubuntu 22.04 environment with about 100 GB of disk space works well for
-      this Learning Path, and supported host architectures include AArch64 (arm64) and ARMv7.
-  - question: Which Ubuntu releases are supported as Yocto build hosts right now?
-    answer: >-
-      Tested build hosts include Ubuntu 20.04 LTS (AArch64) and Ubuntu 22.04 LTS (AArch64). As
-      of publication, Ubuntu 24.04 LTS is not a supported Yocto build host OS.
-  - question: How do I register a Debian or Ubuntu device for UltraEdge?
-    answer: >-
-      Log in to the Uncloud Dashboard and navigate to Device Management, then choose New Device.
-      This initializes and registers your edge device in the Uncloud ecosystem for subsequent
-      UltraEdge installation and management.
-  - question: How do I deploy and validate a sample microservice on UltraEdge?
-    answer: >-
-      Download a sample MPAC file from the Tinkerblox support repository and install it on your
-      device using the Tinkerblox CLI. Use the CLI to inspect system state and observe the microservice’s
-      runtime behavior.
-  - question: Do I need Docker or Kubernetes to run workloads in this Learning Path?
-    answer: >-
-      No. UltraEdge uses a lean, deterministic execution stack, and the procedures use the Tinkerblox
-      CLI rather than Docker or Kubernetes.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Tinkerblox
 
diff --git a/content/learning-paths/cross-platform/topdown-compare/_index.md b/content/learning-paths/cross-platform/topdown-compare/_index.md
index 2d8e6026cc..1ff11a5549 100644
--- a/content/learning-paths/cross-platform/topdown-compare/_index.md
+++ b/content/learning-paths/cross-platform/topdown-compare/_index.md
@@ -16,63 +16,9 @@ prerequisites:
     - Familiarity with performance analysis on Linux systems using Perf and PMU counters
     - Access to Arm Neoverse V2 and Intel x86 Linux systems to run the code example
     - Basic understanding of CPU pipeline concepts and performance bottlenecks
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:53:36Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 5f4b05e3d962476c67c4a4400c8fa71f04412f3f0354d5c3123f38af57791304
-  summary_generated_at: '2026-06-01T21:20:45Z'
-  summary_source_hash: 5f4b05e3d962476c67c4a4400c8fa71f04412f3f0354d5c3123f38af57791304
-  faq_generated_at: '2026-06-02T21:53:36Z'
-  faq_source_hash: 5f4b05e3d962476c67c4a4400c8fa71f04412f3f0354d5c3123f38af57791304
-  summary: >-
-    This advanced Learning Path shows how to compare Arm Neoverse and Intel x86 top-down performance
-    analysis on Linux using PMU counters. You will review Intel’s multilevel hierarchical model
-    and Arm’s two-stage approach for Neoverse V2, then build and run a backend-bound C benchmark
-    with GCC or Clang. Using Linux Perf on x86 and topdown-tool on Arm, you will collect and contrast
-    Retiring, Bad Speculation, Frontend Bound, and Backend Bound metrics, and evaluate differences
-    in slot-based accounting across the two architectures. Prerequisites include familiarity with
-    Perf and PMU counters, access to both an Intel x86 and an Arm Neoverse V2 Linux system, and
-    a basic understanding of CPU pipelines. Estimated time to complete is about 30 minutes.
-  faqs:
-  - question: What do I need before running the cross-platform example?
-    answer: >-
-      You need access to both an Arm Neoverse V2 Linux system and an Intel x86 Linux system. Familiarity
-      with Perf and PMU counters, and a basic understanding of CPU pipelines and bottlenecks,
-      are expected.
-  - question: Which tools should I install on each platform?
-    answer: >-
-      Install GCC or Clang and Perf on both systems. On Arm systems, also install topdown-tool;
-      use your Linux distribution’s package manager for installation information.
-  - question: How do I build and run the provided benchmark?
-    answer: >-
-      Copy the example source to a file named core-bound-div-chain.c and compile it with GCC or
-      Clang. Run the resulting executable with an iterations argument as indicated by the code
-      comment: ./core-bound-div-chain <iterations>.
-  - question: What result should I expect when I run the benchmark?
-    answer: >-
-      The benchmark is intended to be backend/core-bound via an FP64 divide chain. Collect measurements
-      with Perf on x86 and topdown-tool on Arm, and examine the Backend Bound, Frontend Bound,
-      Bad Speculation, and Retiring categories.
-  - question: How should I compare results across Arm and Intel given different counters and slot
-      models?
-    answer: >-
-      Counter names and formulas differ, and Intel uses issue-slot accounting while Neoverse V2
-      uses eight rename slots per cycle. Focus on comparing the shared top-level categories and
-      methodology rather than one-to-one event mappings; details will differ for other Neoverse
-      processors.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author:
     - Jason Andrews
diff --git a/content/learning-paths/cross-platform/vectorization-comparison/_index.md b/content/learning-paths/cross-platform/vectorization-comparison/_index.md
index 8f8d740d89..997c8e7a37 100644
--- a/content/learning-paths/cross-platform/vectorization-comparison/_index.md
+++ b/content/learning-paths/cross-platform/vectorization-comparison/_index.md
@@ -15,60 +15,9 @@ learning_objectives:
 prerequisites:
     - Familiarity with vector extensions, SIMD programming, and compiler intrinsics
     - Access to Linux systems with Neon and SVE support
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:54:01Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 26450ae17f7ed4242c52456c2780ffce5fad36b56dfc4a8482e8f236f855e134
-  summary_generated_at: '2026-06-01T21:21:21Z'
-  summary_source_hash: 26450ae17f7ed4242c52456c2780ffce5fad36b56dfc4a8482e8f236f855e134
-  faq_generated_at: '2026-06-02T21:54:01Z'
-  faq_source_hash: 26450ae17f7ed4242c52456c2780ffce5fad36b56dfc4a8482e8f236f855e134
-  summary: >-
-    This advanced Learning Path shows how to migrate x86-64 SIMD code to Arm64 by mapping Intel
-    SSE/AVX/AMX to Arm Neon, SVE, and SME. You review migration strategies using autovectorization,
-    intrinsics, or library substitution, then work through a SAXPY kernel implemented in plain
-    C and with vector extensions on both Arm (Neon, SVE) and x86 (AVX2, AVX-512). On a Linux system
-    with Neon and SVE support, you build and run each version using GCC or Clang and observe how
-    vector width influences throughput. The expected outcome is an understanding of how Arm vector
-    extensions relate to x86 equivalents and a practical plan for porting existing SIMD code.
-    No additional prerequisites are listed beyond those stated; estimated duration is about 30
-    minutes.
-  faqs:
-  - question: What do I need before running the examples?
-    answer: >-
-      You should be comfortable with SIMD programming and compiler intrinsics, and have access
-      to Linux systems with Neon and SVE support. GCC or Clang are used to build the examples.
-  - question: Which compiler should I use to build the code?
-    answer: >-
-      Use GCC or Clang as listed in the Learning Path tools. The steps show how to build and run
-      the Arm and x86 variants of the SAXPY example.
-  - question: How do I map x86 SIMD intrinsics to Arm equivalents?
-    answer: >-
-      The overview explains how SSE, AVX, and AMX map to Arm Neon, SVE, and SME. Use this mapping
-      to guide intrinsics substitution or decide when autovectorization or libraries are more
-      appropriate.
-  - question: What result should I expect when I run the SAXPY variants?
-    answer: >-
-      You will build and run C, Neon, SVE, AVX2, and AVX-512 versions of a SAXPY kernel that computes
-      y[i] = a * x[i] + y[i]. The run results let you compare SIMD behavior and see how vector
-      width affects throughput.
-  - question: When should I use a library instead of writing intrinsics?
-    answer: >-
-      If a tuned library provides the operation (for example, BLAS for SAXPY), prefer the library.
-      The intrinsics-based examples are provided for learning and comparison.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author:
     - Jason Andrews
diff --git a/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md b/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md
index 2b2f04a354..f3d5de29f2 100644
--- a/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md
+++ b/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md
@@ -12,61 +12,9 @@ learning_objectives:
 
 prerequisites:
     - An Arm computer running Linux and a recent version of Clang or the GNU compiler (gcc) installed.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:54:37Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 14a22194d3fc73ebebb62db9c7cfbeabe0bd9acdaf340b2df52072be65d98655
-  summary_generated_at: '2026-06-01T21:21:48Z'
-  summary_source_hash: 14a22194d3fc73ebebb62db9c7cfbeabe0bd9acdaf340b2df52072be65d98655
-  faq_generated_at: '2026-06-02T21:54:37Z'
-  faq_source_hash: 14a22194d3fc73ebebb62db9c7cfbeabe0bd9acdaf340b2df52072be65d98655
-  summary: >-
-    This advanced Learning Path guides C/C++ developers on Arm Linux through restructuring data
-    from Array-of-Structures to Structure-of-Arrays to make SIMD vectorization more effective.
-    You will study data layout and alignment issues (such as 3D vec3 triplets versus 4-wide float
-    operations), incrementally modify a particle simulation, and progress to hand-written SIMD
-    using Arm Neon intrinsics. The path also references practical examples with Neon and SVE intrinsics.
-    Working on an Arm computer with GCC or Clang, you will create successive source files (simulation1.c
-    to simulation4.c) that illustrate alignment fixes, boundary checks, manual intrinsics, and
-    SoA transformations. The expected outcome is a clear understanding of why data layout matters
-    for SIMD on Arm and how to restructure code accordingly.
-  faqs:
-  - question: What do I need before running the examples?
-    answer: >-
-      You need an Arm computer running Linux with a recent version of Clang or GCC installed.
-      No other prerequisites are explicitly listed.
-  - question: How do I know if my current data layout is blocking vectorization?
-    answer: >-
-      If operations are organized as x, y, z triplets, the compiler may not emit SIMD instructions
-      because 32-bit float SIMD requires 4 elements. The path shows a memory layout diagram of
-      the object struct and highlights a 12-byte alignment issue that interferes with vectorization.
-  - question: Which files do I edit and in what order?
-    answer: >-
-      You start from simulation1.c, copy it to simulation2.c to add boundary checks and new types
-      (such as ctr4 and a box constant), then copy to simulation3.c for hand-written SIMD. Finally,
-      you create simulation4.c from provided code to study a Structure-of-Arrays version.
-  - question: When should I switch to hand-written intrinsics, and which ones are used?
-    answer: >-
-      If the compiler is not vectorizing as much as it could, the path has you convert the program
-      to hand-written SIMD in simulation3.c. This uses Arm Neon intrinsics and includes the <arm_neon.h>
-      header.
-  - question: Does this Learning Path cover both Neon and SVE intrinsics?
-    answer: >-
-      Yes. The description states practical examples using Neon and SVE intrinsics, though the
-      step-by-step code shown uses Neon explicitly.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Konstantinos Margaritis
 
diff --git a/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md b/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
index f6e9a2328b..0d55633e47 100644
--- a/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
+++ b/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
@@ -18,61 +18,9 @@ prerequisites:
     - An installation of [WindowsPerf](/install-guides/wperf).
     - An installation of [Visual Studio](/install-guides/vs-woa/).
     - An installation of [Git](/install-guides/git-woa/).
-  
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:55:45Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: bf887ef2481b51cf6c32e49e3eb1d5577346b7b9b1e4d6a604c559597b5306f0
-  summary_generated_at: '2026-06-01T21:22:34Z'
-  summary_source_hash: bf887ef2481b51cf6c32e49e3eb1d5577346b7b9b1e4d6a604c559597b5306f0
-  faq_generated_at: '2026-06-02T21:55:45Z'
-  faq_source_hash: bf887ef2481b51cf6c32e49e3eb1d5577346b7b9b1e4d6a604c559597b5306f0
-  summary: >-
-    This introductory path shows how to sample and profile CPU instructions on Windows on Arm
-    using WindowsPerf with the Arm Statistical Profiling Extension (SPE), demonstrated on a CPython
-    workload. You will install and use the SPE-enabled WindowsPerf build, verify that your Windows
-    on Arm machine supports SPE, and build CPython from source for AArch64 with Visual Studio
-    and Git. The steps pin the CPython debug binary to a specific core, run a large integer computation,
-    and use WindowsPerf sampling and record commands to collect and explore SPE events (such as
-    load events) from the workload. By the end, you will understand the basics of SPE sampling
-    and have hands-on experience collecting instruction-level samples for a native Windows on
-    Arm application.
-  faqs:
-  - question: What do I need before running the examples?
-    answer: >-
-      You need a Windows on Arm machine with CPU support for Arm SPE, WindowsPerf (driver and
-      wperf CLI) installed, Visual Studio, and Git. These are explicitly listed in the Setup step.
-  - question: How do I check if my Arm CPU supports SPE?
-    answer: >-
-      The Setup step includes guidance on verifying CPU support for Arm SPE. Follow that section
-      before proceeding with sampling or recording.
-  - question: Which WindowsPerf build should I use for SPE?
-    answer: >-
-      WindowsPerf release 3.8.0 includes a separate build with Arm SPE support located in the
-      SPE/ subdirectory of the release assets. Use that build when following the SPE steps.
-  - question: What workload is used to exercise CPython during sampling?
-    answer: >-
-      The path uses a debug-built CPython (python_d.exe) to compute 10**10**100, and pins the
-      process to CPU core 1. The Windows start command is used to launch and pin the process as
-      shown in the steps.
-  - question: In the wperf record example, what does the “--” mean and what data is captured?
-    answer: >-
-      The double dash separates wperf options from the arguments passed to the profiled program
-      (python_d.exe). The example records Arm SPE load events using arm_spe_0/ld=1/ on core 1
-      for 5 seconds, producing a recording you can inspect.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Przemyslaw Wirkus
 
diff --git a/content/learning-paths/cross-platform/woa_azure/_index.md b/content/learning-paths/cross-platform/woa_azure/_index.md
index 186f253084..fbd93a6389 100644
--- a/content/learning-paths/cross-platform/woa_azure/_index.md
+++ b/content/learning-paths/cross-platform/woa_azure/_index.md
@@ -14,57 +14,9 @@ learning_objectives:
 prerequisites:
     - An Azure Cloud account.
     - An RDP client to connect to your Windows on Arm instance.  For more info on RDP clients, see [Remote Desktop clients for Remote Desktop Services and remote PCs](https://learn.microsoft.com/en-us/windows-server/remote/remote-desktop-services/clients/remote-desktop-clients) to get started.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:56:12Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 367566dfec0a76685b1504c2c1a996e50c55e67b2f4c5515057c0f0f1384ef98
-  summary_generated_at: '2026-06-01T21:23:14Z'
-  summary_source_hash: 367566dfec0a76685b1504c2c1a996e50c55e67b2f4c5515057c0f0f1384ef98
-  faq_generated_at: '2026-06-02T21:56:12Z'
-  faq_source_hash: 367566dfec0a76685b1504c2c1a996e50c55e67b2f4c5515057c0f0f1384ef98
-  summary: >-
-    Learn how to deploy a Windows on Arm virtual machine in Microsoft Azure and connect to it
-    using Remote Desktop. This introductory path guides you through signing in to Azure, using
-    the Azure Marketplace to locate Arm-based images, creating a Windows on Arm VM, and establishing
-    an RDP session. You will also see how to discover other Arm-based offerings, with a note that
-    the same flow applies if you choose a Linux image instead of Windows. Prerequisites are an
-    Azure Cloud account and an RDP client. By the end, you will have a running Windows on Arm
-    instance in Azure.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an Azure Cloud account and an RDP client. You can sign in using either your personal
-      subscription or your organization’s subscription.
-  - question: Where do I start creating the Windows on Arm VM in Azure?
-    answer: >-
-      Sign in to your Azure account and use the Azure Marketplace to select a Windows on Arm image,
-      then create a virtual machine from that listing. The steps walk you through initiating the
-      VM from the Marketplace.
-  - question: How do I discover Arm-based image offerings in Azure?
-    answer: >-
-      Open the Azure Image Marketplace and review the available Arm-based listings. The path highlights
-      how to locate Windows on Arm and other Arm-based images.
-  - question: How do I connect to the VM after it is created?
-    answer: >-
-      Use your RDP client to connect to the Windows on Arm instance. The path uses RDP for access
-      so you can sign in to the Windows session.
-  - question: Can I use the same instructions to deploy a Linux image on Arm?
-    answer: >-
-      Yes. Select a Linux distribution instead of Windows during image selection, as noted in
-      the path.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md b/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
index 673905518c..f6991189cf 100644
--- a/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
+++ b/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
@@ -16,61 +16,9 @@ learning_objectives:
 prerequisites:
     - At least two local Cortex-A devices running Linux, such as Raspberry Pi 4 or Pi 5. You can also use Arm servers or cloud instances
     - Experience with ROS 2 applications
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:56:40Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: a56dce27c6a846bcf35b6e9505142f1445b22ff71530f1189700049d7b14e994
-  summary_generated_at: '2026-06-01T21:23:51Z'
-  summary_source_hash: a56dce27c6a846bcf35b6e9505142f1445b22ff71530f1189700049d7b14e994
-  faq_generated_at: '2026-06-02T21:56:40Z'
-  faq_source_hash: a56dce27c6a846bcf35b6e9505142f1445b22ff71530f1189700049d7b14e994
-  summary: >-
-    Learn to build and deploy a distributed Eclipse Zenoh system on Arm Linux devices, including
-    Raspberry Pi 4/5 and Arm servers or cloud instances. You will install the Rust-based Zenoh
-    stack, build core examples, and run a two-node publish/subscribe test, then add in-memory
-    storage and querying using a Zenoh daemon with z_put and z_get. The path also shows how to
-    containerize Zenoh with Docker to streamline multi-node distribution and repeatable testing.
-    Prerequisites include at least two Cortex-A devices running Linux and experience with ROS
-    2 applications. By the end, you can stand up and validate a basic multi-node Zenoh deployment
-    on Arm.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need at least two local Cortex-A devices running Linux, such as Raspberry Pi 4 or 5.
-      You can also use Arm servers or cloud instances. Experience with ROS 2 applications is expected.
-  - question: Do I have to use Docker to deploy across multiple devices?
-    answer: >-
-      No. You can copy the compiled binaries from ~/zenoh/target/release/ to each device. The
-      path also shows how to containerize with Docker for streamlined and consistent multi-node
-      testing.
-  - question: How do I know the Zenoh build on Raspberry Pi completed correctly?
-    answer: >-
-      After building Zenoh and its core examples, you should see release binaries under ~/zenoh/target/release/.
-      You will use these binaries in the deployment and example steps to confirm they run on your
-      devices.
-  - question: What network setup and topics are used in the pub/sub example?
-    answer: >-
-      Run the example across two devices on the same local network. The subscriber listens on
-      the key expression demo/example/**, and you should see it receive messages published under
-      that prefix.
-  - question: How do I validate the storage and query example is working?
-    answer: >-
-      Start the Zenoh daemon with in-memory storage, publish values with z_put, and retrieve them
-      with z_get. Being able to query previously published data—even after the publisher is offline—confirms
-      the storage engine is functioning.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: 
     - Odin Shen
diff --git a/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md b/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
index c7f5d7f90a..b7aaab5ed7 100644
--- a/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
@@ -16,58 +16,9 @@ prerequisites:
     - Some familiarity with Verilog
     - Basic understanding of System on Chip design
     - A 'Zybo Z7-10' development board 
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:57:22Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: d8c48c293b2d316d5e68e18ab9e81157ad114a64cf2efa6951374a55d25238d6
-  summary_generated_at: '2026-06-01T21:24:18Z'
-  summary_source_hash: d8c48c293b2d316d5e68e18ab9e81157ad114a64cf2efa6951374a55d25238d6
-  faq_generated_at: '2026-06-02T21:57:22Z'
-  faq_source_hash: d8c48c293b2d316d5e68e18ab9e81157ad114a64cf2efa6951374a55d25238d6
-  summary: >-
-    This Learning Path guides you through designing and integrating a custom AXI-Lite peripheral
-    with the Cortex-A9 Processing System on a Zybo Z7-10 board using Xilinx Vivado, then generating
-    a bitstream and writing a bare-metal C application in Vitis to read board switches and drive
-    LEDs. You will set up a Windows-based workspace, create and package a new AXI-Lite peripheral,
-    connect GPIO-style ports to the Zynq PS, and build a simple end-to-end system that demonstrates
-    LEDs reflecting switch states. This introductory path assumes some Verilog and basic SoC design
-    knowledge and requires a Zybo Z7-10. Estimated time to complete is about 60 minutes.
-  faqs:
-  - question: What do I need before starting this Learning Path?
-    answer: >-
-      You need a Zybo Z7-10 development board, some familiarity with Verilog, and a basic understanding
-      of System on Chip design. The flow targets a bare-metal application on a Cortex-A9.
-  - question: What project setup should I use in Vivado?
-    answer: >-
-      Create a new RTL Project in Vivado. On Windows, place your workspace in a path without spaces
-      (for example, C:/Workspace).
-  - question: Which option should I use to create the custom AXI-Lite peripheral?
-    answer: >-
-      In Vivado, select Tools -> Create and Package New IP, then choose the option to create a
-      new AXI4 peripheral. Provide a name for the IP and accept the default IP location if appropriate.
-  - question: How do I expose LEDs and switches from the custom peripheral?
-    answer: >-
-      Create ports in the block design: an led output (4 bits) and an sw input, then connect them
-      appropriately in the Vivado diagram. Ensure directions and widths match the intended board
-      connections.
-  - question: What steps complete the design and what should I expect when running the application?
-    answer: >-
-      Create the HDL Wrapper and generate the bitstream in Vivado, then use the Xilinx Vitis IDE
-      to write and run a bare-metal C program on the Cortex-A9. The program reads the switch state
-      and lights the LEDs based on the status of the switches.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md b/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
index 7ecbbfce39..ef564dd8f7 100644
--- a/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
@@ -19,66 +19,9 @@ prerequisites:
     - A SEGGER J-Link debug probe (included in the DevKit)
     - A development machine running macOS on Apple Silicon with Visual Studio Code installed
     - An AWS account or access to an Arm-based cloud instance for native Arm compilation
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:57:47Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 8585bb59efa67b3a92314e4382339fc5c0a14bb458931c0d98f2748379003857
-  summary_generated_at: '2026-06-01T21:24:49Z'
-  summary_source_hash: 8585bb59efa67b3a92314e4382339fc5c0a14bb458931c0d98f2748379003857
-  faq_generated_at: '2026-06-02T21:57:47Z'
-  faq_source_hash: 8585bb59efa67b3a92314e4382339fc5c0a14bb458931c0d98f2748379003857
-  summary: >-
-    This advanced Learning Path guides you through deploying a MobileNetV2 image classification
-    model to the Alif Ensemble E8 DevKit and running inference on the Ethos‑U85 NPU from the Cortex‑M55
-    High‑Performance core. You will compile the model with ExecuTorch’s ahead‑of‑time compiler
-    on an Arm‑based cloud instance, build ExecuTorch static libraries for a bare‑metal target,
-    create a CMSIS project in VS Code by cloning a Blinky template, integrate SEGGER RTT, and
-    adjust memory and linker settings. By the end, you will flash the firmware, run real‑time
-    inference on a test image, and verify results over RTT. Prerequisites include C/C++ experience,
-    the E8 DevKit with J‑Link, macOS on Apple Silicon with VS Code, and Arm‑based cloud access;
-    estimated time is 120 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need C/C++ and embedded development experience, an Alif Ensemble E8 DevKit with a USB-C
-      cable, and a SEGGER J-Link (included with the DevKit). You also need a macOS machine on
-      Apple Silicon with Visual Studio Code, plus an AWS account or access to an Arm-based cloud
-      instance.
-  - question: Why should I build on an Arm-based cloud instance instead of my local host?
-    answer: >-
-      ExecuTorch’s Arm backend build scripts are designed for native Arm compilation, and components
-      like the Vela compiler and CMSIS-NN target Arm. Using a Graviton-based EC2 instance avoids
-      the complexity of cross-compilation and lets you compile the model and build the ExecuTorch
-      static libraries natively.
-  - question: When creating the firmware project, which components must be included?
-    answer: >-
-      Duplicate the Blinky example to a new CMSIS project (mv2_runner) and include the ExecuTorch
-      libraries, the compiled MobileNetV2 model, and SEGGER RTT for debug output. Update the project
-      references so they point to mv2_runner rather than the original Blinky.
-  - question: How should I configure memory and linker settings for this workload?
-    answer: >-
-      Reconfigure MRAM allocation, stack/heap sizes, and the linker script to match the ML workload.
-      The embedded model is about 3.7 MB (MRAM/flash), the runtime and application code add roughly
-      800 KB, and inference needs approximately 7.6 MB of SRAM for memory pools and intermediate
-      tensors.
-  - question: What result should I expect after flashing, and how do I verify it?
-    answer: >-
-      The application initializes the Ethos-U85, loads the MobileNetV2 model via ExecuTorch, runs
-      inference on an embedded test image, and prints the classification result. Use SEGGER RTT
-      to view and verify the output in real time.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Gabriel Peterson
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md b/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
index 2dbce416a1..5719b371e4 100644
--- a/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
@@ -19,59 +19,9 @@ prerequisites:
     - A [Raspberry Pi Pico](https://www.raspberrypi.com/products/raspberry-pi-pico/) board
     - A [PIR sensor](https://www.amazon.com/HiLetgo-HC-SR501-Infrared-Sensor-Arduino/dp/B07KZW86YR/ref=sr_1_3?keywords=pir+sensor&qid=1698432931&sr=8-3) for detecting motion
     - A [peizo-electric buzzer](https://www.amazon.com/mxuteuk-Electronic-Computers-Printers-Components/dp/B07VK1GJ9X/ref=sr_1_4?crid=2FAXYI17HZKDB&keywords=piezo+buzzer&qid=1698432968&sprefix=peizo%2Caps%2C148&sr=8-4) for signaling motion
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T21:58:43Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 3ddb97eb97a4c7ede7951410086198ee793a9d452a79b607f211873971bd375d
-  summary_generated_at: '2026-06-01T21:25:21Z'
-  summary_source_hash: 3ddb97eb97a4c7ede7951410086198ee793a9d452a79b607f211873971bd375d
-  faq_generated_at: '2026-06-02T21:58:43Z'
-  faq_source_hash: 3ddb97eb97a4c7ede7951410086198ee793a9d452a79b607f211873971bd375d
-  summary: >-
-    Build a motion-detection device on a Raspberry Pi Pico (RP2040 Cortex‑M0+) using the Arduino
-    IDE on baremetal. This introductory Learning Path explains the differences between application
-    and embedded stacks, then walks you through writing a simple embedded application, adding
-    hardware interrupt handlers for a PIR motion sensor, and running it on the Pico with a piezo‑electric
-    buzzer to signal motion. You will practice interrupt-driven programming on Arm Cortex‑M and
-    deploy to real hardware in about 60 minutes. Prerequisites: Arduino IDE with the RP2040 board
-    support package installed, a Raspberry Pi Pico, a PIR sensor, and a piezo‑electric buzzer.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      Install the Arduino IDE with the RP2040 board support package and have a Raspberry Pi Pico,
-      a PIR sensor, and a piezo-electric buzzer. No other prerequisites are explicitly listed.
-  - question: How do I know the Arduino IDE is ready for RP2040 development?
-    answer: >-
-      Verify that the RP2040 board support package is installed so you can build and upload for
-      the Raspberry Pi Pico. The Learning Path assumes you are using Arduino IDE configured for
-      RP2040.
-  - question: Is an RTOS used, or is this bare-metal Arduino on RP2040?
-    answer: >-
-      This project runs on baremetal and uses hardware interrupts. The Learning Path also explains
-      how embedded stacks and RTOS-based designs differ from traditional application stacks.
-  - question: What result should I expect when I run the program on the Pico?
-    answer: >-
-      A simple motion-detection device: when the PIR sensor detects movement, the application
-      responds via an interrupt and signals using the piezo-electric buzzer. You will run this
-      on the Raspberry Pi Pico.
-  - question: What should I check if the buzzer doesn’t sound when motion is detected?
-    answer: >-
-      Confirm you can program the Raspberry Pi Pico from the Arduino IDE and that the PIR sensor
-      and buzzer are correctly connected for the GPIOs used in your program. Also check that your
-      interrupt handler is attached to the PIR input as described in the steps.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Michael Hall
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/armds/_index.md b/content/learning-paths/embedded-and-microcontrollers/armds/_index.md
index 2db9b8b978..8a4b4dc161 100644
--- a/content/learning-paths/embedded-and-microcontrollers/armds/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/armds/_index.md
@@ -13,60 +13,9 @@ learning_objectives:
 
 prerequisites:
     - Some familiarity with embedded programming is assumed
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:00:01Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 0103f51d42c230dbe75ff5b78ac15a33dfd2f2c0f4906fb665a8dd681512d2e1
-  summary_generated_at: '2026-06-01T21:25:49Z'
-  summary_source_hash: 0103f51d42c230dbe75ff5b78ac15a33dfd2f2c0f4906fb665a8dd681512d2e1
-  faq_generated_at: '2026-06-02T22:00:01Z'
-  faq_source_hash: 0103f51d42c230dbe75ff5b78ac15a33dfd2f2c0f4906fb665a8dd681512d2e1
-  summary: >-
-    Learn how to get productive with Arm Development Studio by importing and building an example
-    bare-metal project, then debugging it on a Fixed Virtual Platform (FVP) or on hardware using
-    a DSTREAM debug probe. Starting from a working, licensed installation, you launch the IDE,
-    open the workspace, and use the supplied Cortex-M3 FVP (a digital twin of the MPS2+ AN385
-    platform) with a ready-to-use .launch configuration to step through the code without target
-    hardware. The path also shows how to select a different Arm Compiler for Embedded version
-    at the project level. Some familiarity with embedded programming is assumed.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      Have Arm Development Studio installed with a valid license configured. The path assumes
-      some familiarity with embedded programming.
-  - question: How do I launch the IDE and set up the workspace?
-    answer: >-
-      Start the IDE from your OS applications menu or run the armds_ide command. On first launch,
-      accept the default workspace configuration by clicking Finish; the workspace is a base directory
-      on your host.
-  - question: Can I run the example without hardware, and which FVP does it target?
-    answer: >-
-      Yes. Arm Development Studio provides FVPs, and the supplied Cortex-M FVPs are digital twins
-      of the MPS2+ platform programmed for Cortex-M3 (AN385). If you have hardware and a DSTREAM
-      probe, you can choose to run on the board instead.
-  - question: Where is the FVP debug configuration and how do I use it?
-    answer: >-
-      The project includes startup_Cortex-M3_AC6_FVP.launch in the project folder. Double-click
-      it to inspect settings; it is a ready-to-use configuration to start an FVP debug session
-      from the IDE.
-  - question: How do I select a different Arm Compiler for Embedded version for my project?
-    answer: >-
-      Install the required compiler version using the Arm Compiler for Embedded install guide.
-      Then open Project Properties and adjust the C/C++ Build settings to select the desired compiler
-      version; Development Studio ships with the latest available at its release.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/asm/_index.md b/content/learning-paths/embedded-and-microcontrollers/asm/_index.md
index 406d018225..9eb945b319 100644
--- a/content/learning-paths/embedded-and-microcontrollers/asm/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/asm/_index.md
@@ -3,59 +3,9 @@ title: Write Arm Assembler functions
 
 minutes_to_complete: 60
 description: Learn how to write mixed C and assembly programs for Cortex-M microcontrollers using Keil MDK, following Arm Procedure Call Standard conventions.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:01:17Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 24245102b7b6cefd0d4f67fbfaff1fb27c913de33665929ec3cb15293a1b3b51
-  summary_generated_at: '2026-06-01T21:26:11Z'
-  summary_source_hash: 24245102b7b6cefd0d4f67fbfaff1fb27c913de33665929ec3cb15293a1b3b51
-  faq_generated_at: '2026-06-02T22:01:17Z'
-  faq_source_hash: 24245102b7b6cefd0d4f67fbfaff1fb27c913de33665929ec3cb15293a1b3b51
-  summary: >-
-    Learn to write mixed C and assembly for Cortex-M microcontrollers using Keil MDK, following
-    the Arm Procedure Call Standard. You will set up a bare-metal Cortex-M4 project either in
-    Keil Studio (VS Code, CMSIS Solution) or in μVision, target the ARMCM4 device, and, in μVision,
-    add CMSIS Core and Device Startup components and use the Models Cortex-M Debugger with the
-    Cortex-M4 Fixed Virtual Platform. You will implement assembly subroutines (my_strcpy and my_capitalize),
-    call them from C, and step through execution to understand their operation. This introductory
-    path expects some familiarity with C/Assembly and an installed Keil MDK, and takes about 60
-    minutes to complete.
-  faqs:
-  - question: Which Keil environment should I use, and what setup steps differ?
-    answer: >-
-      You can use Keil Studio (VS Code) or µVision. In Keil Studio, create a CMSIS Solution (csolution),
-      select the ARMCM4 target, choose Blank Solution, and ensure Arm Compiler 6 is selected.
-      In µVision, create a new project, select ARMCM4, and add CMSIS > Core and Device > Startup.
-  - question: How do I run the example on a Cortex-M4 Fixed Virtual Platform instead of hardware?
-    answer: >-
-      This path uses the Cortex‑M4 FVP provided with MDK. In µVision, set the Debug option to
-      Models Cortex-M Debugger and open Settings to configure it for the FVP.
-  - question: How do I add the main C file in each environment?
-    answer: >-
-      In µVision, right‑click Source Group 1, choose Add New Item, select C file (.c), and name
-      it main.c. In Keil Studio, open main.c within the Source Files group of your CMSIS Solution.
-  - question: What assembly functions do I implement and how are they called?
-    answer: >-
-      Implement my_strcpy(const char *src, char *dst) and my_capitalize(char *str). The main C
-      function creates character arrays and calls these subroutines to copy and capitalize a string.
-  - question: What calling convention should the assembly subroutines follow?
-    answer: >-
-      Write the subroutines to conform to the Arm Procedure Call Standard, using Arm register
-      calling conventions. This enables the C code to call the assembly routines as shown in the
-      example.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
index 8980565c42..be2a37a0ac 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
@@ -17,61 +17,9 @@ prerequisites:
     - An Arm Virtual Hardware account
     - A Linux machine with root access
     - Some familiarity with embedded Linux
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:02:28Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 983afd3fcc565266c8f12588086e36d736540e23d0e748c94b5d2a45ab53d6ab
-  summary_generated_at: '2026-06-01T21:26:34Z'
-  summary_source_hash: 983afd3fcc565266c8f12588086e36d736540e23d0e748c94b5d2a45ab53d6ab
-  faq_generated_at: '2026-06-02T22:02:28Z'
-  faq_source_hash: 983afd3fcc565266c8f12588086e36d736540e23d0e748c94b5d2a45ab53d6ab
-  summary: >-
-    This introductory Learning Path shows how to prepare a custom Balena OS image, run it on Arm
-    Virtual Hardware as a virtual Raspberry Pi 4, and deploy a pre-built IoT application from
-    Balena Hub. Working from a Linux machine with root access, you create a Balena Cloud fleet,
-    start a Raspberry Pi 4 instance in Arm Virtual Hardware, and upload the balenaos_rpi4b.zip
-    firmware. You then deploy a Grafana dashboard from Balena Hub to view the state of your device.
-    Prerequisites include a Balena Cloud account, an Arm Virtual Hardware account, and some familiarity
-    with embedded Linux. By the end, you will have a managed virtual device and a running application
-    in about 30 minutes.
-  faqs:
-  - question: What do I need before I start?
-    answer: >-
-      You need a Balena Cloud account, an Arm Virtual Hardware account, and a Linux machine with
-      root access. A free Balena Cloud account supports up to 10 devices, and this path uses one
-      device. If you create a new AVH account, you are automatically enrolled in a free 30-day
-      trial.
-  - question: When and why do I create a fleet in Balena Cloud?
-    answer: >-
-      After signing up for Balena Cloud, create a fleet. A fleet groups devices and acts as the
-      single deployment target for your applications.
-  - question: In Arm Virtual Hardware, which device should I select and how do I provide the OS
-      image?
-    answer: >-
-      From the AVH dashboard, click Create Device and select Raspberry Pi 4. When prompted for
-      firmware, choose Upload your own firmware and provide the balenaos_rpi4b.zip image you prepared.
-  - question: How do I open Balena Hub and which example application should I deploy?
-    answer: >-
-      Use the Balena Hub button in the top right of the Balena Cloud dashboard to open Balena
-      Hub. Go to Apps and search for balena-app, which deploys a Grafana dashboard showing the
-      state of your Balena OS device.
-  - question: Can I follow this path without using the hosted Balena Cloud service?
-    answer: >-
-      OpenBalena can deploy Balena applications without the hosted service, but this Learning
-      Path uses Balena Cloud. Follow the steps as written to use the hosted workflow.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Michael Hall
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
index fb366eaaeb..a60f83f965 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
@@ -15,58 +15,9 @@ prerequisites:
     - An Amazon AWS account
     - An Arm Virtual Hardware account
     - Some familiarity with embedded Linux
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:03:50Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 85845fd4a47960bca053aed8d87c1d1442a7f5de0be5caff349fc92c6980b07e
-  summary_generated_at: '2026-06-01T21:27:06Z'
-  summary_source_hash: 85845fd4a47960bca053aed8d87c1d1442a7f5de0be5caff349fc92c6980b07e
-  faq_generated_at: '2026-06-02T22:03:50Z'
-  faq_source_hash: 85845fd4a47960bca053aed8d87c1d1442a7f5de0be5caff349fc92c6980b07e
-  summary: >-
-    This introductory Learning Path guides embedded Linux developers through running a virtual
-    Raspberry Pi 4 on Arm Virtual Hardware and deploying AWS IoT Greengrass components to it.
-    You will create or use existing accounts for Arm Virtual Hardware and AWS, start a Raspberry
-    Pi Arm Virtual Hardware instance, set up AWS IoT Greengrass Core on the device, and use the
-    AWS IoT console to define and launch a Greengrass deployment of pre-built components. The
-    steps focus on essential configuration and deployment actions in a Linux environment. Prerequisites
-    include an Amazon AWS account, an Arm Virtual Hardware account, and some familiarity with
-    embedded Linux.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an Amazon AWS account, an Arm Virtual Hardware (AVH) account, and some familiarity
-      with embedded Linux. These are the only explicit prerequisites.
-  - question: Will I be charged by AWS or Arm Virtual Hardware during this tutorial?
-    answer: >-
-      AWS requires a credit card, but this Learning Path uses free tier only and can be completed
-      without incurring charges. New AVH accounts are automatically enrolled in a free 30-day
-      trial.
-  - question: Which virtual device does this Learning Path use?
-    answer: >-
-      It uses a Raspberry Pi 4 virtual device provided by Arm Virtual Hardware. You will start
-      this virtual device as part of the steps.
-  - question: Where do I create the AWS IoT Greengrass deployment?
-    answer: >-
-      In the AWS console, open the IoT Core service and navigate to Manage -> Greengrass devices
-      -> Deployments. Click Create to start a new Greengrass deployment.
-  - question: How do I change what runs on the device after deployment?
-    answer: >-
-      Modify the Greengrass deployment to change component configurations, add components, or
-      remove components. Deployments are designed to be updated in place.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Michael Hall
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
index a24926787f..52f4043489 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
@@ -15,62 +15,9 @@ learning_objectives:
 
 prerequisites:
     - Some familiarity with embedded programming is assumed
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:04:34Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: bd39d50c93219201ead5de5da627447a91a82ea9c65e232350facd0c3516bedc
-  summary_generated_at: '2026-06-01T21:27:33Z'
-  summary_source_hash: bd39d50c93219201ead5de5da627447a91a82ea9c65e232350facd0c3516bedc
-  faq_generated_at: '2026-06-02T22:04:34Z'
-  faq_source_hash: bd39d50c93219201ead5de5da627447a91a82ea9c65e232350facd0c3516bedc
-  summary: >-
-    This introductory Learning Path guides embedded developers through building and running Matter
-    reference examples on Arm Virtual Hardware, demonstrating communication between two Raspberry
-    Pi 4 virtual targets, and automating development with GitHub Actions on Linux. You will instantiate
-    AVH instances, fork and clone the connectedhomeip repository, run an example application,
-    and configure a self-hosted runner with a simplified workflow. You will also integrate the
-    AVH API—using JavaScript in this path—and add a GitHub secret to drive chip-tool commands
-    automatically. Prerequisites include an Arm Virtual Hardware 3rd Party Hardware account, a
-    GitHub account, and a Personal Access Token enabled to update GitHub Action workflows. Some
-    familiarity with embedded programming is assumed.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an Arm Virtual Hardware 3rd Party Hardware user account and a GitHub account. Generate
-      a GitHub Personal Access Token with permission to Update GitHub Action workflows and save
-      it locally. Some familiarity with embedded programming is assumed.
-  - question: Which Arm Virtual Hardware targets should I create, and how many?
-    answer: >-
-      Prepare Raspberry Pi 4 instances of Arm Virtual Hardware. You will use multiple instances
-      to demonstrate communication between two virtual hardware targets.
-  - question: How do I get the Matter sources into my AVH instances?
-    answer: >-
-      Fork the public connectedhomeip repository to your personal GitHub account. From the console
-      of each AVH instance, clone your fork so you can build and run the examples there.
-  - question: What should I do before configuring GitHub Actions in the repository?
-    answer: >-
-      If the lighting-app is still running, stop it with Ctrl+C. Then in .github/workflows, remove
-      the existing workflow files so you can add the new workflow used by this path with a self-hosted
-      runner.
-  - question: How do I enable API-based control of AVH in the workflow, and what result should
-      I expect?
-    answer: >-
-      Generate an AVH API Token from Profile > API and add it as a GitHub secret. The workflow
-      is extended (using JavaScript) to transmit chip-tool commands to your virtual devices via
-      the AVH API.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
index fe2f5fc24e..ed6f9a5ce8 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
@@ -16,59 +16,9 @@ prerequisites:
     - Some familiarity with embedded programming 
     - Some familiarity with AI/ML software development 
     - An Amazon Web Services(AWS) [account](https://aws.amazon.com/) to subscribe [Arm Virtual Hardware](https://aws.amazon.com/marketplace/pp/prodview-urbpq7yo5va7g) Amazon Machine Image(AMI)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:05:16Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 398077be1406d0787b0a5d8dc254472da0d125b51b0907769cd4a3516ce9111b
-  summary_generated_at: '2026-06-01T21:28:14Z'
-  summary_source_hash: 398077be1406d0787b0a5d8dc254472da0d125b51b0907769cd4a3516ce9111b
-  faq_generated_at: '2026-06-02T22:05:16Z'
-  faq_source_hash: 398077be1406d0787b0a5d8dc254472da0d125b51b0907769cd4a3516ce9111b
-  summary: >-
-    This introductory Learning Path shows how to export a PaddlePaddle inference model for text
-    recognition, compile it with TVMC, and deploy it on the Arm Corstone-300 Fixed Virtual Platform
-    (FVP) with Cortex-M55 using Arm Virtual Hardware. You will work with a PaddleOCR text recognition
-    model in a bare-metal target environment, using tools listed in the path such as TVMC, GCC,
-    Paddle, and Arm Virtual Hardware. The steps include an OCR overview and an end-to-end workflow
-    from model preparation through final execution on the FVP. Prerequisites include basic familiarity
-    with embedded and AI/ML development and an AWS account to subscribe to the Arm Virtual Hardware
-    AMI. Expected duration is about 30 minutes.
-  faqs:
-  - question: What do I need before running the workflow?
-    answer: >-
-      You need an AWS account to subscribe to the Arm Virtual Hardware AMI, basic familiarity
-      with embedded programming, and some experience with AI/ML development. No other prerequisites
-      are explicitly listed.
-  - question: Do I need to train a model, or does this use a pre-trained PaddlePaddle model?
-    answer: >-
-      The steps deploy pre-trained PaddlePaddle models. You export a Paddle inference model and
-      compile it with TVMC before deployment.
-  - question: Which Arm platform and runtime does this target?
-    answer: >-
-      The deployment targets the Corstone-300 FVP with an Arm Cortex-M55 processor, included with
-      Arm Virtual Hardware. The operating system context is bare-metal.
-  - question: How do I start the environment on AWS?
-    answer: >-
-      Begin by launching the Arm Virtual Hardware AMI on AWS. The Learning Path then guides you
-      through the end-to-end flow from model export to execution on the Corstone-300 FVP.
-  - question: What result should I expect after completing the steps?
-    answer: >-
-      You should complete model export and TVMC compilation and see the PaddleOCR text recognition
-      model execute on the Corstone-300 FVP with Cortex-M55. Successful final execution on the
-      FVP indicates the deployment worked.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Liliya Wu
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
index b29c3cd9a2..e94465dd1a 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
@@ -13,58 +13,9 @@ learning_objectives:
 prerequisites:
     - A valid [AWS](https://aws.amazon.com/) account
     - Some familiarity with Python
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:06:31Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: aaff31b8917320c53825f3e7410b703bc7c7e273b1963fd80727b6b874f50566
-  summary_generated_at: '2026-06-01T21:28:41Z'
-  summary_source_hash: aaff31b8917320c53825f3e7410b703bc7c7e273b1963fd80727b6b874f50566
-  faq_generated_at: '2026-06-02T22:06:31Z'
-  faq_source_hash: aaff31b8917320c53825f3e7410b703bc7c7e273b1963fd80727b6b874f50566
-  summary: >-
-    This introductory Learning Path guides you to create and integrate a virtual LED peripheral
-    using the Virtual IO (VIO) interface in Arm Virtual Hardware (AVH) to simulate real-world
-    peripherals. You will work in an AWS environment by launching the AVH Amazon Machine Image
-    (AMI), install the Tkinter Python package, and use an example project that demonstrates connecting
-    a virtual LED to a bare-metal application. The steps focus on using AVH virtual interfaces
-    and navigating the provided leds_example project. Intended for developers new to AVH and relevant
-    to Cortex-M and Corstone use cases, prerequisites include a valid AWS account and some familiarity
-    with Python; no other requirements are explicitly listed. Estimated completion time is about
-    20 minutes.
-  faqs:
-  - question: What do I need before running the example?
-    answer: >-
-      You need a valid AWS account and some familiarity with Python. No other prerequisites are
-      explicitly listed.
-  - question: How do I launch the environment used in this Learning Path?
-    answer: >-
-      Launch the Arm Virtual Hardware AMI in your AWS account. For full instructions, refer to
-      the Arm Virtual Hardware install guide.
-  - question: How do I install the Tkinter dependency in the AVH instance?
-    answer: >-
-      In the AVH terminal, install it with: sudo apt install -y python3-tk. This provides the
-      Tkinter Python interface to Tcl/Tk used by the example.
-  - question: How do I obtain the example project files?
-    answer: >-
-      In your AVH terminal, clone the example project repository and navigate into the leds_example
-      directory. The steps guide you through cloning and changing to the correct directory.
-  - question: Do I need physical hardware to test the LED peripheral?
-    answer: >-
-      No. The example uses AVH Virtual Interfaces, specifically the Virtual IO (VIO) interface,
-      to simulate a real-world LED peripheral.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md b/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
index 99443bc267..bb75c33aef 100644
--- a/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
@@ -19,63 +19,9 @@ learning_objectives:
 prerequisites:
     - A machine with Python 3 and Visual Studio Code installed
     - An active Azure account with sufficient permissions to create resources (such as IoT Hub, Functions, and Cosmos DB)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:07:49Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 0e653bdbf5e5b08a6a00ba68e5ed215a0082e22c634d7d632d088851d48bb01c
-  summary_generated_at: '2026-06-01T21:29:13Z'
-  summary_source_hash: 0e653bdbf5e5b08a6a00ba68e5ed215a0082e22c634d7d632d088851d48bb01c
-  faq_generated_at: '2026-06-02T22:07:49Z'
-  faq_source_hash: 0e653bdbf5e5b08a6a00ba68e5ed215a0082e22c634d7d632d088851d48bb01c
-  summary: >-
-    This advanced Learning Path guides you through building an end-to-end IoT solution in Azure
-    for Arm devices using Python and Visual Studio Code. You will set up Azure IoT Hub, register
-    a device, and stream telemetry using the Azure IoT SDK. The path shows how to process data
-    with Azure Stream Analytics, persist it in Azure Cosmos DB, trigger alerts and aggregate readings
-    with Azure Functions, and publish aggregated results to a public-facing web app hosted on
-    Azure Blob Storage. The steps target Windows, Linux, and macOS. Prerequisites include Python
-    3, Visual Studio Code, and an Azure account with permissions to create IoT Hub, Functions,
-    and Cosmos DB resources.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need Python 3, Visual Studio Code, and an active Azure account with permissions to create
-      resources such as IoT Hub, Azure Functions, and Cosmos DB. Development can be done on Windows,
-      Linux, or macOS.
-  - question: Do I need physical Arm hardware, or can I simulate device telemetry?
-    answer: >-
-      The path includes a Python-based telemetry simulator that generates sensor readings. Physical
-      hardware requirements are not explicitly listed, though the content references streaming
-      from an Arm64-powered IoT device.
-  - question: Which Azure services will I create and how are they used in the workflow?
-    answer: >-
-      You will use Azure IoT Hub for device communication, Azure Stream Analytics to process and
-      route telemetry, and Azure Cosmos DB to store data. Azure Functions monitor thresholds and
-      aggregate readings, and results are published to a web app hosted on Azure Blob Storage.
-  - question: How do I know my simulator is successfully sending data to Azure IoT Hub?
-    answer: >-
-      After configuring your Python app to connect securely to IoT Hub, you should observe continuous,
-      real-time telemetry streaming as described in the steps. Subsequent Stream Analytics inputs
-      and queries confirm that messages from IoT Hub are being received and processed.
-  - question: What outcome should I expect after configuring Stream Analytics and Cosmos DB?
-    answer: >-
-      Stream Analytics will process and route incoming telemetry, and the data will be persisted
-      into Cosmos DB. This stored data enables the next steps: Azure Functions for threshold-based
-      alerts and aggregation, followed by publishing aggregated results to a public-facing Blob
-      Storage web app.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md b/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md
index d4c6253c86..3f5faff16b 100644
--- a/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md
@@ -16,59 +16,9 @@ learning_objectives:
 
 prerequisites:
     - Some familiarity with embedded programming is assumed
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:08:50Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 6521f17d1a10ab8871d13917923f7f64320f69301b4c0c5f5b528163d3cb43c6
-  summary_generated_at: '2026-06-01T21:29:37Z'
-  summary_source_hash: 6521f17d1a10ab8871d13917923f7f64320f69301b4c0c5f5b528163d3cb43c6
-  faq_generated_at: '2026-06-02T22:08:50Z'
-  faq_source_hash: 6521f17d1a10ab8871d13917923f7f64320f69301b4c0c5f5b528163d3cb43c6
-  summary: >-
-    Build and run a bare-metal Armv8-A “Hello World” on a Fixed Virtual Platform, then extend
-    it with minimal boot code, UART output, and basic exception handling. You will use Arm Development
-    Studio or the standalone Arm Compiler for Embedded with Arm Fixed Virtual Platforms (FVP),
-    targeting the FVP_Base_AEMvA model that implements four processors. The steps include creating
-    a project, adding a reset handler to park secondary cores and run on one, replacing semihosting-based
-    printf with PL011 UART output, and enabling asynchronous exceptions with GICv3 and a timer
-    interrupt routed to EL3. Some familiarity with embedded programming is assumed. Estimated
-    time to complete is about 60 minutes.
-  faqs:
-  - question: What tools do I need before starting?
-    answer: >-
-      Install Arm Development Studio and configure your license, or install Arm Compiler for Embedded
-      and Arm Fixed Virtual Platforms individually. The Learning Path also references an example
-      Docker image that includes these tools.
-  - question: Which Fixed Virtual Platform should I use to run the example?
-    answer: >-
-      Use the FVP_Base_AEMvA Architecture Envelope Model. It is a generic Arm Architecture platform
-      implementing 4 processors.
-  - question: How do I ensure the application runs on a single core after reset?
-    answer: >-
-      Create a minimal reset handler at EL3 that reads MPIDR_EL1 to identify the core and parks
-      all but one processor. The application then executes on the selected processor.
-  - question: How do I know if printf is using semihosting and how do I redirect output?
-    answer: >-
-      Import the symbol __use_no_semihosting to detect or disable semihosting. Modify the code
-      to send output to the PL011 UART provided by the FVP; note that semihosting uses HLT and
-      would halt on real hardware without a debugger.
-  - question: How are interrupts configured in the event-driven example?
-    answer: >-
-      Asynchronous exceptions are enabled and routed to EL3. You initialize GICv3 in gic.s and
-      configure the timer as an interrupt source.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md b/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
index 7fc69566cc..c6165af514 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
@@ -16,60 +16,9 @@ learning_objectives:
 prerequisites:
     - A valid account with [Arm Virtual Hardware](https://app.avh.arm.com/login)
     - An Arm Linux host machine (if you want to build your own runtime and container image)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:09:54Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 3394bf994039e441d57dced2abb64d725077d4672e0e68dc71f1de50e58f0408
-  summary_generated_at: '2026-06-01T21:29:58Z'
-  summary_source_hash: 3394bf994039e441d57dced2abb64d725077d4672e0e68dc71f1de50e58f0408
-  faq_generated_at: '2026-06-02T22:09:54Z'
-  faq_source_hash: 3394bf994039e441d57dced2abb64d725077d4672e0e68dc71f1de50e58f0408
-  summary: >-
-    This introductory path shows how to deploy containerized embedded applications and firmware
-    to a Cortex-M core from a Linux-based Cortex-A application core using the OCI-compatible hybrid-runtime
-    with containerd and K3s on Arm Virtual Hardware. You provision an i.MX 8M Plus model in AVH,
-    review the hybrid-runtime components, run a Hello World firmware container via containerd’s
-    io.containerd.hybrid runtime, and verify creation with ctr commands. You also set up a single-node
-    K3s cluster configured to use containerd with selected components disabled for embedded use.
-    Objectives include building the hybrid-runtime components and a firmware container image.
-    Prerequisites are an AVH account and, if you will build locally, an Arm Linux host machine.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      You need a valid Arm Virtual Hardware account. If you plan to build your own runtime and
-      container image, you also need access to an Arm Linux host machine.
-  - question: Which Arm Virtual Hardware device should I create?
-    answer: >-
-      Create a device in the Default Project and select the i.MX 8M Plus platform. This model
-      runs four Cortex-A53 processors and is used for the hybrid edge setup.
-  - question: Which runtime should I specify when starting a container with containerd?
-    answer: >-
-      Use the hybrid runtime by passing --runtime io.containerd.hybrid to ctr run. The example
-      image is ghcr.io/smarter-project/hybrid-runtime/hello_world_imx8mp:latest with a container
-      name such as test.
-  - question: How do I verify that the container started correctly with containerd?
-    answer: >-
-      Run ctr c ls to list containers. You should see your container (for example, test) with
-      the hello_world_imx8mp:latest image and the io.containerd.hybrid runtime.
-  - question: How should I install and configure K3s for this demo?
-    answer: >-
-      Set INSTALL_K3S_EXEC for a single-node server and include the provided flags to disable
-      traefik, metrics-server, coredns, and local-storage, set flannel-backend=none, cluster-dns
-      to 169.254.0.2, and point to containerd via --container-runtime-endpoint. Then run: curl
-      -sfL https://get.k3s.io | INSTALL_K3S_EXEC=$INSTALL_K3S_EXEC sh -s -
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Basma El Gaabouri
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md
index e85b8a262a..b94cc9b257 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md
@@ -13,64 +13,9 @@ learning_objectives:
 prerequisites:
     - An installation of [Arm Keil MDK](/install-guides/mdk) or [Arm Development Studio](/install-guides/armds) (MDK recommended)
     - Some familiarity with CMSIS is assumed
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:10:56Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: d8c73d658fa7a8051131276b8567cbd7376aac3b8f6e87c8ecdf224443c3ef99
-  summary_generated_at: '2026-06-01T21:30:25Z'
-  summary_source_hash: d8c73d658fa7a8051131276b8567cbd7376aac3b8f6e87c8ecdf224443c3ef99
-  faq_generated_at: '2026-06-02T22:10:56Z'
-  faq_source_hash: d8c73d658fa7a8051131276b8567cbd7376aac3b8f6e87c8ecdf224443c3ef99
-  summary: >-
-    Learn how to create, build, and debug a basic RTX5 RTOS application using Keil μVision in
-    Keil MDK. You will install or update CMSIS packs, initialize RTX5 via the CMSIS-RTOS2 API
-    (including SysTick setup using SystemCoreClockUpdate), implement main() and an app_main thread
-    that launches three RTOS threads, then build and run the project on an FVP from within the
-    IDE. You will observe RTOS activity with the RTX RTOS watch window and route printf output,
-    using Event Recorder when semihosting is unavailable. The path is introductory, targets Cortex‑M
-    development, and includes notes for Arm Development Studio users. Prerequisites are an installation
-    of Keil MDK or Arm Development Studio (MDK recommended) and some familiarity with CMSIS.
-  faqs:
-  - question: What do I need installed before running the steps, and which IDE should I use?
-    answer: >-
-      Install Arm Keil MDK or Arm Development Studio; Keil MDK is recommended. Some familiarity
-      with CMSIS is assumed. If you use Arm Keil Studio for Visual Studio Code, follow the separate
-      path for Keil Studio (VS Code).
-  - question: How do I install the required CMSIS components for the project?
-    answer: >-
-      Open the Pack Installer and install the latest CMSIS packs. The Learning Path assumes you
-      use the most up-to-date CMSIS content.
-  - question: Which source files do I create, and where do I add them in the project?
-    answer: >-
-      Create main.c and app_main.c by right-clicking the Source folder under the FVP target, choosing
-      Add new item, and selecting C file (.c). main.c contains the system and RTX5 initialization;
-      app_main starts and manages additional threads.
-  - question: How do I build, start the FVP, and observe the RTOS during debug in Keil MDK?
-    answer: >-
-      Save all files, build with F7, then click Debug (Ctrl+F5) to launch the FVP and enter debug
-      mode. Use View > Watch Windows > RTX RTOS to inspect RTOS features and View > Serial Windows
-      > Debug (printf) for printf output. Click Run (F5) to start and Stop when finished.
-  - question: How do I enable Event Recorder for printf output in Keil MDK, and when should I
-      use it?
-    answer: >-
-      Because Keil MDK does not support semihosting here, use CMSIS-View Event Recorder for printf
-      functionality. In Manage Run-Time Environment, enable CMSIS-View > Event Recorder (DAP variant),
-      set CMSIS-Compiler > STDOUT (API) to Event recorder, and enable CMSIS-Compiler > Core. In
-      Arm Development Studio, Event Recorder and Component Viewer are not supported, so skip this
-      section.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md
index 0d728cb44c..9898ed1c8d 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md
@@ -15,59 +15,9 @@ learning_objectives:
 prerequisites:
     - Installation of [Arm Keil Studio for VS Code](/install-guides/keilstudio_vs)
     - Some familiarity with CMSIS is assumed
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:11:43Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: f4d1ab3448590c5654e2479937c9eec3e378d05229b29a45b8675139f40ac177
-  summary_generated_at: '2026-06-01T21:30:42Z'
-  summary_source_hash: f4d1ab3448590c5654e2479937c9eec3e378d05229b29a45b8675139f40ac177
-  faq_generated_at: '2026-06-02T22:11:43Z'
-  faq_source_hash: f4d1ab3448590c5654e2479937c9eec3e378d05229b29a45b8675139f40ac177
-  summary: >-
-    Learn to create, configure, and debug a basic RTX5 RTOS application for Arm Cortex-M using
-    Keil Studio for VS Code and the CMSIS-RTOS2 API. You will set up a new csolution project,
-    configure the Run-Time Environment (including C Startup), initialize the kernel by setting
-    up SysTick with SystemCoreClockUpdate(), and implement an app_main thread that creates multiple
-    RTOS threads. The steps target the supplied Cortex-M4 Fixed Virtual Platform (FVP), with build
-    and debug driven from the VS Code environment. By the end, you can build, run, and observe
-    thread output in the Debug Console. Prerequisites are installation of Arm Keil Studio for
-    VS Code and some familiarity with CMSIS.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      Install Arm Keil Studio for VS Code. Some familiarity with CMSIS is assumed.
-  - question: Which target does this use, and can I run it on other hardware?
-    answer: >-
-      The steps are written for the supplied Cortex-M4 Fixed Virtual Platform (FVP). You can also
-      run the project on other devices supported by CMSIS-Pack.
-  - question: What project should I create and what initial setup is required?
-    answer: >-
-      Create a csolution project in Keil Studio for VS Code. When configuring the project's Run-Time
-      Environment, add the system initialization code (C Startup).
-  - question: How do I set up the OS and create threads?
-    answer: >-
-      Configure the SysTick timer using SystemCoreClockUpdate(), then initialize and start RTX5.
-      Implement app_main to create threads with the CMSIS-RTOS2 API (the example uses three threads,
-      but the number and names are flexible).
-  - question: How do I build, debug, and verify that it works?
-    answer: >-
-      In the CMSIS Extension view, save your files and click the hammer icon to build. Start debugging
-      with the Debug icon or the Run and Debug view, select your configured debug connection to
-      launch the FVP, and expect to see thread messages printed in the Debug Console once the
-      OS is initialized.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md b/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
index b042eca518..f523ed22c3 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
@@ -18,63 +18,9 @@ prerequisites:
     - Working knowledge of C.
     - Prior exposure to CMSIS-DSP.
     - Python installed on your machine.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:13:13Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 69526ee8b840c8f5d77d49349d2f0cc7ff4594fec5e4311984ef72a0672d3462
-  summary_generated_at: '2026-06-01T21:31:12Z'
-  summary_source_hash: 69526ee8b840c8f5d77d49349d2f0cc7ff4594fec5e4311984ef72a0672d3462
-  faq_generated_at: '2026-06-02T22:13:13Z'
-  faq_source_hash: 69526ee8b840c8f5d77d49349d2f0cc7ff4594fec5e4311984ef72a0672d3462
-  summary: >-
-    This advanced Learning Path shows how to prototype DSP algorithms in Python using the CMSIS-DSP
-    Python package and understand how the Python API maps to the CMSIS-DSP C implementation for
-    Arm Cortex-M and Cortex-A platforms. On Linux, Windows, or macOS, you will set up a Python
-    virtual environment, install cmsisdsp, Jupyter, and NumPy, and use a Jupyter notebook to load
-    a sample “yes/no” audio file from an Arm repository. You will implement a simple energy-based
-    voice activity detector, apply overlapping Hanning windows, and build a NumPy reference for
-    a noise-suppression workflow. Prerequisites include familiarity with Python and DSP concepts,
-    working knowledge of C, prior exposure to CMSIS-DSP, and Python installed.
-  faqs:
-  - question: What do I need before running the notebook?
-    answer: >-
-      You need Python installed, familiarity with Python and DSP concepts, working knowledge of
-      C, and prior exposure to CMSIS-DSP. The path targets Linux, Windows, and macOS. No additional
-      prerequisites are explicitly listed.
-  - question: Should I create a Python virtual environment and which packages do I install?
-    answer: >-
-      Yes, the steps use a Python virtual environment. Install cmsisdsp (which also installs NumPy)
-      and then install the jupyter package.
-  - question: Where does the sample audio come from and how is it used?
-    answer: >-
-      The notebook loads a yesno.wav file from an Arm demo repository on GitHub using urlopen.
-      You play it in the notebook with an Audio widget to inspect the noisy speech used in the
-      subsequent steps.
-  - question: How do I know my VAD and noise suppression steps are working?
-    answer: >-
-      You implement a simple energy-based VAD with a manually tuned threshold and then build a
-      NumPy reference for noise suppression using overlapping windows and a Hanning window via
-      dsp.arm_hanning_f32. The path relies on iterative tuning and listening/inspection in the
-      notebook; specific validation criteria beyond this are not explicitly listed.
-  - question: How does the Python code relate to the CMSIS-DSP C implementation on Arm cores?
-    answer: >-
-      The CMSIS-DSP Python package provides APIs that map to CMSIS-DSP C functions, helping you
-      prototype in Python before building and porting to C. The underlying C library is optimized
-      for Arm Cortex-M and Cortex-A, including DSP extensions on M4/M7, Helium on M55/M85, and
-      Neon on Cortex-A55 and other Cortex-A cores.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Christophe Favergeon
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md b/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
index 99384fc816..9e971025a2 100644
--- a/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
@@ -15,60 +15,9 @@ learning_objectives:
 
 prerequisites:
     - Basic knowledge and familiarity with Cortex-M processors.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:14:04Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 0b7a5895a87de83903c223215845b6c840602663838c0682f46e50d139d69c59
-  summary_generated_at: '2026-06-01T21:31:37Z'
-  summary_source_hash: 0b7a5895a87de83903c223215845b6c840602663838c0682f46e50d139d69c59
-  faq_generated_at: '2026-06-02T22:14:04Z'
-  faq_source_hash: 0b7a5895a87de83903c223215845b6c840602663838c0682f46e50d139d69c59
-  summary: >-
-    This introductory path shows how to implement context switching on Arm Cortex-M processors
-    in a bare-metal environment using the Memory Protection Unit (MPU) and the SysTick exception.
-    You will build and run an open-source example from the Armv8-M Memory Model and MPU User Guide
-    repository that demonstrates simple real-time kernel context switching between two threads
-    using MPU regions. The workflow uses Arm Development Studio 2022.1 with Arm Compiler for Embedded
-    6.18, Fast Models Fixed Virtual Platforms 11.18, and CMSIS 5.8.0. By completing the steps,
-    you will understand context switching basics, program the MPU, apply SysTick with context
-    switching operations, and successfully build and run the example. Basic familiarity with Cortex-M
-    is expected.
-  faqs:
-  - question: Where do I get the example project used in this Learning Path?
-    answer: >-
-      The source code is available in the GitHub repository that accompanies the Armv8-M Memory
-      Model and MPU User Guide. The example demonstrates simple real-time kernel context switching
-      between two threads.
-  - question: Which tool versions should I use to build and run the example?
-    answer: >-
-      Use Arm Development Studio 2022.1, Arm Compiler for Embedded 6.18, Fast Models Fixed Virtual
-      Platforms (FVP) 11.18, and CMSIS 5.8.0 as listed in the Learning Path.
-  - question: Where is the example intended to run?
-    answer: >-
-      The example targets a bare-metal environment on Arm Cortex-M processors. The listed tools
-      include Fast Models Fixed Virtual Platforms (FVP) 11.18 for running the example.
-  - question: How do MPU and SysTick feature in the example?
-    answer: >-
-      You will program MPU regions and use the SysTick exception as part of the context switching
-      operations. The example shows switching between two threads using these features.
-  - question: What should I check if the project does not build or run as expected?
-    answer: >-
-      Confirm you are using the specified tool versions and CMSIS 5.8.0. Also ensure you are building
-      the example from the GitHub repository associated with the Armv8-M Memory Model and MPU
-      User Guide.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Uma Ramalingam
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md b/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
index 28b0128483..9dbc1ac96e 100644
--- a/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
@@ -13,56 +13,9 @@ learning_objectives:
 
 prerequisites:
     - Basic familiarity with Keil MDK
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:15:46Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: f989929b11c48df42c2701dc71516cec0b8637b4c639c3dbbf6ac5e7440c8a56
-  summary_generated_at: '2026-06-01T21:31:57Z'
-  summary_source_hash: f989929b11c48df42c2701dc71516cec0b8637b4c639c3dbbf6ac5e7440c8a56
-  faq_generated_at: '2026-06-02T22:15:46Z'
-  faq_source_hash: f989929b11c48df42c2701dc71516cec0b8637b4c639c3dbbf6ac5e7440c8a56
-  summary: >-
-    Learn to configure and run code coverage in Keil MDK using Fixed Virtual Platforms (FVPs)
-    for Cortex-M targets. You will import and build the CMSIS-RTOS2 Blinky (uVision Simulator)
-    example for ARMCM3 from the Pack Installer, set up debugging on the supplied Cortex-M FVP,
-    execute the application, and view the Code Coverage report to see which code paths your tests
-    exercise, such as all cases of a C switch statement. This introductory path assumes basic
-    familiarity with Keil MDK and takes about 15 minutes. By the end, you can run a project on
-    an FVP and understand the basics of the coverage report.
-  faqs:
-  - question: Do I need real target hardware to follow this path?
-    answer: >-
-      No. While MDK can perform code coverage with FVPs or real hardware, this Learning Path uses
-      the supplied Cortex-M FVP, so you can complete it without hardware.
-  - question: What do I need before I start?
-    answer: >-
-      You must have Keil MDK installed, and basic familiarity with MDK is assumed. No other explicit
-      prerequisites are listed.
-  - question: Which device and example should I select in the Pack Installer?
-    answer: >-
-      In the Devices tree, select ARM > ARM Cortex M3 > ARMCM3. Then open the Examples tab and
-      copy the CMSIS-RTOS2 Blinky (uVision Simulator) example, open it in MDK, and build.
-  - question: Can I use a different project instead of the CMSIS-RTOS2 Blinky example?
-    answer: >-
-      Yes. You can perform code coverage on any project that runs on a suitable target, but this
-      path uses a standard RTX example that runs on the supplied Cortex-M FVP.
-  - question: What should I look for in the Code Coverage report?
-    answer: >-
-      The report highlights which areas of your code were executed by your tests. A common check
-      is verifying that all cases in a C switch statement have been exercised.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md b/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
index 4b4cbf9704..8ab830b85f 100644
--- a/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
@@ -14,60 +14,9 @@ learning_objectives:
 
 prerequisites:
     - Basic familiarity with Python and the command line
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:16:41Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 9d9e4c170fa318a9875c888c2c812ceb27c59f56c4b0dd7e0ae87dd31bb5783c
-  summary_generated_at: '2026-06-01T21:38:11Z'
-  summary_source_hash: 9d9e4c170fa318a9875c888c2c812ceb27c59f56c4b0dd7e0ae87dd31bb5783c
-  faq_generated_at: '2026-06-02T22:16:41Z'
-  faq_source_hash: 9d9e4c170fa318a9875c888c2c812ceb27c59f56c4b0dd7e0ae87dd31bb5783c
-  summary: >-
-    Learn how to establish peer-to-peer device-to-device communication at the edge using the Device
-    Connect Edge SDK in a Python environment, with no hardware required. You will build two simulated
-    devices on the same mesh: a sensor that publishes temperature and humidity readings, and a
-    threshold monitor that subscribes and raises an alert when a configurable limit is crossed.
-    Along the way, you will work with the SDK’s developer model (DeviceDriver, decorators, and
-    DeviceRuntime) and see how discovery, pub/sub, and RPC fit together. The walkthrough uses
-    uv to manage the project and dependencies, and includes using Device Connect agent tools to
-    discover devices and invoke their RPCs. Target platforms include Linux, macOS, and Windows.
-    Prerequisite: basic familiarity with Python and the command line. Estimated time: about 25
-    minutes.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      You should be comfortable with Python and the command line. The steps support Linux, macOS,
-      and Windows, and no hardware is required.
-  - question: Do I need a broker or cloud service to complete the device-to-device setup?
-    answer: >-
-      No. The walkthrough stands up peer-to-peer communication between two devices with no broker
-      or cloud service in between.
-  - question: Which tool is used to manage the Python project and dependencies?
-    answer: >-
-      The walkthrough uses uv to manage the project and its Python dependencies. uv will resolve
-      a compatible Python for the environment.
-  - question: How are devices defined and brought online with Device Connect?
-    answer: >-
-      You subclass DeviceDriver from device_connect_edge.drivers and annotate methods and properties
-      with primitives. DeviceRuntime brings the driver online and wires it into discovery, pub/sub,
-      and RPC.
-  - question: How do I know the two simulated devices are discoverable and callable?
-    answer: >-
-      You will use the Device Connect agent tools to discover both devices on the mesh and invoke
-      their RPCs. Successful discovery and RPC calls indicate the setup is working as intended.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author:
     - Kavya Sri Chennoju
diff --git a/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md b/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md
index 1c873b2ff3..2471e7d5bf 100644
--- a/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md
@@ -16,62 +16,9 @@ prerequisites:
     - A development machine with git installed
     - Basic familiarity with command-line tools
     - (Optional) A Raspberry Pi for testing a full device-to-device (D2D) setup
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:17:42Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: c31d398d3ce965a4193fca45cd025182d788a2fc603fbe8c828dfbd5a1c599ba
-  summary_generated_at: '2026-06-01T21:38:36Z'
-  summary_source_hash: c31d398d3ce965a4193fca45cd025182d788a2fc603fbe8c828dfbd5a1c599ba
-  faq_generated_at: '2026-06-02T22:17:42Z'
-  faq_source_hash: c31d398d3ce965a4193fca45cd025182d788a2fc603fbe8c828dfbd5a1c599ba
-  summary: >-
-    Learn to connect AI agents to Arm-based edge devices using Device Connect for structured device
-    access and Strands for agent orchestration. You will set up a Python environment from source
-    by cloning the strands-labs/robots repository and running its setup script to install dependencies
-    and create a Python 3.12 virtual environment. Then you will start a simulated robot that registers
-    on the local network and use Device Connect agent tools with the robot_mesh Strands tool to
-    discover and invoke it. An optional section runs Zenoh, etcd, and a registry via Docker and
-    connects a Raspberry Pi for a full device-to-device setup. Target platforms are Linux and
-    macOS; prerequisites are Git and basic command-line skills. Core steps take about 30 minutes.
-  faqs:
-  - question: What do I need before cloning the repository?
-    answer: >-
-      Use a Linux or macOS machine with git installed and basic command-line familiarity. Docker
-      is only required for the optional infrastructure section. A Raspberry Pi is optional if
-      you want to test a full device-to-device setup.
-  - question: How do I set up the Python environment?
-    answer: >-
-      Clone the robots repository and run the provided setup.sh script. The script installs uv,
-      creates a Python 3.12 virtual environment, and installs all required packages; then source
-      the environment as directed in the steps.
-  - question: Which option should I choose for device discovery and control?
-    answer: >-
-      Choose the single-machine option to follow a conceptual implementation using two terminal
-      windows on your machine. Choose the real hardware option if you have an external device;
-      your machine acts as the agent machine and the external device serves as the remote device.
-  - question: How do I know the agent discovered the robot?
-    answer: >-
-      After starting the simulated robot, it registers on the local network and is discovered
-      automatically by the agent. Use the Device Connect agent tools and the robot_mesh Strands
-      tool to list and invoke the robot.
-  - question: What changes when I run with the full Device Connect infrastructure?
-    answer: >-
-      You will run a Zenoh router, an etcd state store, and a registry service on your machine
-      using Docker, then connect a Raspberry Pi on the same network as the remote device. This
-      goes beyond the local-only discovery used in the earlier section.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: 
     - Annie Tallund
diff --git a/content/learning-paths/embedded-and-microcontrollers/docker/_index.md b/content/learning-paths/embedded-and-microcontrollers/docker/_index.md
index b358b91b88..fc2e79ce1e 100644
--- a/content/learning-paths/embedded-and-microcontrollers/docker/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/docker/_index.md
@@ -13,57 +13,9 @@ learning_objectives:
     - Test the image
 
 prerequisites:
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:19:02Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: a4b522c46c68d3c6f5c4af7c76b00c7bde48bb638147c201376bc19a4de79cca
-  summary_generated_at: '2026-06-01T21:39:09Z'
-  summary_source_hash: a4b522c46c68d3c6f5c4af7c76b00c7bde48bb638147c201376bc19a4de79cca
-  faq_generated_at: '2026-06-02T22:19:02Z'
-  faq_source_hash: a4b522c46c68d3c6f5c4af7c76b00c7bde48bb638147c201376bc19a4de79cca
-  summary: >-
-    Build a containerized Arm embedded development environment by creating a Dockerfile, constructing
-    an Ubuntu-based Docker image that includes Arm Compiler for Embedded and a library of Fixed
-    Virtual Platforms (FVPs), and testing the image. This introductory path is aimed at embedded
-    software developers new to Docker and focuses on a basic build-and-run setup for bare-metal
-    development. The host machine can be Windows or Linux, and Linux users may need sudo for Docker
-    commands. Before starting, install Docker for your host and download the installation packages
-    you will copy into the image. By the end, you will have a tested Docker image suitable for
-    running compiler and FVP tasks.
-  faqs:
-  - question: What do I need before running docker build?
-    answer: >-
-      Install Docker for your host platform and download the installation packages for Arm Compiler
-      for Embedded and the Fixed Virtual Platforms you plan to include. These files are copied
-      into the image during the build.
-  - question: Which host operating systems can I use to follow this path?
-    answer: >-
-      You can use Windows or Linux as the host. On Linux, you may need to prefix Docker commands
-      with sudo because the Docker daemon runs as root.
-  - question: What base operating system does the container use?
-    answer: >-
-      The Docker image uses Ubuntu as the operating system inside the container.
-  - question: What will the resulting Docker image contain?
-    answer: >-
-      It will contain Arm Compiler for Embedded and a library of Fixed Virtual Platforms (FVPs).
-      This provides a basic build and run environment for Arm embedded development.
-  - question: How do I know the image works after the build?
-    answer: >-
-      The steps include testing the containerized environment. You should be able to run the container
-      and exercise the compiler and FVPs without errors.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/edge/_index.md b/content/learning-paths/embedded-and-microcontrollers/edge/_index.md
index dbbd80f0f4..574a306905 100644
--- a/content/learning-paths/embedded-and-microcontrollers/edge/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/edge/_index.md
@@ -18,62 +18,9 @@ prerequisites:
   - An [Edge Impulse Studio](https://studio.edgeimpulse.com/signup) account.
   - The [Arduino IDE](/install-guides/arduino-pico/) with the RP2040 board support package installed on your computer.
   - An [Arduino Nano RP2040 Connect board](https://store.arduino.cc/products/arduino-nano-rp2040-connect-with-headers).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:20:08Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 8a7ec29d10a89649662ebb0fa4f14712984786902b6e7343a195abb1f3cbc00b
-  summary_generated_at: '2026-06-01T21:39:32Z'
-  summary_source_hash: 8a7ec29d10a89649662ebb0fa4f14712984786902b6e7343a195abb1f3cbc00b
-  faq_generated_at: '2026-06-02T22:20:08Z'
-  faq_source_hash: 8a7ec29d10a89649662ebb0fa4f14712984786902b6e7343a195abb1f3cbc00b
-  summary: >-
-    This introductory Learning Path guides you through building a TinyML audio command demo on
-    the Arduino Nano RP2040 Connect. You will use Edge Impulse to collect and preprocess audio
-    data, train a simple voice-command classifier, and export a library for deployment. After
-    setting up the Arduino IDE with RP2040 board support, you will integrate the generated library
-    into a new sketch, build, and upload to the board to run real-time inference on a bare-metal
-    Cortex-M class microcontroller. Prerequisites include an Edge Impulse Studio account, the
-    Arduino IDE with RP2040 support, and an Arduino Nano RP2040 Connect. By the end, you will
-    have an LED that turns on and off when it recognizes the words “on” and “off,” in about 90
-    minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an Arduino Nano RP2040 Connect, the Arduino IDE with the RP2040 board support package
-      installed, and an Edge Impulse Studio account. If you are an absolute beginner, complete
-      the Arduino on Raspberry Pi Pico path first.
-  - question: Which platform and tools does this project use?
-    answer: >-
-      It targets the Arduino Nano RP2040 Connect (RP2040 on Arm Cortex-M) running bare-metal.
-      You will use Edge Impulse for data collection and model training, and the Arduino IDE to
-      build and upload the sketch.
-  - question: How do I get the Edge Impulse model into my Arduino sketch?
-    answer: >-
-      Train a voice-command classification model in Edge Impulse, then use the library generated
-      by Edge Impulse. Add that library to your Arduino sketch before building and uploading to
-      the board.
-  - question: What result should I expect after deployment?
-    answer: >-
-      Your board will run real-time audio inference and control an LED based on predictions. Saying
-      "on" or "off" should toggle the LED accordingly.
-  - question: What should I check if the LED does not react to voice commands?
-    answer: >-
-      Verify the RP2040 board support is installed and the correct board is selected in the Arduino
-      IDE. Ensure the Edge Impulse–generated library is included in your sketch, rebuild the model
-      in Edge Impulse if needed, and re-upload the program.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Bright Edudzi Gershon Kordorwu
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/_index.md b/content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/_index.md
index 83b5c21ef2..4ae50aadeb 100644
--- a/content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/_index.md
@@ -23,11 +23,9 @@ prerequisites:
     - A supported Arm-based edge device (Raspberry Pi 5, Nvidia Jetson, Qualcomm Dragonwing QC6490) or an AWS EC2 Arm instance
     - An SSH client and familiarity with the Linux command line
     - Basic understanding of ML concepts
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 author: Doug Anson
 
 ### Tags
diff --git a/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md b/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md
index 342fb210ec..f2c1453939 100644
--- a/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md
@@ -15,60 +15,9 @@ prerequisites:
     - Familiarity with ML concepts
     - Familiarity with C programming on microcontrollers
     - STM32 B-L475E-IOT01A2 board
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:21:11Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 66024a2e3da90dcda298bf66b5de07952ed1e355d22172bc2812c46ad4fcda7f
-  summary_generated_at: '2026-06-01T21:40:03Z'
-  summary_source_hash: 66024a2e3da90dcda298bf66b5de07952ed1e355d22172bc2812c46ad4fcda7f
-  faq_generated_at: '2026-06-02T22:21:11Z'
-  faq_source_hash: 66024a2e3da90dcda298bf66b5de07952ed1e355d22172bc2812c46ad4fcda7f
-  summary: >-
-    This Learning Path walks you through building a convolutional neural network for image classification
-    using the CIFAR-10 dataset in a Jupyter Notebook environment set up with Anaconda, then deploying
-    and running it on an Arm Cortex-M–based STM32 B-L475E-IOT01A2 (STM32L4 Discovery) board. You
-    will import the trained model into an STM32CubeMX project using STM32Cube.AI, target a bare-metal
-    configuration, and exercise the model on hardware with a provided ST Python tool that sends
-    images to the board. It is aimed at advanced embedded developers. Prerequisites include familiarity
-    with ML concepts, C programming on microcontrollers, and access to the specified STM32 board.
-    The steps note using X-CUBE-AI 7.0.0 for the testing tool.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      You need an STM32 B-L475E-IOT01A2 board, familiarity with ML concepts, and familiarity with
-      C programming on microcontrollers. No other explicit prerequisites are listed.
-  - question: How do I open and run the training notebook?
-    answer: >-
-      From an Anaconda Prompt, run "jupyter notebook" and open lab.ipynb from the extracted project
-      files. Click Run to execute each cell; In[] means not started, In[*] means running, and
-      In[N] indicates the cell completed.
-  - question: Which dataset and model are used for training?
-    answer: >-
-      The model is a CNN trained on the CIFAR-10 dataset, which contains 60,000 images across
-      10 categories. The model takes an RGB image as input and predicts its category.
-  - question: Which STM32Cube tools and versions should I use during deployment?
-    answer: >-
-      Install STM32CubeMX using the Windows installer and add the STM32Cube.AI extension. Select
-      X-CUBE-AI 7.0.0, as the provided testing tool was written for this version and later versions
-      may not connect successfully.
-  - question: How do I run the testing tool and what if the board is not detected?
-    answer: >-
-      Activate your Anaconda environment (conda activate ml_lab), install opencv-python, protobuf==3.20,
-      and tqdm==4.50.2, then run "python ui_python_ai_runner.py" from the Misc folder. If the
-      board is not detected, press the black reset button on the board and try again.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/intro/_index.md b/content/learning-paths/embedded-and-microcontrollers/intro/_index.md
index 478f244182..7b44fff018 100644
--- a/content/learning-paths/embedded-and-microcontrollers/intro/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/intro/_index.md
@@ -13,60 +13,9 @@ learning_objectives:
 
 prerequisites:
     - None
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:22:16Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: ac69e979101f6befe55abee1429299c163c70b9a886d87a8f64779babd9fe5af
-  summary_generated_at: '2026-06-01T21:40:30Z'
-  summary_source_hash: ac69e979101f6befe55abee1429299c163c70b9a886d87a8f64779babd9fe5af
-  faq_generated_at: '2026-06-02T22:22:16Z'
-  faq_source_hash: ac69e979101f6befe55abee1429299c163c70b9a886d87a8f64779babd9fe5af
-  summary: >-
-    This introductory Learning Path explains where Arm architecture appears in microcontrollers
-    and helps you identify hardware options for software development on Arm Cortex-M processors.
-    You will review common use contexts, then compare evaluation boards (starter kits) for early
-    development with other small-form-factor boards and modules that can be designed into final
-    products. The content is oriented to bare-metal and RTOS environments and focuses on selection
-    and understanding rather than hands-on tooling. No explicit prerequisites are listed. After
-    completing the path, you should be able to describe where Cortex-M microcontrollers are used
-    and choose suitable board types for prototyping or product integration, with pointers to additional
-    reading and training.
-  faqs:
-  - question: Do I need any prerequisites or hardware to start this Learning Path?
-    answer: >-
-      No prerequisites are listed. The content helps you understand Cortex‑M use cases and discover
-      suitable hardware; you do not need a board to follow the overview.
-  - question: How do evaluation boards differ from edge computing boards or SBCs?
-    answer: >-
-      Evaluation boards (starter kits) are used for early software development, prototyping, and
-      demonstrations, and are typically used stand‑alone. Edge boards, modules, or SBCs can be
-      designed directly into a final product and often use small form factors, with features such
-      as debug interfaces.
-  - question: Which operating environments are in scope for the examples and guidance?
-    answer: >-
-      The path targets microcontroller development on bare‑metal and RTOS operating environments.
-  - question: Will this help if I’m migrating an application from another architecture?
-    answer: >-
-      Yes, it provides context on where Arm microcontrollers are used and how to find Cortex‑M
-      hardware options. It is not a detailed migration guide.
-  - question: Where can I find additional learning resources after finishing?
-    answer: >-
-      The path lists example books on Cortex‑M processors, a free Arm Helium Technology (M‑Profile
-      Vector Extension) e‑book for Cortex‑M, and Arm on‑demand training that includes M‑Profile
-      architecture.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md b/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
index 070d964128..9045c14f30 100644
--- a/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
@@ -16,61 +16,9 @@ learning_objectives:
 prerequisites:
     - Basic knowledge of Machine Learning concepts
     - A Linux computer
-
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:23:20Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: aa49e4a1651367965e79d36c5f6af16e9b341c392d48cf051f24cbb21070e972
-  summary_generated_at: '2026-06-01T21:40:55Z'
-  summary_source_hash: aa49e4a1651367965e79d36c5f6af16e9b341c392d48cf051f24cbb21070e972
-  faq_generated_at: '2026-06-02T22:23:20Z'
-  faq_source_hash: aa49e4a1651367965e79d36c5f6af16e9b341c392d48cf051f24cbb21070e972
-  summary: >-
-    This introductory path explains what differentiates TinyML from other AI domains and why Arm-based
-    edge devices are a good fit. You set up a Linux-hosted TinyML environment using PyTorch, ExecuTorch,
-    and the Corstone-320 Fixed Virtual Platform (FVP), a pre-silicon virtual platform that models
-    Cortex-M processors and Arm Ethos-U NPUs. The steps include installing and configuring ExecuTorch,
-    running scripts to provision the Corstone-320 FVP, and defining a small PyTorch feedforward
-    network that you export with ExecuTorch tooling to validate the setup. By the end, you can
-    describe TinyML trade-offs, identify suitable Arm devices, and work with a basic TinyML sandbox
-    on Linux. Prerequisites: basic ML knowledge and a Linux computer.
-  faqs:
-  - question: What do I need before running the setup?
-    answer: >-
-      You need basic knowledge of Machine Learning concepts and a Linux computer. No other explicit
-      prerequisites are listed.
-  - question: Do I need physical Arm hardware to complete this path?
-    answer: >-
-      No. The Corstone-320 Fixed Virtual Platform provides a virtual representation of Arm-based
-      microcontrollers so you can develop and test before boards are available.
-  - question: What does the Corstone-320 FVP provide for this workflow?
-    answer: >-
-      It is a pre-silicon software development environment designed for AI and ML workloads, with
-      support for Arm Ethos-U NPUs and Cortex-M processors. It enables early software validation
-      and development for embedded AI applications.
-  - question: How do I validate that ExecuTorch and the environment are installed correctly?
-    answer: >-
-      Follow the steps to run the setup scripts for the Corstone-320 reference package and then
-      execute the provided example. Being able to run the example without errors indicates the
-      environment is ready.
-  - question: What code artifact will I create in the modeling step?
-    answer: >-
-      You will create a Python file (simple_nn.py) that defines a small feedforward neural network
-      for a classification task. The example uses PyTorch export utilities and ExecuTorch conversion
-      APIs to target edge execution.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dominica Abena O. Amanfo
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md b/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
index 2d3cdcdf51..70f04af3ad 100644
--- a/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
@@ -14,59 +14,9 @@ learning_objectives:
 prerequisites:
     - Some familiarity with embedded programming
     - An AWS account (required for Arm Virtual Hardware)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:25:19Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 11fc64eb1a0595b1d8e625e465be9fa87a4de04f59492004204ccbe61a92a5b7
-  summary_generated_at: '2026-06-01T21:41:45Z'
-  summary_source_hash: 11fc64eb1a0595b1d8e625e465be9fa87a4de04f59492004204ccbe61a92a5b7
-  faq_generated_at: '2026-06-02T22:25:19Z'
-  faq_source_hash: 11fc64eb1a0595b1d8e625e465be9fa87a4de04f59492004204ccbe61a92a5b7
-  summary: >-
-    This introductory path shows how to build Open-IoT-SDK examples and run them on Corstone-300
-    virtual hardware using Arm Virtual Hardware. You set up an AVH instance, install the required
-    Python environment, then build and run a keyword example to observe ML inference logs on a
-    bare-metal or RTOS stack. The flow highlights how Arm Trusted Firmware-M and the Arm ML Evaluation
-    Kit integrate within Arm Total Solutions for IoT, and how the keyword and speech examples
-    can connect to AWS IoT. Tools listed include Arm Virtual Hardware, FVP, and Arm Compiler for
-    Embedded. Prerequisites are some embedded programming familiarity and an AWS account (required
-    for AVH). Estimated time is about 30 minutes.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      Some familiarity with embedded programming is expected, and you need an AWS account to use
-      Arm Virtual Hardware. No other prerequisites are explicitly listed.
-  - question: How do I set up Arm Virtual Hardware and install the required software?
-    answer: >-
-      Create and set up your AVH instance by following the Arm Virtual Hardware install guide.
-      In the AVH instance, run: sudo apt update; sudo apt install python3.8-venv -y; sudo cp /usr/local/bin/pip3.8
-      /usr/bin.
-  - question: How do I build and run the keyword example?
-    answer: >-
-      From the project, run: ./ats.sh build-n-run keyword. The build takes a few minutes and runs
-      on Corstone-300 virtual hardware within AVH.
-  - question: What result should I expect in the terminal when the example runs successfully?
-    answer: >-
-      Look for logs such as "ML interface initialised" and inference output showing a label and
-      score, for example: label: on, score: 0.996127; threshold: 0.700000. You may also see markers
-      like ML_HEARD_O.
-  - question: How is AWS connectivity used in the examples, and what should I configure?
-    answer: >-
-      The keyword and speech examples implement AWS cloud connectivity. You can create an AWS
-      thing to send data from the simulated Corstone-300 device to AWS IoT cloud services.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md b/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
index 742090a3d7..1efb596698 100644
--- a/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
@@ -15,61 +15,9 @@ prerequisites:
     - A [Jetson Orin Nano](https://developer.nvidia.com/embedded/learn/jetson-orin-nano-devkit-user-guide/index.html)
     - A microSD card (64GB UHS-1 or larger is recommended)
     - A MIPI CSI-2 camera, with a 22 pin connector on at least one end
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:25:46Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: fdf582f1b54f372768a2c896e1da152c50d22c3bd238848253cdbe18cc68222d
-  summary_generated_at: '2026-06-01T21:42:08Z'
-  summary_source_hash: fdf582f1b54f372768a2c896e1da152c50d22c3bd238848253cdbe18cc68222d
-  faq_generated_at: '2026-06-02T22:25:46Z'
-  faq_source_hash: fdf582f1b54f372768a2c896e1da152c50d22c3bd238848253cdbe18cc68222d
-  summary: >-
-    This introductory path shows how to bring up a Jetson Orin Nano on Linux with a MIPI CSI-2
-    camera and run real-time object detection using DetectNet and TensorRT. You will download
-    the latest Jetson Orin Nano developer kit image from the NVIDIA site and write it to a microSD
-    card with balenaEtcher, then clone and launch the jetson-inference Docker container. From
-    there, you will run DetectNet on a live CSI camera stream and on image files, including adjusting
-    detection thresholds. Required hardware is a Jetson Orin Nano, a 64GB (UHS-1 recommended)
-    microSD card, and a MIPI CSI-2 camera with a 22‑pin connector. The estimated completion time
-    is about 60 minutes.
-  faqs:
-  - question: What do I need before starting the setup?
-    answer: >-
-      You need a Jetson Orin Nano, a microSD card (64GB UHS-1 or larger is recommended), and a
-      MIPI CSI-2 camera with a 22-pin connector. No other prerequisites are explicitly listed.
-  - question: How do I write the Jetson image to the microSD card?
-    answer: >-
-      Download the Jetson Orin Nano Developer Kit image from the NVIDIA developer website (expand
-      the Jetson Xavier NX & Orin Nano section, then select the Jetson Orin Nano Developer Kit).
-      Use balenaEtcher, choose Flash from file, and select the downloaded zip file without unzipping
-      it.
-  - question: How do I download and start the jetson-inference Docker container?
-    answer: >-
-      Clone the repository with git clone --recursive --depth=1 https://github.com/dusty-nv/jetson-inference,
-      change into the jetson-inference directory, and run docker/run.sh to download and launch
-      the container.
-  - question: How do I check that the Docker container is running and find its ID?
-    answer: >-
-      Run sudo docker ps -q to print the container ID. A hex string (for example, 174055df45cd)
-      indicates the container is active.
-  - question: How do I run DetectNet on the live camera and adjust sensitivity?
-    answer: >-
-      Inside the container, change to build/aarch64/bin and run ./detectnet csi://0 to process
-      the live camera stream. Adjust sensitivity with --threshold (default 0.5). The first run
-      can take longer, and you should see object labels rendered in real time.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Gabriel Peterson
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md b/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
index f4207de80b..c12490793f 100644
--- a/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
@@ -14,56 +14,9 @@ learning_objectives:
 prerequisites:
     - Some familiarity with embedded programming is assumed
     - An [Arm Account](https://developer.arm.com/register) is required
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:27:21Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: f3a01adf18ad93027b6ab61ceb5b0c470e6b5c298f9d4f944989a96ff64eec81
-  summary_generated_at: '2026-06-01T21:42:40Z'
-  summary_source_hash: f3a01adf18ad93027b6ab61ceb5b0c470e6b5c298f9d4f944989a96ff64eec81
-  faq_generated_at: '2026-06-02T22:27:21Z'
-  faq_source_hash: f3a01adf18ad93027b6ab61ceb5b0c470e6b5c298f9d4f944989a96ff64eec81
-  summary: >-
-    This introductory Learning Path shows how to import and build an example Cortex-M project
-    in Keil Studio Cloud and run it on Arm Virtual Hardware. Using the browser-based IDE with
-    Arm Compiler for Embedded and CMSIS, you learn the core workflow to build, optionally debug,
-    and execute the example without requiring a physical board. Some familiarity with embedded
-    programming is assumed, and an Arm Account (or an existing Mbed account) is required to sign
-    in. If you later connect a board over USB, use a desktop browser with WebUSB support such
-    as Google Chrome or Microsoft Edge (Chromium). The path is designed to take about 30 minutes.
-  faqs:
-  - question: What do I need before I can access Keil Studio Cloud?
-    answer: >-
-      You need an Arm Account to sign in. If you already have an Mbed account, you can use it
-      to access Keil Studio. Some familiarity with embedded programming is assumed.
-  - question: Which browser should I use, especially if I plan to connect a board over USB?
-    answer: >-
-      For USB-connected boards, use a desktop browser that supports WebUSB: Google Chrome or Microsoft
-      Edge (Chromium). All other features are supported in the latest versions of Chrome, Edge,
-      Opera, Safari, and Firefox.
-  - question: Can I complete this Learning Path without physical hardware?
-    answer: >-
-      Yes. One of the objectives is to run the example on Arm Virtual Hardware, so you can run
-      the project in a virtual environment.
-  - question: How do I check if my development board is supported by Keil Studio Cloud?
-    answer: >-
-      Go to keil.arm.com and click the Hardware menu to see the list of supported hardware.
-  - question: What targets and tools are used in the example project?
-    answer: >-
-      The path targets Cortex-M devices and uses Arm Compiler for Embedded, Arm Virtual Hardware,
-      and CMSIS. Examples may be bare-metal or RTOS-based.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Christopher Seidl 
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md b/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
index ba85ef9ae8..b049587a6e 100644
--- a/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
@@ -19,60 +19,9 @@ prerequisites:
     - A computer running Linux or macOS.
     - A USB-C cable for the board's **DBG** serial connection.
     - A USB-C power supply/cable for the board's **POWER** port.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:28:35Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 567387e8e513458a360f74c14d3f4d02c4af392bc573620e605c93976e4d8b4f
-  summary_generated_at: '2026-06-01T21:43:12Z'
-  summary_source_hash: 567387e8e513458a360f74c14d3f4d02c4af392bc573620e605c93976e4d8b4f
-  faq_generated_at: '2026-06-02T22:28:35Z'
-  faq_source_hash: 567387e8e513458a360f74c14d3f4d02c4af392bc573620e605c93976e4d8b4f
-  summary: >-
-    This Learning Path shows how to bring up Linux on the NXP FRDM i.MX 93 board and prepare it
-    for on-device development. You will boot and log in over the DBG serial console, create a
-    non-root user with sudo access, connect to WiFi using ConnMan, and transfer files to the board
-    with OpenSSH scp or a USB drive. An optional step configures the WiFi driver to load at boot
-    so ConnMan can reconnect automatically after a reboot. It targets embedded developers and
-    ML engineers working with Arm Cortex-A55–based hardware. Prerequisites include the FRDM i.MX
-    93 board, a Linux or macOS host, and USB-C cables for power and serial. Estimated time: 120
-    minutes.
-  faqs:
-  - question: What do I need before powering the board?
-    answer: >-
-      You need an NXP FRDM i.MX 93 board, a Linux or macOS host computer, a USB-C cable for the
-      DBG serial connection, and a USB-C power supply for the POWER port. These are the explicit
-      prerequisites.
-  - question: How do I access the Linux console on the board?
-    answer: >-
-      Connect your host to the board’s DBG serial port over USB-C and use a serial console tool
-      such as picocom. You will boot the board and log in over the serial console as described
-      in the steps.
-  - question: Which tool should I use to connect to WiFi, and how do I verify it worked?
-    answer: >-
-      Use ConnMan (via connmanctl) to join your WiFi network. To verify connectivity, run ifconfig
-      and look for the WiFi interface (often mlan0) and its inet address.
-  - question: How do I transfer files to the board during development?
-    answer: >-
-      Use scp over WiFi by targeting the board’s IP address and destination path. If WiFi is unavailable,
-      you can move files with a USB drive.
-  - question: What should I check if WiFi does not reconnect after a reboot?
-    answer: >-
-      Load the WiFi driver module after boot using the provided modprobe command so ConnMan can
-      reconnect to the saved network. Give it up to a minute to establish a link, then confirm
-      with ifconfig.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Waheed Brown
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md b/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md
index bdab012c39..b0fd05bbfd 100644
--- a/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md
@@ -13,63 +13,9 @@ learning_objectives:
 prerequisites:
     - A Linux-based x86-64 host computer with Arm Development Studio installed.
     - Basic understanding of Assembly and C programming.
-   
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:29:22Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 5943d817827bef274f175f7c14249cad769b2160094b3c65d7476aca6cbf0a1d
-  summary_generated_at: '2026-06-01T21:43:52Z'
-  summary_source_hash: 5943d817827bef274f175f7c14249cad769b2160094b3c65d7476aca6cbf0a1d
-  faq_generated_at: '2026-06-02T22:29:22Z'
-  faq_source_hash: 5943d817827bef274f175f7c14249cad769b2160094b3c65d7476aca6cbf0a1d
-  summary: >-
-    This introductory Learning Path shows how to boot a Linux software stack on Arm Fixed Virtual
-    Platforms (FVPs) and then debug Trusted Firmware-A (TF-A) and the Linux kernel using Arm Development
-    Studio. You will configure TF-A build flags to include cpu_ops for CPU-specific initialization,
-    adjust the device tree for CPU FVPs by removing unsupported PCI and SMMU nodes and setting
-    correct CPU affinity, launch the stack on an FVP, and verify expected build outputs and UART
-    logs. The target environment is a Linux-based x86-64 host with Arm Development Studio installed.
-    FVPs are fast, functional simulation models of Arm hardware, so you can develop and debug
-    without physical silicon. Estimated time to complete is approximately 60 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Linux-based x86-64 host computer with Arm Development Studio installed, and a
-      basic understanding of Assembly and C programming. No other prerequisites are explicitly
-      listed.
-  - question: How should I modify the device tree for CPU FVPs?
-    answer: >-
-      Remove PCI and SMMU nodes and ensure CPU affinity values are set correctly. Leaving PCI
-      or SMMU nodes in place can cause a kernel panic during boot on CPU FVPs.
-  - question: How do I confirm that cpu_ops is enabled in my TF-A build?
-    answer: >-
-      Follow the steps to configure TF-A build flags to include cpu_ops support for your CPU.
-      If the proper cpu_ops are missing, Linux may fail to boot; the steps describe enabling the
-      correct CPU-specific implementations.
-  - question: What result should I expect from the build output?
-    answer: >-
-      In the output directory (for example, output/aemfvp-a/aemfvp-a/), expect files such as Image
-      and Image.defconfig, often as symlinks to the component outputs. If these are missing, revisit
-      the build and configuration steps.
-  - question: How do I run and debug the software stack on an FVP?
-    answer: >-
-      Use the provided command templates, substituting <SRC_PATH> and <PATH_TO_LOG>, and capture
-      the UART output to verify the boot. For debugging, Arm Development Studio v2022.2 or later
-      is recommended for DWARF 5 support; launch the IDE and follow the steps to create a debug
-      configuration for TF-A and the Linux kernel.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Qixiang Xu
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md b/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md
index 762d19fa81..377b72adcb 100644
--- a/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md
@@ -15,57 +15,9 @@ learning_objectives:
 
 prerequisites:
     - A Raspberry Pi 5 running Raspberry Pi OS.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:30:54Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: aa6252610c0603acfdfc8d4555bb7da93cbdd5b9d92ba140c5dc5f63d23e2b07
-  summary_generated_at: '2026-06-01T21:44:40Z'
-  summary_source_hash: aa6252610c0603acfdfc8d4555bb7da93cbdd5b9d92ba140c5dc5f63d23e2b07
-  faq_generated_at: '2026-06-02T22:30:54Z'
-  faq_source_hash: aa6252610c0603acfdfc8d4555bb7da93cbdd5b9d92ba140c5dc5f63d23e2b07
-  summary: >-
-    This introductory Learning Path guides you through running a local LLM chatbot on a Raspberry
-    Pi 5. You install the Python version of llama.cpp on Raspberry Pi OS (64-bit), download a
-    model from Hugging Face, assess model memory size and performance, and run the model using
-    Python bindings. The 8GB RAM Raspberry Pi 5 is preferred for exploring an LLM, and with minor
-    modifications the approach can be adapted to other Arm Linux computers. By the end, you will
-    have a working local chatbot and a basic understanding of its resource and performance characteristics
-    on this device. Estimated time to complete is about 30 minutes. No additional prerequisites
-    are explicitly listed beyond a Raspberry Pi 5 running Raspberry Pi OS.
-  faqs:
-  - question: How should I prepare the SD card and which Raspberry Pi OS build should I choose?
-    answer: >-
-      Use Raspberry Pi Imager as recommended in the Raspberry Pi documentation to prepare the
-      SD card. Install the 64-bit version of Raspberry Pi OS for this Learning Path.
-  - question: Do I need the 8GB RAM Raspberry Pi 5 model?
-    answer: >-
-      The 8GB RAM Raspberry Pi 5 model is preferred for exploring an LLM. The Learning Path requires
-      a Raspberry Pi 5 running Raspberry Pi OS.
-  - question: Can I follow these steps on another Arm Linux computer?
-    answer: >-
-      Yes, the instructions can be used on any Arm Linux computer with minor modifications. The
-      Learning Path focuses on Raspberry Pi 5 as the primary target.
-  - question: Where do I obtain the model and how is it executed?
-    answer: >-
-      You will download an LLM from Hugging Face. It is run using the Python version of llama.cpp
-      through its Python bindings.
-  - question: What result should I expect after completing the steps, and how long will it take?
-    answer: >-
-      In about 30 minutes, you will have a local LLM chatbot running on your Raspberry Pi 5 using
-      Python. You will also assess the model’s memory size and performance on your device.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/migration/_index.md b/content/learning-paths/embedded-and-microcontrollers/migration/_index.md
index 6cdebb04ee..9fc81546b7 100644
--- a/content/learning-paths/embedded-and-microcontrollers/migration/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/migration/_index.md
@@ -16,61 +16,9 @@ prerequisites:
     - Introductory understanding of software containers
     - Knowledge about building workflows
     - Access to an aarch64 or x86_64 machine running Linux
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:32:21Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 81a15ff0d5579be9529a8c9c444be57521ebc48bbf5234f042f5a47a8a709b5c
-  summary_generated_at: '2026-06-01T21:45:31Z'
-  summary_source_hash: 81a15ff0d5579be9529a8c9c444be57521ebc48bbf5234f042f5a47a8a709b5c
-  faq_generated_at: '2026-06-02T22:32:21Z'
-  faq_source_hash: 81a15ff0d5579be9529a8c9c444be57521ebc48bbf5234f042f5a47a8a709b5c
-  summary: >-
-    This advanced Learning Path guides you through migrating an x86_64 Linux application to aarch64
-    using a practical porting methodology. You will set up an aarch64 GCC development environment
-    in a Docker container on a Linux host, analyze a Sobel filter workload implemented as non-SIMD
-    C++, x86_64 intrinsics, and OpenCV, and iteratively port code to Arm, including translating
-    intrinsics to Neon using SIMDe. You will build and run the application and evaluate console
-    timing results and image outputs. Emulation, remote hardware, or physical Arm hardware can
-    be used; physical hardware is not required. Prerequisites include introductory container knowledge,
-    familiarity with build workflows, and access to an aarch64 or x86_64 Linux machine. The path
-    also introduces using Arm compilers and libraries.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an introductory understanding of software containers, knowledge about building
-      workflows, and access to a Linux machine on either aarch64 or x86_64. This is an advanced
-      topic aimed at developers migrating Linux workloads.
-  - question: Can I complete this Learning Path without physical Arm hardware?
-    answer: >-
-      Yes. Physical Arm hardware is not required; you can use emulation or remote hardware to
-      run the aarch64 application.
-  - question: Which compiler and environment should I use for the port?
-    answer: >-
-      The example uses GCC and recommends matching the original GCC version when possible. Set
-      up an aarch64 GCC development container with Docker and run all build and test commands
-      inside that container.
-  - question: How should I handle x86 SIMD intrinsics during the port?
-    answer: >-
-      Use SIMD Everywhere (SIMDe) to port AVX intrinsics. This enables keeping a single source
-      base while targeting aarch64.
-  - question: What result should I expect when I run the ported application?
-    answer: >-
-      The program prints execution time measurements in microseconds for the non-SIMD, SIMD, and
-      OpenCV implementations, and opens four image windows including the original and processed
-      outputs. The example runs on CPU only (no hardware acceleration).
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Kasper Mecklenburg
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md b/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md
index 7adbd58171..756beaaa2d 100644
--- a/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md
@@ -14,61 +14,9 @@ learning_objectives:
 prerequisites:
     - Some familiarity with embedded programming
     - A Linux host machine running Ubuntu
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:33:38Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: acf43df3c6f344b55bb413b7483d60e26d0e137489ac148bca64500e443140ab
-  summary_generated_at: '2026-06-01T21:46:02Z'
-  summary_source_hash: acf43df3c6f344b55bb413b7483d60e26d0e137489ac148bca64500e443140ab
-  faq_generated_at: '2026-06-02T22:33:38Z'
-  faq_source_hash: acf43df3c6f344b55bb413b7483d60e26d0e137489ac148bca64500e443140ab
-  summary: >-
-    This Learning Path shows how to build sample applications from the Arm Machine Learning Evaluation
-    Kit (MLEK) and run them on an Arm Ecosystem Fixed Virtual Platform (FVP) for bare-metal ML
-    development on microcontrollers. You will set up the Corstone-320 FVP, build the MLEK examples
-    using a supported toolchain (such as GCC or Arm Compiler for Embedded), and locate the generated
-    .axf images in the cmake-*/bin directory. You then launch an application on the FVP, selecting
-    the binary with -a and configuring the Ethos-U NPU MACs using -C mps4_board.subsystem.ethosu.num_macs
-    so they match the build. Targets include Cortex-M55 with Ethos-U85. Prerequisites are familiarity
-    with embedded programming and an Ubuntu Linux host (20.04 or 22.04 on x86_64 recommended).
-    Estimated time: about 30 minutes.
-  faqs:
-  - question: What do I need on my host machine before running the steps?
-    answer: >-
-      Use a Linux host running Ubuntu; 20.04 or 22.04 is recommended. The instructions have been
-      tested on x86_64 and assume some familiarity with embedded programming.
-  - question: Which FVP should I install to run the examples?
-    answer: >-
-      Install the Corstone-320 Ecosystem FVP on your local machine. You can download Arm Ecosystem
-      FVPs from the Arm Developer website and follow the Fast Model and Fixed Virtual Platform
-      install guide.
-  - question: Where will the built binaries be located after compiling MLEK?
-    answer: >-
-      The built examples are .axf files found under a cmake-*/bin directory, which depends on
-      your build configuration. An example path is similar to cmake-build-mps4-sse-320-ethos-u85-256-gnu/bin/.
-  - question: How do I choose and run a specific example on the FVP?
-    answer: >-
-      Use the -a option to specify the application image (.axf) to load when launching the FVP.
-      Configure the Ethos-U component using -C mps4_board.subsystem.ethosu.num_macs to match your
-      build.
-  - question: What Arm IP and reference system do these examples target?
-    answer: >-
-      The examples let you investigate the software stack and evaluate performance on Cortex-M55
-      and Ethos-U85. They are run on Arm Corstone reference systems, such as the Corstone-320
-      FVP; similar steps apply to other platforms.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md b/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md
index fd971ae2b1..1cf18e4037 100644
--- a/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md
@@ -7,60 +7,9 @@ armips:
 - Cortex-M
 - Ethos-U
 - Corstone
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:35:12Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 0cfaa21be515b980097232ed38092b80d046df308120887e5503513a3384a1d7
-  summary_generated_at: '2026-06-01T21:46:50Z'
-  summary_source_hash: 0cfaa21be515b980097232ed38092b80d046df308120887e5503513a3384a1d7
-  faq_generated_at: '2026-06-02T22:35:12Z'
-  faq_source_hash: 0cfaa21be515b980097232ed38092b80d046df308120887e5503513a3384a1d7
-  summary: >-
-    This introductory path helps embedded developers plan Machine Learning workflows on Arm Cortex-M
-    with Ethos-U by choosing suitable physical and virtual targets, identifying core tools, and
-    locating example applications. You will compare development options that include Corstone-based
-    designs such as the MPS3 FPGA Prototyping Board and virtual platforms like Arm Virtual Hardware
-    and Fixed Virtual Platforms (FVPs). The path outlines host setup on an x86_64 Windows or Linux
-    machine, noting that some tools work only on Linux and that the Arm ML Evaluation Kit (MLEK)
-    is not fully supported on Windows. By the end, you will be prepared to select a target, set
-    up a bare-metal toolchain (GCC or Arm Compiler for Embedded), and find relevant examples to
-    study.
-  faqs:
-  - question: I don’t have an Ethos-U board—what platform should I start with?
-    answer: >-
-      Use a virtual platform. The path highlights virtual options such as FVP and Arm Virtual
-      Hardware, which let you begin ML development without physical hardware.
-  - question: Can I follow this path on Windows, or do I need Linux?
-    answer: >-
-      An x86_64 machine running Windows or Linux is suitable, but the Arm ML Evaluation Kit is
-      not fully supported on Windows and some required tools are Linux-only. If you plan to use
-      MLEK extensively, Linux is recommended.
-  - question: Which compilers can I use to build ML applications for Cortex-M and Ethos-U?
-    answer: >-
-      You can build C/C++ applications with GCC or Arm Compiler for Embedded. These toolchains
-      are appropriate for the targets described in the path.
-  - question: What physical hardware options exist today for Ethos-U development?
-    answer: >-
-      Readily available development boards with Ethos-U are currently limited. The Arm MPS3 FPGA
-      Prototyping Board can be programmed with Corstone reference system images to support ML
-      development.
-  - question: Does this path assume bare-metal or an RTOS, and what prior experience is needed?
-    answer: >-
-      The path targets bare-metal development. It assumes some familiarity with microcontroller
-      software development.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md b/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md
index 5aa2cb9cfb..55dfa63990 100644
--- a/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md
@@ -13,62 +13,9 @@ learning_objectives:
 
 prerequisites:
     - Some familiarity with embedded debug
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:35:33Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: d2c403c7fd1001dbd985697c9eb018951a955358c2be39c727183a87a3dfdf51
-  summary_generated_at: '2026-06-01T21:47:16Z'
-  summary_source_hash: d2c403c7fd1001dbd985697c9eb018951a955358c2be39c727183a87a3dfdf51
-  faq_generated_at: '2026-06-02T22:35:33Z'
-  faq_source_hash: d2c403c7fd1001dbd985697c9eb018951a955358c2be39c727183a87a3dfdf51
-  summary: >-
-    This introductory Learning Path shows how to add new debug targets in Arm Development Studio
-    for both virtual platforms and physical development boards. You will create debugger connections
-    to Arm Fast Models for bare-metal software bring-up and to boards via the Arm DSTREAM family
-    of probes. The steps outline when to use each DSTREAM variant and how to connect over USB
-    or Ethernet, so you can attach the debugger to Cortex-A, Cortex-R, Cortex-M, or Neoverse based
-    systems. It assumes Arm Development Studio and Arm Fast Models are installed and that you
-    have some familiarity with embedded debug. After completing the path, you will have working
-    debug configurations for your chosen target.
-  faqs:
-  - question: What do I need installed before creating a Fast Models debug connection in Arm Development
-      Studio?
-    answer: >-
-      It is assumed that Arm Development Studio and Arm Fast Models are installed, and that you
-      have some familiarity with embedded debug. Installation steps are not covered in this path.
-  - question: Do I need a physical development board to follow this path?
-    answer: >-
-      Not for the virtual platform step; Fast Models let you connect the debugger to a model as
-      if it were real hardware. For the hardware step, you will use an Arm DSTREAM probe with
-      a development board.
-  - question: Which DSTREAM probe should I choose for my board?
-    answer: >-
-      DSTREAM-ST provides full debug over JTAG and SWD, plus on-chip and low-bandwidth (4-bit)
-      external trace. If you require higher-bandwidth trace and your SoC and platform support
-      it, select DSTREAM-PT, DSTREAM-HT, or DSTREAM-XT.
-  - question: Should I connect DSTREAM to my host over USB or Ethernet?
-    answer: >-
-      The DSTREAM family supports both high-speed USB and Ethernet connections to the host. Use
-      whichever is available and appropriate for your setup.
-  - question: What result should I expect after creating each debug configuration?
-    answer: >-
-      For Fast Models, the Arm Development Studio debugger should attach to the virtual platform
-      and let you interact with it like real hardware. For a development board, the debugger should
-      connect through DSTREAM and provide debug (and trace, where supported) according to the
-      probe and target capabilities.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md b/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
index 3215bacfef..c27a8de00e 100644
--- a/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
@@ -17,61 +17,9 @@ prerequisites:
     - Completion of [Use Linux on an NXP FRDM i.MX 93 board](/learning-paths/embedded-and-microcontrollers/linux-nxp-board/) (Linux setup, login access, and file transfer)
     - Basic knowledge of Machine Learning concepts
     - A host computer to compile ExecuTorch libraries
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:36:25Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: be2b3571d4d2ed75a16bc97589abc22c970d83be868b7e94bb60962a4ba853da
-  summary_generated_at: '2026-06-01T21:47:49Z'
-  summary_source_hash: be2b3571d4d2ed75a16bc97589abc22c970d83be868b7e94bb60962a4ba853da
-  faq_generated_at: '2026-06-02T22:36:25Z'
-  faq_source_hash: be2b3571d4d2ed75a16bc97589abc22c970d83be868b7e94bb60962a4ba853da
-  summary: >-
-    This Learning Path guides you through deploying ExecuTorch on the NXP FRDM i.MX 93 to accelerate
-    inference with the Arm Ethos-U65. You will bring up a custom executor_runner firmware on the
-    Cortex-M33 using Linux RemoteProc from the Linux-based application processor, compile ExecuTorch
-    .pte models for Ethos-U65, and run them on the device. The steps cover board boot and serial
-    console access, setting up a consistent build environment (including an Ubuntu container on
-    macOS), and building and installing ExecuTorch. You will produce an executor_runner ELF and
-    a .pte model and see how these components work across Cortex-A, Cortex-M, and the NPU. Prerequisites
-    include the FRDM i.MX 93 board, a USB cable, basic ML knowledge, prior Linux setup on the
-    board, and a host computer.
-  faqs:
-  - question: What do I need before running the steps on the FRDM i.MX 93?
-    answer: >-
-      You need an NXP FRDM i.MX 93 board, a suitable USB cable, a host computer to compile ExecuTorch
-      libraries, and basic ML knowledge. Complete the Learning Path “Use Linux on an NXP FRDM
-      i.MX 93 board” to set up Linux, serial console access, and file transfer.
-  - question: How should I set up the ExecuTorch build environment on macOS?
-    answer: >-
-      Use an Ubuntu Docker container on macOS to build ExecuTorch. This container is a build-only
-      environment that produces prebuilt ExecuTorch libraries and .pte model files you later move
-      onto the FRDM i.MX 93.
-  - question: How do I connect to the board’s serial console, especially on macOS?
-    answer: >-
-      Connect your host to the board’s DEBUG USB-C port and open a serial terminal. On macOS,
-      install the Silicon Labs USB-to-UART driver and picocom via Homebrew before connecting.
-  - question: How can I verify that ExecuTorch installed correctly in my environment?
-    answer: >-
-      After running the installation, check that the package is present with: pip list | grep
-      executorch. If it appears in the list, the install succeeded.
-  - question: Which artifacts do I deploy, and how do they run on this heterogeneous system?
-    answer: >-
-      Deploy a .pte model compiled for Ethos-U65 and an executor_runner ELF firmware for the Cortex-M33.
-      Linux on the Cortex-A side uses RemoteProc to bring up the firmware, which loads the model
-      and invokes the NPU for accelerated inference.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: 
 - Waheed Brown
diff --git a/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md b/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
index 8913232e35..306f0124c9 100644
--- a/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
@@ -14,61 +14,9 @@ learning_objectives:
 prerequisites:
     - A good understanding of [CMSIS-Packs](https://open-cmsis-pack.github.io/Open-CMSIS-Pack-Spec/main/html/index.html).
     - A CMSIS-Pack that contains device support and was created for CMSIS v5.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:37:02Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 8039812773c6c1ac45cceb4039cee801e627d67871b625283c1bb5b3fa2960b3
-  summary_generated_at: '2026-06-01T21:48:37Z'
-  summary_source_hash: 8039812773c6c1ac45cceb4039cee801e627d67871b625283c1bb5b3fa2960b3
-  faq_generated_at: '2026-06-02T22:37:02Z'
-  faq_source_hash: 8039812773c6c1ac45cceb4039cee801e627d67871b625283c1bb5b3fa2960b3
-  summary: >-
-    This path shows maintainers how to migrate a CMSIS v5-based CMSIS-Pack with device support
-    to CMSIS v6 and update example projects for compatibility. You will update device support
-    by switching from assembly-based startup to C-based startup files and creating scatter files,
-    then migrate example projects from Arm Compiler 5 to Arm Compiler 6 and convert them to the
-    CMSIS-Toolbox project standard (csolution/cproject). CMSIS v6 supports Arm Compiler for Embedded
-    v6+, Arm GNU Toolchain v12+, LLVM v16+, and IAR EWARM v9.30+, and this path uses Arm Compiler
-    for Embedded v6. Prerequisites are a solid understanding of CMSIS-Packs and a CMSIS v5 device-support
-    pack. Target environments include Baremetal and RTOS.
-  faqs:
-  - question: Which toolchains can I use for CMSIS v6, and which one is used in this path?
-    answer: >-
-      CMSIS v6 supports Arm Compiler for Embedded (v6 and above), Arm GNU Toolchain (v12 and above),
-      LLVM (v16 and above), and IAR Embedded Workbench for Arm (v9.30 and above). This Learning
-      Path uses Arm Compiler for Embedded v6.
-  - question: What do I need before running the migration steps?
-    answer: >-
-      You need a good understanding of CMSIS-Packs and a CMSIS-Pack with device support that was
-      created for CMSIS v5. This path targets maintainers responsible for such packs.
-  - question: What changes are required in device support when moving to CMSIS v6?
-    answer: >-
-      Switch from assembly-based startup code to C-based startup files and create scatter files.
-      These updates prepare the device support for CMSIS v6 compatibility.
-  - question: My example projects use Arm Compiler 5. What should I do first?
-    answer: >-
-      Migrate the projects to Arm Compiler for Embedded v6 before attempting conversion to the
-      new CMSIS-Toolbox project format. In µVision, install the newly created device family pack,
-      set the compiler to use default version 6 under Options for Target > Target, and configure
-      defines in the C/C++ [AC6] tab.
-  - question: When can I convert projects to the CMSIS-Toolbox csolution/cproject format?
-    answer: >-
-      After migrating your examples to Arm Compiler for Embedded v6, they can be automatically
-      converted to the CMSIS-Toolbox project standard (csolution/cproject). The path assumes this
-      conversion follows the compiler migration.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Christopher Seidl
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md b/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md
index 08ac701917..d3bd705ba5 100644
--- a/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md
@@ -15,61 +15,9 @@ learning_objectives:
 prerequisites:
     - A CMSIS v5 based project.
     - A basic understanding of the CMSIS-Pack system.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:37:32Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 7a192506fc8a15423d1257fe92e7655053e38be85aad8310dae29602e483fbba
-  summary_generated_at: '2026-06-01T21:48:57Z'
-  summary_source_hash: 7a192506fc8a15423d1257fe92e7655053e38be85aad8310dae29602e483fbba
-  faq_generated_at: '2026-06-02T22:37:32Z'
-  faq_source_hash: 7a192506fc8a15423d1257fe92e7655053e38be85aad8310dae29602e483fbba
-  summary: >-
-    Learn how to migrate CMSIS v5 projects to CMSIS v6 for Cortex-M targets on bare-metal or RTOS.
-    Verify your toolchain (Arm Compiler for Embedded v6+, Arm GNU Toolchain v12+, LLVM v16+, or
-    IAR Embedded Workbench for Arm v9.30+; Arm Compiler v5 is not supported), install the required
-    CMSIS-Packs (ARM.CMSIS.6.0.0, ARM.Cortex_DFP.1.0.0, ARM.CMSIS-RTX.5.8.0), and update device
-    selection using the Cortex_DFP mappings. If your project used Keil.ARM_Compiler, install ARM.CMSIS-View.1.1.0
-    and ARM.CMSIS_Compiler.2.0. A troubleshooting section addresses common issues including missing
-    devices, RTE component errors, linker messages, and RTX5 runtime problems. Prerequisites:
-    a CMSIS v5 project and basic CMSIS-Pack knowledge.
-  faqs:
-  - question: Which toolchain versions are supported for CMSIS v6?
-    answer: >-
-      Use one of the following: Arm Compiler for Embedded v6 and above, Arm GNU Toolchain v12
-      and above, LLVM Toolchain v16 and above, or IAR Embedded Workbench for Arm v9.30 and above.
-      Arm Compiler v5 is not supported, and earlier versions of the listed toolchains are not
-      guaranteed to work.
-  - question: Which CMSIS-Packs do I need when migrating from CMSIS v5 to v6?
-    answer: >-
-      Install ARM.CMSIS.6.0.0, ARM.Cortex_DFP.1.0.0, and ARM.CMSIS-RTX.5.8.0. These replace the
-      monolithic ARM.CMSIS.5.x.x pack for CMSIS v6.
-  - question: I used the Keil.ARM_Compiler pack. Which packs replace it for CMSIS v6?
-    answer: >-
-      Install ARM.CMSIS-View.1.1.0 and ARM.CMSIS_Compiler.2.0. The Keil.ARM_Compiler pack is deprecated
-      and its content has moved into these packs.
-  - question: How do I map my CMSIS v5 device to the Cortex_DFP pack?
-    answer: >-
-      Switch your device selection to a supported device in the Cortex_DFP pack using the provided
-      mapping table. For example, ARMCM4/ARMCM4_FP maps to ARMCM4 (SP_FPU, MPU) and ARMCM7/ARMCM7_SP/ARMCM7_DP
-      maps to ARMCM7 (DP_FPU, MPU).
-  - question: What should I do if I’m using a Keil MDK v5 uvprojx project?
-    answer: >-
-      Use the project format conversion guidance to move from uvprojx to the Open-CMSIS-Pack csolution
-      format. Follow the referenced learning path to import, convert, and build in Keil Studio
-      for VS Code or on the command line.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Christopher Seidl
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md b/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md
index 6b34ca262d..b4b2ee078f 100644
--- a/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md
@@ -18,64 +18,9 @@ prerequisites:
   - An Arm-based single board computer (for example, Raspberry Pi 5 running Raspberry Pi OS)
   - Electronic components (breadboard, LEDs, resistors, jumper wires) for GPIO testing
   - Familiarity with Python programming, Raspberry Pi GPIO pinout, and basic electronics
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:37:58Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: a0145c77b3d4a8bb25a32c62adaa3ad378e65fccbe6db88d9a46a569897d238a
-  summary_generated_at: '2026-06-01T21:50:06Z'
-  summary_source_hash: a0145c77b3d4a8bb25a32c62adaa3ad378e65fccbe6db88d9a46a569897d238a
-  faq_generated_at: '2026-06-02T22:37:58Z'
-  faq_source_hash: a0145c77b3d4a8bb25a32c62adaa3ad378e65fccbe6db88d9a46a569897d238a
-  summary: >-
-    This introductory Learning Path guides you through building a fully local, privacy-first smart
-    home assistant on Raspberry Pi 5 with an Arm Cortex-A76 CPU. You install Python and required
-    libraries, set up Ollama to run a local large language model, and validate GPIO by wiring
-    an LED with a resistor to GPIO 17 and controlling it from a Python script. You then clone
-    a GitHub project that initializes devices, exposes a local FastAPI web server, and uses the
-    model’s JSON responses to execute actions from natural-language commands. Prerequisites include
-    a Raspberry Pi 5 running Raspberry Pi OS, basic electronics components, and familiarity with
-    Python and Raspberry Pi GPIO.
-  faqs:
-  - question: What do I need before running the setup?
-    answer: >-
-      You need an Arm-based single board computer such as a Raspberry Pi 5 running Raspberry Pi
-      OS with network connectivity. Have a breadboard, LEDs, 220Ω resistors, and jumper wires
-      for GPIO testing, plus familiarity with Python, the Raspberry Pi GPIO pinout, and basic
-      electronics.
-  - question: How should I connect to my Raspberry Pi 5 to install dependencies?
-    answer: >-
-      Connect the Raspberry Pi 5 to an external display through a micro‑HDMI port for local access.
-      The Learning Path assumes Raspberry Pi OS and network connectivity are already configured.
-  - question: How do I wire and verify the GPIO LED test?
-    answer: >-
-      Connect the LED anode in series with a 220Ω resistor to GPIO 17 (physical pin 11), and connect
-      the cathode to a GND pin. Create and run the testgpio.py script as shown; the LED should
-      respond to the script, confirming the wiring and GPIO control.
-  - question: Where do I get the assistant code and what does the main script do?
-    answer: >-
-      The assistant is available on GitHub; clone the repository and navigate to the project directory
-      as directed in the steps. Running smart_home_assistant.py initializes devices on specific
-      GPIO pins, starts a local web server, and uses a local model via Ollama to parse JSON commands
-      and control devices.
-  - question: How do I interact with the assistant and what behavior should I expect from the
-      LLM?
-    answer: >-
-      You can issue commands from the terminal or use the local web interface started by the script.
-      The Learning Path notes the system can achieve 15+ tokens per second while operating without
-      cloud services for a privacy-first setup.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Fidel Makatia Omusilibwa
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md b/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md
index 6d56f74a69..c1ca946b9b 100644
--- a/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md
@@ -19,61 +19,9 @@ prerequisites:
     - A Raspberry Pi 4 or 5 (earlier models may also work)
     - A microSD card with at least 16GB of storage
     - A Linux compatible USB microphone and USB speakers or a USB audio device with a microphone and speakers
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:38:37Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: ed2034f5c95c4148fca355f6d545219c320f2e73267a0725934c792ebc4c0c59
-  summary_generated_at: '2026-06-01T21:50:55Z'
-  summary_source_hash: ed2034f5c95c4148fca355f6d545219c320f2e73267a0725934c792ebc4c0c59
-  faq_generated_at: '2026-06-02T22:38:37Z'
-  faq_source_hash: ed2034f5c95c4148fca355f6d545219c320f2e73267a0725934c792ebc4c0c59
-  summary: >-
-    This introductory Learning Path guides you through building and running a voice-controlled
-    ChatGPT bot on a Raspberry Pi 4 or 5 using Raspberry Pi OS (64-bit, Linux). You will install
-    the OS with Raspberry Pi Imager, configure and test audio I/O, and create a Python application
-    that listens for a wake word with Porcupine, converts speech to text using Google Speech Recognition,
-    sends text to ChatGPT’s gpt-4-turbo-preview model via API, generates speech via ChatGPT’s
-    text-to-speech API, and plays the audio reply. You will work in a Python virtual environment
-    and use packages including pyaudio, SpeechRecognition, pydub, openai, python-dotenv, and pvporcupine.
-    Prerequisites include a Raspberry Pi, a 16GB microSD card, and a USB microphone and speakers.
-  faqs:
-  - question: What Raspberry Pi hardware and OS do I need before starting?
-    answer: >-
-      Use a Raspberry Pi 4 or 5 (earlier models may also work), a microSD card with at least 16GB,
-      and Raspberry Pi OS (64-bit) installed via Raspberry Pi Imager. You also need a Linux compatible
-      USB microphone and USB speakers or a combined USB audio device.
-  - question: How do I verify my microphone and speakers are set up correctly?
-    answer: >-
-      Plug in the devices, then right-click the speaker icon on the desktop to select your speakers.
-      In a terminal, run arecord -d 5 test.wav to create a short recording; if the file is not
-      created or contains no audio, adjust audio settings manually and retry.
-  - question: Which Python version and packages does the application use?
-    answer: >-
-      Raspberry Pi OS includes Python 3.11.2. Create a virtual environment and install pyaudio,
-      SpeechRecognition, pydub, openai, python-dotenv, and pvporcupine; you can optionally run
-      pip freeze to capture versions for troubleshooting.
-  - question: How do I run and stop the bot?
-    answer: >-
-      Activate your virtual environment, then run python main.py from the project directory. The
-      application runs indefinitely until you press Ctrl+C to stop it.
-  - question: What behavior should I expect when I say the wake word?
-    answer: >-
-      Say “computer,” pause briefly, then ask a question. After detection, the app converts your
-      speech to text, sends it to ChatGPT’s gpt-4-turbo-preview model, converts the reply to speech
-      using ChatGPT’s text-to-speech model, and plays the audio response.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Gabriel Peterson
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
index 2facc0c495..0a1c202501 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
@@ -19,59 +19,9 @@ learning_objectives:
 prerequisites:
     - An Arm Linux machine or an [Arm cloud instance](/learning-paths/servers-and-cloud-computing/csp/).
     - A Raspberry Pi 5.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:39:40Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 9cd2c3082c4874dc596e1b7b59c3dc2143aaf1ce3e66948ab8b6af190ffa3776
-  summary_generated_at: '2026-06-01T21:51:49Z'
-  summary_source_hash: 9cd2c3082c4874dc596e1b7b59c3dc2143aaf1ce3e66948ab8b6af190ffa3776
-  faq_generated_at: '2026-06-02T22:39:40Z'
-  faq_source_hash: 9cd2c3082c4874dc596e1b7b59c3dc2143aaf1ce3e66948ab8b6af190ffa3776
-  summary: >-
-    This introductory Learning Path shows how to compile the Llama 3 large language model with
-    ExecuTorch using a Docker container that runs Raspberry Pi OS on an Arm Linux machine or Arm
-    cloud instance, then deploy it to a Raspberry Pi 5. You will create an isolated Python environment
-    for ExecuTorch, build the binaries required for the device, and review quantization techniques
-    relevant to running LLMs in embedded environments. Finally, you will install the 64-bit Raspberry
-    Pi OS on the Raspberry Pi 5 and run the model, experimenting with prompts and settings to
-    observe behavior on-device. Prerequisites are an Arm Linux machine or Arm cloud instance and
-    a Raspberry Pi 5. Estimated time: 60 minutes.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      You need an Arm Linux machine or an Arm cloud instance, and a Raspberry Pi 5. No other explicit
-      prerequisites are listed.
-  - question: Where do I build the binaries for deployment?
-    answer: >-
-      You build in a Docker container running Raspberry Pi OS on your Arm Linux machine. Inside
-      that container, set up ExecuTorch with an isolated Python environment before compiling the
-      model.
-  - question: Which Raspberry Pi OS should I install on the device?
-    answer: >-
-      Install the 64-bit version of Raspberry Pi OS using the Raspberry Pi documentation. The
-      steps rely on the 64-bit image on the Raspberry Pi 5.
-  - question: Do I need to quantize the Llama 3 model for the Raspberry Pi 5?
-    answer: >-
-      The Learning Path explains quantization and why it is often used to reduce the memory footprint
-      of large models in memory-constrained environments. Follow the guidance in the steps to
-      decide when to apply it.
-  - question: How do I validate that the model is running correctly on the Raspberry Pi 5?
-    answer: >-
-      After deployment, experiment with different prompts and settings on the device as shown
-      in the final section. You should observe the model generating text responses to your prompts.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Annie Tallund
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
index 4966f7b613..4afeecde5a 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
@@ -16,62 +16,9 @@ learning_objectives:
 prerequisites:
     - An Arm computer running Linux. Cloud instances can be used, refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
     - A Raspberry Pi 3 or 4 board
-
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:40:08Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 17721158fe10e97314af364f138c32b7bcf53fdc88fe11fffeb5765f11e26b79
-  summary_generated_at: '2026-06-01T21:52:22Z'
-  summary_source_hash: 17721158fe10e97314af364f138c32b7bcf53fdc88fe11fffeb5765f11e26b79
-  faq_generated_at: '2026-06-02T22:40:08Z'
-  faq_source_hash: 17721158fe10e97314af364f138c32b7bcf53fdc88fe11fffeb5765f11e26b79
-  summary: >-
-    This advanced Learning Path shows how to cut compile time for embedded Linux work by building
-    the MXNet machine learning framework on an Arm Linux server using a Raspberry Pi OS file system,
-    then deploying the result to a Raspberry Pi. You will set up an Arm server (Ubuntu 22.04 was
-    tested), enter the Raspberry Pi OS environment, install build dependencies, and compile MXNet
-    as the pi user. The steps cover exporting the resulting Raspberry Pi image from the server
-    via scp, writing it to an SD card, and testing on a Raspberry Pi 3 or 4. Prerequisites include
-    an Arm computer running Linux (on-premises or cloud) and, optionally, a Raspberry Pi board
-    for testing.
-  faqs:
-  - question: What do I need on the Arm server before starting?
-    answer: >-
-      An Arm Linux server or an Arm cloud instance running Ubuntu is required; the instructions
-      were tested on Ubuntu 22.04. Verify you can use SSH to connect. A Raspberry Pi 3 or 4 is
-      only needed to test the compiled application, and that step is optional if a board is not
-      available.
-  - question: How do I know I am inside the Raspberry Pi OS file system before installing dependencies?
-    answer: >-
-      Proceed when you have a root shell inside the Raspberry Pi OS file system; the prompt appears
-      as #. The steps then run apt to update and install packages in that environment.
-  - question: Which user should compile MXNet, and where should I run the build?
-    answer: >-
-      After installing packages as root, switch to user pi (su pi). Use the pi home directory
-      ($HOME) to build the application.
-  - question: Which packages are required to build MXNet in this path?
-    answer: >-
-      Run apt update/upgrade and install: git, cmake, ninja-build, gfortran, liblapack*, libblas*,
-      libopencv*, libopenblas*, python3-dev, python3-pip, python-dev, and virtualenv. Then install
-      Cython with pip3.
-  - question: How do I transfer the built image and deploy it on a Raspberry Pi?
-    answer: >-
-      From your local machine, use scp with your SSH key and server IP to download the image (for
-      example: scp -i <your-key.pem> ubuntu@<your-ip-addr>:~/2023-02-21-raspios-bullseye-arm64-lite.img
-      .). Write the image to an SD card and insert it into a Raspberry Pi 3 or 4 to test.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md
index d83088c6cf..fdbe493716 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md
@@ -14,59 +14,9 @@ learning_objectives:
 prerequisites:
     - A Raspberry Pi 4 board
     - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:39:04Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 5e5fad1563b67ed38d1cf3399d8e0d62162e76cae8d6848c3be7638f67cd037c
-  summary_generated_at: '2026-06-01T21:51:21Z'
-  summary_source_hash: 5e5fad1563b67ed38d1cf3399d8e0d62162e76cae8d6848c3be7638f67cd037c
-  faq_generated_at: '2026-06-02T22:39:04Z'
-  faq_source_hash: 5e5fad1563b67ed38d1cf3399d8e0d62162e76cae8d6848c3be7638f67cd037c
-  summary: >-
-    This introductory Learning Path walks you through setting up a Raspberry Pi 4 with 64-bit
-    Raspberry Pi OS and an Arm-based cloud instance, then running comparable software examples
-    on both to understand relative performance. You will identify hardware characteristics with
-    uname, build the Linux kernel, and install and run a TensorFlow quickstart using tensorflow-aarch64
-    and tensorflow_io. The path also includes Docker applications, as indicated in the overview.
-    By the end, you will have built and executed multiple examples on the Raspberry Pi 4 and contrasted
-    the results with an Arm cloud server. Prerequisites are a Raspberry Pi 4 and an Arm-based
-    instance from a cloud service provider. Estimated time to complete is about 90 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Raspberry Pi 4 and access to an Arm-based instance from a cloud service provider.
-      Both systems should run 64-bit Linux for the comparisons in this path.
-  - question: Which Raspberry Pi OS should I install and how?
-    answer: >-
-      Install the 64-bit version of Raspberry Pi OS. Use Raspberry Pi Imager on Windows, Linux,
-      or macOS as recommended by the Raspberry Pi documentation.
-  - question: How do I verify that both systems are 64-bit Arm and running Linux?
-    answer: >-
-      Run uname -a on the Raspberry Pi 4 and on the Arm cloud instance. You should see aarch64
-      GNU/Linux in the output; exact kernel details may differ between systems.
-  - question: How do I install and test TensorFlow in this path?
-    answer: >-
-      Install Python and pip with sudo apt install python-is-python3 python3-pip, then run pip
-      install tensorflow-aarch64 tensorflow_io. Validate by running the TensorFlow quickstart
-      code provided in the path.
-  - question: What result should I expect from the Linux kernel compile comparison?
-    answer: >-
-      You will compile the Linux kernel on both platforms to observe relative performance. Recent
-      cloud servers are faster than a Raspberry Pi 4; the goal is to understand the differences,
-      not to achieve a specific metric.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
index cf255da4ff..43e9df2e7d 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
@@ -16,56 +16,9 @@ learning_objectives:
 prerequisites:
     - Raspberry Pi Pico board.
     - Raspberry Pi 3, 4, 400, or 5 as a development computer.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:41:08Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: d9fe3cfc8f7a7092f40786763fc28e371697e4b57dba99b2c9b191dae4273911
-  summary_generated_at: '2026-06-01T21:52:41Z'
-  summary_source_hash: d9fe3cfc8f7a7092f40786763fc28e371697e4b57dba99b2c9b191dae4273911
-  faq_generated_at: '2026-06-02T22:41:08Z'
-  faq_source_hash: d9fe3cfc8f7a7092f40786763fc28e371697e4b57dba99b2c9b191dae4273911
-  summary: >-
-    This introductory path shows how to set up the Raspberry Pi Pico C/C++ SDK on a Raspberry
-    Pi development computer and write bare-metal applications for the Arm Cortex-M0+ on the Pico.
-    You will install the SDK using the pico_setup.sh script from GitHub, build and run a Hello
-    World that prints over USB and blinks the on-board LED with GCC and CMake, measure execution
-    cycles using the SysTick timer by comparing two Fibonacci implementations, and perform interactive
-    debugging over SWD from the command line with gdb. Prerequisites are a Raspberry Pi Pico and
-    a Raspberry Pi 3, 4, 400, or 5 as the host. The estimated time to complete is about 30 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Raspberry Pi Pico board and a Raspberry Pi 3, 4, 400, or 5 to use as the development
-      computer. No other prerequisites are explicitly listed.
-  - question: Which tools does the Pico SDK use to build applications?
-    answer: >-
-      The Pico SDK uses the GCC compiler and CMake to build applications. The installation script
-      is provided as pico_setup.sh in GitHub.
-  - question: How do I know the Hello World example worked?
-    answer: >-
-      The program prints “Hello” over USB and blinks the on-board LED. Seeing the repeated USB
-      printout and the LED toggling confirms a successful build and run.
-  - question: How can I measure the number of cycles a code section takes on the Pico?
-    answer: >-
-      Use the 24-bit SysTick system timer on Cortex-M0+. The example measures cycles while computing
-      the Fibonacci series in two different ways and reports the counts.
-  - question: How can I load and debug without pressing the BOOTSEL button each time?
-    answer: >-
-      Connect the three SWD debug pins on the Raspberry Pi Pico and load programs from the command
-      line. You can then use gdb for interactive debugging over SWD.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md b/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
index dfaf7eb973..1d424b1365 100644
--- a/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
@@ -19,60 +19,9 @@ prerequisites:
     - Basic understanding of Linux kernel development and module programming
     - Arm-based Linux target device (such as a Raspberry Pi, BeagleBone, or similar board) with Secure Shell (SSH) access
     - A host machine that meets [Buildroot system requirements](https://buildroot.org/downloads/manual/manual.html#requirement)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:41:38Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 0b8de63a15d3d77b9c9972103b1317d6c48907875ac625c3d1c1b8db5360879a
-  summary_generated_at: '2026-06-01T21:53:04Z'
-  summary_source_hash: 0b8de63a15d3d77b9c9972103b1317d6c48907875ac625c3d1c1b8db5360879a
-  faq_generated_at: '2026-06-02T22:41:38Z'
-  faq_source_hash: 0b8de63a15d3d77b9c9972103b1317d6c48907875ac625c3d1c1b8db5360879a
-  summary: >-
-    This advanced Learning Path shows how to profile Linux kernel modules on Arm-based systems
-    using Arm Streamline, part of Arm Performance Studio. You will prepare a Buildroot-based environment,
-    implement a simple cache-unfriendly character device as an out-of-tree module, and then integrate
-    the same driver in-tree to profile with the kernel’s vmlinux for symbolized analysis. The
-    path explains Streamline’s sampling workflow and introduces using Statistical Profiling Extension
-    (SPE) for deeper kernel insights. Prerequisites include basic Linux kernel and module development
-    knowledge, an Arm-based Linux target with SSH access, and a host machine that meets Buildroot
-    requirements. Expect to analyze bottlenecks in both out-of-tree and in-tree scenarios in about
-    60 minutes.
-  faqs:
-  - question: What do I need before running the steps on hardware?
-    answer: >-
-      You need an Arm-based Linux target device with SSH access and a host machine that meets
-      the Buildroot system requirements. A basic understanding of Linux kernel development and
-      module programming is also expected.
-  - question: Which system should I use to install Buildroot prerequisites and run the build steps?
-    answer: >-
-      Use an AArch64-based Linux system as your host and run the package installation commands
-      there. The setup step shows updating package lists and installing required dependencies
-      before building.
-  - question: How does the example kernel module create measurable behavior for profiling?
-    answer: >-
-      It traverses a two-dimensional array in column-major order to induce cache misses. This
-      cache-unfriendly pattern helps expose hotspots and memory access inefficiencies in Streamline.
-  - question: What should I add in Streamline to profile an in-tree driver with kernel symbols?
-    answer: >-
-      Add the kernel’s vmlinux file in the capture settings. This enables analysis of function
-      calls, call paths, and specific kernel code sections for the in-tree build.
-  - question: How is the Statistical Profiling Extension (SPE) used in this path?
-    answer: >-
-      SPE is introduced for deeper kernel profiling insights alongside Streamline’s sampling.
-      Use it when available on your Arm-based system to expand the analysis beyond basic metrics.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Yahya Abouelseoud
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md b/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
index a1873356e0..4770b9b24e 100644
--- a/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
@@ -15,60 +15,9 @@ prerequisites:
     - Familiarity with ML concepts
     - Familiarity with C programming on microcontrollers
     - STM32 B-L475E-IOT01A2 board
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:42:04Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: b5714494f00fff407c39e6f2f7bf37de94cde61d7569ba147412fec1b2f537c2
-  summary_generated_at: '2026-06-01T21:53:37Z'
-  summary_source_hash: b5714494f00fff407c39e6f2f7bf37de94cde61d7569ba147412fec1b2f537c2
-  faq_generated_at: '2026-06-02T22:42:04Z'
-  faq_source_hash: b5714494f00fff407c39e6f2f7bf37de94cde61d7569ba147412fec1b2f537c2
-  summary: >-
-    This Learning Path guides you through building a letter recognition neural network in TensorFlow
-    using accelerometer data from an STM32 B-L475E-IOT01A2 board, then deploying it to the device
-    with STM32Cube.AI. You will set up a Python environment with Anaconda, work in a Jupyter notebook
-    to collect data and train a multi-layer perceptron, and create a feature-based model using
-    mean and standard deviation per axis. Finally, you will configure an STM32CubeMX project and
-    run the model on the Arm Cortex-M4–based board in a bare-metal configuration. This advanced
-    path assumes familiarity with ML concepts and C programming on microcontrollers, and access
-    to the specified STM32 board.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      Have the STM32 B-L475E-IOT01A2 board and be comfortable with ML concepts and C programming
-      on microcontrollers. Install Anaconda for the Python environment and download STM32CubeMX;
-      STM32Cube.AI will be used within STM32CubeMX to import the trained model.
-  - question: How should I run the Jupyter notebook steps, and how do I know each cell finished?
-    answer: >-
-      Run the cells in order using the Run button. Jupyter shows In[ ] before execution, In[*]
-      while running, and In[N] when a cell has completed.
-  - question: What data do I train on, and how is it prepared?
-    answer: >-
-      You will use accelerometer data from the STM32 board to recognize letters. The dataset is
-      stored as CSV files in a samples directory and is first used as raw sequences; later you
-      extract features (mean and standard deviation per axis) and retrain.
-  - question: Which model architecture should I define in TensorFlow?
-    answer: >-
-      Define a multi-layer perceptron with three dense layers and dropout using TensorFlow/Keras,
-      as shown in the notebook. Labels are converted to categorical form before training.
-  - question: Which option should I use in STM32CubeMX to target the board and import the model?
-    answer: >-
-      Open STM32CubeMX and use Access to Board Selector to find the B-L475E-IOT01A board and start
-      a new project, then set the project name and location. Under Pinout & Configuration, proceed
-      with setup and use the STM32Cube.AI extension to import the trained ML model.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md b/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md
index 3005a72671..1242047bb7 100644
--- a/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md
@@ -15,56 +15,9 @@ learning_objectives:
 prerequisites:
 - Some familiarity with embedded C programming
 - A machine running Ubuntu Linux
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:43:17Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 8d8f9df559ff1b1570dc98baf640fc2d4c4d225d69f22529e1881b62d4017752
-  summary_generated_at: '2026-06-01T21:54:11Z'
-  summary_source_hash: 8d8f9df559ff1b1570dc98baf640fc2d4c4d225d69f22529e1881b62d4017752
-  faq_generated_at: '2026-06-02T22:43:17Z'
-  faq_source_hash: 8d8f9df559ff1b1570dc98baf640fc2d4c4d225d69f22529e1881b62d4017752
-  summary: >-
-    This introductory Learning Path shows how to build and run the reference Trusted Firmware-M
-    (TF-M) tests and example application on the Corstone-300 Fixed Virtual Platform (FVP). Working
-    in a bare-metal environment for Armv8-M/Armv8.1-M, you use Arm Virtual Hardware FVP to exercise
-    the Secure Processing Environment (SPE) reference implementation aligned with PSA Certified
-    guidelines. The steps assume an Ubuntu 22.04 LTS (Jammy) host and basic familiarity with embedded
-    C. By the end, you will have compiled the supplied TF-M tests and reference example and executed
-    them on the Corstone-300 FVP, providing a practical starting point for secure microcontroller
-    development with TF-M.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      You need a machine running Ubuntu Linux and some familiarity with embedded C programming.
-      No other prerequisites are explicitly listed.
-  - question: Which platform should I use to run the TF-M tests and example?
-    answer: >-
-      Use the Corstone-300 Fixed Virtual Platform (FVP). It is available from the Arm Ecosystem
-      FVP page.
-  - question: Is an RTOS required or is this a bare-metal setup?
-    answer: >-
-      This path targets a bare-metal setup. No operating system is used on the target.
-  - question: Which Ubuntu version is assumed, and what initial setup step should I run?
-    answer: >-
-      The instructions assume Ubuntu 22.04-LTS (jammy). Begin by updating your system package
-      lists with: sudo apt update.
-  - question: What result should I expect after completing the steps, and how long will it take?
-    answer: >-
-      You will build the supplied TF-M tests and reference example and run them on the Corstone-300
-      FVP. The estimated time to complete is about 15 minutes.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md b/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md
index 964696941b..1e8261d96c 100644
--- a/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md
@@ -18,60 +18,9 @@ prerequisites:
   - Familiarity with Python and the PyTorch library
   - Completion of the Learning Path [Introduction to TinyML on Arm using PyTorch and ExecuTorch](/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/)
   - An x86 Linux host machine or VM running Ubuntu 22.04 or later
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:43:51Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 20c500fd5174c9901058676d4619981ee1c8a8cf8e331affea8c0292112651c9
-  summary_generated_at: '2026-06-01T21:54:57Z'
-  summary_source_hash: 20c500fd5174c9901058676d4619981ee1c8a8cf8e331affea8c0292112651c9
-  faq_generated_at: '2026-06-02T22:43:51Z'
-  faq_source_hash: 20c500fd5174c9901058676d4619981ee1c8a8cf8e331affea8c0292112651c9
-  summary: >-
-    This Learning Path walks you through training a small CNN in PyTorch to classify images of
-    the letters R, P, and S into rock, paper, or scissors, exporting the model to an ExecuTorch
-    program (.pte), and running it as a simple interactive mini-game. You then compile and execute
-    the model on the Corstone-320 Fixed Virtual Platform (FVP), completing an end-to-end TinyML
-    workflow for Arm-based edge devices. The path uses synthetic data generation when real data
-    is limited and employs the Ahead-of-Time Arm compiler with delegation to the Ethos-U NPU.
-    Prerequisites include basic ML knowledge, Python/PyTorch familiarity, the prior TinyML Learning
-    Path, and an x86 Ubuntu 22.04+ Linux host.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need basic ML knowledge, familiarity with Python and PyTorch, completion of the “Introduction
-      to TinyML on Arm using PyTorch and ExecuTorch” Learning Path, and an x86 Linux host or VM
-      running Ubuntu 22.04 or later. The path targets the Corstone-320 FVP, so additional Arm
-      hardware is not explicitly required.
-  - question: Where should I create the script and start training?
-    answer: >-
-      Navigate to $HOME/executorch/examples/arm and create rps_tiny.py there. Train and export/play
-      using the provided commands, for example: python rps_tiny.py --epochs 8 --export --play.
-  - question: Do I need a real image dataset to train the model?
-    answer: >-
-      No. The path uses synthetic data generation for training when real data is limited.
-  - question: What artifact should I expect after exporting the model?
-    answer: >-
-      Exporting produces an ExecuTorch program (.pte). You will then compile and build it for
-      the Corstone-320 FVP using the Ahead-of-Time Arm compiler with delegation to the Ethos-U
-      NPU.
-  - question: What should I expect when I run the mini-game or the FVP build?
-    answer: >-
-      The --play option runs an interactive CLI mini-game that classifies the letters R, P, and
-      S as rock, paper, or scissors. The FVP build runs the trained model on a simulated Arm-based
-      edge device to demonstrate on-device inference.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dominica Abena O. Amanfo
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md b/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md
index 817dfa9744..3db1b17e06 100644
--- a/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md
@@ -17,58 +17,9 @@ prerequisites:
     - Familiar with C programming on microcontrollers
     - Comfortable with Windows 
     - NXP LPCXpresso55S69 board
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:45:23Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 6c81f3c9595df1128ca6f4f89446f093826d2242fa789ffa2d37465854be1f8e
-  summary_generated_at: '2026-06-01T21:55:22Z'
-  summary_source_hash: 6c81f3c9595df1128ca6f4f89446f093826d2242fa789ffa2d37465854be1f8e
-  faq_generated_at: '2026-06-02T22:45:23Z'
-  faq_source_hash: 6c81f3c9595df1128ca6f4f89446f093826d2242fa789ffa2d37465854be1f8e
-  summary: >-
-    This introductory path shows how to set up Keil MDK with Arm Compiler for Embedded on Windows
-    and run a bare-metal TrustZone hello world on the NXP LPCXpresso55S69. You will obtain the
-    example using the Keil µVision Pack Installer, which provides two sub-projects (hello_ns and
-    hello_s) representing the non-secure and secure worlds. You will build, run, and start a debug
-    session to examine TrustZone behavior, including security state switching and how non-secure
-    code calls secure functions. Prerequisites include familiarity with C programming on microcontrollers,
-    comfort with Windows, and access to an LPCXpresso55S69 board. The path is designed to be completed
-    in about 20 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      Install Keil MDK and Arm Compiler for Embedded on a Windows machine and connect an NXP LPCXpresso55S69
-      board. You should also be comfortable with C programming on microcontrollers and using Windows.
-  - question: How do I obtain the TrustZone hello world example in Keil μVision?
-    answer: >-
-      Open the Pack Installer, select the LPC55S69 device, and copy the hello_ns and hello_s examples
-      into your workspace. These provide the non-secure and secure sub-projects used in the tutorial.
-  - question: Which project should I open to build and run the example?
-    answer: >-
-      From μVision, choose Project -> Open Project and select the hello_world_s example. This
-      example uses the hello_s (secure) and hello_ns (non-secure) sub-projects.
-  - question: What result should I expect when starting a debug session?
-    answer: >-
-      At reset, the secure startup code runs, and the program counter will be at the start of
-      main() in hello_world_s.c. You can then run or step through the code as directed.
-  - question: How do I explore security state switching and secure function calls?
-    answer: >-
-      Start another debug session and step through the example as described in the path to observe
-      transitions between secure and non-secure states and how secure functions are invoked from
-      the non-secure world.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md b/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
index 32c25f0ce7..78ebcc6ef6 100644
--- a/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
@@ -23,62 +23,9 @@ prerequisites:
     - 18-8 stainless steel socket head screw. 4 per card. [Example part](https://www.mcmaster.com/91292A016/)
     - 18-8 stainless steel hex nut. 4 per card. [Example part](https://www.mcmaster.com/91828A113/)
     - PETG filament. Others can work, but PETG allows some flex without the risk of snapping
-    
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:46:11Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 57e05139cdea1de2f4a4ce239d701f0cef634b6ac11fd437cba47cc071e14098
-  summary_generated_at: '2026-06-01T21:55:43Z'
-  summary_source_hash: 57e05139cdea1de2f4a4ce239d701f0cef634b6ac11fd437cba47cc071e14098
-  faq_generated_at: '2026-06-02T22:46:11Z'
-  faq_source_hash: 57e05139cdea1de2f4a4ce239d701f0cef634b6ac11fd437cba47cc071e14098
-  summary: >-
-    This Learning Path shows you how to 3D print parts and assemble a universal rack mount system
-    for single board computers in a 4U chassis. You will print bay bodies and covers using PETG,
-    cut and prepare 8-32 stainless steel threaded rods, and build chassis bays with nuts, wing
-    nuts, and washers. You then mount SBCs to card plates with screws, standoffs, and hex nuts,
-    and slide the finished assemblies into bay slots. The path lists Fusion 360 as a tool and
-    targets an introductory audience working with Linux-based SBC projects. Expected outcome:
-    a 4U chassis populated with modular bays ready to house multiple SBCs. Prerequisites, including
-    a 3D printer and specific hardware, are explicitly provided.
-  faqs:
-  - question: What do I need before I start printing and assembling the rack?
-    answer: >-
-      You need a 3D printer, PETG filament, a hack saw or chop saw, and a 4U server chassis with
-      the insides removed. Hardware includes 8‑32 stainless threaded rods (cut to 405 mm), #8
-      washers, 8‑32 hex nuts, 8‑32 wing nuts (counts per bay row are listed), and 18‑8 stainless
-      socket head screws and hex nuts for each card.
-  - question: Which filament should I use for the printed parts and why?
-    answer: >-
-      Use PETG. It flexes for parts with squeeze tabs, is non‑toxic so it doesn’t require extra
-      ventilation like ABS, and withstands higher temperatures than PLA.
-  - question: How many printed parts do I need per bay?
-    answer: >-
-      Print bay bodies and bay covers, with the number of each per bay depending on the spacer
-      size you use. Spacers are also required. The exact counts depend on your chosen spacing
-      and are not explicitly listed.
-  - question: How should I prepare and assemble the chassis bays?
-    answer: >-
-      Wash grease off the threaded rods with soap and hot water, then cut each rod to 405 mm.
-      Follow the bay assembly steps to install the nuts, wing nuts, and washers for each bay row.
-  - question: How do I mount an SBC to a card plate and check orientation?
-    answer: >-
-      Insert bolts through the SBC, add standoffs on the back, align the SBC with the appropriate
-      card plate holes, and secure with hex nuts. The design places the SBC’s back edge flush
-      with the grip side of the card plate before you slide the card and plate into a bay slot.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Gabriel Peterson
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md b/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
index acdc2f59d4..6f807733a4 100644
--- a/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
@@ -6,63 +6,9 @@ description: Learn how to debug microcontrollers using µVision with basic run/s
 
 minutes_to_complete: 90
 # Always measured in minutes. Should be an integer, to complete the learning path (not read it).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:47:15Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 27ced3e9f69dbe51e75dcf13999ae1745a994137f31c86d6f17d4de341c7fa2a
-  summary_generated_at: '2026-06-01T21:56:36Z'
-  summary_source_hash: 27ced3e9f69dbe51e75dcf13999ae1745a994137f31c86d6f17d4de341c7fa2a
-  faq_generated_at: '2026-06-02T22:47:15Z'
-  faq_source_hash: 27ced3e9f69dbe51e75dcf13999ae1745a994137f31c86d6f17d4de341c7fa2a
-  summary: >-
-    This advanced Learning Path guides you through debugging Cortex-M software in Arm Keil µVision
-    using a Blinky example on the Corstone-300 Ecosystem FVP. You will build the project, start
-    a debug session, and use run/stop with hardware breakpoints. It then introduces Event Recorder,
-    including printf to the Debug (printf) Viewer, and Serial Wire Viewer for real-time data (note:
-    SWV is not supported in simulation). You will also explore ETM instruction trace on Armv7-M/Armv8-M
-    devices for execution profiling and code coverage analysis, and measure power with ULINKplus
-    using Event Statistics. Prerequisites include familiarity with embedded programming, a Windows
-    machine, an Arm Account, Keil MDK with an active MDK-Community license, and the Corstone-300
-    Ecosystem FVP.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an Arm Account, a Windows machine, Arm Keil MDK with an active MDK-Community license,
-      and the Corstone-300 Ecosystem FVP installed. Some familiarity with embedded programming
-      is assumed. Clone or download the Blinky example project and open Blinky.Debug+AVH.uvprojx
-      in µVision.
-  - question: How can I print debug text without a UART?
-    answer: >-
-      Use the Event Recorder’s printf utility and view output in the Debug (printf) Viewer window.
-      Event Recorder uses CoreSight DAP for data output and requires some system RAM.
-  - question: What should I check if Serial Wire Viewer (SWV) shows no data?
-    answer: >-
-      SWV is not supported in simulation mode. Connect a debug adapter to real target hardware
-      before using SWV.
-  - question: When should I enable ETM Trace, and what results should I expect?
-    answer: >-
-      Enable ETM Trace on Armv7-M/Armv8-M devices that include ETM to capture instruction trace.
-      In µVision, you can review historical execution sequences, perform execution profiling and
-      performance analysis, and generate code coverage. It also helps diagnose issues like pointer
-      problems and illegal instructions or data aborts.
-  - question: How do I measure power with ULINKplus and configure it?
-    answer: >-
-      Use an Arm Keil ULINKplus to add serial-wire debug, CPU core clock measurement, and power
-      measurement to your session. You can create power profiles with Event Statistics and configure
-      ULINKplus using an initialization file (debug script) that runs when debug mode starts.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Christopher Seidl
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md b/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
index e9c063c743..b6d6229b4a 100644
--- a/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
@@ -17,61 +17,9 @@ prerequisites:
     - Install [µVision](/install-guides/mdk/) on your machine.
     - Install [uv2csolution](https://arm-software.github.io/MDK-Toolbox/01_installation/) for the command line flow.
     - The &micro;Vision project must use Arm Compiler 6 as the default toolchain. Arm Compiler 5 is not supported.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:48:13Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 179b0318bbef9cef31ca6bb82de853597a609f61d6868aaaa85cfb40a27a051a
-  summary_generated_at: '2026-06-01T21:57:17Z'
-  summary_source_hash: 179b0318bbef9cef31ca6bb82de853597a609f61d6868aaaa85cfb40a27a051a
-  faq_generated_at: '2026-06-02T22:48:13Z'
-  faq_source_hash: 179b0318bbef9cef31ca6bb82de853597a609f61d6868aaaa85cfb40a27a051a
-  summary: >-
-    This Learning Path shows how to migrate existing µVision uvprojx-based Cortex-M projects to
-    the csolution format required by CMSIS-Toolbox. You will convert projects using three workflows:
-    Keil Studio in VS Code, µVision’s built-in export, and the uv2csolution command-line tool
-    on Windows, Linux, or macOS. The steps highlight what gets generated (for example, .csolution.yaml,
-    .cproject.yaml, and a vcpkg configuration) and how to confirm a successful conversion in the
-    output views. Prerequisites include installed Keil Studio, µVision, and uv2csolution for the
-    CLI flow; the project must use Arm Compiler 6. After conversion, you can use the project with
-    CMSIS-Toolbox or Keil Studio. Estimated time to complete is about 10 minutes.
-  faqs:
-  - question: What do I need installed before running the conversion?
-    answer: >-
-      Install Keil Studio and µVision, and install uv2csolution if you plan to use the command-line
-      flow. The µVision project must use Arm Compiler 6 as the default toolchain; Arm Compiler
-      5 is not supported.
-  - question: How do I start and verify the conversion in Keil Studio?
-    answer: >-
-      In VS Code, open the folder containing the uvprojx, right-click the uvprojx file, and select
-      “Convert µVision project to csolution.” The Output window shows a successful conversion,
-      and the vcpkg configuration file is automatically activated so you will see “Arm Tools”
-      available.
-  - question: What files should I expect after a successful conversion?
-    answer: >-
-      You should see files such as <project>.csolution.yaml, <project>.cproject.yaml, and vcpkg-configuration.json,
-      along with any related support files.
-  - question: How do I export from µVision and confirm it worked?
-    answer: >-
-      Use Project → Export → Save Project to csolution format in µVision. The Build Output window
-      will show a successful conversion, and you can then use the project with CMSIS-Toolbox or
-      Keil Studio.
-  - question: What should I check if my project currently uses Arm Compiler 5?
-    answer: >-
-      Arm Compiler 5 is not supported; set Arm Compiler 6 as the default toolchain in your µVision
-      project before converting.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Christopher Seidl
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md b/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
index 71f4c88b6b..f82edf8a7e 100644
--- a/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
@@ -18,62 +18,9 @@ learning_objectives:
 prerequisites:
     - A basic understanding of the [development tools for Arm Cortex-M](https://developer.arm.com/Tools%20and%20Software/)
     - Command line access to your machine
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:49:26Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 0c3422e1cd571e6abff676c28ec32c2ec69a626a406167f9166e57daa6829252
-  summary_generated_at: '2026-06-01T21:57:53Z'
-  summary_source_hash: 0c3422e1cd571e6abff676c28ec32c2ec69a626a406167f9166e57daa6829252
-  faq_generated_at: '2026-06-02T22:49:26Z'
-  faq_source_hash: 0c3422e1cd571e6abff676c28ec32c2ec69a626a406167f9166e57daa6829252
-  summary: >-
-    Use vcpkg on Linux, Windows, or macOS to create reproducible command-line installations of
-    tools used in Arm Cortex-M development. You will install and initialize vcpkg in each new
-    terminal session, create a vcpkg-configuration.json to ensure consistent, cross-platform tool
-    setup, activate the tools defined by your configuration, and handle license activation for
-    Arm tools using armlm. The path also covers removing vcpkg when you are finished. Prerequisites
-    are a basic understanding of development tools for Arm Cortex-M and command-line access. After
-    completing the steps, you will be able to stand up a consistent tool installation via vcpkg
-    and verify that licensing is active.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a basic understanding of the development tools for Arm Cortex-M and command-line
-      access to your machine. No other explicit prerequisites are listed.
-  - question: Which initialization command should I use on my OS, and when should I run it?
-    answer: >-
-      Run the vcpkg init command in every new Terminal window: Windows (cmd): %USERPROFILE%\.vcpkg\vcpkg-init.cmd,
-      Windows (PowerShell): . ~/.vcpkg/vcpkg-init.ps1, Linux/macOS: . ~/.vcpkg/vcpkg-init. This
-      ensures your shell session is set up to use vcpkg.
-  - question: What is the purpose of vcpkg-configuration.json?
-    answer: >-
-      It ensures a consistent installation of tools across all platforms by selecting the correct
-      binaries for your host OS and architecture. Creating this configuration file is the first
-      step before using the tools.
-  - question: How do I activate the tools and confirm activation worked?
-    answer: >-
-      Use vcpkg-shell activate to activate the tools specified in your vcpkg-configuration.json.
-      You should see a list of artifacts with their Status (for example, "installed") such as
-      Arm distributed Open-CMSIS-Pack CLI tools or Arm Compiler for Embedded. A warning that vcpkg-artifacts
-      is experimental may appear and is shown in the example output.
-  - question: When do I need to activate a license, and how can I verify it?
-    answer: >-
-      Before compiling with Arm Compiler for Embedded, you must install a license. You can activate
-      an MDK-Community license with: armlm activate -product KEMDK-COM0 -server https://mdk-preview.keil.arm.com.
-      Verify the license with armlm inspect, which shows active products in your local cache.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Christopher Seidl
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md b/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md
index 37efc5b417..f2409a9f59 100644
--- a/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md
@@ -16,59 +16,9 @@ learning_objectives:
 prerequisites:
     - Familiarity with basic machine learning concepts
     - A Linux or macOS computer with Python 3 installed
-
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:50:53Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: dcdbc4cecc376ed4c0df9377d13cb4fe792ad7c68acfe0610d75cf41898804ff
-  summary_generated_at: '2026-06-01T21:59:12Z'
-  summary_source_hash: dcdbc4cecc376ed4c0df9377d13cb4fe792ad7c68acfe0610d75cf41898804ff
-  faq_generated_at: '2026-06-02T22:50:53Z'
-  faq_source_hash: dcdbc4cecc376ed4c0df9377d13cb4fe792ad7c68acfe0610d75cf41898804ff
-  summary: >-
-    This introductory Learning Path shows how to evaluate TinyML workloads on Arm virtual hardware
-    before physical boards are available. You will set up an ExecuTorch development environment
-    on Linux or macOS, install and configure the Corstone-320 Fixed Virtual Platform (FVP), and
-    deploy a MobileNet V2 model to exercise the Ethos-U NPU in a virtual system. The steps explain
-    how ExecuTorch uses ahead-of-time compilation and hybrid CPU/NPU execution, then guide you
-    to run the example and visualize execution with the FVP graphical interface. Prerequisites
-    are basic machine learning familiarity and a Linux or macOS host with Python 3. The path is
-    designed to be completed in about 120 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need familiarity with basic machine learning concepts and a Linux or macOS computer
-      with Python 3 installed. The path is introductory and assumes no prior TinyML experience.
-  - question: I’m using macOS—are there extra steps to run the FVP?
-    answer: >-
-      Yes. The path notes additional setup is required on macOS for FVP execution and points to
-      the FVPs-on-Mac GitHub repository for the necessary steps.
-  - question: Where is the example model and how do I run it?
-    answer: >-
-      The MobileNet V2 Python code is located in executorch/examples/models/mobilenet_v2/model.py
-      within your local ExecuTorch repository. You deploy it using the run.sh script with extra
-      parameters as shown in the steps.
-  - question: How do I know the FVP and ExecuTorch setup worked?
-    answer: >-
-      After setup, you should be able to start the Corstone-320 FVP and run an ExecuTorch-compiled
-      model. You can then visualize model execution in the FVP graphical interface.
-  - question: Do I need physical hardware to test Ethos-U NPU performance?
-    answer: >-
-      No. The Corstone-320 FVP simulates an Arm-based embedded system so you can deploy and test
-      TinyML models, including visualization, without any hardware.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Waheed Brown
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md b/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md
index 4f02e263cd..1b042f8dee 100644
--- a/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md
@@ -14,56 +14,9 @@ learning_objectives:
 prerequisites:
     - Some familiarity with embedded Linux.
     - A linux machine running Ubuntu 22.04 with at least 60 GB of disk space.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:52:18Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 3bcfc51edbab56e3c8416f27045a61b069333e6f0cd130e8689b9a4d9fde1dd6
-  summary_generated_at: '2026-06-01T21:59:52Z'
-  summary_source_hash: 3bcfc51edbab56e3c8416f27045a61b069333e6f0cd130e8689b9a4d9fde1dd6
-  faq_generated_at: '2026-06-02T22:52:18Z'
-  faq_source_hash: 3bcfc51edbab56e3c8416f27045a61b069333e6f0cd130e8689b9a4d9fde1dd6
-  summary: >-
-    Learn how to build a minimal Yocto Linux image for a generic 64-bit Arm (Cortex-A class) target
-    and run it under QEMU. Working on a Linux host (Ubuntu 22.04) with at least 60 GB of disk
-    space, you use the Yocto Project—starting from the Poky reference distribution—to configure
-    and produce a bootable image, then launch it on a 64-bit Arm QEMU machine. This introductory
-    path is aimed at developers who want the basics of Yocto for embedded Arm. By the end, you
-    will have built a minimal image and verified it by running it in QEMU. Some familiarity with
-    embedded Linux is expected.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      Use a Linux host running Ubuntu 22.04 with at least 60 GB of disk space. Some familiarity
-      with embedded Linux is also expected.
-  - question: Which Yocto distribution should I use to start the build?
-    answer: >-
-      Poky, the Yocto Project reference distribution, is used as the starting point to build a
-      minimal image. You will work with Yocto recipes as part of this process.
-  - question: Do I need physical Arm hardware to complete this Learning Path?
-    answer: >-
-      No. The image is run on a 64-bit QEMU Arm target, so you can complete the steps without
-      physical Arm hardware.
-  - question: Which target architecture is used when running under QEMU?
-    answer: >-
-      The steps target a generic 64-bit Arm platform (Cortex-A class) and boot the built image
-      under QEMU.
-  - question: What result should I expect after the build, and how do I run it?
-    answer: >-
-      Expect a minimal Yocto Linux image produced by the Yocto build system. You will launch QEMU
-      and boot this image as shown in the steps to validate the build.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md b/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md
index 5d4e5bc326..bf637eeebd 100644
--- a/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md
@@ -19,61 +19,9 @@ prerequisites:
     - A Flexible Printed Circuit (FPC) cable.
     - A USB-C cable.
     - An x86 Linux machine, or a Mac running macOS.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:54:16Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: b0cb917bdcfb601c054e6cac93b2d04aca3ffe84b5a1963288fd7b89dd9bad3b
-  summary_generated_at: '2026-06-01T22:00:17Z'
-  summary_source_hash: b0cb917bdcfb601c054e6cac93b2d04aca3ffe84b5a1963288fd7b89dd9bad3b
-  faq_generated_at: '2026-06-02T22:54:16Z'
-  faq_source_hash: b0cb917bdcfb601c054e6cac93b2d04aca3ffe84b5a1963288fd7b89dd9bad3b
-  summary: >-
-    Build and deploy a YOLO object detection application on the Himax WiseEye2 platform (Arm Cortex-M55
-    with Ethos-U55) using the Seeed Grove Vision AI Module V2. You will prepare a Linux or macOS
-    host, install Python, clone the Himax examples repository, build the Himax SDK to generate
-    a firmware image, and flash the microcontroller using Xmodem. After connecting the OV5647
-    camera via the FPC cable and USB-C, you will run the firmware to view a live camera feed and
-    explore additional models by editing a makefile and selecting a model in the web toolkit.
-    Prerequisites include the Grove Vision AI Module V2, OV5647 camera, FPC and USB-C cables,
-    and an x86 Linux machine or a Mac. Estimated time: 90 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Seeed Grove Vision AI Module V2, an OV5647-62 camera module, a Flexible Printed
-      Circuit (FPC) cable, a USB-C cable, and an x86 Linux machine or a Mac running macOS.
-  - question: Which operating systems are supported, and can I use Windows?
-    answer: >-
-      The path has been validated on Ubuntu 22.04 LTS and macOS. If you use Windows, you can run
-      Ubuntu through Windows Subsystem for Linux 2 (WSL2).
-  - question: How do I clone the Himax project with all required submodules?
-    answer: >-
-      Clone the repository recursively so that subrepositories are included: git clone --recursive
-      https://github.com/HimaxWiseEyePlus/Seeed_Grove_Vision_AI_Module_V2.git. Then change into
-      the cloned directory to proceed with the build steps.
-  - question: How do I install Xmodem for flashing the firmware?
-    answer: >-
-      From the repository root, run: cd $HOME/Seeed_Grove_Vision_AI_Module_V2 and pip install
-      -r xmodem/requirements.txt. Xmodem is used to transfer the compiled firmware image to the
-      microcontroller.
-  - question: How do I select and run different models, such as YOLO object detection?
-    answer: >-
-      Edit the makefile in $HOME/Seeed_Grove_Vision_AI_Module_V2/EPII_CM55M_APP_S/ and set APP_TYPE
-      (for example, tflm_yolov8_od for object detection). Use the corresponding model argument
-      with the --model option in the Xmodem command; after flashing, you will view a live camera
-      feed with the application running.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author:
     - Chaodong Gong
diff --git a/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md b/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md
index e82a925410..ab887e4269 100644
--- a/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md
@@ -15,59 +15,9 @@ learning_objectives:
 prerequisites:
 - Some familiarity with embedded C programming
 - A Linux machine running Ubuntu, or an AWS account to use [Arm Virtual Hardware](https://www.arm.com/products/development-tools/simulation/virtual-hardware)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:56:13Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 89f599ab33721a2d578d4fc39e505b5aed8d930aabac71f22d1ba28bfc4a5cf8
-  summary_generated_at: '2026-06-01T22:00:54Z'
-  summary_source_hash: 89f599ab33721a2d578d4fc39e505b5aed8d930aabac71f22d1ba28bfc4a5cf8
-  faq_generated_at: '2026-06-02T22:56:13Z'
-  faq_source_hash: 89f599ab33721a2d578d4fc39e505b5aed8d930aabac71f22d1ba28bfc4a5cf8
-  summary: >-
-    This Learning Path shows how to build and run Zephyr RTOS applications on the Arm Corstone-300
-    Fixed Virtual Platform (FVP) using Arm Virtual Hardware. You will obtain the Zephyr source,
-    install the Zephyr SDK, build Zephyr sample applications, and execute them on a virtual Corstone-300
-    system targeting Cortex-M. This introductory path is designed for developers getting started
-    with Zephyr on Arm and can be completed in about 30 minutes. Prerequisites are some familiarity
-    with embedded C and either a Linux machine running Ubuntu or an AWS account to use Arm Virtual
-    Hardware. By the end, you will have verified Zephyr builds running on the Corstone-300 FVP.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need some familiarity with embedded C programming and either a Linux machine running
-      Ubuntu or an AWS account to use Arm Virtual Hardware. No other prerequisites are explicitly
-      listed.
-  - question: Do I need physical hardware for this Learning Path?
-    answer: >-
-      No. The applications are run on the Corstone-300 Fixed Virtual Platform using Arm Virtual
-      Hardware, so no physical hardware is required.
-  - question: 'Which environment should I use: local Ubuntu or Arm Virtual Hardware on AWS?'
-    answer: >-
-      Use a local Ubuntu machine if you prefer to run the tools on your own system, or choose
-      an AWS account to access Arm Virtual Hardware in the cloud. The path supports either option
-      as indicated in the prerequisites.
-  - question: What will I build and run in this path?
-    answer: >-
-      You will get the Zephyr source, install the Zephyr SDK, build sample Zephyr applications,
-      and run them on the Corstone-300 FVP.
-  - question: How do I know the application ran correctly on the Corstone-300 FVP?
-    answer: >-
-      You should be able to launch the Corstone-300 FVP and see the sample application run without
-      errors. If it fails, recheck that the Zephyr SDK is installed and the Zephyr source was
-      obtained as shown in the steps.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md b/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md
index b1ca6dd7f3..98aefc5f5b 100644
--- a/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md
@@ -19,60 +19,9 @@ prerequisites:
     - Visual Studio Code
     - A Cortex-M development board
     - Windows 10+ (64-bit), macOS with Homebrew, or Linux (preferably Ubuntu 20.04+)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:57:52Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 808f1c99409f9ca3bb72419eaf950bc097f1c7af6c2dcade6385be97fdbbf713
-  summary_generated_at: '2026-06-01T22:01:28Z'
-  summary_source_hash: 808f1c99409f9ca3bb72419eaf950bc097f1c7af6c2dcade6385be97fdbbf713
-  faq_generated_at: '2026-06-02T22:57:52Z'
-  faq_source_hash: 808f1c99409f9ca3bb72419eaf950bc097f1c7af6c2dcade6385be97fdbbf713
-  summary: >-
-    This introductory path shows how to install and configure the Workbench for Zephyr extension
-    in Visual Studio Code, set up the Zephyr SDK and toolchain, and create, build, and debug Zephyr
-    RTOS applications on Arm Cortex-M boards. You will follow a workflow demonstrated with an
-    NXP FRDM-MCXN947 board, but the same steps apply to any Zephyr-supported Cortex-M target,
-    with board-specific debug runners selected as needed. The path also covers generating memory
-    usage reports to understand ROM and RAM consumption and applying basic optimization techniques.
-    Prerequisites include basic embedded C skills, VS Code, a Cortex-M development board, and
-    a host running Windows 10+ (64-bit), macOS with Homebrew, or Linux (preferably Ubuntu 20.04+).
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need basic familiarity with embedded C, Visual Studio Code, and access to a Cortex-M
-      development board. Use Windows 10+ (64-bit), macOS with Homebrew, or Linux (preferably Ubuntu
-      20.04+) as your host system.
-  - question: How do I know if my Arm Cortex-M board will work for this path?
-    answer: >-
-      The process works for any Zephyr-supported Arm Cortex-M board. The path demonstrates with
-      an NXP FRDM-MCXN947, and you can confirm your board on the Zephyr Supported Boards list.
-  - question: Which debug runner should I use for my board?
-    answer: >-
-      The required runner depends on your board. Follow your board’s Zephyr documentation and
-      the path’s guidance, as you might need to install and select a different debug tool (runner)
-      in Workbench for Zephyr.
-  - question: What result should I expect after I build the sample application in Workbench?
-    answer: >-
-      You should get a successful Zephyr build along with a memory usage report showing ROM and
-      RAM consumption. You can then proceed to live debugging and memory analysis within Workbench.
-  - question: What should I check if the build or debug setup fails?
-    answer: >-
-      Verify that the Workbench for Zephyr extension is installed and that it completed SDK and
-      toolchain setup. Ensure your board is Zephyr-supported and that the appropriate debug runner
-      is configured for your target.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: 
     - Ayoub Bourjilat
diff --git a/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md b/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md
index 9b7976ab0f..229acd3a0d 100644
--- a/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md
+++ b/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md
@@ -17,60 +17,9 @@ learning_objectives:
 prerequisites:
     - A ChromeOS device with the Linux development environment enabled. The Lenovo Chromebook Plus 14 is recommended. 
     - Basic knowledge of the Linux command line
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T22:58:53Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 137e974aee0ccba78e3375a1c8179af392e54a0304cfecdcd53cf4ac5c38917b
-  summary_generated_at: '2026-06-01T22:01:54Z'
-  summary_source_hash: 137e974aee0ccba78e3375a1c8179af392e54a0304cfecdcd53cf4ac5c38917b
-  faq_generated_at: '2026-06-02T22:58:53Z'
-  faq_source_hash: 137e974aee0ccba78e3375a1c8179af392e54a0304cfecdcd53cf4ac5c38917b
-  summary: >-
-    This introductory Learning Path shows how to create and run an Ubuntu 24.04 LXC container
-    on ChromeOS (Crostini) from the Termina shell on an Arm-based Chromebook. You will set up
-    ChromeOS integration for selective folder sharing from the Files app and enable Linux GUI
-    applications through Sommelier, using a minimal desktop environment and a test app to validate.
-    You will also manage the container lifecycle with common LXC commands, including start, stop,
-    exec, list, info, and delete. Prerequisites are a ChromeOS device with the Linux development
-    environment enabled (the Lenovo Chromebook Plus 14 is recommended) and basic Linux command-line
-    familiarity. Estimated time to complete is about 60 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a ChromeOS device with the Linux development environment enabled and basic Linux
-      command-line knowledge. The Lenovo Chromebook Plus 14 is recommended.
-  - question: Where do I run the LXC and setup commands on ChromeOS?
-    answer: >-
-      Run all container management and setup commands in the Termina shell provided by the ChromeOS
-      Linux development environment.
-  - question: How do I start, stop, and access my Ubuntu container, and check its status?
-    answer: >-
-      From Termina, use lxc start u1 to start, lxc stop u1 to stop, and lxc exec u1 -- bash to
-      enter the container shell. Use lxc list to view all containers and lxc info u1 for detailed
-      information such as status and architecture.
-  - question: How do I share folders between ChromeOS and the Ubuntu container?
-    answer: >-
-      In the ChromeOS Files app, right-click a folder and select Share with Linux to make it available
-      to the container. Only folders (not individual files) can be shared, and access is two-way
-      for Linux apps and the command line.
-  - question: How do I enable and test Linux GUI applications from the container?
-    answer: >-
-      Install a minimal desktop environment and configure the display environment variables so
-      applications use Sommelier. You can install a test GUI application (for example, a terminal
-      emulator like terminator) to confirm that windows open in the ChromeOS desktop.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md
index 2989da231c..565261dae4 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md
@@ -18,61 +18,9 @@ prerequisites:
     - Familiarity with Linux command-line tools
     - Experience with Python scripting and virtual environments
     - Basic understanding of reinforcement learning concepts (rewards, policies, episodes)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:00:20Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: eda533c727ea7094202e8784bf1ed2240cfea2573cefc73aca1a86797840778c
-  summary_generated_at: '2026-06-01T22:02:33Z'
-  summary_source_hash: eda533c727ea7094202e8784bf1ed2240cfea2573cefc73aca1a86797840778c
-  faq_generated_at: '2026-06-02T23:00:20Z'
-  faq_source_hash: eda533c727ea7094202e8784bf1ed2240cfea2573cefc73aca1a86797840778c
-  summary: >-
-    This advanced Learning Path shows how to build, configure, and run NVIDIA Isaac Sim and Isaac
-    Lab on an Arm-based NVIDIA DGX Spark system powered by the Grace–Blackwell (GB10) architecture.
-    You will verify the DGX Spark configuration, install required build dependencies, build Isaac
-    Sim, and set up Isaac Lab on top. You will launch and control a sample Cartpole simulation
-    using Python to understand Isaac Sim’s simulation loop. You will then train and evaluate a
-    reinforcement learning policy for the Unitree H1 humanoid robot using Isaac Lab with RSL-RL
-    (PPO). Prerequisites include a DGX Spark with about 50 GB free disk space, Linux command-line
-    skills, experience with Python virtual environments, and a basic understanding of RL concepts.
-    Estimated time to complete is 90 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need access to an NVIDIA DGX Spark system with at least 50 GB of free disk space. Familiarity
-      with Linux command-line tools, Python scripting and virtual environments, and basic RL concepts
-      is expected.
-  - question: How long does installation usually take, and how much storage is required?
-    answer: >-
-      The setup typically takes 15–20 minutes on a DGX Spark system. Plan for approximately 50
-      GB of available disk space.
-  - question: How are Isaac Sim and Isaac Lab arranged in the environment?
-    answer: >-
-      You first build and configure Isaac Sim, then set up Isaac Lab on top of the Isaac Sim environment.
-      The path begins by verifying the DGX Spark configuration and installing required build dependencies.
-  - question: Which simulation do I run first, and how do I confirm it worked?
-    answer: >-
-      You start with the Cartpole environment by launching a pre-built scene in Isaac Sim. Successful
-      setup is indicated by the scene loading and your ability to interact with it programmatically
-      using Python while exploring the simulation loop.
-  - question: Which RL framework and algorithm are used for training the humanoid policy?
-    answer: >-
-      Training uses Isaac Lab’s integration with the RSL-RL library implementing PPO. You configure
-      the task and environment for the Unitree H1 humanoid to walk over rough terrain, then train
-      and evaluate the policy.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author:
     - Johnny Nunez
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md
index 2b8f859c69..ea810e0fce 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md
@@ -19,64 +19,9 @@ prerequisites:
     - Understanding of CUDA programming basics and GPU/CPU compute concepts
     - Basic knowledge of quantized large language models (LLMs) and machine learning inference
     - Experience building software from source using CMake and make
-
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:01:47Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: ada333cc887badfd57815708ef93e172543da74f2c995b46a916817917e92394
-  summary_generated_at: '2026-06-01T22:03:05Z'
-  summary_source_hash: ada333cc887badfd57815708ef93e172543da74f2c995b46a916817917e92394
-  faq_generated_at: '2026-06-02T23:01:47Z'
-  faq_source_hash: ada333cc887badfd57815708ef93e172543da74f2c995b46a916817917e92394
-  summary: >-
-    This Learning Path shows how to build and validate both CUDA-enabled and CPU-only versions
-    of llama.cpp on an Arm-based NVIDIA DGX Spark system with the Grace–Blackwell (GB10) architecture
-    running Linux. You will review GB10 fundamentals, including the Grace CPU with Armv9 Cortex‑X925
-    and Cortex‑A725 cores, verify system readiness (CPU, OS, Blackwell GPU, and CUDA toolkit),
-    compile llama.cpp for GPU and CPU targets, and confirm both builds function on DGX Spark.
-    You then analyze Armv9 SIMD behavior on the Grace CPU using Process Watch, observing instruction
-    usage during quantized LLM inference. Prerequisites include DGX Spark access (15 GB free),
-    Linux CLI skills, CUDA basics, quantized LLM knowledge, and experience building with CMake
-    and make.
-  faqs:
-  - question: What do I need before running the steps on DGX Spark?
-    answer: >-
-      You need access to an NVIDIA DGX Spark system with at least 15 GB of available disk space.
-      Familiarity with Linux and the command line, CUDA programming basics, knowledge of quantized
-      LLMs, and experience building from source with CMake and make are expected.
-  - question: How do I confirm my DGX Spark is ready for building llama.cpp?
-    answer: >-
-      Verify your Grace CPU configuration and operating system, ensure the Blackwell GPU and CUDA
-      drivers are active, and confirm that the CUDA toolkit is installed. The readiness section
-      guides you through these checks before you start building.
-  - question: 'Which llama.cpp build should I use on GB10: GPU or CPU-only?'
-    answer: >-
-      Use the CUDA-enabled GPU build when the Blackwell GPU and a CUDA 13 environment are available
-      for quantized LLM workloads. Use the CPU-only build to run entirely on the Grace CPU, which
-      leverages Armv9 vector capabilities such as SVE2, BFloat16, and I8MM.
-  - question: What result should I expect after completing the builds?
-    answer: >-
-      You will have both a CUDA-enabled and a CPU-only llama.cpp build ready to run on DGX Spark.
-      The Learning Path includes steps to validate that each build functions correctly on the
-      platform.
-  - question: How do I analyze the Armv9 instruction mix during CPU inference?
-    answer: >-
-      Use Process Watch to observe Neon SIMD instruction execution on the Grace CPU. The path
-      also explains why SVE and SVE2 remain inactive under the current kernel configuration during
-      this analysis.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Odin Shen
 
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md
index 6e21cd84df..eaa9e580b9 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md
@@ -15,62 +15,9 @@ learning_objectives:
 
 prerequisites:
     - An NVIDIA DGX Spark system with at least 15 GB of available disk space
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:02:18Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: facf9c504a3caceb62ef898a04a6760e8aafeb7e802e5727cb80d3a7e7e344d0
-  summary_generated_at: '2026-06-01T22:03:59Z'
-  summary_source_hash: facf9c504a3caceb62ef898a04a6760e8aafeb7e802e5727cb80d3a7e7e344d0
-  faq_generated_at: '2026-06-02T23:02:18Z'
-  faq_source_hash: facf9c504a3caceb62ef898a04a6760e8aafeb7e802e5727cb80d3a7e7e344d0
-  summary: >-
-    This advanced Learning Path guides you through building a hybrid Retrieval-Augmented Generation
-    (RAG) pipeline on Arm-based NVIDIA DGX Spark (Grace–Blackwell/GB10). You will set up a Python
-    environment on Linux, prepare the e5-base-v2 embedding model and the Llama 3.1 8B Instruct
-    LLM, load a sample document corpus, and index it with FAISS for vector search on Arm Grace
-    CPUs. You will run GPU-accelerated inference via the llama.cpp REST server on Blackwell GPUs
-    while CPU-managed retrieval orchestrates requests. Finally, you will monitor unified memory
-    behavior and GPU utilization to validate zero-copy data sharing. Prerequisite: an NVIDIA DGX
-    Spark with at least 15 GB free disk space; related llama.cpp background is recommended.
-  faqs:
-  - question: Do I need to complete another Learning Path before starting this one?
-    answer: >-
-      It is recommended to first complete “Unlock quantized LLM performance on Arm-based NVIDIA
-      DGX Spark” to learn about CPU and GPU builds of llama.cpp. That background helps when deploying
-      the RAG solution in this path.
-  - question: What platform and resources are required to follow the steps?
-    answer: >-
-      You need an NVIDIA DGX Spark (Grace–Blackwell/GB10) system running Linux with at least 15
-      GB of available disk space. No other explicit prerequisites are listed.
-  - question: Which models and libraries does the RAG pipeline use?
-    answer: >-
-      The pipeline uses e5-base-v2 for embeddings and Llama 3.1 8B Instruct for generation. It
-      relies on Python, Hugging Face tooling, FAISS for vector search, and the llama.cpp REST
-      server for GPU-accelerated inference.
-  - question: How should I set up the Python environment for this project?
-    answer: >-
-      Create a Python virtual environment and upgrade pip. Then install torch from the PyTorch
-      CPU wheel index along with transformers==4.46.2 and sentence-transformers==2.7 as shown
-      in the steps.
-  - question: How do I verify the pipeline is working and monitor performance?
-    answer: >-
-      After integration, run the RAG model server and issue a query against your document corpus
-      to exercise retrieval and GPU-backed generation. Use the monitoring steps to observe unified
-      memory and GPU utilization from separate terminals and confirm zero-copy data sharing during
-      inference.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Odin Shen
 
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md
index 3b281b1c23..49fefd617d 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md
@@ -17,61 +17,9 @@ learning_objectives:
 prerequisites:
     - An NVIDIA DGX Spark system with at least 15 GB of available disk space
     - A USB microphone for audio input
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:03:30Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 4ccde526ec4dd9fc18672e162e067108c7251161c1da0375a0bb0374a2f3a4ea
-  summary_generated_at: '2026-06-01T22:04:24Z'
-  summary_source_hash: 4ccde526ec4dd9fc18672e162e067108c7251161c1da0375a0bb0374a2f3a4ea
-  faq_generated_at: '2026-06-02T23:03:30Z'
-  faq_source_hash: 4ccde526ec4dd9fc18672e162e067108c7251161c1da0375a0bb0374a2f3a4ea
-  summary: >-
-    This advanced Learning Path guides you through building a private, offline voice chatbot on
-    Arm-based DGX Spark running Linux. You will capture real-time audio from a USB microphone
-    using PyAudio with Voice Activity Detection, transcribe speech locally using faster-whisper
-    on CPU, and generate responses with vLLM for on-device inference. The steps cover installing
-    and validating faster-whisper, constructing a real-time STT pipeline, fine-tuning segmentation
-    parameters, and integrating vLLM to complete the end-to-end system. By the end, you will deploy
-    and run the full pipeline on DGX Spark, with a focus on adjusting segmentation and prompt
-    strategies to balance latency and response quality. Prerequisites include a DGX Spark system
-    with at least 15 GB free disk space and a USB microphone, using Docker and Python.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an NVIDIA DGX Spark system with at least 15 GB of available disk space, a USB microphone,
-      and a Linux environment. The path uses Python and Docker.
-  - question: Which components run on CPU versus GPU in this workflow?
-    answer: >-
-      The path builds a real-time speech-to-text pipeline on the CPU using faster-whisper. It
-      then adds vLLM for local language generation, which runs on GPU.
-  - question: How do I verify that faster-whisper is installed correctly?
-    answer: >-
-      The setup step focuses on confirming that faster-whisper can reliably transcribe audio.
-      Run a short recording or sample through the tool and check that you get accurate text output
-      before proceeding.
-  - question: How is audio captured and segmented for transcription?
-    answer: >-
-      You capture real-time audio with PyAudio and apply VAD-based segmentation and smart turn
-      detection. The path evolves from a simple 10-second recorder to a multithreaded, VAD-enhanced
-      STT engine.
-  - question: What result should I expect when the full pipeline is running?
-    answer: >-
-      Speaking into the microphone yields transcribed text from faster-whisper, and vLLM generates
-      a local response. You can fine-tune segmentation and prompt strategies to improve latency
-      and response quality.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Odin Shen
 
diff --git a/content/learning-paths/laptops-and-desktops/docker-models/_index.md b/content/learning-paths/laptops-and-desktops/docker-models/_index.md
index 33e4a92582..14b3c24931 100644
--- a/content/learning-paths/laptops-and-desktops/docker-models/_index.md
+++ b/content/learning-paths/laptops-and-desktops/docker-models/_index.md
@@ -15,58 +15,9 @@ prerequisites:
     - Docker Desktop (version 4.40 or later) installed on a system with at least 16GB of RAM (recommended).
     - Basic understanding of Docker CLI and concepts.
     - Familiarity with LLM concepts.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:04:02Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: eae0a23635e7a025e1a73baaf5ccbd01f2c031ec76725c68893ca02190e36deb
-  summary_generated_at: '2026-06-01T22:04:54Z'
-  summary_source_hash: eae0a23635e7a025e1a73baaf5ccbd01f2c031ec76725c68893ca02190e36deb
-  faq_generated_at: '2026-06-02T23:04:02Z'
-  faq_source_hash: eae0a23635e7a025e1a73baaf5ccbd01f2c031ec76725c68893ca02190e36deb
-  summary: >-
-    This introductory path shows how to run pre-trained large language models locally on Windows
-    or macOS using Docker Model Runner, an official Docker extension that leverages llama.cpp
-    without requiring you to install AI frameworks. You will start local LLM inference and then
-    use Docker Compose to deploy a simple containerized AI chat application with a Flask frontend
-    and a Model Runner backend. The provided example can interact with local models such as Llama
-    3.2 or Gemma 3. Prerequisites are Docker Desktop 4.40+ (16GB RAM recommended), basic Docker
-    CLI knowledge, and familiarity with LLM concepts. The workflow is designed to run across environments,
-    including Arm-based systems, and takes about 45 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need Docker Desktop version 4.40 or later on Windows or macOS, a system with at least
-      16GB of RAM recommended, basic understanding of Docker CLI and concepts, and familiarity
-      with LLM concepts.
-  - question: Do I need to install any LLM frameworks or toolchains locally?
-    answer: >-
-      No. Docker Model Runner uses llama.cpp under the hood, so you do not need to download, build,
-      or install any LLM frameworks.
-  - question: Will this work on Arm-based systems?
-    answer: >-
-      Yes. Docker Model Runner is designed to run models across different environments, including
-      Arm-based systems; the steps target Windows or macOS with Docker Desktop.
-  - question: Which models can I try with the example chat app?
-    answer: >-
-      The example supports interacting with local AI models such as Llama 3.2 or Gemma 3.
-  - question: What result should I expect after deploying with Docker Compose?
-    answer: >-
-      You will run a simple web-based AI chat application where a Flask frontend communicates
-      with a Docker Model Runner backend. You should be able to enter prompts in the web interface
-      and receive model-generated responses.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/laptops-and-desktops/electron/_index.md b/content/learning-paths/laptops-and-desktops/electron/_index.md
index ef3f5a5286..b7deb2033c 100644
--- a/content/learning-paths/laptops-and-desktops/electron/_index.md
+++ b/content/learning-paths/laptops-and-desktops/electron/_index.md
@@ -15,56 +15,9 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - Node.js for Arm64. You can find the [Node.js installer](https://nodejs.org/dist/v20.10.0/node-v20.10.0-arm64.msi).
     - Any code editor; we recommend using [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:04:46Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 8491a5e83e9e6436721f6078085e9b367121d19fdf228634dba859b3e1a0802a
-  summary_generated_at: '2026-06-01T22:05:20Z'
-  summary_source_hash: 8491a5e83e9e6436721f6078085e9b367121d19fdf228634dba859b3e1a0802a
-  faq_generated_at: '2026-06-02T23:04:46Z'
-  faq_source_hash: 8491a5e83e9e6436721f6078085e9b367121d19fdf228634dba859b3e1a0802a
-  summary: >-
-    This Learning Path shows how to develop a simple Electron desktop application on Windows on
-    Arm (Arm64) and build it for multiple architectures. You will set up a Windows on Arm device
-    or virtual machine with Node.js for Arm64 and a code editor, create an Electron app using
-    web technologies, and configure cross-platform builds. The steps introduce Electron Builder
-    and the required changes to package.json so you can produce builds targeting Arm64 and x64.
-    Designed for an introductory audience, the path takes about 30 minutes and provides a practical
-    workflow for getting an Electron app running on Windows on Arm and preparing multi-architecture
-    outputs.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      Have a Windows on Arm computer (for example, a Lenovo ThinkPad X13s running Windows 11)
-      or a Windows on Arm virtual machine, Node.js for Arm64 installed, and a code editor. Visual
-      Studio Code for Arm64 is recommended.
-  - question: How long should I plan to spend on this Learning Path?
-    answer: >-
-      The estimated time to complete is 30 minutes.
-  - question: How do I add Electron Builder to my project?
-    answer: >-
-      From your project folder, run: npm install electron-builder --save-dev. The console output
-      will be similar to the example shown in the steps and may include npm audit messages.
-  - question: Where do I configure the project for cross-platform builds?
-    answer: >-
-      Modify the package.json file in your project folder as shown in the Learning Path to enable
-      building for multiple architectures.
-  - question: Which architectures will the final build target?
-    answer: >-
-      The build is configured to run on both Arm64 and x64.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md b/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md
index 7b6cd13fd5..6134a80c61 100644
--- a/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md
+++ b/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md
@@ -15,58 +15,9 @@ learning_objectives:
 prerequisites: 
     - A GitHub account. 
     - Familiarity with GitHub Actions.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:06:25Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 7e108d774eb0fd0b2b72fa8b5d65e1efec0ff4ecf3d80d4e511e82cdf3862284
-  summary_generated_at: '2026-06-01T22:05:42Z'
-  summary_source_hash: 7e108d774eb0fd0b2b72fa8b5d65e1efec0ff4ecf3d80d4e511e82cdf3862284
-  faq_generated_at: '2026-06-02T23:06:25Z'
-  faq_source_hash: 7e108d774eb0fd0b2b72fa8b5d65e1efec0ff4ecf3d80d4e511e82cdf3862284
-  summary: >-
-    This introductory Learning Path shows how to automate Windows application builds on Arm architecture
-    using GitHub Arm-hosted runners and GitHub Actions. You will learn what Arm-hosted Windows
-    runners are, how to target them in your workflows, and how to automate builds for a sample
-    rotating 3D cube application that is also used in the Optimize Windows applications using
-    Arm Performance Libraries Learning Path. The steps emphasize running CI on Arm hardware without
-    operating your own infrastructure and introduce options for configuring a larger runner if
-    required. Prerequisites are a GitHub account and familiarity with GitHub Actions. Estimated
-    time to complete is about 20 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a GitHub account and familiarity with GitHub Actions. No other explicit prerequisites
-      are listed.
-  - question: How do I target a GitHub Arm-hosted Windows runner in my workflow?
-    answer: >-
-      Configure your GitHub Actions workflow to run on the Arm-hosted Windows runner as described
-      in the path steps. The workflow will then execute on Arm architecture without additional
-      infrastructure.
-  - question: Do I need to provide my own server or a self-hosted runner?
-    answer: >-
-      No. An Arm-hosted runner is managed by GitHub, so you do not need to provide or manage a
-      server to run your Actions workflows.
-  - question: Which application is used as the example, and where are the detailed build instructions?
-    answer: >-
-      The example is a rotating 3D cube application used in the “Optimize Windows applications
-      using Arm Performance Libraries” Learning Path. This path provides a basic overview; see
-      the referenced Learning Path for detailed build instructions.
-  - question: Can I configure a larger runner if my build needs more resources?
-    answer: >-
-      Yes. The introduction covers how to configure your own larger runner.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: 
     - Pareena Verma
diff --git a/content/learning-paths/laptops-and-desktops/hyper-v/_index.md b/content/learning-paths/laptops-and-desktops/hyper-v/_index.md
index 5871d66c94..9e19f16d73 100644
--- a/content/learning-paths/laptops-and-desktops/hyper-v/_index.md
+++ b/content/learning-paths/laptops-and-desktops/hyper-v/_index.md
@@ -12,56 +12,9 @@ learning_objectives:
 
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 with [Hyper-V](/install-guides/hyper-v/) installed.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:07:08Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 829130636ec6969f791826ef731b38f7bb87c025d910218822a113ecdef62306
-  summary_generated_at: '2026-06-01T22:06:03Z'
-  summary_source_hash: 829130636ec6969f791826ef731b38f7bb87c025d910218822a113ecdef62306
-  faq_generated_at: '2026-06-02T23:07:08Z'
-  faq_source_hash: 829130636ec6969f791826ef731b38f7bb87c025d910218822a113ecdef62306
-  summary: >-
-    This introductory Learning Path shows how to create and manage Arm-based Linux virtual machines
-    using Hyper-V on Windows on Arm devices. Working on Windows 11 version 22H2 or newer with
-    Hyper-V installed, you will use an Ubuntu 24.04 ISO image for Arm as the example Linux distribution,
-    with guidance that can be applied to other distributions. The steps call out a key requirement
-    specific to Windows on Arm: do not use Hyper-V Quick Create. By the end, you will have created
-    a Linux virtual machine in Hyper-V on your Windows on Arm computer (for example, a Lenovo
-    Thinkpad X13s). Estimated time to complete is about 60 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Windows on Arm computer with Hyper-V installed and Windows 11 version 22H2 or
-      newer. A device such as the Lenovo Thinkpad X13s meets the requirement.
-  - question: Which Ubuntu image should I download for this setup?
-    answer: >-
-      Download the Ubuntu 24.04 ISO file for Arm. Make sure you select the Arm build, not an x86
-      image.
-  - question: Can I use Hyper-V Quick Create on Windows on Arm?
-    answer: >-
-      No. Do not use Quick Create with Windows on Arm devices; follow the manual creation steps
-      described in the path.
-  - question: How do I proceed if I want a different Linux distribution?
-    answer: >-
-      Use the same process shown for Ubuntu and obtain the Arm ISO for your chosen distribution.
-      The instructions indicate you can follow the Ubuntu steps for other distributions.
-  - question: How long will this take and what result should I expect?
-    answer: >-
-      Plan for about 60 minutes. You will create an Arm-based Linux virtual machine running under
-      Hyper-V on your Windows on Arm device.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/laptops-and-desktops/intro/_index.md b/content/learning-paths/laptops-and-desktops/intro/_index.md
index 239038956f..f254e45716 100644
--- a/content/learning-paths/laptops-and-desktops/intro/_index.md
+++ b/content/learning-paths/laptops-and-desktops/intro/_index.md
@@ -13,58 +13,9 @@ learning_objectives:
 
 prerequisites:
     - Nothing
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:07:34Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 5a5e4437a7e71182ede45ee091ae49117446fd1c34b02be92df75a41f4fd1f0d
-  summary_generated_at: '2026-06-01T22:06:27Z'
-  summary_source_hash: 5a5e4437a7e71182ede45ee091ae49117446fd1c34b02be92df75a41f4fd1f0d
-  faq_generated_at: '2026-06-02T23:07:34Z'
-  faq_source_hash: 5a5e4437a7e71182ede45ee091ae49117446fd1c34b02be92df75a41f4fd1f0d
-  summary: >-
-    This introductory Learning Path explains where Arm architecture is used in modern laptops
-    and desktops and helps you identify hardware suitable for software development. You will review
-    platform choices across Windows, Linux, and ChromeOS, see which companies supply Arm processors
-    for client systems (Qualcomm, MediaTek, and Rockchip), and explore example Arm-based Chromebooks
-    such as the Lenovo Chromebook Plus 14 with the MediaTek Kompanio Ultra and the Lenovo Duet
-    Gen 9. The path also highlights why using the same Arm architecture locally and on servers
-    or cloud instances can simplify build and test workflows. No explicit prerequisites are listed,
-    and the material is designed for developers new to Arm on laptops and desktops, taking about
-    10 minutes to complete.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      No explicit prerequisites are listed. You can start immediately with the provided guidance.
-  - question: Which operating systems are covered for Arm laptops and desktops?
-    answer: >-
-      Windows, Linux, and ChromeOS are covered. The path points to Arm-based options available
-      on each of these platforms.
-  - question: Which processor vendors are mentioned for Arm-based laptops and desktops?
-    answer: >-
-      Qualcomm, MediaTek, and Rockchip are mentioned as creating processors for laptops and desktops.
-  - question: What Chromebook models are highlighted as Arm-based options?
-    answer: >-
-      The Lenovo Chromebook Plus 14 with the MediaTek Kompanio Ultra is highlighted, along with
-      the Lenovo Duet Gen 9. These are presented as examples of Chromebooks suitable for software
-      development.
-  - question: How does this path help me align my local machine with my server or cloud architecture?
-    answer: >-
-      The background explains that adopting the same architecture locally as in servers and cloud
-      can simplify building and testing. The path then points you to Arm-based laptop and desktop
-      options across common operating systems.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md b/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md
index db3f1424b3..5b38cbcac7 100644
--- a/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md
+++ b/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md
@@ -16,59 +16,9 @@ prerequisites:
 - A Mac with Apple Silicon (M4 generation or newer)
 - Xcode command line tools installed
 - Basic familiarity with using the Terminal and command-line tools
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:07:54Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 3e93048b46c24514a89ccc2f277b53111a419c049b53662e1461be38a22e83f7
-  summary_generated_at: '2026-06-01T22:06:52Z'
-  summary_source_hash: 3e93048b46c24514a89ccc2f277b53111a419c049b53662e1461be38a22e83f7
-  faq_generated_at: '2026-06-02T23:07:54Z'
-  faq_source_hash: 3e93048b46c24514a89ccc2f277b53111a419c049b53662e1461be38a22e83f7
-  summary: >-
-    This introductory path shows how to download, build, and test Arm KleidiCV on macOS using
-    an Apple Silicon Mac (M4 generation or newer). You will compile the library, run its API tests,
-    and verify Scalable Matrix Extensions (SME) backend support, including checking for increased
-    SME performance where available. KleidiCV provides optimized implementations for Arm Neon,
-    SVE2, and SME2 and automatically selects the fastest path for your hardware, so you do not
-    need to change existing CV code. Prerequisites are Xcode command line tools and basic Terminal
-    familiarity. In about 30 minutes, you will confirm a working KleidiCV build and execute example
-    tests on macOS.
-  faqs:
-  - question: What do I need before running the build steps?
-    answer: >-
-      You need a Mac with Apple Silicon (M4 generation or newer), Xcode command line tools installed,
-      and basic familiarity with using the Terminal and command-line tools. No other prerequisites
-      are explicitly listed.
-  - question: How do I run the KleidiCV API test and what result should I expect?
-    answer: >-
-      Run the API test binary at ./build-kleidicv-benchmark-SME/test/api/kleidicv-api-test. The
-      output shows the number of tests run and their results, with lines similar to “Vector length
-      is set to 16 bytes,” a seed value, and test progress markers.
-  - question: How do I verify that the SME backend is enabled and see its impact?
-    answer: >-
-      Follow the steps to enable SME and then run the tests to confirm SME backend support. The
-      Learning Path guides you to verify increased SME performance after enabling SME.
-  - question: Do I need to change my code to use Neon, SVE2, or SME2 with KleidiCV?
-    answer: >-
-      No. KleidiCV automatically detects your hardware and selects the fastest available implementation,
-      so you do not need to adjust your application code.
-  - question: Do I need a specific computer vision framework to complete this path?
-    answer: >-
-      No. You can use KleidiCV with any CV framework, and this path focuses on building and testing
-      KleidiCV itself. The steps also include running KleidiCV and OpenCV tests.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jett Zhou
 
diff --git a/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md b/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md
index 2b804ab3c4..d938895a44 100644
--- a/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md
+++ b/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md
@@ -13,56 +13,9 @@ learning_objectives:
 
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:09:11Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 631134875aac73e168b60b86d1ca7b4e898196f98cb2825a4238f62129d2d862
-  summary_generated_at: '2026-06-01T22:07:27Z'
-  summary_source_hash: 631134875aac73e168b60b86d1ca7b4e898196f98cb2825a4238f62129d2d862
-  faq_generated_at: '2026-06-02T23:09:11Z'
-  faq_source_hash: 631134875aac73e168b60b86d1ca7b4e898196f98cb2825a4238f62129d2d862
-  summary: >-
-    This introductory Learning Path shows how to configure the native LLVM toolchain in Visual
-    Studio to compile a Windows on Arm application, using the open-source PuTTY project as the
-    example. You will set up Visual Studio 2022 or later with LLVM support, install the required
-    32-bit x86 Strawberry Perl package, and then build PuTTY with Clang for Windows on Arm. The
-    path targets developers working on a Windows on Arm computer or a Windows on Arm virtual machine
-    and is designed to be completed in about 60 minutes. By the end, you will have compiled PuTTY
-    natively for Windows on Arm using the LLVM toolchain integrated with Visual Studio.
-  faqs:
-  - question: Do I need Arm hardware, or can I use a virtual machine?
-    answer: >-
-      You can use either. The prerequisites list a Windows on Arm computer such as the Lenovo
-      ThinkPad X13s running Windows 11 or a Windows on Arm virtual machine.
-  - question: Which version of Visual Studio and components are required?
-    answer: >-
-      Use Visual Studio 2022 or higher and install LLVM support in Visual Studio. The steps assume
-      LLVM is available through the Visual Studio installer.
-  - question: Which Strawberry Perl package should I install on Windows on Arm?
-    answer: >-
-      Install the 32-bit x86 version of Strawberry Perl. There is currently no Arm version available.
-  - question: Which compiler and build system are used to compile PuTTY?
-    answer: >-
-      The path uses Clang from the LLVM toolchain within Visual Studio to build a CMake application.
-      The example application is PuTTY.
-  - question: What result should I expect after the build completes?
-    answer: >-
-      You should have a successful build of the PuTTY application for Windows on Arm using the
-      native LLVM toolchain. A completed build produces PuTTY artifacts in your configured build
-      output.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md b/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md
index c64ce37c64..8763d713bf 100644
--- a/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md
+++ b/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md
@@ -15,62 +15,9 @@ prerequisites:
 - A Linux computer.
 - Basic knowledge of how MTE works. Refer to the [Learn about Memory Tagging Extension Learning Path](/learning-paths/mobile-graphics-and-gaming/mte/)
 - Knowledge of how a dynamic memory allocator can be implemented. Refer to [Write a Dynamic Memory Allocator Learning Path](/learning-paths/cross-platform/dynamic-memory-allocator/).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:09:52Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 87d4afe4ce7f0cef113cd61fd712fde073cca0eaafbe86a2066b76a117328d11
-  summary_generated_at: '2026-06-01T22:08:16Z'
-  summary_source_hash: 87d4afe4ce7f0cef113cd61fd712fde073cca0eaafbe86a2066b76a117328d11
-  faq_generated_at: '2026-06-02T23:09:52Z'
-  faq_source_hash: 87d4afe4ce7f0cef113cd61fd712fde073cca0eaafbe86a2066b76a117328d11
-  summary: >-
-    This advanced Learning Path shows how to add Arm Memory Tagging Extension (MTE) to a C dynamic
-    memory allocator on Linux. Using the provided project (CMakeLists.txt, heap.c/.h, mte_utils.c/.h,
-    and main.c), you will enable tagged addressing for the process, request memory with tag storage,
-    and implement tagging in allocator operations. The steps focus on MTE-specific changes and
-    include runnable examples that illustrate how tag checks catch common allocation and use errors.
-    It targets developers who already understand MTE and dynamic memory allocators. Estimated
-    time to complete is 120 minutes. The outcome is a working demo allocator that applies MTE
-    for learning and experimentation.
-  faqs:
-  - question: What do I need before running the code in this Learning Path?
-    answer: >-
-      You need a Linux computer, basic knowledge of how MTE works, and familiarity with how a
-      dynamic memory allocator can be implemented. The referenced Learning Paths on MTE and writing
-      a dynamic memory allocator provide the necessary background.
-  - question: Which source files contain the allocator and MTE-specific logic?
-    answer: >-
-      The project includes CMakeLists.txt, heap.c and heap.h for the allocator, mte_utils.c and
-      mte_utils.h for tag handling helpers, and main.c for the demo application. Review these
-      files to see how tagging is integrated into allocation and use sites.
-  - question: How is MTE enabled and memory with tag storage requested in the allocator?
-    answer: >-
-      Memory with tag storage is not allocated by the kernel by default, so the application must
-      request it. The heap does this in simple_heap_init using prctl(PR_SET_TAGGED_ADDR_CTRL,
-      PR_TAGGED_ADDR_ENABLE | PR_MTE_TCF_SYNC | (0xfffe << PR_MTE_TAG_SHIFT), ...). The provided
-      code shows the exact initialization used.
-  - question: How do I exercise the examples and what result should I expect?
-    answer: >-
-      The demo starts in main.c, where each example exploit is a function you can call from main.
-      When a tagged pointer accesses memory with a mismatched allocation tag, MTE raises an exception,
-      demonstrating how common mistakes are caught.
-  - question: Is the allocator implementation intended for production use?
-    answer: >-
-      No. It is a demo that illustrates concepts and does not make optimal use of MTE from a security
-      or performance perspective. Any production code should be rigorously tested.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: David Spickett
 
diff --git a/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md b/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
index fbc3f8790b..6fa5cc5133 100644
--- a/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
+++ b/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
@@ -15,59 +15,9 @@ learning_objectives:
 prerequisites:
     - A Pinebook Pro laptop
     - A microSD card (8GB or greater; class 10 or faster)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:10:51Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: e1befed4cafab0eaee29a31c2f259a6a90a14d9f8230c570b6c10a9840b761d5
-  summary_generated_at: '2026-06-01T22:08:50Z'
-  summary_source_hash: e1befed4cafab0eaee29a31c2f259a6a90a14d9f8230c570b6c10a9840b761d5
-  faq_generated_at: '2026-06-02T23:10:51Z'
-  faq_source_hash: e1befed4cafab0eaee29a31c2f259a6a90a14d9f8230c570b6c10a9840b761d5
-  summary: >-
-    This advanced Learning Path shows how to install and configure Arch Linux for Arm on a Pinebook
-    Pro, then set up the i3 window manager and optionally configure Neovim for development. You
-    will prepare a bootable microSD card from a second computer (instructions target Linux), install
-    Arch Linux on the Pinebook Pro, and perform user-level i3 configuration, including practical
-    tweaks like setting display brightness. An optional section demonstrates a Neovim-based editing
-    workflow. The focus is turning the Pinebook Pro into an Arm Linux development machine. Prerequisites
-    are a Pinebook Pro and a class‑10 or faster microSD card (8GB or larger); no other explicit
-    prerequisites are listed.
-  faqs:
-  - question: Do I need a second computer to prepare the microSD card, and which OS is covered?
-    answer: >-
-      Yes. You will write the Arch Linux image to the microSD card from a second computer, and
-      the instructions are written for Linux. You can use macOS, but the partitioning steps differ
-      and are not included here.
-  - question: What hardware do I need before starting?
-    answer: >-
-      You need a Pinebook Pro laptop and a microSD card that is at least 8GB and class 10 or faster.
-      These are required to install Arch Linux for Arm.
-  - question: Which account should I use when installing and running the i3 window manager?
-    answer: >-
-      Use your created user account, not root. The instructions use sudo for package installation,
-      and you will run i3 from your user account.
-  - question: How do I set the Pinebook Pro display to maximum brightness under i3?
-    answer: >-
-      Run the command: echo 4095 > /sys/class/backlight/edp-backlight/brightness. This sets the
-      laptop display to maximum brightness.
-  - question: Is the Neovim setup required, and what should I expect the first time I open it?
-    answer: >-
-      The Neovim section is optional. On first launch it looks much like vim, but it is more customizable
-      with Lua extensibility and still supports Vimscript; the majority of vim plugins work as
-      expected.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Gabriel Peterson
 
diff --git a/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md b/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
index 7ed4ee7090..5c2b12cf70 100644
--- a/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
+++ b/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
@@ -16,57 +16,9 @@ learning_objectives:
 prerequisites:
     - Hugging Face account and access token
     - NVIDIA DGX Spark workstation
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:11:51Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: aa2a78baf3e52172e37506c3f75254968d775b4eb516f9696a0a6998aba50e97
-  summary_generated_at: '2026-06-01T22:09:20Z'
-  summary_source_hash: aa2a78baf3e52172e37506c3f75254968d775b4eb516f9696a0a6998aba50e97
-  faq_generated_at: '2026-06-02T23:11:51Z'
-  faq_source_hash: aa2a78baf3e52172e37506c3f75254968d775b4eb516f9696a0a6998aba50e97
-  summary: >-
-    Learn how to fine-tune the Llama 3.2 3B language model on domain data using PyTorch and Hugging
-    Face on an NVIDIA DGX Spark with an Arm-based Grace CPU and a Blackwell GPU. You will configure
-    Docker on Linux, pull a pre-built PyTorch container, prepare a JSONL dataset from Raspberry
-    Pi datasheet content for supervised fine-tuning, and run a provided PyTorch script to train
-    the model. Finally, you will serve the base and fine-tuned models using a vLLM container to
-    compare responses and confirm factual accuracy improvements. Prerequisites are a Hugging Face
-    account with access token and access to a DGX Spark workstation.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Hugging Face account with an access token and an NVIDIA DGX Spark workstation.
-      The Learning Path targets a Linux environment.
-  - question: Do I need to install Docker on DGX Spark?
-    answer: >-
-      No. Docker is pre-installed on the DGX Spark, and you only need to configure permissions
-      as described in the setup step.
-  - question: Which model and dataset format are used for fine-tuning?
-    answer: >-
-      You will fine-tune Llama 3.2 3B. The workflow expects a custom JSONL dataset prepared for
-      supervised fine-tuning.
-  - question: Which containers are used for training and serving?
-    answer: >-
-      You pull a pre-built PyTorch container to run fine-tuning. For inference and comparison,
-      you use an NVIDIA-provided vLLM container.
-  - question: How do I know the fine-tuned model improved factual accuracy?
-    answer: >-
-      Serve both the base and fine-tuned models with vLLM and compare answers to domain questions.
-      For example, after fine-tuning on Raspberry Pi datasheets, the model should answer that
-      the RP2350 supports up to 150 MHz instead of the incorrect 1.8 GHz.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Michael Hall
 
diff --git a/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md b/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
index 633da793d3..a835e93901 100644
--- a/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
+++ b/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
@@ -16,57 +16,9 @@ prerequisites:
     - An Arm64-powered machine, either virtual or physical. This Learning Path demonstration uses an Arm64-powered VM with Ubuntu 22.04.
     - A DockerHub account. You can [set up a free DockerHub account](https://hub.docker.com/signup).
     - A GitHub account. You can [sign up for GitHub](https://github.com/signup).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:12:53Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: a851b2f81b6aac54aef84acf7537a1cc6b99f66ce31ffd26631d6a966401e4ed
-  summary_generated_at: '2026-06-01T22:09:45Z'
-  summary_source_hash: a851b2f81b6aac54aef84acf7537a1cc6b99f66ce31ffd26631d6a966401e4ed
-  faq_generated_at: '2026-06-02T23:12:53Z'
-  faq_source_hash: a851b2f81b6aac54aef84acf7537a1cc6b99f66ce31ffd26631d6a966401e4ed
-  summary: >-
-    This introductory Learning Path shows how to build a GitHub Actions CI/CD pipeline that uses
-    a self-hosted Arm64 runner to compile a .NET application and publish an Arm64 Docker image
-    to DockerHub. You will create a private DockerHub repository, import a starter GitHub repository,
-    configure repository secrets for Docker credentials, and prepare an Arm64 Ubuntu 22.04 runner
-    by installing the .NET SDK and Docker. The workflow builds the container image and pushes
-    it to your DockerHub repository. Prerequisites include an Arm64-powered machine, a GitHub
-    account, and a DockerHub account. The path targets Linux environments and uses .NET and Visual
-    Studio Code.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an Arm64-powered machine (the demonstration uses an Ubuntu 22.04 VM), a DockerHub
-      account, and a GitHub account. No other prerequisites are explicitly listed.
-  - question: Which DockerHub repository settings should I use, and what push command will I see?
-    answer: >-
-      Create a repository named sampleapp and set its visibility to Private. You should see a
-      push command in the form: docker push <YOUR_ACCOUNT_NAME>/sampleapp:tagname.
-  - question: How do I bring the sample application into my GitHub account?
-    answer: >-
-      Use GitHub’s Import repository and provide https://github.com/dawidborycki/arm-lp-ci-cd-net.git
-      as the source URL. Set a repository name (for example, lp-ci-cd-net) and start the import.
-  - question: Which secrets should I add to the GitHub repository?
-    answer: >-
-      Create two secrets that store your DockerHub username and a DockerHub token. These are used
-      by the workflow to authenticate when pushing images.
-  - question: What software must be installed on the self-hosted Arm64 runner?
-    answer: >-
-      Install the .NET SDK and Docker on your Arm64 machine, and keep the OS patched. For Ubuntu
-      22.04, the Learning Path provides Docker installation steps.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/_index.md b/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
index ebaa500f7b..8b47c30af5 100644
--- a/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
@@ -13,58 +13,9 @@ learning_objectives:
 
 prerequisites:
     - A Windows on Arm machine such as the Lenovo Thinkpad X13s, or an [Azure virtual machine](/learning-paths/cross-platform/woa_azure/).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:13:24Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: d24bf30aef690451942789bb66df63b7a3483ecc3cd2c4b3e60ce15fad90cb91
-  summary_generated_at: '2026-06-01T22:10:09Z'
-  summary_source_hash: d24bf30aef690451942789bb66df63b7a3483ecc3cd2c4b3e60ce15fad90cb91
-  faq_generated_at: '2026-06-02T23:13:24Z'
-  faq_source_hash: d24bf30aef690451942789bb66df63b7a3483ecc3cd2c4b3e60ce15fad90cb91
-  summary: >-
-    This Learning Path shows how to build the OpenCV library from source on Windows on Arm and
-    create a small test application using either MSVC or Clang. You will work on a Windows on
-    Arm machine or an Azure virtual machine, install CMake and Git, and, for the MSVC route, use
-    Visual Studio 2022 or higher. The steps clone the OpenCV repository and build version 4.10.0
-    with CMake from the command line. By the end, you will have a working OpenCV build on Windows
-    on Arm and a C++ application linked against it. No additional prerequisites are explicitly
-    listed. The estimated time to complete is about 90 minutes.
-  faqs:
-  - question: What do I need before building OpenCV on Windows on Arm?
-    answer: >-
-      You need a Windows on Arm machine such as the Lenovo Thinkpad X13s, or an Azure virtual
-      machine. Install CMake (tested with 3.28.1) and Git for Windows on Arm. For the MSVC flow,
-      install Visual Studio 2022 or higher; Clang is used in the alternative flow.
-  - question: Which compiler should I use, MSVC or Clang?
-    answer: >-
-      This Learning Path includes separate sections for MSVC and Clang. Choose one compiler and
-      follow the corresponding steps to build OpenCV and the test application.
-  - question: Where do I run the commands to fetch and configure OpenCV?
-    answer: >-
-      Open a Windows PowerShell, clone the OpenCV repository, and check out the 4.10.0 tag. Then
-      use CMake from the command line to run the pre-build configuration.
-  - question: Can I use a newer OpenCV version than 4.10.0?
-    answer: >-
-      The instructions have been tested with OpenCV 4.10.0. You might be able to use a later version,
-      but 4.10.0 is the version verified by this path.
-  - question: What result should I expect after completing the steps?
-    answer: >-
-      You will have a built OpenCV library for Windows on Arm and a test application that uses
-      the library. This provides a working base to start developing OpenCV applications on your
-      device or Azure VM.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Koki Mitsunami
 
diff --git a/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md b/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md
index 0f2df1653c..034f126f09 100644
--- a/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md
@@ -15,60 +15,9 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11
     - A code editor such as [Visual Studio Code for Windows on Arm](https://code.visualstudio.com/docs/?dv=win32arm64user) 
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:14:27Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: fc0d79605640cc8e7a36070a044323d6e278335484949921d0aa4e9cd163d4bd
-  summary_generated_at: '2026-06-01T22:10:37Z'
-  summary_source_hash: fc0d79605640cc8e7a36070a044323d6e278335484949921d0aa4e9cd163d4bd
-  faq_generated_at: '2026-06-02T23:14:27Z'
-  faq_source_hash: fc0d79605640cc8e7a36070a044323d6e278335484949921d0aa4e9cd163d4bd
-  summary: >-
-    Learn how to measure application resource and power usage on Windows on Arm using FFmpeg and
-    PowerShell. You will set up FFmpeg, encode a test video, and run a decoding workload while
-    PowerShell scripts record CPU and memory usage to CSV for later analysis. You will also sample
-    battery status to measure power consumption without external equipment. The workflow includes
-    running the same tests with an x86_64 FFmpeg binary under Windows instruction emulation and
-    an Arm64 native build to compare behavior. Prerequisites are a Windows on Arm device such
-    as a Lenovo Thinkpad X13s running Windows 11 and a code editor such as Visual Studio Code
-    for Windows on Arm. Estimated time: 60 minutes.
-  faqs:
-  - question: What do I need before running the scripts?
-    answer: >-
-      You need a Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 and
-      a code editor such as Visual Studio Code for Windows on Arm. The Learning Path uses FFmpeg
-      and PowerShell.
-  - question: Which FFmpeg binaries should I use for the tests?
-    answer: >-
-      Run the same tests with both the x86_64 binary (using Windows instruction emulation) and
-      the Arm64 native binary. This lets you compare results on the same device.
-  - question: How do I capture CPU and memory usage during decoding, and what output should I
-      expect?
-    answer: >-
-      Use the provided PowerShell script saved as sample_decoding.ps1. It launches the decoding
-      process, periodically records CPU and memory statistics, and writes them to a CSV file.
-  - question: How is power usage measured without extra hardware?
-    answer: >-
-      Use the sample_power.ps1 PowerShell script to sample battery status while the decoding task
-      runs. The script logs readings to a CSV file for analysis.
-  - question: How should I compare results between Arm64 and x86_64 runs?
-    answer: >-
-      Execute identical workloads with each binary and compare the generated CSV files for CPU,
-      memory, and battery metrics. Use these data to benchmark the encoding task and analyze decoding
-      resource usage.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Ruifeng Wang
 
diff --git a/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md b/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
index 9c20570bc9..d174305937 100644
--- a/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
@@ -15,58 +15,9 @@ learning_objectives:
 
 prerequisites:
     - An Arm Linux system with KVM support and a minimum of 8GB RAM and 50GB free disk space
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:15:47Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 915f6eb5e95bd42ed09b727b4599855d965125e67a8761fbd48a124e9e74b1bc
-  summary_generated_at: '2026-06-01T22:11:02Z'
-  summary_source_hash: 915f6eb5e95bd42ed09b727b4599855d965125e67a8761fbd48a124e9e74b1bc
-  faq_generated_at: '2026-06-02T23:15:47Z'
-  faq_source_hash: 915f6eb5e95bd42ed09b727b4599855d965125e67a8761fbd48a124e9e74b1bc
-  summary: >-
-    This introductory path shows how to install and run Windows 11 on Arm virtual machines on
-    an Arm Linux system using QEMU, KVM, and two Bash automation scripts. You will clone a GitHub
-    project, understand the script structure to customize options, and create a complete VM with
-    a single command that stores its data in a directory you choose. You will then launch the
-    VM with a run script that checks status, starts headless when needed, and connects over RDP
-    using Remmina. The path is intended for developers and system administrators building or testing
-    on Windows on Arm. Prerequisite: an Arm Linux host with KVM support, at least 8 GB RAM and
-    50 GB free disk space.
-  faqs:
-  - question: What do I need before running the VM automation scripts?
-    answer: >-
-      An Arm Linux system with KVM support and at least 8GB RAM and 50GB free disk space. This
-      path assumes you will run QEMU/KVM on that host.
-  - question: How do I get the automation scripts onto my Arm Linux system?
-    answer: >-
-      Clone the GitHub repository and change into the project directory: git clone https://github.com/jasonrandrews/win11arm.git;
-      cd win11arm.
-  - question: Which command should I use to create a new Windows on Arm VM quickly?
-    answer: >-
-      Run: ./create-win11-vm.sh all $HOME/win11-vm. This uses default values for all configurable
-      parameters and stores the VM data in $HOME/win11-vm while Windows installs automatically.
-  - question: How do I start and connect to the VM after it is created?
-    answer: >-
-      Run: ./run-win11-vm.sh $HOME/win11-vm. The script checks if the VM is already running, starts
-      it in headless mode if needed, and connects via RDP using Remmina.
-  - question: What should I check if VM creation or startup fails?
-    answer: >-
-      Confirm your system meets the prerequisites and that KVM is available on your Arm Linux
-      host. Verify the VM directory path you pass to the scripts is correct, then re-run the command;
-      the Learning Path includes guidance for troubleshooting common setup and runtime issues.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md b/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
index 25ab162b77..36694403b3 100644
--- a/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
@@ -13,58 +13,9 @@ learning_objectives:
 
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:17:05Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 4069c5bce1ce4b689a7a67d740fc077dc55c9b0bfbab392f5984ba0bdd9e59c3
-  summary_generated_at: '2026-06-01T22:11:42Z'
-  summary_source_hash: 4069c5bce1ce4b689a7a67d740fc077dc55c9b0bfbab392f5984ba0bdd9e59c3
-  faq_generated_at: '2026-06-02T23:17:05Z'
-  faq_source_hash: 4069c5bce1ce4b689a7a67d740fc077dc55c9b0bfbab392f5984ba0bdd9e59c3
-  summary: >-
-    This Learning Path shows how to use Arm64EC on Windows 11 on Arm to build native Arm applications
-    and begin migrating existing x86 or x64 code. Working on a Windows on Arm computer (for example,
-    a Lenovo ThinkPad X13s) with Visual Studio 2022 or later, you will configure projects to target
-    the Arm64EC application binary interface, build and run on-device, and compare the performance
-    of a simple application across different build configurations. The topic is introductory and
-    focused on practical steps developers can follow on Windows, with no additional prerequisites
-    explicitly listed beyond the required hardware and Visual Studio installation.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Windows on Arm computer such as a Lenovo ThinkPad X13s running Windows 11 and
-      Visual Studio 2022 or higher installed. No other prerequisites are explicitly listed.
-  - question: Which option should I use to migrate an existing x86 or x64 application?
-    answer: >-
-      Use Arm64EC to migrate existing x86 or x64 applications to devices using the Arm architecture.
-      The path also covers building new native Arm applications so you can compare configurations.
-  - question: What should I check if I do not see Arm64EC options in Visual Studio?
-    answer: >-
-      Verify that you are using Visual Studio 2022 or higher on a Windows 11 on Arm computer.
-      The Learning Path does not list additional components beyond installing Visual Studio.
-  - question: How do I compare performance across build configurations?
-    answer: >-
-      Build the same simple application using different configurations and then run them to observe
-      differences. The steps guide you through creating those builds and comparing the results;
-      no specific performance targets are stated.
-  - question: How do I verify that my build was successful?
-    answer: >-
-      After building in Visual Studio, you should get a runnable application on your Windows 11
-      on Arm device. Launch it and proceed to the comparison step to confirm it behaves as expected
-      in the selected configuration.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md b/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
index 3fadd21b61..9ed2117791 100644
--- a/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
@@ -16,60 +16,9 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is suitable.
     - Visual Studio 2022 with Arm build tools. [Refer to this guide for the installation steps](https://developer.arm.com/documentation/102528/0100/Install-Visual-Studio).
-    
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:18:19Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 3b3ecd451ed8b634c7fbe194248cdf1d33432633efbcbef5de713495041ff425
-  summary_generated_at: '2026-06-01T22:12:11Z'
-  summary_source_hash: 3b3ecd451ed8b634c7fbe194248cdf1d33432633efbcbef5de713495041ff425
-  faq_generated_at: '2026-06-02T23:18:19Z'
-  faq_source_hash: 3b3ecd451ed8b634c7fbe194248cdf1d33432633efbcbef5de713495041ff425
-  summary: >-
-    This Learning Path shows how to port a Qt-based Python desktop application with C/C++ dependencies
-    to Arm64 on Windows using Arm64EC. You will build the app, create C/C++ DLLs, and port each
-    DLL to Arm64 by configuring Arm64EC targets with both CMake (editing CMakePresets.json) and
-    MSBuild in Visual Studio 2022. Arm64EC allows Arm64 binaries and existing x64 dependencies
-    to run in the same process, enabling staged migration. The target environment is Windows on
-    Arm hardware running Windows 11 or a Windows on Arm virtual machine. Prerequisites are Visual
-    Studio 2022 with Arm build tools and a code editor such as Visual Studio Code for Arm64. Estimated
-    time to complete is about 90 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Windows on Arm computer running Windows 11 or a Windows on Arm virtual machine,
-      any code editor (Visual Studio Code for Arm64 is suitable), and Visual Studio 2022 with
-      Arm build tools installed.
-  - question: 'Which option should I use to port DLLs: CMake or MSBuild?'
-    answer: >-
-      Use the option that matches your project. This path demonstrates both: CMake (used earlier
-      in the path) and MSBuild with Visual Studio 2022.
-  - question: How do I enable Arm64EC for a CMake project in this path?
-    answer: >-
-      Modify the CMakePresets.json file by adding the final statement block shown in the steps
-      to configure the build target for Arm64EC. This config lets you build the DLLs for Arm64EC.
-  - question: How do I set up an MSBuild project for Arm64EC in Visual Studio 2022?
-    answer: >-
-      Create a new Console Application project in Visual Studio 2022 and set the build target
-      to Arm64EC. The steps provide example solution and project names to guide the configuration.
-  - question: What result should I expect after building with Arm64EC?
-    answer: >-
-      Your application can load existing x64 dependencies in the same process as your Arm64 binaries,
-      easing the transition of x64 apps to Arm64. As described in the introduction, this approach
-      can improve app performance without changing code.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md b/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md
index 6d6b10c746..b9da3a4cd5 100644
--- a/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md
@@ -14,56 +14,9 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - '[Qt framework](https://www.qt.io/) or [Qt for Open Source Development](https://www.qt.io/download-open-source)'
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:20:12Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 63389742eced4df89f85bdf56a01e489e52a9702d446b557f8f55312f9d31f20
-  summary_generated_at: '2026-06-01T22:12:37Z'
-  summary_source_hash: 63389742eced4df89f85bdf56a01e489e52a9702d446b557f8f55312f9d31f20
-  faq_generated_at: '2026-06-02T23:20:12Z'
-  faq_source_hash: 63389742eced4df89f85bdf56a01e489e52a9702d446b557f8f55312f9d31f20
-  summary: >-
-    This Learning Path shows how to build and run a Qt-based desktop application on Windows on
-    Arm (WoA) and investigate native Arm64 performance characteristics. You work on a WoA device
-    such as a Lenovo Thinkpad X13s running Windows 11, or a WoA virtual machine, using the Qt
-    framework (including the Qt for Open Source Development option). The content is introductory
-    and is designed to be completed in about 20 minutes. By the end, you will have a Qt application
-    running natively on Arm64 and a foundation for exploring the performance improvements available
-    with native WoA development.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Windows on Arm computer such as a Lenovo Thinkpad X13s running Windows 11, or
-      a Windows on Arm virtual machine. You also need the Qt framework or Qt for Open Source Development.
-  - question: Which Qt package or version should I install for Windows on Arm?
-    answer: >-
-      You can use the Qt framework or Qt for Open Source Development. Qt v6.2 supports native
-      development for Windows on Arm.
-  - question: Can I use a virtual machine instead of physical hardware?
-    answer: >-
-      Yes. A Windows on Arm virtual machine is listed as an alternative to a physical device.
-  - question: Do I need to use Qt Creator for this Learning Path?
-    answer: >-
-      The path references the Qt framework and notes that Qt provides tools including Qt Creator.
-      It does not explicitly require a specific IDE.
-  - question: What result should I expect and how long will it take?
-    answer: >-
-      You will build and run a Qt-based desktop application on Windows on Arm and investigate
-      performance improvements from running natively on Arm64. The estimated time to complete
-      is about 20 minutes.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md b/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
index 0d00b962a1..06e563f2d2 100644
--- a/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
@@ -16,60 +16,9 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - .NET 8 SDK for [arm64](https://dotnet.microsoft.com/en-us/download/dotnet/thank-you/sdk-8.0.100-windows-arm64-installer).
     - Any code editor, we recommend using [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:20:57Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: e60ce02e9d1872da06bc5bfeb18c7ec65f2bc17defb2b75292f930fb3ad41711
-  summary_generated_at: '2026-06-01T22:13:07Z'
-  summary_source_hash: e60ce02e9d1872da06bc5bfeb18c7ec65f2bc17defb2b75292f930fb3ad41711
-  faq_generated_at: '2026-06-02T23:20:57Z'
-  faq_source_hash: e60ce02e9d1872da06bc5bfeb18c7ec65f2bc17defb2b75292f930fb3ad41711
-  summary: >-
-    Follow this advanced, approximately 30-minute Learning Path to build and run an ASP.NET Core
-    8 Web API on Windows on Arm (Arm64). You will create a project that uses dependency injection
-    for services, build it with the .NET 8 SDK for arm64, run it locally, and confirm from console
-    output that the server is listening on localhost. The target environment is a Windows 11 on
-    Arm device such as a Lenovo ThinkPad X13s or a Windows on Arm virtual machine, using any code
-    editor (Visual Studio Code for Arm64 recommended). By the end, you will have a working ASP.NET
-    Core 8 web server suitable as a starting point for headless IoT scenarios on Arm.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      You need a Windows on Arm computer running Windows 11 or a Windows on Arm virtual machine,
-      the .NET 8 SDK for arm64, and a code editor. Visual Studio Code for Arm64 is recommended,
-      but any editor will work.
-  - question: How do I create and run the ASP.NET Core Web API project on Windows on Arm?
-    answer: >-
-      Follow the steps to create the Web API project, then open a command prompt, change to the
-      project folder, and run the application. The path shows using dotnet run to build and start
-      the server.
-  - question: What result should I expect when the server starts successfully?
-    answer: >-
-      The console output will indicate the server is listening on a localhost URL and that the
-      application has started in the Development environment. The example output shows a line
-      like “Now listening on: http://localhost:5203”.
-  - question: What should I check if dotnet run doesn’t show a listening address?
-    answer: >-
-      Confirm the .NET 8 SDK for arm64 is installed and that you are in the project’s directory
-      before running the command. Rebuild from the project folder and review the console output
-      for build errors.
-  - question: How are dependency injection services used in this path?
-    answer: >-
-      You will create services and consume them via ASP.NET Core’s built-in dependency injection.
-      The steps guide you to register and use these services from your Web API.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
index d5eceb11be..281a05f2c0 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
@@ -15,58 +15,9 @@ learning_objectives:
 prerequisites:
     - A Windows-on-Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows-on-Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - Any code editor. Visual Studio Code is suitable.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:21:33Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: cddfb7b83e82f0daa513558b1e7ee09b55c63e2ff95675d67be7d4408d391aa4
-  summary_generated_at: '2026-06-01T22:13:29Z'
-  summary_source_hash: cddfb7b83e82f0daa513558b1e7ee09b55c63e2ff95675d67be7d4408d391aa4
-  faq_generated_at: '2026-06-02T23:21:33Z'
-  faq_source_hash: cddfb7b83e82f0daa513558b1e7ee09b55c63e2ff95675d67be7d4408d391aa4
-  summary: >-
-    This Learning Path shows how to build a Node.js IoT application on Windows on Arm that streams
-    synthesized sensor data to AWS IoT Core over MQTT. You will register a device using the AWS
-    IoT Core “Connect one device” wizard, verify connectivity with the provided ping command,
-    connect an emulator, and send data to the cloud. You will then validate the stream using the
-    MQTT Test Client by subscribing to a specific topic. The path targets developers working on
-    Windows on Arm devices or a Windows-on-Arm virtual machine, uses a code editor (Visual Studio
-    Code is suitable), and takes about 120 minutes. It assumes access to the AWS Console but lists
-    no additional prerequisites.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      Use a Windows-on-Arm computer such as a Lenovo ThinkPad X13s running Windows 11, or a Windows-on-Arm
-      virtual machine, and any code editor (Visual Studio Code is suitable). The path uses Node.js;
-      no other explicit prerequisites are listed.
-  - question: Where do I register and connect the device in AWS IoT Core?
-    answer: >-
-      In the AWS Console, open IoT Core and select Connect one device. The wizard guides you through
-      Register and secure your device and subsequent steps.
-  - question: How do I check network connectivity to AWS IoT Core before sending data?
-    answer: >-
-      Use the ping command shown in the Connect one device wizard to confirm your device can reach
-      the AWS IoT Core endpoint. Verify the ping succeeds before proceeding.
-  - question: Which MQTT topic should I subscribe to in the test client to view messages?
-    answer: >-
-      Subscribe to Emulators/Weather/SensorReadings in the AWS IoT Core MQTT test client. This
-      is where the emulator’s synthesized sensor data is published.
-  - question: How do I know the data stream from the emulator is working?
-    answer: >-
-      After subscribing in the MQTT test client, you should see data from the emulator appear
-      in the message pane. If no messages appear, re-check the connection steps in the wizard.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md
index 6aad915998..994c4811dc 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md
@@ -16,60 +16,9 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).   
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is suitable.
     - Completion of the [Create IoT applications with Windows on Arm and AWS IoT Core](/learning-paths/laptops-and-desktops/win_aws_iot/) Learning Path.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:22:02Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: f25597c6c9e69e09e9a86fa7c02d0ace9c347f293a21a11c00a6d3498300052c
-  summary_generated_at: '2026-06-01T22:13:59Z'
-  summary_source_hash: f25597c6c9e69e09e9a86fa7c02d0ace9c347f293a21a11c00a6d3498300052c
-  faq_generated_at: '2026-06-02T23:22:02Z'
-  faq_source_hash: f25597c6c9e69e09e9a86fa7c02d0ace9c347f293a21a11c00a6d3498300052c
-  summary: >-
-    This Learning Path guides you through configuring AWS IoT Core to parse MQTT messages and
-    store IoT data in Amazon DynamoDB from a Windows on Arm environment. Building on the previously
-    completed weather-station emulator and AWS IoT setup, you will run the IoT application that
-    streams data to AWS IoT Core and create an IoT Core rule (send_message_to_dynamodb) that writes
-    parsed messages to DynamoDB. The path targets Windows on Arm devices and uses a code editor
-    such as Visual Studio Code; .NET is listed in the metadata. Prerequisites include a Windows
-    on Arm PC or VM, any code editor, and completion of the prior “Create IoT applications with
-    Windows on Arm and AWS IoT Core” Learning Path.
-  faqs:
-  - question: What do I need before running these steps?
-    answer: >-
-      You need a Windows on Arm computer such as the Lenovo ThinkPad X13s running Windows 11,
-      or a Windows on Arm virtual machine, and any code editor; Visual Studio Code for Arm64 is
-      suitable. Complete the "Create IoT applications with Windows on Arm and AWS IoT Core" Learning
-      Path to prepare the weather station emulator and connect it to AWS IoT Core.
-  - question: Where do I create the AWS IoT Core rule?
-    answer: >-
-      In AWS IoT Core, go to Message routing and select Rules. Click Create rule to open the Create
-      rule view.
-  - question: What should I name the rule?
-    answer: >-
-      Use send_message_to_dynamodb as the rule name when prompted. Then proceed through the configuration
-      views as described in the steps.
-  - question: Do I need to modify or rebuild the IoT application for this path?
-    answer: >-
-      The path expects you to run the existing IoT application from the prerequisite to stream
-      data to AWS IoT Core. The focus here is on configuring the AWS IoT Core rule that writes
-      to DynamoDB.
-  - question: What result should I expect after completing the configuration?
-    answer: >-
-      The rule parses incoming MQTT messages from AWS IoT Core and writes the data to Amazon DynamoDB.
-      This connects your Arm64-based Windows workload to persistent storage in DynamoDB.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md
index 555a986caa..bacfb83b21 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md
@@ -17,61 +17,9 @@ prerequisites:
     - A Windows on Arm computer such as the a Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).   
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is suitable.
     - Completion of the [Create IoT applications with Windows on Arm and AWS IoT Core](/learning-paths/laptops-and-desktops/win_aws_iot/) Learning Path.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:23:16Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: f685f45be9e5fc05b278e28590f9d421a920d43cc36ccb572545de4eaf4a799a
-  summary_generated_at: '2026-06-01T22:14:31Z'
-  summary_source_hash: f685f45be9e5fc05b278e28590f9d421a920d43cc36ccb572545de4eaf4a799a
-  faq_generated_at: '2026-06-02T23:23:16Z'
-  faq_source_hash: f685f45be9e5fc05b278e28590f9d421a920d43cc36ccb572545de4eaf4a799a
-  summary: >-
-    This Learning Path shows how to process IoT data on Arm64 by connecting AWS IoT Core to an
-    AWS Lambda function from a Windows on Arm device. You will reuse the weather-station IoT emulator
-    from the prerequisite path, create an AWS IoT Core rule to route temperature messages, and
-    implement a Lambda function that checks a threshold and uses Amazon SNS to send email notifications.
-    The target environment is Windows on Arm, using tools such as .NET and Visual Studio Code.
-    By the end, you will have an event-driven flow from IoT Core to Lambda and SNS. Prerequisites
-    include a Windows on Arm machine or VM and completion of the prior AWS IoT Core Learning Path.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Windows on Arm computer or a Windows on Arm virtual machine, a code editor such
-      as Visual Studio Code for Arm64, and completion of the “Create IoT applications with Windows
-      on Arm and AWS IoT Core” Learning Path to set up the weather station emulator and connect
-      it to AWS IoT Core.
-  - question: Where do I create the AWS IoT Core rule that triggers the Lambda function?
-    answer: >-
-      In AWS IoT Core, open Message routing and select Rules. Click Create rule and configure
-      it in the Create rule view.
-  - question: Which AWS services are used and how do they interact in this path?
-    answer: >-
-      AWS IoT Core receives temperature messages from the emulator, an IoT Core rule triggers
-      an AWS Lambda function, and the Lambda function uses Amazon Simple Notification Service
-      (SNS) to send an email when the temperature exceeds a predefined threshold.
-  - question: How do I know the Lambda trigger and notifications are working?
-    answer: >-
-      Publish a temperature value above the defined threshold from the emulator. You should receive
-      an email notification when the Lambda function is invoked by the IoT Core rule.
-  - question: What should I check if I do not receive an email after sending a high temperature
-      reading?
-    answer: >-
-      Confirm the emulator is connected to AWS IoT Core and sending data to the expected topic,
-      verify the IoT Core rule targets your Lambda function, and review the threshold logic in
-      the Lambda implementation and its use of SNS for email delivery.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md
index b87d92abcc..1646ede942 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md
@@ -15,61 +15,9 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).   
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is suitable.
     - Completion of the [Create IoT applications with Windows on Arm and AWS IoT Core](/learning-paths/laptops-and-desktops/win_aws_iot/) Learning Path.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:23:55Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: f5a93346a0fd55659b7c0a6df501db97742ec71755b6443051b654f3ce871cdf
-  summary_generated_at: '2026-06-01T22:14:53Z'
-  summary_source_hash: f5a93346a0fd55659b7c0a6df501db97742ec71755b6443051b654f3ce871cdf
-  faq_generated_at: '2026-06-02T23:23:55Z'
-  faq_source_hash: f5a93346a0fd55659b7c0a6df501db97742ec71755b6443051b654f3ce871cdf
-  summary: >-
-    This Learning Path shows how to implement and test an AWS Lambda function on Windows on Arm
-    that scans and aggregates IoT data stored in Amazon DynamoDB. You will create a Lambda function
-    in the AWS console using the Node.js 20.x runtime, implement the handler as an ES module (index.mjs)
-    to scan a table (SensorReadings) and compute an average temperature value, then deploy and
-    invoke a test event to view results. The path assumes your DynamoDB table already contains
-    records written by an IoT emulator from a prior exercise. Prerequisites include a Windows
-    on Arm computer or VM, a code editor such as Visual Studio Code for Arm64, and completion
-    of the earlier Windows on Arm and AWS IoT Core Learning Path. Estimated time: 45 minutes.
-  faqs:
-  - question: What do I need before running these steps?
-    answer: >-
-      You need a Windows on Arm computer (for example, a Lenovo ThinkPad X13s running Windows
-      11 or a Windows on Arm virtual machine), any code editor such as Visual Studio Code for
-      Arm64, and completion of the “Create IoT applications with Windows on Arm and AWS IoT Core”
-      Learning Path.
-  - question: Which options should I choose when creating the Lambda function?
-    answer: >-
-      In the AWS Lambda console, select Create function, choose Author from scratch, set the Function
-      name to GetAverageTemperature, and select Node.js 20.x as the runtime.
-  - question: Where do I add the code and what file name should I use?
-    answer: >-
-      Paste the code in the Code source section under index.mjs. The .mjs extension indicates
-      the Lambda entry file is an ECMAScript (ES) module.
-  - question: How do I populate data and test the function?
-    answer: >-
-      Launch the IoT emulator to write data to your DynamoDB table, then click Deploy in the function
-      dashboard. Click Test, create a test event named Test, and run it to see execution status
-      and the average temperature in the console.
-  - question: What should I check if the function returns no average or errors?
-    answer: >-
-      Verify the DynamoDB table SensorReadings exists in the eu-central-1 region and contains
-      items with a temperature attribute. Also confirm you completed the prior steps that write
-      records to the table.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md
index 2e32e0939a..51ae1e40d0 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md
@@ -15,63 +15,9 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).   
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is suitable.    
     - Completion of the [Use AWS Lambda for IoT applications](/learning-paths/laptops-and-desktops/win_aws_iot_lambda/) Learning Path.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:24:33Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 32186a4879e98aa113f461d2a2c705dee099404ed2020ef6fdb981a28bb0c0c3
-  summary_generated_at: '2026-06-01T22:15:20Z'
-  summary_source_hash: 32186a4879e98aa113f461d2a2c705dee099404ed2020ef6fdb981a28bb0c0c3
-  faq_generated_at: '2026-06-02T23:24:33Z'
-  faq_source_hash: 32186a4879e98aa113f461d2a2c705dee099404ed2020ef6fdb981a28bb0c0c3
-  summary: >-
-    This Learning Path guides you through hosting a static IoT website on Amazon S3 from a Windows
-    on Arm environment. You will create a simple site (index.html, styles.css, index.js), connect
-    it to an existing GetAverageTemperature AWS Lambda function by retrieving its Function URL,
-    and deploy the site to S3 using AWS CLI v2. The path is intended for advanced developers and
-    takes about 30 minutes. Prerequisites include a Windows on Arm computer or virtual machine,
-    a code editor (Visual Studio Code for Arm64 is suitable), and prior completion of the Use
-    AWS Lambda for IoT applications Learning Path. Tools referenced include Node.js and Visual
-    Studio Code.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Windows on Arm computer or a Windows on Arm virtual machine, a code editor (Visual
-      Studio Code for Arm64 is suitable), and completion of the "Use AWS Lambda for IoT applications"
-      Learning Path. Node.js is listed among the tools.
-  - question: How should I structure the static website files, and what does each file do?
-    answer: >-
-      Create a folder (for example, IoTPage) with three files: index.html, styles.css, and index.js.
-      The HTML defines the page structure, the CSS handles styling, and index.js contains logic
-      to fetch data from AWS Lambda and display it on the page.
-  - question: Where do I find the AWS Lambda Function URL to use in my website?
-    answer: >-
-      In the AWS Lambda console, open the GetAverageTemperature function, go to the Configuration
-      tab, and select Function URL, then create the Function URL. Ensure the GetAverageTemperature
-      function is prepared as described in the related Learning Path that integrates AWS Lambda
-      with DynamoDB.
-  - question: How do I set up AWS CLI to deploy to Amazon S3?
-    answer: >-
-      Install AWS CLI version 2, create an AWS CLI user, and generate access keys following the
-      AWS CLI authentication tutorial. Run aws configure and provide your access key details,
-      then use the CLI to deploy the website to S3.
-  - question: How do I know the website is working after deployment?
-    answer: >-
-      When the site loads, it should call your configured AWS Lambda Function URL and display
-      the retrieved IoT data on the page. If no data appears, verify that the Function URL in
-      index.js matches the URL shown in the Lambda console.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_cef/_index.md b/content/learning-paths/laptops-and-desktops/win_cef/_index.md
index ff1b4f66f6..f6f101f2c5 100644
--- a/content/learning-paths/laptops-and-desktops/win_cef/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_cef/_index.md
@@ -14,58 +14,9 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - Visual Studio 2022.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:25:17Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 5df8cc6d859c505ad79f8297056b06233956c92203a6d2352d9be8e498a42547
-  summary_generated_at: '2026-06-01T22:15:54Z'
-  summary_source_hash: 5df8cc6d859c505ad79f8297056b06233956c92203a6d2352d9be8e498a42547
-  faq_generated_at: '2026-06-02T23:25:17Z'
-  faq_source_hash: 5df8cc6d859c505ad79f8297056b06233956c92203a6d2352d9be8e498a42547
-  summary: >-
-    This introductory Learning Path guides you through creating and building a Chromium Embedded
-    Framework (CEF) desktop application on Windows on Arm using CMake. Working in Visual Studio
-    2022 on a Windows on Arm device or a Windows on Arm virtual machine, you will set up a C++
-    CEF project and then modify and style the application using HTML, JavaScript, and CSS. The
-    focus is on building the project for Arm-based Windows systems (Arm Cortex-A) and applying
-    basic UI changes with familiar web technologies. Prerequisites are a Windows on Arm computer
-    such as a Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm VM, and Visual Studio
-    2022. Estimated time: about 30 minutes.
-  faqs:
-  - question: What do I need before starting this Learning Path?
-    answer: >-
-      You need a Windows on Arm computer such as the Lenovo ThinkPad X13s running Windows 11,
-      or a Windows on Arm virtual machine, and Visual Studio 2022. These are the only explicit
-      prerequisites.
-  - question: Which tools and languages will I use to build the application?
-    answer: >-
-      You will use CMake with Visual Studio 2022 to configure and build a CEF project written
-      in C++. You will also work with HTML, JavaScript, and CSS to modify and style the application.
-  - question: What environment does the resulting application target?
-    answer: >-
-      The application targets Windows on Arm (WoA) devices. The metadata indicates an Arm Cortex-A
-      CPU class for the platform.
-  - question: What result should I expect when I finish the steps?
-    answer: >-
-      You will have created and built a CEF project on Windows on Arm and applied basic styling
-      or modifications using web technologies. Expect a CEF-based desktop application that incorporates
-      web content.
-  - question: Is this suitable if I am new to CEF or Windows on Arm, and how long will it take?
-    answer: >-
-      Yes. The Learning Path is introductory and is designed to be completed in about 30 minutes.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_forms/_index.md b/content/learning-paths/laptops-and-desktops/win_forms/_index.md
index e540839522..96b9c136e5 100644
--- a/content/learning-paths/laptops-and-desktops/win_forms/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_forms/_index.md
@@ -14,56 +14,9 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - Visual Studio 2022 with .NET Desktop Development workload
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:25:52Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: d43413097704af29b9233dfe33fb675ffd5c6d5a172154d6a7542e77d6625c00
-  summary_generated_at: '2026-06-01T22:16:20Z'
-  summary_source_hash: d43413097704af29b9233dfe33fb675ffd5c6d5a172154d6a7542e77d6625c00
-  faq_generated_at: '2026-06-02T23:25:52Z'
-  faq_source_hash: d43413097704af29b9233dfe33fb675ffd5c6d5a172154d6a7542e77d6625c00
-  summary: >-
-    This introductory path shows how to create and build a Windows Forms desktop application in
-    C#/.NET on Windows on Arm using Visual Studio 2022. You will configure build settings, including
-    creating an ARM64 solution platform in Configuration Manager, then run the app under different
-    settings and compare matrix multiplication computation times to observe execution behavior
-    on Arm64. The target environment is a Windows on Arm device or a Windows on Arm virtual machine.
-    Prerequisites are Visual Studio 2022 with the .NET Desktop Development workload and access
-    to Windows on Arm. By the end, you will have a working WinForms app and a basic method to
-    measure code performance on Arm64.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Windows on Arm computer running Windows 11 or a Windows on Arm virtual machine,
-      plus Visual Studio 2022 with the .NET Desktop Development workload installed.
-  - question: Which language and framework does the sample use?
-    answer: >-
-      The application is built with Windows Forms using C# on .NET.
-  - question: How do I switch the project to build for ARM64 in Visual Studio?
-    answer: >-
-      Open the target platform dropdown (default is Any CPU), choose Configuration Manager, select
-      New in Active solution platform, and pick ARM64 in the New Solution Platform dialog.
-  - question: How do I confirm I’m building and running the ARM64 configuration?
-    answer: >-
-      Check that the Active solution platform in Visual Studio shows ARM64 before building and
-      launching the application.
-  - question: What result should I expect when comparing performance settings?
-    answer: >-
-      You will run the application under different build settings and compare the matrix multiplication
-      computation times reported by the app to evaluate execution performance on Arm64.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_net/_index.md b/content/learning-paths/laptops-and-desktops/win_net/_index.md
index 65a8b26486..c0340331a0 100644
--- a/content/learning-paths/laptops-and-desktops/win_net/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_net/_index.md
@@ -12,58 +12,9 @@ learning_objectives:
 
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:26:32Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 9cc8f77f98bc8f4afb7b77344e42615e420be421f85583f9fc4f5ec76516e6eb
-  summary_generated_at: '2026-06-01T22:16:47Z'
-  summary_source_hash: 9cc8f77f98bc8f4afb7b77344e42615e420be421f85583f9fc4f5ec76516e6eb
-  faq_generated_at: '2026-06-02T23:26:32Z'
-  faq_source_hash: 9cc8f77f98bc8f4afb7b77344e42615e420be421f85583f9fc4f5ec76516e6eb
-  summary: >-
-    This introductory Learning Path shows how to build and run a native .NET 6 Windows Presentation
-    Foundation (WPF) application on a Windows on Arm system. You will prepare your environment
-    by installing Visual Studio 2022 or later with the .NET desktop development workload, then
-    build and execute the application on a Windows on Arm computer or a Windows on Arm virtual
-    machine. The steps focus on installing the required tools and validating a working WPF app
-    running natively on Arm. Prerequisites are a Windows on Arm computer (for example, a Lenovo
-    ThinkPad X13s running Windows 11) or a Windows on Arm VM; no other explicit prerequisites
-    are listed. Estimated time to complete is about 20 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need access to a Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows
-      11, or a Windows on Arm virtual machine. Install Visual Studio 2022 or higher.
-  - question: Which Visual Studio components should I install?
-    answer: >-
-      Install the .NET desktop development workload component in Visual Studio. This is the required
-      workload for this Learning Path.
-  - question: How do I add the .NET desktop development workload to an existing Visual Studio
-      installation?
-    answer: >-
-      Open the Windows Start Menu, launch Visual Studio Installer, and select Modify. On the Workloads
-      tab, select the .NET desktop development workload.
-  - question: Can I use a Windows on Arm virtual machine instead of physical hardware?
-    answer: >-
-      Yes. A Windows on Arm virtual machine is listed as an acceptable environment for this Learning
-      Path.
-  - question: What result should I expect after completing the steps, and how long will it take?
-    answer: >-
-      You will build and run a .NET 6 Windows Presentation Foundation (WPF) application on a Windows
-      on Arm machine. The estimated time to complete is about 20 minutes.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/laptops-and-desktops/win_net8/_index.md b/content/learning-paths/laptops-and-desktops/win_net8/_index.md
index 0b788a76b5..0d7be43b52 100644
--- a/content/learning-paths/laptops-and-desktops/win_net8/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_net8/_index.md
@@ -16,59 +16,9 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - .NET 8 SDK for [x64](https://dotnet.microsoft.com/en-us/download/dotnet/thank-you/sdk-8.0.100-windows-x64-installer) and [arm64](https://dotnet.microsoft.com/en-us/download/dotnet/thank-you/sdk-8.0.100-windows-arm64-installer).
     - Any code editor, we recommend using [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:27:37Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 218fd5b33e104360d5de9f632f9338e2f24041ea70f7a01fad0af6be2a11619c
-  summary_generated_at: '2026-06-01T22:17:04Z'
-  summary_source_hash: 218fd5b33e104360d5de9f632f9338e2f24041ea70f7a01fad0af6be2a11619c
-  faq_generated_at: '2026-06-02T23:27:37Z'
-  faq_source_hash: 218fd5b33e104360d5de9f632f9338e2f24041ea70f7a01fad0af6be2a11619c
-  summary: >-
-    This introductory path shows how to build, run, and benchmark .NET 8 Console applications
-    on Windows on Arm, with a focus on measuring execution performance on Arm64. You will set
-    up your development environment, verify your .NET installation, clone a sample repository,
-    and implement custom benchmarks using System.Diagnostics.Stopwatch. Prerequisites include
-    a Windows on Arm device or VM, the .NET 8 SDK for both x64 and arm64, and any code editor
-    (Visual Studio Code for Arm64 is recommended). In about 20 minutes, you will be able to run
-    the sample app and create simple, repeatable measurements to understand how your .NET code
-    performs on Windows on Arm.
-  faqs:
-  - question: What do I need before running the benchmarks?
-    answer: >-
-      You need a Windows on Arm computer or a Windows on Arm virtual machine, the .NET 8 SDK for
-      both x64 and arm64, and a code editor (Visual Studio Code for Arm64 is recommended). These
-      are listed in the prerequisites.
-  - question: How do I verify that .NET 8 is installed correctly on Windows on Arm?
-    answer: >-
-      Follow the “Before you begin” step to check your .NET installation. If anything is missing,
-      install the .NET 8 SDK for both x64 and arm64 as listed in the prerequisites.
-  - question: How do I get the sample application used in this Learning Path?
-    answer: >-
-      Clone the repository by running: git clone https://github.com/dawidborycki/Arm64.Performance.DotNet.git.
-      The sample is a .NET console application created with dotnet new console.
-  - question: How are the custom benchmarks implemented in this path?
-    answer: >-
-      They use the System.Diagnostics.Stopwatch class. The sample includes a PerformanceHelper
-      static class with reusable timing methods and a PerformanceTests static class to organize
-      test code.
-  - question: How should I compare performance between x64 and Arm64 on Windows on Arm?
-    answer: >-
-      Install both the x64 and arm64 .NET 8 SDKs as listed, then run the same Stopwatch-based
-      benchmarks as directed in the steps. Compare the reported execution times to observe differences.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md b/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md
index 607a99a5c2..988dae16c7 100644
--- a/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md
@@ -14,60 +14,9 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - Visual Studio 2022 with .NET Multi-platform App UI development and Universal Windows Platform development installed.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:28:34Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 037509ebca3a7c5a58bef247532919cd8196c7993e946ce519585cdc82073daa
-  summary_generated_at: '2026-06-01T22:17:25Z'
-  summary_source_hash: 037509ebca3a7c5a58bef247532919cd8196c7993e946ce519585cdc82073daa
-  faq_generated_at: '2026-06-02T23:28:34Z'
-  faq_source_hash: 037509ebca3a7c5a58bef247532919cd8196c7993e946ce519585cdc82073daa
-  summary: >-
-    This path shows how to create and build a cross-platform .NET MAUI application on Windows
-    on Arm and measure code execution performance uplift on Arm64. Using Visual Studio 2022, you
-    will start a new MAUI project, add C# helper classes to generate pseudo-random double-precision
-    vectors and compute a*b+c, measure execution time with a PerformanceHelper, and present results
-    in a list view. Prerequisites are a Windows on Arm computer such as a Lenovo Thinkpad X13s
-    running Windows 11, or a Windows on Arm virtual machine, plus Visual Studio 2022 with .NET
-    Multi-platform App UI development and Universal Windows Platform development installed. The
-    path is introductory and takes about 30 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      Use a Windows on Arm computer such as a Lenovo ThinkPad X13s running Windows 11, or a Windows
-      on Arm virtual machine. Install Visual Studio 2022 with the .NET Multi-platform App UI development
-      and Universal Windows Platform development workloads.
-  - question: Which Visual Studio components should I install?
-    answer: >-
-      Install Visual Studio 2022 with the .NET Multi-platform App UI development workload and
-      the Universal Windows Platform development workload. These are explicitly listed prerequisites.
-  - question: Which project type should I create in Visual Studio?
-    answer: >-
-      Create a .NET MAUI project. The Learning Path focuses on building and running it on Windows
-      on Arm.
-  - question: What code will I add to measure performance and what does it compute?
-    answer: >-
-      You will add a PerformanceHelper class to measure code execution time and a VectorHelper
-      class that implements AdditionOfProduct, computing a*b+c over pseudo-random double-precision
-      vectors. A list view displays the processing results.
-  - question: How do I know the performance measurement part worked?
-    answer: >-
-      Build and run the app on Windows on Arm and check that the UI displays processing results
-      and execution times. The Learning Path does not specify expected numbers; you use the reported
-      timings to observe Arm64 performance.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md b/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md
index 74e1f19ec6..4243b30aad 100644
--- a/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md
@@ -12,61 +12,9 @@ learning_objectives:
     - Run inference with a Phi-3 model using ONNX Runtime with KleidiAI acceleration.
 prerequisites:
     - A Windows on Arm computer such as a Lenovo Thinkpad X13 running Windows 11, or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:29:03Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 606702e7afc9a29976d027eceab021570cf3f91ec408ef2dd2051df0b05d9bda
-  summary_generated_at: '2026-06-01T22:17:48Z'
-  summary_source_hash: 606702e7afc9a29976d027eceab021570cf3f91ec408ef2dd2051df0b05d9bda
-  faq_generated_at: '2026-06-02T23:29:03Z'
-  faq_source_hash: 606702e7afc9a29976d027eceab021570cf3f91ec408ef2dd2051df0b05d9bda
-  summary: >-
-    This Learning Path shows how to build ONNX Runtime with the Generate() API on Windows on Arm
-    and run inference on the Phi-3 Mini (3.3B) model with KleidiAI acceleration. You will clone
-    and build ONNX Runtime and the Generate() API from source using Visual Studio and CMake, then
-    download the short-context (4K) Phi-3 Mini ONNX model (quantized to 4-bits) and execute a
-    simple model runner that reports performance metrics. The target environment is a Windows
-    on Arm device or a Windows on Arm virtual machine. Tools used include Visual Studio, C++,
-    Python, Git, CMake, and ONNX Runtime. Outcome: a working build and a validated Phi-3 inference
-    run on WoA.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need access to a Windows on Arm computer such as a Lenovo ThinkPad X13 running Windows
-      11, or a Windows on Arm virtual machine. No other explicit prerequisites are listed.
-  - question: Which Phi-3 model variant should I use in this path?
-    answer: >-
-      Use the short-context (4K) Phi-3 Mini (3.3B) model in ONNX format, quantized to 4-bits.
-      This version consumes less memory than the long-context (128K) model and is the variant
-      used in the steps.
-  - question: How is the ONNX Runtime Generate() API used here?
-    answer: >-
-      You build the Generate() API from source and use it for text generation with Phi-3. It handles
-      pre- and post-processing, inference with ONNX Runtime (including logits processing), search
-      and sampling, and KV cache management.
-  - question: How do I know the build and run were successful?
-    answer: >-
-      You should be able to run the simple model runner without build errors, see generated model
-      outputs, and observe reported performance metrics. These results indicate a successful build
-      and inference run.
-  - question: Do I need extra configuration to use KleidiAI acceleration?
-    answer: >-
-      The path runs inference with KleidiAI acceleration, but specific configuration steps beyond
-      building ONNX Runtime and the Generate() API are not explicitly listed. Follow the provided
-      build and run instructions.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Barbara Corriero
 
diff --git a/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md b/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md
index 7bc307d10e..34b88d4a7d 100644
--- a/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md
@@ -15,60 +15,9 @@ learning_objectives:
 prerequisites:
     - Familiarity with C++ development and compiling programs from the command line
     - A Windows on Arm machine with [Visual Studio](/install-guides/vs-woa/) and the C++ desktop development tools installed
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:29:33Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: b975cc83674410ec50ca49a31744eee4ac5a63c994dd28e46ed84f7afda0568d
-  summary_generated_at: '2026-06-01T22:18:09Z'
-  summary_source_hash: b975cc83674410ec50ca49a31744eee4ac5a63c994dd28e46ed84f7afda0568d
-  faq_generated_at: '2026-06-02T23:29:33Z'
-  faq_source_hash: b975cc83674410ec50ca49a31744eee4ac5a63c994dd28e46ed84f7afda0568d
-  summary: >-
-    This Learning Path guides you through applying Profile-Guided Optimization (PGO) to C++ code
-    and measuring the impact with Google Benchmark on Windows on Arm. You start by understanding
-    PGO fundamentals, then create a baseline microbenchmark of an integer division function. Using
-    MSVC on a Windows on Arm system, you build an instrumented binary, run it to collect profile
-    data, and rebuild using that profile to produce a PGO-optimized binary. You then compare benchmark
-    results between the baseline and optimized builds. Prerequisites are C++ command-line experience
-    and a Windows on Arm machine with Visual Studio and the C++ desktop development tools installed.
-    Estimated time: about 30 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Windows on Arm machine with Visual Studio and the C++ desktop development tools
-      installed. Familiarity with C++ and compiling from the command line is expected.
-  - question: Which build environment should I use on Windows on Arm?
-    answer: >-
-      Open an ARM64 Native Tools Command Prompt and use PowerShell if instructed. Navigate to
-      your project directory and set any environment variables (such as VCPKG) as shown in the
-      steps.
-  - question: What does the baseline benchmark measure, and why was it chosen?
-    answer: >-
-      The baseline measures an integer division operation. Division is used because it typically
-      has higher latency and lower throughput than addition, subtraction, or multiplication, making
-      changes measurable.
-  - question: How do I apply PGO here, and how do I know it worked?
-    answer: >-
-      You build an instrumented binary, run it to collect profile data, and rebuild using that
-      profile with MSVC. You then run Google Benchmark to compare the optimized build against
-      the baseline and observe the measured differences.
-  - question: Do I need to install Google Benchmark before starting?
-    answer: >-
-      No. The path first introduces Google Benchmark, then guides you through setting up your
-      environment and running your first benchmark in the following section.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Tom Dunkle
 
diff --git a/content/learning-paths/laptops-and-desktops/win_python/_index.md b/content/learning-paths/laptops-and-desktops/win_python/_index.md
index 6866468435..aa42916436 100644
--- a/content/learning-paths/laptops-and-desktops/win_python/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_python/_index.md
@@ -15,55 +15,9 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/). 
     - Any code editor, we recommend using [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user).
     - Visual Studio 2022 with Arm build tools. [Refer to this guide for the installation steps](https://developer.arm.com/documentation/102528/0100/Install-Visual-Studio)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:30:23Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: d953214fe33ad89a683f79e7c29ce9348e5169e6529b808804329cb35060b431
-  summary_generated_at: '2026-06-01T22:18:32Z'
-  summary_source_hash: d953214fe33ad89a683f79e7c29ce9348e5169e6529b808804329cb35060b431
-  faq_generated_at: '2026-06-02T23:30:23Z'
-  faq_source_hash: d953214fe33ad89a683f79e7c29ce9348e5169e6529b808804329cb35060b431
-  summary: >-
-    This introductory path shows how to build native Python applications on Windows on Arm and
-    work with platform-dependent packages using Arm64. Using a Windows on Arm PC or virtual machine,
-    a code editor (Visual Studio Code for Arm64 recommended), and Visual Studio 2022 with Arm
-    build tools, you will create a small NumPy-based application that synthesizes noisy sine waves,
-    runs FFTs for varying input sizes, and measures execution time. You will examine the platform-specificity
-    of Python packages and use native Arm64 builds where applicable. By the end, you will have
-    a working sample.py and timing results to analyze on an Arm64 Windows environment.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Windows on Arm computer (for example, a Lenovo ThinkPad X13s running Windows
-      11) or a Windows on Arm virtual machine, a code editor such as Visual Studio Code for Arm64,
-      and Visual Studio 2022 with Arm build tools.
-  - question: Can I use a Windows on Arm virtual machine instead of physical hardware?
-    answer: >-
-      Yes. The prerequisites explicitly list a Windows on Arm virtual machine as an option.
-  - question: Do I need Visual Studio 2022 if I plan to edit code in VS Code?
-    answer: >-
-      Yes. Visual Studio 2022 with Arm build tools is listed as a prerequisite, while Visual Studio
-      Code for Arm64 is the recommended editor.
-  - question: What should I create and what does the sample application do?
-    answer: >-
-      Create a file named sample.py. It uses NumPy to generate noisy sine waves, runs FFTs over
-      multiple input sizes, and measures execution time.
-  - question: Where can I find the complete sample code?
-    answer: >-
-      The Learning Path references that the complete code is available on GitHub.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_python_onnx/_index.md b/content/learning-paths/laptops-and-desktops/win_python_onnx/_index.md
index d4d9eb210b..293f0f5f09 100644
--- a/content/learning-paths/laptops-and-desktops/win_python_onnx/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_python_onnx/_index.md
@@ -18,11 +18,9 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/). 
     - Any code editor like [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user).    
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md b/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md
index 0c3f8e6e9c..3e57764911 100644
--- a/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md
@@ -14,61 +14,9 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 Version 22H2 which has [Windows Sandbox enabled](/install-guides/windows-sandbox-woa).
     - A valid [GitHub account](https://github.com/) to complete this Learning Path.
-    
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:31:07Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 055e3a3d8c0dc717e2246e3e8e7ebb00c7d2cea3ff9faabf7125ca1ebfff7a31
-  summary_generated_at: '2026-06-01T22:19:03Z'
-  summary_source_hash: 055e3a3d8c0dc717e2246e3e8e7ebb00c7d2cea3ff9faabf7125ca1ebfff7a31
-  faq_generated_at: '2026-06-02T23:31:07Z'
-  faq_source_hash: 055e3a3d8c0dc717e2246e3e8e7ebb00c7d2cea3ff9faabf7125ca1ebfff7a31
-  summary: >-
-    This Learning Path shows how to use Windows Sandbox on a Windows on Arm PC as a self-hosted
-    Arm64 GitHub Actions runner, then run a CI/CD workflow that builds and runs a .NET 8 Windows
-    Presentation Foundation (WPF) sample solving the Traveling Salesman Problem. You will configure
-    the sandboxed runner and use the workflow definition in .github/workflows/dotnet_sandbox.yml
-    to trigger builds manually or on pushes to the main branch. The focus is on getting a working
-    pipeline that executes inside Windows Sandbox. Prerequisites are a Windows on Arm computer
-    (for example, a Lenovo ThinkPad X13s) running Windows 11 Version 22H2 with Windows Sandbox
-    enabled, and a GitHub account.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 Version
-      22H2 with Windows Sandbox enabled, and a valid GitHub account. Follow the Windows Sandbox
-      enablement guide linked in the prerequisites.
-  - question: Which GitHub Actions runner is configured in this Learning Path?
-    answer: >-
-      You will configure a self-hosted Arm64 runner inside Windows Sandbox on your Windows on
-      Arm machine. This runner executes the jobs defined in your workflow.
-  - question: Where is the workflow file located and how is it triggered?
-    answer: >-
-      The workflow is defined in .github/workflows/dotnet_sandbox.yml. It runs on push events
-      to the main branch and can also be triggered manually from the Actions tab.
-  - question: What result should I expect when I run the pipeline?
-    answer: >-
-      The pipeline builds a .NET 8 WPF sample application and verifies it runs on your Windows
-      Sandbox self-hosted runner. The workflow should complete successfully with jobs executed
-      on the self-hosted Arm64 runner, and it may publish the app as configured.
-  - question: What should I check if my jobs are queued and do not run in Windows Sandbox?
-    answer: >-
-      Confirm Windows Sandbox is enabled and that you have configured and started the self-hosted
-      runner in the Sandbox as described in the steps. Also ensure the repository contains .github/workflows/dotnet_sandbox.yml
-      and you triggered the workflow as specified.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md b/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md
index 6c291bba2a..8d75bf4b57 100644
--- a/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md
@@ -15,59 +15,9 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).    
     - Refer to [Visual Studio 2022 with Arm build tools](/install-guides/vs-woa).
-    
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:31:50Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: cacf4c6fc3cd3c2a9ab194f7a04981fd4820fca5482317a06c6b9fa02a1c9da2
-  summary_generated_at: '2026-06-01T22:19:36Z'
-  summary_source_hash: cacf4c6fc3cd3c2a9ab194f7a04981fd4820fca5482317a06c6b9fa02a1c9da2
-  faq_generated_at: '2026-06-02T23:31:50Z'
-  faq_source_hash: cacf4c6fc3cd3c2a9ab194f7a04981fd4820fca5482317a06c6b9fa02a1c9da2
-  summary: >-
-    This introductory Learning Path shows how to create a C/C++ Win32 DLL, use it from a Windows
-    console application, and port the library to Arm64 for Windows on Arm. You work on a Windows
-    on Arm device such as a Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm virtual
-    machine, using Visual Studio 2022 with Arm build tools. The steps include a brief overview
-    of Armv8-A Arm64 concepts and focus on building and running the console app that consumes
-    your DLL on Arm64. By the end, you will have exercised a practical migration workflow for
-    a Win32 library to Arm64.
-  faqs:
-  - question: What do I need installed before starting?
-    answer: >-
-      Use a Windows on Arm computer or a Windows on Arm virtual machine and refer to the Visual
-      Studio 2022 with Arm build tools installation guide. No other prerequisites are explicitly
-      listed.
-  - question: Can I complete this on a virtual machine instead of physical hardware?
-    answer: >-
-      Yes. The prerequisites explicitly allow a Windows on Arm virtual machine as an alternative
-      to a Windows on Arm device.
-  - question: What will I build and target by the end?
-    answer: >-
-      You will create a C/C++ Win32 DLL and a Windows console application that uses it, with both
-      projects targeting Arm64 on Windows on Arm.
-  - question: How do I choose the correct build target for Arm64?
-    answer: >-
-      The steps show how to configure your projects in Visual Studio 2022 with Arm build tools
-      to build for Arm64. Follow the project configuration guidance provided in the path.
-  - question: What should I check if my Arm64 build fails or the app cannot load the DLL?
-    answer: >-
-      Verify that Visual Studio 2022 with Arm build tools is installed, the project target is
-      set to Arm64, and you are building and running on a Windows on Arm environment. Revisit
-      the configuration steps to confirm the platform settings.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_winui3/_index.md b/content/learning-paths/laptops-and-desktops/win_winui3/_index.md
index 1ae1f72408..8636a7e1da 100644
--- a/content/learning-paths/laptops-and-desktops/win_winui3/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_winui3/_index.md
@@ -14,58 +14,9 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - Visual Studio 2022 with .NET desktop development and Universal Windows Platform development installed.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:32:13Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 383dcf17bce86b24a2d9c8d0982fa6e9ddf954955c16b420463ada951753dbfa
-  summary_generated_at: '2026-06-01T22:20:00Z'
-  summary_source_hash: 383dcf17bce86b24a2d9c8d0982fa6e9ddf954955c16b420463ada951753dbfa
-  faq_generated_at: '2026-06-02T23:32:13Z'
-  faq_source_hash: 383dcf17bce86b24a2d9c8d0982fa6e9ddf954955c16b420463ada951753dbfa
-  summary: >-
-    This Learning Path shows how to create and build a Windows UI Library (WinUI 3) application
-    in C#/.NET using Visual Studio 2022 on Windows on Arm, then compare code execution performance
-    on Arm64 versus x64. You will configure Visual Studio for Release builds, select the target
-    architecture, launch the app, and use matrix multiplication to measure and compare computation
-    times across the two architectures. Prerequisites are a Windows on Arm computer (or a Windows
-    on Arm virtual machine) and Visual Studio 2022 with the .NET desktop development and Universal
-    Windows Platform development workloads installed. Designed for an introductory audience focused
-    on migration to Arm, the path takes about 30 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Windows on Arm device such as a Lenovo ThinkPad X13s running Windows 11 or a
-      Windows on Arm virtual machine. Install Visual Studio 2022 with the .NET desktop development
-      and Universal Windows Platform development workloads.
-  - question: Which Visual Studio settings should I use to build and run for each architecture?
-    answer: >-
-      Set the Configuration to Release. Then choose the target architecture as x64 or ARM64 and
-      select Arm64.WinUIApp (Package) when targeting ARM64.
-  - question: How do I run the performance comparison between x64 and ARM64?
-    answer: >-
-      Launch the application for x64 first, perform the matrix multiplication calculations as
-      described, then switch the architecture to ARM64 and repeat. Record the computation times
-      to compare the results.
-  - question: How do I confirm I built the app for ARM64?
-    answer: >-
-      In Visual Studio, verify the Configuration is set to Release and the Architecture dropdown
-      shows ARM64. Ensure the startup item is Arm64.WinUIApp (Package) before running.
-  - question: Can I complete this Learning Path without a physical Arm device?
-    answer: >-
-      Yes. A Windows on Arm virtual machine is listed as an acceptable environment in the prerequisites.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_wpf/_index.md b/content/learning-paths/laptops-and-desktops/win_wpf/_index.md
index a49fc4b945..0f119d9bfd 100644
--- a/content/learning-paths/laptops-and-desktops/win_wpf/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_wpf/_index.md
@@ -14,59 +14,9 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - Visual Studio 2022 with .NET desktop development installed.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:33:26Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: cc1a494e7b03672122ff84073b677a93659eca7df601b3f77c7e7fd536cc1af9
-  summary_generated_at: '2026-06-01T22:20:26Z'
-  summary_source_hash: cc1a494e7b03672122ff84073b677a93659eca7df601b3f77c7e7fd536cc1af9
-  faq_generated_at: '2026-06-02T23:33:26Z'
-  faq_source_hash: cc1a494e7b03672122ff84073b677a93659eca7df601b3f77c7e7fd536cc1af9
-  summary: >-
-    This Learning Path shows how to create and build a Windows Presentation Foundation (WPF) desktop
-    application on Windows on Arm and compare execution times between ARM64 and x86_64 builds
-    using Visual Studio 2022. You will work with WPF and XAML to define the UI, then use Visual
-    Studio’s Configuration Manager to add an ARM64 Solution Platform and run the app under different
-    settings to measure code execution performance uplift on Arm64. The target environment is
-    a Windows on Arm computer (or a Windows on Arm virtual machine) with the .NET desktop development
-    workload installed. By the end, you will have built and executed a WPF app and gathered timing
-    comparisons across build configurations.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Windows on Arm computer (such as a Lenovo ThinkPad X13s) or a Windows on Arm
-      virtual machine, and Visual Studio 2022 with .NET desktop development installed. No additional
-      prerequisites are explicitly listed.
-  - question: Which Visual Studio option do I use to target ARM64?
-    answer: >-
-      Use the Any CPU drop-down, choose Configuration Manager, then select New from the Active
-      Solution Platform menu. In the New Solution Platform window, choose ARM64 and click OK.
-  - question: Do I also need an x86_64 configuration for comparison?
-    answer: >-
-      Yes. The procedure prepares both ARM64 and x86_64 builds so you can compare computation
-      times. Repeat the New Solution Platform steps to create the additional architecture.
-  - question: How do I run the app to compare execution times across configurations?
-    answer: >-
-      Select the desired platform in Active Solution Platform, build, and launch the app from
-      Visual Studio. Run it under each configuration and compare the computation times as instructed
-      in the steps.
-  - question: How do I know the app is running as ARM64 rather than x86_64?
-    answer: >-
-      Ensure ARM64 is selected as the Active Solution Platform before building and launching.
-      The app will run using the architecture of the active platform you selected.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md b/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md
index 4287c8b81e..5c1dca6602 100644
--- a/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md
@@ -15,57 +15,9 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - Visual Studio 2022 with .NET desktop development and Universal Windows Platform development installed.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:34:00Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 16b7f8c67050ea336402791c0ecca2c688d5da9373c571986e12dcf646c8093c
-  summary_generated_at: '2026-06-01T22:20:57Z'
-  summary_source_hash: 16b7f8c67050ea336402791c0ecca2c688d5da9373c571986e12dcf646c8093c
-  faq_generated_at: '2026-06-02T23:34:00Z'
-  faq_source_hash: 16b7f8c67050ea336402791c0ecca2c688d5da9373c571986e12dcf646c8093c
-  summary: >-
-    This introductory Learning Path shows how to create and build a Xamarin Forms application
-    on Windows on Arm using Visual Studio 2022. You will apply the Model-View-ViewModel (MVVM)
-    pattern by adding a Models folder and a DataPoint2d class to structure app logic, and measure
-    code execution performance uplift on Arm64. The path targets developers exploring cross-platform
-    development with C# and .NET while running on Arm-based Windows systems. Prerequisites are
-    a Windows on Arm computer such as a Lenovo ThinkPad X13s or a Windows on Arm virtual machine,
-    and Visual Studio 2022 with the .NET desktop development and Universal Windows Platform workloads
-    installed. By the end, you will have built the app and measured code execution on Arm64.
-  faqs:
-  - question: What do I need installed before starting on Windows on Arm?
-    answer: >-
-      Use a Windows on Arm computer or a Windows on Arm virtual machine, and install Visual Studio
-      2022 with the .NET desktop development and Universal Windows Platform development workloads.
-      No other prerequisites are explicitly listed.
-  - question: Can I complete this Learning Path using a virtual machine instead of physical hardware?
-    answer: >-
-      Yes. A Windows on Arm virtual machine is listed as an acceptable environment.
-  - question: Which Visual Studio workloads should I select for this Xamarin Forms project?
-    answer: >-
-      Install the .NET desktop development and Universal Windows Platform development workloads
-      in Visual Studio 2022 as specified in the prerequisites.
-  - question: Where should I place the DataPoint2d model when implementing MVVM?
-    answer: >-
-      Create a folder named Models in your Arm64.MobileApp.XamarinForms project and add a new
-      class file named DataPoint2d.cs inside it. This establishes the Model part of the MVVM structure.
-  - question: How will I measure code execution performance uplift on Arm64 in this path?
-    answer: >-
-      The Learning Path includes steps to measure code execution performance uplift on Arm64.
-      Follow the provided instructions in the steps to perform and interpret the measurement.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md b/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md
index 1fda2e8dc7..aa3f600f50 100644
--- a/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md
@@ -13,61 +13,9 @@ learning_objectives:
 
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:34:43Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: f058f628cefb7766ef0728e594c9ef5b57bde97c1850395786d54cc591271503
-  summary_generated_at: '2026-06-01T22:21:28Z'
-  summary_source_hash: f058f628cefb7766ef0728e594c9ef5b57bde97c1850395786d54cc591271503
-  faq_generated_at: '2026-06-02T23:34:43Z'
-  faq_source_hash: f058f628cefb7766ef0728e594c9ef5b57bde97c1850395786d54cc591271503
-  summary: >-
-    This introductory path guides you through setting up Visual Studio 2022 on a Windows on Arm
-    device, creating and running a simple console application, and then building and profiling
-    a sample that renders a rotating 3D cube. You will install Git for Windows on Arm to clone
-    the SpinTheCubeInGDI repository, open its Visual Studio solution, and review core components
-    such as shape generation, rotation, and drawing. The final steps install and deploy Arm Performance
-    Libraries and explore performance differences after using these math libraries compared to
-    multithreaded code. Prerequisite: a Windows on Arm computer such as a Lenovo ThinkPad X13s
-    running Windows 11. Estimated time to complete is about 60 minutes.
-  faqs:
-  - question: Which Visual Studio edition should I install on Windows on Arm?
-    answer: >-
-      Any of the three Visual Studio 2022 editions can be used. The Community Edition is a free,
-      fully featured option suitable for individual developers.
-  - question: How do I create the initial Windows on Arm project in Visual Studio?
-    answer: >-
-      From the Start window, select Create a new project, choose Console App, provide a project
-      name, and click Create. Then build and run the project to verify your setup.
-  - question: How do I get the SpinTheCubeInGDI example used in this path?
-    answer: >-
-      Install Git for Windows on Arm if needed, navigate to an empty directory, and clone the
-      repository: git clone https://github.com/arm/SpinTheCubeInGDI.git. This repository contains
-      the Visual Studio solution for the example.
-  - question: How do I open and run the spinning cube example in Visual Studio?
-    answer: >-
-      In Windows File Explorer, double-click SpinTheCubeInGDI.sln to open the solution in Visual
-      Studio. Build and run it to see the rotating 3D cube; the project includes shape generation,
-      rotation, and drawing logic implemented in SpinTheCubeInGDI.cpp.
-  - question: How do I use Arm Performance Libraries with this example?
-    answer: >-
-      Follow the Arm Performance Libraries install guide to set up the libraries on Windows. After
-      installation, use the project to explore differences when numerical routines are backed
-      by Arm Performance Libraries, which provide BLAS, LAPACK, FFT, and sparse implementations
-      built with OpenMP.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Odin Shen
 
diff --git a/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md b/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md
index a37b82338b..70af30b905 100644
--- a/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md
@@ -15,61 +15,9 @@ prerequisites:
     - Some familiarity with CI/CD concepts is assumed
     - Valid GitHub account
     - Microsoft Azure account (if using virtual machine)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:35:23Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 828e4022f387698d2736d94010de5d93efa9f38ffd3a2e4f15252410e9ccedb2
-  summary_generated_at: '2026-06-01T22:22:06Z'
-  summary_source_hash: 828e4022f387698d2736d94010de5d93efa9f38ffd3a2e4f15252410e9ccedb2
-  faq_generated_at: '2026-06-02T23:35:23Z'
-  faq_source_hash: 828e4022f387698d2736d94010de5d93efa9f38ffd3a2e4f15252410e9ccedb2
-  summary: >-
-    Set up a GitHub self-hosted runner on a Windows on Arm machine or cloud instance and run a
-    minimal GitHub Actions workflow to validate a basic CI/CD flow on this platform. You will
-    create a new GitHub repository, configure the runner on Windows on Arm, and use the Actions
-    Simple workflow to generate a minimal blank.yml under .github/workflows (optionally renamed,
-    for example to hello.yml) that executes a hello world command on the Windows Arm VM. Prerequisites
-    are a valid GitHub account, a Microsoft Azure account if you use a virtual machine, and some
-    familiarity with CI/CD concepts. This introductory path is intended for developers interested
-    in running CI on Windows on Arm and can be completed in about 30 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a valid GitHub account and some familiarity with CI/CD concepts. If you plan to
-      use a virtual machine, you also need a Microsoft Azure account.
-  - question: Can I use a virtual machine instead of physical Windows on Arm hardware?
-    answer: >-
-      Yes. You can use a cloud instance as the Windows on Arm host; if you choose a virtual machine,
-      an Azure account is required.
-  - question: How do I create the repository used for testing the workflow?
-    answer: >-
-      Log in to GitHub in your browser, select New to create a repository, give it a name, and
-      click Create Repository. This repo will host the workflow you run on the Windows on Arm
-      runner.
-  - question: How do I set up the Windows on Arm self-hosted runner, and what does it do?
-    answer: >-
-      Follow the path’s runner preparation steps to configure a GitHub self-hosted runner on your
-      Windows on Arm machine or cloud instance. The runner is the machine that executes your GitHub
-      Actions jobs.
-  - question: How do I create and run the sample GitHub Actions workflow, and what file should
-      I expect?
-    answer: >-
-      In your repository, select Actions, choose the Simple workflow option, and click Configure.
-      GitHub creates .github/workflows/blank.yml, which you can optionally rename (for example,
-      hello.yml); this minimal workflow runs a hello world command to validate the setup.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md
index 9971c94891..b60769889d 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md
@@ -16,60 +16,9 @@ learning_objectives:
 prerequisites:
   - A desktop or laptop running Windows on Arm.
   - Visual Studio 2022 Community Edition, WindowsPerf, WindowsPerf Visual Studio extension, and Windows Performance Analyzer (WPA) installed. 
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:36:56Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 1dd709b14537e06c80b9cdee58729cfa7066f44f0de4ceabe5450b33288731aa
-  summary_generated_at: '2026-06-02T02:37:03Z'
-  summary_source_hash: 1dd709b14537e06c80b9cdee58729cfa7066f44f0de4ceabe5450b33288731aa
-  faq_generated_at: '2026-06-02T23:36:56Z'
-  faq_source_hash: 1dd709b14537e06c80b9cdee58729cfa7066f44f0de4ceabe5450b33288731aa
-  summary: >-
-    This introductory Learning Path shows how to install and use the WindowsPerf Visual Studio
-    extension on Windows on Arm to generate counting and sampling reports and analyze performance
-    data in Windows Performance Analyzer (WPA). You configure Visual Studio 2022 Community Edition
-    with WindowsPerf, the WindowsPerf extension, and the WPA tooling, then run counting and sampling
-    sessions from within Visual Studio. You review results in Visual Studio and in WPA, and, if
-    your hardware supports it, explore the SPE subset of the sampling feature. By the end, you
-    can produce and examine WindowsPerf reports as part of a Windows on Arm development workflow.
-    No additional prerequisites are listed beyond the required tools.
-  faqs:
-  - question: What do I need installed before I start?
-    answer: >-
-      You need a Windows on Arm desktop or laptop with Visual Studio 2022 Community Edition, WindowsPerf,
-      the WindowsPerf Visual Studio extension, and Windows Performance Analyzer (WPA). The Learning
-      Path provides install guides for each tool.
-  - question: How do I open and configure the counting settings in Visual Studio?
-    answer: >-
-      In Visual Studio 2022, open the View menu and select Counting Settings to open the dialog.
-      From there, configure the counting parameters as shown in the Learning Path.
-  - question: How do I generate a counting report and review it in WPA?
-    answer: >-
-      After configuring counting, generate a report in Visual Studio and explore the data in the
-      IDE. You can then review the report in Windows Performance Analyzer (WPA) using the WindowsPerf
-      WPA plugin described in the Learning Path.
-  - question: Where do I find the sampling tools and set sampling preferences?
-    answer: >-
-      Open the View menu in Visual Studio 2022 and select Sampling Explorer. In the Sampling Explorer
-      window, use the Configure the sampling command icon to set your preferences.
-  - question: What should I check if the SPE feature does not work on my system?
-    answer: >-
-      The SPE section requires hardware that supports the Arm Statistical Profiling Extension.
-      If your CPU does not support SPE, this feature will not function and you should proceed
-      with the general Sampling feature instead.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: 
   - Nader Zouaoui
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf/_index.md
index f8953271fb..6456a4c2c4 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf/_index.md
@@ -13,58 +13,9 @@ learning_objectives:
 
 prerequisites:
     - Windows on Arm desktop or development machine
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:36:06Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 61a6390d42ce6bfc37a3bb981710dc23b8566070733f7979f9ec37bc63ab7d78
-  summary_generated_at: '2026-06-02T02:36:31Z'
-  summary_source_hash: 61a6390d42ce6bfc37a3bb981710dc23b8566070733f7979f9ec37bc63ab7d78
-  faq_generated_at: '2026-06-02T23:36:06Z'
-  faq_source_hash: 61a6390d42ce6bfc37a3bb981710dc23b8566070733f7979f9ec37bc63ab7d78
-  summary: >-
-    This introductory Learning Path shows how to install WindowsPerf on a Windows on Arm desktop
-    or development machine and generate sample CPU profiling reports. You will use the wperf command-line
-    interface to count ARM64 PMU events with wperf stat and to collect samples with wperf sample
-    and wperf record, producing example outputs at function, basic block, or instruction granularity.
-    The steps focus on practical, minimal commands and a cheat sheet to help you run counting
-    and sampling quickly. By the end, you will have WindowsPerf installed and be able to execute
-    basic profiling runs and view sample results. No additional prerequisites are explicitly listed
-    beyond a Windows on Arm machine.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Windows on Arm desktop or development machine. No additional prerequisites are
-      explicitly listed.
-  - question: Which wperf command should I use for counting versus sampling?
-    answer: >-
-      Use wperf stat for counting occurrences of PMU events. Use wperf sample or wperf record
-      for sampling to analyze where events occur in your code.
-  - question: How do I limit a count to a specific core and time window?
-    answer: >-
-      The cheat sheet includes an example: wperf stat -e inst_spec,vfp_spec,ase_spec,ld_spec -c
-      0 --timeout 3. This counts the listed events on core 0 for 3 seconds.
-  - question: What result should I expect from counting and sampling runs?
-    answer: >-
-      Counting provides aggregate totals of selected PMU events. Sampling reports event frequencies
-      attributed to program locations at the function, basic block, and/or instruction levels.
-  - question: Where can I find example PMU events and metrics to try?
-    answer: >-
-      Refer to the WindowsPerf cheat sheet, which shows practical examples including events like
-      inst_spec, vfp_spec, ase_spec, ld_spec and a metric example such as imix with an additional
-      event.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md
index 776c13e4e7..a55e72a3b2 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md
@@ -16,59 +16,9 @@ learning_objectives:
 prerequisites:
     - Windows on Arm desktop or development machine with [WindowsPerf installed](/install-guides/wperf)
     - Windows x86_64 desktop machine with [Visual Studio 2022 Community Edition](https://visualstudio.microsoft.com/vs/) installed.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:37:32Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: e23de84e7e05d771072ab799f5cc802476fd5e3c23da41a202c9c50fe9980e25
-  summary_generated_at: '2026-06-02T02:37:39Z'
-  summary_source_hash: e23de84e7e05d771072ab799f5cc802476fd5e3c23da41a202c9c50fe9980e25
-  faq_generated_at: '2026-06-02T23:37:32Z'
-  faq_source_hash: e23de84e7e05d771072ab799f5cc802476fd5e3c23da41a202c9c50fe9980e25
-  summary: >-
-    This Learning Path shows how to use WindowsPerf to sample a native Windows on Arm workload
-    by building CPython from sources for the ARM64 target and analyzing its runtime. You will
-    create a debug build, run CPython interactively, pin python_d.exe to a selected core, and
-    collect both counting and sampling data to locate hot code paths using PMU event frequencies.
-    The path also shows how to streamline the workflow with the WindowsPerf record command to
-    spawn and pin the process and forward arguments. Prerequisites include a Windows on Arm machine
-    with WindowsPerf installed and a Windows x86_64 desktop with Visual Studio 2022 Community
-    Edition. After completing, you will understand basic sampling and the WindowsPerf command
-    line for this scenario.
-  faqs:
-  - question: What do I need before running the examples?
-    answer: >-
-      You need a Windows on Arm desktop or development machine with WindowsPerf installed, and
-      a Windows x86_64 desktop with Visual Studio 2022 Community Edition installed. The sampling
-      examples are run on a native ARM64 Windows on Arm machine.
-  - question: Which CPython build should I use during the sampling exercises?
-    answer: >-
-      Use the debug build of CPython targeting ARM64 that you built from sources in the previous
-      step. The examples reference these pre-built ARM64 debug binaries.
-  - question: Which WindowsPerf command should I use to spawn and pin CPython to a core?
-    answer: >-
-      Use the record command with the -c option to pin to a specific core. You can specify the
-      process with --pe_file or place the process to spawn at the end of the wperf command.
-  - question: How do I pass command-line arguments to my program when using WindowsPerf?
-    answer: >-
-      Place all application arguments after the WindowsPerf options. They are passed verbatim
-      to the spawned program.
-  - question: What result should I expect when I run counting and sampling on the Googolplex workload?
-    answer: >-
-      Counting provides aggregate event counts, while sampling reports frequencies of PMU events.
-      Together they help you see hot locations in the CPython runtime image under the chosen workload.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Przemyslaw Wirkus
 
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
index ce1e369a53..d318718c85 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
@@ -13,57 +13,9 @@ learning_objectives:
 
 prerequisites:
     - A Windows on Arm laptop with WindowsPerf, Windows Performance Analyzer (WPA), and the WPA plugin installed.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:38:01Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 1fd4d85855933a5dfc5edf5ae3abf5f4388d94c9ee69aff12b77eb766e88301f
-  summary_generated_at: '2026-06-02T02:38:21Z'
-  summary_source_hash: 1fd4d85855933a5dfc5edf5ae3abf5f4388d94c9ee69aff12b77eb766e88301f
-  faq_generated_at: '2026-06-02T23:38:01Z'
-  faq_source_hash: 1fd4d85855933a5dfc5edf5ae3abf5f4388d94c9ee69aff12b77eb766e88301f
-  summary: >-
-    This Learning Path shows how to take performance data collected with WindowsPerf on a Windows
-    on Arm laptop and analyze it in Windows Performance Analyzer (WPA) using the WPA plugin. You
-    will generate a .json report from a WindowsPerf wperf stat run, import that file into WPA,
-    and use the plugin to visualize timeline and telemetry data. The focus is practical: connect
-    WindowsPerf output to WPA and inspect the resulting views. Prerequisites are explicitly listed:
-    a Windows on Arm laptop with WindowsPerf, Windows Performance Analyzer, and the WPA plugin
-    installed. The path is introductory and designed to be completed in about 15 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Windows on Arm laptop with WindowsPerf, Windows Performance Analyzer (WPA), and
-      the WPA plugin installed. The WPA plugin install guide includes installation of WPA.
-  - question: How do I create the .json file that WPA will import?
-    answer: >-
-      Run the WindowsPerf wperf stat command on your Windows on Arm machine and save the output
-      as a .json file using the --output option. This .json file is the input for WPA.
-  - question: Where should I run the wperf stat command?
-    answer: >-
-      Run wperf stat on a Windows on Arm machine. The .json output from that run is what you will
-      import into WPA.
-  - question: How do I know the import into WPA worked?
-    answer: >-
-      After importing the .json file, use the WPA plugin to view timeline and telemetry data.
-      If you can open the file and see these views, the import succeeded.
-  - question: What should I check if I do not see the plugin views in WPA?
-    answer: >-
-      Verify that the WPA plugin is installed and that you imported a .json file generated by
-      WindowsPerf using --output. Recreate the .json with wperf stat if needed and try the import
-      again.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Alaaeddine Chakroun
 
diff --git a/content/learning-paths/laptops-and-desktops/wsl2/_index.md b/content/learning-paths/laptops-and-desktops/wsl2/_index.md
index 97871cac32..1eadda70cb 100644
--- a/content/learning-paths/laptops-and-desktops/wsl2/_index.md
+++ b/content/learning-paths/laptops-and-desktops/wsl2/_index.md
@@ -18,61 +18,9 @@ learning_objectives:
 
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11. 
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:38:39Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 03d816ff419e6e5b1533f95e9d4ffa087b9c0c9aefeee112f67b70223c8aedc3
-  summary_generated_at: '2026-06-02T02:38:49Z'
-  summary_source_hash: 03d816ff419e6e5b1533f95e9d4ffa087b9c0c9aefeee112f67b70223c8aedc3
-  faq_generated_at: '2026-06-02T23:38:39Z'
-  faq_source_hash: 03d816ff419e6e5b1533f95e9d4ffa087b9c0c9aefeee112f67b70223c8aedc3
-  summary: >-
-    This Learning Path shows how to configure and run Windows Subsystem for Linux (WSL) on Windows
-    on Arm computers to support Linux and cloud-native development. You will set up WSL with various
-    Linux distributions, run graphical Linux applications on Windows 11, enable systemd so services
-    start automatically, and use SSH when remote access is required. You will also configure remote
-    desktop access with RDP and VNC, learn multiple options for running Visual Studio Code, and
-    import or export WSL file systems for backup. The intended audience is developers using Windows
-    on Arm systems; no additional prerequisites are explicitly listed beyond a Windows 11 device
-    such as a Lenovo ThinkPad X13s. Estimated time to complete is about 90 minutes.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      You need a Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11.
-      No other explicit prerequisites are listed.
-  - question: How do I know systemd is enabled and running in my WSL distribution?
-    answer: >-
-      Add systemd=true to /etc/wsl.conf, terminate the distribution, and restart it. Then run
-      systemctl list-unit-files --type=service to confirm systemd-managed services are available;
-      services such as SSH and docker will start automatically when systemd is enabled.
-  - question: How can I run and verify a graphical Linux application on Windows 11?
-    answer: >-
-      Install the application from the Linux command line in WSL (for example, terminator on Ubuntu
-      22.04) and launch it. A new window should appear on your Windows desktop, and the app will
-      show on the Windows taskbar with a penguin icon.
-  - question: Do I need SSH to move files between Windows and WSL on the same machine?
-    answer: >-
-      No. WSL mounts the Windows C: drive at /mnt/c, so you can copy files directly (for example,
-      cp /mnt/c/Users/<username>/Downloads/<filename> .). Use SSH only if you need to access WSL
-      from a different machine.
-  - question: What should I check if RDP does not display the Linux desktop?
-    answer: >-
-      Verify xfce4 and xrdp are installed, set XFCE4 as the default session (echo xfce4-session
-      > ~/.xsession), and restart the xrdp service. Check systemctl status xrdp and start it if
-      it is not running.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md b/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md
index 0f854b249f..8b13bce038 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md
@@ -16,63 +16,9 @@ prerequisites:
     - An appropriate Android device (e.g., Google Pixel 8) supporting the required Vulkan extensions.
     - Knowledge of the Vulkan API.
     - A Vulkan application that creates and uses images. This Learning Path shows how to use an API Sample in the [Khronos Vulkan Samples repository](https://github.com/KhronosGroup/Vulkan-Samples/blob/main/scripts/README.adoc#generate-api-sample) as an example.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:39:11Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: e4512ad20b372f616409199c51a38d95cb116ec6cf49d9004348d6bb8ee4014d
-  summary_generated_at: '2026-06-02T02:39:32Z'
-  summary_source_hash: e4512ad20b372f616409199c51a38d95cb116ec6cf49d9004348d6bb8ee4014d
-  faq_generated_at: '2026-06-02T23:39:11Z'
-  faq_source_hash: e4512ad20b372f616409199c51a38d95cb116ec6cf49d9004348d6bb8ee4014d
-  summary: >-
-    This Learning Path shows how to enable and verify Arm Fixed Rate Compression (AFRC) in Vulkan
-    applications on Android. You will check for VK_EXT_image_compression_control support (and
-    VK_EXT_image_compression_control_swapchain for swapchain images), query whether specific VkImage
-    configurations support fixed-rate compression, and request AFRC by chaining VkImageCompressionControlEXT
-    at image creation. The steps reference a Vulkan API Sample from the Khronos Vulkan Samples
-    repository as a test application. Prerequisites include an Android device (for example, Google
-    Pixel 8) that supports the required Vulkan extensions, familiarity with the Vulkan API, and
-    a Vulkan app that creates and uses images. By the end, you can confirm that compression is
-    applied to reduce memory footprint and bandwidth.
-  faqs:
-  - question: How do I know if my Android device supports the required Vulkan extensions for AFRC?
-    answer: >-
-      Use vkEnumerateDeviceExtensionProperties to look for VK_EXT_image_compression_control, and
-      include VK_EXT_image_compression_control_swapchain if you need swapchain images. If these
-      are not listed, the device does not meet the prerequisites for this path.
-  - question: Where do I enable the Vulkan extensions in my application?
-    answer: >-
-      Add the required extension names to VkDeviceCreateInfo::ppEnabledExtensionNames before calling
-      vkCreateDevice. This enables VK_EXT_image_compression_control (and the swapchain variant
-      when needed) for use in your Vulkan device.
-  - question: How do I query whether a specific image setup supports fixed-rate compression?
-    answer: >-
-      Define your intended VkImageCreateInfo properties (such as format, type, tiling, and usage)
-      and use them to query support on your platform before creating the image. The path shows
-      using those properties to drive the support check.
-  - question: How do I request fixed-rate compression at image creation time?
-    answer: >-
-      Provide a VkImageCompressionControlEXT structure in the pNext chain of VkImageCreateInfo
-      and set flags = VK_IMAGE_COMPRESSION_FIXED_RATE_DEFAULT_EXT. Then create the VkImage with
-      these settings applied.
-  - question: What result should I expect when verifying that compression was applied?
-    answer: >-
-      VK_EXT_image_compression_control can be used to verify whether default compression was applied
-      and to confirm your fixed-rate request. The verification indicates the compression chosen
-      for the image based on device support and your request.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jose-Emilio Munoz-Lopez
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md b/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md
index 57d4f4ded5..6281356b62 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md
@@ -13,62 +13,9 @@ learning_objectives:
 
 prerequisites:
     - A computer running Arm Linux or macOS with Docker installed
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:39:57Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: dee181248ecc8cb40e3ad76642fdb216cbf1e7610dde2f2605ba04f111b8926a
-  summary_generated_at: '2026-06-02T02:40:04Z'
-  summary_source_hash: dee181248ecc8cb40e3ad76642fdb216cbf1e7610dde2f2605ba04f111b8926a
-  faq_generated_at: '2026-06-02T23:39:57Z'
-  faq_source_hash: dee181248ecc8cb40e3ad76642fdb216cbf1e7610dde2f2605ba04f111b8926a
-  summary: >-
-    Build and run AI-powered camera pipeline applications on Arm using SME2 with KleidiAI and
-    KleidiCV. You will clone the ai-camera-pipelines repository with Git LFS, build a Docker container,
-    compile the C++ pipelines, apply background blur, denoising, and low-light effects, and run
-    provided benchmark binaries to exercise the hot loop and observe improvements from KleidiCV
-    and KleidiAI. The steps target an Arm64 system with SME2 support, with instructions tested
-    on Ubuntu 24.04. Prerequisites list a computer running Arm Linux or macOS with Docker installed,
-    plus Git and Git LFS. After completing the path, you can build, run, and benchmark these real-time
-    camera effects.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      Use an Arm64 machine with SME2 support; the instructions were tested on Ubuntu 24.04. The
-      prerequisites list a computer running Arm Linux or macOS with Docker installed, plus Git
-      and Git LFS.
-  - question: Which repository do I clone and why is Git LFS required?
-    answer: >-
-      Clone git.gitlab.arm.com/kleidi/kleidi-examples/ai-camera-pipelines.git. Git LFS is needed
-      to fetch the large files referenced by the project.
-  - question: How do I build the container used to compile the pipelines?
-    answer: >-
-      Build the Docker image from docker/Dockerfile with tag ai-camera-pipelines, passing the
-      build args DOCKERHUB_MIRROR=docker.io and CI_UID=$(id -u), targeting the docker/ directory.
-      Then start a shell in the container to compile the pipelines as shown in the steps.
-  - question: How do I run a background blur or other effect and verify success?
-    answer: >-
-      Create a Python virtual environment, install numpy, opencv-python, pillow, and torch, then
-      run the provided binaries from the bin directory. For background blur, run cinematic_mode
-      with resources/test_input.png and expect an output image like test_output_cinematic_mode.png.
-  - question: How do I run benchmarks and what result should I expect?
-    answer: >-
-      Use the benchmark executables: cinematic_mode_benchmark, low_light_image_enhancement_benchmark,
-      and neural_denoiser_temporal_benchmark_4K. They run the core processing loop multiple times
-      and demonstrate improvements enabled by KleidiCV (OpenCV kernels on Arm) and KleidiAI (LiteRT+XNNPack
-      inference micro-kernels).
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Arnaud de Grandmaison
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md b/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
index 5a92f75523..105b93a5b5 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
@@ -19,62 +19,9 @@ prerequisites:
     - A debuggable build of your application. 
     - Arm Performance Studio installed. Follow the [Arm Performance Studio install guide](/install-guides/ams) for instructions.
     - Android SDK Platform tools installed. Required for the Android Debug bridge (adb).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:41:00Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 3d1a743c1b3ee617f52191fc9c9fd33a9d44454ac079f00b6f532e2865103377
-  summary_generated_at: '2026-06-02T02:40:50Z'
-  summary_source_hash: 3d1a743c1b3ee617f52191fc9c9fd33a9d44454ac079f00b6f532e2865103377
-  faq_generated_at: '2026-06-02T23:41:00Z'
-  faq_source_hash: 3d1a743c1b3ee617f52191fc9c9fd33a9d44454ac079f00b6f532e2865103377
-  summary: >-
-    This introductory path shows Android developers how to start profiling apps on devices with
-    Mali-based GPUs using Arm Performance Studio. You will install the tools, connect an Android
-    device over adb, explore a provided Streamline example capture, then profile your own debuggable
-    build and generate a Performance Advisor HTML report using streamline-cli. The walkthrough
-    focuses on Streamline and Performance Advisor basics. Prerequisites include an Android device,
-    a debuggable app using OpenGL ES 2.0–3.2 or Vulkan 1.0–1.2 (Android 10+ for OpenGL ES, Android
-    9+ for Vulkan), Arm Performance Studio installed, Android SDK Platform Tools (adb), and Python
-    3.8 or later for the Performance Advisor script.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an Android device, a debuggable build of your application, Arm Performance Studio
-      installed, and the Android SDK Platform Tools (adb). For Performance Advisor’s connection
-      script, install Python 3.8 or later.
-  - question: Which graphics APIs and Android versions are supported?
-    answer: >-
-      Arm Performance Studio supports OpenGL ES versions 2.0 to 3.2 and Vulkan versions 1.0 to
-      1.2. For OpenGL ES applications your device must run Android 10 or later; for Vulkan applications
-      your device must run Android 9 or later.
-  - question: How do I connect my Android device in Streamline?
-    answer: >-
-      Launch the Performance Studio Hub and open Streamline, then in the Start view select Android
-      (adb) and choose your device. Streamline installs the gatord daemon and connects to the
-      device; if your device is not listed, check that adb from the Android SDK Platform Tools
-      is installed.
-  - question: How do I open the example Streamline capture?
-    answer: >-
-      In Streamline, select File > Import, then choose Import Streamline Sample Captures and select
-      the Android example. After import, double-click the report in Streamline Data to view it.
-  - question: How do I generate a Performance Advisor report from a Streamline capture?
-    answer: >-
-      From a terminal, navigate to the capture and run the streamline-cli command with the -pa
-      option on the .apc file. The capture is processed and an HTML report is generated, with
-      warnings shown where applicable.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md b/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md
index f1ceaabf0f..899713235e 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md
@@ -17,62 +17,9 @@ prerequisites:
     - A debuggable build of your application. 
     - Download and install Arm Performance Studio from [Product Download Hub](https://developer.arm.com/downloads/view/MOBST-PRO0). It is supported on Windows, Linux, and macOS host platforms.
     - Download and install [Android SDK Platform tools](https://developer.android.com/studio/releases/platform-tools.html). Required for [Android Debug bridge (adb)](https://developer.android.com/studio/command-line/adb).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:42:06Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: d5406f24ab38522b21e348ed3829ede7b1bcdce28e81ec4fddebbec280d2cb89
-  summary_generated_at: '2026-06-02T02:41:30Z'
-  summary_source_hash: d5406f24ab38522b21e348ed3829ede7b1bcdce28e81ec4fddebbec280d2cb89
-  faq_generated_at: '2026-06-02T23:42:06Z'
-  faq_source_hash: d5406f24ab38522b21e348ed3829ede7b1bcdce28e81ec4fddebbec280d2cb89
-  summary: >-
-    This introductory Learning Path shows how to use Frame Advisor in Arm Performance Studio to
-    capture a significant frame from an Android application and analyze where time is spent. You
-    will connect a supported device, start a trace from Frame Advisor, and examine the captured
-    frame’s render passes and draw calls, including primitive counts. You will use the Render
-    Graph to understand how data flows between passes and to spot attachments that do not contribute
-    to the final output, and the Content Metrics view to identify complex meshes by sorting and
-    navigating to high-primitive draw calls. Prerequisites include a debuggable build, Arm Performance
-    Studio on Windows, Linux, or macOS, Android SDK Platform-tools (adb), and an Android device
-    running OpenGL ES 2.0–3.2 (Android 10+) or Vulkan 1.0–1.2 (Android 9+).
-  faqs:
-  - question: What do I need before running Frame Advisor?
-    answer: >-
-      You need a supported Android device, a debuggable build of your app, Arm Performance Studio
-      installed on Windows, Linux, or macOS, and Android SDK Platform Tools (adb). Frame Advisor
-      supports OpenGL ES 2.0–3.2 on Android 10+ and Vulkan 1.0–1.2 on Android 9+.
-  - question: How do I start a capture trace from my device?
-    answer: >-
-      Open Frame Advisor and choose New Trace. Select your connected device and the target application,
-      switch the API to Vulkan if needed, then click Next to start the capture session; the app
-      launches automatically on the device.
-  - question: How do I know the capture and analysis worked?
-    answer: >-
-      When analysis completes, the Analysis screen appears with the Frame Hierarchy listing captured
-      frames, render passes, and draw calls. You can step through draw calls to see how the scene
-      is built.
-  - question: Which view helps me find unused render passes or attachments?
-    answer: >-
-      Use the render graph. It visualizes how data flows between render passes and resources so
-      you can spot passes or attachments that are not used in the final output to the swapchain.
-  - question: How can I locate the most complex meshes in my scene?
-    answer: >-
-      Open the Content Metrics view, select Draws, and sort by the highest number of primitives
-      (Prims). Right-click a top entry and choose Navigate to call to select it in the Frame Hierarchy
-      and view it in the Framebuffers view.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Julie Gaskin
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md
index ff4b577039..ce0b487a7c 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md
@@ -15,59 +15,9 @@ prerequisites:
     - An Android development environment with Android Studio installed
     - An Android phone for testing, in Developer Mode, with USB cable for connection
     - Basic familiarity with Kotlin and Android app development
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:42:41Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: e63ac917c218aa5e5981f96a9821567d0f8ba9761d5ce6e4c9d7b6dea7ed5c5f
-  summary_generated_at: '2026-06-02T02:41:57Z'
-  summary_source_hash: e63ac917c218aa5e5981f96a9821567d0f8ba9761d5ce6e4c9d7b6dea7ed5c5f
-  faq_generated_at: '2026-06-02T23:42:41Z'
-  faq_source_hash: e63ac917c218aa5e5981f96a9821567d0f8ba9761d5ce6e4c9d7b6dea7ed5c5f
-  summary: >-
-    Build a simple Android chatbot app that runs a local LLM on-device using Arm’s AI Chat library.
-    You will create a new Android Studio project, verify google() and mavenCentral() repositories,
-    add the library dependency, design a basic chat UI, and implement MainActivity in Kotlin to
-    load a GGUF model and stream chat responses. The library wraps llama.cpp with Arm CPU optimizations
-    for GGUF models. You will download a mobile-friendly GGUF, such as google_gemma-3-4b-it-Q4_0.gguf,
-    sized for your device and run the app on a physical Android phone in Developer Mode. Prerequisites
-    include Android Studio, a USB-connected Android phone, and basic familiarity with Kotlin and
-    Android app development. Reference implementations include the Arm AI Chat app on Google Play.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      Install Android Studio, have an Android phone in Developer Mode with a USB cable, and be
-      comfortable with basic Kotlin and Android app development. No other prerequisites are explicitly
-      listed.
-  - question: Which repositories should be in settings.gradle.kts to resolve the AI Chat library?
-    answer: >-
-      Ensure the top-level repositories include google() and mavenCentral(). This allows Gradle
-      to find the AI Chat library from Maven Central.
-  - question: Where do I add the AI Chat dependency and what is the coordinate?
-    answer: >-
-      Add the dependency in the app module’s build file (app/build.gradle.kts), not the project-level
-      file. Use implementation "com.arm:ai-chat:0.1.0".
-  - question: How do I choose a mobile-compatible GGUF model, and is there an example?
-    answer: >-
-      Pick a model that is significantly smaller than your device’s RAM to leave room for the
-      OS and other apps. A good example provided is google_gemma-3-4b-it-Q4_0.gguf.
-  - question: What result should I expect when I run the app, and how do I know it’s working?
-    answer: >-
-      The app should load your selected GGUF model on-device and produce streamed chat responses
-      in the UI. If the build cannot resolve the library, re-check that google() and mavenCentral()
-      are configured and that the dependency was added to app/build.gradle.kts.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Ben Clark
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
index a1dba23d66..8bb7f19652 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
@@ -15,63 +15,9 @@ learning_objectives:
 prerequisites:
     - Basic C++ knowledge
     - Android Studio with Android Emulator
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:43:24Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: fa04ff97605ae93b17c056c397c37f6fc17cf6f4853bfabe374610ede002e3df
-  summary_generated_at: '2026-06-02T02:42:22Z'
-  summary_source_hash: fa04ff97605ae93b17c056c397c37f6fc17cf6f4853bfabe374610ede002e3df
-  faq_generated_at: '2026-06-02T23:43:24Z'
-  faq_source_hash: fa04ff97605ae93b17c056c397c37f6fc17cf6f4853bfabe374610ede002e3df
-  summary: >-
-    This introductory Learning Path shows how to build and integrate real-time image processing
-    pipelines with Halide on Android. You start by installing and configuring Halide, then build
-    a camera pipeline that captures frames using OpenCV, applies Gaussian (binomial) blur and
-    thresholding, and measures performance while exploring Halide scheduling (parallelization
-    and tiling). You then apply operator fusion and learn when to materialize intermediates with
-    compute_root() or compute_at(), using print_loop_nest() to inspect the schedule. Next, you
-    perform ahead-of-time cross-compilation on the host to generate a library for Android (for
-    example, arm64-v8a), and integrate it into an Android app written in Kotlin using Android
-    Studio. Prerequisites are basic C++ knowledge and Android Studio with Android Emulator.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      You need basic C++ knowledge and Android Studio with Android Emulator. No other prerequisites
-      are explicitly listed.
-  - question: What result should I expect from the initial pipeline, and how do I confirm it worked?
-    answer: >-
-      The pipeline applies Gaussian blur followed by thresholding to produce a binary output that
-      highlights prominent features. If you see smoothed frames and a clear binary image derived
-      from the captured frames, the pipeline is running as intended. You will also measure performance
-      as part of the steps.
-  - question: Which Halide scheduling options will I use, and how can I inspect the schedule?
-    answer: >-
-      You will explore parallelization and tiling to improve throughput. Use print_loop_nest()
-      to see how Halide arranges the computation loops under your chosen schedule.
-  - question: When should I use operator fusion versus materializing intermediates?
-    answer: >-
-      Use fusion to compute stages inside their consumers to reduce memory traffic and improve
-      cache efficiency. Materialize intermediates with compute_root() or compute_at() for large
-      filters or when results are reused by multiple stages.
-  - question: Where does Android compilation happen, and what target should I build for?
-    answer: >-
-      Compilation occurs on the host using Halide’s ahead-of-time cross-compilation to produce
-      an Android pipeline library. The example targets an ABI such as arm64-v8a and avoids building
-      Halide or performing JIT on the device, preparing the library for integration into a Kotlin
-      Android app.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Éliás Bálint, Dawid Borycki, Steve Suzuki
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_neon/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_neon/_index.md
index ece58cd5b4..575bdd980d 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_neon/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_neon/_index.md
@@ -19,11 +19,9 @@ learning_objectives:
 prerequisites:
     - A x86_64 or Apple M1 development machine with Android Studio installed.
     - A 64-bit Arm powered smartphone running Android.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 author: Dawid Borycki
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md
index bc39132807..e0c4d8afc9 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md
@@ -14,59 +14,9 @@ learning_objectives:
 prerequisites:
     - A development machine with [Android Studio](https://developer.android.com/studio) installed. 
     - An Android smartphone.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:44:45Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 2137cec46fe8d57f11e9e4011306a140bba7d8a5b2326a746193c1794a148f75
-  summary_generated_at: '2026-06-02T02:42:50Z'
-  summary_source_hash: 2137cec46fe8d57f11e9e4011306a140bba7d8a5b2326a746193c1794a148f75
-  faq_generated_at: '2026-06-02T23:44:45Z'
-  faq_source_hash: 2137cec46fe8d57f11e9e4011306a140bba7d8a5b2326a746193c1794a148f75
-  summary: >-
-    Build an introductory Android camera app that uses OpenCV to process images on an Arm-based
-    smartphone. Working in Android Studio on Windows, you create a Kotlin project, integrate the
-    OpenCV library, enable camera permissions, and capture real-time frames using JavaCameraView.
-    You then manage Mat objects and implement adaptive thresholding with OpenCV’s Imgproc.adaptiveThreshold,
-    controlled by a simple UI toggle for real-time processing. The result is a runnable app on
-    an Android smartphone that demonstrates live camera capture and basic computer vision processing.
-    No additional prerequisites are explicitly listed beyond Android Studio on your development
-    machine and an Android smartphone. Estimated time to complete is about 30 minutes.
-  faqs:
-  - question: Which Android Studio version should I use for this path?
-    answer: >-
-      The example uses Android Studio Jellyfish | 2023.3.1 Patch 1. The Learning Path does not
-      list other versions, so follow the steps as shown with that release.
-  - question: Do I need to develop on Windows to follow the steps?
-    answer: >-
-      Yes, the target operating system for the development machine in this Learning Path is Windows.
-      The instructions and tooling are presented with that environment in mind.
-  - question: Should I use Kotlin or Java for the project?
-    answer: >-
-      The steps configure the project to use Kotlin and show edits in MainActivity.kt. Java is
-      listed among the tools, but the provided code examples use Kotlin.
-  - question: How do I know OpenCV is integrated correctly?
-    answer: >-
-      Follow the steps to add the OpenCV library and imports like org.opencv.imgproc.Imgproc.
-      A successful build and the ability to run the app with JavaCameraView and adaptive thresholding
-      indicate the integration is working.
-  - question: What result should I expect when I run the app on my phone?
-    answer: >-
-      You should see a camera preview from JavaCameraView. When you check the provided checkbox,
-      adaptive thresholding is applied to the live frames; unchecking it shows the unprocessed
-      preview.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md
index e1fb83da93..f0259c6342 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md
@@ -16,59 +16,9 @@ prerequisites:
    - A development machine with [Android Studio](https://developer.android.com/studio) installed. 
    - An Android smartphone.
    - Familiarity with OpenCV, review [Create Computer Vision Applications with OpenCV on Android Devices](/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/) before starting.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:45:29Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 3a120620bbc56e796aa45fbe01aa1455fbee085a1a4cf0d055416b7e95e72d0d
-  summary_generated_at: '2026-06-02T02:43:15Z'
-  summary_source_hash: 3a120620bbc56e796aa45fbe01aa1455fbee085a1a4cf0d055416b7e95e72d0d
-  faq_generated_at: '2026-06-02T23:45:29Z'
-  faq_source_hash: 3a120620bbc56e796aa45fbe01aa1455fbee085a1a4cf0d055416b7e95e72d0d
-  summary: >-
-    Build an introductory Android app that detects faces in real time using OpenCV. Working in
-    Android Studio on Windows or macOS, you will create a Kotlin project, add OpenCV, retrieve
-    camera frames, and apply a Haar cascade classifier using a pre-trained XML file. The steps
-    focus on the essentials for camera access and classical face detection with OpenCV on Android
-    devices, relevant to Arm Cortex-A based smartphones. Prerequisites include Android Studio
-    on your development machine, an Android smartphone, and familiarity with OpenCV (with a recommended
-    prior Learning Path for review). The estimated time to complete is about 30 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need Android Studio installed on a Windows or macOS machine, an Android smartphone,
-      and familiarity with OpenCV. The path recommends reviewing the “Create Computer Vision Applications
-      with OpenCV on Android Devices” Learning Path first.
-  - question: Do I need a specific version of Android Studio?
-    answer: >-
-      The example uses Android Studio Jellyfish | 2023.3.1 Patch 1. If you use a different version,
-      expect minor UI differences when creating the project.
-  - question: Which Haar cascade file should I use and how is it included?
-    answer: >-
-      This path uses OpenCV’s pre-trained Haar cascades, which are XML files. The steps indicate
-      which cascade to use and how to include it in your project.
-  - question: How do I know OpenCV is correctly added and camera frames are being read?
-    answer: >-
-      After following the setup steps, you should be able to build the project and retrieve camera
-      frames via OpenCV without errors. If frame retrieval works, proceed to the face detection
-      step with the Haar cascade.
-  - question: What should I check if faces are not being detected?
-    answer: >-
-      Confirm that the correct Haar cascade XML file is included and loaded, and that valid camera
-      frames are being passed to the classifier. Revisit the steps to ensure OpenCV integration
-      and frame retrieval are configured as shown.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
index 4265973fcb..96c20e5dbb 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
@@ -16,60 +16,9 @@ prerequisites:
     - A development machine with [Android Studio](https://developer.android.com/studio) installed. 
     - Familiarity with Android development concepts.
     - An Android smartphone.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:46:04Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 8e05fb124ad18194fba2ed83adae3e52673b2fad41af998ca8d0aa8cb9785f5e
-  summary_generated_at: '2026-06-02T02:43:45Z'
-  summary_source_hash: 8e05fb124ad18194fba2ed83adae3e52673b2fad41af998ca8d0aa8cb9785f5e
-  faq_generated_at: '2026-06-02T23:46:04Z'
-  faq_source_hash: 8e05fb124ad18194fba2ed83adae3e52673b2fad41af998ca8d0aa8cb9785f5e
-  summary: >-
-    This introductory Android Learning Path shows how to build an OpenCV-based app accelerated
-    with KleidiCV. You will create a new Android Studio project, add OpenCV with KleidiCV support,
-    define a simple UI, and implement image processing on an input image. The app uses an ImageOperation
-    enum (for tasks such as Gaussian blur, resizing, and rotation), an ImageProcessor that works
-    with OpenCV Mat objects, and a PerformanceMetrics class that reports statistics like average
-    and standard deviation. The path targets Android development in Android Studio using Kotlin
-    or Java. Prerequisites include Android Studio, familiarity with Android development concepts,
-    and an Android smartphone. Estimated completion time is about 45 minutes.
-  faqs:
-  - question: What do I need before running through the steps?
-    answer: >-
-      You need a development machine with Android Studio installed, familiarity with Android development
-      concepts, and an Android smartphone. No other prerequisites are explicitly listed.
-  - question: Which Android Studio version is referenced in the example?
-    answer: >-
-      The example uses Android Studio Ladybug 2024.2.1, Patch 3. If you are using a different
-      version, menu names or screens may vary slightly from the instructions.
-  - question: Where should I place the test image, and does it have to be PNG?
-    answer: >-
-      Create an assets folder under src/main and add an img.png file there. The app will convert
-      the image as needed, and any image file can be used; the Learning Path uses a cameraman
-      image.
-  - question: Which files do I edit to define the UI and application logic?
-    answer: >-
-      Replace the contents of app/src/main/res/layout/activity_main.xml to define the UI. For
-      logic, create the ImageOperation enum, ImageProcessor class, and PerformanceMetrics class
-      as outlined in the steps.
-  - question: What result should I expect when I run the app on my device?
-    answer: >-
-      The app processes the bundled image using operations such as Gaussian blur, resizing, and
-      rotation. It also displays performance metrics, including average and standard deviation,
-      for the executed operations.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md
index fba89a4db4..f9cc57da74 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md
@@ -15,60 +15,9 @@ prerequisites:
     - Knowledge of Single instruction Multi Data (SIMD)
     - Knowledge of [Neon](https://developer.arm.com/documentation/102474/latest)
     - Knowledge of [Scalable Vector Extension (SVE)](https://developer.arm.com/documentation/101726/4-0)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:46:40Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: be91053c5abcdd669ff8da7e66b2f717c4adaac27b3fb44ea073b69a68d2dba7
-  summary_generated_at: '2026-06-02T02:44:16Z'
-  summary_source_hash: be91053c5abcdd669ff8da7e66b2f717c4adaac27b3fb44ea073b69a68d2dba7
-  faq_generated_at: '2026-06-02T23:46:40Z'
-  faq_source_hash: be91053c5abcdd669ff8da7e66b2f717c4adaac27b3fb44ea073b69a68d2dba7
-  summary: >-
-    This Learning Path guides you through enabling Scalable Vector Extension 2 (SVE2) in Android
-    Studio and implementing a native Android NDK example that computes vector fused multiply-add
-    (a * b + c) using SVE2 intrinsics. You will write C++ code to generate pseudo-random input
-    data, add helper functions, and create a reusable measureExecutionTime template to time N
-    invocations of FMA implementations with and without SVE2 on a 64-bit Arm smartphone running
-    Android. The introduction places SVE2 in the context of the ARMv9-A architecture. Prerequisites
-    include Android Studio on an x86_64 or Apple development machine, a 64-bit Arm Android device,
-    and familiarity with SIMD, Neon, and SVE. The expected outcome is a working project that builds,
-    runs, and compares execution times.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need Android Studio installed on an x86_64 or Apple development machine and access to
-      a 64-bit Arm smartphone running Android. Prior knowledge of SIMD, Neon, and SVE is expected.
-  - question: How do I enable SVE2 support in Android Studio for this project?
-    answer: >-
-      Follow the step titled “Enable SVE2 support in Android Studio” to configure the project
-      so SVE2 intrinsics compile for your NDK code. The path guides you through the necessary
-      project changes to build and run on a 64-bit Arm device.
-  - question: Which source file do I modify to add the FMA and timing code?
-    answer: >-
-      You will modify native-lib.cpp located under app/cpp/. This file is updated to implement
-      the FMA routines and the measureExecutionTime template function.
-  - question: How is performance measured, and what result should I expect to see?
-    answer: >-
-      A measureExecutionTime template function runs N invocations of the FMA implementations with
-      and without SVE2 intrinsics and returns their execution times. You should see timing results
-      that let you compare the two paths; specific numbers are not provided.
-  - question: Can I complete this path without a physical Arm-based Android device?
-    answer: >-
-      A 64-bit Arm-powered smartphone running Android is listed as a prerequisite. The path does
-      not list an emulator or non-Arm alternative.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
index 2c1953f42a..45ffdd8c2d 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
@@ -24,61 +24,9 @@ prerequisites:
     - Android Studio.
     - Arm Performance Studio.
     - Python 3.10 or later.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:47:07Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 8f58429f12e40b53e8a2e0d7eb260f22fbb7ac869ca64554a906ddcd28c9fd75
-  summary_generated_at: '2026-06-02T02:44:35Z'
-  summary_source_hash: 8f58429f12e40b53e8a2e0d7eb260f22fbb7ac869ca64554a906ddcd28c9fd75
-  faq_generated_at: '2026-06-02T23:47:07Z'
-  faq_source_hash: 8f58429f12e40b53e8a2e0d7eb260f22fbb7ac869ca64554a906ddcd28c9fd75
-  summary: >-
-    This Learning Path shows how to integrate Dawn WebGPU into a C++-based Android Game Activity,
-    render a simple 3D object using WebGPU APIs, and profile the application with Arm Streamline.
-    You will set up a development environment on macOS, Linux, or Windows with Android Studio
-    (including the NDK), Arm Performance Studio, Blender, and Python 3.10, and use an Android
-    phone in developer mode. The steps introduce WebGPU fundamentals, create and configure the
-    Android Studio project, add Dawn and renderer sources, build and run the app, and capture
-    and analyze profiling data. By the end, you have a working WebGPU Android application and
-    a Streamline capture to review.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need Android Studio, Arm Performance Studio, Python 3.10 or later, and Blender installed
-      on your development machine. You also need an Android phone in developer mode. Basic knowledge
-      of graphics APIs and experience developing Android graphics applications are expected.
-  - question: Which Android Studio project template should I start with?
-    answer: >-
-      Create a new project using the Game Activity (C++) template. Name the project dawnwebgpu
-      and accept the default selections until the project is created in ~/AndroidStudioProjects.
-  - question: How should I set up the Android SDK and NDK for this project?
-    answer: >-
-      Install the latest Android Studio and the Android NDK. In Settings > Languages & Frameworks
-      > Android SDK, enable the Android 14.0 (UpsideDownCake) platform, then use the SDK Tools
-      tab to install the required tools including the NDK.
-  - question: After integrating Dawn, which project files do I keep or add?
-    answer: >-
-      Delete all files from the top-level cpp directory except CMakeLists.txt. Add webgpuRenderer.cpp
-      and webgpuRenderer.h, and use the provided commands to copy in a new main.cpp and the WebGPU
-      renderer files.
-  - question: When should I profile the app with Streamline and what is the expected outcome?
-    answer: >-
-      After the application builds and runs on your Android device, use Arm Performance Studio’s
-      Streamline to profile it. You will capture and analyze profiling data to understand the
-      app’s behavior as described in the steps.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author:
     - Varun Chari
diff --git a/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md b/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
index 29a275ec9e..3c5ffbd646 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
@@ -15,62 +15,9 @@ prerequisites:
 - A computer capable of running [Unreal Engine 5.3 or later version](https://www.unrealengine.com/en-US/download).
 - An Android mobile device that has a Mali GPU with hardware ray tracing support.
 - A USB cable to connect the mobile device to your computer.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:47:45Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: ef70ed2089132df657bd1ebfb9a66a39934d8a118b1e73974148ce11c104fef5
-  summary_generated_at: '2026-06-02T02:45:10Z'
-  summary_source_hash: ef70ed2089132df657bd1ebfb9a66a39934d8a118b1e73974148ce11c104fef5
-  faq_generated_at: '2026-06-02T23:47:45Z'
-  faq_source_hash: ef70ed2089132df657bd1ebfb9a66a39934d8a118b1e73974148ce11c104fef5
-  summary: >-
-    This introductory Learning Path shows Unreal Engine developers how to improve hardware ray
-    tracing with Lumen on Android devices powered by Arm Mali GPUs, including Immortalis series.
-    In approximately 30 minutes, you learn the basics of ray tracing and acceleration structures,
-    then apply best practices in Unreal Engine 5.3 or later to get the most from Lumen on Arm
-    devices. You will trim the acceleration structure by excluding non‑contributing and very small
-    actors, use instancing so BLAS data is shared, and minimize mesh overlap. The path uses Unreal
-    Editor Ray Tracing Debug tools such as the Instance Overlap view and r.RayTracing.Debug.PickerDomain
-    to inspect changes. Prerequisites are a machine that can run Unreal Engine 5.3+, an Android
-    device with hardware ray tracing on a Mali GPU, and a USB cable.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a computer capable of running Unreal Engine 5.3 or later, an Android device with
-      a Mali GPU that supports hardware ray tracing, and a USB cable to connect the device to
-      your computer. No other prerequisites are explicitly listed.
-  - question: Should I enable Lumen hardware ray tracing before following these optimizations?
-    answer: >-
-      If you are not familiar with Lumen and global illumination, review the guidance on enabling
-      hardware ray tracing for Lumen on Android devices before proceeding. This path focuses on
-      optimization once hardware ray tracing is available.
-  - question: How do I exclude actors that don’t help lighting from ray tracing?
-    answer: >-
-      In Unreal Editor, use the actor details panel to turn off the appropriate ray tracing visibility
-      for objects that do not contribute to lighting and for very small actors. This reduces geometry
-      in the acceleration structure and can cut noise in indirect lighting.
-  - question: How can I check and use instancing to improve efficiency?
-    answer: >-
-      Instanced actors share geometry in the BLAS, reducing memory and improving cache behavior.
-      To inspect instancing, run the command r.RayTracing.Debug.PickerDomain 1 and use the Ray
-      Tracing Debug Picker in the Unreal Editor.
-  - question: How do I identify and reduce mesh overlap in the acceleration structure?
-    answer: >-
-      Open the Instance Overlap view under Ray Tracing Debug to visualize overlap in your level.
-      Aim for tight actor bounding boxes with minimal empty space to lower traversal cost.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Owen Wu
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md b/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md
index 54011fc894..4239dec95d 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md
@@ -14,60 +14,9 @@ learning_objectives:
 prerequisites:
     - A Windows x86_64 development machine with at least 16GB of RAM.
     - An Android phone with at least 8GB of RAM. This learning path was tested on Samsung Galaxy S24.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:48:15Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: deeb745d72457138cb81252c421205cd8dfb43b5e29ceee3a15c5842cc90cc33
-  summary_generated_at: '2026-06-02T02:45:40Z'
-  summary_source_hash: deeb745d72457138cb81252c421205cd8dfb43b5e29ceee3a15c5842cc90cc33
-  faq_generated_at: '2026-06-02T23:48:15Z'
-  faq_source_hash: deeb745d72457138cb81252c421205cd8dfb43b5e29ceee3a15c5842cc90cc33
-  summary: >-
-    This advanced Learning Path guides you through cross-compiling ONNX Runtime and its generate()
-    API for Android on a Windows x86_64 host, then running a Phi-3 model on an Arm-based (Cortex-A)
-    smartphone. You will set up Android Studio, the Android NDK (tested with 27.3.13750724), Python
-    3.13, CMake 4.1.0, and Ninja 1.12.1; build ONNX Runtime and the onnxruntime-genai Generate()
-    API; prepare and run a Phi-3-mini model; and view performance metrics using a command-line
-    model runner. You will also build and run a Kotlin-based Android chat demo from the onnxruntime-inference-examples
-    repository. Prerequisites include a Windows machine with at least 16GB RAM and an Android
-    phone with at least 8GB RAM (tested on Samsung Galaxy S24).
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Windows x86_64 development machine with at least 16GB of RAM and an Android phone
-      with at least 8GB of RAM. This path was tested on a Samsung Galaxy S24. The operating systems
-      used are Windows and Android.
-  - question: Which software versions should I install for the build environment?
-    answer: >-
-      Install Android Studio (latest recommended), Android NDK tested with version 27.3.13750724,
-      Python 3.13, CMake tested with version 4.1.0, and Ninja tested with version 1.12.1. These
-      versions are referenced in the steps.
-  - question: What is the build target for ONNX Runtime and the generate() API?
-    answer: >-
-      You cross-compile ONNX Runtime and the generate() API for Android CPU. The steps use the
-      Android NDK toolchain during the build.
-  - question: Where should the CMake toolchain file point when building the model runner?
-    answer: >-
-      Set -DCMAKE_TOOLCHAIN_FILE to the android.toolchain.cmake file inside your installed Android
-      NDK. The example path in the steps references NDK 27.3.13750724 under the Android SDK; update
-      it to match your local installation.
-  - question: What result should I expect when running the benchmark on the phone?
-    answer: >-
-      The benchmark prepares and runs a Phi-3-mini model on your Android device. You should be
-      able to view performance metrics produced by the model runner.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Koki Mitsunami
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md b/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md
index c3f7d6bca8..3b8dc6f73a 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md
@@ -19,63 +19,9 @@ prerequisites:
     - Familiarity with Android development concepts.
     - Basic knowledge of Modern Android Architecture. See [Modern Android App Architecture](https://developer.android.com/courses/pathways/android-architecture).
     - Basic knowledge of Kotlin programming language, including [Coroutines](https://kotlinlang.org/docs/coroutines-overview.html) and [Kotlin Flows](https://kotlinlang.org/docs/flow.html).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:49:17Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 13ff69755e51c6d7c355616f95703b3f2cefaedcf1f8dbeab31fe2e3db9aec74
-  summary_generated_at: '2026-06-02T02:46:24Z'
-  summary_source_hash: 13ff69755e51c6d7c355616f95703b3f2cefaedcf1f8dbeab31fe2e3db9aec74
-  faq_generated_at: '2026-06-02T23:49:17Z'
-  faq_source_hash: 13ff69755e51c6d7c355616f95703b3f2cefaedcf1f8dbeab31fe2e3db9aec74
-  summary: >-
-    Build a hands-free selfie Android app that runs on a recent Arm-powered Android phone using
-    MediaPipe multimodal AI, Kotlin Flows, CameraX, and an MVVM architecture. You will set up
-    Android Studio, connect a device with USB debugging, manage camera permissions, add MediaPipe
-    dependencies, and incorporate Jetpack Lifecycle components. The path shows how to combine
-    MediaPipe face landmark detection and gesture recognition, access camera features with CameraX,
-    and handle multiple asynchronous data streams with SharedFlow and StateFlow. Prerequisites
-    include Android Studio, a front-facing camera device, familiarity with Android development
-    and Modern Android Architecture, and basic Kotlin knowledge (Coroutines and Flows). Estimated
-    time to complete is about 120 minutes.
-  faqs:
-  - question: What do I need before running the app on a device?
-    answer: >-
-      Install Android Studio on your development machine and have a recent Arm-powered Android
-      phone with a front-facing camera and a USB data cable. You should be familiar with Android
-      development, Modern Android Architecture, Kotlin Coroutines, and Kotlin Flows.
-  - question: How do I know my Android Studio setup is complete before coding?
-    answer: >-
-      Open Android Studio, accept license agreements, download all required assets, and choose
-      the default or recommended settings. These steps prepare the environment used throughout
-      the Learning Path.
-  - question: How do I set up and verify device debugging over USB?
-    answer: >-
-      Enable USB debugging on your device, then connect it by USB and tap OK on the Allow USB
-      debugging dialog. Check Always allow from this computer so Android Studio can deploy and
-      debug the app without repeated prompts.
-  - question: Which option should I use to access the camera in this app?
-    answer: >-
-      Use JetPack CameraX to access camera features. Camera permissions are handled in a dedicated
-      step before running the app on your device.
-  - question: How do I add MediaPipe and handle UI state and events?
-    answer: >-
-      Add MediaPipe dependencies by updating libs.versions.toml and project settings as shown
-      in the steps that introduce MediaPipe Solutions and Tasks. Manage UI state with ViewModel
-      and Jetpack Lifecycle, and use SharedFlow and StateFlow to emit and observe UI events and
-      state across multiple subscribers.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Han Yin
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md b/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md
index e5cf5bbb63..e2a634e8e0 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md
@@ -21,61 +21,9 @@ prerequisites:
     - Android Debug Bridge (adb) installed on your device. Follow the steps in [adb](https://developer.android.com/tools/adb)  to install Android SDK Platform Tools. The adb tool is included in this package.
     - Java 17 JDK. Follow the steps in [Java 17 JDK](https://www.oracle.com/java/technologies/javase/jdk17-archive-downloads.html) to download and install JDK for host.
     - Python 3.10.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:49:55Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 688b5b6eddc0480fa732308802d225696371c22a468bb6b140c6b74908222bbc
-  summary_generated_at: '2026-06-02T02:46:55Z'
-  summary_source_hash: 688b5b6eddc0480fa732308802d225696371c22a468bb6b140c6b74908222bbc
-  faq_generated_at: '2026-06-02T23:49:55Z'
-  faq_source_hash: 688b5b6eddc0480fa732308802d225696371c22a468bb6b140c6b74908222bbc
-  summary: >-
-    Learn how to build and deploy a simple LLM-based Android chat app using ExecuTorch with XNNPACK
-    and KleidiAI on Arm smartphones. You will set up an ExecuTorch development environment, prepare
-    the Llama 3.2 1B Instruct model, and understand how KleidiAI kernels and the i8mm feature
-    accelerate quantized LLMs, along with the role of 4-bit groupwise PTQ. The path covers building
-    the ExecuTorch runtime and JNI libraries, cross-compiling a Llama runner with the Android
-    NDK, and running benchmarks on device. Prerequisites include an Apple M1/M2 or Linux host,
-    an Arm-powered Android phone with i8mm (both with 16GB RAM), USB, adb, Java 17 JDK, and Python
-    3.10. Estimated time: about 60 minutes.
-  faqs:
-  - question: Do I need macOS or Linux for the host, and what resources are required?
-    answer: >-
-      Use an Apple M1/M2 development machine with Android Studio installed, or a Linux machine
-      with at least 16GB of RAM. Python 3.10 and Java 17 JDK are also required.
-  - question: What Android device requirements should I confirm before starting?
-    answer: >-
-      Use an Arm-powered smartphone running Android that includes the i8mm feature and has 16GB
-      of RAM. You also need a USB cable and adb (from Android SDK Platform Tools) installed on
-      your host.
-  - question: When setting up ExecuTorch, should I use a Python virtual environment or Conda?
-    answer: >-
-      Create an isolated Python environment for ExecuTorch; you can use either a Python virtual
-      environment or a Conda environment. You only need one of these options.
-  - question: How do I obtain and prepare the Llama model used in this path?
-    answer: >-
-      Request access to Llama from Meta’s Llama Downloads page, accept the Responsible Use Guide,
-      and use the provided 24-hour download link. Install the llama-stack package from pip, then
-      download the Llama 3.2 1B Instruct model following the provided steps.
-  - question: What should I set before cross-compiling the Llama runner for Android, and what
-      outputs should I expect?
-    answer: >-
-      Set ANDROID_NDK to your NDK path and ensure the CMake android.toolchain.cmake file is available.
-      The build produces the ExecuTorch runtime with KleidiAI, associated libraries (including
-      JNI libraries), and a Llama runner binary for Android.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: 
     - Varun Chari
diff --git a/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md b/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md
index 7edd6dd1c9..bb4f2e5289 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md
@@ -21,65 +21,9 @@ prerequisites:
     - Java 17 JDK. Follow the steps in [Java SE 17 Archive Downloads](https://www.oracle.com/java/technologies/javase/jdk17-archive-downloads.html) to download and install JDK for your host
     - Python 3.10 or later
     - A [Hugging Face](https://huggingface.co/) account with access to Meta Llama models
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:50:22Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 9ccf2cdd6406694d85c553204479d7ff1f688512056a3c76d4c85266cdc418b1
-  summary_generated_at: '2026-06-02T02:47:30Z'
-  summary_source_hash: 9ccf2cdd6406694d85c553204479d7ff1f688512056a3c76d4c85266cdc418b1
-  faq_generated_at: '2026-06-02T23:50:22Z'
-  faq_source_hash: 9ccf2cdd6406694d85c553204479d7ff1f688512056a3c76d4c85266cdc418b1
-  summary: >-
-    Learn to build and deploy an on-device customer support chatbot for Android using Meta’s Llama
-    3.2 and the ExecuTorch runtime with KleidiAI integrated through XNNPACK on Arm. You set up
-    a development environment on macOS or Linux, install ExecuTorch in a Python virtual environment,
-    obtain the Llama 3.2 1B Instruct model, and export it to .pte for on-device inference. You
-    then cross-compile ExecuTorch and a Llama runner with the Android NDK and CMake, enabling
-    KleidiAI kernels for Arm chips with the i8mm feature, and run the model on an Arm-powered
-    Android phone to verify inference performance. Prerequisites include a compatible host, an
-    Android device with i8mm and 16GB RAM, adb, Java 17 JDK, Python 3.10+, and a Hugging Face
-    account with Llama access.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a macOS (Apple M1/M2/M3) or Linux machine with at least 16GB RAM, and an Arm-powered
-      Android smartphone with the i8mm feature and 16GB RAM. Also prepare a USB cable, adb (Android
-      SDK Platform Tools), Java 17 JDK, Python 3.10 or later, and a Hugging Face account with
-      access to Meta Llama models.
-  - question: Should I use a Python virtual environment for ExecuTorch, and which Python version
-      is required?
-    answer: >-
-      Yes, the best practice is to create an isolated Python virtual environment before installing
-      ExecuTorch dependencies. Use Python 3.10 or later.
-  - question: How do I obtain and prepare the Llama model for ExecuTorch?
-    answer: >-
-      Request access on Meta’s Llama Downloads page, accept the Responsible Use Guide, and use
-      the time-limited download link you receive. Install the llama-stack package from pip, download
-      the model, and export it to .pte format optimized for on-device inference as described in
-      the path.
-  - question: Which Llama model variant does this path use, and can I try others?
-    answer: >-
-      This path uses the Llama 3.2 1B Instruct model. The same instructions apply to other variants
-      with minimal modification.
-  - question: How do I build and run the chatbot on Android, and how do I confirm it works?
-    answer: >-
-      Set the ANDROID_NDK path, ensure the CMake Android toolchain file is available, then use
-      CMake to cross-compile ExecuTorch and libraries with KleidiAI and build the Llama runner
-      for Android. Deploy to the phone, run the model, and follow the path’s steps to verify on-device
-      inference performance without cloud dependency.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Parichay Das
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md b/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md
index 1a086e62b4..c310b69c0a 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md
@@ -18,59 +18,9 @@ prerequisites:
     - Android Studio installed on your development computer.
     - A USB cable to connect your computer to your Google Pixel 8.
     - Android Debug Bridge (adb) installed on your device. If needed, follow the steps in the [Android Debug Bridge](https://developer.android.com/tools/adb) documentation.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:51:15Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 95d4fb855552939d1a0e9cccfe46333692ea291ee85dd08b816321db29ceebdc
-  summary_generated_at: '2026-06-02T02:48:04Z'
-  summary_source_hash: 95d4fb855552939d1a0e9cccfe46333692ea291ee85dd08b816321db29ceebdc
-  faq_generated_at: '2026-06-02T23:51:15Z'
-  faq_source_hash: 95d4fb855552939d1a0e9cccfe46333692ea291ee85dd08b816321db29ceebdc
-  summary: >-
-    This Learning Path shows how to detect and debug memory safety bugs in Android applications
-    using Arm Memory Tagging Extension (MTE) on a Google Pixel 8. You will clone an Android MTE
-    Test app from GitHub, open it in Android Studio, explore common native memory bug patterns,
-    enable or disable MTE via the AndroidManifest, then build and debug the app on a connected
-    Pixel 8. The path targets advanced developers and takes about 20 minutes. Prerequisites include
-    a Google Pixel 8 smartphone, Android Studio on your development computer, a USB cable, and
-    Android Debug Bridge (adb) installed.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Google Pixel 8 smartphone, Android Studio on your development computer, a USB
-      cable, and adb. If adb is not installed, follow the Android Debug Bridge documentation linked
-      in the prerequisites.
-  - question: How do I get the MTE Test app project into Android Studio?
-    answer: >-
-      Clone the repository from GitHub using the provided git clone command, then launch Android
-      Studio and open the cloned project. The path guides you to view the project files as needed.
-  - question: Which file do I edit to enable or disable MTE?
-    answer: >-
-      Use the AndroidManifest.xml in the app module. Switch the project view to Project Files,
-      navigate to app -> src -> main -> res, and open AndroidManifest.xml to apply the settings
-      shown in the steps.
-  - question: How do I run and debug the app on my Pixel 8?
-    answer: >-
-      Connect the Pixel 8 via USB, ensure it appears in the device selector in Android Studio,
-      and press the Debug button to build and start debugging. On the device, you will see a startup
-      message, then the app interface appears for you to continue debugging.
-  - question: What should I check if my Pixel 8 does not appear in Android Studio?
-    answer: >-
-      Verify the USB connection and confirm that adb is installed as listed in the prerequisites.
-      Reconnect the device and reopen the project if it still does not show up.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Roberto Lopez Mendez
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/gemma4-kleidiai-sme2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/gemma4-kleidiai-sme2/_index.md
index be3d875037..fab685379a 100755
--- a/content/learning-paths/mobile-graphics-and-gaming/gemma4-kleidiai-sme2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/gemma4-kleidiai-sme2/_index.md
@@ -18,11 +18,9 @@ learning_objectives:
 prerequisites:
     - A SME2 device (macOS M4 on Apple Silicon)
     - Git, Homebrew, and Xcode Command Line Tools
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 author: Annie Tallund
 
 ### Tags
diff --git a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md
index 91ea0769cf..2ebedb897a 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md
@@ -13,57 +13,9 @@ learning_objectives:
 prerequisites:
     - A game project that uses advanced rendering features (such as hardware ray tracing) that stretch the performance capabilities of everyday smartphones.
     - A development machine with Git installed.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:51:46Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: b194edd6ff4ffc5e39e7daa995271d2ed81ec31c8e88ddf1b23a54eb06dd1d06
-  summary_generated_at: '2026-06-02T02:48:29Z'
-  summary_source_hash: b194edd6ff4ffc5e39e7daa995271d2ed81ec31c8e88ddf1b23a54eb06dd1d06
-  faq_generated_at: '2026-06-02T23:51:46Z'
-  faq_source_hash: b194edd6ff4ffc5e39e7daa995271d2ed81ec31c8e88ddf1b23a54eb06dd1d06
-  summary: >-
-    Learn how to install and integrate Arm Accuracy Super Resolution (Arm ASR)—a mobile-optimized
-    temporal upscaling technique derived from AMD Fidelity Super Resolution 2 v2.2.2—into Android
-    game projects. You will add the ASR plugin to an Unreal Engine project (UE 5.3–5.5 recommended)
-    and complete common setup tasks, or integrate the generic ASR library into a custom engine
-    using either a quick standalone backend or a tight renderer backend. You will manage how ASR
-    upscales content by configuring quality presets, shader variants, and input resources. Prerequisites
-    are a game project that pushes smartphone performance (for example, with hardware ray tracing)
-    and a development machine with Git installed. Estimated completion time is about 40 minutes.
-  faqs:
-  - question: Which Unreal Engine versions should I use for this Learning Path?
-    answer: >-
-      Unreal Engine 5.3–5.5 is recommended. The Arm ASR plugin is available for UE 5.3, 5.4, and
-      5.5.
-  - question: What do I need before running the steps?
-    answer: >-
-      Have a game project that uses advanced rendering features that push everyday smartphones,
-      and a development machine with Git installed. The path targets Android.
-  - question: I’m not using Unreal Engine—how can I integrate Arm ASR?
-    answer: >-
-      Use the generic library. You can choose Quick Integration with the standalone backend or
-      Tight Integration using your engine’s backend/renderer.
-  - question: What configuration areas will I manage when integrating ASR?
-    answer: >-
-      You will work with quality presets, shader variants and extensions, and input resources.
-      These areas control how ASR upscales your content.
-  - question: How is Arm ASR related to AMD FSR2?
-    answer: >-
-      Arm ASR is a mobile-optimized temporal upscaling technique derived from AMD Fidelity Super
-      Resolution 2 v2.2.2, with optimizations for resource-constrained mobile gaming.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Julie Gaskin
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md
index 640bd12433..962b53a197 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md
@@ -14,60 +14,9 @@ prerequisites:
     - Basic knowledge of game engines and programming concepts
     - Recent Android device, such as a mobile phone or tablet
     - Desktop computer capable of running Unity
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:52:25Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 23df12c0e165a4120fa25b5573437121bc7af141376758ccd051ddac14e180fb
-  summary_generated_at: '2026-06-02T02:48:56Z'
-  summary_source_hash: 23df12c0e165a4120fa25b5573437121bc7af141376758ccd051ddac14e180fb
-  faq_generated_at: '2026-06-02T23:52:25Z'
-  faq_source_hash: 23df12c0e165a4120fa25b5573437121bc7af141376758ccd051ddac14e180fb
-  summary: >-
-    This introductory path shows how to set up Unity for Android, build and deploy a simple sample
-    to a real device, and begin investigating performance with the Unity Profiler. You will install
-    the latest Unity with Android Build Support, open a provided scene that includes a small C#
-    script, switch the project to the Android platform, and run it on a recent Android phone or
-    tablet. You then launch the Profiler in the editor and on a connected device to review CPU,
-    graphics, and memory timelines and start diagnosing why a basic scene runs slowly. Prerequisites
-    are basic game‑engine knowledge and a desktop capable of running Unity; the estimated time
-    to complete is about 30 minutes.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      You need basic knowledge of game engines and programming concepts, a recent Android device
-      (phone or tablet), and a desktop computer capable of running Unity. No other explicit prerequisites
-      are listed.
-  - question: Which Unity components should I install to target Android?
-    answer: >-
-      Install the latest version of Unity and add Android Build Support. The setup step in the
-      path calls out both items before you open the sample project.
-  - question: How do I open and inspect the sample project and scene?
-    answer: >-
-      Extract the sample project from the path, open it in Unity, and double-click SampleScene
-      in the Project tab. Select the Cube object to view the attached Spin.cs script.
-  - question: How do I switch the project to Android and build for my device?
-    answer: >-
-      Open File -> Build Profile to access the window where you switch the active platform to
-      Android. Then use the Build Settings workflow described in the steps to produce and deploy
-      the Android build.
-  - question: Should I profile in the editor or on my Android device?
-    answer: >-
-      Use the Unity Profiler in the editor for quick, high-level checks, then profile on your
-      Android device to reflect end-user characteristics. Expect a per-frame timeline with CPU,
-      graphics, and memory data when profiling is active.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Joshua Marshall-Law
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md
index 96ad9b3723..5b9f841c58 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md
@@ -12,61 +12,9 @@ learning_objectives:
 prerequisites:
     - Familiarity with Godot
     - Familiarity with Arm Performance Studio tools
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:52:55Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 44e7b5fe3fda52267dc1957319439895293276602b1f533de5665adaf6f7cafe
-  summary_generated_at: '2026-06-02T02:49:25Z'
-  summary_source_hash: 44e7b5fe3fda52267dc1957319439895293276602b1f533de5665adaf6f7cafe
-  faq_generated_at: '2026-06-02T23:52:55Z'
-  faq_source_hash: 44e7b5fe3fda52267dc1957319439895293276602b1f533de5665adaf6f7cafe
-  summary: >-
-    Learn how to profile Android games built with Godot using Arm Performance Studio. You install
-    the Arm Performance Studio Integration extension from the Godot Asset Library, then add annotations
-    in GDScript with the PerformanceStudio class, including single markers, regions, and threaded
-    channels. You visualize these annotations in Streamline and Performance Advisor to correlate
-    game events with CPU and GPU activity on Arm-based Android devices, including Mali GPUs. The
-    path is introductory, takes about 15 minutes, and targets Godot 4.3 or later on Windows, macOS,
-    or Linux. Prerequisites include familiarity with Godot and Arm Performance Studio tools. By
-    the end, you will have a project instrumented for profiling and ready to analyze with Arm’s
-    tools.
-  faqs:
-  - question: Which Godot versions support the Arm Performance Studio extension?
-    answer: >-
-      The extension is compatible with Godot 4.3 and later.
-  - question: How do I install the Arm Performance Studio Integration in my Godot project?
-    answer: >-
-      Open your project, select AssetLib, search for "Arm Performance Studio Integration," then
-      double-click the result and choose Download. When prompted, you can change the install folder
-      before completing the installation.
-  - question: How do I add a basic marker and where will I see it?
-    answer: >-
-      Create an instance of the PerformanceStudio class in your script and call marker("Label").
-      These markers appear on the Streamline timeline to help correlate game behavior with performance
-      data.
-  - question: How do I define a performance region and how is it reported?
-    answer: >-
-      Emit a pair of markers with labels prefixed by "Region Start <Name>" and "Region End <Name>".
-      Regions appear on the frame rate analysis chart in the Performance Advisor report, with
-      dedicated charts for each region at the end of the report.
-  - question: When should I use channels, and what do they capture?
-    answer: >-
-      Use channels for threaded, duration-based annotations tied to a specific software thread.
-      Define a PerformanceStudio_Channel, then add annotations with labels (and optional color)
-      to trace tasks like asset loading or enemy spawning.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Albin Bernhardsson, Julie Gaskin
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md b/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md
index f814b6bfa6..bd5ed172e1 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md
@@ -13,59 +13,9 @@ prerequisites:
 - A computer capable of running [Unreal Engine 5.3 or later version](https://www.unrealengine.com/en-US/download).
 - An Android mobile device that has a Mali GPU with hardware ray tracing support.
 - A USB cable to connect the mobile device to your computer.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:53:37Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 3f35558e0399cb4c851b120eaa2217a63c48336cbba96d23500d1b751e4aec63
-  summary_generated_at: '2026-06-02T02:49:43Z'
-  summary_source_hash: 3f35558e0399cb4c851b120eaa2217a63c48336cbba96d23500d1b751e4aec63
-  faq_generated_at: '2026-06-02T23:53:37Z'
-  faq_source_hash: 3f35558e0399cb4c851b120eaa2217a63c48336cbba96d23500d1b751e4aec63
-  summary: >-
-    This introductory Learning Path guides Unreal Engine developers through enabling hardware
-    ray tracing for Lumen on Android devices with Arm Mali GPUs, including those based on Immortalis-G715
-    or G720. You will first review Lumen and global illumination, then configure an Unreal Engine
-    5.3+ project to use Lumen for Global Illumination and Reflections. The steps cover Android-specific
-    requirements for Lumen’s hardware ray tracing, including enabling the SM5 shader format (via
-    Support Vulkan Desktop [Experimental]) and selecting deferred shading mode, with an option
-    to enable Lumen via a Post Process Volume. Prerequisites include a computer capable of running
-    Unreal Engine, an Android device with a Mali GPU that supports hardware ray tracing, and a
-    USB cable.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a computer capable of running Unreal Engine 5.3 or later, an Android mobile device
-      with a Mali GPU that supports hardware ray tracing, and a USB cable to connect the device
-      to your computer.
-  - question: Where do I enable Lumen for Global Illumination and Reflections?
-    answer: >-
-      Open Project Settings, go to Engine - Rendering, and select Lumen in the Global Illumination
-      section and also select Lumen in the Reflections section.
-  - question: Can I enable Lumen per scene instead of project-wide?
-    answer: >-
-      Yes. Add a Post Process Volume actor to your scene and select Lumen in the Global Illumination
-      section of the volume’s details panel.
-  - question: How do I enable the SM5 shader format for Android?
-    answer: >-
-      In Project Settings under Platforms - Android, enable Support Vulkan Desktop (Experimental)
-      to activate SM5 shader format support.
-  - question: Which shading path should I choose when using Lumen?
-    answer: >-
-      Use deferred shading. Lumen exclusively supports deferred shading mode, which you configure
-      under Engine - Rendering in Project Settings.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Owen Wu
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md b/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md
index 6d2e83b0c4..d48a3afa0f 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md
@@ -10,56 +10,9 @@ learning_objectives:
 
 prerequisites:
     - None
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:54:18Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: ab171b1ff6cfed7bce1cb8e50d4d9aeb4bee1972e8a409203cb438f94086a320
-  summary_generated_at: '2026-06-02T02:50:02Z'
-  summary_source_hash: ab171b1ff6cfed7bce1cb8e50d4d9aeb4bee1972e8a409203cb438f94086a320
-  faq_generated_at: '2026-06-02T23:54:18Z'
-  faq_source_hash: ab171b1ff6cfed7bce1cb8e50d4d9aeb4bee1972e8a409203cb438f94086a320
-  summary: >-
-    This introductory Learning Path helps developers new to Arm identify Android smartphones suitable
-    for software development and performance analysis. You will learn how to read device specifications
-    to confirm an Arm-based CPU (such as Cortex-A) and to check for Arm Mali or Arm Immortalis
-    GPUs. The path highlights that different devices offer varying levels of performance analysis
-    depending on the CPU and GPU, and introduces tools such as Arm Performance Studio for Mobile
-    for analyzing mobile application performance. No explicit prerequisites are listed. By the
-    end, you will be better equipped to select mobile hardware aligned with your development and
-    analysis needs.
-  faqs:
-  - question: How do I know if a smartphone I’m considering uses Arm hardware?
-    answer: >-
-      Almost all modern smartphones have Arm-based CPUs. Check the device specifications for an
-      Arm Mali or Arm Immortalis GPU to confirm the graphics processor.
-  - question: Which devices should I consider if I plan to analyze performance?
-    answer: >-
-      Different mobile devices offer differing levels of performance analysis depending on the
-      CPU and GPU used. Availability and depth of performance counters can vary by device.
-  - question: Do I need to install any tools or set up accounts before starting this path?
-    answer: >-
-      No explicit prerequisites are listed. This path focuses on identifying suitable mobile hardware
-      rather than installing tools.
-  - question: How does Arm Performance Studio for Mobile fit into this path?
-    answer: >-
-      It is mentioned as a tool that can help optimize mobile application performance once you
-      have hardware. This path does not cover installing or using the tool.
-  - question: What platform does this target, and how long will it take?
-    answer: >-
-      The path targets Android devices and takes about 5 minutes to complete.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md b/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md
index 945191cabc..381910f9eb 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md
@@ -15,61 +15,9 @@ prerequisites:
     - Basic understanding of general matrix multiplication (GEMM) and matmul operations
     - Basic understanding of quantization concepts for neural networks
     - (Optional) Access to an Arm CPU with SME2 support (Linux or Android) for hands-on verification steps
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:54:55Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 5a94138f74e7b2266c35dbd555f9966ca0ff29fdbd1842d4724e0da0cd4e46db
-  summary_generated_at: '2026-06-02T02:50:20Z'
-  summary_source_hash: 5a94138f74e7b2266c35dbd555f9966ca0ff29fdbd1842d4724e0da0cd4e46db
-  faq_generated_at: '2026-06-02T23:54:55Z'
-  faq_source_hash: 5a94138f74e7b2266c35dbd555f9966ca0ff29fdbd1842d4724e0da0cd4e46db
-  summary: >-
-    This advanced Learning Path explains how KleidiAI implements matrix multiplication microkernels
-    for quantized inference on Arm CPUs using SME2 INT8 MOPA instructions. You will decode a specific
-    SME2 matmul microkernel, understand its tiling and packing parameters (mr, nr, bl, kr), and
-    trace how quantized GGML Q4_0 weights from llama.cpp are repacked and consumed. Using a simplified
-    example with FP32 activations [16,64] and Q4_0 weights [64,64], you will connect normal matmul
-    semantics to the SME2 inner loop and see where MOPA instructions appear. Optional hands-on
-    steps include source inspection and disassembly on Linux or Android systems with SME2 support.
-    Prerequisites are basic GEMM/matmul and quantization knowledge; no other explicit prerequisites
-    are listed.
-  faqs:
-  - question: Do I need a device with SME2 support to follow this Learning Path?
-    answer: >-
-      No. SME2-capable hardware is optional and only required for the hands-on verification steps
-      such as disassembly. The core explanations and examples can be followed without hardware.
-  - question: How do I verify that SME2 INT8 MOPA instructions are used in the microkernel?
-    answer: >-
-      The path shows where these instructions appear in the inner loop and suggests basic checks
-      via source inspection. If you have SME2 hardware, you can optionally confirm via disassembly.
-  - question: Which llama.cpp operations route through the SME2 matmul microkernel in this context?
-    answer: >-
-      The heavy matmul work in attention (K/Q/V projections) and feed-forward network (FFN) layers
-      can run through the SME2 matmul microkernel. In these cases, the LHS activations are FP32
-      and the RHS weights use GGML Q4_0.
-  - question: Which tiling and packing parameters should I pay attention to?
-    answer: >-
-      Focus on mr, nr, bl, and kr, which define the output tile shape and inner-loop step sizes.
-      The microkernel computes C in tiles and expects inputs to be quantized and packed accordingly.
-  - question: What SVL and matrix sizes does the example assume, and how do I interpret 1vlx4vl?
-    answer: >-
-      The example assumes an SME2 streaming vector length (SVL) of 512 bits and a simplified matmul
-      of LHS [16, 64] by RHS [64, 64]. The 1vlx4vl suffix means each inner-loop iteration computes
-      a 1VL × 4VL submatrix of the output, with exact element counts depending on the hardware
-      SVL.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Zenon Zhilong Xiu
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md b/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md
index 13717362c5..8a0a8b4c66 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md
@@ -14,60 +14,9 @@ learning_objectives:
 prerequisites:
     - An x86_64 Linux machine running Ubuntu with approximately 500 MB of free space, or a docker daemon that can build and run a provided x86_64 Dockerfile.
     - An Android phone with support for i8mm (tested on Google Pixel 8 Pro).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:55:26Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 0e51db9ceb25c31c42608f3c6744a4fa8f9d995805ed44a7d7fc83324889ea12
-  summary_generated_at: '2026-06-02T02:50:53Z'
-  summary_source_hash: 0e51db9ceb25c31c42608f3c6744a4fa8f9d995805ed44a7d7fc83324889ea12
-  faq_generated_at: '2026-06-02T23:55:26Z'
-  faq_source_hash: 0e51db9ceb25c31c42608f3c6744a4fa8f9d995805ed44a7d7fc83324889ea12
-  summary: >-
-    Learn to cross-compile and run LLM inference on Android using Google AI Edge’s MediaPipe with
-    XNNPACK and KleidiAI-enhanced Arm i8mm. Starting from an x86_64 Ubuntu host (or a provided
-    Docker setup), you install Android SDK/NDK and Bazel prerequisites, build a CPU inference
-    engine, and run the Gemma 2B model on an Android device that supports i8mm (tested on Google
-    Pixel 8 Pro). You then benchmark inference with the i8mm build flag enabled and disabled to
-    compare performance. The path targets advanced Android developers and uses tools including
-    MediaPipe, XNNPACK, Bazel, Android SDK/NDK, and Hugging Face. Expected outcomes are a working
-    inference binary and benchmark results on your device.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an x86_64 Linux machine running Ubuntu with about 500 MB free space or a Docker
-      daemon to build and run the provided x86_64 Dockerfile, plus an Android phone that supports
-      i8mm (tested on Google Pixel 8 Pro).
-  - question: Should I install dependencies with Docker or directly on Ubuntu?
-    answer: >-
-      The path provides two options: build a Docker container with the dependencies or install
-      them directly on an x86_64 Ubuntu machine. Choose Docker if you prefer a contained setup;
-      use native installation if you already work on Ubuntu.
-  - question: Which Bazel options target Android Arm64 and enable i8mm?
-    answer: >-
-      Use --config=android_arm64 to target Android Arm64 and --define=xnn_enable_arm_i8mm=true
-      to enable i8mm. These flags are applied when building the inference tool.
-  - question: How do I confirm the inference engine built correctly?
-    answer: >-
-      After the build completes, check for the binary in bazel-bin/mediapipe/tasks/cc/genai/inference/c/.
-      The executable is named llm_inference_engine_cpu_main.
-  - question: What result should I expect when running inference and benchmarking?
-    answer: >-
-      The inference tool runs an LLM on the Android device and produces output from an initial
-      prompt. For benchmarking, you will cross-compile with and without the i8mm build flag to
-      demonstrate performance differences using KleidiAI through XNNPACK.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: 
     - Pareena Verma
diff --git a/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md b/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md
index b44485f77b..560854f42e 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md
@@ -13,57 +13,9 @@ learning_objectives:
 prerequisites:
     - A development machine running Ubuntu or Debian Linux with `x86_64` architecture
     - An Android device with an Arm GPU
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:55:55Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 68cdc7315795b6ffa794c0a1fd4cf325ab39ebb65e23efd27b7760ece76a2ec9
-  summary_generated_at: '2026-06-02T02:51:15Z'
-  summary_source_hash: 68cdc7315795b6ffa794c0a1fd4cf325ab39ebb65e23efd27b7760ece76a2ec9
-  faq_generated_at: '2026-06-02T23:55:55Z'
-  faq_source_hash: 68cdc7315795b6ffa794c0a1fd4cf325ab39ebb65e23efd27b7760ece76a2ec9
-  summary: >-
-    Learn to build the libGPUInfo C++ library with the Android NDK and run an example application
-    on an Android device to query configuration details of Arm Mali or Arm Immortalis GPUs. Working
-    from a Debian or Ubuntu x86_64 host, you will install the Android NDK, use adb to deploy and
-    run the sample, and read GPU feature and performance-level information reported by the device.
-    The outcome is the ability to retrieve device-specific GPU configuration at runtime to inform
-    application settings. Prerequisites are a development machine running Ubuntu or Debian Linux
-    and an Android device with an Arm GPU; no additional prerequisites are explicitly listed.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a development machine running Ubuntu or Debian Linux with x86_64 architecture and
-      an Android device with an Arm GPU. The steps use the Android NDK and adb.
-  - question: Which Android GPUs and devices does this target?
-    answer: >-
-      The example targets Arm Mali and Arm Immortalis GPUs on Android. Use an Android device that
-      includes an Arm GPU.
-  - question: Does this Learning Path include installing the Android NDK and using adb?
-    answer: >-
-      Yes. You download and install the Android NDK and use adb as part of building libGPUInfo
-      and running the example on a connected device.
-  - question: What result should I expect from the example application?
-    answer: >-
-      The example queries the device to read Arm GPU hardware configuration information. The results
-      identify available features and performance levels.
-  - question: How would I use libGPUInfo in my own application?
-    answer: >-
-      libGPUInfo is a C++ library that can be integrated into applications to gather Arm GPU configuration
-      details at runtime. You can use this information to guide runtime settings and application
-      complexity choices.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md b/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md
index 3a0104b91b..70b645662a 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md
@@ -15,62 +15,9 @@ learning_objectives:
 prerequisites:
 - An Arm64 Linux development machine 
 - An Android device that supports Arm SME2 architecture features - see this [list of devices with SME2 support](/learning-paths/cross-platform/multiplying-matrices-with-sme2/1-get-started/#devices)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:56:24Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: a744c8118a5a3cb97eee7bee271f768bdd71b4286e723c38a7b4ff6cd9e08d18
-  summary_generated_at: '2026-06-02T02:51:34Z'
-  summary_source_hash: a744c8118a5a3cb97eee7bee271f768bdd71b4286e723c38a7b4ff6cd9e08d18
-  faq_generated_at: '2026-06-02T23:56:24Z'
-  faq_source_hash: a744c8118a5a3cb97eee7bee271f768bdd71b4286e723c38a7b4ff6cd9e08d18
-  summary: >-
-    This advanced Learning Path shows how to accelerate LiteRT (Lite Runtime) model inference
-    on Android by enabling KleidiAI with Scalable Matrix Extension 2 (SME2) via XNNPACK, then
-    validating the results with the benchmark_model tool. You will examine how LiteRT, XNNPACK,
-    and KleidiAI fit together; create LiteRT models that match the subset of operators currently
-    accelerated by SME2; build two benchmark_model binaries (one with KleidiAI+SME2 and one baseline
-    using Neon micro-kernels); and run benchmarks on an SME2-capable Android device. Prerequisites
-    are an Arm64 Linux development machine and an Android device with SME2 support (a device list
-    is linked in the path). By the end, you can compare benchmark outputs to evaluate SME2 acceleration
-    for your models.
-  faqs:
-  - question: What do I need before building and benchmarking?
-    answer: >-
-      You need an Arm64 Linux development machine and an Android device that supports Arm SME2.
-      You also need a LiteRT model (for example, fc_fp32.tflite) and two benchmark_model binaries
-      built with and without KleidiAI and SME2.
-  - question: How do I check if my Android device supports SME2?
-    answer: >-
-      On the device, use an ADB shell and run cat /proc/cpuinfo. Look for a feature entry indicating
-      SME2 support, and you can also refer to the linked list of SME2-capable devices in the prerequisites.
-  - question: Which parts of my LiteRT model are accelerated through KleidiAI SME2?
-    answer: >-
-      Only a subset of KleidiAI SME2 micro-kernels has been integrated into XNNPACK. Supported
-      operator data types and quantization configurations are listed in the path; other operators
-      use XNNPACK’s default implementation.
-  - question: Why build two versions of the benchmark_model tool?
-    answer: >-
-      You build one version with KleidiAI and SME2 enabled and another without SME2 to establish
-      a Neon-based baseline. Running both lets you evaluate and validate the acceleration provided
-      by SME2-enabled micro-kernels.
-  - question: What should I check if my benchmark does not reflect SME2 acceleration?
-    answer: >-
-      Confirm the device reports SME2 support and that your model uses operators and data types
-      covered by the integrated SME2 micro-kernels. If not supported, LiteRT will use XNNPACK’s
-      default implementation during inference.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jiaming Guo
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md b/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md
index 6f0c11498b..ac8886542b 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md
@@ -15,60 +15,9 @@ learning_objectives:
 prerequisites:
   - An x86_64 Linux host machine running Ubuntu, with at least 15 GB of free disk space
   - An Arm64 target system with support for SME or SME2 - see the Learning Path [Devices with native SME2 support](/learning-paths/cross-platform/multiplying-matrices-with-sme2/1-get-started/#devices)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:56:51Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: b55d902389806f5e84e674e5ba1f1f2d9e38af370c289ad344eff2dad3475df1
-  summary_generated_at: '2026-06-02T02:51:58Z'
-  summary_source_hash: b55d902389806f5e84e674e5ba1f1f2d9e38af370c289ad344eff2dad3475df1
-  faq_generated_at: '2026-06-02T23:56:51Z'
-  faq_source_hash: b55d902389806f5e84e674e5ba1f1f2d9e38af370c289ad344eff2dad3475df1
-  summary: >-
-    This Learning Path shows how to benchmark KleidiAI micro-kernels in ExecuTorch on Arm64 platforms
-    that support SME or SME2. You will set up an isolated Python environment on an x86_64 Ubuntu
-    host, cross-compile ExecuTorch for AArch64 with XNNPACK and KleidiAI (including SME/SME2),
-    and build and export quantized benchmark models for Fully Connected and Conv2d operators.
-    On an Arm64 target with SME/SME2, you will run workloads with executor_runner, measure throughput
-    and latency, and collect ETDump traces. You will then use the ExecuTorch Inspector API to
-    inspect ETRecord and ETDump files to understand kernel-level behavior across GEMM variants.
-    Prerequisites are explicitly listed in the path.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an x86_64 Linux host running Ubuntu with at least 15 GB of free disk space and
-      an Arm64 target device that supports SME or SME2. The path targets Linux environments.
-  - question: Should I use a Python virtual environment, and how long should it stay active?
-    answer: >-
-      Yes. Create and activate a Python virtual environment before building ExecuTorch, and keep
-      it activated while completing the steps so dependencies install and run in the correct location.
-  - question: Which toolchain should I install to cross-compile ExecuTorch for AArch64?
-    answer: >-
-      Install the GNU Arm cross-compilation toolchain on your x86_64 host along with Ninja as
-      the CMake build backend. The path cross-compiles ExecuTorch with XNNPACK and KleidiAI enabled
-      for the Arm64 target.
-  - question: How do I know if KleidiAI micro-kernels are being used for my operators?
-    answer: >-
-      ExecuTorch automatically dispatches to KleidiAI within XNNPACK when an operator’s data types
-      and quantization match supported configurations. You can confirm by collecting ETDump data
-      and inspecting it with the ExecuTorch Inspector API.
-  - question: What results should I expect after running executor_runner?
-    answer: >-
-      executor_runner runs kernel workloads and collects ETDump profiling data. Use the ExecuTorch
-      Inspector API to examine ETRecord and ETDump files and review kernel-level behavior, along
-      with throughput and latency measurements produced during the runs.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Qixiang Xu
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md b/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md
index f1fe45cc2e..ff7827e0b9 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md
@@ -16,59 +16,9 @@ prerequisites:
     - Basic understanding of PyTorch and machine learning concepts
     - A development machine running Ubuntu 22.04, with a CUDA-capable NVIDIA® GPU
     - CUDA Toolkit version 11.8 or later
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:57:36Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: e145caae5b20f7165251d88e334ba22d90c8b35270902778a2b6fe0ed0b250f6
-  summary_generated_at: '2026-06-02T02:52:24Z'
-  summary_source_hash: e145caae5b20f7165251d88e334ba22d90c8b35270902778a2b6fe0ed0b250f6
-  faq_generated_at: '2026-06-02T23:57:36Z'
-  faq_source_hash: e145caae5b20f7165251d88e334ba22d90c8b35270902778a2b6fe0ed0b250f6
-  summary: >-
-    This advanced Learning Path shows how to fine-tune and evaluate a Neural Super Sampling (NSS)
-    upscaler using PyTorch with Arm’s Model Gym API and CLI on Ubuntu 22.04. You will set up a
-    Python 3.10+ environment, install required system packages, clone open-source example notebooks,
-    and launch a Jupyter Notebook to configure training and evaluation with hardware-aware optimization.
-    The workflow exports models in .vgf format, which you then inspect using Model Explorer with
-    the VGF adapter to review architecture, tensor shapes, and graph connectivity. The final section
-    demonstrates how to register and train your own model via the Python API by subclassing BaseNGModel.
-    Prerequisites include basic PyTorch knowledge, a CUDA-capable NVIDIA GPU, and CUDA Toolkit
-    11.8 or later.
-  faqs:
-  - question: What do I need before running the notebooks?
-    answer: >-
-      You need basic familiarity with PyTorch and machine learning, a development machine running
-      Ubuntu 22.04 with a CUDA-capable NVIDIA GPU, and CUDA Toolkit 11.8 or later.
-  - question: How do I set up Python and system dependencies on Ubuntu?
-    answer: >-
-      Verify Python 3.10+ with: python3 --version. Then install dependencies with: sudo apt update
-      followed by sudo apt install python3-venv python-is-python3 gcc make python3-dev -y.
-  - question: How do I get the example notebooks used in this Learning Path?
-    answer: >-
-      Clone the open-source examples repository from GitHub using git clone. The exact repository
-      URL is provided in the setup step of the Learning Path.
-  - question: What result should I expect after training the NSS model?
-    answer: >-
-      You will produce a fine-tuned NSS model and export it as a .vgf file using the Model Gym
-      toolchain. The .vgf can be opened in Model Explorer for inspection with the VGF adapter.
-  - question: Can I integrate my own model into Model Gym?
-    answer: >-
-      Yes. Create a Python class that inherits from BaseNGModel, register it with the toolkit,
-      and use the same Python API to run training, evaluation, and export as demonstrated for
-      NSS.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Annie Tallund
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md b/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md
index 410689944f..a15b349a53 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md
@@ -11,55 +11,9 @@ learning_objectives:
     
 prerequisites:
     - An AArch64 Linux development machine. Cloud instances can be used, refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:58:26Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: a25cb73b70716e397d9477f8ab9729f4a094143d276e4de68cf8c33b273bb1ff
-  summary_generated_at: '2026-06-02T02:52:58Z'
-  summary_source_hash: a25cb73b70716e397d9477f8ab9729f4a094143d276e4de68cf8c33b273bb1ff
-  faq_generated_at: '2026-06-02T23:58:26Z'
-  faq_source_hash: a25cb73b70716e397d9477f8ab9729f4a094143d276e4de68cf8c33b273bb1ff
-  summary: >-
-    Learn how to build and run a small C program on AArch64 Linux to explore the Arm Memory Tagging
-    Extension (MTE). MTE, available in Armv8.5-A and Armv9-A processors, helps detect memory safety
-    issues such as buffer overflows and use-after-free. In about 20 minutes, you will follow practical
-    steps to compile and execute an example that illustrates how MTE works on a recent AArch64
-    system. The only explicit prerequisite is an AArch64 Linux development machine; cloud instances
-    from Arm cloud service providers are suitable. QEMU is listed among the tools. After completing
-    the path, you will have an introductory understanding of MTE based on a working example.
-  faqs:
-  - question: What do I need before running the example C program?
-    answer: >-
-      You need an AArch64 Linux development machine. No other explicit prerequisites are listed.
-  - question: Can I use a cloud-based AArch64 instance for this path?
-    answer: >-
-      Yes. Cloud instances can be used, and the path references a list of Arm cloud service providers.
-  - question: Is QEMU required for this Learning Path?
-    answer: >-
-      QEMU is listed among the tools. The core activity is to build and run a small C program
-      on AArch64 Linux; follow the steps to see if QEMU is used in your setup.
-  - question: How do I know if my environment supports MTE?
-    answer: >-
-      MTE is a feature of Armv8.5-A and Armv9-A processors, and the path suggests using a recent
-      AArch64 Linux machine. The steps do not provide a specific detection command.
-  - question: What result should I expect when I build and run the example?
-    answer: >-
-      The example demonstrates how MTE detects memory safety issues like buffer overflows and
-      use-after-free. Expect behavior that shows MTE in action; the exact output is determined
-      by the provided example.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md b/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md
index 54cccd5091..9d5979f55e 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md
@@ -17,61 +17,9 @@ prerequisites:
     - A Google Pixel 8 smartphone
     - A USB cable to connect your Google Pixel 8 to your desktop machine
     - Android Debug Bridge (adb) installed on your device. Follow the steps in https://developer.android.com/tools/adb to install Android SDK Platform Tools. The adb tool is included in this package.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:59:06Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: b6a9aa558ec5062c829d61b28fb92e25d5517249c011eb29f03cc36775b6f9af
-  summary_generated_at: '2026-06-02T02:53:24Z'
-  summary_source_hash: b6a9aa558ec5062c829d61b28fb92e25d5517249c011eb29f03cc36775b6f9af
-  faq_generated_at: '2026-06-02T23:59:06Z'
-  faq_source_hash: b6a9aa558ec5062c829d61b28fb92e25d5517249c011eb29f03cc36775b6f9af
-  summary: >-
-    This Learning Path shows how to enable Arm’s Memory Tagging Extension (MTE) on a Google Pixel
-    8, trigger memory-bug crashes using a test app, and examine the resulting Android bug report.
-    You will enable Developer options, turn on MTE on the device, install MTE_test.apk, and use
-    the app to execute code with common memory bugs so that MTE forces a crash. You then capture
-    a Bug report from the Developer options menu, unzip the generated archive on your desktop,
-    and review diagnostics—especially tombstone files—to understand where and why the crash occurred.
-    Prerequisites are a Pixel 8, a USB cable, and adb from Android SDK Platform Tools. Estimated
-    time: about 10 minutes.
-  faqs:
-  - question: What do I need before enabling MTE on my Pixel 8?
-    answer: >-
-      You need a Google Pixel 8 smartphone, a USB cable, and Android Debug Bridge (adb) installed
-      from the Android SDK Platform Tools. These are the only prerequisites explicitly listed.
-  - question: How do I turn on Developer options to access MTE settings?
-    answer: >-
-      Go to Settings -> About phone -> Build number and tap the Build number seven times until
-      you see “You are now a developer!”. Then return to System and open Developer options.
-  - question: How do I confirm that MTE is active after I enable it?
-    answer: >-
-      Install and run MTE_test.apk and press any button in the app to execute code with a memory
-      bug. If MTE is enabled, the app will crash and the bug report will include MTE-specific
-      information about the violation.
-  - question: How do I capture a bug report after the test app crashes?
-    answer: >-
-      Open Developer options and select the Bug report option, then tap Report to start generation.
-      You can watch the progress on the device; the result is a zip file that you can move to
-      your desktop and unzip.
-  - question: Which files should I inspect in the bug report, and why might the filename include
-      'husky'?
-    answer: >-
-      After unzipping, review the main bugreport text file and the tombstone file under FS/data/tombstones
-      for detailed crash information. “Husky” is the code name for Google Pixel 8 Pro and may
-      appear in the generated bug report filename.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Roberto Lopez Mendez
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md b/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md
index c2409c6942..78d2a61d0b 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md
@@ -17,61 +17,9 @@ prerequisites:
     - Unreal Engine 5.5 installed
     - Visual Studio with C++ game development tools
     - A C++ Unreal project (such as the Third Person template)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-02T23:59:40Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 5ca059af36f0ba375701e76ce82b7803f01ae6beab313336b333bfbdebaa3b1a
-  summary_generated_at: '2026-06-02T02:53:51Z'
-  summary_source_hash: 5ca059af36f0ba375701e76ce82b7803f01ae6beab313336b333bfbdebaa3b1a
-  faq_generated_at: '2026-06-02T23:59:40Z'
-  faq_source_hash: 5ca059af36f0ba375701e76ce82b7803f01ae6beab313336b333bfbdebaa3b1a
-  summary: >-
-    Learn how to capture high-quality frame datasets from Unreal Engine 5.5 gameplay using the
-    Neural Graphics Data Capture plugin on Windows. You will install and enable the plugin in
-    a C++ Unreal project, wire up a Level Blueprint to start (C) and stop (V) capture, run in
-    Standalone Game mode to record frames at the correct dimensions, and verify the exported dataset.
-    The path also introduces key capture settings, including UpscalingRatio, SupersamplingRatio,
-    FixedFrameRate, camera cut thresholds, and output path controls (DatasetDir, CaptureName).
-    Prerequisites include Windows 11, Unreal Engine 5.5, Visual Studio with C++ game development
-    tools, and a C++ project. Suitable for developers preparing data for Neural Super Sampling
-    and related temporal upscalers.
-  faqs:
-  - question: What do I need before running the capture workflow?
-    answer: >-
-      You need Windows 11, Unreal Engine 5.5 installed, Visual Studio with C++ game development
-      tools, and a C++ Unreal project. A template project like Third Person is suitable.
-  - question: How do I install and enable the Neural Graphics Data Capture plugin in my project?
-    answer: >-
-      Clone the plugin’s GitHub repository, then copy the NeuralGraphicsDataCapture folder into
-      your project’s Plugins directory. Open the project so the module compiles via Visual Studio,
-      then enable the plugin in Unreal.
-  - question: How do I set up hotkeys to start and stop capture?
-    answer: >-
-      Open your Level Blueprint and paste the prepared snippet provided by this Learning Path
-      (downloaded on Windows via PowerShell wget). The snippet binds C to start capture and V
-      to stop capture.
-  - question: Where can I configure capture parameters and output locations?
-    answer: >-
-      Adjust settings in NGDCRenderingSettings and NGDCExportSettings. You can set UpscalingRatio,
-      SupersamplingRatio, FixedFrameRate, camera cut thresholds, and control DatasetDir and CaptureName
-      for output organization.
-  - question: What should I check if my captured frame dimensions look wrong?
-    answer: >-
-      Use Standalone Game from the Play mode menu instead of New Editor Window (PIE). PIE can
-      produce frame dimensions that differ from expected output sizes.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: 
 - Annie Tallund
diff --git a/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md b/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md
index 8f2faa0ec5..52b6c9e1a7 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md
@@ -18,63 +18,9 @@ prerequisites:
     - Windows 11
     - Unreal Engine 4.27 or 5.4 or 5.6 (with the Templates and Feature Pack enabled)
     - Visual Studio (with Desktop Development with C++ and .NET desktop build tools)
-
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:00:50Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 7ffbac59b340f3ef5119d2f5ba4a786fd15d521bb294f3a4213b083c9b4ce23d
-  summary_generated_at: '2026-06-02T02:54:18Z'
-  summary_source_hash: 7ffbac59b340f3ef5119d2f5ba4a786fd15d521bb294f3a4213b083c9b4ce23d
-  faq_generated_at: '2026-06-03T00:00:50Z'
-  faq_source_hash: 7ffbac59b340f3ef5119d2f5ba4a786fd15d521bb294f3a4213b083c9b4ce23d
-  summary: >-
-    This Learning Path shows how to configure ML Extensions for Vulkan emulation and enable Arm
-    Neural Super Sampling (NSS) in Unreal Engine on Windows 11. You will install the Vulkan SDK
-    and activate the ML Emulation Layer with Vulkan Configurator, download the NSS Unreal plugin
-    (including its VGF model), create a C++ Third Person template project, and run the level to
-    visualize real-time upscaling. The steps explain how Arm enables neural graphics in Unreal
-    and how to verify NSS using console commands and plugin settings, with optional frame capture
-    in RenderDoc for analysis. Prerequisites include Windows 11, Unreal Engine 4.27 or 5.4 or
-    5.6, and Visual Studio with C++ and .NET desktop build tools. Estimated time: about 30 minutes.
-  faqs:
-  - question: Which Unreal Engine versions should I use for this path?
-    answer: >-
-      The prerequisites list Unreal Engine 4.27 or 5.4 or 5.6. The Unreal Engine 5.5 plugin is
-      deprecated; refer to the plugin repository documentation for details.
-  - question: Do I need the Vulkan SDK, and how are the ML emulation layers enabled?
-    answer: >-
-      Yes. The Vulkan SDK is required to use the Vulkan Configurator, which sets up the emulation
-      layers used to run ML extensions for Vulkan workloads. The Vulkan layer configuration activates
-      the ML Emulation Layer so it runs with the Unreal Engine plugin.
-  - question: Where do I get the NSS plugin and what does it include?
-    answer: >-
-      Download the latest release .zip from the Neural Super Sampling Unreal Engine Plugin GitHub
-      repository. The release package contains the plugin and the VGF model file; extract it on
-      your Windows machine.
-  - question: How do I verify that NSS is active and view its output in Unreal?
-    answer: >-
-      Press Play, then run ShowFlag.VisualizeTemporalUpscaler 1 to see NSS listed in the rendering
-      summary (use 0 to hide it). To visualize the model’s output in real time, run r.NSS.Debug
-      1. You can also view and configure the active neural network model under Project Settings
-      > Plugins > Neural Super Sampling.
-  - question: When should I use RenderDoc during this workflow?
-    answer: >-
-      Use RenderDoc to capture and inspect frames when you see unexpected visual output or need
-      to analyze the rendering sequence. It lets you step through Vulkan API calls, examine shader
-      inputs/outputs, and review resource state, with additional features available for Arm GPUs.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Annie Tallund
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md b/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md
index 2f1f1dddc9..bfb68635a7 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md
@@ -18,61 +18,9 @@ prerequisites:
   - Basic familiarity with PyTorch or TensorFlow
   - An Arm64 device such as a Raspberry Pi or Android smartphone
   - Android Studio (required only for the final deployment section)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:01:17Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: aba1cea365c0c61e84fb545ada15a64ed52c099f1252dc6312f88ee82385d2d0
-  summary_generated_at: '2026-06-02T02:54:49Z'
-  summary_source_hash: aba1cea365c0c61e84fb545ada15a64ed52c099f1252dc6312f88ee82385d2d0
-  faq_generated_at: '2026-06-03T00:01:17Z'
-  faq_source_hash: aba1cea365c0c61e84fb545ada15a64ed52c099f1252dc6312f88ee82385d2d0
-  summary: >-
-    This advanced Learning Path shows how to build, optimize, and deploy ONNX models for Arm64
-    platforms using ONNX Runtime. You will create a small digit-recognition CNN in Python, export
-    it to ONNX, validate numerical parity with ONNX Runtime, and apply model optimization techniques
-    such as layer fusion. The steps target Arm64 devices including Raspberry Pi, Arm-based servers,
-    Windows on Arm, and Android, with Apple Silicon suitable for development. You will culminate
-    in deploying an optimized model inside an Android application, using the CPU execution provider
-    on edge devices and NNAPI on Android. Prerequisites include Python 3.10 or 3.11, basic PyTorch
-    or TensorFlow familiarity, an Arm64 device, and Android Studio for the final deployment stage.
-  faqs:
-  - question: Which Python version should I install for this Learning Path?
-    answer: >-
-      Use Python 3.10 or 3.11. Prebuilt ONNX Runtime packages for Arm platforms don't yet support
-      Python 3.12.
-  - question: Which Arm64 hardware can I use, and can I develop on macOS or Windows on Arm?
-    answer: >-
-      You can use Raspberry Pi 4/5 (64-bit OS), Jetson (Arm64 CPU; GPU via CUDA if using NVIDIA
-      stack), or Arm-based servers. Apple Silicon (macOS/Arm64) and Windows on Arm are suitable
-      for development, with deployment to Arm64 Linux later.
-  - question: How do I know ONNX Runtime is using the expected execution provider on my device?
-    answer: >-
-      In the setup step you verify that ONNX Runtime detects and uses the available execution
-      providers. On edge Arm64 devices the CPU execution provider is used; on Android the NNAPI
-      execution provider is targeted.
-  - question: Do I need to prepare a dataset before training the digit recognizer?
-    answer: >-
-      No manual collection is required. The path guides you to generate a custom Sudoku digit
-      dataset, starting from a Hugging Face parquet dataset.
-  - question: What artifacts should I expect after training and evaluation, and when is the model
-      ready for Android deployment?
-    answer: >-
-      You will have a PyTorch checkpoint and an exported ONNX model, with numerical parity verified
-      against ONNX Runtime. After reviewing evaluation outputs such as the confusion matrix and
-      visual diagnostics, proceed to integrate the optimized model into the Android application.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md b/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md
index 6735630a04..7eb6d5c471 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md
@@ -13,57 +13,9 @@ learning_objectives:
 prerequisites:
     - Understanding of vertex attributes.
     - Familiarity with Arm Frame Advisor (part of Arm Performance Studio).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:02:15Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 586a505543df4237c943ea47210d735fafe7d5ed2c6ad18b1b99227987ef9b15
-  summary_generated_at: '2026-06-02T02:55:06Z'
-  summary_source_hash: 586a505543df4237c943ea47210d735fafe7d5ed2c6ad18b1b99227987ef9b15
-  faq_generated_at: '2026-06-03T00:02:15Z'
-  faq_source_hash: 586a505543df4237c943ea47210d735fafe7d5ed2c6ad18b1b99227987ef9b15
-  summary: >-
-    This advanced Learning Path guides Android graphics developers through diagnosing and improving
-    vertex data efficiency on Arm GPUs. Using Arm Frame Advisor (part of Arm Performance Studio),
-    you will profile frames to analyze the Vertex Memory Efficiency metric, identify low-efficiency
-    draw calls (for example, shadow map passes), and apply vertex representation optimizations
-    in your C/C++ rendering code. The focus is on understanding what drives poor VME on Arm Immortalis
-    and Mali devices and making targeted changes to reduce vertex bandwidth pressure. Prerequisites
-    include knowledge of vertex attributes and familiarity with Arm Frame Advisor. Estimated time
-    to complete is about 10 minutes.
-  faqs:
-  - question: How do I know if Vertex Memory Efficiency is low in my frame?
-    answer: >-
-      Profile your frame with Arm Frame Advisor and inspect the VME metric for each draw call.
-      Low VME values indicate inefficient vertex data usage that warrants investigation.
-  - question: What should I check if the shadow map draw calls report low VME?
-    answer: >-
-      Review the vertex representations and attributes used by those draw calls. The Learning
-      Path guides you through approaches to address inefficiency identified by Frame Advisor.
-  - question: What do I need before running the steps in this path?
-    answer: >-
-      You should understand vertex attributes and be familiar with Arm Frame Advisor, which is
-      part of Arm Performance Studio. The content assumes an advanced level of graphics knowledge.
-  - question: Which platforms and GPUs does this apply to?
-    answer: >-
-      The Learning Path targets Android applications running on Arm GPUs, including Arm Immortalis
-      and Mali. The analysis is performed with Arm Frame Advisor.
-  - question: How do I validate that my changes improved vertex efficiency?
-    answer: >-
-      Re-profile the frame in Arm Frame Advisor and compare VME for the affected draw calls before
-      and after your changes. An increase in VME indicates improved vertex efficiency.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author:
     - Andrew Kilroy
diff --git a/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md
index d19582ea00..4ab6c57f0b 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md
@@ -16,63 +16,9 @@ prerequisites:
     - A Linux host machine (x86_64 or aarch64) for building llama.cpp with the Arm GNU Toolchain
     - Git, CMake, and Android Debug Bridge (ADB) installed on your host machine
     - An Android device with Arm SME2 support for running and profiling the executable
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:02:40Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 4962524245ec5e5e07d1207537208a22c2e9569f252f36d758c4f828d2104272
-  summary_generated_at: '2026-06-02T02:55:26Z'
-  summary_source_hash: 4962524245ec5e5e07d1207537208a22c2e9569f252f36d758c4f828d2104272
-  faq_generated_at: '2026-06-03T00:02:40Z'
-  faq_source_hash: 4962524245ec5e5e07d1207537208a22c2e9569f252f36d758c4f828d2104272
-  summary: >-
-    This advanced Learning Path shows you how to build a statically linked llama.cpp (llama-cli)
-    with Arm KleidiAI and Scalable Matrix Extension 2 (SME2) to measure LLM inference performance
-    on Android. You cross-compile on a Linux host (x86_64 or aarch64) using the Linux-hosted Arm
-    GNU Toolchain and GCC 14.2 or later, then deploy to an SME2-capable Android device via ADB.
-    Along the way you trace how acceleration flows from llama.cpp through the ggml-cpu backend
-    into KleidiAI microkernels that can use SME2, I8MM, or DotProd, and learn how to verify that
-    SME2 kernels are active. You then compare performance with SME2 enabled and disabled using
-    a 3B-parameter GGUF model. Prerequisites include knowledge of KleidiAI and SME2, plus Git,
-    CMake, and ADB.
-  faqs:
-  - question: What do I need before building and running this path?
-    answer: >-
-      You need a Linux host machine (x86_64 or aarch64), the Linux‑hosted Arm GNU Toolchain, Git,
-      CMake, and ADB. You also need an Android device with Arm SME2 support and prior knowledge
-      of KleidiAI and SME2.
-  - question: Which compiler and target should I use to enable SME2 in llama.cpp?
-    answer: >-
-      Use the aarch64 GCC cross‑compile toolchain with the aarch64‑none‑linux‑gnu‑ prefix from
-      the Linux‑hosted Arm GNU Toolchain. GCC version 14.2 or later is required for SME2, and
-      the build produces a statically linked llama-cli.
-  - question: How do I put the model and binary onto the Android device?
-    answer: >-
-      ADB is the recommended way to transfer files and open a shell on the device. Download the
-      Llama-3.2-3B-Instruct-Q4_0.gguf model from Hugging Face using curl on your host, then use
-      ADB to move both the model and the built binary to the device.
-  - question: How do I verify that SME2 microkernels are being used during inference?
-    answer: >-
-      The steps show how to confirm SME2 microkernels are active and trace the selection path
-      from llama.cpp through ggml‑cpu to KleidiAI. Follow the verification guidance when running
-      on the SME2‑capable Android device.
-  - question: What should I check if SME2 is not selected at runtime?
-    answer: >-
-      Verify the target Android device supports Arm SME2 and that you built with GCC 14.2+ and
-      the SME2‑enabled configuration. If SME2 is unavailable, the KleidiAI integration may select
-      I8MM or DotProd microkernels depending on hardware support.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Zenon Zhilong Xiu
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md
index 1a00d03e95..3b02b2adcd 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md
@@ -16,61 +16,9 @@ prerequisites:
     - An Android device with Arm SME2 support
     - Basic understanding of machine learning model inference
     - Familiarity with Android NDK and cross-compilation
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:03:23Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 1645a1c65527da010edd6fefddad3c865eac0f9cad417ba59709a57bb9dc847a
-  summary_generated_at: '2026-06-02T02:55:45Z'
-  summary_source_hash: 1645a1c65527da010edd6fefddad3c865eac0f9cad417ba59709a57bb9dc847a
-  faq_generated_at: '2026-06-03T00:03:23Z'
-  faq_source_hash: 1645a1c65527da010edd6fefddad3c865eac0f9cad417ba59709a57bb9dc847a
-  summary: >-
-    This Learning Path shows how to build ONNX Runtime for Android with KleidiAI micro-kernels
-    and Arm Scalable Matrix Extension 2 (SME2) support, then profile model performance to assess
-    acceleration. You will cross-compile ONNX Runtime (v1.23.2) using the Android NDK (r26b or
-    newer, with r27 recommended), CMake, and Ninja on a Linux environment, and run benchmarks
-    on an Android device that supports SME2. Using onnxruntime_perf_test and a ResNet-50 v2 model,
-    you will measure execution, see how MLAS dispatches to KleidiAI kernels when SME2 is detected,
-    and compare standard versus SME2-optimized runs. Prerequisites include an SME2-capable Android
-    device, basic ML inference knowledge, and familiarity with the Android NDK and cross-compilation.
-  faqs:
-  - question: What do I need before building ONNX Runtime for Android in this path?
-    answer: >-
-      Install the Android NDK r26b or newer (r27 recommended), and ensure CMake and Ninja are
-      in your PATH. You also need an Android device with Arm SME2 support, plus familiarity with
-      NDK cross-compilation and basic model inference.
-  - question: Which ONNX Runtime version is used and how do I check it out?
-    answer: >-
-      This path uses ONNX Runtime v1.23.2. Clone the repository and checkout v1.23.2 as shown
-      in the build step.
-  - question: How does ONNX Runtime select KleidiAI SME2 kernels at runtime?
-    answer: >-
-      ORT’s MLAS checks CPU capabilities for SME2 and, when available, dispatches to KleidiAI
-      micro-kernels. Examples include Conv and GEMM via ArmKleidiAI::MlasConv, ArmKleidiAI::MlasGemmBatch,
-      and ArmKleidiAI::MlasDynamicQGemmBatch.
-  - question: How do I prepare the example model on the device for profiling?
-    answer: >-
-      Download the ResNet-50 v2 package, push it to /data/local/tmp with adb, and extract it on
-      the device in that directory. You then run profiling with onnxruntime_perf_test against
-      the extracted model files.
-  - question: What should I check if I don’t observe SME2-optimized execution?
-    answer: >-
-      Verify the Android device supports Arm SME2, because MLAS only dispatches to KleidiAI when
-      SME2 is detected. Without SME2, ONNX Runtime uses its default kernels (for example, Neon),
-      and SME2-focused comparisons will not apply.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Zenon Zhilong Xiu
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md b/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md
index 8f57d6e34d..c91bba24e1 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md
@@ -15,64 +15,9 @@ prerequisites:
     - For profiling the ML inference, [Arm NN ExecuteNetwork](https://github.com/ARM-software/armnn/releases) or [ExecuTorch](https://github.com/pytorch/executorch).
     - For profiling the application, [Arm Performance Studio with Streamline](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio).
     - Android Studio Profiler.
-  
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:04:25Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 01138f6538c655f866fb034a983461b2ac9b793142775465233b2ec8ec18d0c5
-  summary_generated_at: '2026-06-02T02:56:03Z'
-  summary_source_hash: 01138f6538c655f866fb034a983461b2ac9b793142775465233b2ec8ec18d0c5
-  faq_generated_at: '2026-06-03T00:04:25Z'
-  faq_source_hash: 01138f6538c655f866fb034a983461b2ac9b793142775465233b2ec8ec18d0c5
-  summary: >-
-    Learn how to profile ML model execution times and end-to-end application behavior on Arm-powered
-    Android devices using Arm Performance Studio (Streamline), Android Studio Profiler, and framework-level
-    tools. This introductory path shows how to identify bottlenecks across CPU, memory, cache,
-    and GPU counters with a sampling profiler, monitor Android app memory usage and leaks, and
-    extract per-layer timings from ML models. You will connect an Arm-based Android smartphone
-    via USB, run profiling sessions, and interpret timeline and layer-level outputs. Prerequisites
-    include an Arm-powered Android smartphone with a USB cable, Android Studio Profiler, Arm Performance
-    Studio with Streamline, and either Arm NN ExecuteNetwork or ExecuTorch. Estimated time to
-    complete is about 60 minutes.
-  faqs:
-  - question: What do I need before running the profiling steps?
-    answer: >-
-      You need an Arm-powered Android smartphone and a USB cable. For inference profiling, have
-      Arm NN ExecuteNetwork or ExecuTorch. For application profiling, install Arm Performance
-      Studio with Streamline and use Android Studio Profiler.
-  - question: How do I set up Android Studio Profiler to examine memory?
-    answer: >-
-      Open your project in Android Studio, go to View > Tool Windows > Profiler to open the Profiler
-      window. Connect your device in Developer Mode via USB and select your app’s process. You
-      can then monitor memory usage and look for leaks.
-  - question: Which profiler should I use for system behavior versus memory analysis?
-    answer: >-
-      Use Streamline (part of Arm Performance Studio) for system-wide sampling of performance
-      metrics with low overhead. Use Android Studio Profiler to focus on app memory usage and
-      leak detection.
-  - question: What output should I expect from Arm NN ExecuteNetwork when profiling a LiteRT model?
-    answer: >-
-      ExecuteNetwork runs the model outside the rest of the app and reports per-layer timings
-      and other useful information. This helps pinpoint bottlenecks inside the network. If you
-      are using LiteRT without Arm NN, treat the output as indicative rather than definitive.
-  - question: Which performance metrics does Streamline provide during sampling?
-    answer: >-
-      Streamline samples system counters such as memory, CPU activity and cycles, cache misses,
-      and many parts of the GPU. It also provides a timeline view to visualize how these metrics
-      evolve during execution.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Ben Clark
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md b/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md
index ec2752ca47..7d77ba40ff 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md
@@ -16,59 +16,9 @@ prerequisites:
     - Desktop computer capable of running Unity
     - Basic knowledge of Unity and programming concepts
     - The setup described in the Learning Path [Get started with Unity on Android](/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:04:52Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 9950a58160736e0c60ad80ea602262347e2ba8a37a00e29f25dc96709026ea1f
-  summary_generated_at: '2026-06-02T02:56:23Z'
-  summary_source_hash: 9950a58160736e0c60ad80ea602262347e2ba8a37a00e29f25dc96709026ea1f
-  faq_generated_at: '2026-06-03T00:04:52Z'
-  faq_source_hash: 9950a58160736e0c60ad80ea602262347e2ba8a37a00e29f25dc96709026ea1f
-  summary: >-
-    This introductory Learning Path guides Unity developers through deploying a sample app to
-    an Android device, collecting frame-level performance data with the Unity Profiler, and comparing
-    captures in the Profile Analyzer. You will create a blank 3D (URP) Core project, import a
-    sample from the Unity Asset Store, and run three code paths—Plain, Burst, and Neon with Arm
-    Neon intrinsics—to observe differences. The steps emphasize recording datasets for the unoptimized
-    and Neon modes, then loading them into the Analyzer to visualize and compare results. Prerequisites
-    are a recent Android phone or tablet, a desktop capable of running Unity, basic Unity/programming
-    knowledge, and the setup from Get started with Unity on Android.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      Have a recent Android device, a desktop capable of running Unity, and basic Unity/programming
-      knowledge. Complete the setup described in Get started with Unity on Android before proceeding.
-  - question: Which Unity project template should I use when creating the project?
-    answer: >-
-      Create a blank project in Unity Hub using the 3D (URP) Core template. Even though the sample
-      is a project, you will import it into this blank project.
-  - question: How are the Profiler and Profile Analyzer used differently in this path?
-    answer: >-
-      You will use the Profiler to record data over a series of frames and drill into specific
-      frames and timings. Then you will load the captured data into the Profile Analyzer to visualize
-      and compare datasets.
-  - question: Which sample modes should I run, and what do they represent?
-    answer: >-
-      The sample has three modes: Plain (unoptimized), Burst (code tagged for the Burst compiler
-      to enable auto-vectorization), and Neon (uses Arm Neon intrinsics). You will collect data
-      from the unoptimized (Plain) and optimized (Neon) versions for comparison.
-  - question: How should I run the sample on the device during data collection?
-    answer: >-
-      Run the app in landscape orientation, as it works best on Android in this mode. The sample
-      displays information in the bottom-right of the screen.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Joshua Marshall-Law
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md b/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md
index cb56ab0efa..9f4997e439 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md
@@ -15,62 +15,9 @@ learning_objectives:
 prerequisites:
     - Basic PyTorch model training and evaluation experience
     - A development machine with Python 3.10+ and PyTorch installed that runs ExecuTorch
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:05:22Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 56d9d0fe9606e42281a2d2be52992c7a4b6846b208376c92e7fe3094647d1c70
-  summary_generated_at: '2026-06-02T02:56:51Z'
-  summary_source_hash: 56d9d0fe9606e42281a2d2be52992c7a4b6846b208376c92e7fe3094647d1c70
-  faq_generated_at: '2026-06-03T00:05:22Z'
-  faq_source_hash: 56d9d0fe9606e42281a2d2be52992c7a4b6846b208376c92e7fe3094647d1c70
-  summary: >-
-    This advanced Learning Path guides ML developers through applying post-training quantization
-    (PTQ) and quantization-aware training (QAT) to PyTorch models using TorchAO PT2E APIs, then
-    exporting INT8 models to the .vgf format via the ExecuTorch Arm backend. You start with a
-    complete, runnable CIFAR-10-based example to generate a VGF artifact intended for Arm hardware
-    with dedicated neural accelerators (NX), export to TOSA, and validate the graph using Google’s
-    Model Explorer. The steps cover environment setup, PTQ and QAT workflows, and graph inspection
-    to spot issues such as unexpected layout conversions. Prerequisites include basic PyTorch
-    training/evaluation experience and a machine with Python 3.10+ and PyTorch that runs ExecuTorch
-    on Linux, macOS, or Windows.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need basic PyTorch model training and evaluation experience and a development machine
-      with Python 3.10+ and PyTorch installed that runs ExecuTorch. The path supports Linux, macOS,
-      and Windows.
-  - question: Should I start with PTQ or QAT in this workflow?
-    answer: >-
-      Start with PTQ using the end-to-end CIFAR-10 example to quickly generate a .vgf artifact
-      and validate the export path. Then extend the same example with QAT to compare PTQ and QAT
-      outputs using the same model and data.
-  - question: Where will the .vgf files be generated, and what result should I expect?
-    answer: >-
-      Running the provided example produces a .vgf artifact as part of the ExecuTorch Arm backend
-      export. The path uses default output directories such as ./output/ for PTQ and ./output_qat/
-      for QAT.
-  - question: How do I inspect the exported graph and what should I look for?
-    answer: >-
-      Install and launch Model Explorer with the VGF adapter, then open the .vgf files from the
-      output directories. Check for unexpected layout conversions (for example, extra transpose
-      operations) and operators you did not intend to run on your GPU.
-  - question: Can I apply this quantization and export flow to my own model?
-    answer: >-
-      Yes. After running the CIFAR-10 example end to end, reuse the same PTQ (and optionally QAT)
-      logic with your model and calibration data to export your own .vgf artifact.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author:
 - Richard Burton
diff --git a/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md b/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md
index b4e3420ece..24d1b02ce9 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md
@@ -15,67 +15,9 @@ prerequisites:
     - An appropriate Android device that supports the required Vulkan extensions (for example, Vivo X100).
     - Knowledge of the Vulkan API.
     - A Vulkan renderer. Most code is generic and should be easy to incorporate into any deferred PBR renderer.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:05:48Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 78f862a7c46fd4591fec45f91d040eb3f1093291c0a7328dddd2203dad72db3f
-  summary_generated_at: '2026-06-02T02:57:13Z'
-  summary_source_hash: 78f862a7c46fd4591fec45f91d040eb3f1093291c0a7328dddd2203dad72db3f
-  faq_generated_at: '2026-06-03T00:05:48Z'
-  faq_source_hash: 78f862a7c46fd4591fec45f91d040eb3f1093291c0a7328dddd2203dad72db3f
-  summary: >-
-    Learn how to add ray tracing to Android renderers using the Vulkan ray tracing API. This Learning
-    Path explains core concepts, compares the ray tracing pipeline and ray query approaches, shows
-    how to create acceleration structures, and uses bindless materials to access hit data efficiently.
-    You will implement basic effects for realistic shadows, reflections, and refractions in an
-    existing Vulkan renderer. The target is Android devices that support the required Vulkan extensions;
-    Immortalis GPUs (such as Immortalis-G715, Immortalis-G720, and Immortalis-G925) support ray
-    tracing, while support on some Mali G7-series devices varies by phone model. Prerequisites
-    include a compatible Android device (for example, Vivo X100), knowledge of the Vulkan API,
-    and access to a renderer, ideally a deferred PBR design.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an appropriate Android device that supports the required Vulkan extensions (for
-      example, a Vivo X100), prior knowledge of the Vulkan API, and a Vulkan renderer. The material
-      is written so most code can be integrated into a deferred PBR renderer.
-  - question: How do I know if my Android device or GPU supports Vulkan ray tracing?
-    answer: >-
-      Immortalis GPUs such as Arm Immortalis-G715, Immortalis-G720, and Immortalis-G925 support
-      ray tracing. Some Arm Mali G7‑series GPUs after Mali‑G715 may or may not support it, depending
-      on the phone model. Vulkan uses the same ray tracing API on PC and mobile, so you can prototype
-      on PC and deploy to Android.
-  - question: Which Vulkan approach should I use to launch rays?
-    answer: >-
-      The path introduces two options: the ray tracing pipeline (VK_KHR_ray_tracing_pipeline)
-      and ray queries. The ray tracing pipeline is a more driver‑managed approach with dedicated
-      shader stages such as Ray Generation and Intersection. Choose the approach that best fits
-      your renderer; the path covers them conceptually.
-  - question: What acceleration structures will I build for ray tracing?
-    answer: >-
-      You will represent the scene using VK_KHR_acceleration_structure. These implementation‑defined,
-      typically tree‑like structures accelerate intersection tests, and the API provides options
-      to control topology and balancing. Constructing them is the first step before launching
-      rays.
-  - question: Are bindless materials required for the examples?
-    answer: >-
-      No. VK_EXT_descriptor_indexing (a core feature since Vulkan 1.2) is independent of ray tracing,
-      but it simplifies accessing data for intersected objects by letting shaders index arrays
-      of buffers and textures with dynamic, non‑uniform indices. It helps organize resources in
-      lookup tables.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Iago Calvo Lista
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md b/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md
index 14eb38a876..8ffe96b231 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md
@@ -14,62 +14,9 @@ prerequisites:
     - Frame Advisor, part of Arm Performance Studio, installed. Refer to the [Arm Performance Studio](/install-guides/ams/) install guide. 
     - If you wish to analyze your own applications you will need a supported Android device.
     - Some basic familiarity with Frame Advisor. Review the [Frame Advisor](/learning-paths/mobile-graphics-and-gaming/ams/fa/) section in [Get started with Arm Performance Studio for mobile](/learning-paths/mobile-graphics-and-gaming/ams/).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:06:38Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: e2f6f3005c119e6f6fe97612d4e2600849106f244bce729d56bfeeedb30637c2
-  summary_generated_at: '2026-06-02T02:57:43Z'
-  summary_source_hash: e2f6f3005c119e6f6fe97612d4e2600849106f244bce729d56bfeeedb30637c2
-  faq_generated_at: '2026-06-03T00:06:38Z'
-  faq_source_hash: e2f6f3005c119e6f6fe97612d4e2600849106f244bce729d56bfeeedb30637c2
-  summary: >-
-    Learn to analyze Android graphics workloads using Frame Advisor’s Render Graph view in Arm
-    Performance Studio. You will capture GPU data with Streamline Performance Analyzer, then inspect
-    the directed acyclic graph of workloads and resources to find GPU‑heavy sections, spot unused
-    resources, and detect unwanted execution nodes. The path explains render graph concepts, shows
-    how to generate a capture, and demonstrates actionable fixes such as removing unnecessary
-    API calls. It applies to applications using OpenGL ES or Vulkan. Prerequisites include having
-    Frame Advisor installed; a supported Android device is needed if you plan to analyze your
-    own applications. Basic familiarity with Frame Advisor is recommended. Estimated time to complete
-    is about 30 minutes on Linux, Windows, or macOS hosts.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      Install Frame Advisor (part of Arm Performance Studio). If you plan to analyze your own
-      applications, use a supported Android device. Basic familiarity with Frame Advisor is recommended;
-      review the “Get started with Arm Performance Studio for mobile” section.
-  - question: Which Streamline capture settings should I use to record GPU data for the render
-      graph?
-    answer: >-
-      In Streamline’s Start view, open Configure Capture and enable GPU data collection. For an
-      Arm GPU, deselect “Use advanced mode” and select the “Capture Arm GPU” checkbox.
-  - question: What result should I expect from the Render Graph view?
-    answer: >-
-      You will see a directed acyclic graph of nodes and edges that summarizes GPU workloads (execution
-      nodes) and resources for a single frame. It shows how data flows between passes and where
-      outputs are consumed.
-  - question: What should I check if the graph shows resources that are never consumed?
-    answer: >-
-      Identify outputs from a render or transfer node that have no downstream consumers in the
-      graph. These indicate data written but not used in the frame and are candidates for review
-      or removal in your application.
-  - question: How do I decide whether an execution node can be removed?
-    answer: >-
-      If all outputs from a node are unnecessary, the computation is unnecessary and you can remove
-      the corresponding API calls. Make changes carefully and verify the application after adjustments.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Mark Thurman
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md
index 5b063184a2..9a80f2d136 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md
@@ -17,61 +17,9 @@ prerequisites:
     - A Linux-based x86 or macOS development machine with at least 8 GB of RAM and 50 GB of disk space (tested on Ubuntu 22.04 with x86_64).
     - A [HuggingFace](https://huggingface.co/) account.
     - An Android phone in [developer mode](https://developer.android.com/studio/debug/dev-options) and a cable to connect it to your development machine.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:07:29Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 388cab0dcb284c29fe2ad82f2b2934d9243c6356b2885d26db0a97c1a1d4d393
-  summary_generated_at: '2026-06-02T02:58:12Z'
-  summary_source_hash: 388cab0dcb284c29fe2ad82f2b2934d9243c6356b2885d26db0a97c1a1d4d393
-  faq_generated_at: '2026-06-03T00:07:29Z'
-  faq_source_hash: 388cab0dcb284c29fe2ad82f2b2934d9243c6356b2885d26db0a97c1a1d4d393
-  summary: >-
-    This Learning Path shows how to take the Stable Audio Open Small text-to-audio model from
-    Hugging Face, convert its submodules to LiteRT (.tflite), build LiteRT from the TensorFlow
-    repository using Bazel, and compile a simple C++ application for Arm-based Android (arm64-v8a)
-    with the Android NDK and CMake. You will run the app on an Android smartphone to generate
-    an audio snippet from a text prompt. The path assumes a Linux-based x86 or macOS development
-    machine, a Hugging Face account, and an Android device in developer mode with a USB cable.
-    macOS is mentioned as a platform, but the provided steps focus on Android deployment.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Linux-based x86 or macOS development machine with at least 8 GB RAM and 50 GB
-      of disk, a Hugging Face account, and an Android phone in developer mode with a USB cable.
-      These are the only explicit prerequisites listed.
-  - question: Which model files do I download from Hugging Face, and how do I verify them?
-    answer: >-
-      Download model_config.json and model.ckpt from the Stable Audio Open Small page and copy
-      them into your workspace. Verify that both files exist in the workspace before proceeding.
-  - question: Which tool versions are required for the environment setup?
-    answer: >-
-      Install Android NDK r27b or newer, Python 3.10 or newer (tested with 3.10), and CMake 3.16.0
-      or newer (tested with 3.28.1). LiteRT is built using Bazel, but a specific Bazel version
-      is not listed.
-  - question: How are the model components converted to LiteRT format?
-    answer: >-
-      You will clone a repository that provides scripts to convert the model’s three submodules
-      into LiteRT (.tflite) and generate the inference application. Follow the steps to run these
-      scripts after downloading the model assets.
-  - question: What result should I expect when running the Android app, and how do I configure
-      the build?
-    answer: >-
-      Configure CMake with the Android NDK toolchain, set ANDROID_ABI=arm64-v8a, and pass the
-      TensorFlow include and library paths. The app takes a text prompt and outputs an audio file;
-      successful generation confirms the pipeline is working.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author:
     - Nina Drozd
diff --git a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md
index 1abfbb16e0..254804b9fa 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md
@@ -15,62 +15,9 @@ prerequisites:
     - A Linux-based x86 or macOS development machine with at least 8 GB of RAM and 50 GB of disk space (tested on Ubuntu 22.04 with x86_64 and macOS with Apple Silicon)
     - A [Hugging Face](https://huggingface.co/) account
     - An Android phone in [developer mode](https://developer.android.com/studio/debug/dev-options) with at least 8 GB of RAM and a cable to connect it to your development machine
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:08:09Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 7970846a399ddcdb488d90bf19cce3c6b74df6348e88914aed83661b2597fe7b
-  summary_generated_at: '2026-06-02T02:58:48Z'
-  summary_source_hash: 7970846a399ddcdb488d90bf19cce3c6b74df6348e88914aed83661b2597fe7b
-  faq_generated_at: '2026-06-03T00:08:09Z'
-  faq_source_hash: 7970846a399ddcdb488d90bf19cce3c6b74df6348e88914aed83661b2597fe7b
-  summary: >-
-    This Learning Path shows how to download the Stable Audio Open Small model from Hugging Face,
-    convert it to ExecuTorch (.pte), and build an audio generation application targeting Arm CPUs.
-    You will set up a Python 3.10+ environment, install ExecuTorch 1.0.0, and build with CMake;
-    Android builds use the Android NDK, and macOS (Apple Silicon) uses ExecuTorch with XNNPack
-    and Arm KleidiAI. You will then run the application on an Android smartphone or macOS to generate
-    short audio snippets. Prerequisites include a Linux-based x86 or macOS development machine
-    with 8 GB RAM and 50 GB disk space, a Hugging Face account, and an Android phone in developer
-    mode with at least 8 GB RAM and a cable; Android devices should have an Arm CPU with FEAT_DotProd
-    (dotprod).
-  faqs:
-  - question: What do I need before running the conversion and build steps?
-    answer: >-
-      Use a Linux-based x86 or macOS development machine with at least 8 GB RAM and 50 GB of disk
-      space, and sign in to a Hugging Face account. For Android, enable developer mode on a phone
-      with at least 8 GB RAM and an Arm CPU that supports FEAT_DotProd; Python 3.10+ and CMake
-      3.16+ are required, and the Android NDK is referenced (version not fully specified in the
-      excerpt).
-  - question: Which ExecuTorch installation option should I use?
-    answer: >-
-      You can install executorch==1.0.0 from PyPI, which is the simplest path. Alternatively,
-      clone the ExecuTorch repository, check out v1.0.0, and run the provided installation script.
-  - question: How should I set up the Python environment for conversion?
-    answer: >-
-      Create and activate a Python 3.10 virtual environment in the audiogen-et directory to isolate
-      dependencies. Then install ExecuTorch before running the conversion step.
-  - question: How do I know the model conversion to ExecuTorch succeeded?
-    answer: >-
-      The conversion produces a .pte file for Stable Audio Open Small. Proceed to the build steps
-      once this file is created.
-  - question: What should I check if the Android build or run fails?
-    answer: >-
-      Confirm you are targeting an Arm64 Android device with FEAT_DotProd and sufficient memory
-      (8 GB recommended) and that developer mode is enabled. Ensure required tools like CMake
-      and the Android NDK are installed, and follow the cross-compilation steps for Android.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author:
     - Adnan AlSinan
diff --git a/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md b/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md
index 225a725d38..12f0c6ec6c 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md
@@ -16,59 +16,9 @@ prerequisites:
     - A microSD card (16GB or greater; class 10 or faster)
     - An ethernet connection
     - A mouse and keyboard connected to the Orange Pi
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:08:53Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: dea8c3e15cca703f075c8fa9ba3daf873a90e3eb2ee9975dd05b973dad744b54
-  summary_generated_at: '2026-06-02T02:59:30Z'
-  summary_source_hash: dea8c3e15cca703f075c8fa9ba3daf873a90e3eb2ee9975dd05b973dad744b54
-  faq_generated_at: '2026-06-03T00:08:53Z'
-  faq_source_hash: dea8c3e15cca703f075c8fa9ba3daf873a90e3eb2ee9975dd05b973dad744b54
-  summary: >-
-    This introductory Learning Path shows how to install Droid OS on an Arm-based Orange Pi 5,
-    build a Unity game for Android, and deploy the resulting APK to the board. You will use a
-    Windows PC to obtain the Orange Pi OS (Droid) TF Card image from the Orange Pi 5 support page
-    and write it to a microSD card with SDDiskTool, using 7‑Zip as needed for archives. Then you
-    will configure Unity Build Settings for Android, add Android Build Support in Unity Hub if
-    required, and produce an APK. Finally, you will transfer the APK to the Orange Pi 5 (for example
-    via USB, microSD, or a cloud drive over Ethernet) and install it. Prerequisites are explicitly
-    listed.
-  faqs:
-  - question: Do I need a Windows PC to flash Droid OS to the microSD card?
-    answer: >-
-      Yes. The Orange Pi imaging software used in this path is only available for Windows, so
-      the flashing step must be done on a Windows PC.
-  - question: Where do I download the correct Droid OS image for Orange Pi 5?
-    answer: >-
-      Go to the Orange Pi 5 support page, select Orange Pi OS (Droid) > TF Card Image, and download
-      the latest release. An example filename provided is OrangePI-OS_Droid_orangepi5_en_v0.0.6_beta.tar.gz.
-  - question: Which Unity components are required to build for the Orange Pi 5?
-    answer: >-
-      In Unity Hub, add the Android Build Support module for the Unity version used by your project.
-      In Build Settings, select Android (Unity may prompt a restart), and ensure all needed Android
-      subcomponents are included.
-  - question: What microSD card should I use for Droid OS on the Orange Pi 5?
-    answer: >-
-      Use a microSD card that is 16GB or larger and Class 10 or faster. This capacity and speed
-      are listed as prerequisites for the path.
-  - question: How can I move my Unity APK onto the Orange Pi 5?
-    answer: >-
-      You can copy the APK via a USB thumb drive if the file systems are compatible, place it
-      directly on the microSD card if formats allow, or upload it to a cloud drive and download
-      it from Droid OS on the board.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Gabriel Peterson
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md b/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md
index 129ff4b844..ffa31a4697 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md
@@ -14,59 +14,9 @@ learning_objectives:
 prerequisites:
     - Familiarity with Unity and the Unity Profiler
     - Familiarity with Arm Performance Studio tools
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:09:23Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 4a990e957658b03bac9306d5f8e6955734cc1d3b063f43c38ac525ed1bf95b79
-  summary_generated_at: '2026-06-02T02:59:56Z'
-  summary_source_hash: 4a990e957658b03bac9306d5f8e6955734cc1d3b063f43c38ac525ed1bf95b79
-  faq_generated_at: '2026-06-03T00:09:23Z'
-  faq_source_hash: 4a990e957658b03bac9306d5f8e6955734cc1d3b063f43c38ac525ed1bf95b79
-  summary: >-
-    Learn how to install Arm integration packages in Unity to profile games targeting Android
-    devices with Arm CPUs and GPUs. In about 20 minutes, you add the System Metrics Mali package
-    to enable Arm GPU hardware counters in the Unity Profiler (supported in Unity 2021.2 and later)
-    and integrate annotations that appear in Arm Performance Studio tools, Streamline and Performance
-    Advisor. You work on Windows, macOS, or Linux. By the end, you can configure Unity to display
-    Arm GPU metrics and annotate your project with markers and custom counters to add context
-    in Arm Performance Studio. Prerequisites include familiarity with Unity, the Unity Profiler,
-    and Arm Performance Studio tools.
-  faqs:
-  - question: Do I need a specific Unity version to view Arm GPU metrics?
-    answer: >-
-      Yes. The System Metrics Mali package is supported in Unity versions 2021.2 and later.
-  - question: How do I install the System Metrics Mali package in Unity?
-    answer: >-
-      Open Window > Package Manager, click the + button, and choose Add package by name. Enter
-      com.unity.profiling.systemmetrics.mali to add the package.
-  - question: What result should I expect in the Unity Profiler after installing the Mali metrics
-      package?
-    answer: >-
-      You will be able to read and display GPU hardware counters from Arm GPUs in the Unity Profiler.
-  - question: How do I enable annotations for Arm Performance Studio from my Unity project?
-    answer: >-
-      Use the Arm Performance Studio Unity integration package to add annotations. It lets you
-      mark the timeline with events and custom counters that provide context alongside performance
-      data in Streamline and are visible to Performance Advisor.
-  - question: What should I check if the Mali metrics package is not available or GPU metrics
-      do not appear?
-    answer: >-
-      Verify that your project uses Unity 2021.2 or later and that you added the package by name
-      as com.unity.profiling.systemmetrics.mali. After installation, open the Unity Profiler to
-      view the Arm GPU hardware counters.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Julie Gaskin
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md b/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md
index 5181b7679a..faae791184 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md
@@ -17,62 +17,9 @@ prerequisites:
     - Recent Android device, such as a mobile phone or tablet
     - Desktop computer capable of running Unity
     - Unity version compatible with Unity Burst compiler 1.5 or later
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:10:08Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: f17ab3c4216495b88cee75a81612c11a4727d874114f914edf97c467f0d1c125
-  summary_generated_at: '2026-06-02T03:00:21Z'
-  summary_source_hash: f17ab3c4216495b88cee75a81612c11a4727d874114f914edf97c467f0d1c125
-  faq_generated_at: '2026-06-03T00:10:08Z'
-  faq_source_hash: f17ab3c4216495b88cee75a81612c11a4727d874114f914edf97c467f0d1c125
-  summary: >-
-    This advanced Learning Path guides you through using Arm Neon intrinsics in Unity C# scripts
-    for Android, compiled with the Unity Burst compiler, and measuring results with the Unity
-    Profiler and Analyzer tools. You will install Unity with Android build support, open a provided
-    sample, configure an unoptimized baseline, then enable Burst and Neon intrinsics to compare
-    performance across versions. The path was written using Unity v6.3 and Burst 1.8.28, though
-    any Unity version compatible with Burst 1.5 or later is suitable. Prerequisites include basic
-    Unity and C# knowledge, a desktop capable of running Unity, and a recent Android device for
-    testing. Expect to complete the steps in around 90 minutes.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      You need basic knowledge of Unity and C#, a recent Android device, a desktop capable of
-      running Unity, and a Unity version compatible with the Burst compiler 1.5 or later. Android
-      build support for Unity is required.
-  - question: Which Unity and Burst versions are assumed?
-    answer: >-
-      Use a Unity version compatible with Burst 1.5 or later. The Learning Path was written using
-      Unity v6.3 and Burst 1.8.28.
-  - question: How do I enable the Burst package in my Unity project?
-    answer: >-
-      Open Window > Package Manager, set the Packages filter to Unity Registry, search for "Burst,"
-      select it, and install or enable it. Follow the project setup described to allow Burst to
-      compile the targeted code paths.
-  - question: How do I switch the sample project between unoptimized, Burst, and Neon modes?
-    answer: >-
-      Edit Assets/BurstNeonCollisions/Scripts/CollisionCalculationScript.cs and set the codeMode
-      constant (for example, Mode.Plain for unoptimized as shown). The Neon version will not function
-      correctly on computers without Neon support, so run and profile that mode on an Android
-      device.
-  - question: How do I validate that the performance comparison worked?
-    answer: >-
-      Use the Unity Profiler and Analyzer to capture data for each mode—unoptimized, Burst, and
-      Neon—on your Android device. You should see separate measurements that let you compare the
-      collision-detection workload across modes.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Ben Clark, Joshua Marshall-Law
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md b/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md
index b1d97c3a4d..9690eb55ee 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md
@@ -14,60 +14,9 @@ prerequisites:
 - A computer capable of running Unity. (Instructions are for Windows, but could be adapted to other platforms.)
 - An Android mobile device that has a 64-bit processor and supports at least Android 8.
 - A USB cable to connect the mobile device to your computer.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:10:44Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 9cd19738cbe9cde618125b309bd4f2c57ad1799e807251fdaed09707bea2781d
-  summary_generated_at: '2026-06-02T03:01:07Z'
-  summary_source_hash: 9cd19738cbe9cde618125b309bd4f2c57ad1799e807251fdaed09707bea2781d
-  faq_generated_at: '2026-06-03T00:10:44Z'
-  faq_source_hash: 9cd19738cbe9cde618125b309bd4f2c57ad1799e807251fdaed09707bea2781d
-  summary: >-
-    This Learning Path shows how to use Unity’s Machine Learning Agents toolkit inside a Unity
-    project that can be deployed to Arm-powered Android devices. You will install Unity (via Unity
-    Hub), open the provided Dr Arm sample project, review how observations and actions map to
-    a neural network “brain,” and prepare the scene and scripts for the training stage. The toolkit
-    includes a C# API and Python scripts; you will need Python and a few extra libraries before
-    running training, but you can begin by setting up Unity. Prerequisites include a computer
-    capable of running Unity (instructions target Windows), an Android device with a 64-bit processor
-    running Android 8 or later, and a USB cable. Deployment and profiling are covered in separate
-    Learning Paths.
-  faqs:
-  - question: Do I need to install Python before I start, or can I begin with Unity only?
-    answer: >-
-      You will need Python and some additional libraries before the training stage, but to get
-      started quickly you can install Unity first. The C# API is used inside Unity, while Python
-      scripts run outside Unity during training.
-  - question: Which Unity components should I install through Unity Hub?
-    answer: >-
-      Install Unity via the Unity Hub and include Visual Studio Community Edition with the Unity
-      support module. The Hub helps manage multiple Unity installations and add required support
-      modules.
-  - question: Which scene should I open in the Dr Arm project to follow the steps?
-    answer: >-
-      Open Assets -> #DevSummit2022 -> Scenes and load the Level DevSummit2022 scene. Ignore the
-      “Ready to Play” version and use this incomplete scene to apply the ML setup changes.
-  - question: What Android device requirements should I check before proceeding?
-    answer: >-
-      Use an Android device with a 64-bit processor running Android 8 or later and have a USB
-      cable to connect it to your computer. These are the explicitly listed prerequisites.
-  - question: Does this Learning Path include Android deployment and profiling steps?
-    answer: >-
-      No. Instructions for deploying Unity games to Arm-powered Android devices and profiling
-      them are provided in separate Learning Paths.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Arm
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md b/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md
index f9be54e755..d00284e997 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md
@@ -16,59 +16,9 @@ learning_objectives:
 prerequisites:
     - A development machine with [Android Studio](https://developer.android.com/studio) installed.
     - A smartphone running Android with support for `i8mm` and `dotprod` instructions.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:11:12Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: e7d95c8e7210be2fe4520fe94ccbaa3e437cd0edfa5caca549afaee22fb8b377
-  summary_generated_at: '2026-06-02T03:01:36Z'
-  summary_source_hash: e7d95c8e7210be2fe4520fe94ccbaa3e437cd0edfa5caca549afaee22fb8b377
-  faq_generated_at: '2026-06-03T00:11:12Z'
-  faq_source_hash: e7d95c8e7210be2fe4520fe94ccbaa3e437cd0edfa5caca549afaee22fb8b377
-  summary: >-
-    This Learning Path guides you through running Vision Transformer (ViT) inference on Android
-    using the Mobile Neural Network (MNN) framework and KleidiAI micro-kernels. You will download
-    a Vision LLM from Hugging Face, prepare the Qwen vision model, convert it to MNN, and build
-    a demo Android app from the Vision Language Models repository in Android Studio to create
-    an APK. You also compile command-line binaries, push an example image to the device with adb,
-    and run inference. Finally, you benchmark runs with and without KleidiAI kernels to compare
-    performance. Prerequisites include Android Studio and an Android smartphone with i8mm and
-    dotprod support. The path is introductory and takes about 30 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need Android Studio installed on your development machine and a smartphone running Android
-      that supports i8mm and dotprod instructions. No other prerequisites are explicitly listed.
-  - question: Which NDK and CMake versions are used, and how do I install them?
-    answer: >-
-      This path was tested with NDK 28.0.12916984 and CMake 4.0.0-rc1. Install the NDK via Android
-      Studio (Tools > SDK Manager > SDK Tools > NDK (Side by side)), and on Ubuntu/Debian install
-      CMake and git‑lfs with: sudo apt update and sudo apt install cmake git-lfs -y.
-  - question: Where do I get the source code for the Android demo app?
-    answer: >-
-      Clone the examples repository with: git clone https://gitlab.arm.com/kleidi/kleidi-examples/vision-language-models.
-      Open the project in Android Studio and build to generate an APK.
-  - question: How is the model prepared for use with MNN?
-    answer: >-
-      You will download a Vision LLM from Hugging Face and convert it to the MNN format. The setup
-      steps prepare the Qwen vision model as part of this process.
-  - question: How do I run the benchmark and what input image should I use?
-    answer: >-
-      Build the command-line ViT demo and prepare an example image named example.png. Push it
-      to the device with adb push example.png /data/local/tmp, then follow the steps to compare
-      inference runs with and without KleidiAI micro-kernels.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author:
     - Shuheng Deng
diff --git a/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md b/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md
index 134dc239bd..25e0a1f044 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md
@@ -17,61 +17,9 @@ prerequisites:
     - An Android phone with support for SME (Scalable Matrix Extension) instructions, required for SME performance checking
     - This Learning Path was tested on a Vivo X300 Pro.
     - A development machine with [Android Studio](https://developer.android.com/studio) installed.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:11:41Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 192f5919261cfef88c107576dc444d2db3a07fbad98100d9c758bb75670a5a00
-  summary_generated_at: '2026-06-02T03:02:05Z'
-  summary_source_hash: 192f5919261cfef88c107576dc444d2db3a07fbad98100d9c758bb75670a5a00
-  faq_generated_at: '2026-06-03T00:11:41Z'
-  faq_source_hash: 192f5919261cfef88c107576dc444d2db3a07fbad98100d9c758bb75670a5a00
-  summary: >-
-    Build and run a multimodal Voice Assistant on Android and explore how KleidiAI and SME2 can
-    accelerate its performance. You will set up Android Studio and supporting command-line tools
-    (cmake, python3, git, adb), clone the Real-Time-Voice-Assistant repository, compile the project
-    in Android Studio, and deploy it to a USB-connected phone in developer mode. The application
-    implements a Speech-to-Text → LLM (via Llama.cpp) → Text-to-Speech pipeline, with KleidiAI
-    micro-kernels and SME2 highlighted for Arm-specific acceleration on supported hardware. Prerequisites
-    include an Android phone with i8mm and SME support and a development machine with Android
-    Studio. This introductory path takes about 30 minutes and results in a working app and a clear
-    understanding of the acceleration points.
-  faqs:
-  - question: What do I need before starting?
-    answer: >-
-      An Android phone that supports the i8mm Arm architecture feature and SME (Scalable Matrix
-      Extension) instructions, and a development machine with Android Studio installed. This path
-      was tested on a Vivo X300 Pro and uses a USB connection to deploy via adb.
-  - question: Which command-line tools should I install and why?
-    answer: >-
-      Install cmake, python3, git, and adb. Python is used by the project to fetch dependencies
-      and models, and adb is required to communicate with and control the Android device.
-  - question: How do I build the app in Android Studio?
-    answer: >-
-      Open the downloaded project in Android Studio and click the Make Module VoiceAssistant.app
-      button (hammer icon). Android Studio will build the application with the default settings.
-  - question: How do I install and run the app on my phone?
-    answer: >-
-      Enable developer mode on the Android device, connect it via USB, and select it as the target
-      in Android Studio. Click Run to transfer and start the application on the phone.
-  - question: How are KleidiAI, SME2, and Llama.cpp used in this application?
-    answer: >-
-      The application combines local LLM inference and speech recognition optimized for Arm CPUs
-      using Llama.cpp and the KleidiAI library of tuned micro-kernels. SME support is required
-      for SME performance checking, and the path focuses on using KleidiAI and SME2 to accelerate
-      the workload on supported devices.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author:
     - Arnaud de Grandmaison
diff --git a/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md b/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md
index 550cff1f2f..f6e8c1817d 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md
@@ -18,60 +18,9 @@ prerequisites:
     - Python 3.9 or later for programming.
     - A working microphone for voice input.
     - Basic Python and command-line knowledge.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:12:36Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 2d8fca8838e759c3098358f131fb4b0884b0d80d5fdb2fd68719bd09fa4c3d32
-  summary_generated_at: '2026-06-02T03:02:45Z'
-  summary_source_hash: 2d8fca8838e759c3098358f131fb4b0884b0d80d5fdb2fd68719bd09fa4c3d32
-  faq_generated_at: '2026-06-03T00:12:36Z'
-  faq_source_hash: 2d8fca8838e759c3098358f131fb4b0884b0d80d5fdb2fd68719bd09fa4c3d32
-  summary: >-
-    Build an end-to-end, on-device voice assistant on Arm that understands both speech and emotion.
-    You will set up an isolated Python environment (Linux, Windows, or macOS), install dependencies
-    including ffmpeg for Whisper, and create a baseline pipeline that records from a microphone,
-    transcribes with Whisper, and queries a locally hosted LLM via llama.cpp. You then train a
-    HuBERT-based sentiment classifier on the RAVDESS dataset (neutral, happy, angry), export the
-    model to ONNX, and apply post-training quantization for on-device inference with ONNX Runtime.
-    Finally, you integrate sentiment inference into the voice-to-LLM flow to generate context-aware
-    responses. Prerequisites include Python 3.9+, a working microphone, and basic Python/CLI skills.
-    Estimated time: 90 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need Python 3.9 or later, a working microphone, and basic Python and command-line knowledge.
-      No other prerequisites are explicitly listed.
-  - question: Which operating systems are supported, and how should I set up the environment?
-    answer: >-
-      The instructions support Ubuntu, macOS, and Windows. You will create a project workspace
-      and use an isolated UV environment, and install required system tools first; ffmpeg is required
-      by Whisper for audio decoding.
-  - question: What result should I expect when the baseline voice-to-LLM pipeline is working?
-    answer: >-
-      After recording audio from your microphone, Whisper transcribes it to text and sends the
-      text to a locally hosted LLM. You should see the model’s response displayed.
-  - question: Which dataset and sentiment labels are used for training the classifier?
-    answer: >-
-      Training uses the RAVDESS dataset with three sentiment classes: neutral, happy, and angry.
-      The same approach can be extended to more classes or other datasets.
-  - question: How do I verify the ONNX conversion and quantization steps?
-    answer: >-
-      You should obtain an exported ONNX model and a quantized version with reduced file size
-      for on-device inference with ONNX Runtime. If export fails, ensure the trained HuBERT checkpoint
-      from the previous section exists and can be loaded; ONNX export may take a few seconds.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Bhanu Arya
 
diff --git a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md
index 79edf0a2f3..590af7981d 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md
@@ -16,65 +16,9 @@ prerequisites:
     - Visual Studio 2022
     - Visual Studio workload - Desktop development with C++
     - Visual Studio workload - .NET desktop build tools
-
-
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:13:13Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: af4f1c8964e0970c187643bcd0330a63a6ce66c38a2e6b4d2fc17857a12a3d7f
-  summary_generated_at: '2026-06-02T03:03:08Z'
-  summary_source_hash: af4f1c8964e0970c187643bcd0330a63a6ce66c38a2e6b4d2fc17857a12a3d7f
-  faq_generated_at: '2026-06-03T00:13:13Z'
-  faq_source_hash: af4f1c8964e0970c187643bcd0330a63a6ce66c38a2e6b4d2fc17857a12a3d7f
-  summary: >-
-    This Learning Path shows how to enable neural graphics workflows on Windows by using ML Extensions
-    for Vulkan. You install the ML Emulation Layers to simulate VK_ARM_data_graph and VK_ARM_tensors,
-    set up build tools (CMake, Python 3, Git), and use Visual Studio 2022 on a Windows 11 development
-    machine. You then build and run the Vulkan Samples fork, starting with the Simple Tensor and
-    Data Graph example that executes a 2D average pooling operation via a data graph pipeline.
-    You also run an end-to-end inference test with the Scenario Runner from Arm’s ML SDK for Vulkan,
-    and debug or inspect frames with RenderDoc. By the end, you can run sample workloads using
-    the ML extensions and analyze their execution.
-  faqs:
-  - question: What do I need installed before building and running the samples?
-    answer: >-
-      Use a Windows 11 development machine with Visual Studio 2022 and the Desktop development
-      with C++ and .NET desktop build tools workloads. Install CMake (3.12+), Python 3, and Git,
-      then download the ML Emulation Layers for Vulkan. You can verify tools with commands like
-      cmake --version and python3 --version.
-  - question: Which Vulkan ML extensions does this path use, and how are they enabled?
-    answer: >-
-      The path uses VK_ARM_data_graph and VK_ARM_tensors. These are enabled on your machine by
-      installing the ML Emulation Layers for Vulkan, which simulate the extensions so the samples
-      can run.
-  - question: How do I get and build the first sample?
-    answer: >-
-      Clone Arm’s fork of Vulkan Samples on the tensor_and_data_graph branch with submodules as
-      shown in the steps. Build it with the tools you installed; the Simple Tensor and Data Graph
-      sample demonstrates a 2D average pooling operation via a data graph pipeline.
-  - question: How do I run a complete inference test beyond the simple sample?
-    answer: >-
-      Use the Scenario Runner from Arm’s ML SDK for Vulkan. The Learning Path points to Arm’s
-      Hugging Face page where you can download binaries and assets that demonstrate the ML extensions
-      in action.
-  - question: When should I use RenderDoc with these samples, and what can I inspect?
-    answer: >-
-      Use RenderDoc to capture frames when you need to visualize and debug ML-integrated rendering.
-      You can step through frames, inspect Vulkan API calls, view shader inputs and outputs, examine
-      tensors, and review GPU resource states and memory usage.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Annie Tallund
 
diff --git a/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md b/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
index a65cd16837..f4105a63f3 100644
--- a/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
@@ -16,62 +16,9 @@ prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an on-premise Arm server.
     - Basic understanding of Python and prompt engineering.
     - Understanding of LLM fundamentals.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:14:25Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 275878b5aa4c27dee394da12ad8b80e4c0d0235b3ddce66b1fe3f0e056ef3da9
-  summary_generated_at: '2026-06-02T03:03:34Z'
-  summary_source_hash: 275878b5aa4c27dee394da12ad8b80e4c0d0235b3ddce66b1fe3f0e056ef3da9
-  faq_generated_at: '2026-06-03T00:14:25Z'
-  faq_source_hash: 275878b5aa4c27dee394da12ad8b80e4c0d0235b3ddce66b1fe3f0e056ef3da9
-  summary: >-
-    This Learning Path shows how to build and deploy an AI agent application on Arm servers using
-    llama.cpp, llama-cpp-python, and llama-cpp-agent with KleidiAI optimization. You will configure
-    an Arm-optimized environment on Ubuntu 22.04 LTS, build llama.cpp, download a quantized Llama
-    3.1 8B model, and implement a Python script (agent.py) that adds custom functions and exercises
-    function calling. The path targets Arm-based cloud or on-prem servers and was tested on an
-    AWS EC2 Graviton3 m7g.xlarge instance. Plan for at least 4 CPU cores, 16 GB RAM, and 32 GB
-    disk. Prerequisites include basic Python and prompt engineering skills and understanding of
-    LLM fundamentals. By the end, you will have a working, Arm-optimized AI agent suitable for
-    application integration.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      Use an Arm-based server running Ubuntu 22.04 LTS with at least 4 CPU cores, 16 GB RAM, and
-      32 GB disk space. You should also have a basic understanding of Python, prompt engineering,
-      and LLM fundamentals.
-  - question: Which environment or instance type is assumed?
-    answer: >-
-      Any Arm-based instance from a supported cloud service provider or an on-premise Arm server
-      that meets the resource requirements. The instructions were tested on an AWS EC2 Graviton3
-      m7g.xlarge instance.
-  - question: Which model is used in the example and how is it referenced?
-    answer: >-
-      The example uses a quantized Llama 3.1 8B model. Ensure you download this model and that
-      the model_path in agent.py points to the location where you stored it.
-  - question: Do I need special configuration to use KleidiAI optimizations?
-    answer: >-
-      This path sets up llama.cpp and llama-cpp-python optimized for Arm with KleidiAI as part
-      of the procedure. Follow the steps in order on Ubuntu 22.04 LTS; no additional configuration
-      beyond what is shown is listed.
-  - question: How do I know the AI agent is working after I create agent.py?
-    answer: >-
-      When you run the script, the agent should load the quantized Llama 3.1 8B model and select
-      predefined functions based on your input. You should see outputs that reflect function calls
-      and responses generated by the model.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Andrew Choi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/aks/_index.md b/content/learning-paths/servers-and-cloud-computing/aks/_index.md
index ace34c3b2a..7700d9fb27 100644
--- a/content/learning-paths/servers-and-cloud-computing/aks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/aks/_index.md
@@ -14,58 +14,9 @@ learning_objectives:
 prerequisites:
     - An Azure account
     - A machine with [Terraform](/install-guides/terraform/), [Azure CLI](/install-guides/azure-cli), and [Kubectl](/install-guides/kubectl/) installed
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:15:11Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 01c5cc8930650a8d81d67d4c48b62e0f9d7b1ebfc2d09a552e11046fb982fc52
-  summary_generated_at: '2026-06-02T03:03:59Z'
-  summary_source_hash: 01c5cc8930650a8d81d67d4c48b62e0f9d7b1ebfc2d09a552e11046fb982fc52
-  faq_generated_at: '2026-06-03T00:15:11Z'
-  faq_source_hash: 01c5cc8930650a8d81d67d4c48b62e0f9d7b1ebfc2d09a552e11046fb982fc52
-  summary: >-
-    This Learning Path shows how to automate the creation of an Arm-based Azure Kubernetes Service
-    (AKS) cluster using Terraform and then deploy a WordPress example workload backed by MySQL.
-    You will target Azure Dpsv5 virtual machines featuring Ampere Altra Arm-based processors and
-    use the Azure CLI and kubectl alongside Terraform. The steps include provisioning the AKS
-    cluster and applying three Kubernetes manifests (kustomization.yaml, mysql-deployment.yaml,
-    and wordpress-deployment.yaml) adapted from the Kubernetes WordPress tutorial. Prerequisites
-    are an Azure account and a machine with Terraform, Azure CLI, and kubectl installed; the instructions
-    target a Linux environment. By the end, you will have a running AKS cluster and a deployed
-    WordPress example.
-  faqs:
-  - question: What do I need before running the Terraform deployment?
-    answer: >-
-      You need an Azure account and a machine with Terraform, Azure CLI, and kubectl installed.
-      The Learning Path assumes these tools are ready before you start.
-  - question: Which Azure VM series is used for Arm-based AKS nodes in this path?
-    answer: >-
-      The path uses the Azure Dpsv5 Virtual Machine series featuring Ampere Altra Arm-based processors.
-      AKS can run on this series to provide Arm-based compute.
-  - question: Can I run the setup steps from my local computer or a virtual machine?
-    answer: >-
-      Yes. Any computer with the required tools installed can be used, including your desktop,
-      laptop, or a virtual machine.
-  - question: What files do I create to deploy the WordPress example?
-    answer: >-
-      You will create three Kubernetes YAML files: kustomization.yaml, mysql-deployment.yaml,
-      and wordpress-deployment.yaml. These are modified from the Kubernetes WordPress Tutorial.
-  - question: How do I know I’m ready to deploy WordPress to the cluster?
-    answer: >-
-      You should have an AKS cluster already running from the previous topic. Once the cluster
-      is provisioned, proceed to create the YAML files and deploy the example workload.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md b/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
index 6aae02c36f..badda3de19 100644
--- a/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
@@ -18,65 +18,9 @@ prerequisites:
   - Basic familiarity with Python
   - Basic understanding of data formats such as Parquet or ORC
   - Familiarity with Linux command-line operations
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:15:38Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 90b638385267e085ea07a70a1c23f16578aa3caaeabeca1f651bcdcac5bf38fb
-  summary_generated_at: '2026-06-02T03:04:20Z'
-  summary_source_hash: 90b638385267e085ea07a70a1c23f16578aa3caaeabeca1f651bcdcac5bf38fb
-  faq_generated_at: '2026-06-03T00:15:38Z'
-  faq_source_hash: 90b638385267e085ea07a70a1c23f16578aa3caaeabeca1f651bcdcac5bf38fb
-  summary: >-
-    This Learning Path shows how to deploy Apache Arrow and Arrow Flight on Arm-based Google Cloud
-    C4A Axion instances for high-throughput columnar analytics and low-latency data transport.
-    You will provision a c4a-standard-4 arm64 VM running Linux (SUSE Linux Enterprise Server),
-    configure Google Cloud firewall rules for MinIO and Arrow Flight, install Arrow and MinIO,
-    and assemble a single-node analytics stack. Using Python, you will read and write Parquet
-    and ORC datasets stored in MinIO and explore predicate pushdown and column pruning. The path
-    also integrates Arrow Flight and includes guidance to validate performance benefits on Arm-based
-    infrastructure. Prerequisites include a GCP account with billing enabled and basic familiarity
-    with Python, Parquet/ORC, and the Linux command line.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Google Cloud Platform (GCP) account with billing enabled, basic familiarity with
-      Python, a basic understanding of Parquet or ORC, and comfort with Linux command-line operations.
-      No other prerequisites are explicitly listed.
-  - question: Which Google Cloud machine type and operating system are used?
-    answer: >-
-      You will create an Axion C4A arm64 virtual machine using the c4a-standard-4 type, which
-      provides 4 vCPUs and 16 GB of memory. The environment is based on SUSE Linux Enterprise
-      Server (SLES) for arm64.
-  - question: Which firewall ports should I open for MinIO and Arrow Flight?
-    answer: >-
-      Open port 9000 for the MinIO S3 API as listed in the path. Additional ports for the MinIO
-      Web UI and Arrow Flight are required; follow the port list provided in the firewall setup
-      step.
-  - question: How is MinIO used, and how does Apache Arrow access data?
-    answer: >-
-      MinIO provides S3-compatible object storage for analytical datasets. Apache Arrow uses its
-      Dataset API to read and write Parquet and ORC files stored in MinIO.
-  - question: What result should I expect after the analysis and Arrow Flight steps, and how can
-      I validate success?
-    answer: >-
-      You should be able to store datasets in MinIO and use Apache Arrow to read and write Parquet
-      and ORC while exploring predicate pushdown and column pruning. You also set up and run an
-      Arrow Flight server for low-latency data transport, with access allowed by your firewall
-      rules. The path includes validation of performance benefits on Arm-based C4A, but it does
-      not provide specific metrics.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
index 555b14fc55..adfbd2d509 100644
--- a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
@@ -16,57 +16,9 @@ learning_objectives:
 prerequisites:
     - An [AWS account](https://aws.amazon.com/) with permission to launch Graviton4 (`c8g.4xlarge` or larger) instances
     - Basic familiarity with Linux and SSH
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:16:17Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 964c1e6a87810dca686a7b3218433ce09d31bd92882db10af355e8c50bb6091c
-  summary_generated_at: '2026-06-02T03:04:42Z'
-  summary_source_hash: 964c1e6a87810dca686a7b3218433ce09d31bd92882db10af355e8c50bb6091c
-  faq_generated_at: '2026-06-03T00:16:17Z'
-  faq_source_hash: 964c1e6a87810dca686a7b3218433ce09d31bd92882db10af355e8c50bb6091c
-  summary: >-
-    Follow a concise workflow to deploy Arcee’s AFM-4.5B small language model on Arm-based AWS
-    Graviton4 using Llama.cpp. You will launch a Graviton4 EC2 instance (c8g.4xlarge or larger),
-    configure a Linux environment with system packages and a Python virtual environment, build
-    Llama.cpp from source, download the AFM-4.5B model from Hugging Face, quantize it, and run
-    inference. The path includes evaluating model quality using perplexity. Prerequisites are
-    an AWS account with permission to launch Graviton4 instances, at least 128 GB of available
-    storage, and basic Linux and SSH familiarity. The estimated time to complete is about 30 minutes.
-  faqs:
-  - question: Do I need specific AWS access or resources before starting?
-    answer: >-
-      Yes. You need an AWS account with permission to launch Graviton4 EC2 instances and at least
-      128 GB of available storage. Basic familiarity with Linux and SSH is also expected.
-  - question: Which EC2 instance type should I launch for this workflow?
-    answer: >-
-      Use an Arm-based AWS Graviton4 instance of type c8g.4xlarge or larger. The steps assume
-      a Linux environment on this instance.
-  - question: How do I connect to the EC2 instance?
-    answer: >-
-      Create an SSH key pair in the EC2 console as part of the provisioning steps. You will use
-      this key pair to establish an SSH connection to your Graviton4 instance.
-  - question: Which Llama.cpp repository should I use for AFM-4.5B?
-    answer: >-
-      Use the standard upstream repository at https://github.com/ggerganov/llama.cpp. Arcee AI
-      has contributed the necessary modeling code upstream, so no custom fork is required.
-  - question: What are the main steps after provisioning the instance?
-    answer: >-
-      Install system packages and a Python environment, then build Llama.cpp from source. Next,
-      download the AFM-4.5B model from Hugging Face, quantize it, run inference with Llama.cpp,
-      and evaluate quality by measuring perplexity.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Julien Simon
 
diff --git a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
index 22ac19416c..dc14eb7a69 100644
--- a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
@@ -16,59 +16,9 @@ learning_objectives:
 prerequisites:
     - A [Google Cloud account](https://console.cloud.google.com/) with permission to launch Axion (`c4a-standard-16` or larger) instances
     - Basic familiarity with Linux and SSH
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:16:57Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: b2f60d2c31ef380e6e6ef3028671ad9242dd51c98f4a192f2da32bb05b94e185
-  summary_generated_at: '2026-06-02T03:05:06Z'
-  summary_source_hash: b2f60d2c31ef380e6e6ef3028671ad9242dd51c98f4a192f2da32bb05b94e185
-  faq_generated_at: '2026-06-03T00:16:57Z'
-  faq_source_hash: b2f60d2c31ef380e6e6ef3028671ad9242dd51c98f4a192f2da32bb05b94e185
-  summary: >-
-    This Learning Path guides you through deploying Arcee’s AFM-4.5B small language model on Arm-based
-    Google Cloud Axion instances using Llama.cpp. You will provision a Linux Compute Engine VM
-    (c4a-standard-16 or larger), install system and Python dependencies, build Llama.cpp from
-    source, download the model from Hugging Face, quantize it, and run inference. You will also
-    evaluate model quality by measuring perplexity. It targets developers and ML engineers and
-    is scoped to about 30 minutes. Prerequisites include a Google Cloud account with permission
-    and quota to launch Axion instances, at least 128 GB of available storage, and basic familiarity
-    with Linux and SSH.
-  faqs:
-  - question: What do I need in my Google Cloud project before launching the VM?
-    answer: >-
-      You need permission and sufficient quota to launch a Google Cloud Axion instance of type
-      c4a-standard-16 (or larger). Ensure at least 128 GB of available storage for the model and
-      dependencies.
-  - question: Which Llama.cpp repository should I clone for AFM-4.5B support?
-    answer: >-
-      Use the standard Llama.cpp repository: git clone https://github.com/ggerganov/llama.cpp.
-      AFM-4.5B support is available because Arcee AI contributed the necessary modeling code upstream.
-  - question: Do I need a Hugging Face account or token to download AFM-4.5B?
-    answer: >-
-      The Learning Path states that you will download the AFM-4.5B model from Hugging Face, but
-      it does not explicitly list whether a Hugging Face account or token is required. Follow
-      the steps as provided in the path.
-  - question: Why create a Python virtual environment for Llama.cpp, and how is it set up here?
-    answer: >-
-      A virtual environment isolates dependencies and prevents conflicts. In this path, you create
-      one with virtualenv env-llama-cpp before installing the required Python packages.
-  - question: What result should I expect after completing the steps?
-    answer: >-
-      You will have built Llama.cpp on a Google Cloud Axion Arm64 VM, downloaded and quantized
-      AFM-4.5B, and run inference. You will also evaluate model quality by measuring perplexity.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Julien Simon
 
diff --git a/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
index a8303065b5..a38fab5195 100644
--- a/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
@@ -20,62 +20,9 @@ prerequisites:
   - Basic familiarity with [Kubernetes concepts](https://kubernetes.io/docs/concepts/)
   - Basic understanding of Git and GitHub workflows
   - Familiarity with basic Linux command-line usage
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:17:35Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: c6106f21859f5a8e04bbd579db47c7177bb5371d4c7f306054e56e81ed9e89a1
-  summary_generated_at: '2026-06-02T03:05:29Z'
-  summary_source_hash: c6106f21859f5a8e04bbd579db47c7177bb5371d4c7f306054e56e81ed9e89a1
-  faq_generated_at: '2026-06-03T00:17:35Z'
-  faq_source_hash: c6106f21859f5a8e04bbd579db47c7177bb5371d4c7f306054e56e81ed9e89a1
-  summary: >-
-    Learn how to deploy and manage applications on Arm-based Google Kubernetes Engine (GKE) using
-    Argo CD and GitOps. You will provision an Arm-based SUSE Linux Enterprise Server VM on a Google
-    Axion C4A instance, create and connect to a GKE cluster running on Arm64 nodes, and install
-    Argo CD using official manifests. The path covers configuring browser and CLI access, deploying
-    a production-ready NGINX application from a Git repository, and enabling automated sync, pruning,
-    and self-healing. By the end, you will verify application health and access to services on
-    GKE. Prerequisites include a GCP account with billing enabled, basic Kubernetes and Git/GitHub
-    knowledge, and Linux CLI familiarity.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Google Cloud Platform (GCP) account with billing enabled and basic familiarity
-      with Kubernetes, Git/GitHub workflows, and Linux command-line usage. The path uses a SUSE
-      Linux Arm64 VM that you provision on Google Cloud as part of the steps.
-  - question: Which Google Cloud VM and OS are used for the setup host?
-    answer: >-
-      The example provisions a Google Axion C4A VM using the c4a-standard-4 machine type (4 vCPUs,
-      16 GB memory) running SUSE Linux Enterprise Server (SLES) for Arm64. This VM is used to
-      prepare and interact with the GKE environment.
-  - question: What type of GKE cluster should I create for this path?
-    answer: >-
-      Create a production-ready Arm64 GKE cluster running on Axion (C4A) nodes to support GitOps
-      deployments with Argo CD. The steps guide you to prepare the cluster from the SLES Arm64
-      VM.
-  - question: How do I know Argo CD is installed and accessible?
-    answer: >-
-      Argo CD is installed using the official upstream manifests into a dedicated namespace. You
-      should be able to access the Argo CD UI in a browser, retrieve the admin credentials, and
-      authenticate with the Argo CD CLI.
-  - question: What repository do I need for the GitOps deployment?
-    answer: >-
-      You need a GitHub repository to store the GitOps manifests; an empty repository is sufficient
-      to start. Argo CD continuously reconciles the cluster to match the desired state defined
-      in this repo.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md b/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
index 480f3c2d59..b90e551239 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
@@ -14,58 +14,9 @@ learning_objectives:
 prerequisites:
     - Access to an x86 and an Arm cloud instance (virtual machine).
     - Proficiency in C++ programming.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:18:10Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 3a4bc8b2ab548be507b6d528844b0593b27f2dab3ab3c9649e807abdf4887ce0
-  summary_generated_at: '2026-06-02T03:06:00Z'
-  summary_source_hash: 3a4bc8b2ab548be507b6d528844b0593b27f2dab3ab3c9649e807abdf4887ce0
-  faq_generated_at: '2026-06-03T00:18:10Z'
-  faq_source_hash: 3a4bc8b2ab548be507b6d528844b0593b27f2dab3ab3c9649e807abdf4887ce0
-  summary: >-
-    This Learning Path helps experienced C++ developers port concurrent code from x86 to Arm by
-    explaining the C++ memory model, highlighting key memory ordering differences, and demonstrating
-    how subtle races can appear on Arm. You will run a simple race condition example on both x86
-    and Arm cloud instances (Linux), with an example using an Arm-based AWS t4g.xlarge instance
-    running Ubuntu 22.04 LTS, though other instance types can be used. You will use ThreadSanitizer
-    (TSan) to detect infrequent data races and learn best practices for writing correct C++ on
-    Arm. Prerequisites include access to both an x86 and an Arm VM and proficiency in C++. Estimated
-    time to complete is about 45 minutes.
-  faqs:
-  - question: What do I need before running the example?
-    answer: >-
-      You need access to both an x86 and an Arm cloud instance (virtual machine) and proficiency
-      in C++ programming. The Learning Path assumes a Linux environment.
-  - question: Which Arm instance and OS are used in the walkthrough?
-    answer: >-
-      The example uses an AWS t4g.xlarge instance running Ubuntu 22.04 LTS. You can use other
-      Arm instance types if preferred.
-  - question: Which compiler/toolchain should I use for ThreadSanitizer (TSan)?
-    answer: >-
-      Use a recent version of the clang toolchain that includes TSan support. TSan instruments
-      the code at compile time to detect data races.
-  - question: How do I know if the race condition has been reproduced?
-    answer: >-
-      Expect differences in program behavior between x86 and Arm due to memory ordering, as illustrated
-      by the example. When you run with TSan, it will report data races if they are present, including
-      details to help you debug.
-  - question: What operating system is assumed for this Learning Path?
-    answer: >-
-      Linux is the target operating system. The example specifically references Ubuntu 22.04 LTS
-      on an Arm instance.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md b/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
index 25c1bfbdb0..8faf618b95 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
@@ -17,62 +17,9 @@ prerequisites:
     - An AI-powered IDE such as VS Code, Copilot in VS Code, Kiro (IDE or CLI) or Codex
     - Basic familiarity with Docker and C/C++ development
     - Access to an Arm-based cloud instance or local Arm computer running Linux or macOS
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:18:38Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: b870ac5160d35ddb51955ca8379493e172787ced8125cde8ae79f7700e653a87
-  summary_generated_at: '2026-06-02T03:06:30Z'
-  summary_source_hash: b870ac5160d35ddb51955ca8379493e172787ced8125cde8ae79f7700e653a87
-  faq_generated_at: '2026-06-03T00:18:38Z'
-  faq_source_hash: b870ac5160d35ddb51955ca8379493e172787ced8125cde8ae79f7700e653a87
-  summary: >-
-    This Learning Path guides you through automating x86-to-Arm application migration using the
-    Arm MCP Server. You will connect an AI-powered IDE or agent to the MCP Server to run AI-assisted
-    checks on Docker images for arm64 support, refactor C++ (including SIMD intrinsics cases)
-    with the Arm Cloud Migration Agent in GitHub Copilot, and validate the migrated application
-    in Docker on Arm-based systems. You also configure the same migration workflow in other agentic
-    tools. Prerequisites include an AI-enabled IDE (for example VS Code with Copilot, Kiro, or
-    Codex), basic Docker and C/C++ knowledge, and access to an Arm-based Linux or macOS system.
-    Estimated time to complete is about 20 minutes.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      Have an AI-powered IDE (for example, VS Code with GitHub Copilot, Kiro, or Codex), basic
-      familiarity with Docker and C/C++ development, and access to an Arm-based cloud instance
-      or a local Arm machine running Linux or macOS.
-  - question: How do I check if a Docker base image supports arm64 during migration?
-    answer: >-
-      Use natural language prompts with the Arm MCP Server to ask about arm64 compatibility. This
-      avoids manual manifest inspection and returns an AI-assisted compatibility assessment you
-      can act on.
-  - question: I’m not using GitHub Copilot—how do I follow the migration workflow?
-    answer: >-
-      Skip to the section on configuring other agentic systems and set up persistent instructions
-      (such as steering documents or prompt files) for your tool. The goal is to let your AI assistant
-      use the Arm MCP Server to execute the same multi-step migration workflow.
-  - question: What should I do if my C++ code uses x86 SIMD intrinsics?
-    answer: >-
-      Use the Arm Cloud Migration Agent in GitHub Copilot to guide refactoring from SSE/AVX/AVX2
-      intrinsics to Arm Neon or SVE equivalents. Follow the agent’s structured steps to address
-      architecture-specific vector code.
-  - question: How do I validate the migrated C++ application on Arm?
-    answer: >-
-      Run and validate the application in Docker on an Arm-based system as outlined in the path.
-      You should be able to execute the container on Arm Linux or macOS and confirm the application
-      runs as expected.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Joe Stech
 
diff --git a/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md b/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
index 84293a520b..9da852cdfb 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
@@ -18,63 +18,9 @@ prerequisites:
     - Working knowledge of C programming
     - Familiarity with Linux development environments and basic embedded or cloud deployment concepts
     - Experience building applications with GCC and CMake
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:19:04Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 49b3f2b1464efb20f0c8fbcff46e9f30910d856567ca01c01dc348aa1c5740d1
-  summary_generated_at: '2026-06-02T03:06:57Z'
-  summary_source_hash: 49b3f2b1464efb20f0c8fbcff46e9f30910d856567ca01c01dc348aa1c5740d1
-  faq_generated_at: '2026-06-03T00:19:04Z'
-  faq_source_hash: 49b3f2b1464efb20f0c8fbcff46e9f30910d856567ca01c01dc348aa1c5740d1
-  summary: >-
-    This advanced Learning Path shows how to migrate a C application between Arm platforms using
-    Kiro Arm SoC Migration Power. You install Kiro IDE on your local machine, enable the Migration
-    Power, and use an Arm MCP server deployed as a Docker-based backend for Arm-specific guidance.
-    You build and validate a sensor-monitor application on an AWS Graviton3 source platform, then
-    follow an AI-guided workflow—discovery, architecture analysis, abstraction design, and platform-specific
-    implementation—to target a Raspberry Pi 5. Finally, you validate on both platforms using testing
-    recommendations for functional correctness, platform compatibility, hardware interaction,
-    and performance comparison. Prerequisites include access to both platforms, C experience,
-    Linux familiarity, and the ability to build with GCC and CMake. Estimated time is 60 minutes.
-  faqs:
-  - question: What do I need before running the migration workflow?
-    answer: >-
-      You need access to both source and target Arm platforms (for example, AWS Graviton3 and
-      Raspberry Pi 5), working knowledge of C, familiarity with Linux development, and experience
-      with GCC and CMake. Basic embedded or cloud deployment concepts are also assumed.
-  - question: How do I set up Kiro and the required backend services?
-    answer: >-
-      Install Kiro IDE on your local machine, enable Kiro Arm SoC Migration Power, and run the
-      Arm MCP server as a containerized backend using Docker. You also provision an AWS Graviton3
-      instance to serve as the source platform for the example.
-  - question: Which application and platforms are used in the example?
-    answer: >-
-      The example uses a sensor-monitor application. The migration demonstrates moving from AWS
-      Graviton3 (Neoverse) to Raspberry Pi 5 (Cortex-A76), though the workflow applies to other
-      Arm-to-Arm scenarios.
-  - question: How do I know the analysis phase is working during migration?
-    answer: >-
-      The Migration Power highlights platform-specific and hardware-dependent code and guides
-      abstraction boundaries. Use these findings to design and implement a hardware abstraction
-      layer before adding platform-specific implementations.
-  - question: What should I check to confirm the migration is successful?
-    answer: >-
-      Use the Power’s testing recommendations on both source and target platforms to verify functional
-      correctness, confirm platform compatibility, validate hardware interaction, and compare
-      performance characteristics. Migration is complete when these checks pass on both environments.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Daniel Schleicher
 
diff --git a/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md b/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
index 5f016ad1f6..b95191ffa5 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
@@ -16,58 +16,9 @@ learning_objectives:
 
 prerequisites:
   - Access to an Arm-based Linux system running Ubuntu, Debian, or CentOS.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:19:30Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 67004eb9a75926144eb6f75124104972b3cf20a57a22b698c9359d20db3eca02
-  summary_generated_at: '2026-06-02T03:07:24Z'
-  summary_source_hash: 67004eb9a75926144eb6f75124104972b3cf20a57a22b698c9359d20db3eca02
-  faq_generated_at: '2026-06-03T00:19:30Z'
-  faq_source_hash: 67004eb9a75926144eb6f75124104972b3cf20a57a22b698c9359d20db3eca02
-  summary: >-
-    This Learning Path shows how to install and boot a Linux kernel configured for 64K page size
-    on Arm-based systems to improve memory efficiency and performance for memory‑intensive workloads.
-    You will learn the role of page size, how Arm64 differs from x86, and how page size impacts
-    efficiency and performance. The steps cover checking the current page size, switching to a
-    64K kernel on Ubuntu 22.04 LTS or later, Debian 11 “Bullseye” or later (compiled from source),
-    and CentOS 9 or later, then confirming the change and optionally reverting to a 4K kernel.
-    Prerequisite: access to an Arm-based Linux system running Ubuntu, Debian, or CentOS. The path
-    uses bash and is introductory.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need access to an Arm-based Linux system running Ubuntu, Debian, or CentOS. No other
-      explicit prerequisites are listed.
-  - question: Which Linux distributions and versions are covered?
-    answer: >-
-      Ubuntu 22.04 LTS or later, Debian 11 “Bullseye” or later, and CentOS 9 or later. The steps
-      are distribution-specific.
-  - question: How do I check my current memory page size and kernel?
-    answer: >-
-      Run getconf PAGESIZE and uname -r. A first line of 4096 indicates a 4K base-page-size kernel;
-      if it is different, you are already using a non-4K page size.
-  - question: On Debian, do I need to compile a 64K kernel and which source should I use?
-    answer: >-
-      Yes. Debian does not provide a 64K kernel package, so you must compile from source; you
-      can use kernel.org or the Debian source package, and this path uses the Debian source package.
-  - question: How do I verify the 64K page size is active, and can I revert to 4K?
-    answer: >-
-      Re-run getconf PAGESIZE after booting the new kernel; it should no longer report 4096 and
-      should reflect the 64K configuration. The path includes an optional step to revert to the
-      default 4K page size kernel.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Geremy Cohen
 
diff --git a/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md b/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
index 41b5d018f1..7e1a6b23a0 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
@@ -13,62 +13,9 @@ learning_objectives:
     - Use the Linux perf_event_open system call to instrument event counters in code
 prerequisites:
     - An Arm computer running Linux. A bare metal or cloud metal instance is best because they expose more counters. You can use a virtual machine (VM), but fewer counters may be available. These instructions have been tested on the `a1.metal` instance type.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:20:03Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
-  summary_generated_at: '2026-06-02T03:07:54Z'
-  summary_source_hash: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
-  faq_generated_at: '2026-06-03T00:20:03Z'
-  faq_source_hash: 70ed68f472841d24321968188948c5c661a48445c0b07e54535d538233e048e3
-  summary: >-
-    Learn how to access Arm hardware performance counters (PMU) and the system counter from user
-    space on Linux. You will measure time using the system counter with small assembly snippets
-    (MRS/MSR), instrument event counters with PAPI, and use the Linux perf_event_open system call
-    to read both single counters and groups (without multiplexing). The path covers installing
-    PAPI, setting environment variables (PAPI_DIR and, if needed, LD_LIBRARY_PATH), enabling user-space
-    access to counters, and building example programs with GCC. Target environment is an Arm computer
-    running Linux; bare-metal or cloud metal instances expose more counters, and the steps were
-    tested on the a1.metal instance type. You will finish with working code that reads hardware
-    and system counter values.
-  faqs:
-  - question: What do I need before running the examples?
-    answer: >-
-      You need an Arm computer running Linux. A bare metal or cloud metal instance is recommended
-      because it exposes more counters, while a VM may provide fewer counters. The instructions
-      were tested on an a1.metal instance.
-  - question: How do I decide between using the system counter, PAPI, or perf_event_open?
-    answer: >-
-      Use the system counter via MRS/MSR if you only need to measure time or cycles from user
-      space. Use PAPI to instrument event counters in application code, or use the perf_event_open
-      system call to read hardware event counters directly.
-  - question: Which environment variables and permissions are required for the PAPI steps?
-    answer: >-
-      Set PAPI_DIR to the PAPI installation path, and you might need to add $PAPI_DIR/lib to LD_LIBRARY_PATH.
-      The steps also include enabling user space access to counters using a sudo command to change
-      a kernel setting.
-  - question: What does the perf_event_open section demonstrate, and does it support multiplexing?
-    answer: >-
-      It provides two examples: reading a single hardware counter and reading a group of counters
-      without multiplexing. perf_event_open does not support multiplexing.
-  - question: What should I check if I cannot access certain hardware counters?
-    answer: >-
-      Confirm that user space access to counters has been enabled as shown in the steps. Also
-      note that VMs may expose fewer counters; using a bare metal or cloud metal instance typically
-      provides more.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Julio Suarez
 
diff --git a/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md b/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
index 9e8aca2f20..c0667e88f1 100644
--- a/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
@@ -14,58 +14,9 @@ learning_objectives:
 prerequisites:
     - An Amazon Web Services (AWS) account
     - A local computer with Docker, AWS CLI, and AWS Copilot CLI installed
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:20:41Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: ced3882cf8be11a55304d2697f450a2c862a81d87317ab42298148755e9f272c
-  summary_generated_at: '2026-06-02T03:08:36Z'
-  summary_source_hash: ced3882cf8be11a55304d2697f450a2c862a81d87317ab42298148755e9f272c
-  faq_generated_at: '2026-06-03T00:20:41Z'
-  faq_source_hash: ced3882cf8be11a55304d2697f450a2c862a81d87317ab42298148755e9f272c
-  summary: >-
-    This Learning Path shows how to package a multi-architecture container and deploy it to AWS
-    Fargate using the AWS Copilot CLI, configured to run on AWS Graviton processors. You will
-    containerize an example service, use copilot init to build locally, push the image to Amazon
-    ECR, and provision a load balanced web service on Fargate. It explains Copilot’s default amd64
-    behavior and where to set the architecture to Arm for Graviton. Prerequisites are an AWS account
-    and a local machine with Docker, AWS CLI, and AWS Copilot CLI installed. The guide is applicable
-    to Linux and macOS users.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an AWS account and a local environment with Docker, AWS CLI, and the AWS Copilot
-      CLI installed. The path targets Linux, and the steps note macOS is also applicable.
-  - question: What architecture does Copilot use by default, and how does this affect deploying
-      on Graviton?
-    answer: >-
-      Copilot defaults to amd64. To run on AWS Graviton processors with Fargate, you must explicitly
-      set the architecture to Arm as described in the steps.
-  - question: How do I deploy the sample service with Copilot?
-    answer: >-
-      Use the copilot init command shown in the path to build from your Dockerfile, create a Load
-      Balanced Web Service, and deploy to an environment. Copilot builds locally, pushes the image
-      to Amazon ECR, and provisions the Fargate resources.
-  - question: Can I use an existing container image instead of building from a Dockerfile?
-    answer: >-
-      Yes. Use the --image option instead of --dockerfile, and ensure the image is multi-architecture.
-  - question: What result should I expect after a successful deployment?
-    answer: >-
-      A running service on AWS Fargate with the image stored in Amazon ECR, configured as a Load
-      Balanced Web Service. Copilot will have created the required infrastructure in the specified
-      environment.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md b/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
index 7dffcdc01f..158a9180a1 100644
--- a/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
@@ -14,57 +14,9 @@ learning_objectives:
 prerequisites:
     - An Amazon Web Services (AWS) [account](https://aws.amazon.com/)
     - A computer with [Terraform](/install-guides/terraform) installed
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:21:25Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 087a4d56914e5b53cec5ad17e8d682e72d04ba6b394237c081efe4a3f939e04e
-  summary_generated_at: '2026-06-02T03:09:04Z'
-  summary_source_hash: 087a4d56914e5b53cec5ad17e8d682e72d04ba6b394237c081efe4a3f939e04e
-  faq_generated_at: '2026-06-03T00:21:25Z'
-  faq_source_hash: 087a4d56914e5b53cec5ad17e8d682e72d04ba6b394237c081efe4a3f939e04e
-  summary: >-
-    This Learning Path shows how to automate the provisioning of Arm-based AWS Graviton instances
-    using Terraform, with access provided through a Jump Server (bastion) for secure infrastructure
-    management. You will use Terraform Cloud to define and deploy EC2 resources on AWS and work
-    with reusable infrastructure-as-code files that you can adapt for future Learning Paths. Prerequisites
-    are an AWS account and a computer with Terraform installed; any desktop, laptop, or VM with
-    the required tools is suitable. By the end, you will have Arm instances deployed on AWS with
-    jump server access and a foundation for modifying the provided Terraform for related exercises.
-    Estimated time to complete is about 60 minutes.
-  faqs:
-  - question: What do I need before running the Terraform steps?
-    answer: >-
-      You need an AWS account and a computer with Terraform installed. Any computer with the required
-      tools can be used.
-  - question: Does this path use Terraform Cloud or local Terraform?
-    answer: >-
-      The steps use Terraform Cloud to automate the creation of Arm instances. Follow the instructions
-      in the path to run the workflow in Terraform Cloud.
-  - question: What infrastructure gets created by the configuration?
-    answer: >-
-      It provisions AWS EC2 Arm instances (Graviton) and sets up access through a Jump Server
-      (bastion). The Jump Server provides a supervised, secure channel between networks.
-  - question: How do I access the deployed instances?
-    answer: >-
-      Access is provided via the Jump Server (bastion). Traffic is funneled through this intermediary
-      host to add a security barrier between networks.
-  - question: Can I reuse or modify the Terraform files for other Learning Paths?
-    answer: >-
-      Yes. The Terraform files are intended as a platform you can adapt to support other Learning
-      Paths that require one or more server nodes.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
index 45e754df5e..f13f5fdb81 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
@@ -16,62 +16,9 @@ prerequisites:
     - An Azure subscription with permissions to create resource groups, virtual machines, and networking resources
     - Azure CLI installed on your local machine - see the [Azure CLI install guide](/install-guides/azure-cli/)
     - An SSH key pair for authentication
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:21:51Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 1682c0527bb49c417263286e2aba7c82fb9a27fa315ecc6b9ca10978b69cc236
-  summary_generated_at: '2026-06-02T03:09:38Z'
-  summary_source_hash: 1682c0527bb49c417263286e2aba7c82fb9a27fa315ecc6b9ca10978b69cc236
-  faq_generated_at: '2026-06-03T00:21:51Z'
-  faq_source_hash: 1682c0527bb49c417263286e2aba7c82fb9a27fa315ecc6b9ca10978b69cc236
-  summary: >-
-    Learn how to automate the provisioning of Arm64-based Azure Cobalt 100 virtual machines using
-    Azure Resource Manager (ARM) templates and the Azure CLI. You will author a JSON template
-    with parameters, variables, resources, and outputs to define a Linux VM, networking, security
-    settings, and SSH access. The path shows how to specify Arm64 and choose a Cobalt 100 VM size,
-    deploy the template to a resource group, and verify the VM by connecting over SSH and checking
-    uname -m for aarch64. Prerequisites include an Azure subscription with sufficient permissions,
-    the Azure CLI installed, and an SSH key pair. By the end, you can reproducibly deploy a Cobalt
-    100 VM and validate the Arm64 environment.
-  faqs:
-  - question: What do I need before running the template?
-    answer: >-
-      You need an Azure subscription with permissions to create resource groups, virtual machines,
-      and networking resources, the Azure CLI installed, and an SSH key pair. These are the only
-      explicit prerequisites listed.
-  - question: Which Azure region and VM size should I use for Cobalt 100?
-    answer: >-
-      Create a resource group in your preferred region, then query available VM SKUs in that region
-      and filter for the Dpsv6 series to find Cobalt 100 sizes. The Learning Path shows an az
-      vm list-skus command and grep filter you can run to confirm availability.
-  - question: How is the ARM template structured and how do I customize it?
-    answer: >-
-      The template is organized into $schema, contentVersion, parameters, variables, resources,
-      and outputs. You customize deployments by defining inputs in parameters, computing values
-      in variables, and specifying resources that include the VM, networking, security settings,
-      and SSH authentication.
-  - question: How do I get the VM’s public IP to connect over SSH?
-    answer: >-
-      Use the public IP address recorded during the deployment step or retrieve it from your template
-      outputs. The path then uses that IP with your SSH key to connect.
-  - question: What result should I expect after deployment, and how do I verify Arm64?
-    answer: >-
-      You should have a Linux VM powered by Cobalt 100 with its networking and SSH access configured
-      in your resource group. After connecting via SSH, run uname -m and expect aarch64; lscpu
-      will show additional CPU details.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
index fee958b574..efd13fa818 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
@@ -15,57 +15,9 @@ prerequisites:
     - A Microsoft Azure account. 
     - A GitHub account.
     - A machine with [Terraform](/install-guides/terraform/),[Azure CLI](/install-guides/azure-cli), and [Kubectl](/install-guides/kubectl/) installed.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:22:28Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: b5bd3abde8d9dd3146e0f11f4819ac510417ad7f707b4d0970c02fd63801b358
-  summary_generated_at: '2026-06-02T03:10:03Z'
-  summary_source_hash: b5bd3abde8d9dd3146e0f11f4819ac510417ad7f707b4d0970c02fd63801b358
-  faq_generated_at: '2026-06-03T00:22:28Z'
-  faq_source_hash: b5bd3abde8d9dd3146e0f11f4819ac510417ad7f707b4d0970c02fd63801b358
-  summary: >-
-    This Learning Path shows how to configure a self-hosted GitHub Actions Arm64 runner on an
-    Azure Cobalt 100 VM, create an Arm-based Azure Kubernetes Service (AKS) cluster with Terraform,
-    and deploy a .NET 8 web application using GitHub Actions CI/CD. You will work on Linux and
-    use Azure CLI, kubectl, and Terraform to automate infrastructure and deployment on Microsoft’s
-    Armv9 Neoverse-N2–based Cobalt 100 instances (Dpsv6/Dplsv6/Epsv6). By the end, you will have
-    a running application on AKS triggered from a self-hosted runner. Prerequisites are a Microsoft
-    Azure account, a GitHub account, and a machine with Terraform, Azure CLI, and kubectl installed.
-    Estimated time to complete is about 60 minutes.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      You need a Microsoft Azure account, a GitHub account, and a machine with Terraform, Azure
-      CLI, and kubectl installed. The procedures use Linux.
-  - question: Which Azure Cobalt 100 VM series should I use for the runner or AKS nodes?
-    answer: >-
-      The path does not prescribe a specific series. Azure Cobalt 100 offers general-purpose Dpsv6
-      and Dplsv6 VMs and a memory-optimized Epsv6 series; select from these in the steps as appropriate.
-  - question: Can I use GitHub-hosted Arm64 runners instead of a self-hosted runner?
-    answer: >-
-      GitHub-hosted Arm64 runners are generally available for Team and Enterprise Cloud accounts.
-      This path demonstrates using a self-hosted Arm64 runner on an Azure Cobalt 100 VM.
-  - question: What does the Terraform configuration create?
-    answer: >-
-      An Azure Kubernetes Service (AKS) cluster with Arm-based Azure Cobalt 100 nodes. The cluster
-      is the target environment for deploying the .NET application.
-  - question: What should I expect after the GitHub Actions workflow runs?
-    answer: >-
-      The .NET 8-based web application is built and deployed to the AKS cluster using the self-hosted
-      Arm64 runner. The steps guide you through the CI/CD pipeline to reach this state.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pranay Bakre
 
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
index 0ccbaf278f..f410c93e95 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
@@ -15,60 +15,9 @@ learning_objectives:
 prerequisites:
     - An Azure account
     - A computer with Terraform installed
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:23:24Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 6713af3d70887933cf54c7fa36bdc88d71a51fa34fb29a4ce90636ff1de624c7
-  summary_generated_at: '2026-06-02T03:10:40Z'
-  summary_source_hash: 6713af3d70887933cf54c7fa36bdc88d71a51fa34fb29a4ce90636ff1de624c7
-  faq_generated_at: '2026-06-03T00:23:24Z'
-  faq_source_hash: 6713af3d70887933cf54c7fa36bdc88d71a51fa34fb29a4ce90636ff1de624c7
-  summary: >-
-    This Learning Path shows how to automate the creation of Arm-based virtual machines on Microsoft
-    Azure using Terraform and Terraform Cloud. You will deploy Azure Arm VMs (Neoverse) and configure
-    access through a Jump Server (bastion host), using provided Terraform code you can adapt for
-    future Learning Paths. The steps require an Azure account and a computer with Terraform installed;
-    any desktop, laptop, or VM with the required tools will work. Positioned as an introductory
-    topic for developers new to deploying Arm instances on Azure with Terraform, the same instructions
-    can be used to deploy Linux as well. By the end, you will have automated infrastructure and
-    a controlled access path to your instances.
-  faqs:
-  - question: What do I need before running the Terraform steps?
-    answer: >-
-      You need an Azure account and a computer with Terraform installed. Any desktop, laptop,
-      or virtual machine with the required tools can be used. You will also need access to the
-      Azure portal.
-  - question: Which Terraform workflow does this Learning Path use?
-    answer: >-
-      It uses Terraform Cloud to automate the instantiation of Arm instances on Azure. The provided
-      Terraform files form the basis of the deployment.
-  - question: Can I deploy Linux or Windows on Arm with these instructions?
-    answer: >-
-      The same instructions can be used to deploy Linux. A related guide for deploying a Windows
-      on Arm virtual machine on Microsoft Azure is referenced.
-  - question: How is access to the deployed VMs provided?
-    answer: >-
-      Access is provided via a Jump Server (also known as a bastion host). The Jump Server funnels
-      traffic through firewalls using a supervised secure channel to create a barrier between
-      networks.
-  - question: What should I expect to have at the end of this Learning Path?
-    answer: >-
-      You will have Arm virtual machines deployed on Azure and access set up through a Jump Server.
-      You will also have Terraform files that you can modify and reuse as a platform for other
-      Learning Paths.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
index 17fd726456..0dd1100955 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
@@ -18,60 +18,9 @@ learning_objectives:
 prerequisites:
     - A [Microsoft Azure](https://azure.microsoft.com/) account with permission to create resources, including instances using Cobalt 100 processors
     - A Linux machine with [QEMU](https://www.qemu.org/download/) and the [Azure CLI](/install-guides/azure-cli/) installed and authenticated
-    
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:24:06Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 413467e581e3561425229d0f6118975dbb596d6a9494544a54f0b9ab22c1b89d
-  summary_generated_at: '2026-06-02T03:11:18Z'
-  summary_source_hash: 413467e581e3561425229d0f6118975dbb596d6a9494544a54f0b9ab22c1b89d
-  faq_generated_at: '2026-06-03T00:24:06Z'
-  faq_source_hash: 413467e581e3561425229d0f6118975dbb596d6a9494544a54f0b9ab22c1b89d
-  summary: >-
-    This advanced Learning Path guides you through building a custom Azure Linux 3.0 image for
-    Arm and deploying it on Microsoft Azure Cobalt 100 processors. You will use QEMU on a Linux
-    host to create a raw disk image, install Azure Linux 3.0 from an AArch64 ISO, convert the
-    disk to a fixed-size VHD, upload it to Azure, and register it in Azure Shared Image Gallery.
-    Finally, you will create an Azure VM using the Azure CLI and your custom image. Prerequisites
-    include an Azure account with permissions for Cobalt 100 resources and a Linux machine with
-    QEMU and the Azure CLI installed and authenticated. Estimated time: about 120 minutes.
-  faqs:
-  - question: What do I need before running these steps?
-    answer: >-
-      You need a Microsoft Azure account with permission to create resources, including instances
-      using Cobalt 100 processors. You also need a Linux machine with QEMU and the Azure CLI installed
-      and authenticated.
-  - question: Which Azure Linux ISO and architecture should I use with QEMU?
-    answer: >-
-      Use the Azure Linux 3.0 AArch64 (Arm64) ISO. The Learning Path points to the Azure Linux
-      3.0 project README, which includes links to ISO downloads.
-  - question: What artifacts should I have before uploading to Azure?
-    answer: >-
-      After installing Azure Linux 3.0 in QEMU, you should have a raw disk image that you convert
-      to a fixed-size VHD. The VHD file is the artifact you upload to Azure.
-  - question: How is the VHD registered so I can reuse it to create VMs?
-    answer: >-
-      You upload the VHD to Azure Blob Storage using the Azure CLI, then register it in Azure
-      Shared Image Gallery. This process produces a custom image you can reference by its image
-      ID.
-  - question: How do I launch a VM on Cobalt 100 using my custom image?
-    answer: >-
-      Use az vm create with the image ID from the Shared Image Gallery and specify the VM size
-      targeting Cobalt 100 Arm-based processors. Provide the resource group, VM name, image ID,
-      size, admin username, and optionally generate an SSH key as shown in the example.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md b/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
index 7ddeddefad..1e7cab8840 100644
--- a/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
@@ -13,60 +13,9 @@ learning_objectives:
 
 prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an on-premise Arm server.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:24:45Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: a4cf1d9161b3a32e29694415762eda419752e1c3144662d5e131b6553f0a58e3
-  summary_generated_at: '2026-06-02T03:11:52Z'
-  summary_source_hash: a4cf1d9161b3a32e29694415762eda419752e1c3144662d5e131b6553f0a58e3
-  faq_generated_at: '2026-06-03T00:24:45Z'
-  faq_source_hash: a4cf1d9161b3a32e29694415762eda419752e1c3144662d5e131b6553f0a58e3
-  summary: >-
-    Learn to deploy and evaluate Hugging Face Sentiment Analysis models with PyTorch on Arm servers
-    running Linux. You will run three NLP models using the Sentiment Analysis pipeline, then enable
-    BFloat16 fast math kernels on Arm Neoverse-based AWS Graviton3 processors to measure performance
-    uplift. The instructions target Ubuntu 22.04 LTS on an Arm server with at least four cores
-    and 8GB of RAM, and have been tested on AWS Graviton3 (c7g). You will use Python, PyTorch,
-    and Hugging Face to complete the workflow. Prerequisite: access to an Arm-based instance from
-    a cloud provider or an on-premise Arm server.
-  faqs:
-  - question: What do I need before running the examples?
-    answer: >-
-      You need an Arm-based instance from a cloud service provider or an on-prem Arm server. The
-      instructions assume Ubuntu 22.04 LTS with at least four cores and 8GB RAM and have been
-      tested on AWS Graviton3 (c7g) instances.
-  - question: Which platforms can I use for this path?
-    answer: >-
-      You can use an Arm-based instance from AWS, Microsoft Azure, Google Cloud, or Oracle, or
-      an on-prem Arm server. The procedure is written for any Arm server running Ubuntu 22.04
-      LTS.
-  - question: What should I install first to follow the steps?
-    answer: >-
-      Install PyTorch on your Arm machine. PyTorch is the framework used to deploy and run the
-      Hugging Face NLP models in this path.
-  - question: How do I know the sentiment analysis models ran successfully?
-    answer: >-
-      You should be able to execute the Sentiment Analysis pipeline for three Hugging Face models
-      in PyTorch and capture performance measurements. The path then has you compare results before
-      and after enabling BFloat16 fast math kernels.
-  - question: How do I enable and validate BFloat16 fast math kernels on Graviton3?
-    answer: >-
-      Follow the steps to enable support for BFloat16 fast math kernels on Arm Neoverse-based
-      AWS Graviton3 processors. Validate by re-running the same workloads and comparing the measured
-      performance uplift reported in the path.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md b/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
index 2417a60dca..4efe64fe57 100644
--- a/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
@@ -16,61 +16,9 @@ learning_objectives:
 prerequisites:
 - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from an appropriate
   cloud service provider.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:25:22Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 1701b37580fe5d012a5e6fd322307742656a748dfb766fd48914011167386e95
-  summary_generated_at: '2026-06-02T03:12:13Z'
-  summary_source_hash: 1701b37580fe5d012a5e6fd322307742656a748dfb766fd48914011167386e95
-  faq_generated_at: '2026-06-03T00:25:22Z'
-  faq_source_hash: 1701b37580fe5d012a5e6fd322307742656a748dfb766fd48914011167386e95
-  summary: >-
-    Learn how to implement and benchmark bitmap scanning for database workloads on Arm-based cloud
-    instances running Linux. You will build a simple bitmap data structure and multiple scanning
-    routines in C—covering scalar, Neon, and SVE—and add a benchmarking harness to compare their
-    behavior. The steps focus on Arm Neoverse servers, with examples targeting Neoverse V2–based
-    instances such as AWS Graviton4, so you can measure performance differences across implementations.
-    This introductory path is aimed at database developers and performance engineers; the only
-    explicit prerequisite is access to an Arm-based instance from a cloud provider such as AWS,
-    Microsoft Azure, Google Cloud, or Oracle.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      Provision an Arm-based instance from an appropriate cloud service provider running Linux.
-      For the SVE sections, use a Neoverse V2–based server such as AWS Graviton4.
-  - question: Where do I put the code for this Learning Path?
-    answer: >-
-      Create a file named bitvector_scan_benchmark.c with a text editor and copy in the provided
-      code sections. This single file will contain the bit vector data structure, scalar implementations,
-      Neon and SVE versions, and the benchmarking code.
-  - question: Which bitmap scanning implementations will I build and compare?
-    answer: >-
-      You will implement a per-bit scalar baseline, an optimized scalar approach for sparse data,
-      and vectorized versions using Neon and SVE. These are all placed in the same C source file
-      for side-by-side benchmarking.
-  - question: What results should I expect from the benchmarking step?
-    answer: >-
-      The benchmarking framework times multiple iterations using CLOCK_MONOTONIC and reports how
-      many set-bit positions are found. You will be able to compare the relative performance of
-      the scalar, Neon, and SVE implementations; specific numeric results are not provided.
-  - question: How do I validate that all implementations are correct?
-    answer: >-
-      Use the same generated bitmaps and compare the counts (and positions if captured) returned
-      by each implementation. The Learning Path includes helper functions to generate and analyze
-      bitmaps so you can verify consistency across scalar, Neon, and SVE scans.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md b/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
index f99c0ebc02..3550fba98a 100644
--- a/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
@@ -21,65 +21,9 @@ prerequisites:
     - Linux kernel version 6.14 or later for Arm Statistical Profiling Extension ([SPE profiling](/learning-paths/servers-and-cloud-computing/bolt-demo/spe/))
     - GCC version 13.3 or later to compile the example program ([GCC](/install-guides/gcc/) )
     - A system with with sufficient hardware performance counters to use the [TopDown](/install-guides/topdown-tool) methodology. This typically requires running on bare metal rather than a virtualized environment.
-
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:26:35Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 8654b656131bf1d529e11d85f874f7a81c01f7207340d2a606b6fd2d80bfad04
-  summary_generated_at: '2026-06-02T03:13:32Z'
-  summary_source_hash: 8654b656131bf1d529e11d85f874f7a81c01f7207340d2a606b6fd2d80bfad04
-  faq_generated_at: '2026-06-03T00:26:35Z'
-  faq_source_hash: 8654b656131bf1d529e11d85f874f7a81c01f7207340d2a606b6fd2d80bfad04
-  summary: >-
-    This introductory Learning Path shows how to assess AArch64 programs for code layout optimization
-    and apply LLVM BOLT to a deliberately inefficient, BubbleSort-based example on Linux. You
-    install LLVM BOLT (22.1.0 or later), prepare a small workspace, compile the example with GCC
-    13.3 or later, and collect profiles using BRBE, SPE, instrumentation, and PMU event sampling.
-    Using a subset of Arm TopDown indicators, you check for front-end bound behavior and poor
-    instruction locality, then run BOLT to reorganize code layout. You finish by evaluating the
-    effect using performance metrics and the collected profiling data. Prerequisites include an
-    AArch64 Linux system with perf, recent kernels for BRBE/SPE, and sufficient hardware counters
-    (typically on bare metal).
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an AArch64 Linux system with perf installed, GCC 13.3 or later, and sufficient
-      hardware performance counters for TopDown analysis. BRBE profiling requires Linux 6.17 or
-      later, and SPE profiling requires Linux 6.14 or later.
-  - question: Which BOLT version should I install, and what if my package manager provides an
-      older one?
-    answer: >-
-      Use LLVM BOLT 22.1.0 or later to access the required options and SPE profiling support.
-      If your package manager is older, install BOLT from a prebuilt LLVM release and verify the
-      installed version before continuing.
-  - question: How should I set up the example and organize outputs?
-    answer: >-
-      Download bsort.cpp into a working directory and create subdirectories named out, prof, and
-      heatmap. The out directory stores output binaries, while prof and heatmap hold profile data
-      and generated visualizations.
-  - question: How do I know if my application is a good candidate for BOLT?
-    answer: >-
-      Use hardware performance metrics and the Arm TopDown methodology to look for front-end bound
-      behavior and poor code locality. If instruction delivery is inefficient, the program is
-      a strong candidate for BOLT code layout optimization.
-  - question: What does BRBE profiling capture and why is it useful here?
-    answer: >-
-      BRBE records the most recent taken branches in a circular buffer (typically 32 or 64 entries,
-      depending on hardware). This edge-based, low-overhead data is well-suited for BOLT to derive
-      code layout profiles.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Paschalis Mpeis
 
diff --git a/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md b/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
index 4bb678c1a1..eb9614320f 100644
--- a/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
@@ -15,58 +15,9 @@ learning_objectives:
 
 prerequisites:
   - An Arm-based Linux system with [BOLT](/install-guides/bolt/) and [Linux Perf](/install-guides/perf/) installed
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:27:14Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 84a8b96fe7df302e0a2a6e4645bbb6170b45a3e0b55e0ea3682ec47663d34819
-  summary_generated_at: '2026-06-02T03:14:28Z'
-  summary_source_hash: 84a8b96fe7df302e0a2a6e4645bbb6170b45a3e0b55e0ea3682ec47663d34819
-  faq_generated_at: '2026-06-03T00:27:14Z'
-  faq_source_hash: 84a8b96fe7df302e0a2a6e4645bbb6170b45a3e0b55e0ea3682ec47663d34819
-  summary: >-
-    This advanced path shows how to instrument and optimize Arm application binaries and shared
-    libraries on Linux using BOLT and Linux perf. You will build the MySQL server (mysqld) from
-    source, create an instrumented binary, run read- and write-heavy workloads to collect profiles,
-    and merge the profiles to broaden coverage before applying BOLT optimizations. You will also
-    rebuild OpenSSL to produce instrumentable libssl.so and libcrypto.so, optimize these libraries
-    with BOLT, and integrate them into the application. Finally, you will use Sysbench with --time=0
-    --events=10000 to compare baseline, isolated, and merged optimization scenarios. Prerequisite:
-    an Arm-based Linux system with BOLT and Linux perf installed.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an Arm-based Linux system with BOLT and Linux Perf installed. The path builds and
-      instruments MySQL and OpenSSL from source during the steps.
-  - question: How do I generate profiles for BOLT to use with mysqld?
-    answer: >-
-      Instrument the MySQL server binary with BOLT, then run targeted workloads to collect profile
-      data. The result is workload-specific .fdata files that BOLT uses to optimize code layout.
-  - question: When should I merge profiles, and what does that produce?
-    answer: >-
-      After creating separate profiles for read-heavy and write-only workloads, merge them to
-      broaden code coverage. The merged profile is then used to optimize the final mysqld binary.
-  - question: What should I do if libssl.so or libcrypto.so are stripped and lack relocations?
-    answer: >-
-      Rebuild OpenSSL from source and include relocations so BOLT can instrument and optimize
-      the libraries. The path shows configuring OpenSSL with the linker option -Wl,--emit-relocs.
-  - question: How do I compare baseline and BOLT-optimized results?
-    answer: >-
-      Use Sysbench with --time=0 --events=10000 and run consistent read-only, write-only, and
-      read+write tests. Compare the baseline binaries to BOLT-optimized binaries and to runs that
-      include optimized shared libraries.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Gayathri Narayana Yegna Narayanan
 
diff --git a/content/learning-paths/servers-and-cloud-computing/bolt/_index.md b/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
index b7cc59f217..62d042b7a7 100644
--- a/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
@@ -14,61 +14,9 @@ learning_objectives:
 prerequisites:
     - An Arm based system running Linux with [BOLT](/install-guides/bolt/) and [Linux Perf](/install-guides/perf/) installed. The Linux kernel should be version 5.15 or later. Earlier kernel versions can be used, but some Linux Perf features may be limited or not available. For [SPE](./bolt-spe) the version should be 6.14 or later.
     - (Optional) A second, more powerful Linux system to build the software executable and run BOLT.
-
-generate_summary_faq: false
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:25:57Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
-  summary_generated_at: '2026-06-02T03:12:45Z'
-  summary_source_hash: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
-  faq_generated_at: '2026-06-03T00:25:57Z'
-  faq_source_hash: 2e9ac8a3c73b7d3d59fe6ba20fb6d61fc2b7e5e9320aaadc20af0a8bbb3ff959
-  summary: >-
-    This Learning Path shows how to build, profile, and post-link optimize an Arm Linux executable
-    with BOLT. You will collect runtime profiles on an Arm-based target using Linux Perf (via
-    samples, ETM, or SPE), convert the profile into the format BOLT expects, and run BOLT to produce
-    a new optimized binary with improved code layout. You can work on a single Arm Linux system
-    or split tasks across two systems, using a second, more powerful Linux host for building and
-    running BOLT if preferred. Prerequisites include an Arm system running Linux with BOLT and
-    Linux Perf installed, a kernel version 5.15 or later (earlier versions may limit Perf features),
-    and for SPE, version 6.14 or later. Estimated time: about 30 minutes.
-  faqs:
-  - question: Do I need one or two Linux systems for this workflow?
-    answer: >-
-      You can complete all steps on a single Arm Linux system. Alternatively, profile on an Arm
-      Linux target system and use a second, more powerful Linux system to build the executable
-      and run BOLT.
-  - question: 'Which profiling option should I choose: samples, ETM, or SPE?'
-    answer: >-
-      Use the samples method for a straightforward profile, ETM if ETM tracing is available, or
-      SPE when you need SPE branch information. The SPE workflow requires Linux Perf version 6.14
-      or later; follow the dedicated steps for each option.
-  - question: What versions of Linux kernel and Perf are required before I start?
-    answer: >-
-      Use a Linux kernel version 5.15 or later; earlier kernels can work but some Perf features
-      may be limited or unavailable. For SPE, use Linux Perf version 6.14 or later, and the prerequisites
-      note that 6.14 or later is required for SPE.
-  - question: How do I collect the performance profile and verify that it worked?
-    answer: >-
-      For samples, run: perf record -e cycles:u -o perf.data -- ./executable. For ETM, run: perf
-      record -e cs_etm//u -o perf.data -- ./executable. Perf reports the total number of samples
-      and/or the perf.data size; confirm that perf.data is created.
-  - question: What does BOLT produce after profiling, and how is it used?
-    answer: >-
-      After collecting perf.data, convert the profile to BOLT’s format and run BOLT to create
-      a new optimized executable. The optimized binary is saved separately, and the expected outcome
-      is improved performance compared to the original executable.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jonathan Davies
 
diff --git a/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
index 4a210b6589..81e4b2a256 100644
--- a/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
@@ -18,62 +18,9 @@ prerequisites:
   - Basic Linux system administration skills, including how to create users, install packages, and manage services
   - Familiarity with [Docker](https://docs.docker.com/get-started/) and container concepts
   - A [GitHub account](https://github.com/join) to host your application repository
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:27:49Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 4bda206717eef380430009f859826d9bcf820442d13492cd3c22a114561e2917
-  summary_generated_at: '2026-06-02T03:15:44Z'
-  summary_source_hash: 4bda206717eef380430009f859826d9bcf820442d13492cd3c22a114561e2917
-  faq_generated_at: '2026-06-03T00:27:49Z'
-  faq_source_hash: 4bda206717eef380430009f859826d9bcf820442d13492cd3c22a114561e2917
-  summary: >-
-    This Learning Path shows how to use Buildkite on Arm-based Google Axion C4A virtual machines
-    to build and publish multi-architecture Docker images. You will provision a c4a-standard-4
-    VM on Google Cloud running Ubuntu or SUSE Linux Enterprise Server, install Docker, Docker
-    Buildx, and the Buildkite agent, and create a simple Flask-based Python application with a
-    Dockerfile. You then configure a Buildkite pipeline to produce a multi-architecture image
-    for Arm and x86 and push it to Docker Hub, before starting the application and verifying it
-    runs. Prerequisites include a GCP account with billing enabled, a GitHub account, basic Linux
-    administration skills, and familiarity with Docker. Estimated time to complete is about 40
-    minutes.
-  faqs:
-  - question: What do I need before provisioning the Google Axion C4A VM?
-    answer: >-
-      You need a Google Cloud Platform account with billing enabled, basic Linux administration
-      skills, familiarity with Docker, and a GitHub account to host your repository. These are
-      the only explicit prerequisites listed.
-  - question: Which instance type and operating systems does this path use?
-    answer: >-
-      The steps use a c4a-standard-4 instance with 4 vCPUs and 16 GB of memory in the Google Cloud
-      Console. The VM can run either Ubuntu or SUSE Linux Enterprise Server.
-  - question: How do I install the Buildkite agent on the C4A VM?
-    answer: >-
-      Update packages and install the listed prerequisites using your distribution’s package manager
-      (apt on Ubuntu, zypper on SUSE), then run the provided one-line Buildkite installer. The
-      path shows the exact commands for each supported distribution.
-  - question: How do I know my Buildkite agent is ready to run jobs?
-    answer: >-
-      Create an agent token in your Buildkite organization, configure the agent with that token,
-      and assign it to a queue. In the Buildkite UI, verify the agent shows as online and is listed
-      in the configured queue.
-  - question: What does the pipeline build and where is it published?
-    answer: >-
-      The pipeline uses Docker Buildx to build a multi-architecture Docker image for Arm and x86
-      from your Flask app’s Dockerfile and then pushes it to Docker Hub. The repository contains
-      the Dockerfile and app.py used by the build.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
index f32d3243d0..4cdeee6691 100644
--- a/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
@@ -16,58 +16,9 @@ learning_objectives:
 prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Familiarity with Cassandra architecture, replication, and [Cassandra partitioning and event-driven I/O](https://cassandra.apache.org/doc/stable/cassandra/architecture/)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:28:19Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: b8268d4df3b62aeb9943b750b436a936134d47745fa71db6cc08db8c84eb37be
-  summary_generated_at: '2026-06-02T03:16:24Z'
-  summary_source_hash: b8268d4df3b62aeb9943b750b436a936134d47745fa71db6cc08db8c84eb37be
-  faq_generated_at: '2026-06-03T00:28:19Z'
-  faq_source_hash: b8268d4df3b62aeb9943b750b436a936134d47745fa71db6cc08db8c84eb37be
-  summary: >-
-    Follow this introductory path to provision a Google Cloud Axion C4A Arm64 virtual machine,
-    install Apache Cassandra with Java 17 on SUSE or Ubuntu, validate basic database operations,
-    and benchmark read/write performance using cassandra-stress. You will create a C4A instance
-    (the example uses c4a-standard-4), start Cassandra, confirm health via logs and nodetool,
-    and use cqlsh for baseline keyspace/table operations before running cassandra-stress. This
-    path targets developers moving Cassandra workloads to Arm on Google Cloud and takes about
-    30 minutes. Prerequisites are a Google Cloud account with billing enabled and familiarity
-    with Cassandra architecture, replication, and partitioning/event-driven I/O.
-  faqs:
-  - question: What do I need before provisioning the VM on Google Cloud?
-    answer: >-
-      You need a Google Cloud Platform account with billing enabled. Familiarity with Cassandra
-      architecture, replication, and partitioning/event-driven I/O is expected.
-  - question: Which Google Cloud machine type is used in this guide?
-    answer: >-
-      The steps use an Axion C4A instance with the c4a-standard-4 machine type (4 vCPUs, 16 GB
-      memory) created from the Google Cloud Console.
-  - question: Which Linux distributions does the installation cover?
-    answer: >-
-      The installation shows how to set up Cassandra on Ubuntu or SUSE Linux. The learning objectives
-      emphasize SUSE on Arm64 (C4A).
-  - question: How do I verify that Cassandra started correctly?
-    answer: >-
-      Start Cassandra in the background and check the system.log for the message “Startup complete.”
-      Then run nodetool status to confirm the node is up before proceeding.
-  - question: How do I confirm cassandra-stress is available and what does it test?
-    answer: >-
-      cassandra-stress is included in the Cassandra distribution under tools/bin; list that directory
-      and check the tool’s help to confirm it’s present. It measures performance for write, read,
-      and mixed workloads as used in this path.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
index 311b020df5..05280a733a 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
@@ -15,58 +15,9 @@ learning_objectives:
 
 prerequisites:
     - An AArch64 or x86_64 computer running Linux or macOS. You can use cloud instances, refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:28:52Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 3947ebbac742399e70001e41ac5face92819bed0deb55aba3e2414f98432023e
-  summary_generated_at: '2026-06-02T03:16:50Z'
-  summary_source_hash: 3947ebbac742399e70001e41ac5face92819bed0deb55aba3e2414f98432023e
-  faq_generated_at: '2026-06-03T00:28:52Z'
-  faq_source_hash: 3947ebbac742399e70001e41ac5face92819bed0deb55aba3e2414f98432023e
-  summary: >-
-    This Learning Path shows how to bring up the Arm Confidential Compute Architecture (CCA) reference
-    software stack on an Armv-A AEM Fixed Virtual Platform (FVP) with Realm Management Extension
-    (RME) support using a pre-built Docker image (armswdev/cca-learning-path:cca-simulation-v3).
-    You will create a Realm that runs a guest Linux virtual machine, inject and run a simple application
-    inside that Realm, and obtain a CCA attestation token from the guest. You also run the CCA
-    stack with Memory Encryption Contexts (MEC). The path targets developers on AArch64 or x86_64
-    hosts running Linux or macOS and is introductory, with an estimated completion time of about
-    120 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      Use an AArch64 or x86_64 computer running Linux or macOS and install Docker Engine. You
-      can use cloud instances; a list of Arm cloud service providers is referenced.
-  - question: Which Docker image should I pull, and how do I verify it downloaded?
-    answer: >-
-      Pull armswdev/cca-learning-path:cca-simulation-v3. Verify with docker image list and check
-      that the image appears with its ID and sizes.
-  - question: What runs inside the Realm, and what result should I expect regarding attestation?
-    answer: >-
-      A guest Linux virtual machine runs inside the Realm. As part of the steps, you will obtain
-      a CCA attestation token from the virtual guest.
-  - question: How do I run my own application inside the Realm in this example?
-    answer: >-
-      Inject the application into the guest filesystem of the Realm. The path demonstrates this
-      with a simple hello application that runs under the Realm’s protections.
-  - question: When do I use Memory Encryption Contexts (MEC), and what does it change?
-    answer: >-
-      The MEC section shows how to run the CCA software stack using MEC after downloading the
-      same container. MEC extends RME to support multiple encryption contexts in the Realm Physical
-      Address Space, with each access tagged by a MECID.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author:
     - Pareena Verma
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
index cc63a945b5..791b5b601c 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
@@ -18,60 +18,9 @@ prerequisites:
     - Completion of [Get Started with CCA Attestation and Veraison](/learning-paths/servers-and-cloud-computing/cca-veraison) Learning Path
     - Completion of the [Run an application in a Realm using the Arm Confidential Computing Architecture (CCA)](/learning-paths/servers-and-cloud-computing/cca-container/) Learning Path
     - Completion of the [Run an end-to-end Attestation Flow](/learning-paths/servers-and-cloud-computing/cca-essentials/) Learning Path
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:29:35Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: e21f51feb101ad90245ecceab95fe13e5eb61958faf24dff818eddd11f484b9a
-  summary_generated_at: '2026-06-02T03:17:31Z'
-  summary_source_hash: e21f51feb101ad90245ecceab95fe13e5eb61958faf24dff818eddd11f484b9a
-  faq_generated_at: '2026-06-03T00:29:35Z'
-  faq_source_hash: e21f51feb101ad90245ecceab95fe13e5eb61958faf24dff818eddd11f484b9a
-  summary: >-
-    This advanced Learning Path explains how Arm CCA Realms interact with I/O devices, contrasting
-    VirtIO paravirtualized attach with secure physical device attach. You will review what a Realm
-    is, how the Realm Management Extension (RME) isolates Realm memory, and when SWIOTLB bounce
-    buffers are used. A hands-on exercise uses Docker to run the CCA Key Broker demo inside a
-    Realm and employs kernel tracing to confirm bounce buffer activity for VirtIO network I/O.
-    The path also describes how PCIe‑TDISP and PCIe‑IDE support secure device attach and attestation.
-    It targets developers on AArch64 or x86_64 systems running Linux or macOS, including Arm cloud
-    instances, and assumes completion of three prerequisite CCA Learning Paths.
-  faqs:
-  - question: What do I need before running the exercise?
-    answer: >-
-      Use an AArch64 or x86_64 computer running Linux or macOS, or a cloud instance from the Arm
-      cloud service providers page. Complete the CCA Attestation and Veraison, Run an application
-      in a Realm using CCA, and Run an end-to-end Attestation Flow Learning Paths.
-  - question: How is attestation covered when discussing secure physical device attach?
-    answer: >-
-      The Learning Path describes how PCIe‑TDISP and PCIe‑IDE support secure physical device attach
-      with attestation. It builds on prior attestation knowledge from the prerequisite Learning
-      Paths.
-  - question: How do I start the Key Broker server (KBS) used in the exercise?
-    answer: >-
-      Pull and run the Docker image armswdev/cca-learning-path:cca-key-broker-v2. The steps provide
-      the exact docker pull and docker run commands.
-  - question: How do I confirm that SWIOTLB bounce buffers are being used inside the Realm?
-    answer: >-
-      Follow the exercise to enable kernel tracing in the Realm while generating VirtIO network
-      I/O with the Key Broker demo. You should observe trace evidence indicating SWIOTLB activity
-      for the transfers.
-  - question: How can I check network interfaces during the exercise?
-    answer: >-
-      Use the ip -c a command as shown in the steps to list network interfaces and verify the
-      environment during the demo.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Arnaud de Grandmaison
 
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
index b87d5f5cc9..23d4c31137 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
@@ -15,60 +15,9 @@ prerequisites:
     - An AArch64 or x86_64 computer running Linux. You can use cloud instances, see this list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
     - Completion of [Get Started with CCA Attestation and Veraison](/learning-paths/servers-and-cloud-computing/cca-veraison) Learning Path.
     - Completion of the [Run an application in a Realm using the Arm Confidential Computing Architecture (CCA)](/learning-paths/servers-and-cloud-computing/cca-container/) Learning Path.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:30:18Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: b032debbdfe82cbd017812cf671907520b146fd8684afce0c45c91e7f2287e18
-  summary_generated_at: '2026-06-02T03:18:08Z'
-  summary_source_hash: b032debbdfe82cbd017812cf671907520b146fd8684afce0c45c91e7f2287e18
-  faq_generated_at: '2026-06-03T00:30:18Z'
-  faq_source_hash: b032debbdfe82cbd017812cf671907520b146fd8684afce0c45c91e7f2287e18
-  summary: >-
-    This advanced Learning Path guides you through running an end-to-end attestation flow with
-    Arm’s Confidential Computing Architecture (CCA). You will deploy a simple workload inside
-    a confidential Linux realm on an Armv9-A AEM Base Fixed Virtual Platform (FVP) with Realm
-    Management Extension (RME) support, then connect it to attestation services so confidential
-    data is released only after the realm’s isolation is assessed. Using Docker and Veraison,
-    you will run a minimal, educational Key Broker Server (KBS) and integrate it with the realm.
-    A Linux host (AArch64 or x86_64) is required, and prior completion of the CCA attestation/Veraison
-    and CCA realm application Learning Paths is expected.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      You need a Linux computer on AArch64 or x86_64; cloud instances are acceptable. You must
-      also complete the “Get Started with CCA Attestation and Veraison” and “Run an application
-      in a Realm using the Arm Confidential Computing Architecture (CCA)” Learning Paths.
-  - question: Which FVP and Arm features does the example require?
-    answer: >-
-      Use the Armv9-A AEM Base Fixed Virtual Platform (FVP) with support for RME extensions. The
-      target compute environment is a Linux realm.
-  - question: How do I run the Key Broker Server (KBS) used in this path?
-    answer: >-
-      A pre-built Docker container image for the KBS is provided, and you will pull the image
-      and run the container. The KBS comes from the Veraison project and is intentionally minimal
-      for educational use, not for production.
-  - question: What result should I expect when attestation succeeds?
-    answer: >-
-      Attestation assesses whether the realm offers a provable level of confidential isolation.
-      When it succeeds, confidential data can be released to the Linux realm for processing as
-      part of the end-to-end flow.
-  - question: How long does this take and which tools will I use?
-    answer: >-
-      The estimated time to complete is about 120 minutes. You will use GCC, FVP, RME, CCA, Docker,
-      Veraison, and a runbook on a Linux host.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: 
     - Arnaud de Grandmaison
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
index 8861248607..875d20cd9f 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
@@ -14,58 +14,9 @@ learning_objectives:
 prerequisites:
   - An AArch64 or x86_64 computer running Linux or macOS. Cloud-based instances can also be used; see the [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/)
   - Completion of the [Run an end-to-end Attestation with Arm CCA and Trustee](/learning-paths/servers-and-cloud-computing/cca-trustee) Learning Path
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:31:07Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 649957b07b2bde05bc11047bd12bfc4f697c1a55eef1c3a25b5fafc95cda893d
-  summary_generated_at: '2026-06-02T03:19:07Z'
-  summary_source_hash: 649957b07b2bde05bc11047bd12bfc4f697c1a55eef1c3a25b5fafc95cda893d
-  faq_generated_at: '2026-06-03T00:31:07Z'
-  faq_source_hash: 649957b07b2bde05bc11047bd12bfc4f697c1a55eef1c3a25b5fafc95cda893d
-  summary: >-
-    Learn to deploy a Confidential Container from an encrypted image inside an Arm CCA Realm using
-    Trustee for attestation-based authorization. Working on the Armv9-A AEM Base Fixed Virtual
-    Platform (FVP) with RME support, you will start the Trustee services (AS, KBS, RVPS) and a
-    local Docker registry, publish an encrypted image, then launch and verify the container running
-    in a Realm. The path includes an overview of Confidential Containers and how Arm CCA attestation
-    integrates with Trustee. Prerequisites are an AArch64 or x86_64 Linux or macOS host (cloud
-    instances are acceptable) and completion of the prior CCA + Trustee attestation path. Tools
-    include FVP, RME, CCA, Docker, Veraison, Trustee, and Kata Containers.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      Use an AArch64 or x86_64 computer running Linux or macOS; a cloud-based instance is also
-      acceptable. Complete the “Run an end-to-end Attestation with Arm CCA and Trustee” Learning
-      Path first.
-  - question: Which platform does the container run on in this workflow?
-    answer: >-
-      The container runs on an Armv9-A AEM Base FVP with RME support. The procedure is FVP-based
-      and does not specify running on physical hardware.
-  - question: Which services must be started before launching the confidential container?
-    answer: >-
-      Start the Trustee services (AS, KBS, RVPS) and a local Docker registry. The steps also guide
-      you to install Docker if it is not already present.
-  - question: How do I create and publish the encrypted container image?
-    answer: >-
-      Follow the steps to encrypt the image and push it to the local Docker registry. The Learning
-      Path provides the exact sequence to publish the encrypted image.
-  - question: How do I know the container is running inside an Arm CCA Realm?
-    answer: >-
-      After launching the workload, the Learning Path includes a verification step to confirm
-      it is running inside an Arm CCA Realm. Follow those checks to validate success.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author:
   - Anton Antonov
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
index 68b0e4ec96..e0c1ebb39f 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
@@ -15,58 +15,9 @@ prerequisites:
   - An AArch64 or x86_64 computer running Linux or macOS; you can use cloud instances - see the [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/)
   - Completion of the [Get started with CCA attestation and Veraison](/learning-paths/servers-and-cloud-computing/cca-veraison) Learning Path
   - Completion of the [Run an end-to-end attestation flow with Arm CCA](/learning-paths/servers-and-cloud-computing/cca-essentials/) Learning Path
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:31:41Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 563456f5d151c7ef59bf458f6ac971c321077475a0a78d3b5a3885b97157ba9e
-  summary_generated_at: '2026-06-02T03:20:03Z'
-  summary_source_hash: 563456f5d151c7ef59bf458f6ac971c321077475a0a78d3b5a3885b97157ba9e
-  faq_generated_at: '2026-06-03T00:31:41Z'
-  faq_source_hash: 563456f5d151c7ef59bf458f6ac971c321077475a0a78d3b5a3885b97157ba9e
-  summary: >-
-    This Learning Path shows how to run an end-to-end attestation flow using Arm Confidential
-    Computing Architecture (CCA) and Trustee services. On a Linux or macOS host (AArch64 or x86_64),
-    you will use an Armv9-A AEM Base Fixed Virtual Platform (FVP) with RME extensions to launch
-    a Linux realm, deploy a simple workload, and connect it to Trustee services (AS, KBS, RVPS)
-    with Docker. You will generate attestation evidence, see an initial secret request fail under
-    policy, endorse the realm initial measurement (RIM), re-attest, and retrieve the secret. Prerequisites
-    include completing the CCA attestation and Veraison and CCA end-to-end Learning Paths. Estimated
-    time: 60 minutes.
-  faqs:
-  - question: What do I need before running the exercises?
-    answer: >-
-      You need an AArch64 or x86_64 computer running Linux or macOS. Complete the “Get started
-      with CCA attestation and Veraison” and “Run an end-to-end attestation flow with Arm CCA”
-      Learning Paths first.
-  - question: Can I use a cloud instance as the host machine?
-    answer: >-
-      Yes. You can use cloud instances; see the Arm cloud service providers link referenced in
-      the prerequisites.
-  - question: Which FVP and realm environment does this path use?
-    answer: >-
-      You will deploy a simple workload in a CCA realm on an Armv9-A AEM Base FVP that has support
-      for RME extensions. The target compute environment is a Linux realm.
-  - question: Which Trustee components are started during the flow?
-    answer: >-
-      You will run the Trustee services: AS, KBS, and RVPS. These are used in the attestation
-      flow and policy-controlled secret release.
-  - question: What result should I expect when I request a secret?
-    answer: >-
-      The first request intentionally fails due to attestation policy. After endorsing the realm
-      initial measurement (RIM) and re-attesting, the request succeeds and the secret is retrieved.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author:
   - Anton Antonov
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
index 4ffd978bee..6a3aeac70a 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
@@ -13,61 +13,9 @@ learning_objectives:
 prerequisites:
     - An [AWS account](/learning-paths/servers-and-cloud-computing/csp/aws/) with access to AWS services.
     - An x86 computer running Ubuntu or Arch Linux, authorized for AWS access. If you're using another build environment, you'll need to configure the toolchains for cross-compilation.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:32:36Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 55b8032ceaf735d53b1157102a3a1da5aa5cb686e9ec51cf4896ded66d9bf263
-  summary_generated_at: '2026-06-02T03:21:13Z'
-  summary_source_hash: 55b8032ceaf735d53b1157102a3a1da5aa5cb686e9ec51cf4896ded66d9bf263
-  faq_generated_at: '2026-06-03T00:32:36Z'
-  faq_source_hash: 55b8032ceaf735d53b1157102a3a1da5aa5cb686e9ec51cf4896ded66d9bf263
-  summary: >-
-    This advanced Learning Path shows how to build and deploy a scalable Arm CCA attestation verifier
-    on AWS using Veraison. You will prepare your AWS account, install and authenticate the AWS
-    CLI, create a public domain in Route 53 and an HTTPS certificate, then use Veraison’s bootstrap
-    process to clone sources and launch an automated deployment that typically completes in 30–60
-    minutes. After the services are online, you will provision Arm CCA platform endorsements using
-    the Linaro endorsement tool so Veraison can verify CCA attestation tokens. The path targets
-    Linux and assumes an active AWS account and an x86 machine running Ubuntu or Arch Linux; other
-    build environments require cross-compilation setup.
-  faqs:
-  - question: What do I need before starting the deployment?
-    answer: >-
-      You need an AWS account with access to AWS services and an x86 computer running Ubuntu or
-      Arch Linux that is authorized for AWS access. The path assumes administrator-level privileges
-      for your AWS account.
-  - question: How should I authenticate the AWS CLI before deploying Veraison?
-    answer: >-
-      Set up your local environment to authenticate with AWS before you begin the deployment.
-      Follow the AWS documentation to install the latest AWS CLI and configure authentication.
-  - question: Do I need a public domain, and how is it used?
-    answer: >-
-      Yes. You create a domain in Route53 because the Veraison services are published on the internet
-      over HTTPS using RESTful APIs, and they need a domain to be accessible. You also create
-      a certificate for the chosen domain.
-  - question: What should I expect when running the Veraison deployment?
-    answer: >-
-      The process is highly automated and typically takes 30 to 60 minutes as several AWS resources
-      are created. You start with a bootstrap step that clones the Veraison source from GitHub
-      and sets up your build environment, including dependencies.
-  - question: How do I add CCA platform endorsements so the verifier can process tokens?
-    answer: >-
-      Clone the Linaro endorsement tool from the provided Git server, configure it for AWS, and
-      use it to provision the CCA platform endorsements. This enables the deployed Veraison services
-      to act as a verifier for Arm CCA attestation tokens.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Paul Howard
 
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md
index f752b6f672..14df720201 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md
@@ -17,62 +17,9 @@ learning_objectives:
 
 prerequisites:
     - An Arm-based or x86 computer running Ubuntu. You can use a server instance from a cloud service provider of your choice.
-
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:32:10Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 9e6dee3aef79c1c65cc1a1f4fe3528b9c5542d8046f0b059ef703079ef964d77
-  summary_generated_at: '2026-06-02T03:20:26Z'
-  summary_source_hash: 9e6dee3aef79c1c65cc1a1f4fe3528b9c5542d8046f0b059ef703079ef964d77
-  faq_generated_at: '2026-06-03T00:32:10Z'
-  faq_source_hash: 9e6dee3aef79c1c65cc1a1f4fe3528b9c5542d8046f0b059ef703079ef964d77
-  summary: >-
-    Learn how to work with Arm Confidential Computing Architecture (CCA) attestation by obtaining
-    an example CCA attestation token, inspecting its contents with command-line tools on Ubuntu,
-    and evaluating it using a publicly hosted Veraison-based verifier from Linaro. The path covers
-    key concepts including Trusted Execution Environments and how Armv9 Realm Management Extension
-    (RME) provides the secure boundary, then moves into hands-on token formats and workflows.
-    You will install the Go language to run the required tools. No explicit prerequisites beyond
-    an Arm-based or x86 Ubuntu system are listed, and a cloud instance can be used. In about 30
-    minutes, you will be able to parse a CCA token and submit it to an attestation verification
-    service, and understand the purpose of the open-source Veraison project.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an Arm-based or x86 computer running Ubuntu. A server instance from a cloud service
-      provider is acceptable. No other explicit prerequisites are listed.
-  - question: How do I install Go for this Learning Path?
-    answer: >-
-      The steps start by removing any existing Go installation, then download and extract Go 1.23.3
-      using the provided commands. You then export the installation path and add it to your PATH
-      as shown in the instructions.
-  - question: What is Veraison used for here?
-    answer: >-
-      Veraison provides the verification components and tools used to evaluate CCA attestation
-      tokens. It originated within Arm and is now an open-source project within the Confidential
-      Computing Consortium.
-  - question: How do I obtain and inspect the example CCA attestation token?
-    answer: >-
-      You will obtain an example token in the steps and use command-line tools to inspect its
-      contents. This gives hands-on experience with the token format and common attestation data.
-  - question: Which service should I use to verify the token, and what tokens does it support?
-    answer: >-
-      Use the publicly hosted Linaro attestation verifier service for pre-silicon CCA platforms
-      such as FVP. It verifies CCA attestation tokens from emulated Arm platforms, including the
-      example token used in this exercise.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Paul Howard
 
diff --git a/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
index 3ccf786ad2..80dd117313 100644
--- a/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
@@ -21,63 +21,9 @@ prerequisites:
     [jobs](https://circleci.com/docs/guides/orchestrate/jobs-steps/),
     [resource classes](https://circleci.com/docs/guides/execution-managed/resource-class-overview/), and 
     [runners](https://circleci.com/docs/guides/execution-runner/runner-overview/)
-
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:33:06Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: ec9cdca7aa9a5670f54ea3646f965149460d8e66d9d5d904e1b6dcf09867df39
-  summary_generated_at: '2026-06-02T03:22:01Z'
-  summary_source_hash: ec9cdca7aa9a5670f54ea3646f965149460d8e66d9d5d904e1b6dcf09867df39
-  faq_generated_at: '2026-06-03T00:33:06Z'
-  faq_source_hash: ec9cdca7aa9a5670f54ea3646f965149460d8e66d9d5d904e1b6dcf09867df39
-  summary: >-
-    Set up a SUSE Linux Arm64 virtual machine on Google Cloud C4A with Axion processors and run
-    CircleCI Arm-native CI/CD workflows using self-hosted machine runners. You will provision
-    a c4a instance via the Google Cloud Console, install the CircleCI CLI and Machine Runner on
-    SUSE, create a custom resource class in the CircleCI dashboard, and target it from a workflow.
-    The path also includes creating a simple Node.js demo app and testing workflows locally to
-    understand job execution on Arm64 runners. Prerequisites include a GCP account with billing
-    enabled, basic Linux command line, Node.js/npm familiarity, and a basic understanding of CircleCI
-    workflows, jobs, resource classes, and runners. Estimated time to complete is about 45 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Google Cloud Platform account with billing enabled, plus basic familiarity with
-      the Linux command line, Node.js and npm. You should also understand CircleCI concepts such
-      as workflows, jobs, resource classes, and runners.
-  - question: Which Google Cloud VM type and OS should I use for the self-hosted runner?
-    answer: >-
-      Provision a SUSE Linux (Arm64) VM using the C4A series, specifically c4a-standard-4 in the
-      Google Cloud Console. This VM runs on Google’s Axion processors based on Arm Neoverse-V2
-      cores.
-  - question: How is the CircleCI CLI used in this path?
-    answer: >-
-      The CLI lets you validate CircleCI configuration, run jobs locally, and manage runners from
-      the terminal. You will install it on SUSE Arm64 to test workflows and interact with your
-      setup.
-  - question: How do resource classes direct jobs to my Arm VM?
-    answer: >-
-      You create a custom resource class in the CircleCI dashboard that links your self-hosted
-      runner to your organization. Reference this resource class in your workflow so jobs target
-      the SUSE Arm64 VM.
-  - question: How do I know the self-hosted runner is working with my Node.js demo workflow?
-    answer: >-
-      Run the provided CircleCI workflow that specifies your custom Arm resource class; the job
-      should execute on the SUSE Arm64 VM. You can also use the CircleCI CLI to test and validate
-      the configuration locally.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md b/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md
index 59ac0471a5..7d0c380cc1 100644
--- a/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md
@@ -15,60 +15,9 @@ prerequisites:
   - An [AWS account](https://aws.amazon.com/free/) with billing enabled
   - A CircleCI account
   - Basic understanding of CircleCI workflows, jobs and resource classes
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:33:29Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: e04982fd668765fa2ab25f943f020b8d2b4a5e9b5ff3a51b7431cc4d93730180
-  summary_generated_at: '2026-06-02T03:22:34Z'
-  summary_source_hash: e04982fd668765fa2ab25f943f020b8d2b4a5e9b5ff3a51b7431cc4d93730180
-  faq_generated_at: '2026-06-03T00:33:29Z'
-  faq_source_hash: e04982fd668765fa2ab25f943f020b8d2b4a5e9b5ff3a51b7431cc4d93730180
-  summary: >-
-    Learn how to set up CircleCI self-hosted machine runners on AWS EC2 Graviton (Arm64) to execute
-    CI/CD jobs natively on Arm. You will create a Linux Arm64 VM on an m6g.large instance, install
-    the CircleCI CLI, register a resource class in the CircleCI dashboard, and install and configure
-    the machine runner. Finally, you verify the setup by running a simple workflow and test computation
-    on the runner. This introductory path targets developers and DevOps engineers using CircleCI,
-    Bash, and Git. Prerequisites include an AWS account with billing enabled, a CircleCI account,
-    and a basic understanding of CircleCI workflows, jobs, and resource classes. Estimated time:
-    about 30 minutes.
-  faqs:
-  - question: Which EC2 instance type and OS should I use for this setup?
-    answer: >-
-      The steps use an AWS Graviton Arm64 instance with the m6g.large type. Choose an appropriate
-      Linux AMI, such as an Ubuntu AMI, during instance creation.
-  - question: What do I need before launching the instance and configuring CircleCI?
-    answer: >-
-      You need an AWS account with billing enabled, a CircleCI account, and a basic understanding
-      of CircleCI workflows, jobs, and resource classes. No other prerequisites are explicitly
-      listed.
-  - question: How do I install the CircleCI CLI on the Graviton instance?
-    answer: >-
-      Update your package index and install tools like curl, tar, gzip, coreutils, gpg, and git.
-      Then download and extract the CircleCI CLI binary as described in the steps.
-  - question: How do I register and link a self-hosted runner to my CircleCI organization?
-    answer: >-
-      Create a resource class in the CircleCI Web Dashboard, which uniquely identifies your runner
-      and links it to your namespace. If you do not have an organization, create one first to
-      access the dashboard features.
-  - question: How is the CircleCI machine runner installed on the EC2 instance?
-    answer: >-
-      Add the official CircleCI package repository for Debian/Ubuntu on Arm64, then install the
-      CircleCI Runner via apt and configure it to use your resource class. Follow the path steps
-      to complete the configuration.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Annie Tallund
 
diff --git a/content/learning-paths/servers-and-cloud-computing/clair/_index.md b/content/learning-paths/servers-and-cloud-computing/clair/_index.md
index 7428ba7a62..4a64481f0e 100644
--- a/content/learning-paths/servers-and-cloud-computing/clair/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/clair/_index.md
@@ -13,60 +13,9 @@ learning_objectives:
 
 prerequisites:
     - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an Arm server with recent versions of Docker and Go installed.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:33:54Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: e742eb44fa108bfcc4ee5a241414e6aa05e1fc0e1cceb589fb78a080f59b0d38
-  summary_generated_at: '2026-06-02T03:23:30Z'
-  summary_source_hash: e742eb44fa108bfcc4ee5a241414e6aa05e1fc0e1cceb589fb78a080f59b0d38
-  faq_generated_at: '2026-06-03T00:33:54Z'
-  faq_source_hash: e742eb44fa108bfcc4ee5a241414e6aa05e1fc0e1cceb589fb78a080f59b0d38
-  summary: >-
-    This Learning Path shows how to install and run Clair on Arm-based Linux servers to scan container
-    images and generate vulnerability reports. You will deploy Clair using both combined (single-process)
-    and distributed (separate indexer, matcher, notifier) models, then use the clairctl CLI to
-    submit image manifests for static analysis. The path targets advanced developers working with
-    containers on Arm infrastructure, including Arm instances from major cloud providers. Prerequisites
-    are an Arm server or cloud instance running Linux with recent versions of Docker and Go installed;
-    the instructions are tested on Ubuntu. By the end, you will have a running Clair deployment
-    and can produce vulnerability reports from your images.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      Use an Arm-based instance from a cloud provider or an Arm server running Linux, with recent
-      versions of Docker and Go installed. The instructions are tested on Ubuntu; other Linux
-      distributions may require adjustments.
-  - question: Which Clair deployment model should I use?
-    answer: >-
-      Use the combined deployment if you want the simplest setup, as all Clair components run
-      in a single process. Choose the distributed deployment if you want to run the indexer, matcher,
-      and notifier as separate services.
-  - question: How do I know when Clair is ready to scan images?
-    answer: >-
-      Wait 5–10 minutes after starting Clair before submitting manifests so vulnerabilities can
-      populate in the PostgreSQL database. Submitting too early can produce a clean (empty) report.
-  - question: How do I submit a container image for scanning?
-    answer: >-
-      With Clair running (combined or distributed), use clairctl to submit a manifest to your
-      deployment. The Learning Path steps guide you to generate a vulnerability report from this
-      submission.
-  - question: What result should I expect after submitting a manifest?
-    answer: >-
-      Clair performs static analysis of the image layers and returns a vulnerability report. It
-      does not run the container image as part of the analysis.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
index a80760bb46..edae70cd2d 100644
--- a/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
@@ -20,59 +20,9 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with [ClickHouse](https://clickhouse.com/)
   - Basic understanding of databases and SQL
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:34:45Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: e77cd1373236f39f360afe6844d642fde290960ccdad26544ff1e3342f2a980c
-  summary_generated_at: '2026-06-02T03:24:37Z'
-  summary_source_hash: e77cd1373236f39f360afe6844d642fde290960ccdad26544ff1e3342f2a980c
-  faq_generated_at: '2026-06-03T00:34:45Z'
-  faq_source_hash: e77cd1373236f39f360afe6844d642fde290960ccdad26544ff1e3342f2a980c
-  summary: >-
-    Follow this introductory Learning Path to deploy ClickHouse on Arm-based Google Cloud Axion
-    C4A virtual machines and build a real-time analytics pipeline. You will provision a SUSE Linux
-    (Arm64) VM using the c4a-standard-4 type, configure a firewall rule for TCP 8123, and install
-    ClickHouse and the Google Cloud CLI. You will create Pub/Sub resources (including a logs-topic)
-    and IAM roles, then implement a streaming ETL with Apache Beam and run it on Google Dataflow
-    to ingest events into ClickHouse. Finally, you will validate end-to-end ingestion and run
-    baseline and analytical queries to measure and report latency, including p95, on Axion processors.
-    Prerequisites are a GCP account with billing enabled and basic familiarity with ClickHouse
-    and SQL.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Google Cloud Platform account with billing enabled. Basic familiarity with ClickHouse
-      and a basic understanding of databases and SQL are also listed.
-  - question: Which VM type and OS should I use on Google Cloud?
-    answer: >-
-      Use a Google Axion C4A instance with the c4a-standard-4 machine type (4 vCPUs, 16 GB memory).
-      The VM runs SUSE SLES on Arm64.
-  - question: Which network port must be opened for this setup?
-    answer: >-
-      Create a VPC firewall rule to allow inbound TCP traffic on port 8123. The steps guide you
-      through doing this in the Google Cloud Console.
-  - question: How should I configure Pub/Sub for ingestion?
-    answer: >-
-      Create a Pub/Sub topic named logs-topic using default settings. The path also covers setting
-      up the required IAM so Dataflow and the VM can communicate with Pub/Sub.
-  - question: What outcome should I expect after deployment and configuration?
-    answer: >-
-      You will ingest real-time data from Pub/Sub into ClickHouse using Dataflow and validate
-      end-to-end data flow. You will also run baseline and analytical query benchmarks and measure
-      query latency, including p95, on Axion processors.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md b/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
index 0eb6ef33c2..b7344f0c29 100644
--- a/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
@@ -12,59 +12,9 @@ learning_objectives:
 
 prerequisites:
     - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an on-premise Arm server.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:34:16Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 0d1390198cf2f0e87f509c5269fc2405cffcb2e57311024150d47e60a3273178
-  summary_generated_at: '2026-06-02T03:24:05Z'
-  summary_source_hash: 0d1390198cf2f0e87f509c5269fc2405cffcb2e57311024150d47e60a3273178
-  faq_generated_at: '2026-06-03T00:34:16Z'
-  faq_source_hash: 0d1390198cf2f0e87f509c5269fc2405cffcb2e57311024150d47e60a3273178
-  summary: >-
-    This Learning Path shows how to install ClickHouse on an Arm-based cloud instance or Arm server
-    running Ubuntu for Arm, then measure query latency with ClickBench using a web‑analytics dataset.
-    It is an introductory, hands-on path for developers evaluating ClickHouse on Arm to choose
-    appropriate instance configurations across cloud providers or on‑premises. You will set up
-    ClickHouse, run ClickBench to capture processing times, and use the results to inform instance
-    selection for your workloads. Prerequisites include access to an Arm-based instance and sufficient
-    storage for the dataset; no additional tools beyond ClickHouse and ClickBench are listed.
-    Expected duration is about 45 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an Arm-based instance from a cloud service provider or an on-premise Arm server.
-      Ensure it runs a recent version of Ubuntu for Arm and has enough storage for the web-analytics
-      dataset used in the benchmark.
-  - question: Which cloud platforms can I use for the Arm instance?
-    answer: >-
-      You can use Arm-based instances from AWS, Microsoft Azure, Google Cloud, or Oracle. An on-premise
-      Arm server is also suitable.
-  - question: Which operating system should I run on the instance?
-    answer: >-
-      Use a recent version of Ubuntu for Arm. The path assumes a Linux environment.
-  - question: What result should I expect after running ClickBench?
-    answer: >-
-      ClickBench reports processing time (query latency) for ClickHouse on the web-analytics workload.
-      You can use these measurements to evaluate performance and inform your instance configuration
-      choices.
-  - question: What should I check if the benchmark fails or seems unusually slow?
-    answer: >-
-      Confirm you are using an Arm-based instance with a recent Ubuntu for Arm and that sufficient
-      storage is available for the dataset. Also ensure the dataset required by the steps is present
-      before running ClickBench.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
index 8472f87213..9d0030b9ad 100644
--- a/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
@@ -15,61 +15,9 @@ learning_objectives:
 prerequisites:
     - A Microsoft Azure subscription with permissions to create virtual machines and networking resources
     - Basic familiarity with SSH
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:35:17Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: e4eb84292a669a8d73bff4b63d2fe3f70382dd873d023fc8ff24db5ad6178ab8
-  summary_generated_at: '2026-06-02T03:25:26Z'
-  summary_source_hash: e4eb84292a669a8d73bff4b63d2fe3f70382dd873d023fc8ff24db5ad6178ab8
-  faq_generated_at: '2026-06-03T00:35:17Z'
-  faq_source_hash: e4eb84292a669a8d73bff4b63d2fe3f70382dd873d023fc8ff24db5ad6178ab8
-  summary: >-
-    This Learning Path walks you through deploying a Linux-based Cobalt 100 virtual machine on
-    Microsoft Azure, connecting via SSH, and configuring Network Security Group (NSG) rules to
-    expose an application port for testing. Using the Azure Portal, you create an Arm-based VM
-    powered by Microsoft’s Cobalt 100 (Armv9 Neoverse-N2), open inbound TCP ports 22 and 8080,
-    and verify external connectivity to the newly opened port. You will copy the VM’s public IP,
-    establish an SSH session, and optionally start a temporary HTTP server to confirm reachability.
-    Prerequisites include an Azure subscription with permissions to create VMs and networking
-    resources, and basic familiarity with SSH. Azure Portal and Azure CLI are listed tools; the
-    steps focus on the Portal.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Microsoft Azure subscription with permissions to create virtual machines and
-      networking resources, and basic familiarity with SSH. No other prerequisites are explicitly
-      listed.
-  - question: Which Cobalt 100 VM series should I choose during creation?
-    answer: >-
-      Azure offers Cobalt 100–powered VMs in Dpsv6 and Dplsv6 (general-purpose) and Epsv6 (memory-optimized)
-      series. Select the series that aligns with your general-purpose or memory-optimized needs.
-  - question: How do I find the public IP to SSH into the VM?
-    answer: >-
-      Open the VM’s Overview page in the Azure Portal and copy the Public IP address. Use that
-      address in your SSH command.
-  - question: What SSH command and username should I use to connect?
-    answer: >-
-      From a terminal, run: ssh -i [path to your pem file] azureuser@[public IP], replacing the
-      placeholders with your key path and the VM’s public IP. Use azureuser unless you specified
-      a different admin username during VM creation.
-  - question: How do I open and test an application port like 8080?
-    answer: >-
-      Add an inbound rule in the VM’s Network Security Group to allow TCP 8080, typically scoped
-      to your IP for testing. Start your application (or a temporary HTTP server) on the VM listening
-      on 8080, then access the VM on that port from your client to verify connectivity.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Joe Stech
 
diff --git a/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md b/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
index e97794ea52..2dec123e04 100644
--- a/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
@@ -13,58 +13,9 @@ learning_objectives:
 prerequisites:
     - An [AWS account](/learning-paths/servers-and-cloud-computing/csp/aws/) for accessing AWS cloud services.
     - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or any Arm server, laptop, or single-board computer running [Docker](/install-guides/docker/) used to run the created images
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:35:39Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: e7ea48aaea0e25f624ea1d77c6252b0e537fdaeca3aacca560d7bc9d103bbbb3
-  summary_generated_at: '2026-06-02T03:25:50Z'
-  summary_source_hash: e7ea48aaea0e25f624ea1d77c6252b0e537fdaeca3aacca560d7bc9d103bbbb3
-  faq_generated_at: '2026-06-03T00:35:39Z'
-  faq_source_hash: e7ea48aaea0e25f624ea1d77c6252b0e537fdaeca3aacca560d7bc9d103bbbb3
-  summary: >-
-    Automate building Arm AArch64 Docker images with AWS CodeBuild using a GitHub project, then
-    publish them to Docker Hub and the Amazon ECR Public Gallery and run them on any Arm system
-    with Docker installed. The path targets Linux and uses AWS Graviton-backed CodeBuild to create
-    images for Arm. You will also validate your runtime environment by checking uname -m returns
-    aarch64 and then pull and run the completed images. Prerequisites include an AWS account and
-    access to an Arm-based instance or any Arm server, laptop, or single-board computer with Docker
-    installed. This advanced, 30‑minute path is aimed at developers comfortable with Docker and
-    CI/CD concepts.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an AWS account and an Arm-based system with Docker installed to run the created
-      images. The path assumes Linux and mentions prior Docker experience is helpful, but no other
-      prerequisites are explicitly listed.
-  - question: How do I verify that my machine is Arm AArch64 before running the images?
-    answer: >-
-      On Linux, run uname -m. The expected output is aarch64; if you see a different result, you
-      are not on a 64-bit Arm Linux machine.
-  - question: Where will the built Docker images be published?
-    answer: >-
-      The images are published to the Amazon ECR Public Gallery and Docker Hub. Both images are
-      identical.
-  - question: When should I pull and run the images on my Arm machine?
-    answer: >-
-      Wait until the AWS CodeBuild process completes. Once complete, you can pull and run the
-      images from either Docker Hub or ECR on any Arm system with Docker installed.
-  - question: Do I need a GitHub repository to follow this path?
-    answer: >-
-      Yes. The path uses a GitHub project integrated with AWS CodeBuild to automate Docker image
-      creation.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/codec/_index.md b/content/learning-paths/servers-and-cloud-computing/codec/_index.md
index 58607bb771..d6a5a6ccae 100644
--- a/content/learning-paths/servers-and-cloud-computing/codec/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/codec/_index.md
@@ -16,57 +16,9 @@ learning_objectives:
 prerequisites:
 - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from an appropriate
   cloud service provider. This Learning Path has been verified on AWS EC2 and Oracle cloud services, running `Ubuntu Linux 20.04.`
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:36:00Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 908a750f2a891a0c4c9d1183c2130ac5ac0fdcfb4a45185cef6ed6da47c9aaa9
-  summary_generated_at: '2026-06-02T03:26:25Z'
-  summary_source_hash: 908a750f2a891a0c4c9d1183c2130ac5ac0fdcfb4a45185cef6ed6da47c9aaa9
-  faq_generated_at: '2026-06-03T00:36:00Z'
-  faq_source_hash: 908a750f2a891a0c4c9d1183c2130ac5ac0fdcfb4a45185cef6ed6da47c9aaa9
-  summary: >-
-    Build and run the x265 H.265 encoder on Arm servers and benchmark its performance across different
-    video resolutions and encoding presets. You will use an Arm-based cloud instance—verified
-    on AWS EC2 and Oracle Cloud Services—running Ubuntu Linux 20.04, install GCC, CMake, and required
-    packages, then compile x265 and execute the same video under varied configurations to observe
-    performance impact. The open-source libx265 includes optimizations for Arm Neoverse with Neon,
-    and optimized code is available on Bitbucket. This introductory path focuses on practical
-    build-and-run steps so you finish with a working x265 on Arm and comparative measurements.
-    Estimated time to complete is about 10 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      An Arm-based instance from a cloud service provider. This Learning Path has been verified
-      on AWS EC2 and Oracle Cloud, running Ubuntu Linux 20.04.
-  - question: Which packages should I install to build x265 on Ubuntu?
-    answer: >-
-      Update apt and install wget, git, cmake, cmake-curses-gui, and build-essential. You also
-      need GCC for your Arm Linux distribution.
-  - question: Where do the Arm optimizations for x265 come from?
-    answer: >-
-      The path uses the open-source libx265, which includes optimizations for Arm Neoverse platforms
-      with Neon support. The optimized code is available on Bitbucket.
-  - question: How will I measure the performance impact of different settings?
-    answer: >-
-      You will run x265 on the same video using various resolutions and encoding presets. Compare
-      the results to assess the performance impact of those choices.
-  - question: Which operating systems and platforms are validated for these steps?
-    answer: >-
-      The steps target Linux and have been verified on Ubuntu 20.04 running on Arm-based servers
-      from AWS EC2 and Oracle Cloud. Other operating systems are not explicitly listed.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/codec1/_index.md b/content/learning-paths/servers-and-cloud-computing/codec1/_index.md
index 4aa435b4d3..e4520a382b 100644
--- a/content/learning-paths/servers-and-cloud-computing/codec1/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/codec1/_index.md
@@ -1,59 +1,9 @@
 ---
 title: Run the AV1 and VP9 codecs on Arm Linux
 description: Learn how to build and run the AV1 and VP9 video codecs on Arm Linux systems with performance benchmarking across various resolutions and encoding configurations.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:36:28Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: c0643a788cdb0b3e33fe645fbb61d99a1899806e3ee197541c1eb8134b2876c1
-  summary_generated_at: '2026-06-02T03:27:16Z'
-  summary_source_hash: c0643a788cdb0b3e33fe645fbb61d99a1899806e3ee197541c1eb8134b2876c1
-  faq_generated_at: '2026-06-03T00:36:28Z'
-  faq_source_hash: c0643a788cdb0b3e33fe645fbb61d99a1899806e3ee197541c1eb8134b2876c1
-  summary: >-
-    Learn how to build and run the AV1 (libaom) and VP9 (libvpx) video codecs on Arm Linux, then
-    benchmark them on example videos using multiple resolutions and encoding configurations. You
-    will install build dependencies such as CMake and the GNU compiler, obtain the codec sources,
-    compile on an Arm server or Arm-based cloud instance, and execute encoding and decoding workloads.
-    The reference implementations include Arm-focused optimizations, including use of Neon and
-    SVE2 on Arm Neoverse platforms. By the end, you will be able to run these codecs on your Arm
-    system and record performance results. No further prerequisites are listed beyond access to
-    an Arm Linux environment.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need access to an Arm Linux system or an Arm-based instance from a cloud service provider.
-      No other explicit prerequisites are listed.
-  - question: Which codecs and libraries are used in this path?
-    answer: >-
-      AV1 is built and run using the libxaom reference implementation, and VP9 is built and run
-      using libvpx. Both libraries support encoding and decoding.
-  - question: Which development tools do I need to install to build the codecs?
-    answer: >-
-      You need various development tools including CMake and the GNU compiler. The steps provide
-      installation instructions for the required packages.
-  - question: Where do I obtain the source code for the codecs?
-    answer: >-
-      For VP9, the path clones libvpx from https://chromium.googlesource.com/webm/libvpx. For
-      AV1, the reference implementation and Arm-optimized code for libxaom are available on Google
-      Git.
-  - question: What results should I expect after completing the path?
-    answer: >-
-      You will have built the AV1 and VP9 codecs on Arm Linux and run them on example videos at
-      various resolutions and encodings. You will also collect performance measurements to compare
-      configurations, with notes on Arm Neoverse optimizations using Neon and SVE2.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Odin Shen
 
diff --git a/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
index 9cd11f02ad..5d4051ff47 100644
--- a/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
@@ -16,59 +16,9 @@ learning_objectives:
 prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled  
   - Basic familiarity with [Couchbase](https://www.couchbase.com/)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:36:56Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 0a7d76b8c944a50073c7524813acf83948155c7c61d9c92f9202eae1192c9600
-  summary_generated_at: '2026-06-02T03:28:12Z'
-  summary_source_hash: 0a7d76b8c944a50073c7524813acf83948155c7c61d9c92f9202eae1192c9600
-  faq_generated_at: '2026-06-03T00:36:56Z'
-  faq_source_hash: 0a7d76b8c944a50073c7524813acf83948155c7c61d9c92f9202eae1192c9600
-  summary: >-
-    Follow this introductory path to deploy Couchbase Server on Arm-based Google Cloud Axion C4A
-    virtual machines and run basic performance checks. You will provision a SUSE Linux Enterprise
-    Server (SLES) VM on the c4a-standard-4 machine type, create a Google Cloud firewall rule to
-    open TCP port 8091, install Couchbase on Arm64, and initialize the cluster from the web console
-    by creating a test bucket and confirming node health. You then benchmark Couchbase using YCSB
-    workloads to record operations per second, memory utilization, and disk behavior on the Arm
-    platform. Prerequisites are a GCP account with billing enabled and basic familiarity with
-    Couchbase.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Google Cloud Platform account with billing enabled and basic familiarity with
-      Couchbase. No additional prerequisites are explicitly listed.
-  - question: Which Google Cloud VM type and OS should I use?
-    answer: >-
-      Provision an Arm-based Google Axion C4A VM using the c4a-standard-4 machine type (4 vCPUs,
-      16 GB memory). The Learning Path targets SUSE Linux Enterprise Server (Arm64).
-  - question: How do I allow and test access to the Couchbase Web Console?
-    answer: >-
-      Create a VPC firewall rule in Google Cloud Console to allow inbound TCP port 8091. Then
-      open http://VM_PUBLIC_IP:8091 in your browser to reach the console.
-  - question: How do I know Couchbase installed correctly on the VM?
-    answer: >-
-      You should be able to access the Couchbase Web Console, complete the initial cluster setup,
-      see your node reported as healthy, and create a test bucket. These checks confirm the deployment
-      is ready for benchmarking.
-  - question: What should I capture when running the YCSB benchmarks?
-    answer: >-
-      Measure and record operations per second (ops/sec), memory utilization, and disk performance
-      for the Couchbase workload on the Arm-based instance. The Learning Path guides you to prepare
-      the bucket and run YCSB workloads to collect these results.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md b/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
index fc33141574..7d48c0f50a 100644
--- a/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
@@ -13,62 +13,9 @@ learning_objectives:
 prerequisites:
     - Basic understanding of C++.
     - Basic understanding of compilers.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:37:16Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 22f845b8ea4dbb9ffc63fafb76c17c79aedde14a28417d49e1ab0833bbbc1eba
-  summary_generated_at: '2026-06-02T03:28:45Z'
-  summary_source_hash: 22f845b8ea4dbb9ffc63fafb76c17c79aedde14a28417d49e1ab0833bbbc1eba
-  faq_generated_at: '2026-06-03T00:37:16Z'
-  faq_source_hash: 22f845b8ea4dbb9ffc63fafb76c17c79aedde14a28417d49e1ab0833bbbc1eba
-  summary: >-
-    Learn how to apply g++ compiler optimization flags when building C++ applications for Arm-based
-    servers. You will provision and connect to an AWS Graviton4 (r8g.xlarge) instance running
-    Ubuntu 24.04 LTS, then build and run a sample C++ program on Linux while selecting an appropriate
-    target architecture and optimization strategy. The path also reviews Neoverse-based instance
-    generations (for example, Graviton3/V1 and Graviton4/V2) to inform choices like the -march
-    flag for portability, size, or focusing on a specific CPU. This introductory, 60‑minute path
-    assumes a basic understanding of C++ and compilers and focuses on compiling for a specific
-    Arm target and managing optimizations with g++.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a basic understanding of C++ and compilers, and access to an AWS account to create
-      a Graviton4 (r8g.xlarge) instance running Ubuntu 24.04 LTS. You also need a way to connect
-      to the instance.
-  - question: Which -march value should I use for my build?
-    answer: >-
-      Choose the lowest Arm architecture among the systems you plan to run on if you need portability.
-      If you want the highest performance on a specific processor, target that processor (for
-      example, AWS Graviton4) instead.
-  - question: How do I know my environment and compiler are ready?
-    answer: >-
-      After connecting to the instance, the path has you run commands to confirm the OS and compiler
-      setup on Ubuntu 24.04 LTS. Proceed once you have verified that your build environment is
-      available.
-  - question: What result should I expect after I build and run the example?
-    answer: >-
-      You will produce a compiled C++ application built with the selected g++ optimization flags
-      for an Arm target and run it on the AWS Graviton4 instance. The path helps you compare choices
-      such as portability versus targeting a specific CPU or optimizing for size.
-  - question: Can I follow this on other Arm-based cloud instances?
-    answer: >-
-      Most cloud providers offer Arm-based instances on Neoverse, but the hands-on steps use AWS
-      Graviton4. If you plan to run across different Arm servers, select an -march value that
-      matches the lowest target in your set.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md b/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md
index d5107caf21..e94b09f09d 100644
--- a/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md
@@ -13,62 +13,9 @@ learning_objectives:
 prerequisites:
     - Basic C++ understanding.
     - Access to an Arm-based Linux machine.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:37:40Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 4e7de348514d0b5a742fa47bb85a2a5814fa9bd587478e7ef08365d16273f6bf
-  summary_generated_at: '2026-06-02T03:29:36Z'
-  summary_source_hash: 4e7de348514d0b5a742fa47bb85a2a5814fa9bd587478e7ef08365d16273f6bf
-  faq_generated_at: '2026-06-03T00:37:40Z'
-  faq_source_hash: 4e7de348514d0b5a742fa47bb85a2a5814fa9bd587478e7ef08365d16273f6bf
-  summary: >-
-    Learn to measure and tune C++ code on Arm-based Linux systems using Profile-Guided Optimization
-    (PGO) and Google Benchmark. You will compile an instrumented binary with GCC/G++ using -fprofile-generate,
-    run it to emit profile data (.gcda), and rebuild with -fprofile-use to create a profile-tuned
-    executable. An integer division example demonstrates microbenchmarking and comparing baseline
-    versus PGO builds with Google Benchmark. The path also shows how to integrate PGO into a Makefile
-    and a GitHub Actions workflow, with cautions on when PGO is appropriate. Prerequisites are
-    basic C++ knowledge and access to an Arm-based Linux machine. Estimated time is 15 minutes;
-    the approach applies to Arm environments, including cloud instances on AWS, Microsoft Azure,
-    Google Cloud, or Oracle.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need basic C++ understanding and access to an Arm-based Linux machine. The path uses
-      GCC/G++ and Google Benchmark to build and run the examples.
-  - question: Which compiler options should I use for PGO with GCC/G++ and in what order?
-    answer: >-
-      First compile with -fprofile-generate to create an instrumented binary, then run that binary
-      to collect profile data. Recompile the program with -fprofile-use to apply the collected
-      data during optimization.
-  - question: How do I know the profiling run succeeded and where are the files?
-    answer: >-
-      After running the instrumented binary, expect profile data files (typically .gcda) to appear
-      in the same directory. Their presence indicates that execution generated the data needed
-      for the -fprofile-use rebuild.
-  - question: What will I benchmark in this path and why that example?
-    answer: >-
-      You will benchmark a simple integer division operation. Division is chosen because it is
-      typically more expensive than addition, subtraction, or multiplication, making performance
-      differences easier to observe.
-  - question: When should I apply PGO in my project or CI workflow?
-    answer: >-
-      Use PGO for performance-critical code that is heavily influenced by runtime behavior, and
-      consider integrating it via a Makefile or GitHub Actions. Be aware that PGO adds build steps
-      and time, and it may not be ideal for early-stage development or highly variable workloads.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md b/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
index 055f2f5c9d..d5660f9fd7 100644
--- a/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
@@ -13,60 +13,9 @@ learning_objectives:
 prerequisites:
     - Access to Arm Performix
     - Basic understanding of C++
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:38:16Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 58dba071f70b4f85b87b3bd27b3a7ba3ff985f80079a42e05b797a998aeaf104
-  summary_generated_at: '2026-06-02T03:30:14Z'
-  summary_source_hash: 58dba071f70b4f85b87b3bd27b3a7ba3ff985f80079a42e05b797a998aeaf104
-  faq_generated_at: '2026-06-03T00:38:16Z'
-  faq_source_hash: 58dba071f70b4f85b87b3bd27b3a7ba3ff985f80079a42e05b797a998aeaf104
-  summary: >-
-    This Learning Path shows how to find code hotspots in C++ applications running on Arm Linux
-    systems using Arm Performix on Arm Neoverse. You will build and run a C++11 Mandelbrot example
-    that generates a 1920×1080 bitmap, profile baseline performance with the Code Hotspots recipe,
-    and read the resulting flame graph to identify functions that dominate CPU time. The steps
-    then use those insights to focus potential improvements, such as investigating calls like
-    __hypot within Mandelbrot::getIterations. This is an introductory path aimed at developers
-    and performance engineers. Prerequisites are access to Arm Performix and a basic understanding
-    of C++. By the end, you will be able to run the recipe and pinpoint CPU-intensive functions
-    for deeper analysis.
-  faqs:
-  - question: Which Arm Performix feature should I run to find hotspots?
-    answer: >-
-      Use the Code Hotspots recipe. It samples execution and produces a flame graph that highlights
-      the functions consuming the most CPU time.
-  - question: What do I need before running the steps?
-    answer: >-
-      You need access to Arm Performix and a basic understanding of C++. The example runs on an
-      Arm Linux system, as described by the Learning Path.
-  - question: What do I build and what output should I expect from the example?
-    answer: >-
-      You will build a C++11 program that computes the Mandelbrot set and writes a 1920×1080 bitmap
-      image. The source is provided so you can rebuild, profile, and relate flame graph results
-      back to specific functions and loops.
-  - question: How do I know profiling worked?
-    answer: >-
-      After running the Code Hotspots recipe, you should see a flame graph that clearly shows
-      the hottest functions. In the example, __hypot appears as a hotspot invoked by Mandelbrot::getIterations.
-  - question: What should I check if the image file is missing when profiling under Arm Performix?
-    answer: >-
-      The Learning Path notes that myplot.draw() uses a relative path (./images/green.bmp) and
-      that Arm Performix launches the binary in a different location. Follow the step guidance
-      to ensure the output is written to the intended directory.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/csp/_index.md b/content/learning-paths/servers-and-cloud-computing/csp/_index.md
index 07fdd78c50..6590ebca7f 100644
--- a/content/learning-paths/servers-and-cloud-computing/csp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/csp/_index.md
@@ -1,57 +1,9 @@
 ---
 title: Get started with Arm-based cloud instances
 description: Learn how to start an Arm-based virtual machine instance from major cloud service providers and verify the Arm architecture is being used.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:38:48Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 9e94b69eabf35677c48db812bb85ea9cef184efc078a826b96954f540a45e915
-  summary_generated_at: '2026-06-02T03:30:48Z'
-  summary_source_hash: 9e94b69eabf35677c48db812bb85ea9cef184efc078a826b96954f540a45e915
-  faq_generated_at: '2026-06-03T00:38:48Z'
-  faq_source_hash: 9e94b69eabf35677c48db812bb85ea9cef184efc078a826b96954f540a45e915
-  summary: >-
-    This introductory Learning Path shows how to launch a Linux virtual machine on Arm-based instances
-    from major cloud providers and confirm that it is running on Arm architecture. You will use
-    each provider’s standard VM service: AWS EC2 with Graviton, Microsoft Azure Virtual Machines
-    (Azure Cobalt 100 or previous Ampere generations), Google Cloud Compute Engine with Axion
-    C4A (example c4a-standard-4), Oracle Cloud Infrastructure compute with Ampere, and Alibaba
-    Cloud ECS. The steps focus on selecting an Arm-based machine type and performing a brief post-launch
-    verification. An active account with your chosen provider is required; no other prerequisites
-    are explicitly listed. The path takes about 15 minutes to complete.
-  faqs:
-  - question: What do I need before starting?
-    answer: >-
-      You need an account with your preferred cloud service provider. The path uses provider consoles
-      and documentation to guide VM creation.
-  - question: Which instance types should I choose to get an Arm VM on each cloud?
-    answer: >-
-      Use AWS EC2 with Graviton, Azure Arm-based VMs (Cobalt 100 or Ampere generations), Google
-      Cloud Axion C4A (for example c4a-standard-4), Oracle Cloud Infrastructure with Ampere, and
-      Alibaba Cloud ECS with Arm-based processors.
-  - question: Which operating system is used in the examples?
-    answer: >-
-      Linux is used for the examples in this Learning Path.
-  - question: How do I verify that the VM is Arm-based once it’s running?
-    answer: >-
-      Follow the verification step in the path to confirm that the instance reports an Arm CPU
-      architecture. The process is performed from the running Linux VM.
-  - question: What result should I expect after completing the steps?
-    answer: >-
-      You will have a running Linux VM on your chosen cloud that is using an Arm-based processor,
-      and you will have verified the architecture on the instance.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md b/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
index 3b00135d53..03aa0e493b 100644
--- a/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
@@ -13,57 +13,9 @@ learning_objectives:
 
 prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud provider or an on-premise Arm server. This Learning Path was tested on an AWS Graviton4 r8g.24xlarge instance.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:39:18Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: f600fcba0adea12ff1b8b092e75de553577d940dc3bf632e9a247cec22d364a4
-  summary_generated_at: '2026-06-02T03:31:44Z'
-  summary_source_hash: f600fcba0adea12ff1b8b092e75de553577d940dc3bf632e9a247cec22d364a4
-  faq_generated_at: '2026-06-03T00:39:18Z'
-  faq_source_hash: f600fcba0adea12ff1b8b092e75de553577d940dc3bf632e9a247cec22d364a4
-  summary: >-
-    This Learning Path shows how to deploy and run the DeepSeek-R1 671B language model on Arm-based
-    servers using llama.cpp with quantization for CPU inference. You will clone and build llama.cpp,
-    download a pre-quantized DeepSeek-R1 model from Hugging Face, start the llama.cpp server,
-    and access it via an OpenAI-compatible API. The instructions target Ubuntu 24.04 LTS on an
-    Arm server with at least 64 cores, 512 GB RAM, and 400 GB of disk space; they were tested
-    on an AWS Graviton4 r8g.24xlarge instance. By the end, you will have a running chatbot on
-    your Arm CPU and benchmark its performance. Prerequisite: an Arm-based instance from a cloud
-    provider or an on-prem Arm server.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      Use an Arm-based server running Ubuntu 24.04 LTS with at least 64 CPU cores, 512 GB RAM,
-      and 400 GB of disk space. An Arm-based instance from a cloud provider or an on-prem Arm
-      server is suitable; the instructions were tested on an AWS Graviton4 r8g.24xlarge instance.
-  - question: Where do I get the DeepSeek-R1 model and what format is expected?
-    answer: >-
-      Download a pre-quantized DeepSeek-R1 model from Hugging Face as directed in the Learning
-      Path. The steps assume a pre-quantized artifact appropriate for llama.cpp.
-  - question: How do I start and access the model server during this Learning Path?
-    answer: >-
-      After building llama.cpp, start its server mode as shown in the steps. The server provides
-      an OpenAI-compatible API and can be accessed locally or over the network from another machine.
-  - question: Do I need any extra tools to query or work with the API responses?
-    answer: >-
-      Yes. The steps require jq for this section; install it with: sudo apt install jq -y.
-  - question: What should I check if the llama.cpp server binary is missing?
-    answer: >-
-      The server executable is built when you run make in the previous section. Ensure you completed
-      the llama.cpp build step before attempting to start the server.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author:
     - Tianyu Li
diff --git a/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md b/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md
index f8e246771f..d54ec0edd7 100644
--- a/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md
@@ -14,61 +14,9 @@ learning_objectives:
 prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an Arm Linux server.
     - Familiarity with Linux.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:39:53Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: a1ec216948e7cfd4fc52815196bb3b99ab4e76c9c756aa9e9a8e3216ef5e7ce4
-  summary_generated_at: '2026-06-02T03:32:18Z'
-  summary_source_hash: a1ec216948e7cfd4fc52815196bb3b99ab4e76c9c756aa9e9a8e3216ef5e7ce4
-  faq_generated_at: '2026-06-03T00:39:53Z'
-  faq_source_hash: a1ec216948e7cfd4fc52815196bb3b99ab4e76c9c756aa9e9a8e3216ef5e7ce4
-  summary: >-
-    This introductory Learning Path shows how to monitor and microbenchmark storage on Arm-based
-    Linux systems. You will review storage fundamentals and key workload attributes (IOPS, I/O
-    size, throughput, read/write ratio, and access patterns), analyze a real workload using FFMPEG
-    on an AWS t4g.medium (Graviton2) instance, and then install and run fio to benchmark SSD-based
-    block devices. The steps use iostat, iotop, and pidstat to observe I/O behavior and identify
-    bottlenecks. An example demonstrates attaching and identifying two AWS EBS volumes (io2 and
-    gp2) before testing. Prerequisites are an Arm-based cloud instance or Arm Linux server and
-    familiarity with Linux. Expected outcomes include describing data flow, monitoring storage
-    activity, and running fio microbenchmarks.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an Arm-based instance from a cloud service provider or an Arm Linux server, and
-      familiarity with Linux. No other explicit prerequisites are listed.
-  - question: Can I use a cloud provider other than AWS?
-    answer: >-
-      Yes. The prerequisite allows any Arm-based instance from a cloud service provider, but the
-      example steps use AWS. Setup details for other providers are not explicitly listed.
-  - question: Which instance type and example workload are used in the path?
-    answer: >-
-      The example uses an AWS t4g.medium (Graviton2) instance with two vCPUs and 4 GiB of memory.
-      FFMPEG is used as a real workload to analyze I/O behavior.
-  - question: Which block storage devices are benchmarked and how are they created?
-    answer: >-
-      Two SSD-based EBS volumes are used: an io2 volume (8 GiB, 400 provisioned IOPS, same Availability
-      Zone as the instance) and a gp2 volume. They are created in the AWS Console and added to
-      the EC2 instance before identifying them on the system.
-  - question: How should I monitor and validate storage behavior while running fio?
-    answer: >-
-      Use iostat, iotop, and pidstat to observe activity and relate results to workload attributes
-      such as IOPS, I/O size, throughput, read/write ratio, and random vs. sequential access.
-      If results look incorrect or devices are missing, verify the volumes are in the same Availability
-      Zone as the instance and properly attached.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md
index ce5081bcf8..1d50b56d9d 100644
--- a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md
@@ -16,59 +16,9 @@ prerequisites:
     - Access to Meta's gated repository for the Llama 3.1 model family and a Hugging Face token to download models
     - Familiarity with the Learning Path [Deploy a Large Language Model (LLM) chatbot with llama.cpp using KleidiAI on Arm servers](/learning-paths/servers-and-cloud-computing/llama-cpu)
     - Familiarity with AWS
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:40:36Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 6ac0c7cf1ab4b3efa680acbf1e349b858bee5dc7992baf6689e1f293a83060a4
-  summary_generated_at: '2026-06-02T03:33:12Z'
-  summary_source_hash: 6ac0c7cf1ab4b3efa680acbf1e349b858bee5dc7992baf6689e1f293a83060a4
-  faq_generated_at: '2026-06-03T00:40:36Z'
-  faq_source_hash: 6ac0c7cf1ab4b3efa680acbf1e349b858bee5dc7992baf6689e1f293a83060a4
-  summary: >-
-    Learn to run distributed LLM inference with llama.cpp across multiple Arm-based AWS Graviton4
-    instances on Linux. You will set up a master (main) host and worker nodes, download a Meta
-    Llama 3.1 model, convert safetensors to a single GGUF file, quantize 16-bit weights to 4-bit,
-    configure node coordination using llama.cpp’s distributed RPC feature, verify connectivity,
-    and run the model across machines. This introductory path targets developers with some llama.cpp
-    experience and familiarity with AWS. Prerequisites include three AWS c8g.4xlarge instances,
-    Python 3 on each, and access to Meta’s gated repository with a Hugging Face token. The expected
-    outcome is a working multi-node CPU inference run of a large quantized model.
-  faqs:
-  - question: What AWS resources do I need before starting?
-    answer: >-
-      You need three AWS c8g.4xlarge instances with at least 500 GB of EBS storage, running Linux.
-      This path targets Arm-based AWS Graviton4.
-  - question: Which model is used and how is it prepared?
-    answer: >-
-      The steps download Meta’s Llama 3.1 70B model, convert the safetensors files into a single
-      GGUF file, and quantize the 16-bit GGUF weights to 4-bit. The resulting 4-bit GGUF file
-      is what llama.cpp loads for inference.
-  - question: How do I register worker nodes on the master node?
-    answer: >-
-      After setting up the workers, export the worker_ips environment variable on the master using
-      entries like ip:50052. You can find each instance’s IP address in the AWS console.
-  - question: How do I verify that the master can reach a worker node?
-    answer: >-
-      From the master node, run a telnet command to the worker’s IP on port 50052. If the backend
-      server is set up correctly on the worker, you should see the backend server output.
-  - question: What access and prior knowledge do I need to download and run the model?
-    answer: >-
-      You need Python 3 installed on each instance, access to Meta’s gated repository for the
-      Llama 3.1 family, and a Hugging Face token. Familiarity with AWS and the Learning Path on
-      deploying a llama.cpp chatbot using KleidiAI is also expected.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: 
     - Aryan Bhusari
diff --git a/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md
index d9a10fd157..adc71975fe 100644
--- a/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md
@@ -20,63 +20,9 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled  
   - Basic familiarity with [Django](https://www.djangoproject.com/)
   - Basic understanding of containers and Kubernetes concepts
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:41:54Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 6675df4c91126b157dcbf39c96a773130f1a95e2f5680913979da96f6f6c97cd
-  summary_generated_at: '2026-06-02T03:34:24Z'
-  summary_source_hash: 6675df4c91126b157dcbf39c96a773130f1a95e2f5680913979da96f6f6c97cd
-  faq_generated_at: '2026-06-03T00:41:54Z'
-  faq_source_hash: 6675df4c91126b157dcbf39c96a773130f1a95e2f5680913979da96f6f6c97cd
-  summary: >-
-    This Learning Path shows how to deploy a production-grade Django REST API on Google Cloud
-    using Arm-based Axion compute. You will provision Arm64 Axion C4A virtual machines and GKE
-    node pools, package the application into an Arm-native Docker image, push it to Google Artifact
-    Registry, and deploy on GKE using Kubernetes manifests (Deployment, Service, ConfigMap, Secrets).
-    The path integrates Django with Cloud SQL (PostgreSQL) over private IP and Memorystore (Redis),
-    exposes the service via a LoadBalancer, and validates connectivity to both services. You also
-    set up a SUSE Linux Enterprise Server VM, open port 8000, run the Django development server,
-    and benchmark Gunicorn on Arm with ApacheBench to measure throughput and p95 latency. Prerequisites
-    are a GCP account with billing enabled and basic familiarity with Django, containers, and
-    Kubernetes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Google Cloud Platform account with billing enabled. The path assumes basic familiarity
-      with Django and a basic understanding of containers and Kubernetes.
-  - question: How do I run and reach the Django development server on the Axion VM?
-    answer: >-
-      You will install Python 3.11 on a SUSE Linux Enterprise Server VM, create a Django project,
-      and start the development server. Then, create a firewall rule to allow inbound traffic
-      on port 8000 so you can access the server from your browser.
-  - question: Which container and registry steps are included before deploying to GKE?
-    answer: >-
-      You will package the Django REST API into an Arm-native Docker container and push the image
-      to Google Artifact Registry. These steps prepare the application for deployment on Arm64
-      GKE node pools.
-  - question: Which Kubernetes resources and exposure method are used on GKE?
-    answer: >-
-      The deployment uses Kubernetes manifests including a Deployment, Service, ConfigMap, and
-      Secrets. The application is exposed externally using a LoadBalancer Service on Arm64 Axion
-      node pools.
-  - question: How does the app connect to managed data services and how is performance evaluated?
-    answer: >-
-      Django is integrated with Cloud SQL (PostgreSQL) over private IP and Memorystore (Redis)
-      for caching and sessions, with steps to validate application connectivity. Performance is
-      measured using ApacheBench to report throughput and p95 latency against Gunicorn on Arm.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/django/_index.md b/content/learning-paths/servers-and-cloud-computing/django/_index.md
index 18f6a496d8..eee96882fa 100644
--- a/content/learning-paths/servers-and-cloud-computing/django/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/django/_index.md
@@ -16,60 +16,9 @@ prerequisites:
     - Sudo access to install dependencies and to modify system configuration files.
     - Be comfortable with SSH/Linux terminal and basic system administration tasks.
     - To install both [Nginx](/learning-paths/servers-and-cloud-computing/nginx/) and [PostgreSQL](/learning-paths/servers-and-cloud-computing/postgresql/)
-
-generate_summary_faq: false
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:41:18Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
-  summary_generated_at: '2026-06-02T03:33:56Z'
-  summary_source_hash: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
-  faq_generated_at: '2026-06-03T00:41:18Z'
-  faq_source_hash: c316c81de911ecd7f8e517f4ae5e5006d66a637199b8952fe195a74f3456a5e0
-  summary: >-
-    Build and deploy a simple Django web application on Arm-based Linux machines using Nginx and
-    PostgreSQL. This introductory path uses Ubuntu 22.04 LTS and walks you through creating a
-    Django project, configuring its PostgreSQL database settings, creating the database and user,
-    deploying behind Nginx, and verifying the application is working. You can run the steps on
-    an Arm instance from AWS, Microsoft Azure, Google Cloud, or Oracle, on an on-premises Arm
-    server, or on a Linux VM on your Arm device. Prerequisites include sudo access, comfort with
-    SSH and basic Linux administration, and the ability to install Nginx and PostgreSQL.
-  faqs:
-  - question: What environment do I need to run this?
-    answer: >-
-      Use an Arm-based instance from a cloud provider, an on-premises Arm server, or a Linux VM
-      on your Arm device. The instructions use Ubuntu 22.04 LTS and are the same regardless of
-      the Arm machine type.
-  - question: Do I need a specific Python version or a virtual environment?
-    answer: >-
-      Ubuntu 22.04 provides Python 3.10, which you can use, or you may optionally install a newer
-      Python via the Deadsnakes PPA. The steps assume you are working in a terminal with a Python
-      virtual environment activated.
-  - question: Do I need to install Nginx and PostgreSQL before deploying?
-    answer: >-
-      Yes. Installing both Nginx and PostgreSQL is listed as a prerequisite for this path. Follow
-      the referenced Learning Paths for Nginx and PostgreSQL if you need installation guidance.
-  - question: How do I know the Django project was created correctly?
-    answer: >-
-      After running django-admin startproject myproject, you should see a myproject directory
-      with manage.py and a myproject package containing asgi.py, __init__.py, settings.py, urls.py,
-      and wsgi.py. You can start the development server from the project directory to quickly
-      validate it runs.
-  - question: Which PostgreSQL settings should I use and how do I create the database?
-    answer: >-
-      In settings.py set ENGINE to django.db.backends.postgresql with NAME myprojectdb, USER usr,
-      PASSWORD mypassword, HOST as localhost or your machine’s IP, and PORT 5432. Open the PostgreSQL
-      prompt with sudo -u postgres psql and create the database and user to match those values.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Diego Russo
 
diff --git a/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md b/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
index 364cb2400b..9edcc52555 100644
--- a/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
@@ -13,60 +13,9 @@ learning_objectives:
 
 prerequisites:
     - Any [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider (CSP), or an on-premise Arm server with at least 400GB of RAM and 800 GB of disk space.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:42:37Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 82716c2de19d18a154c85d03b7f2ec01839284262914f7bb3ad04b18a105379d
-  summary_generated_at: '2026-06-02T03:35:11Z'
-  summary_source_hash: 82716c2de19d18a154c85d03b7f2ec01839284262914f7bb3ad04b18a105379d
-  faq_generated_at: '2026-06-03T00:42:37Z'
-  faq_source_hash: 82716c2de19d18a154c85d03b7f2ec01839284262914f7bb3ad04b18a105379d
-  summary: >-
-    This Learning Path shows how to build and benchmark the Deep Learning Recommendation Model
-    (DLRM) on Arm Neoverse V2 processors using PyTorch and MLPerf. You will prepare a Linux Arm-based
-    cloud instance or on‑prem server, obtain data and model weights with rclone, and use provided
-    scripts to run a modified DLRMv2 benchmark. The path uses PyTorch 2.9.0+cpu with Arm-focused
-    optimizations and Docker-based tooling where applicable. By the end, you will have built DLRM,
-    executed the MLPerf benchmark tailored for Arm systems, and inspected the resulting performance
-    outputs. Prerequisites include an Arm-based instance (AWS or Google Cloud) or on‑prem Arm
-    server with at least 400GB RAM and 800GB of disk space.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      Use any Arm-based instance from a cloud service provider such as AWS or Google Cloud, or
-      an on-premise Arm server. The prerequisites are at least 400GB of RAM and 800GB of disk
-      space on a Linux system.
-  - question: Which operating system and processors does this target?
-    answer: >-
-      The steps assume Linux and target Arm Neoverse V2 CPUs. The procedures and benchmarks are
-      written for Arm-based systems.
-  - question: How do I download the DLRM data and model weights?
-    answer: >-
-      Create data and model directories in your home folder, then install rclone using the provided
-      installation script. Run rclone config and use it to download the required datasets and
-      weights as shown in the steps.
-  - question: Which frameworks and versions are used to run the benchmark?
-    answer: >-
-      You will run a modified MLPerf benchmark for DLRM using PyTorch 2.9.0+cpu. The steps use
-      a repository of scripts tailored for Arm-based systems.
-  - question: How do I run the benchmark and confirm it completed successfully?
-    answer: >-
-      Clone the provided repository and run the included scripts to execute the DLRM benchmark.
-      After the run, inspect the generated results as directed in the Learning Path to validate
-      completion.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: 
     - Phalani Paladugu
diff --git a/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md b/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
index e3e4f04c66..786a5f4a54 100644
--- a/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
@@ -22,66 +22,9 @@ prerequisites:
     - A GitHub account with a personal access token
     - A machine with at least 8 GB RAM (16 GB recommended)
     - Basic familiarity with Docker, C++, and SIMD intrinsics concepts
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:43:28Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 80785022032bf4e3c65da682e698940a212ee1ee77386698889a9fafbed9f823
-  summary_generated_at: '2026-06-02T03:37:07Z'
-  summary_source_hash: 80785022032bf4e3c65da682e698940a212ee1ee77386698889a9fafbed9f823
-  faq_generated_at: '2026-06-03T00:43:28Z'
-  faq_source_hash: 80785022032bf4e3c65da682e698940a212ee1ee77386698889a9fafbed9f823
-  summary: >-
-    This advanced path shows how to use the Docker MCP Toolkit with the Arm MCP Server and GitHub
-    Copilot in VS Code to automate migration of a containerized C++ app from x86 AVX2 intrinsics
-    to Arm64 Neon. You will enable the MCP Toolkit in Docker Desktop, connect the MCP Gateway
-    to VS Code, and configure the Arm, GitHub, and Sequential Thinking MCP servers. Using a provided
-    demo repository, you will scan for x86-specific code, generate Neon equivalents, create a
-    pull request, and review changes. Finally, you will build for linux/arm64 with docker buildx
-    and run the benchmark to validate output on Arm64. Prerequisites include Docker Desktop 4.59+,
-    VS Code with GitHub Copilot, a GitHub PAT, and basic Docker/C++ and SIMD knowledge. Estimated
-    time: 45 minutes on Linux or macOS.
-  faqs:
-  - question: What do I need before starting the migration steps?
-    answer: >-
-      You need Docker Desktop 4.59 or later with MCP Toolkit enabled, VS Code with the GitHub
-      Copilot extension, a GitHub account with a Personal Access Token that allows repository
-      access, a machine with at least 8 GB RAM (16 GB recommended), and basic familiarity with
-      Docker, C++, and SIMD intrinsics concepts.
-  - question: Which MCP servers should I configure, and how do I make them available to Copilot
-      in VS Code?
-    answer: >-
-      Configure the Arm MCP Server, GitHub MCP Server, and Sequential Thinking MCP Server in the
-      Docker MCP Toolkit. In VS Code, ensure the MCP_DOCKER server is running (Extensions > MCP_DOCKER
-      > Start Server) so GitHub Copilot can invoke these servers through the MCP Gateway.
-  - question: Where do I get the demo application and open it in VS Code?
-    answer: >-
-      Clone the repository with: git clone https://github.com/JoeStech/docker-blog-arm-migration
-      and cd into docker-blog-arm-migration. Open it in VS Code with: code .
-  - question: How do I direct GitHub Copilot to perform the x86-to-Arm64 migration?
-    answer: >-
-      Open GitHub Copilot Chat in VS Code and paste the provided prompt that instructs it to use
-      the Arm MCP Server tools for migration. Copilot will scan for x86-specific dependencies
-      and intrinsics, automate AVX2-to-Neon conversions using the Arm MCP Server knowledge base,
-      and propose changes via a pull request using the GitHub MCP Server.
-  - question: What result should I expect after building and running the Arm64 container?
-    answer: >-
-      After building with docker buildx for --platform linux/arm64 and running the container,
-      the benchmark output should indicate it’s running on Arm64 with NEON optimizations and display
-      matrix multiplication timings and a result sum. This confirms the migrated code executes
-      on Arm64 as intended.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Ajeet Singh Raina
 
diff --git a/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md b/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md
index 94c3ae8273..241a02fdf1 100644
--- a/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md
@@ -19,59 +19,9 @@ prerequisites:
     - Basic knowledge of C and C#
     - GCC installed (Linux) or access to a cross-compiler
     - OrchardCore application created using the .NET CLI or Visual Studio
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:44:01Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 08d8f0c86625ef41476d3a8b24bad9b0a0820797022ef847bf9bb17a976726a7
-  summary_generated_at: '2026-06-02T03:38:20Z'
-  summary_source_hash: 08d8f0c86625ef41476d3a8b24bad9b0a0820797022ef847bf9bb17a976726a7
-  faq_generated_at: '2026-06-03T00:44:01Z'
-  faq_source_hash: 08d8f0c86625ef41476d3a8b24bad9b0a0820797022ef847bf9bb17a976726a7
-  summary: >-
-    Learn how to migrate and run an OrchardCore CMS .NET application on Azure Cobalt 100 Arm-based
-    virtual machines. You will build and run the app on Ubuntu 24.04 with port 8080 open, integrate
-    a simple C shared library that is invoked from C# via DllImport, and configure .NET AnyCPU
-    so the same build runs on both Arm and x86. The path also reviews .NET version choices and
-    support status to help you evaluate behavior on Arm. Prerequisites include an Azure account
-    with VM permissions, .NET SDK 8.0 or later, GCC or a cross-compiler, basic C and C# knowledge,
-    and an OrchardCore app created with the .NET CLI or Visual Studio.
-  faqs:
-  - question: What do I need in Azure before I start?
-    answer: >-
-      You need a Microsoft Azure account with permissions to deploy virtual machines. The path
-      assumes you can create and configure an Azure Cobalt 100 instance.
-  - question: Which VM image and network settings should I use for the OrchardCore app?
-    answer: >-
-      Launch an Azure Cobalt 100 (Arm-based) VM running Ubuntu 24.04 and open port 8080 to the
-      internet. If you need help creating the VM, see the Create an Azure Cobalt 100 VM Learning
-      Path.
-  - question: What tools and project setup are required on the VM?
-    answer: >-
-      Install .NET SDK 8.0 or later and ensure GCC is available on Linux (or use a cross-compiler).
-      You should also have an OrchardCore application created using the .NET CLI or Visual Studio,
-      and basic knowledge of C and C#.
-  - question: How do I build the C shared library and verify it is called from .NET?
-    answer: >-
-      Compile the C source with: gcc -shared -o libmylib.so -fPIC mylib.c, which produces libmylib.so.
-      Call the function from C# via DllImport; when invoked, it prints: Hello from the C library!.
-  - question: How do I run the same build on both Arm and x86 machines?
-    answer: >-
-      Use .NET’s AnyCPU configuration to produce an architecture-agnostic build. The path shows
-      how to configure and run the OrchardCore app so it can execute on Arm-based cloud VMs as
-      well as x86 systems.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Joe Stech
 
diff --git a/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
index 4e565c064c..c6312b3a01 100644
--- a/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
@@ -17,61 +17,9 @@ prerequisites:
   - Basic knowledge of Linux command-line operations
   - Familiarity with SSH and remote server access
   - Basic understanding of cloud infrastructure and monitoring concepts
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:44:58Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 4ef29931bf19dc95bb586440796381725c271ef1b953dcf46d00ad9617eabbb1
-  summary_generated_at: '2026-06-02T03:39:49Z'
-  summary_source_hash: 4ef29931bf19dc95bb586440796381725c271ef1b953dcf46d00ad9617eabbb1
-  faq_generated_at: '2026-06-03T00:44:58Z'
-  faq_source_hash: 4ef29931bf19dc95bb586440796381725c271ef1b953dcf46d00ad9617eabbb1
-  summary: >-
-    This Learning Path shows how to monitor Azure Cobalt 100 Arm64 virtual machines with Dynatrace.
-    You will create an Azure VM in the Dpsv6 series, install Dynatrace OneAgent on Ubuntu 24.04
-    LTS Arm64, and configure Dynatrace ActiveGate as a secure gateway to the Dynatrace SaaS platform.
-    You will open TCP port 9999 in the Azure Network Security Group to allow ActiveGate traffic,
-    then verify host and application visibility by monitoring system resources, processes, and
-    services, and validating with a sample NGINX workload. Prerequisites include an Azure account
-    with access to Cobalt 100 instances, basic Linux command-line skills, SSH familiarity, and
-    a basic understanding of cloud and monitoring concepts.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Microsoft Azure account with access to Cobalt 100 based instances (Dpsv6), basic
-      Linux command-line skills, familiarity with SSH, and a basic understanding of cloud infrastructure
-      and monitoring concepts. The path connects to a Dynatrace SaaS environment, but specific
-      account details are not explicitly listed.
-  - question: Which Azure VM type and operating system should I use?
-    answer: >-
-      Use a general-purpose VM in the Dpsv6 series running on Azure Cobalt 100 processors. The
-      installation steps target Ubuntu 24.04 LTS Arm64.
-  - question: How do I allow Dynatrace ActiveGate traffic to the VM?
-    answer: >-
-      Create a Network Security Group rule in the Azure Portal to allow inbound TCP traffic on
-      port 9999. Apply the rule to the NSG attached to the VM’s network interface or subnet.
-  - question: How do I know if OneAgent and ActiveGate are installed correctly?
-    answer: >-
-      After installation, OneAgent runs as a host monitoring agent, connects to your Dynatrace
-      SaaS environment, and begins monitoring system processes and services automatically. ActiveGate
-      runs as a system service, listens on port 9999, and communicates with Dynatrace.
-  - question: What result should I expect when validating with the sample NGINX workload?
-    answer: >-
-      You should see NGINX detected in Dynatrace with process and service monitoring data from
-      the Arm64 VM. This confirms that application monitoring is functioning through OneAgent
-      and, if configured, via ActiveGate.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/ecs/_index.md b/content/learning-paths/servers-and-cloud-computing/ecs/_index.md
index 0af006bfd9..01838b32ab 100644
--- a/content/learning-paths/servers-and-cloud-computing/ecs/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ecs/_index.md
@@ -14,57 +14,9 @@ learning_objectives:
 prerequisites:
     - An AWS account
     - A computer with Docker, AWS CLI, and Terraform installed
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:45:33Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: ef5f9e7c8844b20b9044b43f4758bc1d74374521093d7738a7f8832d21f1dcac
-  summary_generated_at: '2026-06-02T03:40:50Z'
-  summary_source_hash: ef5f9e7c8844b20b9044b43f4758bc1d74374521093d7738a7f8832d21f1dcac
-  faq_generated_at: '2026-06-03T00:45:33Z'
-  faq_source_hash: ef5f9e7c8844b20b9044b43f4758bc1d74374521093d7738a7f8832d21f1dcac
-  summary: >-
-    Learn to deploy containerized applications on Amazon Elastic Container Service (ECS) using
-    Fargate with AWS Graviton processors. You will create an ECS cluster, configure required identity
-    settings, and run a container task on Arm-based infrastructure. The path also shows how to
-    automate the same workflow with Terraform by incrementally building a main.tf file, including
-    creating an Amazon ECR repository and deploying an example Nginx service. This introductory,
-    Linux-focused path targets developers new to ECS on Graviton. Prerequisites are an AWS account
-    and a computer with Docker, AWS CLI, and Terraform installed. By the end, you will have a
-    running ECS task on Fargate and a Terraform configuration that reproduces the deployment.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an AWS account and a computer with Docker, AWS CLI, and Terraform installed. The
-      path targets Linux.
-  - question: Do I need to manage EC2 instances for this deployment?
-    answer: >-
-      No. The path uses the Fargate launch type, which is serverless, so you do not provision
-      or maintain EC2 instances.
-  - question: Which architecture should my container image target to run on AWS Graviton?
-    answer: >-
-      Build your container image for the Arm architecture. Fargate supports AWS Graviton processors
-      so your containers can run on Arm.
-  - question: Where will I store and pull my container images in this workflow?
-    answer: >-
-      The path creates a repository in Amazon Elastic Container Registry (ECR). The Terraform
-      section builds a main.tf that sets up ECR and uses it for the ECS deployment.
-  - question: What result should I expect after completing the Terraform section?
-    answer: >-
-      You will have a main.tf that automates the same steps for deploying Nginx on ECS. This includes
-      provisioning the required ECS resources and using an ECR repository for the container image.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md b/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
index 286c79183b..5768ddb4db 100644
--- a/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
@@ -15,57 +15,9 @@ prerequisites:
     - An [AWS account](https://aws.amazon.com/). Create an account if needed.
     - A computer with [Amazon eksctl CLI](/install-guides/eksctl) and [kubectl](/install-guides/kubectl/)installed.
     - Docker installed on local computer [Docker](/install-guides/docker)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:46:43Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: e32bdb090c422d1fb5bb1f9bd3af56c55fdf8989e6a7fe6a101e90dcb6f3eadd
-  summary_generated_at: '2026-06-02T03:43:01Z'
-  summary_source_hash: e32bdb090c422d1fb5bb1f9bd3af56c55fdf8989e6a7fe6a101e90dcb6f3eadd
-  faq_generated_at: '2026-06-03T00:46:43Z'
-  faq_source_hash: e32bdb090c422d1fb5bb1f9bd3af56c55fdf8989e6a7fe6a101e90dcb6f3eadd
-  summary: >-
-    This Learning Path shows how to build and deploy a multi-architecture container application
-    for x86/amd64 and arm64 on Amazon EKS using docker buildx and docker manifest. You will create
-    a hybrid EKS cluster with both x86 and Arm-based (Graviton) nodes, then build images for each
-    architecture and understand the key nuances of multi-arch container builds. The environment
-    assumes Linux, and you need an AWS account plus eksctl, kubectl, and Docker installed locally.
-    By the end, you will have deployed a multi-arch application to a single EKS cluster that can
-    run across both architectures. The topic is advanced and is designed for developers targeting
-    multi-arch Kubernetes on AWS.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an AWS account and a Linux machine with eksctl, kubectl, and Docker installed.
-      No other prerequisites are explicitly listed.
-  - question: Which tools are used to build multi-architecture images, and where do I run them?
-    answer: >-
-      You will use docker buildx and docker manifest. These are run with your local Docker installation.
-  - question: How is the Amazon EKS cluster set up for multiple architectures?
-    answer: >-
-      You create a hybrid EKS cluster that includes both x86/amd64 and Arm-based (Graviton) nodes.
-      This lets you run workloads across both architectures in a single cluster.
-  - question: What result should I expect after deployment?
-    answer: >-
-      A multi-architecture container application runs on a single Amazon EKS cluster that supports
-      both arm64 and amd64. The image you build is suitable for both architectures using a multi-arch
-      manifest.
-  - question: What should I check if the application only runs on one node type?
-    answer: >-
-      Confirm that you built images for both amd64 and arm64 and that your docker manifest includes
-      both. Also verify your EKS cluster has both x86 and Arm-based (Graviton) nodes available.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pranay Bakre
 
diff --git a/content/learning-paths/servers-and-cloud-computing/eks/_index.md b/content/learning-paths/servers-and-cloud-computing/eks/_index.md
index dfc3ad5b43..0cb5f3d3e9 100644
--- a/content/learning-paths/servers-and-cloud-computing/eks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/eks/_index.md
@@ -13,60 +13,9 @@ learning_objectives:
 
 prerequisites:
     - An Amazon Web Services (AWS) [account](https://aws.amazon.com/)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:45:59Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 4f1c448eef66300e024bda27c420f9746047c0c4b76e8556d3d8693382206055
-  summary_generated_at: '2026-06-02T03:42:15Z'
-  summary_source_hash: 4f1c448eef66300e024bda27c420f9746047c0c4b76e8556d3d8693382206055
-  faq_generated_at: '2026-06-03T00:45:59Z'
-  faq_source_hash: 4f1c448eef66300e024bda27c420f9746047c0c4b76e8556d3d8693382206055
-  summary: >-
-    Provision an Amazon EKS cluster on Arm-based Graviton instances and deploy a WordPress application
-    with a MySQL database. Working from a machine with the AWS CLI, EKS CLI, and Kubernetes CLI
-    installed, you will configure AWS credentials, create the cluster, and use three Kubernetes
-    YAML files (kustomization.yaml, mysql-deployment.yaml, and wordpress-deployment.yaml) to deploy
-    the application with kubectl. The path is introductory and aimed at developers new to Kubernetes
-    on AWS. It focuses on practical setup and deployment steps, including setting a MySQL password
-    via Kustomize. An AWS account is required; no other explicit prerequisites are listed. Estimated
-    time to complete is about 60 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an AWS account and must configure your AWS access key ID and secret access key.
-      Install the EKS CLI, AWS CLI, and Kubernetes CLI, and confirm you can run the aws, ekscli,
-      and kubectl commands.
-  - question: Which machine can I use to run the setup?
-    answer: >-
-      Any computer with the required tools installed can be used. The operating system listed
-      for this path is Linux.
-  - question: How do I create an EKS cluster on Arm-based instances?
-    answer: >-
-      Follow the Create an EKS cluster step to provision an Amazon EKS cluster on Arm-based Graviton
-      instances. You will use the EKS CLI together with the AWS CLI during this step.
-  - question: Which files are required to deploy WordPress and where do I set the MySQL password?
-    answer: >-
-      You need kustomization.yaml, mysql-deployment.yaml, and wordpress-deployment.yaml. In kustomization.yaml,
-      the secretGenerator named mysql-pass sets the database password using a literal such as
-      password=YourPassword.
-  - question: How do I apply the deployment and know it targets my EKS cluster?
-    answer: >-
-      Use kubectl with the kustomization.yaml that references the MySQL and WordPress resources.
-      Ensure kubectl is configured to communicate with your newly created EKS cluster before applying
-      the files.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md
index 1c2c0dad52..ddaa14a4ee 100644
--- a/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md
@@ -16,61 +16,9 @@ learning_objectives:
 prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free?utm_source=google&hl=en) account with billing enabled
   - Familiarity with networking concepts and the [Envoy architecture](https://www.envoyproxy.io/docs/envoy/latest/)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:48:02Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: f36b1e9a45b6ec29d8041b70997550d917322cf9cdd456db26083169d21175ed
-  summary_generated_at: '2026-06-02T03:44:25Z'
-  summary_source_hash: f36b1e9a45b6ec29d8041b70997550d917322cf9cdd456db26083169d21175ed
-  faq_generated_at: '2026-06-03T00:48:02Z'
-  faq_source_hash: f36b1e9a45b6ec29d8041b70997550d917322cf9cdd456db26083169d21175ed
-  summary: >-
-    This Learning Path shows how to deploy Envoy Proxy on Google Cloud Axion C4A Arm64 virtual
-    machines built on Arm Neoverse V2 cores, then validate and benchmark it. You will provision
-    a c4a-standard-4 instance (4 vCPUs, 16 GB) in the Google Cloud Console, install Envoy v1.30.0
-    on RHEL 9 using the official static Arm64 binary, and run a minimal configuration that forwards
-    traffic to httpbin.org to verify a 200 OK response on port 10000. You will also build and
-    use Siege to generate HTTP load and record availability, throughput, response time, and failure
-    rates, comparing results on Arm64 (AArch64) and x86_64. Prerequisites include a GCP account
-    with billing enabled and familiarity with networking and Envoy architecture.
-  faqs:
-  - question: What do I need before provisioning the C4A VM on GCP?
-    answer: >-
-      You need a Google Cloud Platform account with billing enabled, plus familiarity with networking
-      concepts and the Envoy architecture. For general GCP setup assistance, see the Learning
-      Path Getting started with Google Cloud Platform.
-  - question: Which C4A machine type is used, and where do I create it?
-    answer: >-
-      The path uses c4a-standard-4 (4 vCPUs, 16 GB memory). Create it in the Google Cloud Console
-      under Compute Engine > VM instances by selecting Create instance and choosing the C4A machine
-      type.
-  - question: What Envoy build is installed on the C4A instance?
-    answer: >-
-      Envoy Proxy v1.30.0 is installed on RHEL 9 using the official static Arm64 (AArch64) binary.
-      You install required dependencies and then download the binary with curl to /usr/local/bin/envoy.
-  - question: How do I validate Envoy after installation, and what result should I expect?
-    answer: >-
-      Create a minimal Envoy configuration, start Envoy with it, and issue a request using curl.
-      Envoy should listen on port 10000, forward requests to httpbin.org, and return a 200 OK
-      response.
-  - question: How do I run the benchmarks and what metrics does Siege report?
-    answer: >-
-      Build Siege from source after installing Development Tools, then run load tests against
-      Envoy. Siege reports availability, throughput, response time, and failure rates; repeat
-      the same procedure on Arm64 and x86_64 to compare results.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/envoy/_index.md b/content/learning-paths/servers-and-cloud-computing/envoy/_index.md
index c7d2d07a2a..9390450b94 100644
--- a/content/learning-paths/servers-and-cloud-computing/envoy/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/envoy/_index.md
@@ -14,57 +14,9 @@ learning_objectives:
 prerequisites:
     - To run Envoy as a web server, you will need at least one [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an on-premises Arm server.
     - Network settings (firewalls and security groups) which allow communication on port 22 (SSH) and port 80 (HTTP).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:47:28Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: d492b6dce11b7d8f6591ff3b3ce9aa2c382a6a7a749add228c3ac1f6ae57e218
-  summary_generated_at: '2026-06-02T03:43:48Z'
-  summary_source_hash: d492b6dce11b7d8f6591ff3b3ce9aa2c382a6a7a749add228c3ac1f6ae57e218
-  faq_generated_at: '2026-06-03T00:47:28Z'
-  faq_source_hash: d492b6dce11b7d8f6591ff3b3ce9aa2c382a6a7a749add228c3ac1f6ae57e218
-  summary: >-
-    This Learning Path shows how to build, install, and run Envoy on Arm-based Linux servers and
-    configure it as a basic web server for traffic management. You will provision an Arm instance
-    in the cloud (AWS, Microsoft Azure, Google Cloud, or Oracle) or use an on-premises Arm server,
-    ensure network access on SSH (22) and HTTP (80), and then set up Envoy with a sample configuration
-    to run it as a service. The steps focus on practical setup and conclude with checks to verify
-    Envoy is working correctly. Aimed at an introductory audience, the path is designed to be
-    completed in about 60 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need at least one Arm-based instance from a cloud service provider or an on-premises
-      Arm server. Ensure your network settings (firewalls and security groups) allow communication
-      on port 22 (SSH) and port 80 (HTTP).
-  - question: Which platforms can I use for the Arm-based instance?
-    answer: >-
-      You can use Arm-based instances from AWS, Microsoft Azure, Google Cloud, or Oracle. An on-premises
-      Arm server also works for this Learning Path.
-  - question: Which operating system do the steps target?
-    answer: >-
-      The steps target Linux. Ensure your Arm instance is running a Linux distribution.
-  - question: How do I run Envoy as a service in this path?
-    answer: >-
-      You will create a sample configuration file at configs/config-http.yaml and use it to start
-      Envoy. The sample config defines a listener on port 80 with an HTTP connection manager.
-  - question: What should I check if I cannot reach the Envoy web server?
-    answer: >-
-      Verify that your security groups and firewalls allow inbound traffic on port 80 and that
-      SSH access on port 22 is permitted for management. Also confirm that Envoy is running with
-      the provided configuration file.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Zhengjun Xing
 
diff --git a/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md
index 8469d8e4eb..ceb4c9fd69 100644
--- a/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md
@@ -15,57 +15,9 @@ learning_objectives:
 prerequisites:
     - Cloud or bare-metal installation of an Envoy service
     - Review [Learn how to deploy Envoy](/learning-paths/servers-and-cloud-computing/envoy/) if you do not already have an Envoy setup
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:48:49Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: cbbcc8fa33f864e422f8db7d58fba32c26f6070be2846b246837c63ccf37e1c7
-  summary_generated_at: '2026-06-02T03:45:17Z'
-  summary_source_hash: cbbcc8fa33f864e422f8db7d58fba32c26f6070be2846b246837c63ccf37e1c7
-  faq_generated_at: '2026-06-03T00:48:49Z'
-  faq_source_hash: cbbcc8fa33f864e422f8db7d58fba32c26f6070be2846b246837c63ccf37e1c7
-  summary: >-
-    Learn how to tune Envoy on Arm servers running Linux—on bare metal or Arm instances from AWS,
-    Microsoft Azure, Google Cloud, or Oracle—using Transparent Huge Pages (THP) and Profile-Guided
-    Optimization (PGO). You will review kernel parameters that affect Envoy, check THP configuration
-    (with an Ubuntu example), and rebuild Envoy with Bazel and LLVM/Clang to apply PGO, using
-    the latest compiler and a recent Bazel as recommended. This advanced path expects an existing
-    Envoy service; if you do not have one, follow the Deploy Envoy Learning Path first. By the
-    end, you will have applied THP settings and produced a PGO-built Envoy binary.
-  faqs:
-  - question: What do I need before running these tuning steps?
-    answer: >-
-      You need a cloud or bare-metal installation of an Envoy service. If you do not already have
-      Envoy set up, review Learn how to deploy Envoy.
-  - question: Which environments does this Learning Path target?
-    answer: >-
-      Linux on Arm servers, including Arm Neoverse in the cloud (AWS, Microsoft Azure, Google
-      Cloud, Oracle) or on bare metal. The guidance is for developers running Envoy on Arm.
-  - question: How do I check my Linux kernel configuration for THP on Ubuntu?
-    answer: >-
-      Run: cat /boot/config-$(uname -r) to inspect your kernel configuration. Use this to verify
-      settings relevant to Transparent Huge Pages.
-  - question: Which toolchain should I use to build Envoy with PGO?
-    answer: >-
-      Build Envoy using Bazel and LLVM/Clang, and use the latest compiler version. It is advisable
-      to build Bazel from the most recent source; refer to the LLVM and Clang documentation for
-      details.
-  - question: What performance improvement should I expect from THP or PGO?
-    answer: >-
-      The Learning Path notes that applying THP can result in an 18% enhancement in performance,
-      and PGO can result in a 10% enhancement. These figures are presented as general guidance.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Zhengjun Xing
 
diff --git a/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md b/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md
index 2cb5d15260..127d033f8d 100644
--- a/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md
@@ -18,59 +18,9 @@ prerequisites:
     - Some familiarity with reading and writing basic C code and AArch64 assembly code.
     - Some familiarity with running linux command line commands.
     - Some familiarity with using a gdb debugger.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:49:39Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 02eedcebf59a8ab506d4ebbf5bbe20ead367cf3c0700950db5a9059d2f671853
-  summary_generated_at: '2026-06-02T03:46:23Z'
-  summary_source_hash: 02eedcebf59a8ab506d4ebbf5bbe20ead367cf3c0700950db5a9059d2f671853
-  faq_generated_at: '2026-06-03T00:49:39Z'
-  faq_source_hash: 02eedcebf59a8ab506d4ebbf5bbe20ead367cf3c0700950db5a9059d2f671853
-  summary: >-
-    This advanced Learning Path shows how stack buffer overflow exploits work on AArch64 Linux
-    by building and analyzing small, controlled examples. You will create a Docker-based lab on
-    an Arm machine using an Ubuntu 22.04 image that installs Clang and GDB and disables ASLR for
-    repeatable experiments. Working through C programs, you will inspect compiler-generated stack
-    frame layouts, see how an out-of-bounds write can overwrite a saved return address, and construct
-    a minimal end-to-end exploit that redirects control flow to an attacker-chosen value without
-    crashing. Prerequisites include an Arm computer running Linux with Docker installed, plus
-    familiarity with C, AArch64 assembly, the Linux command line, and GDB.
-  faqs:
-  - question: What do I need before running the exercises?
-    answer: >-
-      You need an Arm computer running Linux with Docker installed. You should be comfortable
-      with basic C and AArch64 assembly, Linux command line commands, and using gdb.
-  - question: Why does the Dockerfile disable ASLR, and what happens if I skip that step?
-    answer: >-
-      ASLR is an on-by-default mitigation that would block some of the experiments in this path.
-      The Dockerfile sets kernel.randomize_va_space to 0 so you can reproduce the stack layouts
-      and control-flow redirection reliably.
-  - question: Where should I save the example source files when using Docker?
-    answer: >-
-      Save the files in the directory where you run docker run. You can create or edit these files
-      outside the container.
-  - question: Which tools will I use inside the container to build and inspect the examples?
-    answer: >-
-      The container installs Clang and gdb. You will compile the provided C programs with Clang
-      and may use gdb as part of inspecting behavior during the steps.
-  - question: How do I know if the control-flow redirection worked?
-    answer: >-
-      The goal is to modify program behavior without causing a crash by overwriting the saved
-      return address with a chosen value. You should see the program’s behavior change as described
-      in the steps rather than terminating with a fault.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Kristof Beyls
 
diff --git a/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md b/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
index fa3323b42b..e26a1f59e1 100644
--- a/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
@@ -15,60 +15,9 @@ prerequisites:
     - Access to an Arm-based cloud instance with support for the Arm Statistical Profiling Extension (SPE).
     - A basic understanding of cache coherency and its impact on performance.
     - Familiarity with Linux Perf tools.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:50:12Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: dde32f5d14a877313a8b0aafec58acb09cd1e77f4fc9138b9e1bd6d9fedf250a
-  summary_generated_at: '2026-06-02T03:47:24Z'
-  summary_source_hash: dde32f5d14a877313a8b0aafec58acb09cd1e77f4fc9138b9e1bd6d9fedf250a
-  faq_generated_at: '2026-06-03T00:50:12Z'
-  faq_source_hash: dde32f5d14a877313a8b0aafec58acb09cd1e77f4fc9138b9e1bd6d9fedf250a
-  summary: >-
-    Learn how to detect and address false sharing on Arm-based cloud systems using Linux perf
-    C2C and the Arm Statistical Profiling Extension (SPE). You will set up a Linux environment
-    on an Arm Neoverse-based instance with SPE support, verify kernel and tool access to the required
-    performance events, and compile a multithreaded C example that contrasts cache-aligned and
-    unaligned data. Using perf stat and perf c2c, you will compare the two builds, investigate
-    cache line behavior, and trace memory contention to source lines. Prerequisites include access
-    to an Arm-based cloud instance with SPE, a basic understanding of cache coherency, and familiarity
-    with Linux perf tools. No additional prerequisites are explicitly listed.
-  faqs:
-  - question: How do I know if my cloud instance supports Arm SPE?
-    answer: >-
-      Follow the setup steps to check both hardware and kernel support for SPE and to validate
-      that Linux perf can access the required events. Choose an Arm-based instance that exposes
-      SPE to the OS.
-  - question: Which cloud platforms can I use for this path?
-    answer: >-
-      You can use an Arm-based instance on AWS, Microsoft Azure, Google Cloud, or Oracle, as long
-      as the instance supports Arm SPE.
-  - question: Which perf commands will I use during the analysis?
-    answer: >-
-      You will use perf stat to compare the runtime and metrics of aligned and unaligned binaries,
-      and perf c2c to record and analyze cache line behavior and memory contention.
-  - question: What result should I expect from the false sharing example?
-    answer: >-
-      After compiling and running both versions, expect a runtime difference and c2c analysis
-      that highlights cache line contention in the unaligned case. The steps show how to relate
-      those findings back to the source code.
-  - question: What should I check if perf c2c does not show the expected events?
-    answer: >-
-      Verify that SPE is enabled and supported by your hardware and kernel, that the perf tools
-      are installed, and that perf can access the necessary performance monitoring events as described
-      in the setup section.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md b/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md
index 88cadb4d67..ee66cc6b23 100644
--- a/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md
@@ -15,61 +15,9 @@ learning_objectives:
 prerequisites:
     - An AWS account with permissions to create EC2 instances
     - Familiarity with basic Linux administration and SSH
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:50:51Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 978d3da8668e38758c138313c31809f3de5a8cefd1b7c2b47a536c0ee364b692
-  summary_generated_at: '2026-06-02T03:48:10Z'
-  summary_source_hash: 978d3da8668e38758c138313c31809f3de5a8cefd1b7c2b47a536c0ee364b692
-  faq_generated_at: '2026-06-03T00:50:51Z'
-  faq_source_hash: 978d3da8668e38758c138313c31809f3de5a8cefd1b7c2b47a536c0ee364b692
-  summary: >-
-    This advanced Learning Path guides you through building custom Linux kernels with tuxmake,
-    provisioning Arm-based AWS EC2 instances, and benchmarking multiple kernel versions using
-    Fastpath. You will set up three machines: a CPU-optimized kernel build host, a Fastpath host
-    on Ubuntu 24.04 LTS to orchestrate testing, and a System Under Test (SUT) on Ubuntu 24.04
-    LTS to run workloads. You will generate a YAML benchmark plan (plan.yaml), execute benchmarks,
-    and analyze collected results to compare kernel performance across versions. Prerequisites
-    include an AWS account with permissions to create EC2 instances and familiarity with basic
-    Linux administration and SSH. The estimated time to complete is about 90 minutes.
-  faqs:
-  - question: What do I need before provisioning the EC2 instances?
-    answer: >-
-      You need an AWS account with permissions to create EC2 instances. The path assumes familiarity
-      with basic Linux administration and SSH.
-  - question: Which EC2 instance types and images are used for each role?
-    answer: >-
-      The example build host and SUT use AWS Graviton m6g.12xlarge instances. The Fastpath host
-      is a separate EC2 instance using the Ubuntu 24.04 LTS (Arm) AMI, and the SUT also runs Ubuntu
-      24.04 LTS; other instance details beyond these examples are not explicitly listed.
-  - question: Can I use the AWS Management Console or the AWS CLI to create the instances?
-    answer: >-
-      You can use either the AWS Management Console or the AWS CLI to perform the EC2 instance
-      creation steps. The Learning Path supports both approaches.
-  - question: Where are kernels built and which tools are used?
-    answer: >-
-      Kernels are built on the kernel build host using tuxmake. The Learning Path then prepares
-      those kernels for testing with Fastpath.
-  - question: How do I generate and run the Fastpath benchmark plan, and what should I expect?
-    answer: >-
-      You use a provided helper script to generate a YAML plan (plan.yaml) that defines the SUT,
-      kernels to deploy, and workloads to run. Executing the plan on the Fastpath host installs
-      each kernel on the SUT, runs benchmarks, collects results, and enables you to compare performance
-      across kernel versions; connectivity between the Fastpath host and SUT is validated during
-      setup.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Geremy Cohen
 
diff --git a/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md b/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md
index 75f4ee49fb..0626facbc3 100644
--- a/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md
@@ -14,59 +14,9 @@ learning_objectives:
 prerequisites:
     - Access to an [AWS Graviton4, Google Axion, or Azure Cobalt 100 virtual machine from a cloud service provider](/learning-paths/servers-and-cloud-computing/csp/)
     - Some familiarity with SIMD programming and SVE intrinsics
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:51:30Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: a67c552b76df6323eccc331c9146d0fcbdb8ad222fe81dbf2e1c2339c7609b61
-  summary_generated_at: '2026-06-02T03:49:04Z'
-  summary_source_hash: a67c552b76df6323eccc331c9146d0fcbdb8ad222fe81dbf2e1c2339c7609b61
-  faq_generated_at: '2026-06-03T00:51:30Z'
-  faq_source_hash: a67c552b76df6323eccc331c9146d0fcbdb8ad222fe81dbf2e1c2339c7609b61
-  summary: >-
-    Learn how to implement the exponential function on Arm Neoverse processors using SVE intrinsics
-    and then refine it with the FEXPA instruction. You will review range reduction and polynomial
-    approximation trade-offs, write a C implementation with SVE intrinsics, and build it with
-    gcc on a cloud VM. The path lists Linux and macOS, shows installing gcc on Linux, and was
-    tested on an AWS Graviton4 r8g.medium instance. You will apply FEXPA to reduce the polynomial
-    degree needed for a target precision, with SME support noted for integrating the approximation
-    into matrix computation paths. Prerequisites include access to an AWS Graviton4, Google Axion,
-    or Azure Cobalt 100 VM and some familiarity with SIMD programming and SVE intrinsics.
-  faqs:
-  - question: What do I need before running the example?
-    answer: >-
-      You need access to an AWS Graviton4, Google Axion, or Azure Cobalt 100 virtual machine,
-      plus some familiarity with SIMD programming and SVE intrinsics. The path uses gcc on Linux
-      or macOS.
-  - question: Which instance type should I pick, and what was used to validate the steps?
-    answer: >-
-      You can use an Arm-based VM from AWS Graviton4, Google Axion, or Azure Cobalt 100. The steps
-      were tested on an AWS Graviton4 r8g.medium instance.
-  - question: How do I set up the build environment and source file?
-    answer: >-
-      Install gcc; the steps show using apt on Linux to install it. Then create the exp_sve.c
-      file with the provided SVE-based implementation.
-  - question: What changes when I enable FEXPA compared to the initial SVE implementation?
-    answer: >-
-      You begin with a polynomial approximation using SVE intrinsics, then apply FEXPA for hardware-accelerated
-      exponential computation. With FEXPA, the approximation can reach a specified target precision
-      using a lower-degree polynomial than alternative implementations.
-  - question: I’m on macOS—what should I do if the Linux package commands don’t work?
-    answer: >-
-      macOS is listed as supported, but the explicit setup commands use Linux’s apt. Use a C compiler
-      available on macOS; the path does not provide macOS-specific install commands.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: 
 - Arnaud Grasset
diff --git a/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md
index 653829b889..08cbd4e14f 100644
--- a/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md
@@ -15,61 +15,9 @@ learning_objectives:
 prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with [Apache Flink](https://flink.apache.org/) and its runtime environment
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:53:07Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: adcf2a1b8a4a77e5834e14a40e46e27b0cbe5e440fbf732a4366ce486d7fafb7
-  summary_generated_at: '2026-06-02T03:51:50Z'
-  summary_source_hash: adcf2a1b8a4a77e5834e14a40e46e27b0cbe5e440fbf732a4366ce486d7fafb7
-  faq_generated_at: '2026-06-03T00:53:07Z'
-  faq_source_hash: adcf2a1b8a4a77e5834e14a40e46e27b0cbe5e440fbf732a4366ce486d7fafb7
-  summary: >-
-    Learn how to deploy Apache Flink on Google Cloud C4A virtual machines powered by Axion processors
-    (Arm Neoverse-V2) using a SUSE Linux Arm64 environment. You will provision a c4a-standard-4
-    VM through the Google Cloud Console, install Java 17 and Flink, and validate your setup by
-    starting the Flink cluster and running a baseline job. The path then guides you to install
-    Maven and benchmark Flink using the official JMH-based flink-benchmarks suite, including the
-    Remote Channel Throughput Benchmark. By the end, you will have a working Flink environment
-    on Arm-based Google Cloud infrastructure and baseline microbenchmark results. Prerequisites
-    are a GCP account with billing enabled and basic familiarity with Flink.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      You need a Google Cloud Platform account with billing enabled and basic familiarity with
-      Apache Flink and its runtime. Sudo access on the VM is implied because the steps install
-      packages and place files under system directories.
-  - question: Which Google Cloud VM and OS should I create for the exercises?
-    answer: >-
-      Create an Axion C4A Arm instance, using the c4a-standard-4 machine type in the Google Cloud
-      Console under Compute Engine > VM Instances. The steps assume a SUSE SLES Arm64 virtual
-      machine.
-  - question: Which Java version is required on the VM?
-    answer: >-
-      Install Java 17 (OpenJDK) along with the development package on the SUSE system. The steps
-      use zypper to install java-17-openjdk and java-17-openjdk-devel.
-  - question: Where should I install Flink and how do I confirm it works?
-    answer: >-
-      The path downloads and installs the official Flink distribution under /opt on the VM. You
-      will validate the installation by starting the Flink cluster and running a baseline job
-      to confirm the JobManager and TaskManager execute successfully.
-  - question: Which benchmarks will I run and how are they executed?
-    answer: >-
-      You will clone the official apache/flink-benchmarks repository, build it with Maven, and
-      run JMH-based microbenchmarks. The steps demonstrate running the Remote Channel Throughput
-      Benchmark to assess Flink performance on the C4A instance.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/flink/_index.md b/content/learning-paths/servers-and-cloud-computing/flink/_index.md
index d6d3647570..5f9700d833 100644
--- a/content/learning-paths/servers-and-cloud-computing/flink/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/flink/_index.md
@@ -13,59 +13,9 @@ learning_objectives:
 
 prerequisites:
 - An Arm based instance server from a cloud service provider.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:52:15Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 974d71b1ee968e3aeabe900bfdd52ae4fbfdd0ca7dea420e6c1fc01f5475e8c1
-  summary_generated_at: '2026-06-02T03:50:28Z'
-  summary_source_hash: 974d71b1ee968e3aeabe900bfdd52ae4fbfdd0ca7dea420e6c1fc01f5475e8c1
-  faq_generated_at: '2026-06-03T00:52:15Z'
-  faq_source_hash: 974d71b1ee968e3aeabe900bfdd52ae4fbfdd0ca7dea420e6c1fc01f5475e8c1
-  summary: >-
-    This Learning Path shows how to install and run Apache Flink on an Arm-based Linux server
-    and benchmark its stream processing performance using the Nexmark suite. You will set up Java,
-    configure a Flink Standalone Cluster, prepare the Nexmark environment (including Maven and
-    SSH), and execute benchmark queries. The steps use common Linux tooling and Flink/Nexmark
-    scripts to start the cluster, set up the benchmark, and run queries, with optional additional
-    query runs. The target audience is developers using Flink on Arm servers. A prerequisite is
-    an Arm-based instance from a cloud service provider. The estimated time to complete is about
-    30 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      Provision an Arm-based Linux instance from a cloud provider such as AWS, Microsoft Azure,
-      Google Cloud, or Oracle. You will also need Java installed because Flink runs on the JVM.
-  - question: Which Java version should I install for this setup?
-    answer: >-
-      Install a JDK 11, using either Oracle JDK or OpenJDK. Nexmark requires JDK 1.8+ tools, so
-      JDK 11 satisfies both Flink and Nexmark needs.
-  - question: What are the Nexmark setup requirements I must have in place?
-    answer: >-
-      You need a Flink standalone cluster, JDK 1.8.x or higher, and ssh with sshd running for
-      the scripts that manage remote components. Maven must be installed, and any required environment
-      variables for the scripts should be configured.
-  - question: Where do I run the commands to start Flink and the benchmark?
-    answer: >-
-      Run them on the master node. Use these scripts in order: ~/flink-benchmark/flink-1.17.2/bin/start-cluster.sh,
-      then ~/flink-benchmark/nexmark-flink/bin/setup_cluster.sh, and finally ~/flink-benchmark/nexmark-flink/bin/run_query.sh.
-  - question: What should I check if the Nexmark scripts fail to start components?
-    answer: >-
-      Verify that sshd is running, Java is installed and available, and Maven is installed. Ensure
-      the Flink standalone cluster is set up and any required environment variables for the scripts
-      are defined.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Ying Yu
 
diff --git a/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md b/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md
index 42bcfcebf6..3deabe8071 100644
--- a/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md
@@ -17,61 +17,9 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with Python
   - Basic understanding of machine learning pipelines
-
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:53:45Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 85359b9210812675169f149c86bf11a1736ab838c011d18e876b246463d297ee
-  summary_generated_at: '2026-06-02T03:53:08Z'
-  summary_source_hash: 85359b9210812675169f149c86bf11a1736ab838c011d18e876b246463d297ee
-  faq_generated_at: '2026-06-03T00:53:45Z'
-  faq_source_hash: 85359b9210812675169f149c86bf11a1736ab838c011d18e876b246463d297ee
-  summary: >-
-    This Learning Path shows how to build and run an introductory machine learning workflow on
-    Arm-based Google Cloud C4A Axion processors using Flyte for orchestration and gRPC for distributed
-    service communication. You will provision a c4a-standard-4 Arm64 VM in Google Cloud, prepare
-    a SUSE Linux Enterprise Server (SLES) development environment, install Flyte and gRPC tools,
-    implement a gRPC feature engineering service, and create a Flyte workflow that loads data,
-    preprocesses it, generates features via the service, trains a model, and evaluates results.
-    It targets Linux on Arm infrastructure and takes about 30 minutes. Prerequisites include a
-    GCP account with billing enabled, plus basic Python and ML pipeline familiarity.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      You need a Google Cloud Platform account with billing enabled, basic familiarity with Python,
-      and a basic understanding of machine learning pipelines. No other prerequisites are explicitly
-      listed.
-  - question: Which Google Cloud VM type should I create for the exercises?
-    answer: >-
-      Use the c4a-standard-4 machine type on Google Axion C4A, which provides 4 vCPUs and 16 GB
-      of memory. This VM hosts the Flyte ML workflow and gRPC applications.
-  - question: Which operating system and architecture are used on the VM?
-    answer: >-
-      The development environment uses a SUSE Linux Enterprise Server (SLES) arm64 virtual machine.
-      The tools run natively on the Arm-based Axion C4A processors.
-  - question: How does the Flyte workflow interact with the gRPC feature engineering service?
-    answer: >-
-      The Flyte workflow calls the external gRPC-based feature engineering service during execution
-      to generate features used by downstream tasks. This integrates distributed services directly
-      into the pipeline.
-  - question: What result should I expect after running the workflow?
-    answer: >-
-      The pipeline loads a dataset, preprocesses it, generates features via the gRPC service,
-      trains a machine learning model, and evaluates the model’s performance. You will have a
-      working example of a Flyte-orchestrated ML workflow running on Axion C4A.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
index 72496fa1e2..dd2abeae82 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
@@ -20,59 +20,9 @@ prerequisites:
     - 'Docker Extension for Visual Studio Code: https://code.visualstudio.com/docs/containers/overview'
     - 'C# Extension for Visual Studio Code: https://marketplace.visualstudio.com/items?itemName=ms-dotnettools.csharp'
     - '[Install Docker on Arm64](/install-guides/docker/docker-desktop/)'
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:54:23Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 94866800acca2c5f9cd89f76f972af1a343aad5777b6844d49fac09eb764f580
-  summary_generated_at: '2026-06-02T03:53:58Z'
-  summary_source_hash: 94866800acca2c5f9cd89f76f972af1a343aad5777b6844d49fac09eb764f580
-  faq_generated_at: '2026-06-03T00:54:23Z'
-  faq_source_hash: 94866800acca2c5f9cd89f76f972af1a343aad5777b6844d49fac09eb764f580
-  summary: >-
-    This introductory path shows how to deploy a .NET application on Arm64 in Microsoft Azure.
-    You will create a Linux Arm64 virtual machine, connect over SSH using Azure Cloud Shell, install
-    the .NET 7 SDK and git, then build and run the app. You will configure the VM’s network security
-    group to expose the application over the Internet. Next, you will containerize the People.WebApp
-    with a Dockerfile in Visual Studio Code and push the resulting image to Azure Container Registry.
-    Prerequisites include an Azure subscription, Visual Studio Code with the Docker and C# extensions,
-    and Docker on Arm64. By the end, you have a running app and an image stored in ACR.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an Azure subscription, Visual Studio Code with the Docker and C# extensions, and
-      Docker installed on an Arm64 system. These prerequisites are listed so you can build the
-      app locally and containerize it before pushing to Azure Container Registry.
-  - question: How do I connect to the VM and which IP address should I use?
-    answer: >-
-      Connect over SSH using Azure Cloud Shell from the portal. Always use the public IP address
-      of your own VM shown in the Azure portal and not the sample IP provided in the tutorial
-      text.
-  - question: Which SDK and tools are installed on the VM to build the app?
-    answer: >-
-      You install the .NET 7 SDK using the dotnet-install.sh script and also install git to clone
-      the application sources. These are used to build and run the .NET application on the VM.
-  - question: How will the application be accessible from the internet?
-    answer: >-
-      You will configure the VM’s network security group to expose the application. This step
-      opens access so the running app can be reached externally.
-  - question: Where should I build the Docker image and how is it published to Azure?
-    answer: >-
-      You can containerize the People.WebApp using Visual Studio Code on a Windows on Arm device
-      (via WSL) or on the previously created VM, then push the local Docker image to Azure Container
-      Registry. The application sources are cloned from https://github.com/dawidborycki/People.WebApp.git.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md
index f58c172606..cd395a099f 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md
@@ -14,60 +14,9 @@ learning_objectives:
 prerequisites:
     - 'Azure subscription. Use this link to sign up for a free account: https://azure.microsoft.com/en-us/free/.'
     - 'Complete the [first learning path](/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1) of this series.'  
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:55:15Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 3823ad3df8ae868acfd59b5b5b541240624572fe739aaf2275185d5cd3578032
-  summary_generated_at: '2026-06-02T03:54:18Z'
-  summary_source_hash: 3823ad3df8ae868acfd59b5b5b541240624572fe739aaf2275185d5cd3578032
-  faq_generated_at: '2026-06-03T00:55:15Z'
-  faq_source_hash: 3823ad3df8ae868acfd59b5b5b541240624572fe739aaf2275185d5cd3578032
-  summary: >-
-    This introductory Learning Path shows how to create an Azure Container Instance (ACI) and
-    run a Docker container on Microsoft Azure. You will provision ACI through the Azure Portal
-    and Cloud Shell, enable the Admin account in Azure Container Registry (ACR) when deploying
-    from ACR, and verify the containerized ASP.NET sample application by browsing to the instance’s
-    public IP on port 8080. At the time of writing, ACI was not yet compatible with Arm64 Docker
-    containers, so the steps use a sample image from the Microsoft Container Registry. Prerequisites
-    are an active Azure subscription and completion of the first part of this series. The path
-    can be followed from Linux or Windows.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      You need an active Azure subscription and you must complete the first learning path in this
-      series. No other explicit prerequisites are listed.
-  - question: Which container image should I use for Azure Container Instances in this path?
-    answer: >-
-      Use the sample ASP.NET application image from the Microsoft Container Registry: mcr.microsoft.com/dotnet/samples:aspnetapp.
-      At the time of writing, Azure Container Instances was not yet compatible with arm64 Docker
-      containers.
-  - question: Where do I run the Azure CLI commands shown in the steps?
-    answer: >-
-      Open the Azure Portal and launch Cloud Shell using the icon in the top-right corner. Run
-      the provided commands directly in Cloud Shell.
-  - question: How do I enable and verify the Azure Container Registry Admin account?
-    answer: >-
-      Enable the Admin account in your Azure Container Registry because it is required by Azure
-      Container Instances when deploying from ACR. In Cloud Shell, run az acr list -o table and
-      check the ADMIN ENABLED column to confirm.
-  - question: How do I access the running application and what port should I use?
-    answer: >-
-      In the Container Instance Overview tab, copy the public IP address and open it in a browser
-      using port 8080 (for example, http://IP_ADDRESS:8080). If the deployment succeeded, the
-      application should load in the browser.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
index ba46b2d871..f55cc42f3d 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
@@ -13,59 +13,9 @@ learning_objectives:
 prerequisites:
     - 'Azure subscription. Use this link to sign up for a free account: https://azure.microsoft.com/en-us/free/.'
     - 'Complete the [first](/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1) and [second](/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2) learning paths of this series.'
-  
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:55:54Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: a3347a62c3322d88b80fc7848fa5ee1d92ee86333c2a21891b958286f8b70935
-  summary_generated_at: '2026-06-02T03:55:07Z'
-  summary_source_hash: a3347a62c3322d88b80fc7848fa5ee1d92ee86333c2a21891b958286f8b70935
-  faq_generated_at: '2026-06-03T00:55:54Z'
-  faq_source_hash: a3347a62c3322d88b80fc7848fa5ee1d92ee86333c2a21891b958286f8b70935
-  summary: >-
-    This introductory Learning Path shows how to create an Azure Kubernetes Service (AKS) cluster
-    backed by arm64-based virtual machines, connect to it, and deploy a containerized application.
-    You will provision the cluster in the Azure Portal with integration to Azure Container Registry,
-    then use Azure Cloud Shell and kubectl to access and manage it. Deployment is driven by Kubernetes
-    YAML that defines a Deployment and a Service. Tools listed include Docker, Kubernetes, and
-    ASP.NET Core, targeting Linux. Prerequisites are an active Azure subscription and completion
-    of the first and second parts of this series. By the end, you will have an AKS cluster on
-    Arm64 with an application running on it.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an Azure subscription and you must complete the first and second learning paths
-      in this series. No other explicit prerequisites are listed.
-  - question: How do I connect to the AKS cluster once it’s created?
-    answer: >-
-      Open Azure Cloud Shell and run: az aks get-credentials -g rg-arm64 -n aks-people. After
-      the command completes, manage the cluster with kubectl.
-  - question: Where do the container images for deployment come from?
-    answer: >-
-      The cluster is created with integration to Azure Container Registry. Images stored in that
-      registry are available to the cluster for deployment as shown in this path.
-  - question: What result should I expect after applying the Kubernetes YAML?
-    answer: >-
-      The Deployment creates one or more Pods, and the Service exposes the application. You should
-      see the Pods running and the Service present in the cluster.
-  - question: What should I check if kubectl commands fail after connecting?
-    answer: >-
-      Confirm you ran az aks get-credentials in Azure Cloud Shell and used the correct resource
-      group and cluster name (for example, rg-arm64 and aks-people). If issues persist, re-run
-      the command and try again from Cloud Shell as shown in the path.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md
index 192fa0e02b..1616ac5b20 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md
@@ -17,61 +17,9 @@ prerequisites:
     - 'A free Pulumi account and Pulumi CLI (details provided in this learning path)'
     - 'Node.js (details provided in this learning path)'
     - 'Azure CLI (details provided in this learning path)'
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:56:53Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 1510c787979257e191196e13999e98c20ca24d0857f72075649c28520c74c576
-  summary_generated_at: '2026-06-02T03:56:05Z'
-  summary_source_hash: 1510c787979257e191196e13999e98c20ca24d0857f72075649c28520c74c576
-  faq_generated_at: '2026-06-03T00:56:53Z'
-  faq_source_hash: 1510c787979257e191196e13999e98c20ca24d0857f72075649c28520c74c576
-  summary: >-
-    Learn how to use Infrastructure as Code with Pulumi to automate Azure resource deployment
-    on Windows. You will install and configure Node.js, the Pulumi CLI, and the Azure CLI, then
-    create a Pulumi TypeScript project in Visual Studio Code. The path shows the Pulumi project
-    structure and how to declare an Azure Resource Group and an Azure Container Instance that
-    runs a sample container image. By the end, you will provision the required Azure resources
-    with Pulumi. Prerequisites include an Azure subscription, Visual Studio Code, a free Pulumi
-    account with the Pulumi CLI, Node.js, and the Azure CLI. No additional prerequisites are explicitly
-    listed.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Windows environment, an Azure subscription (a free account link is provided),
-      Visual Studio Code, a free Pulumi account with the Pulumi CLI, Node.js, and the Azure CLI.
-      The setup step in the path provides installer links.
-  - question: Which installers should I use on Windows?
-    answer: >-
-      Install Node.js for Arm64 using the MSI linked in the setup step, then install the Pulumi
-      CLI and the Azure CLI using the Windows installers provided. Follow the path’s setup instructions
-      in order.
-  - question: Which Pulumi runtime and language does this path use?
-    answer: >-
-      The project uses TypeScript on Node.js with Pulumi’s Azure Native provider. You will edit
-      index.ts to declare Azure resources.
-  - question: After creating the Pulumi app, what should I see in the project?
-    answer: >-
-      Open the azure-aci folder in Visual Studio Code and expect a typical Node.js layout plus
-      Pulumi.yaml. Pulumi.yaml contains the global project configuration such as name, runtime,
-      and description.
-  - question: What result should I expect after updating index.ts and deploying?
-    answer: >-
-      The Pulumi deployment provisions an Azure Resource Group and an Azure Container Instance
-      using a sample ASP.NET Docker image. You should see these resources created in your Azure
-      subscription.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/servers-and-cloud-computing/funasr/_index.md b/content/learning-paths/servers-and-cloud-computing/funasr/_index.md
index 4624551be0..303fc9449a 100644
--- a/content/learning-paths/servers-and-cloud-computing/funasr/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/funasr/_index.md
@@ -13,60 +13,9 @@ learning_objectives:
 
 prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider, or a local Arm Linux computer with at least 8 CPUs and 16GB of RAM.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:57:43Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 410ec28d257ce7aa1308f11a741aa87baea80daf1acb113c4149761144269022
-  summary_generated_at: '2026-06-02T03:56:56Z'
-  summary_source_hash: 410ec28d257ce7aa1308f11a741aa87baea80daf1acb113c4149761144269022
-  faq_generated_at: '2026-06-03T00:57:43Z'
-  faq_source_hash: 410ec28d257ce7aa1308f11a741aa87baea80daf1acb113c4149761144269022
-  summary: >-
-    Deploy the ModelScope FunASR Chinese ASR model on Arm-based Linux servers to enable real-time
-    transcription, punctuation restoration, and sentiment analysis. This introductory path walks
-    you through the essentials of ModelScope and FunASR, including installing FunASR via pip and
-    using it from Python to run speech recognition tasks. You will learn how to leverage open-source
-    large language models and tools for Chinese ASR, and describe approaches to accelerate ModelScope
-    models on Arm servers. The target environment is Ubuntu 22.04 LTS (or later) on an Arm-based
-    instance or local Arm Linux machine with at least 8 CPUs, 16GB RAM, and 30GB disk. Estimated
-    time to complete is about 60 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      Use an Arm-based server or a local Arm Linux computer running Ubuntu 22.04 LTS or later
-      with at least 8 CPU cores, 16GB of RAM, and 30GB of disk space. This environment is required
-      for the examples in the Learning Path.
-  - question: Which FunASR version should I install and how?
-    answer: >-
-      Install FunASR version 1.2.3 using the command: pip3 install funasr==1.2.3. The examples
-      in this Learning Path use 1.2.3, and results might vary with other versions.
-  - question: Can I run this on a cloud provider and which ones are suitable?
-    answer: >-
-      Yes. Use an Arm-based instance from a cloud service provider; AWS, Microsoft Azure, Google
-      Cloud, and Oracle are listed options, or use a local Arm Linux machine.
-  - question: How do I know FunASR is working correctly after installation?
-    answer: >-
-      Run the speech recognition example provided in the Learning Path and confirm that an audio
-      input produces transcribed text output. FunASR provides a simple interface for transcription
-      that you can use to validate your setup.
-  - question: What output should I expect from the deployment?
-    answer: >-
-      You should be able to perform real-time Chinese speech-to-text transcription with punctuation
-      restoration and sentiment analysis using FunASR. The steps guide you through enabling these
-      capabilities on an Arm-based server.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Odin Shen
 
diff --git a/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
index 9e287b042f..b7d39b7572 100644
--- a/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
@@ -17,60 +17,9 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with [Kubernetes](https://kubernetes.io/)
   - Familiarity with container concepts ([Docker](https://www.docker.com/))
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:58:45Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 85d3d64e0eb0c3cfa0eee29cf1e4199049a6dcbf69634e578dad2b5443ca2b7f
-  summary_generated_at: '2026-06-02T03:57:32Z'
-  summary_source_hash: 85d3d64e0eb0c3cfa0eee29cf1e4199049a6dcbf69634e578dad2b5443ca2b7f
-  faq_generated_at: '2026-06-03T00:58:45Z'
-  faq_source_hash: 85d3d64e0eb0c3cfa0eee29cf1e4199049a6dcbf69634e578dad2b5443ca2b7f
-  summary: >-
-    Learn how to provision a Google Cloud C4A virtual machine powered by Axion (Arm Neoverse-V2)
-    and install Gardener on SUSE Linux Enterprise Server (Arm64). You will set up Gardener Local,
-    deploy Garden, Seed, and Shoot clusters using Kubernetes in Docker (KinD), and validate functionality
-    by deploying workloads into a Shoot cluster. The path uses tools including Kubernetes, Docker,
-    KinD, Helm, and kube-bench, and includes baseline security benchmarking against CIS Kubernetes
-    guidelines. Prerequisites are a Google Cloud account with billing enabled plus basic familiarity
-    with Kubernetes and Docker. The steps focus on a c4a-standard-4 VM configuration suitable
-    for running Gardener Local on Arm.
-  faqs:
-  - question: What do I need before creating the Axion C4A VM on Google Cloud?
-    answer: >-
-      You need a Google Cloud Platform account with billing enabled. Basic familiarity with Kubernetes
-      and Docker is also assumed.
-  - question: Which VM type and operating system does this path use for Gardener?
-    answer: >-
-      You will use a c4a-standard-4 instance (4 vCPUs, 16 GB memory) on Google Cloud C4A with
-      SUSE Linux Enterprise Server. This configuration is sufficient for running Gardener Local
-      with Garden, Seed, and Shoot clusters.
-  - question: Do the Garden, Seed, and Shoot clusters run in the cloud or locally?
-    answer: >-
-      They run locally on the C4A VM using Kubernetes in Docker (KinD). The path deploys Garden,
-      Seed, and Shoot clusters without requiring a separate managed Kubernetes service.
-  - question: How do I point kubectl at the Gardener Local cluster to validate the setup?
-    answer: >-
-      Set the KUBECONFIG environment variable to $PWD/example/gardener-local/kind/local/kubeconfig.
-      Then follow the verification steps to check cluster health and confirm Garden and Shoot
-      resources report Ready states.
-  - question: What should be ready before running kube-bench, and what output should I expect?
-    answer: >-
-      Ensure Gardener Local is running with Garden and Shoot clusters in Ready state and Docker
-      is available. kube-bench will check the cluster against CIS Kubernetes benchmarks and produce
-      a baseline security report.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md b/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
index 8d1c46ed26..797f81761c 100644
--- a/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
@@ -15,57 +15,9 @@ learning_objectives:
 prerequisites:
     - An Arm Linux system (cloud instance, on-premises hardware, or a virtual machine)
     - A recent version of the [GCC toolchain](/install-guides/gcc/)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T00:59:35Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 2e53b87d4dc7a7d1984e3bbe035038a60e619aea2f5793cb3082f3b59084bbf9
-  summary_generated_at: '2026-06-02T03:57:53Z'
-  summary_source_hash: 2e53b87d4dc7a7d1984e3bbe035038a60e619aea2f5793cb3082f3b59084bbf9
-  faq_generated_at: '2026-06-03T00:59:35Z'
-  faq_source_hash: 2e53b87d4dc7a7d1984e3bbe035038a60e619aea2f5793cb3082f3b59084bbf9
-  summary: >-
-    This introductory Learning Path shows how to enable and use GCC link-time optimization (LTO)
-    on an Arm Linux system to improve application performance by optimizing across compilation
-    units. You will learn how LTO works, when to apply it, and how to build with -flto during
-    both compilation and linking. The steps cover deploying LTO with GCC on Linux and evaluating
-    performance and code size trade-offs, with context on standardized benchmarks that illustrate
-    potential gains. Prerequisites are an Arm Linux environment (cloud, on-premises, or VM) and
-    a recent GCC toolchain. After completing the path, you will be able to configure LTO in your
-    builds and compare results against non-LTO builds.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an Arm Linux system (cloud instance, on‑premises hardware, or a virtual machine)
-      and a recent version of the GCC toolchain. No other prerequisites are explicitly listed.
-  - question: Which GCC flags do I use to enable LTO?
-    answer: >-
-      Pass -flto during both compilation and linking. The examples also use -O2 alongside -flto.
-  - question: Do I need to compile every translation unit with -flto?
-    answer: >-
-      Yes. In a stepwise build, compile each translation unit with -flto so the object files embed
-      LTO information, and then link with -flto to trigger whole‑program optimization.
-  - question: Can I build a small program with a single gcc command?
-    answer: >-
-      Yes. For small programs, the path notes you can simplify the build into a single gcc invocation
-      that both compiles and links with -flto.
-  - question: How should I evaluate the impact of LTO on my workload?
-    answer: >-
-      The path discusses evaluating performance and code size trade‑offs and references SPEC CPU2017
-      integer rate as a standardized way to illustrate potential gains. Actual results will depend
-      on your application.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Victor Do Nascimento
 
diff --git a/content/learning-paths/servers-and-cloud-computing/gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/gcp/_index.md
index d39099a9ce..cde01c65f2 100644
--- a/content/learning-paths/servers-and-cloud-computing/gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gcp/_index.md
@@ -15,57 +15,9 @@ prerequisites:
     - A [Google Cloud account](https://console.cloud.google.com/). Create an account if needed.
     - A computer with [Terraform](/install-guides/terraform) installed.
     - A computer with [Google Cloud CLI](/install-guides/gcloud) installed.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:00:09Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 24e2ffcf9b7cda919e6e864f18bb9424c0c328242c92f491c1b284e317ed7e1c
-  summary_generated_at: '2026-06-02T03:58:46Z'
-  summary_source_hash: 24e2ffcf9b7cda919e6e864f18bb9424c0c328242c92f491c1b284e317ed7e1c
-  faq_generated_at: '2026-06-03T01:00:09Z'
-  faq_source_hash: 24e2ffcf9b7cda919e6e864f18bb9424c0c328242c92f491c1b284e317ed7e1c
-  summary: >-
-    Learn to automate the deployment of Arm-based virtual machines on Google Cloud Platform using
-    Terraform, with secure access configured through a Jump Server (bastion host). You will generate
-    an SSH key pair, obtain GCP user credentials so Terraform can authenticate, and apply Terraform
-    files that serve as a base for future Learning Paths that need one or more server nodes. This
-    introductory, Linux-based path targets developers new to Arm VMs on GCP and takes about 20
-    minutes. By the end, you will have Terraform-managed infrastructure that deploys Arm instances
-    on GCP and provides access via a Jump Server, along with reusable Terraform code you can modify
-    for related tasks.
-  faqs:
-  - question: What do I need before running the Terraform steps?
-    answer: >-
-      You need a Google Cloud account and a computer with Terraform and the Google Cloud CLI installed.
-      These are the only explicit prerequisites listed.
-  - question: Do I need to generate a new SSH key pair, and where should it be located?
-    answer: >-
-      Generate an SSH key pair with ssh-keygen if you do not already have one. If you have keys
-      in the ~/.ssh directory, you can skip key generation and use the existing pair.
-  - question: How do I authenticate Terraform with my Google Cloud project?
-    answer: >-
-      Obtain GCP user credentials by following the provided guide so Terraform can communicate
-      with GCP. This authentication step is required before running Terraform.
-  - question: What gets created when I apply the Terraform configuration?
-    answer: >-
-      The configuration deploys Arm-based virtual machine instances on GCP and provides access
-      via a Jump Server (bastion). Any additional resources are not explicitly listed.
-  - question: How do I access the deployed Arm instances after provisioning?
-    answer: >-
-      Use SSH via the Jump Server (bastion) with the SSH key pair you generated or reused. The
-      Learning Path explains how to configure this bastion-based access.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md b/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
index ee1be45070..cbda5b2084 100644
--- a/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
@@ -12,58 +12,9 @@ learning_objectives:
 
 prerequisites:
 - An Arm computer running Linux. You can use a cloud instance, refer to [Get started with Arm-based cloud instances](/learning-paths/servers-and-cloud-computing/csp/).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:01:02Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 85a0a44bde6f217bdfc7fd0660ed6a86279b24c7ede302307ef6efb2626d83d9
-  summary_generated_at: '2026-06-02T03:59:40Z'
-  summary_source_hash: 85a0a44bde6f217bdfc7fd0660ed6a86279b24c7ede302307ef6efb2626d83d9
-  faq_generated_at: '2026-06-03T01:01:02Z'
-  faq_source_hash: 85a0a44bde6f217bdfc7fd0660ed6a86279b24c7ede302307ef6efb2626d83d9
-  summary: >-
-    This introductory Learning Path shows how to download and run Geekbench on Arm Linux systems
-    to benchmark CPU performance. You will install and execute Geekbench, obtain single-core and
-    multi-core scores, and use the results to compare different Arm configurations when selecting
-    hardware for your workload. The path targets Arm computers running Linux, including cloud
-    instances, and takes about 15 minutes to complete. Tools include Geekbench (with Preview Versions
-    available for Linux on Arm) and a Runbook. By the end, you will be able to run Geekbench on
-    an Arm Linux system, interpret the reported core scores, and apply them to basic hardware
-    selection decisions.
-  faqs:
-  - question: What do I need before running this benchmark?
-    answer: >-
-      You need an Arm computer running Linux. A cloud instance is acceptable; refer to Get started
-      with Arm-based cloud instances.
-  - question: Which Geekbench package should I download for Arm Linux?
-    answer: >-
-      Use a Geekbench Preview Version for Linux on Arm. Check the Geekbench downloads area for
-      the appropriate Arm Linux build.
-  - question: What result should I expect after a successful run?
-    answer: >-
-      Geekbench reports a single-core score, a multi-core score, and individual performance scores.
-      You will use these values to assess and compare systems.
-  - question: How should I compare different Arm systems using Geekbench?
-    answer: >-
-      Run Geekbench on each system you want to evaluate and compare the reported single-core,
-      multi-core, and individual performance scores. Use these comparisons to help determine a
-      suitable hardware configuration for your workload.
-  - question: Can I use an operating system other than Linux for this path?
-    answer: >-
-      This path targets Linux on Arm. Geekbench provides downloads for additional operating systems,
-      but those are not covered here.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md b/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
index 9d58723f1a..53955b9850 100644
--- a/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
@@ -17,62 +17,9 @@ prerequisites:
     - A GitHub account with access to Arm-hosted GitHub runners.
     - A Docker Hub account for storing container images.
     - Familiarity with the concepts of ML and continuous integration and deployment (CI/CD).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:01:51Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 642b67e7ca3717f499ab73efedd924eeab29a0055fad81c2d60fda993dcea195
-  summary_generated_at: '2026-06-02T04:00:17Z'
-  summary_source_hash: 642b67e7ca3717f499ab73efedd924eeab29a0055fad81c2d60fda993dcea195
-  faq_generated_at: '2026-06-03T01:01:51Z'
-  faq_source_hash: 642b67e7ca3717f499ab73efedd924eeab29a0055fad81c2d60fda993dcea195
-  summary: >-
-    This Learning Path shows how to automate an end-to-end MLOps workflow on Linux using Arm-hosted
-    GitHub runners and GitHub Actions. You will fork an example repository, set up workflows to
-    train and test a PyTorch model on the German Traffic Sign Recognition Benchmark (GTSRB) dataset,
-    and save the trained model as a workflow artifact. You will compare inference performance
-    by switching from a PyTorch 2.3.0 Docker image compiled with OpenBLAS to a oneDNN backend
-    with the Arm Compute Library (ACL). Finally, you will containerize the model using the provided
-    Dockerfile, push the image to Docker Hub, and deploy the container for API-based access. Prerequisites
-    include GitHub access to Arm-hosted runners, a Docker Hub account, and familiarity with ML
-    and CI/CD.
-  faqs:
-  - question: What do I need before running the workflows?
-    answer: >-
-      You need a GitHub account with access to Arm-hosted GitHub runners and a Docker Hub account.
-      Familiarity with ML and CI/CD is expected, and the path targets Linux.
-  - question: Where should I fork the example repository, and what if the name conflicts?
-    answer: >-
-      Fork https://github.com/Arm-Labs/gh_armrunner_mlops_gtsrb into a GitHub Organization or
-      Team where you have access to Arm-hosted GitHub runners. If a repository with the same name
-      already exists there, rename it during the fork.
-  - question: Which workflow trains the model and what should I expect as output?
-    answer: >-
-      The training is automated by .github/workflows/train-model.yml, which runs scripts/train_model.py
-      inside a PyTorch 2.3.0 Docker image compiled with OpenBLAS. When it completes, the trained
-      model is saved as a workflow artifact.
-  - question: How do I compare inference performance across PyTorch backends?
-    answer: >-
-      Use the comparison step to change the backend used for testing to oneDNN with Arm Compute
-      Library (ACL) and run the workflow to measure inference time. Compare those results with
-      the OpenBLAS-based run.
-  - question: How do I containerize and publish the trained model, and how is deployment validated?
-    answer: >-
-      Build an image using the Dockerfile in the repository and push it to Docker Hub; the Dockerfile
-      uses armswdev/pytorch-arm-neoverse:r24.07-torch-2.3.0-onednn-acl as the base. After deployment,
-      access the model using API calls as described in the steps.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author:
     - Pareena Verma
diff --git a/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md b/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
index a7666041a3..2f86b3eee4 100644
--- a/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
@@ -13,58 +13,9 @@ learning_objectives:
 prerequisites:
     - An [Amazon Web Services account](/learning-paths/servers-and-cloud-computing/csp/aws/).
     - A GitHub account (personal or organizational).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:02:59Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: cc72ef1fe1fde9f4f9ea0769c0e04d749731b8073b2efc0e65d7f608665abc2d
-  summary_generated_at: '2026-06-02T04:01:04Z'
-  summary_source_hash: cc72ef1fe1fde9f4f9ea0769c0e04d749731b8073b2efc0e65d7f608665abc2d
-  faq_generated_at: '2026-06-03T01:02:59Z'
-  faq_source_hash: cc72ef1fe1fde9f4f9ea0769c0e04d749731b8073b2efc0e65d7f608665abc2d
-  summary: >-
-    This Learning Path shows how to install RunsOn, a self-hosted runner manager, in your AWS
-    account to run GitHub Actions on Arm-based AWS EC2 instances. You will set up RunsOn using
-    AWS CloudFormation and a GitHub App, then modify your workflow files to target Arm runners,
-    including AWS Graviton instances based on Arm Neoverse processors. The steps highlight account
-    setup, installation flow, and the minimal workflow changes needed to launch Arm runners, which
-    typically come online in under 30 seconds. Prerequisites are an AWS account and a GitHub account.
-    The path is introductory and designed to be completed in about 15 minutes on Linux.
-  faqs:
-  - question: What do I need before running the installation?
-    answer: >-
-      You need an AWS account and a GitHub account. It is best to install RunsOn in its own AWS
-      sub-account for isolation and security.
-  - question: How do I install RunsOn in my AWS account?
-    answer: >-
-      Log in to the AWS console for the target account, then follow the official RunsOn installation
-      guide to create the CloudFormation stack and the GitHub app. Use the link at the top of
-      the guide to obtain your license key before proceeding.
-  - question: Which EC2 instance types and Arm processors can I use for runners?
-    answer: >-
-      You can select any instance types offered by AWS, including Arm instances with AWS Graviton
-      processors. With Graviton, you can run on Neoverse N1, Neoverse V1, and Neoverse V2 processors.
-  - question: How do I change my GitHub Actions workflow to target an Arm runner?
-    answer: >-
-      Edit the runs-on setting in your workflow file. For example, replace runs-on: ubuntu-22.04
-      with runs-on entries that include runner=1cpu-linux-arm64 and run-id=${{ github.run_id }}
-      to invoke a new Arm runner in your AWS account.
-  - question: What outcome and timing should I expect after triggering a workflow?
-    answer: >-
-      After installation, new runners launch in less than 30 seconds and your job should start
-      shortly. The runner will be an AWS EC2 Arm instance with 1 vCPU running Ubuntu 22.04.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Cyril Rohr
 
diff --git a/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md b/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md
index 7e958f02a2..80da4aac46 100644
--- a/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md
@@ -14,58 +14,9 @@ learning_objectives:
 prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free?utm_source=google&hl=en) account with billing enabled
   - A GitHub account; you can [sign up for GitHub](https://github.com/signup)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:03:50Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: d5df467355a7792f7a04eafc4a6bbd2410353aca000cd2b1aec74a7ed93a9270
-  summary_generated_at: '2026-06-02T04:01:33Z'
-  summary_source_hash: d5df467355a7792f7a04eafc4a6bbd2410353aca000cd2b1aec74a7ed93a9270
-  faq_generated_at: '2026-06-03T01:03:50Z'
-  faq_source_hash: d5df467355a7792f7a04eafc4a6bbd2410353aca000cd2b1aec74a7ed93a9270
-  summary: >-
-    This Learning Path shows how to provision a Google Axion C4A Arm virtual machine on Google
-    Cloud and use it as a self-hosted runner for GitHub Actions. You will create a c4a-standard-4
-    instance from the Google Cloud Console, install Git and the GitHub CLI on Linux, authenticate
-    with GitHub, and register the runner so workflows execute on Arm infrastructure. To validate
-    the setup, you will deploy a basic CI workflow that installs and starts NGINX when changes
-    are pushed to the main branch. Prerequisites are a Google Cloud account with billing enabled
-    and a GitHub account; no other prerequisites are explicitly listed.
-  faqs:
-  - question: What do I need before creating the VM and runner?
-    answer: >-
-      You need a Google Cloud Platform account with billing enabled and a GitHub account. No other
-      prerequisites are explicitly listed.
-  - question: Which Google Cloud machine type is used in the steps?
-    answer: >-
-      The path uses the c4a-standard-4 machine type (4 vCPUs, 16 GB memory) from the Google Axion
-      C4A family. Other sizes are not covered in the instructions.
-  - question: Which operating system is assumed on the VM?
-    answer: >-
-      The steps assume a Linux VM on an Arm64 Google Axion C4A instance. A specific Linux distribution
-      is not explicitly listed.
-  - question: How do I set up the self-hosted runner on the VM?
-    answer: >-
-      Install Git and GitHub CLI with apt, configure your Git identity, authenticate with GitHub,
-      and register the runner as shown in the steps. This enables your CI/CD workflows to target
-      the self-hosted Arm runner.
-  - question: How do I verify that the workflow executed on the Arm runner?
-    answer: >-
-      Push to the main branch to trigger the provided workflow, which installs and starts NGINX
-      on the self-hosted runner. A successful job run and a started NGINX service on the VM indicate
-      it executed on your Arm infrastructure.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Annie Tallund
 
diff --git a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
index e46388d2f7..09abe96a40 100644
--- a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
@@ -16,64 +16,9 @@ prerequisites:
     - A [Google Cloud account](https://console.cloud.google.com/) with billing enabled
     - A local Linux or macOS computer with Docker, Kubernetes CLI (kubectl), Google Cloud CLI (gcloud), and Git installed, or access to Google Cloud Shell
     - Basic familiarity with Docker, Kubernetes, and gcloud
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:05:58Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: cf981c67553824c7c57b6dda9c2953ac80926750676ac101e939f6bbff655ab5
-  summary_generated_at: '2026-06-02T04:03:32Z'
-  summary_source_hash: cf981c67553824c7c57b6dda9c2953ac80926750676ac101e939f6bbff655ab5
-  faq_generated_at: '2026-06-03T01:05:58Z'
-  faq_source_hash: cf981c67553824c7c57b6dda9c2953ac80926750676ac101e939f6bbff655ab5
-  summary: >-
-    This advanced Learning Path walks you through migrating a microservices application from x86
-    to Arm on Google Kubernetes Engine using multi-architecture container images and Google Axion
-    processors. You will prepare Dockerfiles for arm64, create a dual-architecture GKE standard
-    cluster with separate amd64 and arm64 node pools, and build and publish images to Artifact
-    Registry with Docker Buildx. You will deploy Online Boutique on amd64 and migrate to arm64
-    using Kustomize overlays, with optional automation using Cloud Build and Skaffold. Prerequisites
-    include a billing-enabled Google Cloud account and a Linux or macOS environment with Docker,
-    kubectl, gcloud, and Git installed, or access to Cloud Shell. Estimated time to complete is
-    about 90 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Google Cloud account with billing enabled and either a local Linux or macOS system
-      with Docker, kubectl, gcloud, and Git installed, or access to Google Cloud Shell. Basic
-      familiarity with Docker, Kubernetes, and gcloud is expected.
-  - question: Which GKE cluster configuration and networking are used?
-    answer: >-
-      You will create a GKE standard cluster with two node pools: one amd64 and one arm64. GKE
-      uses VPC-native (IP aliasing) with two secondary ranges for Pods and Services; for the default
-      VPC these ranges are created automatically.
-  - question: Which Online Boutique services require Dockerfile changes for multi-architecture
-      builds?
-    answer: >-
-      Four services need updates: emailservice, recommendationservice, loadgenerator, and cartservice.
-      The changes ensure the correct compiler headers and runtime libraries are present for each
-      architecture.
-  - question: How are the multi-architecture images built and published?
-    answer: >-
-      Images are built with Docker Buildx on the cluster using separate BuildKit pods per architecture,
-      so no QEMU emulation is required. The resulting multi-architecture images are pushed to
-      Google Artifact Registry.
-  - question: How do I deploy on amd64 first and then migrate to Arm?
-    answer: >-
-      Update the base Kubernetes manifests to reference your Artifact Registry images, then create
-      Kustomize overlays that select nodes by CPU architecture. Deploy to the amd64 node pool
-      first, then apply the arm64 overlay to migrate to the Arm node pool.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: 
    - Rani Chowdary Mandepudi
diff --git a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
index 697c4ddb00..93ab277bfd 100644
--- a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
@@ -16,58 +16,9 @@ prerequisites:
     - A [Google Cloud account](https://console.cloud.google.com/). Create an account if needed.
     - A computer with [Google Cloud CLI](/install-guides/gcloud) and [kubectl](/install-guides/kubectl/)installed.
     - An existing Google Kubernetes Engine (GKE) cluster with x86-based nodes
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:05:07Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 50715640292b60ea4216ee2211140c4212592a27356d628d945e83d9deb7fdcc
-  summary_generated_at: '2026-06-02T04:02:44Z'
-  summary_source_hash: 50715640292b60ea4216ee2211140c4212592a27356d628d945e83d9deb7fdcc
-  faq_generated_at: '2026-06-03T01:05:07Z'
-  faq_source_hash: 50715640292b60ea4216ee2211140c4212592a27356d628d945e83d9deb7fdcc
-  summary: >-
-    This Learning Path shows how to extend an existing x86-based Google Kubernetes Engine (GKE)
-    cluster with Arm-based Google Axion nodes and rebuild an x86 application for multi-architecture
-    support. You will add C4A virtual machine nodes (based on Google Axion with Armv9 Neoverse
-    V2 CPUs), rebuild your container to run on Arm, and use Kubernetes taints and tolerations
-    to schedule pods on architecture-specific nodes. The path targets Linux and uses Google Cloud
-    with Kubernetes tooling, including the Google Cloud CLI and kubectl. By the end, you will
-    run a multi-arch application across both Arm and x86 within a single GKE cluster and validate
-    placement through scheduling controls.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Google Cloud account, Google Cloud CLI, kubectl installed on your local machine,
-      and an existing Google Kubernetes Engine (GKE) cluster with x86-based nodes.
-  - question: Which VM type should I use for the Arm-based node pool?
-    answer: >-
-      Use the C4A family of virtual machines. These nodes are based on Google Axion, built using
-      the Armv9 Neoverse V2 CPU.
-  - question: How do I rebuild my existing x86 application for multi-architecture?
-    answer: >-
-      The path guides you to rebuild your container image so it supports both Arm and x86. You
-      then deploy the new multi-arch image to the hybrid GKE cluster.
-  - question: How will I control which pods run on Arm versus x86 nodes?
-    answer: >-
-      You will add taints to the Arm-based nodes and apply matching tolerations to your workloads
-      so only compatible pods schedule there. The steps show how to configure these settings for
-      architecture-specific placement.
-  - question: How do I know the application is running on the intended architecture?
-    answer: >-
-      Use kubectl to inspect pod placement and confirm pods are scheduled onto Arm-based nodes
-      and x86 nodes as configured. The path explains the checks to perform after deployment.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pranay Bakre
 
diff --git a/content/learning-paths/servers-and-cloud-computing/gke/_index.md b/content/learning-paths/servers-and-cloud-computing/gke/_index.md
index e74d53e216..d07ef0379e 100644
--- a/content/learning-paths/servers-and-cloud-computing/gke/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gke/_index.md
@@ -12,59 +12,9 @@ learning_objectives:
 prerequisites:
     - A Google Cloud account
     - A computer with the following tools installed`:` Terraform, Google Cloud CLI (gcloud), Kubernetes CLI (kubectl)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:04:26Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 7c3d37545c93db584d6e0ce88d8feaf0bf690e0c8786b664a6643be713d0336f
-  summary_generated_at: '2026-06-02T04:01:56Z'
-  summary_source_hash: 7c3d37545c93db584d6e0ce88d8feaf0bf690e0c8786b664a6643be713d0336f
-  faq_generated_at: '2026-06-03T01:04:26Z'
-  faq_source_hash: 7c3d37545c93db584d6e0ce88d8feaf0bf690e0c8786b664a6643be713d0336f
-  summary: >-
-    Automate the creation of an Arm-based Kubernetes cluster on Google Cloud using Terraform.
-    This advanced Learning Path focuses on deploying Google Kubernetes Engine (GKE) on Tau T2A
-    virtual machines powered by Ampere Altra Arm-based processors. You will prepare a Linux environment
-    with Terraform, the Google Cloud CLI (gcloud), and kubectl, create a new Google Cloud project,
-    and use infrastructure-as-code to provision the cluster for container orchestration. The expected
-    outcome is a deployed Arm-based GKE cluster managed via Terraform. Prerequisites are a Google
-    Cloud account and a computer with Terraform, gcloud, and kubectl installed; no other prerequisites
-    are explicitly listed.
-  faqs:
-  - question: What do I need before running the Terraform configuration?
-    answer: >-
-      You need a Google Cloud account and a computer with Terraform, Google Cloud CLI (gcloud),
-      and kubectl installed. The Learning Path lists Linux as the operating system and notes that
-      any computer with the required tools can be used.
-  - question: How do I ensure the GKE nodes are Arm-based?
-    answer: >-
-      Configure the cluster to use the Tau T2A VM family in GKE. Tau T2A is powered by Ampere
-      Altra Arm-based processors.
-  - question: Will I create a new Google Cloud project or use an existing one?
-    answer: >-
-      The steps include creating a new Google Cloud project before provisioning the cluster with
-      Terraform. Using an existing project is not explicitly listed.
-  - question: What result should I expect when the Terraform apply completes?
-    answer: >-
-      A GKE cluster will be deployed on Google Cloud with Arm-based nodes (Tau T2A). You can then
-      interact with the cluster using kubectl.
-  - question: Does this Learning Path cover deploying workloads or only cluster creation?
-    answer: >-
-      The objective is to automate the deployment of an Arm-based GKE cluster using Terraform.
-      Additional tasks such as deploying applications or tearing down the cluster are not explicitly
-      listed.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md b/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
index d12177dbfb..1790f600e1 100644
--- a/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
@@ -14,59 +14,9 @@ learning_objectives:
 prerequisites:
 - An Arm based instance from a cloud service provider.
 - Review the learning path on [LSE](/learning-paths/servers-and-cloud-computing/lse/)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:06:30Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 74952d68380c7540306543b0a7520f6960f673dd55ad5f164365fcfe1470380e
-  summary_generated_at: '2026-06-02T04:04:16Z'
-  summary_source_hash: 74952d68380c7540306543b0a7520f6960f673dd55ad5f164365fcfe1470380e
-  faq_generated_at: '2026-06-03T01:06:30Z'
-  faq_source_hash: 74952d68380c7540306543b0a7520f6960f673dd55ad5f164365fcfe1470380e
-  summary: >-
-    This advanced path shows how to rebuild and install glibc with Armv8-A Large System Extensions
-    (LSE) on an Arm server running Linux, then benchmark the impact on MongoDB. You will build
-    MongoDB 5.3.2 from source to run with the LSE-enabled glibc, drive workloads using YCSB, and
-    compare results against a No-LSE baseline. The steps focus on measuring throughput and runtime
-    characteristics and provide guidance on when LSE can deliver a measurable uplift for multi-threaded
-    workloads. Prerequisites are an Arm-based instance from a cloud service provider and a prior
-    review of the LSE learning path. Expected duration is about 60 minutes.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      You need an Arm-based instance from a cloud service provider running Linux. You should also
-      review the separate Learning Path on LSE before starting. This is an advanced topic intended
-      for experienced developers.
-  - question: Do I need to rebuild glibc on the instance, and why?
-    answer: >-
-      Yes. The steps have you build and install glibc with LSE so library routines can use LSE
-      atomic operations available on ARMv8-A, which you will then evaluate with workloads.
-  - question: Which MongoDB version is used and how is it installed?
-    answer: >-
-      MongoDB 5.3.2 is built from source using the provided commands. You clone the repository,
-      check out r5.3.2, install the listed dependencies, and build with SCons.
-  - question: How do I run and validate the benchmarks with and without LSE?
-    answer: >-
-      You run YCSB against MongoDB configured to use the newly built glibc with LSE, then repeat
-      with a No-LSE configuration. Compare the YCSB output, focusing on lines such as [OVERALL]
-      Throughput(ops/sec), as shown in the examples.
-  - question: What result should I expect from the No-LSE baseline?
-    answer: >-
-      YCSB prints summary lines including overall runtime, throughput in ops/sec, and GC metrics.
-      The path provides a sample No-LSE output format that you can use to compare against the
-      LSE run.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Ying Yu
 
diff --git a/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md b/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
index be2e97f36f..f82a16a408 100644
--- a/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
@@ -14,56 +14,9 @@ learning_objectives:
 prerequisites:
     - A [Google Cloud account](https://console.cloud.google.com/). This Learning Path can be run on any cloud provider or on-premises, but it focuses on Google Cloud’s Axion Arm64-based instances.
     - A local machine with [Google Cloud CLI](/install-guides/gcloud/) installed
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:07:15Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: aa78434138f08b424352302e92a1cd40d8297459bc65202715dcb41c40de6057
-  summary_generated_at: '2026-06-02T04:05:11Z'
-  summary_source_hash: aa78434138f08b424352302e92a1cd40d8297459bc65202715dcb41c40de6057
-  faq_generated_at: '2026-06-03T01:07:15Z'
-  faq_source_hash: aa78434138f08b424352302e92a1cd40d8297459bc65202715dcb41c40de6057
-  summary: >-
-    Provision Arm64 and x86_64 Linux VM instances on Google Cloud and use Go benchmarking tools
-    to compare performance across architectures. You will create an Arm-based c4a-standard-4 and
-    an Intel Emerald Rapids c4-standard-8 instance, install Go, Sweet, and Benchstat on both,
-    then run Go Benchmarks with Sweet and analyze results with Benchstat (text or CSV). Prerequisites
-    are a Google Cloud account and the Google Cloud CLI on your local machine. The path focuses
-    on Google Cloud’s Axion Arm64-based instances but can also be run on other clouds or on-premises.
-    Estimated time to complete is about 60 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Google Cloud account and the Google Cloud CLI installed on your local machine.
-      No other explicit prerequisites are listed.
-  - question: Which VM types should I create for the comparison?
-    answer: >-
-      Create an Arm-based c4a-standard-4 VM named "c4a" and an Intel-based Emerald Rapids c4-standard-8
-      VM named "c4". The steps show how to launch each in the Google Cloud console.
-  - question: Do I install Go, Sweet, and Benchstat on both VMs, and where should I run the install?
-    answer: >-
-      Yes, install on both VMs. The steps assume you run the installation from your home directory
-      ($HOME), which results in a $HOME/benchmarks/sweet directory.
-  - question: How do I execute and compare the benchmarks?
-    answer: >-
-      Run sweet on each VM to generate raw performance data. Then use benchstat to compare results
-      from the different VMs.
-  - question: What output should I expect from Benchstat?
-    answer: >-
-      Benchstat compares results to highlight performance differences and outputs text by default.
-      It can also produce CSV output.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Geremy Cohen
 
diff --git a/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md
index d1a3d157cd..f11e98f41f 100644
--- a/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md
@@ -15,62 +15,9 @@ prerequisites:
     - A [Microsoft Azure](https://azure.microsoft.com/) account with access to Azure Cobalt 100 Arm-based instances (Dpsv6-series)
     - Basic familiarity with the [Go programming language](https://go.dev/) and cloud deployment practices
     - Understanding of Linux command line and virtual machine management
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:07:53Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 46a0a3df799ac473f56d3820390af405b3301758cf15685a250a04c4854aba9e
-  summary_generated_at: '2026-06-02T04:05:37Z'
-  summary_source_hash: 46a0a3df799ac473f56d3820390af405b3301758cf15685a250a04c4854aba9e
-  faq_generated_at: '2026-06-03T01:07:53Z'
-  faq_source_hash: 46a0a3df799ac473f56d3820390af405b3301758cf15685a250a04c4854aba9e
-  summary: >-
-    This introductory Learning Path guides you through provisioning an Arm64 Azure Cobalt 100
-    (Dpsv6-series) virtual machine using the Azure portal with Ubuntu Pro 24.04 LTS, installing
-    the Go toolchain, deploying a simple Go web server for baseline validation, and running performance
-    tests with go test -bench (and -benchmem). You will use the official Arm64 Go distribution,
-    confirm compilation, networking, and runtime on the VM, and perform basic benchmarking, with
-    objectives that include comparing results on both x86_64 and Arm64 virtual machines. Prerequisites
-    include an Azure account with access to Cobalt 100 instances, familiarity with Go and cloud
-    deployment practices, and understanding of the Linux command line and VM management. After
-    completing the path, you can provision, deploy, and benchmark Go workloads on Azure Cobalt
-    100.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      You need a Microsoft Azure account with access to Azure Cobalt 100 Arm-based instances (Dpsv6-series),
-      basic familiarity with Go and cloud deployment practices, and an understanding of the Linux
-      command line and virtual machine management.
-  - question: Which VM series and operating system image should I choose?
-    answer: >-
-      Use a general-purpose Dpsv6-series virtual machine and select Ubuntu Pro 24.04 LTS (Arm64)
-      as the base image in the Azure portal.
-  - question: Which Go distribution should I install on the Arm64 VM?
-    answer: >-
-      Download the official Arm64-optimized Go distribution from the Go website and install it
-      on Ubuntu Pro 24.04 LTS. The steps guide you to fetch the tarball directly from go.dev.
-  - question: What result should I expect from the baseline Go web server test?
-    answer: >-
-      You will build and run a simple Go web application that serves an HTML page, confirming
-      that compilation, networking, and runtime execution work correctly on the Azure Cobalt 100
-      Arm64 VM.
-  - question: How do I run and interpret the performance benchmarks, and compare with x86_64?
-    answer: >-
-      Use go test -bench to run benchmarks and add -benchmem to capture memory usage, which reports
-      ns/op, B/op, and allocs/op. To compare architectures, run the same benchmark suite on an
-      x86_64 VM and evaluate the reported metrics side by side.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md
index c24cc8ba1d..6e5f6599cb 100644
--- a/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md
@@ -21,60 +21,9 @@ prerequisites:
   - Basic familiarity with [Kubernetes concepts](https://kubernetes.io/docs/concepts/)
   - Basic understanding of [Helm](https://helm.sh/docs/topics/architecture/) and Kubernetes manifests
   - Familiarity with basic Linux command-line usage
-    
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:08:45Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 87e9d25d5fb1f45d126aa4ca2fa1e13d6a470f2bcdc70968aa4622790106e629
-  summary_generated_at: '2026-06-02T04:06:17Z'
-  summary_source_hash: 87e9d25d5fb1f45d126aa4ca2fa1e13d6a470f2bcdc70968aa4622790106e629
-  faq_generated_at: '2026-06-03T01:08:45Z'
-  faq_source_hash: 87e9d25d5fb1f45d126aa4ca2fa1e13d6a470f2bcdc70968aa4622790106e629
-  summary: >-
-    Follow this introductory, hands-on path to install and validate Helm on Arm-based Google Cloud
-    Axion C4A virtual machines running SUSE Linux Enterprise Server. You will provision a C4A
-    instance, install Docker, kubectl, Helm, and KinD, and verify Helm workflows on a local Arm64
-    Kubernetes cluster. Next, you create and connect to a Google Kubernetes Engine (GKE) cluster
-    on Arm-based nodes and deploy PostgreSQL, Redis, and NGINX using official Helm charts. You
-    will perform install, upgrade, rollback, and uninstall operations, check application readiness
-    and service access, and observe Helm behavior under concurrent CLI operations. Prerequisites
-    include a GCP account with billing enabled and basic familiarity with Kubernetes, Helm, and
-    the Linux command line.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Google Cloud Platform account with billing enabled, basic familiarity with Kubernetes
-      concepts, a basic understanding of Helm and Kubernetes manifests, and comfort with the Linux
-      command line.
-  - question: Which Google Cloud machine type is used for the C4A VM in this path?
-    answer: >-
-      The steps use the c4a-standard-4 machine type, which provides 4 vCPUs and 16 GB of memory.
-  - question: Which tools are installed on the SUSE Arm64 VM to prepare for Helm testing?
-    answer: >-
-      You install Docker, kubectl, Helm, and KinD, and enable the SUSE Containers Module. This
-      setup lets you create and verify a local Kubernetes cluster for validating Helm workflows.
-  - question: How do I confirm that Helm and the chart repository are set up correctly?
-    answer: >-
-      Add the Bitnami chart repository and run a repository update. You should see output indicating
-      that "bitnami" was added and the repositories were successfully updated.
-  - question: What is deployed to GKE, and how does that differ from the local KinD cluster?
-    answer: >-
-      On GKE you deploy PostgreSQL, Redis, and NGINX using official Helm charts, and verify readiness
-      and service access. The earlier KinD-based cluster is used only for local validation before
-      targeting GKE; verify kubectl availability with the command kubectl version -.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/intro/_index.md b/content/learning-paths/servers-and-cloud-computing/intro/_index.md
index 9fec89077a..addcccec71 100644
--- a/content/learning-paths/servers-and-cloud-computing/intro/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/intro/_index.md
@@ -12,58 +12,9 @@ learning_objectives:
 
 prerequisites:
     - None
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:09:37Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: d2786f42b505823253ae16c647ca2e03c154f371b7c326210fde8523b12a8188
-  summary_generated_at: '2026-06-02T04:07:02Z'
-  summary_source_hash: d2786f42b505823253ae16c647ca2e03c154f371b7c326210fde8523b12a8188
-  faq_generated_at: '2026-06-03T01:09:37Z'
-  faq_source_hash: d2786f42b505823253ae16c647ca2e03c154f371b7c326210fde8523b12a8188
-  summary: >-
-    This short, introductory Learning Path helps software developers new to Arm understand where
-    Arm architecture appears in servers and cloud computing and how to find Arm-based hardware
-    for development. In about 10 minutes, it outlines how server vendors and cloud service providers
-    use Arm Neoverse processors for data center and on‑premises workloads, with an emphasis on
-    Linux environments. You will learn that cloud providers offer Arm instances based on Neoverse
-    and how creating a CSP account can be a practical first step, often with pay‑as‑you‑go access.
-    By the end, you can identify where Arm fits in server and cloud stacks and locate hardware
-    options to start exploring. No explicit prerequisites are listed.
-  faqs:
-  - question: Do I need my own Arm server to follow this path?
-    answer: >-
-      No. There are no prerequisites, and the path explains that creating an account with a cloud
-      service provider is the easiest way to try Arm instances.
-  - question: Which operating system does this path assume?
-    answer: >-
-      Linux is the target environment mentioned in the metadata.
-  - question: How do I choose an Arm-based instance in the cloud?
-    answer: >-
-      The path states that cloud providers offer Arm instances based on Neoverse processors. When
-      browsing your provider’s catalog, select an Arm-based option to evaluate.
-  - question: Does this path include step-by-step migration or tuning guidance?
-    answer: >-
-      No. It introduces where Arm is used and how to find Arm-based hardware, but it does not
-      provide detailed migration procedures or performance tuning steps.
-  - question: What outcome should I expect, and how long will it take?
-    answer: >-
-      In about 10 minutes, you will be able to identify where Arm is used in servers and cloud
-      computing and locate Arm-based hardware from CSPs and server vendors. You will also understand
-      common starting points to access Arm hardware through pay‑as‑you‑go models and introductory
-      credits.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md b/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
index e388716528..35f04228dd 100644
--- a/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
@@ -16,62 +16,9 @@ learning_objectives:
 prerequisites:
     - An Arm computer running Linux
     - Some familiarity with the Linux command line
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:10:27Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 6fc608d45c4fdf85a77bddd4f8c26b05a7950d1086e578a8be0dd637feeb5d79
-  summary_generated_at: '2026-06-02T04:07:50Z'
-  summary_source_hash: 6fc608d45c4fdf85a77bddd4f8c26b05a7950d1086e578a8be0dd637feeb5d79
-  faq_generated_at: '2026-06-03T01:10:27Z'
-  faq_source_hash: 6fc608d45c4fdf85a77bddd4f8c26b05a7950d1086e578a8be0dd637feeb5d79
-  summary: >-
-    Learn how to analyze and adjust network interrupt (IRQ) distribution on Arm Linux servers
-    to improve network workload performance. You will inspect the current IRQ layout, experiment
-    with different IRQ management patterns using scripts provided in the Learning Path, and configure
-    a distribution strategy appropriate for your workload, including making changes persistent.
-    The guidance reflects observations across multiple cloud platforms and VM sizes, with specific
-    notes for smaller systems (16 vCPUs or fewer). This introductory path is aimed at developers
-    and performance engineers with an Arm computer running Linux and basic command-line familiarity.
-    In about 20 minutes, you will be able to evaluate IRQ placement and apply a practical distribution
-    approach for your system.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      You need an Arm computer running Linux and some familiarity with the Linux command line.
-      No additional prerequisites are explicitly listed.
-  - question: How do I know how my NIC IRQs are currently distributed?
-    answer: >-
-      The steps show you how to understand and analyze the current IRQ configuration on your Arm
-      Linux system. You will review how network interrupts are assigned across CPU cores before
-      making changes.
-  - question: Which IRQ distribution strategies can I try, and how are they applied?
-    answer: >-
-      The Learning Path presents multiple IRQ distribution strategies and provides scripts to
-      implement them on your systems. You will experiment with assigning network IRQs to specific
-      cores to improve cache locality and reduce contention, depending on your workload.
-  - question: How should I choose a strategy for my system size or workload?
-    answer: >-
-      Effectiveness depends on system size and workload characteristics, and there is no single
-      best approach. For systems with 16 vCPUs or less, recommendations include concentrating
-      network IRQs on one or two CPU cores.
-  - question: How do I make my IRQ configuration persistent and confirm it worked?
-    answer: >-
-      The Learning Path covers implementing persistent IRQ management solutions so your configuration
-      survives reboots. You can validate changes by repeating the analysis steps and observing
-      IRQ distribution under your workload.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Kiel Friedt
 
diff --git a/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md b/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
index 53887cb314..435dd54d64 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
@@ -16,58 +16,9 @@ prerequisites:
     - An Arm-based instance from a cloud service provider, or an on-premise Arm server.
     - Basic understanding of Java.
     - An [installation of Java](/install-guides/java/) on your machine.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:11:10Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: f57dd99bad280f64f53071373519e2d3ba8ae50b94fe1ad0a9cc40d02b458b9b
-  summary_generated_at: '2026-06-02T04:08:34Z'
-  summary_source_hash: f57dd99bad280f64f53071373519e2d3ba8ae50b94fe1ad0a9cc40d02b458b9b
-  faq_generated_at: '2026-06-03T01:11:10Z'
-  faq_source_hash: f57dd99bad280f64f53071373519e2d3ba8ae50b94fe1ad0a9cc40d02b458b9b
-  summary: >-
-    Learn to monitor, interpret, and tune Java Garbage Collection on Arm-based Linux servers.
-    Using an Arm instance on AWS, Microsoft Azure, Google Cloud, Oracle, or an on‑premise Arm
-    server, you will verify your JDK with java --version, review the differences among commonly
-    used production collectors, and run a small Java program that rapidly fills the heap to expose
-    GC behavior. The path shows how to select a collector for your application and adjust core
-    parameters, with guidance on updating to a recent LTS JDK. Prerequisites include basic Java
-    knowledge and a working Java installation. Estimated time: 45 minutes.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      You need an Arm-based instance from a cloud provider (or an on-premise Arm server), a basic
-      understanding of Java, and a working Java installation. Linux is the target operating system.
-  - question: How do I check which JDK version I am using?
-    answer: >-
-      Run the command: java --version. The output shows your JDK release and build details so
-      you can proceed with the appropriate GC options.
-  - question: How do I find which Garbage Collectors are available with my JDK?
-    answer: >-
-      Different JDK versions ship with different collectors, so first confirm your version with
-      java --version. Then follow the identification step in the path to see which collectors
-      your JDK includes.
-  - question: How do I use the example application to observe GC behavior?
-    answer: >-
-      Create the provided HeapUsageExample.java file and run it to allocate a large number of
-      objects and fill the heap. This makes GC activity easy to observe while you vary GC choices
-      and tuning parameters.
-  - question: What should I do if I’m on an older JDK release?
-    answer: >-
-      Update to a recent long-term-support JDK, because newer releases include GC improvements.
-      Use java --version to verify the upgrade before repeating your measurements.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
index 8e5b95e561..a3d142998a 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
@@ -13,59 +13,9 @@ learning_objectives:
 
 prerequisites:
     - A [Google Cloud](https://cloud.google.com/) account with access to Axion based instances (C4A).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:12:09Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: e6f73fbca45a1be1644f79bbcfbaaec964e31f1aab236e9a872c72a6fd5e4b66
-  summary_generated_at: '2026-06-02T04:09:25Z'
-  summary_source_hash: e6f73fbca45a1be1644f79bbcfbaaec964e31f1aab236e9a872c72a6fd5e4b66
-  faq_generated_at: '2026-06-03T01:12:09Z'
-  faq_source_hash: e6f73fbca45a1be1644f79bbcfbaaec964e31f1aab236e9a872c72a6fd5e4b66
-  summary: >-
-    Learn how to deploy and evaluate a Java workload on Google Cloud Axion instances built on
-    Armv9 Neoverse V2. You will create an Arm-based VM using the gcloud CLI, install Java on Ubuntu
-    24.04, and build and deploy the Spring Petclinic application. The path then uses jmeter (with
-    a provided JMX file) to exercise the application, compare JDK versions, and test common JVM
-    performance optimization flags. You can also compare Axion results with previous-generation
-    Google Cloud Arm instances. This introductory path targets developers running Java on Arm
-    in Google Cloud. Prerequisite: a Google Cloud account with access to Axion-based (C4A) instances.
-  faqs:
-  - question: What do I need before creating the VM?
-    answer: >-
-      You need a Google Cloud account with access to Axion-based instances (C4A). No other explicit
-      prerequisites are listed.
-  - question: Which method should I use to create the Axion VM?
-    answer: >-
-      There are multiple options: Google Cloud console, the gcloud CLI, or Infrastructure as Code.
-      This guide uses the gcloud CLI.
-  - question: How do I connect to the instance, and which OS is used?
-    answer: >-
-      Use the Google Cloud console’s SSH button to open a shell to the VM. The guide uses an Ubuntu
-      24.04 image on the Axion instance.
-  - question: Which Java package should I install and how do I verify it?
-    answer: >-
-      Install the default JRE using apt and verify with java -version. The example output shows
-      an OpenJDK 21.x release.
-  - question: What application and tool are used for performance testing, and how should I run
-      the tests?
-    answer: >-
-      You deploy the Spring Petclinic application and test it with jmeter using the JMX file in
-      the spring-petclinic repo. Open a new SSH terminal so the running application is not interrupted,
-      then compare results across JDK versions and common JVM flags; comparing Axion to previous-generation
-      Arm instances is optional.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Joe Stech
 
diff --git a/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
index 283846d89a..c5cb10f7fe 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
@@ -13,59 +13,9 @@ learning_objectives:
 
 prerequisites:
     - A [Microsoft Azure](https://azure.microsoft.com/) account with access to Cobalt 100 based instances (Dpsv6)
-
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:13:27Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 58b0bb9f6e4190029d7edc65bdb04a597c7539de55a5e51ed59e88b174e527c3
-  summary_generated_at: '2026-06-02T04:09:50Z'
-  summary_source_hash: 58b0bb9f6e4190029d7edc65bdb04a597c7539de55a5e51ed59e88b174e527c3
-  faq_generated_at: '2026-06-03T01:13:27Z'
-  faq_source_hash: 58b0bb9f6e4190029d7edc65bdb04a597c7539de55a5e51ed59e88b174e527c3
-  summary: >-
-    Provision an Arm-based Azure Cobalt 100 virtual machine using the Azure portal, install Java
-    on Ubuntu Pro 24.04 LTS (Arm64), and measure application performance with JVM-aware microbenchmarks.
-    This introductory path is aimed at developers migrating Java workloads to Arm and walks through
-    creating a Cobalt 100 (Dpsv6) VM, installing the JRE and JDK via the Ubuntu package manager,
-    validating the installation, and running a simple Tomcat-like Java baseline before benchmarking
-    with JMH. You will learn how to set up the environment, run baseline tests, and execute JMH
-    to assess Java performance on Arm. A Microsoft Azure account with access to Cobalt 100 instances
-    is required. Estimated time to complete: 30 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Microsoft Azure account with access to Cobalt 100 based instances (Dpsv6). No
-      other explicit prerequisites are listed.
-  - question: How should I create the VM and which OS image should I choose?
-    answer: >-
-      Use the Azure portal to create an Arm64 VM with the Cobalt 100 processor. Select Ubuntu
-      Pro 24.04 LTS as the base image, following the VM creation steps in the portal.
-  - question: Which Java package should I install on Ubuntu Pro 24.04 LTS (Arm64)?
-    answer: >-
-      Install OpenJDK using the default-jdk package, which provides both the JRE and JDK. Run:
-      sudo apt update and then sudo apt install -y default-jdk.
-  - question: Why start with a Tomcat-like baseline instead of deploying a full Tomcat server?
-    answer: >-
-      A Tomcat-like baseline lets you measure how efficiently raw Java executes simple operations
-      before adding server components. Full servers introduce complexity such as request parsing,
-      thread management, and I/O handling.
-  - question: How will I benchmark the Java code and what results should I look for?
-    answer: >-
-      You will use JMH (Java Microbenchmark Harness) to run JVM-aware microbenchmarks. JMH accounts
-      for JIT and warmup and enables you to measure throughput for small code snippets.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
index 56e9508e4c..7707f3f44e 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
@@ -14,60 +14,9 @@ learning_objectives:
 prerequisites:
   - Access to both Arm-based and x86-based computers running Ubuntu (you can use cloud-based server instances)
   - Basic familiarity with Java applications and performance profiling using flame graphs
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:14:15Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 655180d91bb9b9cf571840b59d8943c1d2c73d8f8aea647db57dc2704ede3af8
-  summary_generated_at: '2026-06-02T04:10:23Z'
-  summary_source_hash: 655180d91bb9b9cf571840b59d8943c1d2c73d8f8aea647db57dc2704ede3af8
-  faq_generated_at: '2026-06-03T01:14:15Z'
-  faq_source_hash: 655180d91bb9b9cf571840b59d8943c1d2c73d8f8aea647db57dc2704ede3af8
-  summary: >-
-    Learn how to analyze Java application performance on Arm Neoverse-based Linux servers by benchmarking
-    a Tomcat deployment and generating flame graphs. You will set up Apache Tomcat, drive HTTP
-    load with wrk2, and profile on the same Arm machine using two approaches: async-profiler and
-    a perf-based Java agent (libperf-jvmti) with the FlameGraph toolkit. The path uses OpenJDK
-    21 and focuses on producing actionable flame graphs to help identify bottlenecks under load.
-    Prerequisites include access to Arm- and x86-based Ubuntu systems (cloud instances are acceptable)
-    and basic familiarity with Java applications and flame graph profiling. Estimated time to
-    complete is about 30 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need access to both Arm-based and x86-based computers running Ubuntu (cloud instances
-      are acceptable) and basic familiarity with Java applications and flame-graph-based profiling.
-      The path uses OpenJDK 21, Apache Tomcat, async-profiler, FlameGraph, and wrk2.
-  - question: Can I perform the steps on an x86 server?
-    answer: >-
-      The procedures target an Arm Neoverse-based Linux server for profiling. Access to an x86
-      Ubuntu system is listed as a prerequisite, but the profiling steps in this path are executed
-      on the Arm machine.
-  - question: Where should I run async-profiler relative to Tomcat?
-    answer: >-
-      Install and run async-profiler on the same Arm-based Linux machine where Tomcat is running
-      to ensure accurate profiling.
-  - question: How are flame graphs generated with the Java agent approach?
-    answer: >-
-      Configure Tomcat to load the libperf-jvmti.so JVMTI agent so perf can record stacks with
-      Java method names, then use the FlameGraph toolkit to build the flame graph. This complements
-      the async-profiler method provided earlier in the path.
-  - question: Do I need to generate load during profiling, and how should I do that?
-    answer: >-
-      Yes. The path sets up a Tomcat benchmark and uses wrk2 to simulate HTTP load while you collect
-      profiles, so the flame graphs reflect realistic request handling.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: 
   - Ying Yu
diff --git a/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md b/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
index 4730be8821..6873a0f698 100644
--- a/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
@@ -20,59 +20,9 @@ prerequisites:
     - A [Google Cloud Platform](https://cloud.google.com/) account with access to Arm-based virtual machine instances
     - Basic understanding of Linux command line
     - Familiarity with CI/CD concepts and [Jenkins fundamentals](https://www.jenkins.io/doc/book/pipeline/)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:14:54Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 525d893a796e8e4eb5cc7a9d5996bd7d55a35f8fe413f87b9a275a214d77626f
-  summary_generated_at: '2026-06-02T04:11:14Z'
-  summary_source_hash: 525d893a796e8e4eb5cc7a9d5996bd7d55a35f8fe413f87b9a275a214d77626f
-  faq_generated_at: '2026-06-03T01:14:54Z'
-  faq_source_hash: 525d893a796e8e4eb5cc7a9d5996bd7d55a35f8fe413f87b9a275a214d77626f
-  summary: >-
-    This Learning Path guides you through deploying Jenkins LTS on Arm-based cloud servers and
-    validating Arm-native CI/CD pipelines. You provision an Azure Cobalt 100 (Dpsv6) virtual machine
-    using the Azure console with Ubuntu Pro 24.04 LTS, and a Google Cloud C4A instance powered
-    by Axion processors on SUSE Linux. You configure cloud firewall rules to expose Jenkins on
-    TCP port 8080, install Jenkins with OpenJDK 17 on Arm64, and verify the setup via service
-    checks, UI access, and pipeline execution. You then run Arm-native Jenkins pipelines, including
-    Docker-based workflows. No additional prerequisites are listed beyond cloud account access,
-    basic Linux skills, and familiarity with CI/CD and Jenkins fundamentals.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an Azure account with access to Cobalt 100-based Dpsv6 instances and a Google Cloud
-      account with access to Arm-based VMs. You should be comfortable with the Linux command line
-      and basic CI/CD and Jenkins concepts.
-  - question: Which VM types and operating systems are used in this path?
-    answer: >-
-      On Azure, you provision an Arm64 VM from the Dpsv6 (Cobalt 100) series using Ubuntu Pro
-      24.04 LTS. On Google Cloud, you provision an Arm-based SUSE Linux VM on the C4A family powered
-      by Axion processors.
-  - question: How do I expose the Jenkins web UI to my browser?
-    answer: >-
-      Open TCP port 8080. On Azure, create an NSG rule for the VM’s network interface or subnet;
-      on Google Cloud, create a VPC firewall rule allowing inbound TCP 8080 to the instance.
-  - question: How do I validate that Jenkins installed correctly on the Azure VM?
-    answer: >-
-      Confirm the Jenkins service is running, then access the web UI on port 8080. The setup verifies
-      Jenkins on Arm64 (aarch64) with Java 17 as part of the installation outcome.
-  - question: What should I check if I plan to run Docker-based pipelines?
-    answer: >-
-      This path includes CI use cases that use Docker-based pipelines on Arm64. Follow the steps
-      when they introduce Docker workflows and ensure your environment meets those requirements.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
index 0e37ec09ab..e49ce1771a 100644
--- a/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
@@ -16,59 +16,9 @@ prerequisites:
     - A [Microsoft Azure](https://azure.microsoft.com/) account with access to Cobalt 100 based instances (Dpsv6)
     - Basic understanding of Linux command line
     - Familiarity with the [Apache Kafka architecture](https://kafka.apache.org/) and deployment practices on Arm64 platforms
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:16:31Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: d510dd43a485e1c2fac404ef9a2e00b5be2449b99b1095c9a1841cd2fcba9b0e
-  summary_generated_at: '2026-06-02T04:12:27Z'
-  summary_source_hash: d510dd43a485e1c2fac404ef9a2e00b5be2449b99b1095c9a1841cd2fcba9b0e
-  faq_generated_at: '2026-06-03T01:16:31Z'
-  faq_source_hash: d510dd43a485e1c2fac404ef9a2e00b5be2449b99b1095c9a1841cd2fcba9b0e
-  summary: >-
-    Provision an Arm-based Microsoft Azure Cobalt 100 (Dpsv6) virtual machine using the Azure
-    portal, install Apache Kafka on Ubuntu Pro 24.04 LTS (arm64), and validate end-to-end messaging
-    before running official Kafka benchmarks. You will set up Java, deploy Kafka, start the broker
-    in KRaft mode, and perform a baseline producer/consumer test to confirm the environment is
-    working. Finally, use Kafka’s bundled performance tools to measure throughput and latency
-    on the Arm64 VM. This advanced path targets developers migrating Kafka workloads to Arm on
-    Azure. Prerequisites include an Azure account with access to Cobalt 100 instances, basic Linux
-    command-line skills, and familiarity with Kafka architecture and Arm64 deployment practices.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      You need a Microsoft Azure account with access to Cobalt 100 based instances (Dpsv6), basic
-      Linux command-line skills, and familiarity with the Apache Kafka architecture and deployment
-      on Arm64. No other prerequisites are explicitly listed.
-  - question: Which Azure VM size and OS image should I select?
-    answer: >-
-      Use a Dpsv6 series virtual machine based on the Arm-based Cobalt 100 CPU and select Ubuntu
-      Pro 24.04 LTS (Arm64) as the base image. The steps use the Azure portal to create the VM.
-  - question: Do I need ZooKeeper for this Kafka setup?
-    answer: >-
-      No. Kafka 4.1.0 in KRaft mode integrates the control and data planes and removes the need
-      for ZooKeeper, simplifying deployment.
-  - question: How do I know the baseline test worked?
-    answer: >-
-      Start the Kafka broker in KRaft mode and run the producer and consumer in separate terminals.
-      Successful end-to-end message production and consumption indicates the setup is working.
-  - question: Which tools are used for benchmarking and what should be running first?
-    answer: >-
-      Use kafka-producer-perf-test.sh and kafka-consumer-perf-test.sh bundled with Kafka to measure
-      throughput, latency, and end-to-end efficiency. Ensure your Kafka broker is already active
-      in a separate terminal before running these tools.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/kafka/_index.md b/content/learning-paths/servers-and-cloud-computing/kafka/_index.md
index ffae3df504..0e46f0959b 100644
--- a/content/learning-paths/servers-and-cloud-computing/kafka/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kafka/_index.md
@@ -16,59 +16,9 @@ learning_objectives:
 
 prerequisites:
     - Seven physical Arm machines or cloud instances with either Ubuntu or Debian installed. 
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:15:30Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: b7f36df881c2c436143c1e5579d2c54cb5930f52998904098829c52fa7290f38
-  summary_generated_at: '2026-06-02T04:11:38Z'
-  summary_source_hash: b7f36df881c2c436143c1e5579d2c54cb5930f52998904098829c52fa7290f38
-  faq_generated_at: '2026-06-03T01:15:30Z'
-  faq_source_hash: b7f36df881c2c436143c1e5579d2c54cb5930f52998904098829c52fa7290f38
-  summary: >-
-    This advanced Learning Path guides you through deploying a production-style Kafka event streaming
-    cluster on Arm-based Linux servers. You will install and configure a three-node ZooKeeper
-    ensemble and a three-node Kafka cluster on Ubuntu or Debian, then validate the setup by creating
-    a topic and writing/reading events from a client node. The path also covers automating deployment
-    on AWS Graviton processors using Terraform and Ansible, with objectives that include automation
-    on Google Cloud. You need seven Arm machines or cloud instances and appropriate network ports
-    opened. After about 90 minutes, you will have a working Kafka cluster on Arm and a repeatable
-    deployment approach for cloud environments.
-  faqs:
-  - question: What do I need before running the setup?
-    answer: >-
-      You need seven physical Arm machines or cloud instances running Ubuntu or Debian. Ensure
-      ports 8080, 2888, 3888, 2181, and 9092 are open in the security groups for these machines.
-  - question: How should I assign roles to the seven machines?
-    answer: >-
-      Use three machines for the ZooKeeper cluster, three machines for the Kafka cluster, and
-      one machine as the client.
-  - question: Which configuration values do I change on Kafka nodes to connect to ZooKeeper?
-    answer: >-
-      Edit config/server.properties on each Kafka node and replace zk_1_ip, zk_2_ip, and zk_3_ip
-      with the IP addresses of your three ZooKeeper nodes.
-  - question: Where do I run the validation and what result should I expect?
-    answer: >-
-      Install Kafka on the client machine, create a topic, write events to it, and read them back.
-      Successfully reading the events you produced confirms the Kafka cluster is working.
-  - question: Which options are available for automated deployment on cloud platforms?
-    answer: >-
-      The path covers automated deployment on AWS and Google Cloud. On AWS, Terraform and Ansible
-      are used to deploy a three-node ZooKeeper cluster, a three-node Kafka cluster, and one client
-      on AWS Graviton, and you should have the required tools installed on a computer you can
-      run them from.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md b/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
index bb3f6a3c19..6b75feed63 100644
--- a/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
@@ -16,61 +16,9 @@ prerequisites:
   - A running Kubernetes cluster (local or cloud)
   - Kubectl and Helm installed 
   - Access to the Kedify Service dashboard to obtain your Organization ID and API key (sign up at [Kedify dashboard](https://dashboard.kedify.io/))
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:17:17Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 6ff33f6dfaf25e926a0ce8756b4e564e5aa37112e5425f9c3dce13f772b54145
-  summary_generated_at: '2026-06-02T04:12:58Z'
-  summary_source_hash: 6ff33f6dfaf25e926a0ce8756b4e564e5aa37112e5425f9c3dce13f772b54145
-  faq_generated_at: '2026-06-03T01:17:17Z'
-  faq_source_hash: 6ff33f6dfaf25e926a0ce8756b4e564e5aa37112e5425f9c3dce13f772b54145
-  summary: >-
-    This Learning Path shows how to enable event-driven autoscaling for HTTP workloads on Kubernetes
-    using KEDA and Kedify. You will use Helm to add the Kedify chart repository and install three
-    charts—the KEDA (Kedify build), the HTTP Scaler, and the Kedify Agent—then verify they are
-    running. If needed, you will install an ingress controller (NGINX) and target arm64 nodes.
-    Next, you will deploy a sample web service, expose it via Kubernetes Ingress, rely on Kedify’s
-    autowiring to route traffic, and generate load to observe scale-out, scale-in, and scale-to-zero
-    behavior. It targets Linux and works on local or cloud clusters (EKS, GKE, AKS). Prerequisites
-    include kubectl, Helm, a running cluster, and Kedify dashboard credentials.
-  faqs:
-  - question: What do I need before I start the installation?
-    answer: >-
-      You need a running Kubernetes cluster (local or cloud), kubectl and Helm installed, and
-      access to the Kedify Service dashboard to obtain your Organization ID and API key. The path
-      targets Linux.
-  - question: Do I need an ingress controller, and which one is used here?
-    answer: >-
-      Yes, an ingress controller is required to handle HTTP traffic. This path installs the NGINX
-      Ingress Controller with Helm and targets arm64 nodes; if your cluster already has a working
-      ingress controller, you can skip this step.
-  - question: Which Helm charts are installed to enable HTTP autoscaling?
-    answer: >-
-      You install three charts from the Kedify repository: KEDA (Kedify build) for event-driven
-      autoscaling, the HTTP Scaler for HTTP-based scaling, and the Kedify Agent to connect your
-      cluster to Kedify’s cloud service.
-  - question: How do I know Kedify and KEDA are running correctly?
-    answer: >-
-      The Learning Path includes a verification step to check that the Kedify and KEDA components
-      are running in your cluster. Follow those checks before proceeding to application deployment.
-  - question: What behavior should I expect when testing the sample HTTP app?
-    answer: >-
-      After deploying the app and enabling autoscaling with a scaled object, generating HTTP load
-      should trigger scale out. When the app becomes idle, you should observe scale in, including
-      scale-to-zero.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Zbynek Roubalik
 
diff --git a/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md b/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
index 9ce127a2ab..bbd79b12e5 100644
--- a/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
@@ -16,61 +16,9 @@ prerequisites:
     - Basic Machine Learning knowledge.
     - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider, an on-premises Arm server, or a Linux virtual machine on your Arm device. 
     - Familiarity with SSH, the Linux command line, and basic system administration tasks.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:17:46Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 830556dfd51aaa2dd95ee957e64306bab369297f7bc66325e7eb509f541f683c
-  summary_generated_at: '2026-06-02T04:13:36Z'
-  summary_source_hash: 830556dfd51aaa2dd95ee957e64306bab369297f7bc66325e7eb509f541f683c
-  faq_generated_at: '2026-06-03T01:17:46Z'
-  faq_source_hash: 830556dfd51aaa2dd95ee957e64306bab369297f7bc66325e7eb509f541f683c
-  summary: >-
-    This introductory Learning Path shows how to create, train, and evaluate a simple neural network
-    on Arm servers using Keras Core with TensorFlow, PyTorch, and JAX backends. You work on Ubuntu
-    22.04 LTS on an Arm-based instance or server, including cloud instances from AWS, Microsoft
-    Azure, Google Cloud, or Oracle. After installing the required Python environment, you write
-    and run a compact ml.py script that defines and compiles a model in Keras Core, then train,
-    evaluate, and generate predictions using different backends. The path targets Linux and takes
-    about 30 minutes. Prerequisites include basic machine learning knowledge and familiarity with
-    SSH, the Linux command line, and basic system administration tasks.
-  faqs:
-  - question: What environment should I prepare before starting?
-    answer: >-
-      Use an Arm-based machine running Ubuntu 22.04 LTS: a cloud instance on AWS, Microsoft Azure,
-      Google Cloud, or Oracle; an on-premises Arm server; or a Linux VM on your Arm device. Access
-      it via SSH (for remote servers) or open a terminal locally.
-  - question: Which Python version should I use on Ubuntu 22.04, and do I need pip and venv?
-    answer: >-
-      Ubuntu 22.04 includes Python 3.10 by default, which you can use, or you can install a newer
-      Python version. In either case, install the python3-pip and python3-venv packages to manage
-      dependencies and an isolated environment.
-  - question: How do I switch between TensorFlow, PyTorch, and JAX backends in Keras Core?
-    answer: >-
-      Keras Core supports multiple backends and the Learning Path runs the same model with TensorFlow,
-      PyTorch, and JAX. Follow the step that specifies how to choose the backend before executing
-      the script; the exact selection method is provided there.
-  - question: What script do I run, and what should I expect as output?
-    answer: >-
-      You will create an ml.py script that defines a simple model with Keras Core, then compile,
-      train, evaluate, and generate predictions. When it runs successfully, you should see training
-      progress and evaluation results, followed by prediction output.
-  - question: What input shape and data type does the example model expect?
-    answer: >-
-      The example model uses an input shape of 784 elements with dtype float16. If your data has
-      a different shape or dtype, adjust the Input layer in the script accordingly.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: 
     - Diego Russo
diff --git a/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md b/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
index d60204d94a..341416727f 100644
--- a/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
@@ -17,61 +17,9 @@ prerequisites:
     - An Arm cloud instance with at least 24 vCPUs and 200 GB of free storage running Ubuntu 24.04 LTS
     - Understanding of kernel images and modules
     - Familiarity with GRUB bootloader and initramfs
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:18:47Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 004436cf14ff0056136889c58d676295c6dd2088f06f57f397a07ec9004e6b44
-  summary_generated_at: '2026-06-02T04:14:17Z'
-  summary_source_hash: 004436cf14ff0056136889c58d676295c6dd2088f06f57f397a07ec9004e6b44
-  faq_generated_at: '2026-06-03T01:18:47Z'
-  faq_source_hash: 004436cf14ff0056136889c58d676295c6dd2088f06f57f397a07ec9004e6b44
-  summary: >-
-    Learn how to build and install custom Linux kernels on Arm cloud instances using TuxMake.
-    You will provision an Ubuntu 24.04 LTS Arm server (minimum 24 vCPUs and 200 GB free storage),
-    configure a build environment, compile specific kernel versions, and install or package the
-    resulting kernels. The path covers standard workflows for general-purpose kernels as well
-    as configurations for 64 KB page sizes. It also explains how to produce Fastpath-enabled builds
-    for testing workflows, which are build-only. Examples use AWS, but the steps apply to any
-    provider offering 64-bit Arm Ubuntu instances. By the end, you will be able to build, install,
-    and prepare kernels for Fastpath validation on Arm cloud machines.
-  faqs:
-  - question: What do I need on my Arm cloud instance before starting?
-    answer: >-
-      Use an Ubuntu 24.04 LTS Arm instance with at least 24 vCPUs and 200 GB of free storage,
-      and ensure you have SSH access. The example uses AWS, but any provider offering 64-bit Arm
-      Ubuntu instances is suitable.
-  - question: How do I choose which Linux kernel version to build with TuxMake?
-    answer: >-
-      Specify your desired version with the --tags flag. The versions shown in the examples (such
-      as v6.18.1) are valid but arbitrary, so you can substitute the version you need.
-  - question: What result should I expect from a standard TuxMake build workflow?
-    answer: >-
-      Standard workflows produce general-purpose kernels suitable for production deployment, development
-      testing, or packaging. You can build for direct installation on the instance or create artifacts
-      for downstream packaging, and configure options such as 64 KB page sizes.
-  - question: What is the correct workflow for Fastpath builds?
-    answer: >-
-      Fastpath builds are build-only; do not combine --fastpath true (or the demo shortcut) with
-      --kernel-install or any --install-from commands. Build with Fastpath enabled, then copy
-      the flat artifacts (for example, the kernel image and modules) to your Fastpath test environment.
-  - question: What should I check if compilation is very slow or runs out of memory?
-    answer: >-
-      Use a sufficiently large Arm instance because smaller instances take longer or can run out
-      of memory during compilation. Meeting the minimum of 24 vCPUs and ample free storage helps
-      avoid resource-related build failures.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Geremy Cohen
 
diff --git a/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md b/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
index 81e9299b13..997b913a05 100644
--- a/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
@@ -15,61 +15,9 @@ learning_objectives:
 
 prerequisites:
     - A running Kubernetes cluster accessible with `kubectl`.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:19:42Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 43e4e28b88b9721ca94457418f3b4887d0099017578a54da1db5fcdc11f4a122
-  summary_generated_at: '2026-06-02T04:14:53Z'
-  summary_source_hash: 43e4e28b88b9721ca94457418f3b4887d0099017578a54da1db5fcdc11f4a122
-  faq_generated_at: '2026-06-03T01:19:42Z'
-  faq_source_hash: 43e4e28b88b9721ca94457418f3b4887d0099017578a54da1db5fcdc11f4a122
-  summary: >-
-    Learn how to assess and migrate Kubernetes container images to Arm-compatible versions using
-    KubeArchInspect. You will install KubeArchInspect on Linux, ensure kubectl is configured to
-    your cluster, run kubearchinspect images to inventory running images, and generate a report
-    by querying source registries for available architectures. You will analyze the results using
-    clear indicators (arm64 supported, not available, available in newer version, or error) and
-    make configuration changes to upgrade images that include Arm support. This introductory path
-    targets developers who want to confirm Arm readiness for their workloads and can be applied
-    to Kubernetes clusters, including those on major cloud providers. Prerequisite: a running
-    Kubernetes cluster accessible with kubectl. Estimated time: 15 minutes.
-  faqs:
-  - question: What do I need before running KubeArchInspect?
-    answer: >-
-      You need a running Kubernetes cluster and kubectl configured to access it. Install kubearchinspect
-      on a Linux environment. No other explicit prerequisites are listed.
-  - question: Which command should I use to generate the image report?
-    answer: >-
-      Run: kubearchinspect images. This connects to your cluster and produces a report of images
-      in use and their available architectures.
-  - question: How does KubeArchInspect determine whether an image supports Arm?
-    answer: >-
-      It queries the image’s source registry and checks which architectures are available. The
-      report highlights whether arm64 is present for each image.
-  - question: How do I interpret the output symbols in the report?
-    answer: >-
-      A green tick (✅) means the image already supports arm64. A red cross (❌) means arm64 is
-      not available; a blue up (🆙) indicates a newer version includes arm64; a red cross mark
-      (🚫) signals an error occurred when checking the image, which may indicate a registry connectivity
-      issue.
-  - question: What should I do after running the report?
-    answer: >-
-      Use the results to plan configuration changes that upgrade images to versions with arm64
-      support. Prioritize images marked with 🆙 for straightforward upgrades, and review ❌ entries
-      to determine your next steps.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md b/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
index 79cfd87410..dafd8751db 100644
--- a/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
@@ -12,58 +12,9 @@ learning_objectives:
 
 prerequisites:
     - A computer with [Terraform](/install-guides/terraform/) and the [AWS CLI](/install-guides/aws-cli/) installed. 
-    
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:20:35Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 22fa96e29143f2e0dc0c204919422914b3f70d63ddf833cf1067fbf67b525aa0
-  summary_generated_at: '2026-06-02T04:15:30Z'
-  summary_source_hash: 22fa96e29143f2e0dc0c204919422914b3f70d63ddf833cf1067fbf67b525aa0
-  faq_generated_at: '2026-06-03T01:20:35Z'
-  faq_source_hash: 22fa96e29143f2e0dc0c204919422914b3f70d63ddf833cf1067fbf67b525aa0
-  summary: >-
-    This introductory Learning Path shows how to deploy AWS Lambda functions on AWS Graviton processors
-    using Terraform. From a Linux host with Terraform and the AWS CLI installed, you will provision
-    Lambda functions configured with the arm64 architecture and implement examples in both Node.js
-    and Python. The steps demonstrate selecting the runtime, specifying the target architecture
-    in Terraform, and reusing the workflow across languages, including a simple Python function
-    that assembles a greeting message from event fields. By the end, you will be able to deploy
-    Lambda functions on Graviton with Terraform and adapt the same approach for either runtime.
-    No other prerequisites are explicitly listed. Estimated time: 30 minutes.
-  faqs:
-  - question: Which architecture should I select in Terraform to run the function on Graviton?
-    answer: >-
-      Choose arm64 for the function architecture in your Terraform configuration. This setting
-      deploys the Lambda function on Graviton processors.
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a computer with Terraform and the AWS CLI installed. No other explicit prerequisites
-      are listed, and the path targets Linux.
-  - question: Can I reuse the same deployment approach for Python and Node.js?
-    answer: >-
-      Yes. Follow the Node.js deployment workflow and, for Python, replace the Node.js code with
-      the provided Python Lambda function.
-  - question: How do I know the sample Python function is behaving as expected?
-    answer: >-
-      The Python handler constructs a message using the event’s first_name and last_name fields.
-      When invoked with those fields, expect a response containing the formatted greeting.
-  - question: What should I check if Terraform deployment does not work as expected?
-    answer: >-
-      Verify that Terraform and the AWS CLI are installed and accessible. Also confirm that the
-      Lambda function architecture is set to arm64 as shown in the path.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md b/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
index d67daf8d8e..a2968830cb 100644
--- a/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
@@ -14,58 +14,9 @@ learning_objectives:
 prerequisites:
     - An Arm server or virtual machine instance from a cloud service provider with Ubuntu installed
     - Knowledge of how to build a MySQL server and run the sysbench benchmark test
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:21:05Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 66d13edff1371295f0e88a618e924ca67b85bcc8aa72034d1e582a0b267f0d05
-  summary_generated_at: '2026-06-02T04:16:07Z'
-  summary_source_hash: 66d13edff1371295f0e88a618e924ca67b85bcc8aa72034d1e582a0b267f0d05
-  faq_generated_at: '2026-06-03T01:21:05Z'
-  faq_source_hash: 66d13edff1371295f0e88a618e924ca67b85bcc8aa72034d1e582a0b267f0d05
-  summary: >-
-    This Learning Path shows how to enable libhugetlbfs on an Arm server running Ubuntu Linux
-    and measure its impact on memory-intensive workloads. You will configure hugepages so application
-    text, data, malloc, and shared memory can use larger pages, then apply the approach to MySQL
-    by modifying its build flags and benchmarking with sysbench to compare results. The target
-    environment is an Arm server or a cloud VM (for example, from AWS, Microsoft Azure, Google
-    Cloud, or Oracle) with Ubuntu installed. This advanced path expects familiarity with building
-    MySQL and running sysbench. Tools referenced include GCC and MySQL. Estimated time to complete
-    is about 60 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an Arm server or virtual machine with Ubuntu installed, plus knowledge of how to
-      build a MySQL server and run the sysbench benchmark test. No additional prerequisites are
-      explicitly listed.
-  - question: Can I use a cloud VM for this Learning Path?
-    answer: >-
-      Yes. An Arm-based instance from AWS, Microsoft Azure, Google Cloud, or Oracle with Ubuntu
-      installed meets the environment requirement.
-  - question: Where do I add libhugetlbfs build options when compiling MySQL?
-    answer: >-
-      Add the options to both -DCMAKE_C_FLAGS and -DCMAKE_CXX_FLAGS during the MySQL build configuration
-      as described in the steps. The path shows the required flags to enable libhugetlbfs.
-  - question: Do I need to change both build and run settings for MySQL?
-    answer: >-
-      Yes. The steps explain how to modify both the build and the run of the MySQL server to enable
-      libhugetlbfs.
-  - question: How should I evaluate the effect of enabling libhugetlbfs?
-    answer: >-
-      Run your baseline workload, then enable libhugetlbfs and repeat the same test to compare
-      results. For MySQL, you are expected to use sysbench to measure before-and-after performance.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Bolt Liu
 
diff --git a/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md b/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
index f4ff7aab1d..ca2c207aa0 100644
--- a/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
@@ -13,59 +13,9 @@ learning_objectives:
 
 prerequisites:
     - An AWS Graviton4 r8g.16xlarge instance to test Arm performance optimizations, or any [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an on-premise Arm server.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:21:51Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: d82aa4faeb599a3a1ac12d477204ebe0a4ddb99564bedced86ca0fc4851e17b9
-  summary_generated_at: '2026-06-02T04:16:47Z'
-  summary_source_hash: d82aa4faeb599a3a1ac12d477204ebe0a4ddb99564bedced86ca0fc4851e17b9
-  faq_generated_at: '2026-06-03T01:21:51Z'
-  faq_source_hash: d82aa4faeb599a3a1ac12d477204ebe0a4ddb99564bedced86ca0fc4851e17b9
-  summary: >-
-    Deploy a pre-quantized Llama‑3.1‑8B chatbot on an Arm server using llama.cpp with KleidiAI,
-    and expose it through an OpenAI‑compatible API. You will download and build llama.cpp, fetch
-    the pre‑quantized model from Hugging Face, run it on your Arm CPU, and measure performance.
-    The path targets Ubuntu 24.04 LTS on Arm with a minimum of 4 CPU cores, 8 GB RAM, and 32 GB
-    disk; it was tested on an AWS Graviton4 r8g.16xlarge instance but can run on other Arm‑based
-    instances or on‑prem Arm servers. You will also install jq and start the llama.cpp server,
-    which listens on port 8080 and can be accessed by OpenAI‑style clients over the network.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      Use an Arm server running Ubuntu 24.04 LTS with at least four cores, 8 GB RAM, and 32 GB
-      of disk. The instructions were tested on an AWS Graviton4 r8g.16xlarge instance, but any
-      Arm-based instance or on-prem Arm server meeting these resources is acceptable.
-  - question: Which LLM model should I download for this setup?
-    answer: >-
-      Download a pre-quantized Llama 3.1 model from Hugging Face. The Learning Path guides you
-      to use that model with llama.cpp on your Arm CPU.
-  - question: How do I start and access the OpenAI-compatible server?
-    answer: >-
-      After building llama.cpp (via make in a prior step), start the server binary; it listens
-      on port 8080. You can submit requests using an OpenAI-compatible API from the same machine
-      or over the network.
-  - question: Is any extra package required to interact with the API responses?
-    answer: >-
-      Yes. Install jq to work with JSON responses (for example, using sudo apt install jq -y on
-      Ubuntu). This helps format and inspect the server’s OpenAI-compatible output.
-  - question: Can I measure performance during inference, and how is it covered?
-    answer: >-
-      Yes. The Learning Path includes running the pre-quantized model on your Arm CPU and measuring
-      performance as part of the procedure; it does not list additional tools beyond those in
-      the steps.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author:
     - Pareena Verma
diff --git a/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md b/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
index 3f01b20bed..c6eb5defbd 100644
--- a/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
@@ -18,59 +18,9 @@ prerequisites:
     - A basic understanding of Python and ML concepts.
     - A basic understanding of Streamlit.
     - A basic understanding of LLM fundamentals.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:22:45Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 3e8307a5daf435c21f3cecff635ac51724a63ff9ea4307082d869601563b4204
-  summary_generated_at: '2026-06-02T04:17:37Z'
-  summary_source_hash: 3e8307a5daf435c21f3cecff635ac51724a63ff9ea4307082d869601563b4204
-  faq_generated_at: '2026-06-03T01:22:45Z'
-  faq_source_hash: 3e8307a5daf435c21f3cecff635ac51724a63ff9ea4307082d869601563b4204
-  summary: >-
-    This Learning Path shows how to deploy a production-ready, vision-enabled chatbot on Arm-based
-    servers using Google Cloud Axion. You will build a Flask backend that downloads a Llama 3.2‑Vision
-    model from Hugging Face, applies 4‑bit quantization, and serves inference with PyTorch and
-    Transformers, and a Streamlit frontend that accepts images and text prompts. The instructions
-    target Ubuntu 24.04 LTS and were tested on a Google Cloud c4a-standard-64 instance; an Arm
-    server with at least 32 CPU cores is required. You will launch the web UI on port 8501 and
-    monitor and analyze inference on Arm CPUs. Prerequisites include basic Python/ML, Streamlit,
-    LLM fundamentals, and familiarity with REST and web services.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      You need a Google Cloud Axion compute instance or any Arm-based instance with at least 32
-      CPU cores. Familiarity with REST APIs and web services, basic Python and ML concepts, Streamlit,
-      and LLM fundamentals is expected.
-  - question: Which environment is targeted and what instance was used for testing?
-    answer: >-
-      The steps are tailored for Arm servers running Ubuntu 24.04 LTS. They were tested on a Google
-      Cloud c4a-standard-64 instance.
-  - question: Which model is used and how is it prepared for inference?
-    answer: >-
-      The backend downloads the Llama 3.2‑Vision model from Hugging Face and performs 4‑bit quantization.
-      It then serves the model with PyTorch on Arm CPUs.
-  - question: How do I access the web application once the services are running?
-    answer: >-
-      Open your browser to http://[your instance ip]:8501. On Google Cloud, you may need to allow
-      inbound TCP traffic on port 8501 in your instance’s firewall rules.
-  - question: What result should I expect to validate that inference is working?
-    answer: >-
-      From the Streamlit UI, upload an image and enter a text prompt; the app should return a
-      generated text response that uses the image as context. The backend Flask service streams
-      the model’s output to the frontend.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Nobel Chowdary Mandepudi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
index 3a0e0872c0..72650f04fd 100644
--- a/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
@@ -19,61 +19,9 @@ prerequisites:
     - Understanding of transformer models
     - Knowledge of Arm Streamline usage
     - An Arm Neoverse or Cortex-A hardware platform running Linux or Android
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:23:50Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 36bf3c45f0b38350714ba41ea88c1551b33f6d299a65b3b0d6668fee2d88d835
-  summary_generated_at: '2026-06-02T04:18:16Z'
-  summary_source_hash: 36bf3c45f0b38350714ba41ea88c1551b33f6d299a65b3b0d6668fee2d88d835
-  faq_generated_at: '2026-06-03T01:23:50Z'
-  faq_source_hash: 36bf3c45f0b38350714ba41ea88c1551b33f6d299a65b3b0d6668fee2d88d835
-  summary: >-
-    Learn how to profile llama.cpp inference on Arm CPUs using Arm Streamline. This advanced path
-    guides you to integrate Streamline Annotation Markers and Annotation Channels into the llama.cpp
-    codebase to visualize and analyze the Prefill and Decode stages, and to perform operator-level
-    timing during token generation. You will build llama-cli, configure the gator daemon, and
-    prepare your Arm Neoverse or Cortex-A target running Linux or Android with the required executables
-    and model files. By the end, you will capture and interpret Streamline data and evaluate multi-core
-    and multi-thread execution characteristics. Prerequisites include familiarity with llama.cpp,
-    transformer models, and Arm Streamline.
-  faqs:
-  - question: What do I need before running the profiling steps?
-    answer: >-
-      You need an Arm Neoverse or Cortex-A hardware platform running Linux or Android, plus a
-      basic understanding of llama.cpp, transformer models, and Arm Streamline usage. These are
-      listed prerequisites for this Learning Path.
-  - question: Which option should I use to visualize the Prefill and Decode stages?
-    answer: >-
-      Use Streamline’s Annotation Marker feature and insert markers in llama.cpp to tag the Prefill
-      and Decode stages. These markers appear in the Streamline timeline to correlate performance
-      data with token generation phases.
-  - question: How can I analyze operator-level performance during token generation?
-    answer: >-
-      Use Streamline Annotation Channels to group related operations and track their timing as
-      separate visual channels. This lets you examine execution time per node in the compute graph
-      and see concurrent operations over time.
-  - question: How do I evaluate multi-core or multi-thread execution in this path?
-    answer: >-
-      Capture a Streamline profile while running llama.cpp and use your annotations to correlate
-      activity during Prefill and Decode across threads and cores. The Learning Path guides you
-      through assessing multi-core and multi-thread execution on Arm CPUs.
-  - question: What should I check if Streamline is not collecting data from my target?
-    answer: >-
-      Verify that the gator daemon is configured and running on the target system. Also ensure
-      the required executables and model files are present on the device before capturing data.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: 
     - Zenon Zhilong Xiu
diff --git a/content/learning-paths/servers-and-cloud-computing/lse/_index.md b/content/learning-paths/servers-and-cloud-computing/lse/_index.md
index 7c12568933..c4db833a32 100644
--- a/content/learning-paths/servers-and-cloud-computing/lse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/lse/_index.md
@@ -13,57 +13,9 @@ learning_objectives:
 
 prerequisites:
     - An [AWS account](/learning-paths/servers-and-cloud-computing/csp/aws/) to access instance types with different AWS Graviton processors. If you don't have an AWS account, you can substitute other Arm Linux computers.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:24:38Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 9610291239b88c67e21055f94cd03fe7cf80f7d875d53ebe0d8de7837ce99fa7
-  summary_generated_at: '2026-06-02T04:18:56Z'
-  summary_source_hash: 9610291239b88c67e21055f94cd03fe7cf80f7d875d53ebe0d8de7837ce99fa7
-  faq_generated_at: '2026-06-03T01:24:38Z'
-  faq_source_hash: 9610291239b88c67e21055f94cd03fe7cf80f7d875d53ebe0d8de7837ce99fa7
-  summary: >-
-    This Learning Path introduces Large System Extensions (LSE) on Arm processors and shows how
-    to check whether your application and toolchain use LSE for atomic operations. You will build
-    and run a short C example on a Linux system to observe multi-threaded atomic increments and
-    verify if the compiler emits LSE instructions. The path is introductory and relevant to developers
-    targeting Arm servers based on Neoverse, using AWS Graviton instances or another Arm Linux
-    machine. You will use GCC and follow a runbook to create the sample, compile it, and assess
-    LSE usage. No additional prerequisites are explicitly listed beyond access to an Arm Linux
-    environment; an AWS account is suggested for convenient access to Arm instances.
-  faqs:
-  - question: What do I need before running the example?
-    answer: >-
-      You need access to an Arm Linux computer. An AWS account is recommended to use instance
-      types with different AWS Graviton processors, but you can substitute other Arm Linux systems
-      if you prefer.
-  - question: Which compiler should I use to build the example program?
-    answer: >-
-      Use GCC on your Arm Linux computer. The Learning Path uses GCC to build the C example that
-      exercises atomic operations.
-  - question: How do I know if my build is using Large System Extensions?
-    answer: >-
-      The steps guide you to build and run an example and then verify whether the compiler generated
-      LSE instructions. Follow the verification instructions in the path to confirm LSE usage.
-  - question: Can I complete this Learning Path without an AWS account?
-    answer: >-
-      Yes. If you do not have an AWS account, you can use any other Arm Linux computer as a substitute.
-  - question: What result should I expect after running the example program?
-    answer: >-
-      You will compile and run a multithreaded C program that uses atomic operations. The expected
-      outcome is that you can determine whether LSE instructions were generated for the example.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md b/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
index e78dc571cd..47278d0dc1 100644
--- a/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
@@ -16,59 +16,9 @@ learning_objectives:
 prerequisites:
     - Cloud service provider accounts for each service you want to use including AWS, Azure, and GCP
     - A local computer with [Docker](/install-guides/docker/), [Terraform](/install-guides/terraform/), [AWS CLI](/install-guides/aws-cli/), [Azure CLI](/install-guides/azure-cli/), [Google Cloud CLI](/install-guides/gcloud/), and [Ansible](/install-guides/ansible/) installed
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:25:42Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: db4ce1c59ad10bd127b4990c82ce876311d7dc7e1ad5d39ec132daf82ceb8b79
-  summary_generated_at: '2026-06-02T04:19:21Z'
-  summary_source_hash: db4ce1c59ad10bd127b4990c82ce876311d7dc7e1ad5d39ec132daf82ceb8b79
-  faq_generated_at: '2026-06-03T01:25:42Z'
-  faq_source_hash: db4ce1c59ad10bd127b4990c82ce876311d7dc7e1ad5d39ec132daf82ceb8b79
-  summary: >-
-    Learn how to deploy MariaDB on Arm-based cloud infrastructure across AWS, Microsoft Azure,
-    and Google Cloud using Terraform, Ansible, Docker, and Amazon RDS. You will provision single
-    virtual machines on each provider with automation, deploy MariaDB in a Docker container using
-    Ansible, and create a managed MariaDB database with Amazon RDS via Terraform. The steps target
-    Linux hosts and assume cloud accounts for the services you plan to use. Work from a local
-    computer with Docker, Terraform, the AWS/Azure/Google Cloud CLIs, and Ansible installed. This
-    introductory path takes about 90 minutes and results in MariaDB running on Arm servers and
-    as a managed RDS service.
-  faqs:
-  - question: What do I need installed locally before starting?
-    answer: >-
-      Install Docker, Terraform, AWS CLI, Azure CLI, Google Cloud CLI, and Ansible on your local
-      computer. You also need cloud accounts for the services you plan to use.
-  - question: Can I follow only the sections for the cloud provider I use?
-    answer: >-
-      Yes. The Learning Path includes AWS, Azure, and GCP; complete the sections that match the
-      accounts and services you have available.
-  - question: Which tools does each deployment method use?
-    answer: >-
-      EC2, Azure, and GCP VM deployments use Terraform and Ansible to provision a single Arm-based
-      instance and install MariaDB. The Amazon RDS section uses Terraform. The Docker section
-      uses Ansible to deploy a MariaDB container.
-  - question: What additional setup is required for the Docker-based deployment?
-    answer: >-
-      You need a cloud instance, VM, or physical machine with Ubuntu installed, running, and ready
-      to deploy MariaDB. Ansible must be installed locally, and you can reuse the same SSH key
-      pair.
-  - question: What credentials are required for the Amazon RDS section?
-    answer: >-
-      An AWS account is required along with an AWS access key ID and secret access key. You also
-      need Terraform and the AWS CLI installed on the computer you use to run the steps.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/memcached/_index.md b/content/learning-paths/servers-and-cloud-computing/memcached/_index.md
index fd08bd8c08..3acdc0578c 100644
--- a/content/learning-paths/servers-and-cloud-computing/memcached/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memcached/_index.md
@@ -13,57 +13,9 @@ learning_objectives:
 
 prerequisites:
 - An Arm based instance from an appropriate cloud service provider.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:26:50Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: b42ca5cc8f4db6ba6019f3720a5ef46119aa403a2baaef5362e874f898ce0419
-  summary_generated_at: '2026-06-02T04:20:04Z'
-  summary_source_hash: b42ca5cc8f4db6ba6019f3720a5ef46119aa403a2baaef5362e874f898ce0419
-  faq_generated_at: '2026-06-03T01:26:50Z'
-  faq_source_hash: b42ca5cc8f4db6ba6019f3720a5ef46119aa403a2baaef5362e874f898ce0419
-  summary: >-
-    This introductory Learning Path shows how to install and run Memcached on an Arm-based Ubuntu
-    Linux cloud instance and measure its performance with the open-source memtier_benchmark tool.
-    You will provision an Arm server (tested on AWS and Oracle Cloud), install gcc and required
-    development libraries such as libevent, and set up the packages needed to build and run the
-    benchmark. By the end, you will have Memcached running and will execute a benchmark workload
-    to generate performance results for your environment. Prerequisite: access to an Arm-based
-    instance from a cloud service provider; no other explicit prerequisites are listed.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need access to an Arm-based instance from a cloud service provider running Ubuntu Linux.
-      Install gcc on the instance by following the GNU compiler install guide.
-  - question: Which cloud platforms are referenced in this Learning Path?
-    answer: >-
-      The steps have been tested on AWS and Oracle Cloud. Any appropriate Arm-based cloud instance
-      running Ubuntu Linux is suitable.
-  - question: Which packages should I install to prepare for memcached and the benchmark?
-    answer: >-
-      Install libevent-dev for memcached. For memtier_benchmark, install build-essential, autoconf,
-      automake, libpcre3-dev, libevent-dev, pkg-config, zlib1g-dev, libssl-dev, wget, and git.
-  - question: Which benchmark tool is used to measure memcached performance?
-    answer: >-
-      The path uses the open-source memtier_benchmark. You install its required build and runtime
-      dependencies and then run it against your memcached service.
-  - question: How do I know the setup worked?
-    answer: >-
-      After starting memcached, run memtier_benchmark; a successful connection and benchmark output
-      indicate the service is running and being exercised. The benchmark will report performance
-      metrics you can review.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md b/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
index 418ba36db9..a4dcfde2b0 100644
--- a/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
@@ -17,61 +17,9 @@ prerequisites:
 - An Azure portal [account](https://azure.microsoft.com/en-in/get-started/azure-portal)
 - A Google Cloud [account](https://console.cloud.google.com/)
 - A machine with [Terraform](/install-guides/terraform/), [AWS CLI](/install-guides/aws-cli), [Google Cloud CLI](/install-guides/gcloud), [Azure CLI](/install-guides/azure-cli), [AWS IAM authenticator](https://docs.aws.amazon.com/eks/latest/userguide/install-aws-iam-authenticator.html), and [Ansible](/install-guides/ansible/) installed
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:27:30Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 4efe46aaf4580a6b8f263b69e8f072211bfd24fcf7f7a885689c927fc05a4c63
-  summary_generated_at: '2026-06-02T04:20:49Z'
-  summary_source_hash: 4efe46aaf4580a6b8f263b69e8f072211bfd24fcf7f7a885689c927fc05a4c63
-  faq_generated_at: '2026-06-03T01:27:30Z'
-  faq_source_hash: 4efe46aaf4580a6b8f263b69e8f072211bfd24fcf7f7a885689c927fc05a4c63
-  summary: >-
-    Learn how to deploy Memcached as a cache for MySQL and PostgreSQL on Arm-based cloud instances
-    using Terraform and Ansible. You will provision Linux instances on AWS, Microsoft Azure, and
-    Google Cloud, then install and configure Memcached to serve as a cache layer for your database
-    workload. The path includes sections for MySQL on AWS, Azure, and GCP, and for PostgreSQL
-    on AWS and Azure; PostgreSQL on GCP is not explicitly listed in the provided steps. No explicit
-    prior Terraform knowledge is required, but related automation guides are referenced. Prerequisites
-    include cloud accounts and a machine with Terraform, AWS CLI, Google Cloud CLI, Azure CLI,
-    AWS IAM authenticator, and Ansible installed. Estimated time to complete is 60 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need AWS, Azure, and Google Cloud accounts, and a machine with Terraform, AWS CLI, Google
-      Cloud CLI, Azure CLI, AWS IAM authenticator, and Ansible installed. These tools are required
-      to provision infrastructure and configure Memcached.
-  - question: Which database and cloud combinations are covered in the sections?
-    answer: >-
-      MySQL on AWS, Azure, and Google Cloud Arm-based instances, and PostgreSQL on AWS and Azure.
-      PostgreSQL on Google Cloud is listed in the objectives but is not explicitly shown in the
-      provided section excerpts.
-  - question: Where do I run Terraform and Ansible from?
-    answer: >-
-      From any computer that has the required tools installed; a desktop or laptop is suitable.
-      The deployed target instances run Linux.
-  - question: I'm new to Terraform—what should I read first?
-    answer: >-
-      Each cloud-specific section recommends reviewing the corresponding “Automate [cloud] instance
-      creation using Terraform” guide before you start. Use the AWS, Azure, or GCP guide referenced
-      by the section you plan to follow.
-  - question: What result should I expect after completing a section?
-    answer: >-
-      A running Arm-based cloud instance with Memcached configured to act as a cache for the chosen
-      database (MySQL or PostgreSQL). The deployment is performed with Terraform and configured
-      with Ansible as described in the section.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md b/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
index 4db10435b4..50f11ff106 100644
--- a/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
@@ -18,64 +18,9 @@ prerequisites:
     - Two or more Arm Linux systems with root or sudo access. The examples use AWS Graviton2 and Graviton4 instances, but other systems are possible
     - Arm System Characterization Tool (ASCT) installed on each system
     - A good understanding of CPU memory subsystems, including cache hierarchies, cache lines, and DRAM in the memory hierarchy
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:28:09Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: d61c9ddcef1c7ffe12b3ee818852fb3f0a62a90cec451692c1186cf2c01de625
-  summary_generated_at: '2026-06-02T04:21:33Z'
-  summary_source_hash: d61c9ddcef1c7ffe12b3ee818852fb3f0a62a90cec451692c1186cf2c01de625
-  faq_generated_at: '2026-06-03T01:28:09Z'
-  faq_source_hash: d61c9ddcef1c7ffe12b3ee818852fb3f0a62a90cec451692c1186cf2c01de625
-  summary: >-
-    This advanced Learning Path shows how to characterize the CPU-side memory subsystem on Arm
-    Neoverse-based Linux systems using the Arm System Characterization Tool (ASCT). You will identify
-    CPU topology, cache hierarchy, and NUMA layout, then measure cache and memory latency with
-    a pointer-chase benchmark. You will also measure single-core and multi-core streaming bandwidth
-    across L1, L2, last-level cache, and DRAM, evaluate latency under bandwidth pressure, and
-    examine coherency latency. Examples use AWS Graviton2 and Graviton4, but any two or more Arm
-    Linux systems with ASCT installed and root or sudo access can be used. By the end, you can
-    compare results across Arm systems and draw practical conclusions.
-  faqs:
-  - question: What do I need before running these tests?
-    answer: >-
-      You need two or more Arm Linux systems with root or sudo access, and ASCT installed on each
-      system. The examples use AWS Graviton2 and Graviton4, but other Arm systems are possible.
-      A good understanding of cache hierarchies and DRAM is assumed.
-  - question: How do I identify core, cache, and NUMA topology on my system?
-    answer: >-
-      Use standard Linux tools to determine the CPU topology, cache hierarchy, and NUMA configuration
-      before testing. This context helps you interpret where cache-level transitions and bandwidth
-      limits should appear in the results.
-  - question: Which ASCT benchmarks should I run to measure latency and bandwidth?
-    answer: >-
-      Run the pointer-chase benchmark to measure dependent-load latency at each level of the memory
-      hierarchy. Use the single-core bandwidth sweep to measure per-core streaming bandwidth,
-      then run the multi-core peak-bandwidth and loaded-latency benchmarks to characterize scaling
-      and contention.
-  - question: How do I know the latency and bandwidth measurements are reasonable?
-    answer: >-
-      Expect step-like increases in latency as working sets exceed L1, then L2, then LLC and fall
-      into DRAM, and look for bandwidth plateaus consistent with each level. Pointer chasing defeats
-      hardware prefetching and out-of-order execution, so the latency curves should reflect true
-      dependent-load behavior.
-  - question: How should I compare results across systems like Graviton2 and Graviton4?
-    answer: >-
-      Run the same ASCT benchmarks under similar conditions on each system, then compare latency
-      curves, bandwidth sweeps, and the points where scaling saturates. Use these comparisons
-      to draw conclusions about cache hierarchy behavior and shared-resource limits across generations.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md b/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
index 68361c3709..c1769859d4 100644
--- a/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
@@ -18,61 +18,9 @@ prerequisites:
     - Familiarity with Arm assembly language, and the ability to find relevant information on Arm assembly instructions.
     - Familiarity with general-purpose registers.
     - Familiarity with memory barriers, including Acquire-Release Semantics.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:28:34Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 6aa61de638be961339d958345225d696ff2b83b8f9cab22bf956f9bf2d15c1aa
-  summary_generated_at: '2026-06-02T04:22:41Z'
-  summary_source_hash: 6aa61de638be961339d958345225d696ff2b83b8f9cab22bf956f9bf2d15c1aa
-  faq_generated_at: '2026-06-03T01:28:34Z'
-  faq_source_hash: 6aa61de638be961339d958345225d696ff2b83b8f9cab22bf956f9bf2d15c1aa
-  summary: >-
-    This advanced Learning Path guides you through testing and validating thread synchronization
-    in the Arm memory model on Linux using Herd7, Litmus7, and Arm hardware. You will create and
-    run litmus tests, including an abbreviated MP.litmus example, to compare formal model predictions
-    against observed hardware behavior. The exercises focus on Arm ISA acquire-release semantics
-    with LDAR and STLR, and show how to compare results from different synchronization approaches.
-    The path is intended for developers with knowledge of memory consistency models, thread synchronization,
-    Arm assembly, general-purpose registers, and memory barriers (including acquire-release semantics).
-    No additional prerequisites are explicitly listed beyond these skills.
-  faqs:
-  - question: Do I need access to Arm hardware, and what operating system is used?
-    answer: >-
-      Yes, testing on Arm hardware is part of the Learning Path. The target operating system is
-      Linux, and no specific hardware platform or distribution is explicitly listed.
-  - question: Which tools should I use for modeling versus running on hardware?
-    answer: >-
-      Use Herd7 to test snippets against the formal definition of the Arm memory model, and Litmus7
-      to run litmus tests on Arm hardware. A Runbook structures the steps; diy7 is referenced
-      only in additional resources.
-  - question: How do I start with a litmus test in this path?
-    answer: >-
-      Follow the Herd7 and Litmus7 primer to create the provided abbreviated MP.litmus example
-      as test.litmus. Run it with Herd7 to confirm the syntax is correct and to produce results
-      you can later compare with hardware runs.
-  - question: Which Arm synchronization instructions are covered in the examples?
-    answer: >-
-      The path focuses on acquire-release ordering using LDAR (load-acquire) and STLR (store-release).
-      Other atomic instructions like CAS, SWP, LDADD, and STADD are mentioned but are outside
-      the scope of this Learning Path.
-  - question: What results should I expect to compare when I finish?
-    answer: >-
-      You will compare the observed outcomes of different thread synchronization approaches between
-      the formal model (Herd7) and runs on Arm hardware (Litmus7). Specific expected result values
-      are not listed in the context.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Julio Suarez
 
diff --git a/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md b/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
index 59ba1c9499..b140b4b046 100644
--- a/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
@@ -13,59 +13,9 @@ learning_objectives:
 prerequisites:
     - Basic understanding of networking principles such as Transmission Control Protocol/Internet Protocol (TCP/IP) and User Datagram Protocol (UDP).
     - Access to two [Arm-based cloud instances](/learning-paths/servers-and-cloud-computing/csp/).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:29:10Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: a67d36fb650b77c170c15f193049490286a3f097801d0c79d701d3f5610fe1dc
-  summary_generated_at: '2026-06-02T04:23:38Z'
-  summary_source_hash: a67d36fb650b77c170c15f193049490286a3f097801d0c79d701d3f5610fe1dc
-  faq_generated_at: '2026-06-03T01:29:10Z'
-  faq_source_hash: a67d36fb650b77c170c15f193049490286a3f097801d0c79d701d3f5610fe1dc
-  summary: >-
-    Learn to microbenchmark and tune network performance on Arm-based Linux systems using iPerf3
-    and Linux traffic control (tc). You will provision two Arm-based instances—such as AWS EC2
-    with Graviton within a VPC or equivalent Arm-based VMs from other cloud providers—and run
-    TCP/UDP tests in cloud-to-cloud and local-to-cloud scenarios. The steps show how to start
-    iPerf3, simulate latency and packet loss with tc, and adjust basic Linux kernel parameters,
-    then compare results across environments. This introductory path assumes a basic understanding
-    of TCP/IP and UDP and access to two Arm-based cloud instances. By the end, you can run accurate
-    iPerf3 tests, model adverse network conditions, and apply simple tunables to evaluate behavior.
-  faqs:
-  - question: What do I need before running the tests?
-    answer: >-
-      You need two Arm-based Linux cloud instances and a basic understanding of TCP/IP and UDP.
-      Ensure the systems can reach each other over the network, and if you use AWS, the setup
-      follows EC2 instances within a VPC.
-  - question: How do I start the iPerf3 server and confirm it’s ready?
-    answer: >-
-      Run iperf3 -s on the server node. You should see “Server listening on 5201” by default;
-      if that port is in use, start the server with -p to select another port.
-  - question: Can I use a cloud provider other than AWS for this Learning Path?
-    answer: >-
-      Yes. While the setup examples use AWS EC2 with Graviton, you can use Linux virtual machines
-      from other cloud service providers.
-  - question: How do I simulate latency or packet loss with tc and which interface should I modify?
-    answer: >-
-      First, identify the network interface on the client system using ip addr show. Apply tc
-      rules (such as delay or loss) to that interface to simulate different network conditions.
-  - question: What should I check if a local-to-cloud test cannot connect?
-    answer: >-
-      Update the cloud server’s security group to allow incoming TCP connections from your local
-      machine. Also ensure iPerf3 is installed on the local system as described in the iPerf3
-      installation guide.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md b/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
index e6c765466a..420741dadc 100644
--- a/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
@@ -15,59 +15,9 @@ learning_objectives:
 
 prerequisites:
     - Access to an [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) for testing and validation.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:29:38Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 56a38d1d742dd301d48e9ad61ff00e07048559f3f646815e22937113243adcdb
-  summary_generated_at: '2026-06-02T04:24:02Z'
-  summary_source_hash: 56a38d1d742dd301d48e9ad61ff00e07048559f3f646815e22937113243adcdb
-  faq_generated_at: '2026-06-03T01:29:38Z'
-  faq_source_hash: 56a38d1d742dd301d48e9ad61ff00e07048559f3f646815e22937113243adcdb
-  summary: >-
-    Use migrate-ease to scan your source code for architecture-specific issues before migrating
-    applications to Arm-based servers. This introductory, Linux-focused path shows how to set
-    up dependencies, clone the migrate-ease repository, and run a code scan that targets AArch64,
-    including an example that analyzes Protobuf v2.5.0 and writes a JSON report. Migrate-ease
-    is a read-only tool designed to examine x86_64-oriented code and suggest changes for AArch64
-    on Linux, and it runs on either x86_64 or Arm AArch64 machines. The path emphasizes identifying
-    architecture-dependent code and common migration challenges. An Arm-based instance is required
-    for testing and validation. Estimated completion time is about 45 minutes.
-  faqs:
-  - question: What do I need before running migrate-ease?
-    answer: >-
-      You need access to an Arm-based instance for testing and validation. You also need a Linux
-      machine (x86_64 or Arm AArch64) to run the tool, with Git and Python 3 available as shown
-      in the setup steps.
-  - question: Can I run migrate-ease on x86_64, or do I need an Arm machine?
-    answer: >-
-      You can run migrate-ease on either x86_64 or Arm AArch64 Linux systems. The tool targets
-      migration to AArch64 but does not require Arm hardware to perform the analysis.
-  - question: Which packages should I install on my distro before cloning the tool?
-    answer: >-
-      On Ubuntu 22.04 or Debian 13: python3, python3-pip, python3-venv, unzip, libmagic1, and
-      git. On Fedora 42: python3, python3-pip, unzip, and git.
-  - question: Which command does the path use to scan the Protobuf v2.5.0 source and write a report?
-    answer: >-
-      Run: python3 -m cpp --git-repo https://github.com/protocolbuffers/protobuf.git --branch
-      v2.5.0 --output result.json --march armv8-a protobuf. This analyzes the specified branch
-      and writes findings to a JSON file.
-  - question: What result should I expect, and how do I verify it?
-    answer: >-
-      Expect a JSON report named result.json containing AArch64-related findings. Verify the file
-      was created and populated; migrate-ease is read-only and will not modify your source code.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: 
     - Odin Shen
diff --git a/content/learning-paths/servers-and-cloud-computing/migration/_index.md b/content/learning-paths/servers-and-cloud-computing/migration/_index.md
index 10bae98514..d2f9a817d1 100644
--- a/content/learning-paths/servers-and-cloud-computing/migration/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/migration/_index.md
@@ -15,59 +15,9 @@ learning_objectives:
 
 prerequisites:
     - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:30:21Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 6b2b985c39579c4d0855c8c28b610ba558e25b451a6b755475ff4393e2810670
-  summary_generated_at: '2026-06-02T04:24:49Z'
-  summary_source_hash: 6b2b985c39579c4d0855c8c28b610ba558e25b451a6b755475ff4393e2810670
-  faq_generated_at: '2026-06-03T01:30:21Z'
-  faq_source_hash: 6b2b985c39579c4d0855c8c28b610ba558e25b451a6b755475ff4393e2810670
-  summary: >-
-    Learn the essentials of migrating applications to Arm servers on Linux. This introductory
-    path guides you to set up an Arm-based development machine (typically a cloud instance), analyze
-    application dependencies, and review common migration challenges and scenarios. It provides
-    practical, language-specific guidance for C/C++ on Arm Neoverse with current compilers, Java
-    on Arm (including areas to investigate for JVM performance), and Go (with emphasis on using
-    recent releases). You also learn where to check third‑party software support using the Software
-    Ecosystem Dashboard for Arm and the AWS Graviton Technical Guide. The only explicit prerequisite
-    is access to an Arm-based instance from a cloud service provider.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an Arm-based instance from a cloud service provider running Linux. A Linux Arm
-      development machine can also be set up using a virtual machine such as Multipass.
-  - question: Which C/C++ compiler versions should I use on Arm Neoverse?
-    answer: >-
-      Use the latest GCC or Clang/LLVM available for your Linux distribution. If a newer version
-      is available beyond the distribution default, install that newer version.
-  - question: How should I install Java on Arm Linux, and are there JVM options to consider?
-    answer: >-
-      There are several ways to install Java on Arm Linux; refer to the Java install guide linked
-      from the path. Java runs well on Arm, and you should review which JVM flags impact performance.
-  - question: Which Go version should I install for Arm servers?
-    answer: >-
-      Install the latest Go compiler and toolchain. Go 1.18 introduced a significant performance
-      improvement, so staying current is recommended; refer to Go releases and the Go install
-      guide.
-  - question: Where can I check if my application’s dependencies or ISV software support Arm?
-    answer: >-
-      Use the Software Ecosystem Dashboard for Arm to review supported software. The AWS Graviton
-      Technical Guide also lists ISV products with Arm support, and both resources accept GitHub
-      issues for feedback.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
index 4cab9758e5..9416ebc2f6 100644
--- a/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
@@ -15,57 +15,9 @@ prerequisites:
     - A basic understanding of a RAG pipeline.
     - An AWS Graviton3 C7g.2xlarge instance, or any [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp) from a cloud service provider or an on-premise Arm server.
     - A [Zilliz account](https://zilliz.com/cloud?utm_source=partner&utm_medium=referral&utm_campaign=2024-10-24_web_arm-dev-hub-data-loading_arm), which you can sign up for with a free trial.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:30:54Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 2eb252b19b7535fbb776a3153bfc9f70b6217088e5b4b55deb56d01393abaf3b
-  summary_generated_at: '2026-06-02T04:25:25Z'
-  summary_source_hash: 2eb252b19b7535fbb776a3153bfc9f70b6217088e5b4b55deb56d01393abaf3b
-  faq_generated_at: '2026-06-03T01:30:54Z'
-  faq_source_hash: 2eb252b19b7535fbb776a3153bfc9f70b6217088e5b4b55deb56d01393abaf3b
-  summary: >-
-    Build a Retrieval-Augmented Generation application on Arm-based servers using Zilliz Cloud
-    for vector search and llama.cpp for LLM inference. You will create a Dedicated Zilliz Cloud
-    cluster on AWS using Arm-based machines, then build and run a local llama.cpp server that
-    exposes an OpenAI-compatible API with the Llama‑3.1‑8B model. In Python, prepare embeddings
-    and call the local LLM to perform an online RAG query, validating by printing the embedding
-    dimension and sample values. Prerequisites: basic RAG knowledge, access to an Arm-based instance
-    (for example, an AWS Graviton3 C7g.2xlarge or other Arm server), a Zilliz account, and Linux.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a basic understanding of a RAG pipeline, access to an Arm-based server (for example,
-      an AWS Graviton3 C7g.2xlarge or any Arm-based instance from a cloud provider or on-prem),
-      and a Zilliz Cloud account. The environment is Linux.
-  - question: Which Zilliz Cloud cluster should I create for this path?
-    answer: >-
-      Create a Dedicated cluster deployed in AWS using Arm-based machines. You can alternatively
-      use self-hosted Milvus, but this is more complicated to set up.
-  - question: Do I need to request access to the Llama 3.1 model before launching llama.cpp?
-    answer: >-
-      Yes. Before using the Llama 3.1-8B model, visit the Llama website and fill in the form to
-      request access.
-  - question: Do I need an OpenAI API key when using the OpenAI SDK with the local llama.cpp server?
-    answer: >-
-      No. Because the LLM service is running locally via llama.cpp, you do not need to provide
-      an API key.
-  - question: What output should I see when I test the embedding model in the Python script?
-    answer: >-
-      The script prints the embedding dimension and the first few elements. The example output
-      shows 384 followed by several floating-point values.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Chen Zhang
 
diff --git a/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
index 2f65426b9c..c99e2fdc3d 100644
--- a/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
@@ -17,61 +17,9 @@ prerequisites:
   - A [Microsoft Azure account](https://azure.microsoft.com/) with access to Cobalt 100-based instances (Dpsv6)
   - Familiarity with SSH and remote server access
   - Basic understanding of cloud storage concepts
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:31:21Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 6c438573edec90b3aa4135ace38cd3ae604c58658c1949117ca80dbdd21394bd
-  summary_generated_at: '2026-06-02T04:26:22Z'
-  summary_source_hash: 6c438573edec90b3aa4135ace38cd3ae604c58658c1949117ca80dbdd21394bd
-  faq_generated_at: '2026-06-03T01:31:21Z'
-  faq_source_hash: 6c438573edec90b3aa4135ace38cd3ae604c58658c1949117ca80dbdd21394bd
-  summary: >-
-    This Learning Path shows how to deploy a single-node, S3-compatible MinIO server on an Arm-based
-    Azure Cobalt 100 virtual machine and verify it end to end. You will provision a Dpsv6 instance
-    (Ubuntu 24.04), open MinIO ports 9000 and 9001 in the Azure Network Security Group, connect
-    over SSH, and install and configure MinIO. You will generate a 1 GB test dataset, benchmark
-    large-object upload throughput using MinIO tooling, and validate S3 API compatibility with
-    the boto3 Python SDK. The path is introductory, takes about 30 minutes, and is intended for
-    developers and platform/DevOps engineers with an Azure account, SSH familiarity, and basic
-    cloud storage knowledge.
-  faqs:
-  - question: Which provisioning method, VM size, and OS are used in this path?
-    answer: >-
-      The steps use the Azure Portal to create an Arm-based Azure Cobalt 100 virtual machine from
-      the Dpsv6 series. The architecture shows Ubuntu 24.04 as the OS for the VM. Other provisioning
-      methods exist, but this path focuses on the Azure Portal workflow.
-  - question: Which network ports must I open for MinIO, and where do I configure them?
-    answer: >-
-      Open TCP ports 9000 and 9001 in the Azure Network Security Group (NSG) attached to the VM’s
-      network interface or subnet. You configure these rules in the Azure Portal under your VM’s
-      Networking settings.
-  - question: How do I connect to the Azure Cobalt 100 VM?
-    answer: >-
-      Use SSH with the private key you downloaded and the VM’s public IP address. For example:
-      ssh -i <your-key>.pem azureuser@<VM-IP>.
-  - question: How do I run the throughput benchmark and what result should I expect to see?
-    answer: >-
-      Create a 1 GB dataset with dd, then measure upload time using time mc cp --recursive dataset
-      local/ as shown. dd prints record counts and the total bytes written; time reports the command’s
-      duration. The path does not specify expected performance values.
-  - question: How is S3 API compatibility validated in this path?
-    answer: >-
-      You use the Python boto3 SDK to interact with MinIO and confirm S3 compatibility. Ensure
-      Python and boto3 are available, then run simple bucket and object operations as directed
-      in the steps.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md b/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
index d4a30c2f97..62c93ed32e 100644
--- a/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
@@ -14,57 +14,9 @@ learning_objectives:
 prerequisites:
 - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from an appropriate
   cloud service provider or an on-premise Arm server.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:31:54Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 973ba6c1e00fc67e1702606412124d8172e071b9b677a1df53c3be66210bf704
-  summary_generated_at: '2026-06-02T04:26:55Z'
-  summary_source_hash: 973ba6c1e00fc67e1702606412124d8172e071b9b677a1df53c3be66210bf704
-  faq_generated_at: '2026-06-03T01:31:54Z'
-  faq_source_hash: 973ba6c1e00fc67e1702606412124d8172e071b9b677a1df53c3be66210bf704
-  summary: >-
-    Set up an Arm-based Linux server and benchmark machine learning inference using TensorFlow
-    and the MLPerf Inference benchmark suite from MLCommons. You will launch an Arm instance running
-    Ubuntu 20.04 or 22.04, install required system and Python packages, then configure and run
-    TensorFlow with the MLPerf Inference suite to measure performance. This introductory path
-    has been tested on AWS and Oracle cloud platforms and also applies to on-premise Arm servers.
-    The only explicit prerequisite is access to an Arm-based instance. By the end, you will have
-    executed MLPerf Inference on your Arm server and obtained benchmark results in about 20 minutes.
-  faqs:
-  - question: What do I need before running the benchmarks?
-    answer: >-
-      You need an Arm-based instance from a cloud service provider or an on-premise Arm server
-      running Ubuntu 20.04 or Ubuntu 22.04. This path has been tested on AWS and Oracle. No other
-      explicit prerequisites are listed.
-  - question: Which Ubuntu version should I choose for this path?
-    answer: >-
-      Use Ubuntu 20.04 or Ubuntu 22.04, as shown in the steps. Both were tested on AWS and Oracle.
-  - question: Which packages do I install to prepare the environment?
-    answer: >-
-      Update apt and install build-essential, python3-pip, and git, then use pip to install the
-      Python packages listed in the steps (for example, opencv-python-headless and Cython). Follow
-      the exact apt-get and pip commands provided.
-  - question: How are TensorFlow and MLPerf Inference used here?
-    answer: >-
-      You will install and run TensorFlow on your Arm server, then use the MLPerf Inference benchmark
-      suite from MLCommons to test ML inference performance.
-  - question: How long will this take and what result should I expect?
-    answer: >-
-      The path is estimated to take about 20 minutes. On completion, you will have run TensorFlow
-      and executed the MLPerf Inference suite to produce benchmark output on your Arm server.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
index ada73fca78..b4d8e10fa8 100644
--- a/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
@@ -15,60 +15,9 @@ learning_objectives:
 prerequisites:
     - A [Microsoft Azure](https://azure.microsoft.com/) account with access to Cobalt 100 (Dpsv6) instances
     - Familiarity with the [MongoDB architecture](https://www.mongodb.com/) and deployment practices on Arm64 platforms
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:33:04Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: f270cfc90245c0090685fabf5cbebf62a714edbf34e1ea86c3df0bfbb4699ea4
-  summary_generated_at: '2026-06-02T04:28:12Z'
-  summary_source_hash: f270cfc90245c0090685fabf5cbebf62a714edbf34e1ea86c3df0bfbb4699ea4
-  faq_generated_at: '2026-06-03T01:33:04Z'
-  faq_source_hash: f270cfc90245c0090685fabf5cbebf62a714edbf34e1ea86c3df0bfbb4699ea4
-  summary: >-
-    This Learning Path shows how to run MongoDB on Arm-based Microsoft Azure Cobalt 100 virtual
-    machines. You will provision a Dpsv6 instance using the Azure console with Ubuntu Pro 24.04
-    LTS (Arm64), install MongoDB and mongosh, and validate the deployment. The steps include baseline
-    checks such as service health, a quick storage test with fio, and CRUD verification, followed
-    by monitoring database activity with mongotop and using mongostat for additional runtime metrics.
-    By the end, you will have a working MongoDB setup on Cobalt 100 and initial observations from
-    light benchmarking on Arm64. Prerequisites are an Azure account with access to Cobalt 100
-    and familiarity with MongoDB architecture and Arm64 deployments.
-  faqs:
-  - question: What do I need before creating the Azure VM?
-    answer: >-
-      You need a Microsoft Azure account with access to Cobalt 100 (Dpsv6) instances. Familiarity
-      with MongoDB architecture and deployment practices on Arm64 platforms is also expected.
-  - question: Which Azure VM series and OS image should I select?
-    answer: >-
-      Use the Dpsv6 general-purpose series for the Arm-based Cobalt 100 processor. The path targets
-      Ubuntu Pro 24.04 LTS (Arm64).
-  - question: How do I verify that MongoDB was installed and is working?
-    answer: >-
-      Start mongod locally, check service health, and connect with mongosh to validate CRUD operations.
-      Run a quick storage baseline with fio and perform light query, index, and concurrency checks.
-  - question: How is access control handled during the exercises and how can I enable remote access
-      later?
-    answer: >-
-      For this exercise, access control is disabled by default and mongod should remain bound
-      to 127.0.0.1. To accept remote connections later, set --bind_ip (or bindIp in the config)
-      and enable authorization.
-  - question: How do I monitor MongoDB activity and what should be running first?
-    answer: >-
-      Use mongotop (and mongostat) to observe real-time activity, ensuring mongod is running locally
-      and that the long_system_load.js script is generating traffic. The path includes benchmark
-      results from Azure Arm64 VMs as a latency reference.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
index cfac2beeae..1150633727 100644
--- a/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
@@ -14,58 +14,9 @@ learning_objectives:
 
 prerequisites:
      - A [Google Cloud Platform (GCP)](https://cloud.google.com/free?utm_source=google&hl=en) account with billing enabled
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:33:34Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: abbdde22271151fb1485c54bafe463e7797a0b913c7d4c99245d2914a3f9bb82
-  summary_generated_at: '2026-06-02T04:28:40Z'
-  summary_source_hash: abbdde22271151fb1485c54bafe463e7797a0b913c7d4c99245d2914a3f9bb82
-  faq_generated_at: '2026-06-03T01:33:34Z'
-  faq_source_hash: abbdde22271151fb1485c54bafe463e7797a0b913c7d4c99245d2914a3f9bb82
-  summary: >-
-    Learn how to deploy MongoDB on Arm-based Google Axion C4A virtual machines and benchmark it
-    with the Yahoo Cloud Serving Benchmark (YCSB). You will create a c4a-standard-4 VM in Google
-    Cloud using the Console, install MongoDB and mongosh on Red Hat Enterprise Linux with Arm64
-    (aarch64) binaries for RHEL 9, and verify the server locally. Then you will build YCSB’s MongoDB
-    binding from source (Maven/Java 11), load a starter dataset, and run workloads to capture
-    a quick baseline and benchmark results. The C4A family uses Google’s Axion CPU based on Arm
-    Neoverse‑V2 cores. Prerequisite: a Google Cloud Platform account with billing enabled.
-  faqs:
-  - question: What do I need before creating the VM on Google Cloud?
-    answer: >-
-      You need a Google Cloud Platform (GCP) account with billing enabled. All setup and deployment
-      takes place in your GCP project.
-  - question: Which VM configuration does this path use for Axion C4A?
-    answer: >-
-      The steps create an Arm-based C4A VM using the c4a-standard-4 machine type (4 vCPUs, 16
-      GB memory). You create it in the Google Cloud Console under Compute Engine by selecting
-      the C4A series.
-  - question: Which operating system and MongoDB package are assumed?
-    answer: >-
-      The installation targets Red Hat Enterprise Linux on Arm. The steps fetch the Arm64 (aarch64)
-      MongoDB binaries for RHEL 9.3.
-  - question: How do I verify that MongoDB is running correctly?
-    answer: >-
-      Connect locally with mongosh using mongodb://127.0.0.1:27017. Create a test database and
-      collection, perform basic CRUD operations, and record a quick insert-time baseline.
-  - question: How do I install and run YCSB for MongoDB, and what data size is loaded initially?
-    answer: >-
-      Install git, Maven, and Java 11, clone the YCSB repository, and build the MongoDB binding
-      with Maven. Use YCSB to load the starter dataset, which defaults to 1,000 records, and then
-      run the workloads to benchmark MongoDB.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Annie Tallund
 
diff --git a/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md b/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
index 692be7cce7..8a4403dfea 100644
--- a/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
@@ -1,63 +1,8 @@
 ---
 title: Analyze the performance of MongoDB on Arm servers
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:32:28Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: b52a1b60a74e19f2a3b60b8a77199ad5369c1ccd3b4d5ce0252a169d9f6123fd
-  summary_generated_at: '2026-06-02T04:27:23Z'
-  summary_source_hash: b52a1b60a74e19f2a3b60b8a77199ad5369c1ccd3b4d5ce0252a169d9f6123fd
-  faq_generated_at: '2026-06-03T01:32:28Z'
-  faq_source_hash: b52a1b60a74e19f2a3b60b8a77199ad5369c1ccd3b4d5ce0252a169d9f6123fd
-  summary: >-
-    Learn how to install MongoDB Community Edition 8.0 on Arm-based Linux servers and evaluate
-    database performance using Yahoo Cloud Serving Benchmark (YCSB). You will provision an Arm
-    instance from a cloud provider (such as AWS, Microsoft Azure, Google Cloud, or Oracle) and
-    deploy MongoDB on supported distributions including Ubuntu 20.04/22.04/24.04, RHEL/CentOS
-    8/9, and Amazon Linux 2023. The path covers configuring a three-node MongoDB replica set,
-    installing supporting packages (Maven, Make, GCC), and running common YCSB workloads (95/5,
-    100/0, 90/10) with warm-up and load-tuning guidance. An alternative Java-based MongoDB performance
-    test tool using OpenJDK is also included. By the end, you can measure and compare MongoDB
-    performance on Arm in about 30 minutes.
-  faqs:
-  - question: Which Linux distributions are supported for installing MongoDB Community Edition
-      8.0 in this path?
-    answer: >-
-      Ubuntu 20.04, 22.04, and 24.04; RHEL/CentOS 8 and 9; and Amazon Linux 2023 are listed as
-      supported. Refer to the Platform Support Matrix for additional details.
-  - question: How should I structure the MongoDB environment for testing with YCSB?
-    answer: >-
-      Use two parts: one instance running YCSB and one or more instances running MongoDB. The
-      recommended setup is a three-node replica set of equal-sized nodes, with one primary (the
-      target for test traffic) and two secondary nodes.
-  - question: What additional packages are required to run YCSB, and how do I install them on
-      Ubuntu?
-    answer: >-
-      Additional software packages are required for YCSB. On Ubuntu, install maven, make, and
-      gcc using: sudo apt install -y maven make gcc.
-  - question: Which YCSB workloads should I run, for how long, and how do I know the system is
-      exercised enough?
-    answer: >-
-      Common workloads are 95/5, 100/0, and 90/10, with 95/5 recommended for real-world testing.
-      After loading the dataset, run the test for about five minutes to warm up, then target high
-      CPU utilization (90%+) by adjusting threads, operationscount, and recordscount.
-  - question: Is there an alternative to YCSB for testing MongoDB performance in this path?
-    answer: >-
-      Yes. The MongoDB performance test tool is an open source Java application that measures
-      latency and throughput across operations like Inserts, Updates, Deletes, Counts, and Finds.
-      To use it, install the appropriate OpenJDK run-time environment.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/mpi/_index.md b/content/learning-paths/servers-and-cloud-computing/mpi/_index.md
index 10fc8181fc..6ae0c5c09b 100644
--- a/content/learning-paths/servers-and-cloud-computing/mpi/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mpi/_index.md
@@ -16,62 +16,9 @@ prerequisites:
     - General knowledge about distributed parallelism (MPI)
     - Some understanding of C, Python, and Linux commands
     - An Arm computer running Linux. Cloud instances can be used, refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:34:00Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 300794f4010658d945212c74c5257635d620cbb6230cac0de92bf23e4e9e29fe
-  summary_generated_at: '2026-06-02T04:29:07Z'
-  summary_source_hash: 300794f4010658d945212c74c5257635d620cbb6230cac0de92bf23e4e9e29fe
-  faq_generated_at: '2026-06-03T01:34:00Z'
-  faq_source_hash: 300794f4010658d945212c74c5257635d620cbb6230cac0de92bf23e4e9e29fe
-  summary: >-
-    This advanced Learning Path is for HPC developers building MPI applications on Arm-based Linux
-    servers or cloud instances. You will install and validate Linaro Forge, then build, debug,
-    and profile a parallel matrix multiplication example implemented in C, Fortran, and Python.
-    The steps show how to compile with -O0 -g -fsanitize=address to expose bugs and memory issues,
-    use gdb and Forge for debugging, and compare profiling results across compiler options and
-    alternative libraries, including Arm Performance Libraries for common math routines. The path
-    was tested on Ubuntu 20.04 and assumes general MPI knowledge plus some familiarity with C,
-    Python, and Linux commands. Cloud instances from AWS, Microsoft Azure, Google Cloud, or Oracle
-    may be used.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an Arm computer running Linux; cloud instances from AWS, Microsoft Azure, Google
-      Cloud, or Oracle can be used. General MPI knowledge and some familiarity with C, Python,
-      and Linux commands are expected. The instructions are tested on Ubuntu 20.04; other distributions
-      may require adjustments.
-  - question: How do I verify that Linaro Forge installed correctly?
-    answer: >-
-      Run ddt --version. If the command is not found or does not report a version, revisit the
-      Linaro Forge install guide and confirm your PATH and environment are set.
-  - question: Where is the example application and which languages are available?
-    answer: >-
-      The parallel matrix multiplication application is in the src directory. Implementations
-      are provided in C, Fortran, and Python, and each contains a bug that must be fixed.
-  - question: Which build flags should I use for debugging and where do I set them?
-    answer: >-
-      Edit make.def in the src directory and set CFLAGS = -O0 -g -fsanitize=address. This disables
-      compiler optimizations, adds debug symbols, and enables AddressSanitizer to help find memory
-      issues.
-  - question: How should I approach profiling and comparing alternatives?
-    answer: >-
-      Profile a baseline build with -O0, then enable compiler optimizations and compare results.
-      You can also try alternative coding approaches and libraries implementing equivalent functions,
-      including Arm Performance Libraries for common math routines.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Florent Lebeau
 
diff --git a/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md b/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md
index 929df63977..c98d3893aa 100644
--- a/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md
@@ -2,61 +2,9 @@
 title: Control floating-point accuracy modes in Arm Performance Libraries
 
 minutes_to_complete: 20
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:34:28Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: a10683fdd14359e93056dc4ba24581856833765c64915979d0466a06887e0755
-  summary_generated_at: '2026-06-02T04:29:42Z'
-  summary_source_hash: a10683fdd14359e93056dc4ba24581856833765c64915979d0466a06887e0755
-  faq_generated_at: '2026-06-03T01:34:28Z'
-  faq_source_hash: a10683fdd14359e93056dc4ba24581856833765c64915979d0466a06887e0755
-  summary: >-
-    Learn how to control floating-point accuracy for vectorized math functions in Libamath, a
-    component of Arm Performance Libraries, on Linux. This path introduces IEEE-754 representation,
-    Units in the Last Place (ULP), and the ULP error metric used to assess function accuracy.
-    You will see how Libamath offers multiple accuracy modes, how to recognize them by function-name
-    suffixes (for example, _u10 for results within 1 ULP), and how to select a mode that balances
-    precision and speed for your workload. A concise C example invokes the Neon single-precision
-    exp function across modes and computes ULP error using a provided helper header. Prerequisite:
-    an Arm computer running Linux with Arm Performance Libraries 25.04 or newer installed.
-  faqs:
-  - question: What do I need before running the example code?
-    answer: >-
-      You need an Arm computer running Linux with Arm Performance Libraries version 25.04 or newer
-      installed. The path uses C code and Libamath, and assumes a typical GCC-based environment.
-  - question: How do I select a specific Libamath accuracy mode in my code?
-    answer: >-
-      Accuracy modes are encoded in the function symbol suffix. For example, a suffix of _u10
-      indicates a high-accuracy variant (≤ 1 ULP); other modes are exposed via their documented
-      suffixes when available.
-  - question: How is ULP error computed when checking results?
-    answer: >-
-      ULP error is defined as |want − got| divided by ULP(want). Because it scales with floating-point
-      spacing, it provides a more meaningful accuracy measure than absolute error across different
-      magnitudes.
-  - question: What files should I have to build the example?
-    answer: >-
-      Create example.c using the provided code and ensure ulp_error.h from the previous section
-      is available. The example includes amath.h and calls Libamath Neon single-precision exp
-      variants to compare accuracy.
-  - question: What should I check if the build fails with missing headers or vector types?
-    answer: >-
-      Verify Arm Performance Libraries 25.04+ is installed and accessible, and that you included
-      both amath.h and ulp_error.h. Build on an Arm Linux system; the example uses AArch64 vector
-      calling conventions and Neon vector types.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Joana Cruz
 
diff --git a/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md b/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md
index 6f74c330d4..debb670954 100644
--- a/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md
@@ -16,64 +16,9 @@ learning_objectives:
 prerequisites:
     - An [Azure account](https://azure.microsoft.com/en-us/free/)
     - A local machine with [`jq`](https://jqlang.org/download/), [`curl`](https://curl.se/download.html), [`wrk`](https://github.com/wg/wrk), [Azure CLI](/install-guides/azure-cli/), and [`kubectl`](/install-guides/kubectl/) installed
-   
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:35:04Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: d765afadfe5155f0ecd5bd08373fa12464b2ee5ebb1f8c7831039eb07eba8831
-  summary_generated_at: '2026-06-02T04:31:12Z'
-  summary_source_hash: d765afadfe5155f0ecd5bd08373fa12464b2ee5ebb1f8c7831039eb07eba8831
-  faq_generated_at: '2026-06-03T01:35:04Z'
-  faq_source_hash: d765afadfe5155f0ecd5bd08373fa12464b2ee5ebb1f8c7831039eb07eba8831
-  summary: >-
-    This Learning Path walks you through building a hybrid Azure Kubernetes Service (AKS) cluster
-    with both Arm-based and x86 node pools on Linux, then deploying nginx using a multi-architecture
-    image to each architecture. You authenticate with Azure CLI, create the cluster with two node
-    pools, and verify access. You also create a small utility script to streamline kubectl operations
-    and testing. The path adds a namespace and a shared ConfigMap with performance-optimized nginx
-    settings, then creates architecture-specific deployments and LoadBalancer services. You validate
-    that pods land on the intended nodes and use wrk to exercise traffic and compare behavior
-    across architectures. Plan about 60 minutes to complete. Prerequisites are an Azure account
-    and a local machine with jq, curl, wrk, Azure CLI, and kubectl installed.
-  faqs:
-  - question: What do I need before running the setup?
-    answer: >-
-      You need an Azure account and a local machine with jq, curl, wrk, Azure CLI, and kubectl
-      installed. The path assumes a Linux environment.
-  - question: How do I know my AKS cluster includes both x86 and Arm nodes?
-    answer: >-
-      During cluster creation, you provision two distinct node pools—one x86 and one Arm—and then
-      verify connectivity. You will confirm that both node types are available from your AKS environment
-      using the tools introduced in the steps.
-  - question: Which files set up nginx on Intel, and what should I expect after applying them?
-    answer: >-
-      You use namespace.yaml and nginx-configmap.yaml along with the Intel-specific deployment
-      manifest described in the steps. The result is an nginx deployment in a dedicated namespace
-      and a load balancer service that exposes nginx to the Internet.
-  - question: How is the Arm nginx deployment created and exposed?
-    answer: >-
-      Applying arm_nginx.yaml creates nginx-arm-deployment and nginx-arm-svc. It pulls a multi-architecture
-      nginx image from DockerHub, schedules a pod on the Arm node, mounts the shared ConfigMap
-      at /etc/nginx/nginx.conf, and exposes it via a load balancer service targeting pods with
-      app: nginx-multiarch and arch: arm labels.
-  - question: How do I compare performance between the x86 and Arm nginx instances?
-    answer: >-
-      Use the provided utility script and tools like wrk to send requests to each load-balanced
-      service and observe results. Validate that both endpoints respond as expected, then compare
-      behavior and performance across architectures.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author:
     - Geremy Cohen
diff --git a/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md b/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md
index 4154204734..604832e7ec 100644
--- a/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md
@@ -15,61 +15,9 @@ prerequisites:
     - A [Google Cloud account](https://console.cloud.google.com/).
     - A local machine with [Google Cloud CLI](/install-guides/gcloud/) and [kubectl](/install-guides/kubectl/) installed.
     - The [GKE Cloud Plugin](https://cloud.google.com/kubernetes-engine/docs/how-to/cluster-access-for-kubectl#gcloud) installed.
-   
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:35:29Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: cd31c217eaeab6faad263728c446ee188cd9d542904d87ae7bfd5120713dfaa4
-  summary_generated_at: '2026-06-02T04:32:13Z'
-  summary_source_hash: cd31c217eaeab6faad263728c446ee188cd9d542904d87ae7bfd5120713dfaa4
-  faq_generated_at: '2026-06-03T01:35:29Z'
-  faq_source_hash: cd31c217eaeab6faad263728c446ee188cd9d542904d87ae7bfd5120713dfaa4
-  summary: >-
-    This Learning Path shows how to extend a Google Kubernetes Engine (GKE) cluster with Arm-based
-    nodes and deploy Ollama using a single multi-architecture container image. You begin with
-    an amd64 node running an Ollama Deployment and Service, then add an arm64 node pool and mirror
-    the deployment to form a hybrid cluster. You create the Kubernetes namespace, apply architecture-specific
-    services, and exercise a multi-architecture service that can route to either backend. You
-    validate by pinging the service, pulling models, and running LLM inferences while observing
-    which pod and node serve requests. Prerequisites are a Google Cloud account, gcloud, kubectl,
-    and the GKE Cloud Plugin on Linux or macOS.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Google Cloud account and a local machine with the Google Cloud CLI, kubectl,
-      and the GKE Cloud Plugin installed. The steps target Linux and macOS.
-  - question: How is the initial amd64 deployment organized in Kubernetes?
-    answer: >-
-      You create an ollama namespace, then deploy an Ollama Deployment and Service for amd64.
-      This simulates an existing cluster before adding Arm nodes.
-  - question: What settings should I use when adding the Arm node pool?
-    answer: >-
-      In the GKE console, select the ollama-on-multiarch cluster, choose Add node pool, name it
-      arm64-pool, set Size to 1, and specify the us-central1-a location. Follow the step guidance
-      to complete node settings.
-  - question: How do I verify that requests can reach either architecture in the hybrid cluster?
-    answer: >-
-      Use the provided script: ./model_util.sh multiarch hello. The response includes the pod
-      and deployment that handled the request, and repeating the command may route to different
-      pods.
-  - question: How do I compare amd64 and arm64 behavior and performance in this setup?
-    answer: >-
-      Validate by pinging services, pulling models, and running inferences on both the amd64 and
-      arm64 deployments. Use the multiarch service to observe request routing or target each architecture’s
-      service to compare results.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author:
     - Geremy Cohen
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md
index f5f8694df3..67f4e40716 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md
@@ -14,60 +14,9 @@ learning_objectives:
 prerequisites:
     - A [Microsoft Azure](https://azure.microsoft.com/) account with access to Cobalt 100 based instances (Dpsv6)
     - Familiarity with relational databases and the basics of [MySQL](https://dev.mysql.com/doc/refman/8.0/en/introduction.html)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:36:24Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: b9bc1d1f965e4fdeeb9552d853330c201e00597829420c8a0397fa425c3231c4
-  summary_generated_at: '2026-06-02T04:33:49Z'
-  summary_source_hash: b9bc1d1f965e4fdeeb9552d853330c201e00597829420c8a0397fa425c3231c4
-  faq_generated_at: '2026-06-03T01:36:24Z'
-  faq_source_hash: b9bc1d1f965e4fdeeb9552d853330c201e00597829420c8a0397fa425c3231c4
-  summary: >-
-    Learn how to provision an Arm64 virtual machine on Microsoft Azure Cobalt 100 (Neoverse-N2)
-    using the Azure Portal with Ubuntu Pro 24.04 LTS, deploy and secure MySQL, validate the service,
-    and run baseline benchmarks with mysqlslap. Aimed at developers migrating MySQL applications
-    from x86_64 to Arm, this introductory path focuses on Dpsv6 series VMs and walks through installation,
-    configuration, and functional checks to confirm the database is ready for use. You will also
-    perform baseline testing with mysqlslap to understand MySQL behavior on Azure Arm64. Prerequisites
-    include an Azure account with access to Cobalt 100 instances (Dpsv6) and familiarity with
-    relational databases and MySQL basics.
-  faqs:
-  - question: Which Azure VM size and base image should I use?
-    answer: >-
-      Use a general-purpose VM in the Dpsv6 series (Cobalt 100, Arm64) and select Ubuntu Pro 24.04
-      LTS as the base image. The path provisions the VM via the Azure Portal.
-  - question: Can I create the VM with Azure CLI or IaC instead of the Azure Portal?
-    answer: >-
-      Yes, Azure CLI and IaC are common alternatives, but this path demonstrates the Azure Portal
-      workflow. If you prefer CLI or IaC, you can adapt the same choices (Dpsv6, Ubuntu Pro 24.04
-      LTS), though those steps are not covered here.
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Microsoft Azure account with access to Cobalt 100 based instances (Dpsv6). Familiarity
-      with relational databases and the basics of MySQL is also expected.
-  - question: How do I know MySQL started and is ready for use?
-    answer: >-
-      Start and enable MySQL using systemctl as shown in the validation step. Then perform the
-      functional checks to confirm queries run, users can authenticate, and the environment is
-      correctly configured for cloud workloads.
-  - question: How do I benchmark MySQL in this setup, and what does mysqlslap measure?
-    answer: >-
-      Use the built-in mysqlslap tool to run baseline tests on the Azure Cobalt 100 (Arm64) VM.
-      It simulates multiple clients and reports read/write throughput, query response times, and
-      overall MySQL server performance under different workloads.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql-lns-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql-lns-azure/_index.md
index 1ad0e6879b..9dfaafd2bd 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql-lns-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql-lns-azure/_index.md
@@ -19,12 +19,9 @@ learning_objectives:
 prerequisites:
     - A [Microsoft Azure](https://azure.microsoft.com/) account with access to Cobalt 100 based instances (Dpsv6)
     - Basic familiarity with SSH and MySQL command-line tools
-
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 author: Doug Anson
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql/_index.md
index 4c8d3b53ce..de275dcbd2 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql/_index.md
@@ -12,59 +12,9 @@ learning_objectives:
 prerequisites:
     - An Arm based instance from a cloud service provider, or an on-premise Arm server.
     - If you do not have an Arm node, the next section discusses some options.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:35:55Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: b9b2ed7f58611c9bc7e1867fe06700f3197eb095ddc62d91c00814021967cf72
-  summary_generated_at: '2026-06-02T04:33:29Z'
-  summary_source_hash: b9b2ed7f58611c9bc7e1867fe06700f3197eb095ddc62d91c00814021967cf72
-  faq_generated_at: '2026-06-03T01:35:55Z'
-  faq_source_hash: b9b2ed7f58611c9bc7e1867fe06700f3197eb095ddc62d91c00814021967cf72
-  summary: >-
-    This introductory Learning Path shows how to deploy MySQL on Arm-based Linux systems and interact
-    with it using the MySQL client CLI. You will review common deployment options on Arm, including
-    bare metal, cloud VMs, and managed SQL services from providers such as AWS, Microsoft Azure,
-    Google Cloud, and Oracle. The practical steps focus on installing, configuring, and checking
-    a MySQL instance, then running basic interactions from a CLI. Prerequisites include access
-    to an Arm-based instance from a cloud service provider or an on-premise Arm server; if you
-    do not have one, the path discusses options. Estimated time to complete is about 30 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need access to a Linux system on an Arm-based instance from a cloud provider or an on‑premise
-      Arm server. No other explicit prerequisites are listed.
-  - question: I don’t have an Arm node—what should I do?
-    answer: >-
-      The path discusses options to obtain Arm capacity, including Arm Cloud VMs and a separate
-      learning path for getting started with Arm-based cloud instances. Cloud providers referenced
-      include AWS, Microsoft Azure, Google Cloud, and Oracle.
-  - question: Which deployment approach should I choose for MySQL on Arm?
-    answer: >-
-      This path introduces multiple options: bare metal, cloud VMs, and cloud providers’ SQL services.
-      Choose based on your available infrastructure and whether you prefer managing MySQL yourself
-      or using a managed service.
-  - question: How do I know the installation worked?
-    answer: >-
-      The steps include checking the installation and interacting with the database using the
-      MySQL client CLI tool. You should be able to connect and run simple SQL commands to validate
-      the deployment.
-  - question: Does this path cover performance tuning?
-    answer: >-
-      No. If you already know how to deploy MySQL and want to focus on performance, follow the
-      separate “Learn how to Tune MySQL” learning path.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md
index 634412529a..47c49af936 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md
@@ -12,61 +12,9 @@ learning_objectives:
 prerequisites:
     - Basic knowledge of [MySQL databases](https://www.mysql.com/)
     - Two Arm servers running Ubuntu 22.04, one for the MySQL server and the other for the Sysbench client
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:37:09Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: e473c0724855bbbc59481822130f7dc5e1684593527a6b5688939f84cd32963d
-  summary_generated_at: '2026-06-02T04:34:09Z'
-  summary_source_hash: e473c0724855bbbc59481822130f7dc5e1684593527a6b5688939f84cd32963d
-  faq_generated_at: '2026-06-03T01:37:09Z'
-  faq_source_hash: e473c0724855bbbc59481822130f7dc5e1684593527a6b5688939f84cd32963d
-  summary: >-
-    This Learning Path shows how to benchmark MySQL on Arm Linux using Sysbench and apply profile-guided
-    optimization (PGO) with GCC. You will build, configure, and run a MySQL server on one Arm
-    server running Ubuntu 22.04, then build and run Sysbench on a second Arm Linux system. On
-    the client, you also build and install MySQL to provide the libraries required by Sysbench.
-    You will run Sysbench against the server, rebuild MySQL to generate and then use profile data,
-    and examine the resulting performance changes. Prerequisites include basic MySQL knowledge
-    and access to two Arm servers (200 GB disk on the server, 30 GB on the client).
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need two Arm servers running Ubuntu 22.04: one for the MySQL server and one for the
-      Sysbench client. Ensure at least 200 GB of free disk space on the server and 30 GB on the
-      client. Basic knowledge of MySQL is also required.
-  - question: Which packages should I install to build MySQL on Ubuntu 22.04?
-    answer: >-
-      Install: git, make, automake, libtool, bison, pkg-config, cmake, g++, openssl, libssl-dev,
-      libncurses5-dev, libtirpc-dev, rpcsvc-proto, libaio-dev, libssl-dev. These packages are
-      used to build, install, and run the MySQL server from source.
-  - question: Why do I need to build MySQL on the Sysbench client as well?
-    answer: >-
-      Sysbench requires MySQL libraries to build and run the MySQL tests. On the client system
-      you only build and install MySQL to provide these libraries; you do not configure or run
-      the MySQL server there.
-  - question: Can I use a different Linux distribution or Ubuntu version?
-    answer: >-
-      The steps assume Ubuntu 22.04 on Arm, but other Linux distributions and Ubuntu versions
-      may also work. The path does not list specific adjustments for other distributions.
-  - question: How is PGO applied to MySQL in this path, and which compiler is used?
-    answer: >-
-      The path uses GCC to apply PGO: first rebuild MySQL with profile generation to collect data,
-      then rebuild with profile use to apply the collected profiles. The initial installation
-      referenced is at /home/mysql/mysql_install_8.0.33, and the PGO workflow creates two additional
-      installations (one for profile collection and one for profile use).
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Bolt Liu
 
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md
index eb2b4aaae3..11deaa7062 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md
@@ -10,59 +10,9 @@ learning_objectives:
 
 prerequisites:
     - Bare-metal or cloud [installation of MySQL](/learning-paths/servers-and-cloud-computing/mysql/)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:37:44Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: faa914d636e03296c6b65ba146745c543326955ec457577f047eec51aa6a3733
-  summary_generated_at: '2026-06-02T04:34:44Z'
-  summary_source_hash: faa914d636e03296c6b65ba146745c543326955ec457577f047eec51aa6a3733
-  faq_generated_at: '2026-06-03T01:37:44Z'
-  faq_source_hash: faa914d636e03296c6b65ba146745c543326955ec457577f047eec51aa6a3733
-  summary: >-
-    This advanced Learning Path guides you through tuning MySQL for better performance on Arm-based
-    (Neoverse) cloud VMs running Linux. You will review system-level considerations such as storage
-    technology and filesystem choices, then apply MySQL server settings using configuration files
-    under the mysqld group or the mysqld command line, with a preference for version-controlled
-    config files. The guidance is workload-focused: start from defaults, change only when needed,
-    and evaluate results. Tools and concepts include MySQL, SQL, InnoDB, and a runbook approach
-    to track changes. Prerequisite: a bare-metal or cloud installation of MySQL from the referenced
-    setup path. Estimated time to complete is about 30 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an existing MySQL installation on either bare-metal or in the cloud, as referenced
-      by the prerequisite Learning Path. No other explicit prerequisites are listed.
-  - question: Which platforms and Arm targets does this path focus on?
-    answer: >-
-      It targets Arm-based VMs in AWS, Microsoft Azure, Google Cloud, and Oracle. The Arm focus
-      is Neoverse.
-  - question: How should I choose storage and filesystem for MySQL?
-    answer: >-
-      Locally attached SSD storage generally performs best, though network storage can also perform
-      well. Start with the xfs filesystem, with ext4 as an alternative, and evaluate disk scheduling
-      and other options for your workload.
-  - question: Where should I place MySQL tuning parameters, and can I use command-line options?
-    answer: >-
-      Place configuration under the mysqld group in a MySQL configuration file, following the
-      Specifying Program Options section of the MySQL documentation. You can set options on the
-      mysqld command line, but configuration files are preferred for version control.
-  - question: Should I change many MySQL settings at once?
-    answer: >-
-      No. It is usually best to leave most settings at their defaults and change them only when
-      you suspect or know they affect your workload, since there is no one-size-fits-all configuration.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Julio Suarez
 
diff --git a/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md b/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md
index 06405c6774..bf3be1b04f 100644
--- a/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md
@@ -18,65 +18,9 @@ prerequisites:
     - Familiarity with Linux command-line tools and basic scripting
     - Understanding of firmware boot stages and SoC-level architecture
     - Docker installed, or a GitHub Codespaces-compatible development environment
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:38:16Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 65336a8f8d1d11c4b127ea13982a581afffa94cbfd1af10a9e4df2becbc34b5a
-  summary_generated_at: '2026-06-02T04:35:16Z'
-  summary_source_hash: 65336a8f8d1d11c4b127ea13982a581afffa94cbfd1af10a9e4df2becbc34b5a
-  faq_generated_at: '2026-06-03T01:38:16Z'
-  faq_source_hash: 65336a8f8d1d11c4b127ea13982a581afffa94cbfd1af10a9e4df2becbc34b5a
-  summary: >-
-    This advanced Learning Path shows how to develop and validate firmware pre-silicon for Arm
-    Neoverse CSS‑V3 using the RD‑V3 reference design and Arm Fixed Virtual Platforms (FVPs). You
-    will examine the CSS‑V3 architecture and coordinated boot sequence (TF‑A, RSE, SCP/MCP/LCP,
-    UEFI/GRUB, Linux), set up a containerized build environment, sync sources with a pinned repo
-    manifest, then build and boot the RD‑V3 firmware stack on an FVP. The steps include mapping
-    UART consoles, interpreting boot logs, and bringing the stack to a Linux shell with Buildroot.
-    You will also modify platform control firmware and run a dual‑chip RD‑V3‑R1 simulation. This
-    path takes about 90 minutes and assumes an Arm Neoverse‑based Linux machine, Docker or Codespaces,
-    and prior firmware knowledge.
-  faqs:
-  - question: What do I need before running the build and simulation steps?
-    answer: >-
-      You need access to an Arm Neoverse‑based Linux machine with at least 80 GB of free storage,
-      Docker installed or a GitHub Codespaces‑compatible environment, and familiarity with Linux
-      command‑line tools and basic scripting. An understanding of firmware boot stages and SoC‑level
-      architecture is also required.
-  - question: Which FVP model version should I use with my RD‑V3 release tag?
-    answer: >-
-      Each RD‑V3 release tag maps to a specific FVP version. For example, the RD‑INFRA‑2025.07.03
-      tag is designed to work with FVP version 11.29.35; consult the RD‑V3 Release Tags to select
-      and install the matching model.
-  - question: What result should I expect when the FVP simulation completes successfully?
-    answer: >-
-      The simulation brings up the full firmware stack from BL1 to a Linux shell using Buildroot.
-      You should see boot logs across the mapped UART consoles for components including TF‑A,
-      RSE, SCP/MCP/LCP, and UEFI/GRUB, ending at a Linux shell prompt.
-  - question: How do I diagnose issues if the boot sequence stalls?
-    answer: >-
-      Use the mapped UART consoles and boot logs to identify the active or failing stage and verify
-      the expected handoffs across TF‑A, RSE, SCP/MCP/LCP, and UEFI. The steps show how to interpret
-      logs to verify bring‑up and locate boot‑stage issues.
-  - question: What is different about running the dual‑chip RD‑V3‑R1 simulation, and what should
-      I verify?
-    answer: >-
-      RD‑V3‑R1 models a dual‑chip platform with two application processors and a Management Control
-      Processor (Cortex‑M7) for cross‑die management. You will launch the dual‑chip simulation
-      and verify AP/MCP coordination and the chiplet‑style boot flow.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author:
     - Odin Shen
diff --git a/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md b/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md
index b8e0fe8832..8ca0187a47 100644
--- a/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md
@@ -14,62 +14,9 @@ prerequisites:
     - A Windows on Arm machine, for example the Lenovo Thinkpad X13s running Windows 11 to build the .NET Aspire project.    
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from AWS or GCP.
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is an example of a suitable editor.    
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:38:53Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 5a5fd9b66a09de71009ccb098c7ef08a848463a8a7956643020ab1fb1db22fbb
-  summary_generated_at: '2026-06-02T04:36:04Z'
-  summary_source_hash: 5a5fd9b66a09de71009ccb098c7ef08a848463a8a7956643020ab1fb1db22fbb
-  faq_generated_at: '2026-06-03T01:38:53Z'
-  faq_source_hash: 5a5fd9b66a09de71009ccb098c7ef08a848463a8a7956643020ab1fb1db22fbb
-  summary: >-
-    This introductory Learning Path guides you through creating, running, modifying, and deploying
-    a .NET Aspire application using a Windows on Arm development machine and Arm-based virtual
-    machines on AWS and Google Cloud. You will verify .NET 8.0 or later, install the Aspire workload,
-    generate and run the project (including trusting the HTTPS development certificate and using
-    the Aspire dashboard), and add a computation service to simulate intensive work. The path
-    then shows how to deploy to an Arm-powered EC2 instance, such as AWS Graviton; Google Cloud
-    Arm-based VMs are also targeted. Prerequisites include a Windows on Arm device, an Arm-based
-    instance from AWS or GCP, and a code editor (for example, Visual Studio Code for Arm64).
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Windows on Arm machine (for example, a Lenovo ThinkPad X13s running Windows 11),
-      access to an Arm-based instance on AWS or GCP, and a code editor. Visual Studio Code for
-      Arm64 is an example of a suitable editor.
-  - question: How do I check my .NET version and install the Aspire workload?
-    answer: >-
-      Open a PowerShell terminal and run dotnet --version to confirm .NET 8.0 or later is installed.
-      Then install the Aspire workload with dotnet workload install aspire and wait for the download
-      and installation to complete without errors.
-  - question: How do I run the application locally and confirm it started correctly?
-    answer: >-
-      First trust the HTTPS development certificate by running dotnet dev-certs https --trust.
-      Then change into the project directory and run dotnet run --project NetAspire.Arm.AppHost;
-      you should see build output, an Aspire version line, and messages such as “Distributed application
-      starting.”
-  - question: Where do I add the computational code, and what does it do?
-    answer: >-
-      Add a new file named ComputationService.cs in the NetAspire.Arm.ApiService project. The
-      provided code performs matrix multiplication to mimic computationally intensive work.
-  - question: Which cloud targets are supported, and how do I begin with AWS?
-    answer: >-
-      The path targets Arm-based VMs on AWS and Google Cloud. For AWS, sign in to the AWS Management
-      Console, navigate to the EC2 service, and choose an Arm-powered instance type such as those
-      based on AWS Graviton.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md
index 66656b22ff..9ff2eda354 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md
@@ -14,59 +14,9 @@ learning_objectives:
 
 prerequisites:
     - A [Microsoft Azure](https://azure.microsoft.com/) account with access to Cobalt 100 based instances (Dpsv6)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:39:34Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: e6f6f4d843b4974d1c1d67a35009b4428d08bb356d26c08a441f82726e002fb2
-  summary_generated_at: '2026-06-02T04:37:18Z'
-  summary_source_hash: e6f6f4d843b4974d1c1d67a35009b4428d08bb356d26c08a441f82726e002fb2
-  faq_generated_at: '2026-06-03T01:39:34Z'
-  faq_source_hash: e6f6f4d843b4974d1c1d67a35009b4428d08bb356d26c08a441f82726e002fb2
-  summary: >-
-    This Learning Path shows how to deploy and validate NGINX on an Arm-based Microsoft Azure
-    Cobalt 100 virtual machine. Using the Azure portal, you create a general-purpose Dpsv6 Arm64
-    VM with Ubuntu Pro 24.04 LTS, install and enable NGINX, and replace the default page with
-    a simple static site to confirm the server is working. You then install ApacheBench (ab) to
-    run baseline NGINX performance tests and review the output, with a sample result from a D4ps_v6
-    configuration. The path is introductory and Linux-focused, takes about 30 minutes, and is
-    intended for system administrators and developers. Prerequisite: an Azure account with access
-    to Cobalt 100 (Dpsv6) instances.
-  faqs:
-  - question: What do I need before I start creating the VM on Azure?
-    answer: >-
-      You need a Microsoft Azure account with access to Cobalt 100 based instances (Dpsv6). This
-      access is required to select the Arm64 Cobalt 100 VM used in the steps.
-  - question: Which Azure VM series and OS image should I select?
-    answer: >-
-      Use a general-purpose D-series VM in the Dpsv6 size series and choose Ubuntu Pro 24.04 LTS
-      as the base image for Arm64.
-  - question: Can I use Azure CLI or IaC instead of the portal to create the VM?
-    answer: >-
-      There are multiple ways to create a Cobalt 100 VM, but this Learning Path uses the Azure
-      portal. CLI and IaC workflows are not covered here.
-  - question: How do I know NGINX is installed and serving content?
-    answer: >-
-      After installation and enabling, NGINX should serve its default welcome page. Then create
-      /var/www/my-static-site with a simple HTML file to replace the default page and confirm
-      it is delivered by the server.
-  - question: How do I install and verify ApacheBench (ab) on Ubuntu Pro 24.04 LTS?
-    answer: >-
-      Install the apache2-utils package and verify the tool with ab -V. You can then run a basic
-      benchmark and review the key metrics, with a sample result provided for an Azure D4ps_v6
-      instance.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx/_index.md
index 5b3cd197d8..507097e6bd 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx/_index.md
@@ -14,58 +14,9 @@ prerequisites:
     - To create a file server you will need at least one [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or one on-premises Arm server.
     - To create a reverse proxy or API gateway you will need at least three Arm based instances from a cloud service provider or at least three on-premises Arm servers.
     - Network settings (firewalls and security groups) which allow communication on port 22 (SSH) and port 443 (HTTPS).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:39:16Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: c5e077458808373c8ce9660235716b5bb55e4e7eb8b6300c162c041ef1c96cb0
-  summary_generated_at: '2026-06-02T04:36:50Z'
-  summary_source_hash: c5e077458808373c8ce9660235716b5bb55e4e7eb8b6300c162c041ef1c96cb0
-  faq_generated_at: '2026-06-03T01:39:16Z'
-  faq_source_hash: c5e077458808373c8ce9660235716b5bb55e4e7eb8b6300c162c041ef1c96cb0
-  summary: >-
-    Deploy the open source Nginx on Arm-based Linux servers and configure it as a minimal HTTPS
-    static file server and as a reverse proxy and API gateway. You will first install Nginx using
-    a package manager and review its build configuration, then optionally build Nginx from source
-    with the features you need. Next, you will create a key and certificate, add a basic Nginx
-    configuration, and start the server. Finally, you will set up a third node to act as a reverse
-    proxy and API gateway that load balances across two upstream file servers. Prerequisites include
-    Arm-based instances (AWS, Microsoft Azure, Google Cloud, or Oracle) or on-prem Arm servers,
-    and network access on ports 22 and 443. No other explicit prerequisites are listed.
-  faqs:
-  - question: Which Nginx edition does this path use?
-    answer: >-
-      The path uses the open source version of Nginx. Nginx Plus is mentioned for context but
-      is not used here.
-  - question: How many Arm-based instances do I need to complete the exercises?
-    answer: >-
-      You need at least one instance to create a static file server. For the reverse proxy and
-      API gateway, you need at least three instances: two file servers and one reverse proxy/API
-      gateway node.
-  - question: Should I install Nginx from a package manager or build from source?
-    answer: >-
-      The path covers both approaches. It recommends inspecting the build configuration of a prebuilt
-      package first to inform which features you enable when compiling from source.
-  - question: What network settings should I configure before starting?
-    answer: >-
-      Ensure your firewalls and security groups allow communication on port 22 (SSH) and port
-      443 (HTTPS). These are required for access and for serving HTTPS.
-  - question: What should be ready before configuring the reverse proxy and API gateway?
-    answer: >-
-      Set up two static file servers using the earlier section. The third node will run the reverse
-      proxy/API gateway and load balance across the two upstream file servers.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Julio Suarez
 
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
index 4a493e4f34..2ec4274188 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
@@ -15,62 +15,9 @@ learning_objectives:
 prerequisites:
     - A cloud or bare-metal installation of a Nginx file server or load balancer.
     - If you do not already have a Nginx setup, a review of [Learn how to deploy Nginx](/learning-paths/servers-and-cloud-computing/nginx/).
-
-generate_summary_faq: false
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:40:13Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 10f13dee3459577fcadf5966cf80d990f3396321189f638432a3308699e773a8
-  summary_generated_at: '2026-06-02T04:38:02Z'
-  summary_source_hash: 10f13dee3459577fcadf5966cf80d990f3396321189f638432a3308699e773a8
-  faq_generated_at: '2026-06-03T01:40:13Z'
-  faq_source_hash: 10f13dee3459577fcadf5966cf80d990f3396321189f638432a3308699e773a8
-  summary: >-
-    This advanced Learning Path shows how to tune Nginx on Arm-based Linux servers in about 60
-    minutes. You will review how Linux kernel parameters, compiler and library choices, and Nginx
-    configuration affect performance. The steps walk through tuned examples for a static file
-    server (/etc/nginx/nginx.conf) and a Reverse Proxy/API Gateway (/etc/nginx/conf.d/loadbalancer.conf),
-    and present a practical method to test changes using wrk2. A cloud or bare-metal Nginx file
-    server or load balancer is required to follow along; if you do not already have one, first
-    review Learn how to deploy Nginx. By the end, you will be able to apply workload-aware tuning
-    and validate the impact with targeted load testing.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a cloud or bare-metal installation of an Nginx file server or load balancer on
-      Linux. If you do not already have a setup, review the “Learn how to deploy Nginx” Learning
-      Path first.
-  - question: How do I list and change the Linux kernel networking parameters mentioned in the
-      tuning guidance?
-    answer: >-
-      Run sudo sysctl -a to list kernel parameters. You can change values in /etc/sysctl.conf
-      or apply them using the sysctl command; see the Linux source admin-guide and networking
-      documentation for parameter details.
-  - question: Which Nginx configuration files will I tune?
-    answer: >-
-      You will work with the top-level /etc/nginx/nginx.conf and, for Reverse Proxy and API Gateway
-      use cases, /etc/nginx/conf.d/loadbalancer.conf. The Learning Path focuses on performance-relevant
-      directives in these files.
-  - question: Do I have to use wrk2 for performance testing?
-    answer: >-
-      No. If you already have a performance test methodology for your deployment, you can skip
-      the wrk2 section; otherwise, the path shows how wrk2 is typically used for testing Nginx
-      at Arm.
-  - question: What result should I expect after tuning, and how do I validate it?
-    answer: >-
-      There is no single recommended setting set; outcomes depend on your workload and use case.
-      Validate by measuring before and after with your test methodology (or wrk2) and compare
-      results for your specific scenario.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Julio Suarez
 
diff --git a/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md b/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md
index f227fa4099..f647cf45a7 100644
--- a/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md
@@ -11,56 +11,9 @@ learning_objectives:
 
 prerequisites:
     - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an on-premise Arm server.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:40:53Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 81bdee16a18b09da91a7514eb70368771b251ed3f8ec657ec105ca10ecead038
-  summary_generated_at: '2026-06-02T04:38:40Z'
-  summary_source_hash: 81bdee16a18b09da91a7514eb70368771b251ed3f8ec657ec105ca10ecead038
-  faq_generated_at: '2026-06-03T01:40:53Z'
-  faq_source_hash: 81bdee16a18b09da91a7514eb70368771b251ed3f8ec657ec105ca10ecead038
-  summary: >-
-    Learn how to run a Hugging Face Natural Language Processing (NLP) model with PyTorch on Arm
-    servers. Using an Arm-based cloud instance or on-prem Arm server running Ubuntu 22.04 LTS,
-    you will install PyTorch, load an NLP model from Hugging Face, execute the model on an Arm
-    AArch64 CPU, and use the PyTorch profiler to analyze its execution time. The path focuses
-    on practical setup and measurement using Python, PyTorch, and Hugging Face. No explicit prerequisites
-    are listed beyond access to an Arm-based server. This introductory Learning Path is designed
-    to be completed in about 20 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need access to an Arm based instance from a cloud service provider or an on-premise
-      Arm server. No other explicit prerequisites are listed.
-  - question: Which operating system should my server use?
-    answer: >-
-      The instructions are written for an Arm server running Ubuntu 22.04 LTS on Linux. Other
-      operating systems are not covered in this path.
-  - question: Can I use AWS, Microsoft Azure, Google Cloud, or Oracle Cloud for this?
-    answer: >-
-      Yes. You can use an Arm based instance from any of these cloud service providers, or an
-      on-premise Arm server.
-  - question: Do I need a GPU to run the model?
-    answer: >-
-      No. This path focuses on deploying and running the model on an Arm AArch64 CPU. GPU use
-      is not covered.
-  - question: How do I know the deployment and profiling worked?
-    answer: >-
-      You should be able to run inference on the model and collect execution-time data using the
-      PyTorch profiler. Seeing profiler output for the model run indicates success.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md
index fe2466d6e0..bd91822b01 100644
--- a/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md
@@ -18,59 +18,9 @@ learning_objectives:
 prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Familiarity with networking concepts and [Node.js event-driven architecture](https://nodejs.org/en/docs/guides/event-loop-timers-and-nexttick)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:41:19Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 800eed835763098d4b369789d10de642bbdbaa390e8bfe0cb6ea2c4c78f7ecbd
-  summary_generated_at: '2026-06-02T04:39:16Z'
-  summary_source_hash: 800eed835763098d4b369789d10de642bbdbaa390e8bfe0cb6ea2c4c78f7ecbd
-  faq_generated_at: '2026-06-03T01:41:19Z'
-  faq_source_hash: 800eed835763098d4b369789d10de642bbdbaa390e8bfe0cb6ea2c4c78f7ecbd
-  summary: >-
-    Learn how to deploy and evaluate Node.js on Google Cloud C4A virtual machines powered by Axion
-    processors built on Arm Neoverse-V2 cores. You will provision a SUSE Linux Enterprise Server
-    VM (for example, c4a-standard-4) in the Google Cloud Console, install and manage Node.js with
-    Node Version Manager (NVM), validate the runtime with baseline REPL and HTTP server tests,
-    and benchmark using Autocannon on Arm64 (AArch64). This introductory path is aimed at developers
-    migrating Node.js workloads from x86_64 to Arm on GCP. Prerequisites include a GCP account
-    with billing enabled and familiarity with networking concepts and the Node.js event-driven
-    architecture.
-  faqs:
-  - question: What do I need before provisioning the VM?
-    answer: >-
-      You need a Google Cloud Platform account with billing enabled. Familiarity with networking
-      concepts and Node.js’s event-driven architecture is also expected.
-  - question: Which Google Cloud instance type and OS image are used in the steps?
-    answer: >-
-      The path uses Google Cloud C4A Arm-based instances, with c4a-standard-4 (4 vCPUs, 16 GB
-      memory) shown as an example. The VM runs SUSE Linux Enterprise Server on Arm64 (AArch64).
-  - question: How do I install Node.js on the Arm VM?
-    answer: >-
-      Use Node Version Manager (NVM). Run the provided NVM install script, load NVM in your shell,
-      then install and select a Node.js version using the official Node.js packages.
-  - question: How do I confirm the Node.js setup before benchmarking?
-    answer: >-
-      Start the Node.js REPL and print a message such as "Hello from Node.js" to verify the runtime.
-      Then run the simple HTTP server baseline test to confirm the server starts and responds.
-  - question: What should I expect from the Autocannon benchmark, and what should I check if it
-      fails?
-    answer: >-
-      Autocannon reports metrics such as throughput and latency for your HTTP server on Arm64.
-      If it fails to reach the server, first confirm the baseline HTTP server is running and reachable,
-      then rerun the benchmark.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md b/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md
index 0376a8c7a7..74b7876613 100644
--- a/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md
@@ -11,57 +11,9 @@ learning_objectives:
 prerequisites:
     - An OCI account
     - A computer with Terraform installed
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:41:50Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 3ee5dd8d8edeb5dcb1e3be7bb09cdf0b1b2694f0e74e9eb3c6c2f17912399808
-  summary_generated_at: '2026-06-02T04:39:57Z'
-  summary_source_hash: 3ee5dd8d8edeb5dcb1e3be7bb09cdf0b1b2694f0e74e9eb3c6c2f17912399808
-  faq_generated_at: '2026-06-03T01:41:50Z'
-  faq_source_hash: 3ee5dd8d8edeb5dcb1e3be7bb09cdf0b1b2694f0e74e9eb3c6c2f17912399808
-  summary: >-
-    Learn how to automate the creation of Arm (Neoverse) virtual machine instances on Oracle Cloud
-    Infrastructure (OCI) using Terraform. This Learning Path is aimed at developers new to deploying
-    Arm instances on OCI and uses Terraform to define and provision the required resources. The
-    command examples assume you work from a Linux environment, though any computer with the required
-    tools can be used. You need an OCI account and a computer with Terraform installed. In about
-    60 minutes, you will run Terraform to stand up an Arm VM on OCI and understand the basic workflow
-    for repeatable infrastructure deployment.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      You need an Oracle Cloud Infrastructure (OCI) account and a computer with Terraform installed.
-      The commands are written for a Linux environment.
-  - question: Do I have to use Linux to follow the commands?
-    answer: >-
-      The command format assumes you are working on a Linux machine. Any computer can be used
-      if it has the required tools installed.
-  - question: Is there anything I should review before starting with OCI?
-    answer: >-
-      You may want to review the Learning Path “Getting Started with Oracle OCI” before you begin.
-      It helps establish the basics you will build on here.
-  - question: How long does this take and what experience level is expected?
-    answer: >-
-      The estimated time to complete is 60 minutes. The skill level is listed as Advanced, but
-      the topic is positioned as introductory for developers new to deploying Arm instances on
-      OCI using Terraform.
-  - question: What result should I expect after completing the steps?
-    answer: >-
-      You will have automated the creation of Arm virtual machine instances on OCI using Terraform.
-      The outcome is a repeatable infrastructure-as-code workflow for provisioning Arm-based VMs.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Frédéric -lefred- Descamps
 
diff --git a/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md
index 365ca25e09..71763641ff 100644
--- a/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md
@@ -14,60 +14,9 @@ prerequisites:
     - A [Microsoft Azure](https://azure.microsoft.com/) account with access to Cobalt 100 based instances (Dpsv6)
     - Basic understanding of Python and machine learning concepts
     - Familiarity with [ONNX Runtime](https://onnxruntime.ai/docs/) and Azure cloud services
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:43:11Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 84ba887426f3ca45b698c79028023d80cbf0a06e6bc2fb71fcbe924943078dd8
-  summary_generated_at: '2026-06-02T04:41:17Z'
-  summary_source_hash: 84ba887426f3ca45b698c79028023d80cbf0a06e6bc2fb71fcbe924943078dd8
-  faq_generated_at: '2026-06-03T01:43:11Z'
-  faq_source_hash: 84ba887426f3ca45b698c79028023d80cbf0a06e6bc2fb71fcbe924943078dd8
-  summary: >-
-    Provision an Arm-based Azure Cobalt 100 (Dpsv6) virtual machine using the Azure portal and
-    Ubuntu Pro 24.04 LTS, then set up a clean Python environment to run ONNX Runtime with a SqueezeNet
-    1.0 INT8 model. You will validate your setup by performing a simple baseline latency test
-    in Python and then run onnxruntime_perf_test for more systematic benchmarking on Arm64. This
-    introductory path targets developers deploying ONNX-based applications on Arm-based machines
-    and takes about 60 minutes. Prerequisites include an Azure account with access to Cobalt 100
-    instances, basic Python and machine learning knowledge, and familiarity with ONNX Runtime
-    and Azure services.
-  faqs:
-  - question: What do I need before provisioning the VM?
-    answer: >-
-      You need a Microsoft Azure account with access to Cobalt 100 based instances (Dpsv6). Basic
-      understanding of Python and machine learning, and familiarity with ONNX Runtime and Azure
-      cloud services, are also assumed.
-  - question: When creating the VM, which size series and OS image should I choose?
-    answer: >-
-      Use a general-purpose D-series Dpsv6 Arm64 VM and select Ubuntu Pro 24.04 LTS as the base
-      image. The Learning Path guides you through creation using the Azure portal.
-  - question: Can I use the Azure CLI or IaC to create the VM instead of the portal?
-    answer: >-
-      There are multiple ways to create a Cobalt 100 VM, but this Learning Path uses the Azure
-      portal. Other methods are not covered in the steps.
-  - question: How should I prepare the Python environment for ONNX Runtime on the VM?
-    answer: >-
-      Install Python 3, pip, and venv on Ubuntu Pro 24.04 LTS, then create and activate a virtual
-      environment as shown in the steps. This prepares the environment to run ONNX models with
-      ONNX Runtime.
-  - question: How do I run and validate the SqueezeNet INT8 baseline and benchmark?
-    answer: >-
-      Use the provided baseline.py to load squeezenet-int8.onnx and time a single inference to
-      confirm ONNX Runtime is working. Then run onnxruntime_perf_test for detailed statistics;
-      successful runs finish without errors and report latency or benchmark metrics.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma    
 
diff --git a/content/learning-paths/servers-and-cloud-computing/onnx/_index.md b/content/learning-paths/servers-and-cloud-computing/onnx/_index.md
index 154783ed35..c2cde3421e 100644
--- a/content/learning-paths/servers-and-cloud-computing/onnx/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/onnx/_index.md
@@ -14,60 +14,9 @@ prerequisites:
     - Basic understanding of Python and machine learning concepts.
     - Familiarity with ONNX Runtime and Azure cloud services.
     - Knowledge of Large Language Model (LLM) fundamentals.
-
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:42:17Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: a9e547c4a9f99e1c7bfbfe582032ede11eb8ff5a74dbb7a93bada275ac435220
-  summary_generated_at: '2026-06-02T04:40:35Z'
-  summary_source_hash: a9e547c4a9f99e1c7bfbfe582032ede11eb8ff5a74dbb7a93bada275ac435220
-  faq_generated_at: '2026-06-03T01:42:17Z'
-  faq_source_hash: a9e547c4a9f99e1c7bfbfe582032ede11eb8ff5a74dbb7a93bada275ac435220
-  summary: >-
-    This advanced Learning Path guides you through quantizing and deploying Microsoft’s Phi-4-mini
-    model with ONNX Runtime on Arm-based Azure Cobalt 100 virtual machines running Ubuntu 24.04
-    LTS. You will build and configure ONNX Runtime, convert and quantize the model, and create
-    a minimal Python chatbot server (phi4.py) using onnxruntime_genai. You will run prompts and
-    analyze performance on Neoverse N2–based Cobalt 100 instances, observing metrics such as tokens
-    per second and time to first token. Prerequisites include an Arm-based cloud instance (tested
-    on an Azure Cobalt 100 VM), familiarity with Python, ONNX Runtime, Azure services, and LLM
-    fundamentals. By the end, you can interactively serve Phi-4-mini inference on Azure Arm CPUs.
-  faqs:
-  - question: What kind of Azure instance should I use to follow this path?
-    answer: >-
-      Use an Arm-based instance. The steps were tested on an Azure Cobalt 100 Dpls_v6 VM with
-      32 cores, 64GB of RAM, and 32GB of disk space.
-  - question: Which operating system and environment are the instructions written for?
-    answer: >-
-      The procedures target Linux and were validated on Ubuntu 24.04 LTS running on Azure Cobalt
-      100 servers.
-  - question: Do I need to quantize the Phi-4-mini model before running inference?
-    answer: >-
-      Yes. The setup includes quantizing and converting Phi-4-mini before deploying it with ONNX
-      Runtime.
-  - question: How do I run the chatbot server and which arguments matter?
-    answer: >-
-      Create the provided phi4.py script and run it with a model path (args.model_path) to your
-      converted Phi-4-mini model. You can also set an execution provider (args.execution_provider)
-      and enable verbose or timing output if needed.
-  - question: How do I know the deployment worked and what results should I expect?
-    answer: >-
-      After starting the server, send a text prompt and check the terminal for generated tokens
-      and performance metrics. You should see tokens/second and time to first token; the example
-      output shows about 57 tokens/s and ~0.2 s to first token.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Nobel Chowdary Mandepudi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md b/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md
index 0ac502909c..71524d42a2 100644
--- a/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md
@@ -17,62 +17,9 @@ prerequisites:
   - At least 80 GB free disk space and 48 GB RAM
   - Working knowledge of Docker, Git, and common Linux terminal tools
   - Basic understanding of the server firmware stack (such as UEFI, BMC, and TF-A)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:43:38Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: dec913a7a15e82ebf129e2ac64cba8d9140694840f8f9e817fb091b0c82c4cab
-  summary_generated_at: '2026-06-02T04:41:47Z'
-  summary_source_hash: dec913a7a15e82ebf129e2ac64cba8d9140694840f8f9e817fb091b0c82c4cab
-  faq_generated_at: '2026-06-03T01:43:38Z'
-  faq_source_hash: dec913a7a15e82ebf129e2ac64cba8d9140694840f8f9e817fb091b0c82c4cab
-  summary: >-
-    This advanced Learning Path shows how to build and simulate OpenBMC and UEFI firmware pre-silicon
-    on the Arm Neoverse RD-V3 r1 Fixed Virtual Platform (FVP). You will set up a Docker-based
-    build environment, compile OpenBMC and host UEFI images, launch the RD-V3 FVP, and observe
-    the boot across multiple UART consoles. You will validate host–BMC communication using UART
-    and Serial over LAN (SoL), access the host console through the OpenBMC web UI, and implement
-    a custom IPMI command in C++ for validation. Prerequisites include an Arm Neoverse-based Ubuntu
-    22.04 LTS system with 80 GB free disk space, 48 GB RAM, and familiarity with Docker, Git,
-    and Linux tools. Tools include OpenBMC, Yocto/BitBake, FVP, C/C++, and ipmitool.
-  faqs:
-  - question: What do I need before running the builds?
-    answer: >-
-      Use an Arm Neoverse-based Linux machine running Ubuntu 22.04 LTS with at least 80 GB free
-      disk space and 48 GB RAM. You should be comfortable with Docker, Git, common Linux terminal
-      tools, and have a basic understanding of UEFI, BMC, and TF-A.
-  - question: How do I know the RD-V3 FVP booted OpenBMC and UEFI correctly?
-    answer: >-
-      After launching the FVP, you should see multiple UART consoles for subsystems such as Neoverse
-      V3, Cortex-M55, Cortex-M7, and the Cortex-A BMC. Successful boot is indicated by visible
-      boot logs on these consoles for both the BMC and the host UEFI firmware.
-  - question: What should I check if the UART console windows do not appear?
-    answer: >-
-      The simulation opens multiple graphical UART terminals and requires a desktop session. If
-      you connect over SSH only, these consoles will not render; switch to a desktop environment
-      or an appropriate session that supports GUI windows.
-  - question: How do I access the host console through OpenBMC?
-    answer: >-
-      Use OpenBMC Serial over LAN (SoL). Create a virtual UART bridge with socat between the host-side
-      and BMC-side ports (for example, tcp:localhost:5005 to tcp:localhost:5067), verify the mappings,
-      then open the host console from the BMC web UI.
-  - question: How do I add and validate a custom IPMI command in OpenBMC?
-    answer: >-
-      Implement a custom IPMI command handler in C++, package it with Yocto/BitBake, and rebuild
-      the OpenBMC image. Run it on the FVP and confirm it returns the expected simple string response,
-      using the steps provided (you can invoke it with ipmitool).
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author:
   - Odin Shen
diff --git a/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md b/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md
index f562ee9494..1e3139984c 100644
--- a/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md
@@ -16,61 +16,9 @@ learning_objectives:
 prerequisites:
     - An Arm Linux (aarch64) server, such as an AWS Graviton3 instance
     - Basic understanding of C++ and CMake
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:44:04Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 778ac2a8b8ad9ffd665f6f0be545541090465f355094c354cc693e784d27559a
-  summary_generated_at: '2026-06-02T04:42:23Z'
-  summary_source_hash: 778ac2a8b8ad9ffd665f6f0be545541090465f355094c354cc693e784d27559a
-  faq_generated_at: '2026-06-03T01:44:04Z'
-  faq_source_hash: 778ac2a8b8ad9ffd665f6f0be545541090465f355094c354cc693e784d27559a
-  summary: >-
-    Learn to profile and accelerate a C++ data-processing workload on Arm Linux (aarch64) using
-    Arm Performix and OpenRNG from Arm Performance Libraries. You will build and run a baseline
-    application, use Performix Code Hotspots to identify the most impactful functions to optimize,
-    then integrate OpenRNG’s vector API to speed up random number generation. Finally, you will
-    run a microbenchmark sweep to measure runtime across input sizes from 2^8 to 2^15 elements
-    and compare baseline versus accelerated builds. This introductory path targets C++ developers
-    and assumes access to an Arm Linux server, such as an AWS Graviton3 instance, plus basic knowledge
-    of C++ and CMake.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an Arm Linux (aarch64) server, such as an AWS Graviton3 instance, and a basic understanding
-      of C++ and CMake. No other explicit prerequisites are listed.
-  - question: Which packages should I install, and what if I’m not using Amazon Linux?
-    answer: >-
-      Install git, cmake, g++, environment-modules, and python3 with your package manager. The
-      steps use dnf on Amazon Linux 2023, but you can substitute apt on Ubuntu or Debian.
-  - question: How do I decide which function to optimize after running the baseline?
-    answer: >-
-      Use Arm Performix Code Hotspots to profile the entire program with hardware performance
-      counters and identify the routines with the highest impact. This avoids relying on manual
-      timers that might miss hotspots like both generateDistribution and min_length.
-  - question: When integrating OpenRNG, which API should I use and what changes am I making?
-    answer: >-
-      Use OpenRNG’s Vector Statistical Library (VSL) API to generate Gaussian values in bulk via
-      a stream object. Replace the baseline’s one-sample-at-a-time generation with this vectorized,
-      bulk generation.
-  - question: What result should I expect from the microbenchmark sweep, and how do I compare
-      builds?
-    answer: >-
-      The microbenchmark isolates generateDistribution and times it in microseconds across sizes
-      from 2^8 to 2^15. Run both the baseline and accelerated builds and compare the recorded
-      times to see how the speedup from OpenRNG scales with input size.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/openshift/_index.md b/content/learning-paths/servers-and-cloud-computing/openshift/_index.md
index 5bcea5ed98..dcbde44876 100644
--- a/content/learning-paths/servers-and-cloud-computing/openshift/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openshift/_index.md
@@ -13,61 +13,9 @@ prerequisites:
   - Red Hat OpenShift Pipelines (Tekton) Operator installed in your cluster
   - Familiarity with the `oc` CLI, container fundamentals, and basic Tekton concepts (Task, Pipeline, PipelineRun)
   - Cluster access with cluster-admin or equivalent permissions to configure nodes and pipelines
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:44:23Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 7972fb230273ab1809c6f0917c4bbc49934f495feb2649da665d67bf71a45a3f
-  summary_generated_at: '2026-06-02T04:43:07Z'
-  summary_source_hash: 7972fb230273ab1809c6f0917c4bbc49934f495feb2649da665d67bf71a45a3f
-  faq_generated_at: '2026-06-03T01:44:23Z'
-  faq_source_hash: 7972fb230273ab1809c6f0917c4bbc49934f495feb2649da665d67bf71a45a3f
-  summary: >-
-    Learn how to use Red Hat OpenShift Pipelines (Tekton) on AWS to migrate existing OpenShift
-    applications from x86 compute nodes to Arm 64-bit (arm64) nodes and build multi-architecture
-    container images. You will assess workload compatibility, enable multi-architecture support
-    in OpenShift, configure Arm64 nodes, rebuild and verify images, and transition deployments
-    safely. The example uses the OpenShift Pipelines Tutorial as a baseline running on x86 infrastructure.
-    Prerequisites include an AWS account with an OpenShift 4.18 cluster on x86 compute nodes,
-    the OpenShift Pipelines (Tekton) Operator installed, cluster-admin access, and familiarity
-    with the oc CLI, container fundamentals, and core Tekton concepts. Estimated time to complete
-    is about 30 minutes.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      You need an AWS account with an OpenShift 4.18 cluster running x86 compute nodes, the Red
-      Hat OpenShift Pipelines (Tekton) Operator installed, and cluster-admin or equivalent permissions.
-      Familiarity with the oc CLI, container fundamentals, and basic Tekton concepts (Task, Pipeline,
-      PipelineRun) is also required.
-  - question: Which environment does the example start from?
-    answer: >-
-      It uses the OpenShift Pipelines Tutorial as the baseline, running on an OpenShift 4.18 cluster
-      on AWS with x86 compute nodes. The procedures assume this x86 starting point.
-  - question: Do I need Arm64 worker nodes already available?
-    answer: >-
-      Not explicitly. The Learning Path shows how to configure Arm64 nodes and enable multi-architecture
-      support as part of the migration steps.
-  - question: How do I know my application can run on Arm (arm64)?
-    answer: >-
-      Begin with the assessment step to confirm workload compatibility with the 64-bit Arm architecture.
-      Proceed only after verifying that your applications can run on arm64.
-  - question: What result should I expect after completing the steps?
-    answer: >-
-      You will rebuild and verify container images with multi-architecture support and transition
-      deployments to Arm-based nodes on AWS. The steps focus on a safe migration using OpenShift
-      Pipelines.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jeff Young
 
diff --git a/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md
index 27c6917cd9..c83f450156 100644
--- a/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md
@@ -20,59 +20,9 @@ prerequisites:
   - Basic knowledge of Linux command-line operations
   - Familiarity with SSH and remote server access
   - Basic understanding of cloud computing and virtualization concepts
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:44:49Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 42628afa923ce6e89693bdfc72469ac0803610c3decb6cd4f36bd1a003874d0d
-  summary_generated_at: '2026-06-02T04:43:52Z'
-  summary_source_hash: 42628afa923ce6e89693bdfc72469ac0803610c3decb6cd4f36bd1a003874d0d
-  faq_generated_at: '2026-06-03T01:44:49Z'
-  faq_source_hash: 42628afa923ce6e89693bdfc72469ac0803610c3decb6cd4f36bd1a003874d0d
-  summary: >-
-    Learn how to deploy OpenStack on Arm-based Microsoft Azure Cobalt 100 (Arm64) virtual machines.
-    You will provision an Azure Dpsv6 series VM and use DevStack to bring up a single-node development
-    environment with core services. Then you will prepare a second Ubuntu 24.04 Arm64 VM with
-    two NICs and a data disk, and use Kolla-Ansible to deploy containerized OpenStack services.
-    Along the way, you will configure networking and storage, launch and manage instances, and
-    access OpenStack via the CLI and Horizon dashboard. Prerequisites include an Azure account
-    with access to Cobalt 100 instances, basic Linux command-line skills, familiarity with SSH,
-    and a basic understanding of cloud and virtualization concepts.
-  faqs:
-  - question: What do I need before I start?
-    answer: >-
-      You need a Microsoft Azure account with access to Cobalt 100 based instances (Dpsv6), basic
-      Linux command-line skills, familiarity with SSH and remote access, and a basic understanding
-      of cloud computing and virtualization concepts.
-  - question: Which Azure VM size and disk setup should I use for the DevStack deployment?
-    answer: >-
-      Use a single-NIC D4ps_v6 instance with at least 80 GB of disk. The steps in this Learning
-      Path use the Azure Portal to create the VM.
-  - question: Can I run DevStack and Kolla-Ansible on the same VM?
-    answer: >-
-      No. You can't run DevStack and Kolla-Ansible on the same VM; the Kolla-Ansible deployment
-      must run on a separate Azure VM.
-  - question: What specifications and OS are required for the Kolla-Ansible host?
-    answer: >-
-      Create a separate Azure VM with 4 vCPUs (8 recommended), 16 GB RAM (recommended), a 100
-      GB OS disk, a 32 GB data disk (for Cinder/Docker), and two NICs. Use Ubuntu 24.04 on Arm64.
-  - question: After deployment, how do I access OpenStack and what should I expect to be running?
-    answer: >-
-      Access OpenStack using the CLI and the Horizon dashboard. The environment runs core services
-      such as Nova, Neutron, Keystone, and Glance on Arm64 and allows launching virtual machine
-      instances.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md b/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md
index 3c32b8a08e..0769317b66 100644
--- a/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md
@@ -15,59 +15,9 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with Python and Flask
   - Basic understanding of containers and Kubernetes concepts
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:45:22Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: cb4227a3f8375d6ae63801891703d6e615bdc60a01fca099a2f76453775ef460
-  summary_generated_at: '2026-06-02T04:44:29Z'
-  summary_source_hash: cb4227a3f8375d6ae63801891703d6e615bdc60a01fca099a2f76453775ef460
-  faq_generated_at: '2026-06-03T01:45:22Z'
-  faq_source_hash: cb4227a3f8375d6ae63801891703d6e615bdc60a01fca099a2f76453775ef460
-  summary: >-
-    This Learning Path guides you through deploying and observing a Python Flask microservice
-    on Arm64-based Google Cloud C4A Axion processors. You will provision a c4a-standard-4 VM running
-    SUSE Linux, configure GCP firewall rules for the service and observability endpoints, and
-    prepare container tooling to run an instrumented Flask app that emits OpenTelemetry traces
-    and metrics. You then deploy the OpenTelemetry Collector and integrate Prometheus and Jaeger
-    so metrics and traces flow from the service through the collector to their UIs. By the end,
-    you can generate and analyze telemetry for the service on Arm infrastructure. Prerequisites
-    include a GCP account with billing enabled and basic Python/Flask and container/Kubernetes
-    familiarity.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Google Cloud Platform account with billing enabled, basic familiarity with Python
-      and Flask, and a basic understanding of containers and Kubernetes concepts.
-  - question: Which Google Cloud VM and operating system does this path use?
-    answer: >-
-      You will create a Google Axion C4A Arm-based VM using the c4a-standard-4 machine type with
-      4 vCPUs and 16 GB of memory. The setup uses an arm64-based SUSE Linux virtual machine.
-  - question: Which firewall ports should I open and why?
-    answer: >-
-      Open TCP ports: 8080 for the Flask application, 16686 for the Jaeger UI, 9090 for the Prometheus
-      UI, 4317 for OTLP gRPC ingestion, and 4318 for OTLP HTTP ingestion.
-  - question: How are the telemetry components connected in this setup?
-    answer: >-
-      The Flask microservice uses the OpenTelemetry SDK to emit telemetry to the OpenTelemetry
-      Collector. The Collector routes metrics to Prometheus and traces to Jaeger.
-  - question: How do I validate that telemetry is flowing end-to-end?
-    answer: >-
-      Access the Prometheus UI on port 9090 and the Jaeger UI on port 16686 to verify data from
-      the Flask service. Generate requests to the Flask app on port 8080 to produce new traces
-      and metrics.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/pac/_index.md b/content/learning-paths/servers-and-cloud-computing/pac/_index.md
index 9f85efca7d..7cf96119f2 100644
--- a/content/learning-paths/servers-and-cloud-computing/pac/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pac/_index.md
@@ -13,60 +13,9 @@ learning_objectives:
 prerequisites:
     - An Arm based instance from a cloud service provider, or an on-premise Arm server.
     - If needed, review [Get started with Arm-based cloud instances](/learning-paths/servers-and-cloud-computing/csp/) to learn how to deploy Arm in the cloud. These learning paths also point to more advanced learning paths that show how to automate the deployment of Arm instances at different cloud providers.
-    
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:45:44Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: fa699cafa5c0d998a63de306371bfbe47680c7453b28fc4b35d4fe2671902b23
-  summary_generated_at: '2026-06-02T04:45:16Z'
-  summary_source_hash: fa699cafa5c0d998a63de306371bfbe47680c7453b28fc4b35d4fe2671902b23
-  faq_generated_at: '2026-06-03T01:45:44Z'
-  faq_source_hash: fa699cafa5c0d998a63de306371bfbe47680c7453b28fc4b35d4fe2671902b23
-  summary: >-
-    Use a Linux Arm server to explore Arm Pointer Authentication (PAC) by building and analyzing
-    a small, vulnerable C program. You will compile the application with and without PAC, inspect
-    the generated instructions, and use pwntools to exploit the non-PAC binary (main_nopac) to
-    redirect control flow to an unintended function that launches a shell, then compare behavior
-    with PAC enabled to see how the protection changes the outcome. This advanced path targets
-    Arm-based instances in the cloud or on-premise and takes about 30 minutes. Prerequisite: access
-    to an Arm-based instance; if needed, consult the referenced Get started with Arm-based cloud
-    instances learning paths.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need access to a Linux Arm-based instance from a cloud provider or an on‑prem Arm server.
-      No other prerequisites are explicitly listed.
-  - question: Can I use any cloud provider for the Arm instance?
-    answer: >-
-      Yes. You can use an Arm-based instance from AWS, Microsoft Azure, Google Cloud, or Oracle,
-      or use an on‑prem Arm server.
-  - question: Which tools do I install to run the exploit code?
-    answer: >-
-      Install pwntools and its dependencies as shown in the steps. The path uses Python 3 and
-      pip to set up pwntools.
-  - question: Which binary should I target when running the exploit?
-    answer: >-
-      Target the application built without Pointer Authentication, referred to as main_nopac in
-      the steps.
-  - question: What result should I expect when the exploit works, and how do I compare with Pointer
-      Authentication enabled?
-    answer: >-
-      A successful exploit will execute func2(), print "Hello from func2!", and spawn a shell.
-      Then build the Pointer Authentication version and follow the steps to inspect the generated
-      instructions and compare behavior.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md b/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md
index 495e6fb009..36cfc24a95 100644
--- a/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md
@@ -18,64 +18,9 @@ prerequisites:
     - Access to an Arm-based cloud instance running Linux, such as an AWS Graviton3 instance
     - Access to Arm Performix configured with the remote Arm target. See the [Arm Performix install guide](/install-guides/performix/) for setup instructions
     - Basic understanding of C++
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:46:30Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 0599665da13e9e1a8b017b0dac87c63f323ef769b1a5be62a60a32a36bc82696
-  summary_generated_at: '2026-06-02T04:46:21Z'
-  summary_source_hash: 0599665da13e9e1a8b017b0dac87c63f323ef769b1a5be62a60a32a36bc82696
-  faq_generated_at: '2026-06-03T01:46:30Z'
-  faq_source_hash: 0599665da13e9e1a8b017b0dac87c63f323ef769b1a5be62a60a32a36bc82696
-  summary: >-
-    Use an AI coding assistant with the Arm MCP Server to run Arm Performix Code Hotspots on a
-    C++ application and act on the results on Arm Neoverse. You configure a GitHub Copilot prompt
-    file to launch profiling on a remote Linux-based Arm instance, interpret flame graph output,
-    and apply agent-suggested changes to the Mandelbrot example, such as a squared-magnitude check,
-    raw double arithmetic instead of std::complex, and compiling with -O3. Prerequisites include
-    familiarity with configuring the Arm MCP Server in an AI assistant (or completion of the related
-    Learning Path), access to an Arm-based cloud instance (for example, AWS Graviton3) with Arm
-    Performix set up for the target, and basic C++ knowledge.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      You need familiarity with configuring the Arm MCP Server in an AI coding assistant (or completion
-      of the referenced Learning Path), access to an Arm-based Linux cloud instance such as an
-      AWS Graviton3 instance, access to Arm Performix configured with the remote Arm target, and
-      a basic understanding of C++.
-  - question: Do I have to use Visual Studio Code and GitHub Copilot?
-    answer: >-
-      The steps use Visual Studio Code with GitHub Copilot as the example AI assistant. Equivalent
-      configurations for other AI agents (Kiro and OpenAI Codex) are referenced at the end of
-      the section.
-  - question: Which prompt file should I use to run the Code Hotspots recipe?
-    answer: >-
-      Use the Arm MCP arm-hotspots-optimization prompt file with GitHub Copilot. It drives the
-      Code Hotspots recipe through the MCP Server, confirms your target details, runs the collection,
-      and returns structured profiling results.
-  - question: How do I know Arm Performix can reach my remote Arm target?
-    answer: >-
-      You will build the Mandelbrot C++ application on the remote server and follow a step that
-      confirms Performix can access the target. Complete this confirmation before launching the
-      Code Hotspots run.
-  - question: What result should I expect, and what optimizations are applied?
-    answer: >-
-      Expect structured profiling output and a flame graph that highlights the hottest functions
-      in the Mandelbrot application. The path applies AI-suggested changes: replacing std::abs
-      with a squared-magnitude check, replacing std::complex<double> with raw double arithmetic,
-      and rebuilding with -O3; the agent can edit the remote source via SSH through the MCP Server.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md b/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md
index 74313af587..d8a2f03c51 100644
--- a/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md
@@ -16,60 +16,9 @@ prerequisites:
     - An Arm Neoverse-based server running Linux (bare-metal or cloud bare-metal instance preferred for access to hardware performance counters)
     - Familiarity with Linux command line
     - Basic understanding of CPU performance concepts
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:47:08Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: ec027f6022cc86a644a71a7d2016bf4601e2c4ac41570e861e9f124468b228ae
-  summary_generated_at: '2026-06-02T04:47:05Z'
-  summary_source_hash: ec027f6022cc86a644a71a7d2016bf4601e2c4ac41570e861e9f124468b228ae
-  faq_generated_at: '2026-06-03T01:47:08Z'
-  faq_source_hash: ec027f6022cc86a644a71a7d2016bf4601e2c4ac41570e861e9f124468b228ae
-  summary: >-
-    Analyze and improve a Linux application’s performance on Arm Neoverse-based servers using
-    Arm Performix Runbook. You will configure a Performix connection, build a C Mandelbrot set
-    generator, then run the CPU Microarchitecture recipe to identify pipeline bottlenecks and
-    the Instruction Mix recipe to examine instruction types and SIMD utilization. Using these
-    insights, apply vectorization and compiler flags and compare performance profiles to measure
-    execution changes. Target environment: an Arm Neoverse server running Linux, with bare-metal
-    or cloud bare-metal preferred for access to hardware performance counters. This introductory
-    path assumes familiarity with the Linux command line and basic CPU performance concepts and
-    is designed to be completed in about 60 minutes.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      You need a Linux system on an Arm Neoverse-based server, with bare-metal or cloud bare-metal
-      access preferred for hardware performance counters. You should be comfortable with the Linux
-      command line and have a basic understanding of CPU performance concepts.
-  - question: How do I know the sample Mandelbrot application built and runs correctly?
-    answer: >-
-      The program generates a 1920×1080 bitmap image of the fractal when it runs successfully.
-      Use this output as a quick validation before launching Arm Performix analyses.
-  - question: Which option should I select for the Instruction Mix recipe?
-    answer: >-
-      Choose Dynamic for the Analysis Mode. This path uses the Dynamic mode to report instruction
-      types and SIMD utilization.
-  - question: What should I look for in the CPU Microarchitecture recipe results?
-    answer: >-
-      Identify which instruction pipeline stages dominate program latency. Use those findings
-      to focus subsequent changes on the most impactful bottlenecks.
-  - question: How do I confirm whether my workload is using SIMD, and what if it isn’t?
-    answer: >-
-      Run the Instruction Mix recipe and review the SIMD utilization alongside the integer and
-      floating-point instruction counts. If SIMD usage is absent, proceed with vectorization and
-      appropriate compiler flags, then compare performance profiles to measure the effect.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author:
 - Brendan Long
diff --git a/content/learning-paths/servers-and-cloud-computing/performix-system-characterization/_index.md b/content/learning-paths/servers-and-cloud-computing/performix-system-characterization/_index.md
index 676a9a13ef..5a7680afa0 100644
--- a/content/learning-paths/servers-and-cloud-computing/performix-system-characterization/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/performix-system-characterization/_index.md
@@ -18,11 +18,9 @@ learning_objectives:
 
 prerequisites:
     - A Arm Linux target machine accessible via SSH to characterize.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 author:
 - Brendan Long
 - David Wong
diff --git a/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md
index dcabef896d..9b1cf97ec0 100644
--- a/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md
@@ -16,59 +16,9 @@ learning_objectives:
 prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with web servers and PHP scripting
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:48:24Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 719ec8966a776cc5ee97782b7ef7cc2b989a8616d14cb36b9847c9cbd2f16446
-  summary_generated_at: '2026-06-02T04:47:26Z'
-  summary_source_hash: 719ec8966a776cc5ee97782b7ef7cc2b989a8616d14cb36b9847c9cbd2f16446
-  faq_generated_at: '2026-06-03T01:48:24Z'
-  faq_source_hash: 719ec8966a776cc5ee97782b7ef7cc2b989a8616d14cb36b9847c9cbd2f16446
-  summary: >-
-    Follow this introductory path to deploy and validate a PHP stack on Arm-based Google Cloud
-    C4A virtual machines built on Axion processors. You will provision a SUSE Linux Enterprise
-    Server instance (c4a-standard-4), install PHP, Apache, and common PHP extensions, and configure
-    PHP-FPM. The path guides you through running baseline HTTP server tests to confirm the setup
-    and benchmarking PHP performance with PHPBench on Arm64. It is intended for developers migrating
-    PHP workloads from x86_64 to Arm on Google Cloud. Prerequisites include a Google Cloud Platform
-    account with billing enabled and basic familiarity with web servers and PHP scripting.
-  faqs:
-  - question: What do I need before provisioning the instance on Google Cloud?
-    answer: >-
-      You need a Google Cloud Platform account with billing enabled and basic familiarity with
-      web servers and PHP scripting. The path references a separate Learning Path for general
-      GCP setup support.
-  - question: Which Google Cloud VM configuration does this path use?
-    answer: >-
-      You will create a Google Cloud C4A Arm-based Axion VM using the c4a-standard-4 machine type
-      (four vCPUs, 16 GB memory). Provisioning is performed in the Google Cloud Console.
-  - question: Which operating system and architecture are targeted?
-    answer: >-
-      The path uses SUSE Linux Enterprise Server on an Arm64 Google Cloud C4A instance. Steps
-      refer to installing and configuring software on a SUSE Arm-based virtual machine.
-  - question: How do I install the PHP stack on the SUSE instance?
-    answer: >-
-      Update the system with zypper and then install PHP, PHP-FPM, Apache, and commonly used PHP
-      extensions. The steps use zypper refresh, zypper update, and zypper install to add the required
-      packages.
-  - question: How do I validate the setup and what should I look for in benchmarks?
-    answer: >-
-      Configure a PHP-FPM pool, connect it to Apache, and run baseline HTTP server tests to verify
-      FastCGI and dynamic PHP are working. For benchmarking, use PHPBench and review metrics like
-      mode time, variance, and throughput on sample operations such as string and array handling.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md b/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md
index dfdd79e971..ec36ea83b3 100644
--- a/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md
@@ -16,61 +16,9 @@ prerequisites:
     - Experience with multi-threaded programming in C++ and Python
     - Understanding of build systems and computer architecture concepts
     - Familiarity with Linux command-line tools
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:49:12Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 2fdb45e72a36eb114250486b88d603cc8687b9f6b44bb5bb656e25c37d9795a9
-  summary_generated_at: '2026-06-02T04:47:41Z'
-  summary_source_hash: 2fdb45e72a36eb114250486b88d603cc8687b9f6b44bb5bb656e25c37d9795a9
-  faq_generated_at: '2026-06-03T01:49:12Z'
-  faq_source_hash: 2fdb45e72a36eb114250486b88d603cc8687b9f6b44bb5bb656e25c37d9795a9
-  summary: >-
-    This advanced Learning Path teaches you how to control where your workloads run on many-core
-    Arm-based Linux systems by setting CPU affinity for processes and threads. You will pin threads
-    to specific cores using taskset and source-level changes in C++ and Python, create a single-threaded
-    Python benchmark and C++ examples, and use perf (and Google Benchmark where applicable) to
-    measure cache behavior and compare default scheduling with pinned execution. You will also
-    evaluate throughput versus latency consistency and apply CPU affinity strategies for co-located
-    workloads. The path runs on any Arm Linux system with four or more CPU cores; an example uses
-    an AWS Graviton 3 m7g.4xlarge with Ubuntu 24.04 LTS, and you will check NUMA topology with
-    lscpu. Prerequisites are explicitly listed.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an Arm Linux system with four or more CPU cores. Experience with multi-threaded
-      C++ and Python, build systems, computer architecture concepts, and familiarity with Linux
-      command-line tools is expected.
-  - question: Do I have to use the AWS Graviton3 instance mentioned in the setup?
-    answer: >-
-      No. The steps work on any Arm Linux system with four or more cores; the AWS Graviton3 m7g.4xlarge
-      on Ubuntu 24.04 LTS (Neoverse V1) is provided as an example.
-  - question: How do I check whether my system has a single NUMA node before choosing cores?
-    answer: >-
-      Run lscpu | grep -i numa. On the example m7g.4xlarge instance, all 16 cores are reported
-      in the same NUMA node.
-  - question: How do I validate that thread pinning changed behavior?
-    answer: >-
-      Compare runs before and after pinning using the provided benchmarks and use perf to measure
-      cache performance differences. Use the results to assess throughput and latency consistency
-      trade-offs.
-  - question: When is thread pinning most useful in this Learning Path?
-    answer: >-
-      Pinning is presented as a fine-tuning technique for workloads that aim to consume as many
-      CPU cycles as possible while co-located with other workloads. Use it when you want more
-      consistent execution by constraining where threads run.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md b/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md
index b2482b0b53..21ffab7130 100644
--- a/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md
@@ -14,62 +14,9 @@ learning_objectives:
 prerequisites:
     - An Arm-based computer running Linux.
     - Some familiarity with Linux application performance analysis.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:50:24Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 3ec6ae40daff557dd05cd092ff7d6b5a63954a1618605030a443f56fe5ffe490
-  summary_generated_at: '2026-06-02T04:48:11Z'
-  summary_source_hash: 3ec6ae40daff557dd05cd092ff7d6b5a63954a1618605030a443f56fe5ffe490
-  faq_generated_at: '2026-06-03T01:50:24Z'
-  faq_source_hash: 3ec6ae40daff557dd05cd092ff7d6b5a63954a1618605030a443f56fe5ffe490
-  summary: >-
-    This Learning Path shows how to instrument C/C++ applications on Arm-based Linux systems for
-    precise, code-level performance analysis using the PMUv3 plugin. You will prepare the plugin,
-    enable user-space access to Arm PMUv3 performance counters, and instrument one or multiple
-    code sections to collect fine-grained metrics. The path demonstrates running a single collection
-    over any of 15 event groups (bundles) and using a Python tool to plot raw PMU event values
-    alongside KPIs such as MPKI, stalls, and IPC for visualization. Prerequisites include an Arm-based
-    computer running Linux and some familiarity with Linux application performance analysis; no
-    additional prerequisites are explicitly listed.
-  faqs:
-  - question: How do I enable and verify userspace access to the PMU counters?
-    answer: >-
-      Run: sudo sysctl kernel/perf_user_access=1. Verify with: cat /proc/sys/kernel/perf_user_access
-      and expect a value of 1. This setting enables access until the next reboot.
-  - question: How should I organize my directories before instrumenting code?
-    answer: >-
-      Keep three parallel directories: the Linux kernel source tree, the PMUv3 plugin source code,
-      and a test directory for integrating the plugin into an application. If you use a different
-      layout, adjust build commands to locate headers and libraries accordingly.
-  - question: Which events and metrics can I collect in a single run?
-    answer: >-
-      You can collect raw event values and performance metrics for any of the 15 event groups
-      (bundles) in a single run. The results can later be plotted with KPIs such as MPKI, stalls,
-      and IPC.
-  - question: How do I instrument multiple sections of code in C?
-    answer: >-
-      Include the two required headers, initialize the plugin with pmuv3_bundle_init() using the
-      desired bundle number, then use start and stop functions with markers to identify each profiled
-      segment. Cleanup steps are the same as for the single-section scenario.
-  - question: How do I set up the Python environment to plot and analyze results?
-    answer: >-
-      On Ubuntu, install python-is-python3, python3-pip, and python3-venv, create and activate
-      a virtual environment, then pip install pandas, pyyaml, matplotlib, and PyPDF2. Download
-      the provided Python application to generate plots of raw PMU events and KPIs from your collected
-      data.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Gayathri Narayana Yegna Narayanan
 
diff --git a/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md
index 48d6c9d1fa..c1eccc0840 100644
--- a/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md
@@ -17,63 +17,9 @@ prerequisites:
   - Basic knowledge of Linux command-line operations
   - Familiarity with SSH and remote server access
   - Basic understanding of databases and SQL
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:52:07Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 57827f45473867b3b5039a9cbca04d2c6d8a93e177ca0ab053999517389014b5
-  summary_generated_at: '2026-06-02T04:48:52Z'
-  summary_source_hash: 57827f45473867b3b5039a9cbca04d2c6d8a93e177ca0ab053999517389014b5
-  faq_generated_at: '2026-06-03T01:52:07Z'
-  faq_source_hash: 57827f45473867b3b5039a9cbca04d2c6d8a93e177ca0ab053999517389014b5
-  summary: >-
-    Deploy PostgreSQL on Arm-based Microsoft Azure Cobalt 100 virtual machines and validate it
-    for transactional and analytical workloads in about 30 minutes. You will provision a Dpsv6
-    VM, install PostgreSQL on Ubuntu 24.04 Pro Arm64, configure the service for remote access,
-    and load a relational schema with transactional data. The path then runs analytical SQL queries,
-    benchmarks using pgbench, and monitors query execution with built-in PostgreSQL tools such
-    as pg_stat_statements. You will also add indexes and apply basic tuning for better query execution
-    on Arm. Prerequisites include an Azure account with access to Cobalt 100 instances, basic
-    Linux CLI skills, SSH familiarity, and a basic understanding of databases and SQL.
-  faqs:
-  - question: What do I need in Azure before creating the VM?
-    answer: >-
-      You need a Microsoft Azure account with access to Cobalt 100-based instances (Dpsv6). The
-      path also assumes basic Linux, SSH, and SQL knowledge.
-  - question: Which option should I use to provision the Cobalt 100 VM?
-    answer: >-
-      The path walks through creating the VM in the Azure Portal and targets general-purpose Dpsv6
-      instances. You can also use the Azure CLI or an IaC tool, but the instructions focus on
-      the Portal workflow.
-  - question: How do I confirm PostgreSQL is installed and ready for connections?
-    answer: >-
-      At the end of the installation section, PostgreSQL is installed, running as a service, configured
-      for remote access, and ready for application workloads. You can verify by connecting as
-      the postgres user to the appdb database.
-  - question: What schema and data are created before running queries?
-    answer: >-
-      The path creates a relational schema with two tables to simulate a transactional application
-      and loads sample transactional data. You work in the appdb database and then run analytical
-      SQL queries using the application user.
-  - question: What should I expect after running the pgbench initialization, and how do I monitor
-      queries?
-    answer: >-
-      Running pgbench -i -s 50 appdb creates standard benchmarking tables and loads data for testing,
-      with output indicating the initialization steps. For monitoring and tuning, you use PostgreSQL
-      built-in extensions such as pg_stat_statements and apply indexing techniques described in
-      the path.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md b/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md
index 446d231973..2e51bded10 100644
--- a/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md
@@ -12,58 +12,9 @@ learning_objectives:
 prerequisites:
     - An Arm based instance from a cloud service provider, or an on-premise Arm server.
     - If you do not have an Arm node, the next section discusses some options.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:51:17Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: a60cc2148a83f919467076e9d5a49fc4c9cec85670a919c7ba044cba72bfe219
-  summary_generated_at: '2026-06-02T04:48:33Z'
-  summary_source_hash: a60cc2148a83f919467076e9d5a49fc4c9cec85670a919c7ba044cba72bfe219
-  faq_generated_at: '2026-06-03T01:51:17Z'
-  faq_source_hash: a60cc2148a83f919467076e9d5a49fc4c9cec85670a919c7ba044cba72bfe219
-  summary: >-
-    This introductory Learning Path shows how to deploy PostgreSQL on Arm-based infrastructure
-    running Linux. In about 30 minutes, you will review deployment choices on Arm, including bare
-    metal, cloud VMs, and SQL services from AWS, Microsoft Azure, Google Cloud, and Oracle. You
-    will consider installation and configuration options, learn how to check your database, and
-    use the psql client tool to interact with PostgreSQL. A prerequisite is access to an Arm-based
-    instance from a cloud service provider or an on-premise Arm server; if you do not yet have
-    an Arm node, the path outlines options. The content targets Arm server platforms, including
-    Neoverse-based systems.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      You need access to an Arm-based instance from a cloud provider or an on-premise Arm server.
-      The path targets Linux. If you do not have an Arm node, the next section discusses options.
-  - question: Which Arm deployment options does this path cover?
-    answer: >-
-      It discusses deploying PostgreSQL on bare metal, on cloud virtual machines, and via managed
-      SQL services. Cloud providers listed include AWS, Microsoft Azure, Google Cloud, and Oracle.
-  - question: Will I use the psql client, and for what?
-    answer: >-
-      Yes. You will use the psql client tool to interact with the PostgreSQL database, run SQL,
-      and validate connectivity.
-  - question: How do I know my PostgreSQL installation is working?
-    answer: >-
-      The steps include configuring and checking your PostgreSQL database, then connecting with
-      psql. Successful connection and basic SQL interaction indicate that the database is running.
-  - question: Can I skip any sections if I already have experience or hardware?
-    answer: >-
-      If you already know how to deploy PostgreSQL, you can skip this path and explore the Learn
-      how to Tune PostgreSQL path. If you already have an Arm system, you can skip the subsection
-      about Arm deployment options and continue reading.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md
index c3f973c681..7871205b48 100644
--- a/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md
@@ -10,60 +10,9 @@ learning_objectives:
 
 prerequisites:
     - Bare-metal or cloud [installation of PostgreSQL](/learning-paths/servers-and-cloud-computing/postgresql/)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:52:41Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: f30f7fb50000ae7ee28e46d7cbe4e73ba232c3daad2d559cfa41996d630cb936
-  summary_generated_at: '2026-06-02T04:49:21Z'
-  summary_source_hash: f30f7fb50000ae7ee28e46d7cbe4e73ba232c3daad2d559cfa41996d630cb936
-  faq_generated_at: '2026-06-03T01:52:41Z'
-  faq_source_hash: f30f7fb50000ae7ee28e46d7cbe4e73ba232c3daad2d559cfa41996d630cb936
-  summary: >-
-    This advanced Learning Path guides developers and DevOps engineers through tuning PostgreSQL
-    on Linux, with relevance to Arm Neoverse-based servers and common cloud providers. You will
-    review system considerations such as storage technology and file system selection (with xfs
-    as a good starting point), apply PostgreSQL configuration changes via configuration files
-    (including connection and prepared transaction settings), and measure their impact using HammerDB
-    TPROC-C. The content emphasizes that tuning is workload-specific and should be validated with
-    testing. A bare-metal or cloud installation of PostgreSQL is required, and you need a machine
-    or cloud node with PostgreSQL installed and configured for the test steps. Estimated time
-    to complete is about 30 minutes.
-  faqs:
-  - question: What do I need before running the tuning and tests?
-    answer: >-
-      You need a physical machine or a cloud node with PostgreSQL installed and configured. The
-      prerequisite is a bare-metal or cloud installation of PostgreSQL.
-  - question: How should I apply the provided PostgreSQL configuration parameters?
-    answer: >-
-      The parameters shown can be pasted directly into a PostgreSQL configuration file. The path
-      references the Setting Parameters documentation for ways to set these values.
-  - question: Which storage and file system options should I start with?
-    answer: >-
-      Storage technology and file system format can significantly impact performance. In general,
-      locally attached SSDs perform best, but network-based storage can also perform well; xfs
-      is a good start. You should study and experiment with options for your workload.
-  - question: Do I need to use HammerDB if I already have a performance test?
-    answer: >-
-      No. Skip the HammerDB section if you already have a performance test methodology; otherwise,
-      the path demonstrates testing with HammerDB TPROC-C.
-  - question: Should I increase max_connections or max_prepared_transactions?
-    answer: >-
-      Increase these only if your use case requires high client connection counts or prepared
-      transactions. The path notes that max_connections does not directly impact query performance
-      but helps avoid rejecting client requests; test any changes.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Julio Suarez
 
diff --git a/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md b/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
index a0022257b7..8eebfa751a 100644
--- a/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
@@ -12,61 +12,9 @@ learning_objectives:
 prerequisites:
     - An Arm-based system (bare metal server, cloud instance, or developer board) running Linux with kernel version 5.8.0 or later.
     - Root access, or the ability to run the sudo command.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:53:34Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: ed85d6171f3c05bfdf1b396065fb0c73ce88724ff63fd1c3c8a93d4c27dd59f8
-  summary_generated_at: '2026-06-02T04:49:56Z'
-  summary_source_hash: ed85d6171f3c05bfdf1b396065fb0c73ce88724ff63fd1c3c8a93d4c27dd59f8
-  faq_generated_at: '2026-06-03T01:53:34Z'
-  faq_source_hash: ed85d6171f3c05bfdf1b396065fb0c73ce88724ff63fd1c3c8a93d4c27dd59f8
-  summary: >-
-    This Learning Path shows you how to build and run the Process Watch tool on an Arm-based Linux
-    machine to monitor, in real time, whether workloads use specific Arm instructions and features.
-    You will install required build dependencies (such as CMake, Clang/LLVM, and libelf), clone
-    the Process Watch repository with submodules, and run the tool—preferably as root or by configuring
-    capabilities and sysctl settings for non-root use. It explains how Process Watch samples retired
-    instructions via Linux perf_events and a BPF program, and how to interpret output fields like
-    PID, NAME, NEON, SVE, and SVE2. You will compile and run a simple C workload to observe instruction
-    usage, including a no-optimization case. Prerequisites are an Arm-based Linux system (kernel
-    5.8+), with root or sudo access.
-  faqs:
-  - question: What do I need before running the steps in this Learning Path?
-    answer: >-
-      Use an Arm-based system running Linux with kernel version 5.8.0 or later, and have root
-      access or the ability to use sudo. No other prerequisites are explicitly listed.
-  - question: Which packages should I install on Ubuntu 20.04 or later?
-    answer: >-
-      Run: sudo apt-get update, then sudo apt-get install libelf-dev cmake clang llvm llvm-dev
-      -y. These provide CMake, Clang/LLVM, and libelf required to build Process Watch.
-  - question: How should I clone the Process Watch repository to include all submodules?
-    answer: >-
-      Clone with submodules using: git clone --recursive https://github.com/intel/processwatch.git.
-      The --recursive option ensures all submodules are fetched.
-  - question: Should I run Process Watch as root, or can I enable it for non-root users?
-    answer: >-
-      Running as root is recommended. To allow a non-root user, run these as root: sudo setcap
-      CAP_PERFMON,CAP_BPF=+ep ./processwatch, sudo sysctl -w kernel.perf_event_paranoid=-1, and
-      sudo sysctl kernel.unprivileged_bpf_disabled=0.
-  - question: How do I run Process Watch and interpret its output for NEON or SVE usage?
-    answer: >-
-      View options with: sudo ./processwatch -h, then run the tool and observe columns like FPARMv8,
-      NEON, SVE, SVE2, %TOTAL, and TOTAL. Create and run the provided C workload with different
-      optimization settings; the NEON and SVE columns indicate whether those instruction sets
-      are being exercised.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Graham Woodward
 
diff --git a/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md b/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md
index f46af0aa79..f6f1e7442b 100644
--- a/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md
@@ -11,63 +11,9 @@ learning_objectives:
 
 prerequisites:
     - An Arm Neoverse-based (N1, N2 or V1) computer running Linux. For your host OS, you can use Amazon Linux 2023 or newer, Debian 10 or newer, RHEL 8 or newer, or Ubuntu 20.04 or newer.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:54:25Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: ef12378069d6f3538d8daefb5da7fc8e8ce4196277928d372745d12fde6e46a1
-  summary_generated_at: '2026-06-02T04:50:40Z'
-  summary_source_hash: ef12378069d6f3538d8daefb5da7fc8e8ce4196277928d372745d12fde6e46a1
-  faq_generated_at: '2026-06-03T01:54:25Z'
-  faq_source_hash: ef12378069d6f3538d8daefb5da7fc8e8ce4196277928d372745d12fde6e46a1
-  summary: >-
-    This introductory Learning Path shows how to profile applications on Arm Neoverse-based Linux
-    servers using Streamline CLI tools and Arm’s top-down performance methodology. You begin by
-    checking hardware-assisted profiling support with Arm Sysreport, examining perf counters and
-    SPE availability (best results are on systems with at least 6 CPU counters). You then capture
-    raw samples with sl-record, preprocess with sl-analyze, and format function-attributed metrics
-    with sl-format.py. The path explains Frontend, Backend, and Retire concepts, demonstrates
-    interpreting Retiring%, FE bound%, Bad spec%, and BE bound% in a sample report, and provides
-    a short optimization checklist. Prerequisite: an Arm Neoverse (N1, N2, or V1) Linux system;
-    supported host OS options include Amazon Linux 2023+, Debian 10+, RHEL 8+, or Ubuntu 20.04+.
-  faqs:
-  - question: What do I need before running the profiling steps?
-    answer: >-
-      You need an Arm Neoverse-based (N1, N2, or V1) computer running Linux. Supported host OS
-      options include Amazon Linux 2023 or newer, Debian 10 or newer, RHEL 8 or newer, or Ubuntu
-      20.04 or newer.
-  - question: How do I know if my system supports hardware-assisted profiling?
-    answer: >-
-      Run the Arm Sysreport utility as described in the referenced guide. In the report, perf
-      counters shows how many CPU counters are available and perf sampling indicates if SPE is
-      available; systems with at least 6 available CPU counters provide better profiles.
-  - question: Do I need to rebuild my application before profiling?
-    answer: >-
-      Yes. Build your application with debug information so the profiler can map instructions
-      to source code and attribute metrics to functions.
-  - question: Which Streamline CLI tools should I run and in what order?
-    answer: >-
-      Use sl-record to capture raw sampled data, sl-analyze to generate function-attributed counters
-      and metrics, and sl-format.py to produce a human-readable report. Follow this sequence for
-      each profiling run.
-  - question: What result should I expect, and how do I interpret low Retiring%?
-    answer: >-
-      After sl-format.py, expect a functions report with top-down metrics: Retiring%, FE bound%,
-      Bad spec%, and BE bound%. A low Retiring% indicates inefficient use of processing resources;
-      if a function is frontend bound with high instruction cache miss rate, the checklist suggests
-      applying profile-guided optimization to reduce less important code.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Julie Gaskin
 
diff --git a/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md
index e0d7990d46..ef7541a117 100644
--- a/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md
@@ -10,59 +10,9 @@ learning_objectives:
   - Install Puppet on a SUSE Arm64 C4A instance
   - Verify Puppet by applying a test manifest and confirming successful resource creation on Arm64  
   - Benchmark Puppet by measuring catalog compile time, apply speed, and resource usage on Arm64
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:54:50Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: aeb6a390b5134af2f851e36db17c5d3578d4697b3c372af86c6bd5176765e087
-  summary_generated_at: '2026-06-02T04:51:08Z'
-  summary_source_hash: aeb6a390b5134af2f851e36db17c5d3578d4697b3c372af86c6bd5176765e087
-  faq_generated_at: '2026-06-03T01:54:50Z'
-  faq_source_hash: aeb6a390b5134af2f851e36db17c5d3578d4697b3c372af86c6bd5176765e087
-  summary: >-
-    Learn how to deploy and validate Puppet on Arm-based Google Cloud C4A virtual machines powered
-    by Axion processors. You will provision a SUSE Linux Arm64 VM (c4a-standard-4), install Puppet
-    by setting up dependencies and building Ruby 3.1.4 from source, then verify the installation
-    by checking Puppet and Facter versions, applying a simple manifest, and confirming system
-    facts collection. The path concludes with a standalone benchmark of Puppet on Arm64 to measure
-    catalog compile time, apply speed, and resource usage—without a Puppet Master. Prerequisites
-    include a GCP account with billing enabled and basic familiarity with Puppet. Expected duration
-    is about 30 minutes.
-  faqs:
-  - question: What do I need before provisioning the VM?
-    answer: >-
-      You need a Google Cloud Platform account with billing enabled and basic familiarity with
-      Puppet. No other prerequisites are explicitly listed.
-  - question: Which Google Cloud machine type and OS should I select?
-    answer: >-
-      Use the c4a-standard-4 machine type (4 vCPUs, 16 GB memory) and a SUSE Linux Arm64 (SUSE
-      Linux Enterprise Server) image. The VM is created from the Google Cloud Console under Compute
-      Engine.
-  - question: Do I need to build Ruby, and which version is used?
-    answer: >-
-      Yes. You will install required development tools and libraries, then build Ruby 3.1.4 from
-      source to prepare the environment for Puppet and avoid compatibility issues.
-  - question: How do I verify that Puppet installed correctly?
-    answer: >-
-      Run a version check such as puppet --version (the example output shown is 8.10.0), then
-      run basic Puppet commands. Apply a simple manifest and confirm that resources are created
-      and Facter reports system facts.
-  - question: Does the benchmark require a Puppet Master, and what does it measure?
-    answer: >-
-      No. The benchmark runs standalone on the SUSE Arm64 VM and measures local execution, including
-      catalog compile time, apply speed, and resource usage on Arm64.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md b/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md
index 1e570eed2e..83ac92531b 100644
--- a/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md
@@ -14,57 +14,9 @@ learning_objectives:
 
 prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) with at least 16 CPUs from a cloud service provider or an on-premise Arm server.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:55:09Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 1c5be7bfc7785ae3b06c60210c06324f00aed7ba75145a0fa65425e6005d4f06
-  summary_generated_at: '2026-06-02T04:51:34Z'
-  summary_source_hash: 1c5be7bfc7785ae3b06c60210c06324f00aed7ba75145a0fa65425e6005d4f06
-  faq_generated_at: '2026-06-03T01:55:09Z'
-  faq_source_hash: 1c5be7bfc7785ae3b06c60210c06324f00aed7ba75145a0fa65425e6005d4f06
-  summary: >-
-    Learn how to run a Meta Llama 3.1 LLM chatbot on Arm-based servers using PyTorch and KleidiAI
-    INT4 kernels. You will use an Ubuntu 24.04 LTS Arm instance with at least 16 cores, 64 GB
-    RAM, and 50 GB disk; the steps were tested on an AWS Graviton4 r8g.4xlarge. The path covers
-    downloading the model from the Meta Hugging Face repository, applying 4-bit quantization,
-    running CPU inference with PyTorch, and exposing the service through a Streamlit frontend
-    backed by the Torchchat framework. You will also measure performance metrics for the inference
-    run. Estimated time to complete is about 30 minutes. No additional explicit prerequisites
-    are listed beyond access to an Arm-based server.
-  faqs:
-  - question: What infrastructure and OS should I use to follow this path?
-    answer: >-
-      Use an Arm server running Ubuntu 24.04 LTS with at least 16 cores, 64 GB of RAM, and around
-      50 GB of disk space. The instructions were tested on an AWS Graviton4 r8g.4xlarge instance.
-  - question: Do I need a GPU to run the example?
-    answer: >-
-      No. The Learning Path runs LLM inference on an Arm-based CPU using PyTorch.
-  - question: Where do I obtain the model used in the example?
-    answer: >-
-      Download the Meta Llama 3.1 model from the Meta Hugging Face repository as shown in the
-      steps.
-  - question: How is quantization performed, and what role does KleidiAI play?
-    answer: >-
-      The model is 4-bit quantized using optimized INT4 KleidiAI Kernels for PyTorch. This setup
-      is used to run the LLM on Arm-based CPUs.
-  - question: Which packages are required for the frontend, and how do I avoid HTTP client issues?
-    answer: >-
-      Activate the torch_env virtual environment, install openai version 1.45.0, and roll back
-      httpx to a version before 0.28. Then start the backend server and launch the Streamlit app
-      to access the chatbot in your browser.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author:
     - Nikhil Gupta
diff --git a/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md
index 8e79c1e3c5..93c70ca17a 100644
--- a/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md
@@ -18,59 +18,9 @@ prerequisites:
   - Basic familiarity with Python
   - Basic understanding of machine learning embeddings
   - Familiarity with Linux command-line operations
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:55:47Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 8b180acd30b3b00484b6e7302b61fd8c3669ef83ff7fca41972d24c5df2682b7
-  summary_generated_at: '2026-06-02T04:52:49Z'
-  summary_source_hash: 8b180acd30b3b00484b6e7302b61fd8c3669ef83ff7fca41972d24c5df2682b7
-  faq_generated_at: '2026-06-03T01:55:47Z'
-  faq_source_hash: 8b180acd30b3b00484b6e7302b61fd8c3669ef83ff7fca41972d24c5df2682b7
-  summary: >-
-    This Learning Path shows how to deploy the Qdrant vector database on Arm-based Google Cloud
-    C4A Axion processors, generate text embeddings with Sentence Transformers in Python, and run
-    semantic similarity search to power a simple chatbot retrieval system. You will provision
-    a c4a-standard-4 Arm64 VM in Google Compute Engine, prepare a SLES Linux environment, install
-    and run Qdrant, create and index embeddings, and issue vector queries. Tools used include
-    Qdrant, Python, Sentence Transformers, and Docker. Prerequisites are a GCP account with billing
-    enabled, basic Python skills, a basic understanding of embeddings, and familiarity with the
-    Linux command line. The path is introductory and designed to complete in about 30 minutes.
-  faqs:
-  - question: Do I need anything set up in Google Cloud before I start?
-    answer: >-
-      Yes. You need a Google Cloud Platform account with billing enabled to provision the Axion
-      C4A VM used in this Learning Path.
-  - question: Which Google Cloud instance and operating system should I create?
-    answer: >-
-      Create a c4a-standard-4 Arm-based VM (4 vCPUs, 16 GB memory) and use a SUSE Linux Enterprise
-      Server (SLES) arm64 image to host Qdrant.
-  - question: How do I confirm that Qdrant is installed and running on the VM?
-    answer: >-
-      Start Qdrant and check that the service or container initializes without errors and is reachable
-      from your client code on the VM. The steps guide you through deploying Qdrant on the Arm64
-      instance.
-  - question: Which Sentence Transformers model should I use to generate embeddings?
-    answer: >-
-      A specific model is not explicitly listed. Use a Sentence Transformers model suitable for
-      text embeddings to follow the embedding and indexing steps.
-  - question: What result should I expect when I run a semantic similarity query?
-    answer: >-
-      You should see a ranked list of the most relevant documents by meaning and context rather
-      than exact keyword matches. Successful results indicate your embeddings were stored and
-      indexed correctly in Qdrant.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md
index 87f0ac2755..31f0a1ad4d 100644
--- a/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md
@@ -18,61 +18,9 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic understanding of message queues and messaging concepts (publishers, consumers)
   - Familiarity with Linux command-line operations
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:56:11Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: b4392f1cf38fa1f3b6c633c7ae1e8d30a51ea8d4e635287586b084cb0d8a556b
-  summary_generated_at: '2026-06-02T04:53:31Z'
-  summary_source_hash: b4392f1cf38fa1f3b6c633c7ae1e8d30a51ea8d4e635287586b084cb0d8a556b
-  faq_generated_at: '2026-06-03T01:56:11Z'
-  faq_source_hash: b4392f1cf38fa1f3b6c633c7ae1e8d30a51ea8d4e635287586b084cb0d8a556b
-  summary: >-
-    Learn how to deploy RabbitMQ on Arm64 infrastructure across Microsoft Azure and Google Cloud.
-    You will provision Arm-based Linux virtual machines on Azure Cobalt 100 (Dpsv6) and Google
-    Cloud C4A with Axion processors, install RabbitMQ 4.2.0 with the required Erlang (OTP 26)
-    on Ubuntu Pro 24.04 for Azure, and configure a SUSE SLES VM on GCP. The path covers baseline
-    validation steps for RabbitMQ, including service and version checks, and setting up a GCP
-    firewall rule to expose the management interface (TCP 15672). It targets engineers migrating
-    messaging workloads and uses RabbitMQ, Erlang, Python, and pika. Prerequisites include Azure
-    and GCP accounts, basic messaging concepts, and Linux command-line familiarity.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Microsoft Azure account with access to Cobalt 100-based Dpsv6 instances, a Google
-      Cloud account with billing enabled, a basic understanding of message queuing concepts, and
-      familiarity with Linux command-line operations.
-  - question: Which Azure VM series and creation method does this path use?
-    answer: >-
-      The path uses Azure Cobalt 100 Dpsv6 instances and creates the VM through the Azure console.
-      Other methods like the Azure CLI or IaC are mentioned but are not used in the walkthrough.
-  - question: How do I verify RabbitMQ and Erlang after installation on Azure?
-    answer: >-
-      Check the service status with sudo systemctl status rabbitmq and confirm the Erlang runtime
-      with the provided erl -eval command (OTP 26). The path also includes a step to confirm the
-      installed RabbitMQ version.
-  - question: How do I expose the RabbitMQ management interface on GCP?
-    answer: >-
-      Create a VPC firewall rule that allows TCP port 15672. In Google Cloud Console, go to VPC
-      Network > Firewall > Create firewall rule, name it (for example, allow-tcp-15672), select
-      your network, and allow ingress on TCP 15672.
-  - question: What should I check if baseline validation fails?
-    answer: >-
-      Verify that the RabbitMQ service is running, confirm Erlang is OTP 26 using the provided
-      command, and follow the RabbitMQ version check step. If accessing the management UI on GCP,
-      ensure the firewall rule for TCP 15672 is correctly configured.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/rag/_index.md b/content/learning-paths/servers-and-cloud-computing/rag/_index.md
index 741458a1ee..8ec3a3d17f 100644
--- a/content/learning-paths/servers-and-cloud-computing/rag/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rag/_index.md
@@ -18,61 +18,9 @@ prerequisites:
     - Familiarity with REST APIs and web services.
     - Basic knowledge of vector databases.
     - Understanding of LLM fundamentals.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:56:43Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: d9435cc1dc1fe65432d83934e69993853dd3f294f66f98e9bcfb5f81e6ac6d5f
-  summary_generated_at: '2026-06-02T04:54:26Z'
-  summary_source_hash: d9435cc1dc1fe65432d83934e69993853dd3f294f66f98e9bcfb5f81e6ac6d5f
-  faq_generated_at: '2026-06-03T01:56:43Z'
-  faq_source_hash: d9435cc1dc1fe65432d83934e69993853dd3f294f66f98e9bcfb5f81e6ac6d5f
-  summary: >-
-    Build and deploy a Retrieval Augmented Generation (RAG) chatbot on Arm-based Google Cloud
-    Axion processors using llama-cpp-python with KleidiAI. You will provision an Arm server running
-    Ubuntu 22.04 LTS, set up a Python backend that integrates an LLM with the FAISS vector database
-    and Hugging Face embeddings, apply 4-bit quantization, and expose REST endpoints. You will
-    also create a Streamlit web interface for document upload and chat, then access the application
-    via your instance’s external URL and review inference performance metrics. This advanced path
-    targets an Arm instance with at least 16 cores, 8GB RAM, and 32GB disk, and assumes familiarity
-    with Python, ML and LLM fundamentals, REST APIs, and vector databases.
-  faqs:
-  - question: What do I need before running this on Google Cloud Axion?
-    answer: >-
-      Use an Arm server instance with at least 16 cores, 8GB of RAM, and 32GB of disk space. The
-      instructions target Ubuntu 22.04 LTS. You should also be comfortable with Python, ML concepts,
-      REST APIs, vector databases, and LLM fundamentals.
-  - question: Which ports and URLs are used by the backend and frontend?
-    answer: >-
-      The frontend is accessed at http://[your instance ip]:8501. The frontend is configured to
-      call the backend at http://localhost:5000. If you access the frontend externally, you may
-      need to allow inbound TCP traffic on port 8501.
-  - question: How do I know the RAG pipeline is working after I start the servers?
-    answer: >-
-      Upload documents or PDFs in the Streamlit UI and submit a query that should reference those
-      documents. The backend integrates LlamaCpp with FAISS and Hugging Face embeddings, so responses
-      should include context drawn from your uploaded content.
-  - question: How is model performance addressed in this Learning Path?
-    answer: >-
-      You will apply 4-bit quantization with llama-cpp-python and monitor/analyze inference performance
-      metrics as part of the deployment. The provided scripts include logging and callbacks to
-      surface runtime behavior.
-  - question: Do I need a specific LLM or a GPU to complete the steps?
-    answer: >-
-      The path uses open-source LLMs via llama-cpp-python and does not specify a single required
-      model. The prerequisites do not list any GPU requirement.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Nobel Chowdary Mandepudi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/ran/_index.md b/content/learning-paths/servers-and-cloud-computing/ran/_index.md
index a375e8cf13..91767b9997 100644
--- a/content/learning-paths/servers-and-cloud-computing/ran/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ran/_index.md
@@ -14,54 +14,9 @@ learning_objectives:
 prerequisites:
 - An Arm computer running Linux. Cloud instances can be used, refer to the list of
   [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:57:23Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 2b329c566f7fea90010c52f1ce1cb11bccf9d32cf604aaa720bfca5ef1f85fae
-  summary_generated_at: '2026-06-02T04:55:40Z'
-  summary_source_hash: 2b329c566f7fea90010c52f1ce1cb11bccf9d32cf604aaa720bfca5ef1f85fae
-  faq_generated_at: '2026-06-03T01:57:23Z'
-  faq_source_hash: 2b329c566f7fea90010c52f1ce1cb11bccf9d32cf604aaa720bfca5ef1f85fae
-  summary: >-
-    This introductory Learning Path shows how to build and install the Arm RAN Acceleration Library
-    (ArmRAL) on an Arm-based Linux system and then exercise it to test your platform’s capabilities.
-    You will use a development machine—either a local Arm server, laptop, or desktop, or an Arm-based
-    cloud instance—compile the open-source BSD-licensed library with GCC, and run it to validate
-    the environment. The path is designed for developers new to ArmRAL and 5G RAN acceleration
-    and focuses on practical build-and-run steps that take about 15 minutes. No additional prerequisites
-    are explicitly listed beyond access to an Arm computer running Linux.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an Arm computer running Linux and a development environment on that machine. You
-      can use a local Arm server, laptop, or desktop, or an Arm-based cloud instance.
-  - question: Can I use an Arm-based cloud instance instead of local hardware?
-    answer: >-
-      Yes. You can use an Arm-based instance from a cloud service provider; see the list of Arm
-      cloud service providers referenced in the prerequisites.
-  - question: Which operating system do the instructions target?
-    answer: >-
-      Linux on Arm. A specific distribution is not explicitly listed in the provided context.
-  - question: Which compiler is used to build ArmRAL in this path?
-    answer: >-
-      GCC is used to build the library according to the tools listed for this Learning Path.
-  - question: What result should I expect after completing the steps?
-    answer: >-
-      You will have ArmRAL built and installed, and you will run basic tests to check your platform’s
-      capabilities. A successful outcome is a clean build and tests completing without errors.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Ronan Synnott
 
diff --git a/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md
index 6879dff642..d35942fb3b 100644
--- a/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md
@@ -15,61 +15,9 @@ learning_objectives:
 prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with Python and distributed systems concepts
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:57:59Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 0877f5f233ec8a50172f4bb9388ad38ae9b890a4d049679ca6ece083d760d872
-  summary_generated_at: '2026-06-02T04:56:29Z'
-  summary_source_hash: 0877f5f233ec8a50172f4bb9388ad38ae9b890a4d049679ca6ece083d760d872
-  faq_generated_at: '2026-06-03T01:57:59Z'
-  faq_source_hash: 0877f5f233ec8a50172f4bb9388ad38ae9b890a4d049679ca6ece083d760d872
-  summary: >-
-    This Learning Path shows how to deploy and run distributed AI workloads with Ray on Google
-    Cloud Axion C4A Arm-based VMs. You will provision a c4a-standard-4 instance (4 vCPUs, 16 GB)
-    running SUSE Linux Enterprise Server (SLES) Arm64, configure a firewall rule for the Ray Dashboard
-    and Ray Serve API, install Ray and required dependencies, and initialize a single-node Ray
-    cluster. You will run parallel tasks with Ray Core, perform distributed training and hyperparameter
-    tuning using Ray Train and Ray Tune, and deploy an API with Ray Serve to validate end-to-end
-    execution. Prerequisites are a GCP account with billing enabled and basic familiarity with
-    Python and distributed systems. Estimated time: 30 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Google Cloud Platform (GCP) account with billing enabled and basic familiarity
-      with Python and distributed systems concepts. The path provisions the required Arm-based
-      VM during the steps.
-  - question: Which VM type should I create for this path?
-    answer: >-
-      Create a Google Axion C4A Arm VM using the c4a-standard-4 machine type, which provides 4
-      vCPUs and 16 GB of memory. This instance will host your Ray application.
-  - question: Which Ray components will I use, and for what?
-    answer: >-
-      Use Ray Core to run distributed tasks and parallel workloads. Use Ray Train and Ray Tune
-      for distributed training and hyperparameter tuning, and Ray Serve to deploy a scalable API
-      and validate end-to-end execution.
-  - question: How do I expose the Ray Dashboard and Ray Serve endpoints?
-    answer: >-
-      In the Google Cloud Console, go to VPC Network > Firewall and create a firewall rule that
-      allows the required ports for the Ray Dashboard and Ray Serve API. If you need help with
-      GCP setup, see the Learning Path Getting started with Google Cloud Platform.
-  - question: How do I verify that Ray is set up correctly?
-    answer: >-
-      Run the provided Python script that calls a @ray.remote function and aggregates results
-      with ray.get. You should see output like “Results: [...]” containing the squared numbers
-      from the sample.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md
index dd5461ea90..f6b3ed311a 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md
@@ -17,60 +17,9 @@ prerequisites:
   - Basic knowledge of Linux command-line operations
   - Familiarity with SSH and remote server access
   - Basic understanding of databases, caching, and messaging systems
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:59:01Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 9b306bc318491834d0d74dd75221b24081acc20dac7993ccdbd1cb40ba6158ac
-  summary_generated_at: '2026-06-02T04:58:08Z'
-  summary_source_hash: 9b306bc318491834d0d74dd75221b24081acc20dac7993ccdbd1cb40ba6158ac
-  faq_generated_at: '2026-06-03T01:59:01Z'
-  faq_source_hash: 9b306bc318491834d0d74dd75221b24081acc20dac7993ccdbd1cb40ba6158ac
-  summary: >-
-    Learn how to deploy Redis on Azure Cobalt 100 Arm64 virtual machines running Linux, then build
-    and validate real-time messaging and event-driven processing on Arm. You will provision a
-    Cobalt 100 VM in the Dpsv6 series (via the Azure Portal, with options to use the CLI or IaC),
-    install and configure Redis, implement Pub/Sub for low-latency messaging, and use Redis Streams
-    with consumer groups to create scalable pipelines. You will simulate workloads with Python
-    and benchmark throughput and latency to validate performance on Arm-based infrastructure.
-    Prerequisites include an Azure account with Cobalt 100 access, basic Linux and SSH skills,
-    and familiarity with databases and messaging.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Microsoft Azure account with access to Cobalt 100 based instances (Dpsv6), basic
-      Linux command-line skills, familiarity with SSH, and a basic understanding of databases,
-      caching, and messaging systems.
-  - question: Which Azure VM type and creation method should I use?
-    answer: >-
-      The Learning Path focuses on general-purpose Cobalt 100 Arm-based virtual machines in the
-      Dpsv6 series. You can create the VM via the Azure Portal (used in this path), or use the
-      Azure CLI or an infrastructure as code tool if that better fits your workflow.
-  - question: How do I confirm I’m using an Arm-based Cobalt 100 VM?
-    answer: >-
-      During provisioning, ensure you select a Cobalt 100 Arm64 instance in the Dpsv6 series.
-      The path targets an Arm-based Linux VM on Azure Cobalt 100.
-  - question: Do I need Python, and where is it used?
-    answer: >-
-      Yes. Python is used to simulate workloads when validating and benchmarking Redis in the
-      final section.
-  - question: What result should I expect after completing the examples and benchmarks?
-    answer: >-
-      You will have Redis installed and running on a Cobalt 100 VM, with working Pub/Sub messaging
-      and Streams using consumer groups. You will also collect throughput and latency measurements
-      to validate Redis performance on Arm infrastructure.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md b/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md
index f43aa5c61a..545cf79b56 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md
@@ -14,60 +14,9 @@ learning_objectives:
 prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with [Redis](https://redis.io/) 
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:59:24Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: eb008543ea4248b231be5e6345546164533edf2bc149613693095812bbbba3d9
-  summary_generated_at: '2026-06-02T04:58:42Z'
-  summary_source_hash: eb008543ea4248b231be5e6345546164533edf2bc149613693095812bbbba3d9
-  faq_generated_at: '2026-06-03T01:59:24Z'
-  faq_source_hash: eb008543ea4248b231be5e6345546164533edf2bc149613693095812bbbba3d9
-  summary: >-
-    This Learning Path guides you through deploying Redis for data searching on Google Cloud C4A
-    virtual machines powered by Axion processors (Arm Neoverse-V2 cores). You will provision a
-    SUSE Linux (SLES) Arm64 instance in Compute Engine, build and install Redis from source with
-    TLS support, verify the server using redis-cli, and run baseline data insertion and retrieval
-    tests. You will then measure Redis SET and GET throughput and latency using the official redis-benchmark
-    tool on Arm64. It is introductory in scope and intended for developers working with Redis-based
-    data searching on Linux/Arm64. Prerequisites are a Google Cloud account with billing enabled
-    and basic familiarity with Redis.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      You need a Google Cloud Platform account with billing enabled and basic familiarity with
-      Redis. No other explicit prerequisites are listed.
-  - question: Which Google Cloud instance and OS should I use?
-    answer: >-
-      Use the C4A family with the c4a-standard-4 machine type (4 vCPUs, 16 GB memory). Provision
-      a SUSE SLES Arm64 virtual machine from the Google Cloud Console.
-  - question: How is Redis installed on the SUSE Arm64 VM?
-    answer: >-
-      You install build prerequisites using zypper, then download Redis 8.2.2 from the official
-      GitHub repository and build from source. Building from source ensures compatibility on Arm
-      and enables TLS support.
-  - question: How do I start Redis and confirm it is running?
-    answer: >-
-      Start the server in the background with redis-server & and verify responsiveness with redis-cli
-      ping, which should return PONG. The steps then insert and retrieve sample data to validate
-      baseline functionality.
-  - question: How do I benchmark Redis and what results should I look for?
-    answer: >-
-      Use the official redis-benchmark tool; the path demonstrates SET testing with redis-benchmark
-      -t set -n 100000 -c 50 and also measures GET. Review requests per second and latency metrics
-      reported by the tool.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/redis/_index.md b/content/learning-paths/servers-and-cloud-computing/redis/_index.md
index a6b62bd0a9..8e139357c5 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis/_index.md
@@ -13,57 +13,9 @@ prerequisites:
 
     - An Arm based instance from a cloud service provider, or an on-premise Arm server.
     - If you do not have an Arm node, the next section discusses some options.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:58:39Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 7b9906a3c16ebd3c6b41618be7db76d7a97cc4b16c4da927257fb39a66753e12
-  summary_generated_at: '2026-06-02T04:57:15Z'
-  summary_source_hash: 7b9906a3c16ebd3c6b41618be7db76d7a97cc4b16c4da927257fb39a66753e12
-  faq_generated_at: '2026-06-03T01:58:39Z'
-  faq_source_hash: 7b9906a3c16ebd3c6b41618be7db76d7a97cc4b16c4da927257fb39a66753e12
-  summary: >-
-    Deploy Redis on Arm is an introductory, 30-minute path that guides you through installing,
-    configuring, and connecting to Redis on an Arm-based Linux instance. You will learn about
-    Redis deployment configurations and set up a single-node server, including adjusting the default
-    binding so the service is reachable beyond localhost on the default port 6379. The path applies
-    to Arm virtual machines from major cloud providers (AWS, Microsoft Azure, Google Cloud, Oracle)
-    or an on-premise Arm server. The outcome is a running Redis instance on Arm and a clear understanding
-    of the core setup choices; tuning and advanced configuration are covered in a separate path.
-    Prerequisite: access to an Arm node.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need access to an Arm based instance on a cloud service provider or an on-premise Arm
-      server running Linux. If you do not have an Arm node, the next section discusses options.
-  - question: Which cloud providers can I use for the Arm instance?
-    answer: >-
-      You can use AWS, Microsoft Azure, Google Cloud, or Oracle. The path targets Arm-based virtual
-      machines on these platforms.
-  - question: How do I enable remote access to my single-node Redis server?
-    answer: >-
-      By default Redis binds to 127.0.0.1 on port 6379. To accept remote connections, set the
-      bind option in redis.conf to 0.0.0.0.
-  - question: What port does Redis use in this setup?
-    answer: >-
-      Redis runs on port 6379 by default. The path focuses on adjusting the bind address for a
-      single-node deployment; changing the port is not explicitly listed.
-  - question: What should I do after I have Redis running with the default configuration?
-    answer: >-
-      Once Redis is working, follow the Learn how to Tune Redis learning path. It is recommended
-      for improving the configuration beyond the default setup.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Elham Harirpoush
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md b/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md
index 3c0e011d32..f7b876c70f 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md
@@ -14,60 +14,9 @@ prerequisites:
     - An Azure portal [account](https://azure.microsoft.com/en-in/get-started/azure-portal)
     - A Google Cloud [account](https://console.cloud.google.com/)
     - A machine with [Terraform](/install-guides/terraform/), [AWS CLI](/install-guides/aws-cli), [Google Cloud CLI](/install-guides/gcloud), [Azure CLI](/install-guides/azure-cli), [AWS IAM authenticator](https://docs.aws.amazon.com/eks/latest/userguide/install-aws-iam-authenticator.html), and [Ansible](/install-guides/ansible/) installed
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T01:59:56Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 9146285feb38ee45171ac234f605eee08467bbf675a77df231a6dc63c38282f2
-  summary_generated_at: '2026-06-02T04:59:18Z'
-  summary_source_hash: 9146285feb38ee45171ac234f605eee08467bbf675a77df231a6dc63c38282f2
-  faq_generated_at: '2026-06-03T01:59:56Z'
-  faq_source_hash: 9146285feb38ee45171ac234f605eee08467bbf675a77df231a6dc63c38282f2
-  summary: >-
-    Deploy Redis as a cache for MySQL and PostgreSQL on Arm Neoverse-based Linux virtual machines
-    across AWS, Microsoft Azure, and Google Cloud. Using Terraform and Ansible, you will provision
-    cloud instances and configure Redis as a caching layer for your databases. The path provides
-    provider-specific sections; MySQL deployments are covered on AWS, Azure, and GCP, and PostgreSQL
-    deployments on AWS and Azure. Prerequisites include active accounts on the three clouds and
-    a machine with Terraform, AWS CLI, Google Cloud CLI, Azure CLI, AWS IAM authenticator, and
-    Ansible installed. Expect to complete the hands-on steps in about 90 minutes and finish with
-    repeatable automation for your target platform.
-  faqs:
-  - question: What do I need before running the deployment steps?
-    answer: >-
-      You need AWS, Azure, and Google Cloud accounts, plus Terraform, AWS CLI, Azure CLI, Google
-      Cloud CLI, AWS IAM authenticator, and Ansible installed. You can run the steps from any
-      computer that has these tools installed.
-  - question: Which section should I follow for my database and cloud provider?
-    answer: >-
-      Use the MySQL sections for AWS, Azure, or Google Cloud. Use the PostgreSQL sections for
-      AWS or Azure.
-  - question: I am new to Terraform—what should I read before starting?
-    answer: >-
-      Each section references an introductory guide: Automate AWS EC2 instance creation using
-      Terraform, Automate Azure instance creation using Terraform, or Automate GCP instance creation
-      using Terraform. Review the guide that matches your target cloud before proceeding.
-  - question: What result should I expect, and how long will it take?
-    answer: >-
-      Expect a provisioned Arm-based Linux instance on your chosen cloud with Redis configured
-      as a cache for the selected database. The Learning Path is designed to take approximately
-      90 minutes.
-  - question: Is there a section for deploying Redis as a cache for PostgreSQL on Google Cloud?
-    answer: >-
-      A PostgreSQL section for Google Cloud is not explicitly listed in the provided steps. Follow
-      the available PostgreSQL sections for AWS or Azure.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md
index ed0bfe7782..3ff2430bb6 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md
@@ -13,60 +13,9 @@ learning_objectives:
 prerequisites:
     - Cloud or bare-metal installation of an Redis file server
     - Review [Learn how to deploy Redis](/learning-paths/servers-and-cloud-computing/redis/) if you do not already have Redis setup
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:00:24Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: a6ed103f2d5a00f4cb6c5df1c0812eb68bbad8746d3f351adc959c6880002bbc
-  summary_generated_at: '2026-06-02T05:00:17Z'
-  summary_source_hash: a6ed103f2d5a00f4cb6c5df1c0812eb68bbad8746d3f351adc959c6880002bbc
-  faq_generated_at: '2026-06-03T02:00:24Z'
-  faq_source_hash: a6ed103f2d5a00f4cb6c5df1c0812eb68bbad8746d3f351adc959c6880002bbc
-  summary: >-
-    This advanced Learning Path shows how to tune Redis on Arm-based servers built on Neoverse,
-    running Linux in the cloud (AWS, Microsoft Azure, Google Cloud, Oracle) or on bare metal.
-    You will review Linux kernel parameters along with compiler and OpenSSL settings that can
-    impact Redis performance, then apply guidance to tune a Redis configuration file for deployment.
-    The material emphasizes workload-specific choices rather than a single preset and introduces
-    practical mechanisms such as /proc and sysctl for memory-related adjustments. Prerequisite:
-    a cloud or bare-metal installation of a Redis file server; if Redis is not set up, review
-    Learn how to deploy Redis first. Estimated time to complete: 30 minutes.
-  faqs:
-  - question: What do I need before running the tuning steps?
-    answer: >-
-      You need a cloud or bare-metal installation of a Redis file server. If you do not already
-      have Redis set up, review Learn how to deploy Redis before starting.
-  - question: Where do I change Linux memory-related kernel parameters during this path?
-    answer: >-
-      You can change them temporarily through the /proc filesystem or permanently using the sysctl
-      command. The path discusses these options as part of general guidance.
-  - question: How should I decide which kernel, compiler, and OpenSSL settings to use?
-    answer: >-
-      There is no one-size-fits-all configuration; the right choices depend on your client request
-      profile and use case. Use the provided guidance to evaluate and select settings that match
-      your workload characteristics.
-  - question: Which Redis configuration does this path focus on?
-    answer: >-
-      It focuses on Redis file configuration and references the Configure Redis single-node section
-      of the Learn how to deploy Redis path. Cluster-specific configuration is not explicitly
-      listed.
-  - question: Can I follow these steps on my preferred cloud provider?
-    answer: >-
-      Yes. The path targets Linux on Arm-based servers and can be used in cloud or bare-metal
-      environments, including AWS, Microsoft Azure, Google Cloud, and Oracle; provider-specific
-      instructions are not detailed.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Elham Harirpoush
 
diff --git a/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md b/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md
index c349cdf04a..10a8be5067 100644
--- a/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md
@@ -15,64 +15,9 @@ prerequisites:
     - An Arm Neoverse Reference Design (RD) Software Stack.
     - A Fixed Virtual Platform (FVP).
     - A basic understanding of Neoverse Reference Design (RD) platform boot.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:00:58Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: d060151c5f6ac7a743fb53cd3d2a10aa23f8f09245122a199a84ae17a8ab5ff9
-  summary_generated_at: '2026-06-02T05:00:55Z'
-  summary_source_hash: d060151c5f6ac7a743fb53cd3d2a10aa23f8f09245122a199a84ae17a8ab5ff9
-  faq_generated_at: '2026-06-03T02:00:58Z'
-  faq_source_hash: d060151c5f6ac7a743fb53cd3d2a10aa23f8f09245122a199a84ae17a8ab5ff9
-  summary: >-
-    Learn how to debug the Neoverse N2 Reference Design firmware stack using Arm Development Studio
-    on Linux. This path shows how to create a debug connection to an associated Fixed Virtual
-    Platform (FVP), step through early firmware stages, and work with SCP/LCP/RSE and Arm TF-A
-    (BL1 and BL31). You adjust SCP build settings for easier debugging, apply a BL1 workaround
-    to allow early attachment, and use the Functions view to set precise breakpoints. The path
-    is advanced, takes about 30 minutes, and assumes Arm Development Studio with a valid license,
-    the Neoverse RD-N2 Software Stack, an FVP, and a basic understanding of the Neoverse RD platform
-    boot sequence.
-  faqs:
-  - question: What do I need before running the debug steps?
-    answer: >-
-      You need Arm Development Studio with a valid license, the Neoverse RD-N2 Reference Design
-      Software Stack, an associated FVP, and a basic understanding of RD platform boot. The environment
-      targets Linux.
-  - question: Which optimization flag should I use for SCP firmware debug, and how do I change
-      it?
-    answer: >-
-      SCP firmware debug uses -Og by default, which can optimize variables in ways that hinder
-      debugging. To switch to -O0, edit rd-infra/scp/cmake/Toolchain/<compiler>-Baremetal.cmake
-      and change string(APPEND CMAKE_${language}_FLAGS_DEBUG_INIT "-Og") to use "-O0".
-  - question: Why can’t I start the debugger at BL1, and what’s the workaround?
-    answer: >-
-      RSE CPU wait hold means AP cores are powered off, so you cannot start the debugger until
-      RSE powers them. As a workaround, modify BL1 to spin on entry by adding a "b ." in the bl1_entrypoint
-      function at <workspace>/rd-infra/tf-a/bl1/aarch64/bl1_entrypoint.S.
-  - question: How do I set a breakpoint for BL31?
-    answer: >-
-      Open the Functions view in Arm Development Studio, search for bl31_entrypoint, and set a
-      breakpoint there. Continue execution and observe the console as TF-A advances from BL1 to
-      BL2 to BL31, where the breakpoint will be hit.
-  - question: How do I add symbols to debug BL33/UEFI?
-    answer: >-
-      First boot the FVP once without debugging to capture symbol locations and relocation addresses
-      from the Non-secure AP console. Repeat the same actions in the same order during debugging,
-      and use the recorded addresses; the log files are stored under your workspace in the rd-infr
-      directory as indicated in the steps.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Daniel Nguyen
 
diff --git a/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md b/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md
index a1a7bb3b85..bb9936db1a 100644
--- a/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md
@@ -14,62 +14,9 @@ prerequisites:
     - Some understanding of the [Reference Design software stack architecture](https://neoverse-reference-design.docs.arm.com/en/latest/about/software_stack.html).
     - Some understanding of the Linux command line.
     - Optionally a basic understanding of Docker and containers.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:01:19Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 8f2515b7c416a821bd397a77297adc263397a8b3cb86e9bb34e646735a21f854
-  summary_generated_at: '2026-06-02T05:02:22Z'
-  summary_source_hash: 8f2515b7c416a821bd397a77297adc263397a8b3cb86e9bb34e646735a21f854
-  faq_generated_at: '2026-06-03T02:01:19Z'
-  faq_source_hash: 8f2515b7c416a821bd397a77297adc263397a8b3cb86e9bb34e646735a21f854
-  summary: >-
-    Learn how to set up a Linux host, build, and test the Neoverse Reference Design (RD-N2) firmware
-    stack using containers and an Arm Ecosystem FVP. You will prepare an Ubuntu 22.04 AArch64
-    or x86_64 machine, then build a busybox root filesystem and a firmware stack that includes
-    TF-A, UEFI, SCP, and a lightweight OS loader to exercise the UEFI ExitBootServices transition.
-    Finally, you will validate the build by booting it on the RD-N2 FVP. Tools referenced include
-    Docker, Arm Ecosystem FVPs, Arm Development Studio, and Runbook. Prerequisites include familiarity
-    with the Reference Design software stack architecture and the Linux command line, plus optional
-    Docker basics. Expect to allocate 64GB disk and 32GB RAM (48GB recommended) and about 30 minutes
-    to complete.
-  faqs:
-  - question: Which host platforms and OS versions can I use?
-    answer: >-
-      Use either an AArch64 or x86_64 host machine running Ubuntu Linux 22.04. Other host operating
-      systems are not listed.
-  - question: How much disk space and memory do I need to sync and build the software stack?
-    answer: >-
-      Allocate at least 64 GB of free disk space and 32 GB of RAM. 48 GB of RAM is recommended
-      for the build.
-  - question: How do I launch the build environment and start the build?
-    answer: >-
-      Launch the container using the provided script (for example: bash ./container-scripts/container.sh
-      -v /home/ubuntu/rd-infra/ run). Then run the build script from the build-scripts/ directory
-      inside the container as instructed in the steps.
-  - question: Which FVP should I download for testing, and how do I install it?
-    answer: >-
-      Download the Neoverse N2 Reference Design FVP from Arm Ecosystem FVPs, for example with:
-      wget https://developer.arm.com/-/cdn-downloads/permalink/FVPs-Neoverse-Infrastructure/RD-N2/FVP_RD_N2_11.25_23_Linux64.tgz.
-      Unpack it (tar -xf …) and run the installer with: ./FVP_RD_N2.sh --i-agree-to-the-contained-eula
-      --no-interactive, then export the path to the model binary in the MODEL environment variable.
-  - question: What result should I expect when I test the firmware on the FVP?
-    answer: >-
-      The firmware implementation should build and boot on the RD‑N2 FVP into a lightweight BusyBox
-      shell. This path exercises the UEFI ExitBootServices transition to validate the firmware
-      stack.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: 
     - Tom Pilar
diff --git a/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md b/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md
index 6b3b5d6c3a..b119fd42ca 100644
--- a/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md
@@ -2,60 +2,9 @@
 title: Enable reproducible math functions across vector extensions with Arm Performance Libraries
 
 minutes_to_complete: 10
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:01:52Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: d643c9ef25aa5442aec65ac6a8f48264d4789f8e24ffd6270c2efacce48dd8fc
-  summary_generated_at: '2026-06-02T05:03:04Z'
-  summary_source_hash: d643c9ef25aa5442aec65ac6a8f48264d4789f8e24ffd6270c2efacce48dd8fc
-  faq_generated_at: '2026-06-03T02:01:52Z'
-  faq_source_hash: d643c9ef25aa5442aec65ac6a8f48264d4789f8e24ffd6270c2efacce48dd8fc
-  summary: >-
-    This Learning Path shows you how to enable and use reproducible math functions in Libamath,
-    a component of Arm Performance Libraries, on Linux-based Arm systems. You will learn what
-    numerical reproducibility means and where it matters, then configure Libamath so supported
-    functions produce bitwise-identical floating‑point results across scalar, Neon (AdvSIMD),
-    and SVE implementations while operating in the default accuracy mode (within 3.5 ULP). The
-    hands-on example verifies reproducibility using the single‑precision expf function across
-    vector paths. Prerequisites include an Arm computer running Linux with Arm Performance Libraries
-    26.01 or newer, and a C compiler such as GCC or Clang.
-  faqs:
-  - question: What do I need before running the example?
-    answer: >-
-      You need an Arm computer running Linux with Arm Performance Libraries version 26.01 or newer
-      installed, and a C compiler such as GCC or Clang. No other prerequisites are explicitly
-      listed.
-  - question: Which vector extensions are covered by reproducibility in this path?
-    answer: >-
-      On Linux, Libamath supports bitwise-reproducible results across scalar, Neon (AdvSIMD),
-      and SVE implementations for a subset of math functions.
-  - question: Which math functions are reproducible in Libamath?
-    answer: >-
-      Reproducibility is provided for a subset of Libamath functions on Linux. This path demonstrates
-      expf; the complete list of supported functions is not explicitly listed here.
-  - question: How do I compile and link the example against Arm Performance Libraries?
-    answer: >-
-      Set CC to your compiler (for example, gcc), and export C_INCLUDE_PATH, LIBRARY_PATH, and
-      LD_LIBRARY_PATH to point to $ARMPL_DIR/include and $ARMPL_DIR/lib. Then build your C code
-      so it includes Libamath headers and links against the Arm Performance Libraries.
-  - question: What result should I expect when verifying reproducibility?
-    answer: >-
-      For the same inputs, the floating-point results should be bitwise identical across scalar,
-      Neon, and SVE code paths for supported Libamath functions. These routines operate in the
-      default accuracy mode, within 3.5 ULP of the correctly rounded value.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Joana Cruz
 
diff --git a/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md b/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md
index 4acb216984..b3b9a4d36e 100644
--- a/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md
@@ -13,56 +13,9 @@ learning_objectives:
 prerequisites:
     - An aarch64 or x86_64 computer running Ubuntu 22.04. Cloud instances can be used, refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
     - If you use a client application to access your computer running Ubuntu, make sure that X11 forwarding is enabled.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:02:26Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 180803cf9c6e34c0dda76cafdfcd0ce67edfaca4563f57ae0c36bfefd2199ae9
-  summary_generated_at: '2026-06-02T05:04:08Z'
-  summary_source_hash: 180803cf9c6e34c0dda76cafdfcd0ce67edfaca4563f57ae0c36bfefd2199ae9
-  faq_generated_at: '2026-06-03T02:02:26Z'
-  faq_source_hash: 180803cf9c6e34c0dda76cafdfcd0ce67edfaca4563f57ae0c36bfefd2199ae9
-  summary: >-
-    Build and run the Arm Confidential Compute Architecture (CCA) reference software stack on
-    an Armv-A AEM Base FVP with RME support, then create a guest Linux virtual machine inside
-    a Realm. This introductory path targets developers exploring CCA and uses GCC, FVP, RME, CCA,
-    and a Runbook. You will work on Ubuntu 22.04 on aarch64 or x86_64, including cloud instances,
-    with X11 forwarding enabled if you access the machine via a client application. Allocate at
-    least 30 GB of free disk space and install git, gcc, telnet, xterm, net-tools, and build-essential.
-    Estimated time to complete is about two hours.
-  faqs:
-  - question: What do I need on my Ubuntu host before building the Arm CCA stack?
-    answer: >-
-      Use Ubuntu 22.04 on aarch64 or x86_64 with at least 30 GB of free disk space. Install git,
-      gcc, telnet, xterm, net-tools, and build-essential before starting.
-  - question: Which FVP should I use to run the CCA stack?
-    answer: >-
-      Use the Armv-A AEM Base FVP with support for RME extensions. The steps in the path specify
-      the required FVP configuration.
-  - question: Can I complete this Learning Path on a cloud instance?
-    answer: >-
-      Yes. Cloud instances can be used; the path links to a list of Arm cloud service providers.
-  - question: Do I need to enable X11 forwarding?
-    answer: >-
-      Enable X11 forwarding if you use a client application to access your Ubuntu machine. This
-      supports any steps that open X11 applications such as xterm.
-  - question: What outcome should I expect when everything runs correctly?
-    answer: >-
-      You will build and run the Arm CCA reference software stack on the FVP and create a Realm
-      that hosts a guest Linux virtual machine. This demonstrates the CCA flow from build to launching
-      a Realm-based VM.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md b/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md
index 79f98b396b..0923911e71 100644
--- a/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md
@@ -13,56 +13,9 @@ learning_objectives:
 prerequisites:
     - Any Arm Neoverse N2-based or Arm Neoverse V2-based instance running Ubuntu 22.04 LTS from a cloud service provider or an on-premise Arm server. 
     - For the server, at least four cores and 16GB of RAM, with disk storage configured up to at least 32 GB. 
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:02:59Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 27e17ea322cc7dc117f24d9d2007fbc0e6b498840b4097b859b1f376b8d8c9a6
-  summary_generated_at: '2026-06-02T05:05:13Z'
-  summary_source_hash: 27e17ea322cc7dc117f24d9d2007fbc0e6b498840b4097b859b1f376b8d8c9a6
-  faq_generated_at: '2026-06-03T02:02:59Z'
-  faq_source_hash: 27e17ea322cc7dc117f24d9d2007fbc0e6b498840b4097b859b1f376b8d8c9a6
-  summary: >-
-    This introductory Learning Path guides you through running a Large Language Model (LLM) chatbot
-    on an Arm-based CPU using rtp-llm. You will build rtp-llm, set up Python 3.10 with micromamba,
-    install Bazelisk and build tools, then download the Qwen2-0.5B-Instruct model from Hugging
-    Face. You will start the rtp-llm server and send OpenAI-compatible API requests so applications
-    can interact with the model locally or over the network. The target environment is an Arm
-    Neoverse N2- or V2-based Ubuntu 22.04 LTS server with at least 4 CPU cores, 16 GB RAM, and
-    32 GB storage. By the end, you will have a working LLM chatbot service running on an Arm server.
-  faqs:
-  - question: What hardware and OS do I need before running the steps?
-    answer: >-
-      Use an Arm Neoverse N2- or V2-based server running Ubuntu 22.04 LTS with at least four cores,
-      16GB of RAM, and 32GB of disk. This can be a cloud instance or an on-premise Arm server.
-  - question: Which Python version and location does the rtp-llm build expect?
-    answer: >-
-      The build expects Python 3.10 installed at /opt/conda310. The steps use micromamba to create
-      this environment.
-  - question: Which tools do I need to build rtp-llm?
-    answer: >-
-      Install bazelisk to build rtp-llm, and install git, gcc, g++, and build-essential. These
-      packages are used to fetch sources and compile the project.
-  - question: Which model will I run and how is it obtained?
-    answer: >-
-      You will run the Qwen2-0.5B-Instruct model. It is downloaded from Hugging Face in the steps.
-  - question: How do I interact with the model after starting the server?
-    answer: >-
-      Use the rtp-llm server program and submit requests through its OpenAI-compatible API. Install
-      jq to follow the API examples in the steps. The server can be accessed over the network
-      from another machine.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Tianyu Li
 
diff --git a/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md b/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md
index b34974c5b3..2b5d3f4170 100644
--- a/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md
@@ -15,58 +15,9 @@ learning_objectives:
 prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with Ruby programming, the Rails framework, and the [PostgreSQL Relational Database](https://www.postgresql.org/)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:03:26Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 092602df259461e2b22dbf0909a682fbacd59464eea38ab901f38cd0b8c6efd3
-  summary_generated_at: '2026-06-02T05:06:21Z'
-  summary_source_hash: 092602df259461e2b22dbf0909a682fbacd59464eea38ab901f38cd0b8c6efd3
-  faq_generated_at: '2026-06-03T02:03:26Z'
-  faq_source_hash: 092602df259461e2b22dbf0909a682fbacd59464eea38ab901f38cd0b8c6efd3
-  summary: >-
-    This Learning Path guides you through deploying Ruby on Rails on Arm-based Google Cloud C4A
-    virtual machines powered by Axion processors. You will provision a SUSE Linux Enterprise Server
-    instance—illustrated with the c4a-standard-4 type via Google Cloud Console—install Ruby, Rails,
-    and supporting packages, and set up PostgreSQL including development headers required by the
-    pg gem. You will validate a Rails app’s connectivity to PostgreSQL and run Ruby’s built-in
-    Benchmark library to measure execution time for inserts, queries, and CPU tasks on Arm64.
-    Prerequisites are a Google Cloud Platform account with billing enabled and basic familiarity
-    with Ruby, Rails, and PostgreSQL. Estimated time to complete is about 40 minutes.
-  faqs:
-  - question: What do I need before running this Learning Path?
-    answer: >-
-      You need a Google Cloud Platform (GCP) account with billing enabled. Basic familiarity with
-      Ruby, Rails, and PostgreSQL is also expected.
-  - question: Which Google Cloud machine type and OS does this path use?
-    answer: >-
-      You will create a Google Axion C4A Arm VM using the c4a-standard-4 machine type (4 vCPUs,
-      16 GB memory) in the Google Cloud Console. The instance runs SUSE Linux Enterprise Server
-      (SLES) on Arm64.
-  - question: Where in Google Cloud Console do I create the C4A instance?
-    answer: >-
-      Navigate to Compute Engine > VM Instances and select Create Instance. Choose the C4A Arm-based
-      machine type during configuration.
-  - question: How should I prepare SUSE SLES for installing Ruby on Rails?
-    answer: >-
-      Update system packages first using zypper (for example, sudo zypper update). Then install
-      Ruby, Rails, and the essential development tools as directed in the steps.
-  - question: Which PostgreSQL packages are needed for Rails on SUSE SLES?
-    answer: >-
-      Install postgresql-server and postgresql-devel. The development headers are required so
-      the pg gem can compile and allow Rails to communicate with PostgreSQL.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md
index a370274b62..11b7c5719b 100644
--- a/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md
@@ -16,58 +16,9 @@ learning_objectives:
 prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled  
   - Basic familiarity with [Rust](https://www.rust-lang.org/)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:04:00Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: c3dd7a0ff9c645635e41b2d9110f4c52de627b752e33c3c6775e1d2e3ffc381d
-  summary_generated_at: '2026-06-02T05:07:00Z'
-  summary_source_hash: c3dd7a0ff9c645635e41b2d9110f4c52de627b752e33c3c6775e1d2e3ffc381d
-  faq_generated_at: '2026-06-03T02:04:00Z'
-  faq_source_hash: c3dd7a0ff9c645635e41b2d9110f4c52de627b752e33c3c6775e1d2e3ffc381d
-  summary: >-
-    This introductory Learning Path shows how to deploy and benchmark Rust on Google Cloud C4A
-    virtual machines powered by Arm-based Axion processors (Arm Neoverse-V2 cores). You will provision
-    a SUSE SLES Arm64 instance in the Google Cloud Console (for example, c4a-standard-4 with 4
-    vCPUs and 16 GB memory), install Rust with rustup and essential build tools, verify the toolchain
-    by building and running a simple program, and run cargo bench with Criterion to measure execution
-    speed and stability. It targets developers working on Linux/Arm64 in Google Cloud and takes
-    about 30 minutes. Prerequisites are a GCP account with billing enabled and basic Rust familiarity.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Google Cloud Platform (GCP) account with billing enabled and basic familiarity
-      with Rust. No other explicit prerequisites are listed.
-  - question: Which VM type and OS should I create on Google Cloud?
-    answer: >-
-      Use a Google Axion C4A Arm instance, specifically the c4a-standard-4 machine type in the
-      Google Cloud Console. The Learning Path provisions a SUSE SLES Arm64 environment on this
-      instance.
-  - question: How do I install Rust and build tools on the SUSE Arm64 VM?
-    answer: >-
-      Update the system with zypper and install curl, gcc, and make. Then install Rust using rustup
-      to prepare the environment for building and benchmarking Rust applications.
-  - question: How do I verify that the Rust toolchain is working?
-    answer: >-
-      Create a new project with cargo new hello, then run it with cargo run. A successful build
-      prints the standard Hello, world! message and shows normal Cargo compile and run output.
-  - question: How do I set up and run benchmarks with Criterion?
-    answer: >-
-      Create a new project, add criterion = "0.5" to Cargo.toml, and define a bench target (for
-      example, my_benchmark with harness = false). Place your benchmark code under benches/ and
-      run cargo bench to execute Criterion and collect performance measurements on Arm64.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md b/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md
index aa02f9ae8c..4f62ac7e81 100644
--- a/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md
@@ -13,59 +13,9 @@ learning_objectives:
 prerequisites:
     - An AWS account.
     - A computer with Docker, Terraform, the Amazon eksctl command-line interface, and kubectl installed.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:04:23Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 3d9b35d09c97557dcde807bb83f994f8c3701c0d109c0a9bb388acc603d81b16
-  summary_generated_at: '2026-06-02T05:07:57Z'
-  summary_source_hash: 3d9b35d09c97557dcde807bb83f994f8c3701c0d109c0a9bb388acc603d81b16
-  faq_generated_at: '2026-06-03T02:04:23Z'
-  faq_source_hash: 3d9b35d09c97557dcde807bb83f994f8c3701c0d109c0a9bb388acc603d81b16
-  summary: >-
-    Build an end-to-end sentiment analysis workflow on an Arm-based Amazon EKS cluster. You will
-    deploy a text classification model with Apache Spark, index and analyze posts from X using
-    Elasticsearch, and explore results through a Kibana dashboard. The path also adds cluster
-    observability by deploying Prometheus and Grafana dashboards to track CPU and RAM usage of
-    Kubernetes nodes. Prerequisites include an AWS account and a Linux workstation with Docker,
-    Terraform, eksctl, and kubectl installed; the steps also use the AWS CLI (configured with
-    access keys) and Java. You will clone a provided GitHub repository to bootstrap the environment
-    and validate results in Kibana and Grafana.
-  faqs:
-  - question: What do I need before running the setup commands?
-    answer: >-
-      You need an AWS account and a Linux computer with Docker, Terraform, eksctl, kubectl, the
-      AWS CLI, and Java installed. The path assumes these tools are available on your machine.
-  - question: How do I provide AWS credentials for the deployment tools?
-    answer: >-
-      Generate AWS access keys and configure the AWS CLI following the AWS Credentials Install
-      Guide. The CLI must be authenticated before creating or managing EKS resources.
-  - question: Where do I get the code and configurations used in this path?
-    answer: >-
-      Clone the repository: git clone https://github.com/koleini/spark-sentiment-analysis.git.
-      Work from the cloned directory as the Learning Path steps reference files from that repo.
-  - question: Which dashboards will I use and what data should I expect to see?
-    answer: >-
-      Use Kibana to explore posts on X stored in Elasticsearch through customizable visualizations.
-      Use Grafana, backed by Prometheus, to view Kubernetes metrics such as CPU and memory usage
-      of nodes.
-  - question: How do I know the deployment succeeded?
-    answer: >-
-      You should have a text classification model running on EKS with Apache Spark, a Kibana dashboard
-      to analyze X posts, and Grafana dashboards showing CPU and RAM usage. If any part is missing,
-      repeat the corresponding deployment step in the path.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: 
     - Pranay Bakre
diff --git a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md
index 6ad39daf30..0800c8276c 100644
--- a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md
@@ -12,60 +12,9 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).   
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is suitable.    
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:04:56Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 0a4dd65c6d0c7eb79479217b351e3d6c5b8917512d510283fd4d8387ff1339f5
-  summary_generated_at: '2026-06-02T05:08:42Z'
-  summary_source_hash: 0a4dd65c6d0c7eb79479217b351e3d6c5b8917512d510283fd4d8387ff1339f5
-  faq_generated_at: '2026-06-03T02:04:56Z'
-  faq_source_hash: 0a4dd65c6d0c7eb79479217b351e3d6c5b8917512d510283fd4d8387ff1339f5
-  summary: >-
-    Learn to set up the Serverless Framework on a Windows on Arm system and deploy an AWS Lambda
-    function using an introductory, step-by-step workflow. You will install Node.js (version 18.20.3
-    or later) and npm, install the Serverless Framework CLI, configure AWS credentials, and use
-    the interactive serverless command to create a new service with the AWS / Node.js / Simple
-    Function template. The target environment is Windows 11 on Arm hardware or a Windows on Arm
-    virtual machine, using any code editor such as Visual Studio Code for Arm64. By the end, you
-    will have created a basic Serverless project and deployed a Lambda function to AWS. Prerequisites
-    include a Windows on Arm computer, a code editor, and AWS credentials.
-  faqs:
-  - question: What do I need before running the setup steps?
-    answer: >-
-      Use a Windows on Arm computer (or Windows on Arm VM) with Windows 11, and install Node.js
-      18.20.3 or later with npm. Any code editor works; Visual Studio Code for Arm64 is suitable.
-      You also need AWS credentials to deploy to AWS.
-  - question: How do I install the Serverless Framework on Windows on Arm?
-    answer: >-
-      After installing Node.js 18.20.3 or greater, open a terminal or command prompt and run:
-      npm install -g serverless. This adds the Serverless Framework globally so you can use the
-      serverless command.
-  - question: How do I start creating the project and choose the correct template?
-    answer: >-
-      Run the serverless command in a terminal to start the wizard. Use the arrow keys to select
-      the AWS / Node.js / Simple Function template and press Enter.
-  - question: What does the wizard generate for me?
-    answer: >-
-      It scaffolds a new Serverless service configured for a simple Node.js Lambda function targeting
-      AWS. You will then proceed to deploy the function as shown in the steps.
-  - question: How do I know my AWS credentials are ready for deployment?
-    answer: >-
-      This path includes configuring AWS credentials, which are required to deploy to AWS. Ensure
-      you have valid AWS credentials created and available locally before running the deployment
-      steps.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md
index d7368f606a..114b9a1c0b 100644
--- a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md
@@ -13,62 +13,9 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).   
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is suitable.
     - Completion of [Deploy AWS services using the Serverless Framework](/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:05:18Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 2120b6bedf6ea5ae02d8b353a6485f307bcb39d0055c29d9e8a39df37a8b946e
-  summary_generated_at: '2026-06-02T05:09:22Z'
-  summary_source_hash: 2120b6bedf6ea5ae02d8b353a6485f307bcb39d0055c29d9e8a39df37a8b946e
-  faq_generated_at: '2026-06-03T02:05:18Z'
-  faq_source_hash: 2120b6bedf6ea5ae02d8b353a6485f307bcb39d0055c29d9e8a39df37a8b946e
-  summary: >-
-    Learn to define and deploy a small AWS serverless application that integrates AWS Lambda with
-    DynamoDB using the Serverless Framework. You will declare a service that provisions a DynamoDB
-    table for sample sensor data, two Lambda functions (one to write temperatures and one to return
-    an average), and an IAM role with the necessary read/write permissions, then deploy everything
-    with a single serverless deploy command. This introductory path takes about 30 minutes. Prerequisites
-    include a Windows on Arm computer or Windows on Arm virtual machine, a code editor such as
-    Visual Studio Code for Arm64, and completion of the prior Serverless Framework on AWS Learning
-    Path. The path uses Node.js and Visual Studio Code.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      Use a Windows on Arm computer or a Windows on Arm virtual machine, and have a code editor;
-      Visual Studio Code for Arm64 is suitable. Complete the “Deploy AWS services using the Serverless
-      Framework” Learning Path first. This path uses Node.js and Visual Studio Code.
-  - question: Which AWS resources does this service create?
-    answer: >-
-      It creates a DynamoDB table to store timestamps and randomly generated temperatures, two
-      AWS Lambda functions, and an IAM role. One Lambda writes temperature data to the table,
-      and the other retrieves the average temperature.
-  - question: Which command do I use to deploy and where should I run it?
-    answer: >-
-      Run serverless deploy from a terminal in the AwsServerlessDynamoDbLambda directory. The
-      Serverless Framework will validate your serverless.yml and deploy the declared resources
-      to AWS.
-  - question: What result should I expect after running the deploy command?
-    answer: >-
-      The deploy command should complete without errors and provision the DynamoDB table, Lambda
-      functions, and IAM role as defined in serverless.yml. The framework handles the orchestration
-      of these resources for you.
-  - question: What should I check if deployment fails?
-    answer: >-
-      Confirm you are in the AwsServerlessDynamoDbLambda folder and that your service is declared
-      as described. Ensure your serverless.yml is valid and that you have completed the prerequisite
-      Learning Path.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md
index 9c3333251a..9cd0a20a56 100644
--- a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md
@@ -13,60 +13,9 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).   
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is suitable.
     - Completion of the Learning Path that shows you how to [Deploy AWS services using the Serverless Framework](/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:05:45Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: a31c6f9d674bf41cee16066708c884ca587289e181b7754ff75cdbd85bdb30e3
-  summary_generated_at: '2026-06-02T05:10:00Z'
-  summary_source_hash: a31c6f9d674bf41cee16066708c884ca587289e181b7754ff75cdbd85bdb30e3
-  faq_generated_at: '2026-06-03T02:05:45Z'
-  faq_source_hash: a31c6f9d674bf41cee16066708c884ca587289e181b7754ff75cdbd85bdb30e3
-  summary: >-
-    Build and deploy a multi-resource serverless application on AWS using the Serverless Framework.
-    You will declare a service that provisions an Amazon S3 bucket to host a static website, a
-    DynamoDB table for sample sensor data, two AWS Lambda functions (one to write temperatures
-    and one to return an average), and the required IAM role. You will add website files (including
-    index.html) under your Serverless project and deploy the stack using Serverless Framework
-    commands. This introductory path targets developers using Windows on Arm and assumes completion
-    of the introductory Serverless Framework on AWS Learning Path. Estimated time to complete
-    is about 60 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      Have a Windows on Arm computer or a Windows on Arm virtual machine, a code editor such as
-      Visual Studio Code for Arm64, and completion of the Learning Path on deploying AWS services
-      with the Serverless Framework. This path uses Node.js and npm.
-  - question: Where should I create the website files?
-    answer: >-
-      Create a subfolder under the folder where you created the serverless project (for example,
-      AwsServerlessDynamoDbLambdaS3). Inside that website folder, create index.html as shown in
-      the steps.
-  - question: Which AWS resources does the service declare and deploy?
-    answer: >-
-      A DynamoDB table for hypothetical sensor data, two AWS Lambda functions (one writes temperatures,
-      the other retrieves the average), an IAM role granting the functions access to the table,
-      and an S3 bucket to host the static website.
-  - question: From which directory and with which commands do I deploy?
-    answer: >-
-      Open a terminal and navigate to the AwsServerlessDynamoDbLambda folder. Run npm install
-      --save-dev serverless, then run serverless deploy.
-  - question: What result should I expect after deployment?
-    answer: >-
-      You should see packaging and deployment logs, including a line like "Deploying 'AwsServerlessDynamoDbLambdaS3'
-      to stage 'dev' (us-east-1)". This indicates the service and its AWS resources were deployed.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Dawid Borycki
 
diff --git a/content/learning-paths/servers-and-cloud-computing/snappy/_index.md b/content/learning-paths/servers-and-cloud-computing/snappy/_index.md
index 5948dd8f82..695737f14d 100644
--- a/content/learning-paths/servers-and-cloud-computing/snappy/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/snappy/_index.md
@@ -14,57 +14,9 @@ learning_objectives:
 prerequisites:
 - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from an appropriate
   cloud service provider.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:06:13Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 62edadd9a4163e020b55d33f541a13ceb604c3700ba56787b650563c446a3b0d
-  summary_generated_at: '2026-06-02T05:10:41Z'
-  summary_source_hash: 62edadd9a4163e020b55d33f541a13ceb604c3700ba56787b650563c446a3b0d
-  faq_generated_at: '2026-06-03T02:06:13Z'
-  faq_source_hash: 62edadd9a4163e020b55d33f541a13ceb604c3700ba56787b650563c446a3b0d
-  summary: >-
-    This Learning Path guides you through installing and running lzbench with Snappy and Zstandard
-    to measure compression library performance on Arm servers. It targets Linux and has been tested
-    on AWS EC2 and Oracle OCI Arm-based instances running Ubuntu 20.04, with Snappy and Zstandard
-    also supported on Amazon Linux 2, RHEL/CentOS 8, and Ubuntu 22.04/20.04/18.04. You will install
-    required packages (gcc, g++, unzip, make), install lzbench, and run benchmarks to collect
-    performance measurements on a 64-bit Arm instance. It is introductory and intended for developers
-    using compression libraries on Arm Neoverse-based servers. The only explicit prerequisite
-    is access to an Arm-based cloud instance, and it takes about 10 minutes to complete.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need access to an Arm-based instance from an appropriate cloud service provider. The
-      steps have been tested on AWS EC2 and Oracle OCI Arm-based servers running Ubuntu 20.04.
-  - question: Which Linux distributions are supported for Snappy and Zstandard in this path?
-    answer: >-
-      Amazon Linux 2, RHEL/CentOS 8, and Ubuntu 18.04, 20.04, and 22.04 are supported. The detailed
-      steps were validated on Ubuntu 20.04.
-  - question: Which packages should I install on the instance before building or running lzbench?
-    answer: >-
-      Install GNU gcc and g++ for your Arm Linux distribution, along with unzip and make. On Ubuntu,
-      the packages are gcc, g++, unzip, and make.
-  - question: Which compression libraries are benchmarked and how are they executed?
-    answer: >-
-      The path benchmarks Snappy and Zstandard using lzbench. You install lzbench and run it to
-      measure these libraries on your Arm-based server.
-  - question: What result should I expect after running the benchmarks?
-    answer: >-
-      You should obtain lzbench performance measurements for Snappy and Zstandard on your instance.
-      Use these results to assess compression performance on a 64-bit Arm AWS EC2 environment.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md b/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md
index 766a68dade..b627f3b056 100644
--- a/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md
@@ -13,60 +13,9 @@ learning_objectives:
 prerequisites:
     - An Arm-based instance from a cloud provider, or an Arm server running Ubuntu 20.04 or 22.04.
     - A basic understanding of Snort's operation and configuration.
-    
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:06:38Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 95da7df14cf36fe01e00868356cf117aa46007ac32e61b0df64d2eea28a5466e
-  summary_generated_at: '2026-06-02T05:11:39Z'
-  summary_source_hash: 95da7df14cf36fe01e00868356cf117aa46007ac32e61b0df64d2eea28a5466e
-  faq_generated_at: '2026-06-03T02:06:38Z'
-  faq_source_hash: 95da7df14cf36fe01e00868356cf117aa46007ac32e61b0df64d2eea28a5466e
-  summary: >-
-    Learn how to install Snort 3 on an Arm-based Linux server and configure it to use multithreading
-    for processing capture files. You will adjust Snort’s Lua configuration to set the number
-    of packet-processing threads, prepare the system by enabling Transparent HugePages and setting
-    CPU isolation and affinity via GRUB, set up a rule set, and download PCAPs to test and measure
-    performance. The path targets developers with a basic understanding of Snort and applies to
-    Arm platforms, including Neoverse. It runs on Ubuntu 20.04 or 22.04 on an Arm server or an
-    Arm-based cloud instance such as AWS EC2. Estimated time to complete is about 45 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an Arm-based instance from a cloud provider, or an Arm server running Ubuntu 20.04
-      or 22.04, and a basic understanding of Snort’s operation and configuration. No other explicit
-      prerequisites are listed.
-  - question: Which platforms and services can I use for the Arm instance?
-    answer: >-
-      You can use Arm-based instances from AWS, Microsoft Azure, Google Cloud, or Oracle. The
-      tools list includes AWS EC2, but the procedure does not require a specific provider.
-  - question: How do I enable multithreading in Snort 3?
-    answer: >-
-      Edit the Snort 3 Lua configuration to specify the number of threads that process network
-      traffic. The steps show where to set the thread count for a single Snort instance.
-  - question: How do I configure CPU affinity and memory settings before testing?
-    answer: >-
-      Append the provided kernel parameter line to /etc/default/grub to enable Transparent HugePages
-      (THP) and set CPU isolation and affinity. The path includes an example for systems with
-      CPUs 0–95, pinning CPUs 0–9 to Snort; adjust the CPU numbers for your hardware.
-  - question: What should I expect when processing PCAP files with multithreading enabled?
-    answer: >-
-      Snort 3 will concurrently process packets from the capture files using multiple threads
-      within one instance. You will measure performance as described in the steps, and alerts
-      will be produced according to your configured rule set.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Preema Merlin Dsouza
 
diff --git a/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md
index 6041f80d59..011ec82886 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md
@@ -15,63 +15,9 @@ prerequisites:
     - A [Microsoft Azure](https://azure.microsoft.com/) account with access to Cobalt 100 based instances (Dpsv6)
     - A machine with [Docker](/install-guides/docker/) installed
     - Familiarity with distributed computing concepts and the [Apache Spark architecture](https://spark.apache.org/docs/latest/)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:07:30Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 009f2219f1b949be39b4a1dd43a5e4c1ceb5bb273d9a11c3c155315160d593ac
-  summary_generated_at: '2026-06-02T05:12:43Z'
-  summary_source_hash: 009f2219f1b949be39b4a1dd43a5e4c1ceb5bb273d9a11c3c155315160d593ac
-  faq_generated_at: '2026-06-03T02:07:30Z'
-  faq_source_hash: 009f2219f1b949be39b4a1dd43a5e4c1ceb5bb273d9a11c3c155315160d593ac
-  summary: >-
-    Learn how to deploy and validate Apache Spark on Microsoft Azure Cobalt 100 (Arm-based) virtual
-    machines using Azure Linux 3.0. You will provision an Arm64 VM via the Azure portal, choose
-    between running Spark in an Azure Linux 3.0 Docker container or on a custom-image VM, and
-    install the required components (Java, Python, and Spark). The path includes a simple PySpark
-    functional test and guidance to run a suite of Spark benchmarks to understand performance
-    on the Cobalt 100 platform. This advanced path targets developers migrating Spark workloads
-    from x86_64 to Arm. Prerequisites include an Azure account with access to Cobalt 100 (Dpsv6),
-    a machine with Docker installed, and familiarity with distributed computing and the Apache
-    Spark architecture.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need a Microsoft Azure account with access to Cobalt 100 instances (Dpsv6), a machine
-      with Docker installed, and familiarity with distributed computing concepts and the Apache
-      Spark architecture.
-  - question: How do I make sure I’m creating the correct Arm64 VM in Azure?
-    answer: >-
-      Use the Azure portal to create a virtual machine and select a Cobalt 100-based Arm64 size
-      such as Dpsv6. Then choose the image appropriate for your deployment as shown in the steps.
-  - question: Should I deploy Spark in an Azure Linux 3.0 Docker container or on a custom-image
-      VM?
-    answer: >-
-      This Learning Path supports both options on Arm64. Pick one approach and follow the corresponding
-      instructions for an Azure Linux 3.0 container or a VM created from a custom Azure Linux
-      3.0 image.
-  - question: Which packages do I install before setting up Spark, and how do I verify Java?
-    answer: >-
-      Install Java 17 (runtime and devel), Python 3, pip, and common tools such as git and maven
-      using tdnf as shown. Verify the installation by running java -version; it should report
-      OpenJDK 17.
-  - question: How do I validate that Spark is working after installation?
-    answer: >-
-      Create the provided test_spark.py, run it as directed in the steps, and confirm that df.show()
-      prints the sample rows. This verifies that Spark can initialize, process a small job, and
-      exit on the Arm64 environment.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md
index 0e7c0c3c37..235f4f1e9f 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md
@@ -14,59 +14,9 @@ learning_objectives:
 prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free?utm_source=google&hl=en) account with billing enabled
   - Familiarity with distributed computing concepts and the [Apache Spark architecture](https://spark.apache.org/docs/latest/)
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:07:54Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: a4ac73af7e8959a546a66ab776c0bca8b60957e6f1729aa00fd78783e6594c0b
-  summary_generated_at: '2026-06-02T05:13:16Z'
-  summary_source_hash: a4ac73af7e8959a546a66ab776c0bca8b60957e6f1729aa00fd78783e6594c0b
-  faq_generated_at: '2026-06-03T02:07:54Z'
-  faq_source_hash: a4ac73af7e8959a546a66ab776c0bca8b60957e6f1729aa00fd78783e6594c0b
-  summary: >-
-    Learn how to deploy Apache Spark on Arm-based Google Axion C4A virtual machines in Google
-    Cloud. You will provision a c4a-standard-4 instance with RHEL 9, install Java, Scala, Maven,
-    and Spark, then validate the setup by running a simple Scala Spark job. The path concludes
-    with running Spark’s built-in SQL micro-benchmarks using the SBT-based framework to produce
-    results you can use to compare Arm64 C4A performance with x86_64 platforms. This path targets
-    developers evaluating migration of Spark workloads to Arm Neoverse-V2–based systems. Prerequisites
-    include a GCP account with billing enabled and familiarity with distributed computing and
-    the Apache Spark architecture.
-  faqs:
-  - question: What do I need before creating the VM?
-    answer: >-
-      You need a Google Cloud Platform account with billing enabled. Familiarity with distributed
-      computing concepts and the Apache Spark architecture is expected.
-  - question: Which VM configuration and OS image should I use on GCP?
-    answer: >-
-      Use a Google Axion C4A Arm VM with the c4a-standard-4 machine type (4 vCPUs, 16 GB memory).
-      The Learning Path uses Red Hat Enterprise Linux 9 as the base image.
-  - question: How do I access the instance to install Spark and its dependencies?
-    answer: >-
-      SSH into the C4A VM you created in the Google Cloud Console. From there, install Java, Scala,
-      Maven, and Apache Spark on the RHEL 9 system.
-  - question: How do I confirm that my Spark installation works on the C4A VM?
-    answer: >-
-      Create and run a simple Scala Spark job that parallelizes a small dataset and performs a
-      basic transformation and action. Successful execution with the expected output indicates
-      the installation is correct.
-  - question: How are the performance benchmarks run and what do they measure?
-    answer: >-
-      Clone the Apache Spark source and use the SBT-based framework to run the built-in SQL micro-benchmarks.
-      These cover areas such as SQL execution, aggregations, joins, and data source reads and
-      can be used to compare Arm64 results with x86_64 runs.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/spark-velox-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/spark-velox-cobalt/_index.md
index 13cb5aa9d5..cf29528a64 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark-velox-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark-velox-cobalt/_index.md
@@ -21,11 +21,9 @@ prerequisites:
   - Basic knowledge of Linux command-line operations
   - Familiarity with SSH and remote server access
   - Basic understanding of distributed systems and Apache Spark
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 author: Pareena Verma
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/spark/_index.md b/content/learning-paths/servers-and-cloud-computing/spark/_index.md
index 144abf9ee5..9824de61b5 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark/_index.md
@@ -12,56 +12,9 @@ learning_objectives:
 prerequisites:
     - An Amazon Web Services (AWS) [account](https://aws.amazon.com/)
     - A machine with [Terraform](/install-guides/terraform/), [AWS CLI](/install-guides/aws-cli/), [AWS IAM authenticator](https://docs.aws.amazon.com/eks/latest/userguide/install-aws-iam-authenticator.html), and [Ansible](/install-guides/ansible/) installed 
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:07:05Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 7a612e45aa64ac93fa9a1fe83da8a7c63ffbc3a4b159184b1a7b04fd46e8ee4b
-  summary_generated_at: '2026-06-02T05:12:06Z'
-  summary_source_hash: 7a612e45aa64ac93fa9a1fe83da8a7c63ffbc3a4b159184b1a7b04fd46e8ee4b
-  faq_generated_at: '2026-06-03T02:07:05Z'
-  faq_source_hash: 7a612e45aa64ac93fa9a1fe83da8a7c63ffbc3a4b159184b1a7b04fd46e8ee4b
-  summary: >-
-    Deploy a single-node Apache Spark environment on an AWS Graviton2 EC2 instance using Terraform
-    and Ansible on Linux. This Learning Path focuses on automating instance creation with Terraform
-    and configuring Spark with Ansible, targeting Arm Neoverse-based Graviton2 hardware on AWS.
-    It is presented as an advanced topic and estimated to take about 60 minutes. Prerequisites
-    include an AWS account and a local setup with Terraform, AWS CLI, AWS IAM authenticator, and
-    Ansible. If you are new to Terraform, review the Automate AWS EC2 instance creation using
-    Terraform Learning Path before starting. By the end, you will have deployed a single Spark
-    instance on AWS Graviton2.
-  faqs:
-  - question: What do I need before running the deployment?
-    answer: >-
-      You need an AWS account and a machine with Terraform, AWS CLI, AWS IAM authenticator, and
-      Ansible installed. These are the explicit prerequisites for the Learning Path.
-  - question: Do I need prior Terraform experience to follow this path?
-    answer: >-
-      If you are new to Terraform, you should review the Automate AWS EC2 instance creation using
-      Terraform Learning Path first. This will help you follow the provisioning steps more easily.
-  - question: What result should I expect after completing the steps?
-    answer: >-
-      You will deploy a single Apache Spark instance on an AWS EC2 instance using AWS Graviton2.
-      The deployment is automated with Terraform and Ansible.
-  - question: Which operating system and platform does this deployment target?
-    answer: >-
-      The deployment targets Linux on AWS. The EC2 instance is based on AWS Graviton2 (Arm Neoverse).
-  - question: Do I need to choose a specific AWS instance type or region?
-    answer: >-
-      A specific instance type or region is not explicitly listed. Use the Terraform configuration
-      provided in the Learning Path and ensure the EC2 instance uses AWS Graviton2.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md b/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md
index cc3b0f955d..8432152738 100644
--- a/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md
@@ -13,60 +13,9 @@ prerequisites:
     - An Arm Linux computer running Docker
     - An AWS account
     - A Remote.It account
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:08:16Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: d3fa3b409ed7cf2ecb521fa4d19af8411718909881861ef3885214824031b541
-  summary_generated_at: '2026-06-02T05:13:51Z'
-  summary_source_hash: d3fa3b409ed7cf2ecb521fa4d19af8411718909881861ef3885214824031b541
-  faq_generated_at: '2026-06-03T02:08:16Z'
-  faq_source_hash: d3fa3b409ed7cf2ecb521fa4d19af8411718909881861ef3885214824031b541
-  summary: >-
-    Learn how to run multiple services in a single container with Supervisor and access that container
-    for debugging and testing without opening SSH ports or changing AWS security groups. You will
-    update a Dockerfile to add Supervisor, enable SSH, and install and configure Remote.It, then
-    build and run the container on an Arm Linux system using Docker. The path then demonstrates
-    launching the container on AWS ECS with a Fargate launch type using the AWS Copilot CLI and
-    connecting to it through Remote.It. Prerequisites are an Arm Linux computer running Docker,
-    an AWS account, and a Remote.It account. After completing the steps, you can reach running
-    containers for debug and test using Supervisor and Remote.It.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an Arm Linux computer running Docker, an AWS account, and a Remote.It account.
-      No additional prerequisites are explicitly listed.
-  - question: Which changes should I make in the Dockerfile to run multiple services and enable
-      access?
-    answer: >-
-      Install and configure Supervisor, OpenSSH (with password login), and Remote.It, and add
-      a Supervisor configuration file. The example uses Ubuntu 24.04 and also installs common
-      debug/test utilities.
-  - question: How do I access a container running in AWS Fargate without changing security groups?
-    answer: >-
-      Use the AWS Copilot CLI to launch the container on AWS ECS with Fargate, then connect to
-      it using Remote.It. This avoids opening any port for SSH access.
-  - question: How do I know the container is ready to accept SSH via Remote.It?
-    answer: >-
-      After building and running the image with the provided Supervisor configuration, both the
-      SSH daemon and Remote.It should start in the container. You should be able to initiate a
-      Remote.It session and open an SSH shell.
-  - question: Can I adapt this approach to other container runtimes besides AWS Fargate?
-    answer: >-
-      Yes. The example demonstrates AWS ECS with Fargate, but you can adapt the technique to any
-      container runtime environment.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/sve/_index.md b/content/learning-paths/servers-and-cloud-computing/sve/_index.md
index ea7b608988..1c3ec4ba24 100644
--- a/content/learning-paths/servers-and-cloud-computing/sve/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sve/_index.md
@@ -13,60 +13,9 @@ learning_objectives:
 prerequisites:
     - General knowledge about SIMD processing, vectorization or Arm Neon.
     - An Arm computer running Linux. Cloud instances can be used, refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:08:37Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 7b2074e39243a5cd0ccb6a01317c700a4797d8c66353f39aa4197237b6672027
-  summary_generated_at: '2026-06-02T05:14:53Z'
-  summary_source_hash: 7b2074e39243a5cd0ccb6a01317c700a4797d8c66353f39aa4197237b6672027
-  faq_generated_at: '2026-06-03T02:08:37Z'
-  faq_source_hash: 7b2074e39243a5cd0ccb6a01317c700a4797d8c66353f39aa4197237b6672027
-  summary: >-
-    This introductory Learning Path shows how to port SIMD code to Arm Scalable Vector Extension
-    (SVE) on Linux. You will compare Neon and SVE to understand how SVE reduces fixed-length vector
-    constraints, compile C and Fortran code for SVE-capable Arm processors using the GNU toolchain,
-    and run SVE instructions on any Armv8-A system using QEMU or the Arm Instruction Emulator
-    (ArmIE) when dedicated SVE hardware is unavailable. You will build and run a small example
-    and inspect compiler vectorization via disassembly. Prerequisites are general SIMD or Arm
-    Neon knowledge and access to an Arm Linux machine; cloud instances from AWS, Microsoft Azure,
-    Google Cloud, or Oracle can be used. Estimated time to complete is about 30 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need general knowledge of SIMD processing and Arm Neon, and access to an Arm computer
-      running Linux. Arm-based cloud instances can be used; see the listed cloud service providers.
-  - question: Which GCC options enable SVE for my build?
-    answer: >-
-      Use -march=armv8-a+sve when compiling (for example, gcc -march=armv8-a+sve myapp.c -o myapp_c.out
-      or gfortran -march=armv8-a+sve myapp.f90 -o myapp_f90.out). Autovectorization with GCC is
-      enabled with -O3 and can be disabled with -fno-tree-vectorize.
-  - question: How can I run SVE instructions if my system lacks SVE hardware?
-    answer: >-
-      Use QEMU or the Arm Instruction Emulator (ArmIE). The path demonstrates both approaches
-      on an Armv8-A system running Ubuntu 22.04 without SVE support.
-  - question: How do I know if the compiler vectorized my code?
-    answer: >-
-      The steps have you compare the disassembly of a simple program with and without autovectorization
-      enabled. You should observe differences in the generated code when building with -O3 versus
-      disabling vectorization.
-  - question: What should I consider when moving from Neon to SVE?
-    answer: >-
-      Neon uses 32 fixed 128-bit vector registers (V0–V31) for integer and floating-point types,
-      while SVE reduces restrictions related to fixed-length vector sizes. The path introduces
-      these differences to guide porting decisions.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Florent Lebeau
 
diff --git a/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md b/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md
index 726646bd46..2006e34363 100644
--- a/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md
@@ -15,60 +15,9 @@ learning_objectives:
 
 prerequisites:
 - Access to an [AWS Graviton4, Google Axion, or Azure Cobalt 100 virtual machine](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:09:11Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: cc3f88ce181b1614f959497a24fafe736b26e55615792faab6b5dce29fac8d77
-  summary_generated_at: '2026-06-02T05:15:37Z'
-  summary_source_hash: cc3f88ce181b1614f959497a24fafe736b26e55615792faab6b5dce29fac8d77
-  faq_generated_at: '2026-06-03T02:09:11Z'
-  faq_source_hash: cc3f88ce181b1614f959497a24fafe736b26e55615792faab6b5dce29fac8d77
-  summary: >-
-    Implement and benchmark scalar and SVE2 MATCH-based search functions on Arm Neoverse servers
-    to evaluate vectorized search performance on Linux. Working on a cloud VM with SVE2 support—AWS
-    Graviton4, Google Axion, or Azure Cobalt 100—you will compare scalar and vectorized approaches,
-    measure performance, and analyze speedups and efficiency. The path introduces the purpose
-    and function of SVE2 MATCH instructions and contrasts them with a scalar implementation. Tools
-    and technologies referenced include SVE2, Neon, and Runbook. No explicit prerequisites are
-    listed beyond access to one of the specified cloud instances. By the end, you can assess when
-    to apply SVE2 MATCH for search tasks and interpret your benchmarking results.
-  faqs:
-  - question: What do I need before running the exercises?
-    answer: >-
-      You need access to an AWS Graviton4, Google Axion, or Azure Cobalt 100 virtual machine.
-      The steps target a Linux environment. No other explicit prerequisites are listed.
-  - question: Which cloud instance should I choose to use SVE2 MATCH?
-    answer: >-
-      Use one of the Arm Neoverse-based instances listed in the prerequisites: AWS Graviton4,
-      Google Axion, or Azure Cobalt 100. The Learning Path focuses on running SVE2-based code
-      on these servers.
-  - question: What will I implement and benchmark during the path?
-    answer: >-
-      You will implement a scalar search function and a vectorized version using SVE2 MATCH instructions.
-      You will then benchmark both to compare performance and analyze speedups on the target instance.
-  - question: How do I know my results are correct or meaningful?
-    answer: >-
-      You should obtain timing results for both scalar and SVE2-based implementations and observe
-      a performance comparison. The path expects analysis of speedups and efficiency, but no specific
-      numbers are provided.
-  - question: Is Neon or Runbook required, or is the focus only on SVE2 MATCH?
-    answer: >-
-      SVE2, Neon, and Runbook are listed as tools, but the core tasks center on SVE2 MATCH versus
-      scalar implementations. Neon- or Runbook-specific steps are not explicitly detailed in the
-      provided context.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md b/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md
index 0b96fccabd..405019957f 100644
--- a/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md
@@ -12,62 +12,9 @@ learning_objectives:
 
 prerequisites:
     - An Arm-based system (bare metal server, cloud instance, developer board) running Linux 
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:09:35Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: d15c3083cb881838f6500eb56dfafb636d73bb282484a7f1b8f4a855dda37fb4
-  summary_generated_at: '2026-06-02T05:16:32Z'
-  summary_source_hash: d15c3083cb881838f6500eb56dfafb636d73bb282484a7f1b8f4a855dda37fb4
-  faq_generated_at: '2026-06-03T02:09:35Z'
-  faq_source_hash: d15c3083cb881838f6500eb56dfafb636d73bb282484a7f1b8f4a855dda37fb4
-  summary: >-
-    Use Sysreport to quickly assess the performance-related capabilities of an Arm Linux system
-    and decide what to configure before profiling. This introductory path walks you through running
-    the command-line tool on Arm Cortex-A and Neoverse-based platforms, including cloud instances
-    (AWS, Microsoft Azure, Google Cloud, Oracle), bare metal servers, developer boards, and Raspberry
-    Pi devices. You will verify access to the system shell, confirm Python (invoked as python3)
-    and Git are available, run Sysreport, and analyze its on-screen summary of hardware and operating
-    system configuration. By the end, you can identify which performance analysis features are
-    present or enabled and determine any configuration changes needed to improve performance information
-    collection. Estimated time to complete: about 10 minutes.
-  faqs:
-  - question: What do I need before running Sysreport on my Arm system?
-    answer: >-
-      You need an Arm-based system running Linux and the ability to log in via SSH or use a local
-      console, with comfort on the Linux command line. The path asks you to confirm that Python
-      and Git are installed.
-  - question: Which Python command should I use for the steps?
-    answer: >-
-      The path assumes Python is invoked with the python3 command. If your environment uses a
-      different command, adjust accordingly.
-  - question: How do I confirm Python is installed?
-    answer: >-
-      Run python3 --version and look for a version string, for example “Python 3.9.5.” If no version
-      is shown, Python may not be installed or python3 may not be the correct command on your
-      system.
-  - question: What result should I expect after running Sysreport?
-    answer: >-
-      Sysreport prints an on-screen summary of system configuration oriented toward performance
-      analysis. It includes hardware and operating system details and indicates which performance
-      features are available and enabled.
-  - question: What should I check if a feature I expected is missing in the report?
-    answer: >-
-      Use the report to decide whether to switch to a different system or make configuration changes
-      to reach the desired state for performance analysis. The path guides you to examine the
-      output and consider changes where needed.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: James Whitaker
 
diff --git a/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
index 8dcb92efe6..27999b45bf 100644
--- a/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
@@ -13,58 +13,9 @@ learning_objectives:
 prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with [TensorFlow](https://www.tensorflow.org/)
-
-generate_summary_faq: false
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:10:01Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 2c20d30d25a0bb871bdb935d18f7ad8e0740139948133dbd728e10f0add0f94e
-  summary_generated_at: '2026-06-02T05:17:09Z'
-  summary_source_hash: 2c20d30d25a0bb871bdb935d18f7ad8e0740139948133dbd728e10f0add0f94e
-  faq_generated_at: '2026-06-03T02:10:01Z'
-  faq_source_hash: 2c20d30d25a0bb871bdb935d18f7ad8e0740139948133dbd728e10f0add0f94e
-  summary: >-
-    Provision an Arm-based SUSE Linux Enterprise Server (SLES) VM on Google Cloud C4A (Axion,
-    Neoverse-V2) and set up a working TensorFlow environment on Arm64. You will create a c4a-standard-4
-    instance, install Python 3.11 with pip and virtual environment support, and install TensorFlow
-    on SLES. The path guides you to verify the installation by listing available devices and running
-    basic TensorFlow operations and a simple training test on the CPU. You then benchmark ResNet50,
-    MobileNetV2, and InceptionV3 with tf.keras using dummy data to measure average inference time
-    and throughput. Prerequisites are a GCP account with billing enabled and basic familiarity
-    with TensorFlow. Estimated time to complete is about 30 minutes.
-  faqs:
-  - question: What do I need before running this Learning Path, and how long will it take?
-    answer: >-
-      You need a Google Cloud Platform (GCP) account with billing enabled and basic familiarity
-      with TensorFlow. The path is introductory and is designed to take about 30 minutes.
-  - question: Which VM configuration and OS should I select on Google Cloud to match the steps?
-    answer: >-
-      Choose the C4A series and the c4a-standard-4 machine type (4 vCPUs, 16 GB memory). Use a
-      SUSE Linux Enterprise Server (SLES) Arm64 image, then set your region and zone.
-  - question: Which Python version is used and how do I install the prerequisites for TensorFlow?
-    answer: >-
-      The steps install Python 3.11, pip, and virtual environment support using zypper on SLES.
-      After that, you install TensorFlow and continue with testing and benchmarking on Arm64.
-  - question: How do I verify that TensorFlow is correctly installed and recognizes the hardware?
-    answer: >-
-      Run: python -c "import tensorflow as tf; print(tf.config.list_physical_devices())". On most
-      VMs you should see a CPU device listed; the baseline section also runs simple ops and a
-      small training test.
-  - question: What models are benchmarked and what metrics are collected in this path?
-    answer: >-
-      You benchmark ResNet50, MobileNetV2, and InceptionV3 using tf.keras with dummy input data.
-      The procedure measures average inference time and throughput on the CPU of the Arm-based
-      VM.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md b/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md
index 1443563bbb..1afb43cf21 100644
--- a/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md
@@ -11,56 +11,9 @@ learning_objectives:
 
 prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an on-premise Arm server.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:10:25Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 0aaee75d902b938e63e37eca421dd28b91f583e784acdf3cccb1e20b8dda71bd
-  summary_generated_at: '2026-06-02T05:17:40Z'
-  summary_source_hash: 0aaee75d902b938e63e37eca421dd28b91f583e784acdf3cccb1e20b8dda71bd
-  faq_generated_at: '2026-06-03T02:10:25Z'
-  faq_source_hash: 0aaee75d902b938e63e37eca421dd28b91f583e784acdf3cccb1e20b8dda71bd
-  summary: >-
-    Learn how to run a text classification workflow with ThirdAI on Arm servers running Linux.
-    You will provision an Arm-based instance in the cloud (AWS, Microsoft Azure, Google Cloud,
-    or Oracle) or use an on-prem Arm server, install Python and ThirdAI, create a virtual environment,
-    and follow an introductory example to train, evaluate, and deploy a model. The steps use Ubuntu
-    commands, though other Linux distributions can be used. By the end, you will have a trained
-    and evaluated text classifier and know how to invoke ThirdAI’s high-level APIs to make predictions.
-    No explicit prerequisites are listed beyond access to an Arm-based server.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need access to an Arm-based instance from a cloud service provider or an on-premise
-      Arm server. The steps assume a Linux environment.
-  - question: Can I follow the instructions on Linux distributions other than Ubuntu?
-    answer: >-
-      Yes. The instructions show Ubuntu commands, but you can use other Linux distributions.
-  - question: Which setup commands prepare Python and an isolated environment?
-    answer: >-
-      Install python3-pip and python3-venv, then create and activate a virtual environment. The
-      terminal prompt showing a (thirdai) prefix indicates the environment is active.
-  - question: How do I install and activate ThirdAI for this example?
-    answer: >-
-      Install the package inside the virtual environment with pip3 install thirdai. The evaluation
-      script includes a thirdai.licensing.activate(...) call; use that line as shown in the example.
-  - question: How do I evaluate the trained model and what result should I expect?
-    answer: >-
-      Use the provided evaluate.py script to load the saved model (sentiment_analysis.model) and
-      run evaluation on the test file (amazon_polarity_test.csv). The script reports categorical_accuracy
-      and includes a sample prediction; specific metric values are not listed.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: ThirdAI
 
diff --git a/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md
index b9812fa63d..1f48d6322d 100644
--- a/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md
@@ -14,59 +14,9 @@ learning_objectives:
 prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with SQL, Python, and Grafana
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:10:49Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: edf5bebb0440c110723c87ca27e5f4b21e4da588e4208b5391f7091ae93cb73d
-  summary_generated_at: '2026-06-02T05:18:38Z'
-  summary_source_hash: edf5bebb0440c110723c87ca27e5f4b21e4da588e4208b5391f7091ae93cb73d
-  faq_generated_at: '2026-06-03T02:10:49Z'
-  faq_source_hash: edf5bebb0440c110723c87ca27e5f4b21e4da588e4208b5391f7091ae93cb73d
-  summary: >-
-    Deploy a live sensor dashboard on Google Cloud Axion C4A Arm instances by provisioning a c4a-standard-4
-    VM running SUSE Linux Enterprise Server (SLES) Arm64, building PostgreSQL 15 with the TimescaleDB
-    2.25.0 extension from source, and configuring access for Grafana on TCP port 3000. You will
-    simulate real-time sensor data with a Python script using psycopg2, create a TimescaleDB hypertable
-    along with continuous aggregates and retention policies, and visualize the stream in a Grafana
-    dashboard that auto-refreshes. The path concludes with validating end-to-end data flow from
-    ingestion through TimescaleDB to Grafana. Prerequisites: a GCP account with billing enabled
-    and basic familiarity with SQL, Python, and Grafana.
-  faqs:
-  - question: What do I need before provisioning the VM on Google Cloud?
-    answer: >-
-      You need a Google Cloud Platform account with billing enabled. Basic familiarity with SQL,
-      Python, and Grafana is expected.
-  - question: Which Google Cloud VM and operating system are used in this path?
-    answer: >-
-      You will create a c4a-standard-4 instance (4 vCPUs, 16 GB memory) on Google Axion C4A. The
-      VM runs SUSE Linux Enterprise Server (SLES) on Arm64.
-  - question: Why does the path build TimescaleDB from source on Arm64, and which versions are
-      used?
-    answer: >-
-      Building from source ensures the TimescaleDB extension is fully optimized for Arm64. The
-      environment uses PostgreSQL 15 with the TimescaleDB 2.25.0 extension.
-  - question: Which firewall port should I open, and what is it for?
-    answer: >-
-      Open TCP port 3000 in a Google Cloud VPC firewall rule. This exposes the Grafana interface
-      used to view the time-series dashboards.
-  - question: How do I know the ingestion and visualization are working?
-    answer: >-
-      The Python sensor script (using psycopg2) continuously writes readings into a TimescaleDB
-      hypertable, and Grafana automatically refreshes to display the data. Successful completion
-      shows end-to-end flow from ingestion through TimescaleDB to Grafana visualization.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md b/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md
index 776116438b..e2ea0933a5 100644
--- a/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md
@@ -13,65 +13,9 @@ learning_objectives:
 
 prerequisites:
     - An Arm Neoverse N1 computer running Linux. A bare metal or cloud metal instance is best because they expose more counters. You can use a virtual machine (VM), but it may offer fewer counters and some commands might not succeed. These instructions have been tested on the `a1.metal` instance type.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:11:21Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: e6a652fd0b32796433a380012a79def743bc5452a52ffae785007977a2a8e3e0
-  summary_generated_at: '2026-06-02T05:19:33Z'
-  summary_source_hash: e6a652fd0b32796433a380012a79def743bc5452a52ffae785007977a2a8e3e0
-  faq_generated_at: '2026-06-03T02:11:21Z'
-  faq_source_hash: e6a652fd0b32796433a380012a79def743bc5452a52ffae785007977a2a8e3e0
-  summary: >-
-    Learn how to analyze Linux application performance on Arm Neoverse N1 using the Arm Telemetry
-    Solution and Linux perf. You will build a slightly modified DynamoRIO stride benchmark, collect
-    sampling and counting data, and interpret commonly used hardware metrics. Following the Telemetry
-    Solution install guide, you will set up the required tools (including Python and perf) and
-    use g++ to compile the example. You will then enable software prefetching with compile-time
-    defines, rerun measurements, and compare results to assess the impact of the change. The steps
-    target an Arm Neoverse N1 system; bare metal or cloud metal instances are recommended, and
-    results vary by hardware. Estimated time: about 60 minutes.
-  faqs:
-  - question: Do I need a bare-metal Neoverse N1 system, or can I use a VM?
-    answer: >-
-      Use an Arm Neoverse N1 computer running Linux. A bare metal or cloud metal instance is best
-      because they expose more counters; a VM may offer fewer counters and some commands might
-      not succeed. These instructions have been tested on the a1.metal instance type.
-  - question: Which tools must be installed before I build and profile the example?
-    answer: >-
-      Follow the Arm Telemetry Solution install guide to install the required tools on your Arm
-      Neoverse server; this includes Python and Linux perf. You also need the GNU C++ compiler
-      (g++), as described in the GNU Compiler install guide.
-  - question: What application is used as the example, and what does it measure?
-    answer: >-
-      The example is the DynamoRIO stride benchmark, a pointer-chasing micro-benchmark that accesses
-      values in a 16 MB array with positions determined by the chased pointers. The provided code
-      is slightly modified to increase the number of iterations. The original source is at https://github.com/DynamoRIO/dynamorio/blob/master/clients/drcachesim/tests/stride_benchmark.cpp.
-  - question: Can I run this Learning Path on hardware other than the N1SDP, and how will results
-      differ?
-    answer: >-
-      Yes. The white paper uses the Neoverse N1 Software Development Platform (N1SDP), which differs
-      from Neoverse N1 servers and cloud instances, so your results will be different. You can
-      also run on single board computers with Cortex-A76 processors such as Raspberry Pi 5, Khadas
-      Edge2, or Orange Pi 5; the example output provided is from a Khadas Edge2.
-  - question: How do I enable and tune software prefetching in the sample application?
-    answer: >-
-      Recompile the application with two compile-time defines to enable data prefetching, as shown
-      in the steps. You can experiment with values of DIST to observe performance impact; the
-      white paper shows performance saturating at DIST=40 on N1SDP, but your results will vary
-      by hardware.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md b/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md
index e72f409c13..627bc5d2c4 100644
--- a/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md
@@ -12,58 +12,9 @@ learning_objectives:
 
 prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an on-premise Arm server.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:11:47Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: d25121278f9dd40e2fd19d988e35866c6bfa70cfd0b93e2ec4506c8ad8a26d88
-  summary_generated_at: '2026-06-02T05:20:50Z'
-  summary_source_hash: d25121278f9dd40e2fd19d988e35866c6bfa70cfd0b93e2ec4506c8ad8a26d88
-  faq_generated_at: '2026-06-03T02:11:47Z'
-  faq_source_hash: d25121278f9dd40e2fd19d988e35866c6bfa70cfd0b93e2ec4506c8ad8a26d88
-  summary: >-
-    Learn to measure PyTorch inference on Arm-based servers using the PyTorch Benchmarks suite.
-    You will install the benchmarks on Ubuntu 22.04 LTS, run model inference tests with Python
-    and PyTorch, and compare performance between eager mode and torch.compile across NLP, vision,
-    and recommender workloads. The instructions were tested on AWS Graviton3 (c7g.4xlarge) and
-    apply to any Arm server meeting a baseline of 4 CPU cores and 8 GB RAM, whether provisioned
-    on AWS, Microsoft Azure, Google Cloud, Oracle, or on-premises. Prerequisite: access to an
-    Arm-based instance or Arm server. Estimated time to complete: about 20 minutes.
-  faqs:
-  - question: What do I need before running the benchmarks?
-    answer: >-
-      You need access to an Arm-based instance from a cloud service provider or an on-premise
-      Arm server. The instructions target Ubuntu 22.04 LTS and the example assumes at least four
-      cores and 8GB of RAM.
-  - question: Can I run this on AWS, Azure, Google Cloud, or Oracle Cloud?
-    answer: >-
-      Yes. Any Arm-based instance from these cloud providers works, and the steps apply to any
-      Arm server running Ubuntu 22.04 LTS.
-  - question: How do I know the PyTorch Benchmarks suite installed correctly?
-    answer: >-
-      After installation, you will be able to run the benchmark suite and see inference timing
-      output for selected PyTorch models. The Learning Path guides you through producing and reviewing
-      those results.
-  - question: Which PyTorch execution modes should I compare?
-    answer: >-
-      You will compare inference performance between PyTorch eager mode and torch.compile mode.
-      Follow the steps to run both and examine the reported latency.
-  - question: What results should I expect to collect and for which model types?
-    answer: >-
-      You will measure inference latency for PyTorch models in NLP, vision, and recommender categories.
-      The outcome is a side-by-side comparison of latency between eager and torch.compile runs.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md
index 51e124d22c..1958e397c0 100644
--- a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md
@@ -12,61 +12,9 @@ learning_objectives:
 prerequisites:
     - Some familiarity with performance analysis.
     - The ability to read Arm assembly code.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:12:45Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 523ff99ccb8d13543f64839fa21040191d2a9ff7ce7c78fa590e8775fac754a6
-  summary_generated_at: '2026-06-02T05:21:57Z'
-  summary_source_hash: 523ff99ccb8d13543f64839fa21040191d2a9ff7ce7c78fa590e8775fac754a6
-  faq_generated_at: '2026-06-03T02:12:45Z'
-  faq_source_hash: 523ff99ccb8d13543f64839fa21040191d2a9ff7ce7c78fa590e8775fac754a6
-  summary: >-
-    This advanced Learning Path shows how to describe common non-cache PMU events and understand
-    why specific C and Arm assembly sequences trigger them on the Arm Neoverse N2 core. You will
-    run compact examples that exercise Topdown Methodology L1 metrics, TLB effectiveness and walks,
-    and operation mix groups (SIMD, scalar floating point, integer, branch, load, store), then
-    examine the resulting PMU counts. The examples require a way to print to a console and can
-    run in simulation or on hardware; on Linux you may see slight variations due to OS overhead.
-    Prerequisites are familiarity with performance analysis and the ability to read Arm assembly.
-    Estimated time to complete is about 30 minutes.
-  faqs:
-  - question: What do I need before running the examples?
-    answer: >-
-      You should be comfortable with performance analysis and able to read Arm assembly. You also
-      need an environment that can print to a console (printf support) to view event counts.
-  - question: Which execution environment should I use for the code?
-    answer: >-
-      You can use any simulation environment or hardware with printf support. The provided examples
-      were run bare-metal in EL3; running under Linux is also possible but may introduce slight
-      variations in PMU counts.
-  - question: How do I know the ITLB-related events were exercised correctly?
-    answer: >-
-      The ITLB example uses self-modifying code to execute an instruction from a previously unaccessed
-      address, causing an ITLB miss and walk. After running it, check that counts for events such
-      as PMU_EVENT_L1I_TLB, PMU_EVENT_L1I_TLB_REFILL, PMU_EVENT_L2D_TLB, PMU_EVENT_L2D_TLB_REFILL,
-      PMU_EVENT_ITLB_WALK, and PMU_EVENT_INST_RETIRED increase as expected.
-  - question: What result should I expect from the SIMD operation mix example?
-    answer: >-
-      The example demonstrates SIMD activity and, in the provided run, produced counts like INST_SPEC
-      = 12, ASE_SPEC = 1, and ASE_INST_SPEC = 3. Your values may differ slightly, especially if
-      running under an operating system.
-  - question: Where can I find the definitions and behavior of the PMU events used here?
-    answer: >-
-      Refer to the Arm Neoverse N2 PMU guide for event behavior details. Additional Neoverse core
-      PMU guides are available on developer.arm.com.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Johanna Skinnider
 
diff --git a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md
index 44357354e3..c0e77b4c59 100644
--- a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md
@@ -13,62 +13,9 @@ learning_objectives:
 prerequisites:
     - Knowledge of performance analysis. 
     - The ability to read Arm assembly code.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:12:13Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 1b309980bef983966d948036e309e132b56f767161967187e72c6a9f81f17dce
-  summary_generated_at: '2026-06-02T05:21:26Z'
-  summary_source_hash: 1b309980bef983966d948036e309e132b56f767161967187e72c6a9f81f17dce
-  faq_generated_at: '2026-06-03T02:12:13Z'
-  faq_source_hash: 1b309980bef983966d948036e309e132b56f767161967187e72c6a9f81f17dce
-  summary: >-
-    This advanced Learning Path shows how simple C and assembly code patterns trigger common cache
-    Performance Monitoring Unit (PMU) events on Arm Neoverse, with a focus on the Neoverse N2
-    core, in a Linux environment. You will review example snippets that issue stores to Normal
-    Cacheable memory and see how they map to PMU metric groups for L1 data and instruction caches,
-    the unified L2 cache, and the last-level (LL) cache. The steps explain why events such as
-    L1D_CACHE_REFILL, L1D_CACHE, and INST_RETIRED are observed in common scenarios, and include
-    example event counts to compare against. Prerequisites are knowledge of performance analysis
-    and the ability to read Arm assembly. Estimated time to complete is 30 minutes.
-  faqs:
-  - question: What do I need before running the examples?
-    answer: >-
-      You should be comfortable with performance analysis and able to read Arm assembly. The content
-      targets Linux and focuses on the Neoverse N2 core. No other explicit prerequisites are listed.
-  - question: Which PMU events are used to evaluate each cache level?
-    answer: >-
-      L1 Data Cache: PMU_EVENT_L1D_CACHE_REFILL, PMU_EVENT_L1D_CACHE, PMU_EVENT_INST_RETIRED.
-      L1 Instruction Cache: PMU_EVENT_L1I_CACHE_REFILL, PMU_EVENT_L1I_CACHE, PMU_EVENT_INST_RETIRED,
-      PMU_EVENT_INST_SPEC. L2 Unified Cache: PMU_EVENT_L2D_CACHE_REFILL, PMU_EVENT_L2D_CACHE,
-      PMU_EVENT_L2D_CACHE_WR, PMU_EVENT_L2D_CACHE_RD, PMU_EVENT_L1D_CACHE_WR, PMU_EVENT_INST_RETIRED.
-      LL Cache: PMU_EVENT_LL_CACHE_RD, PMU_EVENT_LL_CACHE_MISS_RD, PMU_EVENT_INST_RETIRED.
-  - question: How do the code samples trigger the intended cache PMU events?
-    answer: >-
-      They execute stores to Normal Cacheable memory to allocate and access cache lines. To highlight
-      L2 activity, the examples first fill the L1 D-cache with many stores; LL activity can appear
-      when stores cause writebacks or involve shared cache lines.
-  - question: How do I know if my run matched the expected behavior?
-    answer: >-
-      Compare your observed PMU event counts and relationships to the examples shown in the path.
-      For instance, the L1 D-cache section provides example counts you can use as a reference.
-  - question: What should I check if LL cache events remain low or zero?
-    answer: >-
-      LL cache activity is highlighted when excessive stores lead to writebacks into the LL cache
-      or when there are shared cache lines. Ensure your workload issues enough stores, as illustrated,
-      to overflow earlier cache levels and reach the LL cache.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Johanna Skinnider
 
diff --git a/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md b/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md
index 803f8a0aa6..68fa7d960b 100644
--- a/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md
@@ -16,61 +16,9 @@ prerequisites:
   - Familiarity with CI/CD concepts
   - Basic knowledge of Linux command-line operations
   - Familiarity with GitHub Actions runners
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:13:05Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 13bba1dee1c948f8b751a71479a5106014a2c21dab6ce3968a08bed9e3363de1
-  summary_generated_at: '2026-06-02T05:22:29Z'
-  summary_source_hash: 13bba1dee1c948f8b751a71479a5106014a2c21dab6ce3968a08bed9e3363de1
-  faq_generated_at: '2026-06-03T02:13:05Z'
-  faq_source_hash: 13bba1dee1c948f8b751a71479a5106014a2c21dab6ce3968a08bed9e3363de1
-  summary: >-
-    This Learning Path guides you through building and scanning multi-architecture container images
-    with Trivy on Microsoft Azure Cobalt 100 Arm64 virtual machines. You will provision a Dpsv6
-    series VM via the Azure Portal, configure Docker Buildx, create a demo container, push a multi-architecture
-    image to Docker Hub, install and verify Trivy on Ubuntu, run local vulnerability scans, and
-    generate reports. It also covers configuring self-hosted GitHub Actions Arm runners and adding
-    severity-based security gates to CI pipelines. Prerequisites include an Azure account with
-    access to Cobalt 100 instances, Docker knowledge, familiarity with CI/CD and GitHub Actions
-    runners, basic Linux command-line skills, and a Docker Hub account.
-  faqs:
-  - question: What do I need before starting this Learning Path?
-    answer: >-
-      You need a Microsoft Azure account with access to Cobalt 100 instances (Dpsv6), Docker installed
-      with basic containerization knowledge, familiarity with CI/CD concepts and GitHub Actions
-      runners, and basic Linux command-line skills. For the build and scan steps, ensure you have
-      an Arm64 Ubuntu VM running on Cobalt 100 and a Docker Hub account.
-  - question: Which Azure VM size and operating system should I use?
-    answer: >-
-      Use a general-purpose Dpsv6 series VM with the Arm-based Azure Cobalt 100 processor. The
-      steps use an Arm64 Ubuntu VM.
-  - question: Can I create the VM with Azure CLI or infrastructure as code instead of the Portal?
-    answer: >-
-      Yes, Azure CLI and IaC are common options. This Learning Path focuses on using the Azure
-      Portal to create the Cobalt 100 VM.
-  - question: How do I build a multi-architecture container image on the VM?
-    answer: >-
-      You will configure Docker Buildx for multi-architecture builds, create a demo container
-      application, and push the resulting image to Docker Hub. The steps guide you through enabling
-      Buildx and validating the build on the Arm64 VM.
-  - question: What should I expect from Trivy scanning and how is it used in CI?
-    answer: >-
-      You will install and verify Trivy on the Arm64 VM, run local vulnerability scans, and generate
-      reports with findings categorized by severity. In CI pipelines, you will configure self-hosted
-      GitHub Actions Arm runners and enforce security gates based on vulnerability severity.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md b/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md
index 988c9e96a1..c9fea9159b 100644
--- a/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md
@@ -16,61 +16,9 @@ prerequisites:
     - An Arm Neoverse-based bare-metal server running Ubuntu 24.04 to run Apache Tomcat
     - Access to an x86_64 bare-metal server running Ubuntu 24.04 to run `wrk2`
     - Basic familiarity with Java applications
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:13:37Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 9f2b3f6c5e76ecb754de5c0fd84278ff71f71c5c7906acdc06911f2feff1f5fd
-  summary_generated_at: '2026-06-02T05:23:06Z'
-  summary_source_hash: 9f2b3f6c5e76ecb754de5c0fd84278ff71f71c5c7906acdc06911f2feff1f5fd
-  faq_generated_at: '2026-06-03T02:13:37Z'
-  faq_source_hash: 9f2b3f6c5e76ecb754de5c0fd84278ff71f71c5c7906acdc06911f2feff1f5fd
-  summary: >-
-    This advanced Learning Path shows how to benchmark and tune an HTTP network workload on Arm
-    Neoverse-based bare‑metal servers using Apache Tomcat, wrk2, and OpenJDK 21 on Ubuntu 24.04.
-    You will set up Tomcat on an Arm Neoverse host and wrk2 on an x86_64 host, establish a reproducible
-    baseline (file‑descriptor limits, logging, thread counts, and a fixed core set), then apply
-    targeted tuning: adjust NIC queue counts to match available CPUs, improve NUMA locality by
-    running Tomcat on the NIC’s NUMA node, and compare IOMMU strict versus passthrough modes.
-    Validated on an AWS c8g.metal‑48xl instance, the expected outcome is a clear, repeatable process
-    to measure and refine throughput and latency for your workload.
-  faqs:
-  - question: What do I need before running the benchmark?
-    answer: >-
-      You need an Arm Neoverse-based bare-metal server with Ubuntu 24.04 to run Apache Tomcat,
-      and an x86_64 bare-metal server with Ubuntu 24.04 to run wrk2. Basic familiarity with Java
-      applications is assumed. Tomcat runs on OpenJDK 21.
-  - question: Do I need to raise file descriptor limits on both the client and server?
-    answer: >-
-      Yes. Increase the file-descriptor limit on both the Tomcat server and the wrk2 client to
-      avoid running out under load (for example, set it to 65535).
-  - question: How should I choose the NIC queue count during tuning?
-    answer: >-
-      Match the number of NIC transmit/receive queues to the number of CPUs you keep online. Reducing
-      queues when using a small CPU set helps distribute interrupts more evenly and can stabilize
-      throughput and latency on Arm Neoverse systems.
-  - question: How do I decide where to place Tomcat for NUMA locality?
-    answer: >-
-      Use numactl -H to inspect NUMA topology and relative latencies; cross‑NUMA latency is higher
-      than intra‑NUMA. Place Tomcat on the NIC’s NUMA node and align worker threads with the cores
-      on that node.
-  - question: How do I compare IOMMU strict mode with passthrough?
-    answer: >-
-      Update the kernel command line via GRUB to set iommu.strict=0 and iommu.passthrough=1, then
-      reboot and benchmark again. Compare results with strict mode enabled and select the configuration
-      that performs best for your workload.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Ying Yu, Ker Liu, Rui Chang
 
diff --git a/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md
index 5421336c50..35839c63e1 100644
--- a/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md
@@ -14,60 +14,9 @@ learning_objectives:
 prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with [TypeScript](https://www.typescriptlang.org/) and Node.js runtime environment
-
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:14:07Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: ee805f703acd45612c9a94e60c6322866dc1c8ff25d48de2fb4cd9067948c93e
-  summary_generated_at: '2026-06-02T05:23:36Z'
-  summary_source_hash: ee805f703acd45612c9a94e60c6322866dc1c8ff25d48de2fb4cd9067948c93e
-  faq_generated_at: '2026-06-03T02:14:07Z'
-  faq_source_hash: ee805f703acd45612c9a94e60c6322866dc1c8ff25d48de2fb4cd9067948c93e
-  summary: >-
-    Provision a SUSE Linux Enterprise Server (SLES) VM on Google Cloud’s Arm-based C4A instances
-    powered by Axion processors, install a TypeScript toolchain, validate it, and benchmark it.
-    You will create a c4a-standard-4 VM via the Google Cloud Console, then install Node.js, npm,
-    TypeScript, and ts-node on an Arm64 environment. After initializing a minimal project, you
-    will compile and run a simple TypeScript file to confirm the setup. Finally, you will implement
-    a JMH-style benchmark using Node.js perf_hooks to collect average runtime across multiple
-    iterations. Prerequisites are a GCP account with billing enabled and basic familiarity with
-    TypeScript and Node.js. Estimated time to complete is about 30 minutes.
-  faqs:
-  - question: What do I need before creating the VM on Google Cloud?
-    answer: >-
-      You need a Google Cloud Platform (GCP) account with billing enabled. Basic familiarity with
-      TypeScript and the Node.js runtime is assumed.
-  - question: Which machine type and OS should I use for the instance?
-    answer: >-
-      Use the c4a-standard-4 machine type, which provides four virtual CPUs and 16 GB of memory.
-      Provision a SUSE Linux Enterprise Server (SLES) Arm64 VM from the Google Cloud Console under
-      Compute Engine > VM Instances.
-  - question: Which packages are installed to run TypeScript on the SUSE Arm64 VM?
-    answer: >-
-      You install Node.js, npm, TypeScript, and ts-node. These components enable you to develop,
-      compile, and run TypeScript code on the Arm64 instance.
-  - question: How do I verify the TypeScript environment is working?
-    answer: >-
-      Create a minimal project, then create, compile, and run a simple TypeScript file. Successful
-      compilation and execution confirm the environment is ready for benchmarking.
-  - question: What result should I expect from the benchmarking step?
-    answer: >-
-      The JMH-style benchmark implemented with Node.js perf_hooks runs multiple iterations and
-      reports an average runtime. This produces stable, repeatable performance data for your workload
-      on the C4A Arm64 VM.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md b/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md
index fdfc3c7e9d..ff06b65acc 100644
--- a/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md
@@ -12,59 +12,9 @@ learning_objectives:
 prerequisites:
     - Access to both an Arm and an x86-based cloud instance.
     - Intermediate understanding of C++, compilers, and Linux. 
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:14:44Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 035bd363fc8ae772acf52d7e392f2db27087267706aeec86b4098c497e5fe91d
-  summary_generated_at: '2026-06-02T05:24:08Z'
-  summary_source_hash: 035bd363fc8ae772acf52d7e392f2db27087267706aeec86b4098c497e5fe91d
-  faq_generated_at: '2026-06-03T02:14:44Z'
-  faq_source_hash: 035bd363fc8ae772acf52d7e392f2db27087267706aeec86b4098c497e5fe91d
-  summary: >-
-    This Learning Path guides C and C++ developers through migrating applications that depend
-    on optimized performance libraries from x86 to Arm Architecture on Linux. You will compare
-    the C++ standard library with performance libraries, set up an Arm-based AWS instance running
-    Ubuntu 22.04 LTS, install build tools and Arm Performance Libraries, and use libamath to access
-    optimized math routines. You will then port a basic application that uses Intel’s Vector Statistics
-    Library (VSL) to AArch64 using OpenRNG as a drop-in replacement. Prerequisites include access
-    to both an Arm and an x86 cloud instance and intermediate knowledge of C++, compilers, and
-    Linux. Estimated time to complete is about 60 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need access to both an Arm-based and an x86-based cloud instance, plus an intermediate
-      understanding of C++, compilers, and Linux. No additional prerequisites are explicitly listed.
-  - question: Which Arm instance and OS are used in the setup example?
-    answer: >-
-      The example uses an Arm-based AWS instance, such as t4g.2xlarge, running Ubuntu 22.04 LTS.
-      You connect to the instance via SSH before installing packages.
-  - question: Which compiler should I use to build the examples?
-    answer: >-
-      The setup installs GCC and G++ from the Ubuntu repositories for building the examples. Although
-      Arm Compiler for Linux is listed as a tool, the walkthrough uses GCC.
-  - question: How do I install Arm Performance Libraries on the instance?
-    answer: >-
-      After connecting via SSH, run apt update and install gcc, g++, and make, then download and
-      install Arm Performance Libraries using the commands provided in the Learning Path. For
-      details, follow the Arm Performance Libraries install guide referenced in the steps.
-  - question: How do I replace Intel Vector Statistics Library when migrating to AArch64?
-    answer: >-
-      Use OpenRNG, included with Arm Performance Libraries 24.04, as a drop-in replacement for
-      Intel’s Vector Statistics Library. It supports a range of RNG types and utilities to help
-      transition existing code to Arm.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Kieran Hejmadi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/vLLM-quant/_index.md b/content/learning-paths/servers-and-cloud-computing/vLLM-quant/_index.md
index 656e026c8b..723332b07b 100644
--- a/content/learning-paths/servers-and-cloud-computing/vLLM-quant/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vLLM-quant/_index.md
@@ -21,11 +21,9 @@ prerequisites:
     - An Arm-based server or cloud instance running with at least 32 CPU cores, 64 GB RAM, and 32 GB of available disk space.
     - Familiarity with Python and basic understanding of transformer models and quantization techniques.
     - An active Hugging Face account with access to the target model.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 author: 
    - Rani Chowdary Mandepudi
    - Phalani Paladugu
diff --git a/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md b/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md
index b0a302ceab..03c9d6f97a 100644
--- a/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md
@@ -13,57 +13,9 @@ learning_objectives:
 
 prerequisites:
     - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an Arm server with Ubuntu 20.04 or Ubuntu 22.04 installed.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:15:15Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: beb244c7766c474b47942b86f1cdd0d124c1cccb74dbe40f0b6c0917d0b3d31a
-  summary_generated_at: '2026-06-02T05:24:57Z'
-  summary_source_hash: beb244c7766c474b47942b86f1cdd0d124c1cccb74dbe40f0b6c0917d0b3d31a
-  faq_generated_at: '2026-06-03T02:15:15Z'
-  faq_source_hash: beb244c7766c474b47942b86f1cdd0d124c1cccb74dbe40f0b6c0917d0b3d31a
-  summary: >-
-    Learn how to migrate regex-based workloads from Hyperscan to Arm by installing and running
-    Vectorscan on an Arm-based Ubuntu instance, then integrating it with Snort 3. You will set
-    up on Ubuntu 20.04 or 22.04, install Snort 3 and its dependencies, and run Snort 3 with Vectorscan
-    on capture files to measure performance. This introductory path targets developers familiar
-    with Hyperscan who want to adopt Arm, including Arm-based cloud instances from AWS, Microsoft
-    Azure, Google Cloud, or Oracle, or an Arm server. The steps are concise and practical, designed
-    to complete in about 15 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an Arm-based instance from a cloud service provider or an Arm server with Ubuntu
-      20.04 or Ubuntu 22.04 installed. No other explicit prerequisites are listed.
-  - question: Should I install Hyperscan or Vectorscan on Arm?
-    answer: >-
-      Install Vectorscan. Hyperscan runs only on x86_64, and Vectorscan is the architecture-inclusive
-      fork that supports Arm.
-  - question: Can I use a cloud instance from AWS, Microsoft Azure, Google Cloud, or Oracle?
-    answer: >-
-      Yes. Any Arm-based instance from these cloud providers is in scope, as long as it runs Ubuntu
-      20.04 or Ubuntu 22.04.
-  - question: Which Ubuntu versions are these steps intended for?
-    answer: >-
-      Ubuntu 20.04 and Ubuntu 22.04 on Arm are explicitly listed and are the tested environments
-      for this Learning Path.
-  - question: What result should I expect after completing the steps?
-    answer: >-
-      You will have Vectorscan installed and running on your Arm instance, and Snort 3 installed
-      and run with Vectorscan on capture files. You will also measure performance as directed
-      in the steps.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md b/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
index 45b7d358cd..857c5a7558 100644
--- a/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
@@ -16,64 +16,9 @@ learning_objectives:
 prerequisites:
     - An Arm-based Linux server (Ubuntu 22.04+ recommended) with a minimum of 32 vCPUs, 64 GB RAM, and 64 GB free disk space
     - Python 3.12 and basic familiarity with Hugging Face Transformers and quantization
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:16:14Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 487e9829c6e08798ad01be7a01f192e10e99cb06b71108575f1919c134eece02
-  summary_generated_at: '2026-06-02T05:25:49Z'
-  summary_source_hash: 487e9829c6e08798ad01be7a01f192e10e99cb06b71108575f1919c134eece02
-  faq_generated_at: '2026-06-03T02:16:14Z'
-  faq_source_hash: 487e9829c6e08798ad01be7a01f192e10e99cb06b71108575f1919c134eece02
-  summary: >-
-    This Learning Path shows how to build an aarch64-optimized vLLM with oneDNN and the Arm Compute
-    Library on an Arm-based Linux server, set up runtime dependencies (including PyTorch and llmcompressor),
-    quantize the DeepSeek‑V2‑Lite model to INT4, and serve both INT4 and BF16 variants through
-    OpenAI‑compatible endpoints. You will configure key vLLM batching parameters (max_model_len
-    and max_num_batched_tokens) and evaluate accuracy using the LM Evaluation Harness to compare
-    BF16 and INT4 deployments. Prerequisites include an Arm-based Ubuntu 22.04+ server with at
-    least 32 vCPUs, 64 GB RAM, 64 GB free disk space, and Python 3.12 with basic Hugging Face
-    and quantization familiarity. Estimated time to complete is about 60 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      Use an Arm-based Linux server (Ubuntu 22.04+ recommended) with at least 32 vCPUs, 64 GB
-      RAM, and 64 GB free disk space. Install Python 3.12 and be comfortable with Hugging Face
-      Transformers and basic quantization concepts.
-  - question: How do I build and verify vLLM is optimized for aarch64 with oneDNN and ACL?
-    answer: >-
-      Follow the build step to target aarch64 and include oneDNN and the Arm Compute Library.
-      You validate the build by running inference as described in the path to confirm the binary
-      loads and serves a model.
-  - question: Which packages do I install to quantize the model, and why are they needed?
-    answer: >-
-      Install compressed-tensors and llmcompressor as shown in the quantization step. compressed-tensors
-      provides tensor storage and compression utilities for quantized formats, and llmcompressor
-      supplies quantization utilities compatible with Hugging Face Transformers and vLLM to quantize
-      deepseek-ai/DeepSeek‑V2‑Lite to INT4.
-  - question: How should I set vLLM batch sizing parameters when serving the model?
-    answer: >-
-      Use max_model_len to cap tokens per request and max_num_batched_tokens to bound total tokens
-      across concurrent requests. These parameters determine memory usage and how effectively
-      CPU threads are saturated; choose values based on expected prompt/generation lengths and
-      concurrency on your server.
-  - question: How do I evaluate accuracy for BF16 vs INT4 with LM Evaluation Harness?
-    answer: >-
-      Install the LM Evaluation Harness with vLLM support, then run benchmarks against your BF16
-      and INT4 models served by vLLM. Compare the reported metrics across precisions; results
-      vary based on your CPU, datasets, and runtime settings.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author:
    - Nikhil Gupta
diff --git a/content/learning-paths/servers-and-cloud-computing/vllm/_index.md b/content/learning-paths/servers-and-cloud-computing/vllm/_index.md
index 2f1d4bba34..959498727c 100644
--- a/content/learning-paths/servers-and-cloud-computing/vllm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vllm/_index.md
@@ -13,60 +13,9 @@ learning_objectives:
 
 prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider, or a local Arm Linux computer with at least 8 CPUs and 16 GB RAM.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:15:37Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: db5987ec7987375d9b51de843b0e1faf0e61731b14c5a563d19aaad26f50f6bb
-  summary_generated_at: '2026-06-02T05:25:21Z'
-  summary_source_hash: db5987ec7987375d9b51de843b0e1faf0e61731b14c5a563d19aaad26f50f6bb
-  faq_generated_at: '2026-06-03T02:15:37Z'
-  faq_source_hash: db5987ec7987375d9b51de843b0e1faf0e61731b14c5a563d19aaad26f50f6bb
-  summary: >-
-    Learn to build vLLM from source on an Arm-based Ubuntu 24.04 LTS server, verify BFloat16 support,
-    and run both local batch inference and an OpenAI-compatible server. The path uses a Qwen model
-    from Hugging Face and shows how vLLM automatically downloads models on first run, with optional
-    Hugging Face token authentication for gated models. You can follow the steps on an Arm instance
-    from AWS, Microsoft Azure, Google Cloud, or Oracle, or on a local Arm Linux machine with at
-    least 8 CPUs, 16 GB RAM, and 50 GB of storage. By the end, you will run batch prompts locally
-    and serve requests through a local OpenAI-compatible API. No additional prerequisites are
-    explicitly listed.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      Use an Arm server running Ubuntu 24.04 LTS with at least 8 cores, 16 GB RAM, and 50 GB disk
-      space. You also need a processor that supports BFloat16. You can use an Arm-based instance
-      from a supported cloud provider or a local Arm Linux computer.
-  - question: How do I know if my Arm CPU supports BFloat16?
-    answer: >-
-      Run: lscpu | grep bf16. If the Flags are printed, your processor includes BFloat16 support
-      as required by the steps.
-  - question: Do I need to download the model from Hugging Face ahead of time?
-    answer: >-
-      No. vLLM downloads the required model automatically on first run. For models that require
-      access approval or terms, authenticate with Hugging Face using huggingface-cli login and
-      a token generated from your Hugging Face account.
-  - question: Which model is used in this Learning Path?
-    answer: >-
-      A Qwen LLM from the Hugging Face Hub is used. The path guides you to obtain it via vLLM’s
-      first-run download.
-  - question: When should I use batch inference versus the OpenAI-compatible server?
-    answer: >-
-      Use batch inference for a quick local run from Python. Start the OpenAI-compatible server
-      when you need an API endpoint on your Arm server; this avoids external APIs and supports
-      the privacy, cost, and offline advantages described in the steps.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Jason Andrews
 
diff --git a/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md b/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
index 276df168a4..0afff69b1b 100644
--- a/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
@@ -1,58 +1,8 @@
 ---
 title: Run the vvenc H.266 encoder on Arm servers
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:16:42Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 4ed20056b2d82bd2c9ab1afe3d74657df9ac09808592d843c1b143626a8668ac
-  summary_generated_at: '2026-06-02T05:26:21Z'
-  summary_source_hash: 4ed20056b2d82bd2c9ab1afe3d74657df9ac09808592d843c1b143626a8668ac
-  faq_generated_at: '2026-06-03T02:16:42Z'
-  faq_source_hash: 4ed20056b2d82bd2c9ab1afe3d74657df9ac09808592d843c1b143626a8668ac
-  summary: >-
-    Learn how to build and run the open-source VVenC (vvenc) H.266/VVC encoder on Arm-based Linux
-    servers to encode a real 1080p video and measure performance. This introductory path targets
-    Arm Neoverse platforms and highlights available optimizations in vvenc for Neon and SVE/SVE2,
-    with optimized code in the project’s GitHub repository. You will build the vvenc project and
-    run an encode on an Arm server to gather performance measurements. Prerequisites are an Arm
-    Linux system or an Arm-based cloud instance; the path was tested on an Arm Neoverse N2-based
-    Alibaba Cloud ECS instance (g8y) running Ubuntu 22.04. Estimated completion time is about
-    20 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an Arm Linux system or an Arm-based instance from a cloud service provider. This
-      Learning Path has been tested on a Neoverse N2-based Alibaba Cloud ECS (g8y) running Ubuntu
-      22.04. No other prerequisites are explicitly listed.
-  - question: Which cloud platforms can I use for the Arm instance?
-    answer: >-
-      The path lists AWS, Microsoft Azure, Google Cloud, and Oracle as cloud service providers.
-      It was tested on an Alibaba Cloud ECS instance with a Neoverse N2 CPU and Ubuntu 22.04.
-  - question: Where do I get the encoder source and which tool will I run?
-    answer: >-
-      The optimized code for Arm Neoverse platforms is available in the vvenc GitHub repository.
-      You will build the project and run the vvenc encoder to process a real 1080p video.
-  - question: Do I need Neon or SVE/SVE2 to follow this path?
-    answer: >-
-      The encoder includes optimizations for Arm Neoverse that use Neon and SVE/SVE2 instructions.
-      The path does not list specific instruction-set requirements as prerequisites.
-  - question: What result should I expect after running vvenc on a 1080p video?
-    answer: >-
-      You should complete an encode of a real 1080p video and gather performance measurements
-      as directed. The steps focus on building vvenc, running the encoder, and measuring performance
-      on the Arm server.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Willen Yang
 
diff --git a/content/learning-paths/servers-and-cloud-computing/whisper/_index.md b/content/learning-paths/servers-and-cloud-computing/whisper/_index.md
index c0359a1576..fe3e6bb3df 100644
--- a/content/learning-paths/servers-and-cloud-computing/whisper/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/whisper/_index.md
@@ -17,59 +17,9 @@ prerequisites:
     - Basic knowledge of Python.
     - Familiarity with machine learning concepts.
     - Familiarity with the fundamentals of the Whisper ASR Model.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:17:11Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: e130630b5ae4626641779d0b66d3c1c132b90899cd3d6db07a46df8957c4da38
-  summary_generated_at: '2026-06-02T05:26:50Z'
-  summary_source_hash: e130630b5ae4626641779d0b66d3c1c132b90899cd3d6db07a46df8957c4da38
-  faq_generated_at: '2026-06-03T02:17:11Z'
-  faq_source_hash: e130630b5ae4626641779d0b66d3c1c132b90899cd3d6db07a46df8957c4da38
-  summary: >-
-    This Learning Path shows how to run the OpenAI Whisper ASR model on Arm-based cloud servers
-    using Hugging Face Transformers. You will install the required Python dependencies, configure
-    environment variables to enable Arm-friendly execution, and run the whisper-large-v3-turbo
-    model as an application that accepts audio input and generates a text transcript. The steps
-    target Ubuntu 24.04 LTS on an Arm instance with 32 cores, at least 8GB RAM, and 32GB of disk;
-    they were tested on an AWS Graviton4 c8g.8xlarge. You will also evaluate transcript generation
-    times. This introductory path assumes basic Python skills, familiarity with ML concepts, and
-    Whisper fundamentals, and takes about 15 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an Arm-based server running Ubuntu 24.04 LTS with 32 cores, at least 8GB of RAM,
-      and 32GB of disk space. You should also have basic Python knowledge, familiarity with machine
-      learning concepts, and fundamentals of the Whisper ASR model.
-  - question: Which Whisper model and libraries does this path use?
-    answer: >-
-      The path runs the whisper-large-v3-turbo model. It uses Hugging Face Transformers in Python
-      to load and execute the model.
-  - question: Which settings will I change to improve performance on Arm CPUs?
-    answer: >-
-      You will configure environment variables that enable low-level, Arm-targeted kernels to
-      accelerate key parts of inference. The specific variables to set are provided in the steps
-      before you run the model.
-  - question: What result should I expect after running the demo?
-    answer: >-
-      You will provide an audio input and receive a text transcript generated by Whisper. The
-      steps also guide you to evaluate transcript generation times.
-  - question: Where were these steps tested?
-    answer: >-
-      The procedure was tested on an AWS Graviton4 c8g.8xlarge instance. The instructions are
-      designed for Arm servers running Ubuntu 24.04 LTS.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Nobel Chowdary Mandepudi
 
diff --git a/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md b/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
index 565103b6dd..16e4e61eb7 100644
--- a/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
@@ -6,57 +6,9 @@ minutes_to_complete: 30
 prerequisites:
     - An OCI account
     - An Arm compute instance deployed on OCI with Oracle Linux
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:17:46Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 46f2645fb6ef298508af93569554315cfa2564be9891acabf1c874c94e52d70d
-  summary_generated_at: '2026-06-02T05:27:17Z'
-  summary_source_hash: 46f2645fb6ef298508af93569554315cfa2564be9891acabf1c874c94e52d70d
-  faq_generated_at: '2026-06-03T02:17:46Z'
-  faq_source_hash: 46f2645fb6ef298508af93569554315cfa2564be9891acabf1c874c94e52d70d
-  summary: >-
-    This introductory Learning Path shows how to install MySQL Community Server and WordPress
-    on an Arm virtual machine running Oracle Linux in Oracle Cloud Infrastructure (OCI), targeting
-    an always free tier Arm shape. You will follow practical steps to set up the database and
-    application stack on an Arm (Ampere) compute instance. The path notes that an Arm instance
-    can be deployed through the OCI Console or Terraform. Prerequisites are an OCI account and
-    an Arm compute instance on OCI with Oracle Linux. On completion, you will have MySQL and WordPress
-    installed on your OCI Arm server. The estimated time to complete is about 30 minutes.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      You need an Oracle Cloud Infrastructure (OCI) account and an Arm compute instance deployed
-      on OCI with Oracle Linux. The path suggests reviewing “Getting Started with Oracle OCI”
-      if you want a quick orientation before you begin.
-  - question: Which OCI shape and operating system should I use for the instance?
-    answer: >-
-      Use an always free tier Arm shape in OCI targeting an Arm (Ampere) compute instance. The
-      prerequisite specifies Oracle Linux as the operating system.
-  - question: How can I provision the Arm compute instance?
-    answer: >-
-      You can deploy the instance through the OCI Console or by using Terraform. The Learning
-      Path supports either approach.
-  - question: Which software will I install during this Learning Path?
-    answer: >-
-      You will install MySQL Community Server and WordPress on the Arm virtual machine. These
-      are the only tools explicitly listed.
-  - question: What result should I expect, and how long will it take?
-    answer: >-
-      Plan for about 30 minutes to complete. By the end, MySQL Community Server and WordPress
-      will be installed on your Arm server running in OCI.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Frédéric -lefred- Descamps
 
diff --git a/content/learning-paths/servers-and-cloud-computing/zlib/_index.md b/content/learning-paths/servers-and-cloud-computing/zlib/_index.md
index 0357ddeb71..f55725b00e 100644
--- a/content/learning-paths/servers-and-cloud-computing/zlib/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/zlib/_index.md
@@ -14,58 +14,9 @@ learning_objectives:
 
 prerequisites:
 - An Arm Linux computer or an [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider running Ubuntu 22.04 or Ubuntu 24.04.
-
-generate_summary_faq: false
-
-rerun_summary: true
-rerun_faqs: true
-
-# START generated_summary_faq
-generated_summary_faq:
-  template_version: summary-faq-v3
-  generated_at: '2026-06-03T02:18:20Z'
-  generator: ai
-  ai_assisted: true
-  ai_review_required: true
-  model: gpt-5
-  prompt_template: summary-faq-v3
-  source_hash: 8916f83f621505dc5406f14e70d04e702ea304950e34b4b76dc1bbc987bcc4e3
-  summary_generated_at: '2026-06-02T05:27:47Z'
-  summary_source_hash: 8916f83f621505dc5406f14e70d04e702ea304950e34b4b76dc1bbc987bcc4e3
-  faq_generated_at: '2026-06-03T02:18:20Z'
-  faq_source_hash: 8916f83f621505dc5406f14e70d04e702ea304950e34b4b76dc1bbc987bcc4e3
-  summary: >-
-    Build and use zlib-ng on an Arm Linux server to take advantage of Neon SIMD and ARMv8 CRC32
-    enhancements for compression-heavy workloads. You will compile zlib-ng in zlib-compatible
-    mode, run example applications as a drop-in replacement for the system zlib, and compare a
-    Python file-compression workload before and after switching to zlib-ng. The path also shows
-    how to install and use Linux perf to analyze where time is spent, including enabling access
-    to PMU registers and kernel symbols. Prerequisite: an Arm Linux computer or Arm-based cloud
-    instance running Ubuntu 22.04 or 24.04; no other explicit prerequisites are listed.
-  faqs:
-  - question: What do I need before running the steps?
-    answer: >-
-      Use an Arm Linux computer or an Arm-based cloud instance running Ubuntu 22.04 or Ubuntu
-      24.04. The steps use sudo to install packages and adjust perf settings.
-  - question: Which zlib-ng build mode should I use for a drop-in replacement?
-    answer: >-
-      Build zlib-ng in zlib-compatible mode. This enables the zlib API so existing applications
-      can use zlib-ng without source changes.
-  - question: What result should I expect after running the Python compression example?
-    answer: >-
-      You will compress a large file with Python and measure the time difference when using zlib-ng
-      versus the system zlib. The outcome is a measured performance comparison rather than a specific
-      numeric target.
-  - question: Which packages are installed during this Learning Path?
-    answer: >-
-      The steps install python-is-python3 for running Python and the perf tooling via linux-tools-common,
-      linux-tools-generic, and linux-tools-uname -r. These enable running the example and analyzing
-      performance.
-  - question: What should I check if perf reports permission or access errors?
-    answer: >-
-      Follow the steps that allow user access to PMU registers and kernel symbol addresses using
-      the provided sudo commands. After applying those settings, rerun perf to collect data.
-# END generated_summary_faq
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 author: Pareena Verma
 
diff --git a/reports/generated-summary-faq/README.md b/reports/generated-summary-faq/README.md
new file mode 100644
index 0000000000..ebaf04d90a
--- /dev/null
+++ b/reports/generated-summary-faq/README.md
@@ -0,0 +1,38 @@
+# Generated Summary/FAQ Reports
+
+This folder stores local reports and logs created by:
+
+```bash
+./generate-summary-faq
+```
+
+For a category or single-path run, expect files such as:
+
+```text
+<run-name>.txt  terminal-style progress log
+<run-name>.yml  structured report data
+<run-name>.md   Markdown report for human review
+```
+
+For an all-path run, the tool creates a timestamped folder:
+
+```text
+<run-name>/
+  run.txt          aggregate progress log for the whole command
+  run.yml          aggregate structured report for the whole command
+  run.md           aggregate Markdown report for human review
+  automotive.yml   per-category structured report
+  automotive.md    per-category Markdown report
+  ...
+```
+
+The tool also refreshes:
+
+```text
+latest-run.yml
+latest-run.md
+```
+
+These point to the most recent local run. Future generation branches can include
+the relevant reports and logs in a pull request when they are useful for review
+or backtracking.
diff --git a/set-summary-faq-flags b/set-summary-faq-flags
new file mode 100755
index 0000000000..62b1437760
--- /dev/null
+++ b/set-summary-faq-flags
@@ -0,0 +1,5 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+exec "$SCRIPT_DIR/tools/set-summary-faq-flags" "$@"
diff --git a/tools/generate-summary-faq.md b/tools/generate-summary-faq.md
index fccd4fe7d5..0ad0de3ac3 100644
--- a/tools/generate-summary-faq.md
+++ b/tools/generate-summary-faq.md
@@ -1,18 +1,37 @@
 # Generate Summary/FAQ Tool
 
-Use `tools/generate-summary-faq` to generate AI-assisted summary and FAQ content for Learning Path `_index.md` files.
+Use this tool to generate AI-assisted summary and FAQ content for Arm Learning
+Path `_index.md` files. The generated content is stored in each Learning Path
+front matter under `generated_summary_faq` and is intended as a reviewed draft,
+not as automatically approved content.
 
-The tool always uses the configured LLM endpoint. There is no template or offline generation mode.
+The generator always uses the configured LLM endpoint. There is no offline or
+template-only generation mode.
 
-## Prerequisites
+## What This Tool Does
 
-Set your Arm OpenAI proxy key before running:
+The tool can:
+
+- Generate a summary paragraph and five FAQ entries for selected Learning Paths.
+- Store generated content in the matching Learning Path `_index.md`.
+- Reset control flags after a successful write run so paths are not regenerated accidentally.
+- Produce local `.txt`, `.yml`, and `.md` reports for each run.
+- Process all Learning Paths, one category, one path, newly added paths, or edited paths.
+
+The generated block is labeled AI-assisted in the rendered Learning Path page.
+Generated text should still be reviewed by a human contributor before it is
+treated as final site content.
+
+## One-Time Setup
+
+Set the Arm OpenAI proxy key in your shell before running the generator:
 
 ```bash
 export OPENAI_API_KEY="..."
 ```
 
-Optional endpoint configuration:
+The wrapper defaults to the Arm proxy endpoint and `gpt-5`, but you can override
+them if needed:
 
 ```bash
 export OPENAI_BASE_URL="https://openai-api-proxy.geo.arm.com/api/providers/openai/v1/responses/"
@@ -27,56 +46,43 @@ export OPENAI_CA_BUNDLE="/path/to/arm-ca-bundle.pem"
 
 For local convenience, `tools/generate-summary-faq` automatically bypasses TLS
 verification when no CA bundle is configured. This is intended only for local
-testing against the Arm proxy. If you want to force certificate verification,
-use:
+testing against the Arm proxy. To force certificate verification, use:
 
 ```bash
 tools/generate-summary-faq --all --dry-run --verify-tls
 ```
 
-You can also make the bypass explicit:
-
-```bash
-export OPENAI_INSECURE_SKIP_VERIFY="true"
-```
-
-## Common Runs
+## Quick Start
 
-Default full generation for all opted-in Learning Paths:
+Run all currently opted-in Learning Paths and write the generated content:
 
 ```bash
 ./generate-summary-faq
 ```
 
-The shortcut above is equivalent to:
+That shortcut is equivalent to:
 
 ```bash
 tools/generate-summary-faq --all --write
 ```
 
-Dry-run one category:
+Run a dry run first if you only want reports and no file edits:
 
 ```bash
-tools/generate-summary-faq --category automotive
+tools/generate-summary-faq --all --dry-run
 ```
 
-Write generated content for one category:
+Run one category:
 
 ```bash
-tools/generate-summary-faq --category servers-and-cloud-computing --write
+tools/generate-summary-faq --category laptops-and-desktops --write
 ```
 
-Write generated content for all eligible Learning Paths:
-
-```bash
-tools/generate-summary-faq --all --write
-```
-
-Process a single Learning Path:
+Run one Learning Path:
 
 ```bash
 tools/generate-summary-faq \
-  --path content/learning-paths/servers-and-cloud-computing/nginx_tune \
+  --path content/learning-paths/laptops-and-desktops/wsl2 \
   --write
 ```
 
@@ -86,49 +92,116 @@ List available categories:
 tools/generate-summary-faq --list-categories
 ```
 
-## Including or Excluding Learning Paths
+## Control Flags
 
-By default, the generator only processes Learning Paths with this front-matter flag:
+Each Learning Path uses three front-matter fields:
 
 ```yaml
 generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 ```
 
-Set it to `false` to leave a Learning Path out of generated summary/FAQ runs:
+Use the fields this way:
+
+- `generate_summary_faq: true` opts the Learning Path into the next generator run.
+- `generate_summary_faq: false` leaves the Learning Path out of normal generator runs.
+- `rerun_summary: true` forces the summary to be regenerated.
+- `rerun_faqs: true` forces the FAQs to be regenerated.
+
+After a successful write run, the generator resets all three fields to `false`
+for processed Learning Paths. This prevents repeated LLM calls unless a
+contributor intentionally opts the path in again.
+
+New Learning Paths scaffolded from `archetypes/learning-path/_index.md` should
+start with:
 
 ```yaml
-generate_summary_faq: false
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 ```
 
-After a successful write run, the tool resets `generate_summary_faq` to `false`
-for every processed Learning Path. This keeps future runs from reprocessing
-content unless a contributor intentionally opts the path in again.
+If you create a Learning Path by copying an existing folder, confirm these
+fields manually in the copied `_index.md`.
 
-The `rerun_summary` and `rerun_faqs` fields are separate controls. For a
-Learning Path that already has generated summary/FAQ content, both fields can
-stay `false`; the tool will report the path as unchanged and will not send that
-Learning Path to the LLM again.
+## Set Flags
 
-Set one or both rerun flags to force regeneration for an existing generated
-section. After a successful write run, the tool resets both rerun flags to
-`false`:
+Use `set-summary-faq-flags` to prepare paths for generation without editing
+front matter by hand.
 
-```yaml
-rerun_summary: true
-rerun_faqs: true
+Set all Learning Paths to regenerate both sections:
+
+```bash
+./set-summary-faq-flags --all --all-true
 ```
 
-New Learning Paths scaffolded from `archetypes/learning-path/_index.md` start
-with:
+Reset all Learning Paths back to inactive:
 
-```yaml
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
+```bash
+./set-summary-faq-flags --all --all-false
 ```
 
-If you create a Learning Path by copying an existing folder, confirm these
-three fields manually in the copied `_index.md`.
+Set one category to regenerate both sections:
+
+```bash
+./set-summary-faq-flags --category laptops-and-desktops --all-true
+```
+
+Set one Learning Path to regenerate both sections:
+
+```bash
+./set-summary-faq-flags \
+  --path content/learning-paths/laptops-and-desktops/wsl2 \
+  --all-true
+```
+
+Regenerate only FAQs for a category:
+
+```bash
+tools/set-summary-faq-flags \
+  --category servers-and-cloud-computing \
+  --generate-summary-faq true \
+  --rerun-summary false \
+  --rerun-faqs true
+```
+
+Regenerate only the summary for a category:
+
+```bash
+tools/set-summary-faq-flags \
+  --category laptops-and-desktops \
+  --generate-summary-faq true \
+  --rerun-summary true \
+  --rerun-faqs false
+```
+
+## New Or Edited Learning Paths
+
+Most day-to-day use should be branch based. If your feature branch adds new
+Learning Paths, compare it against the latest `origin/main`:
+
+```bash
+git fetch origin main
+git checkout your-feature-branch
+./set-summary-faq-flags --new-since origin/main --all-true
+./generate-summary-faq
+```
+
+If your feature branch edits existing Learning Paths, use `--changed-since`:
+
+```bash
+git fetch origin main
+git checkout your-feature-branch
+./set-summary-faq-flags --changed-since origin/main --all-true
+./generate-summary-faq
+```
+
+`--new-since` finds Learning Paths that exist on your branch but not on
+`origin/main`. `--changed-since` finds Learning Paths changed on your branch
+since `origin/main`, including newly added paths.
+
+## When The LLM Is Called
 
 The LLM is called only when at least one section needs work:
 
@@ -141,103 +214,72 @@ rerun_faqs is true
 existing generated content used a non-AI generator
 ```
 
-## Output
+If a Learning Path already has generated content and both rerun flags are
+`false`, the tool preserves the existing content and does not send that path to
+the LLM.
 
-Each run writes three local artifacts under:
+Draft Learning Paths are skipped.
+
+## Reports And Logs
+
+Each run writes local artifacts under:
 
 ```text
 reports/generated-summary-faq/
 ```
 
-The files are:
+For a category or path run, the files are:
 
 ```text
 <run-name>.txt  progress log and terminal-style output
-<run-name>.yml  structured report with latest run and retained history
-<run-name>.md   local Markdown report with tables for review
+<run-name>.yml  structured report with run details
+<run-name>.md   Markdown report with review tables
 ```
 
-When you run all Learning Paths, the tool still treats that as one parent run,
-but it splits the actual work by top-level category to reduce timeout/context
-risk. In that case, the artifacts are grouped in one run directory:
+For an all-path run, the tool creates one run directory:
 
 ```text
 reports/generated-summary-faq/<run-name>/
-run.txt          aggregate progress log for the whole run
-run.yml          aggregate structured report for the whole run
-run.md           aggregate Markdown report for the whole run
+run.txt          aggregate progress log for the whole command
+run.yml          aggregate structured report for the whole command
+run.md           aggregate Markdown report for the whole command
 automotive.yml   per-category structured report
 automotive.md    per-category Markdown report
 ...
 ```
 
-Use the aggregate `run.md` first. It links to the per-category Markdown reports
-when you need the deeper breakdown.
+Start with the aggregate `run.md`. It links to the per-category Markdown reports
+when you need a deeper breakdown.
 
-Each run also refreshes these stable report snapshots:
+Each run also refreshes:
 
 ```text
 reports/generated-summary-faq/latest-run.yml
 reports/generated-summary-faq/latest-run.md
 ```
 
-Those snapshots point at the most recent local run data, so you do not have to
-guess which timestamped folder was produced last. The per-command terminal log
-remains in the run folder as `run.txt`.
+These files point to the most recent local run, so you do not have to guess
+which timestamped folder was produced last.
 
-Use `--run-name` to make output filenames predictable:
+Open a Markdown report locally:
 
 ```bash
-tools/generate-summary-faq \
-  --category servers-and-cloud-computing \
-  --run-name servers-and-cloud-computing \
-  --write
+open reports/generated-summary-faq/latest-run.md
 ```
 
-That creates:
-
-```text
-reports/generated-summary-faq/servers-and-cloud-computing.txt
-reports/generated-summary-faq/servers-and-cloud-computing.yml
-reports/generated-summary-faq/servers-and-cloud-computing.md
-```
-
-For all Learning Paths:
+Use `--run-name` to make report filenames predictable:
 
 ```bash
 tools/generate-summary-faq \
-  --all \
-  --run-name all-learning-paths-test \
+  --category servers-and-cloud-computing \
+  --run-name servers-and-cloud-computing \
   --write
 ```
 
-That creates:
-
-```text
-reports/generated-summary-faq/all-learning-paths-test/run.txt
-reports/generated-summary-faq/all-learning-paths-test/run.yml
-reports/generated-summary-faq/all-learning-paths-test/run.md
-```
-
-Open the `.md` file locally to review the table-style run overview:
-
-```bash
-open reports/generated-summary-faq/servers-and-cloud-computing.md
-```
-
-The Markdown report is intentionally plain Markdown, so it can also be copied into a page or wired into a local Hugo-only report page later.
-
-For example, to write the Markdown report somewhere else:
-
-```bash
-tools/generate-summary-faq \
-  --category automotive \
-  --markdown-report /tmp/automotive-summary-faq-report.md
-```
-
 ## Timeout Tuning
 
-For larger categories or occasional read timeouts, use smaller prompts and more retries:
+For larger categories or occasional read timeouts, use smaller prompts and more
+retries:
 
 ```bash
 tools/generate-summary-faq \
@@ -248,3 +290,18 @@ tools/generate-summary-faq \
   --excerpt-chars 600 \
   --write
 ```
+
+## Recommended PR Flow
+
+For a normal PR that adds or edits Learning Paths:
+
+1. Update your branch from main.
+2. Set flags for new or changed Learning Paths with `set-summary-faq-flags`.
+3. Run `./generate-summary-faq`.
+4. Review the generated `_index.md` changes and the Markdown report.
+5. Edit or remove generated content if human review finds issues.
+6. Run the site locally and confirm the generated block renders correctly.
+
+For the initial bulk rollout, keep generated summary/FAQ content out of the PR.
+The PR should include the tool, prompts, rendering, and default flags. The first
+fresh generation can happen after the PR lands on main.
diff --git a/tools/generate_summary_faq.py b/tools/generate_summary_faq.py
index 9f85f5897c..864e993583 100644
--- a/tools/generate_summary_faq.py
+++ b/tools/generate_summary_faq.py
@@ -87,6 +87,8 @@
 GENERATED_KEY = "generated_summary_faq"
 MANAGED_START = "# START generated_summary_faq"
 MANAGED_END = "# END generated_summary_faq"
+CONTROL_CHARACTER_PATTERN = re.compile(r"[\x00-\x08\x0b\x0c\x0e-\x1f]")
+SUMMARY_FIRST_PERSON_PATTERN = re.compile(r"(?:\bI\b(?!/)|(?i:\b(?:we|my|our|ours|let's)\b))")
 
 TEMPLATE_VERSION = "summary-faq-v3"
 PROMPT_TEMPLATE_VERSION = "summary-faq-v3"
@@ -459,6 +461,15 @@ def compact_whitespace(value: str) -> str:
     return re.sub(r"\s+", " ", value).strip()
 
 
+def sanitize_generated_text(value: str) -> str:
+    def replacement(match: re.Match[str]) -> str:
+        if match.group(0) == "\x0b":
+            return r"\v"
+        return " "
+
+    return CONTROL_CHARACTER_PATTERN.sub(replacement, value)
+
+
 def strip_markdown_links(text: str) -> str:
     text = re.sub(r"\[([^\]]+)\]\([^)]+\)", r"\1", text)
     text = re.sub(r"`([^`]+)`", r"\1", text)
@@ -604,11 +615,17 @@ def extract_json_object(text: str) -> Dict[str, Any]:
 
 
 def validate_ai_summary_faq(payload: Dict[str, Any]) -> Dict[str, Any]:
-    summary = compact_whitespace(str(payload.get("summary", "")))
+    summary = compact_whitespace(sanitize_generated_text(str(payload.get("summary", ""))))
     raw_faqs = payload.get("faqs")
 
     if not summary:
         raise ValueError("AI response did not include a non-empty summary.")
+    first_person_match = SUMMARY_FIRST_PERSON_PATTERN.search(summary)
+    if first_person_match:
+        raise ValueError(
+            f"AI summary used first-person wording ({first_person_match.group(0)!r}); "
+            "summaries must use an editorial Learning Path voice."
+        )
     if not isinstance(raw_faqs, list) or not raw_faqs:
         raise ValueError("AI response did not include a non-empty faqs list.")
 
@@ -616,8 +633,8 @@ def validate_ai_summary_faq(payload: Dict[str, Any]) -> Dict[str, Any]:
     for raw_faq in raw_faqs:
         if not isinstance(raw_faq, dict):
             continue
-        question = compact_whitespace(str(raw_faq.get("question", "")))
-        answer = compact_whitespace(str(raw_faq.get("answer", "")))
+        question = compact_whitespace(sanitize_generated_text(str(raw_faq.get("question", ""))))
+        answer = compact_whitespace(sanitize_generated_text(str(raw_faq.get("answer", ""))))
         if question and answer:
             faqs.append({"question": question, "answer": answer})
 
diff --git a/tools/prompts/summary_faq_system.md b/tools/prompts/summary_faq_system.md
index 480c6dd5a8..aaf7235add 100644
--- a/tools/prompts/summary_faq_system.md
+++ b/tools/prompts/summary_faq_system.md
@@ -7,6 +7,8 @@ Authoring rules:
 - Treat the supplied Learning Path as the source of truth. If a detail is not present, either omit it or state that it is not explicitly listed.
 - Preserve the intent of the Learning Path author. Do not rewrite the path into a different task, audience, platform, toolchain, or level of difficulty.
 - Follow the supplied authoring guidance from `.github/copilot-instructions.md` and `content/learning-paths/cross-platform/_example-learning-path/`.
+- Treat existing page fields and sections as already visible to the learner. Do not repeat information that is already covered by the title, description, objectives, "What will you learn?", prerequisites, tool/install lists, or setup sections.
+- Use prerequisites, objectives, tags, and metadata only to understand the path and avoid contradictions. Do not restate prerequisites in the summary or FAQ unless the question addresses a specific action or blocker that occurs while following the steps.
 - Prefer concrete verbs such as install, configure, build, deploy, benchmark, profile, debug, validate, or compare when those actions are supported by the context.
 - Do not overstate outcomes. Avoid claims such as "optimize performance" or "ensure compatibility" unless the context shows how the learner does that.
 - Keep the tone clear, practical, and engineering-focused.
@@ -15,16 +17,19 @@ Authoring rules:
 - Do not include citations, markdown headings, YAML, or explanatory notes.
 
 Summary rules:
-- Write one paragraph that helps a developer quickly decide whether the Learning Path is relevant.
-- Include the main task, target environment or platform, important tools, and expected learner outcome when those details are available.
-- Include prerequisites only when they are explicit or strongly implied by the supplied context.
+- Write one paragraph in an editorial Learning Path summary voice, not as the learner and not as a conversational assistant.
+- Never use first-person wording in the summary, including "I", "we", "my", "our", or phrases such as "I will" or "we will".
+- Use second person sparingly in the summary. One or two uses of "you" are acceptable only when it improves clarity; prefer neutral phrasing such as "This Learning Path shows...", "The path guides learners through...", or "Learners configure...".
+- Focus on the main hands-on activity, the decisions learners make, and the result they should be able to recognize after completing the path.
+- Do not summarize prerequisites, audience, tags, or "what you will learn" as a list in prose. If those facts are already explicit elsewhere, avoid them unless they are necessary to explain the hands-on work.
 - Avoid repeating the title unless it is needed for clarity.
 
 FAQ rules:
-- Create questions a real developer might ask while actively following the Learning Path, especially when they are setting up tools, choosing options, running commands, validating results, or deciding what to do next.
-- Favor practical in-the-moment questions about setup decisions, required services, command outcomes, validation steps, expected artifacts, configuration choices, prerequisites that affect execution, and troubleshooting-relevant checks.
-- A small number of before-you-start questions are acceptable only when they help the learner avoid a real blocker, such as missing access, required hardware, required cloud permissions, or assumed technical knowledge.
-- Avoid basic or filler questions such as "What is this Learning Path about?", "Who is this for?", or "What will I learn?" when the context supports more useful workflow-focused questions.
+- Create questions a real developer might ask while actively doing the Learning Path or immediately after completing a step.
+- Favor practical in-the-moment questions about setup choices, command results, validation steps, expected artifacts, configuration decisions, interpreting output, and what to check before moving to the next step.
+- Avoid questions whose answer is just a restatement of the prerequisites, title, description, objectives, audience, or "What will you learn?" content.
+- A before-you-start question is allowed only when it prevents a concrete blocker not already obvious from the prerequisites section.
+- Avoid basic or filler questions such as "What is this Learning Path about?", "Who is this for?", "What will I learn?", "What prerequisites do I need?", or "Do I need prior experience?" when the context supports more useful workflow-focused questions.
 - Prefer questions that begin with phrases a learner might actually think during the path, such as "How do I know...", "What should I check if...", "Which option should I use...", "What do I need before running...", or "What result should I expect...".
 - Do not create corner-case questions, speculative limitations, security guidance, pricing details, or unsupported troubleshooting advice.
 - Every answer must be grounded in the supplied context and should help the reader take the next step.
diff --git a/tools/prompts/summary_faq_user.md b/tools/prompts/summary_faq_user.md
index 13c2a01a8f..fe6f277e68 100644
--- a/tools/prompts/summary_faq_user.md
+++ b/tools/prompts/summary_faq_user.md
@@ -1,6 +1,8 @@
 Generate an AI-assisted summary paragraph and FAQ section for this Arm Learning Path.
 
-Use the Learning Path title, description, audience, objectives, prerequisites, tags, platform metadata, step excerpts, and supplied authoring guidance to produce useful draft content for a developer following the path.
+Use the Learning Path title, description, audience, objectives, prerequisites, tags, platform metadata, step excerpts, and supplied authoring guidance to understand the learner journey and produce useful draft content for a developer following the path.
+
+Important: the generated block appears on the same page as the existing Learning Path metadata and introduction. Do not repeat information that the learner can already see in existing fields or sections, especially prerequisites, objectives, audience, tags, descriptions, "What will you learn?", installation/setup lists, or other front-matter-driven content.
 
 Assume these rules while writing:
 - Use only the Learning Path context below. Do not add facts, tools, commands, prerequisites, performance claims, compatibility claims, or outcomes that are not present.
@@ -8,19 +10,26 @@ Assume these rules while writing:
 - Keep the content useful for human review. The draft should be specific enough to evaluate, but not so detailed that it replaces the Learning Path steps.
 - If the context is thin, be honest and stay high-level rather than filling gaps.
 - Match the complexity of the Learning Path. Introductory paths should stay approachable; advanced paths can use more precise technical language from the context.
+- Treat prerequisites and objectives as background context, not content to rewrite. Only mention them if doing so helps answer a specific step-level concern that is not already obvious from the page.
 
 Summary guidance:
-- Say what the learner will build, configure, measure, deploy, or understand.
+- Write the summary as an Arm Learning Path overview, not from the learner's first-person point of view.
+- Never use first-person wording in the summary, including "I", "we", "my", "our", or "let's".
+- Use "you" sparingly in the summary. Prefer neutral phrasing such as "This Learning Path shows...", "The path guides learners through...", or "Learners configure..." unless second person is clearly more direct.
+- Say what learners build, configure, measure, deploy, debug, validate, or compare as they work through the steps.
 - Mention Arm technologies, tools, operating systems, and cloud platforms only when they appear in the context.
-- If prerequisites are absent, say that no explicit prerequisites are listed.
+- Avoid restating prerequisites, audience, objectives, or title text. The summary should add orientation around the workflow, not duplicate the surrounding page content.
+- If prerequisites are absent, do not comment on that absence unless the steps make a setup dependency unclear.
 - Do not make the summary sound promotional; make it sound like a useful technical overview.
 
 FAQ guidance:
-- Write questions that a real reader would ask while moving through the Learning Path steps, not only questions they would ask before starting.
-- Prioritize questions that help a learner complete the work: required setup, tool or platform choices, command outcomes, what gets created, how to validate success, what skills or access are assumed, and what decisions the learner must make during the procedure.
-- Include before-you-start questions only when they are genuinely useful for preventing a blocker, such as missing prerequisites, permissions, hardware, cloud account access, or required prior knowledge.
+- Write questions that a real reader would ask while moving through the Learning Path steps or checking their work after a step.
+- Prioritize questions that help a learner complete the work: tool or platform choices, command outcomes, what gets created, how to validate success, which file or service to inspect, what decision to make during the procedure, and what to check before continuing.
+- Do not use the FAQ to repeat prerequisites. Avoid questions like "What prerequisites do I need?", "Do I need prior experience?", or "What should I install first?" unless the answer gives specific step-level guidance that is not already covered by the prerequisites or install sections.
+- Include before-you-start questions only when they are genuinely useful for preventing a blocker that is not already clearly covered elsewhere on the page.
 - Avoid generic questions like "What will I learn?", "Who is this for?", or "What is this Learning Path about?" when the context supports more practical workflow questions.
 - Good FAQ questions should feel like something a learner might ask with the Learning Path open in another tab.
+- Good FAQ answers should point the learner toward the next useful check or decision without repeating entire paragraphs from the Learning Path.
 - Avoid far-fetched edge cases. Stay close to common developer concerns raised by the actual steps and metadata.
 - Answer each question directly using only information from the context.
 
diff --git a/tools/set-summary-faq-flags b/tools/set-summary-faq-flags
new file mode 100755
index 0000000000..b795c9349c
--- /dev/null
+++ b/tools/set-summary-faq-flags
@@ -0,0 +1,5 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+exec python3 "$SCRIPT_DIR/set_summary_faq_flags.py" "$@"
diff --git a/tools/set_summary_faq_flags.py b/tools/set_summary_faq_flags.py
new file mode 100644
index 0000000000..3c188cd5e9
--- /dev/null
+++ b/tools/set_summary_faq_flags.py
@@ -0,0 +1,206 @@
+#!/usr/bin/env python3
+"""Set generated summary/FAQ front-matter flags for Learning Paths."""
+
+from __future__ import annotations
+
+import argparse
+import re
+import subprocess
+from pathlib import Path
+
+
+REPO_ROOT = Path(__file__).resolve().parents[1]
+LEARNING_PATH_ROOT = REPO_ROOT / "content" / "learning-paths"
+FLAGS = ("generate_summary_faq", "rerun_summary", "rerun_faqs")
+FRONT_MATTER_PATTERN = re.compile(r"\A---\r?\n(.*?)\r?\n---\r?\n", re.DOTALL)
+
+
+def parse_bool(value: str) -> bool:
+    normalized = value.strip().lower()
+    if normalized in {"true", "t", "yes", "y", "1"}:
+        return True
+    if normalized in {"false", "f", "no", "n", "0"}:
+        return False
+    raise argparse.ArgumentTypeError(f"Expected true or false, got {value!r}.")
+
+
+def bool_text(value: bool) -> str:
+    return "true" if value else "false"
+
+
+def run_git_lines(args: list[str]) -> list[str]:
+    result = subprocess.run(
+        ["git", *args],
+        cwd=REPO_ROOT,
+        check=True,
+        capture_output=True,
+        text=True,
+    )
+    return [line.strip() for line in result.stdout.splitlines() if line.strip()]
+
+
+def learning_path_index_for_repo_path(repo_path: str) -> Path | None:
+    try:
+        relative_path = Path(repo_path).relative_to("content/learning-paths")
+    except ValueError:
+        return None
+
+    if len(relative_path.parts) < 2:
+        return None
+
+    category, slug = relative_path.parts[:2]
+    index_path = LEARNING_PATH_ROOT / category / slug / "_index.md"
+    return index_path if index_path.exists() else None
+
+
+def untracked_learning_path_files() -> list[str]:
+    return run_git_lines(["ls-files", "--others", "--exclude-standard", "--", "content/learning-paths"])
+
+
+def changed_learning_path_files(base_ref: str, diff_filter: str) -> list[str]:
+    return run_git_lines(
+        ["diff", "--name-only", f"--diff-filter={diff_filter}", base_ref, "--", "content/learning-paths"]
+    )
+
+
+def find_index_files_since(base_ref: str, diff_filter: str, include_untracked: bool) -> list[Path]:
+    changed_files = changed_learning_path_files(base_ref, diff_filter)
+    if include_untracked:
+        changed_files.extend(untracked_learning_path_files())
+
+    paths = {
+        index_path
+        for changed_file in changed_files
+        if (index_path := learning_path_index_for_repo_path(changed_file)) is not None
+    }
+    return sorted(paths)
+
+
+def find_index_files(args: argparse.Namespace) -> list[Path]:
+    if args.all:
+        return sorted(LEARNING_PATH_ROOT.glob("*/*/_index.md"))
+
+    if args.category:
+        category_dir = LEARNING_PATH_ROOT / args.category
+        if not category_dir.is_dir():
+            raise SystemExit(f"Category not found: {args.category}")
+        return sorted(category_dir.glob("*/_index.md"))
+
+    if args.new_since:
+        return find_index_files_since(args.new_since, "A", include_untracked=True)
+
+    if args.changed_since:
+        return find_index_files_since(args.changed_since, "ACMR", include_untracked=True)
+
+    paths: list[Path] = []
+    for raw_path in args.path.split(","):
+        path = Path(raw_path.strip())
+        if not path:
+            continue
+        if not path.is_absolute():
+            path = REPO_ROOT / path
+        if path.is_dir():
+            path = path / "_index.md"
+        if not path.exists():
+            raise SystemExit(f"Path not found: {path.relative_to(REPO_ROOT)}")
+        if path.name != "_index.md":
+            raise SystemExit(f"Path is not a Learning Path _index.md file: {path.relative_to(REPO_ROOT)}")
+        paths.append(path)
+    return sorted(set(paths))
+
+
+def replacement_flags(args: argparse.Namespace) -> dict[str, bool]:
+    values = {
+        "generate_summary_faq": args.generate_summary_faq,
+        "rerun_summary": args.rerun_summary,
+        "rerun_faqs": args.rerun_faqs,
+    }
+
+    if args.all_true:
+        values = {flag: True for flag in FLAGS}
+    elif args.all_false:
+        values = {flag: False for flag in FLAGS}
+
+    selected = {flag: value for flag, value in values.items() if value is not None}
+    if not selected:
+        raise SystemExit("Choose --all-true, --all-false, or at least one individual flag value.")
+    return selected
+
+
+def set_front_matter_flag(front_matter: str, flag: str, value: bool) -> tuple[str, bool]:
+    line_pattern = re.compile(rf"^#?[ \t]*{re.escape(flag)}[ \t]*:[ \t]*(?:true|false)[ \t]*$", re.MULTILINE)
+    replacement = f"{flag}: {bool_text(value)}"
+
+    if line_pattern.search(front_matter):
+        updated = line_pattern.sub(replacement, front_matter, count=1)
+        return updated, updated != front_matter
+
+    lines = front_matter.splitlines()
+    insert_at = 0
+    for index, line in enumerate(lines):
+        if re.match(r"^(title|description|minutes_to_complete|who_is_this_for|learning_objectives|prerequisites|author|reviewers|test_maintenance|test_images|weight|layout|draft|hidden|tags|skilllevels|subjects|armips|tools_software_languages|operatingsystems|cloud_service_providers|ci_cd|learning_path_main_image|main_image|additional_search_terms|ignore_connection_issues|generate_summary_faq|rerun_summary|rerun_faqs)\s*:", line):
+            insert_at = index + 1
+    lines.insert(insert_at, replacement)
+    return "\n".join(lines), True
+
+
+def update_file(path: Path, values: dict[str, bool], dry_run: bool) -> bool:
+    text = path.read_text(encoding="utf-8")
+    match = FRONT_MATTER_PATTERN.match(text)
+    if not match:
+        raise SystemExit(f"Missing YAML front matter: {path.relative_to(REPO_ROOT)}")
+
+    front_matter = match.group(1)
+    changed = False
+    for flag, value in values.items():
+        front_matter, flag_changed = set_front_matter_flag(front_matter, flag, value)
+        changed = changed or flag_changed
+
+    if changed and not dry_run:
+        updated_text = f"---\n{front_matter}\n---\n{text[match.end():]}"
+        path.write_text(updated_text, encoding="utf-8")
+
+    return changed
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(
+        description="Set generated summary/FAQ control flags in Learning Path _index.md files."
+    )
+    target = parser.add_mutually_exclusive_group(required=True)
+    target.add_argument("--all", action="store_true", help="Update all Learning Paths.")
+    target.add_argument("--category", help="Update one top-level Learning Path category.")
+    target.add_argument("--path", help="Update one path, _index.md file, or comma-separated path list.")
+    target.add_argument("--new-since", metavar="REF", help="Update Learning Paths added since a git ref.")
+    target.add_argument("--changed-since", metavar="REF", help="Update Learning Paths changed since a git ref.")
+
+    preset = parser.add_mutually_exclusive_group()
+    preset.add_argument("--all-true", action="store_true", help="Set generate/rerun flags to true.")
+    preset.add_argument("--all-false", action="store_true", help="Set generate/rerun flags to false.")
+
+    parser.add_argument("--generate-summary-faq", type=parse_bool, help="Set generate_summary_faq.")
+    parser.add_argument("--rerun-summary", type=parse_bool, help="Set rerun_summary.")
+    parser.add_argument("--rerun-faqs", type=parse_bool, help="Set rerun_faqs.")
+    parser.add_argument("--dry-run", action="store_true", help="Show what would change without editing files.")
+
+    args = parser.parse_args()
+    values = replacement_flags(args)
+    paths = find_index_files(args)
+    changed = 0
+
+    for path in paths:
+        did_change = update_file(path, values, args.dry_run)
+        changed += int(did_change)
+        status = "would update" if args.dry_run and did_change else "updated" if did_change else "unchanged"
+        print(f"{status:12} {path.relative_to(REPO_ROOT)}")
+
+    print(
+        f"\nProcessed {len(paths)} Learning Paths: {changed} "
+        f"{'would change' if args.dry_run else 'changed'}, {len(paths) - changed} unchanged."
+    )
+    print("Set values: " + ", ".join(f"{flag}={bool_text(value)}" for flag, value in values.items()))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

From 5d764e1658044fb8a1689f9d86500121259ff35a Mon Sep 17 00:00:00 2001
From: Christopher Moroney <christopher.moroney1@gmail.com>
Date: Wed, 17 Jun 2026 11:03:39 -0700
Subject: [PATCH 23/23] Update placeholder/layout to avoid potential merge
 conflicts

---
 archetypes/learning-path/_index.md                |  8 ++------
 content/learning-paths/automotive/intro/_index.md |  5 ++---
 .../automotive/openadkit1_container/_index.md     |  8 +++-----
 .../openadkit2_safetyisolation/_index.md          |  9 ++++-----
 .../automotive/system76-auto/_index.md            |  6 +++---
 .../automotive/zenacssdebug/_index.md             |  6 +++---
 .../_example-learning-path/_index.md              |  7 +++----
 .../cross-platform/adler32/_index.md              |  7 +++----
 .../automate-mcp-with-testcontainers/_index.md    |  6 +++---
 .../cross-platform/avh_cicd/_index.md             |  6 +++---
 .../cross-platform/avh_cicd2/_index.md            |  7 +++----
 .../aws-greengrass-pacbti-test/_index.md          |  8 +++++---
 .../build-a-reachy-robot-app-on-pi/_index.md      |  8 +++++---
 .../cross-platform/cca_rme/_index.md              |  9 ++++-----
 .../cpp-loop-size-context/_index.md               |  6 +++---
 .../create-your-own-topo-templates/_index.md      |  7 +++++--
 .../_index.md                                     |  6 ++++--
 .../deploy-ml-model-to-npu-with-topo/_index.md    |  4 ++++
 .../cross-platform/docker-build-cloud/_index.md   |  6 +++---
 .../cross-platform/docker/_index.md               |  6 +++---
 .../dynamic-memory-allocator/_index.md            |  6 +++---
 .../eigen-linear-algebra-on-arm/_index.md         |  7 +++----
 .../cross-platform/ernie_moe_v9/_index.md         |  6 +++---
 .../floating-point-behavior/_index.md             |  7 ++++---
 .../function-multiversioning/_index.md            |  6 +++---
 .../cross-platform/github-arm-runners/_index.md   |  6 +++---
 .../gitlab-managed-runners/_index.md              |  9 +++------
 .../cross-platform/gitlab/_index.md               |  7 +++----
 .../cross-platform/integer-vs-floats/_index.md    |  8 +++-----
 .../cross-platform/intrinsics/_index.md           |  5 +++--
 .../cross-platform/ipexplorer/_index.md           |  6 +++---
 .../cross-platform/kleidiai-explainer/_index.md   | 11 ++++-------
 .../_index.md                                     |  8 +++-----
 .../cross-platform/loop-reflowing/_index.md       |  8 +++-----
 .../cross-platform/matrix/_index.md               |  8 +++-----
 .../cross-platform/mca-godbolt/_index.md          |  7 +++----
 .../cross-platform/mcp-ai-agent/_index.md         |  6 +++---
 .../cross-platform/memory-latency/_index.md       |  6 +++---
 .../cross-platform/multimodel_mnn_v9/_index.md    |  6 +++---
 .../multiplying-matrices-with-sme2/_index.md      |  6 +++---
 .../cross-platform/psa-tfm/_index.md              |  4 ++--
 .../_index.md                                     |  7 +++----
 .../cross-platform/remoteit/_index.md             |  5 +++--
 .../cross-platform/restrict-keyword-c99/_index.md | 10 ++++------
 .../cross-platform/rust_armds/_index.md           |  6 +++---
 .../cross-platform/simd-info-demo/_index.md       |  9 ++++-----
 .../cross-platform/simd-loops/_index.md           |  8 ++++----
 .../cross-platform/simd-on-rust/_index.md         |  7 +++----
 .../sme-executorch-profiling/_index.md            |  9 ++++-----
 .../cross-platform/tinkerblox_ultraedge/_index.md |  9 ++++-----
 .../cross-platform/topdown-compare/_index.md      |  8 ++++----
 .../vectorization-comparison/_index.md            |  8 ++++----
 .../vectorization-friendly-data-layout/_index.md  |  8 +++-----
 .../windowsperf_sampling_cpython_spe/_index.md    |  5 +++--
 .../cross-platform/woa_azure/_index.md            |  6 +++---
 .../cross-platform/zenoh-multinode-ros2/_index.md | 10 ++++------
 .../advanced_soc/_index.md                        |  5 +++--
 .../alif-image-classification/_index.md           |  5 +++--
 .../arduino-pico/_index.md                        |  7 +++----
 .../embedded-and-microcontrollers/armds/_index.md |  7 +++----
 .../embedded-and-microcontrollers/asm/_index.md   |  5 +++--
 .../avh_balena/_index.md                          |  7 +++----
 .../avh_greengrass/_index.md                      |  8 +++-----
 .../avh_matter/_index.md                          |  6 +++---
 .../avh_ppocr/_index.md                           |  7 +++----
 .../avh_vio/_index.md                             |  9 +++------
 .../azure-iot/_index.md                           |  7 +++----
 .../bare-metal/_index.md                          |  6 +++---
 .../_index.md                                     |  9 +++------
 .../cmsis_rtx/_index.md                           |  6 +++---
 .../cmsis_rtx_vs/_index.md                        |  6 +++---
 .../cmsisdsp-dev-with-python/_index.md            |  7 +++----
 .../context-switch-cortex-m/_index.md             |  5 +++--
 .../coverage_mdk/_index.md                        |  6 +++---
 .../device-connect-d2d/_index.md                  | 10 ++++------
 .../device-connect-server/_index.md               |  4 ++++
 .../device-connect-strands/_index.md              |  7 +++----
 .../docker/_index.md                              |  7 +++----
 .../embedded-and-microcontrollers/edge/_index.md  |  7 +++----
 .../edge_impulse_greengrass/_index.md             |  3 ++-
 .../img_nn_stcube/_index.md                       |  6 +++---
 .../embedded-and-microcontrollers/intro/_index.md |  7 +++----
 .../introduction-to-tinyml-on-arm/_index.md       |  8 +++-----
 .../iot-sdk/_index.md                             |  7 +++----
 .../jetson_object_detection/_index.md             |  8 +++-----
 .../keilstudiocloud/_index.md                     |  8 +++-----
 .../linux-nxp-board/_index.md                     | 10 ++++------
 .../linux-on-fvp/_index.md                        |  7 +++----
 .../llama-python-cpu/_index.md                    |  7 +++----
 .../migration/_index.md                           |  5 +++--
 .../embedded-and-microcontrollers/mlek/_index.md  |  7 +++----
 .../nav-mlek/_index.md                            |  6 +++---
 .../new_debug_targets_armds/_index.md             |  7 +++----
 .../observing-ethos-u-on-nxp/_index.md            | 13 +++++--------
 .../pack-migration-cmsis-v6/_index.md             |  8 +++-----
 .../pqc_pqm4/_index.md                            |  6 +++++-
 .../project-migration-cmsis-v6/_index.md          |  6 +++---
 .../raspberry-pi-smart-home/_index.md             |  5 +++--
 .../raspberry_pi_chatgpt_bot/_index.md            |  8 +++-----
 .../rpi-llama3/_index.md                          | 12 +++---------
 .../rpi-mxnet/_index.md                           |  6 +++---
 .../embedded-and-microcontrollers/rpi/_index.md   |  6 +++---
 .../rpi_pico/_index.md                            |  6 +++---
 .../streamline-kernel-module/_index.md            | 10 +++-------
 .../tflow_nn_stcube/_index.md                     |  6 +++---
 .../embedded-and-microcontrollers/tfm/_index.md   |  7 +++----
 .../training-inference-pytorch/_index.md          |  5 +++--
 .../trustzone_nxp_lpc/_index.md                   |  6 +++---
 .../universal-sbc-chassis/_index.md               |  7 +++----
 .../uv_debug/_index.md                            |  9 +++------
 .../uvprojx-conversion/_index.md                  |  9 +++------
 .../vcpkg-tool-installation/_index.md             |  8 +++-----
 .../visualizing-ethos-u-performance/_index.md     |  8 +++-----
 .../yocto_qemu/_index.md                          |  7 +++----
 .../yolo-on-himax/_index.md                       | 11 ++++-------
 .../zephyr/_index.md                              |  7 +++----
 .../zephyr_cs320_mps4/_index.md                   |  4 ++++
 .../zephyr_shell/_index.md                        |  4 ++++
 .../zephyr_vsworkbench/_index.md                  |  9 ++++-----
 .../laptops-and-desktops/chrome-os-lxc/_index.md  |  5 +++--
 .../dgx_persistent_agent/_index.md                |  5 ++++-
 .../dgx_spark_isaac_robotics/_index.md            |  7 ++++---
 .../dgx_spark_llamacpp/_index.md                  |  5 +++--
 .../laptops-and-desktops/dgx_spark_rag/_index.md  |  6 +++---
 .../dgx_spark_voicechatbot/_index.md              |  5 +++--
 .../laptops-and-desktops/docker-models/_index.md  |  5 +++--
 .../laptops-and-desktops/electron/_index.md       |  6 +++---
 .../gh-arm-runners-win/_index.md                  |  7 ++++---
 .../laptops-and-desktops/hyper-v/_index.md        |  6 +++---
 .../laptops-and-desktops/intro/_index.md          |  8 +++-----
 .../kleidicv-on-mac/_index.md                     |  7 +++----
 .../laptops-and-desktops/llvm_putty/_index.md     |  6 +++---
 .../_index.md                                     |  7 +++----
 .../laptops-and-desktops/pinebook-pro/_index.md   |  6 +++---
 .../pytorch-finetuning-on-spark/_index.md         |  5 +++--
 .../self_hosted_cicd_github/_index.md             |  5 +++--
 .../laptops-and-desktops/win-opencv/_index.md     |  8 +++-----
 .../win-resource-ps1/_index.md                    |  9 +++------
 .../win11-vm-automation/_index.md                 |  5 +++--
 .../laptops-and-desktops/win_arm64ec/_index.md    |  6 +++---
 .../win_arm64ec_porting/_index.md                 |  6 +++---
 .../laptops-and-desktops/win_arm_qt/_index.md     |  6 +++---
 .../laptops-and-desktops/win_asp_net8/_index.md   |  6 +++---
 .../laptops-and-desktops/win_aws_iot/_index.md    |  8 ++++----
 .../win_aws_iot_dynamodb/_index.md                |  6 +++---
 .../win_aws_iot_lambda/_index.md                  |  6 +++---
 .../win_aws_iot_lambda_dynamodb/_index.md         |  6 +++---
 .../laptops-and-desktops/win_aws_iot_s3/_index.md |  6 +++---
 .../laptops-and-desktops/win_cef/_index.md        |  6 +++---
 .../laptops-and-desktops/win_forms/_index.md      |  8 ++++----
 .../laptops-and-desktops/win_net/_index.md        |  6 +++---
 .../laptops-and-desktops/win_net8/_index.md       |  6 +++---
 .../laptops-and-desktops/win_net_maui/_index.md   |  6 +++---
 .../win_on_arm_build_onnxruntime/_index.md        |  5 +++--
 .../win_profile_guided_optimisation/_index.md     |  7 +++----
 .../laptops-and-desktops/win_python/_index.md     |  6 +++---
 .../win_python_onnx/_index.md                     |  4 ++--
 .../win_sandbox_dot_net_cicd/_index.md            |  6 +++---
 .../win_win32_dll_porting/_index.md               |  6 +++---
 .../laptops-and-desktops/win_winui3/_index.md     |  8 ++++----
 .../laptops-and-desktops/win_wpf/_index.md        |  8 ++++----
 .../win_xamarin_forms/_index.md                   |  8 ++++----
 .../laptops-and-desktops/windows_armpl/_index.md  |  9 +++------
 .../windows_cicd_github/_index.md                 |  6 +++---
 .../windowsperf-vs-extension/_index.md            |  7 ++++---
 .../laptops-and-desktops/windowsperf/_index.md    |  5 +++--
 .../windowsperf_sampling_cpython/_index.md        |  5 +++--
 .../windowsperf_wpa_plugin/_index.md              |  5 +++--
 .../laptops-and-desktops/wsl2/_index.md           |  6 +++---
 .../mobile-graphics-and-gaming/afrc/_index.md     |  9 +++------
 .../ai-camera-pipelines/_index.md                 |  5 +++--
 .../mobile-graphics-and-gaming/ams/_index.md      |  6 +++---
 .../analyze_a_frame_with_frame_advisor/_index.md  | 10 +++-------
 .../android-ai-chat-lib/_index.md                 |  9 +++------
 .../android_halide/_index.md                      |  9 ++++-----
 .../android_neon/_index.md                        |  8 +++-----
 .../android_opencv_camera/_index.md               |  6 +++---
 .../android_opencv_facedetection/_index.md        |  6 +++---
 .../android_opencv_kleidicv/_index.md             |  6 +++---
 .../android_sve2/_index.md                        |  8 +++-----
 .../android_webgpu_dawn/_index.md                 | 12 +++++-------
 .../_index.md                                     |  8 +++-----
 .../_index.md                                     |  9 +++------
 .../_index.md                                     |  8 +++-----
 .../_index.md                                     | 11 ++++-------
 .../_index.md                                     |  7 +++----
 .../debugging_with_mte_on_pixel8/_index.md        |  8 +++-----
 .../gemma4-kleidiai-sme2/_index.md                |  7 ++-----
 .../get-started-with-arm-asr/_index.md            |  9 +++------
 .../get-started-with-unity-on-android/_index.md   |  8 +++-----
 .../godot_packages/_index.md                      | 12 +++++-------
 .../_index.md                                     |  8 +++-----
 .../mobile-graphics-and-gaming/intro/_index.md    |  7 +++----
 .../kai_sme2_matmul_ukernel_explained/_index.md   | 11 ++++-------
 .../_index.md                                     | 10 ++++------
 .../libgpuinfo/_index.md                          | 10 ++++------
 .../litert-sme/_index.md                          |  9 +++------
 .../_index.md                                     |  8 +++-----
 .../model-training-gym/_index.md                  |  8 ++++----
 .../mobile-graphics-and-gaming/mte/_index.md      | 10 ++++------
 .../mte_on_pixel8/_index.md                       |  8 +++-----
 .../neural-graphics-data-capture-unreal/_index.md | 14 +++++---------
 .../neural-graphics-playbook-evaluate/_index.md   |  4 ++++
 .../nss-unreal/_index.md                          | 11 +++--------
 .../mobile-graphics-and-gaming/onnx/_index.md     |  5 +++--
 .../optimizing-vertex-efficiency/_index.md        |  9 ++++-----
 .../performance_llama_cpp_sme2/_index.md          | 11 ++++-------
 .../_index.md                                     |  9 +++------
 .../preparing-models-for-nt/_index.md             |  4 ++++
 .../profiling-ml-on-arm/_index.md                 |  9 +++------
 .../profiling-unity-apps-on-android/_index.md     |  7 ++-----
 .../quantize-neural-upscaling-models/_index.md    | 11 ++++++-----
 .../ray_tracing/_index.md                         |  7 +++----
 .../render-graph-optimization/_index.md           |  6 +++---
 .../_index.md                                     | 10 ++++------
 .../run-stable-audio-with-executorch/_index.md    | 10 ++++------
 .../unity_on_orange_pi/_index.md                  |  8 +++-----
 .../unity_packages/_index.md                      |  8 +++-----
 .../_index.md                                     | 10 ++++------
 .../using_unity_machine_learning_agents/_index.md |  8 +++-----
 .../_index.md                                     | 12 ++++--------
 .../voice-assistant/_index.md                     |  7 ++++---
 .../voice-sentiment-analysis-with-llm/_index.md   |  6 +++---
 .../vulkan-ml-sample/_index.md                    |  8 +++-----
 .../adler32-kiro/_index.md                        |  6 ++++--
 .../ai-agent-on-cpu/_index.md                     |  9 +++------
 .../servers-and-cloud-computing/aks/_index.md     |  6 ++----
 .../alluxio-cobalt/_index.md                      |  6 ++++--
 .../apache_arrow_and_flight/_index.md             |  9 +++++----
 .../arcee-foundation-model-on-aws/_index.md       |  6 +++---
 .../arcee-foundation-model-on-gcp/_index.md       |  6 +++---
 .../argo-cd-gcp/_index.md                         |  7 ++++---
 .../arm-cpp-memory-model/_index.md                |  8 ++++----
 .../arm-mcp-server/_index.md                      |  9 +++------
 .../arm-soc-migration-learning-path/_index.md     |  5 +++--
 .../arm_linux_page_size/_index.md                 |  7 ++++---
 .../servers-and-cloud-computing/arm_pmu/_index.md |  6 +++---
 .../aws-copilot/_index.md                         |  8 +++-----
 .../aws-terraform/_index.md                       |  8 +++-----
 .../azure-arm-template/_index.md                  |  6 +++---
 .../azure-cobalt-cicd-aks/_index.md               |  6 ++----
 .../azure-terraform/_index.md                     |  7 +++----
 .../azure-vm/_index.md                            |  7 +++----
 .../benchmark-nlp/_index.md                       |  9 ++++-----
 .../bitmap_scan_sve2/_index.md                    |  7 +++----
 .../bolt-demo/_index.md                           |  8 ++------
 .../bolt-merge/_index.md                          |  6 +++---
 .../servers-and-cloud-computing/bolt/_index.md    |  7 +++----
 .../buildkite-gcp/_index.md                       |  5 +++--
 .../cassandra-on-gcp/_index.md                    |  6 +++---
 .../cca-container/_index.md                       |  9 ++++-----
 .../cca-device-attach/_index.md                   |  5 +++--
 .../cca-essentials/_index.md                      |  9 ++++-----
 .../cca-kata/_index.md                            |  6 +++---
 .../cca-trustee/_index.md                         |  9 +++++----
 .../cca-veraison-aws/_index.md                    |  5 +++--
 .../cca-veraison/_index.md                        | 12 +++---------
 .../circleci-gcp/_index.md                        |  7 ++++---
 .../circleci-on-aws/_index.md                     |  9 ++++-----
 .../servers-and-cloud-computing/clair/_index.md   |  7 +++----
 .../clickhouse-gcp/_index.md                      |  7 ++++---
 .../clickhouse/_index.md                          |  6 +++---
 .../servers-and-cloud-computing/cobalt/_index.md  |  8 +++-----
 .../codebuild/_index.md                           |  7 +++----
 .../servers-and-cloud-computing/codec/_index.md   |  6 +++---
 .../servers-and-cloud-computing/codec1/_index.md  |  6 +++---
 .../couchbase-on-gcp/_index.md                    |  6 +++---
 .../cplusplus_compilers_flags/_index.md           |  6 +++---
 .../cpp-profile-guided-optimisation/_index.md     |  7 +++----
 .../cpu_hotspot_performix/_index.md               |  9 +++------
 .../servers-and-cloud-computing/csp/_index.md     |  8 +++-----
 .../deepseek-cpu/_index.md                        | 10 ++++------
 .../deepspeed-on-axion/_index.md                  |  6 +++++-
 .../disk-io-benchmark/_index.md                   |  5 +++--
 .../_index.md                                     | 11 ++++-------
 .../django-on-gcp/_index.md                       |  7 ++++---
 .../servers-and-cloud-computing/django/_index.md  |  8 +++-----
 .../servers-and-cloud-computing/dlrm/_index.md    | 10 ++++------
 .../docker-mcp-toolkit/_index.md                  |  7 ++++---
 .../dotnet-migration/_index.md                    |  9 +++------
 .../dynatrace-azure/_index.md                     |  7 ++++---
 .../servers-and-cloud-computing/ecs/_index.md     |  7 +++----
 .../eks-multi-arch/_index.md                      |  8 ++------
 .../servers-and-cloud-computing/eks/_index.md     |  6 +++---
 .../elasticsearch-on-azure/_index.md              |  5 ++++-
 .../envoy-gcp/_index.md                           |  6 +++---
 .../servers-and-cloud-computing/envoy/_index.md   |  7 +++----
 .../envoy_tune/_index.md                          |  9 ++++-----
 .../_index.md                                     |  9 +++------
 .../false-sharing-arm-spe/_index.md               |  6 +++---
 .../fastpath/_index.md                            |  5 +++--
 .../servers-and-cloud-computing/fexpa/_index.md   | 13 ++++++-------
 .../flink-on-gcp/_index.md                        |  7 ++++---
 .../servers-and-cloud-computing/flink/_index.md   | 10 +++-------
 .../flyte-with-grpc/_index.md                     |  5 +++--
 .../from-iot-to-the-cloud-part1/_index.md         | 10 ++++------
 .../from-iot-to-the-cloud-part2/_index.md         |  8 +++-----
 .../from-iot-to-the-cloud-part3/_index.md         | 10 ++++------
 .../from-iot-to-the-cloud-part4/_index.md         |  9 ++++-----
 .../servers-and-cloud-computing/funasr/_index.md  |  8 +++-----
 .../gardener-gcp/_index.md                        |  7 ++++---
 .../servers-and-cloud-computing/gcc-lto/_index.md |  6 +++---
 .../servers-and-cloud-computing/gcp/_index.md     |  5 ++---
 .../geekbench/_index.md                           |  6 +++---
 .../gh-runners/_index.md                          | 10 ++++------
 .../github-actions-runner/_index.md               |  8 ++++----
 .../github-on-arm/_index.md                       |  6 +++---
 .../gke-multi-arch-axion/_index.md                | 14 +++++---------
 .../gke-multi-arch/_index.md                      |  8 +++-----
 .../servers-and-cloud-computing/gke/_index.md     |  8 ++++----
 .../glibc-with-lse/_index.md                      | 10 +++-------
 .../go-benchmarking-with-sweet/_index.md          |  5 +++--
 .../go-gc-default-settings/_index.md              |  4 ++++
 .../golang-on-azure/_index.md                     |  6 +++---
 .../helm-on-gcp/_index.md                         |  8 ++++----
 .../servers-and-cloud-computing/intro/_index.md   |  7 +++----
 .../irq-tuning-guide/_index.md                    |  7 +++----
 .../java-gc-tuning/_index.md                      |  8 +++-----
 .../java-on-axion/_index.md                       |  8 +++-----
 .../java-on-azure/_index.md                       |  6 +++---
 .../java-perf-flamegraph/_index.md                |  7 ++++---
 .../servers-and-cloud-computing/jenkins/_index.md |  5 +++--
 .../kafka-azure/_index.md                         |  6 +++---
 .../servers-and-cloud-computing/kafka/_index.md   |  7 +++----
 .../kedify-http-autoscaling/_index.md             |  6 +++---
 .../keras-core/_index.md                          | 10 ++++------
 .../kernel-build/_index.md                        |  5 +++--
 .../keycloak-cobalt/_index.md                     |  6 +++++-
 .../kubearchinspect/_index.md                     |  7 +++----
 .../lambda_functions/_index.md                    |  7 +++----
 .../libhugetlbfs/_index.md                        |  7 +++----
 .../llama-cpu/_index.md                           |  9 ++++-----
 .../llama-vision/_index.md                        |  9 ++++-----
 .../llama_cpp_streamline/_index.md                |  7 ++++---
 .../llamaindex-rag-axion/_index.md                |  6 ++++--
 .../longhorn-cobalt/_index.md                     |  6 +++++-
 .../servers-and-cloud-computing/lse/_index.md     |  8 +++-----
 .../servers-and-cloud-computing/mariadb/_index.md |  6 +++---
 .../memcached/_index.md                           |  6 +++---
 .../memcached_cache/_index.md                     |  7 ++-----
 .../memory-subsystem/_index.md                    |  5 +++--
 .../memory_consistency/_index.md                  |  5 +++--
 .../microbenchmark-network-iperf3/_index.md       |  5 +++--
 .../migrate-ease/_index.md                        |  9 ++++-----
 .../migration/_index.md                           |  6 +++---
 .../milvus-rag/_index.md                          |  8 +++-----
 .../minio-cobalt/_index.md                        |  5 +++--
 .../servers-and-cloud-computing/ml-perf/_index.md |  6 +++---
 .../mlflow-axion/_index.md                        |  6 +++++-
 .../mongodb-on-azure/_index.md                    |  6 +++---
 .../mongodb-on-gcp/_index.md                      |  6 +++---
 .../servers-and-cloud-computing/mongodb/_index.md |  7 +++----
 .../servers-and-cloud-computing/mpi/_index.md     |  9 +++------
 .../multi-accuracy-libamath/_index.md             |  7 +++----
 .../multiarch_nginx_on_aks/_index.md              | 10 +++++-----
 .../multiarch_ollama_on_gke/_index.md             | 15 +++++----------
 .../mysql-azure/_index.md                         |  8 ++++----
 .../mysql-lns-azure/_index.md                     |  4 ++--
 .../servers-and-cloud-computing/mysql/_index.md   |  5 +++--
 .../mysql_benchmark/_index.md                     |  6 +++---
 .../mysql_tune/_index.md                          |  6 +++---
 .../neoverse-rdv3-swstack/_index.md               | 10 +++++-----
 .../net-aspire/_index.md                          | 10 ++++------
 .../nginx-on-azure/_index.md                      |  7 +++----
 .../servers-and-cloud-computing/nginx/_index.md   |  6 +++---
 .../nginx_tune/_index.md                          |  7 +++----
 .../nlp-hugging-face/_index.md                    |  9 ++++-----
 .../node-js-gcp/_index.md                         |  9 +++------
 .../oci-terraform/_index.md                       |  8 +++-----
 .../onnx-on-azure/_index.md                       |  8 +++-----
 .../servers-and-cloud-computing/onnx/_index.md    |  6 +++---
 .../openbmc-rdv3/_index.md                        |  7 ++++---
 .../opencv-on-axion/_index.md                     |  6 +++++-
 .../openebs-cobalt/_index.md                      |  6 +++++-
 .../openjdk-pacbti-gcp/_index.md                  |  6 +++++-
 .../openrng-with-performix/_index.md              |  5 +++--
 .../openshift/_index.md                           |  5 +++--
 .../openstack-on-azure/_index.md                  |  6 +++---
 .../opentelemetry/_index.md                       |  9 +++++----
 .../servers-and-cloud-computing/pac/_index.md     |  6 +++---
 .../performix-mcp-agent/_index.md                 |  9 +++------
 .../performix-memory-access/_index.md             |  4 ++++
 .../performix-microarchitecture/_index.md         | 12 ++++++------
 .../performix-system-characterization/_index.md   |  8 ++++----
 .../php-on-gcp/_index.md                          |  7 +++----
 .../pinning-threads/_index.md                     |  7 +++----
 .../pmuv3_plugin_learning_path/_index.md          |  7 +++----
 .../postgresql-cobalt/_index.md                   |  5 +++--
 .../postgresql/_index.md                          |  5 +++--
 .../postgresql_tune/_index.md                     |  7 +++----
 .../processwatch/_index.md                        |  7 +++----
 .../profiling-for-neoverse/_index.md              |  8 +++-----
 .../puppet-on-gcp/_index.md                       |  5 +++--
 .../pytorch-llama/_index.md                       |  9 ++++-----
 .../qdrant-on-axion/_index.md                     |  7 ++++---
 .../quantlib/_index.md                            |  4 +++-
 .../rabbitmq-gcp/_index.md                        |  5 +++--
 .../servers-and-cloud-computing/rag/_index.md     |  7 +++----
 .../servers-and-cloud-computing/ran/_index.md     |  7 +++----
 .../ray-on-axion/_index.md                        |  7 ++++---
 .../redis-cobalt/_index.md                        |  5 +++--
 .../redis-data-searching/_index.md                |  5 +++--
 .../servers-and-cloud-computing/redis/_index.md   |  7 +++----
 .../redis_cache/_index.md                         |  6 ++----
 .../redis_tune/_index.md                          |  9 ++++-----
 .../refinfra-debug/_index.md                      |  5 +++--
 .../refinfra-quick-start/_index.md                |  9 ++++-----
 .../reproducible-libamath/_index.md               |  6 +++---
 .../rme-cca-basics/_index.md                      |  7 +++----
 .../servers-and-cloud-computing/rtp-llm/_index.md |  8 +++-----
 .../ruby-on-rails/_index.md                       |  6 +++---
 .../rust-on-gcp/_index.md                         |  7 ++++---
 .../sentiment-analysis-eks/_index.md              | 11 ++++-------
 .../serverless-framework-aws-intro/_index.md      | 10 ++++------
 .../_index.md                                     | 10 ++++------
 .../serverless-framework-aws-s3/_index.md         |  9 ++++-----
 .../servers-and-cloud-computing/snappy/_index.md  |  6 +++---
 .../snort3-multithreading/_index.md               |  6 +++---
 .../spark-on-azure/_index.md                      |  7 ++++---
 .../spark-on-gcp/_index.md                        |  7 ++++---
 .../spark-velox-cobalt/_index.md                  |  4 ++--
 .../servers-and-cloud-computing/spark/_index.md   |  7 ++++---
 .../spe-on-performix/_index.md                    |  6 ++++--
 .../supervisord/_index.md                         |  9 ++++-----
 .../servers-and-cloud-computing/sve/_index.md     |  7 +++----
 .../sve2-match/_index.md                          |  8 +++-----
 .../sysreport/_index.md                           |  7 +++----
 .../tensorflow-gcp/_index.md                      |  5 +++--
 .../thirdai-sentiment-analysis/_index.md          |  8 +++-----
 .../timescaledb-on-gcp/_index.md                  |  7 ++++---
 .../top-down-n1/_index.md                         |  7 +++----
 .../torchbench/_index.md                          |  7 +++----
 .../triggering-pmu-events-2/_index.md             | 12 ++++--------
 .../triggering-pmu-events/_index.md               | 10 +++-------
 .../trivy-on-gcpp/_index.md                       |  5 +++--
 .../_index.md                                     |  7 ++++---
 .../typescript-on-gcp/_index.md                   |  7 ++++---
 .../using-and-porting-performance-libs/_index.md  |  5 +++--
 .../vLLM-quant/_index.md                          | 12 ++++--------
 .../vectorscan/_index.md                          |  8 +++-----
 .../vllm-acceleration/_index.md                   | 13 ++++++-------
 .../vllm-benchmark-quantisation/_index.md         | 10 +++++-----
 .../servers-and-cloud-computing/vllm/_index.md    |  7 +++----
 .../servers-and-cloud-computing/vvenc/_index.md   |  6 +++---
 .../servers-and-cloud-computing/whisper/_index.md |  9 +++------
 .../wordpress/_index.md                           |  8 +++-----
 .../xgboost-on-axion/_index.md                    |  6 +++++-
 .../servers-and-cloud-computing/zlib/_index.md    |  5 +++--
 tools/generate-summary-faq.md                     | 10 +++++++++-
 tools/set_summary_faq_flags.py                    | 13 +++++++++++--
 450 files changed, 1516 insertions(+), 1739 deletions(-)
 mode change 100644 => 100755 content/learning-paths/embedded-and-microcontrollers/pqc_pqm4/_index.md

diff --git a/archetypes/learning-path/_index.md b/archetypes/learning-path/_index.md
index d0b74a9ada..61c7447549 100644
--- a/archetypes/learning-path/_index.md
+++ b/archetypes/learning-path/_index.md
@@ -13,6 +13,8 @@ prerequisites:
     - PLACEHOLDER PREREQ 1
     - PLACEHOLDER PREREQ 2
 
+author: PLACEHOLDER NAME
+
 # New Learning Paths are opted in for the next manual generated summary/FAQ run.
 # The generator resets this to false after a successful write.
 generate_summary_faq: true
@@ -23,8 +25,6 @@ generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: PLACEHOLDER NAME
-
 ### Tags
 skilllevels: PLACEHOLDER SKILLLEVEL
 subjects: PLACEHOLDER SUBJECT
@@ -37,8 +37,6 @@ tools_software_languages:
 operatingsystems:
     - PLACEHOLDER OS G
 
-
-
 further_reading:
     - resource:
         title: PLACEHOLDER MANUAL 
@@ -53,8 +51,6 @@ further_reading:
         link: PLACEHOLDER GENERAL WEBSITE LINK
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/automotive/intro/_index.md b/content/learning-paths/automotive/intro/_index.md
index af1f7f9f52..d7362086c8 100644
--- a/content/learning-paths/automotive/intro/_index.md
+++ b/content/learning-paths/automotive/intro/_index.md
@@ -15,10 +15,11 @@ prerequisites:
 draft: true
 cascade:
     draft: true
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
-author: Jason Andrews
 
 ### Tags
 skilllevels: Introductory
@@ -31,7 +32,6 @@ operatingsystems:
     - RTOS
 tools_software_languages:
 
-
 further_reading:
     - resource:
         title: Raspberry Pi Pico
@@ -42,7 +42,6 @@ further_reading:
         link: https://microbit.org/
         type: website
 
-
 weight: 1
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
diff --git a/content/learning-paths/automotive/openadkit1_container/_index.md b/content/learning-paths/automotive/openadkit1_container/_index.md
index b690b9bf93..dd2af119e6 100644
--- a/content/learning-paths/automotive/openadkit1_container/_index.md
+++ b/content/learning-paths/automotive/openadkit1_container/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
     - An Arm Neoverse cloud instance, or a local Arm Neoverse Linux computer with at least 16 CPUs and 32GB of RAM
     - Familiarity with Docker and Docker Compose
+
+author: Odin Shen
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Odin Shen
-
 ### Tags
 skilllevels: Introductory
 subjects: Containers and Virtualization
@@ -32,7 +33,6 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
 further_reading:
     - resource:
         title: Autoware OpenAD Kit demo project
@@ -51,8 +51,6 @@ further_reading:
         link: https://docs.ros.org/en/humble/
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md b/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
index 33a092875a..0bbae6a5c4 100644
--- a/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
+++ b/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md
@@ -15,14 +15,15 @@ prerequisites:
     - Access to two Arm-based Neoverse cloud instances, or a local Arm Neoverse Linux system with at least 16 CPUs and 32 GB of RAM
     - Completion of the [Deploy Open AD Kit containerized autonomous driving simulation on Arm Neoverse](/learning-paths/automotive/openadkit1_container/) Learning Path
     - Basic familiarity with Docker
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author: 
     - Odin Shen
     - Julien Jayat
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Advanced
 subjects: Containers and Virtualization
@@ -69,8 +70,6 @@ further_reading:
         title: Eclipse Cyclone DDS 
         link: https://github.com/eclipse-cyclonedds/cyclonedds
         type: documentation
-    
-
 
 ### FIXED, DO NOT MODIFY
 # ================================================================================
diff --git a/content/learning-paths/automotive/system76-auto/_index.md b/content/learning-paths/automotive/system76-auto/_index.md
index 287b2e9553..6985d98f30 100644
--- a/content/learning-paths/automotive/system76-auto/_index.md
+++ b/content/learning-paths/automotive/system76-auto/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 
 prerequisites:
     - A System76 Thelio Astra desktop computer running Ubuntu 24.04. 
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ### Tags
 skilllevels: Introductory
 subjects: Containers and Virtualization
@@ -42,7 +43,6 @@ further_reading:
         link: https://parsec.community/
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/automotive/zenacssdebug/_index.md b/content/learning-paths/automotive/zenacssdebug/_index.md
index b0172cdd0f..84d35f8ca3 100644
--- a/content/learning-paths/automotive/zenacssdebug/_index.md
+++ b/content/learning-paths/automotive/zenacssdebug/_index.md
@@ -17,12 +17,13 @@ prerequisites:
   - Ubuntu 22.04 host machine
   - Arm Development Studio 2024.1 or later with a valid license - for support see the [Install Guide for Arm DS](/install-guides/armds/) 
   - Basic understanding of the Arm Zena CSS software stack, Armv8-A/Armv9-A cores, and Linux
+
+author: Ronan Synnott
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Ronan Synnott
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -46,7 +47,6 @@ further_reading:
       link: https://developer.arm.com/Tools%20and%20Software/Arm%20Development%20Studio
       type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/_example-learning-path/_index.md b/content/learning-paths/cross-platform/_example-learning-path/_index.md
index 34b9b8580c..c9e6588889 100644
--- a/content/learning-paths/cross-platform/_example-learning-path/_index.md
+++ b/content/learning-paths/cross-platform/_example-learning-path/_index.md
@@ -15,12 +15,13 @@ learning_objectives:
 
 prerequisites:
     - A [GitHub](https://github.com/) account
+
+author: Zach Lasiuk
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Zach Lasiuk
-
 ### Tags
 skilllevels: Introductory
 subjects: 
@@ -30,14 +31,12 @@ operatingsystems:
 tools_software_languages:
     - Hugo
 
-
 further_reading:
     - resource:
         title: GitHub Project Repository
         link: https://github.com/ArmDeveloperEcosystem/arm-learning-paths
         type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/adler32/_index.md b/content/learning-paths/cross-platform/adler32/_index.md
index 9ee971a0c6..1dc71413ff 100644
--- a/content/learning-paths/cross-platform/adler32/_index.md
+++ b/content/learning-paths/cross-platform/adler32/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 prerequisites:
     - An Arm computer running Linux with the GNU compiler (gcc) installed.
     - Visual Studio Code with the GitHub Copilot extension installed. 
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -37,7 +38,6 @@ shared_between:
     - laptops-and-desktops
     - mobile-graphics-and-gaming
 
-
 further_reading:
     - resource:
         title: Arm C Language Extensions
@@ -52,7 +52,6 @@ further_reading:
         link: https://developer.arm.com/documentation/den0018/a
         type: Documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md b/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md
index 37f8243843..66f28592d3 100644
--- a/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md
+++ b/content/learning-paths/cross-platform/automate-mcp-with-testcontainers/_index.md
@@ -16,12 +16,13 @@ prerequisites:
     - A computer with [Docker](/install-guides/docker/) and Python 3.11 or later installed
     - Basic familiarity with Python, PyTest, and container concepts
     - Familiarity with the [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) specification
+
+author: Neethu Elizabeth Simon
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Neethu Elizabeth Simon
-
 ### Tags
 skilllevels: Introductory
 subjects: CI-CD
@@ -63,7 +64,6 @@ further_reading:
         link: https://docs.pytest.org/
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/avh_cicd/_index.md b/content/learning-paths/cross-platform/avh_cicd/_index.md
index da122fd716..22b26fcacf 100644
--- a/content/learning-paths/cross-platform/avh_cicd/_index.md
+++ b/content/learning-paths/cross-platform/avh_cicd/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 
 prerequisites:
     - Some familiarity with CI/CD concepts is assumed
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 skilllevels: Introductory
 subjects: CI-CD
@@ -45,7 +46,6 @@ further_reading:
         link: https://arm-software.github.io/AVH/main/examples/html/GetStarted.html
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/avh_cicd2/_index.md b/content/learning-paths/cross-platform/avh_cicd2/_index.md
index a1a7c65f44..7f53e6d0bd 100644
--- a/content/learning-paths/cross-platform/avh_cicd2/_index.md
+++ b/content/learning-paths/cross-platform/avh_cicd2/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
     - This learning path builds on [Integrate Arm Virtual Hardware into CI/CD workflow 1](/learning-paths/cross-platform/avh_cicd/).
     - Valid AWS and GitHub accounts are required
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ##### Tags
 skilllevels: Advanced
 subjects: CI-CD
@@ -46,8 +47,6 @@ further_reading:
         link: https://arm-software.github.io/AVH/main/examples/html/GetStarted.html
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/aws-greengrass-pacbti-test/_index.md b/content/learning-paths/cross-platform/aws-greengrass-pacbti-test/_index.md
index a1b0b86ab4..37bc467da8 100644
--- a/content/learning-paths/cross-platform/aws-greengrass-pacbti-test/_index.md
+++ b/content/learning-paths/cross-platform/aws-greengrass-pacbti-test/_index.md
@@ -2,7 +2,7 @@
 title: Deploy an AWS IoT Greengrass custom component to Arm devices and verify PAC/BTI support
 
 description: Learn how to register Arm devices as AWS IoT Greengrass core devices, build and deploy a custom component, and use MQTT to verify PAC/BTI support across Armv8 and Armv9 platforms.
-    
+
 minutes_to_complete: 30   
 
 who_is_this_for: This Learning Path is for IoT and embedded developers who want to deploy and manage components on Arm devices using AWS IoT Greengrass, and verify PAC/BTI security feature support across different Arm platforms.
@@ -19,11 +19,14 @@ prerequisites:
     - An NVIDIA Jetson Thor device running JetPack 7.1 or later
     - Familiarity with AWS IoT Core and basic cloud concepts
 
-
 author:
     - Varun Chari
     - Doug Anson
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -63,7 +66,6 @@ further_reading:
       link: https://docs.aws.amazon.com/greengrass/v2/developerguide/what-is-iot-greengrass.html
       type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/build-a-reachy-robot-app-on-pi/_index.md b/content/learning-paths/cross-platform/build-a-reachy-robot-app-on-pi/_index.md
index 17803b4371..39cdd15bc6 100644
--- a/content/learning-paths/cross-platform/build-a-reachy-robot-app-on-pi/_index.md
+++ b/content/learning-paths/cross-platform/build-a-reachy-robot-app-on-pi/_index.md
@@ -1,6 +1,6 @@
 ---
 title: Build an edge AI Reachy Mini app with Raspberry Pi, MediaPipe, and MuJoCo
-    
+
 description: Run MediaPipe gesture inference on a Raspberry Pi 5, connect to a Reachy Mini MuJoCo simulation on a development machine, and use a browser dashboard to decide Reachy's fate with a thumbs-up or thumbs-down.
 
 minutes_to_complete: 60
@@ -24,6 +24,10 @@ prerequisites:
 
 author: Matt Cossins
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -65,8 +69,6 @@ further_reading:
         link: https://ai.google.dev/edge/mediapipe/solutions/vision/gesture_recognizer
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/cca_rme/_index.md b/content/learning-paths/cross-platform/cca_rme/_index.md
index 80852da801..5782183afe 100644
--- a/content/learning-paths/cross-platform/cca_rme/_index.md
+++ b/content/learning-paths/cross-platform/cca_rme/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
     - Some understanding of the Arm architecture
     - Arm Development Studio, 2023.0 or later
+
+author: Ronan Synnott
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Ronan Synnott
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -27,7 +28,7 @@ armips:
     - Neoverse
     - Cortex-A
     - Armv9-A
-    
+
 operatingsystems:
     - Linux
     - Android
@@ -39,7 +40,6 @@ tools_software_languages:
     - CCA
     - Runbook
 
-
 ### Cross-platform metadata only
 shared_path: true
 shared_between:
@@ -61,7 +61,6 @@ further_reading:
         link: https://developer.arm.com/documentation/den0126
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md b/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md
index a586d3bcf1..d42316e52f 100644
--- a/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md
+++ b/content/learning-paths/cross-platform/cpp-loop-size-context/_index.md
@@ -15,12 +15,13 @@ learning_objectives:
 
 prerequisites:
     - An Arm computer running Linux. You can also use a virtual machine from a [cloud service provider](/learning-paths/servers-and-cloud-computing/csp/).
+
+author: Kieran Hejmadi
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Kieran Hejmadi
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -49,7 +50,6 @@ further_reading:
         link: https://llvm.org/docs/Vectorizers.html
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/create-your-own-topo-templates/_index.md b/content/learning-paths/cross-platform/create-your-own-topo-templates/_index.md
index ba340cd48a..5c2ffecec7 100644
--- a/content/learning-paths/cross-platform/create-your-own-topo-templates/_index.md
+++ b/content/learning-paths/cross-platform/create-your-own-topo-templates/_index.md
@@ -1,6 +1,6 @@
 ---
 title: Create and deploy a custom Topo Template
-    
+
 description: Understand how to create and modify Topo Templates, allowing you to deploy your projects as containerized workloads to Arm-based Linux targets over SSH.
 
 minutes_to_complete: 30
@@ -23,6 +23,10 @@ prerequisites:
 
 author: Tomas Agustin Gonzalez Orlando
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: Containers and Virtualization
@@ -64,7 +68,6 @@ further_reading:
         link: https://github.com/arm/remoteproc-runtime
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/deploy-containerized-workloads-with-topo/_index.md b/content/learning-paths/cross-platform/deploy-containerized-workloads-with-topo/_index.md
index 5b09ada1f0..843b93eaea 100644
--- a/content/learning-paths/cross-platform/deploy-containerized-workloads-with-topo/_index.md
+++ b/content/learning-paths/cross-platform/deploy-containerized-workloads-with-topo/_index.md
@@ -24,6 +24,10 @@ prerequisites:
 
 author: Matt Cossins
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: Containers and Virtualization
@@ -70,8 +74,6 @@ further_reading:
         link: https://marketplace.visualstudio.com/items?itemName=Arm.topo
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/deploy-ml-model-to-npu-with-topo/_index.md b/content/learning-paths/cross-platform/deploy-ml-model-to-npu-with-topo/_index.md
index fe53b052cf..d60d1c3340 100644
--- a/content/learning-paths/cross-platform/deploy-ml-model-to-npu-with-topo/_index.md
+++ b/content/learning-paths/cross-platform/deploy-ml-model-to-npu-with-topo/_index.md
@@ -28,6 +28,10 @@ prerequisites:
 
 author: Tomas Agustin Gonzalez Orlando
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: Containers and Virtualization
diff --git a/content/learning-paths/cross-platform/docker-build-cloud/_index.md b/content/learning-paths/cross-platform/docker-build-cloud/_index.md
index b0e1dd209e..f4cdbe3845 100644
--- a/content/learning-paths/cross-platform/docker-build-cloud/_index.md
+++ b/content/learning-paths/cross-platform/docker-build-cloud/_index.md
@@ -15,12 +15,13 @@ prerequisites:
     - A computer with Docker installed. This can be Windows, macOS, or Linux. Any architecture can be used. 
     - A GitHub account
     - A Docker Hub account
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ### Tags
 skilllevels: Introductory
 subjects: Containers and Virtualization
@@ -48,7 +49,6 @@ further_reading:
         link: https://www.docker.com/blog/introducing-docker-build-cloud/
         type: blog
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/docker/_index.md b/content/learning-paths/cross-platform/docker/_index.md
index d16c94c448..c59eab3445 100644
--- a/content/learning-paths/cross-platform/docker/_index.md
+++ b/content/learning-paths/cross-platform/docker/_index.md
@@ -16,12 +16,13 @@ learning_objectives:
 prerequisites:
     - A Windows, macOS, or Linux computer with Docker installed, any architecture can be used
     - An Arm Linux server with Docker installed 
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ### Tags
 skilllevels: Introductory
 subjects: Containers and Virtualization
@@ -58,7 +59,6 @@ further_reading:
         link: https://www.docker.com/blog/how-to-rapidly-build-multi-architecture-images-with-buildx
         type: blog
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md b/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md
index c733b0bba1..aa110e7fbd 100644
--- a/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md
+++ b/content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md
@@ -15,12 +15,13 @@ learning_objectives:
 prerequisites:
 - Familiarity with C programming, with a good understanding of pointers.
 - A Linux machine to run the example code.
+
+author: David Spickett
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: David Spickett
-
 test_images:
 - ubuntu:latest
 test_link: null
@@ -54,7 +55,6 @@ shared_between:
     - laptops-and-desktops
     - embedded-and-microcontrollers
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md b/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md
index 34c18dfcd0..a54caa82f5 100644
--- a/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md
+++ b/content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 
 prerequisites:
     - An Arm-based computer running Linux and a recent version of a C++ compiler (Clang or GCC).
+
+author: Konstantinos Margaritis
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Konstantinos Margaritis
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -38,7 +39,6 @@ shared_between:
     - servers-and-cloud-computing
     - mobile-graphics-and-gaming
 
-
 further_reading:
     - resource:
         title: Eigen official Tutorial on Matrix class
@@ -53,7 +53,6 @@ further_reading:
         link: https://www.tensorflow.org/install/source
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/ernie_moe_v9/_index.md b/content/learning-paths/cross-platform/ernie_moe_v9/_index.md
index 34a3d5c1df..de0331764a 100644
--- a/content/learning-paths/cross-platform/ernie_moe_v9/_index.md
+++ b/content/learning-paths/cross-platform/ernie_moe_v9/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 
 prerequisites:
     - An Armv9 device with at least 32 GB of available disk space, for example, Radxa Orion O6
+
+author: Odin Shen
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Odin Shen
-
 ### Tags
 skilllevels: Advanced
 subjects: ML
@@ -54,7 +55,6 @@ further_reading:
         link: /learning-paths/servers-and-cloud-computing/llama_cpp_streamline/
         type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/floating-point-behavior/_index.md b/content/learning-paths/cross-platform/floating-point-behavior/_index.md
index af9793e16a..d021835eaa 100644
--- a/content/learning-paths/cross-platform/floating-point-behavior/_index.md
+++ b/content/learning-paths/cross-platform/floating-point-behavior/_index.md
@@ -15,14 +15,15 @@ learning_objectives:
 prerequisites:
     - Access to an x86 and an Arm Linux machine.
     - Familiarity with floating-point numbers.
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author: 
     - Kieran Hejmadi
     - Jason Andrews
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
diff --git a/content/learning-paths/cross-platform/function-multiversioning/_index.md b/content/learning-paths/cross-platform/function-multiversioning/_index.md
index 7b1d23ffde..b76d6cd69a 100644
--- a/content/learning-paths/cross-platform/function-multiversioning/_index.md
+++ b/content/learning-paths/cross-platform/function-multiversioning/_index.md
@@ -20,12 +20,13 @@ prerequisites:
     - Basic knowledge of loop vectorization.
     - Familiarity with Arm assembly.
     - A LLVM 20 compiler with runtime library support or GCC 16.
+
+author: Alexandros Lamprineas
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Alexandros Lamprineas
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -58,7 +59,6 @@ further_reading:
         link: https://arm-software.github.io/acle/main/acle.html
         type: documentation
 
-
 weight: 1                       # _index.md always has weight of 1 to order correctly
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
diff --git a/content/learning-paths/cross-platform/github-arm-runners/_index.md b/content/learning-paths/cross-platform/github-arm-runners/_index.md
index ae66bc4e4a..a3a52a991c 100644
--- a/content/learning-paths/cross-platform/github-arm-runners/_index.md
+++ b/content/learning-paths/cross-platform/github-arm-runners/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
     - A GitHub account (a Team or Enterprise Cloud plan is required for private repositories).
     - A Docker Hub account.
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ### Tags
 skilllevels: Introductory
 subjects: CI-CD
@@ -52,7 +53,6 @@ further_reading:
         link: https://github.blog/2024-06-03-arm64-on-github-actions-powering-faster-more-efficient-build-systems/
         type: blog
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md b/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md
index 94d39379e9..2c4c71f619 100644
--- a/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md
+++ b/content/learning-paths/cross-platform/gitlab-managed-runners/_index.md
@@ -1,7 +1,6 @@
 ---
 title: Build a CI/CD pipeline using GitLab-hosted Arm runners
 
-
 minutes_to_complete: 30
 
 who_is_this_for: This is an introductory topic for DevOps engineers who want to build CI/CD pipelines on Arm-based infrastructure using GitLab-hosted runners.
@@ -13,16 +12,16 @@ learning_objectives:
     - Configure pipeline stages to use Arm64 runners
     - Build and containerize applications for Arm64 architecture
     - Store container images in GitLab Container Registry
-    
 
 prerequisites:
     - A GitLab account (free tier includes Arm64 runner access)
+
+author: Mohamed Ismail
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Mohamed Ismail
-
 ### Tags
 skilllevels: Introductory
 subjects: CI-CD
@@ -61,8 +60,6 @@ further_reading:
         link: https://learn.arm.com/install-guides/docker/
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/gitlab/_index.md b/content/learning-paths/cross-platform/gitlab/_index.md
index 1c3e11ece5..35332e1452 100644
--- a/content/learning-paths/cross-platform/gitlab/_index.md
+++ b/content/learning-paths/cross-platform/gitlab/_index.md
@@ -17,12 +17,13 @@ prerequisites:
     - A [Google Cloud account](https://console.cloud.google.com/). Create an account if needed.
     - A computer with [Google Cloud CLI](/install-guides/gcloud/) and [kubectl](/install-guides/kubectl/)installed.
     - A valid GitLab account
+
+author: Pranay Bakre
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pranay Bakre
-
 ### Tags
 skilllevels: Advanced
 subjects: Containers and Virtualization
@@ -61,8 +62,6 @@ further_reading:
         link: https://cloud.google.com/kubernetes-engine/docs
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/integer-vs-floats/_index.md b/content/learning-paths/cross-platform/integer-vs-floats/_index.md
index 47ec00b317..72151fd5af 100644
--- a/content/learning-paths/cross-platform/integer-vs-floats/_index.md
+++ b/content/learning-paths/cross-platform/integer-vs-floats/_index.md
@@ -12,12 +12,13 @@ learning_objectives:
 
 prerequisites:
     - An Arm computer running Linux and a recent version of a C++ compiler (Clang or GCC) installed
+
+author: Konstantinos Margaritis
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Konstantinos Margaritis
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -38,7 +39,6 @@ shared_between:
     - servers-and-cloud-computing
     - mobile-graphics-and-gaming
 
-
 further_reading:
     - resource:
         title: Arm Neoverse™ N1 Software Optimization Guide
@@ -61,8 +61,6 @@ further_reading:
         link: https://en.wikipedia.org/wiki/Bfloat16_floating-point_format
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/intrinsics/_index.md b/content/learning-paths/cross-platform/intrinsics/_index.md
index 24f1ab1774..0b76dc42fd 100644
--- a/content/learning-paths/cross-platform/intrinsics/_index.md
+++ b/content/learning-paths/cross-platform/intrinsics/_index.md
@@ -17,12 +17,13 @@ prerequisites:
 - Some understanding of SIMD concepts.
 - An Arm based machine or [cloud instance](/learning-paths/servers-and-cloud-computing/csp/) running Ubuntu Linux.
 - Optionally, an `x86_64` machine also running Ubuntu.
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 test_images:
 - amd64/ubuntu:latest
 - arm64v8/ubuntu:latest
diff --git a/content/learning-paths/cross-platform/ipexplorer/_index.md b/content/learning-paths/cross-platform/ipexplorer/_index.md
index de81bc3bd0..60b0739cc1 100644
--- a/content/learning-paths/cross-platform/ipexplorer/_index.md
+++ b/content/learning-paths/cross-platform/ipexplorer/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 prerequisites:
     - An Arm account that can access IP Explorer
     - (Optional) A Linux machine with the desired compilers installed
+
+author: Ronan Synnott
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Ronan Synnott
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -46,7 +47,6 @@ further_reading:
         link: https://ipexplorer.arm.com/
         type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/kleidiai-explainer/_index.md b/content/learning-paths/cross-platform/kleidiai-explainer/_index.md
index 98a60bb238..c00acba4de 100644
--- a/content/learning-paths/cross-platform/kleidiai-explainer/_index.md
+++ b/content/learning-paths/cross-platform/kleidiai-explainer/_index.md
@@ -9,15 +9,17 @@ learning_objectives:
     - Describe how basic math operations power Large Language Models.
     - Describe how the KleidiAI micro-kernels speed up Generative AI inference performance.
     - Run a basic C++ matrix multiplication example to showcase the speedup that KleidiAI micro-kernels can deliver.
-    
+
 prerequisites:
     - An Arm-based Linux machine that implements the Int8 Matrix Multiplication (*i8mm*) architecture feature. The example in this Learning Path is run on an AWS Graviton 3 instance. Instructions on setting up an Arm-based server are [found here](/learning-paths/servers-and-cloud-computing/csp/aws/).
     - A basic understanding of linear algebra terminology, such as dot product and matrix multiplication.
+
+author: Zach Lasiuk
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Zach Lasiuk
 ### Tags
 skilllevels: Introductory 
 subjects: ML
@@ -39,8 +41,6 @@ shared_between:
     - servers-and-cloud-computing
     - mobile-graphics-and-gaming
 
-
-
 further_reading:
     - resource:
         title: KleidiAI documentation
@@ -51,9 +51,6 @@ further_reading:
         link: https://community.arm.com/arm-community-blogs/b/ai-and-ml-blog/posts/kleidiai
         type: blog
 
-
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/llm-fine-tuning-for-web-applications/_index.md b/content/learning-paths/cross-platform/llm-fine-tuning-for-web-applications/_index.md
index e8261c0d92..ee776e9da3 100644
--- a/content/learning-paths/cross-platform/llm-fine-tuning-for-web-applications/_index.md
+++ b/content/learning-paths/cross-platform/llm-fine-tuning-for-web-applications/_index.md
@@ -26,17 +26,18 @@ prerequisites:
     - An Android smartphone with the i8mm feature and 16GB of RAM.
     - Basic understanding of machine learning and deep learning. 
     - Familiarity with deep learning frameworks such as PyTorch and Hugging Face Transformers. 
+author: Parichay Das
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
-author: Parichay Das
 
 ### Tags
 skilllevels: Introductory
 subjects: ML
 armips:
     - Neoverse
-    
+
 tools_software_languages:
     - LLM
     - Generative AI
@@ -61,9 +62,6 @@ further_reading:
         title: PyTorch Documentation
         link: https://pytorch.org/docs/stable/index.html
         type: documentation
-   
-
-
 
 ### FIXED, DO NOT MODIFY
 # ================================================================================
diff --git a/content/learning-paths/cross-platform/loop-reflowing/_index.md b/content/learning-paths/cross-platform/loop-reflowing/_index.md
index f8729c215e..8bb70eca5b 100644
--- a/content/learning-paths/cross-platform/loop-reflowing/_index.md
+++ b/content/learning-paths/cross-platform/loop-reflowing/_index.md
@@ -11,12 +11,13 @@ learning_objectives:
 
 prerequisites:
     - An Arm computer running Linux and a recent version of Clang or the GNU compiler (gcc) installed.
+
+author: Konstantinos Margaritis
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Konstantinos Margaritis
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -36,7 +37,6 @@ shared_between:
     - laptops-and-desktops
     - mobile-graphics-and-gaming
 
-
 further_reading:
     - resource:
         title: An update on GNU performance
@@ -55,8 +55,6 @@ further_reading:
         link: https://gcc.gnu.org/projects/tree-ssa/vectorization.html
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/matrix/_index.md b/content/learning-paths/cross-platform/matrix/_index.md
index f1c2522d8c..7d2d27fe3e 100644
--- a/content/learning-paths/cross-platform/matrix/_index.md
+++ b/content/learning-paths/cross-platform/matrix/_index.md
@@ -18,12 +18,12 @@ prerequisites:
     - A C++ compiler with C++17 support.
     - A build system [GNU Make](https://www.gnu.org/software/make/) or [Ninja](https://ninja-build.org/).
     - A documentation generator [Doxygen](https://www.doxygen.nl/).
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author: Arnaud de Grandmaison
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 ### Tags
 skilllevels: Advanced
@@ -59,8 +59,6 @@ further_reading:
         link: https://google.github.io/googletest/quickstart-cmake.html
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/mca-godbolt/_index.md b/content/learning-paths/cross-platform/mca-godbolt/_index.md
index 194553fcd2..4b7c580098 100644
--- a/content/learning-paths/cross-platform/mca-godbolt/_index.md
+++ b/content/learning-paths/cross-platform/mca-godbolt/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
     - Familiarity with Arm assembly.
     - LLVM version 16 or newer, which includes support for Neoverse V2.
+
+author: Asher Dobrescu
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Asher Dobrescu
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -49,8 +50,6 @@ further_reading:
         link: https://developer.arm.com/documentation/109898/0300/?lang=en
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/mcp-ai-agent/_index.md b/content/learning-paths/cross-platform/mcp-ai-agent/_index.md
index 23cfa4df0a..a5e5816da0 100644
--- a/content/learning-paths/cross-platform/mcp-ai-agent/_index.md
+++ b/content/learning-paths/cross-platform/mcp-ai-agent/_index.md
@@ -17,12 +17,13 @@ prerequisites:
     - Familiarity with Python programming and prompt engineering techniques.
     - Basic understanding of Large Language Models (LLMs) and how they are used in local inference.
     - Understanding of AI agents and the OpenAI Agent SDK (or similar frameworks).
+
+author: Andrew Choi
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Andrew Choi
-
 skilllevels: Introductory
 subjects: ML
 armips:
@@ -50,7 +51,6 @@ further_reading:
         link: https://openai.github.io/openai-agents-python/
         type: blog
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/memory-latency/_index.md b/content/learning-paths/cross-platform/memory-latency/_index.md
index d7e72b810b..bb12e35095 100644
--- a/content/learning-paths/cross-platform/memory-latency/_index.md
+++ b/content/learning-paths/cross-platform/memory-latency/_index.md
@@ -12,12 +12,13 @@ learning_objectives:
 
 prerequisites:
     - An Arm computer running Linux with recent versions of Clang or GCC installed.
+
+author: Konstantinos Margaritis
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Konstantinos Margaritis
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -57,7 +58,6 @@ further_reading:
         link: https://colin-scott.github.io/personal_website/research/interactive_latency.html
         type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md b/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md
index fcb8db85c0..0d30420517 100644
--- a/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md
+++ b/content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md
@@ -1,7 +1,6 @@
 ---
 title: Build a Multimodal Retail Restocking Assistant on Armv9 With MNN
 
-    
 minutes_to_complete: 90
 
 who_is_this_for: This Learning Path is for developers and engineers who want to run multimodal image, audio, and text models on Armv9 Linux systems using MNN as a portable, CPU-first inference runtime. It is aimed at readers who are comfortable building software from source and want a reproducible on-device workflow without quantization or heterogeneous scheduling.
@@ -17,12 +16,13 @@ prerequisites:
     - An Armv9 Linux device with at least 32 GB of available disk space, for example a Radxa Orion O6
     - Familiarity with the Linux command line, Git, and building C++ projects with CMake
     - Internet access to download source code, model assets, and sample data
+
+author: Odin Shen
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Odin Shen
-
 ### Tags
 skilllevels: Advanced
 subjects: ML
diff --git a/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md b/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md
index f0a73f8fdf..e3984a4c13 100644
--- a/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md
+++ b/content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md
@@ -23,12 +23,13 @@ prerequisites:
     - Installation of Docker for SME2 emulation (if you don't have SME2 available)
     - Installation of Android Development Studio and adb (if you're targeting an Android phone with SME2 support)
     - Compiler support for SME2 instructions (for example, LLVM 18 or later with SME2 backend support)
+
+author: Arnaud de Grandmaison
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Arnaud de Grandmaison
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -99,7 +100,6 @@ further_reading:
         link: https://github.com/ARM-software/abi-aa
         type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/psa-tfm/_index.md b/content/learning-paths/cross-platform/psa-tfm/_index.md
index 400e8ba518..45080f8785 100644
--- a/content/learning-paths/cross-platform/psa-tfm/_index.md
+++ b/content/learning-paths/cross-platform/psa-tfm/_index.md
@@ -18,10 +18,11 @@ learning_objectives:
 prerequisites:
     - Ubuntu host or access to AWS
     - Optional MPS3 FPGA prototyping board
+author: Ronan Synnott
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
-author: Ronan Synnott
 
 ### Tags
 skilllevels: Introductory
@@ -37,7 +38,6 @@ tools_software_languages:
     - FVP
     - GCC
 
-
 ### Cross-platform metadata only
 shared_path: true
 shared_between:
diff --git a/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md b/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md
index b5ef504fa4..6e7de51fed 100644
--- a/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md
+++ b/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/_index.md
@@ -20,12 +20,13 @@ learning_objectives:
 prerequisites:
     - A machine that can run Python3, Visual Studio Code, and Android Studio. 
     - For the OS, you can use Windows, Linux, or macOS.
+
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dawid Borycki
-
 ### Tags
 skilllevels: Advanced
 subjects: ML
@@ -59,8 +60,6 @@ further_reading:
         link: https://code.visualstudio.com
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/remoteit/_index.md b/content/learning-paths/cross-platform/remoteit/_index.md
index cdbd179f1b..dfc88afb86 100644
--- a/content/learning-paths/cross-platform/remoteit/_index.md
+++ b/content/learning-paths/cross-platform/remoteit/_index.md
@@ -16,12 +16,13 @@ prerequisites:
     - A Windows, macOS, or Linux computer which you will use to configure your devices as well as connect to your remote devices.
     - A device/computer to which you would like remote access. A device can be a Windows, Mac, or Linux computer including development kits such as Raspberry Pi or cloud-hosted such as within Arm Virtual Hardware or within AWS. You will need a method to control this device before Remote.It is deployed which can be local access or access via another remote connectivity solution (Remote Desktop, VPN, etc.)
     - Determine if your device that you would like to access remotely also needs to make connections to other Remote.It devices.
+
+author: Brenda Strech
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Brenda Strech
-
 further_reading:
   - resource:
       title: Developer Documentation
diff --git a/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md b/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
index ddbb51c69c..c854c1d471 100644
--- a/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
+++ b/content/learning-paths/cross-platform/restrict-keyword-c99/_index.md
@@ -12,12 +12,13 @@ learning_objectives:
 
 prerequisites:
     - An Arm computer running Linux OS and a recent version of compiler (Clang or GCC) installed
+
+author: Konstantinos Margaritis
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Konstantinos Margaritis
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -38,21 +39,18 @@ shared_between:
     - laptops-and-desktops
     - servers-and-cloud-computing
     - mobile-graphics-and-gaming
-   
 
 further_reading:
     - resource:
         title: How to use the restrict qualifier in C
         link: https://www.oracle.com/solaris/technologies/solaris10-cc-restrict.html
         type: blog
-       
+
     - resource:
         title: Explore the usage of restrict with Godbolt
         link: https://godbolt.org/z/PxWxjc1oh
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/rust_armds/_index.md b/content/learning-paths/cross-platform/rust_armds/_index.md
index 992f0b5bf9..4a42aadcad 100644
--- a/content/learning-paths/cross-platform/rust_armds/_index.md
+++ b/content/learning-paths/cross-platform/rust_armds/_index.md
@@ -14,12 +14,12 @@ learning_objectives:
 prerequisites:
     - An installation of Arm Development Studio.
     - A basic understanding of Rust programming.
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author: Ronan Synnott
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 ### Tags
 skilllevels: Introductory
diff --git a/content/learning-paths/cross-platform/simd-info-demo/_index.md b/content/learning-paths/cross-platform/simd-info-demo/_index.md
index d320db9bc9..b19a99dfa5 100644
--- a/content/learning-paths/cross-platform/simd-info-demo/_index.md
+++ b/content/learning-paths/cross-platform/simd-info-demo/_index.md
@@ -13,14 +13,15 @@ learning_objectives:
 prerequisites:
     - A basic understanding of SIMD.
     - Access to an Arm platform with a SIMD-supported engine, installed with recent versions of a C compiler such as Clang or GCC.
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author: 
     - Georgios Mermigkis
     - Konstantinos Margaritis
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -48,8 +49,6 @@ further_reading:
         link: https://simd.info
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/simd-loops/_index.md b/content/learning-paths/cross-platform/simd-loops/_index.md
index 95b587e88e..7a72195c62 100644
--- a/content/learning-paths/cross-platform/simd-loops/_index.md
+++ b/content/learning-paths/cross-platform/simd-loops/_index.md
@@ -17,14 +17,15 @@ prerequisites:
     - An AArch64 computer running Linux or macOS. You can use cloud instances, refer to [Get started with Arm-based cloud instances](/learning-paths/servers-and-cloud-computing/csp/) for a list of cloud service providers
     - Some familiarity with SIMD programming and Neon intrinsics
     - Recent toolchains that support SVE/SME (GCC 13+ or Clang 16+ recommended)
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author:
     - Alejandro Martinez Vicente
     - Mohamad Najem
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -101,7 +102,6 @@ further_reading:
         link: https://github.com/ARM-software/abi-aa
         type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/simd-on-rust/_index.md b/content/learning-paths/cross-platform/simd-on-rust/_index.md
index e8357aaf42..115a8a00c0 100644
--- a/content/learning-paths/cross-platform/simd-on-rust/_index.md
+++ b/content/learning-paths/cross-platform/simd-on-rust/_index.md
@@ -15,12 +15,13 @@ learning_objectives:
 
 prerequisites:
     - An Arm-based computer with recent versions of a C compiler (Clang or GCC) and a Rust compiler installed
+
+author: Konstantinos Margaritis
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Konstantinos Margaritis
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -41,7 +42,6 @@ shared_between:
     - servers-and-cloud-computing
     - mobile-graphics-and-gaming
 
-
 further_reading:
     - resource:
         title: Rust std::arch documentation
@@ -60,7 +60,6 @@ further_reading:
         link: https://gendignoux.com/blog/2023/01/05/rust-arm-simd-android.html#implicit-feature-detection-beware-of-target-feature
         type: blog
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md b/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md
index 8f7ec32f81..b0740bc305 100644
--- a/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md
+++ b/content/learning-paths/cross-platform/sme-executorch-profiling/_index.md
@@ -18,17 +18,16 @@ prerequisites:
   - An Apple Silicon macOS host with Python 3.9 or later and CMake 3.29 or later
   - Basic familiarity with ExecuTorch or PyTorch
   - Optionally, an Android device with Armv9 and SME2 support for on-device testing (if used, configure power management settings to ensure consistent performance measurements)
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
-author: Jason Zhu, Tyler Mullenbach, Damien Dooley 
-    
 author:
     - Jason Zhu
     - Tyler Mullenbach
     - Damien Dooley
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Advanced
 subjects: ML
diff --git a/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md b/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md
index fe6174ff0f..95df6c7b42 100644
--- a/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md
+++ b/content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md
@@ -12,18 +12,18 @@ learning_objectives:
   - Deploy the MicroPacs on Linux-based compute systems and scale to cloud or data-center environments
   - Optimize performance for edge-cloud scenarios, enabling near real-time data flows
 
-
 prerequisites:
   - Experience using Linux on embedded or SBC platforms
   - Understanding of container runtimes (containerd) and CNI networking
   - Basic knowledge of communication protocols (MQTT, HTTP, and others)
   - Familiarity with edge-cloud architectures and data-flow orchestration
+
+author: Tinkerblox
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Tinkerblox
-
 ### Tags
 skilllevels: Advanced
 subjects: Containers and Virtualization
@@ -35,7 +35,7 @@ cloud_service_providers:
 
 armips:
   - Neoverse
-  
+
 operatingsystems:
   - Linux
   - other
@@ -51,7 +51,6 @@ further_reading:
       link: https://tinkerblox.io 
       type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/topdown-compare/_index.md b/content/learning-paths/cross-platform/topdown-compare/_index.md
index 1ff11a5549..2fe6f43f57 100644
--- a/content/learning-paths/cross-platform/topdown-compare/_index.md
+++ b/content/learning-paths/cross-platform/topdown-compare/_index.md
@@ -16,13 +16,14 @@ prerequisites:
     - Familiarity with performance analysis on Linux systems using Perf and PMU counters
     - Access to Arm Neoverse V2 and Intel x86 Linux systems to run the code example
     - Basic understanding of CPU pipeline concepts and performance bottlenecks
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author:
     - Jason Andrews
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -59,7 +60,6 @@ further_reading:
         link: /learning-paths/servers-and-cloud-computing/arm_pmu/
         type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/vectorization-comparison/_index.md b/content/learning-paths/cross-platform/vectorization-comparison/_index.md
index 997c8e7a37..769b9c5c6d 100644
--- a/content/learning-paths/cross-platform/vectorization-comparison/_index.md
+++ b/content/learning-paths/cross-platform/vectorization-comparison/_index.md
@@ -10,18 +10,18 @@ who_is_this_for: This is an advanced topic for developers migrating vectorized (
 learning_objectives:
      - Identify how Arm vector extensions including Neon, Scalable Vector Extension (SVE), and Scalable Matrix Extension (SME) map to vector extensions from other architectures
      - Plan a migration strategy using autovectorization, intrinsics, or library substitution
-   
 
 prerequisites:
     - Familiarity with vector extensions, SIMD programming, and compiler intrinsics
     - Access to Linux systems with Neon and SVE support
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author:
     - Jason Andrews
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
diff --git a/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md b/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md
index f3d5de29f2..e9766e4830 100644
--- a/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md
+++ b/content/learning-paths/cross-platform/vectorization-friendly-data-layout/_index.md
@@ -12,12 +12,13 @@ learning_objectives:
 
 prerequisites:
     - An Arm computer running Linux and a recent version of Clang or the GNU compiler (gcc) installed.
+
+author: Konstantinos Margaritis
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Konstantinos Margaritis
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -37,7 +38,6 @@ shared_between:
     - laptops-and-desktops
     - mobile-graphics-and-gaming
 
-
 further_reading:
     - resource:
         title: Array of Structures (AoS), Structure of Arrays (SoA)
@@ -52,8 +52,6 @@ further_reading:
         link: https://arm-software.github.io/acle/neon_intrinsics/advsimd.html 
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md b/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
index 7ad1107b56..3980aecad2 100644
--- a/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
+++ b/content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/_index.md
@@ -18,12 +18,13 @@ prerequisites:
     - An installation of [WindowsPerf](/install-guides/wperf/).
     - An installation of [Visual Studio](/install-guides/vs-woa/).
     - An installation of [Git](/install-guides/git-woa/).
+
+author: Przemyslaw Wirkus
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Przemyslaw Wirkus
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
diff --git a/content/learning-paths/cross-platform/woa_azure/_index.md b/content/learning-paths/cross-platform/woa_azure/_index.md
index fbd93a6389..fd48228793 100644
--- a/content/learning-paths/cross-platform/woa_azure/_index.md
+++ b/content/learning-paths/cross-platform/woa_azure/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
     - An Azure Cloud account.
     - An RDP client to connect to your Windows on Arm instance.  For more info on RDP clients, see [Remote Desktop clients for Remote Desktop Services and remote PCs](https://learn.microsoft.com/en-us/windows-server/remote/remote-desktop-services/clients/remote-desktop-clients) to get started.
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 skilllevels: Introductory
 subjects: Containers and Virtualization
@@ -44,7 +45,6 @@ further_reading:
         link: https://azure.microsoft.com/en-us/blog/azure-virtual-machines-with-ampere-altra-arm-based-processors-generally-available/
         type: blog
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md b/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
index f6991189cf..be12027637 100644
--- a/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
+++ b/content/learning-paths/cross-platform/zenoh-multinode-ros2/_index.md
@@ -7,7 +7,6 @@ description: Learn how to build and deploy distributed Zenoh systems on Arm devi
 
 who_is_this_for: This Learning Path is for robotics developers, industrial automation engineers, and IoT system architects who are building distributed, scalable, and low-latency applications. Whether you're using the Robot Operating System (ROS), developing autonomous systems, or designing multi-node communication frameworks, you can use Eclipse Zenoh on Arm-based platforms, both in the cloud and on local devices like Raspberry Pi.
 
-
 learning_objectives: 
     - Understand Zenoh's architecture and how it integrates pub/sub, storage, querying, and computation models
     - Build and run Zenoh examples on both Arm servers and Raspberry Pi
@@ -16,15 +15,16 @@ learning_objectives:
 prerequisites:
     - At least two local Cortex-A devices running Linux, such as Raspberry Pi 4 or Pi 5. You can also use Arm servers or cloud instances
     - Experience with ROS 2 applications
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author: 
     - Odin Shen
     - William Liang
     - ChenYing Kuo
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 skilllevels: Introductory
 subjects: Performance and Architecture
 armips:
@@ -58,8 +58,6 @@ further_reading:
         link: https://github.com/eclipse-zenoh/zenoh-plugin-ros2dds
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md b/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
index b7aaab5ed7..a39c9f36c2 100644
--- a/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/advanced_soc/_index.md
@@ -16,12 +16,13 @@ prerequisites:
     - Some familiarity with Verilog
     - Basic understanding of System on Chip design
     - A 'Zybo Z7-10' development board 
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
diff --git a/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md b/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
index ef564dd8f7..8b2bb4d144 100644
--- a/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md
@@ -19,12 +19,13 @@ prerequisites:
     - A SEGGER J-Link debug probe (included in the DevKit)
     - A development machine running macOS on Apple Silicon with Visual Studio Code installed
     - An AWS account or access to an Arm-based cloud instance for native Arm compilation
+
+author: Gabriel Peterson
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Gabriel Peterson
-
 skilllevels: Advanced
 subjects: ML
 armips:
diff --git a/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md b/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
index 5719b371e4..4706afd0c3 100644
--- a/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/arduino-pico/_index.md
@@ -13,18 +13,18 @@ learning_objectives:
     - Understand how hardware interrupts are used in embedded systems to respond to external changes
     - Add interrupt handlers to an embedded application
 
-
 prerequisites:
     - The [Arduino IDE with the RP2040 board support package](/install-guides/arduino-pico/) installed on your computer
     - A [Raspberry Pi Pico](https://www.raspberrypi.com/products/raspberry-pi-pico/) board
     - A [PIR sensor](https://www.amazon.com/HiLetgo-HC-SR501-Infrared-Sensor-Arduino/dp/B07KZW86YR/ref=sr_1_3?keywords=pir+sensor&qid=1698432931&sr=8-3) for detecting motion
     - A [peizo-electric buzzer](https://www.amazon.com/mxuteuk-Electronic-Computers-Printers-Components/dp/B07VK1GJ9X/ref=sr_1_4?crid=2FAXYI17HZKDB&keywords=piezo+buzzer&qid=1698432968&sprefix=peizo%2Caps%2C148&sr=8-4) for signaling motion
+
+author: Michael Hall
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Michael Hall
-
 ### Tags
 skilllevels: Introductory
 subjects: RTOS Fundamentals
@@ -45,7 +45,6 @@ further_reading:
         link: https://www.raspberrypi.com/documentation/microcontrollers/raspberry-pi-pico.html
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/armds/_index.md b/content/learning-paths/embedded-and-microcontrollers/armds/_index.md
index 8a4b4dc161..cfe6f1abf9 100644
--- a/content/learning-paths/embedded-and-microcontrollers/armds/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/armds/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 
 prerequisites:
     - Some familiarity with embedded programming is assumed
+
+author: Ronan Synnott
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Ronan Synnott
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -49,8 +50,6 @@ further_reading:
         link: https://developer.arm.com/Tools%20and%20Software/DSTREAM-PT
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/asm/_index.md b/content/learning-paths/embedded-and-microcontrollers/asm/_index.md
index 9eb945b319..328d6c2f61 100644
--- a/content/learning-paths/embedded-and-microcontrollers/asm/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/asm/_index.md
@@ -3,12 +3,13 @@ title: Write Arm Assembler functions
 
 minutes_to_complete: 60
 description: Learn how to write mixed C and assembly programs for Cortex-M microcontrollers using Keil MDK, following Arm Procedure Call Standard conventions.
+
+author: Ronan Synnott
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Ronan Synnott
-
 who_is_this_for: This is an introductory topic for software developers who are interested in programming microcontrollers with C/Assembly.
 
 learning_objectives: 
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
index be2a37a0ac..6af31ed57c 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_balena/_index.md
@@ -17,12 +17,13 @@ prerequisites:
     - An Arm Virtual Hardware account
     - A Linux machine with root access
     - Some familiarity with embedded Linux
+
+author: Michael Hall
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Michael Hall
-
 ### Tags
 skilllevels: Introductory
 
@@ -54,8 +55,6 @@ further_reading:
         link: https://hub.balena.io/
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
index a60f83f965..53dcc92ff1 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_greengrass/_index.md
@@ -15,12 +15,13 @@ prerequisites:
     - An Amazon AWS account
     - An Arm Virtual Hardware account
     - Some familiarity with embedded Linux
+
+author: Michael Hall
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Michael Hall
-
 ### Tags
 skilllevels: Introductory
 
@@ -37,7 +38,6 @@ tools_software_languages:
     - AWS IoT Greengrass
     - Raspberry Pi
 
-
 further_reading:
     - resource:
         title: AWS IoT Greengrass CLI documentation 
@@ -52,8 +52,6 @@ further_reading:
         link: https://docs.aws.amazon.com/greengrass/v2/developerguide/what-is-iot-greengrass.html
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
index 52f4043489..2083af8bbe 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_matter/_index.md
@@ -15,12 +15,13 @@ learning_objectives:
 
 prerequisites:
     - Some familiarity with embedded programming is assumed
+
+author: Ronan Synnott
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Ronan Synnott
-
 ### Tags
 skilllevels: Introductory
 subjects: CI-CD
@@ -47,7 +48,6 @@ further_reading:
         link: https://buildwithmatter.com
         type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
index ed6f9a5ce8..5bbad3d88e 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_ppocr/_index.md
@@ -16,12 +16,13 @@ prerequisites:
     - Some familiarity with embedded programming 
     - Some familiarity with AI/ML software development 
     - An Amazon Web Services(AWS) [account](https://aws.amazon.com/) to subscribe [Arm Virtual Hardware](https://aws.amazon.com/marketplace/pp/prodview-urbpq7yo5va7g) Amazon Machine Image(AMI)
+
+author: Liliya Wu
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Liliya Wu
-
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -54,8 +55,6 @@ further_reading:
         link: https://www.arm.com/blogs/blueprint/baidu-paddlepaddle
         type: blog
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md b/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
index e94465dd1a..698ac367b2 100644
--- a/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/avh_vio/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 prerequisites:
     - A valid [AWS](https://aws.amazon.com/) account
     - Some familiarity with Python
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 skilllevels: Introductory
 subjects: Virtual Hardware
@@ -30,16 +31,12 @@ operatingsystems:
 tools_software_languages:
     - Arm Virtual Hardware
 
-
 further_reading:
     - resource:
         title: AVH Virtual Interfaces
         link: https://arm-software.github.io/AVH/main/simulation/html/group__arm__cmvp.html
         type: documentation
 
-
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md b/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
index bb75c33aef..18a237c01a 100644
--- a/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/azure-iot/_index.md
@@ -19,12 +19,13 @@ learning_objectives:
 prerequisites:
     - A machine with Python 3 and Visual Studio Code installed
     - An active Azure account with sufficient permissions to create resources (such as IoT Hub, Functions, and Cosmos DB)
+
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dawid Borycki
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -53,8 +54,6 @@ further_reading:
         link: https://github.com/Azure-Samples/azure-iot-samples-python
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md b/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md
index 3f5faff16b..cfee1074ad 100644
--- a/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/bare-metal/_index.md
@@ -16,12 +16,13 @@ learning_objectives:
 
 prerequisites:
     - Some familiarity with embedded programming is assumed
+
+author: Ronan Synnott
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Ronan Synnott
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -44,7 +45,6 @@ further_reading:
         link: https://developer.arm.com/documentation/100748
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md b/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
index c6165af514..db095da711 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cloud-native-deployment-on-hybrid-edge-systems/_index.md
@@ -12,16 +12,16 @@ learning_objectives:
     - Build a firmware container image.
     - Build the hybrid-runtime components.
 
-
 prerequisites:
     - A valid account with [Arm Virtual Hardware](https://app.avh.arm.com/login)
     - An Arm Linux host machine (if you want to build your own runtime and container image)
+
+author: Basma El Gaabouri
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Basma El Gaabouri
-
 ### Tags
 skilllevels: Introductory
 subjects: Containers and Virtualization
@@ -36,7 +36,6 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
 further_reading:
     - resource:
         title: K3s Quick start Guide 
@@ -51,8 +50,6 @@ further_reading:
         link: https://github.com/smarter-project/hybrid-runtime/tree/main
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md
index 34124b9d35..c45b4e2db9 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 prerequisites:
     - An installation of [Arm Keil MDK](/install-guides/mdk/) or [Arm Development Studio](/install-guides/armds/) (MDK recommended)
     - Some familiarity with CMSIS is assumed
+
+author: Ronan Synnott
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Ronan Synnott
-
 ### Tags
 skilllevels: Introductory
 subjects: RTOS Fundamentals
@@ -41,7 +42,6 @@ further_reading:
         link: https://www.keil.com/pack/doc/compiler/EventRecorder/html/index.html
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md
index 191d9edf1f..b43a26a1db 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cmsis_rtx_vs/_index.md
@@ -15,12 +15,13 @@ learning_objectives:
 prerequisites:
     - Installation of [Arm Keil Studio for VS Code](/install-guides/keilstudio_vs/)
     - Some familiarity with CMSIS is assumed
+
+author: Ronan Synnott
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Ronan Synnott
-
 ### Tags
 skilllevels: Introductory
 subjects: RTOS Fundamentals
@@ -43,7 +44,6 @@ further_reading:
         link: https://www.keil.com/pack/doc/compiler/EventRecorder/html/index.html
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md b/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
index f523ed22c3..64efe63c6f 100644
--- a/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/_index.md
@@ -7,7 +7,6 @@ minutes_to_complete: 45
 
 who_is_this_for: This is an advanced topic for developers looking to integrate the CMSIS-DSP library into their applications using Python.
 
-
 learning_objectives:
     - Use the CMSIS-DSP Python package to prototype DSP algorithms.
     - Understand how the Python API maps to the C implementation.
@@ -18,12 +17,13 @@ prerequisites:
     - Working knowledge of C.
     - Prior exposure to CMSIS-DSP.
     - Python installed on your machine.
+
+author: Christophe Favergeon
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Christophe Favergeon
-
 ### Tags
 skilllevels: Advanced
 subjects: Libraries
@@ -63,7 +63,6 @@ further_reading:
         link: https://github.com/ARM-software/CMSIS-Stream
         type: Open-source project
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md b/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
index 9e971025a2..a41dc5f704 100644
--- a/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/context-switch-cortex-m/_index.md
@@ -15,12 +15,13 @@ learning_objectives:
 
 prerequisites:
     - Basic knowledge and familiarity with Cortex-M processors.
+
+author: Uma Ramalingam
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Uma Ramalingam
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
diff --git a/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md b/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
index 9dbc1ac96e..7fbec6004c 100644
--- a/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/coverage_mdk/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 
 prerequisites:
     - Basic familiarity with Keil MDK
+
+author: Ronan Synnott
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Ronan Synnott
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture 
@@ -45,7 +46,6 @@ further_reading:
         link: https://www.youtube.com/watch?v=XGmSCVgb6EM
         type: video
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md b/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
index eb14c16cf5..22bf1be008 100644
--- a/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/device-connect-d2d/_index.md
@@ -3,7 +3,6 @@ title: Device-to-Device communication with Device Connect
 
 minutes_to_complete: 25
 
-
 who_is_this_for: This is an introductory topic for developers wiring up heterogeneous edge fleets, where devices need a shared way to find each other and a shared way to be controlled by agents. Device Connect provides this communication protocol between agents and devices, and standardizes how devices from different vendors advertise themselves and exchange structured messages, so both peer devices and AI agents can discover and invoke them through the same driver model. You'll use a Raspberry Pi 5 as the example primary edge device, but the same flow works with another device or with your development machine acting as a simulated device.
 
 learning_objectives:
@@ -16,16 +15,15 @@ prerequisites:
     - Basic familiarity with Python and the command line
     - A Raspberry Pi 5, another Linux device, or your development machine to use as the example primary device
     - A development machine on the same local network if you run the example across two machines
-    
-    
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author:
     - Kavya Sri Chennoju
     - Annie Tallund
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: Libraries
diff --git a/content/learning-paths/embedded-and-microcontrollers/device-connect-server/_index.md b/content/learning-paths/embedded-and-microcontrollers/device-connect-server/_index.md
index 83bd1b9e4a..400a795cd6 100644
--- a/content/learning-paths/embedded-and-microcontrollers/device-connect-server/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/device-connect-server/_index.md
@@ -24,6 +24,10 @@ author:
     - Kavya Sri Chennoju
     - Annie Tallund
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: Libraries
diff --git a/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md b/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md
index 02034f15b3..d6ff3c720a 100644
--- a/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/device-connect-strands/_index.md
@@ -19,15 +19,14 @@ prerequisites:
     - A development machine with git installed
     - Basic familiarity with command-line tools
     - (Optional) A Raspberry Pi for testing a full device-to-device (D2D) setup
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author: 
     - Annie Tallund
     - Kavya Sri Chennoju
 
-
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 ### Tags
 skilllevels: Introductory
diff --git a/content/learning-paths/embedded-and-microcontrollers/docker/_index.md b/content/learning-paths/embedded-and-microcontrollers/docker/_index.md
index fc2e79ce1e..f50fe041ac 100644
--- a/content/learning-paths/embedded-and-microcontrollers/docker/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/docker/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
     - Test the image
 
 prerequisites:
+
+author: Ronan Synnott
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Ronan Synnott
-
 ### Tags
 skilllevels: Introductory
 subjects: Containers and Virtualization
@@ -35,7 +36,6 @@ tools_software_languages:
     - Arm Compiler for Embedded
     - Arm Fast Models
 
-
 further_reading:
     - resource:
         title: Docker documentation
@@ -46,7 +46,6 @@ further_reading:
         link: /learning-paths/cross-platform/docker/
         type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/edge/_index.md b/content/learning-paths/embedded-and-microcontrollers/edge/_index.md
index 574a306905..726e65c2cb 100644
--- a/content/learning-paths/embedded-and-microcontrollers/edge/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/edge/_index.md
@@ -18,11 +18,13 @@ prerequisites:
   - An [Edge Impulse Studio](https://studio.edgeimpulse.com/signup) account.
   - The [Arduino IDE](/install-guides/arduino-pico/) with the RP2040 board support package installed on your computer.
   - An [Arduino Nano RP2040 Connect board](https://store.arduino.cc/products/arduino-nano-rp2040-connect-with-headers).
+
+author: Bright Edudzi Gershon Kordorwu
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Bright Edudzi Gershon Kordorwu
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -36,7 +38,6 @@ tools_software_languages:
 operatingsystems:
     - Baremetal
 
-
 further_reading:
     - resource:
         title: TinyML brings AI to smallest Arm devices
@@ -51,8 +52,6 @@ further_reading:
         link: https://docs.edgeimpulse.com/docs/readme/for-beginners
         type: doc
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/_index.md b/content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/_index.md
index 4ae50aadeb..9a463fe599 100644
--- a/content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/_index.md
@@ -23,10 +23,11 @@ prerequisites:
     - A supported Arm-based edge device (Raspberry Pi 5, Nvidia Jetson, Qualcomm Dragonwing QC6490) or an AWS EC2 Arm instance
     - An SSH client and familiarity with the Linux command line
     - Basic understanding of ML concepts
+author: Doug Anson
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
-author: Doug Anson
 
 ### Tags
 skilllevels: Introductory
diff --git a/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md b/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md
index f2c1453939..b2285a9bb8 100644
--- a/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/img_nn_stcube/_index.md
@@ -15,12 +15,13 @@ prerequisites:
     - Familiarity with ML concepts
     - Familiarity with C programming on microcontrollers
     - STM32 B-L475E-IOT01A2 board
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 skilllevels: Advanced
 subjects: ML
@@ -42,7 +43,6 @@ further_reading:
         link: https://www.st.com/resource/en/user_manual/um2052-getting-started-with-stm32-mcu-discovery-kits-software-development-tools-stmicroelectronics.pdf
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/intro/_index.md b/content/learning-paths/embedded-and-microcontrollers/intro/_index.md
index 7b44fff018..de8507e741 100644
--- a/content/learning-paths/embedded-and-microcontrollers/intro/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/intro/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 
 prerequisites:
     - None
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -30,7 +31,6 @@ operatingsystems:
     - RTOS
 tools_software_languages:
 
-
 further_reading:
     - resource:
         title: Raspberry Pi Pico
@@ -41,7 +41,6 @@ further_reading:
         link: https://microbit.org/
         type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md b/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
index 9045c14f30..d87efa5b31 100644
--- a/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/_index.md
@@ -16,12 +16,13 @@ learning_objectives:
 prerequisites:
     - Basic knowledge of Machine Learning concepts
     - A Linux computer
+
+author: Dominica Abena O. Amanfo
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dominica Abena O. Amanfo
-
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -56,9 +57,6 @@ further_reading:
         link: https://developer.arm.com/documentation/109267/0101
         type: documentation
 
-
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md b/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
index 70f04af3ad..1783fba460 100644
--- a/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/iot-sdk/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
     - Some familiarity with embedded programming
     - An AWS account (required for Arm Virtual Hardware)
+
+author: Ronan Synnott
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Ronan Synnott
-
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -35,7 +36,6 @@ tools_software_languages:
     - FVP
     - Arm Compiler for Embedded
 
-
 further_reading:
     - resource:
         title: Open-IoT-SDK
@@ -50,7 +50,6 @@ further_reading:
         link: https://www.arm.com/products/silicon-ip-subsystems/
         type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md b/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
index 1efb596698..7e612b35b4 100644
--- a/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/jetson_object_detection/_index.md
@@ -15,12 +15,13 @@ prerequisites:
     - A [Jetson Orin Nano](https://developer.nvidia.com/embedded/learn/jetson-orin-nano-devkit-user-guide/index.html)
     - A microSD card (64GB UHS-1 or larger is recommended)
     - A MIPI CSI-2 camera, with a 22 pin connector on at least one end
+
+author: Gabriel Peterson
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Gabriel Peterson
-
 ### Tags
 skilllevels: Introductory
 
@@ -37,7 +38,6 @@ tools_software_languages:
     - TensorRT
     - Docker
 
-
 further_reading:
     - resource:
         title: Jetson Inference
@@ -52,8 +52,6 @@ further_reading:
         link: https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md b/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
index c12490793f..c4a36ed9ae 100644
--- a/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/keilstudiocloud/_index.md
@@ -14,12 +14,12 @@ learning_objectives:
 prerequisites:
     - Some familiarity with embedded programming is assumed
     - An [Arm Account](https://developer.arm.com/register) is required
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author: Christopher Seidl 
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 ##### Tags
 skilllevels: Introductory
@@ -52,8 +52,6 @@ further_reading:
         link: https://community.arm.com/arm-community-blogs/b/tools-software-ides-blog/posts/which-keil-tool-should-i-care-about
         type: blog
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md b/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
index b049587a6e..6f20fc3b2b 100644
--- a/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/linux-nxp-board/_index.md
@@ -2,7 +2,7 @@
 title: Use Linux on the NXP FRDM i.MX 93 board
 
 description: Learn how to boot and configure the NXP FRDM i.MX 93 Arm board with Linux, create a user with sudo access, connect to WiFi using ConnMan, and transfer files over the network.
-    
+
 minutes_to_complete: 120
 
 who_is_this_for: This is an introductory topic for embedded developers and ML engineers who want to boot an NXP FRDM i.MX 93 board, connect over serial, enable WiFi, and transfer files for on-device development on Arm.
@@ -19,12 +19,13 @@ prerequisites:
     - A computer running Linux or macOS.
     - A USB-C cable for the board's **DBG** serial connection.
     - A USB-C power supply/cable for the board's **POWER** port.
+
+author: Waheed Brown
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Waheed Brown
-
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -60,9 +61,6 @@ further_reading:
         link: https://developer.arm.com/documentation/109267/0101
         type: documentation
 
-
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md b/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md
index b0fd05bbfd..9e2b64f77a 100644
--- a/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 prerequisites:
     - A Linux-based x86-64 host computer with Arm Development Studio installed.
     - Basic understanding of Assembly and C programming.
+
+author: Qixiang Xu
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Qixiang Xu
-
 ### Tags
 skilllevels: Introductory
 subjects: Embedded Linux
@@ -45,8 +46,6 @@ further_reading:
         link: https://developer.arm.com/documentation/100964/1128/?lang=en
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md b/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md
index 377b72adcb..c5c5735618 100644
--- a/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/llama-python-cpu/_index.md
@@ -15,12 +15,13 @@ learning_objectives:
 
 prerequisites:
     - A Raspberry Pi 5 running Raspberry Pi OS.
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -49,8 +50,6 @@ further_reading:
         link: https://github.com/abetlen/llama-cpp-python
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/migration/_index.md b/content/learning-paths/embedded-and-microcontrollers/migration/_index.md
index 9fc81546b7..57a4483647 100644
--- a/content/learning-paths/embedded-and-microcontrollers/migration/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/migration/_index.md
@@ -16,12 +16,13 @@ prerequisites:
     - Introductory understanding of software containers
     - Knowledge about building workflows
     - Access to an aarch64 or x86_64 machine running Linux
+
+author: Kasper Mecklenburg
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Kasper Mecklenburg
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
diff --git a/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md b/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md
index 756beaaa2d..3db1dccf38 100644
--- a/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/mlek/_index.md
@@ -14,18 +14,18 @@ learning_objectives:
 prerequisites:
     - Some familiarity with embedded programming
     - A Linux host machine running Ubuntu
+
+author: Ronan Synnott
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Ronan Synnott
-
 ### RS: Learning Path hidden until AWS instance updated
 draft: false
 cascade:
     draft: false
 
-
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -51,7 +51,6 @@ further_reading:
         link: https://devsummit.arm.com/flow/arm/devsummit22/sessions-catalog/page/sessions/session/1656589322296001Tbrk
         type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md b/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md
index 1cf18e4037..59dc0e47c5 100644
--- a/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/nav-mlek/_index.md
@@ -7,12 +7,13 @@ armips:
 - Cortex-M
 - Ethos-U
 - Corstone
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 learning_objectives:
 - Understand and select physical and virtual hardware targets for ML application development with Cortex-M and Ethos-U
 - Identify and install software tools used for machine learning applications on microcontrollers
@@ -55,7 +56,6 @@ further_reading:
         link: https://armkeil.blob.core.windows.net/developer/Files/pdf/ethos/Arm_ML_on_Cortex-M_Microcontrollers_v2.pdf
         type: documentation
 
-
 weight: 1
 
 layout: learningpathall
diff --git a/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md b/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md
index 55dfa63990..fb9818cd3e 100644
--- a/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/new_debug_targets_armds/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 
 prerequisites:
     - Some familiarity with embedded debug
+
+author: Ronan Synnott
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Ronan Synnott
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -48,8 +49,6 @@ further_reading:
         link: https://developer.arm.com/Tools%20and%20Software/DSTREAM-PT
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md b/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
index c27a8de00e..166ed3b25f 100644
--- a/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
@@ -17,14 +17,15 @@ prerequisites:
     - Completion of [Use Linux on an NXP FRDM i.MX 93 board](/learning-paths/embedded-and-microcontrollers/linux-nxp-board/) (Linux setup, login access, and file transfer)
     - Basic knowledge of Machine Learning concepts
     - A host computer to compile ExecuTorch libraries
+
+author:
+    - Waheed Brown
+    - Fidel Makatia Omusilibwa
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: 
-- Waheed Brown
-- Fidel Makatia Omusilibwa
-
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -59,13 +60,9 @@ further_reading:
         link: https://developer.arm.com/documentation/109267/0101
         type: documentation
 
-
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
-
diff --git a/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md b/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
index 306f0124c9..e988db78f2 100644
--- a/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/pack-migration-cmsis-v6/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
     - A good understanding of [CMSIS-Packs](https://open-cmsis-pack.github.io/Open-CMSIS-Pack-Spec/main/html/index.html).
     - A CMSIS-Pack that contains device support and was created for CMSIS v5.
+
+author: Christopher Seidl
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Christopher Seidl
-
 ### Tags
 skilllevels: Advanced
 subjects: Libraries
@@ -32,7 +33,6 @@ operatingsystems:
     - Baremetal
     - RTOS
 
-
 further_reading:
     - resource:
         title: Create a Device Family Pack - Hands-On Example
@@ -43,8 +43,6 @@ further_reading:
         link: https://developer.arm.com/documentation/100068/latest/Migrating-from-Arm-Compiler-5-to-Arm-Compiler-for-Embedded-6
         type: Documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/pqc_pqm4/_index.md b/content/learning-paths/embedded-and-microcontrollers/pqc_pqm4/_index.md
old mode 100644
new mode 100755
index 12a1903085..7c78465859
--- a/content/learning-paths/embedded-and-microcontrollers/pqc_pqm4/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/pqc_pqm4/_index.md
@@ -1,6 +1,6 @@
 ---
 title: Implement post-quantum cryptography on Arm Cortex-M4
-    
+
 description: Learn how to implement and test post-quantum cryptographic algorithms on Arm Cortex-M4 microcontrollers using the pqm4 library.
 
 minutes_to_complete: 120
@@ -23,6 +23,10 @@ author:
     - Akash Malik
     - Odin Shen
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Advanced
 subjects: Security
diff --git a/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md b/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md
index d3bd705ba5..1a8bdb5d25 100644
--- a/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/project-migration-cmsis-v6/_index.md
@@ -15,12 +15,13 @@ learning_objectives:
 prerequisites:
     - A CMSIS v5 based project.
     - A basic understanding of the CMSIS-Pack system.
+
+author: Christopher Seidl
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Christopher Seidl
-
 ### Tags
 skilllevels: Advanced
 subjects: Libraries
@@ -33,7 +34,6 @@ operatingsystems:
     - Baremetal
     - RTOS
 
-
 further_reading:
     - resource:
         title: Keil Studio User's Guide
diff --git a/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md b/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md
index b4b2ee078f..30ae311260 100644
--- a/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md
@@ -18,12 +18,13 @@ prerequisites:
   - An Arm-based single board computer (for example, Raspberry Pi 5 running Raspberry Pi OS)
   - Electronic components (breadboard, LEDs, resistors, jumper wires) for GPIO testing
   - Familiarity with Python programming, Raspberry Pi GPIO pinout, and basic electronics
+
+author: Fidel Makatia Omusilibwa
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Fidel Makatia Omusilibwa
-
 skilllevels: Introductory
 subjects: ML
 armips:
diff --git a/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md b/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md
index c1ca946b9b..8aabaf2305 100644
--- a/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/raspberry_pi_chatgpt_bot/_index.md
@@ -19,12 +19,13 @@ prerequisites:
     - A Raspberry Pi 4 or 5 (earlier models may also work)
     - A microSD card with at least 16GB of storage
     - A Linux compatible USB microphone and USB speakers or a USB audio device with a microphone and speakers
+
+author: Gabriel Peterson
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Gabriel Peterson
-
 ### Tags
 skilllevels: Introductory
 
@@ -41,7 +42,6 @@ tools_software_languages:
     - Porcupine
     - Python
 
-
 further_reading:
     - resource:
         title: OpenAI Documentation
@@ -52,8 +52,6 @@ further_reading:
         link: https://picovoice.ai/docs/porcupine/
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
index 0a1c202501..39129a5d2f 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi-llama3/_index.md
@@ -14,17 +14,16 @@ learning_objectives:
     - Describe how to run Llama 3 on a Raspberry Pi 5 using ExecuTorch.
     - Describe techniques for running large language models in an embedded environment.
 
-
-
 prerequisites:
     - An Arm Linux machine or an [Arm cloud instance](/learning-paths/servers-and-cloud-computing/csp/).
     - A Raspberry Pi 5.
+
+author: Annie Tallund
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Annie Tallund
-
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -39,8 +38,6 @@ tools_software_languages:
     - Hugging Face
     - ExecuTorch
 
-
-
 further_reading:
     - resource:
         title: Practical AI for the Raspberry Pi
@@ -59,9 +56,6 @@ further_reading:
         link: https://dev-discuss.pytorch.org/t/run-llama3-8b-on-a-raspberry-pi-5-with-executorch/2048
         type: website
 
-
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
index 4afeecde5a..72e3cbcc95 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi-mxnet/_index.md
@@ -16,12 +16,13 @@ learning_objectives:
 prerequisites:
     - An Arm computer running Linux. Cloud instances can be used, refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
     - A Raspberry Pi 3 or 4 board
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ### Tags
 skilllevels: Advanced
 subjects: Containers and Virtualization
@@ -40,7 +41,6 @@ further_reading:
         link: https://mxnet.apache.org/versions/1.2.1/tutorials/index.html 
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md
index fdbe493716..1dc7e8042f 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
     - A Raspberry Pi 4 board
     - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider.
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ### Tags
 skilllevels: Introductory
 subjects: Embedded Linux
@@ -43,7 +44,6 @@ further_reading:
         link: https://developer.arm.com/documentation/102561
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md b/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
index 43e9df2e7d..5b18d526c9 100644
--- a/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/rpi_pico/_index.md
@@ -16,12 +16,13 @@ learning_objectives:
 prerequisites:
     - Raspberry Pi Pico board.
     - Raspberry Pi 3, 4, 400, or 5 as a development computer.
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -42,7 +43,6 @@ further_reading:
         link: https://www.raspberrypi.com/documentation/microcontrollers/raspberry-pi-pico.html
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md b/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
index 1d424b1365..ac8d5ab648 100644
--- a/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/_index.md
@@ -3,7 +3,6 @@ title: Profile the Linux kernel with Arm Streamline
 
 description: Learn how to profile Linux kernel modules using Arm Streamline to identify performance bottlenecks, analyze both out-of-tree and in-tree modules, and use Statistical Profiling Extension (SPE) for deeper insights.
 
-
 minutes_to_complete: 60
 
 who_is_this_for: This is an advanced topic for developers and performance engineers interested in profiling Linux kernel performance.
@@ -19,12 +18,13 @@ prerequisites:
     - Basic understanding of Linux kernel development and module programming
     - Arm-based Linux target device (such as a Raspberry Pi, BeagleBone, or similar board) with Secure Shell (SSH) access
     - A host machine that meets [Buildroot system requirements](https://buildroot.org/downloads/manual/manual.html#requirement)
+
+author: Yahya Abouelseoud
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Yahya Abouelseoud
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -38,8 +38,6 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
-
 further_reading:
     - resource:
         title: Streamline user guide 
@@ -54,8 +52,6 @@ further_reading:
         link: https://developer.arm.com/Additional%20Resources/Video%20Tutorials/Arm%20Mali%20GPU%20Training%20-%20EP3-3
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md b/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
index 4770b9b24e..4be0669fdc 100644
--- a/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/tflow_nn_stcube/_index.md
@@ -15,12 +15,13 @@ prerequisites:
     - Familiarity with ML concepts
     - Familiarity with C programming on microcontrollers
     - STM32 B-L475E-IOT01A2 board
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 skilllevels: Advanced
 subjects: ML
@@ -42,7 +43,6 @@ further_reading:
         link: https://www.st.com/resource/en/user_manual/um2052-getting-started-with-stm32-mcu-discovery-kits-software-development-tools-stmicroelectronics.pdf
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md b/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md
index 1242047bb7..4f9066437b 100644
--- a/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/tfm/_index.md
@@ -8,19 +8,19 @@ minutes_to_complete: 15
 who_is_this_for: This is an introductory topic for software developers new to Trusted
   Firmware-M.
 
-
 learning_objectives:
 - Build and run the reference TF-M tests and example application.
 
 prerequisites:
 - Some familiarity with embedded C programming
 - A machine running Ubuntu Linux
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 test_images:
 - armswdev/arm-tools:bare-metal-compilers
 test_maintenance: false
@@ -57,7 +57,6 @@ further_reading:
         link: https://www.psacertified.org/
         type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md b/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md
index 1e8261d96c..a64b5ca048 100644
--- a/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md
@@ -18,12 +18,13 @@ prerequisites:
   - Familiarity with Python and the PyTorch library
   - Completion of the Learning Path [Introduction to TinyML on Arm using PyTorch and ExecuTorch](/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/)
   - An x86 Linux host machine or VM running Ubuntu 22.04 or later
+
+author: Dominica Abena O. Amanfo
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dominica Abena O. Amanfo
-
 ### Tags
 skilllevels: Introductory
 subjects: ML
diff --git a/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md b/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md
index 3db1b17e06..715a9755b0 100644
--- a/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/trustzone_nxp_lpc/_index.md
@@ -17,12 +17,13 @@ prerequisites:
     - Familiar with C programming on microcontrollers
     - Comfortable with Windows 
     - NXP LPCXpresso55S69 board
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 skilllevels: Introductory
 subjects: Security 
@@ -45,7 +46,6 @@ further_reading:
         link: https://community.nxp.com/t5/Blogs/Using-LPC55S69-SDK-TrustZone-examples-with-MCUXpresso-IDE-v11-0/ba-p/1131075
         type: blog
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md b/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
index 78ebcc6ef6..fe5492df3a 100644
--- a/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/_index.md
@@ -23,12 +23,13 @@ prerequisites:
     - 18-8 stainless steel socket head screw. 4 per card. [Example part](https://www.mcmaster.com/91292A016/)
     - 18-8 stainless steel hex nut. 4 per card. [Example part](https://www.mcmaster.com/91828A113/)
     - PETG filament. Others can work, but PETG allows some flex without the risk of snapping
+
+author: Gabriel Peterson
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Gabriel Peterson
-
 ### Tags
 skilllevels: Introductory
 
@@ -53,8 +54,6 @@ further_reading:
         link: https://all3dp.com/2/3d-printing-for-beginners-all-you-need-to-know-to-get-started/
         type: blog
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md b/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
index 6f807733a4..e627e46925 100644
--- a/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/uv_debug/_index.md
@@ -6,12 +6,13 @@ description: Learn how to debug microcontrollers using µVision with basic run/s
 
 minutes_to_complete: 90
 # Always measured in minutes. Should be an integer, to complete the learning path (not read it).
+
+author: Christopher Seidl
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Christopher Seidl
-
 who_is_this_for: >
     This is an advanced topic for software developers who want to debug microcontrollers using µVision.
 # One sentence that should indicate exactly who the target audience is (developers in X industries using Y tools/software for Z use-case).
@@ -35,7 +36,6 @@ prerequisites:
     # Previous learning paths                                   (The Learning Path: Getting Started with Arm Virtual Hardware)
     # Particular tools/environments already being initialized   (An EC2 instance with AVH installed)
 
-
 ##### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -48,8 +48,6 @@ tools_software_languages:
     - Keil MDK
     - FVP
 
-
-
 further_reading:
     - resource:
         title: Keil MDK
@@ -72,7 +70,6 @@ further_reading:
         link: https://keil.arm.com/boards
         type: website
 
-
 # ================================================================================
 #       FIXED, DO NOT MODIFY
 # ================================================================================
diff --git a/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md b/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
index b6d6229b4a..b924b0b0ca 100644
--- a/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/uvprojx-conversion/_index.md
@@ -17,12 +17,13 @@ prerequisites:
     - Install [µVision](/install-guides/mdk/) on your machine.
     - Install [uv2csolution](https://arm-software.github.io/MDK-Toolbox/01_installation/) for the command line flow.
     - The &micro;Vision project must use Arm Compiler 6 as the default toolchain. Arm Compiler 5 is not supported.
+
+author: Christopher Seidl
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Christopher Seidl
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -36,8 +37,6 @@ operatingsystems:
     - Linux
     - macOS
 
-
-
 further_reading:
     - resource:
         title: Keil Studio User's Guide
@@ -52,8 +51,6 @@ further_reading:
         link: https://keil.arm.com
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md b/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
index f82edf8a7e..eb12339a5e 100644
--- a/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/vcpkg-tool-installation/_index.md
@@ -18,12 +18,13 @@ learning_objectives:
 prerequisites:
     - A basic understanding of the [development tools for Arm Cortex-M](https://developer.arm.com/Tools%20and%20Software/)
     - Command line access to your machine
+
+author: Christopher Seidl
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Christopher Seidl
-
 ### Tags
 skilllevels: Advanced
 subjects: CI-CD
@@ -39,7 +40,6 @@ operatingsystems:
     - Windows
     - macOS
 
-
 further_reading:
     - resource:
         title: vcpkg documentation
@@ -54,8 +54,6 @@ further_reading:
         link: https://github.com/Arm-Examples#cmsis-toolbox-2.0.0-examples
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md b/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md
index f2409a9f59..a695440bc9 100644
--- a/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md
@@ -16,12 +16,13 @@ learning_objectives:
 prerequisites:
     - Familiarity with basic machine learning concepts
     - A Linux or macOS computer with Python 3 installed
+
+author: Waheed Brown
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Waheed Brown
-
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -58,9 +59,6 @@ further_reading:
         link: https://developer.arm.com/documentation/109267/0101
         type: documentation
 
-
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md b/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md
index 1b042f8dee..42d07a373a 100644
--- a/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/yocto_qemu/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
     - Some familiarity with embedded Linux.
     - A linux machine running Ubuntu 22.04 with at least 60 GB of disk space.
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 skilllevels: Introductory
 subjects: Embedded Linux
@@ -47,8 +48,6 @@ further_reading:
         link: https://www.qemu.org/docs/master/
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md b/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md
index bf637eeebd..fa7996fb11 100644
--- a/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/yolo-on-himax/_index.md
@@ -19,15 +19,16 @@ prerequisites:
     - A Flexible Printed Circuit (FPC) cable.
     - A USB-C cable.
     - An x86 Linux machine, or a Mac running macOS.
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author:
     - Chaodong Gong
     - Alex Su
     - Kieran Hejmadi
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -43,9 +44,6 @@ operatingsystems:
     - Linux
     - macOS
 
-
-
-
 further_reading:
     - resource:
         title: Grove Vision AI Module V2 User Documentation
@@ -56,7 +54,6 @@ further_reading:
         link: https://www.himax.com.tw/products/wiseeye-ai-sensing/wiseeye2-ai-processor/
         type: blog
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md b/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md
index ab887e4269..5176b382ff 100644
--- a/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md
@@ -8,19 +8,19 @@ minutes_to_complete: 30
 who_is_this_for: This is an introductory topic for software developers getting started
   with the Zephyr RTOS.
 
-
 learning_objectives:
 - Build and run Zephyr applications on the Corstone-300
 
 prerequisites:
 - Some familiarity with embedded C programming
 - A Linux machine running Ubuntu, or an AWS account to use [Arm Virtual Hardware](https://www.arm.com/products/development-tools/simulation/virtual-hardware)
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 test_images:
 - amd64/ubuntu:latest
 test_link: null
@@ -52,7 +52,6 @@ further_reading:
         link: https://docs.zephyrproject.org/latest/boards/arm/index.html
         type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/embedded-and-microcontrollers/zephyr_cs320_mps4/_index.md b/content/learning-paths/embedded-and-microcontrollers/zephyr_cs320_mps4/_index.md
index 23052cc10f..e43f6abbec 100644
--- a/content/learning-paths/embedded-and-microcontrollers/zephyr_cs320_mps4/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/zephyr_cs320_mps4/_index.md
@@ -20,6 +20,10 @@ prerequisites:
 
 author: Sue Wu
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 skilllevels: Introductory
 subjects: RTOS Fundamentals
 armips:
diff --git a/content/learning-paths/embedded-and-microcontrollers/zephyr_shell/_index.md b/content/learning-paths/embedded-and-microcontrollers/zephyr_shell/_index.md
index b82b13b22f..e60ed6ce2b 100644
--- a/content/learning-paths/embedded-and-microcontrollers/zephyr_shell/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/zephyr_shell/_index.md
@@ -27,6 +27,10 @@ author:
     - Odin Shen
     - Akash Malik
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 skilllevels: Introductory
 subjects: RTOS Fundamentals
 armips:
diff --git a/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md b/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md
index f89c98de7c..4faa59e558 100644
--- a/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/zephyr_vsworkbench/_index.md
@@ -19,16 +19,15 @@ prerequisites:
     - Visual Studio Code
     - A Cortex-M development board
     - Windows 10+ (64-bit), macOS with Homebrew, or Linux (preferably Ubuntu 20.04+)
-    
-    
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author: 
     - Ayoub Bourjilat
     - Odin Shen
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 skilllevels: Introductory
 subjects: RTOS Fundamentals
 armips:
diff --git a/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md b/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md
index 229acd3a0d..fe859d63bc 100644
--- a/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md
+++ b/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md
@@ -17,12 +17,13 @@ learning_objectives:
 prerequisites:
     - A ChromeOS device with the Linux development environment enabled. The Lenovo Chromebook Plus 14 is recommended. 
     - Basic knowledge of the Linux command line
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ### Tags
 skilllevels: Introductory
 subjects: Containers and Virtualization
diff --git a/content/learning-paths/laptops-and-desktops/dgx_persistent_agent/_index.md b/content/learning-paths/laptops-and-desktops/dgx_persistent_agent/_index.md
index 2c42b59062..b9f3a5d89d 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_persistent_agent/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_persistent_agent/_index.md
@@ -19,6 +19,10 @@ prerequisites:
 
 author: Odin Shen
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Advanced
 subjects: ML
@@ -49,7 +53,6 @@ further_reading:
         link: /learning-paths/laptops-and-desktops/dgx_spark_llamacpp/
         type: Learning Path
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md
index 565261dae4..4d452ad067 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md
@@ -18,9 +18,6 @@ prerequisites:
     - Familiarity with Linux command-line tools
     - Experience with Python scripting and virtual environments
     - Basic understanding of reinforcement learning concepts (rewards, policies, episodes)
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author:
     - Johnny Nunez
@@ -28,6 +25,10 @@ author:
     - Asier Arranz
     - Raymond Lo
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Advanced
 subjects: ML
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md
index ea810e0fce..880021a031 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md
@@ -19,12 +19,13 @@ prerequisites:
     - Understanding of CUDA programming basics and GPU/CPU compute concepts
     - Basic knowledge of quantized large language models (LLMs) and machine learning inference
     - Experience building software from source using CMake and make
+
+author: Odin Shen
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Odin Shen
-
 ### Tags
 skilllevels: Introductory
 subjects: ML
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md
index eaa9e580b9..1de2af16c0 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md
@@ -15,12 +15,13 @@ learning_objectives:
 
 prerequisites:
     - An NVIDIA DGX Spark system with at least 15 GB of available disk space
+
+author: Odin Shen
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Odin Shen
-
 ### Tags
 skilllevels: Advanced
 subjects: ML
@@ -51,7 +52,6 @@ further_reading:
         link: /learning-paths/laptops-and-desktops/dgx_spark_llamacpp/
         type: Learning Path
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md
index 49fefd617d..dee12f6838 100644
--- a/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md
+++ b/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md
@@ -17,12 +17,13 @@ learning_objectives:
 prerequisites:
     - An NVIDIA DGX Spark system with at least 15 GB of available disk space
     - A USB microphone for audio input
+
+author: Odin Shen
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Odin Shen
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
diff --git a/content/learning-paths/laptops-and-desktops/docker-models/_index.md b/content/learning-paths/laptops-and-desktops/docker-models/_index.md
index 14b3c24931..616a853fa1 100644
--- a/content/learning-paths/laptops-and-desktops/docker-models/_index.md
+++ b/content/learning-paths/laptops-and-desktops/docker-models/_index.md
@@ -15,12 +15,13 @@ prerequisites:
     - Docker Desktop (version 4.40 or later) installed on a system with at least 16GB of RAM (recommended).
     - Basic understanding of Docker CLI and concepts.
     - Familiarity with LLM concepts.
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ### Tags
 skilllevels: Introductory
 subjects: Containers and Virtualization
diff --git a/content/learning-paths/laptops-and-desktops/electron/_index.md b/content/learning-paths/laptops-and-desktops/electron/_index.md
index b7deb2033c..faad83711c 100644
--- a/content/learning-paths/laptops-and-desktops/electron/_index.md
+++ b/content/learning-paths/laptops-and-desktops/electron/_index.md
@@ -15,12 +15,13 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - Node.js for Arm64. You can find the [Node.js installer](https://nodejs.org/dist/v20.10.0/node-v20.10.0-arm64.msi).
     - Any code editor; we recommend using [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user).
+
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dawid Borycki
-
 ### Tags
 skilllevels: Introductory
 subjects: Migration to Arm
@@ -47,7 +48,6 @@ further_reading:
         link: https://www.electronjs.org/docs/latest/tutorial/windows-arm
         type: documentation    
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md b/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md
index 6134a80c61..28aebc5c0b 100644
--- a/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md
+++ b/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md
@@ -15,13 +15,14 @@ learning_objectives:
 prerequisites: 
     - A GitHub account. 
     - Familiarity with GitHub Actions.
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author: 
     - Pareena Verma
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: CI-CD
diff --git a/content/learning-paths/laptops-and-desktops/hyper-v/_index.md b/content/learning-paths/laptops-and-desktops/hyper-v/_index.md
index 9e19f16d73..08ff5931f4 100644
--- a/content/learning-paths/laptops-and-desktops/hyper-v/_index.md
+++ b/content/learning-paths/laptops-and-desktops/hyper-v/_index.md
@@ -12,12 +12,13 @@ learning_objectives:
 
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 with [Hyper-V](/install-guides/hyper-v/) installed.
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ### Tags
 skilllevels: Introductory
 subjects: Migration to Arm
@@ -35,7 +36,6 @@ further_reading:
         link: https://learn.microsoft.com/en-us/virtualization/ 
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/intro/_index.md b/content/learning-paths/laptops-and-desktops/intro/_index.md
index f254e45716..0e2edf09c2 100644
--- a/content/learning-paths/laptops-and-desktops/intro/_index.md
+++ b/content/learning-paths/laptops-and-desktops/intro/_index.md
@@ -13,12 +13,12 @@ learning_objectives:
 
 prerequisites:
     - Nothing
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author: Jason Andrews
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 ### Tags
 skilllevels: Introductory
@@ -30,14 +30,12 @@ operatingsystems:
     - ChromeOS
 tools_software_languages:
 
-
 further_reading:
     - resource:
         title: All Chromebooks with Arm Processors 
         link: https://www.linuxmadesimple.info/2019/08/all-chromebooks-with-arm-processors-in.html
         type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md b/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md
index 5b38cbcac7..307edae44f 100644
--- a/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md
+++ b/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md
@@ -16,12 +16,13 @@ prerequisites:
 - A Mac with Apple Silicon (M4 generation or newer)
 - Xcode command line tools installed
 - Basic familiarity with using the Terminal and command-line tools
+
+author: Jett Zhou
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jett Zhou
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -47,8 +48,6 @@ further_reading:
         link: /learning-paths/cross-platform/function-multiversioning/
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md b/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md
index d938895a44..61e65a5eef 100644
--- a/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md
+++ b/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 skilllevels: Introductory
 subjects: Migration to Arm
@@ -40,7 +41,6 @@ further_reading:
         link: https://linaro.atlassian.net/wiki/spaces/LLVM/overview/
         type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md b/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md
index 8763d713bf..de8b2a0146 100644
--- a/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md
+++ b/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md
@@ -15,12 +15,13 @@ prerequisites:
 - A Linux computer.
 - Basic knowledge of how MTE works. Refer to the [Learn about Memory Tagging Extension Learning Path](/learning-paths/mobile-graphics-and-gaming/mte/)
 - Knowledge of how a dynamic memory allocator can be implemented. Refer to [Write a Dynamic Memory Allocator Learning Path](/learning-paths/cross-platform/dynamic-memory-allocator/).
+
+author: David Spickett
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: David Spickett
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -33,14 +34,12 @@ tools_software_languages:
 operatingsystems:
 - Linux
 
-
 further_reading:
     - resource:
         title: LLSoftSecBook Chapter on Stack Buffer Overflows
         link: https://llsoftsec.github.io/llsoftsecbook/#stack-buffer-overflows
         type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md b/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
index 6fa5cc5133..97aa9f4307 100644
--- a/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
+++ b/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
@@ -15,12 +15,13 @@ learning_objectives:
 prerequisites:
     - A Pinebook Pro laptop
     - A microSD card (8GB or greater; class 10 or faster)
+
+author: Gabriel Peterson
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Gabriel Peterson
-
 ### Tags
 skilllevels: Advanced
 subjects: Migration to Arm
@@ -48,7 +49,6 @@ further_reading:
         link: https://wiki.pine64.org/wiki/Pinebook_Pro
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md b/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
index 5c2b12cf70..8d850ee01a 100644
--- a/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
+++ b/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
@@ -16,12 +16,13 @@ learning_objectives:
 prerequisites:
     - Hugging Face account and access token
     - NVIDIA DGX Spark workstation
+
+author: Michael Hall
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Michael Hall
-
 ### Tags
 skilllevels: Advanced
 subjects: ML
diff --git a/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md b/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
index a835e93901..fb3bf851d7 100644
--- a/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
+++ b/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
@@ -16,12 +16,13 @@ prerequisites:
     - An Arm64-powered machine, either virtual or physical. This Learning Path demonstration uses an Arm64-powered VM with Ubuntu 22.04.
     - A DockerHub account. You can [set up a free DockerHub account](https://hub.docker.com/signup).
     - A GitHub account. You can [sign up for GitHub](https://github.com/signup).
+
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dawid Borycki
-
 ### Tags
 skilllevels: Introductory
 subjects: Migration to Arm
diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/_index.md b/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
index 8b47c30af5..281b6f644e 100644
--- a/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 
 prerequisites:
     - A Windows on Arm machine such as the Lenovo Thinkpad X13s, or an [Azure virtual machine](/learning-paths/cross-platform/woa_azure/).
+
+author: Koki Mitsunami
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Koki Mitsunami
-
 ### Tags
 skilllevels: Introductory
 subjects: Migration to Arm
@@ -32,7 +33,6 @@ tools_software_languages:
 operatingsystems:
     - Windows
 
-
 further_reading:
     - resource:
         title: OpenCV website
@@ -47,8 +47,6 @@ further_reading:
         link: https://community.arm.com/arm-community-blogs/b/architectures-and-processors-blog/posts/sve2
         type: blog
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md b/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md
index 034f126f09..d74b669fe0 100644
--- a/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md
@@ -15,12 +15,13 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11
     - A code editor such as [Visual Studio Code for Windows on Arm](https://code.visualstudio.com/docs/?dv=win32arm64user) 
+
+author: Ruifeng Wang
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Ruifeng Wang
-
 ### Tags
 skilllevels: Introductory
 subjects: Migration to Arm
@@ -32,8 +33,6 @@ tools_software_languages:
 operatingsystems:
     - Windows
 
-
-
 further_reading:
     - resource:
         title: Recording for resource-based analysis
@@ -48,8 +47,6 @@ further_reading:
         link: https://learn.microsoft.com/en-us/windows/arm/arm64ec
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md b/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
index d174305937..1ba430382a 100644
--- a/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
@@ -15,12 +15,13 @@ learning_objectives:
 
 prerequisites:
     - An Arm Linux system with KVM support and a minimum of 8GB RAM and 50GB free disk space
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ### Tags
 skilllevels: Introductory
 subjects: Migration to Arm
diff --git a/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md b/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
index 36694403b3..d5815f5887 100644
--- a/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11.
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 skilllevels: Introductory
 subjects: Migration to Arm
@@ -36,7 +37,6 @@ further_reading:
         link: https://learn.microsoft.com/en-us/windows/arm/arm64ec-build
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md b/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
index 9ed2117791..166c134647 100644
--- a/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
@@ -16,12 +16,13 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is suitable.
     - Visual Studio 2022 with Arm build tools. [Refer to this guide for the installation steps](https://developer.arm.com/documentation/102528/0100/Install-Visual-Studio).
+
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dawid Borycki
-
 ### Tags
 skilllevels: Introductory
 subjects: Migration to Arm
@@ -48,7 +49,6 @@ further_reading:
         link: https://devblogs.microsoft.com/windows-music-dev/load-x64-plug-ins-like-vsts-from-your-arm-code-using-arm64ec/
         type: blog    
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md b/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md
index b9da3a4cd5..69356a15d9 100644
--- a/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - '[Qt framework](https://www.qt.io/) or [Qt for Open Source Development](https://www.qt.io/download-open-source)'
+
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dawid Borycki
-
 ### Tags
 skilllevels: Introductory
 subjects: Migration to Arm
@@ -42,7 +43,6 @@ further_reading:
         link: https://doc.qt.io/qt-6/qtexamplesandtutorials.html
         type: documentation    
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md b/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
index 06e563f2d2..861ec9a38f 100644
--- a/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
@@ -16,12 +16,13 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - .NET 8 SDK for [arm64](https://dotnet.microsoft.com/en-us/download/dotnet/thank-you/sdk-8.0.100-windows-arm64-installer).
     - Any code editor, we recommend using [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user).
+
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dawid Borycki
-
 ### Tags
 skilllevels: Advanced
 subjects: Migration to Arm
@@ -47,7 +48,6 @@ further_reading:
         link: https://dotnet.microsoft.com/en-us/apps/aspnet
         type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
index 281a05f2c0..2bd95ba19e 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot/_index.md
@@ -15,12 +15,13 @@ learning_objectives:
 prerequisites:
     - A Windows-on-Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows-on-Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - Any code editor. Visual Studio Code is suitable.
+
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dawid Borycki
-
 ### Tags
 skilllevels: Advanced
 subjects: Migration to Arm
@@ -31,7 +32,7 @@ operatingsystems:
 tools_software_languages:
     - Node.js    
     - Visual Studio
-    
+
 further_reading:
     - resource:
         title: AWS IoT Core Developer Guide
@@ -42,7 +43,6 @@ further_reading:
         link: https://docs.aws.amazon.com/iot/latest/developerguide/sdk-tutorials.html
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md
index 994c4811dc..6d2c4ac220 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_dynamodb/_index.md
@@ -16,12 +16,13 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).   
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is suitable.
     - Completion of the [Create IoT applications with Windows on Arm and AWS IoT Core](/learning-paths/laptops-and-desktops/win_aws_iot/) Learning Path.
+
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dawid Borycki
-
 ### Tags
 skilllevels: Advanced
 subjects: Migration to Arm
@@ -47,7 +48,6 @@ further_reading:
         link: https://docs.aws.amazon.com/iot/latest/developerguide/iot-rules.html
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md
index bacfb83b21..cc0ced5e44 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/_index.md
@@ -17,12 +17,13 @@ prerequisites:
     - A Windows on Arm computer such as the a Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).   
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is suitable.
     - Completion of the [Create IoT applications with Windows on Arm and AWS IoT Core](/learning-paths/laptops-and-desktops/win_aws_iot/) Learning Path.
+
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dawid Borycki
-
 ### Tags
 skilllevels: Advanced
 subjects: Migration to Arm
@@ -48,7 +49,6 @@ further_reading:
         link: https://docs.aws.amazon.com/whitepapers/latest/aws-overview/introduction.html
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md
index 1646ede942..e075eafeb1 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/_index.md
@@ -15,12 +15,13 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).   
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is suitable.
     - Completion of the [Create IoT applications with Windows on Arm and AWS IoT Core](/learning-paths/laptops-and-desktops/win_aws_iot/) Learning Path.
+
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dawid Borycki
-
 ### Tags
 skilllevels: Advanced
 subjects: Migration to Arm
@@ -46,7 +47,6 @@ further_reading:
         link: https://docs.aws.amazon.com/whitepapers/latest/aws-overview/introduction.html
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md
index 51ae1e40d0..0763385421 100644
--- a/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/_index.md
@@ -15,12 +15,13 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).   
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is suitable.    
     - Completion of the [Use AWS Lambda for IoT applications](/learning-paths/laptops-and-desktops/win_aws_iot_lambda/) Learning Path.
+
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dawid Borycki
-
 ### Tags
 skilllevels: Advanced
 subjects: Migration to Arm
@@ -46,7 +47,6 @@ further_reading:
         link: https://docs.aws.amazon.com/AmazonS3/latest/userguide/developing-s3.html
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/win_cef/_index.md b/content/learning-paths/laptops-and-desktops/win_cef/_index.md
index f6f101f2c5..dba9bea8f0 100644
--- a/content/learning-paths/laptops-and-desktops/win_cef/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_cef/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - Visual Studio 2022.
+
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dawid Borycki
-
 ### Tags
 skilllevels: Introductory
 subjects: Migration to Arm
@@ -44,7 +45,6 @@ further_reading:
         link: https://en.wikipedia.org/wiki/Chromium_Embedded_Framework
         type: website   
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/win_forms/_index.md b/content/learning-paths/laptops-and-desktops/win_forms/_index.md
index 96b9c136e5..d43930abe5 100644
--- a/content/learning-paths/laptops-and-desktops/win_forms/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_forms/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - Visual Studio 2022 with .NET Desktop Development workload
+
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dawid Borycki
-
 ### Tags
 skilllevels: Introductory
 subjects: Migration to Arm
@@ -31,7 +32,7 @@ tools_software_languages:
     - Windows Forms
     - C#
     - .NET
-    
+
 further_reading:
     - resource:
         title: Windows Forms on .NET 8
@@ -42,7 +43,6 @@ further_reading:
         link: https://devblogs.microsoft.com/dotnet/this-arm64-performance-in-dotnet-8/
         type: blog
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/win_net/_index.md b/content/learning-paths/laptops-and-desktops/win_net/_index.md
index c0340331a0..db08f10798 100644
--- a/content/learning-paths/laptops-and-desktops/win_net/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_net/_index.md
@@ -12,12 +12,13 @@ learning_objectives:
 
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 skilllevels: Introductory
 subjects: Migration to Arm
@@ -39,7 +40,6 @@ further_reading:
         link: https://learn.microsoft.com/en-us/dotnet/iot/deployment
         type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/win_net8/_index.md b/content/learning-paths/laptops-and-desktops/win_net8/_index.md
index 0d7be43b52..c58e8fd6ed 100644
--- a/content/learning-paths/laptops-and-desktops/win_net8/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_net8/_index.md
@@ -16,12 +16,13 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - .NET 8 SDK for [x64](https://dotnet.microsoft.com/en-us/download/dotnet/thank-you/sdk-8.0.100-windows-x64-installer) and [arm64](https://dotnet.microsoft.com/en-us/download/dotnet/thank-you/sdk-8.0.100-windows-arm64-installer).
     - Any code editor, we recommend using [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user).
+
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dawid Borycki
-
 ### Tags
 skilllevels: Introductory
 subjects: Migration to Arm
@@ -56,7 +57,6 @@ further_reading:
         link: https://www.codeproject.com/Articles/5367981/NET-Performance-on-Arm64
         type: article
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md b/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md
index 988dae16c7..80ccb2285b 100644
--- a/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_net_maui/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - Visual Studio 2022 with .NET Multi-platform App UI development and Universal Windows Platform development installed.
+
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dawid Borycki
-
 ### Tags
 skilllevels: Introductory
 subjects: Migration to Arm
@@ -46,7 +47,6 @@ further_reading:
         link: https://github.com/dotnet/maui
         type: GitHub repository
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md b/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md
index 4243b30aad..c44ded8a09 100644
--- a/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_on_arm_build_onnxruntime/_index.md
@@ -12,12 +12,13 @@ learning_objectives:
     - Run inference with a Phi-3 model using ONNX Runtime with KleidiAI acceleration.
 prerequisites:
     - A Windows on Arm computer such as a Lenovo Thinkpad X13 running Windows 11, or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
+
+author: Barbara Corriero
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Barbara Corriero
-
 ### Tags
 skilllevels: Advanced
 subjects: ML
diff --git a/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md b/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md
index 34b88d4a7d..c0d5f9debd 100644
--- a/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_profile_guided_optimisation/_index.md
@@ -15,12 +15,13 @@ learning_objectives:
 prerequisites:
     - Familiarity with C++ development and compiling programs from the command line
     - A Windows on Arm machine with [Visual Studio](/install-guides/vs-woa/) and the C++ desktop development tools installed
+
+author: Tom Dunkle
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Tom Dunkle
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -52,8 +53,6 @@ further_reading:
         link: https://learn.arm.com/learning-paths/laptops-and-desktops/
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/win_python/_index.md b/content/learning-paths/laptops-and-desktops/win_python/_index.md
index aa42916436..a01ba6d00f 100644
--- a/content/learning-paths/laptops-and-desktops/win_python/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_python/_index.md
@@ -15,12 +15,13 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/). 
     - Any code editor, we recommend using [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user).
     - Visual Studio 2022 with Arm build tools. [Refer to this guide for the installation steps](https://developer.arm.com/documentation/102528/0100/Install-Visual-Studio)
+
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dawid Borycki
-
 ### Tags
 skilllevels: Introductory
 subjects: Migration to Arm
@@ -42,7 +43,6 @@ further_reading:
         link: https://old.linaro.org/blog/windows-on-arm-now-supported-in-python-3-11-release/
         type: blog    
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/win_python_onnx/_index.md b/content/learning-paths/laptops-and-desktops/win_python_onnx/_index.md
index 293f0f5f09..f75c9b2b0a 100644
--- a/content/learning-paths/laptops-and-desktops/win_python_onnx/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_python_onnx/_index.md
@@ -18,10 +18,11 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/). 
     - Any code editor like [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user).    
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
-author: Dawid Borycki
 
 ### Tags
 skilllevels: Introductory
@@ -44,7 +45,6 @@ further_reading:
         link: https://github.com/onnx/onnx
         type: blog    
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md b/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md
index 2ed5f3b8c0..0469244ab7 100644
--- a/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 Version 22H2 which has [Windows Sandbox enabled](/install-guides/windows-sandbox-woa/).
     - A valid [GitHub account](https://github.com/) to complete this Learning Path.
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 skilllevels: Introductory
 subjects: CI-CD
@@ -42,7 +43,6 @@ further_reading:
         link: https://github.blog/changelog/2022-09-28-github-actions-self-hosted-runners-now-support-windows-arm-hardware/
         type: blog
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md b/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md
index 8d75bf4b57..00ee22823d 100644
--- a/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/_index.md
@@ -15,12 +15,13 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).    
     - Refer to [Visual Studio 2022 with Arm build tools](/install-guides/vs-woa).
+
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dawid Borycki
-
 ### Tags
 skilllevels: Introductory
 subjects: Migration to Arm
@@ -46,7 +47,6 @@ further_reading:
         link: https://devblogs.microsoft.com/windows-music-dev/load-x64-plug-ins-like-vsts-from-your-arm-code-using-arm64ec/
         type: blog    
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/win_winui3/_index.md b/content/learning-paths/laptops-and-desktops/win_winui3/_index.md
index 8636a7e1da..465a114306 100644
--- a/content/learning-paths/laptops-and-desktops/win_winui3/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_winui3/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - Visual Studio 2022 with .NET desktop development and Universal Windows Platform development installed.
+
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dawid Borycki
-
 ### Tags
 skilllevels: Introductory
 subjects: Migration to Arm
@@ -32,7 +33,7 @@ tools_software_languages:
     - C#
     - .NET
     - Visual Studio
-    
+
 further_reading:
     - resource:
         title: Microsoft's Official WinUI 3 Documentation
@@ -43,7 +44,6 @@ further_reading:
         link: https://github.com/Microsoft/WinUI-Gallery
         type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/win_wpf/_index.md b/content/learning-paths/laptops-and-desktops/win_wpf/_index.md
index 0f119d9bfd..0cfa6b66c4 100644
--- a/content/learning-paths/laptops-and-desktops/win_wpf/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_wpf/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - Visual Studio 2022 with .NET desktop development installed.
+
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dawid Borycki
-
 ### Tags
 skilllevels: Introductory
 subjects: Migration to Arm
@@ -32,7 +33,7 @@ tools_software_languages:
     - C#
     - .NET
     - Visual Studio
-    
+
 further_reading:
     - resource:
         title: Windows Presentation Foundation
@@ -43,7 +44,6 @@ further_reading:
         link: https://www.syncfusion.com
         type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md b/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md
index 5c1dca6602..176bcb2a3a 100644
--- a/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md
+++ b/content/learning-paths/laptops-and-desktops/win_xamarin_forms/_index.md
@@ -15,12 +15,13 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).
     - Visual Studio 2022 with .NET desktop development and Universal Windows Platform development installed.
+
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dawid Borycki
-
 ### Tags
 skilllevels: Introductory
 subjects: Migration to Arm
@@ -33,7 +34,7 @@ tools_software_languages:
     - C#
     - .NET
     - Visual Studio
-    
+
 further_reading:
     - resource:
         title: Xamarin Forms
@@ -44,7 +45,6 @@ further_reading:
         link: https://learn.microsoft.com/en-us/xamarin/xamarin-forms/enterprise-application-patterns/mvvm
         type: documentation   
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md b/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md
index aa3f600f50..2450be322b 100644
--- a/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windows_armpl/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11.
+
+author: Odin Shen
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Odin Shen
-
 ### Tags
 skilllevels: Introductory
 subjects: Migration to Arm
@@ -32,16 +33,12 @@ tools_software_languages:
 operatingsystems:
     - Windows
 
-
 further_reading:
     - resource:
         title: Arm Performance Libraries Reference Guide  
         link: https://developer.arm.com/documentation/101004/latest/
         type: documentation
 
-
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md b/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md
index 70af30b905..3c8e487c70 100644
--- a/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windows_cicd_github/_index.md
@@ -15,12 +15,13 @@ prerequisites:
     - Some familiarity with CI/CD concepts is assumed
     - Valid GitHub account
     - Microsoft Azure account (if using virtual machine)
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 skilllevels: Introductory
 subjects: CI-CD
@@ -45,7 +46,6 @@ further_reading:
         link: https://azure.microsoft.com/en-us/blog/azure-virtual-machines-with-ampere-altra-arm-based-processors-generally-available/
         type: blog
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md
index b60769889d..1f69551f95 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/_index.md
@@ -16,13 +16,14 @@ learning_objectives:
 prerequisites:
   - A desktop or laptop running Windows on Arm.
   - Visual Studio 2022 Community Edition, WindowsPerf, WindowsPerf Visual Studio extension, and Windows Performance Analyzer (WPA) installed. 
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author: 
   - Nader Zouaoui
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf/_index.md
index 6456a4c2c4..2cadc3f92c 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 
 prerequisites:
     - Windows on Arm desktop or development machine
+
+author: Ronan Synnott
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Ronan Synnott
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md
index fee2702874..212df1b5c4 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/_index.md
@@ -16,12 +16,13 @@ learning_objectives:
 prerequisites:
     - Windows on Arm desktop or development machine with [WindowsPerf installed](/install-guides/wperf/)
     - Windows x86_64 desktop machine with [Visual Studio 2022 Community Edition](https://visualstudio.microsoft.com/vs/) installed.
+
+author: Przemyslaw Wirkus
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Przemyslaw Wirkus
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md b/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
index d318718c85..17e06c08c8 100644
--- a/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
+++ b/content/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 
 prerequisites:
     - A Windows on Arm laptop with WindowsPerf, Windows Performance Analyzer (WPA), and the WPA plugin installed.
+
+author: Alaaeddine Chakroun
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Alaaeddine Chakroun
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
diff --git a/content/learning-paths/laptops-and-desktops/wsl2/_index.md b/content/learning-paths/laptops-and-desktops/wsl2/_index.md
index 1eadda70cb..05131d94f7 100644
--- a/content/learning-paths/laptops-and-desktops/wsl2/_index.md
+++ b/content/learning-paths/laptops-and-desktops/wsl2/_index.md
@@ -18,12 +18,13 @@ learning_objectives:
 
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11. 
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ### Tags
 skilllevels: Advanced
 subjects: Migration to Arm
@@ -46,7 +47,6 @@ further_reading:
         link: https://devblogs.microsoft.com/visualstudio/arm64-visual-studio/
         type: blog
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md b/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md
index 8b13bce038..6814da3417 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/afrc/_index.md
@@ -16,12 +16,13 @@ prerequisites:
     - An appropriate Android device (e.g., Google Pixel 8) supporting the required Vulkan extensions.
     - Knowledge of the Vulkan API.
     - A Vulkan application that creates and uses images. This Learning Path shows how to use an API Sample in the [Khronos Vulkan Samples repository](https://github.com/KhronosGroup/Vulkan-Samples/blob/main/scripts/README.adoc#generate-api-sample) as an example.
+
+author: Jose-Emilio Munoz-Lopez
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jose-Emilio Munoz-Lopez
-
 ### Tags
 skilllevels: Advanced
 subjects: Graphics
@@ -33,7 +34,6 @@ operatingsystems:
 tools_software_languages:
     - Vulkan
 
-
 further_reading:
     - resource:
         title: AFRC sample and tutorial
@@ -48,9 +48,6 @@ further_reading:
         link: https://developer.arm.com/community/arm-community-blogs/b/mobile-graphics-and-gaming-blog/posts/arm-immortalis-g715-developer-overview
         type: blog
 
-
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md b/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md
index 6281356b62..018c0eadf5 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 
 prerequisites:
     - A computer running Arm Linux or macOS with Docker installed
+
+author: Arnaud de Grandmaison
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Arnaud de Grandmaison
-
 test_images:
     - ubuntu:latest
 test_maintenance: false
diff --git a/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md b/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
index ed5b38c5d6..3a9b8533fb 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/ams/_index.md
@@ -19,12 +19,13 @@ prerequisites:
     - A debuggable build of your application. 
     - Arm Performance Studio installed. Follow the [Arm Performance Studio install guide](/install-guides/ams/) for instructions.
     - Android SDK Platform tools installed. Required for the Android Debug bridge (adb).
+
+author: Ronan Synnott
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Ronan Synnott
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -80,7 +81,6 @@ further_reading:
         link: https://renderdoc.org/docs/index.html
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md b/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md
index 899713235e..8d18691ccf 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/_index.md
@@ -2,7 +2,6 @@
 title: Analyze a frame with Frame Advisor
 description: Learn how to capture frame data from Android applications and analyze performance inefficiencies using Frame Advisor in Arm Performance Studio.
 
-
 minutes_to_complete: 10
 
 who_is_this_for: Android application developers who want to learn how to use Frame Advisor.
@@ -17,12 +16,13 @@ prerequisites:
     - A debuggable build of your application. 
     - Download and install Arm Performance Studio from [Product Download Hub](https://developer.arm.com/downloads/view/MOBST-PRO0). It is supported on Windows, Linux, and macOS host platforms.
     - Download and install [Android SDK Platform tools](https://developer.android.com/studio/releases/platform-tools.html). Required for [Android Debug bridge (adb)](https://developer.android.com/studio/command-line/adb).
+
+author: Julie Gaskin
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Julie Gaskin
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -33,8 +33,6 @@ tools_software_languages:
     - Frame Advisor
 operatingsystems:
     - Android
-   
-
 
 further_reading:
     - resource:
@@ -50,8 +48,6 @@ further_reading:
         link: https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio%20for%20Mobile
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md
index ce0b487a7c..7840d347e8 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_index.md
@@ -2,7 +2,6 @@
 title: Add an LLM to your Android app with Arm's AI Chat library
 description: Learn how to build an Android chatbot app using Arm's AI Chat library to run GGUF models on-device with optimized performance on Arm CPUs.
 
-
 minutes_to_complete: 15
 
 who_is_this_for: This is an introductory topic for developers who want to add a local, on-device LLM chat experience using Arm's AI Chat library, Kotlin, and Android Studio.
@@ -15,12 +14,13 @@ prerequisites:
     - An Android development environment with Android Studio installed
     - An Android phone for testing, in Developer Mode, with USB cable for connection
     - Basic familiarity with Kotlin and Android app development
+
+author: Ben Clark
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Ben Clark
-
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -35,8 +35,6 @@ tools_software_languages:
 operatingsystems:
     - Android
 
-
-
 further_reading:
     - resource:
         title: AI Chat - Explore and evaluate LLMs on Android and ChromeOS
@@ -63,7 +61,6 @@ further_reading:
         link: https://www.arm.com/technologies/sme2
         type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
index cb32b1dee6..037259c790 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_halide/_index.md
@@ -15,15 +15,16 @@ learning_objectives:
 prerequisites:
     - Basic C++ knowledge
     - Android Studio with Android Emulator
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author:
     - Éliás Bálint
     - Dawid Borycki
     - Steve Suzuki
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -40,7 +41,6 @@ tools_software_languages:
     - Android Studio
     - CMake
 
-
 further_reading:
     - resource:
         title: Halide documentation
@@ -55,7 +55,6 @@ further_reading:
         link: https://halide-lang.org/tutorials/
         type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_neon/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_neon/_index.md
index 575bdd980d..cc3fb046e3 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_neon/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_neon/_index.md
@@ -6,7 +6,7 @@ description: Learn how to enable and implement Neon intrinsics in Android NDK ap
 draft: true
 cascade:
     draft: true
-    
+
 minutes_to_complete: 40
 
 who_is_this_for: This is an introductory topic for software developers interested in learning how to use Neon Intrinsics on Arm powered mobile devices running Android. 
@@ -19,10 +19,11 @@ learning_objectives:
 prerequisites:
     - A x86_64 or Apple M1 development machine with Android Studio installed.
     - A 64-bit Arm powered smartphone running Android.
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
-author: Dawid Borycki
 
 ### Tags
 skilllevels: Introductory
@@ -48,9 +49,6 @@ further_reading:
         link: https://developer.arm.com/architectures/instruction-sets/intrinsics/
         type: website
 
-
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md
index e0c4d8afc9..795d8715ca 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
     - A development machine with [Android Studio](https://developer.android.com/studio) installed. 
     - An Android smartphone.
+
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dawid Borycki
-
 ### Tags
 skilllevels: Introductory
 subjects: Graphics
@@ -47,7 +48,6 @@ further_reading:
         link: https://opencv.org/blog/enhanced-opencv-for-android-support-arm-performance-gains/
         type: blog
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md
index f0259c6342..a52e88f212 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/_index.md
@@ -16,12 +16,13 @@ prerequisites:
    - A development machine with [Android Studio](https://developer.android.com/studio) installed. 
    - An Android smartphone.
    - Familiarity with OpenCV, review [Create Computer Vision Applications with OpenCV on Android Devices](/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/) before starting.
+
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dawid Borycki
-
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -49,7 +50,6 @@ further_reading:
         link: https://opencv.org/blog/enhanced-opencv-for-android-support-arm-performance-gains/
         type: blog
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
index 96c20e5dbb..acb1472e25 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_kleidicv/_index.md
@@ -2,7 +2,6 @@
 title: Accelerate an OpenCV-based Android Application with KleidiCV
 description: Learn how to accelerate OpenCV-based Android applications using KleidiCV for enhanced computer vision performance.
 
-
 minutes_to_complete: 45
 
 who_is_this_for: This is an introductory topic for developers who are interested in creating Computer Vision applications with OpenCV and KleidiCV on Android Devices.
@@ -16,12 +15,13 @@ prerequisites:
     - A development machine with [Android Studio](https://developer.android.com/studio) installed. 
     - Familiarity with Android development concepts.
     - An Android smartphone.
+
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dawid Borycki
-
 ### Tags
 skilllevels: Introductory
 subjects: Graphics
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md
index f9cc57da74..32fb45e1e4 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_sve2/_index.md
@@ -15,12 +15,13 @@ prerequisites:
     - Knowledge of Single instruction Multi Data (SIMD)
     - Knowledge of [Neon](https://developer.arm.com/documentation/102474/latest)
     - Knowledge of [Scalable Vector Extension (SVE)](https://developer.arm.com/documentation/101726/4-0)
+
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dawid Borycki
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -49,9 +50,6 @@ further_reading:
         link: https://developer.android.com/studio
         type: website
 
-
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md b/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
index 45ffdd8c2d..b5eafb161c 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/_index.md
@@ -16,7 +16,7 @@ learning_objectives:
     - Build and run a WebGPU Android Application.
     - Profile the application using Streamline.
     - Analyze the profiling data.
-       
+
 prerequisites:
     - Basic knowledge of graphics APIs and experience in developing Android graphics applications.
     - A development machine with Android Studio, Blender, and Arm Streamline installed.
@@ -24,14 +24,15 @@ prerequisites:
     - Android Studio.
     - Arm Performance Studio.
     - Python 3.10 or later.
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author:
     - Varun Chari
     - Albin Bernhardsson
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Advanced
 subjects: Graphics
@@ -48,7 +49,6 @@ operatingsystems:
     - Windows
     - Android
 
-
 further_reading:
     - resource:
         title: WebGPU example application
@@ -79,8 +79,6 @@ further_reading:
         link: https://github.com/samdauwe/webgpu-native-examples
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md b/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
index 3c5ffbd646..44e9b602dc 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/_index.md
@@ -15,12 +15,13 @@ prerequisites:
 - A computer capable of running [Unreal Engine 5.3 or later version](https://www.unrealengine.com/en-US/download).
 - An Android mobile device that has a Mali GPU with hardware ray tracing support.
 - A USB cable to connect the mobile device to your computer.
+
+author: Owen Wu
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Owen Wu
-
 ### Tags
 skilllevels: Introductory
 subjects: Gaming
@@ -32,7 +33,6 @@ operatingsystems:
 tools_software_languages:
     - Unreal Engine
 
-
 further_reading:
     - resource:
         title: Lumen Performance Guide 
@@ -47,8 +47,6 @@ further_reading:
         link: https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md b/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md
index 4239dec95d..6de5331129 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/build-android-chat-app-using-onnxruntime/_index.md
@@ -2,7 +2,6 @@
 title: Build an Android chat application with ONNX Runtime API
 description: Learn how to build ONNX Runtime and the generate() API for Android to run a Phi-3 model on Arm-based smartphones.
 
-
 minutes_to_complete: 60
 
 who_is_this_for: This is an advanced topic for software developers interested in learning how to build an Android chat app with ONNX Runtime and ONNX Runtime Generate() API.
@@ -14,12 +13,13 @@ learning_objectives:
 prerequisites:
     - A Windows x86_64 development machine with at least 16GB of RAM.
     - An Android phone with at least 8GB of RAM. This learning path was tested on Samsung Galaxy S24.
+
+author: Koki Mitsunami
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Koki Mitsunami
-
 ### Tags
 skilllevels: Advanced
 subjects: ML
@@ -36,7 +36,6 @@ operatingsystems:
     - Windows
     - Android
 
-
 further_reading:
     - resource:
         title: ONNX Runtime
@@ -51,8 +50,6 @@ further_reading:
         link: https://newsroom.arm.com/blog/arm-kleidi
         type: blog
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md b/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md
index 3b8dc6f73a..df5fad82a5 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/_index.md
@@ -19,12 +19,13 @@ prerequisites:
     - Familiarity with Android development concepts.
     - Basic knowledge of Modern Android Architecture. See [Modern Android App Architecture](https://developer.android.com/courses/pathways/android-architecture).
     - Basic knowledge of Kotlin programming language, including [Coroutines](https://kotlinlang.org/docs/coroutines-overview.html) and [Kotlin Flows](https://kotlinlang.org/docs/flow.html).
+
+author: Han Yin
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Han Yin
-
 ### Tags
 skilllevels: Advanced
 subjects: ML
@@ -38,7 +39,6 @@ tools_software_languages:
 operatingsystems:
     - Android
 
-
 further_reading:
     - resource:
         title: Completed sample app
@@ -57,8 +57,6 @@ further_reading:
         link: https://android-developers.googleblog.com/2024/10/bring-your-ai-model-to-android-devices.html
         type: blog
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md b/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md
index e2a634e8e0..41e5919b73 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/_index.md
@@ -12,7 +12,6 @@ learning_objectives:
     - Describe how 4-bit groupwise PTQ quantization reduces model size without significantly sacrificing model accuracy.
     - Build and run Llama models using ExecuTorch on your development machine.
     - Build and run an Android Chat app with different Llama models using ExecuTorch on an Arm-based smartphone.
-    
 
 prerequisites:
     - An Apple M1/M2 development machine with Android Studio installed or a Linux machine with at least 16GB of RAM.
@@ -21,14 +20,15 @@ prerequisites:
     - Android Debug Bridge (adb) installed on your device. Follow the steps in [adb](https://developer.android.com/tools/adb)  to install Android SDK Platform Tools. The adb tool is included in this package.
     - Java 17 JDK. Follow the steps in [Java 17 JDK](https://www.oracle.com/java/technologies/javase/jdk17-archive-downloads.html) to download and install JDK for host.
     - Python 3.10.
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author: 
     - Varun Chari
     - Pareena Verma
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -45,7 +45,6 @@ operatingsystems:
     - macOS
     - Android
 
-
 further_reading:
     - resource:
         title: ExecuTorch Overview
@@ -60,8 +59,6 @@ further_reading:
         link: https://github.com/pytorch/executorch/blob/main/examples/README.md
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md b/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md
index bb4f2e5289..20edfb8993 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_index.md
@@ -21,12 +21,13 @@ prerequisites:
     - Java 17 JDK. Follow the steps in [Java SE 17 Archive Downloads](https://www.oracle.com/java/technologies/javase/jdk17-archive-downloads.html) to download and install JDK for your host
     - Python 3.10 or later
     - A [Hugging Face](https://huggingface.co/) account with access to Meta Llama models
+
+author: Parichay Das
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Parichay Das
-
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -43,7 +44,6 @@ operatingsystems:
     - Linux
     - Android
 
-
 further_reading:
     - resource:
         title: ExecuTorch Overview
@@ -62,7 +62,6 @@ further_reading:
         link: /learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/
         type: learning-path
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md b/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md
index c310b69c0a..f9034a7b45 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/debugging_with_mte_on_pixel8/_index.md
@@ -18,12 +18,13 @@ prerequisites:
     - Android Studio installed on your development computer.
     - A USB cable to connect your computer to your Google Pixel 8.
     - Android Debug Bridge (adb) installed on your device. If needed, follow the steps in the [Android Debug Bridge](https://developer.android.com/tools/adb) documentation.
+
+author: Roberto Lopez Mendez
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Roberto Lopez Mendez
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -35,7 +36,6 @@ tools_software_languages:
 operatingsystems:
     - Android
 
-
 further_reading:
     - resource:
         title: MTE User Guide for Android OS
@@ -54,8 +54,6 @@ further_reading:
         link: https://community.arm.com/arm-community-blogs/b/architectures-and-processors-blog/posts/enhanced-security-through-mte
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/gemma4-kleidiai-sme2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/gemma4-kleidiai-sme2/_index.md
index fab685379a..58f670584b 100755
--- a/content/learning-paths/mobile-graphics-and-gaming/gemma4-kleidiai-sme2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/gemma4-kleidiai-sme2/_index.md
@@ -18,10 +18,11 @@ learning_objectives:
 prerequisites:
     - A SME2 device (macOS M4 on Apple Silicon)
     - Git, Homebrew, and Xcode Command Line Tools
+author: Annie Tallund
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
-author: Annie Tallund
 
 ### Tags
 skilllevels: Advanced
@@ -37,8 +38,6 @@ tools_software_languages:
 operatingsystems:
     - macOS
 
-
-
 further_reading:
     - resource:
         title: Arm Scalable Matrix Extension introduction, part 1
@@ -77,8 +76,6 @@ further_reading:
         link: https://huggingface.co/litert-community/Gemma3-4B-IT
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md
index 2ebedb897a..258131fbc8 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 prerequisites:
     - A game project that uses advanced rendering features (such as hardware ray tracing) that stretch the performance capabilities of everyday smartphones.
     - A development machine with Git installed.
+
+author: Julie Gaskin
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Julie Gaskin
-
 ### Tags
 skilllevels: Advanced
 subjects: Graphics
@@ -30,8 +31,6 @@ tools_software_languages:
 operatingsystems:
     - Android
 
-
-
 further_reading:
     - resource:
         title: Arm ASR on Arm Developer Hub
@@ -54,8 +53,6 @@ further_reading:
         link: https://developer.arm.com/documentation/110404/latest/
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md
index 962b53a197..c0f669b74e 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android/_index.md
@@ -14,12 +14,13 @@ prerequisites:
     - Basic knowledge of game engines and programming concepts
     - Recent Android device, such as a mobile phone or tablet
     - Desktop computer capable of running Unity
+
+author: Joshua Marshall-Law
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Joshua Marshall-Law
-
 ### Tags
 skilllevels: Introductory
 subjects: Gaming
@@ -31,15 +32,12 @@ tools_software_languages:
 operatingsystems:
     - Android
 
-
 further_reading:
     - resource:
         title: Profiler overview 
         link: https://docs.unity3d.com/Manual/Profiler.html
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md
index 516f50a723..b256c68b49 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md
@@ -1,6 +1,6 @@
 ---
 title: Profile Android game performance in Godot with Arm Performance Studio
-   
+
 minutes_to_complete: 15
 
 who_is_this_for: This is an introductory topic for Godot developers targeting Android devices who want to optimize game performance on Arm CPUs and Mali GPUs using Arm Performance Studio tools.
@@ -12,14 +12,15 @@ learning_objectives:
 prerequisites:
     - Familiarity with Godot
     - Familiarity with Arm Performance Studio tools
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author:
     - Albin Bernhardsson
     - Julie Gaskin
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -34,7 +35,6 @@ operatingsystems:
     - macOS
     - Linux
 
-
 further_reading:
     - resource:
         title: Get started with Streamline 
@@ -53,8 +53,6 @@ further_reading:
         link: https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md b/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md
index bd5ed172e1..ab16e9abac 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/_index.md
@@ -13,12 +13,13 @@ prerequisites:
 - A computer capable of running [Unreal Engine 5.3 or later version](https://www.unrealengine.com/en-US/download).
 - An Android mobile device that has a Mali GPU with hardware ray tracing support.
 - A USB cable to connect the mobile device to your computer.
+
+author: Owen Wu
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Owen Wu
-
 ### Tags
 skilllevels: Introductory
 subjects: Gaming
@@ -30,7 +31,6 @@ operatingsystems:
 tools_software_languages:
     - Unreal Engine
 
-
 further_reading:
     - resource:
         title: Lumen Global Illumination and Reflections
@@ -45,8 +45,6 @@ further_reading:
         link: https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md b/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md
index d48a3afa0f..af1a7a1e73 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/intro/_index.md
@@ -10,12 +10,13 @@ learning_objectives:
 
 prerequisites:
     - None
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -27,14 +28,12 @@ operatingsystems:
     - Android
 tools_software_languages:
 
-
 further_reading:
     - resource:
         title: Android for Developers
         link: https://developer.android.com/ 
         type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md b/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md
index 381910f9eb..9cd6bd2023 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/kai_sme2_matmul_ukernel_explained/_index.md
@@ -1,6 +1,6 @@
 ---
 title: Understand KleidiAI SME2 matmul microkernels
-    
+
 minutes_to_complete: 40
 
 who_is_this_for: This is an advanced topic for software developers, performance engineers, and AI practitioners.
@@ -15,12 +15,13 @@ prerequisites:
     - Basic understanding of general matrix multiplication (GEMM) and matmul operations
     - Basic understanding of quantization concepts for neural networks
     - (Optional) Access to an Arm CPU with SME2 support (Linux or Android) for hands-on verification steps
+
+author: Zenon Zhilong Xiu
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Zenon Zhilong Xiu
-
 ### Tags
 skilllevels: Advanced
 subjects: ML
@@ -35,8 +36,6 @@ operatingsystems:
     - Android
     - Linux
 
-
-
 further_reading:
     - resource:
         title: Part 1, Arm Scalable Matrix Extension introduction
@@ -54,8 +53,6 @@ further_reading:
         title: Profile llama.cpp performance with Arm Streamline and KleidiAI LLM kernels
         link: /learning-paths/servers-and-cloud-computing/llama_cpp_streamline/
         type: blog
-        
-
 
 ### FIXED, DO NOT MODIFY
 # ================================================================================
diff --git a/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md b/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md
index 8a0a8b4c66..44c6431ed2 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/kleidiai-on-android-with-mediapipe-and-xnnpack/_index.md
@@ -14,15 +14,16 @@ learning_objectives:
 prerequisites:
     - An x86_64 Linux machine running Ubuntu with approximately 500 MB of free space, or a docker daemon that can build and run a provided x86_64 Dockerfile.
     - An Android phone with support for i8mm (tested on Google Pixel 8 Pro).
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author: 
     - Pareena Verma
     - Joe Stech
     - Adnan AlSinan
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Advanced
 subjects: ML
@@ -40,7 +41,6 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
 further_reading:
     - resource:
         title: MediaPipe Solutions Guide 
@@ -55,8 +55,6 @@ further_reading:
         link: https://blog.tensorflow.org/2024/04/faster-dynamically-quantized-inference-with-xnnpack.html
         type: blog
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md b/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md
index 560854f42e..f919f59c75 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/libgpuinfo/_index.md
@@ -9,16 +9,17 @@ who_is_this_for: This is an introductory topic for Android developers who want t
 learning_objectives: 
     - Build the libGPUInfo library using the Android NDK
     - Run an example application to query the configuration details of an Arm Mali or Arm Immortalis GPU
-    
+
 prerequisites:
     - A development machine running Ubuntu or Debian Linux with `x86_64` architecture
     - An Android device with an Arm GPU
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ##### Tags
 
 skilllevels: Introductory
@@ -47,9 +48,6 @@ further_reading:
         link: https://gitlab.arm.com/arm-reference-solutions/arm-reference-solutions-docs/-/tree/master/docs/totalcompute
         type: documentation
 
-
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md b/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md
index 70b645662a..f9b0180ae8 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md
@@ -15,12 +15,13 @@ learning_objectives:
 prerequisites:
 - An Arm64 Linux development machine 
 - An Android device that supports Arm SME2 architecture features - see this [list of devices with SME2 support](/learning-paths/cross-platform/multiplying-matrices-with-sme2/1-get-started/#devices)
+
+author: Jiaming Guo
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jiaming Guo
-
 ### Tags
 skilllevels: Advanced
 subjects: ML
@@ -35,8 +36,6 @@ tools_software_languages:
 operatingsystems:
     - Android
 
-
-
 further_reading:
     - resource:
         title: LiteRT model optimization
@@ -51,8 +50,6 @@ further_reading:
         link: https://github.com/google-ai-edge/LiteRT?tab=readme-ov-file#1--i-have-a-pytorch-model
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md b/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md
index ac8886542b..fa42be5d30 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/measure-kleidiai-kernel-performance-on-executorch/_index.md
@@ -15,12 +15,13 @@ learning_objectives:
 prerequisites:
   - An x86_64 Linux host machine running Ubuntu, with at least 15 GB of free disk space
   - An Arm64 target system with support for SME or SME2 - see the Learning Path [Devices with native SME2 support](/learning-paths/cross-platform/multiplying-matrices-with-sme2/1-get-started/#devices)
+
+author: Qixiang Xu
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Qixiang Xu
-
 ### Tags
 skilllevels: Advanced
 subjects: ML
@@ -36,15 +37,12 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
 further_reading:
     - resource:
         title: Executorch User Guide 
         link: https://docs.pytorch.org/executorch/stable/intro-section.html
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md b/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md
index ff7827e0b9..ac4d97f0a9 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md
@@ -1,7 +1,7 @@
 ---
 title: Fine-tune neural graphics models using Model Gym
 description: Learn how to fine-tune and evaluate Neural Super Sampling (NSS) models using PyTorch and Arm's Model Gym API with hardware-aware optimization.
-   
+
 minutes_to_complete: 45
 
 who_is_this_for: This is an advanced topic for developers exploring neural graphics and interested in training and deploying upscaling models like Neural Super Sampling (NSS) using PyTorch and Arm’s hardware-aware backend.
@@ -16,12 +16,13 @@ prerequisites:
     - Basic understanding of PyTorch and machine learning concepts
     - A development machine running Ubuntu 22.04, with a CUDA-capable NVIDIA® GPU
     - CUDA Toolkit version 11.8 or later
+
+author: Annie Tallund
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Annie Tallund
-
 ### Tags
 skilllevels: Advanced
 subjects: ML
@@ -56,7 +57,6 @@ further_reading:
         link: /learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/
         type: learningpath
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md b/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md
index a15b349a53..b1c9403c12 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/mte/_index.md
@@ -8,15 +8,16 @@ who_is_this_for: This is an introductory topic for developers who want to gain s
 
 learning_objectives: 
     - Run an example C program to gain an introductory understanding of MTE
-    
+
 prerequisites:
     - An AArch64 Linux development machine. Cloud instances can be used, refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ##### Tags
 
 skilllevels: Introductory
@@ -46,9 +47,6 @@ further_reading:
         link: https://youtu.be/Ja9pmZ2NqKE
         type: video
 
-
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md b/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md
index 9d5979f55e..95b219f94a 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/mte_on_pixel8/_index.md
@@ -17,12 +17,13 @@ prerequisites:
     - A Google Pixel 8 smartphone
     - A USB cable to connect your Google Pixel 8 to your desktop machine
     - Android Debug Bridge (adb) installed on your device. Follow the steps in https://developer.android.com/tools/adb to install Android SDK Platform Tools. The adb tool is included in this package.
+
+author: Roberto Lopez Mendez
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Roberto Lopez Mendez
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -35,7 +36,6 @@ tools_software_languages:
 operatingsystems:
     - Android
 
-
 further_reading:
     - resource:
         title: MTE User Guide for Android OS
@@ -54,8 +54,6 @@ further_reading:
         link: https://community.arm.com/arm-community-blogs/b/architectures-and-processors-blog/posts/enhanced-security-through-mte
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md b/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md
index 78d2a61d0b..39223aa32d 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-data-capture-unreal/_index.md
@@ -17,14 +17,15 @@ prerequisites:
     - Unreal Engine 5.5 installed
     - Visual Studio with C++ game development tools
     - A C++ Unreal project (such as the Third Person template)
+
+author:
+    - Annie Tallund
+    - Richard Burton
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: 
-- Annie Tallund
-- Richard Burton
-
 ### Tags
 skilllevels: Introductory
 subjects: Graphics
@@ -38,8 +39,6 @@ tools_software_languages:
 operatingsystems:
     - Windows
 
-
-
 further_reading:
     - resource:
         title: Neural Graphics Data Capture Plugin for Unreal Engine
@@ -62,12 +61,9 @@ further_reading:
         link: https://github.com/arm/neural-graphics-model-gym/blob/main/docs/nss/nss_data_generation.md
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
-
diff --git a/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-playbook-evaluate/_index.md b/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-playbook-evaluate/_index.md
index 5176c22689..d27041ba93 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-playbook-evaluate/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/neural-graphics-playbook-evaluate/_index.md
@@ -17,6 +17,10 @@ prerequisites:
 
 author: Annie Tallund
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: Graphics
diff --git a/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md b/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md
index 52b6c9e1a7..f6189af421 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md
@@ -6,24 +6,23 @@ minutes_to_complete: 30
 
 who_is_this_for: This is an introductory topic for developers experimenting with neural graphics using Unreal Engine® and ML Extensions for Vulkan®.
 
-
 learning_objectives:
     - Understand how Arm enables neural graphics for game development
     - Configure ML extensions for Vulkan emulation
     - Enable Neural Super Sampling (NSS) in Unreal Engine
     - Run and visualize real-time upscaling with NSS
 
-
 prerequisites:
     - Windows 11
     - Unreal Engine 4.27 or 5.4 or 5.6 (with the Templates and Feature Pack enabled)
     - Visual Studio (with Desktop Development with C++ and .NET desktop build tools)
+
+author: Annie Tallund
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Annie Tallund
-
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -38,8 +37,6 @@ tools_software_languages:
 operatingsystems:
     - Windows
 
-
-
 further_reading:
     - resource:
         title: Neural Graphics Development Kit
@@ -58,8 +55,6 @@ further_reading:
         link: https://community.arm.com/arm-community-blogs/b/mobile-graphics-and-gaming-blog/posts/how-arm-neural-super-sampling-works
         type: blog
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md b/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md
index bfb68635a7..6d2e016753 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/onnx/_index.md
@@ -18,12 +18,13 @@ prerequisites:
   - Basic familiarity with PyTorch or TensorFlow
   - An Arm64 device such as a Raspberry Pi or Android smartphone
   - Android Studio (required only for the final deployment section)
+
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dawid Borycki
-
 ### Tags
 skilllevels: Advanced
 subjects: ML
diff --git a/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md b/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md
index 7eb6d5c471..83d0fd2a1d 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/optimizing-vertex-efficiency/_index.md
@@ -13,14 +13,15 @@ learning_objectives:
 prerequisites:
     - Understanding of vertex attributes.
     - Familiarity with Arm Frame Advisor (part of Arm Performance Studio).
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author:
     - Andrew Kilroy
     - Peter Harris
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -55,8 +56,6 @@ further_reading:
         link: https://developer.arm.com/documentation/101897/0304/Vertex-shading/Attribute-layout
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md
index 4ab6c57f0b..f15644dfe3 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_index.md
@@ -1,7 +1,7 @@
 ---
 title: Measure LLM inference performance with KleidiAI and SME2 on Android
 description: Learn how to build llama.cpp with KleidiAI and SME2 support to profile and accelerate LLM inference performance on Android devices.
-    
+
 minutes_to_complete: 40
 
 who_is_this_for: This is an advanced topic for software developers, performance engineers, and AI practitioners 
@@ -16,12 +16,13 @@ prerequisites:
     - A Linux host machine (x86_64 or aarch64) for building llama.cpp with the Arm GNU Toolchain
     - Git, CMake, and Android Debug Bridge (ADB) installed on your host machine
     - An Android device with Arm SME2 support for running and profiling the executable
+
+author: Zenon Zhilong Xiu
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Zenon Zhilong Xiu
-
 ### Tags
 skilllevels: Advanced
 subjects: ML
@@ -35,8 +36,6 @@ operatingsystems:
     - Android
     - Linux
 
-
-
 further_reading:
     - resource:
         title: Arm Scalable Matrix Extension introduction, part 1
@@ -54,8 +53,6 @@ further_reading:
         title: Profile llama.cpp performance with Arm Streamline and KleidiAI LLM kernels
         link: https://learn.arm.com/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/
         type: website
-        
-
 
 ### FIXED, DO NOT MODIFY
 # ================================================================================
diff --git a/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md b/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md
index 3b02b2adcd..cb85bc89bb 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md
@@ -16,12 +16,13 @@ prerequisites:
     - An Android device with Arm SME2 support
     - Basic understanding of machine learning model inference
     - Familiarity with Android NDK and cross-compilation
+
+author: Zenon Zhilong Xiu
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Zenon Zhilong Xiu
-
 ### Tags
 skilllevels: Advanced
 subjects: ML
@@ -36,8 +37,6 @@ operatingsystems:
     - Android
     - Linux
 
-
-
 further_reading:
     - resource:
         title: Arm Scalable Matrix Extension Introduction (Part 1)
@@ -51,8 +50,6 @@ further_reading:
         title: Arm SME2 Introduction (Part 4)
         link: https://developer.arm.com/community/arm-community-blogs/b/architectures-and-processors-blog/posts/part4-arm-sme2-introduction
         type: blog
-        
-
 
 ### FIXED, DO NOT MODIFY
 # ================================================================================
diff --git a/content/learning-paths/mobile-graphics-and-gaming/preparing-models-for-nt/_index.md b/content/learning-paths/mobile-graphics-and-gaming/preparing-models-for-nt/_index.md
index 777f616ac1..fa8741012c 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/preparing-models-for-nt/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/preparing-models-for-nt/_index.md
@@ -20,6 +20,10 @@ prerequisites:
 
 author: Joshua Marshall-Law
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Advanced
 subjects: ML
diff --git a/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md b/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md
index c91bba24e1..023d8b8c0d 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/profiling-ml-on-arm/_index.md
@@ -15,12 +15,13 @@ prerequisites:
     - For profiling the ML inference, [Arm NN ExecuteNetwork](https://github.com/ARM-software/armnn/releases) or [ExecuTorch](https://github.com/pytorch/executorch).
     - For profiling the application, [Arm Performance Studio with Streamline](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio).
     - Android Studio Profiler.
+
+author: Ben Clark
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Ben Clark
-
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -37,16 +38,12 @@ operatingsystems:
     - Android
     - Linux
 
-
 further_reading:
     - resource:
         title: Arm Streamline User Guide  
         link: https://developer.arm.com/documentation/101816/latest/
         type: documentation
 
-
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md b/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md
index 0f8ff84326..ee23d6aec1 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/_index.md
@@ -17,12 +17,12 @@ prerequisites:
     - Basic knowledge of Unity and programming concepts
     - The setup described in the Learning Path [Get started with Unity on Android](/learning-paths/mobile-graphics-and-gaming/get-started-with-unity-on-android)
 
+author: Joshua Marshall-Law
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Joshua Marshall-Law
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -38,7 +38,6 @@ tools_software_languages:
 operatingsystems:
     - Android
 
-
 further_reading:
     - resource:
         title: Unity Profiler documentation
@@ -49,8 +48,6 @@ further_reading:
         link: https://docs.unity3d.com/Packages/com.unity.performance.profile-analyzer@0.4/manual/profiler-analyzer-window.html
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md b/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md
index 9f4997e439..2ff2882e37 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_index.md
@@ -1,7 +1,7 @@
 ---
 title: Quantize neural upscaling models with ExecuTorch
 description: Learn how to apply post-training quantization to PyTorch models using TorchAO and export INT8 models to .vgf format with the ExecuTorch Arm backend.
-    
+
 minutes_to_complete: 60
 
 who_is_this_for: This is an advanced topic for ML developers who want to reduce latency and memory bandwidth by exporting INT8 models to the `.vgf` file format using the ExecuTorch Arm backend.
@@ -15,14 +15,15 @@ learning_objectives:
 prerequisites:
     - Basic PyTorch model training and evaluation experience
     - A development machine with Python 3.10+ and PyTorch installed that runs ExecuTorch
+
+author:
+    - Richard Burton
+    - Annie Tallund
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author:
-- Richard Burton
-- Annie Tallund
-
 ### Tags
 skilllevels: Advanced
 subjects: ML
diff --git a/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md b/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md
index 24d1b02ce9..5e66ce4dc6 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/_index.md
@@ -15,12 +15,13 @@ prerequisites:
     - An appropriate Android device that supports the required Vulkan extensions (for example, Vivo X100).
     - Knowledge of the Vulkan API.
     - A Vulkan renderer. Most code is generic and should be easy to incorporate into any deferred PBR renderer.
+
+author: Iago Calvo Lista
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Iago Calvo Lista
-
 ### Tags
 skilllevels: Advanced
 subjects: Graphics
@@ -32,7 +33,6 @@ operatingsystems:
 tools_software_languages:
     - Vulkan
 
-
 further_reading:
     - resource:
         title: "Arm GPU Best Practices Developer Guide: Ray Tracing"
@@ -53,7 +53,6 @@ further_reading:
         link: https://www.youtube.com/watch?v=OPLTK7RB7co
         type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md b/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md
index 8ffe96b231..8bd45fd4f2 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/_index.md
@@ -14,12 +14,13 @@ prerequisites:
     - Frame Advisor, part of Arm Performance Studio, installed. Refer to the [Arm Performance Studio](/install-guides/ams/) install guide. 
     - If you wish to analyze your own applications you will need a supported Android device.
     - Some basic familiarity with Frame Advisor. Review the [Frame Advisor](/learning-paths/mobile-graphics-and-gaming/ams/fa/) section in [Get started with Arm Performance Studio for mobile](/learning-paths/mobile-graphics-and-gaming/ams/).
+
+author: Mark Thurman
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Mark Thurman
-
 further_reading:
     - resource:
         title: Frame Advisor User Guide
@@ -57,7 +58,6 @@ operatingsystems:
     - macOS
     - Android
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md
index 9a80f2d136..6f80ac2518 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_index.md
@@ -17,9 +17,6 @@ prerequisites:
     - A Linux-based x86 or macOS development machine with at least 8 GB of RAM and 50 GB of disk space (tested on Ubuntu 22.04 with x86_64).
     - A [HuggingFace](https://huggingface.co/) account.
     - An Android phone in [developer mode](https://developer.android.com/studio/debug/dev-options) and a cable to connect it to your development machine.
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author:
     - Nina Drozd
@@ -28,6 +25,10 @@ author:
     - Aude Vuilliomenet
     - Annie Tallund
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -58,9 +59,6 @@ further_reading:
         link: https://arxiv.org/abs/2505.08175
         type: website
 
-
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md
index 254804b9fa..f044e97879 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-with-executorch/_index.md
@@ -15,14 +15,15 @@ prerequisites:
     - A Linux-based x86 or macOS development machine with at least 8 GB of RAM and 50 GB of disk space (tested on Ubuntu 22.04 with x86_64 and macOS with Apple Silicon)
     - A [Hugging Face](https://huggingface.co/) account
     - An Android phone in [developer mode](https://developer.android.com/studio/debug/dev-options) with at least 8 GB of RAM and a cable to connect it to your development machine
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author:
     - Adnan AlSinan
     - Pareena Verma
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -59,9 +60,6 @@ further_reading:
         link: https://gitlab.arm.com/kleidi/kleidiai
         type: website
 
-
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md b/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md
index 12f0c6ec6c..390d9dfcf1 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/unity_on_orange_pi/_index.md
@@ -16,12 +16,13 @@ prerequisites:
     - A microSD card (16GB or greater; class 10 or faster)
     - An ethernet connection
     - A mouse and keyboard connected to the Orange Pi
+
+author: Gabriel Peterson
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Gabriel Peterson
-
 ### Tags
 skilllevels: Introductory
 subjects: Gaming
@@ -49,9 +50,6 @@ further_reading:
         link: https://learn.unity.com/
         type: website
 
-
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md b/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md
index ffa31a4697..7962b259ea 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/unity_packages/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
     - Familiarity with Unity and the Unity Profiler
     - Familiarity with Arm Performance Studio tools
+
+author: Julie Gaskin
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Julie Gaskin
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -34,7 +35,6 @@ operatingsystems:
     - macOS
     - Linux
 
-
 further_reading:
     - resource:
         title: Get started with Streamline 
@@ -57,8 +57,6 @@ further_reading:
         link: https://developer.arm.com/Tools%20and%20Software/Arm%20Mobile%20Studio
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md b/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md
index ef9bb1248e..6d747db917 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/using-neon-intrinsics-to-optimize-unity-on-android/_index.md
@@ -17,14 +17,15 @@ prerequisites:
     - Recent Android device, such as a mobile phone or tablet
     - Desktop computer capable of running Unity
     - Unity version compatible with Unity Burst compiler 1.5 or later
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author:
     - Ben Clark
     - Joshua Marshall-Law
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Advanced
 subjects: Gaming
@@ -40,7 +41,6 @@ tools_software_languages:
 operatingsystems:
     - Android
 
-
 further_reading:
     - resource:
         title: Arm Neon documentation
@@ -51,8 +51,6 @@ further_reading:
         link: https://docs.unity3d.com/Manual/com.unity.burst.html
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md b/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md
index 9ce7a1ca97..0d58e90391 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/_index.md
@@ -14,12 +14,13 @@ prerequisites:
 - A computer capable of running Unity. (Instructions are for Windows, but could be adapted to other platforms.)
 - An Android mobile device that has a 64-bit processor and supports at least Android 8.
 - A USB cable to connect the mobile device to your computer.
+
+author: Annie Tallund
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Annie Tallund
-
 ### Tags
 skilllevels: Advanced
 subjects: Gaming
@@ -30,7 +31,6 @@ operatingsystems:
 tools_software_languages:
     - Unity
 
-
 further_reading:
     - resource:
         title: Using Unity's Machine Learning Agents on Arm on YouTube
@@ -45,8 +45,6 @@ further_reading:
         link: https://developer.arm.com/Tools%20and%20Software/Arm%20Mobile%20Studio
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md b/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md
index d00284e997..4f0a65c85b 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/vision-llm-inference-on-android-with-kleidiai-and-mnn/_index.md
@@ -12,18 +12,18 @@ learning_objectives:
     - Install an Android demo application using the model to run an inference.
     - Compare inference performance with and without KleidiAI Arm-optimized micro-kernels.
 
-
 prerequisites:
     - A development machine with [Android Studio](https://developer.android.com/studio) installed.
     - A smartphone running Android with support for `i8mm` and `dotprod` instructions.
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author:
     - Shuheng Deng
     - Yiyang Fan
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -35,8 +35,6 @@ tools_software_languages:
 operatingsystems:
     - Android
 
-
-
 further_reading:
     - resource:
         title: "MNN: A Universal and Efficient Inference Engine"
@@ -55,8 +53,6 @@ further_reading:
         link: https://github.com/ARM-software/kleidiai
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md b/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md
index 25e0a1f044..6774ac6f5c 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md
@@ -17,14 +17,15 @@ prerequisites:
     - An Android phone with support for SME (Scalable Matrix Extension) instructions, required for SME performance checking
     - This Learning Path was tested on a Vivo X300 Pro.
     - A development machine with [Android Studio](https://developer.android.com/studio) installed.
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author:
     - Arnaud de Grandmaison
     - Nina Drozd
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 skilllevels: Introductory
 subjects: Performance and Architecture
 armips:
diff --git a/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md b/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md
index f6e8c1817d..de5a25723e 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/_index.md
@@ -7,7 +7,6 @@ minutes_to_complete: 90
 
 who_is_this_for: This Learning Path is for developers, ML practitioners, and game developers interested in building on-device AI applications, including voice interfaces, real-time interactions with non-player characters (NPCs), and edge AI systems powered by LLMs on Arm platforms.
 
-
 learning_objectives:
     - Build a voice-to-LLM pipeline using Whisper and llama.cpp.
     - Train a voice sentiment classification model using HuBERT on the RAVDESS dataset.
@@ -18,12 +17,13 @@ prerequisites:
     - Python 3.9 or later for programming.
     - A working microphone for voice input.
     - Basic Python and command-line knowledge.
+
+author: Bhanu Arya
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Bhanu Arya
-
 ### Tags
 skilllevels: Advanced
 subjects: ML
diff --git a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md
index 590af7981d..d26f7d5ac8 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md
@@ -16,12 +16,13 @@ prerequisites:
     - Visual Studio 2022
     - Visual Studio workload - Desktop development with C++
     - Visual Studio workload - .NET desktop build tools
+
+author: Annie Tallund
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Annie Tallund
-
 ### Tags
 skilllevels: Advanced
 subjects: ML
@@ -34,7 +35,6 @@ tools_software_languages:
 operatingsystems:
     - Windows
 
-
 further_reading:
     - resource:
         title: Neural Graphics Development Kit
@@ -57,8 +57,6 @@ further_reading:
         link: https://community.arm.com/arm-community-blogs/b/mobile-graphics-and-gaming-blog/posts/how-arm-neural-super-sampling-works
         type: blog
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/adler32-kiro/_index.md b/content/learning-paths/servers-and-cloud-computing/adler32-kiro/_index.md
index a1c0919d11..799afa68ac 100644
--- a/content/learning-paths/servers-and-cloud-computing/adler32-kiro/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/adler32-kiro/_index.md
@@ -1,7 +1,6 @@
 ---
 title: Optimize an Adler-32 checksum function with SVE intrinsics using the Arm MCP server
 
-
 description: Use the Arm MCP server with an AI coding assistant to incrementally optimize a scalar C Adler-32 checksum function using SVE intrinsics on Arm Neoverse servers.
 
 minutes_to_complete: 45
@@ -22,6 +21,10 @@ prerequisites:
 
 author: Jason Andrews
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 skilllevels: Introductory
 subjects: Performance and Architecture
 cloud_service_providers:
@@ -63,7 +66,6 @@ further_reading:
         link: https://developer.arm.com/documentation/100987/latest/
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md b/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
index f4105a63f3..47130604fa 100644
--- a/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md
@@ -16,12 +16,13 @@ prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an on-premise Arm server.
     - Basic understanding of Python and prompt engineering.
     - Understanding of LLM fundamentals.
+
+author: Andrew Choi
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Andrew Choi
-
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -39,8 +40,6 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
-
 further_reading:
     - resource:
         title: llama.cpp
@@ -51,8 +50,6 @@ further_reading:
         link: https://llama-cpp-agent.readthedocs.io/en/latest/
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/aks/_index.md b/content/learning-paths/servers-and-cloud-computing/aks/_index.md
index 2d0674fd7f..23408235a4 100644
--- a/content/learning-paths/servers-and-cloud-computing/aks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/aks/_index.md
@@ -15,12 +15,12 @@ prerequisites:
     - An Azure account
     - A machine with [Terraform](/install-guides/terraform/), [Azure CLI](/install-guides/azure-cli), and [Kubectl](/install-guides/kubectl/) installed
 
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ### Tags
 skilllevels: Advanced
 subjects: Containers and Virtualization
@@ -61,8 +61,6 @@ further_reading:
         link: https://registry.terraform.io/providers/hashicorp/azurerm/latest/docs/
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/alluxio-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/alluxio-cobalt/_index.md
index 0e08cd0974..cb5b41ec53 100644
--- a/content/learning-paths/servers-and-cloud-computing/alluxio-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/alluxio-cobalt/_index.md
@@ -1,10 +1,8 @@
 ---
 title: Deploy Alluxio on Azure Cobalt 100 Arm64 virtual machines for data orchestration and caching
 
-
 description: Learn how to install and configure Alluxio on an Azure Cobalt 100 Arm64 virtual machine, integrate it with Apache Spark, enable data caching, and benchmark performance improvements for analytics workloads.
 
-   
 minutes_to_complete: 90
 
 who_is_this_for: This is an introductory topic for developers, data engineers, and platform engineers who want to build high-performance data pipelines and analytics systems using Alluxio on Arm-based cloud environments.
@@ -23,6 +21,10 @@ prerequisites:
 
 author: Pareena Verma
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: Containers and Virtualization
diff --git a/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md b/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
index badda3de19..56e5d34e92 100644
--- a/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md
@@ -18,12 +18,13 @@ prerequisites:
   - Basic familiarity with Python
   - Basic understanding of data formats such as Parquet or ORC
   - Familiarity with Linux command-line operations
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ##### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -61,12 +62,12 @@ further_reading:
       title: Arrow Flight documentation
       link: https://arrow.apache.org/docs/format/Flight.html
       type: documentation
-  
+
   - resource:
       title: Apache Parquet documentation
       link: https://parquet.apache.org/documentation/latest/
       type: documentation
-  
+
   - resource:
       title: MinIO documentation
       link: https://min.io/docs
diff --git a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
index adfbd2d509..24be0d3379 100644
--- a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md
@@ -16,12 +16,13 @@ learning_objectives:
 prerequisites:
     - An [AWS account](https://aws.amazon.com/) with permission to launch Graviton4 (`c8g.4xlarge` or larger) instances
     - Basic familiarity with Linux and SSH
+
+author: Julien Simon
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Julien Simon
-
 # Tags
 # Tagging metadata, see the Learning Path guide for the allowed values
 skilllevels: Introductory
@@ -38,7 +39,6 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
 further_reading:
   - resource:
       title: Arcee AI
diff --git a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
index dc14eb7a69..25f4cebe29 100644
--- a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_index.md
@@ -16,12 +16,13 @@ learning_objectives:
 prerequisites:
     - A [Google Cloud account](https://console.cloud.google.com/) with permission to launch Axion (`c4a-standard-16` or larger) instances
     - Basic familiarity with Linux and SSH
+
+author: Julien Simon
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Julien Simon
-
 # Tags
 # Tagging metadata, see the Learning Path guide for the allowed values
 skilllevels: Introductory
@@ -38,7 +39,6 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
 further_reading:
   - resource:
       title: Arcee AI
diff --git a/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
index a38fab5195..7b43d4e22c 100644
--- a/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/argo-cd-gcp/_index.md
@@ -20,12 +20,13 @@ prerequisites:
   - Basic familiarity with [Kubernetes concepts](https://kubernetes.io/docs/concepts/)
   - Basic understanding of Git and GitHub workflows
   - Familiarity with basic Linux command-line usage
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ##### Tags
 skilllevels: Introductory
 subjects: Containers and Virtualization
@@ -42,7 +43,7 @@ tools_software_languages:
   - GKE
   - Git
   - NGINX
-    
+
 operatingsystems:
   - Linux
 
diff --git a/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md b/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
index b90e551239..1a44895f84 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm-cpp-memory-model/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
     - Access to an x86 and an Arm cloud instance (virtual machine).
     - Proficiency in C++ programming.
+
+author: Kieran Hejmadi
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Kieran Hejmadi
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -31,7 +32,7 @@ tools_software_languages:
     - Runbook
 operatingsystems:
     - Linux
-   
+
 further_reading:
     - resource:
         title: C++ Memory Order Reference Manual 
@@ -42,7 +43,6 @@ further_reading:
         link: https://github.com/google/sanitizers/wiki/threadsanitizercppmanual
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md b/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
index 8faf618b95..e4a9f77277 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm-mcp-server/_index.md
@@ -17,12 +17,13 @@ prerequisites:
     - An AI-powered IDE such as VS Code, Copilot in VS Code, Kiro (IDE or CLI) or Codex
     - Basic familiarity with Docker and C/C++ development
     - Access to an Arm-based cloud instance or local Arm computer running Linux or macOS
+
+author: Joe Stech
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Joe Stech
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -36,8 +37,6 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
-
 further_reading:
     - resource:
         title: Arm MCP Server GitHub Repository
@@ -60,8 +59,6 @@ further_reading:
         link: /learning-paths/servers-and-cloud-computing/intro/
         type: learning-path
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md b/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
index 9da852cdfb..efeab103e4 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm-soc-migration-learning-path/_index.md
@@ -18,12 +18,13 @@ prerequisites:
     - Working knowledge of C programming
     - Familiarity with Linux development environments and basic embedded or cloud deployment concepts
     - Experience building applications with GCC and CMake
+
+author: Daniel Schleicher
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Daniel Schleicher
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
diff --git a/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md b/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
index b95191ffa5..f019539281 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm_linux_page_size/_index.md
@@ -16,16 +16,17 @@ learning_objectives:
 
 prerequisites:
   - Access to an Arm-based Linux system running Ubuntu, Debian, or CentOS.
+
+author: Geremy Cohen
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Geremy Cohen
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
-    
+
 armips:
     - Neoverse
 
diff --git a/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md b/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
index 7e1a6b23a0..032e47330b 100644
--- a/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/arm_pmu/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
     - Use the Linux perf_event_open system call to instrument event counters in code
 prerequisites:
     - An Arm computer running Linux. A bare metal or cloud metal instance is best because they expose more counters. You can use a virtual machine (VM), but fewer counters may be available. These instructions have been tested on the `a1.metal` instance type.
+
+author: Julio Suarez
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Julio Suarez
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -48,7 +49,6 @@ further_reading:
         link: https://en.wikipedia.org/wiki/Perf_%28Linux%29
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md b/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
index c0667e88f1..f15511ba11 100644
--- a/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/aws-copilot/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
     - An Amazon Web Services (AWS) account
     - A local computer with Docker, AWS CLI, and AWS Copilot CLI installed
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ### Tags
 skilllevels: Introductory
 subjects: Containers and Virtualization
@@ -32,7 +33,6 @@ operatingsystems:
 tools_software_languages:
     - Docker
 
-
 further_reading:
     - resource:
         title: Introducing AWS Copilot
@@ -47,8 +47,6 @@ further_reading:
         link: https://youtu.be/hBHf241-D2Y?si=ySm0e4VwbgFSoy3s
         type: video
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md b/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
index 7200533388..7427d115f2 100644
--- a/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/aws-terraform/_index.md
@@ -5,7 +5,7 @@ description: Learn how to automate the creation and deployment of AWS Graviton i
 minutes_to_complete: 60   
 
 who_is_this_for: This is an introductory topic for software developers who are new to deploying Arm instances on AWS using Terraform.
- 
+
 learning_objectives: 
     - Automate AWS EC2 instance creation using Terraform
     - Deploy Arm instances on AWS and provide access via Jump Server
@@ -15,12 +15,12 @@ prerequisites:
     - An Amazon Web Services (AWS) [account](https://aws.amazon.com/)
     - A computer with [Terraform](/install-guides/terraform) installed
 
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ### Tags
 skilllevels: Advanced
 subjects: Containers and Virtualization
@@ -44,8 +44,6 @@ further_reading:
         link: https://aws.amazon.com/blogs/aws/new-amazon-ec2-c7g-instances-powered-by-aws-graviton3-processors/
         type: Blog
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
index f13f5fdb81..364aaa8606 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-arm-template/_index.md
@@ -16,12 +16,13 @@ prerequisites:
     - An Azure subscription with permissions to create resource groups, virtual machines, and networking resources
     - Azure CLI installed on your local machine - see the [Azure CLI install guide](/install-guides/azure-cli/)
     - An SSH key pair for authentication
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 skilllevels: Introductory
 subjects: Containers and Virtualization
@@ -49,7 +50,6 @@ further_reading:
         link: /learning-paths/servers-and-cloud-computing/cobalt/
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
index dbfc92d759..8b4cabf81f 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/_index.md
@@ -16,12 +16,12 @@ prerequisites:
     - A GitHub account.
     - A machine with [Terraform](/install-guides/terraform/),[Azure CLI](/install-guides/azure-cli), and [Kubectl](/install-guides/kubectl/) installed.
 
+author: Pranay Bakre
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pranay Bakre
-
 ### Tags
 skilllevels: Advanced
 subjects: Containers and Virtualization
@@ -57,8 +57,6 @@ further_reading:
         link:  https://kubernetes.io/docs/home/
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
index f410c93e95..e71c189185 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-terraform/_index.md
@@ -15,12 +15,13 @@ learning_objectives:
 prerequisites:
     - An Azure account
     - A computer with Terraform installed
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ### Tags
 skilllevels: Advanced
 subjects: Containers and Virtualization
@@ -49,8 +50,6 @@ further_reading:
         link: https://learn.microsoft.com/en-us/azure/bastion/bastion-overview
         type: Documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
index 0dd1100955..33ef8c507c 100644
--- a/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md
@@ -6,7 +6,6 @@ minutes_to_complete: 120
 
 who_is_this_for: This is an advanced topic for developers who want to run Azure Linux 3.0 on Arm-based Cobalt 100 processors in a custom virtual machine.
 
-
 learning_objectives:
   - Use QEMU to create a raw disk image
   - Boot a virtual machine using an AArch64 ISO and install Azure Linux 3.0
@@ -18,12 +17,13 @@ learning_objectives:
 prerequisites:
     - A [Microsoft Azure](https://azure.microsoft.com/) account with permission to create resources, including instances using Cobalt 100 processors
     - A Linux machine with [QEMU](https://www.qemu.org/download/) and the [Azure CLI](/install-guides/azure-cli/) installed and authenticated
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ### Tags
 skilllevels: Advanced
 subjects: Containers and Virtualization
@@ -58,7 +58,6 @@ further_reading:
       link: https://learn.microsoft.com/en-us/azure/virtual-machines/linux/upload-vhd
       type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md b/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
index 1e7cab8840..52d3c82d07 100644
--- a/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/benchmark-nlp/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 
 prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an on-premise Arm server.
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -35,7 +36,7 @@ tools_software_languages:
     - Python
     - PyTorch
     - Hugging Face
-    
+
 further_reading:
     - resource:
         title: Hugging Face Documentation
@@ -54,8 +55,6 @@ further_reading:
         link: https://pytorch.org/docs/stable/index.html
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md b/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
index 4efe64fe57..a0b0ebb6e8 100644
--- a/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md
@@ -6,7 +6,6 @@ minutes_to_complete: 20
 
 who_is_this_for: This is an introductory topic for database developers, performance engineers, and anyone interested in optimizing data processing workloads on Arm-based cloud instances.
 
-
 learning_objectives:
   - Understand bitmap scanning operations in database systems
   - Implement bitmap scanning with scalar, Neon, and SVE instructions
@@ -16,12 +15,12 @@ learning_objectives:
 prerequisites:
 - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from an appropriate
   cloud service provider.
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author: Pareena Verma
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 ### Tags
 skilllevels: Introductory
diff --git a/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md b/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
index f8cb798748..bac4423f6a 100644
--- a/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bolt-demo/_index.md
@@ -6,7 +6,6 @@ minutes_to_complete: 20
 
 who_is_this_for: This is an introductory topic for developers who have compiled an AArch64 Linux application and want to evaluate whether LLVM BOLT can improve its runtime performance.
 
-
 learning_objectives:
     - Identify whether a program is a good candidate for code layout optimization
     - Install LLVM BOLT on Linux
@@ -14,7 +13,6 @@ learning_objectives:
     - Collect profile data using multiple techniques, including BRBE, instrumentation, SPE, and PMU event sampling
     - Evaluate the impact of BOLT optimizations using performance metrics and profiling data
 
-
 prerequisites:
     - An AArch64 system running Linux with [perf](/install-guides/perf/) installed
     - Linux kernel version 6.17 or later to enable Branch Record Buffer Extension ([BRBE profiling](/learning-paths/servers-and-cloud-computing/bolt-demo/brbe/))
@@ -22,12 +20,12 @@ prerequisites:
     - GCC version 13.3 or later to compile the example program ([GCC](/install-guides/gcc/) )
     - A system with with sufficient hardware performance counters to use the [TopDown](/install-guides/topdown-tool) methodology. This typically requires running on bare metal rather than a virtualized environment.
 
+author: Paschalis Mpeis
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Paschalis Mpeis
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -67,8 +65,6 @@ further_reading:
         link: https://developer.arm.com/documentation/ddi0487/latest
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md b/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
index eb9614320f..bbafc9268e 100644
--- a/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bolt-merge/_index.md
@@ -15,12 +15,13 @@ learning_objectives:
 
 prerequisites:
   - An Arm-based Linux system with [BOLT](/install-guides/bolt/) and [Linux Perf](/install-guides/perf/) installed
+
+author: Gayathri Narayana Yegna Narayanan
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Gayathri Narayana Yegna Narayanan
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -44,7 +45,6 @@ further_reading:
         link: https://research.facebook.com/publications/bolt-a-practical-binary-optimizer-for-data-centers-and-beyond/
         type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/bolt/_index.md b/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
index 62d042b7a7..44f98df34b 100644
--- a/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/bolt/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
     - An Arm based system running Linux with [BOLT](/install-guides/bolt/) and [Linux Perf](/install-guides/perf/) installed. The Linux kernel should be version 5.15 or later. Earlier kernel versions can be used, but some Linux Perf features may be limited or not available. For [SPE](./bolt-spe) the version should be 6.14 or later.
     - (Optional) A second, more powerful Linux system to build the software executable and run BOLT.
+
+author: Jonathan Davies
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jonathan Davies
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -44,8 +45,6 @@ further_reading:
         link: https://research.facebook.com/publications/bolt-a-practical-binary-optimizer-for-data-centers-and-beyond/
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
index 81e4b2a256..da34960cd2 100644
--- a/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/_index.md
@@ -18,12 +18,13 @@ prerequisites:
   - Basic Linux system administration skills, including how to create users, install packages, and manage services
   - Familiarity with [Docker](https://docs.docker.com/get-started/) and container concepts
   - A [GitHub account](https://github.com/join) to host your application repository
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ##### Tags
 skilllevels: Introductory
 subjects: CI-CD
diff --git a/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
index 4cdeee6691..5d358c7fb1 100644
--- a/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/_index.md
@@ -6,7 +6,6 @@ minutes_to_complete: 30
 
 who_is_this_for: This is an introductory topic for software developers migrating Cassandra workloads from x86_64 to Arm-based servers, specifically on Google Cloud C4A virtual machines built on Axion processors.
 
-
 learning_objectives:
   - Provision an Arm-based SUSE SLES virtual machine on Google Cloud (C4A with Axion processors)
   - Install and configure Apache Cassandra on a SUSE Arm64 (C4A) instance
@@ -16,12 +15,13 @@ learning_objectives:
 prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Familiarity with Cassandra architecture, replication, and [Cassandra partitioning and event-driven I/O](https://cassandra.apache.org/doc/stable/cassandra/architecture/)
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ##### Tags
 skilllevels: Introductory
 subjects: Databases
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
index 05280a733a..d093d3e3bf 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-container/_index.md
@@ -15,14 +15,15 @@ learning_objectives:
 
 prerequisites:
     - An AArch64 or x86_64 computer running Linux or macOS. You can use cloud instances, refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author:
     - Pareena Verma
     - Arnaud de Grandmaison
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -38,7 +39,6 @@ tools_software_languages:
     - Docker
     - Runbook
 
-
 further_reading:
     - resource:
         title: Learn the architecture - Introducing Arm Confidential Compute Architecture
@@ -61,7 +61,6 @@ further_reading:
         link: https://developer.arm.com/documentation/den0137/latest/
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
index c34692fcab..9958808c73 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md
@@ -18,12 +18,13 @@ prerequisites:
     - Completion of [Get Started with CCA Attestation and Veraison](/learning-paths/servers-and-cloud-computing/cca-veraison/) Learning Path
     - Completion of the [Run an application in a Realm using the Arm Confidential Computing Architecture (CCA)](/learning-paths/servers-and-cloud-computing/cca-container/) Learning Path
     - Completion of the [Run an end-to-end Attestation Flow](/learning-paths/servers-and-cloud-computing/cca-essentials/) Learning Path
+
+author: Arnaud de Grandmaison
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Arnaud de Grandmaison
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
index 7b225c1175..997aa805b9 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-essentials/_index.md
@@ -15,15 +15,16 @@ prerequisites:
     - An AArch64 or x86_64 computer running Linux. You can use cloud instances, see this list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
     - Completion of [Get Started with CCA Attestation and Veraison](/learning-paths/servers-and-cloud-computing/cca-veraison/) Learning Path.
     - Completion of the [Run an application in a Realm using the Arm Confidential Computing Architecture (CCA)](/learning-paths/servers-and-cloud-computing/cca-container/) Learning Path.
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author: 
     - Arnaud de Grandmaison
     - Paul Howard
     - Pareena Verma
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -40,7 +41,6 @@ tools_software_languages:
     - Veraison
     - Runbook
 
-    
 further_reading:
     - resource:
         title: Arm Confidential Compute Architecture
@@ -59,7 +59,6 @@ further_reading:
         link: https://developer.arm.com/documentation/den0137/latest/
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
index 1673dc8559..cd22f344ac 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-kata/_index.md
@@ -15,13 +15,13 @@ prerequisites:
   - An AArch64 or x86_64 computer running Linux or macOS. Cloud-based instances can also be used; see the [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/)
   - Completion of the [Run an end-to-end Attestation with Arm CCA and Trustee](/learning-paths/servers-and-cloud-computing/cca-trustee) Learning Path
 
+author:
+  - Anton Antonov
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author:
-  - Anton Antonov
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
index 40c548ae3a..68cc42600d 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-trustee/_index.md
@@ -1,7 +1,7 @@
 ---
 title: Run an end-to-end attestation flow with Arm CCA and Trustee
 description: Learn how to deploy a CCA realm workload on an FVP and connect it with Trustee services to enable attestation-based confidential data processing.
-   
+
 minutes_to_complete: 60
 
 who_is_this_for: This Learning Path is for software developers who want to run an end-to-end attestation flow using Arm Confidential Compute Architecture (CCA) and Trustee services.
@@ -15,13 +15,14 @@ prerequisites:
   - An AArch64 or x86_64 computer running Linux or macOS; you can use cloud instances - see the [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/)
   - Completion of the [Get started with CCA attestation and Veraison](/learning-paths/servers-and-cloud-computing/cca-veraison/) Learning Path
   - Completion of the [Run an end-to-end attestation flow with Arm CCA](/learning-paths/servers-and-cloud-computing/cca-essentials/) Learning Path
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author:
   - Anton Antonov
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
index 6a3aeac70a..09d50edfb2 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 prerequisites:
     - An [AWS account](/learning-paths/servers-and-cloud-computing/csp/aws/) with access to AWS services.
     - An x86 computer running Ubuntu or Arch Linux, authorized for AWS access. If you're using another build environment, you'll need to configure the toolchains for cross-compilation.
+
+author: Paul Howard
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Paul Howard
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
diff --git a/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md
index 14df720201..a8d92ef36c 100644
--- a/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cca-veraison/_index.md
@@ -4,7 +4,6 @@ description: Learn how to inspect and verify Arm CCA attestation tokens using co
 
 minutes_to_complete: 30
 
-
 who_is_this_for: This Learning Path is for developers who would like to learn about attestation in confidential computing, using Arm's Confidential Computing Architecture (CCA). 
 
 learning_objectives:
@@ -14,15 +13,15 @@ learning_objectives:
     - Use an attestation verification service to evaluate a CCA attestation token.
     - Understand the purpose of the Open-Source Veraison project.
 
-
 prerequisites:
     - An Arm-based or x86 computer running Ubuntu. You can use a server instance from a cloud service provider of your choice.
+
+author: Paul Howard
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Paul Howard
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -35,9 +34,6 @@ tools_software_languages:
     - RME
     - Runbook
 
-
-
-
 further_reading:
     - resource:
         title: RATS architecture (RFC 9334) 
@@ -52,8 +48,6 @@ further_reading:
         link: https://datatracker.ietf.org/doc/draft-ietf-rats-ar4si/
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
index 80dd117313..3e98b6690f 100644
--- a/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/circleci-gcp/_index.md
@@ -1,7 +1,7 @@
 ---
 title: Run CircleCI Arm Native Workflows on a SUSE Arm GCP VM
 description: Learn how to set up CircleCI self-hosted machine runners on Google Cloud Axion C4A SUSE VMs and execute Arm-native CI/CD workflows using custom resource classes.
-    
+
 minutes_to_complete: 45
 
 who_is_this_for: This is an introductory topic for developers and DevOps engineers looking to set up and run CircleCI Arm native workflows on SUSE Linux Arm64 virtual machines (VMs), specifically on Google Cloud C4A with Axion processors, using self-hosted runners.
@@ -21,12 +21,13 @@ prerequisites:
     [jobs](https://circleci.com/docs/guides/orchestrate/jobs-steps/),
     [resource classes](https://circleci.com/docs/guides/execution-managed/resource-class-overview/), and 
     [runners](https://circleci.com/docs/guides/execution-runner/runner-overview/)
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ##### Tags
 skilllevels: Introductory
 subjects: CI-CD
diff --git a/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md b/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md
index 7d0c380cc1..750ffb038c 100644
--- a/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/circleci-on-aws/_index.md
@@ -1,7 +1,7 @@
 ---
 title: Deploy CircleCI Arm Native Workflows on AWS EC2 Graviton
 description: Learn how to install and configure CircleCI self-hosted machine runners on AWS Graviton Arm64 instances to execute CI/CD workflows natively on Arm.
-    
+
 minutes_to_complete: 30
 
 who_is_this_for: This is an introductory topic for developers and DevOps engineers who want to set up and run CircleCI Arm native workflows on Linux Arm64 virtual machines. You'll use AWS EC2 Graviton instances (Neoverse N1) and self-hosted runners. 
@@ -15,12 +15,13 @@ prerequisites:
   - An [AWS account](https://aws.amazon.com/free/) with billing enabled
   - A CircleCI account
   - Basic understanding of CircleCI workflows, jobs and resource classes
+
+author: Annie Tallund
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Annie Tallund
-
 ##### Tags
 skilllevels: Introductory
 subjects: CI-CD
@@ -35,7 +36,6 @@ tools_software_languages:
   - Bash
   - Git
 
-
 operatingsystems:
   - Linux
 
@@ -58,7 +58,6 @@ further_reading:
       link: https://circleci.com/docs/guides/toolkit/local-cli/
       type: documentation
 
-
 weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
diff --git a/content/learning-paths/servers-and-cloud-computing/clair/_index.md b/content/learning-paths/servers-and-cloud-computing/clair/_index.md
index 4a64481f0e..28ffd36c29 100644
--- a/content/learning-paths/servers-and-cloud-computing/clair/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/clair/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 
 prerequisites:
     - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an Arm server with recent versions of Docker and Go installed.
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ### Tags
 skilllevels: Advanced
 subjects: Containers and Virtualization
@@ -46,8 +47,6 @@ further_reading:
         link: https://aws.amazon.com/blogs/aws/new-amazon-ec2-c7g-instances-powered-by-aws-graviton3-processors/
         type: Blog
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
index edae70cd2d..76b947e133 100644
--- a/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md
@@ -1,7 +1,7 @@
 ---
 title: Build a real-time analytics pipeline with ClickHouse on Google Cloud Axion
 description: Learn how to deploy ClickHouse on Google Cloud Axion C4A processors and build a streaming ETL pipeline using Apache Beam, Dataflow, and Pub/Sub for real-time analytics.
-    
+
 minutes_to_complete: 50
 
 who_is_this_for: This is an introductory topic for developers deploying and optimizing ClickHouse on Arm-based Linux environments using Google Cloud C4A virtual machines powered by Axion processors, to evaluate ClickHouse performance and behavior on Arm-based infrastructure.
@@ -20,12 +20,13 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with [ClickHouse](https://clickhouse.com/)
   - Basic understanding of databases and SQL
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ##### Tags
 skilllevels: Introductory
 subjects: Databases
diff --git a/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md b/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
index b7344f0c29..747db8f678 100644
--- a/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/clickhouse/_index.md
@@ -12,12 +12,13 @@ learning_objectives:
 
 prerequisites:
     - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an on-premise Arm server.
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 skilllevels: Introductory
 subjects: Databases
@@ -53,7 +54,6 @@ further_reading:
         link: https://developer.arm.com/community/arm-community-blogs/b/servers-and-cloud-computing-blog/posts/improve-clickhouse-performance-up-to-26-by-using-aws-graviton3
         type: blog
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
index 9d0030b9ad..72bc9c0350 100644
--- a/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cobalt/_index.md
@@ -15,12 +15,13 @@ learning_objectives:
 prerequisites:
     - A Microsoft Azure subscription with permissions to create virtual machines and networking resources
     - Basic familiarity with SSH
+
+author: Joe Stech
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Joe Stech
-
 ### Tags
 # Tagging metadata, see the Learning Path guide for the allowed values
 skilllevels: Introductory
@@ -35,7 +36,6 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
 further_reading:
     - resource:
         title: Azure Cobalt 100 VM documentation
@@ -50,8 +50,6 @@ further_reading:
         link: https://learn.microsoft.com/azure/virtual-network/security-overview
         type: Documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md b/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
index 2dec123e04..9abe7100bc 100644
--- a/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/codebuild/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 prerequisites:
     - An [AWS account](/learning-paths/servers-and-cloud-computing/csp/aws/) for accessing AWS cloud services.
     - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or any Arm server, laptop, or single-board computer running [Docker](/install-guides/docker/) used to run the created images
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ### Tags
 skilllevels: Advanced
 subjects: CI-CD
@@ -43,8 +44,6 @@ further_reading:
         link: https://github.com/aws/aws-codebuild-docker-images 
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/codec/_index.md b/content/learning-paths/servers-and-cloud-computing/codec/_index.md
index d6a5a6ccae..3d8cdcd7f0 100644
--- a/content/learning-paths/servers-and-cloud-computing/codec/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/codec/_index.md
@@ -7,7 +7,6 @@ minutes_to_complete: 10
 who_is_this_for: This is an introductory topic for software developers who want to
   build and run an x265 codec on Arm servers and measure performance.
 
-
 learning_objectives:
 - Build x265 codec on Arm server
 - Run x265 codec on Arm server with the same video of various resolutions and encoding
@@ -16,12 +15,13 @@ learning_objectives:
 prerequisites:
 - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from an appropriate
   cloud service provider. This Learning Path has been verified on AWS EC2 and Oracle cloud services, running `Ubuntu Linux 20.04.`
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 test_images:
 - ubuntu:latest
 test_link: null
diff --git a/content/learning-paths/servers-and-cloud-computing/codec1/_index.md b/content/learning-paths/servers-and-cloud-computing/codec1/_index.md
index e4520a382b..87e1121302 100644
--- a/content/learning-paths/servers-and-cloud-computing/codec1/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/codec1/_index.md
@@ -1,12 +1,13 @@
 ---
 title: Run the AV1 and VP9 codecs on Arm Linux
 description: Learn how to build and run the AV1 and VP9 video codecs on Arm Linux systems with performance benchmarking across various resolutions and encoding configurations.
+
+author: Odin Shen
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Odin Shen
-
 minutes_to_complete: 30
 
 who_is_this_for: This is an introductory topic for software developers who want to
@@ -51,7 +52,6 @@ further_reading:
         link: https://developer.arm.com/community/arm-community-blogs/b/servers-and-cloud-computing-blog/posts/oracle-cloud-infrastructure-arm-based-a1
         type: blog
 
-
 weight: 1
 layout: learningpathall
 learning_path_main_page: "yes"
diff --git a/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
index 5d4051ff47..6a50c6a0c3 100644
--- a/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/_index.md
@@ -2,7 +2,6 @@
 title: Deploy Couchbase on Google Cloud C4A
 description: Learn how to install and configure Couchbase on Google Cloud Axion C4A Arm64 instances and benchmark read/write performance using YCSB workloads.
 
-    
 minutes_to_complete: 30
 
 who_is_this_for: This is an introductory topic for developers deploying Couchbase workloads on Arm Linux environments, specifically using Google Cloud C4A virtual machines (VM) powered by Axion processors. 
@@ -16,12 +15,13 @@ learning_objectives:
 prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled  
   - Basic familiarity with [Couchbase](https://www.couchbase.com/)
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ##### Tags
 skilllevels: Introductory
 subjects: Databases
diff --git a/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md b/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
index 7d48c0f50a..e1ae6353fc 100644
--- a/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cplusplus_compilers_flags/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 prerequisites:
     - Basic understanding of C++.
     - Basic understanding of compilers.
+
+author: Kieran Hejmadi
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Kieran Hejmadi
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -41,7 +42,6 @@ further_reading:
         link: https://community.arm.com/arm-community-blogs/b/operating-systems-blog/posts/runtime-detection-of-cpu-features-on-an-armv8-a-cpu
         type: blog
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md b/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md
index e94b09f09d..ef8748d2f1 100644
--- a/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cpp-profile-guided-optimisation/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 prerequisites:
     - Basic C++ understanding.
     - Access to an Arm-based Linux machine.
+
+author: Kieran Hejmadi
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Kieran Hejmadi
-
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -45,8 +46,6 @@ further_reading:
         link: https://github.com/google/benchmark
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md b/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
index d5660f9fd7..eaafaec6b0 100644
--- a/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 prerequisites:
     - Access to Arm Performix
     - Basic understanding of C++
+
+author: Kieran Hejmadi
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Kieran Hejmadi
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -31,7 +32,6 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
 further_reading:
     - resource:
         title: Optimize application performance using Arm Performix CPU microarchitecture analysis
@@ -46,9 +46,6 @@ further_reading:
         link: https://www.brendangregg.com/flamegraphs.html
         type: blog
 
-
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/csp/_index.md b/content/learning-paths/servers-and-cloud-computing/csp/_index.md
index 6590ebca7f..020703325a 100644
--- a/content/learning-paths/servers-and-cloud-computing/csp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/csp/_index.md
@@ -1,12 +1,13 @@
 ---
 title: Get started with Arm-based cloud instances
 description: Learn how to start an Arm-based virtual machine instance from major cloud service providers and verify the Arm architecture is being used.
+
+author: Ronan Synnott
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Ronan Synnott
-
 minutes_to_complete: 15
 
 who_is_this_for: This is an introductory topic for software developers who are new to Arm-based cloud instances.
@@ -32,8 +33,6 @@ operatingsystems:
     - Linux
 tools_software_languages:
 
-
-
 further_reading:
     - resource:
         title: Cloud computing (arm.com)
@@ -60,7 +59,6 @@ further_reading:
         link: https://developer.oracle.com/arm/
         type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md b/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
index 03aa0e493b..b2592a1db8 100644
--- a/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/deepseek-cpu/_index.md
@@ -13,13 +13,14 @@ learning_objectives:
 
 prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud provider or an on-premise Arm server. This Learning Path was tested on an AWS Graviton4 r8g.24xlarge instance.
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author:
     - Tianyu Li
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -37,7 +38,6 @@ tools_software_languages:
     - Generative AI
     - Python
 
-
 further_reading:
     - resource:
         title: Getting started with DeepSeek-R1
@@ -56,8 +56,6 @@ further_reading:
         link: https://huggingface.co/bartowski/DeepSeek-R1-GGUF 
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/deepspeed-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/deepspeed-on-axion/_index.md
index 61420e9026..1ae62e4841 100644
--- a/content/learning-paths/servers-and-cloud-computing/deepspeed-on-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/deepspeed-on-axion/_index.md
@@ -1,6 +1,6 @@
 ---
 title: Train and benchmark AI workloads with DeepSpeed on Google Cloud C4A Axion VMs
-    
+
 description: Set up PyTorch and DeepSpeed on Google Cloud C4A Axion Arm VMs running SUSE Linux to train neural network models, benchmark AI workloads, and validate scalable CPU-based AI execution on Arm64 processors.
 
 minutes_to_complete: 30
@@ -19,6 +19,10 @@ prerequisites:
 
 author: Pareena Verma
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ##### Tags
 skilllevels: Introductory
 subjects: ML
diff --git a/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md b/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md
index d54ec0edd7..1db5ff4bf7 100644
--- a/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/disk-io-benchmark/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an Arm Linux server.
     - Familiarity with Linux.
+
+author: Kieran Hejmadi
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Kieran Hejmadi
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
diff --git a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md
index 31697d4e30..7b6ef61620 100644
--- a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md
@@ -16,14 +16,15 @@ prerequisites:
     - Access to Meta's gated repository for the Llama 3.1 model family and a Hugging Face token to download models
     - Familiarity with the Learning Path [Deploy a Large Language Model (LLM) chatbot with llama.cpp using KleidiAI on Arm servers](/learning-paths/servers-and-cloud-computing/llama-cpu/)
     - Familiarity with AWS
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author: 
     - Aryan Bhusari
     - Joe Stech
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -38,16 +39,12 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
-
 further_reading:
     - resource:
         title: llama.cpp RPC server code
         link: https://github.com/ggml-org/llama.cpp/tree/master/tools/rpc
         type: Code
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md
index adc71975fe..0b8031e07a 100644
--- a/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/django-on-gcp/_index.md
@@ -1,6 +1,6 @@
 ---
 title: Deploy Django on Arm-based Google Cloud C4A 
-    
+
 minutes_to_complete: 60
 description: Learn how to deploy a production-grade Django REST API on Google Kubernetes Engine with Arm64 Axion node pools integrated with Google Cloud managed data services.
 who_is_this_for: This is an introductory topic for DevOps engineers and software developers who want to deploy, operate, and benchmark a production-grade Django REST API on Google Kubernetes Engine (GKE) running on Arm64 Axion processors, integrated with managed Google Cloud data services
@@ -20,12 +20,13 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled  
   - Basic familiarity with [Django](https://www.djangoproject.com/)
   - Basic understanding of containers and Kubernetes concepts
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ##### Tags
 skilllevels: Introductory
 subjects: Web
diff --git a/content/learning-paths/servers-and-cloud-computing/django/_index.md b/content/learning-paths/servers-and-cloud-computing/django/_index.md
index eee96882fa..62ccab3e0f 100644
--- a/content/learning-paths/servers-and-cloud-computing/django/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/django/_index.md
@@ -16,12 +16,13 @@ prerequisites:
     - Sudo access to install dependencies and to modify system configuration files.
     - Be comfortable with SSH/Linux terminal and basic system administration tasks.
     - To install both [Nginx](/learning-paths/servers-and-cloud-computing/nginx/) and [PostgreSQL](/learning-paths/servers-and-cloud-computing/postgresql/)
+
+author: Diego Russo
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Diego Russo
-
 ### Tags
 skilllevels: Introductory
 subjects: Web
@@ -40,7 +41,6 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
 further_reading:
     - resource:
         title: PostgreSQL Documentation
@@ -55,8 +55,6 @@ further_reading:
         link: https://docs.djangoproject.com/
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md b/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
index 9edcc52555..ce731acb73 100644
--- a/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/dlrm/_index.md
@@ -2,7 +2,6 @@
 title: Building and Benchmarking DLRM on Arm Neoverse V2 with MLPerf
 description: Learn how to build and benchmark the Deep Learning Recommendation Model using PyTorch and MLPerf on Arm Neoverse V2 processors.
 
-
 minutes_to_complete: 90
 
 who_is_this_for: This is an introductory topic for software developers who want to set up a pipeline in the cloud for recommendation models. You'll build and run the Deep Learning Recommendation Model (DLRM) and benchmark its performance using MLPerf and PyTorch.
@@ -13,15 +12,16 @@ learning_objectives:
 
 prerequisites:
     - Any [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider (CSP), or an on-premise Arm server with at least 400GB of RAM and 800 GB of disk space.
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author: 
     - Phalani Paladugu
     - Annie Tallund
     - Pareena Verma
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -47,8 +47,6 @@ further_reading:
         link: https://github.com/mlcommons/inference/tree/master
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md b/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
index 786a5f4a54..d3ab313251 100644
--- a/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/docker-mcp-toolkit/_index.md
@@ -1,6 +1,6 @@
 ---
 title: Automate x86 to Arm Migration with Docker MCP Toolkit, VS Code and GitHub Copilot
-    
+
 description: Learn how to use the Docker MCP Toolkit with the Arm MCP Server and GitHub Copilot to automate container and code migration from x86 to Arm64. Through a hands-on example, migrate a legacy C++ application with AVX2 intrinsics to Arm Neon.
 
 minutes_to_complete: 45
@@ -22,12 +22,13 @@ prerequisites:
     - A GitHub account with a personal access token
     - A machine with at least 8 GB RAM (16 GB recommended)
     - Basic familiarity with Docker, C++, and SIMD intrinsics concepts
+
+author: Ajeet Singh Raina
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Ajeet Singh Raina
-
 ### Tags
 skilllevels: Advanced
 subjects: Containers and Virtualization
diff --git a/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md b/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md
index 241a02fdf1..d463a7af9e 100644
--- a/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/dotnet-migration/_index.md
@@ -2,7 +2,6 @@
 title: Migrate a .NET application to Azure Cobalt 100
 description: Learn how to build and run an OrchardCore CMS .NET application on Azure Cobalt 100 processors, covering AnyCPU configuration and shared C library integration.
 
-  
 minutes_to_complete: 25
 
 who_is_this_for: This is an advanced topic for .NET developers who want to take advantage of the performance and cost benefits of Azure Cobalt processors.
@@ -19,12 +18,13 @@ prerequisites:
     - Basic knowledge of C and C#
     - GCC installed (Linux) or access to a cross-compiler
     - OrchardCore application created using the .NET CLI or Visual Studio
+
+author: Joe Stech
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Joe Stech
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -39,7 +39,6 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
 further_reading:
     - resource:
         title: Orchard Core documentation
@@ -54,8 +53,6 @@ further_reading:
         link: https://learn.microsoft.com/en-us/dotnet/
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
index c6312b3a01..1f5378b857 100644
--- a/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/dynatrace-azure/_index.md
@@ -1,7 +1,7 @@
 ---
 title: Monitor Azure Cobalt 100 Arm64 virtual machines using Dynatrace OneAgent
 description: Learn how to deploy Dynatrace OneAgent on Azure Cobalt 100 Arm64 virtual machines and configure ActiveGate for secure infrastructure and application monitoring.
-    
+
 minutes_to_complete: 30
 
 who_is_this_for: This is an introductory topic for developers, DevOps engineers, and platform engineers who want to implement infrastructure and application monitoring using Dynatrace on Arm-based cloud environments.
@@ -17,12 +17,13 @@ prerequisites:
   - Basic knowledge of Linux command-line operations
   - Familiarity with SSH and remote server access
   - Basic understanding of cloud infrastructure and monitoring concepts
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 skilllevels: Introductory
 subjects: Containers and Virtualization
diff --git a/content/learning-paths/servers-and-cloud-computing/ecs/_index.md b/content/learning-paths/servers-and-cloud-computing/ecs/_index.md
index 01838b32ab..4d67a599ba 100644
--- a/content/learning-paths/servers-and-cloud-computing/ecs/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ecs/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
     - An AWS account
     - A computer with Docker, AWS CLI, and Terraform installed
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ##### Tags
 skilllevels: Introductory
 subjects: Containers and Virtualization
@@ -46,8 +47,6 @@ further_reading:
         link: https://docs.aws.amazon.com/IAM/latest/UserGuide/introduction.html
         type: documentation
 
-
-
 weight: 1                       # _index.md always has weight of 1 to order correctly
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # Indicates this should be surfaced when looking for related content. Only set for _index.md of learning path content.
diff --git a/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md b/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
index ad8a3fd4d7..cf4e99e3f6 100644
--- a/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/eks-multi-arch/_index.md
@@ -16,12 +16,12 @@ prerequisites:
     - A computer with [Amazon eksctl CLI](/install-guides/eksctl) and [kubectl](/install-guides/kubectl/)installed.
     - Docker installed on local computer [Docker](/install-guides/docker)
 
+author: Pranay Bakre
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pranay Bakre
-
 ### Tags
 skilllevels: Advanced
 subjects: Containers and Virtualization
@@ -35,7 +35,6 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
 further_reading:
     - resource:
         title: EKS documentation
@@ -46,9 +45,6 @@ further_reading:
         link: https://docs.aws.amazon.com/AmazonECR/latest/userguide/what-is-ecr.html?pg=ln&sec=hs
         type: documentation
 
-
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/eks/_index.md b/content/learning-paths/servers-and-cloud-computing/eks/_index.md
index 0cb5f3d3e9..029e314422 100644
--- a/content/learning-paths/servers-and-cloud-computing/eks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/eks/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 
 prerequisites:
     - An Amazon Web Services (AWS) [account](https://aws.amazon.com/)
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ### Tags
 skilllevels: Introductory
 subjects: Containers and Virtualization
@@ -47,7 +48,6 @@ further_reading:
         link: https://kubernetes.io/docs/tutorials/stateful-application/mysql-wordpress-persistent-volume/
         type: Blog
 
-
 weight: 1                       # _index.md always has weight of 1 to order correctly
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # Indicates this should be surfaced when looking for related content. Only set for _index.md of learning path content.
diff --git a/content/learning-paths/servers-and-cloud-computing/elasticsearch-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/elasticsearch-on-azure/_index.md
index 489817fbdb..e23ec6d2bc 100644
--- a/content/learning-paths/servers-and-cloud-computing/elasticsearch-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/elasticsearch-on-azure/_index.md
@@ -17,9 +17,12 @@ prerequisites:
     - Basic familiarity with SSH
     - Familiarity with Elasticsearch and ESRally
 
-
 author: Doug Anson
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
diff --git a/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md
index ddaa14a4ee..0fdb4deb51 100644
--- a/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/envoy-gcp/_index.md
@@ -6,7 +6,6 @@ minutes_to_complete: 30
 
 who_is_this_for: This introductory topic for software developers migrating Envoy Proxy workloads from x86_64 to Arm-based servers, specifically on Google Cloud C4A virtual machines built on Axion processors.
 
-
 learning_objectives:
   - Provision an Arm-based C4A VM on Google Cloud Platform (GCP) 
   - Install and configure Envoy Proxy on a C4A instance
@@ -16,12 +15,13 @@ learning_objectives:
 prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free?utm_source=google&hl=en) account with billing enabled
   - Familiarity with networking concepts and the [Envoy architecture](https://www.envoyproxy.io/docs/envoy/latest/)
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ##### Tags
 skilllevels: Introductory
 subjects: Web
diff --git a/content/learning-paths/servers-and-cloud-computing/envoy/_index.md b/content/learning-paths/servers-and-cloud-computing/envoy/_index.md
index 9390450b94..316f306d70 100644
--- a/content/learning-paths/servers-and-cloud-computing/envoy/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/envoy/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
     - To run Envoy as a web server, you will need at least one [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an on-premises Arm server.
     - Network settings (firewalls and security groups) which allow communication on port 22 (SSH) and port 80 (HTTP).
+
+author: Zhengjun Xing
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Zhengjun Xing
-
 ### Tags
 skilllevels: Introductory
 subjects: Web
@@ -45,8 +46,6 @@ further_reading:
         link: https://www.envoyproxy.io/docs/envoy/latest/start/building
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md
index ceb4c9fd69..47b1a2bd0d 100644
--- a/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/envoy_tune/_index.md
@@ -15,12 +15,13 @@ learning_objectives:
 prerequisites:
     - Cloud or bare-metal installation of an Envoy service
     - Review [Learn how to deploy Envoy](/learning-paths/servers-and-cloud-computing/envoy/) if you do not already have an Envoy setup
+
+author: Zhengjun Xing
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Zhengjun Xing
-
 ### Tags
 skilllevels: Advanced
 subjects: Web
@@ -34,7 +35,7 @@ armips:
 tools_software_languages:
     - Envoy  
     - Runbook
-  
+
 operatingsystems:
     - Linux
 
@@ -43,8 +44,6 @@ further_reading:
         title: Envoy Documentation
         link: https://www.envoyproxy.io/docs/envoy/latest
         type: documentation
-    
-
 
 ### FIXED, DO NOT MODIFY
 # ================================================================================
diff --git a/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md b/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md
index 127d033f8d..83686a370d 100644
--- a/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/exploiting-stack-buffer-overflow-aarch64/_index.md
@@ -6,7 +6,6 @@ minutes_to_complete: 120
 
 who_is_this_for: This is an advanced topic for software developers interested in understanding how memory vulnerability-based exploits work on AArch64 and how to defend against them.
 
-
 learning_objectives: 
    - Analyze the stack frame layout to derive which
      field in user input overwrites the return address stored on the stack.
@@ -18,12 +17,13 @@ prerequisites:
     - Some familiarity with reading and writing basic C code and AArch64 assembly code.
     - Some familiarity with running linux command line commands.
     - Some familiarity with using a gdb debugger.
+
+author: Kristof Beyls
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Kristof Beyls
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -38,7 +38,6 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
 further_reading:
     - resource:
         title: Providing protection for complex software 
@@ -53,8 +52,6 @@ further_reading:
         link: https://community.arm.com/arm-community-blogs/b/architectures-and-processors-blog/posts/armv8-1-m-pointer-authentication-and-branch-target-identification-extension
         type: blog
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md b/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
index e26a1f59e1..742dfeb3f6 100644
--- a/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/_index.md
@@ -15,12 +15,13 @@ prerequisites:
     - Access to an Arm-based cloud instance with support for the Arm Statistical Profiling Extension (SPE).
     - A basic understanding of cache coherency and its impact on performance.
     - Familiarity with Linux Perf tools.
+
+author: Kieran Hejmadi
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Kieran Hejmadi
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -37,7 +38,6 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
 further_reading:
     - resource:
         title: Arm Statistical Profiling Extension Whitepaper
diff --git a/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md b/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md
index ee66cc6b23..9b4d39edb8 100644
--- a/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/fastpath/_index.md
@@ -15,12 +15,13 @@ learning_objectives:
 prerequisites:
     - An AWS account with permissions to create EC2 instances
     - Familiarity with basic Linux administration and SSH
+
+author: Geremy Cohen
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Geremy Cohen
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
diff --git a/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md b/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md
index 0626facbc3..a8a1d1e377 100644
--- a/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/fexpa/_index.md
@@ -2,7 +2,6 @@
 title: Optimize exponential functions with FEXPA
 description: Learn how to implement exponential functions using Arm SVE intrinsics with the FEXPA instruction for hardware-accelerated computations on Neoverse processors.
 
-    
 minutes_to_complete: 15
 
 who_is_this_for: This is an introductory topic for developers interested in accelerating exponential function computations using Arm's Scalable Vector Extension (SVE). The FEXPA instruction provides hardware acceleration for exponential calculations on Arm Neoverse processors.
@@ -14,15 +13,16 @@ learning_objectives:
 prerequisites:
     - Access to an [AWS Graviton4, Google Axion, or Azure Cobalt 100 virtual machine from a cloud service provider](/learning-paths/servers-and-cloud-computing/csp/)
     - Some familiarity with SIMD programming and SVE intrinsics
+
+author:
+    - Arnaud Grasset
+    - Claudio Martino
+    - Alexandre Romana
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: 
-- Arnaud Grasset
-- Claudio Martino
-- Alexandre Romana
-
 further_reading:
     - resource:
         title: Arm Optimized Routines
@@ -59,4 +59,3 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
-
diff --git a/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md
index 08cbd4e14f..36df3648e1 100644
--- a/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/flink-on-gcp/_index.md
@@ -1,7 +1,7 @@
 ---
 title: Deploy Apache Flink on Google Cloud C4A (Arm-based Axion VMs)
 description: Learn how to install and configure Apache Flink on Google Cloud Axion C4A Arm64 instances and benchmark stream processing performance with Nexmark.
-    
+
 minutes_to_complete: 30
 
 who_is_this_for: This is an introductory topic for developers deploying and optimizing Apache Flink workloads on Linux/Arm64 environments, specifically using Google Cloud C4A virtual machines powered by Axion processors.
@@ -15,12 +15,13 @@ learning_objectives:
 prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with [Apache Flink](https://flink.apache.org/) and its runtime environment
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ##### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
diff --git a/content/learning-paths/servers-and-cloud-computing/flink/_index.md b/content/learning-paths/servers-and-cloud-computing/flink/_index.md
index 5f9700d833..f268ad6f7d 100644
--- a/content/learning-paths/servers-and-cloud-computing/flink/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/flink/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 
 prerequisites:
 - An Arm based instance server from a cloud service provider.
+
+author: Ying Yu
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Ying Yu
-
 ### Tags
 skilllevels: Introductory
 subjects: Databases
@@ -31,8 +32,6 @@ cloud_service_providers:
 armips:
 - Neoverse
 
-
-
 operatingsystems:
 - Linux
 
@@ -42,8 +41,6 @@ tools_software_languages:
 - Nexmark
 - Runbook
 
-
-
 further_reading:
     - resource:
         title: Flink Manual
@@ -54,7 +51,6 @@ further_reading:
         link: https://github.com/nexmark/nexmark#readme
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md b/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md
index 3deabe8071..a64a70c07a 100644
--- a/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/_index.md
@@ -17,12 +17,13 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with Python
   - Basic understanding of machine learning pipelines
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ##### Tags
 skilllevels: Introductory
 subjects: ML
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
index dd2abeae82..ecca95dd01 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/_index.md
@@ -20,12 +20,13 @@ prerequisites:
     - 'Docker Extension for Visual Studio Code: https://code.visualstudio.com/docs/containers/overview'
     - 'C# Extension for Visual Studio Code: https://marketplace.visualstudio.com/items?itemName=ms-dotnettools.csharp'
     - '[Install Docker on Arm64](/install-guides/docker/docker-desktop/)'
+
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dawid Borycki
-
 ### Tags
 skilllevels: Introductory
 subjects: Containers and Virtualization
@@ -34,7 +35,7 @@ cloud_service_providers:
 
 armips:
     - Neoverse
-    
+
 tools_software_languages:
     - .NET SDK
     - C#
@@ -42,7 +43,6 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
 further_reading:
     - resource:
         title: Terraform on Azure
@@ -57,8 +57,6 @@ further_reading:
         link: https://learn.microsoft.com/en-us/azure/bastion/bastion-overview
         type: Documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md
index 37efde6278..ce1d34f0c8 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/_index.md
@@ -15,12 +15,12 @@ prerequisites:
     - 'Azure subscription. Use this link to sign up for a free account: https://azure.microsoft.com/en-us/free/.'
     - 'Complete the [first learning path](/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1) of this series.'  
 
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dawid Borycki
-
 ### Tags
 skilllevels: Introductory
 subjects: Containers and Virtualization
@@ -29,7 +29,7 @@ cloud_service_providers:
 
 armips:
     - Neoverse
-    
+
 tools_software_languages:
     - ASP.NET Core    
     - Docker
@@ -51,8 +51,6 @@ further_reading:
         link: https://learn.microsoft.com/en-us/azure/container-instances/
         type: Documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
index 35b2c0f062..8a53abbd68 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/_index.md
@@ -14,21 +14,21 @@ prerequisites:
     - 'Azure subscription. Use this link to sign up for a free account: https://azure.microsoft.com/en-us/free/.'
     - 'Complete the [first](/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1) and [second](/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2) learning paths of this series.'
 
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dawid Borycki
-
 ### Tags
 skilllevels: Introductory
 subjects: Containers and Virtualization
 cloud_service_providers:
   - Microsoft Azure
-    
+
 armips:
     - Neoverse
-    
+
 tools_software_languages:
     - ASP.NET Core    
     - Docker
@@ -51,8 +51,6 @@ further_reading:
         link: https://kubernetes.io/docs/reference/kubectl/cheatsheet/
         type: Documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md
index 1616ac5b20..c2500a4e40 100644
--- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/_index.md
@@ -17,12 +17,13 @@ prerequisites:
     - 'A free Pulumi account and Pulumi CLI (details provided in this learning path)'
     - 'Node.js (details provided in this learning path)'
     - 'Azure CLI (details provided in this learning path)'
+
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dawid Borycki
-
 ### Tags
 skilllevels: Introductory
 subjects: Containers and Virtualization
@@ -31,7 +32,7 @@ cloud_service_providers:
 
 armips:
     - Neoverse
-    
+
 tools_software_languages:
     - TypeScript  
     - Docker    
@@ -53,8 +54,6 @@ further_reading:
         link: https://www.terraform.io
         type: Documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/funasr/_index.md b/content/learning-paths/servers-and-cloud-computing/funasr/_index.md
index 303fc9449a..9268d26fa5 100644
--- a/content/learning-paths/servers-and-cloud-computing/funasr/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/funasr/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 
 prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider, or a local Arm Linux computer with at least 8 CPUs and 16GB of RAM.
+
+author: Odin Shen
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Odin Shen
-
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -38,7 +39,6 @@ tools_software_languages:
     - Generative AI
     - Python
 
-
 further_reading:
     - resource:
         title: ModelScope GitHub Repository
@@ -57,8 +57,6 @@ further_reading:
         link: https://community.arm.com/arm-community-blogs/b/servers-and-cloud-computing-blog/posts/neoverse-n2-delivers-leading-price-performance-on-asr
         type: blog
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
index b7d39b7572..5d65d8d52b 100644
--- a/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gardener-gcp/_index.md
@@ -17,12 +17,13 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with [Kubernetes](https://kubernetes.io/)
   - Familiarity with container concepts ([Docker](https://www.docker.com/))
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ##### Tags
 skilllevels: Introductory
 subjects: Containers and Virtualization
@@ -66,7 +67,7 @@ further_reading:
       title: kube-bench security benchmarking tool
       link: https://github.com/aquasecurity/kube-bench
       type: documentation
-    
+
 weight: 1
 layout: "learningpathall"
 learning_path_main_page: "yes"
diff --git a/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md b/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
index 797f81761c..8e60fc0fa1 100644
--- a/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gcc-lto/_index.md
@@ -2,7 +2,6 @@
 title: Optimize performance using Link-Time Optimization with GCC
 description: Learn how to apply link-time optimization with the GCC toolchain to improve application performance by optimizing across compilation units.
 
-    
 minutes_to_complete: 15
 
 who_is_this_for: This is an introductory topic for developers who want to improve application performance using link-time optimization (LTO) with the GCC toolchain.
@@ -15,12 +14,13 @@ learning_objectives:
 prerequisites:
     - An Arm Linux system (cloud instance, on-premises hardware, or a virtual machine)
     - A recent version of the [GCC toolchain](/install-guides/gcc/)
+
+author: Victor Do Nascimento
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Victor Do Nascimento
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
diff --git a/content/learning-paths/servers-and-cloud-computing/gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/gcp/_index.md
index 203b16949d..a916aa97cc 100644
--- a/content/learning-paths/servers-and-cloud-computing/gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gcp/_index.md
@@ -16,12 +16,12 @@ prerequisites:
     - A computer with [Terraform](/install-guides/terraform) installed.
     - A computer with [Google Cloud CLI](/install-guides/gcloud) installed.
 
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ##### Tags
 skilllevels: Introductory
 subjects: Containers and Virtualization
@@ -53,7 +53,6 @@ further_reading:
         link: https://cloud.google.com/solutions/connecting-securely#bastion
         type: documentation
 
-
 weight: 1                       # _index.md always has weight of 1 to order correctly
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # Indicates this should be surfaced when looking for related content. Only set for _index.md of learning path content.
diff --git a/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md b/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
index cbda5b2084..24549092df 100644
--- a/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/geekbench/_index.md
@@ -12,12 +12,13 @@ learning_objectives:
 
 prerequisites:
 - An Arm computer running Linux. You can use a cloud instance, refer to [Get started with Arm-based cloud instances](/learning-paths/servers-and-cloud-computing/csp/).
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 skilllevels: Introductory
 
 subjects: Performance and Architecture
@@ -33,7 +34,6 @@ tools_software_languages:
     - Geekbench
     - Runbook
 
-
 further_reading:
     - resource:
         title: Performance Analysis for Arm vs x86 CPUs in the Cloud
diff --git a/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md b/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
index 53955b9850..6bfb986582 100644
--- a/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gh-runners/_index.md
@@ -17,14 +17,15 @@ prerequisites:
     - A GitHub account with access to Arm-hosted GitHub runners.
     - A Docker Hub account for storing container images.
     - Familiarity with the concepts of ML and continuous integration and deployment (CI/CD).
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author:
     - Pareena Verma
     - Annie Tallund
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: CI-CD
@@ -38,7 +39,6 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
 further_reading:
     - resource:
         title: Arm64 on GitHub Actions - Powering faster, more efficient build systems
@@ -53,8 +53,6 @@ further_reading:
         link: https://github.blog/enterprise-software/ci-cd/streamlining-your-mlops-pipeline-with-github-actions-and-arm64-runners/
         type: blog
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md b/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
index 2f86b3eee4..d1433d5310 100644
--- a/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/github-actions-runner/_index.md
@@ -1,7 +1,7 @@
 ---
 title: Managed, self-hosted Arm runners for GitHub Actions
 description: Learn how to install RunsOn self-hosted runner manager in your AWS account to execute GitHub Actions workflows on Arm runners.
- 
+
 minutes_to_complete: 15
 
 who_is_this_for: This Learning Path is for developers who want to use Arm runners offered by AWS to execute GitHub Actions workflows.
@@ -13,12 +13,13 @@ learning_objectives:
 prerequisites:
     - An [Amazon Web Services account](/learning-paths/servers-and-cloud-computing/csp/aws/).
     - A GitHub account (personal or organizational).
+
+author: Cyril Rohr
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Cyril Rohr
-
 ##### Tags
 skilllevels: Introductory
 subjects: CI-CD
@@ -55,7 +56,6 @@ further_reading:
         link: https://runs-on.com/benchmarks/github-actions-runners/#arm64-runners
         type: website
 
-
 weight: 1                       # _index.md always has weight of 1 to order correctly
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # Indicates this should be surfaced when looking for related content. Only set for _index.md of learning path content.
diff --git a/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md b/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md
index 80da4aac46..82b08c14b7 100644
--- a/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/github-on-arm/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free?utm_source=google&hl=en) account with billing enabled
   - A GitHub account; you can [sign up for GitHub](https://github.com/signup)
+
+author: Annie Tallund
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Annie Tallund
-
 ##### Tags
 skilllevels: Introductory
 subjects: CI-CD
@@ -60,7 +61,6 @@ further_reading:
         link: https://cloud.google.com/compute/docs/instances/create-start-instance
         type: website
 
-
 weight: 1                       # _index.md always has weight of 1 to order correctly
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # Indicates this should be surfaced when looking for related content. Only set for _index.md of learning path content.
diff --git a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
index 09abe96a40..a1a204eae0 100644
--- a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md
@@ -11,18 +11,19 @@ learning_objectives:
     - Build and publish multi-architecture images to Artifact Registry using Docker Buildx
     - Deploy a Kubernetes application on amd64, then migrate to arm64 using Kustomize overlays
     - Automate builds and rollouts with Cloud Build and Skaffold
-    
+
 prerequisites:
     - A [Google Cloud account](https://console.cloud.google.com/) with billing enabled
     - A local Linux or macOS computer with Docker, Kubernetes CLI (kubectl), Google Cloud CLI (gcloud), and Git installed, or access to Google Cloud Shell
     - Basic familiarity with Docker, Kubernetes, and gcloud
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author: 
    - Rani Chowdary Mandepudi
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Advanced
 subjects: Containers and Virtualization
@@ -38,7 +39,6 @@ tools_software_languages:
     - Skaffold
     - Cloud Build
 
-    
 further_reading:
     - resource:
         title: Google Kubernetes Engine documentation
@@ -48,10 +48,6 @@ further_reading:
         title: Create standard clusters and node pools with Arm nodes 
         link: https://cloud.google.com/kubernetes-engine/docs/how-to/create-arm-clusters-nodes
         type: documentation
-    
-    
-
-
 
 ### FIXED, DO NOT MODIFY
 # ================================================================================
diff --git a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
index 5fb927efe6..3d963ffad5 100644
--- a/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gke-multi-arch/_index.md
@@ -16,12 +16,13 @@ prerequisites:
     - A [Google Cloud account](https://console.cloud.google.com/). Create an account if needed.
     - A computer with [Google Cloud CLI](/install-guides/gcloud/) and [kubectl](/install-guides/kubectl/)installed.
     - An existing Google Kubernetes Engine (GKE) cluster with x86-based nodes
+
+author: Pranay Bakre
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pranay Bakre
-
 ### Tags
 skilllevels: Advanced
 subjects: Containers and Virtualization
@@ -38,7 +39,6 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
 further_reading:
     - resource:
         title: Create Arm based clusters and node pools 
@@ -53,8 +53,6 @@ further_reading:
         link: https://cloud.google.com/kubernetes-engine/docs
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/gke/_index.md b/content/learning-paths/servers-and-cloud-computing/gke/_index.md
index d07ef0379e..b137494a14 100644
--- a/content/learning-paths/servers-and-cloud-computing/gke/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/gke/_index.md
@@ -1,7 +1,7 @@
 ---
 title: Create an Arm-based Kubernetes cluster on Google Cloud Platform (GCP)
 description: Learn how to automate the deployment of an Arm-based Google Kubernetes Engine cluster using Terraform for container orchestration.
- 
+
 minutes_to_complete: 60   
 
 who_is_this_for: This is an advanced topic for software developers who want to deploy an Arm-based Kubernetes cluster using Google Kubernetes Engine (GKE).
@@ -12,12 +12,13 @@ learning_objectives:
 prerequisites:
     - A Google Cloud account
     - A computer with the following tools installed`:` Terraform, Google Cloud CLI (gcloud), Kubernetes CLI (kubectl)
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ##### Tags
 skilllevels: Advanced
 subjects: Containers and Virtualization
@@ -51,7 +52,6 @@ further_reading:
         link: https://cloud.google.com/kubernetes-engine/docs
         type: documentation
 
-
 weight: 1                       # _index.md always has weight of 1 to order correctly
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # Indicates this should be surfaced when looking for related content. Only set for _index.md of learning path content.
diff --git a/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md b/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
index 1790f600e1..feb4830ceb 100644
--- a/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
 - An Arm based instance from a cloud service provider.
 - Review the learning path on [LSE](/learning-paths/servers-and-cloud-computing/lse/)
+
+author: Ying Yu
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Ying Yu
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -27,8 +28,6 @@ subjects: Performance and Architecture
 armips:
 - Neoverse
 
-
-
 operatingsystems:
 - Linux
 
@@ -38,8 +37,6 @@ tools_software_languages:
 - MongoDB
 - Runbook
 
-
-
 further_reading:
     - resource:
         title: Arm's LSE for atomics and MySQL
@@ -50,7 +47,6 @@ further_reading:
         link: https://www.mongodb.com/docs/
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md b/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
index f82a16a408..7fec43575c 100644
--- a/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
     - A [Google Cloud account](https://console.cloud.google.com/). This Learning Path can be run on any cloud provider or on-premises, but it focuses on Google Cloud’s Axion Arm64-based instances.
     - A local machine with [Google Cloud CLI](/install-guides/gcloud/) installed
+
+author: Geremy Cohen
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Geremy Cohen
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
diff --git a/content/learning-paths/servers-and-cloud-computing/go-gc-default-settings/_index.md b/content/learning-paths/servers-and-cloud-computing/go-gc-default-settings/_index.md
index 744c0369ac..a8610b8410 100644
--- a/content/learning-paths/servers-and-cloud-computing/go-gc-default-settings/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/go-gc-default-settings/_index.md
@@ -24,6 +24,10 @@ prerequisites:
 
 author: Geremy Cohen
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
diff --git a/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md
index f11e98f41f..78a2dc8faa 100644
--- a/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/golang-on-azure/_index.md
@@ -15,12 +15,13 @@ prerequisites:
     - A [Microsoft Azure](https://azure.microsoft.com/) account with access to Azure Cobalt 100 Arm-based instances (Dpsv6-series)
     - Basic familiarity with the [Go programming language](https://go.dev/) and cloud deployment practices
     - Understanding of Linux command line and virtual machine management
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -50,7 +51,6 @@ further_reading:
         link: https://pkg.go.dev/cmd/go#hdr-Testing_flags
         type: Reference
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md
index 6e5f6599cb..39c0d370e2 100644
--- a/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/helm-on-gcp/_index.md
@@ -6,7 +6,6 @@ minutes_to_complete: 60
 
 who_is_this_for: This is an introductory topic intended for developers who want to get hands-on experience using Helm on Linux Arm64 systems, specifically Google Cloud C4A virtual machines powered by Axion processors.
 
-
 learning_objectives:
   - Provision an Arm-based SUSE Linux Enterprise Server (SLES) virtual machine on Google Cloud (C4A with Axion processors)
   - Install and configure Helm and kubectl on a SUSE Arm64 (C4A) instance
@@ -21,12 +20,13 @@ prerequisites:
   - Basic familiarity with [Kubernetes concepts](https://kubernetes.io/docs/concepts/)
   - Basic understanding of [Helm](https://helm.sh/docs/topics/architecture/) and Kubernetes manifests
   - Familiarity with basic Linux command-line usage
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ##### Tags
 skilllevels: Introductory
 subjects: Containers and Virtualization
@@ -44,7 +44,7 @@ tools_software_languages:
   - PostgreSQL
   - Redis
   - NGINX
-    
+
 operatingsystems:
   - Linux
 
diff --git a/content/learning-paths/servers-and-cloud-computing/intro/_index.md b/content/learning-paths/servers-and-cloud-computing/intro/_index.md
index addcccec71..de8f633252 100644
--- a/content/learning-paths/servers-and-cloud-computing/intro/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/intro/_index.md
@@ -12,12 +12,13 @@ learning_objectives:
 
 prerequisites:
     - None
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -28,14 +29,12 @@ operatingsystems:
 tools_software_languages:
     - Runbook
 
-
 further_reading:
     - resource:
         title: Ampere Computing
         link: https://amperecomputing.com/developers/
         type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md b/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
index 35f04228dd..75f74c1734 100644
--- a/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
@@ -2,7 +2,6 @@
 title: Optimize network interrupt handling on Arm servers
 description: Analyze and optimize interrupt request (IRQ) patterns on Arm Linux servers to improve network workload performance through IRQ distribution strategies.
 
-   
 minutes_to_complete: 20
 
 who_is_this_for: This is an introductory topic for developers and performance engineers who are interested in understanding how network interrupt patterns can impact performance on cloud servers.
@@ -16,12 +15,13 @@ learning_objectives:
 prerequisites:
     - An Arm computer running Linux
     - Some familiarity with the Linux command line
+
+author: Kiel Friedt
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Kiel Friedt
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -33,7 +33,6 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
 further_reading:
     - resource:
         title: Perf for Linux on Arm (LinuxPerf)
diff --git a/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md b/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
index 435dd54d64..174d83d5ed 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
@@ -16,12 +16,13 @@ prerequisites:
     - An Arm-based instance from a cloud service provider, or an on-premise Arm server.
     - Basic understanding of Java.
     - An [installation of Java](/install-guides/java/) on your machine.
+
+author: Kieran Hejmadi
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Kieran Hejmadi
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -39,7 +40,6 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
 further_reading:
     - resource:
         title: OpenJDK Wiki 
@@ -50,8 +50,6 @@ further_reading:
         link: https://www.oracle.com/technical-resources/articles/java/g1gc.html
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
index a3d142998a..511fb108a5 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 
 prerequisites:
     - A [Google Cloud](https://cloud.google.com/) account with access to Axion based instances (C4A).
+
+author: Joe Stech
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Joe Stech
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -34,7 +35,6 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
 further_reading:
     - resource:
         title: Exploring JVM Tuning Flags
@@ -45,8 +45,6 @@ further_reading:
         link: https://docs.oracle.com/en/java/javase/21/docs/specs/man/java.html
         type: blog
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
index c5cb10f7fe..f9825b9d68 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 
 prerequisites:
     - A [Microsoft Azure](https://azure.microsoft.com/) account with access to Cobalt 100 based instances (Dpsv6)
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -53,7 +54,6 @@ further_reading:
       link: https://openjdk.org/projects/code-tools/jmh/
       type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
index 7707f3f44e..483cb98c74 100644
--- a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
@@ -14,14 +14,15 @@ learning_objectives:
 prerequisites:
   - Access to both Arm-based and x86-based computers running Ubuntu (you can use cloud-based server instances)
   - Basic familiarity with Java applications and performance profiling using flame graphs
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author: 
   - Ying Yu
   - Martin Ma
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 # Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
diff --git a/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md b/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
index 6873a0f698..1017c4a36b 100644
--- a/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
@@ -20,12 +20,13 @@ prerequisites:
     - A [Google Cloud Platform](https://cloud.google.com/) account with access to Arm-based virtual machine instances
     - Basic understanding of Linux command line
     - Familiarity with CI/CD concepts and [Jenkins fundamentals](https://www.jenkins.io/doc/book/pipeline/)
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ##### Tags
 skilllevels: Advanced
 subjects: CI-CD
diff --git a/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
index e49ce1771a..d5f607b255 100644
--- a/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
@@ -16,12 +16,13 @@ prerequisites:
     - A [Microsoft Azure](https://azure.microsoft.com/) account with access to Cobalt 100 based instances (Dpsv6)
     - Basic understanding of Linux command line
     - Familiarity with the [Apache Kafka architecture](https://kafka.apache.org/) and deployment practices on Arm64 platforms
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 skilllevels: Advanced
 subjects: Storage
@@ -51,7 +52,6 @@ further_reading:
         link: https://learn.microsoft.com/en-us/samples/azure/azure-quickstart-templates/kafka-ubuntu-multidisks/
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/kafka/_index.md b/content/learning-paths/servers-and-cloud-computing/kafka/_index.md
index 0e46f0959b..5a89973acd 100644
--- a/content/learning-paths/servers-and-cloud-computing/kafka/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kafka/_index.md
@@ -16,12 +16,13 @@ learning_objectives:
 
 prerequisites:
     - Seven physical Arm machines or cloud instances with either Ubuntu or Debian installed. 
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 skilllevels: Advanced
 subjects: Storage
@@ -36,7 +37,6 @@ tools_software_languages:
     - Kafka
     - ZooKeeper
 
-
 further_reading:
     - resource:
         title: Kafka Manual
@@ -55,7 +55,6 @@ further_reading:
         link: https://developer.arm.com/community/arm-community-blogs/b/servers-and-cloud-computing-blog/posts/apache-kafka-benchmarks-on-aws-graviton2
         type: blog
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md b/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
index 6b75feed63..553f729b65 100644
--- a/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
@@ -16,12 +16,13 @@ prerequisites:
   - A running Kubernetes cluster (local or cloud)
   - Kubectl and Helm installed 
   - Access to the Kedify Service dashboard to obtain your Organization ID and API key (sign up at [Kedify dashboard](https://dashboard.kedify.io/))
+
+author: Zbynek Roubalik
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Zbynek Roubalik
-
 ### Tags
 skilllevels: Introductory
 subjects: Containers and Virtualization
@@ -53,7 +54,6 @@ further_reading:
       link: https://keda.sh/
       type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # =============================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md b/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
index bbd79b12e5..cf96f759b8 100644
--- a/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
@@ -16,14 +16,15 @@ prerequisites:
     - Basic Machine Learning knowledge.
     - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider, an on-premises Arm server, or a Linux virtual machine on your Arm device. 
     - Familiarity with SSH, the Linux command line, and basic system administration tasks.
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author: 
     - Diego Russo
     - Leandro Nunes
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -43,7 +44,6 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
 further_reading:
     - resource:
         title: Keras Documentation
@@ -62,8 +62,6 @@ further_reading:
         link: https://jax.readthedocs.io/en/latest/index.html
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md b/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
index 341416727f..98ac9bc1d8 100644
--- a/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
@@ -17,12 +17,13 @@ prerequisites:
     - An Arm cloud instance with at least 24 vCPUs and 200 GB of free storage running Ubuntu 24.04 LTS
     - Understanding of kernel images and modules
     - Familiarity with GRUB bootloader and initramfs
+
+author: Geremy Cohen
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Geremy Cohen
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
diff --git a/content/learning-paths/servers-and-cloud-computing/keycloak-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/keycloak-cobalt/_index.md
index dac7bb83f9..37a77e7268 100644
--- a/content/learning-paths/servers-and-cloud-computing/keycloak-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/keycloak-cobalt/_index.md
@@ -1,6 +1,6 @@
 ---
 title: Deploy Keycloak on Azure Cobalt 100-based Arm64 virtual machines for identity and access management
-    
+
 description: Learn how to install and configure Keycloak on an Azure Cobalt 100 Arm64 virtual machine, integrate it with PostgreSQL, configure OAuth2/OpenID Connect authentication, and secure applications using centralized identity management.
 
 minutes_to_complete: 90
@@ -22,6 +22,10 @@ prerequisites:
 
 author: Pareena Verma
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: Web
diff --git a/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md b/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
index 997b913a05..88bb491bbd 100644
--- a/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
@@ -15,12 +15,13 @@ learning_objectives:
 
 prerequisites:
     - A running Kubernetes cluster accessible with `kubectl`.
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -38,7 +39,6 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
 further_reading:
     - resource:
         title: Kubernetes documentation
@@ -57,7 +57,6 @@ further_reading:
         link: https://cloud.google.com/kubernetes-engine/docs/concepts/arm-on-gke
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md b/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
index dafd8751db..4ccc9bb5fc 100644
--- a/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
@@ -12,12 +12,13 @@ learning_objectives:
 
 prerequisites:
     - A computer with [Terraform](/install-guides/terraform/) and the [AWS CLI](/install-guides/aws-cli/) installed. 
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ### Tags
 skilllevels: Introductory
 subjects: Containers and Virtualization
@@ -47,8 +48,6 @@ further_reading:
         link: https://community.aws/content/2juXXgrDDaUdmi902LHwilBhvNU/aws-lambda-performance-with-java-21-x86-vs-arm64-part-1-initial-measurements-and-comparisons?lang=en
         type: blog
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md b/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
index a2968830cb..b52715c2ee 100644
--- a/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
     - An Arm server or virtual machine instance from a cloud service provider with Ubuntu installed
     - Knowledge of how to build a MySQL server and run the sysbench benchmark test
+
+author: Bolt Liu
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Bolt Liu
-
 skilllevels: Advanced
 subjects: Databases
 cloud_service_providers:
@@ -36,7 +37,6 @@ tools_software_languages:
     - GCC
     - Runbook
 
-
 test_images:
     - ubuntu:latest
 test_link: null
@@ -52,7 +52,6 @@ further_reading:
         link: https://github.com/libhugetlbfs/libhugetlbfs/blob/master/HOWTO
         type: documentation
 
-
 weight: 1
 layout: learningpathall
 learning_path_main_page: 'yes'
diff --git a/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md b/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
index ca2c207aa0..5199907586 100644
--- a/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/llama-cpu/_index.md
@@ -13,15 +13,16 @@ learning_objectives:
 
 prerequisites:
     - An AWS Graviton4 r8g.16xlarge instance to test Arm performance optimizations, or any [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an on-premise Arm server.
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author:
     - Pareena Verma
     - Jason Andrews
     - Zach Lasiuk
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -56,8 +57,6 @@ further_reading:
         link: https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md b/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
index c6eb5defbd..ae7a9b8de0 100644
--- a/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
@@ -18,12 +18,13 @@ prerequisites:
     - A basic understanding of Python and ML concepts.
     - A basic understanding of Streamlit.
     - A basic understanding of LLM fundamentals.
+
+author: Nobel Chowdary Mandepudi
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Nobel Chowdary Mandepudi
-
 ### Tags
 skilllevels: Advanced
 armips:
@@ -38,7 +39,7 @@ tools_software_languages:
     - PyTorch
     - Streamlit
     - Google Axion
-   
+
 further_reading:
     - resource:
         title: Getting started with Llama
@@ -53,8 +54,6 @@ further_reading:
         link: https://blogs.oracle.com/ai-and-datascience/post/democratizing-generative-ai-with-cpu-based-inference
         type: blog
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
index 72650f04fd..a106f7051a 100644
--- a/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
@@ -19,14 +19,15 @@ prerequisites:
     - Understanding of transformer models
     - Knowledge of Arm Streamline usage
     - An Arm Neoverse or Cortex-A hardware platform running Linux or Android
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author: 
     - Zenon Zhilong Xiu
     - Odin Shen
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Advanced
 subjects: ML
diff --git a/content/learning-paths/servers-and-cloud-computing/llamaindex-rag-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/llamaindex-rag-axion/_index.md
index cdb1a65496..d0d2bca575 100644
--- a/content/learning-paths/servers-and-cloud-computing/llamaindex-rag-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/llamaindex-rag-axion/_index.md
@@ -19,6 +19,10 @@ prerequisites:
 
 author: Pareena Verma
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ##### Tags
 skilllevels: Introductory
 subjects: ML
@@ -60,8 +64,6 @@ further_reading:
         link: https://learn.arm.com/learning-paths/servers-and-cloud-computing/csp/google/
         type: documentation
 
-
-
 # ================================================================================
 #       FIXED, DO NOT MODIFY
 # ================================================================================
diff --git a/content/learning-paths/servers-and-cloud-computing/longhorn-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/longhorn-cobalt/_index.md
index 35bc96e68e..a16eaab006 100644
--- a/content/learning-paths/servers-and-cloud-computing/longhorn-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/longhorn-cobalt/_index.md
@@ -1,6 +1,6 @@
 ---
 title: Use Longhorn to deploy persistent storage for Kubernetes workloads on Arm-based Azure virtual machines
-    
+
 description: Learn how to install and configure Longhorn on an Arm64 Azure virtual machine powered by Azure Cobalt 100, deploy Kubernetes persistent storage using Longhorn on K3s, create persistent volumes, and benchmark storage performance for cloud-native workloads.
 
 minutes_to_complete: 60
@@ -21,6 +21,10 @@ prerequisites:
 
 author: Pareena Verma
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: Containers and Virtualization
diff --git a/content/learning-paths/servers-and-cloud-computing/lse/_index.md b/content/learning-paths/servers-and-cloud-computing/lse/_index.md
index c4db833a32..543f98a88b 100644
--- a/content/learning-paths/servers-and-cloud-computing/lse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/lse/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 
 prerequisites:
     - An [AWS account](/learning-paths/servers-and-cloud-computing/csp/aws/) to access instance types with different AWS Graviton processors. If you don't have an AWS account, you can substitute other Arm Linux computers.
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -35,7 +36,6 @@ tools_software_languages:
     - GCC
     - Runbook
 
-    
 further_reading:
     - resource:
         title: Improving Java performance on Neoverse N1 systems
@@ -50,8 +50,6 @@ further_reading:
         link: /learning-paths/servers-and-cloud-computing/glibc-with-lse/
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md b/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
index 47278d0dc1..0d250da06a 100644
--- a/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
@@ -16,11 +16,13 @@ learning_objectives:
 prerequisites:
     - Cloud service provider accounts for each service you want to use including AWS, Azure, and GCP
     - A local computer with [Docker](/install-guides/docker/), [Terraform](/install-guides/terraform/), [AWS CLI](/install-guides/aws-cli/), [Azure CLI](/install-guides/azure-cli/), [Google Cloud CLI](/install-guides/gcloud/), and [Ansible](/install-guides/ansible/) installed
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
 ### Tags
 skilllevels: Introductory
 subjects: Databases
@@ -39,7 +41,6 @@ tools_software_languages:
     - Docker
     - Runbook
 
-
 further_reading:
     - resource:
         title: MariaDB Manual
@@ -58,7 +59,6 @@ further_reading:
         link: https://aws.amazon.com/blogs/database/key-considerations-in-moving-to-graviton2-for-amazon-rds-and-amazon-aurora-databases/
         type: blog
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/memcached/_index.md b/content/learning-paths/servers-and-cloud-computing/memcached/_index.md
index 3acdc0578c..3887145269 100644
--- a/content/learning-paths/servers-and-cloud-computing/memcached/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memcached/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 
 prerequisites:
 - An Arm based instance from an appropriate cloud service provider.
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 test_images:
 - ubuntu:latest
 test_link: https://github.com/armflorentlebeau/arm-learning-paths/actions/runs/4312122327
@@ -48,7 +49,6 @@ further_reading:
         link: https://developer.arm.com/community/arm-community-blogs/b/servers-and-cloud-computing-blog/posts/accelerating-deep-packet-inspection-with-neon-on-arm-neoverse
         type: blog
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md b/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
index deb8db3083..6a915554ec 100644
--- a/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memcached_cache/_index.md
@@ -1,7 +1,6 @@
 ---
 title: Deploy Memcached as a cache for MySQL and PostgreSQL on Arm based servers
 
-
 description: Deploy Memcached as a cache for MySQL and PostgreSQL on Arm servers
 
 minutes_to_complete: 60
@@ -18,13 +17,12 @@ prerequisites:
 - A Google Cloud [account](https://console.cloud.google.com/)
 - A machine with [Terraform](/install-guides/terraform/), [AWS CLI](/install-guides/aws-cli), [Google Cloud CLI](/install-guides/gcloud), [Azure CLI](/install-guides/azure-cli), [AWS IAM authenticator](https://docs.aws.amazon.com/eks/latest/userguide/install-aws-iam-authenticator.html), and [Ansible](/install-guides/ansible/) installed
 
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
-
 test_images:
 - ubuntu:latest
 test_link: https://github.com/armflorentlebeau/arm-learning-paths/actions/runs/4312122327
@@ -53,7 +51,6 @@ further_reading:
         link: https://github.com/memcached/memcached/wiki
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md b/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
index 50f11ff106..3d85b4a9a0 100644
--- a/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memory-subsystem/_index.md
@@ -18,12 +18,13 @@ prerequisites:
     - Two or more Arm Linux systems with root or sudo access. The examples use AWS Graviton2 and Graviton4 instances, but other systems are possible
     - Arm System Characterization Tool (ASCT) installed on each system
     - A good understanding of CPU memory subsystems, including cache hierarchies, cache lines, and DRAM in the memory hierarchy
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 skilllevels: Advanced
 subjects: Performance and Architecture
 armips:
diff --git a/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md b/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
index c1769859d4..1e8eba03be 100644
--- a/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
@@ -18,12 +18,13 @@ prerequisites:
     - Familiarity with Arm assembly language, and the ability to find relevant information on Arm assembly instructions.
     - Familiarity with general-purpose registers.
     - Familiarity with memory barriers, including Acquire-Release Semantics.
+
+author: Julio Suarez
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Julio Suarez
-
 skilllevels: Advanced
 subjects: Performance and Architecture
 armips:
diff --git a/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md b/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
index b140b4b046..18fcd51af2 100644
--- a/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/microbenchmark-network-iperf3/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 prerequisites:
     - Basic understanding of networking principles such as Transmission Control Protocol/Internet Protocol (TCP/IP) and User Datagram Protocol (UDP).
     - Access to two [Arm-based cloud instances](/learning-paths/servers-and-cloud-computing/csp/).
+
+author: Kieran Hejmadi
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Kieran Hejmadi
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
diff --git a/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md b/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
index 420741dadc..51b056603f 100644
--- a/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
@@ -1,7 +1,6 @@
 ---
 title: Migrate applications to Arm servers using migrate-ease
 
-
 minutes_to_complete: 45
 
 who_is_this_for: This is an introductory topic for developers looking to migrate applications to Arm-based servers using migrate-ease, a code analysis tool that scans source code repositories to identify architecture-specific porting issues before migration.
@@ -15,14 +14,15 @@ learning_objectives:
 
 prerequisites:
     - Access to an [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) for testing and validation.
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author: 
     - Odin Shen
     - Jun He
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: Libraries
@@ -50,7 +50,6 @@ further_reading:
         link: /learning-paths/servers-and-cloud-computing/migration/
         type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/migration/_index.md b/content/learning-paths/servers-and-cloud-computing/migration/_index.md
index d2f9a817d1..2198c19d46 100644
--- a/content/learning-paths/servers-and-cloud-computing/migration/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/migration/_index.md
@@ -15,12 +15,13 @@ learning_objectives:
 
 prerequisites:
     - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider.
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ### Tags
 skilllevels: Introductory
 subjects: Libraries
@@ -61,7 +62,6 @@ further_reading:
         link: https://aws.amazon.com/blogs/compute/making-your-go-workloads-up-to-20-faster-with-go-1-18-and-aws-graviton/
         type: blog
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
index 4449a1e105..3af000d60e 100644
--- a/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
@@ -15,12 +15,13 @@ prerequisites:
     - A basic understanding of a RAG pipeline.
     - An AWS Graviton3 C7g.2xlarge instance, or any [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an on-premise Arm server.
     - A [Zilliz account](https://zilliz.com/cloud?utm_source=partner&utm_medium=referral&utm_campaign=2024-10-24_web_arm-dev-hub-data-loading_arm), which you can sign up for with a free trial.
+
+author: Chen Zhang
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Chen Zhang
-
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -40,7 +41,6 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
 further_reading:
     - resource:
         title: Zilliz Documentation
@@ -55,8 +55,6 @@ further_reading:
         link: https://github.com/ggerganov/llama.cpp
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
index c99e2fdc3d..20c4afc740 100644
--- a/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/minio-cobalt/_index.md
@@ -17,12 +17,13 @@ prerequisites:
   - A [Microsoft Azure account](https://azure.microsoft.com/) with access to Cobalt 100-based instances (Dpsv6)
   - Familiarity with SSH and remote server access
   - Basic understanding of cloud storage concepts
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ### Tags
 skilllevels: Introductory
 subjects: Databases 
diff --git a/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md b/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
index 62c93ed32e..4f346b2111 100644
--- a/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
 - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from an appropriate
   cloud service provider or an on-premise Arm server.
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 test_images:
 - ubuntu:latest
 test_link: https://github.com/armflorentlebeau/arm-learning-paths/actions/runs/4312122327
@@ -53,7 +54,6 @@ further_reading:
         link: https://developer.arm.com/community/arm-community-blogs/b/servers-and-cloud-computing-blog/posts/machine-learning-inference-on-aws-graviton3
         type: blog
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/mlflow-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/mlflow-axion/_index.md
index 4aff0442a5..1adc1fa098 100644
--- a/content/learning-paths/servers-and-cloud-computing/mlflow-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mlflow-axion/_index.md
@@ -1,6 +1,6 @@
 ---
 title: Manage the ML lifecycle with MLflow on Google Cloud C4A Axion VM
-    
+
 description: Set up MLflow on Google Cloud C4A Axion Arm VMs running SUSE Linux to track ML experiments, version models with the Model Registry, and deploy a trained model as a REST API.
 
 minutes_to_complete: 30
@@ -19,6 +19,10 @@ prerequisites:
 
 author: Pareena Verma
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ##### Tags
 skilllevels: Introductory
 subjects: ML
diff --git a/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
index b4d8e10fa8..5d75b7ef29 100644
--- a/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
@@ -15,12 +15,13 @@ learning_objectives:
 prerequisites:
     - A [Microsoft Azure](https://azure.microsoft.com/) account with access to Cobalt 100 (Dpsv6) instances
     - Familiarity with the [MongoDB architecture](https://www.mongodb.com/) and deployment practices on Arm64 platforms
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 skilllevels: Introductory
 subjects: Databases
@@ -52,7 +53,6 @@ further_reading:
         link: https://azure.microsoft.com/en-us/solutions/mongodb
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
index 1150633727..f0cf055a37 100644
--- a/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 
 prerequisites:
      - A [Google Cloud Platform (GCP)](https://cloud.google.com/free?utm_source=google&hl=en) account with billing enabled
+
+author: Annie Tallund
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Annie Tallund
-
 ##### Tags
 skilllevels: Introductory
 subjects: Databases
@@ -53,7 +54,6 @@ further_reading:
         link: https://github.com/brianfrankcooper/YCSB/wiki/
         type: documentation
 
-
 weight: 1                       # _index.md always has weight of 1 to order correctly
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # Indicates this should be surfaced when looking for related content. Only set for _index.md of learning path content.
diff --git a/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md b/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
index 8a4403dfea..2a93a6c8f2 100644
--- a/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
@@ -1,11 +1,12 @@
 ---
 title: Analyze the performance of MongoDB on Arm servers
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 minutes_to_complete: 30
 
 who_is_this_for: This is an introductory topic for software developers who want to learn how to deploy and measure MongoDB performance on Arm servers.
@@ -41,7 +42,6 @@ tools_software_languages:
 - GCC
 - Runbook
 
-
 further_reading:
     - resource:
         title: MongoDB Manual
@@ -60,7 +60,6 @@ further_reading:
         link: https://developer.arm.com/community/arm-community-blogs/b/servers-and-cloud-computing-blog/posts/mongodb-performance-on-aws-with-the-arm-graviton2
         type: blog
 
-
 weight: 1
 ---
 
diff --git a/content/learning-paths/servers-and-cloud-computing/mpi/_index.md b/content/learning-paths/servers-and-cloud-computing/mpi/_index.md
index 6ae0c5c09b..c6b091e24f 100644
--- a/content/learning-paths/servers-and-cloud-computing/mpi/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mpi/_index.md
@@ -16,12 +16,13 @@ prerequisites:
     - General knowledge about distributed parallelism (MPI)
     - Some understanding of C, Python, and Linux commands
     - An Arm computer running Linux. Cloud instances can be used, refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
+
+author: Florent Lebeau
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Florent Lebeau
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -42,16 +43,12 @@ tools_software_languages:
     - mpi
     - Runbook
 
-
-
 further_reading:
     - resource:
         title: Parallel Programming for Science Engineering by Victor Eijkhout
         link: https://theartofhpc.com/pcse/
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md b/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md
index c98d3893aa..20ad058d0b 100644
--- a/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md
@@ -2,12 +2,13 @@
 title: Control floating-point accuracy modes in Arm Performance Libraries
 
 minutes_to_complete: 20
+
+author: Joana Cruz
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Joana Cruz
-
 who_is_this_for: This is an introductory topic for developers who want to use the different accuracy modes for vectorized math functions in Libamath, a component of Arm Performance Libraries.
 
 description: Select and apply accuracy modes for vectorized math functions in Libamath to balance performance and precision for your application.
@@ -50,8 +51,6 @@ further_reading:
         link: https://github.com/ARM-software/optimized-routines
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md b/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md
index debb670954..c226bff6ac 100644
--- a/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/multiarch_nginx_on_aks/_index.md
@@ -11,25 +11,25 @@ learning_objectives:
     - Verify nginx deployment and functionality on each architecture
     - Compare performance between x86 and Arm64 nginx instances
     - Learn techniques for deploying multi-architecture Kubernetes workloads
-  
 
 prerequisites:
     - An [Azure account](https://azure.microsoft.com/en-us/free/)
     - A local machine with [`jq`](https://jqlang.org/download/), [`curl`](https://curl.se/download.html), [`wrk`](https://github.com/wg/wrk), [Azure CLI](/install-guides/azure-cli/), and [`kubectl`](/install-guides/kubectl/) installed
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author:
     - Geremy Cohen
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 
 subjects: Containers and Virtualization
 cloud_service_providers:
   - Microsoft Azure
-    
+
 armips:
     - Neoverse
 
diff --git a/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md b/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md
index 604832e7ec..ce99aca829 100644
--- a/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/multiarch_ollama_on_gke/_index.md
@@ -5,7 +5,6 @@ minutes_to_complete: 30
 
 who_is_this_for: This Learning Path is for developers who want to compare the performance of amd64 and arm64 deployments by running inferences on a hybrid GKE cluster using an Ollama multi-architecture container image.
 
-
 learning_objectives:
   - Create a hybrid GKE cluster with amd64 and arm64 nodes.
   - Deploy Ollama services for amd64 and arm64 architectures using a single multi-architecture container image.
@@ -15,20 +14,21 @@ prerequisites:
     - A [Google Cloud account](https://console.cloud.google.com/).
     - A local machine with [Google Cloud CLI](/install-guides/gcloud/) and [kubectl](/install-guides/kubectl/) installed.
     - The [GKE Cloud Plugin](https://cloud.google.com/kubernetes-engine/docs/how-to/cluster-access-for-kubectl#gcloud) installed.
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author:
     - Geremy Cohen
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 
 subjects: Containers and Virtualization
 cloud_service_providers:
   - Google Cloud
-    
+
 armips:
     - Neoverse
 
@@ -75,11 +75,6 @@ further_reading:
       link: https://cloud.google.com/kubernetes-engine/docs/how-to/cluster-access-for-kubectl
       type: documentation
 
-    
-
-
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md
index 67f4e40716..e040c2123c 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql-azure/_index.md
@@ -1,7 +1,6 @@
 ---
 title: Deploy MySQL on Microsoft Azure Cobalt 100 processors
 
-   
 minutes_to_complete: 30
 
 who_is_this_for: This is an introductory topic for developers migrating MySQL applications from x86_64 to Arm.
@@ -14,12 +13,13 @@ learning_objectives:
 prerequisites:
     - A [Microsoft Azure](https://azure.microsoft.com/) account with access to Cobalt 100 based instances (Dpsv6)
     - Familiarity with relational databases and the basics of [MySQL](https://dev.mysql.com/doc/refman/8.0/en/introduction.html)
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 skilllevels: Introductory
 subjects: Databases
@@ -33,7 +33,7 @@ tools_software_languages:
     - MySQL
     - SQL
     - Docker
- 
+
 operatingsystems:
     - Linux
 
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql-lns-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql-lns-azure/_index.md
index bd58bc6390..e528dc6ab3 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql-lns-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql-lns-azure/_index.md
@@ -15,10 +15,11 @@ learning_objectives:
 prerequisites:
     - A [Microsoft Azure](https://azure.microsoft.com/) account with access to Cobalt 100 based instances (Dpsv6)
     - Basic familiarity with SSH and MySQL command-line tools
+author: Doug Anson
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
-author: Doug Anson
 
 ### Tags
 skilllevels: Introductory
@@ -57,7 +58,6 @@ further_reading:
       link: https://github.com/akopytov/sysbench
       type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql/_index.md
index de275dcbd2..9d0358ded3 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql/_index.md
@@ -12,11 +12,13 @@ learning_objectives:
 prerequisites:
     - An Arm based instance from a cloud service provider, or an on-premise Arm server.
     - If you do not have an Arm node, the next section discusses some options.
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
 ### Tags
 skilllevels: Introductory
 subjects: Databases
@@ -47,7 +49,6 @@ further_reading:
         link: https://docs.ansible.com/
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md
index 47c49af936..5fc1c3f06b 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql_benchmark/_index.md
@@ -12,12 +12,13 @@ learning_objectives:
 prerequisites:
     - Basic knowledge of [MySQL databases](https://www.mysql.com/)
     - Two Arm servers running Ubuntu 22.04, one for the MySQL server and the other for the Sysbench client
+
+author: Bolt Liu
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Bolt Liu
-
 skilllevels: Introductory
 subjects: Databases
 cloud_service_providers:
@@ -52,7 +53,6 @@ further_reading:
         link: /learning-paths/servers-and-cloud-computing/mysql/
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1
diff --git a/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md
index 11deaa7062..48039b70c6 100644
--- a/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/mysql_tune/_index.md
@@ -10,12 +10,13 @@ learning_objectives:
 
 prerequisites:
     - Bare-metal or cloud [installation of MySQL](/learning-paths/servers-and-cloud-computing/mysql/)
+
+author: Julio Suarez
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Julio Suarez
-
 skilllevels: Advanced
 subjects: Databases
 cloud_service_providers:
@@ -33,7 +34,6 @@ tools_software_languages:
     - InnoDB
     - Runbook
 
-
 test_images:
     - ubuntu:latest
 test_link: null
diff --git a/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md b/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md
index bf3be1b04f..b7122f6465 100644
--- a/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/_index.md
@@ -12,20 +12,21 @@ learning_objectives:
     - Interpret boot logs to verify bring-up and diagnose boot-stage issues
     - Modify platform control firmware (for example, SCP/MCP) and validate changes via pre-silicon simulation
     - Launch a dual-chip RD-V3-R1 simulation and verify AP/MCP coordination
-   
+
 prerequisites:
     - Access to an Arm Neoverse-based Linux machine (cloud or local) with at least 80 GB of free storage
     - Familiarity with Linux command-line tools and basic scripting
     - Understanding of firmware boot stages and SoC-level architecture
     - Docker installed, or a GitHub Codespaces-compatible development environment
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author:
     - Odin Shen
     - Ann Cheng
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Advanced
 subjects: Containers and Virtualization
@@ -57,7 +58,6 @@ further_reading:
         link: https://git.gitlab.arm.com/infra-solutions/reference-design/infra-refdesign-manifests
         type: gitlab    
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md b/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md
index 8ca0187a47..acb43668c5 100644
--- a/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/net-aspire/_index.md
@@ -14,12 +14,13 @@ prerequisites:
     - A Windows on Arm machine, for example the Lenovo Thinkpad X13s running Windows 11 to build the .NET Aspire project.    
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from AWS or GCP.
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is an example of a suitable editor.    
+
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dawid Borycki
-
 ### Tags
 skilllevels: Introductory
 subjects: Containers and Virtualization
@@ -29,7 +30,7 @@ cloud_service_providers:
 
 armips:
     - Neoverse
-    
+
 tools_software_languages:
     - .NET
     - C# 
@@ -39,7 +40,6 @@ operatingsystems:
     - Windows
     - Linux
 
-
 further_reading:
     - resource:
         title: .NET Aspire Overview
@@ -54,8 +54,6 @@ further_reading:
         link: https://cloud.google.com/products/compute/
         type: Documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md
index 9ff2eda354..b76a93ba4c 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/_index.md
@@ -11,15 +11,15 @@ learning_objectives:
     - Configure and test a static website with NGINX on the virtual machine
     - Run baseline NGINX performance tests with ApacheBench (ab) on Ubuntu Pro 24.04 LTS Arm64
 
-
 prerequisites:
     - A [Microsoft Azure](https://azure.microsoft.com/) account with access to Cobalt 100 based instances (Dpsv6)
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 skilllevels: Introductory
 subjects: Web
@@ -50,7 +50,6 @@ further_reading:
       link: https://docs.nginx.com/nginx/deployment-guides/microsoft-azure/virtual-machines-for-nginx/
       type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx/_index.md
index 507097e6bd..bd9a49fb20 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx/_index.md
@@ -14,12 +14,13 @@ prerequisites:
     - To create a file server you will need at least one [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or one on-premises Arm server.
     - To create a reverse proxy or API gateway you will need at least three Arm based instances from a cloud service provider or at least three on-premises Arm servers.
     - Network settings (firewalls and security groups) which allow communication on port 22 (SSH) and port 443 (HTTPS).
+
+author: Julio Suarez
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Julio Suarez
-
 ### Tags
 skilllevels: Introductory
 subjects: Web
@@ -54,7 +55,6 @@ further_reading:
         link: https://www.nginx.com/blog/deploying-nginx-plus-as-an-api-gateway-part-1/
         type: blog
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
index 2ec4274188..5594088d12 100644
--- a/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nginx_tune/_index.md
@@ -15,12 +15,13 @@ learning_objectives:
 prerequisites:
     - A cloud or bare-metal installation of a Nginx file server or load balancer.
     - If you do not already have a Nginx setup, a review of [Learn how to deploy Nginx](/learning-paths/servers-and-cloud-computing/nginx/).
+
+author: Julio Suarez
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Julio Suarez
-
 ### Tags
 skilllevels: Advanced
 subjects: Web
@@ -47,8 +48,6 @@ further_reading:
         title: Nginx Admin Guide
         link: https://docs.nginx.com/nginx/admin-guide/
         type: documentation
-    
-
 
 ### FIXED, DO NOT MODIFY
 # ================================================================================
diff --git a/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md b/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md
index f647cf45a7..5904b53d1d 100644
--- a/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/_index.md
@@ -11,12 +11,13 @@ learning_objectives:
 
 prerequisites:
     - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an on-premise Arm server.
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -33,7 +34,7 @@ tools_software_languages:
     - Python
     - PyTorch
     - Hugging Face
-    
+
 further_reading:
     - resource:
         title: Hugging Face Documentation
@@ -52,8 +53,6 @@ further_reading:
         link: https://pytorch.org/docs/stable/index.html
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md
index bd91822b01..042220c681 100644
--- a/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/node-js-gcp/_index.md
@@ -1,29 +1,26 @@
 ---
 title:  Deploy Node.js on Google Cloud C4A Arm-based Axion VMs
 
-
-
 minutes_to_complete: 30
 
 who_is_this_for: This is an introductory topic for software developers migrating Node.js workloads from x86_64 to Arm-based servers, specifically on Google Cloud C4A virtual machines built on Axion processors.
 
-
 learning_objectives:
   - Provision an Arm-based SUSE Linux Enterprise Server virtual machine on Google Cloud C4A instances with Axion processors
   - Install and configure Node.js on a SUSE Arm64 (C4A) instance
   - Validate Node.js functionality with baseline HTTP server tests
   - Benchmark Node.js performance using Autocannon on Arm64 (AArch64) architecture 
 
-
 prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Familiarity with networking concepts and [Node.js event-driven architecture](https://nodejs.org/en/docs/guides/event-loop-timers-and-nexttick)
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ##### Tags
 skilllevels: Introductory
 subjects: Web
diff --git a/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md b/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md
index 74b7876613..bba9395e5a 100644
--- a/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/oci-terraform/_index.md
@@ -11,12 +11,13 @@ learning_objectives:
 prerequisites:
     - An OCI account
     - A computer with Terraform installed
+
+author: Frédéric -lefred- Descamps
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Frédéric -lefred- Descamps
-
 ### Tags
 skilllevels: Advanced
 subjects: Containers and Virtualization
@@ -32,7 +33,6 @@ operatingsystems:
 tools_software_languages:
     - Terraform
 
-
 further_reading:
     - resource:
         title: Terraform docs for OCI 
@@ -43,8 +43,6 @@ further_reading:
         link: https://blogs.oracle.com/cloud-infrastructure/post/arm-based-cloud-computing-is-the-next-big-thing-introducing-arm-on-oracle-cloud-infrastructure
         type: blog
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md
index 71763641ff..fa345d1b13 100644
--- a/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/onnx-on-azure/_index.md
@@ -1,7 +1,6 @@
 ---
 title: Deploy SqueezeNet 1.0 INT8 model with ONNX Runtime on Azure Cobalt 100
 
-   
 minutes_to_complete: 60   
 
 who_is_this_for: This Learning Path is for developers deploying ONNX-based applications on Arm-based machines.
@@ -14,12 +13,13 @@ prerequisites:
     - A [Microsoft Azure](https://azure.microsoft.com/) account with access to Cobalt 100 based instances (Dpsv6)
     - Basic understanding of Python and machine learning concepts
     - Familiarity with [ONNX Runtime](https://onnxruntime.ai/docs/) and Azure cloud services
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma    
-
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -54,11 +54,9 @@ further_reading:
         link: https://onnxruntime.ai/docs/performance/tune-performance/profiling-tools.html#in-code-performance-profiling
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
-
diff --git a/content/learning-paths/servers-and-cloud-computing/onnx/_index.md b/content/learning-paths/servers-and-cloud-computing/onnx/_index.md
index c2cde3421e..4f1878501f 100644
--- a/content/learning-paths/servers-and-cloud-computing/onnx/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/onnx/_index.md
@@ -14,12 +14,13 @@ prerequisites:
     - Basic understanding of Python and machine learning concepts.
     - Familiarity with ONNX Runtime and Azure cloud services.
     - Knowledge of Large Language Model (LLM) fundamentals.
+
+author: Nobel Chowdary Mandepudi
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Nobel Chowdary Mandepudi
-
 ### Tags
 skilllevels: Advanced
 armips:
@@ -33,7 +34,6 @@ tools_software_languages:
     - Python
     - ONNX Runtime
 
-
 further_reading:
     - resource:
         title: ONNX Runtime Docs
diff --git a/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md b/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md
index 71524d42a2..12564e8751 100644
--- a/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/_index.md
@@ -17,14 +17,15 @@ prerequisites:
   - At least 80 GB free disk space and 48 GB RAM
   - Working knowledge of Docker, Git, and common Linux terminal tools
   - Basic understanding of the server firmware stack (such as UEFI, BMC, and TF-A)
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author:
   - Odin Shen
   - Ken Zhang
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Advanced
 subjects: Containers and Virtualization
diff --git a/content/learning-paths/servers-and-cloud-computing/opencv-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/opencv-on-axion/_index.md
index 96a87432bb..f5074152e4 100644
--- a/content/learning-paths/servers-and-cloud-computing/opencv-on-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/opencv-on-axion/_index.md
@@ -1,7 +1,7 @@
 ---
 title: Build computer vision pipelines with OpenCV on a Google Cloud C4A Axion VM
 description: Deploy and run OpenCV-based computer vision pipelines on Google Cloud Axion C4A Arm-based VMs, covering image processing, video pipelines, browser-based visualization, and integration with machine learning models.
-  
+
 minutes_to_complete: 45
 
 who_is_this_for: This is an introductory topic for DevOps engineers, software developers, and AI practitioners who want to build and run computer vision pipelines on SUSE Linux Enterprise Server (SLES) Arm64 using OpenCV, process images and videos, visualize outputs in real time, and integrate ML models.
@@ -20,6 +20,10 @@ prerequisites:
 
 author: Pareena Verma
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ##### Tags
 skilllevels: Introductory
 subjects: ML
diff --git a/content/learning-paths/servers-and-cloud-computing/openebs-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/openebs-cobalt/_index.md
index 0245416aec..1fe928bf9c 100644
--- a/content/learning-paths/servers-and-cloud-computing/openebs-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openebs-cobalt/_index.md
@@ -2,7 +2,7 @@
 title: Use OpenEBS for Kubernetes-native persistent storage on Azure Cobalt 100-based Arm64 virtual machines 
 
 description: Learn how to install and configure OpenEBS LocalPV on an Arm64 virtual machine powered by Azure Cobalt 100 using K3s Kubernetes, provision persistent storage dynamically, deploy stateful applications, and validate persistent storage functionality.
-   
+
 minutes_to_complete: 60
 
 who_is_this_for: This is an introductory topic for DevOps engineers, platform engineers, cloud-native developers, and Kubernetes administrators who want to deploy lightweight Kubernetes-native persistent storage on Arm-based cloud infrastructure.
@@ -21,6 +21,10 @@ prerequisites:
 
 author: Pareena Verma
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: Containers and Virtualization
diff --git a/content/learning-paths/servers-and-cloud-computing/openjdk-pacbti-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/openjdk-pacbti-gcp/_index.md
index f4dcc75cdb..b2b7003fcc 100644
--- a/content/learning-paths/servers-and-cloud-computing/openjdk-pacbti-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openjdk-pacbti-gcp/_index.md
@@ -18,6 +18,10 @@ prerequisites:
 
 author: Doug Anson
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -59,4 +63,4 @@ further_reading:
 weight: 1                       # _index.md always has weight of 1 to order correctly
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
----
\ No newline at end of file
+---
diff --git a/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md b/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md
index 1e3139984c..f8d0157032 100644
--- a/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openrng-with-performix/_index.md
@@ -16,12 +16,13 @@ learning_objectives:
 prerequisites:
     - An Arm Linux (aarch64) server, such as an AWS Graviton3 instance
     - Basic understanding of C++ and CMake
+
+author: Kieran Hejmadi
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Kieran Hejmadi
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
diff --git a/content/learning-paths/servers-and-cloud-computing/openshift/_index.md b/content/learning-paths/servers-and-cloud-computing/openshift/_index.md
index dcbde44876..7661d961d5 100644
--- a/content/learning-paths/servers-and-cloud-computing/openshift/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openshift/_index.md
@@ -13,12 +13,13 @@ prerequisites:
   - Red Hat OpenShift Pipelines (Tekton) Operator installed in your cluster
   - Familiarity with the `oc` CLI, container fundamentals, and basic Tekton concepts (Task, Pipeline, PipelineRun)
   - Cluster access with cluster-admin or equivalent permissions to configure nodes and pipelines
+
+author: Jeff Young
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jeff Young
-
 # Tags
 skilllevels: Advanced
 subjects: CI-CD
diff --git a/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md
index c83f450156..30d810bf29 100644
--- a/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/openstack-on-azure/_index.md
@@ -2,7 +2,6 @@
 title: Deploy OpenStack on Azure Cobalt 100 Arm64 Virtual Machine
 description: Deploy OpenStack on Azure Cobalt 100 Arm64 virtual machines using DevStack for development and Kolla-Ansible for containerized production deployments.
 
-    
 minutes_to_complete: 90
 
 who_is_this_for: This learning path is designed for developers, DevOps engineers, and platform engineers who want to deploy and manage OpenStack on Arm-based cloud environments using Kolla-Ansible and DevStack.
@@ -20,12 +19,13 @@ prerequisites:
   - Basic knowledge of Linux command-line operations
   - Familiarity with SSH and remote server access
   - Basic understanding of cloud computing and virtualization concepts
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 skilllevels: Introductory
 subjects: Containers and Virtualization
diff --git a/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md b/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md
index 0769317b66..ccfa58e2b2 100644
--- a/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/opentelemetry/_index.md
@@ -15,12 +15,13 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with Python and Flask
   - Basic understanding of containers and Kubernetes concepts
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ##### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -58,12 +59,12 @@ further_reading:
       title: Prometheus documentation
       link: https://prometheus.io/docs/introduction/overview/
       type: documentation
-  
+
   - resource:
       title: Jaeger documentation
       link: https://www.jaegertracing.io/docs/
       type: documentation
-  
+
   - resource:
       title: Docker documentation
       link: https://docs.docker.com/
diff --git a/content/learning-paths/servers-and-cloud-computing/pac/_index.md b/content/learning-paths/servers-and-cloud-computing/pac/_index.md
index 7cf96119f2..7db500c381 100644
--- a/content/learning-paths/servers-and-cloud-computing/pac/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pac/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 prerequisites:
     - An Arm based instance from a cloud service provider, or an on-premise Arm server.
     - If needed, review [Get started with Arm-based cloud instances](/learning-paths/servers-and-cloud-computing/csp/) to learn how to deploy Arm in the cloud. These learning paths also point to more advanced learning paths that show how to automate the deployment of Arm instances at different cloud providers.
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -34,7 +35,6 @@ operatingsystems:
 tools_software_languages:
     - Runbook
 
-
 further_reading:
     - resource:
         title: Learn the architecture - Providing protection for complex software
diff --git a/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md b/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md
index 36cfc24a95..2b6db6eb98 100644
--- a/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md
@@ -18,12 +18,13 @@ prerequisites:
     - Access to an Arm-based cloud instance running Linux, such as an AWS Graviton3 instance
     - Access to Arm Performix configured with the remote Arm target. See the [Arm Performix install guide](/install-guides/performix/) for setup instructions
     - Basic understanding of C++
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -37,8 +38,6 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
-
 further_reading:
     - resource:
         title: Find code hotspots with Arm Performix
@@ -61,8 +60,6 @@ further_reading:
         link: /learning-paths/servers-and-cloud-computing/performix-microarchitecture/
         type: learning-path
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/performix-memory-access/_index.md b/content/learning-paths/servers-and-cloud-computing/performix-memory-access/_index.md
index 70da52675d..8aed6223be 100644
--- a/content/learning-paths/servers-and-cloud-computing/performix-memory-access/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/performix-memory-access/_index.md
@@ -21,6 +21,10 @@ prerequisites:
 
 author: Kieran Hejmadi
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
diff --git a/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md b/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md
index d8a2f03c51..eb465842cf 100644
--- a/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/performix-microarchitecture/_index.md
@@ -1,7 +1,6 @@
 ---
 title: Optimize application performance using Arm Performix CPU microarchitecture analysis
 
-
 minutes_to_complete: 60
 
 who_is_this_for: This is an introductory topic for software developers who want to learn performance analysis methodologies for Linux applications on Arm Neoverse-based servers.
@@ -16,15 +15,16 @@ prerequisites:
     - An Arm Neoverse-based server running Linux (bare-metal or cloud bare-metal instance preferred for access to hardware performance counters)
     - Familiarity with Linux command line
     - Basic understanding of CPU performance concepts
+
+author:
+    - Brendan Long
+    - Kieran Hejmadi
+    - Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author:
-- Brendan Long
-- Kieran Hejmadi
-- Jason Andrews
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
diff --git a/content/learning-paths/servers-and-cloud-computing/performix-system-characterization/_index.md b/content/learning-paths/servers-and-cloud-computing/performix-system-characterization/_index.md
index 04efc83cff..8218557974 100644
--- a/content/learning-paths/servers-and-cloud-computing/performix-system-characterization/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/performix-system-characterization/_index.md
@@ -15,14 +15,14 @@ learning_objectives:
 prerequisites:
     - A Arm Linux target machine accessible via SSH to characterize.
 
+author:
+    - Brendan Long
+    - David Wong
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author:
-- Brendan Long
-- David Wong
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
diff --git a/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md
index 9b1cf97ec0..35d8e0755b 100644
--- a/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/php-on-gcp/_index.md
@@ -1,7 +1,6 @@
 ---
 title:  Deploy PHP on Google Cloud C4A Arm-based Axion VMs
 
-  
 minutes_to_complete: 30
 
 who_is_this_for: This is an introductory topic for developers migrating Hypertext Preprocessor (PHP) workloads from x86_64 to Arm-based servers, specifically on Google Cloud C4A virtual machines (VM) built on Axion processors.
@@ -12,16 +11,16 @@ learning_objectives:
   - Validate PHP functionality by running baseline HTTP server tests  
   - Benchmark PHP performance using PHPBench on Arm64 architecture 
 
-
 prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with web servers and PHP scripting
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ##### Tags
 skilllevels: Introductory
 subjects: Web
diff --git a/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md b/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md
index ec36ea83b3..92be487ae9 100644
--- a/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pinning-threads/_index.md
@@ -16,12 +16,13 @@ prerequisites:
     - Experience with multi-threaded programming in C++ and Python
     - Understanding of build systems and computer architecture concepts
     - Familiarity with Linux command-line tools
+
+author: Kieran Hejmadi
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Kieran Hejmadi
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -63,8 +64,6 @@ further_reading:
         link: /learning-paths/servers-and-cloud-computing/csp/
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md b/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md
index 21ffab7130..79c95aa486 100644
--- a/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
     - An Arm-based computer running Linux.
     - Some familiarity with Linux application performance analysis.
+
+author: Gayathri Narayana Yegna Narayanan
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Gayathri Narayana Yegna Narayanan
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -34,7 +35,6 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
 further_reading:
     - resource:
         title: Arm Neoverse N2 PMU Guide
@@ -49,7 +49,6 @@ further_reading:
         link: https://developer.arm.com/documentation/109215/0200/?lang=en
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md
index c1eccc0840..c90ea2d91d 100644
--- a/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/postgresql-cobalt/_index.md
@@ -17,12 +17,13 @@ prerequisites:
   - Basic knowledge of Linux command-line operations
   - Familiarity with SSH and remote server access
   - Basic understanding of databases and SQL
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 description: Deploy PostgreSQL on Azure Cobalt 100 Arm64 virtual machines, load a relational schema with transactional data, and benchmark and optimize query performance using pgbench and pg_stat_statements.
 
 ### Tags
diff --git a/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md b/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md
index 2e51bded10..e87d7068c8 100644
--- a/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/postgresql/_index.md
@@ -12,11 +12,13 @@ learning_objectives:
 prerequisites:
     - An Arm based instance from a cloud service provider, or an on-premise Arm server.
     - If you do not have an Arm node, the next section discusses some options.
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
 ### Tags
 skilllevels: Introductory
 subjects: Databases
@@ -44,7 +46,6 @@ further_reading:
         link: https://docs.ansible.com/
         type: documentation 
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md
index 7871205b48..5f82f3de57 100644
--- a/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/postgresql_tune/_index.md
@@ -10,12 +10,13 @@ learning_objectives:
 
 prerequisites:
     - Bare-metal or cloud [installation of PostgreSQL](/learning-paths/servers-and-cloud-computing/postgresql/)
+
+author: Julio Suarez
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Julio Suarez
-
 test_images:
     - ubuntu:latest
 test_maintenance: true
@@ -38,7 +39,6 @@ tools_software_languages:
     - HammerDB
     - Runbook
 
-
 further_reading:
     - resource:
         title: PostgreSQL documentation
@@ -49,7 +49,6 @@ further_reading:
         link: https://dev.to/aws-heroes/postgresql-on-arm-default-page-size-matters-2n7a
         type: blog
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md b/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
index 8eebfa751a..f2ba94c87e 100644
--- a/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/processwatch/_index.md
@@ -12,12 +12,13 @@ learning_objectives:
 prerequisites:
     - An Arm-based system (bare metal server, cloud instance, or developer board) running Linux with kernel version 5.8.0 or later.
     - Root access, or the ability to run the sudo command.
+
+author: Graham Woodward
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Graham Woodward
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -35,7 +36,6 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
 further_reading:
     - resource:
         title: Perf for Linux on Arm (LinuxPerf)
@@ -46,7 +46,6 @@ further_reading:
         link: https://github.com/capstone-engine/capstone
         type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md b/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md
index f6f1e7442b..d33de9e4fa 100644
--- a/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/_index.md
@@ -11,12 +11,13 @@ learning_objectives:
 
 prerequisites:
     - An Arm Neoverse-based (N1, N2 or V1) computer running Linux. For your host OS, you can use Amazon Linux 2023 or newer, Debian 10 or newer, RHEL 8 or newer, or Ubuntu 20.04 or newer.
+
+author: Julie Gaskin
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Julie Gaskin
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -29,7 +30,6 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
 further_reading:
     - resource:
         title: Streamline CLI Tools User Guide 
@@ -44,8 +44,6 @@ further_reading:
         link: https://www.arm.com/products/development-tools/performance/streamline-cli
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md
index ef7541a117..7ca6ce4d0f 100644
--- a/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/puppet-on-gcp/_index.md
@@ -10,12 +10,13 @@ learning_objectives:
   - Install Puppet on a SUSE Arm64 C4A instance
   - Verify Puppet by applying a test manifest and confirming successful resource creation on Arm64  
   - Benchmark Puppet by measuring catalog compile time, apply speed, and resource usage on Arm64
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled  
   - Basic familiarity with [Puppet](https://www.puppet.com/)
diff --git a/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md b/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md
index 83ac92531b..ab3e53096f 100644
--- a/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/pytorch-llama/_index.md
@@ -14,15 +14,16 @@ learning_objectives:
 
 prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) with at least 16 CPUs from a cloud service provider or an on-premise Arm server.
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author:
     - Nikhil Gupta
     - Pareena Verma
     - Nobel Chowdary Mandepudi
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -60,8 +61,6 @@ further_reading:
         link: https://pytorch.org/docs/stable/index.html
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md
index 93c70ca17a..44ed13f627 100644
--- a/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md
@@ -18,12 +18,13 @@ prerequisites:
   - Basic familiarity with Python
   - Basic understanding of machine learning embeddings
   - Familiarity with Linux command-line operations
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ##### Tags
 skilllevels: Introductory
 subjects: Databases
@@ -66,7 +67,7 @@ further_reading:
       title: Vector Databases Explained
       link: https://qdrant.tech/articles/what-is-a-vector-database/
       type: documentation
-    
+
 weight: 1
 layout: "learningpathall"
 learning_path_main_page: yes
diff --git a/content/learning-paths/servers-and-cloud-computing/quantlib/_index.md b/content/learning-paths/servers-and-cloud-computing/quantlib/_index.md
index 8450e5ce05..50d42304f3 100644
--- a/content/learning-paths/servers-and-cloud-computing/quantlib/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/quantlib/_index.md
@@ -1,6 +1,6 @@
 ---
 title: Benchmark QuantLib on Azure Cobalt
-    
+
 description: Learn how to build QuantLib on an Arm-based Azure Cobalt virtual machine and run benchmark workloads to evaluate performance on Arm64 cloud infrastructure.
 
 minutes_to_complete: 60
@@ -21,6 +21,8 @@ prerequisites:
 author: Chris Moroney
 
 generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 ### Tags
 skilllevels: Advanced
diff --git a/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md
index 31f0a1ad4d..c7802dfb63 100644
--- a/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rabbitmq-gcp/_index.md
@@ -18,12 +18,13 @@ prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic understanding of message queues and messaging concepts (publishers, consumers)
   - Familiarity with Linux command-line operations
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ##### Tags
 skilllevels: Introductory
 subjects: Databases
diff --git a/content/learning-paths/servers-and-cloud-computing/rag/_index.md b/content/learning-paths/servers-and-cloud-computing/rag/_index.md
index 8ec3a3d17f..a71e97e8e1 100644
--- a/content/learning-paths/servers-and-cloud-computing/rag/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rag/_index.md
@@ -18,12 +18,13 @@ prerequisites:
     - Familiarity with REST APIs and web services.
     - Basic knowledge of vector databases.
     - Understanding of LLM fundamentals.
+
+author: Nobel Chowdary Mandepudi
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Nobel Chowdary Mandepudi
-
 ### Tags
 skilllevels: Advanced
 armips:
@@ -54,8 +55,6 @@ further_reading:
         link: https://blogs.oracle.com/ai-and-datascience/post/democratizing-generative-ai-with-cpu-based-inference
         type: blog
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/ran/_index.md b/content/learning-paths/servers-and-cloud-computing/ran/_index.md
index 91767b9997..b67014d269 100644
--- a/content/learning-paths/servers-and-cloud-computing/ran/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ran/_index.md
@@ -6,7 +6,6 @@ minutes_to_complete: 15
 who_is_this_for: This is an introductory topic for software developers new to the
   Arm RAN Acceleration Library (ArmRAL).
 
-
 learning_objectives:
 - Build and install the Arm RAN Acceleration Library
 - Test the capabilities of your platform
@@ -14,12 +13,13 @@ learning_objectives:
 prerequisites:
 - An Arm computer running Linux. Cloud instances can be used, refer to the list of
   [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
+
+author: Ronan Synnott
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Ronan Synnott
-
 test_images:
 - ubuntu:latest
 test_link: null
@@ -56,7 +56,6 @@ further_reading:
         link: https://developer.arm.com/community/arm-community-blogs/b/servers-and-cloud-computing-blog/posts/arm-ral-is-now-open-source
         type: blog
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md
index d35942fb3b..b2475672df 100644
--- a/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md
@@ -1,7 +1,7 @@
 ---
 title: Scale AI workloads with Ray on Google Cloud C4A Axion VM
 description: Deploy and run distributed AI workloads using Ray on Google Cloud Axion C4A Arm-based VMs, covering parallel tasks, hyperparameter tuning, and model serving with Ray Core, Train, Tune, and Serve.
-    
+
 minutes_to_complete: 30
 
 who_is_this_for: This is an introductory topic for DevOps engineers, ML engineers, and software developers who want to deploy and run distributed workloads using Ray on SUSE Linux Enterprise Server (SLES) Arm64, execute parallel tasks, perform hyperparameter tuning, and serve models at scale.
@@ -15,12 +15,13 @@ learning_objectives:
 prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with Python and distributed systems concepts
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ##### Tags
 skilllevels: Introductory
 subjects: ML
diff --git a/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md
index f6b3ed311a..d2c629b2ec 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis-cobalt/_index.md
@@ -17,12 +17,13 @@ prerequisites:
   - Basic knowledge of Linux command-line operations
   - Familiarity with SSH and remote server access
   - Basic understanding of databases, caching, and messaging systems
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 ### Tags
 skilllevels: Introductory
diff --git a/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md b/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md
index 545cf79b56..4e70415280 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis-data-searching/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with [Redis](https://redis.io/) 
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ##### Tags
 skilllevels: Introductory
 subjects: Databases
diff --git a/content/learning-paths/servers-and-cloud-computing/redis/_index.md b/content/learning-paths/servers-and-cloud-computing/redis/_index.md
index 8e139357c5..4674cedfe5 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis/_index.md
@@ -13,11 +13,13 @@ prerequisites:
 
     - An Arm based instance from a cloud service provider, or an on-premise Arm server.
     - If you do not have an Arm node, the next section discusses some options.
+
+author: Elham Harirpoush
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Elham Harirpoush
 ### Tags
 skilllevels: Introductory
 subjects: Databases
@@ -34,15 +36,12 @@ tools_software_languages:
     - Redis
     - Runbook
 
-
 further_reading:
     - resource:
         title: Redis documentation
         link: https://redis.io/docs/
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md b/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md
index daff222321..8204a21e4b 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis_cache/_index.md
@@ -15,11 +15,12 @@ prerequisites:
     - A Google Cloud [account](https://console.cloud.google.com/)
     - A machine with [Terraform](/install-guides/terraform/), [AWS CLI](/install-guides/aws-cli), [Google Cloud CLI](/install-guides/gcloud), [Azure CLI](/install-guides/azure-cli), [AWS IAM authenticator](https://docs.aws.amazon.com/eks/latest/userguide/install-aws-iam-authenticator.html), and [Ansible](/install-guides/ansible/) installed
 
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
 ### Tags
 skilllevels: Advanced
 subjects: Databases
@@ -39,15 +40,12 @@ tools_software_languages:
     - MySQL
     - Runbook
 
-
 further_reading:
     - resource:
         title: Redis documentation
         link: https://redis.io/docs/
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md b/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md
index 4df7b54c3a..b8ce1f3749 100644
--- a/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/redis_tune/_index.md
@@ -13,14 +13,15 @@ learning_objectives:
 prerequisites:
     - Cloud or bare-metal installation of a Redis server
     - Review [Learn how to deploy Redis](/learning-paths/servers-and-cloud-computing/redis/) if you do not already have Redis setup
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author:
     - Elham Harirpoush
     - Kelsey Steele
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Advanced
 subjects: Databases
@@ -44,8 +45,6 @@ further_reading:
         link: https://redis.io/docs/
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md b/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md
index 10a8be5067..7a9ca423b7 100644
--- a/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/refinfra-debug/_index.md
@@ -15,12 +15,13 @@ prerequisites:
     - An Arm Neoverse Reference Design (RD) Software Stack.
     - A Fixed Virtual Platform (FVP).
     - A basic understanding of Neoverse Reference Design (RD) platform boot.
+
+author: Daniel Nguyen
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Daniel Nguyen
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
diff --git a/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md b/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md
index bb9936db1a..92b2325b5c 100644
--- a/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/_index.md
@@ -14,14 +14,15 @@ prerequisites:
     - Some understanding of the [Reference Design software stack architecture](https://neoverse-reference-design.docs.arm.com/en/latest/about/software_stack.html).
     - Some understanding of the Linux command line.
     - Optionally a basic understanding of Docker and containers.
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author: 
     - Tom Pilar
     - Daniel Nguyen
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -36,14 +37,12 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
 further_reading:
     - resource:
         title: Neoverse Reference Design Platform Software Documentation
         link: https://neoverse-reference-design.docs.arm.com/en/latest/index.html
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md b/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md
index b119fd42ca..74b220e90e 100644
--- a/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/reproducible-libamath/_index.md
@@ -2,12 +2,13 @@
 title: Enable reproducible math functions across vector extensions with Arm Performance Libraries
 
 minutes_to_complete: 10
+
+author: Joana Cruz
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Joana Cruz
-
 who_is_this_for: This is an introductory topic for developers who want to produce reproducible code across vector extensions using math functions in Libamath, a component of Arm Performance Libraries. 
 
 learning_objectives: 
@@ -47,7 +48,6 @@ further_reading:
         link: /learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/
         type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md b/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md
index b3b9a4d36e..e68799646c 100644
--- a/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rme-cca-basics/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 prerequisites:
     - An aarch64 or x86_64 computer running Ubuntu 22.04. Cloud instances can be used, refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
     - If you use a client application to access your computer running Ubuntu, make sure that X11 forwarding is enabled.
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -33,7 +34,6 @@ tools_software_languages:
     - CCA
     - Runbook
 
-    
 further_reading:
     - resource:
         title: Arm Confidential Compute Architecture
@@ -48,7 +48,6 @@ further_reading:
         link: https://developer.arm.com/documentation/den0126
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md b/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md
index 0923911e71..44f9a0241b 100644
--- a/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rtp-llm/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 prerequisites:
     - Any Arm Neoverse N2-based or Arm Neoverse V2-based instance running Ubuntu 22.04 LTS from a cloud service provider or an on-premise Arm server. 
     - For the server, at least four cores and 16GB of RAM, with disk storage configured up to at least 32 GB. 
+
+author: Tianyu Li
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Tianyu Li
-
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -58,9 +59,6 @@ further_reading:
         title: Get started with Arm-based cloud instances
         link: /learning-paths/servers-and-cloud-computing/csp/
         type: website
-     
-
-
 
 ### FIXED, DO NOT MODIFY
 # ================================================================================
diff --git a/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md b/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md
index 2b5d3f4170..34dc1cbd48 100644
--- a/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/ruby-on-rails/_index.md
@@ -11,16 +11,16 @@ learning_objectives:
   - Validate Ruby on Rails functionality using PostgreSQL as the database  
   - Benchmark Rails performance using the built-in Ruby Benchmark library on Arm64 (Aarch64) architecture
 
-
 prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with Ruby programming, the Rails framework, and the [PostgreSQL Relational Database](https://www.postgresql.org/)
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 skilllevels: Introductory
 subjects: Web
diff --git a/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md
index 11b7c5719b..c16f0a167c 100644
--- a/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md
@@ -16,12 +16,13 @@ learning_objectives:
 prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled  
   - Basic familiarity with [Rust](https://www.rust-lang.org/)
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ##### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -52,7 +53,7 @@ further_reading:
       title: Rust documentation
       link: https://doc.rust-lang.org/stable/
       type: documentation
-  
+
   - resource:
       title: Cargo bench documentation
       link: https://doc.rust-lang.org/cargo/commands/cargo-bench.html
diff --git a/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md b/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md
index 4f62ac7e81..573fae8c95 100644
--- a/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md
@@ -13,9 +13,6 @@ learning_objectives:
 prerequisites:
     - An AWS account.
     - A computer with Docker, Terraform, the Amazon eksctl command-line interface, and kubectl installed.
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author: 
     - Pranay Bakre
@@ -23,6 +20,10 @@ author:
     - Nobel Chowdary Mandepudi
     - Na Li
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Advanced
 subjects: Containers and Virtualization
@@ -36,7 +37,6 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
 further_reading:
     - resource:
         title: EKS documentation
@@ -47,9 +47,6 @@ further_reading:
         link: https://developer.arm.com/community/arm-community-blogs/b/servers-and-cloud-computing-blog/posts/-arm-neoverse-based-kubernetes-clusters
         type: documentation
 
-
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md
index 0800c8276c..aa6e3ace2a 100644
--- a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/_index.md
@@ -12,12 +12,13 @@ learning_objectives:
 prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).   
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is suitable.    
+
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dawid Borycki
-
 ### Tags
 skilllevels: Introductory
 subjects: Web
@@ -26,7 +27,7 @@ cloud_service_providers:
 
 armips:
     - Neoverse
-    
+
 tools_software_languages:
     - Node.js    
     - Visual Studio Code
@@ -34,7 +35,6 @@ tools_software_languages:
 operatingsystems:
     - Windows
 
-
 further_reading:
     - resource:
         title: Serverless Framework
@@ -49,8 +49,6 @@ further_reading:
         link: https://aws.amazon.com/lambda/
         type: Documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md
index 114b9a1c0b..956cbed1cf 100644
--- a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-lambda-dynamodb/_index.md
@@ -13,12 +13,13 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).   
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is suitable.
     - Completion of [Deploy AWS services using the Serverless Framework](/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/).
+
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dawid Borycki
-
 ### Tags
 skilllevels: Introductory
 subjects: Web
@@ -27,7 +28,7 @@ cloud_service_providers:
 
 armips:
     - Neoverse
-    
+
 tools_software_languages:
     - Node.js
     - Visual Studio Code
@@ -37,7 +38,6 @@ operatingsystems:
     - Windows
     - macOS
 
-
 further_reading:
     - resource:
         title: Terraform on Azure
@@ -52,8 +52,6 @@ further_reading:
         link: https://learn.microsoft.com/en-us/azure/bastion/bastion-overview
         type: Documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md
index 9cd0a20a56..b0c4159caf 100644
--- a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-s3/_index.md
@@ -13,12 +13,13 @@ prerequisites:
     - A Windows on Arm computer such as the Lenovo Thinkpad X13s running Windows 11 or a Windows on Arm [virtual machine](/learning-paths/cross-platform/woa_azure/).   
     - Any code editor. [Visual Studio Code for Arm64](https://code.visualstudio.com/docs/?dv=win32arm64user) is suitable.
     - Completion of the Learning Path that shows you how to [Deploy AWS services using the Serverless Framework](/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/).
+
+author: Dawid Borycki
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Dawid Borycki
-
 ### Tags
 skilllevels: Introductory
 subjects: Web
@@ -27,7 +28,7 @@ cloud_service_providers:
 
 armips:
     - Neoverse
-    
+
 tools_software_languages:
     - Node.js
     - Visual Studio Code
@@ -37,7 +38,6 @@ operatingsystems:
     - Windows
     - macOS
 
-
 further_reading:
     - resource:
         title: Serverless Framework
@@ -52,7 +52,6 @@ further_reading:
         link: https://aws.amazon.com/lambda/
         type: Documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/snappy/_index.md b/content/learning-paths/servers-and-cloud-computing/snappy/_index.md
index 695737f14d..41b2ab982e 100644
--- a/content/learning-paths/servers-and-cloud-computing/snappy/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/snappy/_index.md
@@ -6,7 +6,6 @@ minutes_to_complete: 10
 who_is_this_for: This is an introductory topic for software developers using compression
   libraries on Arm servers.
 
-
 learning_objectives:
 - Install and run lzbench with snappy and zstd
 - Measure compression library performance running on 64-bit Arm AWS EC2 instance
@@ -14,12 +13,13 @@ learning_objectives:
 prerequisites:
 - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from an appropriate
   cloud service provider.
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 test_images:
 - ubuntu:latest
 test_link: https://github.com/armflorentlebeau/arm-learning-paths/actions/runs/4312122327
diff --git a/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md b/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md
index b627f3b056..e4a1c0e01e 100644
--- a/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/snort3-multithreading/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 prerequisites:
     - An Arm-based instance from a cloud provider, or an Arm server running Ubuntu 20.04 or 22.04.
     - A basic understanding of Snort's operation and configuration.
+
+author: Preema Merlin Dsouza
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Preema Merlin Dsouza
-
 ### Tags
 skilllevels: Introductory
 subjects: Libraries
@@ -47,7 +48,6 @@ further_reading:
         link: https://files.techmahindra.com/static/img/pdf/next-generation-firewall.pdf
         type: blog
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md
index 011ec82886..6146482155 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md
@@ -15,12 +15,13 @@ prerequisites:
     - A [Microsoft Azure](https://azure.microsoft.com/) account with access to Cobalt 100 based instances (Dpsv6)
     - A machine with [Docker](/install-guides/docker/) installed
     - Familiarity with distributed computing concepts and the [Apache Spark architecture](https://spark.apache.org/docs/latest/)
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -34,7 +35,7 @@ tools_software_languages:
     - Apache Spark
     - Python
     - Docker
- 
+
 operatingsystems:
     - Linux
 
diff --git a/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md
index 235f4f1e9f..9664ee41dd 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark-on-gcp/_index.md
@@ -1,6 +1,6 @@
 ---
 title: Deploy Apache Spark on Google Axion processors
-   
+
 minutes_to_complete: 60
 
 who_is_this_for: This introductory topic is for software developers interested in migrating their Apache Spark workloads from x86_64 platforms to Arm-based platforms, specifically on Google Axion–based C4A virtual machines.  
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free?utm_source=google&hl=en) account with billing enabled
   - Familiarity with distributed computing concepts and the [Apache Spark architecture](https://spark.apache.org/docs/latest/)
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ##### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
diff --git a/content/learning-paths/servers-and-cloud-computing/spark-velox-cobalt/_index.md b/content/learning-paths/servers-and-cloud-computing/spark-velox-cobalt/_index.md
index 18e36b6ee8..6bd95643b1 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark-velox-cobalt/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark-velox-cobalt/_index.md
@@ -17,10 +17,11 @@ learning_objectives:
 prerequisites:
   - A [Microsoft Azure account](https://azure.microsoft.com/) with access to Cobalt 100 based instances (Dpsv6)
   - Basic understanding of distributed systems and Apache Spark
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
-author: Pareena Verma
 
 ### Tags
 skilllevels: Advanced
@@ -38,7 +39,6 @@ tools_software_languages:
   - Gluten
   - Velox
 
-
 operatingsystems:
     - Linux
 
diff --git a/content/learning-paths/servers-and-cloud-computing/spark/_index.md b/content/learning-paths/servers-and-cloud-computing/spark/_index.md
index 9824de61b5..fb99da305f 100644
--- a/content/learning-paths/servers-and-cloud-computing/spark/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spark/_index.md
@@ -8,15 +8,17 @@ who_is_this_for: This is an advanced topic for anyone who wants to deploy Spark
 learning_objectives: 
     - Automate Spark EC2 instance creation using Terraform and Ansible
     - Deploy a single instance of Spark on AWS Graviton2 
-    
+
 prerequisites:
     - An Amazon Web Services (AWS) [account](https://aws.amazon.com/)
     - A machine with [Terraform](/install-guides/terraform/), [AWS CLI](/install-guides/aws-cli/), [AWS IAM authenticator](https://docs.aws.amazon.com/eks/latest/userguide/install-aws-iam-authenticator.html), and [Ansible](/install-guides/ansible/) installed 
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
 ### Tags
 skilllevels: Introductory
 subjects: Databases
@@ -41,7 +43,6 @@ further_reading:
         link: https://aws.amazon.com/blogs/big-data/achieve-up-to-27-better-price-performance-for-spark-workloads-with-aws-graviton2-on-amazon-emr-serverless/
         type: blog 
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/spe-on-performix/_index.md b/content/learning-paths/servers-and-cloud-computing/spe-on-performix/_index.md
index d89d0abdf7..0340cd7628 100644
--- a/content/learning-paths/servers-and-cloud-computing/spe-on-performix/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/spe-on-performix/_index.md
@@ -20,6 +20,10 @@ prerequisites:
 
 author: Kieran Hejmadi
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -59,8 +63,6 @@ further_reading:
         link: https://learn.arm.com/learning-paths/servers-and-cloud-computing/false-sharing-arm-spe/
         type: learning-path
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md b/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md
index 8432152738..03d3cbd640 100644
--- a/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/supervisord/_index.md
@@ -13,12 +13,13 @@ prerequisites:
     - An Arm Linux computer running Docker
     - An AWS account
     - A Remote.It account
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -33,7 +34,7 @@ tools_software_languages:
     - Docker
     - Remote.It
     - Supervisor
-    
+
 further_reading:
     - resource:
         title: Run multiple processes in a container
@@ -48,8 +49,6 @@ further_reading:
         link: https://dev.to/pratapkute/multiple-services-in-a-docker-container-with-supervisord-2g13
         type: blog
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/sve/_index.md b/content/learning-paths/servers-and-cloud-computing/sve/_index.md
index 1c3ec4ba24..f76b88d875 100644
--- a/content/learning-paths/servers-and-cloud-computing/sve/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sve/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 prerequisites:
     - General knowledge about SIMD processing, vectorization or Arm Neon.
     - An Arm computer running Linux. Cloud instances can be used, refer to the list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/).
+
+author: Florent Lebeau
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Florent Lebeau
-
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -66,8 +67,6 @@ further_reading:
         link: https://developer.arm.com/community/arm-community-blogs/b/servers-and-cloud-computing-blog/posts/optimizing-hpcg-for-arm-sve
         type: blog
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md b/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md
index 2006e34363..fdd0e42f31 100644
--- a/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sve2-match/_index.md
@@ -1,12 +1,10 @@
 ---
 title: Accelerate search performance with SVE2 MATCH on Arm servers
 
-    
 minutes_to_complete: 20
 
 who_is_this_for: This is an introductory topic for database developers, performance engineers, and anyone optimizing data processing workloads on Arm-based cloud instances.
 
-
 learning_objectives:
   - Understand the purpose and function of SVE2 MATCH instructions.
   - Implement a search algorithm using both scalar and SVE2-based MATCH approaches.
@@ -15,12 +13,12 @@ learning_objectives:
 
 prerequisites:
 - Access to an [AWS Graviton4, Google Axion, or Azure Cobalt 100 virtual machine](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider.
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author: Pareena Verma
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
 
 ### Tags
 skilllevels: Introductory
diff --git a/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md b/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md
index 405019957f..7dc4891517 100644
--- a/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/sysreport/_index.md
@@ -12,12 +12,13 @@ learning_objectives:
 
 prerequisites:
     - An Arm-based system (bare metal server, cloud instance, developer board) running Linux 
+
+author: James Whitaker
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: James Whitaker
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -36,7 +37,6 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
 further_reading:
     - resource:
         title: Perf for Linux on Arm (LinuxPerf)
@@ -51,7 +51,6 @@ further_reading:
         link: /install-guides/ams/
         type: website
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
index 27999b45bf..520c0dd8a7 100644
--- a/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with [TensorFlow](https://www.tensorflow.org/)
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ##### Tags
 skilllevels: Introductory
 subjects: ML
diff --git a/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md b/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md
index 1afb43cf21..10d6fd48e0 100644
--- a/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/thirdai-sentiment-analysis/_index.md
@@ -11,12 +11,13 @@ learning_objectives:
 
 prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an on-premise Arm server.
+
+author: ThirdAI
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: ThirdAI
-
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -33,7 +34,6 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
 further_reading:
     - resource:
         title: ThirdAI Demos Repository
@@ -44,8 +44,6 @@ further_reading:
         link: https://www.thirdai.com
         type: blog
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md
index 1f48d6322d..eba5272c64 100644
--- a/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_index.md
@@ -1,6 +1,6 @@
 ---
 title: Deploy a live sensor dashboard with TimescaleDB and Grafana on Google Cloud C4A
-    
+
 minutes_to_complete: 45
 
 who_is_this_for: This is an introductory topic for DevOps engineers, database engineers, and software developers who want to deploy and operate TimescaleDB on SUSE Linux Enterprise Server (SLES) Arm64, ingest live time-series sensor data, and visualize it in Grafana.
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with SQL, Python, and Grafana
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ##### Tags
 skilllevels: Introductory
 subjects: Databases
diff --git a/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md b/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md
index e2ea0933a5..4422e19957 100644
--- a/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/top-down-n1/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 
 prerequisites:
     - An Arm Neoverse N1 computer running Linux. A bare metal or cloud metal instance is best because they expose more counters. You can use a virtual machine (VM), but it may offer fewer counters and some commands might not succeed. These instructions have been tested on the `a1.metal` instance type.
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
@@ -29,7 +30,6 @@ tools_software_languages:
     - Telemetry
     - Runbook
 
-
 operatingsystems:
     - Linux
 
@@ -51,7 +51,6 @@ further_reading:
         link: https://www.amazon.com/Computer-Architecture-Quantitative-John-Hennessy/dp/012383872X
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md b/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md
index 627bc5d2c4..d60538f464 100644
--- a/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/torchbench/_index.md
@@ -12,12 +12,13 @@ learning_objectives:
 
 prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an on-premise Arm server.
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -52,8 +53,6 @@ further_reading:
         link: https://pytorch.org/docs/stable/index.html
         type: documentation
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md
index 1958e397c0..e4b12c3173 100644
--- a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events-2/_index.md
@@ -8,16 +8,17 @@ who_is_this_for: This is an advanced topic for software and hardware engineers t
 learning_objectives: 
     - Describe common non-cache PMU events.
     - Understand why specific code triggers specific PMU events on the Neoverse N2 Core.
-   
+
 prerequisites:
     - Some familiarity with performance analysis.
     - The ability to read Arm assembly code.
+
+author: Johanna Skinnider
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Johanna Skinnider
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -30,8 +31,6 @@ tools_software_languages:
     - Assembly
     - Runbook
 
-
-
 further_reading:
     - resource:
         title: Arm Neoverse N2 PMU Guide
@@ -46,9 +45,6 @@ further_reading:
         link: https://developer.arm.com/documentation/109215/0200/?lang=en
         type: documentation
 
-
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md
index c0e77b4c59..adac08bd46 100644
--- a/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/triggering-pmu-events/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 prerequisites:
     - Knowledge of performance analysis. 
     - The ability to read Arm assembly code.
+
+author: Johanna Skinnider
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Johanna Skinnider
-
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
@@ -31,8 +32,6 @@ tools_software_languages:
     - Assembly
     - Runbook
 
-
-
 further_reading:
     - resource:
         title: Arm Neoverse N2 PMU Guide
@@ -47,9 +46,6 @@ further_reading:
         link: https://developer.arm.com/documentation/109215/0200/?lang=en
         type: documentation
 
-
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md b/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md
index 68fa7d960b..4266a9b27f 100644
--- a/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/trivy-on-gcpp/_index.md
@@ -16,12 +16,13 @@ prerequisites:
   - Familiarity with CI/CD concepts
   - Basic knowledge of Linux command-line operations
   - Familiarity with GitHub Actions runners
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 skilllevels: Introductory
 subjects: Containers and Virtualization
diff --git a/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md b/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md
index e357c8c043..fcd0f88a68 100644
--- a/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/tune-network-workloads-on-bare-metal/_index.md
@@ -16,15 +16,16 @@ prerequisites:
     - An Arm Neoverse-based bare-metal server running Ubuntu 24.04 to run Apache Tomcat
     - Access to an x86_64 bare-metal server running Ubuntu 24.04 to run `wrk2`
     - Basic familiarity with Java applications
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author:
     - Ying Yu
     - Ker Liu
     - Rui Chang
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Advanced
 subjects: Performance and Architecture
diff --git a/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md
index 35839c63e1..afd089e324 100644
--- a/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/typescript-on-gcp/_index.md
@@ -1,6 +1,6 @@
 ---
 title: Deploy TypeScript on Google Cloud C4A virtual machines
-    
+
 minutes_to_complete: 30
 
 who_is_this_for: This is an introductory topic for developers deploying and optimizing TypeScript workloads on Arm64 Linux environments, specifically using Google Cloud C4A virtual machines powered by Axion processors.
@@ -14,12 +14,13 @@ learning_objectives:
 prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with [TypeScript](https://www.typescriptlang.org/) and Node.js runtime environment
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ##### Tags
 skilllevels: Introductory
 subjects: Web
diff --git a/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md b/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md
index ff06b65acc..25c0540140 100644
--- a/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md
@@ -12,12 +12,13 @@ learning_objectives:
 prerequisites:
     - Access to both an Arm and an x86-based cloud instance.
     - Intermediate understanding of C++, compilers, and Linux. 
+
+author: Kieran Hejmadi
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Kieran Hejmadi
-
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture
diff --git a/content/learning-paths/servers-and-cloud-computing/vLLM-quant/_index.md b/content/learning-paths/servers-and-cloud-computing/vLLM-quant/_index.md
index 723332b07b..cdd48d47aa 100644
--- a/content/learning-paths/servers-and-cloud-computing/vLLM-quant/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vLLM-quant/_index.md
@@ -16,18 +16,18 @@ learning_objectives:
     - Launch a vLLM server to serve the quantized model.
     - Run single-prompt and batch inference using the vLLM OpenAI-compatible API.
 
-    
 prerequisites:
     - An Arm-based server or cloud instance running with at least 32 CPU cores, 64 GB RAM, and 32 GB of available disk space.
     - Familiarity with Python and basic understanding of transformer models and quantization techniques.
     - An active Hugging Face account with access to the target model.
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 author: 
    - Rani Chowdary Mandepudi
    - Phalani Paladugu
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -47,8 +47,6 @@ tools_software_languages:
     - Python
     - PyTorch
     - OpenBLAS
-    
-
 
 further_reading:
     - resource:
@@ -68,8 +66,6 @@ further_reading:
         link: /learning-paths/servers-and-cloud-computing/vllm/
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md b/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md
index 03c9d6f97a..94b1520d4b 100644
--- a/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vectorscan/_index.md
@@ -5,7 +5,6 @@ minutes_to_complete: 15
 
 who_is_this_for: This is an introductory topic for software developers using Hyperscan who want to migrate to Arm.
 
-
 learning_objectives:
     - Install and run Vectorscan on an Arm-based instance
     - Install and run Snort 3 on your instance
@@ -13,12 +12,13 @@ learning_objectives:
 
 prerequisites:
     - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an Arm server with Ubuntu 20.04 or Ubuntu 22.04 installed.
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 ### Tags
 skilllevels: Introductory
 subjects: Libraries
@@ -49,8 +49,6 @@ further_reading:
         link: https://developer.arm.com/community/arm-community-blogs/b/servers-and-cloud-computing-blog/posts/accelerating-deep-packet-inspection-with-neon-on-arm-neoverse
         type: blog
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md b/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
index 857c5a7558..6074db20ca 100644
--- a/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vllm-acceleration/_index.md
@@ -1,6 +1,6 @@
 ---
 title: Run vLLM inference with INT4 quantization on Arm servers
-    
+
 minutes_to_complete: 60
 
 who_is_this_for: This is an introductory topic for developers interested in building and optimizing vLLM for Arm-based servers. This Learning Path shows you how to quantize large language models (LLMs) to INT4, serve them using an OpenAI-compatible API, and benchmark model accuracy with the LM Evaluation Harness.
@@ -16,13 +16,14 @@ learning_objectives:
 prerequisites:
     - An Arm-based Linux server (Ubuntu 22.04+ recommended) with a minimum of 32 vCPUs, 64 GB RAM, and 64 GB free disk space
     - Python 3.12 and basic familiarity with Hugging Face Transformers and quantization
-generate_summary_faq: true
-rerun_summary: false
-rerun_faqs: false
 
 author:
    - Nikhil Gupta
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -43,7 +44,7 @@ tools_software_languages:
     - Python
     - PyTorch
     - Hugging Face
-    
+
 further_reading:
     - resource:
         title: vLLM Documentation
@@ -66,8 +67,6 @@ further_reading:
         link: https://github.com/EleutherAI/lm-evaluation-harness
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1
diff --git a/content/learning-paths/servers-and-cloud-computing/vllm-benchmark-quantisation/_index.md b/content/learning-paths/servers-and-cloud-computing/vllm-benchmark-quantisation/_index.md
index 50cd2332a7..41a5ddaf37 100644
--- a/content/learning-paths/servers-and-cloud-computing/vllm-benchmark-quantisation/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vllm-benchmark-quantisation/_index.md
@@ -1,6 +1,6 @@
 ---
 title: Run vLLM inference with quantized models and benchmark on Arm servers
-    
+
 minutes_to_complete: 60
 
 who_is_this_for: This is an introductory topic for developers interested in running inference on quantized models. In this Learning Path, you'll learn how to run inference on Llama 3.1-8B and Whisper with and without quantization. You'll then benchmark Llama performance and accuracy with vLLM's bench CLI and the LM Evaluation Harness.
@@ -21,6 +21,10 @@ author:
   - Nikhil Gupta
   - Marek Michałowski
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -34,8 +38,6 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
-
 further_reading:
     - resource:
         title: vLLM Documentation
@@ -58,8 +60,6 @@ further_reading:
         link: https://github.com/EleutherAI/lm-evaluation-harness
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/vllm/_index.md b/content/learning-paths/servers-and-cloud-computing/vllm/_index.md
index 959498727c..069912aca1 100644
--- a/content/learning-paths/servers-and-cloud-computing/vllm/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vllm/_index.md
@@ -13,12 +13,13 @@ learning_objectives:
 
 prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider, or a local Arm Linux computer with at least 8 CPUs and 16 GB RAM.
+
+author: Jason Andrews
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Jason Andrews
-
 ### Tags
 skilllevels: Introductory
 subjects: ML
@@ -52,8 +53,6 @@ further_reading:
         link: https://huggingface.co/models
         type: website
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md b/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
index 0afff69b1b..749a82875f 100644
--- a/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/vvenc/_index.md
@@ -1,11 +1,12 @@
 ---
 title: Run the vvenc H.266 encoder on Arm servers
+
+author: Willen Yang
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Willen Yang
-
 minutes_to_complete: 20
 
 who_is_this_for: This is an introductory topic for software developers who want to build and run the VVenC® (Fraunhofer Versatile Video Encoder) H.266 project on Arm servers and measure the performance.
@@ -52,7 +53,6 @@ further_reading:
         link: https://developer.arm.com/community/arm-community-blogs/b/servers-and-cloud-computing-blog/posts/oracle-cloud-infrastructure-arm-based-a1
         type: blog
 
-
 weight: 1
 layout: learningpathall
 learning_path_main_page: "yes"
diff --git a/content/learning-paths/servers-and-cloud-computing/whisper/_index.md b/content/learning-paths/servers-and-cloud-computing/whisper/_index.md
index fe3e6bb3df..d71128858e 100644
--- a/content/learning-paths/servers-and-cloud-computing/whisper/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/whisper/_index.md
@@ -11,18 +11,18 @@ learning_objectives:
     - Enable performance-enhancing features for running the model on Arm CPUs.
     - Evaluate transcript generation times using Whisper.
 
-
 prerequisites:
     - An [Arm-based compute instance](/learning-paths/servers-and-cloud-computing/intro/) running Ubuntu with 32 cores, 8GB of RAM, and 32GB of disk space.
     - Basic knowledge of Python.
     - Familiarity with machine learning concepts.
     - Familiarity with the fundamentals of the Whisper ASR Model.
+
+author: Nobel Chowdary Mandepudi
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Nobel Chowdary Mandepudi
-
 ### Tags
 skilllevels: Introductory
 armips:
@@ -41,15 +41,12 @@ tools_software_languages:
     - Demo
     - Hugging Face
 
-    
-
 further_reading:
     - resource:
         title: Hugging Face Transformers documentation
         link: https://huggingface.co/transformers/v4.11.3/index.html
         type: documentation
 
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md b/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
index 16e4e61eb7..575697a4a7 100644
--- a/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/wordpress/_index.md
@@ -6,12 +6,13 @@ minutes_to_complete: 30
 prerequisites:
     - An OCI account
     - An Arm compute instance deployed on OCI with Oracle Linux
+
+author: Frédéric -lefred- Descamps
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Frédéric -lefred- Descamps
-
 who_is_this_for: This is an introductory topic for developers who want to install WordPress on Oracle Cloud Infrastructure (OCI) using always free tier.
 
 learning_objectives: 
@@ -33,7 +34,6 @@ tools_software_languages:
     - MySQL
     - WordPress
 
-
 further_reading:
     - resource:
         title: Learn Faster to Grow Faster
@@ -44,8 +44,6 @@ further_reading:
         link: https://blogs.oracle.com/mysql/post/wordpress-with-mysql-on-oci-always-free
         type: blog
 
-
-
 ### FIXED, DO NOT MODIFY
 # ================================================================================
 weight: 1                       # _index.md always has weight of 1 to order correctly
diff --git a/content/learning-paths/servers-and-cloud-computing/xgboost-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/xgboost-on-axion/_index.md
index f6be502b7b..fdd55cbd7a 100644
--- a/content/learning-paths/servers-and-cloud-computing/xgboost-on-axion/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/xgboost-on-axion/_index.md
@@ -1,6 +1,6 @@
 ---
 title: Train and deploy XGBoost models on Google Cloud C4A Axion VM
-    
+
 description: Set up XGBoost on Google Cloud C4A Axion Arm VMs running SUSE Linux to train machine learning models, tune model performance, benchmark large-scale datasets, and deploy trained models as REST APIs.
 
 minutes_to_complete: 90
@@ -19,6 +19,10 @@ prerequisites:
 
 author: Pareena Verma
 
+generate_summary_faq: true
+rerun_summary: false
+rerun_faqs: false
+
 ##### Tags
 skilllevels: Introductory
 subjects: ML
diff --git a/content/learning-paths/servers-and-cloud-computing/zlib/_index.md b/content/learning-paths/servers-and-cloud-computing/zlib/_index.md
index f55725b00e..cc10a6a7f4 100644
--- a/content/learning-paths/servers-and-cloud-computing/zlib/_index.md
+++ b/content/learning-paths/servers-and-cloud-computing/zlib/_index.md
@@ -14,12 +14,13 @@ learning_objectives:
 
 prerequisites:
 - An Arm Linux computer or an [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider running Ubuntu 22.04 or Ubuntu 24.04.
+
+author: Pareena Verma
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
 
-author: Pareena Verma
-
 test_images:
 - ubuntu:latest
 test_link:
diff --git a/tools/generate-summary-faq.md b/tools/generate-summary-faq.md
index 0ad0de3ac3..58d856c0a4 100644
--- a/tools/generate-summary-faq.md
+++ b/tools/generate-summary-faq.md
@@ -97,9 +97,13 @@ tools/generate-summary-faq --list-categories
 Each Learning Path uses three front-matter fields:
 
 ```yaml
+author: Example Author
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
+
+### Tags
 ```
 
 Use the fields this way:
@@ -114,12 +118,16 @@ for processed Learning Paths. This prevents repeated LLM calls unless a
 contributor intentionally opts the path in again.
 
 New Learning Paths scaffolded from `archetypes/learning-path/_index.md` should
-start with:
+place the fields after `author` and before `### Tags`:
 
 ```yaml
+author: PLACEHOLDER NAME
+
 generate_summary_faq: true
 rerun_summary: false
 rerun_faqs: false
+
+### Tags
 ```
 
 If you create a Learning Path by copying an existing folder, confirm these
diff --git a/tools/set_summary_faq_flags.py b/tools/set_summary_faq_flags.py
index 3c188cd5e9..55eeffca3a 100644
--- a/tools/set_summary_faq_flags.py
+++ b/tools/set_summary_faq_flags.py
@@ -136,10 +136,19 @@ def set_front_matter_flag(front_matter: str, flag: str, value: bool) -> tuple[st
         return updated, updated != front_matter
 
     lines = front_matter.splitlines()
-    insert_at = 0
+    insert_at = len(lines)
     for index, line in enumerate(lines):
-        if re.match(r"^(title|description|minutes_to_complete|who_is_this_for|learning_objectives|prerequisites|author|reviewers|test_maintenance|test_images|weight|layout|draft|hidden|tags|skilllevels|subjects|armips|tools_software_languages|operatingsystems|cloud_service_providers|ci_cd|learning_path_main_image|main_image|additional_search_terms|ignore_connection_issues|generate_summary_faq|rerun_summary|rerun_faqs)\s*:", line):
+        if re.match(r"^author\s*:", line):
             insert_at = index + 1
+            while insert_at < len(lines):
+                next_line = lines[insert_at]
+                if re.match(r"^(generate_summary_faq|rerun_summary|rerun_faqs)\s*:", next_line):
+                    insert_at += 1
+                    continue
+                if not next_line.strip() or not next_line.startswith((" ", "\t")):
+                    break
+                insert_at += 1
+            break
     lines.insert(insert_at, replacement)
     return "\n".join(lines), True